diff --git "a/1508.jsonl" "b/1508.jsonl" new file mode 100644--- /dev/null +++ "b/1508.jsonl" @@ -0,0 +1,459 @@ +{"seq_id": "359735254", "text": "#\n# @lc app=leetcode id=994 lang=python3\n#\n# [994] Rotting Oranges\n#\n# https://leetcode.com/problems/rotting-oranges/description/\n#\n# algorithms\n# Easy (46.54%)\n# Total Accepted: 5.7K\n# Total Submissions: 12.3K\n# Testcase Example: '[[2,1,1],[1,1,0],[0,1,1]]'\n#\n# In a given grid, each cell can have one of three values:\n# \n# \n# the value 0 representing an empty cell;\n# the value 1 representing a fresh orange;\n# the value 2 representing a rotten orange.\n# \n# \n# Every minute, any fresh orange that is adjacent (4-directionally) to a rotten\n# orange becomes rotten.\n# \n# Return the minimum number of minutes that must elapse until no cell has a\n# fresh orange.  If this is impossible, return -1 instead.\n# \n# \n# \n# \n# Example 1:\n# \n# \n# \n# \n# Input: [[2,1,1],[1,1,0],[0,1,1]]\n# Output: 4\n# \n# \n# \n# Example 2:\n# \n# \n# Input: [[2,1,1],[0,1,1],[1,0,1]]\n# Output: -1\n# Explanation: The orange in the bottom left corner (row 2, column 0) is never\n# rotten, because rotting only happens 4-directionally.\n# \n# \n# \n# Example 3:\n# \n# \n# Input: [[0,2]]\n# Output: 0\n# Explanation: Since there are already no fresh oranges at minute 0, the\n# answer is just 0.\n# \n# \n# \n# \n# Note:\n# \n# \n# 1 <= grid.length <= 10\n# 1 <= grid[0].length <= 10\n# grid[i][j] is only 0, 1, or 2.\n# \n# \n# \n# \n# \n#\nfrom collections import deque\n\n\nclass Solution(object):\n def orangesRotting(self, grid):\n \"\"\"\n :type grid: List[List[int]]\n :rtype: int\n \"\"\"\n self.minutes = 0\n origins = 0\n q = deque()\n for i in range(len(grid)):\n for j in range(len(grid[0])):\n if grid[i][j] == 1:\n origins += 1\n if grid[i][j] == 2:\n q.append((i, j))\n direction = [(1, 0), (-1, 0), (0, 1), (0, -1)]\n while q and origins > 0:\n l = len(q)\n self.minutes += 1\n for i in range(l):\n x, y = q.popleft()\n for delate_x, delate_y in direction:\n next_x, next_y = x + delate_x, y + delate_y\n if not self.judge(next_x, next_y, grid):\n continue\n grid[next_x][next_y] = 2\n origins -= 1\n if (next_x, next_y) not in q:\n q.append((next_x, next_y))\n return self.minutes if origins == 0 else -1\n \n def judge(self, x, y, grid):\n return x >= 0 and x < len(grid) and y >= 0 and y < len(grid[0]) and grid[x][y] == 1\n\n\nif __name__ == '__main__':\n s = Solution()\n grid = [[0,2]]\n print(s.orangesRotting(grid=grid))\n \n", "sub_path": "Breadth-first Search/easy/994.rotting-oranges.py", "file_name": "994.rotting-oranges.py", "file_ext": "py", "file_size_in_byte": 2632, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "collections.deque", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "408524257", "text": "\"\"\"\r\ncpu_gpu.py\r\nAn OpenCL-OpenCV-Python CPU vs GPU comparison\r\n\"\"\"\r\nimport cv2\r\nimport timeit\r\n\r\n# A simple image pipeline that runs on both Mat and Umat\r\ndef img_cal(img, mode):\r\n if mode=='UMat':\r\n img = cv2.UMat(img)\r\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\r\n img = cv2.GaussianBlur(img, (7, 7), 1.5)\r\n img = cv2.Canny(img, 0, 50)\r\n if type(img) == 'cv2.UMat': \r\n img = cv2.UMat.get(img)\r\n\r\n return img\r\n\r\n# Timing function\r\ndef run(processor, function, n_threads, N):\r\n cv2.setNumThreads(n_threads)\r\n t = timeit.timeit(function, globals=globals(), number=N)/N*1000\r\n print('%s avg. with %d threads: %0.2f ms' % (processor, n, t))\r\n return t\r\n\r\nimg = cv2.imread('test.jpg') \r\nN = 1000\r\nthreads = [1, 16]\r\n\r\nprocessor = {'GPU': \"img_cal(img, 'UMat')\", \r\n 'CPU': \"img_cal(img, '')\"}\r\nresults = {}\r\nfor n in range(8): \r\n for pro in processor.keys():\r\n results[pro,n] = run(processor=pro, \r\n function= processor[pro], \r\n n_threads=n, N=N)\r\n\r\nprint('\\nGPU speed increase over 1 CPU thread [%%]: %0.2f' % \\\r\n (results[('CPU', 1)]/results[('GPU', 1)]*100))\r\nprint('CPU speed increase on 4 threads versus 1 thread [%%]: %0.2f' % \\\r\n (results[('CPU', 1)]/results[('CPU', 16)]*100))\r\nprint('GPU speed increase versus 4 threads [%%]: %0.2f' % \\\r\n (results[('CPU', 4)]/results[('CPU', 1)]*100))", "sub_path": "opencv_cuda_test.py", "file_name": "opencv_cuda_test.py", "file_ext": "py", "file_size_in_byte": 1379, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cv2.UMat", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 12, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.Canny", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.UMat.get", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.UMat", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.setNumThreads", "line_number": 22, "usage_type": "call"}, {"api_name": "timeit.timeit", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "111080289", "text": "from django.contrib import admin\nfrom .models import Photo, Category\n\n\nclass PhotoAdmin(admin.ModelAdmin):\n list_display = [\n '__unicode__', 'photo_rating'\n ]\n fieldsets = [\n (None, {\"fields\": ['photo_name', 'photo_image']}),\n (\"Rating\", {\"fields\": ['photo_rating']}),\n (\"Category\", {\"fields\": ['photo_category']})\n ]\nadmin.site.register(Photo, PhotoAdmin)\n\n\nclass CategoryAdmin(admin.ModelAdmin):\n list_display = [\n '__unicode__',\n ]\n fieldsets = [\n (None, {\"fields\": ['category_name', 'category_icon']})\n ]\nadmin.site.register(Category, CategoryAdmin)\n", "sub_path": "photobook/myapp/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 620, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.contrib.admin.ModelAdmin", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 5, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 14, "usage_type": "call"}, {"api_name": "models.Photo", "line_number": 14, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 14, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 17, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 17, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 24, "usage_type": "call"}, {"api_name": "models.Category", "line_number": 24, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "315872546", "text": "import sys\nimport torch\nfrom transformers import T5ForConditionalGeneration,T5Tokenizer\n\nsummary_model = T5ForConditionalGeneration.from_pretrained('t5-base')\nsummary_tokenizer = T5Tokenizer.from_pretrained('t5-base')\n\ncontent = sys.argv[1]\n\ndef get_summary(text):\n preprocess_text = text.strip().replace(\"\\n\", \"\")\n\n tokenized_text = summary_tokenizer.encode(preprocess_text, add_special_tokens=False, return_tensors=\"pt\")\n\n while len(tokenized_text[0]) > 509:\n tokenized_chunk, tokenized_text = tokenized_text[0][:509], tokenized_text[0][509:]\n\n tokenized_chunk = torch.stack(\n [torch.cat([torch.Tensor([21603]), torch.Tensor([10]), tokenized_chunk, torch.Tensor([1])\n ]).long()])\n\n summary_id = summary_model.generate(tokenized_chunk,\n num_beams=5,\n no_repeat_ngram_size=2,\n min_length=100,\n max_length=512)\n\n tokenized_text = torch.stack([torch.cat([summary_id[0], tokenized_text])])\n\n output = summary_tokenizer.decode(tokenized_text[0], skip_special_tokens=True)\n\n return output\n\n\nsummary = get_summary(content)\n\nprint(summary)", "sub_path": "pysummary.py", "file_name": "pysummary.py", "file_ext": "py", "file_size_in_byte": 1281, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "transformers.T5ForConditionalGeneration.from_pretrained", "line_number": 5, "usage_type": "call"}, {"api_name": "transformers.T5ForConditionalGeneration", "line_number": 5, "usage_type": "name"}, {"api_name": "transformers.T5Tokenizer.from_pretrained", "line_number": 6, "usage_type": "call"}, {"api_name": "transformers.T5Tokenizer", "line_number": 6, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.stack", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "545055597", "text": "\"\"\"\nPython Crash Course, Third Edition https://ehmatthes.github.io/pcc_3e/\nMy notes: https://github.com/egalli64/pythonesque/pcc3\n\nChapter 15 - Rolling Dice with Plotly - Rolling Two Dice\n\"\"\"\nimport plotly.express as px\nfrom e3a_die import Die\n\ndice = (Die(), Die())\n\nresults = []\nfor roll_num in range(1000):\n results.append(dice[0].roll() + dice[1].roll())\n\nfrequencies = []\nvalues = range(2, dice[0].num_sides + dice[1].num_sides + 1)\nfor value in values:\n frequencies.append(results.count(value))\n\ntitle = \"Results of Rolling Two Dice 6 Values 1,000 Times\"\nlabels = {'x': 'Result', 'y': 'Frequency of Result'}\n\nfig = px.bar(x=values, y=frequencies, title=title, labels=labels)\n# each column has its tick\nfig.update_layout(xaxis_dtick=1)\n\nfig.show()\n", "sub_path": "pcc3/ch15/e3c_2dice.py", "file_name": "e3c_2dice.py", "file_ext": "py", "file_size_in_byte": 759, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "e3a_die.Die", "line_number": 10, "usage_type": "call"}, {"api_name": "plotly.express.bar", "line_number": 24, "usage_type": "call"}, {"api_name": "plotly.express", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "622054581", "text": "import tensorflow as tf\nfrom src.base.base_test import BaseTest\nfrom tqdm import tqdm\nimport numpy as np\n\n\nclass SentimentTester(BaseTest):\n def __init__(self, sess, model, data, config, logger):\n super().__init__(sess, model, data, config, logger)\n\n def test(self):\n loop = tqdm(range(self.data.num_batches_test))\n losses = []\n accs = []\n for _ in loop:\n loss, acc = self.test_step()\n losses.append(loss)\n accs.append(acc)\n loss = np.mean(losses)\n acc = np.mean(accs)\n print(\"test_accuracy: \",\n acc * 100, \"% test_loss: \", loss)\n\n def predict(self):\n predictions = np.empty(shape=[0], dtype=int)\n loop = tqdm(range(self.data.num_batches_test))\n for _ in loop:\n prediction = self.predict_step()\n predictions = np.concatenate((predictions, prediction))\n\n return predictions\n\n def predict_step(self):\n batch_x = self.data.next_batch(batch_type=\"unlabeled_test\")\n feed_dict = {self.model.x: batch_x, self.model.is_training: False,\n self.model.seq_len: batch_x.shape[1],\n self.model.keep_prob_lstm_out: 1.0,\n self.model.keep_prob_lstm_recurrent: 1.0, self.model.keep_prob_fc: 1.0}\n\n prediction = self.sess.run([self.model.predictions],\n feed_dict=feed_dict)\n\n return prediction[0]\n\n def test_step(self):\n batch_x, batch_y = self.data.next_batch(batch_type=\"test\")\n\n feed_dict = {self.model.x: batch_x, self.model.y: batch_y, self.model.is_training: False,\n self.model.seq_len: batch_x.shape[1],\n self.model.keep_prob_lstm_out: 1.0,\n self.model.keep_prob_lstm_recurrent: 1.0,\n self.model.keep_prob_fc: 1.0}\n\n loss, acc = self.sess.run([self.model.cross_entropy, self.model.accuracy],\n feed_dict=feed_dict)\n\n return loss, acc\n", "sub_path": "src/testers/sentiment_tester.py", "file_name": "sentiment_tester.py", "file_ext": "py", "file_size_in_byte": 2050, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "src.base.base_test.BaseTest", "line_number": 7, "usage_type": "name"}, {"api_name": "tqdm.tqdm", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 25, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "519874901", "text": "import requests\n\n# time modulu ekle\n# ingilizce kelimeler ekle -> https://dictionary.yandex.net/api/v1/dicservice.json/lookup?key=dict.1.1.20210105T190252Z.1bf16a1a72629b11.f723d8b4f7f900313a0bcb5af8faab64c3fb2e46&lang=en-tr&text=run\n# baslangicta hangi ceviri yapmak istedigini sorsun sonra o sekilde devam etsin\n# Aranan kelimeler ve cevaplar bir database e kaydedilsin\n# Bu program icin bir arayuz tasarla\n# Gecmis sorgular - kayitlar buraya gelsin\n#ingilizce turkce veya turkce ingilizce (veya ingilizce ingilizce) arama yapilabilir\n\n\nwhile True:\n try:\n print(\"-\" * 50)\n ceviri_num = int(input(\"\"\"\n Turkce-Ingilizce ceviri icin 1'e basip Enter a basiniz.\n Ingilizce-Turkce ceviri icin 2'ye basip Enter a basiniz.\n \"\"\"))\n\n if ceviri_num == 1:\n aranan_kelime = input(\"\\nTurkce-Ingilizce ceviri icin Aranan kelimeyi giriniz: \")\n url = (\"https://dictionary.yandex.net/api/v1/dicservice.json/lookup?key=dict.1.1.20210105T190252Z.1bf16a1a72629b11.f723d8b4f7f900313a0bcb5af8faab64c3fb2e46&lang=tr-en&text=\" + aranan_kelime)\n r = requests.get(url)\n data = r.json()\n tr_eng_ilk_anlam = data[\"def\"][0][\"tr\"][0][\"text\"]\n synonims = data[\"def\"][0][\"tr\"][0][\"syn\"]\n print(\"Kelimenin ilk ve en cok kullanilan karsiligi:\\n\")\n print(tr_eng_ilk_anlam)\n print(\"\\nKelimenin esanlamlilari ve kullanilislari:\\n\")\n for i in synonims:\n print(i[\"text\"])\n else:\n aranan_kelime = input(\"\\nIngilizce-Turkce ceviri icin Aranan kelimeyi giriniz: \")\n url = (\"https://dictionary.yandex.net/api/v1/dicservice.json/lookup?key=dict.1.1.20210105T190252Z.1bf16a1a72629b11.f723d8b4f7f900313a0bcb5af8faab64c3fb2e46&lang=en-tr&text=\" + aranan_kelime)\n r = requests.get(url)\n data = r.json()\n eng_tr_ilk_anlam = data[\"def\"][0][\"tr\"][0][\"text\"]\n eng_tr_synonims = data[\"def\"][0][\"tr\"][0][\"syn\"]\n print(\"Kelimenin ilk ve en cok kullanilan karsiligi:\\n\")\n print(eng_tr_ilk_anlam)\n print(\"\\nKelimenin esanlamlilari ve kullanilislari:\\n\")\n for i in eng_tr_synonims:\n print(i[\"text\"])\n\n except:\n print(\"\\nAranan kelime bulunamadi lutfen baska bir kelime giriniz!\")\n", "sub_path": "Apps/Translators/Yandex/Yandex Translator json tr-en dictionary v2.0.py", "file_name": "Yandex Translator json tr-en dictionary v2.0.py", "file_ext": "py", "file_size_in_byte": 2335, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.get", "line_number": 23, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "651597845", "text": "import numpy as np\nfrom matplotlib import pyplot as plt\n\nX = np.linspace(-6, 6, 1024)\nY = np.sinc(X)\n\nX_detail = np.linspace(-3, 3, 1024)\nY_detail = np.sinc(X_detail)\n\nplt.plot(X, Y, c = 'k')\n\nsub_axes = plt.axes([.6, .6, .25, .25])\nsub_axes.plot(X_detail, Y_detail, c = 'k')\n\nplt.setp(sub_axes)\n\nplt.show()", "sub_path": "Aula 02/Exercicio 03 - Resumo - Capitulo 04/Exemplo_05.py", "file_name": "Exemplo_05.py", "file_ext": "py", "file_size_in_byte": 307, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.linspace", "line_number": 4, "usage_type": "call"}, {"api_name": "numpy.sinc", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.sinc", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axes", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "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": "341256010", "text": "import cv2\nimport socket\nfrom termcolor import colored\nfrom time import sleep\nimport base64\nimport zmq\n\ncontext=zmq.Context()\nclient_socket=context.socket(zmq.PUB)\nclient_socket.connect('tcp://localhost:5555')\n\n\n\n\nprint(colored(\"Starting....\",\"cyan\"))\n\n\n\n\nif __name__=='__main__':\n\n camera=input(\"Select the number of the camera ( 0 if error ) :\")\n camera=int(camera)\n\n try:\n vid=cv2.VideoCapture(camera)\n print( colored(f\"Camera number {camera} activated\",'cyan') )\n except Exception:\n print( colored(f\"Error activating camera number {camera}. We will try to activate camera number 0\",'orange'))\n try:\n vid=cv2.VideoCapture(0)\n print( colored(f\"Camera number {0} activated\",'cyan') )\n except Exception:\n print( colored(f\"No camera connected\",'red'))\n\n\n fps=input(\"Select the number of frames per second (between 10 and 60):\")\n fps=int(fps)\n if fps<30 or fps>60:\n fps=60\n\n print(colored(f\"Using {fps} fps\",'blue'))\n\n print(colored(\"Starting streaming\",\"green\"))\n\n while 1:\n try:\n success,img=vid.read()\n encoded,buffer=cv2.imencode('.jpg',img)\n package=base64.b64encode(buffer)\n client_socket.send(package)\n sleep(1/fps)\n except Exception as e:\n print(e)\n break\nelse:\n camera=int(0)\n vid=cv2.VideoCapture(camera) \n print( colored(f\"Camera number {camera} activated\",'cyan') )\n fps=int(60)\n print(colored(f\"Using {fps} fps\",'blue'))\n print(colored(\"Starting streaming\",\"green\"))\n\n while 1:\n try:\n success,img=vid.read()\n encoded,buffer=cv2.imencode('.jpg',img)\n package=base64.b64encode(buffer)\n client_socket.send(package)\n sleep(1/fps)\n except Exception as e:\n print(e)\n break\n\n", "sub_path": "camera-server.py", "file_name": "camera-server.py", "file_ext": "py", "file_size_in_byte": 1890, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "zmq.Context", "line_number": 8, "usage_type": "call"}, {"api_name": "zmq.PUB", "line_number": 9, "usage_type": "attribute"}, {"api_name": "termcolor.colored", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 26, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 27, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 31, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 32, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 34, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 42, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.imencode", "line_number": 49, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 50, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 58, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 59, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 61, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.imencode", "line_number": 67, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 68, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "139062931", "text": "from django.urls import path, re_path\nfrom . import views\n\nurlpatterns = [\n\n # /home/\n path('', views.index, name='index'),\n\n # /f1better/index.html\n path('index.html', views.index, name='index'),\n\n path('register', views.register, name='register'),\n path('register.html', views.register, name='register'),\n\n path('login', views.login, name='login'),\n path('login.html', views.login, name='login'),\n\n path('logout', views.logout, name='logout'),\n]", "sub_path": "f1better/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 474, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.urls.path", "line_number": 7, "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": 13, "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": 18, "usage_type": "call"}]} +{"seq_id": "180657649", "text": "from django.shortcuts import render, redirect, get_object_or_404\nfrom .import forms\nfrom .models import SubsidyTypes\n\n\ndef subsidy_types(request):\n # login_id = request.session['logid']\n model_object = SubsidyTypes.objects.all()\n\n if request.method == 'POST':\n form = forms.SubTypeForms(request.POST, request.FILES)\n if form.is_valid():\n subtypeobj = form.cleaned_data\n subtypename = subtypeobj['subsidy_type_name']\n subtypesub = subtypeobj['subsidy_type_sub_perc']\n subtypeself = subtypeobj['subsidy_type_self_perc']\n sp = SubsidyTypes(subsidy_type_name=subtypename, subsidy_type_sub_perc=subtypesub, subsidy_type_self_perc=subtypeself)\n sp.save()\n return redirect('subsidy_types:SubTypeForms')\n else:\n form = forms.SubTypeForms\n\n return render(request, \"subsidy_types/sub_types.html\", {'form': form, 'data': model_object})\n\n\ndef edit_subtypes(request, pk):\n template = 'subsidy_types/sub_types.html'\n post = get_object_or_404(SubsidyTypes, pk=pk)\n model_object = SubsidyTypes.objects.all()\n if request.method == 'POST':\n form = forms.SubTypeForms(request.POST, instance=post)\n if form.is_valid():\n instance = form.save(commit=False)\n instance.save()\n return redirect('subsidy_types:SubTypeForms')\n else:\n form = forms.SubTypeForms(instance=post)\n context = {\n 'form': form,\n 'post': post,\n 'data': model_object,\n }\n return render(request, template, context)\n\n\ndef delete_subtypes(request, pk):\n post = get_object_or_404(SubsidyTypes, pk=pk)\n post.delete()\n return redirect('subsidy_types:SubTypeForms')\n", "sub_path": "Gconnect/subsidy_types/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1745, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "models.SubsidyTypes.objects.all", "line_number": 8, "usage_type": "call"}, {"api_name": "models.SubsidyTypes.objects", "line_number": 8, "usage_type": "attribute"}, {"api_name": "models.SubsidyTypes", "line_number": 8, "usage_type": "name"}, {"api_name": "models.SubsidyTypes", "line_number": 17, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 19, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 23, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 28, "usage_type": "call"}, {"api_name": "models.SubsidyTypes", "line_number": 28, "usage_type": "argument"}, {"api_name": "models.SubsidyTypes.objects.all", "line_number": 29, "usage_type": "call"}, {"api_name": "models.SubsidyTypes.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "models.SubsidyTypes", "line_number": 29, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 35, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 43, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 47, "usage_type": "call"}, {"api_name": "models.SubsidyTypes", "line_number": 47, "usage_type": "argument"}, {"api_name": "django.shortcuts.redirect", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "345996335", "text": "from io import BytesIO\nimport base64\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom IPython.display import HTML\n# reset to matplotlib defaults rather than seaborn ones\nplt.rcdefaults()\n# Turn off the max column width so the images won't be truncated\npd.set_option('display.max_colwidth', -1)\n#Monkey patch the dataframe so the sparklines are displayed\npd.DataFrame._repr_html_ = lambda self: self.to_html(escape=False)\n\n# Display pandas linebreaks properly\n# Save the original `to_html` function to call it later\npd.DataFrame.base_to_html = pd.DataFrame.to_html\n# Call it here in a controlled way\npd.DataFrame.to_html = (\n lambda df, *args, **kwargs: \n (df.base_to_html(*args, **kwargs)\n .replace(r\"\\n\", \"
\"))\n)\n\n\ndef dist_plot(org_value,\n distribution,\n figsize=(3.5, 1),\n **kwags): \n \"\"\" Draws a matplotlib plot with a kde curve and a line for\n an individual institution.\n \n Parameters\n ----------\n org_value : float\n Value of the individual institution to be highlighted.\n distribution : pandas series\n Values to be used to draw the distribution.\n figsize : tuple, optional\n Size of figure. The default is (3.5, 1).\n **kwags : to be passed to plt.subplots.\n\n Returns\n -------\n plt : matplotlib plot\n\n \"\"\"\n fig, ax = plt.subplots(1,1,figsize=figsize,**kwags)\n sns.kdeplot(distribution,ax=ax,linewidth=0.9)\n ax.axvline(org_value,color='r',linewidth=1)\n ax = remove_clutter(ax)\n return plt\n\ndef sparkline_plot(series,\n figsize=(3.5, 1),\n **kwags):\n \"\"\"\n \n\n Parameters\n ----------\n series : pandas timeseries\n Timeseries to be plotted.\n figsize : tuple, optional\n Size of figure. The default is (3.5, 1).\n **kwags : to be passed to plt.subplots.\n\n Returns\n -------\n plt : matplotlib plot\n\n \"\"\"\n\n fig, ax = plt.subplots(1,1,figsize=figsize,**kwags)\n series.reset_index().plot(ax=ax,linewidth=0.9)\n ax = remove_clutter(ax)\n return plt\n\ndef remove_clutter(ax):\n ax.legend()#_.remove()\n ax.legend_.remove()\n for k,v in ax.spines.items():\n v.set_visible(False)\n ax.tick_params(labelsize=5)\n ax.set_yticks([])\n #ax.set_xticks([])\n ax.xaxis.set_label_text('')\n plt.tight_layout()\n return ax\n\ndef html_plt(plt):\n \"\"\" Converts a matplotlib plot into an html image.\n \n Parameters\n ----------\n plt : matplotlib figure\n\n Returns\n -------\n html_plot : html image\n\n \"\"\"\n img = BytesIO()\n plt.savefig(img, transparent=True)\n plt.close()\n html_plot = ''.format(\n base64.b64encode(img.getvalue()).decode())\n return html_plot\n\ndef get_stats(df,\n measure='measure',\n aggregators=['code']):\n #1 calculate stats\n agg = df.groupby(aggregators).agg(['mean','std','skew'])[measure]\n kurtosis = df.groupby(aggregators).apply(pd.DataFrame.kurt)\n agg['kurtosis'] = kurtosis[measure]\n df = df.join(agg)\n #2 calculate the # of std deviations an entity is away from the mean\n df['z_score'] = (df[measure]-agg['mean'])/agg['std']\n #self['z_score'] = self['z_score'].abs() # change to absolute values\n df = df.dropna()\n return df\n\ndef dist_table(df, column, subset=None):\n if subset is not None:\n index = subset\n else:\n index = df.index\n series = pd.Series(index=index,name='plots')\n for idx in index:\n plot = dist_plot(df.loc[idx,column],\n df.loc[idx[0],column])\n series.loc[idx] = html_plt(plot)\n df = df.join(series, how='right')\n df = df.round(decimals=2)\n return HTML(df.to_html(escape=False))\n\ndef sparkline_table(df, column, subset=None):\n if subset is not None:\n index = subset\n else:\n index = df.index\n series = pd.Series(index=index,name='plots')\n for idx in index:\n plot = sparkline_plot(df.loc[idx,column])\n series.loc[idx] = html_plt(plot)\n df = df.join(series, how='right')\n df = df.round(decimals=2)\n series['one'] = 1\n return series\n", "sub_path": "outliers.py", "file_name": "outliers.py", "file_ext": "py", "file_size_in_byte": 4192, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.pyplot.rcdefaults", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "pandas.set_option", "line_number": 10, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 18, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "seaborn.kdeplot", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "base64.b64encode", "line_number": 106, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 114, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 128, "usage_type": "call"}, {"api_name": "IPython.display.HTML", "line_number": 135, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 142, "usage_type": "call"}]} +{"seq_id": "584312800", "text": "\"\"\"\n Definition of xNVMe Python Distribution Package\n\"\"\"\nimport codecs\nimport glob\nimport os\nfrom setuptools import setup\n\ndef read(*parts):\n \"\"\"Read parts to use a e.g. long_description\"\"\"\n\n here = os.path.abspath(os.path.dirname(__file__))\n\n # intentionally *not* adding an encoding option to open, See:\n # https://github.com/pypa/virtualenv/issues/201#issuecomment-3145690\n with codecs.open(os.path.join(here, *parts), 'r') as pfp:\n return pfp.read()\n\nsetup(\n name=\"pyxnvme\",\n version=\"0.0.12\",\n description=\"xNVMe: cross-platform libraries and tools for NVMe devices\",\n long_description=read('README.rst'),\n author=\"Simon A. F. Lund\",\n author_email=\"simon.lund@samsung.com\",\n# url=\"https://github.com/xnvme/xnvme\",\n license=\"Apache License 2.0\",\n install_requires=[],\n zip_safe=False,\n packages=[\"xnvme\"],\n# package_dir={\"\": \"modules\"},\n data_files=[\n (\"bin\", glob.glob(\"bin/*\")),\n ],\n options={'bdist_wheel':{'universal':True}},\n classifiers=[\n \"Development Status :: 4 - Beta\",\n \"Environment :: Console\",\n \"Intended Audience :: Developers\",\n \"Intended Audience :: System Administrators\",\n \"License :: OSI Approved :: Apache Software License\",\n \"Programming Language :: Python\",\n \"Topic :: Utilities\",\n \"Topic :: Software Development\",\n \"Topic :: Software Development :: Testing\"\n ],\n)\n", "sub_path": "pyxnvme/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1439, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.abspath", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "codecs.open", "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": "setuptools.setup", "line_number": 19, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "570483727", "text": "import click\nfrom datetime import datetime, timedelta, date\nfrom dateutil.relativedelta import relativedelta\n\nfrom mla_bilat_agreements import mla_bilat_agreements\n\n\ndef clean_list(organisations, org_list):\n # Extract the comma-separated organisation symbols and removed\n # leading and trailing spaces (strip function)\n orgs = [item.strip() for item in list(set(organisations.split(',')))]\n if len(orgs) < 2:\n raise click.BadOptionUsage(\n option_name=\"orgs\",\n message=\"A comma separated list of at least two \"\n \"organisations must be provided with option --orgs.\")\n orgs = list(set(orgs))\n for org in orgs:\n if not(org in org_list.keys()):\n raise click.BadOptionUsage(\n option_name=\"orgs\",\n message=\"'{:}' is not in the list of \"\n \"the participating organisations.\"\n .format(org))\n return(orgs)\n\n\ndef all_dates(from_, to_):\n dates = []\n delta = to_ - from_\n for d in range(delta.days + 1):\n day = from_ + timedelta(days=d)\n dates.append(day)\n return dates\n\n\ndef add_years(dates, years):\n new_dates = []\n for d in dates:\n d = d + relativedelta(years=years)\n new_dates.append(d)\n return new_dates\n\n\ndef select_possible_dates(dates, from_, to_):\n date_list = []\n for d in dates:\n morning = datetime.combine(d, datetime.min.time())\n evening = datetime.combine(d, datetime.max.time())\n if (morning >= from_) and (evening <= to_):\n date_list.append(d)\n return(date_list)\n\n\ndef gen_start_dates(from_, months):\n date_list = []\n for x in range(months):\n date_list.append(datetime(from_.year, x+1, 1))\n if (from_.day == 1):\n from__month = from_.month\n else:\n from__month = from_.month + 1\n start_date_list = []\n for d in date_list:\n d = d + relativedelta(months=from__month - 1)\n start_date_list.append(d)\n return start_date_list\n\n\ndef bilateral_agreement(lead, partner, submission_dates):\n \"\"\"Checks if there is a collaboration agreement between the two\n organisations 'lead' and 'partner' in the dates included in\n the 'submission_dates' array. If there is an agreement, checks\n if the identified submission dates are covered by the agreement.\"\"\"\n for agreement in mla_bilat_agreements:\n if (lead in agreement['orgs']) and (partner in agreement['orgs']):\n possible_dates = []\n for d in submission_dates:\n if d >= agreement['from'] and d <= agreement['to']:\n possible_dates.append(d)\n return {'agreement': True, 'possible_dates': possible_dates}\n return {'agreement': False, 'possible_dates': []}\n\n\ndef add_comment(comment, new_string):\n if (comment != ''):\n comment += '\\n'\n return comment + new_string\n", "sub_path": "mla_tools.py", "file_name": "mla_tools.py", "file_ext": "py", "file_size_in_byte": 2910, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "click.BadOptionUsage", "line_number": 13, "usage_type": "call"}, {"api_name": "click.BadOptionUsage", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 32, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.datetime.combine", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 48, "usage_type": "name"}, {"api_name": "datetime.datetime.min.time", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime.min", "line_number": 48, "usage_type": "attribute"}, {"api_name": "datetime.datetime.combine", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 49, "usage_type": "name"}, {"api_name": "datetime.datetime.max.time", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.datetime.max", "line_number": 49, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 58, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 65, "usage_type": "call"}, {"api_name": "mla_bilat_agreements.mla_bilat_agreements", "line_number": 75, "usage_type": "name"}]} +{"seq_id": "120866639", "text": "# -*- coding: utf-8 -*-\nimport numpy as np\n\ntry: range=xrange\nexcept: pass\n\ndef transform_coordinates(x, y, epsg_in, epsg_out):\n \"\"\"\n Transform between any coordinate system.\n\n Requires pyproj\n \"\"\"\n import pyproj\n proj_in = pyproj.Proj(\"+init=EPSG:\"+str(epsg_in))\n proj_out = pyproj.Proj(\"+init=EPSG:\"+str(epsg_out))\n return pyproj.transform(proj_in, proj_out, x, y)\n\n\ndef trim(coords, data, extent, buffer_amount=0.0):\n \"\"\"\n Grid a smaller section of a large dataset taking into\n consideration transformations into various coordinate\n reference systems (CRS)\n \n Parameters\n ----------\n coords : geographical / projected coordinates\n data : values corresponding to coordinates\n extent : box contained within the data\n buffer : amount of buffer to include (default=0.0)\n\n Returns\n -------\n coords_trim : trimmed coordinates\n data_trim : trimmed data array\n \"\"\"\n xmin, xmax, ymin, ymax = extent\n\n # Extract only the data within the extent\n data_mask = np.ones(data.shape[0], dtype=bool)\n\n # Add a 1 percent buffer zone\n x_buffer = buffer_amount*(xmax - xmin)\n y_buffer = buffer_amount*(ymax - ymin)\n\n mask_e = coords[:,0] < xmin - x_buffer\n mask_w = coords[:,0] > xmax + x_buffer\n mask_n = coords[:,1] < ymin - y_buffer\n mask_s = coords[:,1] > ymax + y_buffer\n data_mask[mask_n] = False\n data_mask[mask_s] = False\n data_mask[mask_e] = False\n data_mask[mask_w] = False\n \n data_trim = data[data_mask]\n coords_trim = coords[data_mask]\n\n return coords_trim, data_trim\n\n\n\ndef grid(coords, data, extent, shape=None, epsg_in=None, epsg_out=None, **kwargs):\n \"\"\"\n Grid a smaller section of a large dataset taking into\n consideration transformations into various coordinate\n reference systems (CRS)\n \n Parameters\n ----------\n coords : geographical coordinates\n data : values corresponding to coordinates\n extent : box contained within the data in espg_out\n coordinates\n shape : size of the box (nrows,ncols)\n : if None, shape is estimated from coords spacing\n epsg_in : CRS of data (if transformation is required)\n epsg_out : CRS of grid (if transformation is required)\n kwargs : keyword arguments to pass to griddata from\n : scipy.interpolate.griddata\n \n Returns\n -------\n grid : rectangular section of data bounded by extent\n \"\"\"\n from scipy.interpolate import griddata\n xmin, xmax, ymin, ymax = extent\n \n if type(epsg_in) != type(None):\n xt, yt = transform_coordinates(np.array([xmin, xmin, xmax, xmax]),\\\n np.array([ymin, ymax, ymin, ymax]),\\\n epsg_out, epsg_in)\n # find the coordinates that will completely\n # engulf the extent\n xtmin, xtmax = min(xt), max(xt)\n ytmin, ytmax = min(yt), max(yt)\n else:\n xtmin, xtmax = xmin, xmax\n ytmin, ytmax = ymin, ymax\n\n xtextent = [xtmin, xtmax, ytmin, ytmax]\n\n # trim data - buffer = 1%\n coords_trim, data_trim = trim(coords, data, xtextent, 0.01)\n\n\n if type(epsg_in) != type(None):\n # convert back to output CRS\n xtrim, ytrim = transform_coordinates(coords_trim[:,0],\\\n coords_trim[:,1],\\\n epsg_in, epsg_out)\n coords_trim = np.column_stack([xtrim, ytrim])\n\n\n if shape == None:\n # estimate based on the data spacing\n xunique = np.unique(coords_trim[:,0])\n yunique = np.unique(coords_trim[:,1])\n dx = np.diff(xunique).mean()\n dy = np.diff(yunique).mean()\n nc = int((xtmax - xtmin)/dx)\n nr = int((ytmax - ytmin)/dy)\n print(\"using nrows={}, ncols={} with cell spacing of {}\".format(nr,nc,(dy,dx)))\n else:\n nr, nc = shape\n\n # interpolate\n\n xcoords = np.linspace(xmin, xmax, nc)\n ycoords = np.linspace(ymin, ymax, nr)\n xq, yq = np.meshgrid(xcoords, ycoords)\n\n vq = griddata(coords_trim, data_trim, (xq, yq), **kwargs)\n return vq\n\n\ndef optimise_surfaces(surface1, surface2, sigma):\n \"\"\"\n Optimise the misfit between surface1 and surface2\n\n surface1 and surface2 are normalised between 0 and 1\n and their residual is minimised, weighted by sigma\n\n Parameters\n ----------\n surface1 : starting surface (can be flat)\n surface2 : surface to match to\n sigma : uncertainty of fitting coefficients\n\n Returns\n -------\n surface3 : optimised surface\n\n Notes\n -----\n The Krylov method uses a Krylov approximation for the\n inverse Jacobian as it is suitable for large problems\n \"\"\"\n from scipy.optimize import root\n \n def objective_function(x, x0, sigma_x0):\n return (x - x0)**2/sigma_x0**2\n \n sigma = sigma.ravel()\n \n s1 = surface1.flatten()\n s1 -= s1.min()\n s1 /= s1.max()\n \n s2 = surface2.flatten()\n s2 -= s2.min()\n s2 /= s2.max()\n \n # starting point should be at prior\n x0 = s1\n \n sol = root(objective_function, x0, method='krylov')\n return sol.x.reshape(surface1.shape)\n", "sub_path": "pycurious/mapping.py", "file_name": "mapping.py", "file_ext": "py", "file_size_in_byte": 5245, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pyproj.Proj", "line_number": 14, "usage_type": "call"}, {"api_name": "pyproj.Proj", "line_number": 15, "usage_type": "call"}, {"api_name": "pyproj.transform", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 130, "usage_type": "call"}, {"api_name": "scipy.interpolate.griddata", "line_number": 132, "usage_type": "call"}, {"api_name": "scipy.optimize.root", "line_number": 176, "usage_type": "call"}]} +{"seq_id": "130187454", "text": "from pygame.locals import *\nimport pygame\nimport sys\n\nRED = (255, 0, 0)\nGREEN = (0, 255, 0)\nORANGE = (233, 163, 38)\nWHITE = (255, 255, 255)\nBLACK = (0, 0, 0)\nWINDOW_HEIGHT = 800\nWINDOW_WIDTH = 800\n\n\ndef drawGrid(maze):\n blockSize = 80\n for x in range(0, 800, blockSize):\n for y in range(0, 800, blockSize):\n rect = pygame.Rect(x, y, blockSize, blockSize)\n if maze[y//80][x//80] == 1:\n SCREEN.fill(RED, rect)\n elif maze[y//80][x//80] == 2:\n SCREEN.fill(ORANGE, rect)\n else:\n pygame.draw.rect(SCREEN, BLACK, rect, 1)\n\n\ndef drawAnswerGrid(maze, path):\n blockSize = 80\n for x in range(0, 800, blockSize):\n for y in range(0, 800, blockSize):\n rect = pygame.Rect(x, y, blockSize, blockSize)\n if maze[y//80][x//80] == 1:\n SCREEN.fill(RED, rect)\n else:\n pygame.draw.rect(SCREEN, BLACK, rect, 1)\n for t in path:\n rect = pygame.Rect(t[1] * 80, t[0] * 80, blockSize, blockSize)\n SCREEN.fill(GREEN, rect)\n\n\n# Calculate the shortest path\n\n\nclass Node():\n \"\"\"A node class for A* Pathfinding\"\"\"\n\n def __init__(self, parent=None, position=None):\n self.parent = parent\n self.position = position\n\n self.g = 0\n self.h = 0\n self.f = 0\n\n def __eq__(self, other):\n return self.position == other.position\n\n\ndef astar(maze, start, end):\n start_node = Node(None, start)\n start_node.g = start_node.h = start_node.f = 0\n end_node = Node(None, end)\n end_node.g = end_node.h = end_node.f = 0\n\n # Initialize both open and closed list\n open_list = []\n closed_list = []\n\n # Add the start node\n open_list.append(start_node)\n\n while len(open_list) > 0:\n\n # Get the current node\n current_node = open_list[0]\n current_index = 0\n for index, item in enumerate(open_list):\n if item.f < current_node.f:\n current_node = item\n current_index = index\n\n # Pop current off open list, add to closed list\n open_list.pop(current_index)\n closed_list.append(current_node)\n\n # Found the goal\n if current_node == end_node:\n path = []\n current = current_node\n while current is not None:\n path.append(current.position)\n current = current.parent\n return path[::-1] # Return reversed path\n\n # Generate children\n children = []\n # Adjacent squares\n for new_position in [(0, -1), (0, 1), (-1, 0), (1, 0), (-1, -1), (-1, 1), (1, -1), (1, 1)]:\n\n # Get node position\n node_position = (\n current_node.position[0] + new_position[0], current_node.position[1] + new_position[1])\n\n # Make sure within range\n if node_position[0] > (len(maze) - 1) or node_position[0] < 0 or node_position[1] > (len(maze[len(maze)-1]) - 1) or node_position[1] < 0:\n continue\n\n # Make sure walkable terrain\n if maze[node_position[0]][node_position[1]] != 0:\n continue\n\n # Create new node\n new_node = Node(current_node, node_position)\n\n # Append\n children.append(new_node)\n\n # Loop through children\n for child in children:\n\n # Child is on the closed list\n for closed_child in closed_list:\n if child == closed_child:\n continue\n\n # Create the f, g, and h values\n child.g = current_node.g + 1\n child.h = ((child.position[0] - end_node.position[0]) **\n 2) + ((child.position[1] - end_node.position[1]) ** 2)\n child.f = child.g + child.h\n\n # Child is already in the open list\n for open_node in open_list:\n if child == open_node and child.g > open_node.g:\n continue\n\n # Add the child to the open list\n open_list.append(child)\n\n\nif __name__ == \"__main__\":\n maze = [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]\n pygame.init()\n SCREEN = pygame.display.set_mode((WINDOW_WIDTH, WINDOW_HEIGHT))\n s_e_position = []\n while True:\n SCREEN.fill(WHITE)\n drawGrid(maze)\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n sys.exit()\n\n elif event.type == pygame.MOUSEBUTTONDOWN:\n if event.button == 3:\n x, y = pygame.mouse.get_pos()\n for t in range(0, 800, 80):\n for u in range(0, 800, 80):\n if x > t and x <= t+80 and y > u and y <= u+80:\n maze[u//80][t//80] = 1\n\n if event.button == 1:\n x, y = pygame.mouse.get_pos()\n for t in range(0, 800, 80):\n for u in range(0, 800, 80):\n if x > t and x <= t+80 and y > u and y <= u+80:\n maze[u//80][t//80] = 2\n s_e_position.append([u//80, t//80])\n\n if len(s_e_position) == 2:\n start = tuple(s_e_position[0])\n end = tuple(s_e_position[1])\n\n maze[start[0]][start[1]] = 0\n maze[end[0]][end[1]] = 0\n path = astar(maze, start, end)\n drawAnswerGrid(maze, path)\n\n pygame.display.update()\n\"\"\"\n for i in range(10):\n for j in range(10):\n if maze[i][j] == 2:\n if count == 1:\n start = (i, j)\n print(1)\n if count == 2:\n end = (i, j)\n maze[start[0]][start[1]] = 0\n maze[end[0]][end[1]] = 0\n print(maze)\n path = astar(maze, start, end)\n drawAnswerGrid(maze, path)\n print(path)\n break\n\"\"\"\n", "sub_path": "scripts/maze.py", "file_name": "maze.py", "file_ext": "py", "file_size_in_byte": 6536, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pygame.Rect", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 35, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 35, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.init", "line_number": 152, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 153, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 153, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 158, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 158, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 159, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 160, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 161, "usage_type": "call"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 163, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 165, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 165, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 172, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 172, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 188, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 188, "usage_type": "attribute"}]} +{"seq_id": "314178648", "text": "from wtforms.validators import ValidationError\n\nfrom app import db_session\n\nclass Unique(object):\n def __init__(self, model, field, message=None):\n self.model = model\n self.field = field\n if not message:\n message = u'%s exists already' % (field)\n self.message = message\n\n def __call__(self, form, field):\n check = db_session.query(self.model).filter(self.field == field.data).first()\n if 'id' in form:\n id = form.id.data\n else:\n id = None\n if check and (id is None or id != check.id):\n raise ValidationError(self.message)\n", "sub_path": "app/common/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 628, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "app.db_session.query", "line_number": 14, "usage_type": "call"}, {"api_name": "app.db_session", "line_number": 14, "usage_type": "name"}, {"api_name": "wtforms.validators.ValidationError", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "444972004", "text": "from discord.ext import commands\nfrom BotUtils import REST, getAPIKey, escapeURL, isURL\nimport discord\nfrom datetime import datetime\n\nclass News(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n \n @commands.command(name='news')\n async def NewsAPI(self, ctx, *, query):\n \"\"\"Gets news\"\"\"\n data = await REST(f\"http://newsapi.org/v2/everything?qInTitle={escapeURL(query)}&from={datetime.now().strftime('%Y-%m-%d')}&sortBy=popularity&pageSize=1&apiKey={getAPIKey('newsapi')}\")\n\n if len(data['articles']) == 0:\n await ctx.reply('No articles found.')\n return\n data = data['articles'][0]\n embed = discord.Embed(colour=0xf5c518)\n embed.title = data['title']\n embed.url = data['url']\n embed.description = data['description']\n embed.timestamp = datetime.fromisoformat(data['publishedAt'].replace('Z',''))\n if 'urlToImage' in data and isURL(str(data['urlToImage'])):\n embed.set_image(url=data['urlToImage'])\n embed.set_footer(text=f\"{data['source']['name']} | Written by {data['author']}. Published \")\n await ctx.reply(embed=embed)\n\ndef setup(bot):\n bot.add_cog(News(bot))\n", "sub_path": "cogs/apis/news.py", "file_name": "news.py", "file_ext": "py", "file_size_in_byte": 1208, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 6, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 6, "usage_type": "name"}, {"api_name": "BotUtils.REST", "line_number": 13, "usage_type": "call"}, {"api_name": "BotUtils.escapeURL", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "name"}, {"api_name": "BotUtils.getAPIKey", "line_number": 13, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime.fromisoformat", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "name"}, {"api_name": "BotUtils.isURL", "line_number": 24, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 10, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "387474424", "text": "from flask import Flask, render_template, url_for, abort, request\nfrom queue_model import *\nimport logging\nimport os\nimport time\n\nconfig = configparser.ConfigParser()\nconfig.read(\"config.ini\")\nport = config['Server']['port']\ndebug = config['Server']['debug'].strip() == 'True'\nname = config['Server']['name']\n\napp = Flask(name)\n\n@app.route('/')\ndef index():\n s = QueueModel.select()\n return render_template('index.html', Camera1=s[0].id, Camera2=s[1].id, Camera3=s[3].id,\n Number1=s[0].number_of_people, Number2=s[1].number_of_people,\n Number3=s[3].number_of_people)\n\n#Возвратит кол-во людей в данной очереди\n@app.route('/home/PeopleNumber/', methods=['GET'])\ndef getNearshop(number):\n for i in QueueModel.select():\n if number == i.id:\n amount = i.number_of_people\n\n return(f\"В данной очереди в настоящий момент находится {amount} человек\")\n\nif __name__ == '__main__':\n app.run(port=port, debug=debug, host='0.0.0.0')", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1093, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Flask", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "466108956", "text": "import numpy as np\nimport math\nfrom collections import defaultdict\n\n\nclass Point(object):\n \"\"\"docstring for Point\"\"\"\n def __init__(self, x,y):\n super(Point, self).__init__()\n self.x = x\n self.y = y\n\n def dist(self, other):\n assert isinstance(other, Point)\n \n xd = self.x - other.x\n yd = self.y - other.y\n return math.sqrt( xd**2 + yd**2 )\n\n def __str__(self):\n return \"{},{}\".format(self.x, self.y)\n\n @classmethod\n def from_str(cls, s):\n x,y = map(int, s.split(','))\n return cls(x,y)\n \n\ndef precision_recall(pred_dict, truth_dict, R, N_classes=4):\n \"\"\" returns a N_classes x 3 matrix where the rows correspond to classes\n and the cols correspond to the counts of [correct, predicted, actual]\n \"\"\"\n output = np.zeros((N_classes, 3))\n\n # count true\n for label in truth_dict.values():\n output[label,2] += 1\n\n # count predicted\n for label in pred_dict.values():\n output[label,1] += 1\n\n # load predictions into dict by label\n predictions_by_label = defaultdict(set)\n for coords, label in pred_dict.iteritems():\n pt = Point.from_str(coords)\n predictions_by_label[label].add(pt)\n\n # count correct\n # iterate over true points\n # for each, see if there's a nearby\n # prediction that matches the label\n for coords, label in truth_dict.iteritems():\n pt = Point.from_str(coords)\n\n #print \"Looking for {} near {}\".format(label, pt)\n\n for pred in predictions_by_label[label]:\n #print \"\\tTrying {}\\t{:07.1} away\".format(pred, pt.dist(pred))\n if pt.dist(pred) <= R:\n #print \"Found a correct\"\n output[label,0] += 1\n break\n\n return output\n\ndef partition_based_on_correctness(pred_dict, truth_dict, R):\n ''' given two dictionaries of points, one true, and one predicted,\n this function partitions the predicted points into \"correct\"\n and \"incorrect\" sets for each class.\n '''\n gt_by_label = defaultdict(set)\n for coords, label in truth_dict.iteritems():\n pt = Point.from_str(coords)\n gt_by_label[label].add(pt)\n\n cor_preds_by_label = defaultdict(set)\n inc_preds_by_label = defaultdict(set)\n for coords, label in pred_dict.iteritems():\n pt = Point.from_str(coords)\n\n for truth in gt_by_label[label]:\n if truth.dist(pt) < R:\n cor_preds_by_label[label].add(pt)\n continue\n if pt not in cor_preds_by_label[label]:\n inc_preds_by_label[label].add(pt)\n\n correct = {}\n incorrect = {}\n for label in cor_preds_by_label:\n for pt in cor_preds_by_label[label]:\n correct[str(pt)] = label\n\n for label in inc_preds_by_label:\n for pt in inc_preds_by_label[label]:\n incorrect[str(pt)] = label\n\n return correct, incorrect\n", "sub_path": "old/precision_recall.py", "file_name": "precision_recall.py", "file_ext": "py", "file_size_in_byte": 2931, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "math.sqrt", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 33, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 44, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 72, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 77, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "78733926", "text": "import bpy\nfrom bpy.props import BoolProperty\n\n\n\ndef set_active_tool(tool_name):\n for area in bpy.context.screen.areas:\n if area.type == \"VIEW_3D\":\n override = bpy.context.copy()\n override[\"space_data\"] = area.spaces[0]\n override[\"area\"] = area\n bpy.ops.wm.tool_set_by_id(override, name=tool_name)\n \n\ndef TglCursor(oStartSnap,oEndSnap,oShowCursor):\n Scene = bpy.data.scenes['Scene']\n Snap = Scene.tool_settings.use_snap\n SnapElement = Scene.tool_settings.snap_elements\n Tra = Scene.transform_orientation_slots[0].type\n Pivot = Scene.tool_settings.transform_pivot_point\n ovl = bpy.context.space_data.overlay\n\n if Tra != 'CURSOR':\n set_active_tool(\"builtin.cursor\")\n Scene.transform_orientation_slots[0].type = 'CURSOR'\n ovl.show_cursor = True\n Scene.tool_settings.use_snap = oStartSnap\n Scene.tool_settings.snap_elements = {'VERTEX'}\n Scene.tool_settings.transform_pivot_point = 'CURSOR'\n else:\n Scene.transform_orientation_slots[0].type = 'GLOBAL'\n Scene.tool_settings.use_snap = oEndSnap\n Scene.tool_settings.transform_pivot_point = 'BOUNDING_BOX_CENTER'\n ovl.show_cursor = oShowCursor\n\nclass tglPivot_OT_object(bpy.types.Operator):\n bl_idname = \"view3d.toggle_pivot_mode\"\n bl_label = \"toggle pivot mode\"\n bl_description = \"toggle pivot mode\"\n bl_options = {'REGISTER', 'UNDO'} \n\n bSnap = BoolProperty(default=False, name = \"start Snap\", description = \"Corser Active Snap\")\n eSnap = BoolProperty(default=False, name = \"end Snap\", description = \"Finish Active Snap\")\n oShow = BoolProperty(default=True, name = \"Show Cursor\", description = \"Show cursor\")\n def execute(self, context,):\n\n TglCursor(self.bSnap,self.eSnap,self.oShow)\n\n return {'FINISHED'}\n", "sub_path": "Tgl_Pivot.py", "file_name": "Tgl_Pivot.py", "file_ext": "py", "file_size_in_byte": 1841, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "bpy.context", "line_number": 7, "usage_type": "attribute"}, {"api_name": "bpy.context.copy", "line_number": 9, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 9, "usage_type": "attribute"}, {"api_name": "bpy.ops.wm.tool_set_by_id", "line_number": 12, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 12, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 16, "usage_type": "attribute"}, {"api_name": "bpy.context", "line_number": 21, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 36, "usage_type": "attribute"}, {"api_name": "bpy.props.BoolProperty", "line_number": 42, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 43, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "345183837", "text": "#!/usr/bin/env python3\n\n\"\"\" \nCreated on:\t July, 2020\n@uthor: \t adejonghm\n----------\n\nScript to Manage the JSON file.\n\"\"\"\n\nimport os\nimport json\nimport argparse\nimport libs.jilib as jm\n\n\nif __name__ == \"__main__\":\n\n ap = argparse.ArgumentParser()\n ap.add_argument(\"-f\", \"--file\", required=True, help=\"path to the input JSON file\")\n args = vars(ap.parse_args())\n\n if args['file'].endswith('.json') and os.path.exists(args['file']):\n input_path = args['file']\n else:\n print('ERROR! JSON file not found.')\n os.sys.exit(1)\n\n #### READ JSON FILE ####\n with open(input_path, encoding='utf-8') as file:\n inFile = json.load(file)\n\n outFile = jm.add_item_in_node(inFile, 1, \"backgrdImage\", \"background.jpg\")\n\n #### WRITE NEW JSON FILE ####\n with open('metadata.json', 'w', encoding='utf-8') as file:\n json.dump(outFile, file, indent=2)\n\n print('DONE!')\n", "sub_path": "SignalProcessing/json_manager.py", "file_name": "json_manager.py", "file_ext": "py", "file_size_in_byte": 911, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.sys.exit", "line_number": 27, "usage_type": "call"}, {"api_name": "os.sys", "line_number": 27, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 31, "usage_type": "call"}, {"api_name": "libs.jilib.add_item_in_node", "line_number": 33, "usage_type": "call"}, {"api_name": "libs.jilib", "line_number": 33, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "152977323", "text": "def start(score, game_id):\n #defining every library needed\n import sys\n import pygame\n import os\n import menu\n import astrodoge\n import spacestrike\n import SpaceBound\n import Stranded\n import sumo_smash\n import cheat_sheet\n import soundboard\n import highscore\n from time import sleep\n\n #setting variables\n FPS = 30\n X = 500\n Y = 100\n WHITE = (255, 255, 255)\n BLACK = (0, 0, 0)\n\n #setting the settings of pygame itself\n screen = pygame.display.set_mode((900,900))\n pygame.display.set_caption(\"Welp, you can always try again!\")\n font = pygame.font.Font('resource/fonts/Arcadepix.ttf', 40)\n clock = pygame.time.Clock()\n os.environ['SDL_VIDEO_WINDOW_POS'] = \"%d,%d\" % (X,Y)\n define_location = \"main_menu\"\n \n #initializing pygame's mixer\n # pygame.mixer.init()\n # pygame.mixer.music.load(\"resource/music/main_menu/main_menu.ogg\")\n # pygame.mixer.music.play(-1)\n\n #initiate a image loader\n def load_image(name):\n image = pygame.image.load(name)\n return image\n \n #create a sprite class to animate \n class animated_select_planet(pygame.sprite.Sprite):\n def __init__(self):\n super(animated_select_planet, self).__init__()\n self.images = []\n self.images.append(load_image('resource/images/game_over/tekst/game_over_1.png'))\n self.images.append(load_image('resource/images/game_over/tekst/game_over_2.png'))\n self.images.append(load_image('resource/images/game_over/tekst/game_over_3.png'))\n self.images.append(load_image('resource/images/game_over/tekst/game_over_4.png'))\n self.images.append(load_image('resource/images/game_over/tekst/game_over_5.png'))\n self.images.append(load_image('resource/images/game_over/tekst/game_over_6.png'))\n self.images.append(load_image('resource/images/game_over/tekst/game_over_7.png'))\n self.index = 0\n self.image = self.images[self.index]\n self.rect = pygame.Rect(25, 130, 250, 80)\n\n def update(self):\n self.index += 1\n sleep(0.1)\n if self.index >= len(self.images):\n self.index = 0\n count = 0\n self.image = self.images[self.index]\n\n #start the main menu board.\n def start_game_over():\n #set background\n background = pygame.image.load('resource/images/game_over/game_overbg.png').convert()\n background_rect = background.get_rect()\n screen.blit(background, background_rect)\n buttons_game_over()\n #game over buttons ready\n def buttons_game_over():\n quit_button = pygame.image.load('resource/images/game_over/button_quit.png').convert()\n retry_button = pygame.image.load('resource/images/game_over/button_restart.png').convert()\n quit_rect = quit_button.get_rect()\n retry_rect = quit_button.get_rect()\n screen.blit(quit_button, (325,550))\n screen.blit(retry_button, (325,470))\n #score text to screen\n def text_score(score, highscore): \n scoretext = font.render(\"Your Score \", 1, WHITE)\n score = font.render(\" {0}\".format(score), 1, WHITE)\n scorehightext = font.render(\"Your highscore \", 1, WHITE)\n scorehigh = font.render(\" {0}\".format(highscore), 1, WHITE)\n screen.blit(scoretext, (250, 385))\n screen.blit(score, (250, 415))\n screen.blit(scorehightext, (500, 385))\n screen.blit(scorehigh, (500, 415))\n\n\n\n #beginning of the main loop\n main_loop = True\n soundboard.game_over(score) \n my_sprite = animated_select_planet()\n my_group = pygame.sprite.Group(my_sprite)\n highscore.save(score, game_id)\n \n while main_loop:\n #reset the screen and set screen image's\n screen.fill(BLACK)\n clock.tick(FPS)\n start_game_over()\n my_group.update()\n my_group.draw(screen)\n highscorer = highscore.read(game_id)\n text_score(score, highscorer)\n pygame.display.flip()\n\n #check events\n for evento in pygame.event.get():\n #define event's of quiting the game.\n if evento.type == pygame.QUIT:\n pygame.quit()\n quit()\n #printing every event that's happening within the python script.\n print(evento)\n #Catch mouse position and if it's pressed on the button\n if evento.type == pygame.MOUSEBUTTONDOWN:\n if pygame.mouse.get_pos()[0] >= 325 and pygame.mouse.get_pos()[1] >= 550:\n if pygame.mouse.get_pos()[0] <= 593 and pygame.mouse.get_pos()[1] <= 615:\n menu.start_menu()\n if pygame.mouse.get_pos()[0] >= 315 and pygame.mouse.get_pos()[1] >= 470:\n if pygame.mouse.get_pos()[0] <= 593 and pygame.mouse.get_pos()[1] <= 535:\n cheat_sheet.start(game_id)\n \n pygame.quit()\n quit()\n", "sub_path": "game_over.py", "file_name": "game_over.py", "file_ext": "py", "file_size_in_byte": 5070, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pygame.display.set_mode", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 26, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 27, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 39, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 56, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 60, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 69, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 75, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 75, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 76, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 76, "usage_type": "attribute"}, {"api_name": "soundboard.game_over", "line_number": 96, "usage_type": "call"}, {"api_name": "pygame.sprite.Group", "line_number": 98, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 98, "usage_type": "attribute"}, {"api_name": "highscore.save", "line_number": 99, "usage_type": "call"}, {"api_name": "highscore.read", "line_number": 108, "usage_type": "call"}, {"api_name": "pygame.display.flip", "line_number": 110, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 110, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 113, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 113, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 115, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 116, "usage_type": "call"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 121, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 122, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 122, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 123, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 123, "usage_type": "attribute"}, {"api_name": "menu.start_menu", "line_number": 124, "usage_type": "call"}, {"api_name": "pygame.mouse.get_pos", "line_number": 125, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 125, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 126, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 126, "usage_type": "attribute"}, {"api_name": "cheat_sheet.start", "line_number": 127, "usage_type": "call"}, {"api_name": "pygame.quit", "line_number": 129, "usage_type": "call"}]} +{"seq_id": "222037110", "text": "import scrapy\n\nclass GsocSpider(scrapy.Spider):\n name = 'gsoc'\n\n start_urls = [\n 'https://summerofcode.withgoogle.com/archive/2016/organizations/'\n ]\n\n def parse(self,response):\n base_link = \"https://summerofcode.withgoogle.com\"\n for org in response.xpath(\"//li[@class = 'organization-card__container']\"):\n link = org.xpath(\".//a/@href\").extract_first()\n org_link = base_link + link\n org_name = org.xpath(\".//h4/text()\").extract_first()\n org_image = org.xpath(\".//org-logo/@data\").extract_first()\n yield {\n 'link' : org_link,\n 'name' : org_name,\n 'image': org_image\n }", "sub_path": "gsoc_organizations/spiders/gsoc.py", "file_name": "gsoc.py", "file_ext": "py", "file_size_in_byte": 709, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "scrapy.Spider", "line_number": 3, "usage_type": "attribute"}]} +{"seq_id": "564913172", "text": "import requests\r\nfrom bs4 import BeautifulSoup\r\nfrom csv import DictWriter\r\nimport json\r\nfrom time import sleep\r\n\r\n\r\nurl = 'http://quotes.toscrape.com/'\r\n# Scrapes website for author's quotes, name, bio, and tags and writes into a text file.\r\ndef scrape_to_file(timer):\r\n # Receives user input for file name. We will use that file to record our scraped data.\r\n filename = get_filename() + '.txt'\r\n file = open(filename, 'w', encoding = \"utf-8\")\r\n \r\n # Scrapes from page one, the goal is to scrape until the last page.\r\n page_link = '/page/1'\r\n count = 1\r\n while page_link:\r\n response = requests.get(f'{url}{page_link}')\r\n soup = BeautifulSoup(response.text, 'html.parser')\r\n print(f'Now scraping {url}{page_link}...')\r\n # Searches for each relevant item on the page, and for each item: get it, add it into file.\r\n for each_quote in soup.find_all(class_='quote'):\r\n quote = each_quote.find(class_='text').get_text()\r\n author = each_quote.find(class_='author').get_text()\r\n bio_link = each_quote.find('a')['href']\r\n \r\n # Not every single quote has a tag. This allows our scraper to continue when a tag does not exist. \r\n try:\r\n tag = each_quote.find(class_='tag').get_text()\r\n except AttributeError:\r\n continue\r\n \r\n # Each biography is located in a different url. We will scrape that too.\r\n history = requests.get(f'{url}{bio_link}')\r\n history_soup = BeautifulSoup(history.text, 'html.parser')\r\n biography = history_soup.find(class_='author-description').get_text()\r\n author_born_date = history_soup.find(class_='author-born-date').get_text()\r\n author_born_location = history_soup.find(class_='author-born-location').get_text()\r\n \r\n # Formatting is arbitrary. This format returns 'quote' - 'author', 'born date' in 'location' 'tags':\r\n file.write(quote + ' - ' + author + ', born ' + author_born_date + \r\n ' ' + author_born_location + ' Tags: ' + tag + '\\n' +\r\n biography + '\\n \\n')\r\n \r\n # This print statement is to provide transparency for each action. Shows the program is running.\r\n # If this is annoying, remove this print statement.\r\n print('Scraping...')\r\n \r\n print(f'Page {count} Scraped! Data written to {filename}.') \r\n next_page = soup.find(class_='next')\r\n if next_page:\r\n page_link = next_page.find('a')['href']\r\n count += 1\r\n sleep(timer)\r\n else:\r\n page_link = False\r\n print(f'Website scraping complete! All data has been written to {filename}')\r\n file.close()\r\n \r\n\r\n# Receives the user's choice filename\r\ndef get_filename():\r\n # Make sure to double check, user!\r\n while True:\r\n choice = input('What would you like to name the file? ')\r\n while True:\r\n yes_or_no = input(f'Your filename will be {choice}. Is this what you want? Yes/No ').lower()\r\n if yes_or_no == 'yes' or yes_or_no == 'no':\r\n break\r\n if yes_or_no == 'yes':\r\n break\r\n return choice \r\n\r\n\r\n# Scrape the data into a csv or json file (up to the user)\r\ndef scrape_to_csv_or_json(format, timer):\r\n \r\n filename = get_filename()\r\n file_format = format\r\n complete_filename = filename + file_format\r\n page_link = '/page/1'\r\n count = 1\r\n data = []\r\n while page_link:\r\n response = requests.get(f'{url}{page_link}')\r\n soup = BeautifulSoup(response.text, 'html.parser')\r\n print(f'Now scraping {url}{page_link}...')\r\n # Searches for each relevant item on the page, and for each item: get it, add it into dictionary.\r\n for each_quote in soup.find_all(class_='quote'):\r\n \r\n bio_link = each_quote.find('a')['href']\r\n \r\n # Not every single quote has a tag. This allows our scraper to continue when a tag does not exist. \r\n try:\r\n tag = each_quote.find(class_='tag').get_text()\r\n except AttributeError:\r\n continue\r\n \r\n # Each biography is located in a different url. We will scrape that too.\r\n history = requests.get(f'{url}{bio_link}')\r\n history_soup = BeautifulSoup(history.text, 'html.parser')\r\n data.append({\r\n 'quote': each_quote.find(class_='text').get_text(),\r\n 'author': each_quote.find(class_='author').get_text(),\r\n 'tag': tag,\r\n 'biography': history_soup.find(class_='author-description').get_text(),\r\n 'date_born': history_soup.find(class_='author-born-date').get_text(),\r\n 'location_born': history_soup.find(class_='author-born-location').get_text()\r\n })\r\n\r\n print('Scraping...')\r\n # Continues to scrape until the last page\r\n print(f'Page {count} Scraped!')\r\n next_page = soup.find(class_='next')\r\n if next_page:\r\n page_link = next_page.find('a')['href']\r\n count += 1\r\n sleep(timer)\r\n else:\r\n page_link = False\r\n \r\n if file_format == '.csv': \r\n with open(complete_filename, 'w', encoding = \"utf-8\") as file:\r\n headers = ['quote', 'author', 'tag', 'biography', 'date_born', 'location_born']\r\n csv_writer = DictWriter(file, fieldnames = headers)\r\n csv_writer.writeheader()\r\n for quote in data:\r\n csv_writer.writerow(quote)\r\n print(f'Scraping completed! A .csv file, {complete_filename} has been created')\r\n \r\n elif file_format == '.json':\r\n with open(complete_filename, 'w', encoding = 'utf-8') as file:\r\n json.dump(data, file)\r\n print(f'Scraping completed! A .json file, {complete_filename} has been created')\r\n\r\n# Asks what file they want to save to. \r\ndef welcome():\r\n print('Welcome to my WebScraping project! You can save the data to a .txt file, .csv file, or .json file.')\r\n while True:\r\n choice = input('Take your pick: ').lower()\r\n if choice in 'json' or choice in 'csv' or choice in 'txt':\r\n break\r\n else:\r\n print('The choices are txt, csv, or json')\r\n return '.' + choice \r\n \r\n# Asks if the user wants to use the other 2 formats as well.\r\ndef again():\r\n while True:\r\n yes_or_no = input('Would you like to run the script again and try out the other two formats? ').lower()\r\n if yes_or_no == 'yes' or yes_or_no == 'no':\r\n break\r\n if yes_or_no == 'yes':\r\n return True\r\n else:\r\n return False\r\n\r\n# This will be passed into the sleep() function to determine how long you would like to wait inbetween pages. \r\ndef sleep_timer():\r\n while True:\r\n sleep = input('How long would you like to wait inbetween pages? ')\r\n try:\r\n val = int(sleep)\r\n break\r\n except ValueError:\r\n print('Please give an integer value!')\r\n return val\r\n\r\n# Main method. This is where we'll execute our code.\r\ndef main():\r\n while True:\r\n pick = welcome()\r\n sleep_time = sleep_timer()\r\n if pick in '.txt':\r\n scrape_to_file(sleep_time)\r\n elif pick in '.csv' or pick in '.json':\r\n scrape_to_csv_or_json(pick, sleep_time)\r\n if not again():\r\n break\r\n print('Thanks for using my work.')\r\n \r\n \r\nif __name__ == '__main__':\r\n main()", "sub_path": "WebScrape.py", "file_name": "WebScrape.py", "file_ext": "py", "file_size_in_byte": 7723, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.get", "line_number": 19, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 20, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 35, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 36, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 86, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 87, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 101, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 102, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 119, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 126, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 134, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 162, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 164, "usage_type": "argument"}]} +{"seq_id": "275008350", "text": "#!/usr/bin/env python\n\nimport os\nimport rospy\nimport yaml\nfrom omron_cad_matching.util import *\nfrom omron_cad_matching.train_client import *\n\nclass TrainMultiNode(TrainClient):\n def __init__(self):\n super(TrainMultiNode, self).__init__()\n\n # file settings\n cad_dir = rospy.get_param(\"~cad_dir\")\n conf_dir = rospy.get_param(\"~conf_dir\")\n parts_list = rospy.get_param(\"~parts_list\")\n setting_filename = rospy.get_param(\"~camera_setting_filename\")\n model_dir = rospy.get_param(\"~model_dir\")\n model_name = rospy.get_param(\"~model_name\")\n\n # object search param\n min_dist = rospy.get_param(\"~train_setting/min_dist\")\n max_dist = rospy.get_param(\"~train_setting/max_dist\")\n thread_num = rospy.get_param(\"~train_setting/thread_num\")\n\n # create client of omron cad matching training service server\n self.init_model()\n\n # camera setting\n camera_setting = read_camera_setting_yaml(setting_filename)\n\n # for each parts\n id_map = dict()\n object_id = 0\n for parts in parts_list:\n # read object config\n conf_filename = os.path.join(conf_dir, parts + \".yaml\")\n rospy.loginfo(\"read object config. file = %s\", conf_filename)\n obj_conf = read_object_config_yaml(conf_filename)\n search_setting = get_search_setting(obj_conf, min_dist, max_dist, thread_num)\n\n # train\n cad_filename = os.path.join(cad_dir, parts + \".stl\")\n rospy.loginfo(\"train. cad file = %s\", cad_filename)\n res = self.train_model(cad_filename, camera_setting, search_setting)\n if res.model_id >= 0:\n id_map[obj_conf.object_id] = res.model_id\n\n # save trained model that include template of all parts into a file\n data_filename = os.path.join(model_dir, model_name + \".dat\")\n text_filename = os.path.join(model_dir, model_name + \"_train.txt\")\n rospy.loginfo(\"save model. file = %s\", data_filename)\n self.save_model(data_filename, text_filename)\n\n # save map from object_id to model_id to yaml file \n id_filename = os.path.join(model_dir, model_name + \".yaml\")\n data = dict(id_map = id_map)\n with open(id_filename, 'w') as outfile:\n yaml.dump(data, outfile, default_flow_style=False)\n rospy.loginfo(\"id_map from object_id to model_id = \" + str(id_map))\n\nif __name__ == \"__main__\":\n rospy.init_node('train_multi', anonymous=True, log_level=rospy.INFO)\n node = TrainMultiNode()\n", "sub_path": "catkin_ws/src/omron_cad_matching/scripts/train_multi.py", "file_name": "train_multi.py", "file_ext": "py", "file_size_in_byte": 2664, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "rospy.get_param", "line_number": 14, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 15, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 16, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 17, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 18, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 19, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 22, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 23, "usage_type": "call"}, {"api_name": "rospy.get_param", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "rospy.loginfo", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "rospy.loginfo", "line_number": 44, "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": "os.path.join", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "rospy.loginfo", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "yaml.dump", "line_number": 59, "usage_type": "call"}, {"api_name": "rospy.loginfo", "line_number": 60, "usage_type": "call"}, {"api_name": "rospy.init_node", "line_number": 63, "usage_type": "call"}, {"api_name": "rospy.INFO", "line_number": 63, "usage_type": "attribute"}]} +{"seq_id": "530830767", "text": "\nimport random\nimport sys\nimport time\n\nimport http.client\nimport tornado.escape\n\nfrom PyQt4 import QtGui, QtCore\n\nimport playhouse\n\n\nbuttons = [[None] * 3 for _ in range(3)]\n\nbuffer = []\ndef set_state(x, y, **args):\n global buffer\n buffer += [{'x':x, 'y':y, 'change':args}]\n\ndef commit():\n global buffer\n conn = http.client.HTTPConnection(\"localhost:4711\")\n conn.request(\"POST\", \"/lights\", tornado.escape.json_encode(buffer))\n buffer = []\n\ndef main():\n for i in range(3):\n for j in range(3):\n set_state(i, j, sat=0, hue=0, bri=0)\n commit()\n \n app = QtGui.QApplication(sys.argv)\n \n window = QtGui.QMainWindow()\n window.setWindowTitle(\"Tic tac toe\")\n \n widget = QtGui.QWidget()\n widget.setStyleSheet(\"QPushButton { color: black }\")\n layout = QtGui.QGridLayout()\n \n for row in range(3):\n for column in range(3):\n def clicked(row, column):\n def action():\n do_turn(column, row)\n return action\n \n button = QtGui.QPushButton(\"{}:{}\".format(row, column))\n button.setSizePolicy(QtGui.QSizePolicy.Expanding, QtGui.QSizePolicy.Expanding)\n button.clicked.connect(clicked(row, column))\n button.setStyleSheet(\"QPushButton { background-color: white }\")\n buttons[row][column] = button\n layout.addWidget(button, row, column)\n \n widget.setLayout(layout)\n window.setCentralWidget(widget)\n \n window.show()\n \n app.exec()\n\n\n# ====== #\n\nplayer = 0\ncolors = [0, 45000]\nbutton_colors = [\"red\", \"blue\"]\n\nboard = [[-1, -1, -1],\n [-1, -1, -1],\n [-1, -1, -1]]\n\ntimer_running = False\n\ndef reset():\n global player, board, timer_running\n timer_running = False\n \n for i in range(3):\n for j in range(3):\n set_state(i, j, hue=0, sat=0)\n buttons[j][i].setStyleSheet(\"QPushButton { background-color: white }\")\n commit()\n board = [[-1, -1, -1],\n [-1, -1, -1],\n [-1, -1, -1]]\n player = 0\n\ndef do_turn(x, y):\n global player, timer_running\n if board[y][x] != -1 or timer_running:\n return\n board[y][x] = player\n set_state(x, y, hue=colors[player], sat=255)\n commit()\n buttons[y][x].setStyleSheet(\"QPushButton {{ background-color: {} }}\".format(button_colors[player]))\n \n winner_lamps = set()\n for configuration in [[(y, 0), (y, 1), (y, 2)],\n [(0, x), (1, x), (2, x)],\n [(0, 0), (1, 1), (2, 2)],\n [(0, 2), (1, 1), (2, 0)]]:\n if all(board[i][j] == player for i, j in configuration):\n winner_lamps.update(configuration)\n \n if len(winner_lamps) > 0:\n def set_alert():\n for i, j in winner_lamps:\n set_state(j, i, alert=\"lselect\")\n commit()\n QtCore.QTimer.singleShot(500, set_alert)\n timer_running = True\n QtCore.QTimer.singleShot(5000, reset)\n return\n if all(all(i != -1 for i in j) for j in board):\n reset()\n return\n \n player = 1 - player\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "src/tictactoe.py", "file_name": "tictactoe.py", "file_ext": "py", "file_size_in_byte": 3190, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "http.client.client.HTTPConnection", "line_number": 23, "usage_type": "call"}, {"api_name": "http.client.client", "line_number": 23, "usage_type": "attribute"}, {"api_name": "http.client", "line_number": 23, "usage_type": "name"}, {"api_name": "tornado.escape.escape.json_encode", "line_number": 24, "usage_type": "call"}, {"api_name": "tornado.escape.escape", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tornado.escape", "line_number": 24, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QApplication", "line_number": 33, "usage_type": "call"}, {"api_name": "PyQt4.QtGui", "line_number": 33, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 33, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui.QMainWindow", "line_number": 35, "usage_type": "call"}, {"api_name": "PyQt4.QtGui", "line_number": 35, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QWidget", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt4.QtGui", "line_number": 38, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QGridLayout", "line_number": 40, "usage_type": "call"}, {"api_name": "PyQt4.QtGui", "line_number": 40, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QPushButton", "line_number": 49, "usage_type": "call"}, {"api_name": "PyQt4.QtGui", "line_number": 49, "usage_type": "name"}, {"api_name": "PyQt4.QtGui.QSizePolicy", "line_number": 50, "usage_type": "attribute"}, {"api_name": "PyQt4.QtGui", "line_number": 50, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.QTimer.singleShot", "line_number": 112, "usage_type": "call"}, {"api_name": "PyQt4.QtCore.QTimer", "line_number": 112, "usage_type": "attribute"}, {"api_name": "PyQt4.QtCore", "line_number": 112, "usage_type": "name"}, {"api_name": "PyQt4.QtCore.QTimer.singleShot", "line_number": 114, "usage_type": "call"}, {"api_name": "PyQt4.QtCore.QTimer", "line_number": 114, "usage_type": "attribute"}, {"api_name": "PyQt4.QtCore", "line_number": 114, "usage_type": "name"}]} +{"seq_id": "201697221", "text": "import unittest\n\nimport numpy as np\n\nfrom audiomentations.augmentations.transforms import Trim\nfrom audiomentations.core.composition import Compose\n\n\nclass TestTrim(unittest.TestCase):\n def test_trim(self):\n sample_len = 1024\n samples1 = np.zeros((sample_len,), dtype=np.float32)\n samples2 = np.random.normal(0, 1, size=sample_len).astype(np.float32)\n sample_rate = 16000\n augmenter = Compose([Trim(top_db=20, p=1.0)])\n samples_in = np.hstack((samples1, samples2))\n self.assertEqual(len(samples_in), sample_len * 2)\n samples_out = augmenter(samples=samples_in, sample_rate=sample_rate)\n\n self.assertEqual(samples_out.dtype, np.float32)\n self.assertLess(len(samples_out), sample_len * 2)\n", "sub_path": "tests/test_trim.py", "file_name": "test_trim.py", "file_ext": "py", "file_size_in_byte": 760, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "unittest.TestCase", "line_number": 9, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 13, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 13, "usage_type": "attribute"}, {"api_name": "audiomentations.core.composition.Compose", "line_number": 15, "usage_type": "call"}, {"api_name": "audiomentations.augmentations.transforms.Trim", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 20, "usage_type": "attribute"}]} +{"seq_id": "188187023", "text": "from python.Tracks.TracksDB import TracksDB\nimport xlsxwriter\nimport pandas as pd\n\nstart_year = 1979\nend_year = 2017 # 2011\n\nspawn_lat1 = 30\nspawn_lat2 = 38\nspawn_lon1 = 30\nspawn_lon2 = 38\n\nparent_zone_lon1 = 25\nparent_zone_lon2 = 45\n\nminimum_radius_48 = 350\n\nexcel_headlines = ['Date', 'Lat Daughter', 'Lon Daughter', 'Length', 'SLP Value', 'SLP Gradient',\n 'Radius', 'Max Radius 48', 'RST -6', 'Track Number']\nmonths = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\ndays_in_month = [31, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n\ndf = pd.read_excel('C:/Users/hatzv/Documents/Geography/RSTs/python/Analysis/Results/RST_classification_all_hours_ERA_1979-2016.xlsx', 'Sheet')\n\n# counter = 0\nfinal_list = [excel_headlines]\nfor current_year in range(start_year, end_year+1):\n tr_db = TracksDB(current_year)\n total_tracks = tr_db.get_total_tracks()\n for current_track in range(total_tracks):\n track = tr_db.get_track(current_track)\n if len(track) >= 5: # 24 hours or longer tracks\n first_low = track[0]\n lat_first = tr_db.get_low_lat_degrees(first_low)\n lon_first = tr_db.get_low_lon_degrees(first_low)\n if (lat_first > spawn_lat1) and (lat_first < spawn_lat2) and (lon_first > spawn_lon1) and (lon_first < spawn_lon2): # Inside spawn area\n # Check for radius is first 48 hours. Only those of which are larger han 350Km are used.\n start_radius = tr_db.get_low_radius(track[0])\n max_radius_48 = start_radius\n for low in range(min(len(track), 7)):\n low_num = track[low]\n if low_num > 0:\n radius = tr_db.get_low_radius(low_num)\n if radius > max_radius_48:\n max_radius_48 = radius\n if max_radius_48 >= minimum_radius_48: # Low gets deep enough\n # Find the RST classification 6 hours prior to the low.\n # print(counter)\n time_first_low = tr_db.get_low_time(first_low, return_format='string')\n hour = int(time_first_low[11:])\n day = int(time_first_low[8:10])\n month_int = int(time_first_low[5:7])\n month = months[month_int-1]\n year = int(time_first_low[0:4]) # Not the current_year because each DB file starts in YEAR and ends with YEAR+1\n if year < 2017:\n if hour == 0:\n hour = 18\n day -= 1\n if day == 0:\n month_int -=1\n if month_int == 0:\n day = days_in_month[-1]\n month = months[-1]\n year -= 1\n else:\n day = days_in_month[month_int-1]\n month = months[month_int-1]\n else:\n hour = hour - 6\n\n rst_class_minus_6 = df[(df['Month'] == month) & (df['Day'] == day) & (df['Hour'] == hour)][year]\n rst_class_minus_6 = rst_class_minus_6.get_values()[0]\n if type(rst_class_minus_6) is not str: # In case the date is 1st of March, in which case we look for Feb 28\n rst_class_minus_6 = df[(df['Month'] == month) & (df['Day'] == day-1) & (df['Hour'] == hour)][year]\n rst_class_minus_6 = rst_class_minus_6.get_values()[0]\n\n if rst_class_minus_6 != \"No RST\":\n slp_first = tr_db.get_low_lon_slp_value(first_low)\n gradient_first = tr_db.get_low_gradient(first_low)\n radius_first = tr_db.get_low_radius(first_low)\n track_length = len(track)\n final_list.append([time_first_low, lat_first, lon_first, track_length, slp_first,\n gradient_first, radius_first, max_radius_48, rst_class_minus_6, current_track])\n\n # counter += 1\n\n# Create an new Excel file and add a worksheet.\nworkbook = xlsxwriter.Workbook('RST_lows_after_RST.xlsx')\nworksheet = workbook.add_worksheet()\n\ntotal_cols = len(final_list[1])\nfor row in range(len(final_list)):\n for col in range(total_cols):\n worksheet.write(row, col, final_list[row][col])\n\n# Add a format. Light red fill with dark red text.\nformat1 = workbook.add_format({'bg_color': '#FFC7CE',\n 'font_color': '#9C0006'})\n\n# # Add a format. Green fill with dark green text.\n# format2 = workbook.add_format({'bg_color': '#C6EFCE',\n# 'font_color': '#006100'})\n\n# Write a conditional format over a range.\nworksheet.conditional_format('L1:L'+str(len(final_list)), {'type': 'cell',\n 'criteria': '>=',\n 'value': 10,\n 'format': format1})\n\nworkbook.close()\n", "sub_path": "python/Tracks/find_lows_spawned_after_an_RST.py", "file_name": "find_lows_spawned_after_an_RST.py", "file_ext": "py", "file_size_in_byte": 5349, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_excel", "line_number": 23, "usage_type": "call"}, {"api_name": "python.Tracks.TracksDB.TracksDB", "line_number": 28, "usage_type": "call"}, {"api_name": "xlsxwriter.Workbook", "line_number": 88, "usage_type": "call"}]} +{"seq_id": "533519183", "text": "import asyncio\nimport time\nfrom pathlib import Path\n\nimport aiohttp\n\nfrom Scripts.Downloader.Novel.nparse import WuxiaWorldCo\nimport requests\nimport re\nfrom bs4 import BeautifulSoup\n\nfrom Scripts.Downloader.Novel.services import save_content, save\n\n\nasync def get_content_save_function():\n nparse = WuxiaWorldCo()\n resp = requests.get('https://m.wuxiaworld.co/Lord-of-the-Mysteries/2752246.html')\n print(f'[STATUS CODE] {resp.status_code}'\n f'\\n[CONTENT LENGTH] {resp.headers[\"content-length\"]}')\n\n if resp.ok:\n markup = resp.text\n soup = BeautifulSoup(markup, parser='html.parser', features='lxml')\n content = nparse.parse_content(soup)\n await save_content(content, Path('testfile.chapter'))\n print(f'[CONTENT]\\n{content}')\n\nasync def fetch(session, url):\n stime = time.perf_counter()\n async with session.get(url) as response:\n await response.text()\n return time.perf_counter() - stime\n\nasync def arequest_duration(url):\n async with aiohttp.ClientSession() as session:\n tasks = []\n durations = []\n for i in range(10):\n task = asyncio.create_task(fetch(session, url))\n durations.append(await task)\n # task = asyncio.create_task(fetch(session, url))\n # tasks.append(task)\n # durations = await asyncio.gather(*tasks)\n print(f'[AVERAGE DURATION] {sum(durations) / len(durations)}')\n\ndef request_duration(url):\n durations = []\n for i in range(10):\n start = time.perf_counter()\n content = requests.get(url).text\n duration = time.perf_counter() - start\n durations.append(duration)\n # print(f'[DURATION] {duration:0.2f} s')\n print(f'[AVERAGE DURATION] {sum(durations) / len(durations)}')\n\ndef duration_tests():\n url = 'https://www.google.com/'\n print('[SYNCHRONOUS REQUESTS]')\n request_duration(url)\n\n print('\\n[ASYNCHRONOUS REQUESTS]')\n loop = asyncio.get_event_loop()\n start = time.perf_counter()\n try:\n loop.run_until_complete(arequest_duration(url))\n except Exception as ex:\n print(ex)\n finally:\n print(f'[DURATION] {time.perf_counter() - start:0.2f} s')\n loop.close()\n\nasync def save_data_create_dir_if_not_exists():\n filepath = Path('test_files', 'new.txt')\n await save('', filepath)\n\nif __name__ == '__main__':\n # duration_tests()\n loop = asyncio.get_event_loop()\n loop.run_until_complete(save_data_create_dir_if_not_exists())\n loop.close()\n", "sub_path": "Scripts/Downloader/Novel/tests/function_tests.py", "file_name": "function_tests.py", "file_ext": "py", "file_size_in_byte": 2516, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "Scripts.Downloader.Novel.nparse.WuxiaWorldCo", "line_number": 16, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 17, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 23, "usage_type": "call"}, {"api_name": "Scripts.Downloader.Novel.services.save_content", "line_number": 25, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 25, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 29, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 32, "usage_type": "call"}, {"api_name": "aiohttp.ClientSession", "line_number": 35, "usage_type": "call"}, {"api_name": "asyncio.create_task", "line_number": 39, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 49, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 50, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 51, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 62, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 63, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 69, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 73, "usage_type": "call"}, {"api_name": "Scripts.Downloader.Novel.services.save", "line_number": 74, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "277088970", "text": "import pandas as pd\nfrom sklearn.metrics import accuracy_score,f1_score,confusion_matrix\nimport numpy as np\nimport torch\nfrom sklearn import preprocessing\nimport torch.utils.data as Data\nimport models\nfrom nn import nn_test\nfrom ml import clf_train_test\nimport matplotlib.pyplot as plt\nimport itertools\nimport copy\nfrom pandas import DataFrame\nfrom itertools import combinations\n\nmodel_file = './data/model_gan.pkl'\nisnn = False\ndef main():\n data = pd.read_table('./data/diabetes.txt', sep=',')\n predictors = [f for f in data.columns if f not in ['Outcome']]\n X = np.array(data[predictors])\n Y = np.array(data['Outcome'])\n traincount = int(len(Y) * 0.6)\n scaler = preprocessing.MinMaxScaler()\n X = scaler.fit_transform(X)\n test_x = X[traincount:]\n test_y = Y[traincount:]\n ml_pred = clf_train_test(test_x, test_y, False)\n if isnn:\n X = torch.FloatTensor(X)\n Y = torch.LongTensor(Y)\n nn_test_x = X[traincount:]\n nn_test_y = Y[traincount:]\n test_dataset = Data.TensorDataset(nn_test_x, nn_test_y)\n testloader = Data.DataLoader(\n dataset=test_dataset,\n batch_size=1,\n shuffle=True,\n num_workers=2,\n )\n model = models.init_model(name='nn1')\n model.load_state_dict(torch.load(model_file))\n nn_pred = nn_test(testloader,model)\n ml_pred.append(nn_pred)\n\n pred = np.array(ml_pred)\n\n # bestmodel(0,2,5,6,7)('et','svc','xgb','lgbm','catboost')\n c_all = pred[0] + pred[2] + pred[5]+ pred[6] + pred[7]\n c_all = np.where(c_all > 2.5, 1, 0)\n accuracy = accuracy_score(c_all, test_y)\n f1 = f1_score(c_all, test_y)\n cm = confusion_matrix(c_all, test_y)\n print('c_all accuracy:', accuracy)\n print('c_all f1:', f1)\n class_names = ['样本0', '样本1']\n plot_confusion_matrix(cm,class_names,normalize=True,title='模型融合（GAN）')\n\n# 混淆矩阵绘制\ndef plot_confusion_matrix(cm, classes, normalize=False, title='Confusion matrix', cmap=plt.cm.Blues):\n plt.rcParams['font.sans-serif'] = ['SimHei']\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 print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('真实样本')\n plt.xlabel('预测样本')\n plt.show()\n\n # for c in combinations(pred, 3):\n # c_all = c[0]+c[1]+c[2]\n # c_all = np.where(c_all > 1.5, 1, 0)\n # accuracy = accuracy_score(c_all, test_y)\n # f1 = f1_score(c_all, test_y)\n #\n # for x in c:\n # for i in range(len(pred)):\n # if (np.array(x) == pred[i]).all():\n # print(i)\n #\n # print(' accuracy:', accuracy)\n # print('f1:', f1)\n #\n # for c in combinations(pred, 5):\n # c_all = c[0]+c[1]+c[2]+c[3]+c[4]\n # c_all = np.where(c_all > 2.5, 1, 0)\n # accuracy = accuracy_score(c_all, test_y)\n # f1 = f1_score(c_all, test_y)\n #\n # for x in c:\n # for i in range(len(pred)):\n # if (np.array(x) == pred[i]).all():\n # print(i)\n #\n # print(' accuracy:', accuracy)\n # print('f1:', f1)\n #\n # for c in combinations(pred, 7):\n # c_all = c[0]+c[1]+c[2]+c[3]+c[4]+c[5]+c[6]\n # c_all = np.where(c_all > 3.5, 1, 0)\n # accuracy = accuracy_score(c_all, test_y)\n # f1 = f1_score(c_all, test_y)\n #\n # for x in c:\n # for i in range(len(pred)):\n # if (np.array(x) == pred[i]).all():\n # print(i)\n #\n # print(' accuracy:', accuracy)\n # print('f1:', f1)\n\nif __name__=='__main__':\n main()\n\n", "sub_path": "MLClass/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4291, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_table", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 22, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 24, "usage_type": "name"}, {"api_name": "ml.clf_train_test", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 35, "usage_type": "name"}, {"api_name": "models.init_model", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 42, "usage_type": "call"}, {"api_name": "nn.nn_test", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 50, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 51, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 52, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 60, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 61, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "numpy.newaxis", "line_number": 63, "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.colorbar", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "itertools.product", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.text", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}]} +{"seq_id": "279700236", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu May 24 15:29:57 2018\n\n@author: Luc Deike\n\"\"\"\n\nimport pims\nimport matplotlib.pyplot as plt\nimport comps\n\nfolder = comps.cf('comp3c')+'180522\\\\'\nc = pims.open(folder+'nozzle_zoomIn_ingestedBubbles.cine')\n\ndx = 1.6438267326E-05\n\nf = 117\n\nfig = plt.figure(figsize=(14,7))\nax = fig.add_subplot(111)\nax.imshow(c[f],vmin=0,vmax=500,cmap='gray')\nax.set_axis_off()\n\n'''\nscale bar\n'''\n\nax.plot([1500,1500+0.01/dx],[1500,1500],'-',color='r',lw=3)\nax.text(1800,1600,'1 cm',color='r',fontsize=16)\n\nfig.tight_layout()\nfig.savefig(folder+'bubble_ingestion_example.pdf')", "sub_path": "scripts/OLD/bubble_pump_ingestion_figure.py", "file_name": "bubble_pump_ingestion_figure.py", "file_ext": "py", "file_size_in_byte": 597, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "comps.cf", "line_number": 12, "usage_type": "call"}, {"api_name": "pims.open", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}]} +{"seq_id": "334720588", "text": "#!/usr/bin/env python3\n\nimport json\n\nfrom portscanner.argumentparser import ArgumentParser, help_message\nfrom portscanner.core import ScanController, ScanTarget\n\n\ndef main():\n arg_parser = ArgumentParser()\n\n if not arg_parser.has_valid_args():\n help_message()\n\n st = ScanTarget(arg_parser.ip, arg_parser.methods_ports)\n ps = ScanController(st)\n\n if arg_parser.json:\n results = ps.scan_to_list(arg_parser.threads)\n json_object = json.dumps(results, default=lambda sr: sr.__dict__(), indent=4)\n print(json_object)\n\n else:\n print('Scanning for IP {}'.format(arg_parser.ip))\n print('METHOD\\tPORT\\tSTATUS')\n ps.scan(arg_parser.threads)\n\n\nif __name__ == '__main__':\n try:\n main()\n\n except KeyboardInterrupt:\n exit()\n", "sub_path": "portscanner.py", "file_name": "portscanner.py", "file_ext": "py", "file_size_in_byte": 797, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "portscanner.argumentparser.ArgumentParser", "line_number": 10, "usage_type": "call"}, {"api_name": "portscanner.argumentparser.help_message", "line_number": 13, "usage_type": "call"}, {"api_name": "portscanner.core.ScanTarget", "line_number": 15, "usage_type": "call"}, {"api_name": "portscanner.core.ScanController", "line_number": 16, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "171831784", "text": "import numpy as np\nfrom scipy.stats import multivariate_normal\nfrom scipy.special import logsumexp\nfrom sklearn import cluster\nfrom sklearn.utils import check_array, check_random_state\n\nfrom . import hsmm_core_x as core, hsmm_utils\nfrom .hsmm_utils import log_mask_zero, iter_from_X_lengths\n\n# Base Class for Explicit Duration HSMM\nclass HSMM:\n def __init__(self, n_states=2, n_durations=5, n_iter=20, tol=1e-2, rnd_state=None):\n if not n_states >= 2:\n raise ValueError(\"number of states (n_states) must be at least 2\")\n if not n_durations >= 1:\n raise ValueError(\"number of durations (n_durations) must be at least 1\")\n self.n_states = n_states\n self.n_durations = n_durations\n self.n_iter = n_iter\n self.tol = tol\n self.rnd_state = rnd_state\n\n # _init: initializes model parameters if there are none yet\n def _init(self):\n if not hasattr(self, \"pi\"):\n self.pi = np.full(self.n_states, 1.0 / self.n_states)\n if not hasattr(self, \"tmat\"):\n self.tmat = np.full((self.n_states, self.n_states), 1.0 / (self.n_states - 1))\n for i in range(self.n_states):\n self.tmat[i, i] = 0.0 # no self-transitions in EDHSMM\n self._dur_init() # duration\n\n # _check: check if properties of model parameters are satisfied\n def _check(self):\n # starting probabilities\n self.pi = np.asarray(self.pi)\n if self.pi.shape != (self.n_states, ):\n raise ValueError(\"start probabilities (self.pi) must have shape ({},)\".format(self.n_states))\n if not np.allclose(self.pi.sum(), 1.0):\n raise ValueError(\"start probabilities (self.pi) must add up to 1.0\")\n # transition probabilities\n self.tmat = np.asarray(self.tmat)\n if self.tmat.shape != (self.n_states, self.n_states):\n raise ValueError(\"transition matrix (self.tmat) must have shape ({0}, {0})\".format(self.n_states))\n if not np.allclose(self.tmat.sum(axis=1), 1.0):\n raise ValueError(\"transition matrix (self.tmat) must add up to 1.0\")\n for i in range(self.n_states):\n if self.tmat[i, i] != 0.0: # check for diagonals\n raise ValueError(\"transition matrix (self.tmat) must have all diagonals equal to 0.0\")\n # duration probabilities\n self._dur_check()\n\n # _dur_init: initializes duration parameters if there are none yet\n def _dur_init(self):\n \"\"\"\n arguments: (self)\n return: None\n > initialize the duration parameters\n \"\"\"\n pass # implemented in subclass\n \n # _dur_check: checks if properties of duration parameters are satisfied\n def _dur_check(self):\n \"\"\"\n arguments: (self)\n return: None\n > check the duration parameters\n \"\"\"\n pass # implemented in subclass\n \n # _dur_probmat: compute the probability per state of each duration\n def _dur_probmat(self):\n \"\"\"\n arguments: (self)\n return: duration probability matrix\n \"\"\"\n pass # implemented in subclass\n\n # _dur_mstep: perform m-step for duration parameters\n def _dur_mstep(self):\n \"\"\"\n arguments: (self, new_dur)\n return: None\n > compute the duration parameters\n \"\"\"\n pass # implemented in subclass\n\n # _emission_logprob: compute the log-likelihood per state of each observation\n def _emission_logprob(self):\n \"\"\"\n arguments: (self, X)\n return: logframe\n \"\"\"\n pass # implemented in subclass\n\n # _emission_pre_mstep: prepare m-step for emission parameters\n def _emission_pre_mstep(self):\n \"\"\"\n arguments: (self, gamma, emission_var)\n return: None\n > process gamma and save output to emission_var\n \"\"\"\n pass # implemented in subclass\n\n # _emission_mstep: perform m-step for emission parameters\n def _emission_mstep(self):\n \"\"\"\n arguments: (self, X, emission_var)\n return: None\n > compute the emission parameters\n \"\"\"\n pass # implemented in subclass\n\n # _state_sample: generate 'observation' for given state\n def _state_sample(self):\n \"\"\"\n arguments: (self, state, rnd_state=None)\n return: np.ndarray of length equal to dimension of observation\n > generate sample from state\n \"\"\"\n pass # implemented in subclass\n\n # sample: generate random observation series\n def sample(self, n_samples=5, censoring=1, rnd_state=None):\n # self._init(None) # see \"note for programmers\" in init() in GaussianHSMM\n self._check()\n # setup random state\n if rnd_state is None:\n rnd_state = self.rnd_state\n rnd_checked = check_random_state(rnd_state)\n # adapted from hmmlearn 0.2.3 (see _BaseHMM.score function)\n pi_cdf = np.cumsum(self.pi)\n tmat_cdf = np.cumsum(self.tmat, axis=1)\n dur_cdf = np.cumsum(self._dur_probmat(), axis=1)\n # for first state\n currstate = (pi_cdf > rnd_checked.rand()).argmax() # argmax() returns only the first occurrence\n currdur = (dur_cdf[currstate] > rnd_checked.rand()).argmax() + 1\n if censoring == 0 and currdur > n_samples:\n print(\"SAMPLE: n_samples is too small to contain the first state duration.\")\n return None\n state_sequence = [currstate] * currdur\n X = [self._state_sample(currstate, rnd_checked) for i in range(currdur)] # generate 'observation'\n ctr_sample = currdur\n # for next state transitions\n while ctr_sample < n_samples:\n currstate = (tmat_cdf[currstate] > rnd_checked.rand()).argmax()\n currdur = (dur_cdf[currstate] > rnd_checked.rand()).argmax() + 1\n # test if now in the end of generating samples\n if ctr_sample + currdur > n_samples:\n if censoring == 0:\n break # if without right censoring, do not include exceeding state duration\n else:\n currdur = n_samples - ctr_sample # if with right censoring, cap the samples to n_samples\n state_sequence += [currstate] * currdur\n X += [self._state_sample(currstate, rnd_checked) for i in range(currdur)] # generate 'observation'\n ctr_sample += currdur\n return ctr_sample, np.atleast_2d(X), np.array(state_sequence, dtype=int)\n\n # _core_u_only: container for core._u_only (for multiple observation sequences)\n def _core_u_only(self, logframe):\n n_samples = logframe.shape[0]\n u = np.empty((n_samples, self.n_states, self.n_durations))\n core._u_only(n_samples, self.n_states, self.n_durations,\n logframe, u)\n return u\n\n # _core_forward: container for core._forward (for multiple observation sequences)\n def _core_forward(self, u, logdur, censoring):\n n_samples = u.shape[0]\n if censoring == 0: # without right censoring\n eta = np.empty((n_samples, self.n_states, self.n_durations))\n else: # with right censoring\n eta = np.empty((n_samples + self.n_durations - 1, self.n_states, self.n_durations))\n xi = np.empty((n_samples, self.n_states, self.n_states))\n core._forward(n_samples, self.n_states, self.n_durations,\n log_mask_zero(self.pi),\n log_mask_zero(self.tmat),\n logdur, censoring, eta, u, xi)\n return eta, xi\n\n # _core_backward: container for core._backward (for multiple observation sequences)\n def _core_backward(self, u, logdur, censoring):\n n_samples = u.shape[0]\n beta = np.empty((n_samples, self.n_states))\n betastar = np.empty((n_samples, self.n_states))\n core._backward(n_samples, self.n_states, self.n_durations,\n log_mask_zero(self.pi),\n log_mask_zero(self.tmat),\n logdur, censoring, beta, u, betastar)\n return beta, betastar\n \n # _core_smoothed: container for core._smoothed (for multiple observation sequences)\n def _core_smoothed(self, beta, betastar, censoring, eta, xi):\n n_samples = beta.shape[0]\n gamma = np.empty((n_samples, self.n_states))\n core._smoothed(n_samples, self.n_states, self.n_durations,\n beta, betastar, censoring, eta, xi, gamma)\n return gamma\n \n # _core_viterbi: container for core._viterbi (for multiple observation sequences)\n def _core_viterbi(self, u, logdur, censoring):\n n_samples = u.shape[0]\n state_sequence, log_prob = core._viterbi(n_samples, self.n_states, self.n_durations,\n log_mask_zero(self.pi),\n log_mask_zero(self.tmat),\n logdur, censoring, u)\n return state_sequence, log_prob\n\n # score: log-likelihood computation from observation series\n def score(self, X, lengths=None, censoring=1):\n X = check_array(X)\n # self._init(X)\n self._check()\n logdur = log_mask_zero(self._dur_probmat()) # build logdur\n # main computations\n log_prob = 0\n for i, j in iter_from_X_lengths(X, lengths):\n logframe = self._emission_logprob(X[i:j]) # build logframe\n u = self._core_u_only(logframe)\n _, betastar = self._core_backward(u, logdur, censoring)\n gammazero = log_mask_zero(self.pi) + betastar[0]\n log_prob += logsumexp(gammazero)\n return log_prob\n\n # predict: hidden state & duration estimation from observation series\n def predict(self, X, lengths=None, censoring=1):\n X = check_array(X)\n # self._init(X)\n self._check()\n logdur = log_mask_zero(self._dur_probmat()) # build logdur\n # main computations\n log_prob = 0\n state_sequence = np.empty(X.shape[0], dtype=int) # total n_samples = X.shape[0]\n for i, j in iter_from_X_lengths(X, lengths):\n logframe = self._emission_logprob(X[i:j]) # build logframe\n u = self._core_u_only(logframe)\n iter_state_sequence, iter_log_prob = self._core_viterbi(u, logdur, censoring)\n log_prob += iter_log_prob\n state_sequence[i:j] = iter_state_sequence\n return state_sequence, log_prob\n\n # fit: parameter estimation from observation series\n def fit(self, X, lengths=None, censoring=1):\n X = check_array(X)\n self._init(X)\n self._check()\n # main computations\n for itera in range(self.n_iter):\n score = 0\n pi_num = np.full(self.n_states, -np.inf)\n tmat_num = dur_num = -np.inf\n emission_var = [None] # see \"note for programmers\" in _emission_pre_mstep() in GaussianHSMM\n logdur = log_mask_zero(self._dur_probmat()) # build logdur\n for i, j in iter_from_X_lengths(X, lengths):\n logframe = self._emission_logprob(X[i:j]) # build logframe\n u = self._core_u_only(logframe)\n eta, xi = self._core_forward(u, logdur, censoring)\n beta, betastar = self._core_backward(u, logdur, censoring)\n gamma = self._core_smoothed(beta, betastar, censoring, eta, xi)\n score += logsumexp(gamma[0, :]) # this is the output of 'score' function\n # preparation for reestimation / M-step\n # this will make fit() slower than the previous version :(\n xi = np.resize(xi, (j - i + 1, self.n_states, self.n_states))\n eta = np.resize(eta, (j - i + 1, self.n_states, self.n_durations))\n xi[j - i] = tmat_num\n eta[j - i] = dur_num\n pi_num = logsumexp([pi_num, gamma[0]], axis=0)\n tmat_num = logsumexp(xi, axis=0)\n dur_num = logsumexp(eta, axis=0)\n self._emission_pre_mstep(gamma, emission_var)\n # check for loop break\n if itera > 0 and (score - old_score) < self.tol:\n print(\"FIT: converged at {}th loop.\".format(itera + 1))\n break\n else:\n old_score = score\n # reestimation / M-step\n self.pi = np.exp(pi_num - logsumexp(pi_num))\n self.tmat = np.exp(tmat_num - logsumexp(tmat_num, axis=1)[None].T)\n new_dur = np.exp(dur_num - logsumexp(dur_num, axis=1)[None].T)\n self._dur_mstep(new_dur) # new durations\n self._emission_mstep(X, emission_var[0]) # new emissions\n print(\"FIT: reestimation complete for {}th loop.\".format(itera + 1))\n\n# Sample Subclass: Explicit Duration HSMM with Gaussian Emissions\nclass GaussianHSMM(HSMM):\n def __init__(self, n_states=2, n_durations=5, n_iter=20, tol=1e-2, rnd_state=None):\n super().__init__(n_states, n_durations, n_iter, tol, rnd_state)\n\n def _init(self, X):\n super()._init()\n # note for programmers: for every attribute that needs X in score()/predict()/fit(),\n # there must be a condition 'if X is None' because sample() doesn't need an X, but\n # default attribute values must be initiated for sample() to proceed.\n if True: # always change self.n_dim\n if X is None: # default for sample()\n self.n_dim = 1\n else:\n self.n_dim = X.shape[1]\n if not hasattr(self, \"mean\"):\n if X is None: # default for sample()\n # self.mean = [[0.], [1.], [2.], ...]\n self.mean = np.arange(0., self.n_states)[:, None]\n else:\n kmeans = cluster.KMeans(n_clusters=self.n_states, random_state=self.rnd_state)\n kmeans.fit(X)\n self.mean = kmeans.cluster_centers_\n if not hasattr(self, \"covmat\"):\n if X is None: # default for sample()\n self.covmat = np.repeat(np.identity(self.n_dim)[None], self.n_states, axis=0)\n else:\n # TODO: initial covariance matrices must be computed from X\n self.covmat = np.repeat(np.identity(self.n_dim)[None], self.n_states, axis=0)\n\n def _check(self):\n super()._check()\n # means\n self.mean = np.asarray(self.mean)\n if self.mean.shape != (self.n_states, self.n_dim):\n raise ValueError(\"means (self.mean) must have shape ({}, {})\"\n .format(self.n_states, self.n_dim))\n # covariance matrices\n self.covmat = np.asarray(self.covmat)\n if self.covmat.shape != (self.n_states, self.n_dim, self.n_dim):\n raise ValueError(\"covariance matrices (self.covmat) must have shape ({0}, {1}, {1})\"\n .format(self.n_states, self.n_dim))\n\n def _dur_init(self):\n # non-parametric duration\n if not hasattr(self, \"dur\"):\n self.dur = np.full((self.n_states, self.n_durations), 1.0 / self.n_durations)\n\n def _dur_check(self):\n self.dur = np.asarray(self.dur)\n if self.dur.shape != (self.n_states, self.n_durations):\n raise ValueError(\"duration probabilities (self.dur) must have shape ({}, {})\"\n .format(self.n_states, self.n_durations))\n if not np.allclose(self.dur.sum(axis=1), 1.0):\n raise ValueError(\"duration probabilities (self.dur) must add up to 1.0\")\n\n def _dur_probmat(self):\n # non-parametric duration\n return self.dur\n\n def _dur_mstep(self, new_dur):\n # non-parametric duration\n self.dur = new_dur\n \n def _emission_logprob(self, X):\n # abort EM loop if any covariance matrix is not symmetric, positive-definite.\n # adapted from hmmlearn 0.2.3 (see _utils._validate_covars function)\n for n, cv in enumerate(self.covmat):\n if (not np.allclose(cv, cv.T) or np.any(np.linalg.eigvalsh(cv) <= 0)):\n raise ValueError(\"component {} of covariance matrix is not symmetric, positive-definite.\"\n .format(n))\n # https://www.youtube.com/watch?v=tWoFaPwbzqE&t=1694s\n n_samples = X.shape[0]\n logframe = np.empty((n_samples, self.n_states))\n for i in range(self.n_states):\n multigauss = multivariate_normal(self.mean[i], self.covmat[i])\n for j in range(n_samples):\n logframe[j, i] = log_mask_zero(multigauss.pdf(X[j]))\n return logframe\n \n def _emission_pre_mstep(self, gamma, emission_var):\n # note for programmers: refer to \"emission_var\" as emission_var[0] here. Maybe this\n # is unidiomatic, but this is done to force pass-by-reference to the np.ndarray.\n # note #2: The \"emssion_var\" here is the cumulative concatenation of the gammas of each\n # observation sequence, so most likely you wouldn't modify this for your own subclass.\n if emission_var[0] is None: # initial\n emission_var[0] = gamma\n else:\n old_emitlength = emission_var[0].shape[0]\n emission_var[0] = np.resize(emission_var[0], (old_emitlength + gamma.shape[0], self.n_states))\n emission_var[0][old_emitlength:] = gamma\n\n def _emission_mstep(self, X, emission_var):\n # note for programmers: now refer to \"emission_var\" as it is, here.\n denominator = logsumexp(emission_var, axis=0)\n weight_normalized = np.exp(emission_var - denominator)[None].T\n # compute means (from definition; weighted)\n self.mean = (weight_normalized * X).sum(1)\n # compute covariance matrices (from definition; weighted)\n dist = X - self.mean[:, None]\n self.covmat = ((dist * weight_normalized)[:, :, :, None] * dist[:, :, None]).sum(1)\n \n def _state_sample(self, state, rnd_state=None):\n rnd_checked = check_random_state(rnd_state)\n return rnd_checked.multivariate_normal(self.mean[state], self.covmat[state])\n", "sub_path": "edhsmm/hsmm_base.py", "file_name": "hsmm_base.py", "file_ext": "py", "file_size_in_byte": 18163, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.full", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 45, "usage_type": "call"}, {"api_name": "sklearn.utils.check_random_state", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.atleast_2d", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 174, "usage_type": "call"}, {"api_name": "hsmm_utils.log_mask_zero", "line_number": 176, "usage_type": "call"}, {"api_name": "hsmm_utils.log_mask_zero", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 185, "usage_type": "call"}, {"api_name": "hsmm_utils.log_mask_zero", "line_number": 187, "usage_type": "call"}, {"api_name": "hsmm_utils.log_mask_zero", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 195, "usage_type": "call"}, {"api_name": "hsmm_utils.log_mask_zero", "line_number": 204, "usage_type": "call"}, {"api_name": "hsmm_utils.log_mask_zero", "line_number": 205, "usage_type": "call"}, {"api_name": "sklearn.utils.check_array", "line_number": 211, "usage_type": "call"}, {"api_name": "hsmm_utils.log_mask_zero", "line_number": 214, "usage_type": "call"}, {"api_name": "hsmm_utils.iter_from_X_lengths", "line_number": 217, "usage_type": "call"}, {"api_name": "hsmm_utils.log_mask_zero", "line_number": 221, "usage_type": "call"}, {"api_name": "scipy.special.logsumexp", "line_number": 222, "usage_type": "call"}, {"api_name": "sklearn.utils.check_array", "line_number": 227, "usage_type": "call"}, {"api_name": "hsmm_utils.log_mask_zero", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 233, "usage_type": "call"}, {"api_name": "hsmm_utils.iter_from_X_lengths", "line_number": 234, "usage_type": "call"}, {"api_name": "sklearn.utils.check_array", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 250, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 251, "usage_type": "attribute"}, {"api_name": "hsmm_utils.log_mask_zero", "line_number": 253, "usage_type": "call"}, {"api_name": "hsmm_utils.iter_from_X_lengths", "line_number": 254, "usage_type": "call"}, {"api_name": "scipy.special.logsumexp", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.resize", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.resize", "line_number": 264, "usage_type": "call"}, {"api_name": "scipy.special.logsumexp", "line_number": 267, "usage_type": "call"}, {"api_name": "scipy.special.logsumexp", "line_number": 268, "usage_type": "call"}, {"api_name": "scipy.special.logsumexp", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 278, "usage_type": "call"}, {"api_name": "scipy.special.logsumexp", "line_number": 278, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 279, "usage_type": "call"}, {"api_name": "scipy.special.logsumexp", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 280, "usage_type": "call"}, {"api_name": "scipy.special.logsumexp", "line_number": 280, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 303, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 305, "usage_type": "call"}, {"api_name": "sklearn.cluster", "line_number": 305, "usage_type": "name"}, {"api_name": "numpy.repeat", "line_number": 310, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 310, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 313, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 313, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 318, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 323, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 331, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 334, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.allclose", "line_number": 353, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 353, "usage_type": "call"}, {"api_name": "numpy.linalg.eigvalsh", "line_number": 353, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 353, "usage_type": "attribute"}, {"api_name": "numpy.empty", "line_number": 358, "usage_type": "call"}, {"api_name": "scipy.stats.multivariate_normal", "line_number": 360, "usage_type": "call"}, {"api_name": "hsmm_utils.log_mask_zero", "line_number": 362, "usage_type": "call"}, {"api_name": "numpy.resize", "line_number": 374, "usage_type": "call"}, {"api_name": "scipy.special.logsumexp", "line_number": 379, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 380, "usage_type": "call"}, {"api_name": "sklearn.utils.check_random_state", "line_number": 388, "usage_type": "call"}]} +{"seq_id": "18796405", "text": "import datetime\nimport logging\n\nfrom google.appengine.ext import ndb\n\nimport fb_api\nfrom loc import gmaps_api\nfrom rankings import rankings\nfrom search import search\nfrom nlp import categories\nfrom nlp import event_classifier\nfrom util import dates\nfrom . import event_image\nfrom . import event_locations\n\nDATETIME_FORMAT = \"%Y-%m-%dT%H:%M:%SZ\"\n\n\ndef _event_time_period(db_event):\n return dates.event_time_period(db_event.start_time, db_event.end_time)\n\n\ndef delete_event(db_event):\n search.delete_from_fulltext_search_index(db_event.id)\n db_event.key.delete()\n\n\n# Even if the fb_event isn't updated, sometimes we still need to force a db_event update\ndef need_forced_update(db_event):\n # If the expected time period is not the same as what we've computed and stored, we need to force update\n new_time_period = (db_event.search_time_period != _event_time_period(db_event))\n logging.info(\"Event %s with time %s - %s: has search_time_period %s, expecting %s\", db_event.id, db_event.start_time, db_event.end_time, db_event.search_time_period, _event_time_period(db_event))\n return new_time_period\n\n\ndef update_and_save_fb_events(events_to_update, update_geodata=True):\n for db_event, fb_event in events_to_update:\n logging.info(\"Updating and saving DBEvent %s\", db_event.id)\n _inner_make_event_findable_for_fb_event(db_event, fb_event, update_geodata=update_geodata)\n # We want to save it here, no matter how it was changed.\n db_events = [x[0] for x in events_to_update]\n _save_events(db_events)\n\n\ndef update_and_save_web_events(events_to_update, update_geodata=True):\n for db_event, web_event in events_to_update:\n logging.info(\"Updating and saving DBEvent %s\", db_event.id)\n _inner_make_event_findable_for_web_event(db_event, web_event, update_geodata=update_geodata)\n db_events = [x[0] for x in events_to_update]\n _save_events(db_events)\n\n\ndef _save_events(db_events):\n objects_to_put = list(db_events)\n objects_to_put += [search.DisplayEvent.build(x) for x in db_events]\n # Because some DisplayEvent.build() calls return None (from errors, or from inability)\n objects_to_put = [x for x in objects_to_put if x]\n ndb.put_multi(objects_to_put)\n search.update_fulltext_search_index_batch(db_events)\n\n\ndef _all_attending_count(fb_event):\n if 'info' in fb_event and fb_event['info'].get('attending_count'):\n return fb_event['info']['attending_count']\n else:\n return None\n\ndef _inner_cache_photo(db_event):\n if db_event.json_props is None:\n db_event.json_props = {}\n if db_event.full_image_url:\n try:\n width, height = event_image.cache_image_and_get_size(db_event)\n db_event.json_props['photo_width'] = width\n db_event.json_props['photo_height'] = height\n except (event_image.DownloadError, event_image.NotFoundError):\n logging.exception('Error downloading flyer for event: %s', db_event.id)\n else:\n if 'photo_width' in db_event.json_props:\n del db_event.json_props['photo_width']\n if 'photo_height' in db_event.json_props:\n del db_event.json_props['photo_height']\n\ndef _inner_make_event_findable_for_fb_event(db_event, fb_dict, update_geodata):\n \"\"\"set up any cached fields or bucketing or whatnot for this event\"\"\"\n\n # Update this event with the latest time_period regardless (possibly overwritten below)\n db_event.search_time_period = _event_time_period(db_event)\n\n if fb_dict['empty'] == fb_api.EMPTY_CAUSE_DELETED:\n # If this event has already past, don't allow it to be deleted. We want to keep history!\n if db_event.end_time and db_event.end_time < datetime.datetime.now() - datetime.timedelta(days=2):\n return\n # If we don't have a db_event.end_time, then we've got something messed up, so let's delete the event\n db_event.start_time = None\n db_event.end_time = None\n db_event.search_time_period = None\n db_event.address = None\n db_event.actual_city_name = None\n db_event.city_name = None\n db_event.fb_event = fb_dict\n return\n elif fb_dict['empty'] == fb_api.EMPTY_CAUSE_INSUFFICIENT_PERMISSIONS:\n db_event.search_time_period = _event_time_period(db_event)\n # Don't copy the fb_event over, or any of its fields\n return\n\n # Screw db-normalized form, store this here (and location_geocode down below)\n db_event.fb_event = fb_dict\n if 'owner' in fb_dict['info']:\n db_event.owner_fb_uid = fb_dict['info']['owner']['id']\n else:\n db_event.owner_fb_uid = None\n\n db_event.attendee_count = _all_attending_count(fb_dict)\n\n db_event.start_time = dates.parse_fb_start_time(fb_dict)\n db_event.end_time = dates.parse_fb_end_time(fb_dict)\n db_event.search_time_period = _event_time_period(db_event)\n\n db_event.event_keywords = event_classifier.relevant_keywords(fb_dict)\n db_event.auto_categories = [x.index_name for x in categories.find_styles(fb_dict) + categories.find_event_types(fb_dict)]\n\n _inner_cache_photo(db_event)\n\n if update_geodata:\n # Don't use cached/stale geocode when constructing the LocationInfo here\n db_event.location_geocode = None\n location_info = event_locations.LocationInfo(fb_dict, db_event=db_event)\n _update_geodata(db_event, location_info)\n\n\ndef _inner_make_event_findable_for_web_event(db_event, web_event, update_geodata):\n logging.info(\"Making web_event %s findable.\" % db_event.id)\n db_event.web_event = web_event\n\n db_event.fb_event = None\n db_event.owner_fb_uid = None\n\n db_event.attendee_count = 0 # Maybe someday set attending counts when we fetch them?\n\n db_event.start_time = datetime.datetime.strptime(web_event['start_time'], DATETIME_FORMAT)\n if web_event.get('end_time'):\n db_event.end_time = datetime.datetime.strptime(web_event['end_time'], DATETIME_FORMAT)\n else:\n db_event.end_time = None\n db_event.search_time_period = _event_time_period(db_event)\n\n db_event.event_keywords = event_classifier.relevant_keywords(db_event)\n db_event.auto_categories = [x.index_name for x in categories.find_styles(db_event) + categories.find_event_types(db_event)]\n\n geocode = None\n if web_event.get('location_address'):\n address = event_locations.clean_address(web_event.get('location_address'))\n logging.info(\"Have location address, checking if it is geocodable: %s\", web_event.get('location_address'))\n logging.info(\"Stripping off any floor info, final address is: %s\", address)\n geocode = gmaps_api.lookup_address(address)\n if geocode is None:\n logging.warning(\"Received a location_address that was not geocodeable, treating as empty: %s\", web_event['location_address'])\n if geocode is None:\n if web_event.get('latitude') or web_event.get('longitude'):\n logging.info(\"Have latlong, let's geocode that way: %s, %s\", web_event.get('latitude'), web_event.get('longitude'))\n geocode = gmaps_api.lookup_latlng((web_event.get('latitude'), web_event.get('longitude')))\n if geocode is None:\n if web_event.get('geolocate_location_name'):\n logging.info(\"Have magic geolocate_location_name, checking if it is a place: %s\", web_event.get('geolocate_location_name'))\n geocode = gmaps_api.lookup_location(web_event['geolocate_location_name'])\n if geocode is None:\n if web_event.get('location_name'):\n logging.info(\"Have regular location_name, checking if it is a place: %s\", web_event.get('location_name'))\n geocode = gmaps_api.lookup_location(web_event['location_name'])\n if geocode:\n if 'name' in geocode.json_data:\n web_event['location_name'] = geocode.json_data['name']\n web_event['location_address'] = geocode.json_data['formatted_address']\n logging.info(\"Found an address: %s\", web_event['location_address'])\n # BIG HACK!!!\n if 'Japan' not in web_event['location_address'] and 'Korea' not in web_event['location_address']:\n logging.error(\"Found incorrect address for venue!\")\n latlng = geocode.json_data['geometry']['location']\n web_event['latitude'] = latlng['lat']\n web_event['longitude'] = latlng['lng']\n\n db_event.address = web_event.get('location_address')\n\n _inner_cache_photo(db_event)\n\n if update_geodata:\n # Don't use cached/stale geocode when constructing the LocationInfo here\n db_event.location_geocode = geocode\n location_info = event_locations.LocationInfo(db_event=db_event)\n _update_geodata(db_event, location_info)\n\n\ndef _update_geodata(db_event, location_info):\n # If we got good values from before, don't overwrite with empty values!\n if not db_event.actual_city_name:\n logging.info('NO EVENT LOCATION1: %s', db_event.id)\n logging.info('NO EVENT LOCATION2: %s', location_info)\n logging.info('NO EVENT LOCATION3: %s', location_info.geocode)\n if location_info.actual_city() != db_event.actual_city_name or not db_event.actual_city_name or db_event.city_name == 'Unknown':\n if location_info.geocode:\n db_event.city_name = rankings.get_ranking_location_latlng(location_info.geocode.latlng())\n else:\n db_event.city_name = \"Unknown\"\n db_event.anywhere = location_info.is_online_event()\n db_event.actual_city_name = location_info.actual_city()\n if db_event.actual_city_name:\n db_event.latitude, db_event.longitude = location_info.latlong()\n else:\n db_event.latitude = None\n db_event.longitude = None\n # TODO(lambert): find a better way of reporting/notifying about un-geocodeable addresses\n logging.warning(\"No geocoding results for eid=%s is: %s\", db_event.id, location_info)\n\n # This only grabs the very first result from the raw underlying geocode request, since that's all that's used to construct the Geocode object in memory\n db_event.location_geocode = gmaps_api.convert_geocode_to_json(location_info.geocode)\n\n db_event.country = location_info.geocode.country() if location_info.geocode else None\n", "sub_path": "events/event_updates.py", "file_name": "event_updates.py", "file_ext": "py", "file_size_in_byte": 10185, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "util.dates.event_time_period", "line_number": 20, "usage_type": "call"}, {"api_name": "util.dates", "line_number": 20, "usage_type": "name"}, {"api_name": "search.search.delete_from_fulltext_search_index", "line_number": 24, "usage_type": "call"}, {"api_name": "search.search", "line_number": 24, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 32, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 47, "usage_type": "call"}, {"api_name": "search.search.DisplayEvent.build", "line_number": 55, "usage_type": "call"}, {"api_name": "search.search.DisplayEvent", "line_number": 55, "usage_type": "attribute"}, {"api_name": "search.search", "line_number": 55, "usage_type": "name"}, {"api_name": "google.appengine.ext.ndb.put_multi", "line_number": 58, "usage_type": "call"}, {"api_name": "google.appengine.ext.ndb", "line_number": 58, "usage_type": "name"}, {"api_name": "search.search.update_fulltext_search_index_batch", "line_number": 59, "usage_type": "call"}, {"api_name": "search.search", "line_number": 59, "usage_type": "name"}, {"api_name": "logging.exception", "line_number": 77, "usage_type": "call"}, {"api_name": "fb_api.EMPTY_CAUSE_DELETED", "line_number": 90, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 92, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 92, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 92, "usage_type": "call"}, {"api_name": "fb_api.EMPTY_CAUSE_INSUFFICIENT_PERMISSIONS", "line_number": 103, "usage_type": "attribute"}, {"api_name": "util.dates.parse_fb_start_time", "line_number": 117, "usage_type": "call"}, {"api_name": "util.dates", "line_number": 117, "usage_type": "name"}, {"api_name": "util.dates.parse_fb_end_time", "line_number": 118, "usage_type": "call"}, {"api_name": "util.dates", "line_number": 118, "usage_type": "name"}, {"api_name": "nlp.event_classifier.relevant_keywords", "line_number": 121, "usage_type": "call"}, {"api_name": "nlp.event_classifier", "line_number": 121, "usage_type": "name"}, {"api_name": "nlp.categories.find_styles", "line_number": 122, "usage_type": "call"}, {"api_name": "nlp.categories", "line_number": 122, "usage_type": "name"}, {"api_name": "nlp.categories.find_event_types", "line_number": 122, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 134, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 142, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 142, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 144, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 144, "usage_type": "attribute"}, {"api_name": "nlp.event_classifier.relevant_keywords", "line_number": 149, "usage_type": "call"}, {"api_name": "nlp.event_classifier", "line_number": 149, "usage_type": "name"}, {"api_name": "nlp.categories.find_styles", "line_number": 150, "usage_type": "call"}, {"api_name": "nlp.categories", "line_number": 150, "usage_type": "name"}, {"api_name": "nlp.categories.find_event_types", "line_number": 150, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 155, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 156, "usage_type": "call"}, {"api_name": "loc.gmaps_api.lookup_address", "line_number": 157, "usage_type": "call"}, {"api_name": "loc.gmaps_api", "line_number": 157, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 159, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 162, "usage_type": "call"}, {"api_name": "loc.gmaps_api.lookup_latlng", "line_number": 163, "usage_type": "call"}, {"api_name": "loc.gmaps_api", "line_number": 163, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 166, "usage_type": "call"}, {"api_name": "loc.gmaps_api.lookup_location", "line_number": 167, "usage_type": "call"}, {"api_name": "loc.gmaps_api", "line_number": 167, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 170, "usage_type": "call"}, {"api_name": "loc.gmaps_api.lookup_location", "line_number": 171, "usage_type": "call"}, {"api_name": "loc.gmaps_api", "line_number": 171, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 176, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 179, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 198, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 199, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 200, "usage_type": "call"}, {"api_name": "rankings.rankings.get_ranking_location_latlng", "line_number": 203, "usage_type": "call"}, {"api_name": "rankings.rankings", "line_number": 203, "usage_type": "name"}, {"api_name": "logging.warning", "line_number": 214, "usage_type": "call"}, {"api_name": "loc.gmaps_api.convert_geocode_to_json", "line_number": 217, "usage_type": "call"}, {"api_name": "loc.gmaps_api", "line_number": 217, "usage_type": "name"}]} +{"seq_id": "641580386", "text": "from flask import Flask, render_template, Response\nfrom tensorflow.keras.applications.mobilenet_v2 import preprocess_input\nfrom tensorflow.keras.preprocessing.image import img_to_array\nfrom tensorflow.keras.models import load_model\nfrom imutils.video import VideoStream\nimport numpy as np\nimport imutils\nimport time\nimport cv2\nimport os\n\napp = Flask(__name__,template_folder='Template')\n\ndef detect_and_predict_mask(frame, faceNet, maskNet):\n # grab dimensions of frame then construct a blob from it\n (h, w) = frame.shape[:2]\n blob = cv2.dnn.blobFromImage(frame,1.0, (192,192),\n (104.0,177.0,123.0))\n \n # pass the blob through the network and obtain the face detections\n faceNet.setInput(blob)\n detections = faceNet.forward()\n print(detections.shape)\n \n # init list of faces and corresponding locations, and the list of predictions from our facemask network\n \n faces = []\n locations = []\n predictions = []\n \n # loop over the detections\n for i in range(0, detections.shape[2]):\n # extract the confidence(probability) associated with the detection\n confidence = detections[0,0,i,2]\n \n # filter out weak detections by ensuring the confidence is greater than the minimum confidence\n if confidence > 0.5:\n # compute the (x, y)-coordinates of the bounding box for the object\n box = detections[0,0,i,3:7] * np.array([w,h,w,h])\n (startX, startY, endX, endY) = box.astype('int')\n \n # make sure the bounding boxes fall within the dimensions of the frame\n (startX, startY) = (max(0, startX), max(0, startY))\n (endX, endY) = (min(w-1, endX), min(h-1, endY))\n \n # extract the face Region Of Interest, convert it from BGR to RGB channel ordering, resize it, and preprocess it\n face = frame[startY:endY, startX:endX]\n face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)\n face = cv2.resize(face, (224,224))\n face = img_to_array(face)\n face = preprocess_input(face)\n \n # add the face and bounding boxes to their respective lists\n faces.append(face)\n locations.append((startX, startY, endX, endY))\n \n # only make the predictions if at least one face was detected\n if len(faces) > 0:\n # for faster inference we'll make batch predictions on all faces at the same time rather than one-by-one predictions in the obove for loop\n faces = np.array(faces)\n predictions = maskNet.predict(faces, batch_size=32)\n \n # return tuple of the face locations and their corresponding locations\n return (locations, predictions)\n\n# load our serialized face detector model from disk\nprototxt_path = r\"../face_detector/deploy.prototxt\"\nweightsPath = r\"../face_detector/res10_300x300_ssd_iter_140000.caffemodel\"\nfaceNet = cv2.dnn.readNet(prototxt_path,weightsPath)\n\n# load face mask detector model from disk\nmaskNet = load_model(\"detect_mask.model\")\n\n# init the video stream\nprint(\"[INFO] Starting video stream...\")\n\n\ndef get_frames():\n vs = VideoStream(0).start()\n # loop over the frames from the video stream\n while True:\n # grab the frame from threaded video stream and resize it to have max width of 400 pixels\n frame = vs.read()\n frame = imutils.resize(frame, width=400)\n\n # detect faces in the frame and determ if they are wearing mask, not wearing, or worn incorrectly\n (locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)\n\n # loop over detected fadce locations and their corresponding locations\n for (box, pred) in zip(locs, preds):\n # unpack the bounding box and predictions\n (startX, startY, endX, endY) = box\n (mask, withoutMask) = pred\n\n # determine the class label and color we'll use to draw the bounding box and text\n label = \"Mask\" if mask > withoutMask else \"No Mask\"\n color = (0, 255, 0) if label == \"Mask\" else (0, 0, 255)\n\n # incl probability in the label\n label = \"{}: {:.2f}%\".format(label, max(mask, withoutMask) * 100)\n\n # display the label and bounding box rectangle on the output frame\n cv2.putText(frame,label, (startX, startY-10), cv2.FONT_HERSHEY_SIMPLEX,0.45, color, 2)\n cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)\n\n # show output frame\n cv2.imshow(\"Frame\", frame)\n key = cv2.waitKey(1) & 0xFF\n\n # if 'q' key is pressed, break from the loop\n if key == ord('q'):\n break\n\n # do a bit of cleanup\n cv2.destroyAllWindows()\n vs.stop()\n\n@app.route('/')\ndef index():\n return render_template('index.html')\n@app.route('/video')\ndef video():\n return Response(get_frames(), mimetype='multipart/x-mixed-replace; boundary=frame')\n\nif __name__==\"__main__\":\n app.run(debug=True)", "sub_path": "app/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 4983, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Flask", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.dnn.blobFromImage", "line_number": 17, "usage_type": "call"}, {"api_name": "cv2.dnn", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 48, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.keras.preprocessing.image.img_to_array", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.keras.applications.mobilenet_v2.preprocess_input", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 60, "usage_type": "call"}, {"api_name": "cv2.dnn.readNet", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.dnn", "line_number": 69, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 72, "usage_type": "call"}, {"api_name": "imutils.video.VideoStream", "line_number": 79, "usage_type": "call"}, {"api_name": "imutils.resize", "line_number": 84, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 103, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 103, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 104, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 107, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 108, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 115, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 120, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 123, "usage_type": "call"}]} +{"seq_id": "220381703", "text": "\"\"\"\nPytest unit test configuration for oodi\n\"\"\"\nfrom pathlib import Path\nfrom shutil import copyfile, copytree, rmtree\nfrom typing import Iterator\n\nimport pytest\n\nfrom oodi.client import Oodi\nfrom oodi.codecs.constants import CodecFormat\nfrom oodi.configuration import Configuration\nfrom oodi.library.album import Album\nfrom oodi.library.tree import Library\nfrom oodi.metadata.constants import ALBUMART_SUPPORTED_FILENAMES, BOOKLET_SUPPORTED_FILENAMES\n\nMOCK_MESSAGE = 'Mock message'\n\nMOCK_DATA = Path(__file__).parent.joinpath('mock')\nMOCK_METADATA = MOCK_DATA.joinpath('metadata')\nMOCK_CONFIG_DIRECTORY = MOCK_DATA.joinpath('config/default')\nMOCK_EMPTY_CONFIG_DIRECTORY = MOCK_DATA.joinpath('config/empty')\n\n# Directory with whitenoise samples\nMOCK_WHITENOISE_SAMPLES_PATH = MOCK_DATA.joinpath('samples')\nMOCK_WHITENOISE_SAMPLES_FOLDER_COUNT = 3\nMOCK_METADATA_FILES_COUNT = 8\nMOCK_WHITENOISE_SAMPLES_COUNT = 9\n\n# Mocked album paths that do not exist\nTEST_ALBUM_PATHS = (\n Path('Album/In Library'),\n)\n\n# List of all sample files as standard Path objects from the test data directory\nWHITENOISE_SAMPLE_FILES = [\n item\n for item in list(MOCK_WHITENOISE_SAMPLES_PATH.glob('**/*'))\n if item.is_file()\n]\n\nMOCK_METADATA_FILES = [path for path in MOCK_METADATA.glob('**/*') if path.is_file()]\nMOCK_ALBUMART_FILES = [\n path\n for path in MOCK_METADATA_FILES\n if path.name in ALBUMART_SUPPORTED_FILENAMES\n]\nMOCK_BOOKLET_FILES = [\n path\n for path in MOCK_METADATA_FILES\n if path.name in BOOKLET_SUPPORTED_FILENAMES\n]\n\n\n@pytest.fixture\ndef mock_missing_config_file(monkeypatch, tmpdir) -> Iterator[Path]:\n \"\"\"\n Return a non-existing temporary directory for constant\n oodi.constants.USER_CONFIG_DIRECTORY\n \"\"\"\n missing_config_path = Path(tmpdir.strpath, 'missing-userconfig')\n monkeypatch.setattr(\n 'oodi.constants.USER_CONFIG_DIRECTORY',\n missing_config_path\n )\n yield missing_config_path\n if missing_config_path and missing_config_path.is_dir():\n rmtree(missing_config_path)\n\n\n@pytest.fixture\ndef mock_empty_config_file(monkeypatch) -> Iterator[Path]:\n \"\"\"\n Mock constant oodi.constants.USER_CONFIG_DIRECTORY to return\n mocked directory tests/mock/config/empty with valid but empty\n configuration file\n \"\"\"\n monkeypatch.setattr(\n 'oodi.constants.USER_CONFIG_DIRECTORY',\n MOCK_EMPTY_CONFIG_DIRECTORY,\n )\n yield MOCK_EMPTY_CONFIG_DIRECTORY\n\n\n@pytest.fixture\ndef mock_default_config_file(monkeypatch) -> Iterator[Path]:\n \"\"\"\n Mock constant oodi.constants.USER_CONFIG_DIRECTORY to return\n mocked directory tests/mock/config/default\n \"\"\"\n monkeypatch.setattr(\n 'oodi.constants.USER_CONFIG_DIRECTORY',\n MOCK_CONFIG_DIRECTORY,\n )\n yield MOCK_CONFIG_DIRECTORY\n\n\n@pytest.fixture\ndef missing_tmpdir_directory(tmpdir) -> Iterator[Path]:\n \"\"\"\n Yield missing temporary directory path for unit tests\n \"\"\"\n missing_directory = Path(tmpdir.strpath, 'missing-directory')\n yield missing_directory\n if missing_directory and missing_directory.is_dir():\n rmtree(missing_directory)\n\n\n# pylint: disable=redefined-outer-name,unused-argument\n@pytest.fixture\ndef mock_empty_config(mock_empty_config_file) -> Iterator[Configuration]:\n \"\"\"\n Mock returning Configuration object with mock_empty_config_file fixture\n \"\"\"\n yield Configuration()\n\n\n@pytest.fixture(params=WHITENOISE_SAMPLE_FILES)\ndef mock_sample_file(request) -> Iterator[Path]:\n \"\"\"\n Mock request with full paths to the sample files in test data\n \"\"\"\n yield request.param\n\n\n@pytest.fixture(params=MOCK_METADATA_FILES)\ndef mock_metadata_file(request):\n \"\"\"\n Mock fixture to list all available metadata files in test data\n \"\"\"\n yield request.param\n\n\n@pytest.fixture(params=MOCK_ALBUMART_FILES)\ndef mock_albumart_file(request):\n \"\"\"\n Mock fixture to list all available album art files in test data\n \"\"\"\n yield request.param\n\n\n@pytest.fixture(params=MOCK_BOOKLET_FILES)\ndef mock_booklet_file(request):\n \"\"\"\n Mock fixture to list all available booklet files in test data\n \"\"\"\n yield request.param\n\n\n@pytest.fixture\ndef mock_empty_library(mock_empty_config, tmpdir) -> Iterator[Library]:\n \"\"\"\n Mock returning Library object for tmpdir directory\n \"\"\"\n yield Library(config=mock_empty_config, path=Path(tmpdir.strpath))\n\n\n@pytest.fixture\ndef mock_sample_library(mock_empty_config, tmpdir) -> Iterator[Library]:\n \"\"\"\n Generate a Library object for samples with albumart and bookmark files\n from mock data directory\n \"\"\"\n albumart = MOCK_ALBUMART_FILES[0]\n booklet = MOCK_BOOKLET_FILES[0]\n path = Path(tmpdir.strpath, 'music')\n\n copytree(MOCK_WHITENOISE_SAMPLES_PATH, path)\n copyfile(albumart, path.joinpath(albumart.name))\n copyfile(booklet, path.joinpath(booklet.name))\n for item in path.glob('**/*'):\n if item.is_dir():\n copyfile(albumart, item.joinpath(albumart.name))\n copyfile(booklet, item.joinpath(booklet.name))\n\n yield Library(\n config=mock_empty_config,\n path=path,\n formats=[codec_format.value for codec_format in CodecFormat]\n )\n\n\n@pytest.fixture\ndef oodi_empty_client(mock_empty_config_file) -> Iterator[Oodi]:\n \"\"\"\n Yield Oodi client with mocked empty config\n \"\"\"\n yield Oodi()\n\n\n@pytest.fixture\ndef oodi_default_client(mock_default_config_file) -> Iterator[Oodi]:\n \"\"\"\n Yield Oodi client with mocked default config\n \"\"\"\n yield Oodi()\n\n\n@pytest.fixture(params=TEST_ALBUM_PATHS)\ndef mock_album_relative_path(request) -> Iterator[Path]:\n \"\"\"\n Return iterator for valid album relative paths\n \"\"\"\n yield request.param\n\n\n# pylint: disable=redefined-outer-name\n@pytest.fixture\ndef mock_album(mock_empty_library, mock_album_relative_path) -> Iterator[Album]:\n \"\"\"\n Return mocked Album object\n \"\"\"\n yield Album(mock_empty_library, mock_empty_library.joinpath(mock_album_relative_path))\n", "sub_path": "tests/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 5991, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pathlib.Path", "line_number": 19, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 32, "usage_type": "call"}, {"api_name": "oodi.metadata.constants.ALBUMART_SUPPORTED_FILENAMES", "line_number": 46, "usage_type": "name"}, {"api_name": "oodi.metadata.constants.BOOKLET_SUPPORTED_FILENAMES", "line_number": 51, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 61, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 68, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 55, "usage_type": "attribute"}, {"api_name": "typing.Iterator", "line_number": 56, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 56, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 71, "usage_type": "attribute"}, {"api_name": "typing.Iterator", "line_number": 72, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 72, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 85, "usage_type": "attribute"}, {"api_name": "typing.Iterator", "line_number": 86, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 86, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 103, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 106, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 98, "usage_type": "attribute"}, {"api_name": "typing.Iterator", "line_number": 99, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 99, "usage_type": "name"}, {"api_name": "oodi.configuration.Configuration", "line_number": 115, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 110, "usage_type": "attribute"}, {"api_name": "typing.Iterator", "line_number": 111, "usage_type": "name"}, {"api_name": "oodi.configuration.Configuration", "line_number": 111, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 118, "usage_type": "call"}, {"api_name": "typing.Iterator", "line_number": 119, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 119, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 126, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 134, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 142, "usage_type": "call"}, {"api_name": "oodi.library.tree.Library", "line_number": 155, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 155, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 150, "usage_type": "attribute"}, {"api_name": "typing.Iterator", "line_number": 151, "usage_type": "name"}, {"api_name": "oodi.library.tree.Library", "line_number": 151, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 166, "usage_type": "call"}, {"api_name": "shutil.copytree", "line_number": 168, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 169, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 170, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 173, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 174, "usage_type": "call"}, {"api_name": "oodi.library.tree.Library", "line_number": 176, "usage_type": "call"}, {"api_name": "oodi.codecs.constants.CodecFormat", "line_number": 179, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 158, "usage_type": "attribute"}, {"api_name": "typing.Iterator", "line_number": 159, "usage_type": "name"}, {"api_name": "oodi.library.tree.Library", "line_number": 159, "usage_type": "name"}, {"api_name": "oodi.client.Oodi", "line_number": 188, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 183, "usage_type": "attribute"}, {"api_name": "typing.Iterator", "line_number": 184, "usage_type": "name"}, {"api_name": "oodi.client.Oodi", "line_number": 184, "usage_type": "name"}, {"api_name": "oodi.client.Oodi", "line_number": 196, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 191, "usage_type": "attribute"}, {"api_name": "typing.Iterator", "line_number": 192, "usage_type": "name"}, {"api_name": "oodi.client.Oodi", "line_number": 192, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 199, "usage_type": "call"}, {"api_name": "typing.Iterator", "line_number": 200, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 200, "usage_type": "name"}, {"api_name": "oodi.library.album.Album", "line_number": 213, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 208, "usage_type": "attribute"}, {"api_name": "typing.Iterator", "line_number": 209, "usage_type": "name"}, {"api_name": "oodi.library.album.Album", "line_number": 209, "usage_type": "name"}]} +{"seq_id": "493515854", "text": "from rest_framework import viewsets, mixins\nfrom rest_framework.decorators import action\nfrom rest_framework.response import Response\n\nfrom carts.models import Cart\nfrom carts.serializers import CartSerializer\nfrom utils.func import manage_cart\nfrom utils.status_code import SUCCESS\n\n\nclass CartView(viewsets.GenericViewSet,\n mixins.ListModelMixin,\n mixins.UpdateModelMixin):\n \"\"\"购物车\"\"\"\n queryset = Cart.objects.all()\n serializer_class = CartSerializer\n\n def list(self, request, *args, **kwargs):\n \"\"\"显示购物车商品\"\"\"\n user = request.user\n # 获取当前用户的的购物车信息\n queryset = self.get_queryset().filter(user=user)\n serializer = self.get_serializer(queryset, many=True)\n total_price = 0\n all_select = 1\n for item in queryset:\n # 计算选择商品的总价\n if item.is_select:\n total_price += item.num * item.goods.price\n else:\n all_select = 0\n data = {'carts': serializer.data,\n 'total_price': total_price,\n 'all_select': all_select,\n 'username': user.username,\n 'mobile': user.mobile}\n return Response(data)\n\n def update(self, request, *args, **kwargs):\n \"\"\"修改商品选择\"\"\"\n # 获取到当前的对象实例\n instance = self.get_object()\n instance.is_select = not instance.is_select\n instance.save()\n return Response({'code': SUCCESS[0], 'msg': SUCCESS[1]})\n\n @action(methods=['POST'], detail=False)\n def add_cart(self, request):\n \"\"\"添加商品到购物车\"\"\"\n manage_cart(request, 1)\n return Response({'code': SUCCESS[0], 'msg': SUCCESS[2]})\n\n @action(methods=['POST'], detail=False)\n def sub_cart(self, request):\n \"\"\"删除商品\"\"\"\n manage_cart(request, 0)\n return Response({'code': SUCCESS[0], 'msg': SUCCESS[3]})\n\n @action(methods=['PATCH'], detail=False)\n def change_select(self, request):\n \"\"\"是否全选商品\"\"\"\n user = request.user\n if Cart.objects.filter(user=user, is_select=False).exists():\n # 判断是否有未选择的商品，如果有将选择属性修改为True\n Cart.objects.filter(user=user).update(is_select=True)\n else:\n Cart.objects.filter(user=user).update(is_select=False)\n return Response({'code': SUCCESS[0], 'msg': SUCCESS[1]})\n", "sub_path": "lsj/carts/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2505, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "rest_framework.viewsets.GenericViewSet", "line_number": 11, "usage_type": "attribute"}, {"api_name": "rest_framework.viewsets", "line_number": 11, "usage_type": "name"}, {"api_name": "rest_framework.mixins.ListModelMixin", "line_number": 12, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 12, "usage_type": "name"}, {"api_name": "rest_framework.mixins.UpdateModelMixin", "line_number": 13, "usage_type": "attribute"}, {"api_name": "rest_framework.mixins", "line_number": 13, "usage_type": "name"}, {"api_name": "carts.models.Cart.objects.all", "line_number": 15, "usage_type": "call"}, {"api_name": "carts.models.Cart.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "carts.models.Cart", "line_number": 15, "usage_type": "name"}, {"api_name": "carts.serializers.CartSerializer", "line_number": 16, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 37, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 45, "usage_type": "call"}, {"api_name": "utils.status_code.SUCCESS", "line_number": 45, "usage_type": "name"}, {"api_name": "utils.func.manage_cart", "line_number": 50, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 51, "usage_type": "call"}, {"api_name": "utils.status_code.SUCCESS", "line_number": 51, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 47, "usage_type": "call"}, {"api_name": "utils.func.manage_cart", "line_number": 56, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 57, "usage_type": "call"}, {"api_name": "utils.status_code.SUCCESS", "line_number": 57, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 53, "usage_type": "call"}, {"api_name": "carts.models.Cart.objects.filter", "line_number": 63, "usage_type": "call"}, {"api_name": "carts.models.Cart.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "carts.models.Cart", "line_number": 63, "usage_type": "name"}, {"api_name": "carts.models.Cart.objects.filter", "line_number": 65, "usage_type": "call"}, {"api_name": "carts.models.Cart.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "carts.models.Cart", "line_number": 65, "usage_type": "name"}, {"api_name": "carts.models.Cart.objects.filter", "line_number": 67, "usage_type": "call"}, {"api_name": "carts.models.Cart.objects", "line_number": 67, "usage_type": "attribute"}, {"api_name": "carts.models.Cart", "line_number": 67, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 68, "usage_type": "call"}, {"api_name": "utils.status_code.SUCCESS", "line_number": 68, "usage_type": "name"}, {"api_name": "rest_framework.decorators.action", "line_number": 59, "usage_type": "call"}]} +{"seq_id": "68222501", "text": "#!/usr/bin/env python3\n\nimport argparse\nimport os\nfrom tempfile import TemporaryDirectory\n\nimport skeletonization\nimport skeletonization.doc_utils as docs\n\nROOT_SKELETONIZATION_DIR = os.path.dirname(os.path.dirname(skeletonization.__file__))\n\n\"\"\"Generate Sphinx documentation for the skeletonization module\"\"\"\nDOCS_FOLDER = os.path.join(ROOT_SKELETONIZATION_DIR, \"docs\")\nDOC_WARNING_RATCHET = 200\n\n\ndef main():\n \"\"\"\n Generate Sphinx docs for the skeletonization module.\n \"\"\"\n\n parser = argparse.ArgumentParser(\n description=\"Generate sphinx documentation\")\n parser.add_argument(\n \"--output_folder\",\n help=\"\"\"The folder to output the html documentation tree into.\n If unspecified the docs will be only be generated to check\n for errors/warnings.\"\"\",\n type=str)\n\n docs.build_api_docs(\n os.path.join(ROOT_SKELETONIZATION_DIR, \"\"),\n os.path.join(DOCS_FOLDER, \"api_python\"))\n\n args = parser.parse_args()\n\n with TemporaryDirectory() as td:\n output = args.output_folder if (args.output_folder is not None) else td\n warning_count = docs.build_html_docs(DOCS_FOLDER, output)\n\n print(\"Documentation written to: {}\", output)\n few_enough_warnings = warning_count <= DOC_WARNING_RATCHET\n print(\n \"Documentation Build Test: {} ({}/{})\",\n \"PASSED\" if few_enough_warnings else \"FAILED\",\n warning_count,\n DOC_WARNING_RATCHET)\n\n return 0 if few_enough_warnings else 1\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "run_sphinx_documentation.py", "file_name": "run_sphinx_documentation.py", "file_ext": "py", "file_size_in_byte": 1524, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "skeletonization.__file__", "line_number": 10, "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": "argparse.ArgumentParser", "line_number": 22, "usage_type": "call"}, {"api_name": "skeletonization.doc_utils.build_api_docs", "line_number": 31, "usage_type": "call"}, {"api_name": "skeletonization.doc_utils", "line_number": 31, "usage_type": "name"}, {"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": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 37, "usage_type": "call"}, {"api_name": "skeletonization.doc_utils.build_html_docs", "line_number": 39, "usage_type": "call"}, {"api_name": "skeletonization.doc_utils", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "246127822", "text": "import pandas\nimport turtle\nimport unidecode\n\nscreen = turtle.Screen()\nscreen.title(\"Lietuvos miestų žaidimas\")\nscreen.setup(width=0.99, height=0.99, startx=0, starty=0)\n\ncanvas = screen.getcanvas()\nroot = canvas.winfo_toplevel()\nroot.overrideredirect(1)\n\nimage = \"100 days of code//day 25//lietuvos miestai//lietuvos-žemėlapis-vektorius.gif\"\n\nscreen.addshape(image)\nturtle.shape(image)\n\ndata = pandas.read_csv(\"100 days of code//day 25//lietuvos miestai//50_lietuvos_miestu_pagal_populiacija.csv\")\nall_cities = data.city.to_list()\n# print(data[\"y\"])\n# print(all_cities)\nguessed_cities = []\n\nwhile len(guessed_cities) < 50:\n # def get_mouse_click_coor(x, y):\n # print(x, y)\n\n # turtle.onscreenclick(get_mouse_click_coor)\n\n # turtle.mainloop()\n\n answer_city = screen.textinput(title = f\"{len(guessed_cities)}/50 miestų su didžiausia populiacija\", \n prompt = \"Norint išeiti įveskite 'Baigiau'. Miesto pavadinimas:\").title()\n unidecode.unidecode(answer_city)\n if answer_city == \"Baigiau\":\n cities_left = []\n for city in all_cities:\n if city not in guessed_cities:\n cities_left.append(city)\n df = pandas.DataFrame(cities_left, columns = ['city'])\n df.to_csv(\"100 days of code//day 25//lietuvos miestai//likę_miestai.csv\")\n break\n if answer_city in all_cities and answer_city not in guessed_cities:\n t = turtle.Turtle()\n t.hideturtle()\n t.penup()\n city_data = data[data.city == answer_city]\n print(city_data.x)\n t.goto(int(city_data.x), int(city_data.y))\n t.color(\"black\")\n t.write(answer_city, font = ('Arial', 12, 'normal', 'bold'))\n\n guessed_cities.append(answer_city)\n \n \n\n# If guessed all\nif len(guessed_cities) == 50:\n t = turtle.Turtle()\n t.hideturtle()\n t.penup()\n t.color(\"red\")\n t.goto(-150, 250)\n t.write(\"Atspėjote visus\", font = ('Arial', 48, 'normal', 'bold'))\n \n # screen.mainloop()\n# xcor = data[\"x\"]\n# xc = xcor.to_string(index = False)\n\n# ycor = data[\"y\"]\n# yc = ycor.to_string(index = False)\n\n# print(\"Yes\")\n# turtle.penup()\n# turtle.goto(xc, yc)\n# turtle.write(answer_city, align = \"center\")\n\n# screen.exitonclick()", "sub_path": "100 days of code/day 25/lietuvos miestai/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2293, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "turtle.Screen", "line_number": 5, "usage_type": "call"}, {"api_name": "turtle.shape", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 18, "usage_type": "call"}, {"api_name": "unidecode.unidecode", "line_number": 34, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 40, "usage_type": "call"}, {"api_name": "turtle.Turtle", "line_number": 44, "usage_type": "call"}, {"api_name": "turtle.Turtle", "line_number": 59, "usage_type": "call"}]} +{"seq_id": "533811630", "text": "import requests\nimport shutil\nfrom datetime import datetime\n\ntoday = datetime.now()\ndt = today.strftime(\"%d%m%Y\")\nimg_pre = (\n \"https://epaper.anandabazar.com/epaperimages////\" + dt + \"////\" + dt + \"-md-hr-\"\n)\nimg_post = \"ll.png\"\n\ni = 1\nimage_url = img_pre + str(i) + img_post\nr = requests.get(image_url, stream=True)\nwhile r.status_code == 200:\n r.raw.decode_content = True\n filename = dt + \"_\" + str(i) + \".png\"\n with open(filename, \"wb\") as f:\n shutil.copyfileobj(r.raw, f)\n i += 1\n image_url = img_pre + str(i) + img_post\n r = requests.get(image_url, stream=True, allow_redirects=False)\n", "sub_path": "download_epaper.py", "file_name": "download_epaper.py", "file_ext": "py", "file_size_in_byte": 619, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datetime.datetime.now", "line_number": 5, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 5, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 14, "usage_type": "call"}, {"api_name": "shutil.copyfileobj", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "550380838", "text": "#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\nfrom lxml.html import document_fromstring\nimport re\nfrom helpers.exceptions import UrlParseError\nfrom manga import MultiThreads\n\ndomainUri = 'https://www.viz.com'\n\n\ndef get_main_content(url, get=None, post=None):\n name = get_manga_name(url)\n return get('{}/shonenjump/chapters/{}'.format(domainUri, name))\n\n\ndef get_volumes(content=None, url=None, get=None, post=None):\n items = document_fromstring(content).cssselect('.o_products .chapter-text > a')\n return [i.get('href') for i in items]\n\n\ndef get_archive_name(volume, index: int = None):\n return 'vol_{:0>3}'.format(index)\n\n\ndef get_images(main_content=None, volume=None, get=None, post=None):\n volume_id = re.search('/chapter/[^/]+/(\\d+)', volume)\n params = [\n 'device%5Fid=3',\n # 'page={}',\n 'manga%5Fid={}'.format(volume_id.groups()[0]),\n 'loadermax=1',\n ]\n\n uri = '{}/manga/get_manga_url?'.format(domainUri)\n uri += '&'.join(params)\n\n n = 0\n _img_index = 0\n while n < 199:\n\n _img_index += 1\n content = get('{}&page={}'.format(uri, n)).encode()\n\n parser = document_fromstring(content).cssselect('ImageLoader')\n\n if not len(parser):\n break\n\n t = MultiThreads()\n\n for i in parser:\n img_url = i.get('url')\n if img_url.find('blankpage.jpg') > 0:\n break\n # see manga.py:280\n t.addThread(download_one_file, (img_url,))\n # safe_downloader(img_url, path.join(temp_root_path, 'img_{:0>3}.jpg'.format(_img_index)))\n t.startAll()\n\n n += 2\n\n return [-1]\n\n \"\"\"\n curl 'https://www.viz.com/manga/get_manga_url?device%5Fid=3&page=16&manga%5Fid=6098&loadermax=1' -H 'x-requested-with: ShockwaveFlash/27.0.0.130' -H 'user-agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/60.0.3112.78 Safari/537.36 OPR/47.0.2631.55' -H 'authority: www.viz.com' -H 'referer: https://www.viz.com/shonenjump/chapter/claymore-chapter-7/6098?read=1' --compressed\n \"\"\"\n\n\ndef get_manga_name(url, get=None):\n name = re.search('\\\\.com/shonenjump/chapters/([^/]+)', url)\n if not name:\n return UrlParseError()\n return name.groups()[0]\n \"\"\"\n :param url: str\n :param get: request.get\n :return: str\n \"\"\"\n pass\n\n\ndownload_one_file = lambda x: x\n", "sub_path": "providers/viz_com.py", "file_name": "viz_com.py", "file_ext": "py", "file_size_in_byte": 2381, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "lxml.html.document_fromstring", "line_number": 18, "usage_type": "call"}, {"api_name": "re.search", "line_number": 27, "usage_type": "call"}, {"api_name": "lxml.html.document_fromstring", "line_number": 45, "usage_type": "call"}, {"api_name": "manga.MultiThreads", "line_number": 50, "usage_type": "call"}, {"api_name": "re.search", "line_number": 71, "usage_type": "call"}, {"api_name": "helpers.exceptions.UrlParseError", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "160499762", "text": "# -*- coding: utf-8 -*-\nimport pytest\n\nfrom tfs import *\n\n\nclass TestTFSAPI:\n @pytest.mark.httpretty\n def test_get_workitems(self, tfsapi):\n workitems = tfsapi.get_workitems(work_items_ids=[100, 101])\n\n assert len(workitems) == 2\n assert workitems[0].id == 100\n assert workitems[1].id == 101\n\n @pytest.mark.httpretty\n def test_get_workitem(self, tfsapi):\n workitem = tfsapi.get_workitem(100)\n\n assert isinstance(workitem, Workitem)\n assert workitem.id == 100\n\n @pytest.mark.httpretty\n def test_get_workitems_with_int(self, tfsapi):\n workitems = tfsapi.get_workitems(work_items_ids=100)\n\n assert len(workitems) == 2\n assert workitems[0].id == 100\n assert workitems[1].id == 101\n\n @pytest.mark.httpretty\n def test_get_changesets(self, tfsapi):\n changesets = tfsapi.get_changesets(from_=10, to_=14)\n\n assert len(changesets) == 5\n assert changesets[0].id == 10\n\n @pytest.mark.httpretty\n def test_get_wiql(self, tfsapi):\n wiql_query = \"SELECT *\"\n wiql = tfsapi.run_wiql(wiql_query)\n\n assert isinstance(wiql, Wiql)\n assert wiql.workitem_ids == [100, 101]\n\n @pytest.mark.httpretty\n def test_get_projects(self, tfsapi):\n projects = tfsapi.get_projects()\n\n assert len(projects) == 1\n assert projects[0]['name'] == 'ProjectName'\n\n @pytest.mark.httpretty\n def test_get_project(self, tfsapi):\n projects = tfsapi.get_project('ProjectName')\n\n assert len(projects) == 1\n assert projects[0]['name'] == 'ProjectName'\n\n @pytest.mark.httpretty\n def test_get_teams(self, tfsapi):\n projects = tfsapi.get_projects()\n team = projects[0].team\n\n assert isinstance(team, TFSObject)\n assert team['name'] == 'ProjectName'\n\n @pytest.mark.httpretty\n def test_get_gitrepositories(self, tfsapi):\n repos = tfsapi.get_gitrepositories()\n name = repos[0].data['name']\n\n assert name == 'AnotherRepository'\n\n @pytest.mark.httpretty\n def test_get_gitrepository(self, tfsapi):\n repo = tfsapi.get_gitrepository('AnotherRepository')\n name = repo.data['name']\n\n assert name == 'AnotherRepository'\n\n\nclass TestHTTPClient:\n def test__get_collection(self):\n collection, project = TFSHTTPClient.get_collection_and_project('DefaultCollection')\n assert collection == 'DefaultCollection'\n assert project is None\n\n def test__get_collection_and_project(self):\n collection, project = TFSHTTPClient.get_collection_and_project('DefaultCollection/Project')\n assert collection == 'DefaultCollection'\n assert project == 'Project'\n\n def test__get_collection_and_project_and_team(self):\n collection, project = TFSHTTPClient.get_collection_and_project('DefaultCollection/Project/Team')\n assert collection == 'DefaultCollection'\n assert project == 'Project'\n", "sub_path": "tests/test_connection.py", "file_name": "test_connection.py", "file_ext": "py", "file_size_in_byte": 2966, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pytest.mark", "line_number": 8, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 68, "usage_type": "attribute"}, {"api_name": "pytest.mark", "line_number": 75, "usage_type": "attribute"}]} +{"seq_id": "144971851", "text": "import cv2\nfrom skimage.measure import compare_mse, compare_nrmse, compare_ssim, compare_psnr\nfrom skimage.measure import compare_ssim\nimport csv\nimport os\nfrom random import randint\nimport math\nimport imutils\n\"\"\"\nIn Code References\nhttps://stackoverflow.com/questions/45945258/compare-frame-of-video-with-another-image-python\nhttp://scikit-image.org/docs/stable/api/skimage.measure.html\nhttps://docs.opencv.org/3.2.0/d8/dc8/tutorial_histogram_comparison.html\n\"\"\"\n\n# 1. Mse\ndef getMse(img1,img2):\n\treturn compare_mse(img1,img2)\n\n# 2. PSNR\ndef getPSNR(img1,img2):\n\treturn compare_psnr(img1,img2)\n\n# 3. Histogram\ndef getHistCompare(img1,img2):\n\thist1 = cv2.calcHist([img1],[0],None,[256],[0,256])\n\thist2 = cv2.calcHist([img2],[0],None,[256],[0,256])\n\t#Histogram Bhattacharya Distance\n\treturn cv2.compareHist(hist1,hist2,3)\n\n# 4. SSIM\ndef getSSIM(img1,img2):\n\tgrayA = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\n\tgrayB = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n\t(score, diff) = compare_ssim(grayA, grayB, full=True)\n\tdiff = (diff * 255).astype(\"uint8\")\n\treturn score,diff\n\n# 5. Entropy \ndef getEntropy(img1,img2):\n\tim = cv2.absdiff(img1,img2)\n\treturn sum(sum(sum(im)))\n\n# 6. Average Object Area\n# 7. Number of objects displaced\n\ndef getAvgObjArea(img):\n\tthresh = cv2.threshold(img, 0, 255,\n\tcv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]\n\tcnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,\n\t\tcv2.CHAIN_APPROX_SIMPLE)\n\tcnts = cnts[0] if imutils.is_cv2() else cnts[1]\n\tnumofcnts = 0\n\tarea = 0.0\n\tfor cnt in cnts:\n\t\tarea+=cv2.contourArea(cnt)\n\t\tnumofcnts+=1\n\tif numofcnts ==0:\n\t\treturn 0,0\n\treturn numofcnts,area/numofcnts\n\n\n\ndef getFeatures(im1,im2,isTampered):\n\timg1 = cv2.imread(im1)\n\timg2 = cv2.imread(im2)\n\t# mse = getMse(img1,img2)\n\t# psnr = getPSNR(mse)\n\t# histogram_compare = getHistCompare(img1,img2)\n\tssim,diffImg = getSSIM(img1,img2)\n\t\n\tfeatures ={}\n\tfeatures['mse']=getMse(img1,img2)\n\tfeatures['psnr']=getPSNR(img1,img2)\n\tfeatures['histogram_compare'] = getHistCompare(img1,img2)\n\tfeatures['ssim']=ssim\n\tfeatures['entropy']=getEntropy(img1,img2)\n\tfeatures['avgObjArea'],features['displacedObjects'] = getAvgObjArea(diffImg)\n\tfeatures['class']=isTampered\n\treturn features\n\n\nwith open('data.csv', 'a') as csvfile:\n\tfieldnames = ['mse','psnr','histogram_compare','ssim','entropy','avgObjArea','displacedObjects','class']\t\n\twriter = csv.DictWriter(csvfile, fieldnames=fieldnames)\n\t# writer.writeheader()\n\t\n#\tNon Tampered Frames\n\ti=0\n\twhile i str:\n\t\tregion = config.get_region()\n\t\tbase = \"https://%s.api.riotgames.com\" % region\n\t\treturn base + url\n\n\tdef set_query(self, query_name: str, queries: Union[str, Iterable]) -> 'RiotURL':\n\t\tnew_queries = []\n\t\tif type(queries) is str:\n\t\t\tnew_queries = [queries]\n\t\tfor query in queries:\n\t\t\tnew_queries.append(str(query))\n\t\tself.queries[query_name] = new_queries\n\t\treturn self\n\n\tdef get_url_with_query(self) -> str:\n\t\tfinal_url = self.url\n\t\tfirst = True\n\t\tfor query_name in self.queries.keys():\n\t\t\tif first:\n\t\t\t\tfinal_url += '?'\n\t\t\t\tfirst = False\n\t\t\telse:\n\t\t\t\tfinal_url += '&'\n\t\t\tfinal_url += \"%s=%s\" % (query_name, ','.join(self.queries[query_name]))\n\t\treturn final_url\n\n\tdef request(self, max_retry: int = 5) -> Union[None, Dict]:\n\t\tself.set_query('api_key', config.get_key())\n\t\turl = self.get_url_with_query()\n\n\t\twhile max_retry > 0:\n\t\t\tr = requests.get(url)\n\n\t\t\tif r.status_code == 200:\n\t\t\t\treturn r.json()\n\t\t\telif r.status_code == 404:\n\t\t\t\tlogging.warning(\"Data not found: %s\" % url)\n\t\t\t\treturn None\n\t\t\telif r.status_code == 429:\n\t\t\t\tbackoff = r.headers.get('Retry-After')\n\t\t\t\tif backoff is None:\n\t\t\t\t\tlogging.warning(\"Code 429 with no Retry-After.\")\n\t\t\t\t\tlogging.warning(r.headers)\n\t\t\t\t\tbackoff = 30\n\t\t\t\tbackoff = int(backoff)\n\t\t\t\tlogging.info(\"Backoff for %d seconds.\" % backoff)\n\t\t\t\ttime.sleep(backoff)\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tlogging.error(url)\n\t\t\t\tlogging.error(r.json().get('status'))\n\n\t\t\t\tif r.status_code == 401:\n\t\t\t\t\tlogging.error(\"API token is not included.\")\n\t\t\t\t\traise NotReachableError\n\t\t\t\telif r.status_code == 403:\n\t\t\t\t\tlogging.error(\"API token or URL is not valid.\")\n\t\t\t\t\traise KeyNotValidError\n\t\t\t\telse:\n\t\t\t\t\tlogging.error(\"Unknown error of code %d. Retrying.\" % r.status_code)\n\t\t\t\t\tmax_retry -= 1\n\t\t\t\t\tcontinue\n\n\t\treturn None\n", "sub_path": "api/riot_api.py", "file_name": "riot_api.py", "file_ext": "py", "file_size_in_byte": 2116, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "api.config.get_region", "line_number": 18, "usage_type": "call"}, {"api_name": "api.config", "line_number": 18, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 22, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 22, "usage_type": "name"}, {"api_name": "api.config.get_key", "line_number": 44, "usage_type": "call"}, {"api_name": "api.config", "line_number": 44, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 48, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 53, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 58, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 59, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 62, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 63, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 66, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 67, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 70, "usage_type": "call"}, {"api_name": "api.error.NotReachableError", "line_number": 71, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 73, "usage_type": "call"}, {"api_name": "api.error.KeyNotValidError", "line_number": 74, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 76, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 43, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "443145376", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Feb 12 14:37:28 2020\n\n@author: alex.messina\n\"\"\"\n\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nimport matplotlib as mpl\nimport datetime as dt\nimport numpy as np\nimport os\n#from Rating_curve import *\n## Set Pandas display options\npd.set_option('display.large_repr', 'truncate')\npd.set_option('display.width', 180)\npd.set_option('display.max_rows', 40)\npd.set_option('display.max_columns', 13)\nplt.ion()\n\n## Set Storm start and end\nstorm_start = dt.datetime(2021,3,10,0,0)\nstorm_end = dt.datetime(2021,3,12,23)\n\nuse_recorded_flow = True\n#use_recorded_flow = False\n\ncfs = ['ViaRancho','DELDIOS', 'FELICITA', 'KITCARSON','GREENVALLEY', 'MOONSONG','CLOVERDALE','GUEJITO','SYCAMORE','SDGCRK']\ngpm = ['ElKu','Tazon','Oceans11','Lomica']\n\n#### INDIVIDUAL SITES\n#creeks\nsite_list = ['DELDIOS']\n#site_list = ['FELICITA']\n#site_list = ['KITCARSON']\n#site_list = ['CLOVERDALE']\n#site_list = ['GUEJITO']\n#site_list = ['SDGCRK']\n#site_list = ['MOONSONG']\n#site_list = ['GREENVALLEY']\n#site_list = ['SYCAMORE']\n#outfalls\nsite_list = ['ElKu']\n#site_list = ['ViaRancho']\n#site_list = ['Tazon']\n#site_list = ['Oceans11']\n#site_list = ['Lomica']\n\n\n\n## CREEKS\n#site_list = ['DELDIOS', 'FELICITA', 'KITCARSON','GREENVALLEY', 'MOONSONG','CLOVERDALE','GUEJITO','SYCAMORE','SDGCRK']\n\n## OUTFALLS\n#site_list = ['ElKu','ViaRancho','Tazon','Oceans11','Lomica']\n\n\nfor site in site_list:\n print (site)\n #datadir = 'C:/Users/alex.messina/Documents/GitHub/Sutron_scripts/Data Download/Log backup 5_11_2020/'\n datadir = 'C:/Users/alex.messina/Documents/LinkComm/Log Files/'\n df = pd.DataFrame()\n \n ## Time Series Data\n for fname in [f for f in os.listdir(datadir) if site in f and 'loggrp' in f]:\n print (fname)\n df_ind = pd.read_csv(datadir+fname,index_col=0,header=0,skiprows=[1])\n \n ## Rename flow data column\n if site in gpm:\n flow_units = 'gpm'\n print ('Rename PT Flow to Flow_gpm')\n df_ind = df_ind.rename(columns={'PT Flow':'Flow_gpm','Flow _PT':'Flow_gpm','Flow_PT':'Flow_gpm'})\n if site in cfs:\n flow_units = 'cfs'\n print ('Rename PT Flow to Flow_cfs')\n df_ind = df_ind.rename(columns={'PT Flow':'Flow_cfs','Flow _PT':'Flow_cfs','Flow_PT':'Flow_cfs'}) \n ## Rename Level\n df_ind = df_ind.rename(columns={'PT Level':'Level_PT'}) \n \n if site=='GREENVALLEY':\n df_ind = df_ind.rename(columns={'PT North':'Level_PT_No', 'PT South':'Level_PT_So'}) \n \n \n ## Rename other stuff\n df_ind = df_ind.rename(columns={'Aliquot_Num':'AliquotNum','Curr_pacing':'SamplePacin','FlowVolume':'Incr_Flow'})\n \n ## Combine date and time\n if 'Time' in df_ind.columns:\n df_ind.index = pd.to_datetime(df_ind.index +' '+ df_ind['Time']) \n df_ind.index = pd.to_datetime(df_ind.index)\n ## append to df\n df = df.append(df_ind)#,sort=True)\n \n ##format df\n # Replace error values == -99999\n df = df.replace(-99999,np.nan)\n df = df.replace(-99,np.nan)\n # ensure datetime index and drop duplicates\n df.index = pd.to_datetime(df.index)\n df['Datetime'] = df.index\n df = df.drop_duplicates(subset='Datetime')\n \n # Interpolate battery level\n if 'Battery_950' in df.columns:\n df['Battery_950'] = df['Battery_950'].interpolate('linear',axis=0,limit = 13)\n \n # Interpolate data to fill gap\n for col in ['Level_950','Vel_950','Flow_950']:\n if col in df:\n df[col] = df[col].interpolate('linear',axis=0,limit=3)\n \n if site == 'Tazon':\n df['Flow_gpm'] = df['Flow_950']\n df['Level_PT'] = df['Level_950']\n\n# df['PT Level'].plot()\n \n ## Alarm Data\n events = pd.DataFrame()\n for fname in [f for f in os.listdir(datadir) if site in f and 'events' in f]:\n print (fname)\n df_events = pd.read_csv(datadir+fname,index_col=0,header=0,skiprows=[1])\n if 'Time' in df_ind.columns:\n df_events.index = pd.to_datetime(df_events.index +' '+ df_devents['Time'])\n ## append to df\n events = events.append(df_events,sort=True)\n # ensure datetime index and drop duplicates\n events.index = pd.to_datetime(events.index)\n events['Datetime'] = events.index\n events = events.drop_duplicates(subset='Datetime') \n \n ## Event df's\n ## Aliquots\n aliquots = events[events['Label']=='Triggered S'][['Label','Value']]\n manual_grabs = events[events['Label']=='Trigger Man'][['Label','Value']]\n if site in gpm:\n aliquots['Flow_gpm'] = df['Flow_gpm']\n if site in cfs:\n aliquots['Flow_cfs'] = df['Flow_cfs']\n aliquots = aliquots.dropna()\n aliquots['Datetime'] = aliquots.index \n aliquots['Time between aliquots'] = aliquots['Datetime'].diff()\n aliquots = aliquots.drop('Datetime',1)\n aliquots = aliquots.rename(columns={'Value':'Aliquot#'}) \n aliquots['Aliquot#'] = aliquots['Aliquot#'].astype(int)\n \n \n ## Alarms\n alarm_in = events[events['Label']=='Alarm In'][['Label','Value']]\n alarm_out = events[events['Label']=='Alarm Out'][['Label','Value']]\n ## Bottle changes\n bottle_change = events[events['Label']=='BottleChang'][['Label','Value']]\n\n ## \n ## now resample to 5Min \n# df = df.resample('1Min')#.mean() \n \n \n##%% RECALCULATE FLOWS\n if use_recorded_flow == False:\n ## Rating Curve\n rating_curves = pd.ExcelFile(datadir+'Current_RatingCurves.xlsx')\n rating_curve = rating_curves.parse(sheetname=site,skiprows=1,header=0)\n rating_curve = rating_curve.round(2)\n rating_curve.index = rating_curve['Stage (in)']\n ## From rating curve\n if site == 'GREENVALLEY':\n df['Flow_north_cfs'] = pd.DataFrame(df['Level_PT_No'].apply(lambda x: rating_table(rating_curve,float(x))),columns=['Level_PT_No'])\n df['Flow_south_cfs'] = pd.DataFrame(df['Level_PT_So'].apply(lambda x: rating_table(rating_curve,float(x))),columns=['Level_PT_So'])\n df['Flow_cfs'] = df['Flow_north_cfs'] + df['Flow_south_cfs']\n else:\n df['Flow_cfs'] = pd.DataFrame(level['Result'].apply(lambda x: rating_table(rating_curve,float(x))),columns=['Result'])\n \n#%% PLOT\n fig, ax1 = plt.subplots(1,1,figsize=(16,8))\n fig.suptitle(site,fontsize=14,fontweight='bold')\n \n ## Water Level\n if site=='GREENVALLEY':\n ax1.plot_date(df.index,df['Level_PT_No'],ls='-',marker='None',c='r',label='Water Level from PT North')\n ax1.plot_date(df.index,df['Level_PT_So'],ls='-',marker='None',c='g',label='Water Level from PT South')\n ax1.set_ylim(0, df['Level_PT_No'].max()*1.25)\n else:\n ax1.plot_date(df.index,df['Level_PT'],ls='-',marker='None',c='r',label='Water Level from PT')\n ax1.set_ylim(0, df['Level_PT'].max()*1.25)\n ax1.set_ylabel('Water Level (inches)',color='r',fontsize=14,fontweight='bold')\n ax1.spines['left'].set_color('r')\n ax1.tick_params(axis='y',colors='r',labelsize=14)\n ax1.xaxis.set_major_formatter(mpl.dates.DateFormatter('%A \\n %m/%d/%y %H:%M'))\n \n ## Flow\n ax2 = ax1.twinx()\n if site=='GREENVALLEY' and use_recorded_flow==False:\n print ('GREEN VALLEY recalculated flows')\n ax2.plot_date(df.index,df['Flow_north_cfs'] ,ls='-',marker='None',c='teal',label='Flow from HvF (north)')\n ax2.plot_date(df.index,df['Flow_south_cfs'] ,ls='-',marker='None',c='b',alpha=0.6,label='Flow from HvF (south)')\n ax2.plot_date(df.index,df['Flow_cfs'],ls='-',marker='None',c='b',label='Flow from HvF (Total)')\n else:\n if site in cfs:\n ax2.plot_date(df.index,df['Flow_cfs'],ls='-',marker='None',c='b',label='Flow from HvF')\n ax2.set_ylabel('Flow (cfs)',color='b',fontsize=14,fontweight='bold')\n ## Plot Aliquots\n if len(aliquots) >0:\n ax2.plot_date(aliquots.index,aliquots['Flow_cfs'],ls='None',marker='o',c='k',label='Aliquots')\n for al in aliquots.iterrows():\n #print (al)\n al_num = \"%.0f\"%al[1]['Aliquot#']\n ax2.annotate(al_num,xy=(pd.to_datetime(al[0]),al[1]['Flow_cfs']*1.05),ha='center')\n \n if site in gpm:\n ax2.plot_date(df.index,df['Flow_gpm'],ls='-',marker='None',c='b',label='Flow from HvF')\n ax2.set_ylabel('Flow (gpm)',color='b',fontsize=14,fontweight='bold')\n ax2.set_ylim(0, df['Flow_gpm'].max()*1.1)\n ## Plot Aliquots\n if len(aliquots) >0:\n ax2.plot_date(aliquots.index,aliquots['Flow_gpm'],ls='None',marker='o',c='k',label='Aliquots')\n for al in aliquots.iterrows():\n #print (al)\n al_num = \"%.0f\"%al[1]['Aliquot#']\n ax2.annotate(al_num,xy=(pd.to_datetime(al[0]),al[1]['Flow_gpm']*1.05),ha='center')\n ax2.xaxis.set_major_formatter(mpl.dates.DateFormatter('%A \\n %m/%d/%y %H:%M')) \n # Plot Bottle Changes\n for b_chng in bottle_change.iterrows():\n ax1.axvline(b_chng[0],label='Bottle: '+\"%.0f\"%b_chng[1]['Value'],c='grey',alpha=0.6)\n ax1.annotate('^ Bottle '+\"%.0f\"%b_chng[1]['Value']+' ^',xy=(b_chng[0],5),ha='left',rotation=-90)\n\n ## FMT \n ax2.spines['right'].set_color('b')\n ax2.tick_params(axis='y',colors='b',labelsize=14)\n \n ax1.legend(fontsize=14,ncol=1,loc='upper left')\n ax2.legend(fontsize=14,loc='upper right')\n \n plt.tight_layout()\n plt.subplots_adjust(top=0.95)\n \n ## Zoom to storm\n ax1.set_xlim(storm_start,storm_end)\n if site == 'GREENVALLEY':\n ax1.set_ylim(0, df.loc[storm_start:storm_end,'Level_PT_No'].max()*1.25)\n else:\n ax1.set_ylim(0, df.loc[storm_start:storm_end,'Level_PT'].max()*1.25)\n ax2.set_ylim(0, df.loc[storm_start:storm_end,'Flow_'+flow_units].max()*1.1)\n \n print (aliquots[storm_start:storm_end])\n print ('Minimum time between aliquots: '+ str(aliquots.loc[storm_start:storm_end,'Time between aliquots'].min()))\n \n print ('Peak flow rate: ' + \"%.2f\"%df.loc[storm_start:storm_end,'Flow_'+flow_units].max() + flow_units)\n if site == 'GREENVALLEY':\n print ('Peak stage: ' + \"%.2f\"%df.loc[storm_start:storm_end,'Level_PT_No'].max() + 'inches')\n print ('Peak stage: ' + \"%.2f\"%df.loc[storm_start:storm_end,'Level_PT_So'].max() + 'inches')\n \n else:\n print ('Peak stage: ' + \"%.2f\"%df.loc[storm_start:storm_end,'Level_PT'].max() + 'inches')\n\n\n#%%\nimport mpld3\nhtml_file= open('C:/Users/alex.messina/Documents/GitHub/Sutron_scripts/LakeHodges/Interactive Data Files/'+site+'-flow_data.html',\"w\")\nmpld3.save_html(fig,html_file)\nhtml_file.close()\n\n#%% Scatterplot 950 vs PT LEVEL\n\n#df = df[df.index > dt.datetime(2020,12,26)]\n\nfig,ax = plt.subplots(1,1)\nplt.scatter(df['Level_950'],df['Level_PT'],c='grey',alpha=0.5,label='raw data')\nplt.scatter(df['Level_950'],df['Level_PT']-0.75,c='r',label='-0.75 offset')\nplt.xlabel('Level 950'), plt.ylabel('Level PT')\nplt.xlim(0,18), plt.ylim(0,18)\nplt.plot([0,20],[0,20],ls='--',marker='None',c='grey')\nplt.legend()\n\n\n#%% Scatterplot 950 vs PT FLOW\nfig,ax = plt.subplots(1,1)\nplt.scatter(df['Flow_950'],df['Flow _PT'],c='grey',alpha=0.5)\nplt.scatter(df['Flow_950'],df['Flow _PT'],c='r')\nplt.xlabel('Flow 950'), plt.ylabel('Flow PT')\nplt.xlim(0,18), plt.ylim(0,18)\nplt.plot([0,20],[0,20],ls='--',marker='None',c='grey')\n\n#%% Scatterplot 950 Level Velocity\nfig,ax = plt.subplots(1,1)\nplt.scatter(df['Level_950'],df['Vel_950'],c='r',label='Level_950 vs Vel_950')\nplt.scatter(df['Level_PT'],df['Vel_950'],c='b',label='Level_PT vs Vel_950')\nplt.xlabel('Level 950 and Level_PT'), plt.ylabel('Velocity 950 ')\nplt.xlim(0,18), plt.ylim(0,18)\nplt.plot([0,20],[0,20],ls='--',marker='None',c='grey')\nlegend(loc='upper right')\n\n\n#%% SAVE TO CSV\n# if site == 'GREENVALLEY':\n# df_out = pd.DataFrame({'Level_North_in':level_north['Result'],'Level_South_in':level_south['Result'],'Flow_North_cfs':flow_north['Result'],'Flow_South_cfs':flow_south['Result'],'Flow_cfs':flow['Result']})\n# \n# else:\n# df_out = pd.DataFrame({'Level_in':level['Result'],'Flow_cfs':flow['Result']})\n# df_out = df_out[df_out != -99999.0].dropna()\n# \n# if site == 'SDGCRK':\n# ## just get rid of data prior since it wasn't offset and doesnt matter\n# df_out.ix[:dt.datetime(2020,3,18,8,50)] = np.nan\n# df_out = df_out.dropna()\n# # shift one hour forward to match PDT\n# df_out.ix[:dt.datetime(2020,3,26,15,0)] = df_out.ix[:dt.datetime(2020,3,26,15,0)].set_index(df_out.ix[:dt.datetime(2020,3,26,15,0)].index + dt.timedelta(minutes=60))\n# \n\n \n \n# df_out.to_csv(datadir +'just level and flow/'+ site+'_level_and_flow.csv')\n \n\n\n\n\n\n\n", "sub_path": "Data_visualization_2020.py", "file_name": "Data_visualization_2020.py", "file_ext": "py", "file_size_in_byte": 12847, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.set_option", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.set_option", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ion", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 63, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 66, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 68, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 91, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 99, "usage_type": "attribute"}, {"api_name": "pandas.to_datetime", "line_number": 101, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 121, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 122, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 124, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 126, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 130, "usage_type": "call"}, {"api_name": "pandas.ExcelFile", "line_number": 164, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 170, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 171, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.dates.DateFormatter", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 191, "usage_type": "attribute"}, {"api_name": "pandas.to_datetime", "line_number": 210, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 222, "usage_type": "call"}, {"api_name": "matplotlib.dates.DateFormatter", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.dates", "line_number": 223, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 236, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 237, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 237, "usage_type": "name"}, {"api_name": "mpld3.save_html", "line_number": 262, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 269, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 269, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 270, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 270, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 271, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 271, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 272, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 272, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 272, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 273, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 273, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 273, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 274, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 274, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 275, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 275, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 279, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 279, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 280, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 280, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 281, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 281, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 282, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 282, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 282, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 283, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 283, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 283, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 284, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 284, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 287, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 287, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 288, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 288, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 289, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 289, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 290, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 290, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 290, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 291, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 291, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 291, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 292, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 292, "usage_type": "name"}]} +{"seq_id": "116291843", "text": "import unittest\nimport os\nimport sys\nimport boto3\nimport json\nsys.path.insert(0, './')\nimport add_cluster\nfrom moto import mock_dynamodb2\n\nclass TestAddCluster(unittest.TestCase):\n \"\"\"Testing for add_cluster.py\"\"\"\n\n @mock_dynamodb2\n def test_add_cluster(self):\n \"\"\"Setup DynamoDB tables for hyper-kube-config\n \"\"\"\n\n os.environ[\"DYNAMODB_TABLE_K8_CLUSTERS\"] = \"hyper-kube-config-test\"\n self.dynamodb = boto3.resource('dynamodb', region_name='us-east-1')\n self.dynamodb.create_table(\n AttributeDefinitions=[\n {\n 'AttributeName': 'id',\n 'AttributeType': 'S'\n },\n ],\n TableName='hyper-kube-config-test',\n KeySchema=[\n {\n 'AttributeName': 'id',\n 'KeyType': 'HASH'\n }\n ],\n ProvisionedThroughput={\n 'ReadCapacityUnits': 1,\n 'WriteCapacityUnits': 5\n },\n )\n self.hyper_kube_config_table = self.dynamodb.Table(os.environ[\"DYNAMODB_TABLE_K8_CLUSTERS\"])\n\n\n\nif __name__ == '__main__':\n unittest.main()", "sub_path": "tests/add_cluster_test.py", "file_name": "add_cluster_test.py", "file_ext": "py", "file_size_in_byte": 1192, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.path.insert", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "boto3.resource", "line_number": 19, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 39, "usage_type": "attribute"}, {"api_name": "moto.mock_dynamodb2", "line_number": 13, "usage_type": "name"}, {"api_name": "unittest.main", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "198163241", "text": "import serial\nimport pynmea2\nimport numpy as np\nser = serial.Serial('/dev/ttyUSB0',baudrate=4800)\nser.flushInput()\nwhile True:\n a= ser.readline()\n a = a.decode(\"utf-8\")\n nmeaobj = np.array(a.split(\",\"))\n if nmeaobj[0] == \"$GPRMC\":\n print(\"Latitude = \",str(nmeaobj[3]),str(nmeaobj[4]))\n print(\"Longitude = \",str(nmeaobj[5]),str(nmeaobj[6]))", "sub_path": "gps.py", "file_name": "gps.py", "file_ext": "py", "file_size_in_byte": 365, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "serial.Serial", "line_number": 4, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "426211183", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport numpy as np\nimport scipy as sp\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\nfrom matplotlib import rc\nrc('font',**{'family':'sans-serif','sans-serif':['Computer Modern'],'size':26})\n## for Palatino and other serif fonts use:\n#rc('font',**{'family':'serif','serif':['Palatino']})\nrc('text', usetex=True)\n# matplotlib.rcParams['figure.dpi'] = 400\n\nN=14\ntol = -8\nexact_energy = np.load(\"./pxp,0th_order,e,\"+str(N)+\".npy\")\nexact_overlap = np.load(\"./pxp,0th_order,LW_overlap,\"+str(N)+\".npy\")\nfsa_energy = np.load(\"./pxp,LW_fsa,0th_order,e,\"+str(N)+\".npy\")\nfsa_overlap = np.load(\"./pxp,LW_fsa,0th_order,LW_overlap,\"+str(N)+\".npy\")\nto_del=[]\nfor n in range(0,np.size(exact_overlap,axis=0)):\n if exact_overlap[n] < tol:\n to_del = np.append(to_del,n)\nfor n in range(np.size(to_del,axis=0)-1,-1,-1):\n exact_overlap=np.delete(exact_overlap,to_del[n])\n exact_energy=np.delete(exact_energy,to_del[n])\n \nplt.scatter(exact_energy,exact_overlap)\nplt.scatter(fsa_energy,fsa_overlap,marker=\"x\",color=\"red\",s=100)\nplt.xlabel(r\"$E$\")\nplt.ylabel(r\"$\\log(\\vert \\langle E \\vert H^z, LW \\rangle \\vert^2)$\")\nplt.show()\n\nexact_energy = np.load(\"./pxp,1st_order,e,\"+str(N)+\".npy\")\nexact_overlap = np.load(\"./pxp,1st_order,LW_overlap,\"+str(N)+\".npy\")\nfsa_energy = np.load(\"./pxp,LW_fsa,1st_order,e,\"+str(N)+\".npy\")\nfsa_overlap = np.load(\"./pxp,LW_fsa,1st_order,LW_overlap,\"+str(N)+\".npy\")\nto_del=[]\nfor n in range(0,np.size(exact_overlap,axis=0)):\n if exact_overlap[n] ', methods=['GET', 'POST'])\ndef edit_committee(committee=''):\n if not ModuleAPI.can_write('committee'):\n return abort(403)\n\n path = 'commissie/' + committee\n\n page = Page.get_by_path(path)\n\n form = request.form\n if page:\n revision = page.get_latest_revision()\n form = CommitteeForm(form, revision)\n else:\n revision = None\n form = CommitteeForm()\n\n try:\n url_group_id = int(request.args.get('group_id', None))\n except:\n url_group_id = None\n\n form.group_id.choices = [(group.id, group.name) for group in\n Group.query.order_by(Group.name).all()]\n\n if len(request.form) == 0:\n if revision:\n selected_group_id = revision.group_id\n elif url_group_id is not None:\n selected_group_id = url_group_id\n else:\n selected_group_id = form.group_id.choices[0][0]\n else:\n selected_group_id = int(form.group_id.data)\n\n form.group_id.data = selected_group_id\n\n selected_group = Group.query.get(selected_group_id)\n form.coordinator_id.choices = [\n (user.id, user.name) for user in\n selected_group.users.order_by(User.first_name, User.last_name).all()]\n\n form.nl_title.data = selected_group.name\n\n if form.validate_on_submit():\n committee_nl_title = form.nl_title.data.strip()\n committee_en_title = form.en_title.data.strip()\n\n if not page:\n root_entry_url = url_for('committee.list').rstrip('/')\n root_entry = NavigationEntry.query\\\n .filter(NavigationEntry.url == root_entry_url)\\\n .first()\n\n # Check whether the root navigation entry exists.\n if not root_entry:\n last_root_entry = NavigationEntry.query\\\n .filter(NavigationEntry.parent_id == None)\\\n .order_by(NavigationEntry.position.desc()).first() # noqa\n\n root_entry_position = 1\n if last_root_entry:\n root_entry_position = last_root_entry.position + 1\n\n root_entry = NavigationEntry(\n None, 'Commissies', 'Committees', root_entry_url, False,\n False, root_entry_position)\n\n db.session.add(root_entry)\n db.session.commit()\n\n page = Page(path, 'committee')\n\n # Never needs paid.\n page.needs_paid = False\n\n # Create a navigation entry for the new committee.\n last_navigation_entry = NavigationEntry.query\\\n .filter(NavigationEntry.parent_id == root_entry.id)\\\n .first()\n\n entry_position = 1\n if last_navigation_entry:\n entry_position = last_navigation_entry.position + 1\n\n navigation_entry = NavigationEntry(\n root_entry, committee_nl_title, committee_en_title, '/' + path,\n False, False, entry_position)\n\n db.session.add(navigation_entry)\n db.session.commit()\n\n # Sort these navigation entries.\n NavigationAPI.alphabeticalize(root_entry)\n\n # Assign the navigation entry to the new page (committee).\n page.navigation_entry_id = navigation_entry.id\n\n db.session.add(page)\n db.session.commit()\n\n # Assign read rights to all, and edit rights to BC.\n all_group = Group.query.filter(Group.name == 'all').first()\n bc_group = Group.query.filter(Group.name == 'BC').first()\n\n all_entry = PagePermission(all_group.id, page.id, 1)\n bc_entry = PagePermission(bc_group.id, page.id, 2)\n\n db.session.add(all_entry)\n db.session.add(bc_entry)\n db.session.commit()\n else:\n # If the committee's title has changed, the navigation needs to be\n # updated. Look for the entry, compare the titles, and change where\n # necessary.\n entry = NavigationEntry.query\\\n .filter(NavigationEntry.url == '/' + path).first()\n if entry.title != committee_nl_title:\n entry.title = committee_nl_title\n db.session.add(entry)\n db.session.commit()\n\n group_id = int(form.group_id.data)\n coordinator_id = int(form.coordinator_id.data)\n\n # Add coordinator to BC\n bc_group = Group.query.filter(Group.name == \"BC\").first()\n if bc_group is not None:\n new_coordinator = User.query.filter(\n User.id == coordinator_id).first()\n bc_group.add_user(new_coordinator)\n\n new_revision = CommitteeRevision(\n page, committee_nl_title, committee_en_title,\n form.comment.data.strip(), current_user.id,\n form.nl_description.data.strip(), form.en_description.data.strip(),\n group_id, coordinator_id, form.interim.data)\n\n db.session.add(new_revision)\n db.session.commit()\n\n flash(_('The committee has been saved.'), 'success')\n\n return redirect(url_for('page.get_page', path=path))\n else:\n flash_form_errors(form)\n\n return render_template('committee/edit.htm', page=page,\n form=form, path=path)\n", "sub_path": "app/views/committee.py", "file_name": "committee.py", "file_ext": "py", "file_size_in_byte": 6035, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Blueprint", "line_number": 14, "usage_type": "call"}, {"api_name": "app.utils.committee.get_alphabetical", "line_number": 19, "usage_type": "call"}, {"api_name": "app.utils.committee", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 20, "usage_type": "call"}, {"api_name": "app.utils.ModuleAPI.can_write", "line_number": 25, "usage_type": "call"}, {"api_name": "app.utils.ModuleAPI", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 26, "usage_type": "call"}, {"api_name": "app.models.Page.get_by_path", "line_number": 30, "usage_type": "call"}, {"api_name": "app.models.Page", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 32, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 32, "usage_type": "name"}, {"api_name": "app.forms.CommitteeForm", "line_number": 35, "usage_type": "call"}, {"api_name": "app.forms.CommitteeForm", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "app.models.Group.query.order_by", "line_number": 46, "usage_type": "call"}, {"api_name": "app.models.Group.query", "line_number": 46, "usage_type": "attribute"}, {"api_name": "app.models.Group", "line_number": 46, "usage_type": "name"}, {"api_name": "app.models.Group.name", "line_number": 46, "usage_type": "attribute"}, {"api_name": "flask.request.form", "line_number": 48, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 48, "usage_type": "name"}, {"api_name": "app.models.Group.query.get", "line_number": 60, "usage_type": "call"}, {"api_name": "app.models.Group.query", "line_number": 60, "usage_type": "attribute"}, {"api_name": "app.models.Group", "line_number": 60, "usage_type": "name"}, {"api_name": "app.models.User.first_name", "line_number": 63, "usage_type": "attribute"}, {"api_name": "app.models.User", "line_number": 63, "usage_type": "name"}, {"api_name": "app.models.User.last_name", "line_number": 63, "usage_type": "attribute"}, {"api_name": "flask.url_for", "line_number": 72, "usage_type": "call"}, {"api_name": "app.models.NavigationEntry.query.filter", "line_number": 73, "usage_type": "call"}, {"api_name": "app.models.NavigationEntry.query", "line_number": 73, "usage_type": "attribute"}, {"api_name": "app.models.NavigationEntry", "line_number": 73, "usage_type": "name"}, {"api_name": "app.models.NavigationEntry.url", "line_number": 74, "usage_type": "attribute"}, {"api_name": "app.models.NavigationEntry", "line_number": 74, "usage_type": "name"}, {"api_name": "app.models.NavigationEntry.query.filter", "line_number": 79, "usage_type": "call"}, {"api_name": "app.models.NavigationEntry.query", "line_number": 79, "usage_type": "attribute"}, {"api_name": "app.models.NavigationEntry", "line_number": 79, "usage_type": "name"}, {"api_name": "app.models.NavigationEntry.parent_id", "line_number": 80, "usage_type": "attribute"}, {"api_name": "app.models.NavigationEntry", "line_number": 80, "usage_type": "name"}, {"api_name": "app.models.NavigationEntry.position.desc", "line_number": 81, "usage_type": "call"}, {"api_name": "app.models.NavigationEntry.position", "line_number": 81, "usage_type": "attribute"}, {"api_name": "app.models.NavigationEntry", "line_number": 81, "usage_type": "name"}, {"api_name": "app.models.NavigationEntry", "line_number": 87, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 91, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 91, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 91, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 92, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 92, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 92, "usage_type": "name"}, {"api_name": "app.models.Page", "line_number": 94, "usage_type": "call"}, {"api_name": "app.models.NavigationEntry.query.filter", "line_number": 100, "usage_type": "call"}, {"api_name": "app.models.NavigationEntry.query", "line_number": 100, "usage_type": "attribute"}, {"api_name": "app.models.NavigationEntry", "line_number": 100, "usage_type": "name"}, {"api_name": "app.models.NavigationEntry.parent_id", "line_number": 101, "usage_type": "attribute"}, {"api_name": "app.models.NavigationEntry", "line_number": 101, "usage_type": "name"}, {"api_name": "app.models.NavigationEntry", "line_number": 108, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 112, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 112, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 112, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 113, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 113, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 113, "usage_type": "name"}, {"api_name": "app.utils.NavigationAPI.alphabeticalize", "line_number": 116, "usage_type": "call"}, {"api_name": "app.utils.NavigationAPI", "line_number": 116, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 121, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 121, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 121, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 122, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 122, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 122, "usage_type": "name"}, {"api_name": "app.models.Group.query.filter", "line_number": 125, "usage_type": "call"}, {"api_name": "app.models.Group.query", "line_number": 125, "usage_type": "attribute"}, {"api_name": "app.models.Group", "line_number": 125, "usage_type": "name"}, {"api_name": "app.models.Group.name", "line_number": 125, "usage_type": "attribute"}, {"api_name": "app.models.Group.query.filter", "line_number": 126, "usage_type": "call"}, {"api_name": "app.models.Group.query", "line_number": 126, "usage_type": "attribute"}, {"api_name": "app.models.Group", "line_number": 126, "usage_type": "name"}, {"api_name": "app.models.Group.name", "line_number": 126, "usage_type": "attribute"}, {"api_name": "app.models.PagePermission", "line_number": 128, "usage_type": "call"}, {"api_name": "app.models.PagePermission", "line_number": 129, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 131, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 131, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 131, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 132, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 132, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 132, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 133, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 133, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 133, "usage_type": "name"}, {"api_name": "app.models.NavigationEntry.query.filter", "line_number": 138, "usage_type": "call"}, {"api_name": "app.models.NavigationEntry.query", "line_number": 138, "usage_type": "attribute"}, {"api_name": "app.models.NavigationEntry", "line_number": 138, "usage_type": "name"}, {"api_name": "app.models.NavigationEntry.url", "line_number": 139, "usage_type": "attribute"}, {"api_name": "app.models.NavigationEntry", "line_number": 139, "usage_type": "name"}, {"api_name": "app.db.session.add", "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": "app.models.Group.query.filter", "line_number": 149, "usage_type": "call"}, {"api_name": "app.models.Group.query", "line_number": 149, "usage_type": "attribute"}, {"api_name": "app.models.Group", "line_number": 149, "usage_type": "name"}, {"api_name": "app.models.Group.name", "line_number": 149, "usage_type": "attribute"}, {"api_name": "app.models.User.query.filter", "line_number": 151, "usage_type": "call"}, {"api_name": "app.models.User.query", "line_number": 151, "usage_type": "attribute"}, {"api_name": "app.models.User", "line_number": 151, "usage_type": "name"}, {"api_name": "app.models.User.id", "line_number": 152, "usage_type": "attribute"}, {"api_name": "app.models.User", "line_number": 152, "usage_type": "name"}, {"api_name": "app.models.CommitteeRevision", "line_number": 155, "usage_type": "call"}, {"api_name": "flask_login.current_user.id", "line_number": 157, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 157, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 161, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 161, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 161, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 162, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 162, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 162, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 164, "usage_type": "call"}, {"api_name": "flask_babel._", "line_number": 164, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 166, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 166, "usage_type": "call"}, {"api_name": "app.utils.forms.flash_form_errors", "line_number": 168, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 170, "usage_type": "call"}]} +{"seq_id": "512640232", "text": "from enum import Enum, auto\nimport cv2\nimport numpy as np\n\nclass ColorSpace(Enum):\n BGR = auto()\n RGB = auto()\n HSV = auto()\n GRAY = auto()\n\nclass ImagePreprocessor:\n def __init__(self, original_images, color=ColorSpace.BGR):\n # original images have bgr color space\n # original_images.shape is (image_num, height, width, channel)\n self.original_images = original_images\n self.color = ColorSpace.BGR\n if original_images.shape[3] != 3: # if gray\n self.color = ColorSpace.GRAY\n # size.shape is (width, height)\n def preprocess(self, size, color=None, do_normalize=True):\n images = self.__resize(self.original_images, size)\n \n if self.color == color:\n print(f'Already color space is {color}.')\n elif self.color == ColorSpace.GRAY: # if gray\n print('image color is gray')\n elif color is None:\n pass\n else:\n images = self.__cnvcolor(images, color)\n \n if do_normalize:\n images = self.__normalize(images)\n return images\n\n \n \n def threshold(self, size, color, thresholds):\n images = self.__resize(self.original_images, size)\n if self.color != color:\n images = self.__cnvcolor(images, color)\n str_color = str(color).split('.')[1]\n def _threshold(image):\n res = []\n for channel, image_one_channel in zip(str_color, cv2.split(image)):\n _, image_thresholded = cv2.threshold(image_one_channel, thresholds[channel], 255, cv2.THRESH_BINARY)\n res.append(np.expand_dims(image_thresholded, -1))\n # concatenate channel(e.g. r+g+b -> rgb)\n return np.concatenate(res, -1)\n \n return self.__normalize(np.array([_threshold(x) for x in images]))\n \n \n def __resize(self, images, size):\n return np.asarray([cv2.resize(x, size, interpolation=cv2.INTER_AREA) for x in images], dtype=np.float32)\n \n \n def __cnvcolor(self, images, color):\n if self.color == color:\n return images\n \n convert_enum = None\n \n if self.color == ColorSpace.BGR:\n if color == ColorSpace.RGB:\n convert_enum = cv2.COLOR_BGR2RGB\n elif color == ColorSpace.HSV:\n convert_enum = cv2.COLOR_BGR2HSV\n elif self.color == ColorSpace.RGB:\n if color == ColorSpace.BGR:\n convert_enum = cv2.COLOR_RGB2BGR\n elif color == ColorSpace.HSV:\n convert_enum = cv2.COLOR_RGB2HSV\n \n return np.array([cv2.cvtColor(x, convert_enum) for x in images])\n\n \n def __normalize(self, images):\n if len(images.shape) == 3:\n images = np.expand_dims(images, -1)\n \n normalized_images = np.empty_like(images)\n for i in range(images.shape[3]):\n max_val = np.max(images[:, :, :, i])\n normalized_images[:, :, :, i] = images[:, :, :, i] / max_val\n return normalized_images\n \n \n def normalize(self, images):\n return self.__normalize(images)\n \n \n", "sub_path": "jscas_ai_challenge_2020_data/preprocess.py", "file_name": "preprocess.py", "file_ext": "py", "file_size_in_byte": 3183, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "enum.Enum", "line_number": 5, "usage_type": "name"}, {"api_name": "enum.auto", "line_number": 6, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 7, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 8, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.split", "line_number": 45, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.expand_dims", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 55, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 55, "usage_type": "attribute"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 66, "usage_type": "attribute"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 68, "usage_type": "attribute"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 71, "usage_type": "attribute"}, {"api_name": "cv2.COLOR_RGB2HSV", "line_number": 73, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 75, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.empty_like", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 84, "usage_type": "call"}]} +{"seq_id": "534817771", "text": "\"\"\"empty message\n\nRevision ID: d397e23ee861\nRevises: 6efae7b1c2f5\nCreate Date: 2020-07-21 00:53:57.195159\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'd397e23ee861'\ndown_revision = '6efae7b1c2f5'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('Venue', sa.Column('seeking_description', sa.String(length=500), nullable=True))\n op.add_column('Venue', sa.Column('seeking_talent', sa.Boolean(), nullable=True))\n op.execute('UPDATE \"Venue\" SET seeking_talent=False WHERE seeking_description IS NULL')\n op.alter_column('Venue','seeking_talent',nullable=False)\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_column('Venue', 'seeking_talent')\n op.drop_column('Venue', 'seeking_description')\n # ### end Alembic commands ###\n", "sub_path": "projects/01_fyyur/starter_code/migrations/versions/d397e23ee861_.py", "file_name": "d397e23ee861_.py", "file_ext": "py", "file_size_in_byte": 969, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "alembic.op.add_column", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op.add_column", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Boolean", "line_number": 22, "usage_type": "call"}, {"api_name": "alembic.op.execute", "line_number": 23, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 23, "usage_type": "name"}, {"api_name": "alembic.op.alter_column", "line_number": 24, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 24, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 30, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 30, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 31, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 31, "usage_type": "name"}]} +{"seq_id": "520830760", "text": "from itertools import permutations\n\n\ndef is_same(seq_1: list, seq_2: list):\n for shift in range(len(seq_1)):\n if seq_2 == (seq_1[-shift:] + seq_1[: -shift]):\n return True\n return False\n\n\ndef generate_sequences(ones_num: int, digits_num: int):\n sequences = list()\n zeros_num = digits_num - ones_num\n for sequence in permutations([0] * zeros_num + [1] * ones_num):\n is_add = True\n for seq in sequences:\n if is_same(list(sequence), list(seq)):\n is_add = False\n break\n if is_add:\n sequences.append(sequence)\n return sequences\n\n\ndef find_axes(sequence: list):\n axes = list()\n len_ = len(sequence)\n seq = sequence\n mid = len_ // 2\n if len_ % 2 == 0:\n for shift in range(mid):\n if seq[1:mid] == seq[:mid:-1]:\n axes.append([shift, \"nodes\"])\n if seq[:mid] == seq[:mid - 1:-1]:\n axes.append([shift, \"middles\"])\n seq = seq[-1:] + seq[: -1]\n else:\n for shift in range(len_):\n if seq == seq[::-1]:\n axes.append([len_ - shift - 1, \"mid_node\"])\n seq = seq[-1:] + seq[: -1]\n return axes\n\n\ndef print_axes(sequence: list):\n axis_ = find_axes(sequence)\n for node_ in axis_:\n type_ = node_[1]\n node_1 = node_[0]\n with open(\"result.csv\", 'a') as file:\n if type_ == \"nodes\":\n node_2 = (node_1 + len(sequence) // 2) % len(sequence)\n for i in range(len(sequence)):\n node = sequence[i]\n if i is node_1 or i is node_2:\n file.write(f\"({node})\")\n else:\n file.write(f\"{node}\")\n file.write(\" \")\n if type_ == \"middles\":\n node_2 = (node_1 + len(sequence) // 2) % len(sequence)\n for i in range(len(sequence)):\n node = sequence[i]\n if i is node_1 or i is node_2:\n file.write(\"|\")\n file.write(f\"{node}\")\n file.write(\" \")\n if type_ == \"mid_node\":\n node_2 = (node_1 + 1 + len(sequence) // 2) % len(sequence)\n for i in range(len(sequence)):\n node = sequence[i]\n if i is node_2:\n file.write(f'({node})')\n else:\n file.write(f\"{node}\")\n if i is node_1:\n file.write(\"|\")\n file.write(\" \")\n\n\nwith open(\"result.csv\", 'w') as file:\n file.write(\"Num of digits,Num of ones,Classes\\n\")\nfor n in range(3, 10):\n for k in range(n + 1):\n combinations = generate_sequences(k, n)\n with open(\"result.csv\", 'a') as file:\n file.write(f\"n={n},k = {k},\")\n for sequence_ in combinations:\n print_axes(sequence_)\n with open(\"result.csv\", 'a') as file:\n file.write(f\"\\n\")\n", "sub_path": "Antonov Aleksei/Symmetry/Symmetry.py", "file_name": "Symmetry.py", "file_ext": "py", "file_size_in_byte": 3050, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "itertools.permutations", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "493137542", "text": "#!python3\n'''\n updateProduce.py - corrige os preços em uma planilha de venda de produtos\n'''\nimport openpyxl\n\nwb = openpyxl.load_workbook('produceSales.xlsx')\nsheet = wb['Sheet']\nPRICE_UPDATES = {'Garlic': 4.01, 'Celery': 2.19, 'Lemon': 0.99}\n# Percorre todas as linhas em um loop e atualiza os preços\nfor rowNum in range(2, len(sheet['A'])):\n produceName = sheet.cell(row=rowNum, column=1).value\n if produceName in PRICE_UPDATES:\n sheet.cell(row=rowNum, column=2).value = PRICE_UPDATES[produceName]\n\nwb.save('updatedSales.xlsx')\n", "sub_path": "updateProduce_v2.py", "file_name": "updateProduce_v2.py", "file_ext": "py", "file_size_in_byte": 548, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "openpyxl.load_workbook", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "403641865", "text": "import json\nimport os\nimport requests\n\nfrom django.http.response import HttpResponse\nfrom django.utils.decorators import method_decorator\nfrom django.views import generic\nfrom django.views.decorators.csrf import csrf_exempt\n\nfrom vbl_api import VBLInterface\n\nVBL_URL = 'http://vblcb.wisseq.eu/VBLCB_WebService/data/'\n\nACCESS_TOKEN = os.environ.get('ACCESS_TOKEN', None)\nFACEBOOK_URL = os.environ.get('FACEBOOK_URL', None)\nVERIFY_TOKEN = os.environ.get('VERIFY_TOKEN', None)\n\n\nclass MessengerBotView(generic.View):\n def get(self, request, *args, **kwargs):\n if self.request.GET.get('hub.verify_token', False) == VERIFY_TOKEN:\n return HttpResponse(self.request.GET['hub.challenge'])\n else:\n return HttpResponse('Error, invalid token')\n\n @method_decorator(csrf_exempt)\n def dispatch(self, request, *args, **kwargs):\n return generic.View.dispatch(self, request, *args, **kwargs)\n\n def post(self, request, *args, **kwargs):\n incoming_message = json.loads(self.request.body.decode('utf-8'))\n for entry in incoming_message['entry']:\n for message in entry['messaging']:\n if 'message' in message:\n fb_id = message['sender']['id']\n response = self.create_response(message['message']['text'])\n return self.send_message(fb_id, response)\n return HttpResponse()\n\n def create_response(self, message):\n return 'Your message: %s' % message\n vbl_interface = VBLInterface()\n\n if 'team' in message:\n return vbl_interface.response_team_data(message)\n\n if 'club' in message:\n return vbl_interface.response_club_data(message)\n\n return 'Please provide keywords team or club in your message.'\n\n def send_message(self, fb_id, message):\n post_message_url = '%s?access_token=%s' % (FACEBOOK_URL, ACCESS_TOKEN)\n headers = {\n \"Content-Type\": \"application/json\"\n }\n\n # Send a response to the client.\n values = {\n \"recipient\": {\"id\": fb_id},\n \"message\": {\"text\": message}\n }\n requests.post(post_message_url, headers=headers, data=json.dumps(values))\n\n\ndef home(request):\n vbl_interface = VBLInterface()\n club_teams = vbl_interface.response_club_data('club BBC As ')\n # return HttpResponse(json.dumps(club_teams), content_type=\"application/json\")\n\n my_team = find_team_from_club(club_teams, 'BBC As HSE A')\n team_guid = my_team['guid']\n team_data = load_team_data(team_guid)[0]\n team_competitions = team_data['poules']\n\n my_competition = find_competition_from_team(team_competitions, '3e Provinciale Heren Limburg B')\n competition_guid = my_competition['guid']\n competition_data = load_competition_data(competition_guid)\n\n team_results = load_team_results(team_guid)\n\n return HttpResponse(json.dumps(team_results), content_type=\"application/json\")\n\n\ndef load_team_results(team_guid):\n url = '%sTeamMatchesByGuid?teamGuid=%s' % (VBL_URL, team_guid)\n result_data = requests.get(url).json()\n return result_data\n\n\ndef load_competition_data(competition_guid):\n url = '%spouleByGuid?pouleguid=%s' % (VBL_URL, competition_guid)\n competition_data = requests.get(url).json()\n return competition_data\n\n\ndef find_competition_from_team(team_competitions, competition):\n for team_comp in team_competitions:\n if team_comp['naam'] == competition:\n return team_comp\n\n\ndef find_team_from_club(club_teams, team):\n for club_team in club_teams:\n if club_team['naam'] == team:\n return club_team\n\n\ndef load_team_data(team_guid):\n url = '%sTeamDetailByGuid?teamGuid=%s' % (VBL_URL, team_guid)\n team_data = requests.get(url).json()\n return team_data\n", "sub_path": "vbl_messenger/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3803, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.environ.get", "line_number": 14, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 15, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 16, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}, {"api_name": "django.views.generic.View", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 19, "usage_type": "name"}, {"api_name": "django.http.response.HttpResponse", "line_number": 22, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponse", "line_number": 24, "usage_type": "call"}, {"api_name": "django.views.generic.View.dispatch", "line_number": 28, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 28, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 28, "usage_type": "name"}, {"api_name": "django.utils.decorators.method_decorator", "line_number": 26, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 26, "usage_type": "argument"}, {"api_name": "json.loads", "line_number": 31, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponse", "line_number": 38, "usage_type": "call"}, {"api_name": "vbl_api.VBLInterface", "line_number": 42, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 63, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 63, "usage_type": "call"}, {"api_name": "vbl_api.VBLInterface", "line_number": 67, "usage_type": "call"}, {"api_name": "django.http.response.HttpResponse", "line_number": 82, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 82, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 87, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 93, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 111, "usage_type": "call"}]} +{"seq_id": "150673125", "text": "import pandas as pd\nfrom pandas import DataFrame\nimport matplotlib.pyplot as plt\nfrom sklearn import linear_model\nfrom sklearn import tree\nfrom sklearn import preprocessing\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn import neighbors\nfrom sklearn import neural_network\nfrom sklearn.ensemble import RandomForestRegressor\nimport statsmodels.api as sm\nimport spotipy\nimport spotipy.util as util\nimport random\nimport csv\nimport private\nimport lyricsgenius\nimport textstat\nimport graphviz\n\ndef Train():\n columnNames = ['Name', 'Duration', 'Popularity', 'Key', 'Time Sig', 'Energy', 'Instrumentalness', 'Loudness', 'Tempo', 'LyricSimplicity', 'Sections', 'Singable']\n songs = pd.read_csv(r\"Resources\\TrainingData.csv\")\n trainingDataframe = DataFrame(songs, columns=columnNames)\n #scatterPlot(trainingDataframe)\n\n X = trainingDataframe[['Duration', 'Popularity', 'Key', 'Energy', 'Instrumentalness', 'Tempo', 'LyricSimplicity', 'Sections']]\n Y = trainingDataframe['Singable']\n\n regression = RandomForestRegressor(n_estimators=400, max_features=8, max_depth=None, min_samples_split=2)\n regression = regression.fit(X,Y)\n return regression\n\ndef Predict(regression):\n scope = 'user-library-read playlist-modify-public'\n token = util.prompt_for_user_token(private.spotifyUsername, scope, private.spotifyClientId, private.spotifyClientSecret, \"http://localhost\")\n spot = spotipy.Spotify(auth=token)\n\n if token:\n songUris = []\n playlist = spot.user_playlist_create(private.spotifyUserId, \"Singable\")\n while len(songUris) < 10:\n track = spot.current_user_saved_tracks(1, random.randint(0, 1268))\n song = track['items'][0]['track']\n \n # Don't want duplicate songs in this playlist\n if (song[\"uri\"] not in songUris):\n trackFeatures = spot.audio_features(song[\"uri\"])[0]\n trackAnalysis = spot.audio_analysis(song[\"uri\"])\n name = song[\"name\"]\n artist = song[\"artists\"][0][\"name\"]\n print(name)\n simplicity = LyricDifficulty(GetLyrics(name, artist))\n duration = song[\"duration_ms\"]\n popularity = song[\"popularity\"]\n key = trackFeatures[\"key\"]\n energy = trackFeatures[\"energy\"]\n instrumentalness = trackFeatures[\"instrumentalness\"]\n loudness = trackFeatures[\"loudness\"]\n tempo = trackFeatures[\"tempo\"]\n sections = len(trackAnalysis[\"sections\"])\n\n prediction = regression.predict([[duration, popularity, key, energy, instrumentalness, tempo, simplicity, sections]])\n # Checking for prediction confidence. > 60% and we'll add it to the playlist \n if (float(prediction[0]) > 0.60):\n songUris.append(song[\"uri\"])\n print(name + \" Prediction: \" + str(prediction))\n spot.user_playlist_add_tracks(private.spotifyUserId, playlist[\"id\"], songUris)\n\ndef scatterPlot(dataFrame):\n plt.scatter(dataFrame['Simplicity'], dataFrame['Singable'], color='red')\n plt.title(\"Simple VS Singable\", fontsize=14)\n plt.xlabel(\"Simple\", fontsize=14)\n plt.ylabel(\"Singable\")\n plt.grid(True)\n plt.show()\n\ndef ScrapeSongs():\n scope = 'user-library-read'\n token = util.prompt_for_user_token(private.spotifyUsername, scope, private.spotifyClientId, private.spotifyClientSecret, \"http://localhost\")\n\n if token:\n spot = spotipy.Spotify(auth=token)\n\n # We're grabbing 200 songs total, in batches of 5\n with open(\"TrainingData.csv\", \"w+\", newline='') as file:\n for i in range(0, 40):\n songUris = []\n totalSongs = spot.current_user_saved_tracks(1,0)[\"total\"]\n results = spot.current_user_saved_tracks(5, random.randint(0, totalSongs)) # 1268 is the total amount of songs I have, need to find a way to get this number dynamically\n\n for item in results['items']:\n if (item[\"track\"][\"uri\"] not in songUris):\n songUris.append(item['track']['uri'])\n tracks = spot.tracks(songUris)['tracks']\n trackFeatures = spot.audio_features(songUris)\n\n for i in range(0, len(tracks)):\n trackAnalysis = spot.audio_analysis(songUris[i])\n trackInfo = []\n trackInfo.append(tracks[i][\"name\"])\n trackInfo.append(tracks[i][\"duration_ms\"])\n trackInfo.append(tracks[i][\"popularity\"])\n trackInfo.append(trackFeatures[i][\"key\"])\n trackInfo.append(trackFeatures[i][\"time_signature\"])\n trackInfo.append(trackFeatures[i][\"energy\"])\n trackInfo.append(trackFeatures[i][\"instrumentalness\"])\n trackInfo.append(trackFeatures[i][\"loudness\"])\n trackInfo.append(trackFeatures[i][\"tempo\"])\n trackInfo.append(LyricDifficulty(GetLyrics(tracks[i][\"name\"], tracks[i][\"artists\"][0][\"name\"])))\n trackInfo.append(len(trackAnalysis[\"sections\"]))\n writer = csv.writer(file, delimiter=\",\")\n writer.writerow(trackInfo)\n print(len(songUris))\n\ndef GetLyrics(songName, songArtist):\n try:\n genius = lyricsgenius.Genius(private.geniusAccessToken)\n song = genius.search_song(songName, songArtist)\n if (song is not None):\n return song.lyrics\n return None\n except:\n return None\n\ndef LyricDifficulty(lyrics):\n if (lyrics is not None):\n return textstat.flesch_reading_ease(lyrics)\n return 0\n\n\nregression = Train()\nPredict(regression)\n#ScrapeSongs()\n#print(LyricDifficulty(GetLyrics(\"Hunter\", \"Tonedeff\")))", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5899, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_csv", "line_number": 23, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestRegressor", "line_number": 30, "usage_type": "call"}, {"api_name": "spotipy.util.prompt_for_user_token", "line_number": 36, "usage_type": "call"}, {"api_name": "spotipy.util", "line_number": 36, "usage_type": "name"}, {"api_name": "private.spotifyUsername", "line_number": 36, "usage_type": "attribute"}, {"api_name": "private.spotifyClientId", "line_number": 36, "usage_type": "attribute"}, {"api_name": "private.spotifyClientSecret", "line_number": 36, "usage_type": "attribute"}, {"api_name": "spotipy.Spotify", "line_number": 37, "usage_type": "call"}, {"api_name": "private.spotifyUserId", "line_number": 41, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 43, "usage_type": "call"}, {"api_name": "private.spotifyUserId", "line_number": 68, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "spotipy.util.prompt_for_user_token", "line_number": 80, "usage_type": "call"}, {"api_name": "spotipy.util", "line_number": 80, "usage_type": "name"}, {"api_name": "private.spotifyUsername", "line_number": 80, "usage_type": "attribute"}, {"api_name": "private.spotifyClientId", "line_number": 80, "usage_type": "attribute"}, {"api_name": "private.spotifyClientSecret", "line_number": 80, "usage_type": "attribute"}, {"api_name": "spotipy.Spotify", "line_number": 83, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 90, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 112, "usage_type": "call"}, {"api_name": "lyricsgenius.Genius", "line_number": 118, "usage_type": "call"}, {"api_name": "private.geniusAccessToken", "line_number": 118, "usage_type": "attribute"}, {"api_name": "textstat.flesch_reading_ease", "line_number": 128, "usage_type": "call"}]} +{"seq_id": "561441926", "text": "from datetime import datetime as dt\nfrom sys import _getframe as gf\nfrom requests import get\nfrom bs4 import BeautifulSoup\n\nclass URLHandler(object):\n\n\tdef __init__(self,cfg_file,log,debug): # added debug \n\t\tself.min_h = cfg_file[\"urls_settings\"][\"lower_time_range\"][0]\n\t\tself.min_m = cfg_file[\"urls_settings\"][\"lower_time_range\"][1]\n\t\tself.min_s = cfg_file[\"urls_settings\"][\"lower_time_range\"][2]\n\n\t\tself.max_h = cfg_file[\"urls_settings\"][\"upper_time_range\"][0]\n\t\tself.max_m = cfg_file[\"urls_settings\"][\"upper_time_range\"][1]\n\t\tself.max_s = cfg_file[\"urls_settings\"][\"upper_time_range\"][2]\n\n\t\tself._log_buffer = list() # pushing msg for log inhere\n\t\tself._table_indices = cfg_file[\"table_indices\"]\n\t\tself._cfg = cfg_file\n\t\tself._log = log\n\t\tself._OK = True\n\t\tself.d = debug \n\t\n\tdef isOK(self):\n\t\t\"\"\" Return overall status for emails \"\"\"\n\t\treturn self._OK\n\n\tdef is_time_in_range(self,x):\n\t\tstart = dt.today().replace(hour=self.min_h,minute=self.min_m,second=self.min_s).timestamp()\n\t\tend = dt.today().replace(hour=self.max_h,minute=self.max_m,second=self.max_s).timestamp()\n\t\ts = x.strip().split(\" \")\n\t\tif '' in s:\n\t\t\ts.remove('')\n\t\ts1 = \"{} {}, {} {}\".format(s[1],s[2],s[4],s[3])\n\t\tx = dt.strptime(s1, \"%b %d, %Y %H:%M:%S\").timestamp()\n\t\treturn True if start < x and x < end else False\n\t\n\tdef flowstream_check(self,inst_cnt, str_time):\n\t\t\"\"\" Returns the status for Primary and Secondary Flowstream \"\"\"\n\t\treturn inst_cnt == \"1\" and self.is_time_in_range(str_time)\n\n\tdef wombat_check(self,lines_cnt):\n\t\t\"\"\" Returns the status for Wombat on market data consumption \"\"\"\n\t\treturn lines_cnt in self._table_indices[\"wombat\"][\"check\"]\n\n\tdef subscription_check(self,status):\n\t\t\"\"\" Returns cache status\"\"\"\n\t\treturn status in self._table_indices[\"rai_cache\"][\"check\"]\n\n\tdef raptor_ibm_check(self,status):\n\t\t\"\"\" Returns status for IBM and Raptor connections \"\"\"\n\t\treturn status in self._table_indices[\"ibm_rap_links\"][\"check\"]\n\n\tdef process_req(self):\n\t\tr = \"\" # request\n\t\tself.d.write(\" Line {} - Entering process_req(..)\\n\".format(gf().f_lineno),\"a\")\n\t\tfor i in self._cfg[\"urls\"]:\n\t\t\tif isinstance(self._cfg['urls'][i],str):\n\t\t\t\ttry:\n\t\t\t\t\tself.d.write(\" Line {} - process_req(..), >> FIRST 'try' block\\n\".format(gf().f_lineno),\"a\")\n\t\t\t\t\tr = get(self._cfg[\"urls\"][i])\n\t\t\t\t\tpage = BeautifulSoup(r.text, 'html.parser')\t\n\t\t\t\t\ttable = page.find_all(\"table\")\t\n\t\t\t\t\t# return iterator on rows and pass it to analyze(..)\t\n\t\t\t\t\t# 3 = table index; our page has 4 tables; our value is in the last one\t\t\n\t\t\t\t\trows = iter(table[3].find_all(\"tr\")) \n\t\t\t\t\tx = self.analyze(rows,i)\n\t\t\t\t\tif x[0]:\n\t\t\t\t\t\tlog_msg = x[1].ljust(25) + \" < OK > \".ljust(15) + self._cfg[\"urls\"][i]\n\t\t\t\t\t\tself._log_buffer.append(log_msg + \"\\n\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tlog_msg = x[1].ljust(25) + \" < ERROR > \".ljust(15) + self._cfg[\"urls\"][i]\n\t\t\t\t\t\tself._log_buffer.append(log_msg + \"\\n\")\n\t\t\t\t\t\tself._OK = False\n\t\t\t\texcept BaseException as e:\n\t\t\t\t\tself.d.write(\" Line {} - process_req(..), >> Exception in FIRST 'try'\\n {}\".format(gf().f_lineno,e),\"a\")\n\t\t\telse:\n\t\t\t\tfor j in self._cfg['urls'][i]:\n\t\t\t\t\ttry:\n\t\t\t\t\t\tself.d.write(\" Line {} - process_req(..), >> SECONDS 'try' block\\n\".format(gf().f_lineno),\"a\")\n\t\t\t\t\t\tr = get(j)\n\t\t\t\t\t\tpage = BeautifulSoup(r.text, 'html.parser')\t\n\t\t\t\t\t\ttable = page.find_all(\"table\")\t\t\n\t\t\t\t\t\trows = iter(table[3].find_all(\"tr\")) \n\t\t\t\t\t\tx = self.analyze(rows,j)\n\t\t\t\t\t\tif x[0]:\n\t\t\t\t\t\t\tlog_msg = x[1].ljust(25) + \" < OK > \".ljust(15) + j\n\t\t\t\t\t\t\tself._log_buffer.append(log_msg + \"\\n\")\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tlog_msg = x[1].ljust(25) + \" < ERROR > \".ljust(15) + j\n\t\t\t\t\t\t\tself._log_buffer.append(log_msg + \"\\n\")\n\t\t\t\t\t\t\tself._OK = False\n\t\t\t\t\texcept BaseException as e:\n\t\t\t\t\t\tself.d.write(\" Line {} - process_req(..), >> Exception in SECONDS 'try'\\n {}\".format(gf().f_lineno,e),\"a\")\n\t\tself._dump_log()\n\n\tdef analyze(self,row_iter, url_tag):\n\t\t\"\"\" Returns check result for each individual row \"\"\"\n\t\tself.d.write(\" Line {} - Entering analyze(..)\\n\".format(gf().f_lineno),\"a\")\n\t\tnext(row_iter)\n\t\tprime_cnt = 0\n\t\tfor row in row_iter:\n\t\t\tcells = [x.string.strip() for x in row.find_all(\"td\")]\n\t\t\tif url_tag == \"flowstream_prime\":\n\t\t\t\tif cells[0] in self._table_indices[url_tag][\"check\"]:\n\t\t\t\t\tcnt = cells[1] # instanceCount \n\t\t\t\t\ttime = cells[8] # startTime \n\t\t\t\t\tstat = self.flowstream_check(cnt,time)\n\t\t\t\t\tif not stat:\n\t\t\t\t\t\treturn [False, \"ITRS - Invalid data.\"] \n\t\t\telif url_tag == \"flowstream_second\":\n\t\t\t\tif cells[0] in self._table_indices[url_tag][\"check\"]:\n\t\t\t\t\tcnt = cells[1] # instanceCount \n\t\t\t\t\ttime = cells[8] # startTime \n\t\t\t\t\tstat = self.flowstream_check(cnt,time)\n\t\t\t\t\tif not stat:\n\t\t\t\t\t\treturn [False, \"ITRS - Invalid data.\"] \n\t\t\telif url_tag == \"wombat\":\n\t\t\t\treturn [self.wombat_check(cells[2]), \n\t\t\t\t\"ITRS - Validated.\" if self.wombat_check(cells[2]) else \"ITRS - Invalid data.\"]\n\t\t\telif url_tag == \"rai_cache\":\n\t\t\t\tif self.subscription_check(cells[1]):\n\t\t\t\t\tprime_cnt += 1\n\t\t\telif url_tag == \"ibm_rap_links\":\n\t\t\t\tif not self.raptor_ibm_check(cells[2]):\n\t\t\t\t\treturn [False, \"ITRS - Invalid data.\"] \t\t\n\t\tif url_tag == \"rai_cache\" and prime_cnt != 2:\n\t\t\treturn [False, \"ITRS - Invalid data.\"] \n\t\treturn [True, \"ITRS - Validated.\"]\n\n\tdef _dump_log(self):\n\t\t\"\"\" Writes the status of overall run for emails module \"\"\"\n\t\tself._log.header(\"urls\",65,\"w+\")\n\t\tself._log.write(self._log_buffer, \"a\")", "sub_path": "urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 5280, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datetime.datetime.today", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 29, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 30, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "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": "sys._getframe", "line_number": 56, "usage_type": "call"}, {"api_name": "sys._getframe", "line_number": 60, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 61, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 62, "usage_type": "call"}, {"api_name": "sys._getframe", "line_number": 76, "usage_type": "call"}, {"api_name": "sys._getframe", "line_number": 80, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 81, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 82, "usage_type": "call"}, {"api_name": "sys._getframe", "line_number": 94, "usage_type": "call"}, {"api_name": "sys._getframe", "line_number": 99, "usage_type": "call"}]} +{"seq_id": "490950648", "text": "import base64\nimport functools\nimport getpass\nimport hashlib\nimport json\nimport multiprocessing\nimport pathlib\nimport shutil\n\nimport lz4.frame\nimport requests\nimport tensorflow as tf\nimport tqdm\nfrom PIL import Image\n\nfrom utils.model_logger import logger\n\n\ndef get_session():\n username = input(\"Username:\")\n password = getpass.getpass()\n\n s = requests.Session()\n r = s.get(\"http://13.125.1.208/book/login/?next=/book\")\n s.post(\n \"http://13.125.1.208/book/login/?next=/book\",\n data={\n \"password\": password,\n \"username\": username,\n \"csrfmiddlewaretoken\": r.cookies.get(\"csrftoken\"),\n },\n )\n return s\n\n\ndef save_image(url, directory, filename, file_type):\n directory_path = pathlib.Path(directory)\n file_path = directory_path / filename\n new_label = directory_path.parts[-1]\n\n directory_path.mkdir(exist_ok=True, parents=True)\n\n if file_type:\n if filename in file_type:\n label = file_type[filename][0]\n\n if label != new_label:\n prev_path = directory_path.parents[0] / label / filename\n logger.debug(\"Move from {} to {}\".format(prev_path, file_path))\n try:\n shutil.move(prev_path, file_path)\n except:\n pass\n return\n\n if file_path.exists():\n return\n\n try:\n r = requests.get(url, stream=True)\n except requests.exceptions.ConnectionError:\n print(\"Skipping {}\".format(url))\n return\n\n if r.status_code == 200:\n with open(file_path, \"wb\") as f:\n r.raw.decode_content = True\n shutil.copyfileobj(r.raw, f)\n\n\ndef parse(data, data_type=\"pokemon_yes_no\", black_list=None, file_type=None):\n url = data[\"fields\"][\"url\"]\n filename = url.split(\"/\")[-1]\n\n url_hash = int(hashlib.sha1(url.encode(\"utf-8\")).hexdigest(), 16) % 100\n\n if data_type == \"pokemon_yes_no\":\n label = data[\"fields\"][\"classified\"]\n elif data_type == \"pokemon_classification\":\n label = data[\"fields\"][\"original_label\"]\n else:\n label = data[\"fields\"][\"selected\"]\n\n if url_hash < 90:\n target_path = \"data/{}/train/{}\".format(data_type, label)\n else:\n target_path = \"data/{}/validate/{}\".format(data_type, label)\n\n if black_list and target_path + \"/\" + filename in black_list:\n print(\"Skipping {} since it is listed in blacklist\".format(url))\n return\n\n save_image(url, target_path, filename, file_type)\n\n\ndef validate_image(data_type=\"pokemon_yes_no\"):\n print(\"Validate Images\")\n if pathlib.Path(\"blacklist.json\").exists():\n with open(\"blacklist.json\", \"r\") as f:\n ignore_list = json.load(f)\n else:\n ignore_list = []\n for file_path in pathlib.Path(\"data/\" + data_type + \"/\").glob(\"**/*\"):\n if file_path.is_file():\n str_file_path = str(file_path)\n normalized_str_file_path = str_file_path.replace(\"\\\\\", \"/\")\n if normalized_str_file_path in ignore_list:\n print(\"Skipping {}\".format(file_path))\n file_path.unlink()\n continue\n\n img = tf.io.read_file(str_file_path)\n try:\n tf.image.decode_jpeg(img, channels=3)\n except Exception:\n print(\"Converting\", str_file_path)\n\n try:\n im = Image.open(str_file_path)\n try:\n im.save(str_file_path, \"JPEG\")\n\n except Exception:\n im.close()\n print(\"Converting Failed add to ignore list\")\n file_path.unlink()\n\n if normalized_str_file_path not in ignore_list:\n ignore_list.append(normalized_str_file_path)\n except Exception:\n file_path.unlink()\n\n if normalized_str_file_path not in ignore_list:\n ignore_list.append(normalized_str_file_path)\n\n with open(\"blacklist.json\", \"w\") as w:\n w.write(json.dumps(ignore_list))\n\n\ndef download_pokemon(session, file_type, label=\"yes\"):\n download(\n url=\"http://13.125.1.208/book/pokemon_export/\",\n file_type=file_type,\n label=label,\n session=session,\n )\n\n\ndef download_people(session, file_type, label=\"True\"):\n download(\n url=\"http://13.125.1.208/book/people_result/download/\",\n file_type=file_type,\n label=label,\n data_type=\"people\",\n session=session,\n )\n\n\ndef download(url, session, label=\"True\", data_type=\"pokemon_yes_no\", file_type=None):\n page = 1\n\n black_list_path = pathlib.Path(\"blacklist.json\")\n black_list = None\n if black_list_path.exists():\n with open(black_list_path) as f:\n black_list = json.load(f)\n black_list = set(black_list)\n\n parse_function = functools.partial(\n parse, data_type=data_type, black_list=black_list, file_type=file_type\n )\n with multiprocessing.Pool(5) as pool:\n while True:\n request_url = url + label + \"/\" + str(page)\n results = session.get(url + label + \"/\" + str(page))\n print(request_url)\n\n pickled = base64.b85decode(results.text)\n\n decompressed = lz4.frame.decompress(pickled)\n\n with open(\"./zip.txt\", \"w\") as w:\n w.write(results.text)\n\n data = decompressed.decode(\"utf-8\")\n with open(\"./result.txt\", \"w\") as w:\n w.write(data)\n\n data_json = json.loads(data)\n image_list = data_json[\"image_list\"]\n\n with tqdm.tqdm(total=len(image_list)) as pbar:\n for i, _ in enumerate(pool.imap_unordered(parse_function, image_list)):\n pbar.update()\n page += 1\n if not data_json[\"has_next\"]:\n break\n", "sub_path": "src/train/data_downloader.py", "file_name": "data_downloader.py", "file_ext": "py", "file_size_in_byte": 5958, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "getpass.getpass", "line_number": 21, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 23, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 37, "usage_type": "call"}, {"api_name": "utils.model_logger.logger.debug", "line_number": 49, "usage_type": "call"}, {"api_name": "utils.model_logger.logger", "line_number": 49, "usage_type": "name"}, {"api_name": "shutil.move", "line_number": 51, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 60, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 61, "usage_type": "attribute"}, {"api_name": "shutil.copyfileobj", "line_number": 68, "usage_type": "call"}, {"api_name": "hashlib.sha1", "line_number": 75, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 98, "usage_type": "call"}, {"api_name": "json.load", "line_number": 100, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 103, "usage_type": "call"}, {"api_name": "tensorflow.io.read_file", "line_number": 112, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 112, "usage_type": "attribute"}, {"api_name": "tensorflow.image.decode_jpeg", "line_number": 114, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 114, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 119, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 119, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 137, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 162, "usage_type": "call"}, {"api_name": "json.load", "line_number": 166, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 169, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 172, "usage_type": "call"}, {"api_name": "base64.b85decode", "line_number": 178, "usage_type": "call"}, {"api_name": "lz4.frame.frame.decompress", "line_number": 180, "usage_type": "call"}, {"api_name": "lz4.frame.frame", "line_number": 180, "usage_type": "attribute"}, {"api_name": "lz4.frame", "line_number": 180, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 189, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 192, "usage_type": "call"}]} +{"seq_id": "2606881", "text": "import os.path as osp\nimport pickle\n\nimport mastic.interactions.pi_stacking as pinx\nimport mastic.interactions.hydrogen_bond as hinx\n\nwork_dir = \"/home/salotz/Dropbox/devel/mastic/work/pi_stacking\"\n\n# load the SystemType\nbenzene_system_pkl_path = osp.join(work_dir, \"Benzene_Benzene_SystemType.pkl\")\nwith open(benzene_system_pkl_path, 'rb') as rf:\n Benzene_Benzene_SystemType = pickle.load(rf)\n\n# load the coordinates for the reference benzene\nref_benzene_PDB_path = osp.join(work_dir, \"ref_benzene.pdb\")\n\nfrom rdkit import Chem\n\nref_benzene_rdkit = Chem.MolFromPDBFile(ref_benzene_PDB_path, removeHs=False, sanitize=False)\n\nfrom mastic.interfaces.rdkit import RDKitMoleculeWrapper\n\nbenzene_rdkit_wrapper = RDKitMoleculeWrapper(ref_benzene_rdkit, mol_name=\"benzene\")\n\nref_benzene_coords = benzene_rdkit_wrapper.get_conformer_coords(0)\n\nfrom mastic.interactions.pi_stacking import PiStackingType\n\n# get the interaction space for pi-stacking\npistack_inx_classes = Benzene_Benzene_SystemType.interaction_space([(0,1)], PiStackingType)[(0,1)]\n\n# profile the stacked one that should qualify\nstacked_member_coords = [ref_benzene_coords, test_benzenes['stacked']]\nstacked_system = Benzene_Benzene_SystemType.to_system(stacked_member_coords)\n\n# profile the interactions between the two rings\nstacked_inxs = stacked_system.associations[0].\\\n profile_interactions([PiStackingType],\n interaction_classes=pistack_inx_classes)\\\n [PiStackingType]\n\n# substantiate the systems and profile each one\ntest_inxs = {}\ntest_failed_hits = {}\nfor test_name, test_benzene in test_benzenes.items():\n member_coords = [ref_benzene_coords, test_benzene]\n system = Benzene_Benzene_SystemType.to_system(member_coords)\n\n # profile the interactions between the two rings\n failed_hits, all_inxs = system.associations[0].\\\n profile_interactions([PiStackingType],\n interaction_classes=pistack_inx_classes,\n return_failed_hits=True)\n inxs = all_inxs[PiStackingType]\n test_failed_hits[test_name] = failed_hits\n test_inxs[test_name] = inxs\n", "sub_path": "work/pi_stacking/profile_test_cases.py", "file_name": "profile_test_cases.py", "file_ext": "py", "file_size_in_byte": 2165, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "name"}, {"api_name": "pickle.load", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "name"}, {"api_name": "rdkit.Chem.MolFromPDBFile", "line_number": 19, "usage_type": "call"}, {"api_name": "rdkit.Chem", "line_number": 19, "usage_type": "name"}, {"api_name": "mastic.interfaces.rdkit.RDKitMoleculeWrapper", "line_number": 23, "usage_type": "call"}, {"api_name": "mastic.interactions.pi_stacking.PiStackingType", "line_number": 30, "usage_type": "argument"}, {"api_name": "mastic.interactions.pi_stacking.PiStackingType", "line_number": 38, "usage_type": "name"}, {"api_name": "mastic.interactions.pi_stacking.PiStackingType", "line_number": 40, "usage_type": "name"}, {"api_name": "mastic.interactions.pi_stacking.PiStackingType", "line_number": 51, "usage_type": "name"}, {"api_name": "mastic.interactions.pi_stacking.PiStackingType", "line_number": 54, "usage_type": "name"}]} +{"seq_id": "282814357", "text": "from tempest import openstack\nimport unittest2 as unittest\nfrom nose.plugins.attrib import attr\nimport os\nimport subprocess\nfrom paramiko import SSHClient\nfrom paramiko import AutoAddPolicy\nimport tempest.config\nfrom tempest.common.utils.data_utils import rand_name\nfrom tempest import exceptions\nimport re\n\n\nclass VMstateTest(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n cls.os = openstack.Manager()\n cls.client = cls.os.servers_client\n cls.config = cls.os.config\n cls.image_ref = cls.config.compute.image_ref\n cls.flavor_ref = cls.config.compute.flavor_ref \n cls.login_name = cls.config.compute.login_name\n cls.pswd = cls.config.compute.pswd\n \n def setUp(self):\n self.name = rand_name('server')\n resp, server = self.client.create_server(self.name,\n self.image_ref,\n self.flavor_ref)\n self.server_id = server['id'] \n self.client.wait_for_server_status(self.server_id, 'ACTIVE')\n \n def tearDown(self):\n self.client.delete_server(self.server_id)\n \n @attr(type='positive')\n def test_suspend_resume_server(self): \n \"\"\"The server should have ACTIVE status after the suspend-resume procedure\"\"\" \n self.client.suspend(self.server_id)\n self.client.wait_for_server_status(self.server_id, 'SUSPENDED')\n self.client.resume(self.server_id)\n self.client.wait_for_server_status(self.server_id, 'ACTIVE')\n resp, body=self.client.get_server(self.server_id) \n self.assertEqual(\"ACTIVE\",body['status'])\n \n @attr(type='positive') \n def test_ping_server(self):\n \"\"\"The sever should ping the Internet and other servers from the same subnet\"\"\" \n resp, body=self.client.get_server(self.server_id) \n # Find IP of server\n try:\n (_, network) = body['addresses'].popitem()\n ip = network[0]['addr']\n except KeyError:\n self.fail(\"Failed to retrieve IP address from server entity\")\n \n params = {'status': 'active'}\n data,sbody=self.client.list_servers_with_detail(params) \n servers=[] \n \n # Get the list of active servers from the same subnet\n for i in sbody['servers']: \n (_, network) = i['addresses'].popitem()\n iip = network[0]['addr']\n servers.append(iip) \n \n # regexp\n exp=re.compile(r\"0% packet loss\") \n \n #ssh into server \n ssh=SSHClient()\n ssh.set_missing_host_key_policy(AutoAddPolicy())\n ssh.connect(ip,username=self.login_name,password=self.pswd)\n # Try to ping the internet\n stdin, stdout, stderr=ssh.exec_command(\"ping -c2 8.8.8.8\")\n # Read the output\n bufferdata = stdout.read() \n # Check if internet is available\n if exp.search(bufferdata):\n isping=\"0% packet loss\"\n self.assertEqual(\"0% packet loss\", isping)\n ping = \"\" \n for j in servers: \n stdin, stdout, stderr=ssh.exec_command(\"ping -c2 \"+j) \n buffer = stdout.read()\n if exp.search(buffer):\n ping=\"0% packet loss\"\n self.assertEqual(\"0% packet loss\", ping, \"The severs with ip \"+j+\" is unavailable\")\n \n \n \n \n\n\n\n \n \n \n \n \n \n \n \n ", "sub_path": "tempest/tests/compute/test_server_state.py", "file_name": "test_server_state.py", "file_ext": "py", "file_size_in_byte": 3588, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "unittest2.TestCase", "line_number": 14, "usage_type": "attribute"}, {"api_name": "tempest.openstack.Manager", "line_number": 17, "usage_type": "call"}, {"api_name": "tempest.openstack", "line_number": 17, "usage_type": "name"}, {"api_name": "tempest.common.utils.data_utils.rand_name", "line_number": 26, "usage_type": "call"}, {"api_name": "nose.plugins.attrib.attr", "line_number": 36, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 68, "usage_type": "call"}, {"api_name": "paramiko.SSHClient", "line_number": 71, "usage_type": "call"}, {"api_name": "paramiko.AutoAddPolicy", "line_number": 72, "usage_type": "call"}, {"api_name": "nose.plugins.attrib.attr", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "182602450", "text": "# -*- coding: utf-8 -*-\nfrom django.shortcuts import render_to_response\nfrom django.template import RequestContext\nfrom django.http import HttpResponseRedirect\nfrom django.core.urlresolvers import reverse\nfrom aksharaklp import settings\nfrom aksharaklp.fileuploadapp.models import Document\nfrom aksharaklp.fileuploadapp.forms import DocumentForm\nfrom aksharaklp.fileuploadapp.filereader import read_file\nfrom aksharaklp.fileuploadapp.dataanalyzer import analyze_data\nfrom django.contrib.auth.decorators import login_required\n\ndef list(request):\n # Handle file upload\n if request.method == 'POST':\n form = DocumentForm(request.POST, request.FILES)\n \n if form.is_valid():\n newdoc = Document(docfile = request.FILES['docfile'])\n newdoc.save()\n read_file(settings.PROJECT_ROOT+newdoc.docfile.url)\n\n # Redirect to the document list after POST\n return HttpResponseRedirect(reverse('aksharaklp.fileuploadapp.views.list'))\n else:\n form = DocumentForm() # A empty, unbound form\n\n # Load documents for the list page\n documents = Document.objects.all()\n\n # Render list page with the documents and the form\n return render_to_response(\n 'fileuploadapp/list.html',\n {'documents': documents, 'form': form},\n context_instance=RequestContext(request)\n )\n\ndef analyze(request):\n print(\"Start analysis ===>\");\n #handle data analysis call\n if request.method == 'GET':\n analysis = analyze_data()\n \n # Redirect to the document list \n form = DocumentForm() # A empty, unbound form\n\n # Load documents for the list page\n documents = Document.objects.all()\n\n return render_to_response(\n 'fileuploadapp/list.html',\n {'documents': documents, 'form': form, 'analysis': analysis},\n context_instance=RequestContext(request)\n )", "sub_path": "aksharaklp/aksharaklp/fileuploadapp/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1882, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "aksharaklp.fileuploadapp.forms.DocumentForm", "line_number": 16, "usage_type": "call"}, {"api_name": "aksharaklp.fileuploadapp.models.Document", "line_number": 19, "usage_type": "call"}, {"api_name": "aksharaklp.fileuploadapp.filereader.read_file", "line_number": 21, "usage_type": "call"}, {"api_name": "aksharaklp.settings.PROJECT_ROOT", "line_number": 21, "usage_type": "attribute"}, {"api_name": "aksharaklp.settings", "line_number": 21, "usage_type": "name"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 24, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 24, "usage_type": "call"}, {"api_name": "aksharaklp.fileuploadapp.forms.DocumentForm", "line_number": 26, "usage_type": "call"}, {"api_name": "aksharaklp.fileuploadapp.models.Document.objects.all", "line_number": 29, "usage_type": "call"}, {"api_name": "aksharaklp.fileuploadapp.models.Document.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "aksharaklp.fileuploadapp.models.Document", "line_number": 29, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 32, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 35, "usage_type": "call"}, {"api_name": "aksharaklp.fileuploadapp.dataanalyzer.analyze_data", "line_number": 42, "usage_type": "call"}, {"api_name": "aksharaklp.fileuploadapp.forms.DocumentForm", "line_number": 45, "usage_type": "call"}, {"api_name": "aksharaklp.fileuploadapp.models.Document.objects.all", "line_number": 48, "usage_type": "call"}, {"api_name": "aksharaklp.fileuploadapp.models.Document.objects", "line_number": 48, "usage_type": "attribute"}, {"api_name": "aksharaklp.fileuploadapp.models.Document", "line_number": 48, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 50, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "316790989", "text": "import kawa.spectrum as ksp\nimport kawa.daq as dq\nimport kawa.tools as kt\nimport kawa.myplot as my\nimport matplotlib.pyplot as pl\nimport numpy as np\nimport pandas as pd\nimport os\nmy.defalt()\nfid = dq.pantaADC()\n# --- PATH TO REF AND DATA ---\nhome = os.environ[\"HOME\"]\nrefpath = home + \"/KYTHON/ref/\"\ndatapath = home + \"/mnt/\"\n\ndt = 1e-6\ndef read2d_64(shot,subno,kind = \"Iis\",edge1 = int( 0.24 /dt) ,edge2 = int( 0.54 /dt)):\n fid = dq.pantaADC()\n dagfile = pd.read_csv(refpath + \"64ch.dag\",dtype = str,comment = \"#\")\n dt = 1e-6\n count = -1\n if kind == \"Iis\":\n start = 0\n coef = 1\n elif kind == \"Vf\":\n start = 1\n coef = 20\n for i in range(start,64,2): #i == 0 => ch1 => Iis is odd num. \n count += 1\n if count == 0:\n signal,time = fid.read(shot = shot, subshot = subno ,\n tower = dagfile.loc[i,\"tower\" ].strip(),\n station = dagfile.loc[i,\"station\"].strip(),\n ch = dagfile.loc[i,\"ch\" ].strip(),\n dir=datapath, samplingtime=True,\n start = edge1, end = edge2)\n signal = coef*signal\n ms = np.mean(signal)\n if kind == \"Vf\":\n signal = signal- ms\n elif kind == \"Iis\":\n signal = (signal-ms)/ms\n #signal = signal-np.mean(signal)\n\n #signal = ksg.low_pass(signal,fs = 1e6, fcut =12e3,order = 5)\n\n df = pd.DataFrame(signal)\n else:\n signal = fid.read(shot = shot, subshot = subno ,\n tower = dagfile.loc[i,\"tower\" ].strip(),\n station = dagfile.loc[i,\"station\"].strip(),\n ch = dagfile.loc[i,\"ch\" ].strip(),\n dir=datapath, samplingtime=False,\n start = edge1, end = edge2)\n signal = coef*signal\n ms = np.mean(signal)\n if kind == \"Vf\":\n signal = ( signal- ms )\n elif kind == \"Iis\":\n signal = (signal-ms)/ms\n\n #signal = signal-np.mean(signal)\n #signal = ksg.low_pass(signal,fs = 1e6, fcut =12e3,order = 5)\n df[count] = signal\n phase = np.linspace(0,1,32)\n return phase,time,df\nedge1 = int(0.24/dt)\nedge2 = int(0.54/dt)\n\nshot = \"105394\"\nsub = \"001\"\nphase,time,signals = read2d_64(shot,sub,edge1 = edge1,edge2 = edge2)\nf,m,spec1 = ksp.psd2d(signals,dt = dt,nfft = 1e4)\n\nfig = pl.figure()\nax = fig.add_subplot(111)\ntext = r\"$\\mathsf{ \\frac{\\~{I}_{is}}{ \\langle I_{is} \\rangle }} $\"\nmy.contourf_log(f/1000,m,spec1)\npl.xlim(0,20)\npl.ylim(-5,10)\npl.xlabel(\"Frequency (kHz)\")\npl.ylabel(\"Azimuthal mode number\")\ncbar = pl.colorbar()\ncbar.set_ticks([pow(10,i) for i in range(-12,-2)])\npl.text(0.9, 0.15,r\"$\\mathsf{ \\frac{\\~{I}_{is}}{ \\langle I_{is} \\rangle }} $\", ha='center', va='center', transform=ax.transAxes,\n fontsize = 20,color = \"k\",bbox = {\"facecolor\":\"w\",\"edgecolor\" :\"w\"})\npl.show()", "sub_path": "paper/old/psd2d.py", "file_name": "psd2d.py", "file_ext": "py", "file_size_in_byte": 3147, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "kawa.myplot.defalt", "line_number": 9, "usage_type": "call"}, {"api_name": "kawa.myplot", "line_number": 9, "usage_type": "name"}, {"api_name": "kawa.daq.pantaADC", "line_number": 10, "usage_type": "call"}, {"api_name": "kawa.daq", "line_number": 10, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 12, "usage_type": "attribute"}, {"api_name": "kawa.daq.pantaADC", "line_number": 18, "usage_type": "call"}, {"api_name": "kawa.daq", "line_number": 18, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 65, "usage_type": "call"}, {"api_name": "kawa.spectrum.psd2d", "line_number": 73, "usage_type": "call"}, {"api_name": "kawa.spectrum", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "kawa.myplot.contourf_log", "line_number": 78, "usage_type": "call"}, {"api_name": "kawa.myplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}]} +{"seq_id": "95037375", "text": "from multiprocessing import Process, Value\nimport time, random\n\n# 创建��享内存\nmoney = Value('i', 10000)\n\n# 操作共享内存增加\ndef boy():\n for i in range(30):\n time.sleep(0.2)\n # 对value属性操作即对共享内存操作\n money.value += random.randint(1, 1000)\n\ndef girl():\n for i in range(30):\n time.sleep(0.16)\n money.value -= random.randint(100, 900)\n\nb = Process(target=boy)\ng = Process(target=girl)\nb.start()\ng.start()\nb.join()\ng.join()\n\nprint('月余额：', money.value)\n\nmoney.value = 12000\nprint(money.value) # 打印字符串\n", "sub_path": "day7/value.py", "file_name": "value.py", "file_ext": "py", "file_size_in_byte": 593, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "multiprocessing.Value", "line_number": 5, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 10, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 12, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 16, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 17, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 19, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "73117178", "text": "from tkinter import *\nimport tkinter.messagebox as message_box\nfrom tkinter import ttk\nimport datetime\nimport calendar\nimport os\nimport sqlite3\n\nfrom sqlite3 import Error\n\ntry:\n conn = sqlite3.connect('draft.s3db')\n c = conn.cursor()\nexcept Error as e:\n print(e)\n\nnow = datetime.datetime.now()\n\n# ---- Variables ----\n\nday_of_week = calendar.day_abbr[now.weekday()]\nday = now.day\nmonth = calendar.month_name[now.month]\nyear = now.year\npolicy_path = \"leave_policy.txt\"\nnum_pending_requests = 1\nhalf_day = 0 # 0 for full day, 1 for half day\n# test data\nnum_days = 15\nrequest_id = 1234\nleave_date = \"13/06/19\"\nsubmission_date = \"03/05/19\"\nleave_type = \"Holiday\"\nemp_name = \"Abel Maclead\"\nemp_comment = \"I am going on holiday\"\nsigned_off = \"No\"\nmgr_comment = \"All employees must attend on this date\"\nmgr_name = \"Steven Tasks\"\nviewer_listbox_headers = ('RequestID', 'Date', 'Signed Off')\nmanager_listbox_headers = ('RequestID', 'Date', 'EmpID', 'Name')\n\n\n# ---- Methods ----\n\ndef open_leave_request():\n print(\"Opening Leave Request Form\")\n ShowLeaveRequestForm()\n leaverequestform.mainloop()\n\n\ndef open_request_viewer():\n print(\"Opening Request Viewer\")\n ShowRequestViewerForm()\n requestviewerform.mainloop()\n\n\ndef open_employee_calendar():\n print(\"Opening Employee Calendar\")\n os.startfile(r'EmployeeCalendarForm.py')\n\n\ndef open_policy_viewer():\n print(\"Opening Policy Viewer Form\")\n ShowPolicyViewerForm()\n policyviewerform.mainloop()\n\n\ndef open_manager_calendar():\n print(\"Opening Manager Calendar\")\n # print(\"*Actually opens calendar options form* LIKE A BOSS\")\n # os.startfile(r'CalendarOptionsForm.py')\n os.startfile(r'ManagerCalendarForm.py')\n\n\ndef open_request_manager():\n print(\"Opening Leave Request Manager\")\n os.startfile(r'RequestManagerForm.py')\n\n\ndef close_policy_form():\n policyviewerform.destroy()\n\n\ndef submit_request():\n message_box.showinfo(\"\", \"Leave Request Submitted\")\n leaverequestform.destroy()\n\n\ndef cancel_request():\n print(\"Leave Request Cancelled\")\n leaverequestform.destroy()\n\n\ndef amend_request():\n print(\"Amending Request\")\n ShowLeaveRequestForm()\n leaverequestform.mainloop()\n\n\ndef revoke_request():\n result = message_box.askquestion(\"Revoke Request\", \"Are you sure you want to revoke this request?\", icon='warning')\n\n if result == 'yes':\n message_box.showinfo(\"\", \"Request Revoked\")\n print(\"Request Revoked\")\n else:\n print(\"Request Revoke Canceled\")\n\n\ndef ShowEmployeeDashboardForm():\n global employeedashboardform\n employeedashboardform = Toplevel()\n employeedashboardform.title(\"Employee Leave Dashboard\")\n screen_width = employeedashboardform.winfo_screenwidth()\n screen_height = employeedashboardform.winfo_screenheight()\n empFrmwidth = 900\n empFrmheight = 500\n x = (screen_width / 2) - (empFrmwidth / 2)\n y = (screen_height / 2) - (empFrmheight / 2)\n employeedashboardform.geometry('%dx%d+%d+%d' % (empFrmwidth, empFrmheight, x, y))\n employeedashboardform.resizable(0, 0)\n employeedashboardform.geometry(\"%dx%d+%d+%d\" % (empFrmwidth, empFrmheight, x, y))\n EmployeeDashboardForm()\n\n\ndef ShowManagerDashboardForm():\n global managerdashboardform\n managerdashboardform = Toplevel()\n managerdashboardform.title(\"Manager Leave Dashboard\")\n screen_width = managerdashboardform.winfo_screenwidth()\n screen_height = managerdashboardform.winfo_screenheight()\n manFrmWidth = 900\n manFrmHeight = 650\n x = (screen_width / 2) - (manFrmWidth / 2)\n y = (screen_height / 2) - (manFrmHeight / 2)\n managerdashboardform.geometry('%dx%d+%d+%d' % (manFrmWidth, manFrmHeight, x, y))\n managerdashboardform.resizable(0, 0)\n managerdashboardform.geometry(\"%dx%d+%d+%d\" % (manFrmWidth, manFrmHeight, x, y))\n ManagerDashboardForm()\n\n\ndef ShowPolicyViewerForm():\n global policyviewerform\n policyviewerform = Toplevel()\n policyviewerform.title(\"Leave Policy Viewer\")\n screen_width = policyviewerform.winfo_screenwidth()\n screen_height = policyviewerform.winfo_screenheight()\n viewFrmWidth = 900\n viewFrmHeight = 600\n x = (screen_width / 2) - (viewFrmWidth / 2)\n y = (screen_height / 2) - (viewFrmHeight / 2)\n policyviewerform.geometry('%dx%d+%d+%d' % (viewFrmWidth, viewFrmHeight, x, y))\n policyviewerform.resizable(0, 0)\n policyviewerform.geometry(\"%dx%d+%d+%d\" % (viewFrmWidth, viewFrmHeight, x, y))\n PolicyViewerForm()\n\n\ndef ShowLeaveRequestForm():\n global leaverequestform\n leaverequestform = Toplevel()\n leaverequestform.title(\"Leave Request Form\")\n screen_width = leaverequestform.winfo_screenwidth()\n screen_height = leaverequestform.winfo_screenheight()\n requestFrmWidth = 900\n requestFrmHeight = 550\n x = (screen_width / 2) - (requestFrmWidth / 2)\n y = (screen_height / 2) - (requestFrmHeight / 2)\n leaverequestform.geometry('%dx%d+%d+%d' % (requestFrmWidth, requestFrmHeight, x, y))\n leaverequestform.resizable(0, 0)\n leaverequestform.geometry(\"%dx%d+%d+%d\" % (requestFrmWidth, requestFrmHeight, x, y))\n LeaveRequestForm()\n\n\ndef ShowRequestViewerForm():\n global requestviewerform\n requestviewerform = Toplevel()\n requestviewerform.title(\"Leave Request Viewer Form\")\n screen_width = requestviewerform.winfo_screenwidth()\n screen_height = requestviewerform.winfo_screenheight()\n viewerFrmWidth = 900\n viewerFrmHeight = 500\n x = (screen_width / 2) - (viewerFrmWidth / 2)\n y = (screen_height / 2) - (viewerFrmHeight / 2)\n requestviewerform.geometry('%dx%d+%d+%d' % (viewerFrmWidth, viewerFrmHeight, x, y))\n requestviewerform.resizable(0, 0)\n requestviewerform.geometry(\"%dx%d+%d+%d\" % (viewerFrmWidth, viewerFrmHeight, x, y))\n RequestViewerForm()\n\n\ndef EmployeeDashboardForm():\n ufix_logo = PhotoImage(file=\"UfixLogo.png\")\n mini_calendar = PhotoImage(file=\"calendar.png\")\n\n TopLabels = Frame(employeedashboardform, width=900, height=400, relief=\"raise\")\n TopLabels.pack(side=TOP, padx=20)\n\n TopLabels.grid_rowconfigure(0, weight=1)\n TopLabels.grid_columnconfigure(0, weight=1)\n\n Notification = Frame(employeedashboardform, width=900, height=70, relief=\"raise\")\n Notification.pack(side=TOP, padx=20, pady=20)\n Buttons = Frame(employeedashboardform, width=900, height=180, relief=\"raise\")\n Buttons.pack(side=BOTTOM, fill=BOTH, expand=YES)\n\n Buttons.grid_rowconfigure(0, weight=1)\n Buttons.grid_columnconfigure(0, weight=1)\n\n lbl_title = Label(TopLabels, justify=LEFT, anchor=W, width=100, font=('Arial', 20),\n text=\"Welcome \" + user_name + \"\\n\\nYou currently have \" + str(\n num_days) + \" days of leave remaining.\")\n lbl_title.grid(row=0, column=0)\n\n lbl_notify_expire = Label(Notification, width=100, height=2, font=('Arial', 20), text=\"\")\n lbl_notify_expire.pack()\n\n pic_mini_calendar = Label(TopLabels, anchor=NE, image=mini_calendar)\n\n pic_mini_calendar.grid(row=0, column=3)\n\n pic_ufix_logo = Label(Buttons, anchor=SE, justify=RIGHT, image=ufix_logo)\n\n pic_ufix_logo.grid(row=2, column=3)\n\n lbl_current_date = Label(TopLabels, width=11, bg=\"#fff\", font=('Arial', 15),\n text=day_of_week + \" \" + str(day) + \"\\n\" + month + \"\\n\" + str(year))\n lbl_current_date.grid(row=0, column=3)\n\n btn_createRequest = Button(Buttons, width=15, font=('Arial', 20), text=\"Request Leave\",\n command=open_leave_request)\n btn_createRequest.grid(row=0, column=1, padx=2)\n\n btn_viewRequests = Button(Buttons, width=15, font=('Arial', 20), text=\"View Requests\",\n command=open_request_viewer)\n btn_viewRequests.grid(row=0, column=2, padx=2)\n\n btn_viewCalendar = Button(Buttons, width=15, font=('Arial', 20), text=\"View Calendar\", padx=20,\n command=open_employee_calendar)\n btn_viewCalendar.grid(row=0, column=3, padx=2)\n\n btn_viewPolicy = Button(Buttons, width=20, font=('Arial', 20), text=\"View UFix Ltd. Leave Policy\", padx=20,\n command=open_policy_viewer)\n\n btn_viewPolicy.grid(row=2, column=2)\n\n if days_expiring_soon > 0:\n lbl_notify_expire.configure(bg='#e6586f')\n lbl_notify_expire.configure(fg='white')\n if days_expiring_soon == 1:\n lbl_notify_expire.configure(\n text=\"! You have \" + str(days_expiring_soon) + \" day which need to be booked soon\")\n else:\n lbl_notify_expire.configure(\n text=\"! You have \" + str(days_expiring_soon) + \" days which need to be booked soon\")\n\n\ndef ManagerDashboardForm():\n TopLabels = Frame(managerdashboardform, width=900, height=400, relief=\"raise\")\n TopLabels.pack(side=TOP, padx=20)\n\n TopLabels.grid_rowconfigure(0, weight=1)\n TopLabels.grid_columnconfigure(0, weight=1)\n\n Notification = Frame(managerdashboardform, width=900, height=70, relief=\"raise\")\n Notification.pack(side=TOP, padx=20, pady=20)\n\n Buttons = Frame(managerdashboardform, width=900, height=100, relief=\"raise\")\n Buttons.pack(side=BOTTOM, fill=BOTH, expand=YES)\n\n Buttons.grid_rowconfigure(1, weight=1)\n Buttons.grid_columnconfigure(1, weight=1)\n\n lbl_title = Label(TopLabels, justify=LEFT, anchor=W, width=100, font=('Arial', 20),\n text=\"Welcome \" + user_name + \"\\n\\nYou currently have \" + str(\n num_days) + \" days of leave remaining.\")\n lbl_title.grid(row=0, column=0)\n\n lbl_notify_expire = Label(Notification, width=100, height=2, font=('Arial', 20), text=\"\")\n lbl_notify_expire.pack()\n\n lbl_notify_requests = Label(Notification, width=100, height=2, font=('Arial', 20), text=\"\")\n lbl_notify_requests.pack(pady=4)\n\n mini_calendar = PhotoImage(file=\"calendar.png\")\n pic_mini_calendar = Label(TopLabels, anchor=NE, image=mini_calendar)\n\n pic_mini_calendar.grid(row=0, column=3)\n\n ufix_logo = PhotoImage(file=\"UfixLogo.png\")\n pic_ufix_logo = Label(Buttons, justify=RIGHT, image=ufix_logo)\n\n pic_ufix_logo.grid(row=3, column=2)\n\n lbl_current_date = Label(TopLabels, width=11, bg=\"#fff\", font=('Arial', 15),\n text=day_of_week + \" \" + str(day) + \"\\n\" + month + \"\\n\" + str(year))\n lbl_current_date.grid(row=0, column=3)\n\n btn_createRequest = Button(Buttons, width=15, font=('Arial', 20), text=\"Request Leave\", command=open_leave_request)\n\n btn_createRequest.grid(row=0, column=0, padx=75)\n\n btn_viewRequests = Button(Buttons, width=15, font=('Arial', 20), text=\"View Your Requests\",\n command=open_request_viewer)\n btn_viewRequests.grid(row=2, column=0)\n\n btn_viewCalendar = Button(Buttons, width=15, font=('Arial', 20), text=\"View Calendar\",\n command=open_manager_calendar)\n btn_viewCalendar.grid(row=0, column=1)\n\n btn_manageRequests = Button(Buttons, width=15, font=('Arial', 20), text=\"Manage Requests\",\n command=open_request_manager)\n btn_manageRequests.grid(row=2, column=1)\n\n btn_viewPolicy = Button(Buttons, width=40, font=('Arial', 20), padx=30, text=\"View UFix Ltd. Leave Policy\",\n command=open_policy_viewer)\n btn_viewPolicy.grid(row=3, column=0, columnspan=2)\n\n if days_expiring_soon > 0:\n lbl_notify_expire.configure(bg='#e6586f')\n lbl_notify_expire.configure(fg='white')\n if days_expiring_soon == 1:\n lbl_notify_expire.configure(\n text=\"! You have \" + str(days_expiring_soon) + \" day which need to be booked soon\")\n else:\n lbl_notify_expire.configure(\n text=\"! You have \" + str(days_expiring_soon) + \" days which need to be booked soon\")\n\n if num_pending_requests > 0:\n lbl_notify_requests.configure(bg='#7bbc6e')\n lbl_notify_requests.configure(fg='white')\n if num_pending_requests == 1:\n lbl_notify_requests.configure(text=\"* There is \" + str(num_pending_requests) + \" pending leave request\")\n else:\n lbl_notify_requests.configure(text=\"* There are \" + str(num_pending_requests) + \" pending leave requests\")\n\n\ndef PolicyViewerForm():\n Policy = Frame(policyviewerform, width=900, height=350, relief=\"raise\")\n Policy.pack(side=TOP, padx=20)\n\n CloseButton = Frame(policyviewerform, width=900, height=150, relief=\"raise\")\n CloseButton.pack(side=BOTTOM)\n\n btn_close = Button(CloseButton, width=15, font=('Arial', 20), text=\"Close\", command=close_policy_form)\n btn_close.pack(side=RIGHT, anchor=SE)\n\n txt_policy = Text(Policy, width=76, height=23, font=('Arial', 15))\n txt_policy.pack(side=LEFT, anchor=NW)\n txt_policy.insert(END, open(policy_path).read())\n txt_policy.configure(state=DISABLED)\n txt_scroll = ttk.Scrollbar(Policy, orient=\"vertical\", command=txt_policy.yview)\n\n txt_policy.configure(yscrollcommand=txt_scroll.set)\n\n txt_scroll.pack(side=RIGHT, anchor=E, fill=\"y\", )\n\n\ndef LeaveRequestForm():\n TopLabels = Frame(leaverequestform, width=900, height=400, relief=\"raise\")\n TopLabels.pack(side=TOP, padx=20)\n\n TopLabels.grid_rowconfigure(0, weight=1)\n TopLabels.grid_columnconfigure(0, weight=1)\n\n Options = Frame(leaverequestform, width=450, height=180, relief=\"raise\")\n Options.pack(side=TOP, fill=BOTH, expand=YES)\n\n Options.grid_rowconfigure(0, weight=1)\n Options.grid_columnconfigure(0, weight=1)\n\n Buttons = Frame(leaverequestform, width=800, height=180, relief=\"raise\")\n Buttons.pack(side=LEFT, fill=BOTH, expand=YES)\n\n Buttons.grid_rowconfigure(0, weight=1)\n Buttons.grid_columnconfigure(0, weight=1)\n\n Logo = Frame(leaverequestform, width=100, height=180, relief=\"raise\")\n Logo.pack(side=RIGHT, fill=BOTH, expand=YES)\n\n Logo.grid_rowconfigure(0, weight=1)\n Logo.grid_columnconfigure(0, weight=1)\n\n lbl_title = Label(TopLabels, justify=LEFT, anchor=W, width=100, font=('Arial', 20),\n text=\"You currently have \" + str(num_days) + \" days of leave remaining.\\n\\n\" + \"Request ID: #\" + str(request_id) + \"\\nEmployee ID: \" + str(emp_no))\n lbl_title.grid(row=0, column=0)\n\n mini_calendar = PhotoImage(file=\"calendar.png\")\n pic_mini_calendar = Label(TopLabels, anchor=NE, image=mini_calendar)\n\n pic_mini_calendar.grid(row=0, column=3)\n\n ufix_logo = PhotoImage(file=\"UfixLogo.png\")\n pic_ufix_logo = Label(Logo, anchor=SE, justify=RIGHT, image=ufix_logo)\n\n pic_ufix_logo.grid(row=0, column=0)\n\n lbl_current_date = Label(TopLabels, width=11, bg=\"#fff\", font=('Arial', 15),\n text=day_of_week + \" \" + str(day) + \"\\n\" + month + \"\\n\" + str(year))\n lbl_current_date.grid(row=0, column=3)\n\n lbl_leave_date = Label(Options, width=11, font=('Arial', 20), text=\"Leave Date: \", justify=LEFT)\n lbl_leave_date.grid(row=0, column=0)\n\n lbl_comments = Label(Options, width=11, font=('Arial', 20), text=\"Comments: \", justify=LEFT)\n lbl_comments.grid(row=0, column=2)\n\n lbl_leave_type = Label(Options, width=11, font=('Arial', 20), text=\"Leave Type: \", justify=LEFT)\n lbl_leave_type.grid(row=2, column=0)\n\n btn_cancel = Button(Buttons, width=15, font=('Arial', 20), text=\"Cancel\", command=cancel_request)\n btn_cancel.grid(row=0, column=1, padx=2)\n\n cmb_date_picker = ttk.Combobox(Options, width=15, font=('Arial', 20))\n cmb_date_picker['values'] = \"peekaboo\"\n cmb_date_picker.grid(row=0, column=1)\n\n cmb_leave_type = ttk.Combobox(Options, width=15, font=('Arial', 20))\n cmb_leave_type['values'] = (\"Holiday\", \"Paternity\", \"Emergency\", \"Sickness\", \"Bereavement\")\n cmb_leave_type.grid(row=2, column=1)\n\n txt_comments = Text(Options, width=20, height=5, font=('Arial', 20))\n txt_comments.grid(row=0, column=3, padx=2)\n\n opt_full_day = ttk.Radiobutton(Options, width=20, variable=half_day, value=0, text=\"Full Day\")\n opt_full_day.grid(row=1, column=0, padx=2)\n\n opt_half_day = ttk.Radiobutton(Options, width=20, variable=half_day, value=1, text=\"Half Day\")\n opt_half_day.grid(row=1, column=1, padx=2)\n\n btn_submit = Button(Buttons, width=15, font=('Arial', 20), text=\"Submit\",command=lambda: RequestLeave(half_day, cmb_date_picker.currenttext(), txt_comments.get(\"1.0\", END)))\n btn_submit.grid(row=0, column=0, padx=2)\n\n\ndef RequestLeave(half_day, date, com):\n cursor = conn.execute(\"SELECT ManagerID from Employee Where EmployeeID = ?\", (emp_no,))\n for row in cursor:\n ManID = row[0]\n cursor = conn.execute(\"SELECT RequestID FROM Request ORDER BY RequestID DESC LIMIT 1\")\n for row in cursor:\n RequestID = row[0]\n RequestID = RequestID + 1\n # sql = \"\"\" INSERT INTO Request (?, ?, ?, ?, ?, ?, ?, ?) VALUES (%s, %s, %s, %s, %s, %s, %s) \"\"\"\n # val = (RequestID, emp_no, ManID, half_day, date, 'N', com)\n cursor.execute(\"INSERT INTO Request VALUES (?, ?, ?, ?, ?, ?, ?, ?)\", (RequestID, emp_no, ManID, half_day, date, 'N', com, \"\"))\n conn.commit()\n\n\ndef RequestViewerForm():\n fraListBox = Frame(requestviewerform, width=300, height=500, relief=\"raise\")\n fraListBox.pack(side=LEFT, anchor=NW)\n\n fraInfo = Frame(requestviewerform, width=450, height=500, relief=\"raise\")\n fraInfo.pack(side=RIGHT, anchor=NE)\n\n fraInfo.grid_rowconfigure(0, weight=1)\n fraInfo.grid_columnconfigure(0, weight=1)\n\n fraInfo.grid_rowconfigure(0, weight=1)\n fraInfo.grid_columnconfigure(0, weight=1)\n\n lbl_title = Label(fraInfo, justify=LEFT, anchor=W, width=120, font=('Arial', 18),\n text=\"Details for request # \" + str(\n request_id) + \" submitted on \" + submission_date + \"\\n\\n\" + \"Leave Date: \" + leave_date + \"\\nLeave Type: \" + leave_type + \"\\nYour Comments: \\n\" + emp_comment)\n lbl_title.grid(row=0, column=0, columnspan=4)\n\n lbl_mgr_detail = Label(fraInfo, justify=LEFT, anchor=W, width=120, font=('Arial', 18),\n text=\"\\nSigned off: \" + signed_off + \"\\nManager's Comment:\\n''\" + mgr_comment + \"''\\nManager: \" + mgr_name)\n lbl_mgr_detail.grid(row=1, column=0, columnspan=4)\n\n ufix_logo = PhotoImage(file=\"UfixLogo.png\")\n pic_ufix_logo = Label(fraInfo, anchor=SE, justify=RIGHT, image=ufix_logo)\n\n pic_ufix_logo.grid(row=5, column=3)\n\n lst_leave_req = ttk.Treeview(fraListBox, columns=viewer_listbox_headers, show=\"headings\", height=26)\n lst_leave_req.pack(side=TOP)\n\n lst_leave_req.heading('#1', text='RequestID', anchor=CENTER)\n lst_leave_req.heading('#2', text='Date', anchor=CENTER)\n lst_leave_req.heading('#3', text='Signed Off', anchor=CENTER)\n\n lst_leave_req.column('#1', stretch=YES, minwidth=50, width=100)\n lst_leave_req.column('#2', stretch=YES, minwidth=50, width=100)\n lst_leave_req.column('#3', stretch=YES, minwidth=50, width=100)\n\n cursor = conn.execute(\"SELECT RequestID, LeaveDate, SignedOff from Request Where EmployeeID = ?\", (emp_no,))\n for row in cursor:\n print(row[0])\n lst_leave_req.insert('', 'end', values=((\"1\"), (row[1]), (row[2])))\n\n ttk.Scrollbar(orient=\"vertical\", command=lst_leave_req.yview)\n\n btn_amend = Button(fraInfo, width=15, font=('Arial', 18), text=\"Amend Request\", command=amend_request)\n btn_amend.grid(row=3, column=1, rowspan=8)\n\n btn_revoke = Button(fraInfo, width=15, font=('Arial', 18), text=\"Revoke Request\", command=revoke_request)\n btn_revoke.grid(row=3, column=2, rowspan=8)\n\n\n# ---- Initialization ----\n\n\nglobal emp_no\nemp_no = input(\"Please enter EmpID\")\nManager = \"Not found\"\ncursor = conn.execute(\"SELECT Manager from Employee Where EmployeeID = ?\", (emp_no,))\nfor row in cursor:\n Manager = row[0]\n\n# Get Job role to know which form to load\nif Manager == \"Y\":\n # load Manager Form\n print(\"Opening Manager Form\")\n cursor = conn.execute(\"SELECT Name, Days_Of_Leave, RolloverDays from Employee Where EmployeeID = ?\", (emp_no,))\n for row in cursor:\n user_name = row[0]\n num_days = row[1]\n Rollover = row[2]\n\n days_expiring_soon = (num_days - Rollover) # NOT SURE HOW\n\n ShowManagerDashboardForm()\n managerdashboardform.mainloop()\nelif Manager == \"N\":\n # load Employee Form\n print(\"Opening Employee Form\")\n\n cursor = conn.execute(\"SELECT Name, Days_Of_Leave, RolloverDays from Employee Where EmployeeID = ?\", (emp_no,))\n for row in cursor:\n user_name = row[0]\n num_days = row[1]\n Rollover = row[2]\n\n days_expiring_soon = (num_days - Rollover) # NOT SURE HOW\n\n ShowEmployeeDashboardForm()\n employeedashboardform.mainloop()\nelif Manager == \"Not found\":\n # not found\n print(\"Not found\")\n", "sub_path": "LeaveSystem.py", "file_name": "LeaveSystem.py", "file_ext": "py", "file_size_in_byte": 20633, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sqlite3.connect", "line_number": 12, "usage_type": "call"}, {"api_name": "sqlite3.Error", "line_number": 14, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "attribute"}, {"api_name": "calendar.day_abbr", "line_number": 21, "usage_type": "attribute"}, {"api_name": "calendar.month_name", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.startfile", "line_number": 59, "usage_type": "call"}, {"api_name": "os.startfile", "line_number": 72, "usage_type": "call"}, {"api_name": "os.startfile", "line_number": 77, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 85, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 85, "usage_type": "name"}, {"api_name": "tkinter.messagebox.askquestion", "line_number": 101, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 101, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 104, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 104, "usage_type": "name"}, {"api_name": "tkinter.ttk.Scrollbar", "line_number": 350, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 350, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 412, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 412, "usage_type": "name"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 416, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 416, "usage_type": "name"}, {"api_name": "tkinter.ttk.Radiobutton", "line_number": 423, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 423, "usage_type": "name"}, {"api_name": "tkinter.ttk.Radiobutton", "line_number": 426, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 426, "usage_type": "name"}, {"api_name": "tkinter.ttk.Treeview", "line_number": 474, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 474, "usage_type": "name"}, {"api_name": "tkinter.ttk.Scrollbar", "line_number": 490, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 490, "usage_type": "name"}]} +{"seq_id": "133205130", "text": "from __future__ import print_function\nimport tensorflow.keras as keras\nfrom tensorflow.keras.datasets import mnist\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Dropout, Flatten\nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D\nfrom tensorflow.keras import backend as K\nimport datetime\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport matplotlib.lines as mlines\nimport tensorflow as tf\nimport utils\nimport semantic_drift\n\n# Fully federated, one-to-one model from the initial model\n\n# Hyperparameters\nbatch_size = 50\nepochs = 20\n\n# input image dimensions\nimg_rows, img_cols = 28, 28\n\n\ndef custom_model(input_shape, num_classes):\n model = Sequential()\n model.add(Flatten(input_shape=input_shape))\n model.add(Dense(200, activation='relu'))\n model.add(Dense(200, activation='relu'))\n model.add(Dense(num_classes, activation='softmax'))\n return model\n\ndef compile_model(model): \n # initiate SGD optimizer\n opt = keras.optimizers.SGD(lr=0.1)\n model.compile(loss='mean_squared_error', optimizer=opt, metrics=['accuracy'])\n \ndef fit_model_with_datasets(model, epochs, x_train, y_train):\n now = datetime.datetime.now()\n# print (\"Training date and time : \")\n# print (now.strftime(\"%Y-%m-%d %H:%M:%S\"))\n res = model.fit(x_train, y_train,\n batch_size=batch_size,\n epochs=epochs,\n shuffle=True,\n verbose=0)\n# print (\"Elasped Time: \" + str(datetime.datetime.now() - now))\n return res\n\ndef model_combs(model_list):\n combs = list()\n l = len(model_list)\n for i in range(l):\n for j in range(l):\n if i > j:\n combs.append([model_list[i], model_list[j]])\n return combs\n\ndef run(seed):\n print(\"seed {}\".format(seed))\n \n log_dir = \"logs/fit/\" + datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n \n np.random.seed(seed)\n \n # the data, split between train and test sets\n (x_train, y_train), (x_test, y_test) = mnist.load_data()\n\n if K.image_data_format() == 'channels_first':\n x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols)\n x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols)\n input_shape = (1, img_rows, img_cols)\n else:\n x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)\n input_shape = (img_rows, img_cols, 1)\n\n x_train = x_train.astype('float32')\n x_test = x_test.astype('float32')\n x_train /= 255\n x_test /= 255\n\n global_dataset_size = 0\n local_dataset_size = 60000\n\n X_global = x_train[-global_dataset_size:]\n Y_global = y_train[-global_dataset_size:]\n X_local = x_train[:-global_dataset_size]\n Y_local = y_train[:-global_dataset_size]\n\n X_local_list, Y_local_list = utils.split_training_set(3000, 20, X_local, Y_local)\n\n # convert class vectors to binary class matrices\n num_classes = 10\n Y_global = keras.utils.to_categorical(Y_global, num_classes)\n y_test = keras.utils.to_categorical(y_test, num_classes)\n\n model1 = custom_model(input_shape, num_classes)\n compile_model(model1)\n fit_model_with_datasets(model1, 1, X_global, Y_global)\n\n model_list = list()\n for _ in range(20):\n model_list.append(tf.keras.models.clone_model(model1)) \n model_list[_].set_weights(model1.get_weights())\n\n # sort models according to similarity. We arbitrarily take the model1 as a \"standard\"\n standard_model = tf.keras.models.clone_model(model1)\n standard_model.set_weights(model_list[0].get_weights())\n\n for i in range(len(model_list)):\n compile_model(model_list[i])\n fit_model_with_datasets(model_list[i], (i+1)*10, X_local_list[i], Y_local_list[i])\n\n model_list.sort(key=lambda m : semantic_drift.l2_distance(standard_model, m))\n\n theta_list = [0, 0.5, 1]\n agg_weights_list_per_pi = list()\n dist_list = list()\n\n for model_comp in model_combs(model_list):\n if model_comp[0] is model_comp[1]: #disregard same models\n continue\n weights = [model_comp[0].get_weights(), model_comp[1].get_weights()]\n agg_weights_list = list()\n for theta in theta_list:\n agg_weights = list()\n for weights_list_tuple in zip(*weights):\n agg_weights.append(np.array([np.average(np.array(w), axis=0, weights=[1. - theta, theta]) for w in zip(*weights_list_tuple)]))\n agg_weights_list.append(agg_weights)\n dist_list.append(semantic_drift.l2_distance(model_comp[0], model_comp[1]))\n agg_weights_list_per_pi.append(agg_weights_list)\n\n agg_weights_list_per_pi_sorted = [x for _,x in sorted(zip(dist_list,agg_weights_list_per_pi))]\n model_combs_sorted = [x for _,x in sorted(zip(dist_list, model_combs(model_list)))]\n\n B = np.zeros(len(agg_weights_list_per_pi))\n\n i = 0\n for agg_weights_list in agg_weights_list_per_pi_sorted:\n\n aggr_model = keras.models.clone_model(model1)\n aggr_model.set_weights(agg_weights_list[1])\n compile_model(aggr_model)\n score = aggr_model.evaluate(x=x_test, y=y_test, verbose=0)\n \n aggr_model = keras.models.clone_model(model1)\n aggr_model.set_weights(agg_weights_list[0])\n compile_model(aggr_model)\n comp_score1 = aggr_model.evaluate(x=x_test, y=y_test, verbose=0)\n \n aggr_model = keras.models.clone_model(model1)\n aggr_model.set_weights(agg_weights_list[2])\n compile_model(aggr_model)\n comp_score2 = aggr_model.evaluate(x=x_test, y=y_test, verbose=0)\n \n B[i] = score[0] - min(comp_score1[0], comp_score2[0])\n K.clear_session() #prevent memory leak https://github.com/keras-team/keras/issues/13118\n i += 1\n if i % 10 == 0:\n print(\"{}th iteration\".format(i))\n\n return B, dist_list", "sub_path": "aggregation_experiment_transferred.py", "file_name": "aggregation_experiment_transferred.py", "file_ext": "py", "file_size_in_byte": 6075, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "tensorflow.keras.models.Sequential", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 30, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers.SGD", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers", "line_number": 37, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 37, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 41, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 64, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 64, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.callbacks.TensorBoard", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 65, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 67, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.datasets.mnist.load_data", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.keras.datasets.mnist", "line_number": 70, "usage_type": "name"}, {"api_name": "tensorflow.keras.backend.image_data_format", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 72, "usage_type": "name"}, {"api_name": "utils.split_training_set", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow.keras.utils.to_categorical", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.keras.utils", "line_number": 98, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 98, "usage_type": "name"}, {"api_name": "tensorflow.keras.utils.to_categorical", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.keras.utils", "line_number": 99, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 99, "usage_type": "name"}, {"api_name": "tensorflow.keras.models.clone_model", "line_number": 107, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 107, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.models.clone_model", "line_number": 111, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 111, "usage_type": "attribute"}, {"api_name": "semantic_drift.l2_distance", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 132, "usage_type": "call"}, {"api_name": "semantic_drift.l2_distance", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 140, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.clone_model", "line_number": 145, "usage_type": "call"}, {"api_name": "tensorflow.keras.models", "line_number": 145, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 145, "usage_type": "name"}, {"api_name": "tensorflow.keras.models.clone_model", "line_number": 150, "usage_type": "call"}, {"api_name": "tensorflow.keras.models", "line_number": 150, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 150, "usage_type": "name"}, {"api_name": "tensorflow.keras.models.clone_model", "line_number": 155, "usage_type": "call"}, {"api_name": "tensorflow.keras.models", "line_number": 155, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 155, "usage_type": "name"}, {"api_name": "tensorflow.keras.backend.clear_session", "line_number": 161, "usage_type": "call"}, {"api_name": "tensorflow.keras.backend", "line_number": 161, "usage_type": "name"}]} +{"seq_id": "235293715", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\n\n# Functions\ndef triangle(x, y, c):\n plt . fill([x, x + c, x + c / 2], [y, y, y + c * np.sqrt(3) / 2], \" b \")\n\n\ndef t2s(n, x, y, c):\n print(\"n:\", n, \" / x:\", x, \" / y:\", y, \" / c:\", c, sep='')\n if n == 0:\n triangle(x, y, c)\n else:\n t2s(n - 1, x, y, c / 2)\n t2s(n - 1, x + c / 2, y, c / 2)\n t2s(n - 1, x + c / 2, y + ((np.sqrt(3) / 2) / 2**n), c / 2)\n print()\n\n\n# Main\n\"\"\"\"triangle(0, 0, 0.5)\ntriangle(0.5, 0, 0.5)\ntriangle(0.25, np.sqrt(3) / 4, 0.5)\"\"\"\n\nn = eval(input(\"Niveau du TdS : \"))\nx = 0\ny = 0\nc = 1\nt2s(n, x, y, c)\n\nplt.show()\n", "sub_path": "tp3/tp3ex6.py", "file_name": "tp3ex6.py", "file_ext": "py", "file_size_in_byte": 635, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.pyplot.fill", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "613625282", "text": "import tensorflow as tf\nimport numpy\nimport sparse_conv_gen as sparse\nimport datasets\n\nimport network as net\n\nd_0 = 100\n\nlearning_rate = 2.0\n\nFROM_SAVE = False\n\nnormalize_G = False\nnormalize_D = False\n\nepochs = 10000\n\nn = 4\nm = 3\n\nbatch_size = 16\n\niterator = datasets.celeb_A().batch(batch_size).make_one_shot_iterator()\nZ, z_batch = datasets.random_normal(d_0)\nX = iterator.get_next()\n\ndef batch(batch_size):\n return {**x_batch(batch_size), **z_batch(batch_size)}\n\n\n\nnon_lin_G = net.shift_relu\nnon_lin_D = tf.nn.relu\n\n################# GENERATOR ##########3333\n'''\nxs = tf.lin_space(-1.5, 1.5, 28)\nxs = tf.stack([tf.tile(tf.reshape(xs, [-1, 1]), [1, 28]),\n tf.tile(tf.reshape(xs, [1, -1]), [28, 1])], 2)\nxs = tf.reshape(xs, [-1, 2])\n'''\n[Z1], theta = net.affine([Z], d_0, 500*n)\nZ1 = non_lin_G(Z1)\nif normalize_G:\n [Z1] = net.normalize([Z1])\n\n[V0], theta1 = net.affine([Z1], 500*n, 500*n)\nV0 = non_lin_G(V0)\nif normalize_G:\n [V0] = net.normalize([V0])\n\n[P0], theta2 = net.affine([Z1], 500*n, 2)\n[R0], theta3 = net.affine([Z1], 500*n, 2)\nR0 = tf.reshape(R0, [-1, 1, 2]) + [0.8, 0]\nR0 = 0.8 * R0 / (0.3 + tf.norm(R0, axis=-1, keepdims=True))\n\nscene0 = {\n \"vs\": tf.reshape(V0, [-1, 1, 500*n]),\n \"ps\": tf.reshape(P0, [-1, 1, 2])*0.3,\n \"rs\": R0\n }\n\n\ndef mod_scene(sc):\n sc['rs'] = sc['rs']*2.0\n return sc\n\n\nX0_gen, generator = sparse.sparse_net(scene0, 64, 3.0 / 64.0,\n [500*n, 400*n, 300*n, 200*n, 100*n, 50*n, 20*n], [2, 2, 2, 2, 3, 3],\n normalize_G, non_lin=non_lin_G,\n mod_scene=mod_scene)\n\nX0_gen = non_lin_G(X0_gen)\n\n#[X_gen], theta4 = net.affine([tf.reshape(X0_gen, [batch_size*64*64, -1])], 20*n, 3)\n#X_gen = tf.reshape(X_gen, [-1, 64, 64, 3])\n\n[W_last], theta_W = net.affine([Z1], 500*n, 20*n*3)\n[b_last], theta_b = net.affine([Z1], 500*n, 3)\nX_gen = tf.matmul(tf.reshape(X0_gen, [batch_size, 64*64, -1]), tf.reshape(W_last, [batch_size, -1, 3])) / tf.sqrt(20.0*n)\nX_gen = X_gen + 0.5 + 0.1 * tf.reshape(b_last, [batch_size, 1, 3])\nX_gen = tf.reshape(X_gen, [-1, 64, 64, 3])\n\nX_gen = tf.atan(tf.nn.relu(X_gen)) * 2 / numpy.pi\ntf.summary.image('generated', X_gen, max_outputs=16)\n\ngenerator = generator + theta + theta1 + theta2 + theta3 + theta_W + theta_b\n\n##################### DISCRIMINATOR #######\n\n[X1, X1_gen], vs0 = net.conv_net([X*1.5 - 0.5, X_gen * 1.5 - 0.5], [3, 104*m, 228*m, 428*m, 428*m, 400*m],\n [4, 4, 4, 3, 1], [2, 2, 2, 2, 1],\n \"D_conv\", normalize_D, non_lin_D)\n \nX1 = non_lin_D(X1)\nX1_gen = non_lin_D(X1_gen)\nif normalize_D:\n X1, X1_gen = net.normalize([X1, X1_gen], [0, 1, 2])\nX1 = tf.reshape(X1, [-1, 4*4*400*m])\nX1_gen = tf.reshape(X1_gen, [-1, 4*4*400*m])\n\n[D_real, D_gen], vs1 = net.affine([X1, X1_gen], 4*4*400*m, 1, \"D\")\n \ntf.summary.histogram(\"D_real\", D_real)\ntf.summary.histogram(\"D_gen\", D_gen)\n\n\ndiscriminator = vs0 + vs1\n\n######################### COSTS #############\n'''\nD_cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(None, tf.ones_like(D_real), D_real)) + \\\n tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(None, tf.zeros_like(D_gen), D_gen))\nG_cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(None, tf.ones_like(D_gen), D_gen))\n\n'''\nD_cost = tf.reduce_mean(D_real) - tf.reduce_mean(D_gen)\nG_cost = tf.reduce_mean(D_gen)\n\nD_cost += 0.5 * tf.reduce_mean(tf.square(D_real)) + 0.5 * tf.reduce_mean(tf.square(D_gen))\nG_cost += 0.5 * tf.reduce_mean(tf.square(D_real)) + 0.5 * tf.reduce_mean(tf.square(D_gen))\n\n\ntf.summary.scalar(\"D_cost\", D_cost)\ntf.summary.scalar(\"G_cost\", G_cost)\n\ntf.summary.tensor_summary(\"D_gen\", D_gen)\ntf.summary.tensor_summary(\"D_real\", D_real)\n\n\n\nopt = tf.train.GradientDescentOptimizer(learning_rate)\n\ninfo = []\nstep = []\n\ngrad_G = tf.gradients(G_cost, generator)\ngrad_D = tf.gradients(D_cost, discriminator)\n\ngrads = grad_D + grad_G\n\nstep = step + [opt.apply_gradients(zip(grads, discriminator + generator))]\nfix = net.fix_weights(generator) + net.fix_weights(discriminator)\n'''\nwith tf.Session() as sess:\n sess.run(tf.global_variables_initializer())\n x_gen = sess.run(X_gen, feed_dict=batch(2))\n\nimport matplotlib.pyplot as P\n\nP.imshow(x_gen[0, :, :, 0]);P.show()\n\n'''\nsave_dir = \"CELEB_GAN\"\n\nif FROM_SAVE==False:\n import os\n for file in os.listdir(save_dir):\n os.remove(os.path.join(save_dir, file))\n\n\nmerged = tf.summary.merge_all()\ntrain_writer = tf.summary.FileWriter(save_dir, flush_secs=5)\n\nsaver = tf.train.Saver()\n\nsess = tf.Session()\n\nif FROM_SAVE:\n saver.restore(sess, save_dir + \"/model.ckpt\")\nelse:\n sess.run(tf.global_variables_initializer())\n\n\ntry:\n const_dict = z_batch(batch_size)\n \n for t in range(epochs):\n for _ in range(1):\n _, cost = sess.run([step, D_cost], feed_dict=z_batch(batch_size))\n #sess.run(fix)\n print(cost)\n\n if t % 5 == 0:\n print(\"SAVE IMAGE\")\n summary, x_gen, d_gen, d_real = sess.run([merged, X_gen, D_real, D_gen], feed_dict=const_dict)\n \n train_writer.add_summary(summary, t)\n train_writer.flush()\n \n\nfinally:\n print(\"closing\")\n saver.save(sess, save_dir + '/model.ckpt')\n sess.close()\n train_writer.close()\n\n# tensorboard --logdir=CELEB_GAN --reload_interval=4\n\n\n", "sub_path": "sparse_GAN_celeb.py", "file_name": "sparse_GAN_celeb.py", "file_ext": "py", "file_size_in_byte": 5355, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datasets.celeb_A", "line_number": 24, "usage_type": "call"}, {"api_name": "datasets.random_normal", "line_number": 25, "usage_type": "call"}, {"api_name": "network.shift_relu", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 34, "usage_type": "attribute"}, {"api_name": "network.affine", "line_number": 43, "usage_type": "call"}, {"api_name": "network.normalize", "line_number": 46, "usage_type": "call"}, {"api_name": "network.affine", "line_number": 48, "usage_type": "call"}, {"api_name": "network.normalize", "line_number": 51, "usage_type": "call"}, {"api_name": "network.affine", "line_number": 53, "usage_type": "call"}, {"api_name": "network.affine", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 55, "usage_type": "call"}, {"api_name": "tensorflow.norm", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 60, "usage_type": "call"}, {"api_name": "sparse_conv_gen.sparse_net", "line_number": 70, "usage_type": "call"}, {"api_name": "network.affine", "line_number": 80, "usage_type": "call"}, {"api_name": "network.affine", "line_number": 81, "usage_type": "call"}, {"api_name": "tensorflow.matmul", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.sqrt", "line_number": 82, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 83, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 84, "usage_type": "call"}, {"api_name": "tensorflow.atan", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.nn.relu", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 86, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 86, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.image", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 87, "usage_type": "attribute"}, {"api_name": "network.conv_net", "line_number": 93, "usage_type": "call"}, {"api_name": "network.normalize", "line_number": 100, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 101, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 102, "usage_type": "call"}, {"api_name": "network.affine", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorflow.summary.histogram", "line_number": 106, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 106, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.histogram", "line_number": 107, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 107, "usage_type": "attribute"}, {"api_name": "tensorflow.reduce_mean", "line_number": 119, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 120, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.square", "line_number": 122, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 123, "usage_type": "call"}, {"api_name": "tensorflow.square", "line_number": 123, "usage_type": "call"}, {"api_name": "tensorflow.summary.scalar", "line_number": 126, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 126, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.scalar", "line_number": 127, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 127, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.tensor_summary", "line_number": 129, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 129, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.tensor_summary", "line_number": 130, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 130, "usage_type": "attribute"}, {"api_name": "tensorflow.train.GradientDescentOptimizer", "line_number": 134, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 134, "usage_type": "attribute"}, {"api_name": "tensorflow.gradients", "line_number": 139, "usage_type": "call"}, {"api_name": "tensorflow.gradients", "line_number": 140, "usage_type": "call"}, {"api_name": "network.fix_weights", "line_number": 145, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 160, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path", "line_number": 161, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.merge_all", "line_number": 164, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 164, "usage_type": "attribute"}, {"api_name": "tensorflow.summary.FileWriter", "line_number": 165, "usage_type": "call"}, {"api_name": "tensorflow.summary", "line_number": 165, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 167, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 167, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 169, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 174, "usage_type": "call"}]} +{"seq_id": "491009150", "text": "# -*- coding: utf-8 -*-\n\"\"\"\n@date Created on Wed Jan 20 14:10:24 2016\n@copyright (C) 2015-2016 EOMYS ENGINEERING.\n@author pierre_b\n\"\"\"\n\nimport sys\nfrom random import uniform\n\nfrom PySide2 import QtWidgets\nfrom PySide2.QtTest import QTest\nfrom pyleecan.Classes.Material import Material\nfrom pyleecan.Classes.LamHole import LamHole\nfrom pyleecan.Classes.HoleM57 import HoleM57\nfrom pyleecan.GUI.Dialog.DMatLib.DMatLib import LIB_KEY, MACH_KEY\nfrom pyleecan.GUI.Dialog.DMachineSetup.SMHoleMag.PHoleM57.PHoleM57 import PHoleM57\nfrom Tests.GUI import gui_option # Set unit to m\n\nimport pytest\n\n\nclass TestPHoleM57(object):\n \"\"\"Test that the widget PHoleM57 behave like it should\"\"\"\n\n @pytest.fixture\n def setup(self):\n \"\"\"Run at the begining of every test to setup the gui\"\"\"\n\n if not QtWidgets.QApplication.instance():\n self.app = QtWidgets.QApplication(sys.argv)\n else:\n self.app = QtWidgets.QApplication.instance()\n\n test_obj = LamHole(Rint=0.1, Rext=0.2)\n test_obj.hole = list()\n test_obj.hole.append(\n HoleM57(H1=0.11, H2=0.12, W0=0.13, W1=0.14, W2=0.15, W3=0.17, W4=0.19)\n )\n test_obj.hole.append(\n HoleM57(\n H1=0.11,\n H2=0.12,\n W0=0.13,\n W1=0.14,\n W2=0.15,\n W3=0.17,\n W4=0.19,\n magnet_0=None,\n )\n )\n\n material_dict = {LIB_KEY: list(), MACH_KEY: list()}\n material_dict[LIB_KEY] = [\n Material(name=\"Magnet1\"),\n Material(name=\"Magnet2\"),\n Material(name=\"Magnet3\"),\n ]\n\n widget = PHoleM57(test_obj.hole[0], material_dict)\n widget2 = PHoleM57(test_obj.hole[1], material_dict)\n\n yield {\n \"widget\": widget,\n \"widget2\": widget2,\n \"test_obj\": test_obj,\n \"material_dict\": material_dict,\n }\n\n self.app.quit()\n\n def test_init(self, setup):\n \"\"\"Check that the Widget spinbox initialise to the lamination value\"\"\"\n\n assert setup[\"widget\"].lf_H1.value() == 0.11\n assert setup[\"widget\"].lf_H2.value() == 0.12\n assert setup[\"widget\"].lf_W0.value() == 0.13\n assert setup[\"widget\"].lf_W1.value() == 0.14\n assert setup[\"widget\"].lf_W2.value() == 0.15\n assert setup[\"widget\"].lf_W3.value() == 0.17\n assert setup[\"widget\"].lf_W4.value() == 0.19\n\n assert setup[\"widget\"].w_mat_1.isHidden() == False\n\n setup[\"test_obj\"].hole[0] = HoleM57(\n H1=0.21, H2=0.22, W0=0.23, W1=0.24, W2=0.25, W3=0.27, W4=0.29\n )\n setup[\"widget\"] = PHoleM57(setup[\"test_obj\"].hole[0], setup[\"material_dict\"])\n assert setup[\"widget\"].lf_H1.value() == 0.21\n assert setup[\"widget\"].lf_H2.value() == 0.22\n assert setup[\"widget\"].lf_W0.value() == 0.23\n assert setup[\"widget\"].lf_W1.value() == 0.24\n assert setup[\"widget\"].lf_W2.value() == 0.25\n assert setup[\"widget\"].lf_W3.value() == 0.27\n assert setup[\"widget\"].lf_W4.value() == 0.29\n\n assert setup[\"widget2\"].w_mat_1.isHidden() == True\n\n def test_set_W0(self, setup):\n \"\"\"Check that the Widget allow to update W0\"\"\"\n # Clear the field before writing the new value\n setup[\"widget\"].lf_W0.clear()\n QTest.keyClicks(setup[\"widget\"].lf_W0, \"0.31\")\n setup[\"widget\"].lf_W0.editingFinished.emit() # To trigger the slot\n\n assert setup[\"widget\"].hole.W0 == 0.31\n assert setup[\"test_obj\"].hole[0].W0 == 0.31\n\n def test_set_W1(self, setup):\n \"\"\"Check that the Widget allow to update W1\"\"\"\n setup[\"widget\"].lf_W1.clear()\n QTest.keyClicks(setup[\"widget\"].lf_W1, \"0.32\")\n setup[\"widget\"].lf_W1.editingFinished.emit() # To trigger the slot\n\n assert setup[\"widget\"].hole.W1 == 0.32\n assert setup[\"test_obj\"].hole[0].W1 == 0.32\n\n def test_set_W2(self, setup):\n \"\"\"Check that the Widget allow to update W2\"\"\"\n setup[\"widget\"].lf_W2.clear()\n QTest.keyClicks(setup[\"widget\"].lf_W2, \"0.33\")\n setup[\"widget\"].lf_W2.editingFinished.emit() # To trigger the slot\n\n assert setup[\"widget\"].hole.W2 == 0.33\n assert setup[\"test_obj\"].hole[0].W2 == 0.33\n\n def test_set_W3(self, setup):\n \"\"\"Check that the Widget allow to update W3\"\"\"\n setup[\"widget\"].lf_W3.clear()\n QTest.keyClicks(setup[\"widget\"].lf_W3, \"0.323\")\n setup[\"widget\"].lf_W3.editingFinished.emit() # To trigger the slot\n\n assert setup[\"widget\"].hole.W3 == 0.323\n assert setup[\"test_obj\"].hole[0].W3 == 0.323\n\n def test_set_W4(self, setup):\n \"\"\"Check that the Widget allow to update W4\"\"\"\n setup[\"widget\"].lf_W4.clear()\n QTest.keyClicks(setup[\"widget\"].lf_W4, \"0.334\")\n setup[\"widget\"].lf_W4.editingFinished.emit() # To trigger the slot\n\n assert setup[\"widget\"].hole.W4 == 0.334\n assert setup[\"test_obj\"].hole[0].W4 == 0.334\n\n def test_set_H1(self, setup):\n \"\"\"Check that the Widget allow to update H1\"\"\"\n setup[\"widget\"].lf_H1.clear()\n QTest.keyClicks(setup[\"widget\"].lf_H1, \"0.35\")\n setup[\"widget\"].lf_H1.editingFinished.emit() # To trigger the slot\n\n assert setup[\"widget\"].hole.H1 == 0.35\n assert setup[\"test_obj\"].hole[0].H1 == 0.35\n\n def test_set_H2(self, setup):\n \"\"\"Check that the Widget allow to update H2\"\"\"\n setup[\"widget\"].lf_H2.clear()\n QTest.keyClicks(setup[\"widget\"].lf_H2, \"0.36\")\n setup[\"widget\"].lf_H2.editingFinished.emit() # To trigger the slot\n\n assert setup[\"widget\"].hole.H2 == 0.36\n assert setup[\"test_obj\"].hole[0].H2 == 0.36\n", "sub_path": "Tests/GUI/DMachineSetup/PHole/test_PHoleM57.py", "file_name": "test_PHoleM57.py", "file_ext": "py", "file_size_in_byte": 5746, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "PySide2.QtWidgets.QApplication.instance", "line_number": 30, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QApplication", "line_number": 30, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets", "line_number": 30, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QApplication", "line_number": 31, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 31, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 31, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets.QApplication.instance", "line_number": 33, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets.QApplication", "line_number": 33, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets", "line_number": 33, "usage_type": "name"}, {"api_name": "pyleecan.Classes.LamHole.LamHole", "line_number": 35, "usage_type": "call"}, {"api_name": "pyleecan.Classes.HoleM57.HoleM57", "line_number": 38, "usage_type": "call"}, {"api_name": "pyleecan.Classes.HoleM57.HoleM57", "line_number": 41, "usage_type": "call"}, {"api_name": "pyleecan.GUI.Dialog.DMatLib.DMatLib.LIB_KEY", "line_number": 53, "usage_type": "name"}, {"api_name": "pyleecan.GUI.Dialog.DMatLib.DMatLib.MACH_KEY", "line_number": 53, "usage_type": "name"}, {"api_name": "pyleecan.GUI.Dialog.DMatLib.DMatLib.LIB_KEY", "line_number": 54, "usage_type": "name"}, {"api_name": "pyleecan.Classes.Material.Material", "line_number": 55, "usage_type": "call"}, {"api_name": "pyleecan.Classes.Material.Material", "line_number": 56, "usage_type": "call"}, {"api_name": "pyleecan.Classes.Material.Material", "line_number": 57, "usage_type": "call"}, {"api_name": "pyleecan.GUI.Dialog.DMachineSetup.SMHoleMag.PHoleM57.PHoleM57.PHoleM57", "line_number": 60, "usage_type": "call"}, {"api_name": "pyleecan.GUI.Dialog.DMachineSetup.SMHoleMag.PHoleM57.PHoleM57.PHoleM57", "line_number": 61, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pyleecan.Classes.HoleM57.HoleM57", "line_number": 85, "usage_type": "call"}, {"api_name": "pyleecan.GUI.Dialog.DMachineSetup.SMHoleMag.PHoleM57.PHoleM57.PHoleM57", "line_number": 88, "usage_type": "call"}, {"api_name": "PySide2.QtTest.QTest.keyClicks", "line_number": 103, "usage_type": "call"}, {"api_name": "PySide2.QtTest.QTest", "line_number": 103, "usage_type": "name"}, {"api_name": "PySide2.QtTest.QTest.keyClicks", "line_number": 112, "usage_type": "call"}, {"api_name": "PySide2.QtTest.QTest", "line_number": 112, "usage_type": "name"}, {"api_name": "PySide2.QtTest.QTest.keyClicks", "line_number": 121, "usage_type": "call"}, {"api_name": "PySide2.QtTest.QTest", "line_number": 121, "usage_type": "name"}, {"api_name": "PySide2.QtTest.QTest.keyClicks", "line_number": 130, "usage_type": "call"}, {"api_name": "PySide2.QtTest.QTest", "line_number": 130, "usage_type": "name"}, {"api_name": "PySide2.QtTest.QTest.keyClicks", "line_number": 139, "usage_type": "call"}, {"api_name": "PySide2.QtTest.QTest", "line_number": 139, "usage_type": "name"}, {"api_name": "PySide2.QtTest.QTest.keyClicks", "line_number": 148, "usage_type": "call"}, {"api_name": "PySide2.QtTest.QTest", "line_number": 148, "usage_type": "name"}, {"api_name": "PySide2.QtTest.QTest.keyClicks", "line_number": 157, "usage_type": "call"}, {"api_name": "PySide2.QtTest.QTest", "line_number": 157, "usage_type": "name"}]} +{"seq_id": "592037801", "text": "#-*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.conf.urls import (\n patterns, url\n)\n\nfrom .views import (publish_article, list_articles, search_articles, upload_image)\n\n\nurlpatterns = patterns('articles.views',\n url(r'^list/?$', list_articles, name='list_articles'),\n url(r'^search/$', search_articles, name='search_articles'),\n url(r'^(?P.+)/publish/$', publish_article, name='publish_article'),\n url(r'^upload/image/$', upload_image, name='upload_image'),\n)\n", "sub_path": "djangosrc/pysrc/articles/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 515, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "views.list_articles", "line_number": 12, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "views.search_articles", "line_number": 13, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "views.publish_article", "line_number": 14, "usage_type": "argument"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "views.upload_image", "line_number": 15, "usage_type": "argument"}]} +{"seq_id": "493605072", "text": "#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nSistemi Corporation, copyright, all rights reserved, 2023\nMartin Guthrie\n\nThis is a helper script to test scripts that contain substitutions.\nNormally CLI prism_dev.py will not run a script with substitutions.\nUse this script to process a script that has substitutions and save\na new script that has processed substitutions.\n\nNOTE: the substitutions must be \"set\" within this script below.\n See the # !! MODIFY !! section line ~150\n\n\"\"\"\nimport os\nimport re\nimport json\nimport jstyleson\nimport argparse\n\nimport logging\nlogger = logging.getLogger(\"subs\")\n\n\nSCRIPT_REPLACE_RE = r'\\\"%%.*?\\\"' # \"%%Text\"\n\n\ndef find_sub_items(script_text):\n \"\"\" find items in the script text that are marked for substitution from user input\n The format of fields to be found is \"%%Text\"\n :param script_text:\n :return: a dict of items to be replaced in script\n \"\"\"\n matches = re.finditer(SCRIPT_REPLACE_RE, script_text, re.MULTILINE)\n items = [item.group() for item in matches]\n return list(dict.fromkeys(items)) # removes duplicates\n\n\ndef find_sub_items_replace(script_text, replacements):\n \"\"\" Replace '%%Name' items in script from replacements\n\n - replacements that are of type \"num\", also need to remove surrounding quotes,\n a bit of a hack to do it here, but thats where we are...\n\n :param script_text:\n :param replacements: list of replacement dicts, [{'Lot': '12345'}, ...]\n :return:\n \"\"\"\n script = jstyleson.loads(script_text)\n script_subs = script.pop(\"subs\", {})\n logger.debug(script_subs)\n logger.info(replacements)\n\n def _sub_replace(k, v, t):\n # In order to do the subs correctly, we need to know if the sub is\n # a string or a num, to know whether the quotes (\"\") should be removed or not.\n if t == \"num\":\n return script_text.replace('\"%%{}\"'.format(k), str(v))\n else:\n return script_text.replace(\"%%{}\".format(k), str(v))\n\n for r in replacements:\n for k, v in r.items():\n\n if k not in script_subs:\n logger.error(f\"{k} not in script subs\")\n return None\n\n if not isinstance(v, str):\n logger.error(f\"sub {k} value {v} must be a string\")\n return None\n\n logger.info(f\"{k} -> {v}\")\n script_text = _sub_replace(k, v, script_subs[k][\"type\"])\n\n # inner substitutions\n if \"subs\" in script_subs[k]:\n if v in script_subs[k][\"subs\"]:\n for inner_k in script_subs[k]['subs'][v].keys():\n _type = script_subs[k]['subs'][v][inner_k][\"type\"]\n _val = script_subs[k]['subs'][v][inner_k][\"val\"]\n logger.info(f\"{k} -> {v} {inner_k} --> {_val}\")\n script_text = _sub_replace(inner_k, _val, _type)\n\n return script_text\n\n\ndef parse_args():\n \"\"\"\n :return: args\n \"\"\"\n epilog = \"\"\"\nUsage examples:\n python3 prism_subs.py -w --script public/prism/scripts/example/prod_v0/prod_1.scr \n\n \"\"\"\n parser = argparse.ArgumentParser(description='prism_result_scan',\n formatter_class=argparse.RawDescriptionHelpFormatter,\n epilog=epilog)\n\n parser.add_argument(\"-s\", \"--script\",\n dest=\"script\",\n action=\"store\",\n required=True,\n help=\"Path to script file to sub\")\n\n parser.add_argument(\"-w\", \"--write\",\n dest=\"write\",\n action=\"store_true\",\n help=\"Write output to file\")\n\n args = parser.parse_args()\n return args\n\n\nif __name__ == \"__main__\":\n logging.basicConfig()\n logger.setLevel(logging.INFO)\n\n logger.info(\"!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\")\n logger.info(\"SUBS WITHIN SCRIPT MUST BE MODIFIED TO SUIT THE TARGET SCRIPT\")\n logger.info(\"!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\")\n\n args = parse_args()\n file = args.script\n\n if not os.path.isfile(file):\n logger.error(f\"Unable to find json file {file}\")\n exit(1)\n\n with open(file) as f:\n json_data = f.read()\n\n try:\n # check script formatting by importing it\n script = jstyleson.loads(json_data) # OK\n\n except Exception as e:\n logger.error(e)\n exit(1)\n\n script_text = json.dumps(script, indent=2)\n\n # !! MODIFY !!\n # subs to test, normally this list comes from the GUI or the traveller\n # all the values must be strings\n s = [{\"Lot\": \"12345\"},\n {\"Loc\": \"canada/ontario/milton\"},\n #{\"Loc\": \"us/newyork/buffalo\"},\n {\"TST000Max\": \"9\"}\n ]\n final_script_text = find_sub_items_replace(script_text, s)\n # rename subs key so that prism_dev.py will not error\n final_script_text = final_script_text.replace(\"subs\", \"subs1\", 1)\n logger.info(final_script_text)\n\n if args.write: # save output to file for use with prism_dev.py\n file_out = file.replace(\".scr\", \"_sub.scr\")\n with open(file_out, 'w') as f:\n f.write(final_script_text)\n", "sub_path": "prism_subs.py", "file_name": "prism_subs.py", "file_ext": "py", "file_size_in_byte": 5236, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 23, "usage_type": "call"}, {"api_name": "re.finditer", "line_number": 35, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 35, "usage_type": "attribute"}, {"api_name": "jstyleson.loads", "line_number": 50, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 98, "usage_type": "call"}, {"api_name": "argparse.RawDescriptionHelpFormatter", "line_number": 99, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 118, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 119, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "jstyleson.loads", "line_number": 137, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 143, "usage_type": "call"}]} +{"seq_id": "200585937", "text": "from helpers import *\nimport argparse\nimport os\nimport pickle\n\nparser = argparse.ArgumentParser(description='Image Detection')\nparser.add_argument('-use_trained_model', action = 'store_true')\n\n# Create and print the training dataset\ntrain_dataset = dsets.MNIST(root='../../utils/data', train=True, download=True, transform=transforms.ToTensor())\n# print(\"Downloaded the training dataset:\\n \", train_dataset)\n# Create and print the validating dataset\nvalidation_dataset = dsets.MNIST(root='../../utils/data', train=False, download=True, transform=transforms.ToTensor())\n# print(\"Downloaded the validating dataset:\\n \", validation_dataset)\n\nargs = parser.parse_args()\n\nif(not args.use_trained_model):\n\n\tinput_dimensions = 28*28\n\toutput_dimensions = 10\n\n\t# Create a model\n\tmodel = SoftMax(input_dimensions, output_dimensions)\n\n\t# define an optimizer\n\toptimizer = torch.optim.SGD(model.parameters(), lr = 0.1)\n\t# Define a loss function\n\tcriterion = nn.CrossEntropyLoss()\n\t# Define dataloaders\n\ttrainloader = DataLoader(dataset = train_dataset, batch_size = 100)\n\tvalidationloader = DataLoader(dataset = validation_dataset, batch_size = 5000)\n\n\tPlotParameters(model)\n\tplt.title('Before Training')\n\n\n\tn_epochs = 100\n\tfor epoch in range(n_epochs):\n\t\tprint('Running on epoch {}'.format(epoch), flush = True)\n\t\tfor x, y in trainloader:\n\t\t\toptimizer.zero_grad()\n\t\t\tz = model(x.view(-1, 28 * 28))\n\t\t\tloss = criterion(z, y)\n\t\t\tloss.backward()\n\t\t\toptimizer.step()\n\n\twith open('model/trained_model.pkl', 'wb') as handle:\n\t\tpickle.dump(model, handle, protocol=pickle.HIGHEST_PROTOCOL)\n\nelse:\n\tif(not os.path.isfile('model/trained_model.pkl')):\n\t\tprint('Train the model first')\n\t\tos._exit(1)\n\n\twith open('model/trained_model.pkl', 'rb') as f:\n\t\tmodel = pickle.load(f)\t\n\nPlotParameters(model)\nplt.title('After Training')\n\n# Count the classified and miss classified data using the validation set\ncorrect = 0\nincorrect = 0\nfor (x,y) in validation_dataset:\n\tz = model(x.reshape(-1, 28*28))\n\t_, yhat = torch.max(z, 1)\n\tif(yhat == y):\n\t\tcorrect += 1\n\telse:\n\t\tincorrect += 1\n\nprint(\"Analysis:\")\nprint(\"Correctly classified data count =\", correct)\nprint(\"Incorrectly classified data count =\", incorrect)\nprint(\"Accuracy =\", correct/(correct+incorrect))\n\nplt.show()", "sub_path": "models/Softmax/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 2240, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 6, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 49, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}, {"api_name": "os._exit", "line_number": 54, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "367855099", "text": "\"\"\" Customized learning rate scheduler that are not implemented in pytorch \"\"\"\n\nimport torch\nfrom torch.optim.lr_scheduler import _LRScheduler\n\nclass PolyLR(_LRScheduler):\n \"\"\" Adjust learning rate by \"poly\" policy, refering to paper\n https://arxiv.org/abs/1506.04579\n and serch for \"poly\" in the full-text\n \"\"\"\n def __init__(self, optimizer,\n init_lr= 1e-5, # The initial learning rate\n max_iter= 100, # The maximum number of calling step of this instance\n power= 0.9,\n ):\n self.init_lr = init_lr\n self.max_iter = max_iter\n self.power = power\n self.step_count = -1 # considering super class will call step() once\n super().__init__(optimizer)\n\n def step(self):\n self.step_count += 1\n assert self.step_count <= self.max_iter, \"Call step() should not be more than {} times\".format(self.max_iter)\n\n lr = self.init_lr * (1 - self.step_count / self.max_iter)**self.power\n\n for param_group in self.optimizer.param_groups:\n param_group['lr'] = lr\n\n return lr\n", "sub_path": "vos/algo/lr_scheduler.py", "file_name": "lr_scheduler.py", "file_ext": "py", "file_size_in_byte": 1091, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.optim.lr_scheduler._LRScheduler", "line_number": 6, "usage_type": "name"}]} +{"seq_id": "554619461", "text": "#-*- codeing = utf-8 -*-\nimport requests\nimport re\n# import time\nimport os\nprint('README!!!!!\\nREADME!!!!!\\nREADME!!!!!\\n说明：图片返回数量，范围为1到10，不提供 APIKEY 时固定为1\\nr18参数 0为否，1为是，2为混合\\n不指定关键词填0，若指定关键字，将会返回从插画标题、作者、标签中模糊搜索的结果\\n是否使用 master_1200 缩略图，以节省流量或提升加载速度，0为不使用，���认不使用')\nnumber = int(input('请输入要下载的图片数量：'))\nif number<1 or number>10 :\n print('瞎几把输，给你一张便宜你了')\n number = 1\n# r18yn = int(input('是否r18：'))\n# if r18yn<0 or r18yn>2 :\n# print('?')\n# r18yn = 0\n\nword = input('请输入图片关键词：')\n\nif word =='0':\n word=''\n# print(type(True))\n# size = input('是否要压缩图片：')\n# if size =='0':\n# size = 'false'\n# else:\n# size = 'true'\n# print(size)\ndata = {\n \"apikey\":'', #添加apikey\n # 'r18':r18yn, #添加r18参数 0为否，1为是，2为混合\n 'keyword':word, #若指定关键字，将会返回从插画标题、作者、标签中模糊搜索的结果\n 'num':number, #一次返回的结果数量，范围为1到10，不提供 APIKEY 时固定为1\n # 'size1200':False #是否使用 master_1200 缩略图，以节省流量或提升加载速度\n }\n\n\nresponse = requests.get('https://api.lolicon.app/setu/',params=data)\nhtml = response.text\n# print(html)\nurls1 = re.findall('url\":\"(.*?)\"',html)\nurls = str(urls1)\nurls = re.sub(r'\\\\','',urls)\npattern = 'i.pixiv.cat'\nurls = re.sub(pattern,\"www.pixivdl.net\",urls)\nurl_list = re.sub(\"'\",'',urls)\nurl_list = url_list.replace('[','')\nurl_list = url_list.replace(']','')\n\nurl_list = url_list.strip(',').split(',')\n# print(url_list)\ni = 0\nd = 'D:\\\\setu\\\\'\nfor url in url_list:\n path = d + url.split('/')[-1]\n i += 1\n print('正在下载第%d张图片' % i)\n\n try:\n\n if not os.path.exists(d):\n os.mkdir(d)\n\n if not os.path.exists(path):\n\n r = requests.get(url)\n\n r.raise_for_status()\n\n with open(path, 'wb') as f:\n\n f.write(r.content)\n\n f.close()\n\n print(\"图片保存成功\")\n\n else:\n\n print(\"图片已存在\")\n\n except:\n\n print(\"图片获取失败\")\nprint('图片全部下载完成')\n\n", "sub_path": "nor18.py", "file_name": "nor18.py", "file_ext": "py", "file_size_in_byte": 2400, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.get", "line_number": 36, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 39, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 41, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 43, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 60, "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": "requests.get", "line_number": 64, "usage_type": "call"}]} +{"seq_id": "263840025", "text": "import threading\nimport random\nimport logging\nimport time\n\nlogging.basicConfig(format='%(asctime)s.%(msecs)03d [%(threadName)s] - %(message)s', datefmt='%H:%M:%S', level=logging.INFO)\n\n\"\"\"\n Clase listaFinita, extiende la clase list ([]) de modo que puede establecerse un limite máximo\n al tamaño (cantidad de objetos) de la lista.\n\n Uso:\n Declaración\n lista = listaFinita(Numero_Maximo_Items)\n # Crea una lista VACIA que admitirá hasta un máximo de Numero_Maximo_Items items\n\n El acceso a los elementos es igual que en una lista standard, la diferencia es que\n si se intenta agregar un elemento cuando la lista tiene Numero_Maximo_Items items, dara\n un mensaje de error y terminara el programa.\n\n Ejemplos\n Acceso al elemento i:\n\n a = lista[i]\n\n insertar un elemento en la posicón i.\n lista.insert(i, dato) # si i es mayor que Numero_Maximo_Items termina el programa y da error\n\n o\n\n lista[i] = dato # Si i es mayor que Numero_Maximo_Items termina el programa y da error.\n\n agregar un elemento al final de la lista\n\n lista.append(dato) # Si la lista tiene Numero_Maximo_Items termina el programa y da error.\n\n\"\"\"\n\nclass listaFinita(list):\n\n def __init__(self, max_elementos):\n self.max_elementos = max_elementos\n super().__init__()\n\n def pop(self, index):\n assert len(self) != 0, \"lista vacia\"\n return super().pop(index)\n\n def append(self, item):\n assert len(self) < self.max_elementos,\"lista llena\"\n super().append(item)\n\n def insert(self, index, item):\n assert index < self.max_elementos, \"indice invalido\"\n super().insert(index, item)\n\n def full(self):\n if len(self) == self.max_elementos:\n return True\n else:\n return False\n\n\nclass Productor(threading.Thread):\n paises = [(\"España\",\"Madrid\"),(\"Francia\",\"Paris\"),(\"Italia\",\"Roma\"),(\"Inglaterra\",\"Londres\"),(\"Alemania\",\"Berlin\"),(\"Rusia\",\"Moscu\"),\n (\"Turquia\",\"Istambul\"),(\"China\",\"Pekin\"),(\"Japon\",\"Tokio\"),(\"Emiratos Arabes\",\"Dubai\"),(\"Argentina\",\"Buenos Aires\"),\n (\"Brasil\",\"Brasilia\"),(\"Colombia\",\"Bogota\"),(\"Uruguay\",\"Montevideo\")]\n\n def __init__(self, lista, lockLleno):\n super().__init__()\n self.lista = lista\n self.lockLleno = lockLleno\n\n #Seccion critica debido a, al igual que productor y consumidor del ejercicio anterior, productor quiere consumir un recurso que no se produjo\n def run(self):\n while True:\n self.lockLleno.acquire()\n try:\n while self.lista.full():\n pass\n self.lista.append(self.paises[random.randint(0,len(self.paises)-1)])\n logging.info(f'produjo el item: {self.lista[-1]}')\n time.sleep(random.randint(1,5))\n finally:\n self.lockLleno.release()\n\n\nclass Consumidor(threading.Thread):\n def __init__(self, lista, lockVacio):\n super().__init__()\n self.lista = lista\n self.lockVacio = lockVacio\n\n\n def run(self):\n while True:\n self.lockVacio.acquire()\n try:\n while len(self.lista) == 0:\n pass\n elemento = self.lista.pop(0)\n logging.info(f'La capital de {elemento[0]} es {elemento[1]}')\n time.sleep(random.randint(1,5))\n finally:\n self.lockVacio.release()\n\ndef main():\n hilos = []\n lista = listaFinita(4)\n #a diferencia del consumidor ejecicio primario, en este se realiza de forma polimofica, y se colocan dos Lock\n #en los runs de la clases, para que el productor pueda llenar la lista se aplica antes de que pueda cargar y despues para entregar el recurso\n #se pregunta si esta lleno desactiva el lock\n #a cambio de consumidorque libera una vez el tamaño de la lista sea = 0\n lockLleno = threading.Lock()\n lockVacio = threading.Lock()\n\n for i in range(4):\n productor = Productor(lista, lockLleno)\n consumidor = Consumidor(lista, lockVacio)\n hilos.append(productor)\n hilos.append(consumidor)\n #con logging info, se busca los valores del thread que se pasa como paramentro en la Clase\n logging.info(f'Arrancando productor {productor.name}')\n productor.start()\n\n logging.info(f'Arrancando productor {consumidor.name}')\n consumidor.start()\n #los treads se agregan a una lista, despues iteramos la lista de threads para poder ejec join para que corran juntos\n for h in hilos:\n h.join()\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "Productor_Consumidor_1-Ejercicio2-Resuelto.py", "file_name": "Productor_Consumidor_1-Ejercicio2-Resuelto.py", "file_ext": "py", "file_size_in_byte": 4635, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.basicConfig", "line_number": 6, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 6, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 64, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 81, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 82, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 83, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 83, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 88, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 102, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 103, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 103, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 114, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 115, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 123, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 126, "usage_type": "call"}]} +{"seq_id": "219116895", "text": "\"\"\"\nMain train loop\n\"\"\"\n\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport sys\nimport yaml\nfrom data import Data\nfrom pascal_context_data import PascalContextData\nimport torch.optim as optim\nfrom model import SimpleFCN\nimport pdb\n# reads the config file, returns appropriate instantiation of Data class\ndef _dataset_factory(cfg_file) -> Data:\n\tf = open(cfg_file, 'r')\n\tcfg = yaml.load(f)\n\tf.close()\n\tif cfg['name'] == 'pascal_context':\n\t\treturn PascalContextData(cfg)\n\telse:\n\t\tprint(\"Dataset name not matched\")\n\t\texit(-1)\n\n# reads the config file, returns appropriate instantiation of pytorch Module class\ndef _model_factory(cfg_file) -> torch.nn.Module:\n\tf = open(cfg_file, 'r')\n\tcfg = yaml.load(f)\n\tf.close()\n\tif cfg['name'] == 'simple_fcn':\n\t\treturn SimpleFCN(cfg)\n\telse:\n\t\tprint(\"Model name not matched\")\n\t\texit(-1)\n\n\t\t\ndef train(model, data):\n\t# train the model\n\tmodel.cuda()\n\tnum_batches = data.get_num_batches()\n\toptimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)\n\toptimizer.zero_grad()\n\tcriterion = nn.CrossEntropyLoss()\n\tfor epoch in range(10):\n\t\tdata.shuffle() # shuffle the dataset\n\t\tfor iter in range(num_batches):\n\t\t\t# inputs sholud be N x 224 x 224 x 3\n\t\t\tinputs, labels = data.get_batch(iter)\n\t\t\tinputs, labels = Variable(inputs.cuda()), Variable(labels.cuda())\n\t\t\t# labels and outputs should be N x 28 x 28 x 459\n\t\t\tlabels_reshaped = labels.view(-1)\n\n\t\t\toutputs = model(inputs)\n\t\t\tpdb.set_trace()\n\t\t\toutputs_reshaped = outputs.view(-1,459)\n\t\t\tloss = criterion(outputs_reshaped, labels_reshaped)\n\t\t\tloss.backward()\n\t\t\toptimizer.step()\n\t\t\tprint(loss.data[0])\n\n\t\t\t\nif __name__ == '__main__':\t \n\tdataset_cfg = sys.argv[1]\n\t# does nothing for now\n\tmodel_cfg = sys.argv[2]\n\t# load the appropriate dataset into a container\n\tdata = _dataset_factory(dataset_cfg)\n\tmodel = _model_factory(model_cfg)\n\ttrain(model, data)\n", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 1869, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "yaml.load", "line_number": 18, "usage_type": "call"}, {"api_name": "pascal_context_data.PascalContextData", "line_number": 21, "usage_type": "call"}, {"api_name": "data.Data", "line_number": 16, "usage_type": "name"}, {"api_name": "yaml.load", "line_number": 29, "usage_type": "call"}, {"api_name": "model.SimpleFCN", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "attribute"}, {"api_name": "model.cuda", "line_number": 40, "usage_type": "call"}, {"api_name": "data.get_num_batches", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 42, "usage_type": "name"}, {"api_name": "model.parameters", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "data.shuffle", "line_number": 46, "usage_type": "call"}, {"api_name": "data.get_batch", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 50, "usage_type": "call"}, {"api_name": "pdb.set_trace", "line_number": 55, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 64, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 66, "usage_type": "attribute"}]} +{"seq_id": "222800124", "text": "# --------------------------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for license information.\n# --------------------------------------------------------------------------------------------\n# Generated file, DO NOT EDIT\n# Changes may cause incorrect behavior and will be lost if the code is regenerated.\n# --------------------------------------------------------------------------------------------\n\nfrom msrest.serialization import Model\n\n\nclass WidgetSize(Model):\n \"\"\"WidgetSize.\n\n :param column_span: The Width of the widget, expressed in dashboard grid columns.\n :type column_span: int\n :param row_span: The height of the widget, expressed in dashboard grid rows.\n :type row_span: int\n \"\"\"\n\n _attribute_map = {\n 'column_span': {'key': 'columnSpan', 'type': 'int'},\n 'row_span': {'key': 'rowSpan', 'type': 'int'}\n }\n\n def __init__(self, column_span=None, row_span=None):\n super(WidgetSize, self).__init__()\n self.column_span = column_span\n self.row_span = row_span\n", "sub_path": "vsts/vsts/dashboard/v4_1/models/widget_size.py", "file_name": "widget_size.py", "file_ext": "py", "file_size_in_byte": 1185, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "msrest.serialization.Model", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "349115390", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# Licensed under the terms of the MIT License\n\n\"\"\"\nSetup:\n# pip install slackclient\nCopy your slack bot token into bot_token.txt\nCreate quotes.csv file.\n\nStarting:\n# python qotd.py\n\nAdd bot to a channel by inviting it once you authorize the bot for your slack group.\n\"\"\"\n\nimport datetime\nimport json\nimport logging\nimport random\nimport time\n\nfrom slackclient import SlackClient\n\nlogging.basicConfig(format='%(levelname)s:%(message)s', level=logging.DEBUG)\n\n\ndef MPsetup():\n BOT_NAME = 'mpqotd'\n botuid = '<@U0LJ6Q4S0>: ' # @mpqotd\n token = 'xoxb-49868132742-2Pgt5bXPkSm1GCxhc3ZJg3nj' ### bot token for @mpqotd\n return BOT_NAME, botuid, token\n\n\ndef MBsetup():\n botuid = '<@U1F54AWA3>: ' # @qotd:\n with file('settings.json', 'r') as settingsfile:\n s = json.load(settingsfile)\n BOT_NAME = '<@' + s['bot']['id'] + '>: '\n\n with file('bot_token.txt', 'r') as tokenfile:\n token = tokenfile.read()\n return BOT_NAME, botuid, token\n\n\nwith file('quotes.json', 'r') as quotesfile:\n quotes = [json.loads(line) for line in quotesfile]\n\n### with quotes being JSON, why dont we integrate the attribution of the quote into it?\nwith file('attributions.csv', 'r') as attributionsfile:\n attributions = [line.strip() for line in attributionsfile]\n\ncredsetup = MPsetup\n\n(BOT_NAME, botuid, token) = credsetup()\n\n\n### Slack formatting\n### *bold* `code` _italic_ ~strike~\n\ndef get_bot_idA():\n global BOT_NAME\n api_call = sc.api_call(\"users.list\")\n if api_call.get('ok'):\n # retrieve all users so we can find our bot\n users = api_call.get('members')\n for user in users:\n if 'name' in user and user.get('name') == BOT_NAME:\n ### This is the only diff between our get_bot_io functions, what does it do?\n BOT_NAME = user.get('id')\n return { user['name']:user.get('id') }\n else:\n return \"could not find bot user with the name \" + BOT_NAME\n\n\ndef get_bot_idB():\n api_call = sc.api_call(\"users.list\")\n if api_call.get('ok'):\n # retrieve all users so we can find our bot\n users = api_call.get('members')\n for user in users:\n ### This will return on the first found user, what if there's multiple?\n if 'name' in user and user.get('name') == BOT_NAME:\n return { user['name']:user.get('id') }\n else:\n return \"could not find bot user with the name \" + BOT_NAME\n\n\n### Just to try out different implementations\nget_bot_id = get_bot_idB\n\n\ndef addQuote(msg):\n q = dict()\n q[\"quote\"] = msg['text'].strip('\"“')\n q[\"user\"] = msg['user']\n q[\"time\"] = str(datetime.datetime.utcnow())\n logging.info('Quote is: ' + q[\"quote\"])\n with file('quotes.csv', 'a') as quotesfile:\n logging.debug(\" TRYING TO ADD CONTENT\\n\" + json.dumps(q))\n quotesfile.write(\"\\n\" + json.dumps(q))\n quotes.append(q)\n output = sc.api_call('chat.postMessage', as_user='true', channel=chan,\n text='\\t_*\"' + q[\"quote\"] + '\"*_\\n\\tQuote added. High Five <@' + msg['user'] + '>!')\n logging.debug(output)\n\n\ndef autoping(last, msg):\n ### hardcode the interval to 3 seconds\n now = int(time.time())\n if last + 3 < now:\n sc.server.ping()\n return now\n\n\ndef printQuote(msg):\n output = sc.api_call('chat.postMessage', as_user='true', channel=msg['channel'],\n text='\\t' + random.choice(attributions) + ':\\n\\t\\t_*\"' + random.choice(quotes)[\n \"quote\"] + '\"*_')\n logging.debug(output)\n\n\ndef listQuotes(msg):\n mylist = '\\n'.join('\\t_*' + q[\"quote\"] + '*_' for q in quotes if q is not None)\n output = sc.api_call('chat.postMessage', as_user='true', channel=msg['channel'],\n text='\\t' + mylist + '\\n\\n\\t' + str(len(quotes)) + ' total quotes.')\n logging.debug(output)\n\n\ndef ping(msg):\n logging.debug('calling ping()')\n output = sc.api_call('chat.postMessage', as_user='true', channel=msg['channel'], text=\"PONG!!!\\n\")\n logging.debug(output)\n\n\ndef help(msg):\n output = sc.api_call('chat.postMessage', as_user='true', channel=msg['channel'],\n text=helptext + '\\n\\t' + str(len(quotes)) + ' total quotes.')\n logging.debug(output)\n\n\ncommands = {\n #{'command':s[\"bot\"][\"id\"]+'are you alive', 'response':'_*Yes, I\\'m ALLLIIIVE*_'},\n ### This descriptive format is not consistant, why do outputs are in [] and params in <>?\n 'lol' :{ 'action':printQuote, 'help':'lol [prints random quote]' },\n 'quote':{ 'action':printQuote, 'help':'quote [prints random quote]' },\n 'add' :{ 'action':addQuote, 'help':'add ' },\n 'list' :{ 'action':listQuotes, 'help':'list [prints out all quotes]' },\n 'help' :{ 'action':help, 'help':'help [prints this help text]' },\n 'ping' :{ 'action':ping, 'help':\"ping [pings back, letting you know it's alive]\" },\n}\n\nhelptext = 'Greetings traveler! Commands are:\\n'\nfor c in commands:\n helptext += \"\\t\" + BOT_NAME + commands[c]['help'] + \"\\n\"\ncommands['help']['response'] = helptext\n\n\"\"\"{ u'channel': u'G1FS1CJ84',\nu'team': u'T05311JTT',\nu'text': u'<@U1FRJ3WMU>: lol',\nu'ts': u'1465583194.000034',\nu'type': u'message',\nu'user': u'U0LJ6Q4S0'}\"\"\" ### Typical structure of a command packet\n\n\ndef sendReply(msg):\n msgcontent = msg['text']\n\n ### Disabled the general LOL detector for the time being\n # if 'lol' in text:\n # commands['quote']['action'](chan, msg)\n # return\n\n logging.debug(\"msgcontent ::: %s\" % msgcontent)\n ### Splits the username from commands+params\n fromuser, _, cmdparams = msgcontent.partition(' ')\n ### Splits the cmdparams into cmd and params\n cmd, _, params = cmdparams.partition(' ')\n logging.info('cmd =\"' + cmd + '\"')\n if cmd in commands:\n commands[cmd]['action'](msg)\n\n\n### Different structure for main loop:\n# new_evts = sc.rtm_read()\n# for evt in new_evts:\n# print(evt)\n# if \"type\" in evt:\n# if evt[\"type\"] == \"message\" and \"text\" in evt:\n# message = evt[\"text\"]\n\n\nsc = SlackClient(token)\nlogging.info(\"Connecting as \" + BOT_NAME)\n### Should the sc.rtm_connect be inside of try/except?\nif sc.rtm_connect():\n logging.info(\"...Connected!\")\n logging.debug(\"Bot username:userid %s\", get_bot_id())\n # logging.debug(\"BOT_NAME: %s\", BOT_NAME)\n last_ping = int(time.time())\n while True:\n messages = sc.rtm_read()\n # logging.debug(messages)\n #last_ping = autoping(last_ping)\n for message in messages:\n # logging.debug(message)\n ### simplify all these conditions into a single call function for readability\n if 'type' in message:\n if message['type'] not in ['presence_change', 'user_typing', 'reconnect_url'] \\\n and 'text' in message \\\n and not message['text'].startswith(botuid) \\\n and 'bot_id' not in message:\n logging.debug(message)\n sendReply(message)\n time.sleep(1)\nelse:\n logging.info(\"Connection Failed, invalid token?\")\n\n", "sub_path": "qotd.py", "file_name": "qotd.py", "file_ext": "py", "file_size_in_byte": 7190, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.basicConfig", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 25, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 38, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 97, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 97, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 98, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 100, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 100, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 101, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 105, "usage_type": "call"}, {"api_name": "time.time", "line_number": 110, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 118, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 120, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 127, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 131, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 133, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 139, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 174, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 179, "usage_type": "call"}, {"api_name": "slackclient.SlackClient", "line_number": 193, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 194, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 197, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 198, "usage_type": "call"}, {"api_name": "time.time", "line_number": 200, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 213, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 215, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 217, "usage_type": "call"}]} +{"seq_id": "231737843", "text": "import pygame\nACTIVE_BG = (255, 255, 255)\nINACTIVE_BG = (20, 20, 20)\nFONT = pygame.font.SysFont(\"leelawadee\", 20)\n\nclass InputBox:\n \n def __init__(self, screen, x, y, w, h, text=''):\n self.screen = screen\n self.rect = pygame.Rect(x, y, w, h)\n self.color = INACTIVE_BG\n self.text = text\n self.txt_surface = FONT.render(text, True, self.color)\n self.active = False\n\n def handle_event(self, event):\n if event.type == pygame.MOUSEBUTTONDOWN:\n # event pos is when the mouse was when the even happened\n if self.rect.collidepoint(event.pos):\n self.active = True\n else:\n self.active = False\n \n self.color = ACTIVE_BG if self.active else INACTIVE_BG\n\n if event.type == pygame.KEYDOWN:\n if self.active:\n if event.key == pygame.K_BACKSPACE:\n self.text = self.text[:-1]\n else:\n self.text += str(event.unicode)\n \n # Re-render the text.=\n self.txt_surface = FONT.render(self.text, True, self.color)\n\n def getText(self):\n return self.text\n\n def setText(self, text):\n self.text = text\n self.txt_surface = FONT.render(self.text, True, self.color)\n \n def draw(self):\n # draw text\n self.screen.blit(self.txt_surface, (self.rect.x+5, self.rect.y+5))\n # draw rectangle\n pygame.draw.rect(self.screen, self.color, self.rect, 2)", "sub_path": "blank copy/src/buttons/input_box.py", "file_name": "input_box.py", "file_ext": "py", "file_size_in_byte": 1543, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pygame.font.SysFont", "line_number": 4, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 4, "usage_type": "attribute"}, {"api_name": "pygame.Rect", "line_number": 10, "usage_type": "call"}, {"api_name": "pygame.MOUSEBUTTONDOWN", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pygame.K_BACKSPACE", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 47, "usage_type": "attribute"}]} +{"seq_id": "33106537", "text": "from django import forms\nfrom django.contrib.auth.models import User\nfrom django.utils.translation import gettext_lazy, gettext\n\nfrom . import models\n\nchosen_js = {\"class\": \"chosen-select-contains\"}\n\n\nclass SectionForm(forms.ModelForm):\n class Meta:\n model = models.Section\n exclude = [\n 'shortish_name',\n 'full_name',\n 'full_name_ver1',\n ]\n widgets = {\n 'last_modified_by': forms.HiddenInput(),\n 'head': forms.Select(attrs=chosen_js),\n 'admin': forms.Select(attrs=chosen_js),\n\n }\n\n def __init__(self, *args, **kwargs):\n USER_CHOICES = [(u.id, \"{}, {}\".format(u.last_name, u.first_name)) for u in\n User.objects.all().order_by(\"last_name\", \"first_name\")]\n USER_CHOICES.insert(0, tuple((None, \"---\")))\n\n DIVISION_CHOICES = [(obj.id, \"{} - {}\".format(obj.branch, obj.name)) for obj in\n models.Division.objects.all().order_by(\"branch__region\", \"branch\", \"name\")]\n DIVISION_CHOICES.insert(0, tuple((None, \"---\")))\n\n super().__init__(*args, **kwargs)\n self.fields['head'].choices = USER_CHOICES\n self.fields['division'].choices = DIVISION_CHOICES\n\n\nclass DivisionForm(forms.ModelForm):\n class Meta:\n model = models.Division\n exclude = [\n 'date_last_modified',\n ]\n widgets = {\n 'last_modified_by': forms.HiddenInput(),\n 'head': forms.Select(attrs=chosen_js),\n 'admin': forms.Select(attrs=chosen_js),\n }\n\n def __init__(self, *args, **kwargs):\n BRANCH_CHOICES = [(obj.id, \"{} - {}\".format(obj.region, obj.name)) for obj in\n models.Branch.objects.all().order_by(\"region\", \"name\")]\n BRANCH_CHOICES.insert(0, tuple((None, \"---\")))\n\n super().__init__(*args, **kwargs)\n self.fields['branch'].choices = BRANCH_CHOICES\n\n\nclass BranchForm(forms.ModelForm):\n class Meta:\n model = models.Branch\n exclude = [\n 'date_last_modified',\n ]\n widgets = {\n 'last_modified_by': forms.HiddenInput(),\n 'head': forms.Select(attrs=chosen_js),\n 'admin': forms.Select(attrs=chosen_js),\n }\n\n\nclass RegionForm(forms.ModelForm):\n class Meta:\n model = models.Region\n exclude = [\n 'date_last_modified',\n ]\n widgets = {\n 'last_modified_by': forms.HiddenInput(),\n 'head': forms.Select(attrs=chosen_js),\n 'admin': forms.Select(attrs=chosen_js),\n }\n\n\nclass OrganizationForm(forms.ModelForm):\n class Meta:\n model = models.Organization\n fields = \"__all__\"\n\n\nclass UserCreateForm(forms.Form):\n first_name = forms.CharField(label=gettext_lazy(\"First name\"))\n last_name = forms.CharField(label=gettext_lazy(\"Last name\"))\n email1 = forms.EmailField(label=gettext_lazy(\"Email\"))\n email2 = forms.EmailField(label=gettext_lazy(\"Confirm email address\"))\n\n def clean_email1(self):\n new_email = self.cleaned_data['email1']\n # check to make sure is not a duplicate\n if User.objects.filter(email__iexact=new_email).count() > 0:\n raise forms.ValidationError(gettext(\"This email address already exists in the database.\"))\n # check to make sure is a DFO email\n if new_email.lower().endswith(\"@dfo-mpo.gc.ca\") == False:\n raise forms.ValidationError(gettext(\"The email address provided must be a DFO email address.\"))\n\n # Always return a value to use as the new cleaned data, even if\n # this method didn't change it.\n return new_email\n\n def clean(self):\n cleaned_data = super().clean()\n first_email = cleaned_data.get(\"email1\")\n second_email = cleaned_data.get(\"email2\")\n\n if first_email and second_email:\n # Only do something if both fields are valid so far.\n\n # verify the two emails are the same\n if first_email.lower() != second_email.lower():\n raise forms.ValidationError(gettext(\"Please make sure the two email addresses provided match.\"))\n\n\nclass ScriptForm(forms.ModelForm):\n class Meta:\n model = models.Script\n fields = \"__all__\"\n widgets = {\n 'modified_by': forms.HiddenInput(),\n }\n\n\n\nclass ResponsibilityCenterForm(forms.ModelForm):\n class Meta:\n model = models.ResponsibilityCenter\n fields = \"__all__\"\n\n\nclass ProjectCodeForm(forms.ModelForm):\n class Meta:\n model = models.Project\n fields = \"__all__\"\n", "sub_path": "shared_models/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 4623, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.forms.ModelForm", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 10, "usage_type": "name"}, {"api_name": "django.forms.HiddenInput", "line_number": 19, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 19, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 20, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 20, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 21, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 21, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.all", "line_number": 27, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 27, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 39, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 39, "usage_type": "name"}, {"api_name": "django.forms.HiddenInput", "line_number": 46, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 46, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 47, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 47, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 48, "usage_type": "call"}, {"api_name": "django.forms", "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": "django.forms.HiddenInput", "line_number": 67, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 67, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 68, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 68, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 69, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 69, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 73, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 73, "usage_type": "name"}, {"api_name": "django.forms.HiddenInput", "line_number": 80, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 80, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 81, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 81, "usage_type": "name"}, {"api_name": "django.forms.Select", "line_number": 82, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 82, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 86, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 86, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 92, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 92, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 93, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 93, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 93, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 94, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 94, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 94, "usage_type": "call"}, {"api_name": "django.forms.EmailField", "line_number": 95, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 95, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 95, "usage_type": "call"}, {"api_name": "django.forms.EmailField", "line_number": 96, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 96, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext_lazy", "line_number": 96, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.filter", "line_number": 101, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 101, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 101, "usage_type": "name"}, {"api_name": "django.forms.ValidationError", "line_number": 102, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 102, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext", "line_number": 102, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 105, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 105, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext", "line_number": 105, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 121, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 121, "usage_type": "name"}, {"api_name": "django.utils.translation.gettext", "line_number": 121, "usage_type": "call"}, {"api_name": "django.forms.ModelForm", "line_number": 124, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 124, "usage_type": "name"}, {"api_name": "django.forms.HiddenInput", "line_number": 129, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 129, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 134, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 134, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 140, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 140, "usage_type": "name"}]} +{"seq_id": "630671444", "text": "# Copyright 2013 OpenStack Foundation\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\n\"\"\"\n/template_configs endpoint for Daisy v1 API\n\"\"\"\n\nfrom oslo_log import log as logging\nfrom webob.exc import HTTPBadRequest\nfrom webob.exc import HTTPForbidden\nfrom webob.exc import HTTPNotFound\nimport json\n\nfrom daisy.api import policy\nimport daisy.api.v1\nfrom daisy.api.v1 import controller\nfrom daisy.api.v1 import filters\nfrom daisy.common import exception\nfrom daisy.common import utils\nfrom daisy.common import wsgi\nfrom daisy import i18n\nfrom daisy import notifier\nimport daisy.registry.client.v1.api as registry\nimport daisy.api.backends.common as daisy_cmn\n\nLOG = logging.getLogger(__name__)\n_ = i18n._\n_LE = i18n._LE\n_LI = i18n._LI\n_LW = i18n._LW\nSUPPORTED_PARAMS = daisy.api.v1.SUPPORTED_PARAMS\nSUPPORTED_FILTERS = daisy.api.v1.SUPPORTED_FILTERS\nACTIVE_IMMUTABLE = daisy.api.v1.ACTIVE_IMMUTABLE\n\nCONFIG_ITEMS = ['name', 'config_file', 'service', 'section_name', 'data_type']\n\n\ndef check_template_config_format(template):\n def check_service_format(services):\n \"\"\"\n \"service\": {\n \"compute\": {\"force_type\": \"service\"},\n \"glance\": {\"force_type\": \"none\"}\n }\n \"\"\"\n for service_name, service_value in services.items():\n if service_name not in daisy_cmn.service_map.keys():\n raise HTTPBadRequest(\"service '%s' not in service table\" %\n service_name)\n if 'force_type' not in service_value \\\n or service_value['force_type'] not in ['service', 'node',\n 'none']:\n raise HTTPBadRequest(\"No force_type or error force_type value\"\n \" in service\")\n\n def check_data_type(config):\n if config['data_type'] not in ['int', 'string', 'list', 'boolean',\n 'float', 'ipaddr', 'password']:\n raise HTTPBadRequest(\"data_type '%s' in '%s' not support\" % (\n config['data_type'], config['name']))\n\n if not template:\n raise HTTPBadRequest('Template config is null!')\n\n for value in template.values():\n for item in CONFIG_ITEMS:\n if not value.get(item):\n raise HTTPBadRequest('No service or config file found in '\n 'template config!')\n check_data_type(value)\n check_service_format(value['service'])\n\n\nclass Controller(controller.BaseController):\n \"\"\"\n WSGI controller for template_configs resource in Daisy v1 API\n\n The template_configs resource API is a RESTful web service for\n template_config data.\n The API is as follows::\n\n GET /template_configs -- Returns a set of brief metadata about\n template_configs\n GET /template_configs/detail -- Returns a set of detailed metadata\n about emplate_configs\n HEAD /template_configs/ --\n Return metadata about an template_config with id \n GET /template_configs/ --\n Return template_config data for template_config with id \n POST /template_configs --\n Store template_config data and return metadata about the\n newly-stored template_config\n PUT /template_configs/ --\n Update template_config metadata and/or upload template_config\n data for a previously-reserved template_config\n DELETE /template_configs/ -- Delete the template_config with \n \"\"\"\n\n def __init__(self):\n self.notifier = notifier.Notifier()\n registry.configure_registry_client()\n self.policy = policy.Enforcer()\n\n def _enforce(self, req, action, target=None):\n \"\"\"Authorize an action against our policies\"\"\"\n if target is None:\n target = {}\n try:\n self.policy.enforce(req.context, action, target)\n except exception.Forbidden:\n raise HTTPForbidden()\n\n def _get_filters(self, req):\n \"\"\"\n Return a dictionary of query param filters from the request\n\n :param req: the Request object coming from the wsgi layer\n :retval a dict of key/value filters\n \"\"\"\n query_filters = {}\n for param in req.params:\n if param in SUPPORTED_FILTERS:\n query_filters[param] = req.params.get(param)\n if not filters.validate(param, query_filters[param]):\n raise HTTPBadRequest(_('Bad value passed to filter '\n '%(filter)s got %(val)s')\n % {'filter': param,\n 'val': query_filters[param]})\n return query_filters\n\n def _get_query_params(self, req):\n \"\"\"\n Extracts necessary query params from request.\n\n :param req: the WSGI Request object\n :retval dict of parameters that can be used by registry client\n \"\"\"\n params = {'filters': self._get_filters(req)}\n\n for PARAM in SUPPORTED_PARAMS:\n if PARAM in req.params:\n params[PARAM] = req.params.get(PARAM)\n return params\n\n def _raise_404_if_cluster_deleted(self, req, cluster_id):\n cluster = self.get_cluster_meta_or_404(req, cluster_id)\n if cluster['deleted']:\n msg = _(\"cluster with identifier %s has been deleted.\") % \\\n cluster_id\n raise HTTPNotFound(msg)\n\n @utils.mutating\n def get_template_config(self, req, id):\n \"\"\"\n Returns metadata about an template_config in the HTTP headers of the\n response object\n\n :param req: The WSGI/Webob Request object\n :param id: The opaque template_config identifier\n\n :raises HTTPNotFound if template_config metadata is not\n available to user\n \"\"\"\n self._enforce(req, 'get_template_config')\n template_config_meta = self.get_template_config_meta_or_404(req, id)\n return {'template_config_meta': template_config_meta}\n\n def list_template_config(self, req):\n \"\"\"\n Returns detailed information for all available template_configs\n\n :param req: The WSGI/Webob Request object\n :retval The response body is a mapping of the following form::\n\n {'template_configs': [\n {'id': ,\n 'name': ,\n 'description': ,\n 'created_at': ,\n 'updated_at': ,\n 'deleted_at': |,}, ...\n ]}\n \"\"\"\n self._enforce(req, 'list_template_config')\n params = self._get_query_params(req)\n try:\n template_configs = registry.list_template_config_metadata(\n req.context, **params)\n except exception.Invalid as e:\n raise HTTPBadRequest(explanation=e.msg, request=req)\n return dict(template_configs=template_configs)\n\n @utils.mutating\n def import_template_config(self, req, template_config_meta):\n self._enforce(req, 'import_template_config')\n try:\n template = json.loads(template_config_meta.get('template', None))\n except ValueError as e:\n LOG.error(e.message)\n raise HTTPBadRequest(explanation=e.message, request=req)\n check_template_config_format(template)\n template_config_meta = registry.import_template_config_metadata(\n req.context, template_config_meta)\n return {'template_config_meta': template_config_meta}\n\n\nclass TemplateConfigSetDeserializer(wsgi.JSONRequestDeserializer):\n \"\"\"Handles deserialization of specific controller method requests.\"\"\"\n\n def _deserialize(self, request):\n result = {}\n result[\"template_config_meta\"] = utils.get_dict_meta(request)\n return result\n\n def add_template_config(self, request):\n return self._deserialize(request)\n\n def update_template_config(self, request):\n return self._deserialize(request)\n\n def import_template_config(self, request):\n return self._deserialize(request)\n\n\nclass TemplateConfigSetSerializer(wsgi.JSONResponseSerializer):\n \"\"\"Handles serialization of specific controller method responses.\"\"\"\n\n def __init__(self):\n self.notifier = notifier.Notifier()\n\n def add_template_config(self, response, result):\n template_config_meta = result['template_config_meta']\n response.status = 201\n response.headers['Content-Type'] = 'application/json'\n response.body = self.to_json(\n dict(template_config=template_config_meta))\n return response\n\n def delete_template_config(self, response, result):\n template_config_meta = result['template_config_meta']\n response.status = 201\n response.headers['Content-Type'] = 'application/json'\n response.body = self.to_json(\n dict(template_config=template_config_meta))\n return response\n\n def get_template_config(self, response, result):\n template_config_meta = result['template_config_meta']\n response.status = 201\n response.headers['Content-Type'] = 'application/json'\n response.body = self.to_json(\n dict(template_config=template_config_meta))\n return response\n\n def import_template_config(self, response, result):\n response.status = 201\n response.headers['Content-Type'] = 'application/json'\n response.body = self.to_json(result)\n return response\n\n\ndef create_resource():\n \"\"\"template_configs resource factory method\"\"\"\n deserializer = TemplateConfigSetDeserializer()\n serializer = TemplateConfigSetSerializer()\n return wsgi.Resource(Controller(), deserializer, serializer)\n", "sub_path": "code/daisy/daisy/api/v1/template_configs.py", "file_name": "template_configs.py", "file_ext": "py", "file_size_in_byte": 10481, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "oslo_log.log.getLogger", "line_number": 38, "usage_type": "call"}, {"api_name": "oslo_log.log", "line_number": 38, "usage_type": "name"}, {"api_name": "daisy.i18n._", "line_number": 39, "usage_type": "attribute"}, {"api_name": "daisy.i18n", "line_number": 39, "usage_type": "name"}, {"api_name": "daisy.i18n._LE", "line_number": 40, "usage_type": "attribute"}, {"api_name": "daisy.i18n", "line_number": 40, "usage_type": "name"}, {"api_name": "daisy.i18n._LI", "line_number": 41, "usage_type": "attribute"}, {"api_name": "daisy.i18n", "line_number": 41, "usage_type": "name"}, {"api_name": "daisy.i18n._LW", "line_number": 42, "usage_type": "attribute"}, {"api_name": "daisy.i18n", "line_number": 42, "usage_type": "name"}, {"api_name": "daisy.api.api", "line_number": 43, "usage_type": "attribute"}, {"api_name": "daisy.api", "line_number": 43, "usage_type": "name"}, {"api_name": "daisy.api.api", "line_number": 44, "usage_type": "attribute"}, {"api_name": "daisy.api", "line_number": 44, "usage_type": "name"}, {"api_name": "daisy.api.api", "line_number": 45, "usage_type": "attribute"}, {"api_name": "daisy.api", "line_number": 45, "usage_type": "name"}, {"api_name": "daisy.api.backends.common.service_map.keys", "line_number": 59, "usage_type": "call"}, {"api_name": "daisy.api.backends.common.service_map", "line_number": 59, "usage_type": "attribute"}, {"api_name": "daisy.api.backends.common", "line_number": 59, "usage_type": "name"}, {"api_name": "webob.exc.HTTPBadRequest", "line_number": 60, "usage_type": "call"}, {"api_name": "webob.exc.HTTPBadRequest", "line_number": 65, "usage_type": "call"}, {"api_name": "webob.exc.HTTPBadRequest", "line_number": 71, "usage_type": "call"}, {"api_name": "webob.exc.HTTPBadRequest", "line_number": 75, "usage_type": "call"}, {"api_name": "webob.exc.HTTPBadRequest", "line_number": 80, "usage_type": "call"}, {"api_name": "daisy.api.v1.controller.BaseController", "line_number": 86, "usage_type": "attribute"}, {"api_name": "daisy.api.v1.controller", "line_number": 86, "usage_type": "name"}, {"api_name": "daisy.notifier.Notifier", "line_number": 112, "usage_type": "call"}, {"api_name": "daisy.notifier", "line_number": 112, "usage_type": "name"}, {"api_name": "daisy.registry.client.v1.api.configure_registry_client", "line_number": 113, "usage_type": "call"}, {"api_name": "daisy.registry.client.v1.api", "line_number": 113, "usage_type": "name"}, {"api_name": "daisy.api.policy.Enforcer", "line_number": 114, "usage_type": "call"}, {"api_name": "daisy.api.policy", "line_number": 114, "usage_type": "name"}, {"api_name": "daisy.common.exception.Forbidden", "line_number": 122, "usage_type": "attribute"}, {"api_name": "daisy.common.exception", "line_number": 122, "usage_type": "name"}, {"api_name": "webob.exc.HTTPForbidden", "line_number": 123, "usage_type": "call"}, {"api_name": "daisy.api.v1.filters.validate", "line_number": 136, "usage_type": "call"}, {"api_name": "daisy.api.v1.filters", "line_number": 136, "usage_type": "name"}, {"api_name": "webob.exc.HTTPBadRequest", "line_number": 137, "usage_type": "call"}, {"api_name": "webob.exc.HTTPNotFound", "line_number": 162, "usage_type": "call"}, {"api_name": "daisy.common.utils.mutating", "line_number": 164, "usage_type": "attribute"}, {"api_name": "daisy.common.utils", "line_number": 164, "usage_type": "name"}, {"api_name": "daisy.registry.client.v1.api.list_template_config_metadata", "line_number": 199, "usage_type": "call"}, {"api_name": "daisy.registry.client.v1.api", "line_number": 199, "usage_type": "name"}, {"api_name": "daisy.common.exception.Invalid", "line_number": 201, "usage_type": "attribute"}, {"api_name": "daisy.common.exception", "line_number": 201, "usage_type": "name"}, {"api_name": "webob.exc.HTTPBadRequest", "line_number": 202, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 209, "usage_type": "call"}, {"api_name": "webob.exc.HTTPBadRequest", "line_number": 212, "usage_type": "call"}, {"api_name": "daisy.registry.client.v1.api.import_template_config_metadata", "line_number": 214, "usage_type": "call"}, {"api_name": "daisy.registry.client.v1.api", "line_number": 214, "usage_type": "name"}, {"api_name": "daisy.common.utils.mutating", "line_number": 205, "usage_type": "attribute"}, {"api_name": "daisy.common.utils", "line_number": 205, "usage_type": "name"}, {"api_name": "daisy.common.wsgi.JSONRequestDeserializer", "line_number": 219, "usage_type": "attribute"}, {"api_name": "daisy.common.wsgi", "line_number": 219, "usage_type": "name"}, {"api_name": "daisy.common.utils.get_dict_meta", "line_number": 224, "usage_type": "call"}, {"api_name": "daisy.common.utils", "line_number": 224, "usage_type": "name"}, {"api_name": "daisy.common.wsgi.JSONResponseSerializer", "line_number": 237, "usage_type": "attribute"}, {"api_name": "daisy.common.wsgi", "line_number": 237, "usage_type": "name"}, {"api_name": "daisy.notifier.Notifier", "line_number": 241, "usage_type": "call"}, {"api_name": "daisy.notifier", "line_number": 241, "usage_type": "name"}, {"api_name": "daisy.common.wsgi.Resource", "line_number": 278, "usage_type": "call"}, {"api_name": "daisy.common.wsgi", "line_number": 278, "usage_type": "name"}]} +{"seq_id": "336335453", "text": "import gym\nfrom gym import spaces\nfrom util.constants import *\nfrom util.functions import *\nfrom pygame import *\nfrom pygame.locals import *\nimport numpy as np\n\ndef next_observation(disp, width, height):\n\tnext_state = (surfarray.pixels2d(disp.subsurface((0,0,width,height)).copy())) \n\tnext_state[next_state == 16777215] = 0\n\tnext_state[next_state == 65280] = 1\n\treturn next_state\n\nclass BlockAvoid(gym.Env):\n\tmetadata = {'render.modes': ['human']}\n\n\tdef __init__(self):\n\t\tsuper(BlockAvoid, self).__init__()\n\t\tself.disp = display.set_mode((screen_width, screen_height), 0, 32)\n\t\tself.N_DISCRETE_ACTIONS = 3\n\t\tself.WIDTH = int(screen_width/agent_vision)\n\t\tself.HEIGHT = int(screen_height - ground_height)\n\t\tself.action_space = spaces.Discrete(self.N_DISCRETE_ACTIONS)\n\t\tself.observation_space = spaces.Box(low=0, high=1, shape=(self.WIDTH, self.HEIGHT), dtype=np.uint8)\n\n\tdef step(self, action):\n\t\tglobal character_position\n\t\tif action == 1:\n\t\t\tcharacter_position[1] -= character_jump_power\n\t\telif action == 2:\n\t\t\tcharacter_position[1] += character_jump_power\n\n\t\tif character_position[1]<0:\n\t\t\tcharacter_position[1] = 0\n\t\telif character_position[1] > screen_height-character_dimensions[1]-ground_height:\n\t\t\tcharacter_position[1] = screen_height-character_dimensions[1]-ground_height\n\t\tgenerate_terrain()\n\t\treward = calculate_reward()\n\t\tnext_state = next_observation(self.disp, self.WIDTH, self.HEIGHT)\n\t\tdraw_everything(self.disp, character_position)\n\t\tdone = collision_checker()\n\t\treturn next_state, reward, done, {}\t\t\n \t \n\tdef reset(self):\n\t\tglobal character_position\n\t\tcharacter_position = reset_env()\n\t\treturn next_observation(self.disp, self.WIDTH, self.HEIGHT)", "sub_path": "environment.py", "file_name": "environment.py", "file_ext": "py", "file_size_in_byte": 1675, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "gym.Env", "line_number": 15, "usage_type": "attribute"}, {"api_name": "gym.spaces.Discrete", "line_number": 24, "usage_type": "call"}, {"api_name": "gym.spaces", "line_number": 24, "usage_type": "name"}, {"api_name": "gym.spaces.Box", "line_number": 25, "usage_type": "call"}, {"api_name": "gym.spaces", "line_number": 25, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 25, "usage_type": "attribute"}]} +{"seq_id": "354942626", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# -----------------\n# Реализуйте функцию best_hand, которая принимает на вход\n# покерную \"руку\" (hand) из 7ми карт и возвращает лучшую\n# (относительно значения, возвращаемого hand_rank)\n# \"руку\" из 5ти карт. У каждой карты есть масть(suit) и\n# ранг(rank)\n# Масти: трефы(clubs, C), пики(spades, S), червы(hearts, H), бубны(diamonds, D)\n# Ранги: 2, 3, 4, 5, 6, 7, 8, 9, 10 (ten, T), валет (jack, J), дама (queen, Q), король (king, K), туз (ace, A)\n# Например: AS - туз пик (ace of spades), TH - дестяка черв (ten of hearts), 3C - тройка треф (three of clubs)\n\n# Задание со *\n# Реализуйте функцию best_wild_hand, которая принимает на вход\n# покерную \"руку\" (hand) из 7ми карт и возвращает лучшую\n# (относительно значения, возвращаемого hand_rank)\n# \"руку\" из 5ти карт. Кроме прочего в данном варианте \"рука\"\n# может включать джокера. Джокеры могут заменить карту любой\n# масти и ранга того же цвета, в колоде два джокерва.\n# Черный джокер '?B' может быть использован в качестве треф\n# или пик любого ранга, красный джокер '?R' - в качестве черв и бубен\n# любого ранга.\n\n# Одна функция уже реализована, сигнатуры и описания других даны.\n# Вам наверняка пригодится itertoolsю\n# Можно свободно определять свои функции и т.п.\n# -----------------\n\nfrom itertools import groupby, combinations\n\n\ndef ll(iterable):\n return len(list(iterable))\n\n\ndef all_equals(iterable):\n return ll(groupby(iterable)) == 1\n\n\ndef hand_rank(hand):\n \"\"\"Возвращает значение определяющее ранг 'руки'\"\"\"\n ranks = card_ranks(hand)\n if straight(ranks) and flush(hand):\n return (8, max(ranks))\n elif kind(4, ranks):\n return (7, kind(4, ranks), kind(1, ranks))\n elif kind(3, ranks) and kind(2, ranks):\n return (6, kind(3, ranks), kind(2, ranks))\n elif flush(hand):\n return (5, ranks)\n elif straight(ranks):\n return (4, max(ranks))\n elif kind(3, ranks):\n return (3, kind(3, ranks), ranks)\n elif two_pair(ranks):\n return (2, two_pair(ranks), ranks)\n elif kind(2, ranks):\n return (1, kind(2, ranks), ranks)\n else:\n return (0, ranks)\n\n\ndef card_ranks(hand):\n \"\"\"Возвращает список рангов (его числовой эквивалент),\n отсортированный от большего к меньшему\"\"\"\n total = '23456789TJQKA'\n return sorted([total.index(x[0]) for x in hand], reverse=True)\n\n\ndef flush(hand):\n \"\"\"Возвращает True, если все карты одной масти\"\"\"\n groups = groupby(hand, lambda x: x[1])\n return ll(groups) == 1\n\n\ndef straight(ranks):\n \"\"\"Возвращает True, если отсортированные ранги формируют последовательность 5ти,\n где у 5ти карт ранги идут по порядку (стрит)\"\"\"\n total = '.'.join([str(x) for x in reversed(range(2, 15))])\n return '.'.join([str(x) for x in ranks]) in total\n\n\ndef kind(n, ranks):\n \"\"\"Возвращает первый ранг, который n раз встречается в данной руке.\n Возвращает None, если ничего не найдено\"\"\"\n for rank, group in groupby(ranks):\n if ll(group) == n:\n return rank\n\n\ndef two_pair(ranks):\n \"\"\"Если есть две пары, то возврщает два соответствующих ранга,\n иначе возвращает None\"\"\"\n grouped = groupby(ranks)\n filtered = [rk for rk, gp in grouped if len(list(gp)) == 2]\n result = filtered[:2] if len(filtered) > 1 else None\n return result\n\n\ndef is_better_rank(a, b):\n is_better = False\n for x, y in zip(a, b):\n if x == y:\n continue\n is_better = x > y\n return is_better\n\n\ndef best_hand(hand):\n \"\"\"Из \"руки\" в 7 карт возвращает лучшую \"руку\" в 5 карт \"\"\"\n bhand = hand[:5]\n brank = hand_rank(bhand)\n\n for cur_hand in combinations(hand, 5):\n cur_rank = hand_rank(cur_hand)\n if is_better_rank(cur_rank, brank):\n brank = cur_rank\n bhand = cur_hand\n\n return bhand\n\n\ndef color(kind):\n if kind == 'C' or kind == 'S':\n return 'B'\n return 'R'\n\n\ndef wild_street(hand):\n pass\n\n\ndef wild_n(n, ranks, j_count):\n n = n - j_count\n for rank, group in groupby(ranks):\n if ll(group) >= n:\n return rank\n\n\ndef wild_flush(hand, jokers):\n groups = groupby(hand, lambda x: x[1])\n return ll(groups) == 1 and len(jokers) == 1 and color(hand[0][1]) == jokers[0][1]\n\n\ndef wild_full_house(ranks, hand, jokers):\n if len(jokers) == 2:\n kind(3,)\n\ndef best_wild_hand_one_joker(hand, j_color):\n bhand = None\n brank = None\n\n\n\n return None, None\n\n\ndef best_wild_hand_two_joker(hand):\n return None, None\n\n\ndef best_wild_hand(start_hand):\n jokers = [x for x in start_hand if '?' in x]\n hand = [x for x in start_hand if x not in jokers]\n\n bhand = best_hand(hand)\n brank = hand_rank(bhand)\n\n if len(jokers) == 0:\n return bhand\n\n for cur_hand in combinations(hand, 5):\n for joker in jokers:\n joker_bhand, jocker_brank = best_wild_hand_one_joker(cur_hand, joker)\n if is_better_rank(jocker_brank, brank):\n brank = jocker_brank\n bhand = joker_bhand\n\n if len(jokers) == 2:\n joker_bhand, jocker_brank = best_wild_hand_two_joker(hand)\n if is_better_rank(jocker_brank, brank):\n bhand = joker_bhand\n\n \"\"\"best_hand но с джокерами\"\"\"\n return bhand\n\n\ndef test_best_hand():\n print(\"test_best_hand...\")\n assert (sorted(best_hand(\"6C 7C 8C 9C TC 5C JS\".split()))\n == ['6C', '7C', '8C', '9C', 'TC'])\n assert (sorted(best_hand(\"TD TC TH 7C 7D 8C 8S\".split()))\n == ['8C', '8S', 'TC', 'TD', 'TH'])\n assert (sorted(best_hand(\"JD TC TH 7C 7D 7S 7H\".split()))\n == ['7C', '7D', '7H', '7S', 'JD'])\n print('OK')\n\n\ndef test_best_wild_hand():\n print(\"test_best_wild_hand...\")\n assert (sorted(best_wild_hand(\"6C 7C 8C 9C TC 5C ?B\".split()))\n == ['7C', '8C', '9C', 'JC', 'TC'])\n assert (sorted(best_wild_hand(\"TD TC 5H 5C 7C ?R ?B\".split()))\n == ['7C', 'TC', 'TD', 'TH', 'TS'])\n assert (sorted(best_wild_hand(\"JD TC TH 7C 7D 7S 7H\".split()))\n == ['7C', '7D', '7H', '7S', 'JD'])\n print('OK')\n\n\nif __name__ == '__main__':\n test_best_hand()\n test_best_wild_hand()\n", "sub_path": "hw1_poker/poker.py", "file_name": "poker.py", "file_ext": "py", "file_size_in_byte": 7263, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "itertools.groupby", "line_number": 38, "usage_type": "call"}, {"api_name": "itertools.groupby", "line_number": 73, "usage_type": "call"}, {"api_name": "itertools.groupby", "line_number": 87, "usage_type": "call"}, {"api_name": "itertools.groupby", "line_number": 95, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 115, "usage_type": "call"}, {"api_name": "itertools.groupby", "line_number": 136, "usage_type": "call"}, {"api_name": "itertools.groupby", "line_number": 142, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 173, "usage_type": "call"}]} +{"seq_id": "244631393", "text": "# Import modules\nimport scipy\nimport numpy as np\nimport pandas as pd\nimport sys\nimport os\nimport ipdb\nimport itertools\n\nkb = 0.0019872041 # kcal/(mol deg K)\nkelvin = 273.15 # Kelvin at 0 deg C\n\ndef load_params(model_param_basename='annotations/RNAmap/qMotif_20180302_'):\n \"\"\"Load the model parameters based on the basename\"\"\"\n base_params = pd.read_csv(model_param_basename + 'term1.csv', index_col=0) #.stack().values\n flip_params = pd.read_csv(model_param_basename + 'term2_single.csv', index_col=0) #.stack().values\n dflip_params = pd.read_csv(model_param_basename + 'term2_double.csv', index_col=0, squeeze=True) #.stack().values, 10])\n coupling_params = pd.read_csv(model_param_basename + 'term3.csv', index_col=0, squeeze=True) #.stack().values\n \n return flip_params, base_params, coupling_params, dflip_params\n \n\ndef get_ddG_conversion(temperature):\n return -(temperature+kelvin)*kb\n\ndef perfect_match_ddG_coupling_terms(sequence, base_penalties, coupling_terms, first_coupling, second_coupling):\n # Inputs:\n # first_coupling--set to True if first (7G) coupling should be included if the conditions are met (this is included so that this can be set to false\n # when flips occur in the coupling region, which will likely prevent coupling)\n # second_coupling--set to True if second (7C) coupling should be included if the necessary conditions are met\n # base_penalties--base penalties in partition function space (exp(-ddG_base/kT))\n # coupling_terms--coupling penalties in partition function space (exp(-ddG_base/kT))\n # sequence register that the mutational penalty contribution is being computed for\n # Output: ddG transformed into partition function space for passed sequence\n\n # Initialize to a penalty of 0 kcal: \n ddG = 0\n # Iterate through base penalties\n for i, base in enumerate(sequence):\n if i==8 and sequence[7]!='A':\n # exception at position 8--nothing happens\n continue\n ddG = ddG + base_penalties.loc[i, base]\n\n # Apply coupling corrections\n if first_coupling:\n if ((sequence[4] == 'U' or sequence[4] == 'C') and\n sequence[5] == 'A' and\n sequence[6] == 'G' and\n sequence[7] != 'A'):\n ddG = ddG + coupling_terms.loc['c1']\n if second_coupling:\n if sequence[6] == 'C' and sequence[7] != 'A':\n ddG = ddG + coupling_terms.loc['c2']\n\n return ddG\n\ndef perfect_match_exp_ddG_coupling_terms(sequence, base_penalties_exp, coupling_terms_exp, first_coupling, second_coupling):\n # Inputs:\n # first_coupling--set to True if first (7G) coupling should be included if the conditions are met (this is included so that this can be set to false\n # when flips occur in the coupling region, which will likely prevent coupling)\n # second_coupling--set to True if second (7C) coupling should be included if the necessary conditions are met\n # base_penalties--base penalties in partition function space (exp(-ddG_base/kT))\n # coupling_terms--coupling penalties in partition function space (exp(-ddG_base/kT))\n # sequence register that the mutational penalty contribution is being computed for\n # Output: ddG transformed into partition function space for passed sequence\n\n # Initialize to a penalty of 0 kcal: 10^(0) = 1; initialize to one in partition function space\n ddG = 1\n # Iterate through base penalties\n for i, base in enumerate(sequence):\n if i==8 and sequence[7]!='A':\n # exception at position 8--nothing happens\n continue\n ddG = ddG*base_penalties_exp.loc[i, base]\n\n # Apply coupling corrections\n if first_coupling:\n if ((sequence[4] == 'U' or sequence[4] == 'C') and\n sequence[5] == 'A' and\n sequence[6] == 'G' and\n sequence[7] != 'A'):\n ddG = ddG*coupling_terms_exp.loc['c1']\n if second_coupling:\n if sequence[6] == 'C' and sequence[7] != 'A':\n ddG = ddG*coupling_terms_exp.loc['c2']\n\n return ddG\n\ndef compute_ensemble_ddG_set(single_dG_values, temperature):\n \"\"\"Same as below but better starting with an array. Also assumes inputs are in dG,\n not in 'partition function space'\"\"\"\n ddG_conversion_factor = get_ddG_conversion(temperature)\n return ddG_conversion_factor*np.log(np.exp(single_dG_values/ddG_conversion_factor).sum(axis=1))\n \n\n\ndef compute_ensemble_ddG(single_dG_values, temperature, needs_exponentiating=False):\n # sums the individual contributions ot the partition function to get the compute partition \n # function and then converts that into a ddG for the ensemble\n # Inputs:\n # single_dG_values--a list of the single contributions to the partition function from all possible registers\n # Outputs:\n # final_ddG--final ddG of the ensemble\n ddG_conversion_factor = get_ddG_conversion(temperature)\n\n if needs_exponentiating:\n single_dG_values = np.exp(single_dG_values/ddG_conversion_factor).copy()\n\n # Sum the logged ddG values to compute the partition function \n partition_function = np.sum(single_dG_values)\n\n # Convert the partition function to the ensemble free energy\n \n final_ddG = ddG_conversion_factor*np.log(partition_function)\n\n return final_ddG\n\ndef get_coupling_bool_term1(flip_pos):\n # oonly apply the first coupling term if there is no flip at position 4, 5\n if flip_pos==4 or flip_pos==5:\n return False\n else:\n return True\n \ndef get_coupling_bool_term2(flip_pos):\n # oonly apply the second coupling term if there is no flip at position 5\n if flip_pos==5:\n return False\n else:\n return True\n\ndef get_noflip_registers(sequence, base_penalties, coupling_params):\n \"\"\"for a sequence, find the ddGs for each 1 nt register of the no-flip binding configuration.\"\"\"\n seq_length = 9\n registers = {}\n for i in range(len(sequence)-seq_length+1):\n ddG = perfect_match_ddG_coupling_terms(sequence[i:i+seq_length], base_penalties, coupling_params, True, True)\n registers[('%d:%d'%(i, i+seq_length), '-')] = ddG\n registers = pd.Series(registers)\n return registers\n\ndef get_1flip_registers(sequence, base_penalties, coupling_params, flip_params):\n \"\"\"for a sequence, find the ddGs for each 1 nt register of the 1nt-flip binding configuration.\"\"\"\n possible_flip_positions = flip_params.index.tolist()\n seq_length = 10\n registers = {}\n for i in range(len(sequence)-seq_length+1):\n current_sequence = sequence[i:i+seq_length]\n for flip_pos in possible_flip_positions:\n seq_not_flipped = current_sequence[:flip_pos]+current_sequence[flip_pos+1:]\n flip_base = current_sequence[flip_pos]\n\n dG = (flip_params.loc[flip_pos, flip_base] + # this is the penalty of flipping the residue\n perfect_match_ddG_coupling_terms(seq_not_flipped, base_penalties, coupling_params,\n get_coupling_bool_term1(flip_pos),\n get_coupling_bool_term2(flip_pos)))\n registers[('%d:%d'%(i, i+seq_length), 'pos%d_1nt'%flip_pos)] = dG\n registers = pd.Series(registers)\n return registers\n\ndef get_2flip_registers(sequence, base_penalties, coupling_params, flip_params, double_flip_params):\n \"\"\"for a sequence, find the ddGs for each 1 nt register of the 2nt-flip binding configuration.\"\"\"\n # double flips\n possible_flip_positions = flip_params.index.tolist()\n possible_double_flip_pos = double_flip_params.index.tolist()\n seq_length = 11\n registers = {}\n for i in range(len(sequence)-seq_length+1):\n current_sequence = sequence[i:i+seq_length]\n \n # 2x1nt flips\n for flip_pos1, flip_pos2 in itertools.combinations(possible_flip_positions, 2):\n \n # if the two positions are right next to each other, don't include here because these are the same as double flips\n if np.abs(flip_pos1 - flip_pos2) <= 1:\n continue\n \n seq_not_flipped = current_sequence[:flip_pos1]+current_sequence[flip_pos1+1:flip_pos2] + current_sequence[flip_pos2+1:]\n flip_base1 = current_sequence[flip_pos1]\n flip_base2 = current_sequence[flip_pos2]\n\n dG = (flip_params.loc[flip_pos1, flip_base1] + # this is the penalty of flipping the residue 1\n flip_params.loc[flip_pos2, flip_base2] + # this is the penalty of flipping the residue 2\n perfect_match_ddG_coupling_terms(seq_not_flipped, base_penalties, coupling_params,\n get_coupling_bool_term1(flip_pos1) and get_coupling_bool_term1(flip_pos2),\n get_coupling_bool_term2(flip_pos1) and get_coupling_bool_term2(flip_pos2)))\n \n \n registers[('%d:%d'%(i, i+seq_length), 'pos%d_1nt;pos%d_1nt'%(flip_pos1, flip_pos2))] = dG\n\n \n # 1x2nt flips\n for flip_pos in possible_double_flip_pos:\n\n seq_not_flipped = current_sequence[:flip_pos]+current_sequence[flip_pos+2:]\n dG = (double_flip_params.loc[flip_pos] +\n perfect_match_ddG_coupling_terms(seq_not_flipped, base_penalties, coupling_params,\n get_coupling_bool_term1(flip_pos),\n get_coupling_bool_term2(flip_pos)))\n \n registers[('%d:%d'%(i, i+seq_length), 'pos%d_2nt'%(flip_pos))] = dG\n\n registers = pd.Series(registers)\n return registers\n\n\ndef get_start_and_stop(seq_length, interval_length, i):\n \"\"\"Return the stop and start around nt i.\"\"\"\n start = max(i-interval_length+1, 0)\n stop = min(i+1, seq_length - interval_length+1)\n return start, stop\n \ndef find_energy_for_1nt_sequence_registers(sequence, base_penalties, coupling_params, flip_params, double_flip_params, temperature):\n \"\"\"Find the ensemble energy for each 1 nt register\"\"\"\n linear_binding_ddGs = get_noflip_registers(sequence, base_penalties, coupling_params)\n oneflip_binding_ddGs = get_1flip_registers(sequence, base_penalties, coupling_params, flip_params)\n twoflip_binding_ddGs = get_2flip_registers(sequence, base_penalties, coupling_params, flip_params, double_flip_params)\n seq_length_linear = 9\n seq_length_oneflip = 10\n seq_length_twoflip = 11\n \n ddGs_final = {}\n for i in range(len(sequence)):\n ddGs = {}\n \n # linear binding\n #if seq_length_linear > seq_length_interval:\n # raise ValueError('sequence is too short')\n\n start, stop = get_start_and_stop(len(sequence), seq_length_linear, i)\n for j in range(start, stop):\n key = '%d:%d'%(j, j+seq_length_linear)\n ddGs[key] = linear_binding_ddGs.loc[key]\n \n \n start, stop = get_start_and_stop(len(sequence), seq_length_oneflip, i)\n for j in range(start, stop):\n key = '%d:%d'%(j, j+seq_length_oneflip)\n ddGs[key] = oneflip_binding_ddGs.loc[key]\n \n start, stop = get_start_and_stop(len(sequence), seq_length_twoflip, i)\n for j in range(start, stop):\n key = '%d:%d'%(j, j+seq_length_twoflip)\n ddGs[key] = twoflip_binding_ddGs.loc[key] \n\n try:\n ddGs = pd.concat(ddGs)\n except ValueError:\n ipdb.set_trace()\n # combine into a single ensemble ddG\n ddG = compute_ensemble_ddG(ddGs, temperature, needs_exponentiating=True)\n ddGs_final[i] = ddG\n \n return pd.Series(ddGs_final) \n \n\ndef find_energy_for_11nt_sequence_registers(sequence, base_penalties, coupling_params, flip_params, double_flip_params, temperature):\n \"\"\"Find the ensemble energy for each register\"\"\"\n linear_binding_ddGs = get_noflip_registers(sequence, base_penalties, coupling_params)\n oneflip_binding_ddGs = get_1flip_registers(sequence, base_penalties, coupling_params, flip_params)\n twoflip_binding_ddGs = get_2flip_registers(sequence, base_penalties, coupling_params, flip_params, double_flip_params)\n \n # each register in twoflip_binding_ddGs corresponds to two in oneflip and 3 in noflip\n seq_length_interval = min(11, len(sequence))\n seq_length_linear = 9\n seq_length_oneflip = 10\n seq_length_twoflip = 11\n ddGs_final = {}\n for i in range(len(sequence)-seq_length_interval+1):\n ddGs = {}\n \n # linear binding\n if seq_length_linear > seq_length_interval:\n raise ValueError('sequence is too short')\n \n for j in range(seq_length_interval-seq_length_linear+1):\n key = '%d:%d'%(i+j, i+j+seq_length_linear)\n ddGs[key] = linear_binding_ddGs.loc[key]\n \n # one flip binding\n if seq_length_oneflip <= seq_length_interval: \n for j in range(seq_length_interval-seq_length_oneflip+1):\n key = '%d:%d'%(i+j, i+j+seq_length_oneflip)\n ddGs[key] = oneflip_binding_ddGs.loc[key]\n \n # two flip binding\n if seq_length_twoflip <= seq_length_interval: \n for j in range(seq_length_interval-seq_length_twoflip+1):\n key = '%d:%d'%(i+j, i+j+seq_length_twoflip)\n ddGs[key] = twoflip_binding_ddGs.loc[key]\n \n ddGs = pd.concat(ddGs)\n # combine into a single ensemble ddG\n ddG = compute_ensemble_ddG(ddGs, temperature, needs_exponentiating=True)\n ddGs_final[int(i+np.floor(seq_length_interval/2.))] = ddG\n \n return pd.Series(ddGs_final)\n\ndef additive_PUF_flip_model(passed_sequence, flip_params, base_penalties, coupling_params, double_flip_params, temperature, return_ensemble=False):\n # Inputs\n # passed sequence--sequence to compute the affinity for\n # flip_params--list of single flip param penalties (listed as 3/4A, 3/4C, 3/4G, 3/4U, 4/5A, ...)\n # base_penalties--list of single mutation penalties (listed as 1A, 1C, 1G, 1U, 2A, ...)\n # double_flip_params--list of double flip penalties (listed as 3 double flip, 4 double flip, ...)\n # coupling_params--list of coupling adjustments(listed as 7G adjustment followed by 7C adjustment)\n # Outputs\n # ddG predicted for the pased sequence in kcal/mol\n\n # Compute conversion factor at given Temperature (-T*k*(factor to convert exp to base 10))\n ddG_conversion_factor = get_ddG_conversion(temperature)\n\n # convert penalties from ddG space to \"partition function\" space (should be ordered A, C, G, T)\n flip_params_exp = np.exp(flip_params/ddG_conversion_factor)\n double_flip_params_exp = np.exp(double_flip_params/ddG_conversion_factor)\n base_penalties_exp = np.exp(base_penalties/ddG_conversion_factor)\n coupling_params_exp = np.exp(coupling_params/ddG_conversion_factor)\n\n possible_flip_positions = flip_params_exp.index.tolist()\n possible_double_flip_pos = double_flip_params_exp.index.tolist()\n \n # Convert sequence to a list for easy indexing\n sequence = list(passed_sequence)\n\n # initialize a list to store affinity for each possible register (note that this will store not true ddG values, but ddG values in \"partition function\" space)\n #length_9_ddGs = get_length9_registers(sequence, base_penalties_exp, coupling_params_exp)\n single_ddG_values = []\n registers = []\n for i in range(len(sequence)-8):\n single_ddG_values.append(perfect_match_exp_ddG_coupling_terms(sequence[i:i+9], base_penalties_exp, coupling_params_exp, True, True))\n registers.append('noflip_%d'%i)\n \n # compute the 1 nt flip ddG values\n for i in range(len(sequence)-9):\n current_sequence = sequence[i:i+10]\n for flip_pos in possible_flip_positions:\n seq_not_flipped = current_sequence[:flip_pos]+current_sequence[flip_pos+1:]\n flip_base = current_sequence[flip_pos]\n\n dG = (flip_params_exp.loc[flip_pos, flip_base]* # this is the penalty of flipping the residue\n perfect_match_exp_ddG_coupling_terms(seq_not_flipped, base_penalties_exp, coupling_params_exp,\n get_coupling_bool_term1(flip_pos),\n get_coupling_bool_term2(flip_pos)))\n single_ddG_values.append(dG)\n registers.append('flip_%d;pos_%d'%(i, flip_pos))\n \n # double flips\n for i in range(len(sequence)-10):\n current_sequence = sequence[i:i+11]\n \n # 2x1nt flips\n for flip_pos1, flip_pos2 in itertools.combinations(possible_flip_positions, 2):\n seq_not_flipped = current_sequence[:flip_pos1]+current_sequence[flip_pos1+1:flip_pos2] + current_sequence[flip_pos2+1:]\n flip_base1 = current_sequence[flip_pos1]\n flip_base2 = current_sequence[flip_pos2]\n\n dG = (flip_params_exp.loc[flip_pos1, flip_base1]* # this is the penalty of flipping the residue 1\n flip_params_exp.loc[flip_pos2, flip_base2]* # this is the penalty of flipping the residue 2\n perfect_match_exp_ddG_coupling_terms(seq_not_flipped, base_penalties_exp, coupling_params_exp,\n get_coupling_bool_term1(flip_pos1) and get_coupling_bool_term1(flip_pos2),\n get_coupling_bool_term2(flip_pos1) and get_coupling_bool_term2(flip_pos2)))\n single_ddG_values.append(dG)\n registers.append('doubleflip_%d;pos_%d;pos_%d'%(i, flip_pos1, flip_pos2))\n \n # 1x2nt flips\n for flip_pos in possible_double_flip_pos:\n\n seq_not_flipped = current_sequence[:flip_pos]+current_sequence[flip_pos+2:]\n dG = (double_flip_params_exp.loc[flip_pos]*\n perfect_match_exp_ddG_coupling_terms(seq_not_flipped, base_penalties_exp, coupling_params_exp,\n get_coupling_bool_term1(flip_pos),\n get_coupling_bool_term2(flip_pos)))\n single_ddG_values.append(dG)\n registers.append('doubleflip_%d;pos_%d'%(i, flip_pos))\n\n ddG = compute_ensemble_ddG(single_ddG_values, temperature)\n \n if return_ensemble:\n return ddG, pd.Series(ddG_conversion_factor*np.log(single_ddG_values), index=registers)\n else:\n return ddG\n\ndef interpret_col_names(col_names):\n \"\"\" given the 'register' annotations above, group them more meaningfully\"\"\"\n annotations = pd.Series(1, index=col_names)\n annotations.loc[[idx for idx in col_names if idx.find('noflip')==0 and idx!='noflip_0']] = 2\n annotations.loc[[idx for idx in col_names if idx.find('flip')==0]] = 4\n annotations.loc[[idx for idx in col_names if idx.find('doubleflip')==0]] = 8\n\n return annotations\n\ndef flag_ensemble(ddG_ensemble_vec, cutoff=1):\n \"\"\"Given a set of measurements, find things within cutoff and generate flag with annotations\"\"\"\n close_enough_vec = [idx for idx, val in ddG_ensemble_vec.iteritems() if val < cutoff]\n annotations = interpret_col_names(ddG_ensemble_vec.index.tolist())\n flag = annotations.loc[close_enough_vec].unique().sum()\n return flag\n \ndef determine_seq_occupancy(seq, temperature):\n \"\"\"Break a seq up into 11 nt chunks and return occupancy relative to consensus site for each seq.\"\"\"\n \n", "sub_path": "puflibs/seqmodel_old.py", "file_name": "seqmodel_old.py", "file_ext": "py", "file_size_in_byte": 19521, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 115, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 140, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 159, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 176, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 204, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 248, "usage_type": "call"}, {"api_name": "ipdb.set_trace", "line_number": 250, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 255, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.floor", "line_number": 296, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 298, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 316, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 317, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 352, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 379, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 379, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 385, "usage_type": "call"}]} +{"seq_id": "384923174", "text": "from statistics import mean\nfrom sys import stdin\n\nstudents = {}\nnext(stdin)\nfor line in stdin:\n try:\n name, *marks = line.split()\n students[name] = mean(map(float, marks))\n except ValueError:\n print('{:.2f}'.format(students[line.rstrip()]))\n", "sub_path": "Python/Introduction/finding_the_percentage.py", "file_name": "finding_the_percentage.py", "file_ext": "py", "file_size_in_byte": 269, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.stdin", "line_number": 5, "usage_type": "argument"}, {"api_name": "sys.stdin", "line_number": 6, "usage_type": "name"}, {"api_name": "statistics.mean", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "276379717", "text": "import os\nimport json\nimport argparse\nimport math\nimport numpy as np\nimport copy\nimport torch\nfrom torch import nn, optim\nfrom torch.nn import functional as F\nfrom torch.utils.data import DataLoader\nfrom torch.utils.tensorboard import SummaryWriter\nimport torch.multiprocessing as mp\nimport torch.distributed as dist\nimport matplotlib.pyplot as plt\n\nfrom apex.parallel import DistributedDataParallel as DDP\nfrom apex import amp\n\nfrom data_utils import TextMelLoader, TextMelCollate\nimport models\nimport commons\nimport utils\nfrom text.symbols import symbols\n \n\nglobal_step = 0\n\n\ndef main():\n \"\"\"Assume Single Node Multi GPUs Training Only\"\"\"\n assert torch.cuda.is_available(), \"CPU training is not allowed.\"\n torch.cuda.empty_cache()\n\n n_gpus = torch.cuda.device_count()\n os.environ['MASTER_ADDR'] = 'localhost'\n os.environ['MASTER_PORT'] = '80001'\n\n hps = utils.get_hparams()\n mp.spawn(train_and_eval, nprocs=n_gpus, args=(n_gpus, hps,))\n\n\ndef train_and_eval(rank, n_gpus, hps):\n global global_step\n \n ## Added as part of MSc Thesis - Transformer optimization\n global global_omega\n global prev_l_head_wt\n global prev_l_qry_wt\n \n if rank == 0:\n logger = utils.get_logger(hps.model_dir)\n logger.info(hps)\n utils.check_git_hash(hps.model_dir)\n writer = SummaryWriter(log_dir=hps.model_dir)\n writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, \"eval\"))\n\n dist.init_process_group(backend='nccl', init_method='env://', world_size=n_gpus, rank=rank)\n torch.manual_seed(hps.train.seed)\n torch.cuda.set_device(rank)\n\n train_dataset = TextMelLoader(hps.data.training_files, hps.data)\n train_sampler = torch.utils.data.distributed.DistributedSampler(\n train_dataset,\n num_replicas=n_gpus,\n rank=rank,\n shuffle=True)\n collate_fn = TextMelCollate(1)\n train_loader = DataLoader(train_dataset, num_workers=8, shuffle=False,\n batch_size=hps.train.batch_size, pin_memory=True,\n drop_last=True, collate_fn=collate_fn, sampler=train_sampler)\n if rank == 0:\n val_dataset = TextMelLoader(hps.data.validation_files, hps.data)\n val_loader = DataLoader(val_dataset, num_workers=8, shuffle=False,\n batch_size=hps.train.batch_size, pin_memory=True,\n drop_last=True, collate_fn=collate_fn)\n\n generator = models.FlowGenerator(\n n_vocab=len(symbols) + getattr(hps.data, \"add_blank\", False), \n out_channels=hps.data.n_mel_channels, \n **hps.model).cuda(rank)\n if hps.model.mask_flag == 'Y':\n dim_m = (hps.model.hidden_channels / hps.model.n_heads) * (hps.model.n_heads - len(hps.model.mask_heads))\n else:\n dim_m = hps.model.hidden_channels\n #print(dim_m)\n optimizer_g = commons.Adam(generator.parameters(), scheduler=hps.train.scheduler, dim_model=dim_m, warmup_steps=hps.train.warmup_steps, lr=hps.train.learning_rate, betas=hps.train.betas, eps=hps.train.eps)\n if hps.train.fp16_run:\n generator, optimizer_g._optim = amp.initialize(generator, optimizer_g._optim, opt_level=\"O1\")\n generator = DDP(generator)\n epoch_str = 1\n global_step = 0\n \n ## Added as part of MSc Thesis - Transformer optimization\n global_omega = np.zeros((4, 8), dtype=float)\n prev_l_head_wt = np.zeros((4, 8), dtype=float)\n prev_l_qry_wt = np.zeros((4, 8), dtype=float)\n \n \n try:\n _, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, \"G_*.pth\"), generator, optimizer_g)\n epoch_str += 1\n optimizer_g.step_num = (epoch_str - 1) * len(train_loader)\n optimizer_g._update_learning_rate()\n global_step = (epoch_str - 1) * len(train_loader)\n global_omega = 0\n\n except:\n if hps.train.ddi and os.path.isfile(os.path.join(hps.model_dir, \"ddi_G.pth\")):\n _ = utils.load_checkpoint(os.path.join(hps.model_dir, \"ddi_G.pth\"), generator, optimizer_g)\n loss_train = []\n loss_val = []\n \n best_epoch = 1\n loss_diff = 0.0\n cnt = 0\n\n for epoch in range(epoch_str, hps.train.epochs + 1):\n if rank==0:\n train(rank, epoch, hps, generator, optimizer_g, train_loader, logger, writer,loss_train)\n evaluate(rank, epoch, hps, generator, optimizer_g, val_loader, logger, writer_eval,loss_val)\n \n\n utils.save_checkpoint(generator, optimizer_g, hps.train.learning_rate, epoch, os.path.join(hps.model_dir, \"G_{}.pth\".format(epoch)))\n else:\n train(rank, epoch, hps, generator, optimizer_g, train_loader, None, None)\n \n ## Added as part of MSc Thesis - Transformer optimization\n print(\"Loss: \",loss_train)\n print(\"Loss Val: \",loss_val) \n# loss_diff = abs(loss_val[epoch-1].item() - loss_train[epoch-1].item())\n# # print(\"loss_diff: \",loss_diff)\n# if loss_diff > 0.1:\n# best_epoch = epoch - 1\n# cnt += 1\n# print(\"cnt: \",cnt)\n# else:\n# cnt = 0\n# best_epoch = epoch - 1\n# if cnt > 5:\n# break\n# print(\"loss: \",loss_val)\n# print(\"Best Epoch: \",best_epoch)\n \n fig = plt.figure()\n plt.title(\"Loss vs. Number of Training Epochs\")\n plt.xlabel(\"Epochs\")\n plt.ylabel(\"Loss\")\n plt.plot(range(1,hps.train.epochs + 1),loss_train,label='Train')\n plt.plot(range(1,hps.train.epochs + 1),loss_val,label='Validation')\n# plt.plot(range(1,best_epoch + 2),loss_train,label='Train')\n# plt.plot(range(1,best_epoch + 2),loss_val,label='Validation')\n plt.legend()\n fignm=\"/content/gdrive/MyDrive/Colab Notebooks/Project/glow-tts/logs/fig_\"+str(epoch)+\".png\"\n fig.savefig(fignm)\n print(\"global_omega: \",global_omega)\n print(\"global_omega_idx_sort: \", np.argsort(global_omega))\n arr1 = np.array(global_omega).flatten()\n print(arr1.argsort())\n\ndef train(rank, epoch, hps, generator, optimizer_g, train_loader, logger, writer,loss_train):\n train_loader.sampler.set_epoch(epoch)\n global global_step\n \n ## Added as part of MSc Thesis - Transformer optimization\n global global_omega\n global prev_l_head_wt\n global prev_l_qry_wt\n losses_tot1 = []\n omega = global_omega\n grad = np.zeros((4, 8), dtype=float)\n \n generator.train()\n for batch_idx, (x, x_lengths, y, y_lengths) in enumerate(train_loader):\n x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)\n y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)\n\n\n # Train Generator\n optimizer_g.zero_grad()\n \n (z, z_m, z_logs, logdet, z_mask), (x_m, x_logs, x_mask, l_head_wt, l_qry_wt, l_attn_wt), (attn, logw, logw_) = generator(x, x_lengths, y, y_lengths, gen=False)\n l_mle = commons.mle_loss(z, z_m, z_logs, logdet, z_mask)\n l_length = commons.duration_loss(logw, logw_, x_lengths)\n\n loss_gs = [l_mle, l_length]\n loss_g = sum(loss_gs)\n #print(\"prev_l_qry_wt: \",prev_l_qry_wt)\n if batch_idx == 0:\n losses_tot1 = loss_gs\n else:\n losses_tot1 = [x + y for (x, y) in zip(losses_tot1, loss_gs)]\n \n\n ## Added as part of MSc Thesis - Transformer optimization \n diff_qry = np.abs(l_qry_wt - prev_l_qry_wt)\n #print(\"diff_qry: \",diff_qry)\n grad = (np.abs(l_head_wt - prev_l_head_wt)/ l_head_wt) * diff_qry \n current_size = (batch_idx+1)* hps.train.batch_size #batch_size - 8\n step_size = 1/float(current_size)\n \n #Incremental update for the omega\n omega = omega + step_size*grad \n\n if hps.train.fp16_run:\n with amp.scale_loss(loss_g, optimizer_g._optim) as scaled_loss:\n scaled_loss.backward()\n grad_norm = commons.clip_grad_value_(amp.master_params(optimizer_g._optim), 5)\n else:\n loss_g.backward()\n grad_norm = commons.clip_grad_value_(generator.parameters(), 5)\n optimizer_g.step()\n \n if rank==0:\n if batch_idx % hps.train.log_interval == 0:\n (y_gen, *_), *_ = generator.module(x[:1], x_lengths[:1], gen=True)\n logger.info('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n epoch, batch_idx * len(x), len(train_loader.dataset),\n 100. * batch_idx / len(train_loader),\n loss_gs[0].item()))\n logger.info([x.item() for x in loss_gs] + [global_step, optimizer_g.get_lr()])\n \n scalar_dict = {\"loss/g/total\": loss_g, \"learning_rate\": optimizer_g.get_lr(), \"grad_norm\": grad_norm}\n scalar_dict.update({\"loss/g/{}\".format(i): v for i, v in enumerate(loss_gs)})\n utils.summarize(\n writer=writer,\n global_step=global_step, \n images={\"y_org\": utils.plot_spectrogram_to_numpy(y[0].data.cpu().numpy()), \n \"y_gen\": utils.plot_spectrogram_to_numpy(y_gen[0].data.cpu().numpy()), \n \"attn\": utils.plot_alignment_to_numpy(attn[0,0].data.cpu().numpy()),\n },\n scalars=scalar_dict)\n global_step += 1\n \n ## Added as part of MSc Thesis - Transformer optimization\n prev_l_head_wt = copy.deepcopy(l_head_wt)\n prev_l_qry_wt = copy.deepcopy(l_qry_wt)\n #print(\"global_step: \",global_step)\n global_omega += (1/((hps.train.epochs + 1) - epoch))*omega\n \n losses_tot1 = [x/len(train_loader) for x in losses_tot1]\n #losses_tot1 = [x/2 for x in losses_tot1]\n #loss_tot1 = sum(losses_tot1)\n loss_tot1 = losses_tot1[0]\n loss_train.append(loss_tot1.detach())\n\n if rank == 0:\n logger.info('====> Epoch: {}'.format(epoch))\n\n \ndef evaluate(rank, epoch, hps, generator, optimizer_g, val_loader, logger, writer_eval,loss_val):\n if rank == 0:\n global global_step\n generator.eval()\n losses_tot = []\n with torch.no_grad():\n for batch_idx, (x, x_lengths, y, y_lengths) in enumerate(val_loader):\n x, x_lengths = x.cuda(rank, non_blocking=True), x_lengths.cuda(rank, non_blocking=True)\n y, y_lengths = y.cuda(rank, non_blocking=True), y_lengths.cuda(rank, non_blocking=True)\n\n (z, z_m, z_logs, logdet, z_mask), (x_m, x_logs, x_mask, l_head_wt, l_qry_wt, l_attn_wt), (attn, logw, logw_) = generator(x, x_lengths, y, y_lengths, gen=False)\n l_mle = commons.mle_loss(z, z_m, z_logs, logdet, z_mask)\n l_length = commons.duration_loss(logw, logw_, x_lengths)\n\n loss_gs = [l_mle, l_length]\n loss_g = sum(loss_gs)\n\n if batch_idx == 0:\n losses_tot = loss_gs\n else:\n losses_tot = [x + y for (x, y) in zip(losses_tot, loss_gs)]\n\n if batch_idx % hps.train.log_interval == 0:\n logger.info('Eval Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n epoch, batch_idx * len(x), len(val_loader.dataset),\n 100. * batch_idx / len(val_loader),\n loss_gs[0].item()))\n logger.info([x.item() for x in loss_gs])\n \n \n losses_tot = [x/len(val_loader) for x in losses_tot]\n #loss_tot = sum(losses_tot)\n loss_tot = losses_tot[0]\n loss_val.append(loss_tot.detach())\n scalar_dict = {\"loss/g/total\": loss_tot}\n scalar_dict.update({\"loss/g/{}\".format(i): v for i, v in enumerate(losses_tot)})\n utils.summarize(\n writer=writer_eval,\n global_step=global_step, \n scalars=scalar_dict)\n logger.info('====> Epoch: {}'.format(epoch))\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 10978, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.cuda.is_available", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 31, "usage_type": "attribute"}, {"api_name": "torch.cuda.empty_cache", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.cuda.device_count", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 36, "usage_type": "attribute"}, {"api_name": "utils.get_hparams", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.multiprocessing.spawn", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.multiprocessing", "line_number": 39, "usage_type": "name"}, {"api_name": "utils.get_logger", "line_number": 51, "usage_type": "call"}, {"api_name": "utils.check_git_hash", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.utils.tensorboard.SummaryWriter", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.utils.tensorboard.SummaryWriter", "line_number": 55, "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": "torch.distributed.init_process_group", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.distributed", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.manual_seed", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.cuda.set_device", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 59, "usage_type": "attribute"}, {"api_name": "data_utils.TextMelLoader", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.utils.data.distributed.DistributedSampler", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 62, "usage_type": "attribute"}, {"api_name": "data_utils.TextMelCollate", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 68, "usage_type": "call"}, {"api_name": "data_utils.TextMelLoader", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 73, "usage_type": "call"}, {"api_name": "models.FlowGenerator", "line_number": 77, "usage_type": "call"}, {"api_name": "text.symbols.symbols", "line_number": 78, "usage_type": "argument"}, {"api_name": "commons.Adam", "line_number": 86, "usage_type": "call"}, {"api_name": "apex.amp.initialize", "line_number": 88, "usage_type": "call"}, {"api_name": "apex.amp", "line_number": 88, "usage_type": "name"}, {"api_name": "apex.parallel.DistributedDataParallel", "line_number": 89, "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": "utils.load_checkpoint", "line_number": 100, "usage_type": "call"}, {"api_name": "utils.latest_checkpoint_path", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 108, "usage_type": "call"}, {"api_name": "utils.load_checkpoint", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path", "line_number": 109, "usage_type": "attribute"}, {"api_name": "utils.save_checkpoint", "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": "matplotlib.pyplot.figure", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "numpy.argsort", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 170, "usage_type": "call"}, {"api_name": "commons.mle_loss", "line_number": 182, "usage_type": "call"}, {"api_name": "commons.duration_loss", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 197, "usage_type": "call"}, {"api_name": "apex.amp.scale_loss", "line_number": 205, "usage_type": "call"}, {"api_name": "apex.amp", "line_number": 205, "usage_type": "name"}, {"api_name": "commons.clip_grad_value_", "line_number": 207, "usage_type": "call"}, {"api_name": "apex.amp.master_params", "line_number": 207, "usage_type": "call"}, {"api_name": "apex.amp", "line_number": 207, "usage_type": "name"}, {"api_name": "commons.clip_grad_value_", "line_number": 210, "usage_type": "call"}, {"api_name": "utils.summarize", "line_number": 224, "usage_type": "call"}, {"api_name": "utils.plot_spectrogram_to_numpy", "line_number": 227, "usage_type": "call"}, {"api_name": "utils.plot_spectrogram_to_numpy", "line_number": 228, "usage_type": "call"}, {"api_name": "utils.plot_alignment_to_numpy", "line_number": 229, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 235, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 236, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 255, "usage_type": "call"}, {"api_name": "commons.mle_loss", "line_number": 261, "usage_type": "call"}, {"api_name": "commons.duration_loss", "line_number": 262, "usage_type": "call"}, {"api_name": "utils.summarize", "line_number": 286, "usage_type": "call"}]} +{"seq_id": "596402071", "text": "from svmutil import *\nimport numpy as np\nimport math\nimport random\nimport matplotlib.pyplot as plt\n\n\nclass KernelizedPerceptron_binary():\n\n\tdef __init__(self, path1, path2):\n\n\t\tself.train_list_letter, self.train_list_word = self.parse(path1)\n\t\tself.test_list_letter, self.test_list_word = self.parse(path2)\n\n\n\t\tself.alpha = np.zeros(len(self.train_list_word), dtype = np.float32)\n\t\tself.bias = 0\n\t\tself.maxiter = 20\n\n\t\tself.mistakes_train = np.zeros(self.maxiter, dtype = np.int32)\n\t\tself.mistakes_test = np.zeros(self.maxiter, dtype = np.int32)\n\n\n\tdef parse(self, file):\n\n\t\t# list of the x's\n\t\tlist_word = []\n\t\t# list of the y's\n\t\tlist_letter = []\n\t\t#holds the whole list\n\t\t#list_main = []\n\n\t\tf = open(file)\t\t#code for parsing\n\n\t\tfor line in f:\n\t\t\t# to remove the blank lines\n\t\t\tif line.strip():\n\t\t\t\tg = line.split(\"\\t\")\n\t\t\t\t#convert each element in the list to a list\n\t\t\t\t#extract the label in each x\n\t\t\t\tletter = g[2]\n\t\t\t\t# map the letter to a number \n\t\t\t\t# a -> 1, b -> 2, c -> 3, ...\n\t\t\t\tletter = ord(letter) - 97\n\t\t\t\t#extract the x for each input data\n\t\t\t\tword = list(g[1][2:])\n\t\t\t\t#word = (map(float, word))\n\t\t\t\tword = [float(w) for w in word]\n\t\t\t\tif letter == 0 or letter == 4 or letter == 8 or letter == 14 or letter == 20:\n\t\t\t\t\tletter = 1\n\t\t\t\telse:\n\t\t\t\t\tletter = -1\n\t\t\t\t# print word\n\t\t\t\t# if validation set is being computed\n\t\t\t\tlist_word.append(word)\n\t\t\t\tlist_letter.append(letter)\n\t\t\t\t#list_main.append(g)\n\t\t\n\t\treturn np.array(list_letter, dtype=int), np.array(list_word, dtype=int) #, list_main\n\n\n\tdef classifier_train (self):\n\n\t\tfor iter in range (self.maxiter):\n\t\t\t# print (iter)\n\t\t\tmistake = 0\n\n\t\t\tfor i in range (len(self.train_list_word)):\n\t\t\t\t# now we have to compute the activation function\n\t\t\t\t# computation of activation function\n\t\t\t\ta = 0\n\t\t\t\tfor j in range (len (self.alpha)):\n\t\t\t\t\ta_ = np.dot(self.train_list_word[j], self.train_list_word[i]) + 1\n\n\t\t\t\t\ta__ = math.pow(a_, 2)\n\n\t\t\t\t\ta += self.alpha[j] * a__ + self.bias\n\n\n\t\t\t\t# condition for the mistake check\n\t\t\t\tif self.train_list_letter[i] * a <= 0:\n\t\t\t\t\t\n\t\t\t\t\tmistake += 1\n\t\t\t\t\t# update the alphas\n\t\t\t\t\tself.alpha[i] += self.train_list_letter[i]\n\t\t\t\t\t# update the bias\n\t\t\t\t\tself.bias += self.train_list_letter[i]\n\n\t\t\tself.mistakes_train[iter] = mistake\n\t\t\t# perform the testing after one iteration of training\n\t\t\tself.mistakes_test[iter] = self.classifier_test()\n\n\t\t\n\t\treturn self.alpha, self.bias\n\n\n\tdef classifier_test(self):\n\t\t# for testing set\n\t\tmistake = 0\n\t\t# print (\"testing\")\n\t\tfor i in range (len(self.test_list_word)):\n\n\t\t\ta =0\n\t\t\tfor j in range (len(self.alpha)):\n\t\t\t\n\t\t\t\ta_ = np.dot(self.train_list_word[j], self.test_list_word[i]) + 1\n\n\t\t\t\ta__ = math.pow(a_, 2)\n\n\t\t\t\ta += self.alpha[j] * a__ + self.bias\n\n\t\t\tif self.test_list_letter[i] * a <= 0:\n\n\t\t\t\tmistake += 1\n\t\treturn mistake\n\n\t\t\t\n\n\ndef main():\n\tpath1 = \"/home/goelshivam12/Desktop/ML_Homework/HW#1/OCRdata/ocr_fold0_sm_train.txt\"\n\tpath2 = \"/home/goelshivam12/Desktop/ML_Homework/HW#1/OCRdata/ocr_fold0_sm_test.txt\"\n\n\tclassifier = KernelizedPerceptron_binary(path1, path2)\n\ta, b = classifier.classifier_train()\n\t# print a\n\tprint (classifier.mistakes_train)\n\tprint (classifier.mistakes_test)\n\tprint (len(classifier.train_list_word))\n\tprint (len(classifier.test_list_word))\n\n\tplt.plot(classifier.mistakes_train)\n\tplt.plot(classifier.mistakes_test)\n\tplt.show()\n\n\t# plots the testing accuracy of the dataset\n\t# plt.plot(accuracy_validation)\n\n\t# plt.xlabel(\"C's\", fontsize = 15)\n\t# plt.ylabel(\" Accuracy\", fontsize = 15)\n\t# plt.title(\"Accuracy Curve (SVM)\", fontsize = 25)\n\t# # plt.ylim([0.1, 0.8])\n\t# plt.grid(True)\n\t# plt.legend(['Training', ' Testing', 'Validation'])\n\t# plt.show() \n\n\n\nif __name__ == '__main__':\n\n\tmain()\n\n\n\t\t# compute the training classifier and each time please test its accuracy on the validation data as well as the test data \n\n\n\n\n", "sub_path": "HW#2/code/11483916-GOEL/kernelizedperceptron_bin.py", "file_name": "kernelizedperceptron_bin.py", "file_ext": "py", "file_size_in_byte": 3796, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.zeros", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 20, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 73, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 106, "usage_type": "call"}, {"api_name": "math.pow", "line_number": 108, "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.plot", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}]} +{"seq_id": "487744206", "text": "# ---\n# jupyter:\n# jupytext:\n# formats: ipynb,py:light\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.11.2\n# kernelspec:\n# display_name: Python 3\n# language: python\n# name: python3\n# ---\n\n# + hide_input=false\n# Setup ipytest extension\ntry:\n ipy_str = str(type(get_ipython()))\n if 'zmqshell' in ipy_str or 'terminal' in ipy_str:\n import ipytest\n ipytest.autoconfig()\nexcept:\n pass\n\n\n# + hide_input=false\n# Import our dependencies\n\nimport os\nimport sys\nfrom pathlib import Path\nimport pytest\nimport pandas\nfrom pandas.testing import assert_frame_equal, assert_series_equal\n\nmodule_path = os.path.abspath('../')\nif module_path not in sys.path:\n sys.path.append(module_path)\n\nfrom derived_variables.paidhb import calculate_paid_housing_benefit\n\ndef get_path(filename):\n return os.path.join(module_path, 'tests/fixtures', filename)\n\n# + [markdown] hide_input=false\n# ## Show function definition\n\n# +\n# calculate_paid_housing_benefit??\n# -\n\n# ## Set up our input data from a CSV fixture\n# This reads in a CSV of values and loads it into memory\n#\n\ninput_data = pandas.read_csv(get_path('input_paidhb.csv'), index_col=0)\ninput_data\n\n# ## Set up our expected result from a CSV fixture\n\nexpected_result = pandas.read_csv(get_path('expected_result_paidhb.csv'), index_col=0, squeeze=True)\nexpected_result\n\n# ## Run the model\n\nactual_result = calculate_paid_housing_benefit(input_data)\nactual_result\n\n# +\n# %%run_pytest[clean]\n\n# Check that our actual result matches our expected result\n\ndef test_apply_weighting():\n assert_frame_equal(actual_result, expected_result)\n\n\n# + hide_input=true\n__name__\n", "sub_path": "modules/pandas/tests/test_paidhb.py", "file_name": "test_paidhb.py", "file_ext": "py", "file_size_in_byte": 1727, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "ipytest.autoconfig", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "sys.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 39, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 57, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 62, "usage_type": "call"}, {"api_name": "derived_variables.paidhb.calculate_paid_housing_benefit", "line_number": 67, "usage_type": "call"}, {"api_name": "pandas.testing.assert_frame_equal", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "638837760", "text": "from flask import Blueprint\nfrom flask_restful import Api, Resource\nfrom web.modules.auth.controllers import (AuthorizationController,\n IsAuthenticatedController,\n LogoutController, RefreshController,\n RegistrationController)\n\nbp = Blueprint('oauth', __name__)\napi = Api(bp)\n\n\"\"\"[define routing under oauth module]\n\"\"\"\napi.add_resource(RegistrationController, '/registration')\napi.add_resource(AuthorizationController, '/authorization')\napi.add_resource(RefreshController, '/refresh')\napi.add_resource(IsAuthenticatedController, '/isauthenticated')\napi.add_resource(LogoutController, '/logout')\n", "sub_path": "web/modules/auth/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 718, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Blueprint", "line_number": 8, "usage_type": "call"}, {"api_name": "flask_restful.Api", "line_number": 9, "usage_type": "call"}, {"api_name": "web.modules.auth.controllers.RegistrationController", "line_number": 13, "usage_type": "argument"}, {"api_name": "web.modules.auth.controllers.AuthorizationController", "line_number": 14, "usage_type": "argument"}, {"api_name": "web.modules.auth.controllers.RefreshController", "line_number": 15, "usage_type": "argument"}, {"api_name": "web.modules.auth.controllers.IsAuthenticatedController", "line_number": 16, "usage_type": "argument"}, {"api_name": "web.modules.auth.controllers.LogoutController", "line_number": 17, "usage_type": "argument"}]} +{"seq_id": "569196485", "text": "# Copyright (c) Facebook, Inc. and its affiliates.\r\n#\r\n# This source code is licensed under the MIT license found in the\r\n# LICENSE file in the root directory of this source tree.\r\n\r\nimport logging\r\nimport os\r\n\r\nimport numpy as np\r\nfrom fairseq.data import (\r\n AppendTokenDataset,\r\n ConcatDataset,\r\n DenoisingDataset,\r\n Dictionary,\r\n PrependTokenDataset,\r\n ResamplingDataset,\r\n SortDataset,\r\n TokenBlockDataset,\r\n data_utils,\r\n)\r\nfrom fairseq.data.encoders.utils import get_whole_word_mask\r\nfrom fairseq.tasks import register_task\r\n\r\nfrom .denoising import DenoisingTask\r\n\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\n\r\n@register_task(\"multilingual_denoising\")\r\nclass MultilingualDenoisingTask(DenoisingTask):\r\n @staticmethod\r\n def add_args(parser):\r\n DenoisingTask.add_args(parser)\r\n parser.add_argument(\r\n \"--multilang-sampling-alpha\",\r\n type=float,\r\n default=1.0,\r\n help=\"smoothing alpha for sample ratios across multiple datasets\",\r\n )\r\n parser.add_argument(\"--add-lang-token\", default=False, action=\"store_true\")\r\n parser.add_argument(\r\n \"--langs\", type=str, help=\"language ids we are considering\", default=None\r\n )\r\n parser.add_argument(\r\n \"--no-whole-word-mask-langs\",\r\n type=str,\r\n default=\"\",\r\n metavar=\"N\",\r\n help=\"languages without spacing between words dont support whole word masking\",\r\n )\r\n\r\n @classmethod\r\n def setup_task(cls, args, **kwargs):\r\n \"\"\"Setup the task.\"\"\"\r\n paths = args.data.split(\":\")\r\n assert len(paths) > 0\r\n dictionary = Dictionary.load(os.path.join(paths[0], \"dict.txt\"))\r\n\r\n data_path = paths[0]\r\n if args.langs is None:\r\n languages = sorted(\r\n [\r\n name\r\n for name in os.listdir(data_path)\r\n if os.path.isdir(os.path.join(data_path, name))\r\n ]\r\n )\r\n else:\r\n languages = args.langs.split(\",\")\r\n\r\n if args.add_lang_token:\r\n for lang in languages:\r\n dictionary.add_symbol(\"[{}]\".format(lang))\r\n\r\n logger.info(\"dictionary: {} types\".format(len(dictionary)))\r\n if not hasattr(args, \"shuffle_instance\"):\r\n args.shuffle_instance = False\r\n return cls(args, dictionary)\r\n\r\n def __init__(self, args, dictionary):\r\n super().__init__(args, dictionary)\r\n self.dictionary = dictionary\r\n self.seed = args.seed\r\n\r\n # add mask token\r\n self.mask_idx = self.dictionary.add_symbol(\"\")\r\n self.langs = args.langs\r\n self.args = args\r\n\r\n def _get_sample_prob(self, dataset_lens):\r\n \"\"\"\r\n Get smoothed sampling porbability by languages. This helps low resource\r\n languages by upsampling them.\r\n \"\"\"\r\n prob = dataset_lens / dataset_lens.sum()\r\n smoothed_prob = prob ** self.args.multilang_sampling_alpha\r\n smoothed_prob = smoothed_prob / smoothed_prob.sum()\r\n return smoothed_prob\r\n\r\n def load_dataset(self, split, epoch=1, combine=False, **kwargs):\r\n \"\"\"Load a given dataset split.\r\n\r\n Args:\r\n split (str): name of the split (e.g., train, valid, test)\r\n \"\"\"\r\n paths = self.args.data.split(\":\")\r\n assert len(paths) > 0\r\n data_path = paths[(epoch - 1) % len(paths)]\r\n split_path = os.path.join(data_path, split)\r\n\r\n if self.langs is None:\r\n languages = sorted(\r\n [\r\n name\r\n for name in os.listdir(data_path)\r\n if os.path.isdir(os.path.join(data_path, name))\r\n ]\r\n )\r\n else:\r\n languages = self.langs.split(\",\")\r\n for name in languages:\r\n p = os.path.join(data_path, name)\r\n assert os.path.exists(p), \"data not found: {}\".format(p)\r\n\r\n logger.info(\"Training on {0} languages: {1}\".format(len(languages), languages))\r\n logger.info(\r\n \"Language to id mapping: \", {lang: id for id, lang in enumerate(languages)}\r\n )\r\n\r\n mask_whole_words = get_whole_word_mask(self.args, self.dictionary)\r\n language_without_segmentations = self.args.no_whole_word_mask_langs.split(\",\")\r\n lang_datasets = []\r\n for language in languages:\r\n split_path = os.path.join(data_path, language, split)\r\n\r\n dataset = data_utils.load_indexed_dataset(\r\n split_path,\r\n self.source_dictionary,\r\n self.args.dataset_impl,\r\n combine=combine,\r\n )\r\n if dataset is None:\r\n raise FileNotFoundError(\r\n \"Dataset not found: {} ({})\".format(split, split_path)\r\n )\r\n\r\n end_token = (\r\n self.source_dictionary.index(\"[{}]\".format(language))\r\n if self.args.add_lang_token\r\n else self.source_dictionary.eos()\r\n )\r\n\r\n # create continuous blocks of tokens\r\n dataset = TokenBlockDataset(\r\n dataset,\r\n dataset.sizes,\r\n self.args.tokens_per_sample - 2, # one less for \r\n pad=self.source_dictionary.pad(),\r\n eos=end_token,\r\n break_mode=self.args.sample_break_mode,\r\n )\r\n logger.info(\"loaded {} blocks from: {}\".format(len(dataset), split_path))\r\n\r\n # prepend beginning-of-sentence token (, equiv. to [CLS] in BERT)\r\n dataset = PrependTokenDataset(dataset, self.source_dictionary.bos())\r\n dataset = AppendTokenDataset(dataset, end_token)\r\n\r\n lang_mask_whole_words = (\r\n mask_whole_words\r\n if language not in language_without_segmentations\r\n else None\r\n )\r\n lang_dataset = DenoisingDataset(\r\n dataset,\r\n dataset.sizes,\r\n self.dictionary,\r\n self.mask_idx,\r\n lang_mask_whole_words,\r\n shuffle=self.args.shuffle_instance,\r\n seed=self.seed,\r\n args=self.args,\r\n eos=None\r\n if not self.args.add_lang_token\r\n else self.source_dictionary.index(\"[{}]\".format(language)),\r\n )\r\n lang_datasets.append(lang_dataset)\r\n\r\n dataset_lengths = np.array(\r\n [len(d) for d in lang_datasets],\r\n dtype=float,\r\n )\r\n logger.info(\r\n \"loaded total {} blocks for all languages\".format(\r\n int(dataset_lengths.sum()),\r\n )\r\n )\r\n if split == self.args.train_subset:\r\n # For train subset, additionally up or down sample languages.\r\n sample_probs = self._get_sample_prob(dataset_lengths)\r\n logger.info(\r\n \"Sample probability by language: {}\".format(\r\n {\r\n lang: \"{0:.4f}\".format(sample_probs[id])\r\n for id, lang in enumerate(languages)\r\n }\r\n )\r\n )\r\n size_ratio = (sample_probs * dataset_lengths.sum()) / dataset_lengths\r\n logger.info(\r\n \"Up/Down Sampling ratio by language: {}\".format(\r\n {\r\n lang: \"{0:.2f}\".format(size_ratio[id])\r\n for id, lang in enumerate(languages)\r\n }\r\n )\r\n )\r\n\r\n resampled_lang_datasets = [\r\n ResamplingDataset(\r\n lang_datasets[i],\r\n size_ratio=size_ratio[i],\r\n seed=self.args.seed,\r\n epoch=epoch,\r\n replace=size_ratio[i] >= 1.0,\r\n )\r\n for i, d in enumerate(lang_datasets)\r\n ]\r\n dataset = ConcatDataset(\r\n resampled_lang_datasets,\r\n )\r\n else:\r\n dataset = ConcatDataset(lang_datasets)\r\n lang_splits = [split]\r\n for lang_id, lang_dataset in enumerate(lang_datasets):\r\n split_name = split + \"_\" + languages[lang_id]\r\n lang_splits.append(split_name)\r\n self.datasets[split_name] = lang_dataset\r\n\r\n if split in self.args.valid_subset:\r\n self.args.valid_subset = self.args.valid_subset.replace(\r\n split, \",\".join(lang_splits)\r\n )\r\n\r\n with data_utils.numpy_seed(self.args.seed + epoch):\r\n shuffle = np.random.permutation(len(dataset))\r\n\r\n self.datasets[split] = SortDataset(\r\n dataset,\r\n sort_order=[\r\n shuffle,\r\n dataset.sizes,\r\n ],\r\n )\r\n", "sub_path": "edgelm/fairseq/tasks/multilingual_denoising.py", "file_name": "multilingual_denoising.py", "file_ext": "py", "file_size_in_byte": 9012, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 27, "usage_type": "call"}, {"api_name": "denoising.DenoisingTask", "line_number": 31, "usage_type": "name"}, {"api_name": "denoising.DenoisingTask.add_args", "line_number": 34, "usage_type": "call"}, {"api_name": "denoising.DenoisingTask", "line_number": 34, "usage_type": "name"}, {"api_name": "fairseq.data.Dictionary.load", "line_number": 58, "usage_type": "call"}, {"api_name": "fairseq.data.Dictionary", "line_number": 58, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 117, "usage_type": "call"}, {"api_name": "os.path", "line_number": 117, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 117, "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": "os.path.exists", "line_number": 124, "usage_type": "call"}, {"api_name": "os.path", "line_number": 124, "usage_type": "attribute"}, {"api_name": "fairseq.data.encoders.utils.get_whole_word_mask", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "attribute"}, {"api_name": "fairseq.data.data_utils.load_indexed_dataset", "line_number": 137, "usage_type": "call"}, {"api_name": "fairseq.data.data_utils", "line_number": 137, "usage_type": "name"}, {"api_name": "fairseq.data.TokenBlockDataset", "line_number": 155, "usage_type": "call"}, {"api_name": "fairseq.data.PrependTokenDataset", "line_number": 166, "usage_type": "call"}, {"api_name": "fairseq.data.AppendTokenDataset", "line_number": 167, "usage_type": "call"}, {"api_name": "fairseq.data.DenoisingDataset", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 189, "usage_type": "call"}, {"api_name": "fairseq.data.ResamplingDataset", "line_number": 220, "usage_type": "call"}, {"api_name": "fairseq.data.ConcatDataset", "line_number": 229, "usage_type": "call"}, {"api_name": "fairseq.data.ConcatDataset", "line_number": 233, "usage_type": "call"}, {"api_name": "fairseq.data.data_utils.numpy_seed", "line_number": 245, "usage_type": "call"}, {"api_name": "fairseq.data.data_utils", "line_number": 245, "usage_type": "name"}, {"api_name": "numpy.random.permutation", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 246, "usage_type": "attribute"}, {"api_name": "fairseq.data.SortDataset", "line_number": 248, "usage_type": "call"}, {"api_name": "fairseq.tasks.register_task", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "585067788", "text": "import re\nfrom typing import Any\n\nfrom tidysic.tag import Tag\n\n\nclass FormattedString:\n '''\n A formatted string contains tag keys written in double\n curly brackets, such as `{{artist}}`.\n\n The double brackets are useful if you want to insert text\n that will only be displayed if the tag is not None. For\n instance, the string\n\n `{{track}. }{{title}}`\n\n will become\n\n `1. Intro`\n\n if the `track` tag is defined. Otherwise, it will just\n be\n\n `Intro`\n\n The `year` and `track` tags can be formatted as usual, seeing\n as they are integer values. This way, track numbers may be\n padded using:\n\n `{{track:02d}. }{{title}}`\n '''\n\n def __init__(self, string: str):\n try:\n FormattedString.assert_well_written(string)\n except AssertionError as e:\n raise ValueError(\n f'Could not create FormattedString from {string}: {e}'\n )\n\n self._str = string\n\n def build(self, tags: dict[Tag, Any]) -> str:\n pattern = r'\\{(.*?\\{(\\w+)(:.+?)?\\}.*?)\\}'\n matches = re.findall(pattern, self._str)\n\n return_string = self._str\n\n substitutions = []\n for to_substitute, tag_name, format_spec in matches:\n\n value = None\n tag = Tag[tag_name.capitalize()]\n\n value = tags[tag]\n if tag in {Tag.Year, Tag.Track} and value:\n value = int(value)\n if tag in {Tag.Title, Tag.Artist, Tag.Album} and not value:\n value = f'Unknown {tag.name}'\n\n formattable = to_substitute.replace(\n f'{{{tag_name}{format_spec}}}',\n f'{{{format_spec}}}'\n )\n substitutions.append((\n f'{{{to_substitute}}}',\n formattable.format(value) if value else ''\n ))\n\n for old, new in substitutions:\n return_string = return_string.replace(old, new)\n\n return return_string\n\n @staticmethod\n def assert_well_written(string: str):\n '''\n Runs a series of assert statements that will pass only if the provided\n string has a correct format. Refer to the class' documentation for more\n info on the format.\n\n Args:\n string (str): String whose format to test.\n '''\n bracket_depth = 0\n current_tag_name = ''\n\n for i, char in enumerate(string):\n if char == '{':\n bracket_depth += 1\n assert bracket_depth <= 2, (\n f'Too many opening brackets (col {i})'\n )\n\n elif char == '}':\n bracket_depth -= 1\n assert bracket_depth >= 0, (\n f'Too many closing brackets (col {i})'\n )\n\n if bracket_depth == 0:\n assert current_tag_name in {\n tag.name.lower()\n for tag in Tag\n }, (\n f'Invalid tag name {current_tag_name}'\n )\n current_tag_name = ''\n\n elif bracket_depth == 2:\n current_tag_name += char\n", "sub_path": "tidysic/formatted_string.py", "file_name": "formatted_string.py", "file_ext": "py", "file_size_in_byte": 3185, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "tidysic.tag.Tag", "line_number": 44, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 44, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 46, "usage_type": "call"}, {"api_name": "tidysic.tag.Tag", "line_number": 54, "usage_type": "name"}, {"api_name": "tidysic.tag.Tag.Year", "line_number": 57, "usage_type": "attribute"}, {"api_name": "tidysic.tag.Tag", "line_number": 57, "usage_type": "name"}, {"api_name": "tidysic.tag.Tag.Track", "line_number": 57, "usage_type": "attribute"}, {"api_name": "tidysic.tag.Tag.Title", "line_number": 59, "usage_type": "attribute"}, {"api_name": "tidysic.tag.Tag", "line_number": 59, "usage_type": "name"}, {"api_name": "tidysic.tag.Tag.Artist", "line_number": 59, "usage_type": "attribute"}, {"api_name": "tidysic.tag.Tag.Album", "line_number": 59, "usage_type": "attribute"}, {"api_name": "tidysic.tag.Tag", "line_number": 105, "usage_type": "name"}]} +{"seq_id": "427650503", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# Copyright 2016 Timothy Dozat\n# \n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n# \n# http://www.apache.org/licenses/LICENSE-2.0\n# \n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport sys\nimport time\nimport pickle as pkl\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom lib import models\nfrom lib import optimizers\nfrom lib import rnn_cells\n\nfrom configurable import Configurable\nfrom vocab import Vocab\nfrom dataset import Dataset\nimport contextlib\nfrom subprocess import check_output, CalledProcessError\nimport operator\n\n@contextlib.contextmanager\ndef dummy_context_mgr():\n yield None\n\n#***************************************************************\nclass Network(Configurable):\n \"\"\"\"\"\"\n \n #=============================================================\n def __init__(self, model, *args, **kwargs):\n \"\"\"\"\"\"\n if args:\n if len(args) > 1:\n raise TypeError('Parser takes at most one argument')\n \n kwargs['name'] = kwargs.pop('name', model.__name__)\n super(Network, self).__init__(*args, **kwargs)\n if not os.path.isdir(self.save_dir):\n os.mkdir(self.save_dir)\n with open(os.path.join(self.save_dir, 'config.cfg'), 'w') as f:\n self._config.write(f)\n\n self._global_step = tf.Variable(0., trainable=False, name=\"global_step\")\n self._global_epoch = tf.Variable(0., trainable=False, name=\"global_epoch\")\n\n # todo what is this??\n # self._model = model(self._config, global_step=self.global_step)\n self._model = model(self._config)\n\n self._vocabs = []\n\n if self.conll:\n vocab_files = [(self.word_file, 1, 'Words', self.embed_size),\n (self.tag_file, [3, 4], 'Tags', self.embed_size if self.add_pos_to_input else 0),\n (self.rel_file, 7, 'Rels', 0)]\n elif self.conll2012:\n vocab_files = [(self.word_file, 3, 'Words', self.embed_size),\n (self.tag_file, [5, 4], 'Tags', self.embed_size if self.add_pos_to_input else 0), # auto, gold\n (self.rel_file, 7, 'Rels', 0),\n (self.srl_file, range(14, 50), 'SRLs', 0),\n (self.predicates_file, [10, 4] if self.joint_pos_predicates else 10,\n 'Predicates', self.predicate_embed_size if self.add_predicates_to_input else 0),\n (self.domain_file, 0, 'Domains', 0)]\n\n print(\"Loading vocabs\")\n sys.stdout.flush()\n for i, (vocab_file, index, name, embed_size) in enumerate(vocab_files):\n vocab = Vocab(vocab_file, index, embed_size, self._config,\n name=name,\n cased=self.cased if not i else True,\n use_pretrained=(not i))\n self._vocabs.append(vocab)\n\n print(\"Predicates vocab: \")\n for l, i in sorted(self._vocabs[4].iteritems(), key=operator.itemgetter(1)):\n print(\"%s: %d\" % (l, i))\n print(\"predicate_true_start_idx\", self._vocabs[4].predicate_true_start_idx)\n\n print(\"Loading data\")\n sys.stdout.flush()\n self._trainset = Dataset(self.train_file, self._vocabs, model, self._config, name='Trainset')\n self._validset = Dataset(self.valid_file, self._vocabs, model, self._config, name='Validset')\n self._testset = Dataset(self.test_file, self._vocabs, model, self._config, name='Testset')\n\n self._ops = self._gen_ops()\n self._save_vars = filter(lambda x: u'Pretrained' not in x.name, tf.global_variables())\n self.history = {\n 'train_loss': [],\n 'train_accuracy': [],\n 'valid_loss': [],\n 'valid_accuracy': [],\n 'test_acuracy': 0\n }\n return\n \n #=============================================================\n def train_minibatches(self):\n \"\"\"\"\"\"\n \n return self._trainset.get_minibatches(self.train_batch_size,\n self.model.input_idxs,\n self.model.target_idxs)\n \n #=============================================================\n def valid_minibatches(self):\n \"\"\"\"\"\"\n \n return self._validset.get_minibatches(self.test_batch_size,\n self.model.input_idxs,\n self.model.target_idxs,\n shuffle=False)\n \n #=============================================================\n def test_minibatches(self):\n \"\"\"\"\"\"\n \n return self._testset.get_minibatches(self.test_batch_size,\n self.model.input_idxs,\n self.model.target_idxs,\n shuffle=False)\n \n #=============================================================\n # assumes the sess has already been initialized\n def train(self, sess, profile):\n \"\"\"\"\"\"\n print(\"Training\")\n training_start_time = time.time()\n sys.stdout.flush()\n save_path = os.path.join(self.save_dir, self.name.lower() + '-pretrained')\n saver = tf.train.Saver(self.save_vars, max_to_keep=1, save_relative_paths=True)\n \n n_bkts = self.n_bkts\n train_iters = self.train_iters\n print_every = self.print_every\n validate_every = self.validate_every\n save_every = self.save_every\n current_best = 0.0\n try:\n train_time = 0\n train_loss = 0\n train_log_loss = 0\n train_roots_loss = 0\n train_cycle2_loss = 0\n train_svd_loss = 0\n train_rel_loss = 0\n train_srl_loss = 0\n train_mul_loss = {}\n train_predicate_loss = 0\n train_pos_loss = 0\n n_train_sents = 0\n n_train_correct = 0\n n_train_tokens = 0\n n_train_iters = 0\n n_train_srl_correct = 0\n n_train_srl_count = 0\n n_train_predicate_count = 0\n n_train_predicate_correct = 0\n total_train_iters = 0\n valid_time = 0\n valid_loss = 0\n valid_accuracy = 0\n while total_train_iters < train_iters:\n for j, (feed_dict, _) in enumerate(self.train_minibatches()):\n # train_inputs = feed_dict[self._trainset.inputs]\n train_targets = feed_dict[self._trainset.targets]\n\n start_time = time.time()\n\n if profile:\n pctx.trace_next_step()\n # Dump the profile to '/tmp/train_dir' after the step.\n pctx.dump_next_step()\n\n feed_dict[self._trainset.step] = total_train_iters\n\n _, loss, n_correct, n_tokens, roots_loss, cycle2_loss, svd_loss, log_loss, rel_loss, srl_loss, srl_correct, srl_count, predicate_loss, predicate_count, predicate_correct, pos_loss, pos_correct, multitask_losses, lr, sample_prob = sess.run(self.ops['train_op_srl'], feed_dict=feed_dict)\n total_train_iters += 1\n train_time += time.time() - start_time\n train_loss += loss\n train_log_loss += log_loss\n train_roots_loss += roots_loss\n train_cycle2_loss += cycle2_loss\n train_svd_loss += svd_loss\n train_rel_loss += rel_loss\n train_srl_loss += srl_loss\n train_pos_loss += pos_loss\n train_predicate_loss += predicate_loss\n n_train_predicate_count += predicate_count\n n_train_predicate_correct += predicate_correct\n\n for n, l in multitask_losses.iteritems():\n if n not in train_mul_loss.keys():\n train_mul_loss[n] = 0.\n train_mul_loss[n] += l\n\n n_train_sents += len(train_targets)\n n_train_correct += n_correct\n n_train_tokens += n_tokens\n n_train_srl_correct += srl_correct\n n_train_srl_count += srl_count\n n_train_iters += 1\n self.history['train_loss'].append(loss)\n self.history['train_accuracy'].append(100 * n_correct / n_tokens)\n if total_train_iters == 1 or total_train_iters % validate_every == 0:\n valid_time = 0\n valid_loss = 0\n n_valid_sents = 0\n n_valid_correct = 0\n n_valid_tokens = 0\n with open(os.path.join(self.save_dir, 'sanitycheck.txt'), 'w') as f:\n for k, (feed_dict, _) in enumerate(self.valid_minibatches()):\n inputs = feed_dict[self._validset.inputs]\n targets = feed_dict[self._validset.targets]\n start_time = time.time()\n loss, n_correct, n_tokens, predictions = sess.run(self.ops['valid_op'], feed_dict=feed_dict)\n valid_time += time.time() - start_time\n valid_loss += loss\n n_valid_sents += len(targets)\n n_valid_correct += n_correct\n n_valid_tokens += n_tokens\n self.model.sanity_check(inputs, targets, predictions, self._vocabs, f, feed_dict=feed_dict)\n valid_loss /= k+1\n valid_accuracy = 100 * n_valid_correct / n_valid_tokens\n valid_time = n_valid_sents / valid_time\n self.history['valid_loss'].append(valid_loss)\n self.history['valid_accuracy'].append(valid_accuracy)\n if print_every and total_train_iters % print_every == 0:\n train_loss /= n_train_iters\n train_log_loss /= n_train_iters\n train_roots_loss /= n_train_iters\n train_cycle2_loss /= n_train_iters\n train_svd_loss /= n_train_iters\n train_rel_loss /= n_train_iters\n train_srl_loss /= n_train_iters\n train_predicate_loss /= n_train_iters\n train_pos_loss /= n_train_iters\n train_accuracy = 100 * n_train_correct / n_train_tokens\n train_time = n_train_sents / train_time\n print('%6d) Train loss: %.4f Train acc: %5.2f%% Train rate: %6.1f sents/sec Learning rate: %f Sample prob: %f\\n'\n '\\tValid loss: %.4f Valid acc: %5.2f%% Valid rate: %6.1f sents/sec' %\n (total_train_iters, train_loss, train_accuracy, train_time, lr, sample_prob, valid_loss, valid_accuracy, valid_time))\n print('\\tlog loss: %f\\trel loss: %f\\tsrl loss: %f\\ttrig loss: %f\\tpos loss: %f' % (train_log_loss, train_rel_loss, train_srl_loss, train_predicate_loss, train_pos_loss))\n multitask_losses_str = ''\n for n, l in train_mul_loss.iteritems():\n train_mul_loss[n] = l/n_train_iters\n multitask_losses_str += '\\t%s loss: %f' % (n, train_mul_loss[n])\n print(multitask_losses_str)\n sys.stdout.flush()\n train_time = 0\n train_loss = 0\n n_train_sents = 0\n n_train_correct = 0\n n_train_tokens = 0\n n_train_iters = 0\n train_log_loss = 0\n train_roots_loss = 0\n train_cycle2_loss = 0\n train_rel_loss = 0\n train_predicate_loss = 0\n train_srl_loss = 0\n n_train_srl_correct = 0\n n_train_srl_count = 0\n n_train_predicate_correct = 0\n n_train_predicate_count = 0\n if save_every and (total_train_iters % save_every == 0):\n elapsed_time_str = time.strftime(\"%d:%H:%M:%S\", time.gmtime(time.time()-training_start_time))\n print(\"Elapsed time: %s\" % elapsed_time_str)\n with open(os.path.join(self.save_dir, 'history.pkl'), 'w') as f:\n pkl.dump(self.history, f)\n # only look at non-viterbi decoding if we didn't train w/ crf\n current_score = 0.\n # if not self.viterbi_train:\n # correct = self.test(sess, validate=True)\n # current_score = correct[self.eval_criterion]\n if self.viterbi_decode or self.viterbi_train:\n correct = self.test(sess, viterbi=True, validate=True)\n else:\n correct = self.test(sess, validate=True)\n current_score = correct[self.eval_criterion]\n # las = np.mean(correct[\"LAS\"]) * 100\n # uas = np.mean(correct[\"UAS\"]) * 100\n # print('UAS: %.2f LAS: %.2f' % (uas, las))\n if self.save and current_score > current_best:\n current_best = current_score\n print(\"Writing model to %s\" % (os.path.join(self.save_dir, self.name.lower() + '-trained')))\n saver.save(sess, os.path.join(self.save_dir, self.name.lower() + '-trained'),\n latest_filename=self.name.lower(),\n global_step=self.global_epoch,\n write_meta_graph=False)\n if self.eval_parse:\n with open(os.path.join(self.save_dir, \"parse_results.txt\"), 'w') as parse_results_f:\n print(correct['parse_eval'], file=parse_results_f)\n # with open(os.path.join(self.save_dir, 'history.pkl'), 'w') as f:\n # pkl.dump(self.history, f)\n # self.test(sess, validate=True)\n sess.run(self._global_epoch.assign_add(1.))\n except KeyboardInterrupt:\n try:\n raw_input('\\nPress to save or to exit.')\n except:\n print('\\r', end='')\n sys.exit(0)\n # saver.save(sess, os.path.join(self.save_dir, self.name.lower() + '-trained'),\n # latest_filename=self.name.lower(),\n # global_step=self.global_epoch,\n # write_meta_graph=False)\n # with open(os.path.join(self.save_dir, 'history.pkl'), 'w') as f:\n # pkl.dump(self.history, f)\n # with open(os.path.join(self.save_dir, 'scores.txt'), 'a') as f:\n # pass\n self.test(sess, validate=True)\n return\n\n\n def convert_bilou(self, indices):\n strings = map(lambda i: self._vocabs[3][i], indices)\n converted = []\n started_types = []\n # print(strings)\n for i, s in enumerate(strings):\n label_parts = s.split('/')\n curr_len = len(label_parts)\n combined_str = ''\n Itypes = []\n Btypes = []\n for idx, label in enumerate(label_parts):\n bilou = label[0]\n label_type = label[2:]\n props_str = ''\n if bilou == 'I':\n Itypes.append(label_type)\n props_str = ''\n elif bilou == 'O':\n curr_len = 0\n props_str = ''\n elif bilou == 'U':\n # need to check whether last one was ended\n props_str = '(' + label_type + ('*)' if idx == len(label_parts) - 1 else \"\")\n elif bilou == 'B':\n # need to check whether last one was ended\n props_str = '(' + label_type\n started_types.append(label_type)\n Btypes.append(label_type)\n elif bilou == 'L':\n props_str = ')'\n started_types.pop()\n curr_len -= 1\n combined_str += props_str\n while len(started_types) > curr_len:\n converted[-1] += ')'\n started_types.pop()\n while len(started_types) < len(Itypes) + len(Btypes):\n combined_str = '(' + Itypes[-1] + combined_str\n started_types.append(Itypes[-1])\n Itypes.pop()\n if not combined_str:\n combined_str = '*'\n elif combined_str[0] == \"(\" and combined_str[-1] != \")\":\n combined_str += '*'\n elif combined_str[-1] == \")\" and combined_str[0] != \"(\":\n combined_str = '*' + combined_str\n converted.append(combined_str)\n while len(started_types) > 0:\n converted[-1] += ')'\n started_types.pop()\n return converted\n\n def parens_check(self, srl_preds_str):\n for srl_preds in srl_preds_str:\n parens_count = 0\n for pred in srl_preds:\n for c in pred:\n if c == '(':\n parens_count += 1\n if c == ')':\n parens_count -= 1\n if parens_count < 0:\n return False\n if parens_count != 0:\n return False\n return True\n\n def merge_preds(self, all_preds, dataset):\n # want a sentences x tokens x fields array\n preds_merged = []\n current_sentid = -1\n current_sent_shared = None\n current_srls = []\n current_predicates = None\n merged_indices = []\n examples = 0\n sentences = 0\n predicate_idx = 4\n\n # for each example\n for bkt_idx, idx in dataset._metabucket.data:\n examples += 1\n preds = all_preds[bkt_idx][idx]\n this_sent_id = preds[0, 6]\n # if this_sent_id < 4:\n # print(\"orig preds\", preds)\n # print(\"preds\", preds)\n if this_sent_id != current_sentid:\n sentences += 1\n current_sentid = this_sent_id\n # print(\"processing sent %d\" % current_sentid)\n merged_indices.append((bkt_idx, idx))\n if current_sent_shared is not None:\n # print(\"last sent had: %d/%d preds\" % (len(current_srls), np.sum(current_predicates)))\n # merge and add to merged list\n # print(merged_srls)\n # if len(merged_srls.shape) == 1:\n # merged_srls = np.expand_dims(merged_srls, -1)\n # print(\"merged srls\", len(merged_srls.shape), merged_srls.shape, merged_srls)\n # print(\"current shared\", current_sent_shared.shape, current_sent_shared)\n current_sent_shared[:, predicate_idx] = current_predicates\n if current_srls:\n merged_srls = np.concatenate(current_srls, axis=-1)\n merged_sent = np.concatenate([current_sent_shared, merged_srls], axis=1)\n else:\n merged_sent = current_sent_shared\n preds_merged.append(merged_sent)\n current_sent_shared = preds[:, :17]\n current_srls = []\n current_predicates = np.zeros(current_sent_shared.shape[0])\n if preds.shape[1] > 16:\n # print(current_sent_shared)\n current_srls.append(np.expand_dims(preds[:, -1], -1))\n current_predicates += (preds[:, predicate_idx] > self._vocabs[4].predicate_true_start_idx).astype(np.int32)\n # print(\"predicates\", (preds[:, predicate_idx] > self._vocabs[4].predicate_true_start_idx).astype(np.int32))\n\n # deal with last one\n current_sent_shared[:, predicate_idx] = current_predicates\n if current_srls:\n merged_srls = np.concatenate(current_srls, axis=-1)\n merged_sent = np.concatenate([current_sent_shared, merged_srls], axis=1)\n else:\n merged_sent = current_sent_shared\n preds_merged.append(merged_sent)\n\n print(\"Merged %d examples into %d/%d sentences\" % (examples, len(preds_merged), sentences))\n return preds_merged, merged_indices\n\n \n #=============================================================\n # TODO make this work if lines_per_buff isn't set to 0\n def test(self, sess, viterbi=False, validate=False):\n \"\"\"\"\"\"\n \n if validate:\n filename = self.valid_file\n minibatches = self.valid_minibatches\n dataset = self._validset\n op = self.ops['test_op'][:15]\n else:\n filename = self.test_file\n minibatches = self.test_minibatches\n dataset = self._testset\n op = self.ops['test_op'][15:]\n \n all_predictions = [[]]\n all_sents = [[]]\n bkt_idx = 0\n total_time = 0.\n roots_lt_total = 0.\n roots_gt_total = 0.\n cycles_2_total = 0.\n cycles_n_total = 0.\n not_tree_total = 0.\n srl_correct_total = 0.\n srl_count_total = 0.\n forward_total_time = 0.\n non_tree_preds_total = []\n attention_weights = {}\n attn_correct_counts = {}\n pos_correct_total = 0.\n n_tokens = 0.\n for batch_num, (feed_dict, sents) in enumerate(minibatches()):\n mb_inputs = feed_dict[dataset.inputs]\n mb_targets = feed_dict[dataset.targets]\n forward_start = time.time()\n probs, n_cycles, len_2_cycles, srl_probs, srl_preds, srl_logits, srl_correct, srl_count, srl_predicates, srl_predicate_targets, transition_params, attn_weights, attn_correct, pos_correct, pos_preds = sess.run(op, feed_dict=feed_dict)\n forward_total_time += time.time() - forward_start\n preds, parse_time, roots_lt, roots_gt, cycles_2, cycles_n, non_trees, non_tree_preds, n_tokens_batch = self.model.validate(mb_inputs, mb_targets, probs, n_cycles, len_2_cycles, srl_preds, srl_logits, srl_predicates, srl_predicate_targets, pos_preds, transition_params if viterbi else None)\n n_tokens += n_tokens_batch\n for k, v in attn_weights.iteritems():\n attention_weights[\"b%d:layer%d\" % (batch_num, k)] = v\n for k, v in attn_correct.iteritems():\n if k not in attn_correct_counts:\n attn_correct_counts[k] = 0.\n attn_correct_counts[k] += v\n total_time += parse_time\n roots_lt_total += roots_lt\n roots_gt_total += roots_gt\n cycles_2_total += cycles_2\n cycles_n_total += cycles_n\n not_tree_total += non_trees\n srl_correct_total += srl_correct\n srl_count_total += srl_count\n pos_correct_total += pos_correct\n non_tree_preds_total.extend(non_tree_preds)\n all_predictions[-1].extend(preds)\n all_sents[-1].extend(sents)\n if len(all_predictions[-1]) == len(dataset[bkt_idx]):\n bkt_idx += 1\n if bkt_idx < len(dataset._metabucket):\n all_predictions.append([])\n all_sents.append([])\n\n if self.one_example_per_predicate:\n all_predictions, data_indices = self.merge_preds(all_predictions, dataset)\n else:\n data_indices = dataset._metabucket.data\n # all_predictions = [p for s in all_predictions for p in s]\n\n correct = {'UAS': 0., 'LAS': 0., 'parse_eval': '', 'F1': 0.}\n srl_acc = 0.0\n if self.eval_parse:\n print(\"Total time in prob_argmax: %f\" % total_time)\n print(\"Total time in forward: %f\" % forward_total_time)\n print(\"Not tree: %d\" % not_tree_total)\n print(\"Roots < 1: %d; Roots > 1: %d; 2-cycles: %d; n-cycles: %d\" % (roots_lt_total, roots_gt_total, cycles_2_total, cycles_n_total))\n # ID: Word index, integer starting at 1 for each new sentence; may be a range for multiword tokens; may be a decimal number for empty nodes.\n # FORM: Word form or punctuation symbol.\n # LEMMA: Lemma or stem of word form.\n # UPOSTAG: Universal part-of-speech tag.\n # XPOSTAG: Language-specific part-of-speech tag; underscore if not available.\n # FEATS: List of morphological features from the universal feature inventory or from a defined language-specific extension; underscore if not available.\n # HEAD: Head of the current word, which is either a value of ID or zero (0).\n # DEPREL: Universal dependency relation to the HEAD (root iff HEAD = 0) or a defined language-specific subtype of one.\n # DEPS: Enhanced dependency graph in the form of a list of head-deprel pairs.\n # MISC: Any other annotation.\n\n parse_gold_fname = self.gold_dev_parse_file if validate else self.gold_test_parse_file\n\n # write predicted parse\n parse_pred_fname = os.path.join(self.save_dir, \"parse_preds.tsv\")\n with open(parse_pred_fname, 'w') as f:\n for p_idx, (bkt_idx, idx) in enumerate(data_indices):\n preds = all_predictions[p_idx] if self.one_example_per_predicate else all_predictions[bkt_idx][idx]\n words = all_sents[bkt_idx][idx]\n # sent[:, 6] = targets[tokens, 0] # 5 targets[0] = gold_tag\n # sent[:, 7] = parse_preds[tokens] # 6 = pred parse head\n # sent[:, 8] = rel_preds[tokens] # 7 = pred parse label\n # sent[:, 9] = targets[tokens, 1] # 8 = gold parse head\n # sent[:, 10] = targets[tokens, 2] # 9 = gold parse label\n sent_len = len(words)\n if self.eval_single_token_sents or sent_len > 1:\n for i, (word, pred) in enumerate(zip(words, preds)):\n head = pred[8] + 1\n tok_id = i + 1\n # assert self.tags[datum[6]] == self.tags[pred[7]]\n tup = (\n tok_id, # id\n word, # form\n self.tags[pred[7]], # gold tag\n # self.tags[pred[11]] if self.joint_pos_predicates or self.train_pos else self.tags[pred[4]], # pred tag or auto tag\n str(head if head != tok_id else 0), # pred head\n self.rels[pred[9]] # pred label\n )\n f.write('%s\\t%s\\t_\\t%s\\t_\\t_\\t%s\\t%s\\n' % tup)\n f.write('\\n')\n\n with open(os.devnull, 'w') as devnull:\n try:\n parse_eval = check_output([\"perl\", \"bin/eval.pl\", \"-g\", parse_gold_fname, \"-s\", parse_pred_fname], stderr=devnull)\n short_str = parse_eval.split('\\n')[:3]\n print('\\n'.join(short_str))\n print('\\n')\n correct['parse_eval'] = parse_eval\n correct['LAS'] = short_str[0].split()[9]\n correct['UAS'] = short_str[1].split()[9]\n except CalledProcessError as e:\n print(\"Call to parse eval failed: %s\" % e.output)\n\n if self.eval_by_domain:\n parse_gold_fname_path = '/'.join(parse_gold_fname.split('/')[:-1])\n parse_gold_fname_end = parse_gold_fname.split('/')[-1]\n for d in self._vocabs[5].keys():\n if d not in self._vocabs[5].SPECIAL_TOKENS:\n domain_gold_fname = os.path.join(parse_gold_fname_path, d + '_' + parse_gold_fname_end)\n domain_fname = os.path.join(self.save_dir, '%s_parse_preds.tsv' % d)\n with open(domain_fname, 'w') as f:\n for p_idx, (bkt_idx, idx) in enumerate(data_indices):\n preds = all_predictions[p_idx] if self.one_example_per_predicate else all_predictions[bkt_idx][idx]\n words = all_sents[bkt_idx][idx]\n domain = '-'\n sent_len = len(words)\n if self.eval_single_token_sents or sent_len > 1:\n for i, (word, pred) in enumerate(zip(words, preds)):\n domain = self._vocabs[5][pred[5]]\n head = pred[8] + 1\n tok_id = i + 1\n if domain == d:\n tup = (\n tok_id, # id\n word, # form\n self.tags[pred[7]], # gold tag\n # self.tags[pred[11]] if self.joint_pos_predicates or self.train_pos else self.tags[pred[4]], # pred tag or auto tag\n str(head if head != tok_id else 0), # pred head\n self.rels[pred[9]] # pred label\n )\n f.write('%s\\t%s\\t_\\t%s\\t_\\t_\\t%s\\t%s\\n' % tup)\n if domain == d:\n f.write('\\n')\n with open(os.devnull, 'w') as devnull:\n try:\n parse_eval_d = check_output([\"perl\", \"bin/eval.pl\", \"-g\", domain_gold_fname, \"-s\", domain_fname],\n stderr=devnull)\n short_str_d = map(lambda s: \"%s %s\" % (d, s), parse_eval_d.split('\\n')[:3])\n print('\\n'.join(short_str_d))\n print('\\n')\n # correct['parse_eval'] = parse_eval\n # correct['LAS'] = short_str[0].split()[9]\n # correct['UAS'] = short_str[1].split()[9]\n except CalledProcessError as e:\n print(\"Call to eval failed: %s\" % e.output)\n\n if self.eval_srl:\n # load the real gold preds file\n srl_gold_fname = self.gold_dev_props_file if validate else self.gold_test_props_file\n\n # save SRL gold output for debugging purposes\n srl_sanity_fname = os.path.join(self.save_dir, 'srl_sanity.tsv')\n with open(srl_sanity_fname, 'w') as f, open(filename, 'r') as orig_f:\n for p_idx, (bkt_idx, idx) in enumerate(data_indices):\n # for each word, if predicate print word, otherwise -\n # then all the SRL labels\n data = dataset._metabucket[bkt_idx].data[idx]\n preds = all_predictions[p_idx] if self.one_example_per_predicate else all_predictions[bkt_idx][idx]\n # if len(preds.shape) < 2:\n # preds = np.reshape(preds, [1, preds.shape[0]])\n words = all_sents[bkt_idx][idx]\n num_gold_srls = preds[0, 13]\n num_pred_srls = preds[0, 14]\n srl_preds = preds[:, 15+num_pred_srls+num_gold_srls:]\n srl_golds = preds[:, 15+num_pred_srls:15+num_gold_srls+num_pred_srls]\n srl_preds_bio = map(lambda p: self._vocabs[3][p], srl_preds)\n srl_preds_str = map(list, zip(*[self.convert_bilou(j) for j in np.transpose(srl_preds)]))\n # todo if you want golds in here get it from the props file\n # srl_golds_str = map(list, zip(*[self.convert_bilou(j) for j in np.transpose(srl_golds)]))\n # print(srl_golds_str)\n # print(srl_preds_str)\n for i, (datum, word, pred) in enumerate(zip(data, words, preds)):\n orig_line = orig_f.readline().strip()\n while not orig_line:\n orig_line = orig_f.readline().strip()\n orig_split_line = orig_line.split('\\t')\n docid = orig_split_line[0]\n sentid = orig_split_line[1]\n domain = self._vocabs[5][pred[5]]\n orig_pred = srl_preds_str[i] if srl_preds_str else []\n # gold_pred = srl_golds_str[i] if srl_golds_str else []\n bio_pred = srl_preds_bio[i] if srl_preds_bio else []\n word_str = word\n tag0_str = self.tags[pred[7]] # gold tag\n tag1_str = self.tags[pred[3]] # auto tag\n tag2_str = self.tags[pred[12]] # predicted tag\n # gold_pred = word if np.any([\"(V*\" in p for p in gold_pred]) else '-'\n pred_pred = word if np.any([\"(V*\" in p for p in orig_pred]) else '-'\n # fields = (domain,) + (word_str,) + (tag0_str,) + (tag1_str,) + (tag2_str,) + (gold_pred,) + (pred_pred,) + tuple(bio_pred) + tuple(orig_pred)\n fields = (docid,) + (sentid,) + (word_str,) + (tag0_str,) + (tag1_str,) + (tag2_str,) + (pred_pred,) + tuple(bio_pred) + tuple(orig_pred)\n owpl_str = '\\t'.join(fields)\n f.write(owpl_str + \"\\n\")\n f.write('\\n')\n\n # save SRL output\n srl_preds_fname = os.path.join(self.save_dir, 'srl_preds.tsv')\n # print(\"writing srl preds file: %s\" % srl_preds_fname)\n with open(srl_preds_fname, 'w') as f:\n for p_idx, (bkt_idx, idx) in enumerate(data_indices):\n # for each word, if predicate print word, otherwise -\n # then all the SRL labels\n preds = all_predictions[p_idx] if self.one_example_per_predicate else all_predictions[bkt_idx][idx]\n words = all_sents[bkt_idx][idx]\n # if len(preds.shape) < 2:\n # preds = np.reshape(preds, [1, preds.shape[0]])\n # print(\"preds\", preds)\n num_gold_srls = preds[0, 13]\n num_pred_srls = preds[0, 14]\n srl_preds = preds[:, 15 + num_gold_srls + num_pred_srls:]\n if self.one_example_per_predicate:\n # srl_preds = preds[:, 14 + num_gold_srls + num_pred_srls:]\n predicate_indices = np.where(preds[:, 4] == 1)[0]\n # print(\"predicate indices\", predicate_indices)\n else:\n predicate_indices = preds[0, 15:15+num_pred_srls]\n # print(\"predicate indices\", predicate_indices)\n srl_preds_str = map(list, zip(*[self.convert_bilou(j) for j in np.transpose(srl_preds)]))\n # if len(predicate_indices) == 0:\n # if preds[0,6] < 4:\n # print(\"preds\", preds)\n # print(\"predicate inds\", predicate_indices)\n # print(\"srl_preds_str\", srl_preds_str)\n # print(\"srl_preds\", srl_preds)\n # print(\"words\", words)\n for i, word in enumerate(words):\n pred = srl_preds_str[i] if srl_preds_str else []\n word_str = word if i in predicate_indices else '-'\n fields = (word_str,) + tuple(pred)\n owpl_str = '\\t'.join(fields)\n f.write(owpl_str + \"\\n\")\n if not self.parens_check(np.transpose(srl_preds_str)):\n print(np.transpose(srl_preds_str))\n print(map(lambda i: self._vocabs[3][i], np.transpose(srl_preds)))\n f.write('\\n')\n\n srl_acc = (srl_correct_total / srl_count_total)*100.0\n\n with open(os.devnull, 'w') as devnull:\n try:\n srl_eval = check_output([\"perl\", \"bin/srl-eval.pl\", srl_gold_fname, srl_preds_fname], stderr=devnull)\n print(srl_eval)\n overall_f1 = float(srl_eval.split('\\n')[6].split()[-1])\n correct['F1'] = overall_f1\n except CalledProcessError as e:\n print(\"Call to eval failed: %s\" % e.output)\n\n if self.eval_by_domain:\n srl_gold_fname_path = '/'.join(srl_gold_fname.split('/')[:-1])\n srl_gold_fname_end = srl_gold_fname.split('/')[-1]\n for d in self._vocabs[5].keys():\n if d not in self._vocabs[5].SPECIAL_TOKENS:\n domain_gold_fname = os.path.join(srl_gold_fname_path, d + '_' + srl_gold_fname_end)\n domain_fname = os.path.join(self.save_dir, '%s_srl_preds.tsv' % d)\n with open(domain_fname, 'w') as f:\n for p_idx, (bkt_idx, idx) in enumerate(data_indices):\n # for each word, if predicate print word, otherwise -\n # then all the SRL labels\n # data = dataset._metabucket[bkt_idx].data[idx]\n preds = all_predictions[p_idx] if self.one_example_per_predicate else all_predictions[bkt_idx][idx]\n words = all_sents[bkt_idx][idx]\n num_gold_srls = preds[0, 13]\n num_pred_srls = preds[0, 14]\n srl_preds = preds[:, 15 + num_gold_srls + num_pred_srls:]\n predicate_indices = preds[:, 15:15 + num_pred_srls]\n srl_preds_str = map(list, zip(*[self.convert_bilou(j) for j in np.transpose(srl_preds)]))\n domain = '-'\n for i, (word, p) in enumerate(zip(words, preds)):\n domain = self._vocabs[5][p[5]]\n if domain == d:\n pred = srl_preds_str[i] if srl_preds_str else []\n word_str = word if i in predicate_indices else '-'\n fields = (word_str,) + tuple(pred)\n owpl_str = '\\t'.join(fields)\n f.write(owpl_str + \"\\n\")\n if not self.parens_check(np.transpose(srl_preds_str)):\n print(np.transpose(srl_preds_str))\n print(map(lambda i: self._vocabs[3][i], np.transpose(srl_preds)))\n if domain == d:\n f.write('\\n')\n with open(os.devnull, 'w') as devnull:\n try:\n srl_eval_d = check_output([\"perl\", \"bin/srl-eval.pl\", domain_gold_fname, domain_fname], stderr=devnull)\n # print(srl_eval)\n str_d = srl_eval_d.split('\\n')[6]\n except CalledProcessError as e:\n print(\"Call to eval failed: %s\" % e.output)\n str_d = \"\"\n print(\"%sSRL %s:\" % (\"viterbi \" if viterbi else \"\", d))\n print(str_d)\n\n # with open(os.path.join(self.save_dir, 'scores.txt'), 'a') as f:\n # s, correct = self.model.evaluate(os.path.join(self.save_dir, os.path.basename(filename)), punct=self.model.PUNCT)\n # f.write(s)\n\n if validate and self.multitask_layers != \"\":\n print(\"Attention UAS: \")\n multitask_uas_str = ''\n for k in sorted(attn_correct_counts):\n # todo w/ w/o mask punct\n attn_correct_counts[k] = attn_correct_counts[k] / n_tokens\n multitask_uas_str += '\\t%s UAS: %.2f' % (k, attn_correct_counts[k] * 100)\n print(multitask_uas_str)\n\n if self.save_attn_weights:\n attention_weights = {str(k): v for k, v in attention_weights.iteritems()}\n np.savez(os.path.join(self.save_dir, 'attention_weights'), **attention_weights)\n\n pos_accuracy = (pos_correct_total/n_tokens)*100.0\n correct['POS'] = pos_accuracy\n # if validate:\n # np.savez(os.path.join(self.save_dir, 'non_tree_preds.txt'), non_tree_preds_total)\n # print(non_tree_preds_total)\n # print(non_tree_preds_total, file=f)\n # las = np.mean(correct[\"LAS\"]) * 100\n # uas = np.mean(correct[\"UAS\"]) * 100\n print('UAS: %s LAS: %s' % (correct[\"UAS\"], correct[\"LAS\"]))\n print('POS: %.2f' % pos_accuracy)\n print('SRL acc: %.2f' % (srl_acc))\n print('%sSRL F1: %s' % (\"viterbi \" if viterbi else \"\", correct[\"F1\"]))\n return correct\n \n #=============================================================\n def savefigs(self, sess, optimizer=False):\n \"\"\"\"\"\"\n \n import gc\n import matplotlib as mpl\n mpl.use('Agg')\n import matplotlib.pyplot as plt\n matdir = os.path.join(self.save_dir, 'matrices')\n if not os.path.isdir(matdir):\n os.mkdir(matdir)\n for var in self.save_vars:\n if optimizer or ('Optimizer' not in var.name):\n print(var.name)\n mat = sess.run(var)\n if len(mat.shape) == 1:\n mat = mat[None,:]\n plt.figure()\n try:\n plt.pcolor(mat, cmap='RdBu')\n plt.gca().invert_yaxis()\n plt.colorbar()\n plt.clim(vmin=-1, vmax=1)\n plt.title(var.name)\n plt.savefig(os.path.join(matdir, var.name.replace('/', '-')))\n except ValueError:\n pass\n plt.close()\n del mat\n gc.collect()\n \n #=============================================================\n def _gen_ops(self):\n \"\"\"\"\"\"\n \n optimizer = optimizers.RadamOptimizer(self._config, global_step=self._global_step)\n train_output = self._model(self._trainset)\n\n lr = optimizer.learning_rate\n\n train_op = optimizer.minimize(train_output['loss'])\n\n # These have to happen after optimizer.minimize is called\n valid_output = self._model(self._validset, moving_params=optimizer)\n test_output = self._model(self._testset, moving_params=optimizer)\n\n\n \n ops = {}\n ops['train_op'] = [train_op] + [train_output['loss'],\n train_output['n_correct'],\n train_output['n_tokens']]\n ops['train_op_svd'] = [train_op] + [train_output['loss'],\n train_output['n_correct'],\n train_output['n_tokens'],\n train_output['roots_loss'],\n train_output['2cycle_loss'],\n train_output['svd_loss'],\n train_output['log_loss'],\n train_output['rel_loss']]\n ops['train_op_srl'] = [train_op] + [train_output['loss'],\n train_output['n_correct'],\n train_output['n_tokens'],\n train_output['roots_loss'],\n train_output['2cycle_loss'],\n train_output['svd_loss'],\n train_output['log_loss'],\n train_output['rel_loss'],\n train_output['srl_loss'],\n train_output['srl_correct'],\n train_output['srl_count'],\n train_output['predicate_loss'],\n train_output['predicate_count'],\n train_output['predicate_correct'],\n train_output['pos_loss'],\n train_output['pos_correct'],\n train_output['multitask_losses'],\n lr,\n train_output['sample_prob']]\n ops['valid_op'] = [valid_output['loss'],\n valid_output['n_correct'],\n valid_output['n_tokens'],\n valid_output['predictions']]\n ops['test_op'] = [valid_output['probabilities'],\n valid_output['n_cycles'],\n valid_output['len_2_cycles'],\n valid_output['srl_probs'],\n valid_output['srl_preds'],\n valid_output['srl_logits'],\n valid_output['srl_correct'],\n valid_output['srl_count'],\n valid_output['srl_predicates'],\n valid_output['srl_predicate_targets'],\n valid_output['transition_params'],\n valid_output['attn_weights'],\n valid_output['attn_correct'],\n valid_output['pos_correct'],\n valid_output['pos_preds'],\n test_output['probabilities'],\n test_output['n_cycles'],\n test_output['len_2_cycles'],\n test_output['srl_probs'],\n test_output['srl_preds'],\n test_output['srl_logits'],\n test_output['srl_correct'],\n test_output['srl_count'],\n test_output['srl_predicates'],\n test_output['srl_predicate_targets'],\n test_output['transition_params'],\n test_output['attn_weights'],\n test_output['attn_correct'],\n test_output['pos_correct'],\n test_output['pos_preds'],\n ]\n # ops['optimizer'] = optimizer\n \n return ops\n \n #=============================================================\n # @property\n # def global_step(self):\n # return self._global_step\n @property\n def global_epoch(self):\n return self._global_epoch\n @property\n def model(self):\n return self._model\n @property\n def words(self):\n return self._vocabs[0]\n @property\n def tags(self):\n return self._vocabs[1]\n @property\n def rels(self):\n return self._vocabs[2]\n @property\n def ops(self):\n return self._ops\n @property\n def save_vars(self):\n return self._save_vars\n \n#***************************************************************\nif __name__ == '__main__':\n \"\"\"\"\"\"\n \n import argparse\n \n argparser = argparse.ArgumentParser()\n argparser.add_argument('--test', action='store_true')\n argparser.add_argument('--load', action='store_true')\n argparser.add_argument('--model', default='Parser')\n argparser.add_argument('--matrix', action='store_true')\n argparser.add_argument('--profile', action='store_true')\n argparser.add_argument('--test_eval', action='store_true')\n \n args, extra_args = argparser.parse_known_args()\n cargs = {k: v for (k, v) in vars(Configurable.argparser.parse_args(extra_args)).iteritems() if v is not None}\n \n print('*** '+args.model+' ***')\n model = getattr(models, args.model)\n\n profile = args.profile\n \n # if 'save_dir' in cargs and os.path.isdir(cargs['save_dir']) and not (args.test or args.matrix or args.load):\n # raw_input('Save directory already exists. Press to overwrite or to exit.')\n # if (args.test or args.load or args.matrix) and 'save_dir' in cargs:\n # cargs['config_file'] = os.path.join(cargs['save_dir'], 'config.cfg')\n network = Network(model, **cargs)\n os.system('echo Model: %s > %s/MODEL' % (network.model.__class__.__name__, network.save_dir))\n\n # print variable names (but not the optimizer ones)\n print([v.name for v in network.save_vars if 'Optimizer' not in v.name and 'layer_norm' not in v.name])\n\n config_proto = tf.ConfigProto()\n config_proto.gpu_options.per_process_gpu_memory_fraction = network.per_process_gpu_memory_fraction\n\n # Create options to profile the time and memory information.\n if profile:\n builder = tf.profiler.ProfileOptionBuilder\n opts = builder(builder.time_and_memory()).order_by('micros').build()\n # Create a profiling context, set constructor argument `trace_steps`,\n # `dump_steps` to empty for explicit control.\n with tf.contrib.tfprof.ProfileContext('/tmp/train_dir',\n trace_steps=[],\n dump_steps=[]) if profile else dummy_context_mgr() as pctx:\n with tf.Session(config=config_proto) as sess:\n sess.run(tf.global_variables_initializer())\n if not (args.test or args.matrix):\n if args.load:\n os.system('echo Training: > %s/HEAD' % network.save_dir)\n os.system('git rev-parse HEAD >> %s/HEAD' % network.save_dir)\n saver = tf.train.Saver(var_list=network.save_vars, save_relative_paths=True)\n saver.restore(sess, tf.train.latest_checkpoint(network.load_dir, latest_filename=network.name.lower()))\n if os.path.isfile(os.path.join(network.save_dir, 'history.pkl')):\n with open(os.path.join(network.save_dir, 'history.pkl')) as f:\n network.history = pkl.load(f)\n else:\n os.system('echo Loading: >> %s/HEAD' % network.load_dir)\n os.system('git rev-parse HEAD >> %s/HEAD' % network.save_dir)\n network.train(sess, profile)\n elif args.matrix:\n saver = tf.train.Saver(var_list=network.save_vars, save_relative_paths=True)\n saver.restore(sess, tf.train.latest_checkpoint(network.save_dir, latest_filename=network.name.lower()))\n # TODO make this save pcolor plots of all matrices to a directory in save_dir\n #with tf.variable_scope('RNN0/BiRNN_FW/LSTMCell/Linear', reuse=True):\n # pkl.dump(sess.run(tf.get_variable('Weights')), open('mat0.pkl', 'w'))\n #with tf.variable_scope('RNN1/BiRNN_FW/LSTMCell/Linear', reuse=True):\n # pkl.dump(sess.run(tf.get_variable('Weights')), open('mat1.pkl', 'w'))\n #with tf.variable_scope('RNN2/BiRNN_FW/LSTMCell/Linear', reuse=True):\n # pkl.dump(sess.run(tf.get_variable('Weights')), open('mat2.pkl', 'w'))\n #with tf.variable_scope('MLP/Linear', reuse=True):\n # pkl.dump(sess.run(tf.get_variable('Weights')), open('mat3.pkl', 'w'))\n network.savefigs(sess)\n else:\n os.system('echo Testing: >> %s/HEAD' % network.save_dir)\n os.system('git rev-parse HEAD >> %s/HEAD' % network.save_dir)\n saver = tf.train.Saver(var_list=network.save_vars, save_relative_paths=True)\n print(\"Loading model: \", network.load_dir)\n print(network.name.lower())\n saver.restore(sess, tf.train.latest_checkpoint(network.load_dir, latest_filename=network.name.lower()))\n\n # decode with & without viterbi\n network.test(sess, False, validate=True)\n if network.eval_srl and (network.viterbi_decode or network.viterbi_train):\n network.test(sess, True, validate=True)\n\n # Actually evaluate on test data\n if args.test_eval:\n start_time = time.time()\n network.test(sess, network.viterbi_decode or network.viterbi_train, validate=False)\n print('Parsing took %f seconds' % (time.time() - start_time))\n", "sub_path": "network.py", "file_name": "network.py", "file_ext": "py", "file_size_in_byte": 46864, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "contextlib.contextmanager", "line_number": 41, "usage_type": "attribute"}, {"api_name": "configurable.Configurable", "line_number": 46, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "tensorflow.Variable", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 64, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 86, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 86, "usage_type": "attribute"}, {"api_name": "vocab.Vocab", "line_number": 88, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 95, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 100, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 100, "usage_type": "attribute"}, {"api_name": "dataset.Dataset", "line_number": 101, "usage_type": "call"}, {"api_name": "dataset.Dataset", "line_number": 102, "usage_type": "call"}, {"api_name": "dataset.Dataset", "line_number": 103, "usage_type": "call"}, {"api_name": "tensorflow.global_variables", "line_number": 106, "usage_type": "call"}, {"api_name": "time.time", "line_number": 147, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 148, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 148, "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": "tensorflow.train.Saver", "line_number": 150, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 150, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 187, "usage_type": "call"}, {"api_name": "time.time", "line_number": 198, "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": "time.time", "line_number": 234, "usage_type": "call"}, {"api_name": "time.time", "line_number": 236, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 268, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 268, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 286, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 286, "usage_type": "call"}, {"api_name": "time.time", "line_number": 286, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 288, "usage_type": "call"}, {"api_name": "os.path", "line_number": 288, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 289, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 305, "usage_type": "call"}, {"api_name": "os.path", "line_number": 305, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 306, "usage_type": "call"}, {"api_name": "os.path", "line_number": 306, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 311, "usage_type": "call"}, {"api_name": "os.path", "line_number": 311, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 322, "usage_type": "call"}, {"api_name": "dataset._metabucket", "line_number": 416, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 438, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 439, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 445, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 448, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 449, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 455, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 456, "usage_type": "call"}, {"api_name": "dataset.inputs", "line_number": 499, "usage_type": "attribute"}, {"api_name": "dataset.targets", "line_number": 500, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 501, "usage_type": "call"}, {"api_name": "time.time", "line_number": 503, "usage_type": "call"}, {"api_name": "dataset._metabucket", "line_number": 526, "usage_type": "attribute"}, {"api_name": "dataset._metabucket", "line_number": 533, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 557, "usage_type": "call"}, {"api_name": "os.path", "line_number": 557, "usage_type": "attribute"}, {"api_name": "os.devnull", "line_number": 584, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 586, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 593, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 601, "usage_type": "call"}, {"api_name": "os.path", "line_number": 601, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 602, "usage_type": "call"}, {"api_name": "os.path", "line_number": 602, "usage_type": "attribute"}, {"api_name": "os.devnull", "line_number": 626, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 628, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 636, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 644, "usage_type": "call"}, {"api_name": "os.path", "line_number": 644, "usage_type": "attribute"}, {"api_name": "dataset._metabucket", "line_number": 649, "usage_type": "attribute"}, {"api_name": "numpy.transpose", "line_number": 659, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 680, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 688, "usage_type": "call"}, {"api_name": "os.path", "line_number": 688, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 704, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 709, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 723, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 724, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 725, "usage_type": "call"}, {"api_name": "os.devnull", "line_number": 730, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 732, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 736, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 744, "usage_type": "call"}, {"api_name": "os.path", "line_number": 744, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 745, "usage_type": "call"}, {"api_name": "os.path", "line_number": 745, "usage_type": "attribute"}, {"api_name": "numpy.transpose", "line_number": 757, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 767, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 768, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 769, "usage_type": "call"}, {"api_name": "os.devnull", "line_number": 772, "usage_type": "attribute"}, {"api_name": "subprocess.check_output", "line_number": 774, "usage_type": "call"}, {"api_name": "subprocess.CalledProcessError", "line_number": 777, "usage_type": "name"}, {"api_name": "numpy.savez", "line_number": 798, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 798, "usage_type": "call"}, {"api_name": "os.path", "line_number": 798, "usage_type": "attribute"}, {"api_name": "matplotlib.use", "line_number": 820, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 822, "usage_type": "call"}, {"api_name": "os.path", "line_number": 822, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 823, "usage_type": "call"}, {"api_name": "os.path", "line_number": 823, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 824, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 831, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 831, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pcolor", "line_number": 833, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 833, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 834, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 834, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 835, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 835, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clim", "line_number": 836, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 836, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 837, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 837, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 838, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 838, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 838, "usage_type": "call"}, {"api_name": "os.path", "line_number": 838, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 841, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 841, "usage_type": "name"}, {"api_name": "gc.collect", "line_number": 843, "usage_type": "call"}, {"api_name": "lib.optimizers.RadamOptimizer", "line_number": 849, "usage_type": "call"}, {"api_name": "lib.optimizers", "line_number": 849, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 964, "usage_type": "call"}, {"api_name": "configurable.Configurable.argparser.parse_args", "line_number": 973, "usage_type": "call"}, {"api_name": "configurable.Configurable.argparser", "line_number": 973, "usage_type": "attribute"}, {"api_name": "configurable.Configurable", "line_number": 973, "usage_type": "name"}, {"api_name": "lib.models", "line_number": 976, "usage_type": "argument"}, {"api_name": "{'gc': 'gc', 'mpl': 'matplotlib', 'plt': 'matplotlib.pyplot'}", "line_number": 984, "usage_type": "call"}, {"api_name": "os.system", "line_number": 985, "usage_type": "call"}, {"api_name": "tensorflow.ConfigProto", "line_number": 990, "usage_type": "call"}, {"api_name": "tensorflow.profiler", "line_number": 995, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.tfprof.ProfileContext", "line_number": 999, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 999, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 1002, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 1003, "usage_type": "call"}, {"api_name": "os.system", "line_number": 1006, "usage_type": "call"}, {"api_name": "os.system", "line_number": 1007, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 1008, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 1008, "usage_type": "attribute"}, {"api_name": "tensorflow.train.latest_checkpoint", "line_number": 1009, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 1009, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 1010, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1010, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 1010, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1011, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1011, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 1012, "usage_type": "call"}, {"api_name": "os.system", "line_number": 1014, "usage_type": "call"}, {"api_name": "os.system", "line_number": 1015, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 1018, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 1018, "usage_type": "attribute"}, {"api_name": "tensorflow.train.latest_checkpoint", "line_number": 1019, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 1019, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 1031, "usage_type": "call"}, {"api_name": "os.system", "line_number": 1032, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 1033, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 1033, "usage_type": "attribute"}, {"api_name": "tensorflow.train.latest_checkpoint", "line_number": 1036, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 1036, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 1045, "usage_type": "call"}, {"api_name": "time.time", "line_number": 1047, "usage_type": "call"}]} +{"seq_id": "469065062", "text": "# codinig=utf-8\nimport os,easygui\nimport json\nfrom request_case import PostJira\nclass ManageTxt:\n def __init__(self,filepath):\n self.filepath = filepath\n\n def txt(self):\n txt_path = self.filepath + r'\\temp'\n all_file = os.listdir(txt_path)\n os.chdir(txt_path)\n txt_list = []\n run_list = []\n for j in all_file:\n ext = os.path.splitext(j)\n if ext[1] == '.txt':\n txt_list.append(j)\n count = 1\n for i in txt_list:\n d = ManageTxt(txt_path).HandleTxt(i)\n jira = PostJira.RequestJira().serch(plate=d[0])\n\n if jira != []:\n os.remove(i)\n\n continue\n\n if '续保失败' in str(d[1]):\n os.remove(i)\n continue\n\n # e = easygui.textbox(msg='(%s/%s)车牌[%s]\\n\\n类型为：%s\\n\\n%s秒' % (count,len(txt_list),d[0],d[1],d[2]),text=d[3])\n sum = '第（%s）个车牌：%s' % (count,d[0])\n e = easygui.textbox(msg = sum + '\\n\\n' + str(d[1]) + '\\n\\n' + '耗时%s' % d[2], title = '车辆结果', text = d[3])\n count += 1\n run_list.append(i)\n if e == None:\n break\n\n d_txt = easygui.boolbox(msg='是否删除所有数据？',choices=['删除','取消'])\n if d_txt == True:\n ManageTxt(txt_path).del_txt(txt_path,run_list)\n else:\n pass\n def HandleTxt(self,file):\n fp = open(file)\n plate = fp.readline()\n content = fp.readline()\n xb_time = fp.readline()\n json = fp.read()\n fp.close()\n return plate,content,xb_time,json\n\n def del_txt(self,dfile,run):\n try:\n os.chdir(dfile)\n for i in run:\n print(i)\n os.remove(i)\n if len(run) == 0:\n # 数据清除完毕\n pass\n except Exception as err:\n print('报错啦......报错内容为：%s' % err)\n\nif __name__=='__main__':\n filepath = r'C:\\Users\\Administrator\\Desktop'\n ManageTxt(filepath).txt()", "sub_path": "common/manage_txt.py", "file_name": "manage_txt.py", "file_ext": "py", "file_size_in_byte": 2126, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.listdir", "line_number": 11, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "request_case.PostJira.RequestJira", "line_number": 22, "usage_type": "call"}, {"api_name": "request_case.PostJira", "line_number": 22, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 25, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 30, "usage_type": "call"}, {"api_name": "easygui.textbox", "line_number": 35, "usage_type": "call"}, {"api_name": "easygui.boolbox", "line_number": 41, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 57, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "248900312", "text": "from PIL import Image\nimport numpy as np\nimport os\n\nimg_path = \"./test.png\"\n\nimg = Image.open(img_path)\nprint('img: ',img)\n\nimg_numpy = np.array(img)\nprint('img_numpy.shape: ',img_numpy.shape)\nimg_transpose = img_numpy.transpose(2,1,0)\nprint('img_transpose.shape: ',img_transpose.shape)\n\nimg_transpose.flags.writeable = True\n\nimg = Image.fromarray(img_transpose,mode ='RGB') # mode is necessary\nprint('img: ',type(img))\n\n\n\n", "sub_path": "t0115_numpy_Image.py", "file_name": "t0115_numpy_Image.py", "file_ext": "py", "file_size_in_byte": 423, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "PIL.Image.open", "line_number": 7, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 7, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 10, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 17, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "551956208", "text": "import MDAnalysis as md\n\nimport pandas as pd\n\nfrom plip.structure.preparation import PDBComplex\n\nimport progressbar\n\nimport os\nimport sys\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n\ndef plipmd(topol=None,traj=None):\n\n\tif 'gro' in topol or '.tpr' in topol:\n\t\tprint ('''\\n\\n\n\n\t\t\tWARNING: For analysis using gromacs you SHOULD use .pdb topology\n\n\t\t\tRecomended gromacs (gmx) command to generate a PDB topology file:\\n\n\n\t\t\tgmx trjconv -f xyz.gro -o xyz.pdb -s xyz.tpr\n\n\t\t\t\\n\\n\n\t\t\t''')\n\telse: pass\n\n\ttraj=list(traj.strip('[]').split(','))\n\tu = md.Universe(topol,traj)\n\n\tif len (u.segments.segids) ==1:\n\t\tprint ('''\n\t\t\tWARNING: Only one segment was identified in system topology:{} \\n\\n\n\t\t\t'''.format(list(u.segments.segids)))\n\telse: \n\t\tprint ('\\nINFO: your system contains {} segments with labels: \\n {} \\n\\n'.format(len(u.segments),list(u.segments.segids)))\n\t\tprint ('''\n\t\t\t\\nWARNING: Segments IDs are considered CHAIN names for analysis. Maybe you can consider to chance Segments ID\\n\\n\n\t\t\t''')\n\t\t\n\t\tuser_confirmation=input('Do you want to define chain names to segments (yes/no):\\n>')\n\n\t\tif user_confirmation=='yes':\n\t\t\tchains_from_segments=input('Type the new name (chain) for every segment (SegId) in format: SegId1,A|SegID2,B|...|SegIdn,N:\\n>')\n\t\t\tnames=chains_from_segments.replace(' ','').split('|')\n\t\t\tfor name in names:\n\t\t\t\tsegid=name.split(',')[0]\n\t\t\t\tnewName=name.split(',')[1]\n\t\t\t\tfor segment in u.segments:\n\t\t\t\t\tif segment.segid == segid:\n\t\t\t\t\t\tsegment.segid=newName\n\t\t\tprint ('\\nINFO: your system contains {} segments with labels: \\n {} \\n\\n'.format(len(u.segments),list(u.segments.segids)))\n\t\n\n\tligand_name=input ('\\n\\n1) Type the ResName of your Ligand (must be 3 letter code -example: LIG -):\\n>')\n\n\tsol_name=input ('\\n2) Type the ResName of your Water (must be 3-4 letter code -example: WAT or SOL or TIP3 -):\\n>')\n\n\tfor res in u.residues:\n\t\tif res.resname==sol_name:\n\t\t\tres.resname='HOH'\n\t\tif 'HI' in res.resname or 'HSD' in res.resname:\n\t\t\tres.resname='HIS'\n\t\tif 'CY' in res.resname:\n\t\t\tres.resname='CYS'\n\tfor atom in u.atoms:\n\t\tif atom.name=='OH2':\n\t\t\tatom.name='OW'\n\n\tSystem=u.select_atoms('protein or (resname {} or resname HOH)'.format(ligand_name),updating=True)\n\tSystem=System.select_atoms('protein or resname {} or (around 7 resname {})'.format(ligand_name,ligand_name),updating=True)\n\n\t\n\tfor ts in u.trajectory[0:1]:\n\t\tname='frame_tmp.pdb'\n\t\tPDB= md.Writer(name, multiframe=False)\n\t\tPDB.write(System)\n\t\tplip_job = PDBComplex()\n\t\tplip_job.load_pdb(name) \n\t\tplip_job.analyze()\n\t\tprint ('\\nINFO:',plip_job,'\\n')\n\t\tligand=input('3) Type the name of the ligand in trajectory to analyze (- example: LIG:S:152 -):\\n>')\n\tos.remove(name)\n\n\ttable=pd.DataFrame()\n\tindex=0\n\tprint ('\\nINFO: Your trajectory lenght is:{} steps\\n'.format(range(len(u.trajectory))))\n\tstart=int(input('4) Type the starting STEP to analyze:\\n>'))\n\tfinish=int(input('\\n5) Type the ending STEP to analyze:\\n>'))\n\tbar=progressbar.ProgressBar(max_value=finish)\n\tprint ('\\n\\n----- ----- ----- RUNNING THE ANALYSIS ----- ----- -----\\n\\n')\n\tfor i in range(start,finish):\n\t\tname='frame_tmp.pdb'\n\t\tPDB= md.Writer(name, multiframe=False)\n\t\tfor ts in u.trajectory[i:i+1]:\n\t\t\tPDB.write(System)\n\t\t\tplip_job = PDBComplex()\n\t\t\tplip_job.load_pdb(name) \n\t\t\tplip_job.analyze()\n\t\t\tinteractions = plip_job.interaction_sets[ligand]\n\t\t\tfor interaction in interactions.all_itypes:\n\t\t\t\tinteraction_type=str(type(interaction)).split('.')[-1].replace(\"'>\",\"\")\n\t\t\t\ttable.loc[index,'Frame']=ts.frame\n\t\t\t\ttable.loc[index,'Time']=ts.time\n\t\t\t\ttable.loc[index,'Residue']=interaction.restype+str(interaction.resnr)\n\t\t\t\ttable.loc[index,'Chain']=interaction.reschain\n\t\t\t\ttable.loc[index,'Ligand']=interaction.restype_l+str(interaction.resnr_l)\n\t\t\t\t\n\t\t\t\tif interaction_type == 'hbond':\n\t\t\t\t\ttable.loc[index,'Type']='H-bond'\n\t\t\t\t\ttable.loc[index,'Acceptor']=interaction.atype\n\t\t\t\t\ttable.loc[index,'AcceptorIdx']=interaction.a.idx\n\t\t\t\t\ttable.loc[index,'Donor']=interaction.dtype\n\t\t\t\t\ttable.loc[index,'DonorIdx']=interaction.d.idx\n\t\t\t\t\ttable.loc[index,'DistanceAD']=interaction.distance_ad\n\t\t\t\t\ttable.loc[index,'DistanceAH']=interaction.distance_ah\n\t\t\t\t\ttable.loc[index,'Angle']=interaction.angle\n\t\t\t\t\ttable.loc[index,'Force']=interaction.type\n\t\t\t\t\ttable.loc[index,'ProtIsDon']=interaction.protisdon\n\t\t\t\t\n\t\t\t\telif interaction_type == 'pication':\n\t\t\t\t\ttable.loc[index,'Type']='Pi-cation'\n\t\t\t\t\ttable.loc[index,'Charge']=interaction.charge.type\n\t\t\t\t\ttable.loc[index,'ChargedAtoms']=\",\".join([i.type for i in interaction.charge.atoms])\n\t\t\t\t\ttable.loc[index,'Force']=interaction.type\n\t\t\t\t\ttable.loc[index,'RingType']=interaction.ring.type\n\t\t\t\t\ttable.loc[index,'RingAtoms']=\",\".join([i.type for i in interaction.ring.atoms])\n\t\t\t\t\ttable.loc[index,'RingAtomsIdx']=\",\".join([str(i.idx) for i in interaction.ring.atoms])\n\n\t\t\t\telif interaction_type == 'pistack':\n\t\t\t\t\ttable.loc[index,'Type']='Pi-stacking'\n\t\t\t\t\ttable.loc[index,'StackingType']=interaction.type\n\t\t\t\t\ttable.loc[index,'RecRingType']=interaction.proteinring.type\n\t\t\t\t\ttable.loc[index,'LigRingType']=interaction.ligandring.type\n\t\t\t\t\ttable.loc[index,'RecRingAtoms']=\",\".join([i.type for i in interaction.proteinring.atoms])\n\t\t\t\t\ttable.loc[index,'RecAtomsIdx']=\",\".join([str(i.idx) for i in interaction.proteinring.atoms])\n\t\t\t\t\ttable.loc[index,'LigRingAtoms']=\",\".join([i.type for i in interaction.ligandring.atoms])\n\t\t\t\t\ttable.loc[index,'LigRingAtomsIdx']=\",\".join([str(i.idx) for i in interaction.ligandring.atoms])\n\t\t\t\t\ttable.loc[index,'Distance']=interaction.distance\n\t\t\t\t\ttable.loc[index,'Angle']=interaction.angle\n\t\t\t\t\ttable.loc[index,'Offset']=interaction.offset \n\t\t\t\t\n\t\t\t\telif interaction_type=='saltbridge':\n\t\t\t\t\ttable.loc[index,'Type']='Salt-bridge'\n\t\t\t\t\ttable.loc[index,'NegAtoms']=\",\".join([i.type for i in interaction.negative.atoms])\n\t\t\t\t\ttable.loc[index,'NegAtomsIdx']=\",\".join([str(i.idx) for i in interaction.negative.atoms])\n\t\t\t\t\ttable.loc[index,'PosAtoms']=\",\".join([i.type for i in interaction.positive.atoms])\n\t\t\t\t\ttable.loc[index,'PosAtomsIdx']=\",\".join([str(i.idx) for i in interaction.positive.atoms])\n\t\t\t\t\ttable.loc[index,'Distance']=interaction.distance\n\t\t\t\t\ttable.loc[index,'ProtIsPos']=interaction.protispos\n\t\t\t\t\t\n\t\t\t\telif interaction_type == 'hydroph_interaction':\n\t\t\t\t\ttable.loc[index,'Type']='Hydrophobic'\n\t\t\t\t\ttable.loc[index,'RecAtom']=interaction.bsatom.type\n\t\t\t\t\ttable.loc[index,'RecAtomIdx']=interaction.bsatom.idx\n\t\t\t\t\ttable.loc[index,'LigAtom']=interaction.ligatom.type\n\t\t\t\t\ttable.loc[index,'LigAtomIdx']=interaction.ligatom.idx\n\t\t\t\t\ttable.loc[index,'Distance']=interaction.distance\n\t\t\t\t\t\n\t\t\t\telif interaction_type == 'waterbridge':\n\t\t\t\t\ttable.loc[index,'Type']='Water-bridge'\n\t\t\t\t\ttable.loc[index,'AccType']=interaction.atype\n\t\t\t\t\ttable.loc[index,'DonType']=interaction.dtype\n\t\t\t\t\ttable.loc[index,'WaterIdx']=interaction.water_orig_idx\n\t\t\t\t\ttable.loc[index,'DistanceAWat']=interaction.distance_aw\n\t\t\t\t\ttable.loc[index,'DistanceDWat']=interaction.distance_dw\n\t\t\t\t\ttable.loc[index,'AngleDon']=interaction.d_angle\n\t\t\t\t\ttable.loc[index,'AngleWat']=interaction.w_angle\n\t\t\t\t\ttable.loc[index,'ProtIsDon']=interaction.protisdon\n\n\t\t\t\telif interaction_type == 'halogenbond':\n\t\t\t\t\ttable.loc[index,'Type']='X-bond'\n\t\t\t\t\ttable.loc[index,'Acceptor']=interaction.acctype\n\t\t\t\t\ttable.loc[index,'Donor']=interaction.acctype\n\t\t\t\t\ttable.loc[index,'Distance']=interaction.distance\n\t\t\t\t\ttable.loc[index,'DonAngle']=interaction.don_angle\n\t\t\t\t\ttable.loc[index,'AccAngle']=interaction.acc_angle\n\t \n\t\t\t\telif interaction_type=='metal_complex':\n\t\t\t\t\ttable.loc[index,'Type']='Metal-complex'\n\t\t\t\t\ttable.loc[index,'MetalType']=interaction.metal.type\n\t\t\t\t\ttable.loc[index,'Idx']=interaction.metal.idx\n\t\t\t\t\ttable.loc[index,'TargetType']=interaction.target_type\n\t\t\t\t\ttable.loc[index,'FunctGroup']=interaction.target.fgroup\n\t\t\t\t\ttable.loc[index,'Geometry']=interaction.geometry\n\t\t\t\t\ttable.loc[index,'Distance']=interaction.distance\n\t\t\t\t\ttable.loc[index,'Location']=interaction.location\n\t\t\t\t\n\t\t\t\tindex=index+1 \n\t\tbar.update(i+1)\n\t\tos.remove(name)\n\t\t\n\tprint ('\\n\\n----- ----- ----- SAVING THE RESULTS, PLEASE WAIT ----- ----- -----\\n\\n')\t\n\ttable.set_index(['Frame','Time'], inplace=True)\n\ttable.sort_index(inplace=True)\n\ttable.to_excel('Interactions_Table.xlsx')\n\tprint ('\\n\\n***** ***** ***** ALL DONE, DATA SAVED ON: Interactions_Table.xlsx ***** ***** *****\\n\\n')\t \nif __name__ == \"__main__\":\n\tplipmd(sys.argv[1],sys.argv[2])\n", "sub_path": "Scripts/plipMD_V3.1.py", "file_name": "plipMD_V3.1.py", "file_ext": "py", "file_size_in_byte": 8359, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "warnings.filterwarnings", "line_number": 12, "usage_type": "call"}, {"api_name": "MDAnalysis.Universe", "line_number": 31, "usage_type": "call"}, {"api_name": "MDAnalysis.Writer", "line_number": 78, "usage_type": "call"}, {"api_name": "plip.structure.preparation.PDBComplex", "line_number": 80, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 85, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 87, "usage_type": "call"}, {"api_name": "progressbar.ProgressBar", "line_number": 92, "usage_type": "call"}, {"api_name": "MDAnalysis.Writer", "line_number": 96, "usage_type": "call"}, {"api_name": "plip.structure.preparation.PDBComplex", "line_number": 99, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 193, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 201, "usage_type": "attribute"}]} +{"seq_id": "548682406", "text": "import collections\nimport logging\n\nfrom slvcodec import package, typ_parser, symbolic_math, typs\nfrom slvcodec.typs import ResolutionError\n\n\nlogger = logging.getLogger(__name__)\n\nCLOCK_NAMES = ('clk', 'clock')\n\n\ndef process_files(filenames, must_resolve=True):\n '''\n Takes a list of filenames,\n parses them with the VUnit parser\n and then processes them in slvcodec classes.\n\n The packages references to one another are resolved as\n are the references to types and constants in the entity\n interfaces.\n '''\n entities = {}\n packages = []\n for filename in filenames:\n parsed = package.parsed_from_filename(filename)\n if parsed.entities:\n assert(len(parsed.entities) == 1)\n p = process_parsed_entity(parsed)\n entities[p.identifier] = p\n assert(not parsed.packages)\n if parsed.packages:\n pkg = package.process_parsed_package(parsed)\n packages.append(pkg)\n resolved_packages = package.resolve_packages(packages)\n resolved_entities = dict([(e.identifier, e.resolve(resolved_packages, must_resolve=must_resolve))\n for e in entities.values()])\n return resolved_entities, resolved_packages\n\n\ndef process_parsed_entity(parsed_entity):\n '''\n Processes the parse entity (output from VUnit vhdl_parser)\n into an UnresolvedEntity class.\n '''\n p_generics = parsed_entity.entities[0].generics\n generics = [typs.Generic(\n name=g.identifier,\n typ=typ_parser.process_subtype_indication(g.subtype_indication),\n ) for g in p_generics]\n p_ports = parsed_entity.entities[0].ports\n ports = [Port(\n name=p.identifier,\n direction=p.mode,\n typ=typ_parser.process_subtype_indication(p.subtype_indication),\n ) for p in p_ports]\n gd = dict([(g.name, g) for g in generics])\n pd = collections.OrderedDict([(p.name, p) for p in ports])\n uses = package.get_parsed_package_dependencies(parsed_entity)\n p = UnresolvedEntity(\n identifier=parsed_entity.entities[0].identifier,\n generics=gd,\n ports=pd,\n uses=uses,\n )\n return p\n\n\nclass Port:\n\n def __init__(self, name, direction, typ):\n self.name = name\n self.direction = direction\n self.typ = typ\n\n\nclass UnresolvedEntity:\n '''\n Keeps track of the generics, ports and package dependencies of\n an entity.\n '''\n\n def __init__(self, identifier, generics, ports, uses):\n self.identifier = identifier\n self.generics = generics\n self.ports = ports\n self.uses = uses\n\n def resolve(self, packages, must_resolve=True):\n resolved_uses = package.resolve_uses(self.uses, packages, must_resolve=must_resolve)\n available_types, available_constants = package.combine_packages(\n [u.package for u in resolved_uses.values()])\n available_constants = package.exclusive_dict_merge(\n available_constants, self.generics)\n resolved_ports = collections.OrderedDict()\n for name, port in self.ports.items():\n try:\n if port.typ in available_types:\n resolved_typ = available_types[port.typ]\n elif isinstance(port.typ, str):\n raise Exception('Cannot resolve port typ {}'.format(port.typ))\n else:\n resolved_typ = port.typ.resolve(available_types, available_constants)\n resolved_port = Port(name=port.name, direction=port.direction,\n typ=resolved_typ)\n resolved_ports[name] = resolved_port\n except ResolutionError as e:\n # If we can't resolve and `must_resolve` isn't True then we just\n # skip ports that we can't resolve.\n if must_resolve:\n raise e\n e = Entity(\n identifier=self.identifier,\n generics=self.generics,\n ports=resolved_ports,\n uses=resolved_uses,\n )\n return e\n\n\nclass Entity(object):\n '''\n An entity with all types and constants in the ports resolved.\n '''\n\n resolved = True\n\n def __init__(self, identifier, generics, ports, uses):\n self.identifier = identifier\n self.generics = generics\n self.ports = ports\n self.uses = uses\n\n def __str__(self):\n return 'Entity({})'.format(self.identifier)\n\n def __repr__(self):\n return str(self)\n\n def inputs_to_slv(self, inputs, generics):\n slvs = [] \n for port in self.ports.values():\n if (port.direction == 'in') and (port.name not in CLOCK_NAMES):\n d = inputs.get(port.name, None)\n if d is None:\n w = typs.make_substitute_generics_function(generics)(port.typ.width)\n o = 'U' * symbolic_math.get_value(w)\n else:\n o = port.typ.to_slv(d, generics)\n slvs.append(o)\n slv = ''.join(reversed(slvs))\n return slv\n\n def ports_from_slv(self, slv, generics, direction):\n pos = 0\n outputs = {}\n for port in self.ports.values():\n if (port.direction == direction) and (port.name not in CLOCK_NAMES):\n w = typs.make_substitute_generics_function(generics)(port.typ.width)\n width = symbolic_math.get_value(w)\n intwidth = int(width)\n assert(width == intwidth)\n if pos == 0:\n piece = slv[-intwidth:]\n else:\n piece = slv[-pos-intwidth: -pos]\n pos += intwidth\n o = port.typ.from_slv(piece, generics)\n outputs[port.name] = o\n return outputs\n\n def outputs_from_slv(self, slv, generics):\n slv = slv.strip()\n data = self.ports_from_slv(slv, generics, 'out')\n return data\n\n def inputs_from_slv(self, slv, generics):\n slv = slv.strip()\n data = self.ports_from_slv(slv, generics, 'in')\n return data\n", "sub_path": "slvcodec/entity.py", "file_name": "entity.py", "file_ext": "py", "file_size_in_byte": 6110, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "slvcodec.package.parsed_from_filename", "line_number": 26, "usage_type": "call"}, {"api_name": "slvcodec.package", "line_number": 26, "usage_type": "name"}, {"api_name": "slvcodec.package.process_parsed_package", "line_number": 33, "usage_type": "call"}, {"api_name": "slvcodec.package", "line_number": 33, "usage_type": "name"}, {"api_name": "slvcodec.package.resolve_packages", "line_number": 35, "usage_type": "call"}, {"api_name": "slvcodec.package", "line_number": 35, "usage_type": "name"}, {"api_name": "slvcodec.typs.Generic", "line_number": 47, "usage_type": "call"}, {"api_name": "slvcodec.typs", "line_number": 47, "usage_type": "name"}, {"api_name": "slvcodec.typ_parser.process_subtype_indication", "line_number": 49, "usage_type": "call"}, {"api_name": "slvcodec.typ_parser", "line_number": 49, "usage_type": "name"}, {"api_name": "slvcodec.typ_parser.process_subtype_indication", "line_number": 55, "usage_type": "call"}, {"api_name": "slvcodec.typ_parser", "line_number": 55, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 58, "usage_type": "call"}, {"api_name": "slvcodec.package.get_parsed_package_dependencies", "line_number": 59, "usage_type": "call"}, {"api_name": "slvcodec.package", "line_number": 59, "usage_type": "name"}, {"api_name": "slvcodec.package.resolve_uses", "line_number": 90, "usage_type": "call"}, {"api_name": "slvcodec.package", "line_number": 90, "usage_type": "name"}, {"api_name": "slvcodec.package.combine_packages", "line_number": 91, "usage_type": "call"}, {"api_name": "slvcodec.package", "line_number": 91, "usage_type": "name"}, {"api_name": "slvcodec.package.exclusive_dict_merge", "line_number": 93, "usage_type": "call"}, {"api_name": "slvcodec.package", "line_number": 93, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 95, "usage_type": "call"}, {"api_name": "slvcodec.typs.ResolutionError", "line_number": 107, "usage_type": "name"}, {"api_name": "slvcodec.typs.make_substitute_generics_function", "line_number": 146, "usage_type": "call"}, {"api_name": "slvcodec.typs", "line_number": 146, "usage_type": "name"}, {"api_name": "slvcodec.symbolic_math.get_value", "line_number": 147, "usage_type": "call"}, {"api_name": "slvcodec.symbolic_math", "line_number": 147, "usage_type": "name"}, {"api_name": "slvcodec.typs.make_substitute_generics_function", "line_number": 159, "usage_type": "call"}, {"api_name": "slvcodec.typs", "line_number": 159, "usage_type": "name"}, {"api_name": "slvcodec.symbolic_math.get_value", "line_number": 160, "usage_type": "call"}, {"api_name": "slvcodec.symbolic_math", "line_number": 160, "usage_type": "name"}]} +{"seq_id": "441580114", "text": "from datetime import datetime\nfrom unittest2 import TestCase\nfrom ert.util import ctime\n\n\nclass CTimeTest(TestCase):\n\n def test_c_time(self):\n c_time = ctime(0)\n self.assertEqual(str(c_time), \"1970-01-01 01:00:00\")\n\n date_time = ctime(datetime(1970, 1, 1, 1, 0, 0))\n self.assertEqual(c_time, date_time)\n\n date_time_after = ctime(datetime(1970, 1, 1, 1, 0, 5))\n\n self.assertTrue(date_time_after > date_time)", "sub_path": "devel/python/test/ert_tests/util/test_ctime.py", "file_name": "test_ctime.py", "file_ext": "py", "file_size_in_byte": 451, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "unittest2.TestCase", "line_number": 6, "usage_type": "name"}, {"api_name": "ert.util.ctime", "line_number": 9, "usage_type": "call"}, {"api_name": "ert.util.ctime", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 12, "usage_type": "call"}, {"api_name": "ert.util.ctime", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "20708239", "text": "from bottle import route, run\nfrom bottle import get, post, request, delete # or route\nfrom database import db_connector\n\nconnector = db_connector()\n\n@get('/playlist')\ndef get_playlists():\n from bottle import response\n from json import dumps\n res = connector.read_all_playlists()\n print(res)\n response.content_type = 'application/json'\n return dumps(res)\n\n@get('/playlist/')\ndef get_playlists(id):\n from bottle import response\n from json import dumps\n res = connector.read_one_playlist(id)\n response.content_type = 'application/json'\n return dumps(res)\n\n\n@post('/playlist')\ndef add_playlist():\n playlistname = request.forms.get('playlistname')\n print(\"inserting \"+playlistname+\"in db\")\n connector.insert_playlist_in_db(playlistname)\n\n@get('/video')\ndef get_videos():\n from bottle import response\n from json import dumps\n res = connector.read_all_videos()\n response.content_type = 'application/json'\n return dumps(res)\n\n@post('/video')\ndef add_video():\n playlistid = request.forms.get('playlistid')\n Title = request.forms.get('title')\n Thumbnail = request.forms.get('thumbnail')\n connector.insert_video_in_playlist(playlistid, Title, Thumbnail)\n\n\n@delete('/video//playlist/')\ndef delete_video_from_playlist(videoid, playlistid):\n print(\"{} {}\".format(videoid, playlistid))\n connector.remove_one_video_from_playlist( playlistid, videoid)\n\n\nif __name__ == \"__main__\":\n print(\"starting web server\")\n print(\"Run Server\")\n run(host=\"0.0.0.0\", port=8081, debug=True, reloader=True)\n", "sub_path": "app/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1584, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "database.db_connector", "line_number": 5, "usage_type": "call"}, {"api_name": "bottle.response.content_type", "line_number": 13, "usage_type": "attribute"}, {"api_name": "bottle.response", "line_number": 13, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 14, "usage_type": "call"}, {"api_name": "bottle.get", "line_number": 7, "usage_type": "call"}, {"api_name": "bottle.response.content_type", "line_number": 21, "usage_type": "attribute"}, {"api_name": "bottle.response", "line_number": 21, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 22, "usage_type": "call"}, {"api_name": "bottle.get", "line_number": 16, "usage_type": "call"}, {"api_name": "bottle.request.forms.get", "line_number": 27, "usage_type": "call"}, {"api_name": "bottle.request.forms", "line_number": 27, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 27, "usage_type": "name"}, {"api_name": "bottle.post", "line_number": 25, "usage_type": "call"}, {"api_name": "bottle.response.content_type", "line_number": 36, "usage_type": "attribute"}, {"api_name": "bottle.response", "line_number": 36, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 37, "usage_type": "call"}, {"api_name": "bottle.get", "line_number": 31, "usage_type": "call"}, {"api_name": "bottle.request.forms.get", "line_number": 41, "usage_type": "call"}, {"api_name": "bottle.request.forms", "line_number": 41, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 41, "usage_type": "name"}, {"api_name": "bottle.request.forms.get", "line_number": 42, "usage_type": "call"}, {"api_name": "bottle.request.forms", "line_number": 42, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 42, "usage_type": "name"}, {"api_name": "bottle.request.forms.get", "line_number": 43, "usage_type": "call"}, {"api_name": "bottle.request.forms", "line_number": 43, "usage_type": "attribute"}, {"api_name": "bottle.request", "line_number": 43, "usage_type": "name"}, {"api_name": "bottle.post", "line_number": 39, "usage_type": "call"}, {"api_name": "bottle.delete", "line_number": 47, "usage_type": "call"}, {"api_name": "bottle.run", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "170637426", "text": "import face_recognition\nfrom sklearn import svm\nimport os\nencodings = []\nnames = []\ntrain_dir = os.listdir('./lfw/')\nprint(\"train_dir:\",train_dir)\nfor person in train_dir:\n pix = os.listdir(\"./lfw/\" + person)\n for person_img in pix:\n face = face_recognition.load_image_file(\"./lfw/\" + person + \"/\" + person_img)\n face_locations=face_recognition.face_locations(face)\n print(face_locations)\n if len(face_locations)==1:\n face_enc = face_recognition.face_encodings(face)[0]\n print(\"person：\",person)\n print(\"face_enc:\",face_enc)\n encodings.append(face_enc)\n names.append(person)\n else:\n print(person+\"can not used for tarining\")\nclf = svm.SVC(gamma='scale')\nclf.fit(encodings, names)\ntest_image = face_recognition.load_image_file('Aaron_peirsol-2.jpg')\nface_locations = face_recognition.face_locations(test_image)\nno = len(face_locations)\n\nprint(\"Number of faces detected: \", no)\nprint(\"Found: \\n\")\nfor i in range(no):\n test_image_enc = face_recognition.face_encodings(test_image)[i]\n name = clf.predict([test_image_enc])\n print(*name)\n", "sub_path": "PycharmProjects/0429_new/person2.py", "file_name": "person2.py", "file_ext": "py", "file_size_in_byte": 1189, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.listdir", "line_number": 6, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 9, "usage_type": "call"}, {"api_name": "face_recognition.load_image_file", "line_number": 11, "usage_type": "call"}, {"api_name": "face_recognition.face_locations", "line_number": 12, "usage_type": "call"}, {"api_name": "face_recognition.face_encodings", "line_number": 15, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 22, "usage_type": "call"}, {"api_name": "sklearn.svm", "line_number": 22, "usage_type": "name"}, {"api_name": "face_recognition.load_image_file", "line_number": 24, "usage_type": "call"}, {"api_name": "face_recognition.face_locations", "line_number": 25, "usage_type": "call"}, {"api_name": "face_recognition.face_encodings", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "599733601", "text": "# Copyright (c) 2014-present PlatformIO \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport inspect\nimport json\n\nimport click\nimport jsonrpc\nfrom starlette.endpoints import WebSocketEndpoint\n\nfrom platformio.compat import create_task, get_running_loop, is_bytes\nfrom platformio.proc import force_exit\n\n\nclass JSONRPCServerFactoryBase:\n\n connection_nums = 0\n shutdown_timer = None\n\n def __init__(self, shutdown_timeout=0):\n self.shutdown_timeout = shutdown_timeout\n self.dispatcher = jsonrpc.Dispatcher()\n\n def __call__(self, *args, **kwargs):\n raise NotImplementedError\n\n def addHandler(self, handler, namespace):\n self.dispatcher.build_method_map(handler, prefix=\"%s.\" % namespace)\n\n def on_client_connect(self):\n self.connection_nums += 1\n if self.shutdown_timer:\n self.shutdown_timer.cancel()\n self.shutdown_timer = None\n\n def on_client_disconnect(self):\n self.connection_nums -= 1\n if self.connection_nums < 1:\n self.connection_nums = 0\n\n if self.connection_nums == 0:\n self.shutdown_by_timeout()\n\n async def on_shutdown(self):\n pass\n\n def shutdown_by_timeout(self):\n if self.shutdown_timeout < 1:\n return\n\n def _auto_shutdown_server():\n click.echo(\"Automatically shutdown server on timeout\")\n force_exit()\n\n self.shutdown_timer = get_running_loop().call_later(\n self.shutdown_timeout, _auto_shutdown_server\n )\n\n\nclass WebSocketJSONRPCServerFactory(JSONRPCServerFactoryBase):\n def __call__(self, *args, **kwargs):\n ws = WebSocketJSONRPCServer(*args, **kwargs)\n ws.factory = self\n return ws\n\n\nclass WebSocketJSONRPCServer(WebSocketEndpoint):\n encoding = \"text\"\n factory: WebSocketJSONRPCServerFactory = None\n\n async def on_connect(self, websocket):\n await websocket.accept()\n self.factory.on_client_connect() # pylint: disable=no-member\n\n async def on_receive(self, websocket, data):\n create_task(self._handle_rpc(websocket, data))\n\n async def on_disconnect(self, websocket, close_code):\n self.factory.on_client_disconnect() # pylint: disable=no-member\n\n async def _handle_rpc(self, websocket, data):\n response = jsonrpc.JSONRPCResponseManager.handle(\n data, self.factory.dispatcher # pylint: disable=no-member\n )\n if response.result and inspect.isawaitable(response.result):\n try:\n response.result = await response.result\n response.data[\"result\"] = response.result\n response.error = None\n except Exception as exc: # pylint: disable=broad-except\n if not isinstance(exc, jsonrpc.exceptions.JSONRPCDispatchException):\n exc = jsonrpc.exceptions.JSONRPCDispatchException(\n code=4999, message=str(exc)\n )\n response.result = None\n response.error = exc.error._data # pylint: disable=protected-access\n new_data = response.data.copy()\n new_data[\"error\"] = response.error\n del new_data[\"result\"]\n response.data = new_data\n\n if response.error:\n click.secho(\"Error: %s\" % response.error, fg=\"red\", err=True)\n if \"result\" in response.data and is_bytes(response.data[\"result\"]):\n response.data[\"result\"] = response.data[\"result\"].decode(\"utf-8\")\n\n await websocket.send_text(json.dumps(response.data))\n", "sub_path": "platformio/commands/home/rpc/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 4112, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "jsonrpc.Dispatcher", "line_number": 33, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 63, "usage_type": "call"}, {"api_name": "platformio.proc.force_exit", "line_number": 64, "usage_type": "call"}, {"api_name": "platformio.compat.get_running_loop", "line_number": 66, "usage_type": "call"}, {"api_name": "starlette.endpoints.WebSocketEndpoint", "line_number": 78, "usage_type": "name"}, {"api_name": "platformio.compat.create_task", "line_number": 87, "usage_type": "call"}, {"api_name": "jsonrpc.JSONRPCResponseManager.handle", "line_number": 93, "usage_type": "call"}, {"api_name": "jsonrpc.JSONRPCResponseManager", "line_number": 93, "usage_type": "attribute"}, {"api_name": "inspect.isawaitable", "line_number": 96, "usage_type": "call"}, {"api_name": "jsonrpc.exceptions", "line_number": 102, "usage_type": "attribute"}, {"api_name": "jsonrpc.exceptions.JSONRPCDispatchException", "line_number": 103, "usage_type": "call"}, {"api_name": "jsonrpc.exceptions", "line_number": 103, "usage_type": "attribute"}, {"api_name": "click.secho", "line_number": 114, "usage_type": "call"}, {"api_name": "platformio.compat.is_bytes", "line_number": 115, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 118, "usage_type": "call"}]} +{"seq_id": "383574363", "text": "from flask import Flask, render_template, request\nfrom flask_sqlalchemy import SQLAlchemy\nfrom send_email import send_email\nfrom sqlalchemy import func\n\napp = Flask(__name__)\ndb = SQLAlchemy(app)\n\napp.config['SQLALCHEMY_DATABASE_URI']='postgresql://postgres:3682@localhost/test'\ndb=SQLAlchemy(app)\n\nclass Data(db.Model):\n __tablename__=\"data\"\n id=db.Column(db.Integer, primary_key=True)\n email_=db.Column(db.String(120), unique=True)\n height_=db.Column(db.Integer)\n\n def __init__(self, email_, height_):\n self.email_=email_\n self.height_=height_\n\n@app.route(\"/\")\ndef index():\n return render_template(\"index.html\")\n\n\n@app.route(\"/success/\", methods=['POST'])\ndef success():\n if request.method == 'POST':\n email = request.form[\"email\"]\n height = request.form[\"height\"]\n data= Data(email,height)\n if db.session.query(Data).filter(Data.email_== email).count() == 0:\n db.session.add(data)\n db.session.commit()\n avgerage_height = db.session.query(func.avg(Data.height_)).scalar()\n avgerage_height = round(avgerage_height,1)\n count = db.session.query(Data.height_).count()\n send_email(email,height, avgerage_height, count)\n return render_template(\"success.html\")\n return render_template(\"index.html\", text=\"Email Is Already Exists\")\n\nif __name__ == \"__main__\":\n app.debig=True\n app.run()", "sub_path": "Application_9/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1433, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 7, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 31, "usage_type": "name"}, {"api_name": "sqlalchemy.func.avg", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 36, "usage_type": "name"}, {"api_name": "send_email.send_email", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "219375770", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# This document is free and open-source software, subject to the OSI-approved\n# BSD license below.\n#\n# Copyright (c) 2011 - 2013 Alexis Petrounias ,\n# All rights reserved.\n#\n# Redistribution and use in source and binary forms, with or without\n# modification, are permitted provided that the following conditions are met:\n#\n# * Redistributions of source code must retain the above copyright notice, this\n# list of conditions and the following disclaimer.\n#\n# * Redistributions in binary form must reproduce the above copyright notice,\n# this list of conditions and the following disclaimer in the documentation\n# and/or other materials provided with the distribution.\n#\n# * Neither the name of the author nor the names of its contributors may be used\n# to endorse or promote products derived from this software without specific\n# prior written permission.\n#\n# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS \"AS IS\"\n# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE\n# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE\n# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE\n# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL\n# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR\n# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,\n# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE\n# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.\n\n\nimport os\nfrom io import open\n\nfrom setuptools import find_packages, setup\n\n\ndef read(filename):\n path = os.path.join(os.path.dirname(__file__), filename)\n with open(path, encoding='utf-8') as handle:\n return handle.read()\n\n\nsetup(\n name='django-cte-forest',\n version=__import__('cte_forest').__version__,\n description=(\n 'Django Adjacency-List trees using PostgreSQL'\n ' Common Table Expressions (CTE).'\n ),\n long_description=read('README.rst'),\n maintainer='Matthias Kestenholz',\n maintainer_email='mk@feinheit.ch',\n url='https://github.com/matthiask/django-cte-forest',\n license='BSD License',\n packages=find_packages(\n exclude=['cte_forest_test'],\n ),\n include_package_data=True,\n classifiers=[\n 'Development Status :: 3 - Alpha',\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 'Topic :: Software Development :: Libraries :: Python Modules',\n ],\n zip_safe=False,\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 2873, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"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.dirname", "line_number": 43, "usage_type": "call"}, {"api_name": "io.open", "line_number": 44, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 48, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "521915851", "text": "import numpy as np\nimport gym\nfrom policies import *\nimport math\nfrom scipy.optimize import minimize\nfrom scipy.special import j1\nfrom scipy.optimize import minimize_scalar\nfrom scipy.stats import norm\nimport matplotlib.pyplot as plt\n\nclass HCOPE(object):\n\n def __init__(self,env,policy,eval_policy,rollout_length,delta=0.1):\n self.env = env\n self.policy= policy\n self.eval_policy=eval_policy\n self.rollout_length = rollout_length\n self.w_policy = self.policy.get_weights()\n # Set up maximum and minimum reward in a trajectory\n self.R_max = 200\n self.R_min = 1\n self.delta=delta\n if eval_policy is None:\n self.e_policy = None\n else:\n self.e_policy=self.eval_policy.get_weights()\n\n\n # Method to generate evaluation policy with gaussian noise added to our behaviour policy\n def setup_e_policy(self):\n noise = np.random.normal(0,0.01,self.w_policy.shape)\n self.e_policy = self.w_policy - noise\n self.eval_policy.update_weights(self.e_policy)\n\n def rollout(self,shift = 0.,policy = None, rollout_length = None,render = False):\n \"\"\" \n Performs one rollout of maximum length rollout_length. \n At each time-step it substracts shift from the reward.\n \"\"\"\n total_reward = 0.\n steps = 0\n\n if(rollout_length==None):\n rollout_length=self.rollout_length\n\n ob = self.env.reset()\n for i in range(rollout_length):\n action,prob = policy.act(ob)\n ob, reward, done, _ = self.env.step(action)\n steps += 1\n total_reward += (reward - shift)\n if(render):\n env.render()\n if done:\n break\n \n return total_reward, steps\n\n # Modified rollout method for HCOPE evaluation. Returns probs of each action that were taken in behavorial as well as evaluation policy\n def mod_rollout(self,shift = 0., rollout_length = None,render = False,random =False,greedy=True):\n \"\"\" \n Performs one rollout of maximum length rollout_length. \n At each time-step it substracts shift from the reward.\n \"\"\"\n \n\n total_reward = 0.\n steps = 0\n rewards = []\n probs = []\n eval_probs =[]\n if(rollout_length==None):\n rollout_length=self.rollout_length\n\n ob = self.env.reset()\n for i in range(rollout_length):\n if random== True:\n action = np.random.randint(0,env.action_space.n)\n action,prob = self.policy.act_action(ob,action)\n eval_action,eval_prob = self.eval_policy.act_action(ob,action)\n elif greedy==False:\n action,prob = self.policy.act(ob,greedy=greedy) \n eval_action,eval_prob = self.eval_policy.act_action(ob,action)\n \n else:\n action,prob = self.policy.act(ob)\n \n eval_action,eval_prob = self.eval_policy.act_action(ob,action)\n \n ob, reward, done, _ = self.env.step(action)\n rewards.append(reward- shift)\n probs.append(prob)\n eval_probs.append(eval_prob)\n steps += 1\n total_reward += (reward - shift)\n if(render):\n env.render()\n if done:\n break\n \n return total_reward, steps,rewards,probs,eval_probs\n\n\n # Evaluate any policy\n def evaluate(self,policy=None,shift=0.,n_rollouts=100,render = False):\n self.policy.update_weights(self.w_policy)\n self.policy.update_filter = False\n rewards = []\n for i in range(n_rollouts):\n total_reward,steps = self.rollout(render=render,shift =shift ,policy = policy)\n rewards.append(total_reward) \n\n rewards = np.asarray(rewards)\n rewards = self.normalize_reward(rewards,self.R_min,self.R_max)\n\n return(np.mean(rewards))\n\n\n # Method to normalize trajectory rewards\n def normalize_reward(self, rewards,R_minus,R_plus):\n return (rewards-R_minus)/(R_plus-R_minus)\n\n\n # Method to generate dataset if it is not provided\n def generate_dataset(self,dataset_size = 100,shift = 0.,render=False):\n # Stop updating filter \n self.policy.update_weights(self.w_policy)\n self.policy.update_filter = False\n self.eval_policy.update_weights(self.e_policy)\n self.eval_policy.update_filter = False\n rewards = []\n probs = []\n eval_probs = []\n\n\n for i in range(dataset_size):\n total_reward,steps,rewards_list,probs_list,eval_probs_list = self.mod_rollout(render=render,shift = shift,greedy=False)\n rewards.append(rewards_list)\n probs.append(probs_list)\n eval_probs.append(eval_probs_list) \n\n rewards = np.asarray(rewards)\n probs = np.asarray(probs)\n eval_probs = np.asarray(eval_probs)\n\n # Shuffle our dataset\n permutation = np.random.permutation(probs.shape[0])\n \n rewards = rewards[permutation,:]\n #rewards=self.normalize_reward(rewards,self.R_min,self.R_max)\n\n probs = probs[permutation,:]\n eval_probs =eval_probs[permutation,:]\n\n # Break the dataset into two parts for estimating c* \n d_pre = rewards[:int(0.05*dataset_size),:]\n d_post = rewards[int(0.05*dataset_size):,:]\n \n pi_b_pre = probs[:int(0.05*dataset_size),:]\n pi_b_post = probs[int(0.05*dataset_size):,:]\n\n pi_e_pre = eval_probs[:int(0.05*dataset_size),:]\n pi_e_post = eval_probs[int(0.05*dataset_size):,:]\n\n return [d_pre,d_post,pi_b_pre,pi_b_post,pi_e_pre,pi_e_post]\n\n\n \n def visualize_IS_distribution(self):\n episodes = 1000\n probs=[]\n self.policy.update_weights(self.w_policy)\n self.policy.update_filter = False\n self.eval_policy.update_weights(self.e_policy)\n self.eval_policy.update_filter = False\n\n eval_probs=[]\n for i in range(episodes):\n total_reward,steps,rewards_list,probs_list,eval_probs_list = self.mod_rollout(greedy=False)\n probs.append(probs_list)\n eval_probs.append(eval_probs_list) \n\n \n probs = np.asarray(probs)\n eval_probs = np.asarray(eval_probs)\n\n importance_weight = np.log(np.asarray([ np.prod(np.asarray(eval_probs[i])/np.asarray(probs[i])) for i in range(episodes)], dtype=float))\n plt.hist(importance_weight, color = 'blue', edgecolor = 'black',bins = int(100))\n\n plt.savefig(\"IS_dist.png\")\n \n\n\n def estimate_behavior_policy(self,dataset):\n d_pre,d_post,pi_b_pre,pi_b_post,pi_e_pre,pi_e_post = dataset\n eval_estimate = self.hcope_estimator(d_pre, d_post, pi_b_pre,pi_b_post,pi_e_pre,pi_e_post,self.delta)\n print(\"Estimate of evaluation policy: {}\".format(eval_estimate))\n\n \n def hcope_estimator(self,d_pre, d_post, pi_b_pre,pi_b_post,pi_e_pre,pi_e_post,delta):\n \"\"\"\n d_pre : float, size = (dataset_split,)\n Trajectory rewards from the behavior policy \n\n d_post : float, size = (dataset_size - dataset_split, )\n Trajectory rewards from the behavior policy \n\n delta : float, size = scalar\n 1-delta is the confidence of the estimator\n \n pi_b : Probabilities for respective trajectories in behaviour policy\n\n pi_e : Probabilities for respective trajectories in evaluation policy\n\n RETURNS: lower bound for the mean, mu as per Theorem 1 of Thomas et al. High Confidence Off-Policy Evaluation\n \"\"\"\n \n print(\"Running HCOPE estimator on the evaluation policy..........\")\n\n d_pre = np.asarray(d_pre)\n d_post = np.asarray(d_post)\n n_post = len(d_post)\n n_pre = len(d_pre)\n\n # Estimate c which maximizes the lower bound using estimates from d_pre\n\n c_estimate = 4.0\n print(\"Intial estimate of c {}.\".format(c_estimate))\n\n def f(x):\n n_pre = len(d_pre)\n Y = np.asarray([min(self.normalize_reward(np.sum(d_pre[i]),self.R_min,self.R_max) * np.prod(pi_e_pre[i]/pi_b_pre[i].astype(np.float64)), x) for i in range(n_pre)], dtype=float)\n importance_weights = np.asarray([ np.prod(pi_e_pre[i]/pi_b_pre[i].astype(np.float64)) for i in range(n_pre)], dtype=float)\n # Empirical mean\n EM = np.sum(Y)/n_pre\n #print(EM)\n # Second term\n term2 = (7.*x*np.log(2./delta)) / (3*(n_post-1))\n # print(term2)\n square_term = ((n_pre*np.sum(np.square(Y))) - np.square(np.sum(Y)))\n if square_term<0:\n square_term=0\n # Third term\n term3 = np.sqrt( (((2.*np.log(2./delta))/(n_post*n_pre*(n_pre-1))) * square_term ))\n # print(term3)\n return (-EM+term2+term3) \n\n c_estimate = minimize(f,np.array([c_estimate]),method='BFGS').x\n\n print(\"The estimate for c* was found to be {}.\".format(c_estimate))\n\n # Use the estimated c for computing the maximum lower bound\n c = c_estimate\n\n if ~isinstance(c, list):\n c = np.full((n_post,), c, dtype=float)\n\n \n \n if n_post<=1:\n raise(ValueError(\"The value of 'n' must be greater than 1\"))\n\n\n Y = np.asarray([min(self.normalize_reward(np.sum(d_post[i]),self.R_min,self.R_max) * np.prod(pi_e_post[i]/pi_b_post[i].astype(np.float64)), c[i]) for i in range(len(d_post))], dtype=float)\n importance_weights = np.asarray([ np.prod(pi_e_post[i]/pi_b_post[i].astype(np.float64)) for i in range(n_post)], dtype=float)\n \n # Empirical mean\n c = np.asarray([max(1,i) for i in c])\n\n EM = np.sum(Y/c[0])/(np.sum(1/c))\n\n # Second term\n term2 = (7.*n_post*np.log(2./delta)) / (3*(n_post-1)*np.sum(1/c))\n\n # Third term\n square_term = (n_post*np.sum(np.square(Y/c)) - np.square(np.sum(Y/c)))\n if square_term<0:\n square_term = 0\n term3 = np.sqrt( ((2*np.log(2./delta))/(n_post-1)) * square_term) / np.sum(1/c)\n\n\n # Sanity check on determinant\n\n k1 = (7.*n_post)/(3*(n_post-1)) \n k2 = (n_post*np.sum(np.square(Y/c)) - np.square(np.sum(Y/c)))*(2./(n_post-1))\n k3 = (EM - term2 - term3)*np.sum(1/c) - (np.sum(Y/c))\n\n if(k2-4*k1*k3<0):\n print(\"The estimate of u_ is of zero confidence\")\n else:\n if(-np.sqrt(k2)+np.sqrt(k2-4*k1*k3))<0:\n print(\"The estimate of u_ is of zero confidence\")\n\n # Final estimate\n return EM - term2 - term3\n\n\n\n\n\n\n\n\n\n\n\nif __name__==\"__main__\":\n # Create a gym environment\n env_name = \"MountainCar-v0\"\n env = gym.make(env_name)\n\n # Assuming discrete action space\n action_size = env.action_space.n\n ob_size = env.observation_space.shape[0]\n\n # Create a bilayer mlp with softmax\n policy_params={'type':'bilayer',\n 'ob_filter':'MeanStdFilter',\n 'ob_dim':ob_size,\n 'ac_dim':action_size}\n policy = BilayerPolicy_softmax(policy_params)\n eval_policy = BilayerPolicy_softmax(policy_params)\n\n my_hcope = HCOPE(env,policy,eval_policy,rollout_length = 1000,delta =0.1)\n my_hcope.setup_e_policy()\n\n dataset = my_hcope.generate_dataset(dataset_size=100,shift=-2)\n print(\"Estimate of behavorial policy: {}\".format(my_hcope.evaluate(policy=my_hcope.policy,shift = -2,n_rollouts=100,render =False)))\n\n my_hcope.estimate_behavior_policy(dataset)\n print(\"True estimate of evaluation policy: {}\".format(my_hcope.evaluate(policy=my_hcope.eval_policy,shift = -2,n_rollouts=100,render =False)))\n\n #my_hcope.visualize_IS_distribution()", "sub_path": "Safe-RL/safeRL/HCOPE/hcope.py", "file_name": "hcope.py", "file_ext": "py", "file_size_in_byte": 11883, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.random.normal", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 31, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 78, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 147, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 232, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 233, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 244, "usage_type": "call"}, {"api_name": "scipy.optimize.minimize", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 264, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 265, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 265, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 265, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 270, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 273, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 276, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 276, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 291, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 310, "usage_type": "call"}]} +{"seq_id": "124977854", "text": "import torch\nimport torch.nn as nn\nimport numpy as np\n\nclass LinearBlock(nn.Module):\n\n def __init__(self, in_nodes, out_nodes):\n super(LinearBlock, self).__init__()\n self.layer = nn.utils.weight_norm(nn.Linear(in_nodes, out_nodes), dim = 0)\n\n def forward(self, x):\n x = self.layer(x)\n x = x * torch.sigmoid(x) # SiLU\n return x\n\nclass PINN(nn.Module):\n\n def __init__(self, data, layer_list):\n super(PINN, self).__init__()\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n self.input_layer = nn.utils.weight_norm(nn.Linear(layer_list[0], layer_list[1]), dim = 0)\n self.hidden_layers = self._make_layer(layer_list[1:-1])\n self.output_layer = nn.utils.weight_norm(nn.Linear(layer_list[-2], layer_list[-1]), dim = 0)\n self.data = data\n self.mean = self.data.mean(dim=0).to(device)\n self.sig = torch.sqrt(self.data.var(dim=0)).to(device)\n\n def _make_layer(self, layer_list):\n layers = []\n for i in range(len(layer_list) - 1):\n block = LinearBlock(layer_list[i], layer_list[i + 1])\n layers.append(block)\n return nn.Sequential(*layers)\n\n def forward(self, x):\n x = (x - self.mean) / self.sig\n x = self.input_layer(x)\n x = x * torch.sigmoid(x)\n x = self.hidden_layers(x)\n x = self.output_layer(x)\n return x\n\ndef weights_init(m):\n if isinstance(m, nn.Linear):\n torch.nn.init.xavier_normal_(m.weight)\n\ndef pinn(data, layer_list):\n device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n model = PINN(data, layer_list).to(device)\n model.apply(weights_init)\n print(\"Operation mode: \", device)\n return model\n\ndef fwd_gradients(obj, x):\n dummy = torch.ones_like(obj)\n derivative = torch.autograd.grad(obj, x, dummy, create_graph= True)[0]\n return derivative\n\ndef Navier_Stokes_2D(c, u, v, p, txy, Pec, Rey):\n c_txy = fwd_gradients(c, txy)\n u_txy = fwd_gradients(u, txy)\n v_txy = fwd_gradients(v, txy)\n p_txy = fwd_gradients(p, txy)\n\n c_t = c_txy[:, 0:1]\n c_x = c_txy[:, 1:2]\n c_y = c_txy[:, 2:3]\n u_t = u_txy[:, 0:1]\n u_x = u_txy[:, 1:2]\n u_y = u_txy[:, 2:3]\n v_t = v_txy[:, 0:1]\n v_x = v_txy[:, 1:2]\n v_y = v_txy[:, 2:3]\n p_x = p_txy[:, 1:2]\n p_y = p_txy[:, 2:3]\n\n c_xx = fwd_gradients(c_x, txy)[:, 1:2]\n c_yy = fwd_gradients(c_y, txy)[:, 2:3]\n u_xx = fwd_gradients(u_x, txy)[:, 1:2]\n u_yy = fwd_gradients(u_y, txy)[:, 2:3]\n v_xx = fwd_gradients(v_x, txy)[:, 1:2]\n v_yy = fwd_gradients(v_y, txy)[:, 2:3]\n\n e1 = c_t + (u * c_x + v * c_y) - (1.0 / Pec) * (c_xx + c_yy)\n e2 = u_t + (u * u_x + v * u_y) + p_x - (1.0 / Rey) * (u_xx + u_yy)\n e3 = v_t + (u * v_x + v * v_y) + p_y - (1.0 / Rey) * (v_xx + v_yy)\n e4 = u_x + v_y\n\n return e1, e2, e3, e4\n\ndef Gradient_Velocity_2D(u, v, txy):\n u_txy = fwd_gradients(u, txy)\n v_txy = fwd_gradients(v, txy)\n\n u_x = u_txy[:, 1:2]\n u_y = u_txy[:, 2:3]\n v_x = v_txy[:, 1:2]\n v_y = v_txy[:, 2:3]\n\n return u_x, v_x, u_y, v_y\n\ndef test_data(T_star, X_star, Y_star, C_star, U_star, V_star, P_star):\n snap = np.random.randint(0, 200)\n t_star = T_star[:, snap:snap+1]\n x_star = X_star[:, snap:snap+1]\n y_star = Y_star[:, snap:snap+1]\n c_star = C_star[:, snap:snap+1]\n u_star = U_star[:, snap:snap+1]\n v_star = V_star[:, snap:snap+1]\n p_star = P_star[:, snap:snap+1]\n\n variables_star = torch.FloatTensor(np.concatenate((t_star, x_star, y_star), 1)) # N x 3\n target_star = torch.FloatTensor(np.concatenate((c_star, u_star, v_star, p_star), 1)) # N x 4\n\n return variables_star, target_star\n\ndef relative_error(pred, target):\n return torch.sqrt(torch.mean((pred - target)**2)/torch.mean((target - torch.mean(target))**2)).cpu().numpy()\n\nif __name__ == \"__main__\":\n import numpy as np\n dummy_data = torch.Tensor(np.random.normal(0,1,size=(100,3)))\n layer_list = [3] + 10*[200] + [4]\n model = pinn(dummy_data, layer_list)\n print(model)\n", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 4061, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "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.utils.weight_norm", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 16, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"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": "torch.nn.utils.weight_norm", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 21, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn.utils.weight_norm", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 23, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.sigmoid", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 44, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.nn.init.xavier_normal_", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 48, "usage_type": "attribute"}, {"api_name": "torch.ones_like", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.autograd.grad", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 103, "usage_type": "attribute"}, {"api_name": "torch.FloatTensor", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 122, "usage_type": "attribute"}]} +{"seq_id": "538116693", "text": "##############################################################################\n#\n# Copyright (c) 2001, 2002 Zope Foundation and Contributors.\n# All Rights Reserved.\n#\n# This software is subject to the provisions of the Zope Public License,\n# Version 2.1 (ZPL). A copy of the ZPL should accompany this distribution.\n# THIS SOFTWARE IS PROVIDED \"AS IS\" AND ANY AND ALL EXPRESS OR IMPLIED\n# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS\n# FOR A PARTICULAR PURPOSE.\n#\n##############################################################################\n\"\"\"Row class tests.\n\n$Id$\n\"\"\"\n\nfrom unittest import TestCase, main, makeSuite\n\nclass RowTests(TestCase):\n\n def test_RowClassFactory(self):\n from zope.rdb import RowClassFactory\n\n columns = ('food', 'name')\n data = ('pizza', 'john')\n\n klass = RowClassFactory(columns)\n ob = klass(data)\n\n self.failUnless(ob.food == 'pizza', \"bad row class attribute\")\n self.failUnless(ob.name == 'john', \"bad row class attribute (2)\")\n\n def test_RowClassFactory_Proxied(self):\n from zope.rdb import RowClassFactory\n from zope.security.proxy import ProxyFactory\n from zope.security.interfaces import ForbiddenAttribute\n from zope.security.interfaces import IChecker\n\n columns = ('type', 'speed')\n data = ('airplane', '800km')\n\n klass = RowClassFactory(columns)\n\n ob = klass(data)\n\n proxied = ProxyFactory(ob)\n\n self.failUnless (proxied.type == 'airplane', \"security proxy error\")\n self.failUnless (proxied.speed == '800km', \"security proxy error (2)\")\n self.assertRaises(ForbiddenAttribute, getattr, proxied, '__slots__')\n\n # Indirectly, check the the __Security_checker__ attribute has been\n # applied only to the instance, and not to the class.\n self.assertRaises(ForbiddenAttribute, getattr, proxied, '__bases__')\n proxied_class = ProxyFactory(klass)\n proxied_class.__bases__\n\n # Check __Security_checker__ directly\n self.assertRaises(AttributeError,\n getattr, klass, '__Security_checker__')\n self.assert_(IChecker.providedBy(ob.__Security_checker__))\n\n def test__cmp__(self):\n from zope.rdb import RowClassFactory\n\n columns = ('food', 'name')\n data = ('pizza', 'john')\n\n klass = RowClassFactory(columns)\n ob = klass(data)\n self.assertEqual(ob, ob, \"not equal to self\")\n\n klass2 = RowClassFactory(columns)\n ob2 = klass2(data)\n self.assertEqual(ob, ob2, \"not equal to an identical class\")\n\n columns = ('food', 'surname')\n data = ('pizza', 'john')\n\n klass3 = RowClassFactory(columns)\n ob3 = klass3(data)\n self.assert_(ob < ob3, \"cmp with different columns\")\n\n columns = ('food', 'name')\n data = ('pizza', 'mary')\n\n klass4 = RowClassFactory(columns)\n ob4 = klass4(data)\n self.assert_(ob < ob4, \"cmp with different data\")\n\n def test_InstanceOnlyDescriptor(self):\n from zope.rdb import InstanceOnlyDescriptor\n inst = object() # could be anything\n cls = object # could be any class\n d = InstanceOnlyDescriptor()\n self.assertRaises(AttributeError, d.__get__, inst, cls)\n self.assertRaises(AttributeError, d.__get__, None, cls)\n self.assertRaises(AttributeError, d.__delete__, inst)\n d.__set__(inst, 23)\n self.assertEquals(d.__get__(inst, cls), 23)\n self.assertRaises(AttributeError, d.__get__, None, cls)\n d = InstanceOnlyDescriptor(23)\n self.assertEquals(d.__get__(inst, cls), 23)\n d.__delete__(inst)\n self.assertRaises(AttributeError, d.__get__, inst, cls)\n self.assertRaises(AttributeError, d.__get__, None, cls)\n self.assertRaises(AttributeError, d.__delete__, inst)\n\n\ndef test_suite():\n return makeSuite(RowTests)\n\nif __name__=='__main__':\n main(defaultTest='test_suite')\n", "sub_path": "zope.rdb/branches/3.5/src/zope/rdb/tests/test_row.py", "file_name": "test_row.py", "file_ext": "py", "file_size_in_byte": 4075, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "unittest.TestCase", "line_number": 21, "usage_type": "name"}, {"api_name": "zope.rdb.RowClassFactory", "line_number": 29, "usage_type": "call"}, {"api_name": "zope.rdb.RowClassFactory", "line_number": 44, "usage_type": "call"}, {"api_name": "zope.security.proxy.ProxyFactory", "line_number": 48, "usage_type": "call"}, {"api_name": "zope.security.interfaces.ForbiddenAttribute", "line_number": 52, "usage_type": "name"}, {"api_name": "zope.security.interfaces.ForbiddenAttribute", "line_number": 56, "usage_type": "name"}, {"api_name": "zope.security.proxy.ProxyFactory", "line_number": 57, "usage_type": "call"}, {"api_name": "zope.security.interfaces.IChecker.providedBy", "line_number": 63, "usage_type": "call"}, {"api_name": "zope.security.interfaces.IChecker", "line_number": 63, "usage_type": "name"}, {"api_name": "zope.rdb.RowClassFactory", "line_number": 71, "usage_type": "call"}, {"api_name": "zope.rdb.RowClassFactory", "line_number": 75, "usage_type": "call"}, {"api_name": "zope.rdb.RowClassFactory", "line_number": 82, "usage_type": "call"}, {"api_name": "zope.rdb.RowClassFactory", "line_number": 89, "usage_type": "call"}, {"api_name": "zope.rdb.InstanceOnlyDescriptor", "line_number": 97, "usage_type": "call"}, {"api_name": "zope.rdb.InstanceOnlyDescriptor", "line_number": 104, "usage_type": "call"}, {"api_name": "unittest.makeSuite", "line_number": 113, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 116, "usage_type": "call"}]} +{"seq_id": "202034812", "text": "\n# coding: utf-8\n\n# In[1]:\n\n\nfrom django.shortcuts import render\nfrom datetime import datetime\nfrom .models import Post, Img\n\n# trips/views.py\n\nfrom django.http import HttpResponse\n\nimport tensorflow as tf\nimport cv2, sys, numpy as np, os.path \ncascPath = \"/home/ai_primary_school/mysite/trips/haarcascade_frontalface_default.xml\"\nfaceCascade = cv2.CascadeClassifier(cascPath)\nfrom train import load_model\nfrom train import Model\nmodel = Model()\nmodel.load()\nmodel.predict(cv2.imread('/home/ai_primary_school/mysite/temp/test.jpg'))\n\ndef hello_world(request):\n return render(request, 'hello_world.html', {\n 'current_time': str(datetime.now()),\n })\n\ndef home(request):\n post_list = Post.objects.all()\n return render(request, 'home.html', {\n 'post_list': post_list,\n })\n\ndef post_detail(request, pk):\n post = Post.objects.get(pk=pk)\n return render(request, 'post.html', {'post': post})\n\n\ndef uploadImg(request): # 图片上传函数\n if request.method == 'POST':\n img = Img(img_url=request.FILES.get('img'))\n img.save()\n originPath = \"/home/ai_primary_school/mysite/media/img\"\n desPath = \"/home/ai_primary_school/mysite/temp\"\n for root, dirs, files in os.walk(originPath, topdown=False):\n for name in files: \n temp = cv2.imread(os.path.join(root, name)) \n cv2.imwrite(os.path.join(desPath, \"test.jpg\"), temp)\n return render(request, 'imgupload.html')\n\ndef showImg(request):\n imgDB = Img.objects.raw('SELECT * FROM trips_img ORDER BY 1 DESC LIMIT 1')\n imgs = cv2.imread('/home/ai_primary_school/mysite/temp/test.jpg')\n gray = cv2.cvtColor(imgs, cv2.COLOR_BGR2GRAY)\n result=0\n faces = faceCascade.detectMultiScale(\n gray,\n scaleFactor=1.1,\n minNeighbors=5,\n minSize=(30, 30),\n flags = cv2.CASCADE_SCALE_IMAGE)\n for (x, y, w, h) in faces:\n #框出臉\n image=cv2.rectangle(imgs, (x, y), (x+w, y+h), (0, 255, 0), 2)\n #BGR轉RGB\n image = image[:,:,::-1]\n #將臉譜丟到丟到我們訓練的人臉分類神經網路\n result=model.predict(image)\n \n context = {\n 'imgs' : imgDB,\n 'current_time': result,\n }\n return render(request, 'showImg.html', context)\n\n\n\n\n\n\n\n\n\n", "sub_path": "trips/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2268, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cv2.CascadeClassifier", "line_number": 18, "usage_type": "call"}, {"api_name": "train.Model", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 23, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 27, "usage_type": "name"}, {"api_name": "models.Post.objects.all", "line_number": 31, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 31, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Post.objects.get", "line_number": 37, "usage_type": "call"}, {"api_name": "models.Post.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 37, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 38, "usage_type": "call"}, {"api_name": "models.Img", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.walk", "line_number": 47, "usage_type": "call"}, {"api_name": "os.path", "line_number": 47, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 49, "usage_type": "name"}, {"api_name": "cv2.imwrite", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 50, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 51, "usage_type": "call"}, {"api_name": "models.Img.objects.raw", "line_number": 54, "usage_type": "call"}, {"api_name": "models.Img.objects", "line_number": 54, "usage_type": "attribute"}, {"api_name": "models.Img", "line_number": 54, "usage_type": "name"}, {"api_name": "cv2.imread", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 56, "usage_type": "attribute"}, {"api_name": "cv2.CASCADE_SCALE_IMAGE", "line_number": 63, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 66, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "356031464", "text": "#!/usr/bin/env python\n\n\"\"\"\n@package ion.services.sa.process.test.test_int_data_process_management_service\n@author Maurice Manning\n\"\"\"\nfrom uuid import uuid4\n\nfrom pyon.util.log import log\nfrom pyon.util.ion_time import IonTime\nfrom pyon.public import RT, PRED, OT, LCS\nfrom pyon.core.bootstrap import IonObject\nfrom pyon.core.exception import BadRequest, NotFound\nfrom pyon.util.containers import create_unique_identifier\nfrom pyon.util.containers import DotDict\nfrom pyon.util.arg_check import validate_is_not_none, validate_true\nfrom pyon.ion.resource import ExtendedResourceContainer\nfrom interface.objects import ProcessDefinition\n\nfrom interface.services.sa.idata_process_management_service import BaseDataProcessManagementService\nfrom interface.services.sa.idata_product_management_service import DataProductManagementServiceClient\n\nfrom ion.services.sa.instrument.data_process_impl import DataProcessImpl\n\nfrom ion.util.module_uploader import RegisterModulePreparerPy\nimport os\nimport pwd\n\nclass DataProcessManagementService(BaseDataProcessManagementService):\n\n def on_init(self):\n IonObject(\"Resource\") # suppress pyflakes error\n\n self.override_clients(self.clients)\n\n self.init_module_uploader()\n\n self.get_unique_id = (lambda : uuid4().hex)\n\n def init_module_uploader(self):\n if self.CFG:\n #looking for forms like host=amoeba.ucsd.edu, remotepath=/var/www/release, user=steve\n cfg_host = self.CFG.get_safe(\"service.data_process_management.process_release_host\", None)\n cfg_remotepath = self.CFG.get_safe(\"service.data_process_management.process_release_directory\", None)\n cfg_user = self.CFG.get_safe(\"service.data_process_management.process_release_user\",\n pwd.getpwuid(os.getuid())[0])\n cfg_wwwprefix = self.CFG.get_safe(\"service.data_process_management.process_release_wwwprefix\", None)\n\n if cfg_host is None or cfg_remotepath is None or cfg_wwwprefix is None:\n raise BadRequest(\"Missing configuration items; host='%s', directory='%s', wwwprefix='%s'\" %\n (cfg_host, cfg_remotepath, cfg_wwwprefix))\n\n self.module_uploader = RegisterModulePreparerPy(dest_user=cfg_user,\n dest_host=cfg_host,\n dest_path=cfg_remotepath,\n dest_wwwprefix=cfg_wwwprefix)\n\n\n def override_clients(self, new_clients):\n \"\"\"\n Replaces the service clients with a new set of them... and makes sure they go to the right places\n \"\"\"\n\n #shortcut names for the import sub-services\n if hasattr(self.clients, \"resource_registry\"):\n self.RR = self.clients.resource_registry\n\n #farm everything out to the impls\n\n self.data_process = DataProcessImpl(self.clients)\n\n\n #todo: need to know what object will be worked with here\n def register_data_process_definition(self, process_code=''):\n \"\"\"\n register a process module by putting it in a web-accessible location\n\n @process_code a base64-encoded python file\n \"\"\"\n\n# # retrieve the resource\n# data_process_definition_obj = self.clients.resource_registry.read(data_process_definition_id)\n\n dest_filename = \"process_code_%s.py\" % self.get_unique_id() #data_process_definition_obj._id\n\n #process the input file (base64-encoded .py)\n uploader_obj, err = self.module_uploader.prepare(process_code, dest_filename)\n if None is uploader_obj:\n raise BadRequest(\"Process code failed validation: %s\" % err)\n\n # actually upload\n up_success, err = uploader_obj.upload()\n if not up_success:\n raise BadRequest(\"Upload failed: %s\" % err)\n\n# #todo: save module / class?\n# data_process_definition_obj.uri = uploader_obj.get_destination_url()\n# self.clients.resource_registry.update(data_process_definition_obj)\n\n return uploader_obj.get_destination_url()\n\n def create_data_process_definition(self, data_process_definition=None):\n\n result, _ = self.clients.resource_registry.find_resources(RT.DataProcessDefinition, None, data_process_definition.name, True)\n\n validate_true( len(result) ==0, \"A data process definition named '%s' already exists\" % data_process_definition.name)\n\n #todo: determine validation checks for a data process def\n data_process_definition_id, version = self.clients.resource_registry.create(data_process_definition)\n\n #-------------------------------\n # Process Definition\n #-------------------------------\n # Create the underlying process definition\n process_definition = ProcessDefinition()\n process_definition.name = data_process_definition.name\n process_definition.description = data_process_definition.description\n\n process_definition.executable = {'module':data_process_definition.module, 'class':data_process_definition.class_name}\n process_definition_id = self.clients.process_dispatcher.create_process_definition(process_definition=process_definition)\n\n self.clients.resource_registry.create_association(data_process_definition_id, PRED.hasProcessDefinition, process_definition_id)\n\n return data_process_definition_id\n\n def update_data_process_definition(self, data_process_definition=None):\n # TODO: If executable has changed, update underlying ProcessDefinition\n\n # Overwrite DataProcessDefinition object\n self.clients.resource_registry.update(data_process_definition)\n\n def read_data_process_definition(self, data_process_definition_id=''):\n data_proc_def_obj = self.clients.resource_registry.read(data_process_definition_id)\n return data_proc_def_obj\n\n def delete_data_process_definition(self, data_process_definition_id=''):\n\n self.clients.resource_registry.retire(data_process_definition_id)\n\n def force_delete_data_process_definition(self, data_process_definition_id=''):\n\n processdef_ids, _ = self.clients.resource_registry.find_objects(subject=data_process_definition_id, predicate=PRED.hasProcessDefinition, object_type=RT.ProcessDefinition, id_only=True)\n self._remove_associations(data_process_definition_id)\n self.clients.resource_registry.delete(data_process_definition_id)\n for processdef_id in processdef_ids:\n self.clients.process_dispatcher.delete_process_definition(processdef_id)\n\n\n def find_data_process_definitions(self, filters=None):\n \"\"\"\n @param filters: dict of parameters to filter down\n the list of possible data proc.\n @retval\n \"\"\"\n #todo: add filtering\n data_process_def_list , _ = self.clients.resource_registry.find_resources(RT.DataProcessDefinition, None, None, True)\n return data_process_def_list\n\n def assign_input_stream_definition_to_data_process_definition(self, stream_definition_id='', data_process_definition_id=''):\n \"\"\"Connect the input stream with a data process definition\n \"\"\"\n # Verify that both ids are valid, RR will throw if not found\n stream_definition_obj = self.clients.resource_registry.read(stream_definition_id)\n data_process_definition_obj = self.clients.resource_registry.read(data_process_definition_id)\n\n validate_is_not_none(stream_definition_obj, \"No stream definition object found for stream definition id: %s\" % stream_definition_id)\n validate_is_not_none(data_process_definition_obj, \"No data process definition object found for data process\" \\\n \" definition id: %s\" % data_process_definition_id)\n\n self.clients.resource_registry.create_association(data_process_definition_id, PRED.hasInputStreamDefinition, stream_definition_id)\n\n def unassign_input_stream_definition_from_data_process_definition(self, stream_definition_id='', data_process_definition_id=''):\n \"\"\"\n Disconnect the Data Product from the Data Producer\n\n @param stream_definition_id str\n @param data_process_definition_id str\n @throws NotFound object with specified id does not exist\n \"\"\"\n\n # Remove the link between the Stream Definition resource and the Data Process Definition resource\n associations = self.clients.resource_registry.find_associations(data_process_definition_id, PRED.hasInputStreamDefinition, stream_definition_id, id_only=True)\n validate_is_not_none(associations, \"No Input Stream Definitions associated with data process definition ID \" + str(data_process_definition_id))\n\n for association in associations:\n self.clients.resource_registry.delete_association(association)\n\n def assign_stream_definition_to_data_process_definition(self, stream_definition_id='', data_process_definition_id='', binding=''):\n \"\"\"Connect the output stream with a data process definition\n \"\"\"\n # Verify that both ids are valid, RR will throw if not found\n stream_definition_obj = self.clients.resource_registry.read(stream_definition_id)\n data_process_definition_obj = self.clients.resource_registry.read(data_process_definition_id)\n\n validate_is_not_none(stream_definition_obj, \"No stream definition object found for stream definition id: %s\" % stream_definition_id)\n validate_is_not_none(data_process_definition_obj, \"No data process definition object found for data process\"\\\n \" definition id: %s\" % data_process_definition_id)\n\n self.clients.resource_registry.create_association(data_process_definition_id, PRED.hasStreamDefinition, stream_definition_id)\n data_process_definition_obj.output_bindings[binding] = stream_definition_id\n self.clients.resource_registry.update(data_process_definition_obj)\n\n def unassign_stream_definition_from_data_process_definition(self, stream_definition_id='', data_process_definition_id=''):\n \"\"\"\n Disconnect the Data Product from the Data Producer\n\n @param stream_definition_id str\n @param data_process_definition_id str\n @throws NotFound object with specified id does not exist\n \"\"\"\n\n # Remove the link between the Stream Definition resource and the Data Process Definition resource\n associations = self.clients.resource_registry.find_associations(data_process_definition_id, PRED.hasStreamDefinition, stream_definition_id, id_only=True)\n\n validate_is_not_none(associations, \"No Stream Definitions associated with data process definition ID \" + str(data_process_definition_id))\n for association in associations:\n self.clients.resource_registry.delete_association(association)\n\n\n # ------------------------------------------------------------------------------------------------\n # Working with DataProcess\n\n def create_data_process(self, data_process_definition_id='', in_data_product_ids=None, out_data_products=None, configuration=None):\n \"\"\"\n @param data_process_definition_id: Object with definition of the\n process to apply to the input data product\n @param in_data_product_ids: ID of the input data products\n @param out_data_products: list of IDs of the output data products\n @retval data_process_id: ID of the newly created data process object\n \"\"\"\n\n inform = \"Input Data Product: \"+str(in_data_product_ids)+\\\n \"\\nTransformed by: \"+str(data_process_definition_id)+\\\n \"\\nTo create output Product: \"+str(out_data_products) + \"\\n\"\n log.debug(\"DataProcessManagementService:create_data_process() method called with parameters:\\n\" +\n inform)\n\n #---------------------------------------------------------------------------------------\n # Initialize\n #---------------------------------------------------------------------------------------\n\n configuration = configuration or DotDict()\n\n validate_is_not_none( out_data_products, \"No output data products passed in\")\n\n #---------------------------------------------------------------------------------------\n # Read the data process definition\n #---------------------------------------------------------------------------------------\n data_process_definition = self.read_data_process_definition(data_process_definition_id)\n\n #---------------------------------------------------------------------------------------\n # Read the output bindings from the definition\n #---------------------------------------------------------------------------------------\n\n output_bindings = data_process_definition.output_bindings\n\n #---------------------------------------------------------------------------------------\n # Find the process definition associated with this data process definition.\n # From the process definition, we can get the module and class to run....\n #---------------------------------------------------------------------------------------\n\n procdef_ids,_ = self.clients.resource_registry.find_objects(data_process_definition_id, PRED.hasProcessDefinition, RT.ProcessDefinition, id_only=True)\n if not procdef_ids:\n raise BadRequest(\"Cannot find associated ProcessDefinition for DataProcessDefinition id=%s\" % data_process_definition_id)\n\n process_definition_id = procdef_ids[0]\n\n #---------------------------------------------------------------------------------------\n # Create a data process object and register it\n #---------------------------------------------------------------------------------------\n\n # get the name of the data process and create an IONObject for it\n data_process_name = create_unique_identifier(\"process_\" + data_process_definition.name)\n data_process_obj = IonObject(RT.DataProcess, name=data_process_name)\n\n # register the data process\n data_process_id, version = self.clients.resource_registry.create(data_process_obj)\n\n data_process_obj = self.clients.resource_registry.read(data_process_id)\n\n #---------------------------------------------------------------------------------------\n # Make the necessary associations, registering\n #---------------------------------------------------------------------------------------\n\n #todo check if this assoc is needed?\n # Associate the data process with the data process definition\n self.clients.resource_registry.create_association(data_process_id, PRED.hasProcessDefinition, data_process_definition_id)\n\n # Register the data process instance as a data producer with DataAcquisitionMgmtSvc\n data_producer_id = self.clients.data_acquisition_management.register_process(data_process_id)\n log.debug(\"DataProcessManagementService:create_data_process register process \"\n \"with DataAcquisitionMgmtSvc: data_producer_id: %s (L4-CI-SA-RQ-181)\", str(data_producer_id) )\n\n #---------------------------------------------------------------------------------------\n # Register each output data product with DAMS to create DataProducer links\n #---------------------------------------------------------------------------------------\n output_stream_dict = {}\n\n if out_data_products is None:\n raise BadRequest(\"Data Process must have output product(s) specified %s\", str(data_process_definition_id) )\n\n for binding, output_data_product_id in out_data_products.iteritems():\n\n # check that the product is not already associated with a producer\n producer_ids, _ = self.clients.resource_registry.find_objects(output_data_product_id, PRED.hasDataProducer, RT.DataProducer, True)\n if producer_ids:\n raise BadRequest(\"Data Product should not already be associated to a DataProducer %s hasDataProducer %s\", str(data_process_id), str(producer_ids[0]))\n\n #Assign each output Data Product to this producer resource\n output_data_product_obj = self.clients.resource_registry.read(output_data_product_id)\n if not output_data_product_obj:\n raise NotFound(\"Output Data Product %s does not exist\" % output_data_product_id)\n\n # Associate with DataProcess: register as an output product for this process\n log.debug(\"Link data process %s and output out data product: %s (L4-CI-SA-RQ-260)\", str(data_process_id), str(output_data_product_id))\n self.clients.data_acquisition_management.assign_data_product(input_resource_id= data_process_id,data_product_id= output_data_product_id)\n\n # Retrieve the id of the OUTPUT stream from the out Data Product\n stream_ids, _ = self.clients.resource_registry.find_objects(output_data_product_id, PRED.hasStream, RT.Stream, True)\n\n if not stream_ids:\n raise NotFound(\"No Stream created for output Data Product \" + str(output_data_product_id))\n\n if len(stream_ids) != 1:\n raise BadRequest(\"Data Product should only have ONE stream at this time\" + str(output_data_product_id))\n\n output_stream_dict[binding] = stream_ids[0]\n\n #------------------------------------------------------------------------------------------------------------------------------------------\n #Check for attached objects and put them into the configuration\n #------------------------------------------------------------------------------------------------------------------------------------------\n\n # check for attachments in data process definition\n configuration = self._find_lookup_tables(data_process_definition_id, configuration)\n input_stream_ids = []\n\n if in_data_product_ids:\n for in_data_product_id in in_data_product_ids:\n\n self.clients.resource_registry.create_association(data_process_id, PRED.hasInputProduct, in_data_product_id)\n log.debug(\"Associate data process workflows with source data products %s \"\n \"hasInputProducts %s (L4-CI-SA-RQ-260)\", str(data_process_id), str(in_data_product_ids))\n\n #check if in data product is attached to an instrument, check instrumentDevice and InstrumentModel for lookup table attachments\n instdevice_ids, _ = self.clients.resource_registry.find_subjects(RT.InstrumentDevice, PRED.hasOutputProduct, in_data_product_id, True)\n\n for instdevice_id in instdevice_ids:\n log.debug(\"Instrument device_id assoc to the input data product of this data process: %s (L4-CI-SA-RQ-231)\", str(instdevice_id))\n\n # check for attachments in instrument device\n configuration = self._find_lookup_tables(instdevice_id, configuration)\n instmodel_ids, _ = self.clients.resource_registry.find_objects(instdevice_id, PRED.hasModel, RT.InstrumentModel, True)\n\n for instmodel_id in instmodel_ids:\n # check for attachments in instrument model\n configuration = self._find_lookup_tables(instmodel_id, configuration)\n\n #------------------------------------------------------------------------------------------------------------------------------------------\n # Get the input stream from the input_data_product, which should already be associated with a stream via the Data Producer\n #------------------------------------------------------------------------------------------------------------------------------------------\n input_stream_ids = self._get_input_stream_ids(in_data_product_ids)\n\n #------------------------------------------------------------------------------------------------------------------------------------------\n # Create subscription to the input stream\n #------------------------------------------------------------------------------------------------------------------------------------------\n input_subscription_id = self.clients.pubsub_management.create_subscription(name=data_process_name, stream_ids=input_stream_ids)\n\n #------------------------------------------------------------------------------------------------------------------------------------------\n # Add the subscription id to the data process\n #------------------------------------------------------------------------------------------------------------------------------------------\n data_process_obj.input_subscription_id = input_subscription_id\n\n log.info(\"Launching the process\")\n debug_str = \"\\n\\tQueue Name: %s\\n\\tOutput Streams: %s\\n\\tProcess Definition ID: %s\\n\\tConfiguration: %s\" % (data_process_name, output_stream_dict, process_definition_id, configuration)\n log.debug(debug_str)\n\n pid = self._launch_process(\n queue_name=data_process_name,\n out_streams=output_stream_dict,\n process_definition_id=process_definition_id,\n configuration=configuration)\n\n data_process_obj.process_id = pid\n self.clients.resource_registry.update(data_process_obj)\n return data_process_id\n\n def _get_input_stream_ids(self, in_data_product_ids = None):\n\n input_stream_ids = []\n\n #------------------------------------------------------------------------------------------------------------------------------------------\n # get the streams associated with this IN data products\n #------------------------------------------------------------------------------------------------------------------------------------------\n for in_data_product_id in in_data_product_ids:\n\n # Get the stream associated with this input data product\n stream_ids, _ = self.clients.resource_registry.find_objects(in_data_product_id, PRED.hasStream, RT.Stream, True)\n\n validate_is_not_none( stream_ids, \"No Stream created for this input Data Product \" + str(in_data_product_id))\n validate_is_not_none( len(stream_ids) != 1, \"Input Data Product should only have ONE stream\" + str(in_data_product_id))\n\n # We take for now one stream_id associated with each input data product\n input_stream_ids.append(stream_ids[0])\n\n return input_stream_ids\n\n def _launch_process(self, queue_name='', out_streams=None, process_definition_id='', configuration=None):\n \"\"\"\n Launches the process\n \"\"\"\n\n # ------------------------------------------------------------------------------------\n # Spawn Configuration and Parameters\n # ------------------------------------------------------------------------------------\n\n configuration['process'] = {\n 'queue_name':queue_name,\n 'publish_streams' : out_streams\n }\n\n # ------------------------------------------------------------------------------------\n # Process Spawning\n # ------------------------------------------------------------------------------------\n # Spawn the process\n pid = self.clients.process_dispatcher.schedule_process(\n process_definition_id=process_definition_id,\n configuration=configuration\n )\n validate_is_not_none( pid, \"Process could not be spawned\")\n\n return pid\n\n\n def _find_lookup_tables(self, resource_id=\"\", configuration=None):\n #check if resource has lookup tables attached\n\n configuration = configuration or DotDict()\n\n attachment_objs, _ = self.clients.resource_registry.find_objects(resource_id, PRED.hasAttachment, RT.Attachment, False)\n\n for attachment_obj in attachment_objs:\n\n words = set(attachment_obj.keywords)\n\n if 'DataProcessInput' in words:\n configuration[attachment_obj.name] = attachment_obj.content\n log.debug(\"Lookup table, %s, found in attachment %s\" % (attachment_obj.content, attachment_obj.name))\n else:\n log.debug(\"NO lookup table in attachment %s\" % attachment_obj.name)\n\n return configuration\n\n def update_data_process_inputs(self, data_process_id=\"\", in_stream_ids=None):\n #@TODO: INPUT STREAM VALIDATION\n log.debug(\"Updating inputs to data process '%s'\", data_process_id)\n data_process_obj = self.clients.resource_registry.read(data_process_id)\n subscription_id = data_process_obj.input_subscription_id\n was_active = False \n if subscription_id:\n # get rid of all the current streams\n try:\n log.debug(\"Deactivating subscription '%s'\", subscription_id)\n self.clients.pubsub_management.deactivate_subscription(subscription_id)\n was_active = True\n\n except BadRequest:\n log.info('Subscription was not active')\n\n self.clients.pubsub_management.delete_subscription(subscription_id)\n\n new_subscription_id = self.clients.pubsub_management.create_subscription(data_process_obj.name,\n stream_ids=in_stream_ids)\n data_process_obj.input_subscription_id = new_subscription_id\n\n self.clients.resource_registry.update(data_process_obj)\n\n if was_active:\n log.debug(\"Activating subscription '%s'\", new_subscription_id)\n self.clients.pubsub_management.activate_subscription(new_subscription_id)\n\n \n\n def update_data_process(self,):\n #todo: What are valid ways to update a data process?.\n\n return\n\n def read_data_process(self, data_process_id=\"\"):\n\n data_proc_obj = self.clients.resource_registry.read(data_process_id)\n return data_proc_obj\n\n\n def delete_data_process(self, data_process_id=\"\"):\n\n # Delete the specified DataProcessDefinition object\n data_process_obj = self.read_data_process(data_process_id)\n\n log.debug(\"delete the association with DataProcessDefinition\")\n dpd_assn_ids = self.clients.resource_registry.find_associations(subject=data_process_id, predicate=PRED.hasProcessDefinition, id_only=True)\n for dpd_assn_id in dpd_assn_ids:\n self.clients.resource_registry.delete_association(dpd_assn_id)\n\n self._stop_process(data_process_obj)\n\n\n log.debug(\"Finalizing data products by removing streams associated with the dataset and product\")\n out_products, assocs = self.clients.resource_registry.find_objects(subject=data_process_id, predicate=PRED.hasOutputProduct, id_only=True)\n for out_product, assoc in zip(out_products, assocs):\n data_product_management = DataProductManagementServiceClient()\n data_product_management.remove_streams(out_product)\n log.debug(\"deleting association with output data product '%s'\" % out_product)\n self.clients.resource_registry.delete_association(assoc)\n\n self.clients.data_acquisition_management.unassign_data_product(data_process_id, out_product)\n\n\n log.debug(\"Delete the input product links\")\n inprod_associations = self.clients.resource_registry.find_associations(data_process_id, PRED.hasInputProduct)\n for inprod_association in inprod_associations:\n self.clients.resource_registry.delete_association(inprod_association)\n\n\n try:\n self.deactivate_data_process(data_process_id=data_process_id)\n log.warn('Deleteing activated data process...')\n except BadRequest:\n pass\n\n subscription_id = data_process_obj.input_subscription_id\n self.clients.pubsub_management.delete_subscription(subscription_id)\n data_process_obj.input_subscription_id = None\n\n #unregister the data process in DataAcquisitionMgmtSvc\n self.clients.data_acquisition_management.unregister_process(data_process_id)\n\n # Delete the data process\n self.clients.resource_registry.retire(data_process_id)\n return\n\n def force_delete_data_process(self, data_process_id=\"\"):\n\n # if not yet deleted, the first execute delete logic\n dp_obj = self.read_data_process(data_process_id)\n if dp_obj.lcstate != LCS.RETIRED:\n self.delete_data_process(data_process_id)\n\n self._remove_associations(data_process_id)\n self.clients.resource_registry.delete(data_process_id)\n\n def _stop_process(self, data_process):\n log.debug(\"stopping data process '%s'\" % data_process.process_id)\n pid = data_process.process_id\n self.clients.process_dispatcher.cancel_process(pid)\n\n\n def find_data_process(self, filters=None):\n \"\"\"\n @param filters: dict of parameters to filter down\n the list of possible data proc.\n @retval\n \"\"\"\n #todo: add filter processing\n data_process_list , _ = self.clients.resource_registry.find_resources(RT.DataProcess, None, None, True)\n return data_process_list\n\n def activate_data_process(self, data_process_id=\"\"):\n\n data_process_obj = self.read_data_process(data_process_id)\n log.debug(\"activate_data_process:data_process_obj %s \", str(data_process_obj))\n\n\n# #update the producer context with the activation time and the configuration\n\n\n # todo: update the setting of this context with the return vals from process_dispatcher:schedule_process after convert\n # todo: process_id, process_definition, schedule, configuration\n\n producer_obj = self._get_process_producer(data_process_id)\n producertype = type(producer_obj).__name__\n #todo: producer_obj.producer_context.type_ is returning the base type, not the derived type.\n if producer_obj.producer_context.type_ == OT.DataProcessProducerContext :\n log.debug(\"activate_data_process:activation_time %s \", str(IonTime().to_string()))\n producer_obj.producer_context.activation_time = IonTime().to_string()\n producer_obj.producer_context.configuration = data_process_obj.configuration\n self.clients.resource_registry.update(producer_obj)\n\n subscription_id = data_process_obj.input_subscription_id\n self.clients.pubsub_management.activate_subscription(subscription_id=subscription_id)\n\n def deactivate_data_process(self, data_process_id=\"\"):\n\n data_process_obj = self.read_data_process(data_process_id)\n\n if not data_process_obj.input_subscription_id:\n log.warn(\"data process '%s' has no subscription id to deactivate\", data_process_id)\n return\n\n subscription_obj = self.clients.pubsub_management.read_subscription(data_process_obj.input_subscription_id)\n\n if subscription_obj.activated:\n\n #update the producer context with the deactivation time\n # todo: update the setting of this contect with the return vals from process_dispatcher:schedule_process after convert\n producer_obj = self._get_process_producer(data_process_id)\n producertype = type(producer_obj).__name__\n if producer_obj.producer_context.type_ == OT.DataProcessProducerContext :\n log.debug(\"data_process '%s' (producer '%s'): deactivation_time = %s \",\n data_process_id, producer_obj._id, str(IonTime().to_string()))\n producer_obj.producer_context.deactivation_time = IonTime().to_string()\n self.clients.resource_registry.update(producer_obj)\n\n subscription_id = data_process_obj.input_subscription_id\n log.debug(\"Deactivating subscription '%s'\", subscription_id)\n self.clients.pubsub_management.deactivate_subscription(subscription_id=subscription_id)\n\n\n\n def attach_process(self, process=''):\n \"\"\"\n @param process: Should this be the data_process_id?\n @retval\n \"\"\"\n # TODO: Determine the proper input param\n pass\n\n def _get_process_producer(self, data_process_id=\"\"):\n producer_objs, _ = self.clients.resource_registry.find_objects(subject=data_process_id, predicate=PRED.hasDataProducer, object_type=RT.DataProducer, id_only=False)\n if not producer_objs:\n raise NotFound(\"No Producers created for this Data Process \" + str(data_process_id))\n return producer_objs[0]\n\n\n ############################\n #\n # EXTENDED RESOURCES\n #\n ############################\n\n\n\n def get_data_process_definition_extension(self, data_process_definition_id='', ext_associations=None, ext_exclude=None):\n #Returns an DataProcessDefinition Extension object containing additional related information\n\n if not data_process_definition_id:\n raise BadRequest(\"The data_process_definition_id parameter is empty\")\n\n extended_resource_handler = ExtendedResourceContainer(self)\n\n extended_data_process_definition = extended_resource_handler.create_extended_resource_container(\n extended_resource_type=OT.DataProcessDefinitionExtension,\n resource_id=data_process_definition_id,\n computed_resource_type=OT.DataProcessDefinitionComputedAttributes,\n ext_associations=ext_associations,\n ext_exclude=ext_exclude)\n\n #Loop through any attachments and remove the actual content since we don't need\n # to send it to the front end this way\n #TODO - see if there is a better way to do this in the extended resource frame work.\n if hasattr(extended_data_process_definition, 'attachments'):\n for att in extended_data_process_definition.attachments:\n if hasattr(att, 'content'):\n delattr(att, 'content')\n\n return extended_data_process_definition\n\n def get_data_process_extension(self, data_process_id='', ext_associations=None, ext_exclude=None):\n #Returns an DataProcessDefinition Extension object containing additional related information\n\n if not data_process_id:\n raise BadRequest(\"The data_process_definition_id parameter is empty\")\n\n extended_resource_handler = ExtendedResourceContainer(self)\n\n extended_data_process = extended_resource_handler.create_extended_resource_container(\n extended_resource_type=OT.DataProcessExtension,\n resource_id=data_process_id,\n computed_resource_type=OT.DataProcessComputedAttributes,\n ext_associations=ext_associations,\n ext_exclude=ext_exclude)\n\n #Loop through any attachments and remove the actual content since we don't need\n # to send it to the front end this way\n #TODO - see if there is a better way to do this in the extended resource frame work.\n if hasattr(extended_data_process, 'attachments'):\n for att in extended_data_process.attachments:\n if hasattr(att, 'content'):\n delattr(att, 'content')\n\n return extended_data_process\n\n\n def _remove_associations(self, resource_id=''):\n \"\"\"\n delete all associations to/from a resource\n \"\"\"\n\n # find all associations where this is the subject\n _, obj_assns = self.clients.resource_registry.find_objects(subject=resource_id, id_only=True)\n\n # find all associations where this is the object\n _, sbj_assns = self.clients.resource_registry.find_subjects(object=resource_id, id_only=True)\n\n log.debug(\"pluck will remove %s subject associations and %s object associations\",\n len(sbj_assns), len(obj_assns))\n\n for assn in obj_assns:\n log.debug(\"pluck deleting object association %s\", assn)\n self.clients.resource_registry.delete_association(assn)\n\n for assn in sbj_assns:\n log.debug(\"pluck deleting subject association %s\", assn)\n self.clients.resource_registry.delete_association(assn)\n\n # find all associations where this is the subject\n _, obj_assns = self.clients.resource_registry.find_objects(subject=resource_id, id_only=True)\n\n # find all associations where this is the object\n _, sbj_assns = self.clients.resource_registry.find_subjects(object=resource_id, id_only=True)\n\n log.debug(\"post-deletions, pluck found %s subject associations and %s object associations\",\n len(sbj_assns), len(obj_assns))\n", "sub_path": "ion/services/sa/process/data_process_management_service.py", "file_name": "data_process_management_service.py", "file_ext": "py", "file_size_in_byte": 36908, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "interface.services.sa.idata_process_management_service.BaseDataProcessManagementService", "line_number": 29, "usage_type": "name"}, {"api_name": "pyon.core.bootstrap.IonObject", "line_number": 32, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 38, "usage_type": "call"}, {"api_name": "pwd.getpwuid", "line_number": 46, "usage_type": "call"}, {"api_name": "os.getuid", "line_number": 46, "usage_type": "call"}, {"api_name": "pyon.core.exception.BadRequest", "line_number": 50, "usage_type": "call"}, {"api_name": "ion.util.module_uploader.RegisterModulePreparerPy", "line_number": 53, "usage_type": "call"}, {"api_name": "ion.services.sa.instrument.data_process_impl.DataProcessImpl", "line_number": 70, "usage_type": "call"}, {"api_name": "pyon.core.exception.BadRequest", "line_number": 89, "usage_type": "call"}, {"api_name": "pyon.core.exception.BadRequest", "line_number": 94, "usage_type": "call"}, {"api_name": "pyon.public.RT.DataProcessDefinition", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pyon.public.RT", "line_number": 104, "usage_type": "name"}, {"api_name": "pyon.util.arg_check.validate_true", "line_number": 106, "usage_type": "call"}, {"api_name": "interface.objects.ProcessDefinition", "line_number": 115, "usage_type": "call"}, {"api_name": "pyon.public.PRED.hasProcessDefinition", "line_number": 122, "usage_type": "attribute"}, {"api_name": "pyon.public.PRED", "line_number": 122, "usage_type": "name"}, {"api_name": "pyon.public.PRED.hasProcessDefinition", "line_number": 142, "usage_type": "attribute"}, {"api_name": "pyon.public.PRED", "line_number": 142, "usage_type": "name"}, {"api_name": "pyon.public.RT.ProcessDefinition", "line_number": 142, "usage_type": "attribute"}, {"api_name": "pyon.public.RT", "line_number": 142, "usage_type": "name"}, {"api_name": "pyon.public.RT.DataProcessDefinition", "line_number": 156, "usage_type": "attribute"}, {"api_name": "pyon.public.RT", "line_number": 156, "usage_type": "name"}, {"api_name": "pyon.util.arg_check.validate_is_not_none", "line_number": 166, "usage_type": "call"}, {"api_name": "pyon.util.arg_check.validate_is_not_none", "line_number": 167, "usage_type": "call"}, {"api_name": "pyon.public.PRED.hasInputStreamDefinition", "line_number": 170, "usage_type": "attribute"}, {"api_name": "pyon.public.PRED", "line_number": 170, "usage_type": "name"}, {"api_name": "pyon.public.PRED.hasInputStreamDefinition", "line_number": 182, "usage_type": "attribute"}, {"api_name": "pyon.public.PRED", "line_number": 182, "usage_type": "name"}, {"api_name": "pyon.util.arg_check.validate_is_not_none", "line_number": 183, "usage_type": "call"}, {"api_name": "pyon.util.arg_check.validate_is_not_none", "line_number": 195, "usage_type": "call"}, {"api_name": "pyon.util.arg_check.validate_is_not_none", "line_number": 196, "usage_type": "call"}, {"api_name": "pyon.public.PRED.hasStreamDefinition", "line_number": 199, "usage_type": "attribute"}, {"api_name": "pyon.public.PRED", "line_number": 199, "usage_type": "name"}, {"api_name": "pyon.public.PRED.hasStreamDefinition", "line_number": 213, "usage_type": "attribute"}, {"api_name": "pyon.public.PRED", "line_number": 213, "usage_type": "name"}, {"api_name": "pyon.util.arg_check.validate_is_not_none", "line_number": 215, "usage_type": "call"}, {"api_name": "pyon.util.log.log.debug", "line_number": 235, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 235, "usage_type": "name"}, {"api_name": "pyon.util.containers.DotDict", "line_number": 242, "usage_type": "call"}, {"api_name": "pyon.util.arg_check.validate_is_not_none", "line_number": 244, "usage_type": "call"}, {"api_name": "pyon.public.PRED.hasProcessDefinition", "line_number": 262, "usage_type": "attribute"}, {"api_name": "pyon.public.PRED", "line_number": 262, "usage_type": "name"}, {"api_name": "pyon.public.RT.ProcessDefinition", "line_number": 262, "usage_type": "attribute"}, {"api_name": "pyon.public.RT", "line_number": 262, "usage_type": "name"}, {"api_name": "pyon.core.exception.BadRequest", "line_number": 264, "usage_type": "call"}, {"api_name": "pyon.util.containers.create_unique_identifier", "line_number": 273, "usage_type": "call"}, {"api_name": "pyon.core.bootstrap.IonObject", "line_number": 274, "usage_type": "call"}, {"api_name": "pyon.public.RT.DataProcess", "line_number": 274, "usage_type": "attribute"}, {"api_name": "pyon.public.RT", "line_number": 274, "usage_type": "name"}, {"api_name": "pyon.public.PRED.hasProcessDefinition", "line_number": 287, "usage_type": "attribute"}, {"api_name": "pyon.public.PRED", "line_number": 287, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 291, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 291, "usage_type": "name"}, {"api_name": "pyon.core.exception.BadRequest", "line_number": 300, "usage_type": "call"}, {"api_name": "pyon.public.PRED.hasDataProducer", "line_number": 305, "usage_type": "attribute"}, {"api_name": "pyon.public.PRED", "line_number": 305, "usage_type": "name"}, {"api_name": "pyon.public.RT.DataProducer", "line_number": 305, "usage_type": "attribute"}, {"api_name": "pyon.public.RT", "line_number": 305, "usage_type": "name"}, {"api_name": "pyon.core.exception.BadRequest", "line_number": 307, "usage_type": "call"}, {"api_name": "pyon.core.exception.NotFound", "line_number": 312, "usage_type": "call"}, {"api_name": "pyon.util.log.log.debug", "line_number": 315, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 315, "usage_type": "name"}, {"api_name": "pyon.public.PRED.hasStream", "line_number": 319, "usage_type": "attribute"}, {"api_name": "pyon.public.PRED", "line_number": 319, "usage_type": "name"}, {"api_name": "pyon.public.RT.Stream", "line_number": 319, "usage_type": "attribute"}, {"api_name": "pyon.public.RT", "line_number": 319, "usage_type": "name"}, {"api_name": "pyon.core.exception.NotFound", "line_number": 322, "usage_type": "call"}, {"api_name": "pyon.core.exception.BadRequest", "line_number": 325, "usage_type": "call"}, {"api_name": "pyon.public.PRED.hasInputProduct", "line_number": 340, "usage_type": "attribute"}, {"api_name": "pyon.public.PRED", "line_number": 340, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 341, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 341, "usage_type": "name"}, {"api_name": "pyon.public.RT.InstrumentDevice", "line_number": 345, "usage_type": "attribute"}, {"api_name": "pyon.public.RT", "line_number": 345, "usage_type": "name"}, {"api_name": "pyon.public.PRED.hasOutputProduct", "line_number": 345, "usage_type": "attribute"}, {"api_name": "pyon.public.PRED", "line_number": 345, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 348, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 348, "usage_type": "name"}, {"api_name": "pyon.public.PRED.hasModel", "line_number": 352, "usage_type": "attribute"}, {"api_name": "pyon.public.PRED", "line_number": 352, "usage_type": "name"}, {"api_name": "pyon.public.RT.InstrumentModel", "line_number": 352, "usage_type": "attribute"}, {"api_name": "pyon.public.RT", "line_number": 352, "usage_type": "name"}, {"api_name": "pyon.util.log.log.info", "line_number": 373, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 373, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 375, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 375, "usage_type": "name"}, {"api_name": "pyon.public.PRED.hasStream", "line_number": 397, "usage_type": "attribute"}, {"api_name": "pyon.public.PRED", "line_number": 397, "usage_type": "name"}, {"api_name": "pyon.public.RT.Stream", "line_number": 397, "usage_type": "attribute"}, {"api_name": "pyon.public.RT", "line_number": 397, "usage_type": "name"}, {"api_name": "pyon.util.arg_check.validate_is_not_none", "line_number": 399, "usage_type": "call"}, {"api_name": "pyon.util.arg_check.validate_is_not_none", "line_number": 400, "usage_type": "call"}, {"api_name": "pyon.util.arg_check.validate_is_not_none", "line_number": 429, "usage_type": "call"}, {"api_name": "pyon.util.containers.DotDict", "line_number": 437, "usage_type": "call"}, {"api_name": "pyon.public.PRED.hasAttachment", "line_number": 439, "usage_type": "attribute"}, {"api_name": "pyon.public.PRED", "line_number": 439, "usage_type": "name"}, {"api_name": "pyon.public.RT.Attachment", "line_number": 439, "usage_type": "attribute"}, {"api_name": "pyon.public.RT", "line_number": 439, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 447, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 447, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 449, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 449, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 455, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 455, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 462, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 462, "usage_type": "name"}, {"api_name": "pyon.core.exception.BadRequest", "line_number": 466, "usage_type": "name"}, {"api_name": "pyon.util.log.log.info", "line_number": 467, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 467, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 478, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 478, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 499, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 499, "usage_type": "name"}, {"api_name": "pyon.public.PRED.hasProcessDefinition", "line_number": 500, "usage_type": "attribute"}, {"api_name": "pyon.public.PRED", "line_number": 500, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 507, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 507, "usage_type": "name"}, {"api_name": "pyon.public.PRED.hasOutputProduct", "line_number": 508, "usage_type": "attribute"}, {"api_name": "pyon.public.PRED", "line_number": 508, "usage_type": "name"}, {"api_name": "interface.services.sa.idata_product_management_service.DataProductManagementServiceClient", "line_number": 510, "usage_type": "call"}, {"api_name": "pyon.util.log.log.debug", "line_number": 512, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 512, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 518, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 518, "usage_type": "name"}, {"api_name": "pyon.public.PRED.hasInputProduct", "line_number": 519, "usage_type": "attribute"}, {"api_name": "pyon.public.PRED", "line_number": 519, "usage_type": "name"}, {"api_name": "pyon.util.log.log.warn", "line_number": 526, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 526, "usage_type": "name"}, {"api_name": "pyon.core.exception.BadRequest", "line_number": 527, "usage_type": "name"}, {"api_name": "pyon.public.LCS.RETIRED", "line_number": 545, "usage_type": "attribute"}, {"api_name": "pyon.public.LCS", "line_number": 545, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 552, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 552, "usage_type": "name"}, {"api_name": "pyon.public.RT.DataProcess", "line_number": 564, "usage_type": "attribute"}, {"api_name": "pyon.public.RT", "line_number": 564, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 570, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 570, "usage_type": "name"}, {"api_name": "pyon.public.OT.DataProcessProducerContext", "line_number": 582, "usage_type": "attribute"}, {"api_name": "pyon.public.OT", "line_number": 582, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 583, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 583, "usage_type": "name"}, {"api_name": "pyon.util.ion_time.IonTime", "line_number": 583, "usage_type": "call"}, {"api_name": "pyon.util.ion_time.IonTime", "line_number": 584, "usage_type": "call"}, {"api_name": "pyon.util.log.log.warn", "line_number": 596, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 596, "usage_type": "name"}, {"api_name": "pyon.public.OT.DataProcessProducerContext", "line_number": 607, "usage_type": "attribute"}, {"api_name": "pyon.public.OT", "line_number": 607, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 608, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 608, "usage_type": "name"}, {"api_name": "pyon.util.ion_time.IonTime", "line_number": 609, "usage_type": "call"}, {"api_name": "pyon.util.ion_time.IonTime", "line_number": 610, "usage_type": "call"}, {"api_name": "pyon.util.log.log.debug", "line_number": 614, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 614, "usage_type": "name"}, {"api_name": "pyon.public.PRED.hasDataProducer", "line_number": 628, "usage_type": "attribute"}, {"api_name": "pyon.public.PRED", "line_number": 628, "usage_type": "name"}, {"api_name": "pyon.public.RT.DataProducer", "line_number": 628, "usage_type": "attribute"}, {"api_name": "pyon.public.RT", "line_number": 628, "usage_type": "name"}, {"api_name": "pyon.core.exception.NotFound", "line_number": 630, "usage_type": "call"}, {"api_name": "pyon.core.exception.BadRequest", "line_number": 646, "usage_type": "call"}, {"api_name": "pyon.ion.resource.ExtendedResourceContainer", "line_number": 648, "usage_type": "call"}, {"api_name": "pyon.public.OT.DataProcessDefinitionExtension", "line_number": 651, "usage_type": "attribute"}, {"api_name": "pyon.public.OT", "line_number": 651, "usage_type": "name"}, {"api_name": "pyon.public.OT.DataProcessDefinitionComputedAttributes", "line_number": 653, "usage_type": "attribute"}, {"api_name": "pyon.public.OT", "line_number": 653, "usage_type": "name"}, {"api_name": "pyon.core.exception.BadRequest", "line_number": 671, "usage_type": "call"}, {"api_name": "pyon.ion.resource.ExtendedResourceContainer", "line_number": 673, "usage_type": "call"}, {"api_name": "pyon.public.OT.DataProcessExtension", "line_number": 676, "usage_type": "attribute"}, {"api_name": "pyon.public.OT", "line_number": 676, "usage_type": "name"}, {"api_name": "pyon.public.OT.DataProcessComputedAttributes", "line_number": 678, "usage_type": "attribute"}, {"api_name": "pyon.public.OT", "line_number": 678, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 704, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 704, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 708, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 708, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 712, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 712, "usage_type": "name"}, {"api_name": "pyon.util.log.log.debug", "line_number": 721, "usage_type": "call"}, {"api_name": "pyon.util.log.log", "line_number": 721, "usage_type": "name"}]} +{"seq_id": "556216428", "text": "import speech_recognition as sr\r\nimport pyttsx3 \r\nr=sr.Recognizer()\r\nwith sr.Microphone() as source:\r\n print(\"Hey There!\")\r\n print(\"Welcome to First Aid Bot\")\r\n print(\"Tell Us What Happened\")\r\n print(\"We Would be Really Happy To Help You\")\r\n audio=r.listen(source)\r\n print(\"Thanks! Processing in Process.....................\")\r\ndata=r.recognize_google(audio)\r\nif((\"hurt\" in data) or (\"damage\" in data) or (\"blood\" in data) or (\"bleeding\" in data)) :\r\n pyttsx3.speak(\"Don't worry you will be alright just follow below procedures\")\r\n print(\"You need a bandage and then you will be alright.\")\r\nelif(((\"attack\" in data) or (\"heart attack\" in data) or (\"heart pain\" in data))):\r\n pyttsx3.speak(\"Have the person sit down, rest, and try to keep calm. Loosen any tight clothing. Ask if the person takes any chest pain medicine, such as nitroglycerin, for a known heart condition, and help them take it.\")\r\n pyttsx3.speak(\"Don't worry you will be alright just follow below procedures\")\r\nelif(\"Thanks\" in data) or (\"Thank you\" in data):\r\n print(\"It's was our pleasure to help you!!\")\r\n pyttsx3.speak(\"Don't worry you will be alright just follow below procedures\")\r\n \r\nelse:\r\n pyttsx3.speak(\"Please contact your nearest doctor\")\r\n print(\"---------------------------------------\")\r\n \r\n print(\"---------------------------------------\")\r\n\r\n", "sub_path": "final.py", "file_name": "final.py", "file_ext": "py", "file_size_in_byte": 1421, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "speech_recognition.Recognizer", "line_number": 3, "usage_type": "call"}, {"api_name": "speech_recognition.Microphone", "line_number": 4, "usage_type": "call"}, {"api_name": "pyttsx3.speak", "line_number": 13, "usage_type": "call"}, {"api_name": "pyttsx3.speak", "line_number": 16, "usage_type": "call"}, {"api_name": "pyttsx3.speak", "line_number": 17, "usage_type": "call"}, {"api_name": "pyttsx3.speak", "line_number": 20, "usage_type": "call"}, {"api_name": "pyttsx3.speak", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "572005279", "text": "\nimport logging\nimport re\nfrom collections import namedtuple\nimport cssselect\nimport locale\nfrom amcatclient import AmcatAPI\nfrom lxml import html\nimport requests\nfrom datetime import datetime\n\nfrom rsslib import create_connection\nimport csv\nimport sys\n\n\n\ndef polish(textstring):\n #This function polishes the full text of the articles - it separated the lead from the rest by ||| and separates paragraphs and subtitles by ||.\n lines = textstring.strip().split('\\n')\n lead = lines[0].strip()\n rest = '||'.join( [l.strip() for l in lines[1:] if l.strip()] )\n if rest: result = lead + ' ||| ' + rest\n else: result = lead\n return result.strip()\n\n\ndef get_css(tree, selection, text=True, error=True):\n res = tree.cssselect(selection)\n if len(res) != 1:\n if not error:\n return None\n raise ValueError(\"Selection {selection} yielded {n} results\".format(n=len(res), **locals()))\n return res[0]\n\n\ndef get_links(conn):\n cur = conn.cursor()\n cur.execute(\"SELECT link FROM articles where medium ='telegraaf.nl'\")\n rows = list(cur.fetchall())\n db_links = []\n for row in rows:\n id = row[0]\n db_links.append(id)\n return db_links\n\n\ndef get_meta(conn,url):\n cur = conn.cursor()\n cur.execute(f\"SELECT title, medium, date FROM articles where link ='{url}'\")\n title, medium, date = list(cur.fetchall())[0]\n return {\"title\": title,\n \"publisher\": medium,\n \"date\": date}\n\ndef scrape_article(session, url):\n page = session.get(url)\n if page.status_code == 404:\n return\n page.raise_for_status()\n open(\"/tmp/test7.html\", \"w\").write(page.text)\n tree = html.fromstring(page.text)\n for label in tree.cssselect(\"span.label\"):\n if label.text_content().strip().startswith(\"Liveblog\"):\n return None\n lead_ps = tree.cssselect('p.ArticleIntroBlock__paragraph')\n body_ps = tree.xpath('//div[@data-element=\"articleBodyBlocks\"]/p')\n text = \"\\n\\n\".join(p.text_content() for p in lead_ps + body_ps)\n return {\"text\": text}\n\nCOOKIES = {\n '__cfduid':'d56655838cd13e536c63a84867a1cd55c1585123110',\n 'clientid':\"ck871dfn22m9y568461ch66fv\",\n 'didomi_token':'eyJ1c2VyX2lkIjoiMTcxMTBiMzMtMTBjYS02YTViLWFkNDAtMmQwMGFjNGJlZTY2IiwiY3JlYXRlZCI6IjIwMjAtMDMtMjVUMDc6NTg6MzEuMjA4WiIsInVwZGF0ZWQiOiIyMDIwLTAzLTI1VDA3OjU4OjUwLjk0OFoiLCJ2ZW5kb3JzIjp7ImVuYWJsZWQiOlsiZ29vZ2xlIiwiZmFjZWJvb2siLCJjOm5sLXByb2ZpZWwiXSwiZGlzYWJsZWQiOltdfSwicHVycG9zZXMiOnsiZW5hYmxlZCI6WyJmdW5jdGlvbmVlbCIsInNvY2lhbF9tZWRpYSIsIm5sX3Byb2ZpZWwiLCJjb29raWVzIiwiYWR2ZXJ0aXNpbmdfcGVyc29uYWxpemF0aW9uIiwiY29udGVudF9wZXJzb25hbGl6YXRpb24iLCJhZF9kZWxpdmVyeSIsImFuYWx5dGljcyJdLCJkaXNhYmxlZCI6W119fQ==',\n 'euconsent': 'BOwzpeIOwzphNAHABBNLC--AAAAuhr_7__7-_9_-_f__9uj3Or_v_f__32ccL59v_h_7v-_7fi_20nV4u_1vft9yfk1-5ctDztp507iakivXmqdeb9v_nz3_5pxP78k89r7337Ew_v8_v-b7BCON_YxEiA',\n 'OB-USER-TOKEN': '82e48dea-c07a-420c-a5e2-cece4269fb48',\n 'paywallversion': '1',\n}\n\ndef create_cookie(domain, name, value):\n return {\n \"name\": name,\n \"value\": value,\n \"domain\": domain,\n }\n\nsession = requests.session()\nfor name, value in COOKIES.items():\n session.cookies.set(**create_cookie(\"www.telegraaf.nl\", name, value))\nr = session.get(\"https://www.telegraaf.nl/nieuws/1071777683/pvd-a-ers-houden-samengaan-met-groen-links-af\")\nr.raise_for_status()\n#\n# print(\"aantal nieuwe bevestigde besmettingen\" in r.text)\n# open(\"/tmp/test.html\", \"w\").write(r.text)\n# sys.exit()\n#links = [\"https://www.nu.nl/coronavirus/6039788/kinderen-thuis-in-coronatijd-zoek-de-lichtpuntjes-ga-geen-schooltje-spelen.html\"]\n\n\n\ndb = \"landelijkemedia.db\"\nconn = create_connection(db)\nlinks = get_links(conn)\nfrom amcatclient import AmcatAPI\nc = AmcatAPI(\"http://vu.amcat.nl\")\n#links=['https://www.telegraaf.nl/nieuws/321571165/tientallen-bedolven-door-instorten-quarantainehotel-in-china']\nfor l in links:\n print(l)\n if 'video' in l:\n continue\n if 'redirect' in l:\n continue\n meta = get_meta(conn, l)\n if 'Liveblog' in meta['title']:\n continue\n a = scrape_article(session, l)\n if not a:\n continue\n else:\n a.update(meta)\n c.create_articles(2, 1385, [a])\n\n", "sub_path": "telegraaf_rssfeed.py", "file_name": "telegraaf_rssfeed.py", "file_ext": "py", "file_size_in_byte": 4204, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "lxml.html.fromstring", "line_number": 62, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 62, "usage_type": "name"}, {"api_name": "requests.session", "line_number": 87, "usage_type": "call"}, {"api_name": "rsslib.create_connection", "line_number": 101, "usage_type": "call"}, {"api_name": "amcatclient.AmcatAPI", "line_number": 104, "usage_type": "call"}]} +{"seq_id": "55732878", "text": "from os.path import join, realpath, dirname\nimport os\nfrom difflib import SequenceMatcher\nfrom multiprocessing import Pool\nimport re\nfrom collections import Counter\nimport pandas as pd\n\nfrom models import SourceArtist, CleanedArtist, sqlite_db, data_path\n\n\nmy_data = None\n\n### Part 1\n# a) TODO (complete in models.py)\n\n# b) TODO\nif __name__ == '__main__':\n\tartist_csv = join(dirname(realpath(__file__)), 'data/sothebys_artists.csv')\n\tsqlite_db.init(data_path)\n\tif sqlite_db.connect():\n\t\tprint(\"Connected to database\")\n\n\tsqlite_db.drop_tables([SourceArtist, CleanedArtist])\n\tsqlite_db.create_tables([SourceArtist, CleanedArtist])\n\tprint(\"Database Initialized\")\n\n# c) TODO\n\t#Read data from csv to panda\n\tdata = pd.read_csv(artist_csv)\n\n\t#rename fields of csv\n\tdata = data.rename(index=str, columns={\"name\": \"artist\", \"birth_year\": \"birth\", \"death_year\": \"death\"})\n\tdataDict = data.to_dict('records')\n\n\t#Insert data to database\n\tstep = 150\n\tfor i in range(0, len(dataDict), step):\n\t\tquery = SourceArtist.insert_many(dataDict[i:i+step])\n\t\tquery.execute()\n\n## Part 2\n# a)\n\t#Put data in a dataframe\n\tquery = SourceArtist.select(SourceArtist.artist, SourceArtist.birth, SourceArtist.death)\n\tmy_data = pd.DataFrame(list(query.dicts()))\n\tprint(len(my_data), \"records read from database\")\n\n# b,c)\ndef Data_Extractor(data):\n\t#Regex that breaks sentence where there is no letter\n\tregex_for_name = r\"(\\w+?)\\W\"\n\t#Regex that finds all 4-digit numbers\n\tregex_for_no = r\"([0-9][0-9][0-9][0-9])\"\n\n\tfor index, row in data.iterrows():\n\t\tif index%1000 == 0:\n\t\t\tprint(\"Process\", os.getpid(), \":\", index)\n\n\t\tmatches = re.findall(regex_for_no, row['artist'])\n\t\t#If only one 4 digit put in birth\n\t\tif len(matches) == 1:\n\t\t\tdata.loc[index, \"birth\"] = matches[0]\n\t\telif len(matches) >= 2:\n\t\t\t#Put the two dates into \"birth\" and \"death\"\n\t\t\tif matches[0] < matches[1]:\n\t\t\t\tdata.loc[index, \"birth\"] = matches[0]\n\t\t\t\tdata.loc[index, \"death\"] = matches[1]\n\t\t\telse:\n\t\t\t\tdata.loc[index, \"birth\"] = matches[1]\n\t\t\t\tdata.loc[index, \"death\"] = matches[0]\n\t\t#How to find artist name\n\t\tartist = re.findall(regex_for_name, row['artist'] + ' ')\n\t\tline = ''\n\t\ti = 0\n\t\t#Put in string until digit found(usually the dates)\n\t\twhile i < len(artist) and not artist[i].isdigit():\n\t\t\tif artist[i] not in [\"br\", \"nbsp\", \"b\", \"d\", \"dit\"]:\n\t\t\t\tline = line + artist[i] + \" \"\n\t\t\ti = i + 1\n\t\tif 'by' in line:\n\t\t\tline = line.split('by')[1]\n\t\tdata.loc[index, \"artist\"] = line.strip()\n\treturn data\n\n# Split data into parts\ndef data_splitter(number_of_parts, data):\n\tpart_Size = int(len(data)/number_of_parts)\n\tdata_parts = []\n\tfor i in range(0, number_of_parts-1):\n\t\tdata_parts.append(data.iloc[part_Size*i :part_Size*(i+1), : ])\n\tdata_parts.append(data.iloc[part_Size*(number_of_parts-1):, : ])\n\treturn data_parts\n\n#Run multiple processes\nif __name__ == '__main__':\n\tnumber_of_processes = 8\n\tpool = Pool(number_of_processes)\n\tprint('Cleaning data based on regex parsing...')\n\tmy_data = pd.concat(pool.map(Data_Extractor, data_splitter(number_of_processes, my_data)))\n\tpool.close()\n\tprint('Cleaning done.')\n\tprint('')\n\n# d,e)\n\n#Deduplication based on string similarity\ndef deduplicate(original_row, data_to_check):\n\t#remove first elements from original_row to avoid duplicates during result merging\n\tif len(original_row['birth']) > 0:\n\t\tdel original_row['birth'][0]\n\tif len(original_row['death']) > 0:\n\t\tdel original_row['death'][0]\n\tname_to_check = original_row['name'].pop(0)\n\toriginal_row['count'] -= 1\n\tfor index in range(0, len(data_to_check)):\n\t\tif index < len(data_to_check):\n\t\t\trow = data_to_check.iloc[index]\n\t\t\tratio = SequenceMatcher(None, name_to_check.lower(), row['artist'].lower()).ratio()\n\t\t\tif ratio > 0.8:\n\t\t\t\toriginal_row['name'].append(row['artist'])\n\t\t\t\toriginal_row['birth'].append(row['birth'])\n\t\t\t\toriginal_row['death'].append(row['death'])\n\t\t\t\toriginal_row['count'] += 1\n\t\t\t\tdata_to_check.drop(inplace=True, index=data_to_check.index[index])\n\t\t\t\tdata_to_check.reset_index(inplace=True, drop=True)\n\t\t\t\tindex -= 1\n\t\tindex += 1\n\t#Return both the original_row containing the metrics and the data_to_check containing the deduplicated dataset\n\treturn (original_row, data_to_check)\n\nif __name__ == '__main__':\n\tindex = 0\n\twhile index < len(my_data):\n\t\t#Get the current row from the data\n\t\trow_outter = my_data.iloc[index]\n\n\t\t#Skip names containing numerals (for names containing century)\n\t\tif any(char.isdigit() for char in row_outter['artist']):\n\t\t\tprint(\"Record with artist name\", row_outter['artist'], \"skipped.\")\n\t\t\tmy_data.drop(inplace=True, index=my_data.index[index])\n\t\t\tcontinue\n\n\t\t#Initialize values\n\t\tname = [row_outter['artist']]\n\t\tbirth = []\n\t\tdeath = []\n\t\tcount = 1\n\n\t\t#Check birth and death in order to ensure they are numerals\n\t\tif row_outter['birth'] is not None and isinstance(row_outter['birth'], float):\n\t\t\tbirth.append(row_outter['birth'])\n\t\tif row_outter['death'] is not None and isinstance(row_outter['death'], float):\n\t\t\tdeath.append(row_outter['death'])\n\n\t\t#Print the name being checked and the remaining size of the data\n\t\tprint(\"Artist\", name[0], \"deduplicatation started with\", len(my_data), \"records still remaining in database\")\n\n\t\t#Split the remaining data into parts and run a multithreaded similarity based search on them\n\t\tif __name__ == '__main__':\n\t\t\tsearch_pool = Pool(int(number_of_processes))\n\t\t\targs = []\n\t\t\tparts = data_splitter(int(number_of_processes), my_data.iloc[index+1:, :])\n\t\t\tfor part in parts:\n\t\t\t\targs.append(({'name':name, 'birth':birth, 'death':death, 'count':count}, part))\n\t\t\tsearch_results = search_pool.starmap(deduplicate, args)\n\t\t\tsearch_pool.close()\n\n\t\t#Merge results\n\t\tdata_parts = []\n\t\tfor res in search_results:\n\t\t\t#res[0] is the data exctracted and res[1] is the deduplicated dataset for each partition\n\t\t\tname = name + res[0]['name']\n\t\t\tbirth = birth + res[0]['birth']\n\t\t\tdeath = death + res[0]['death']\n\t\t\tcount += res[0]['count']\n\t\t\tdata_parts.append(res[1])\n\t\tmy_data = pd.concat(data_parts)\n\t\tmy_data.reset_index(inplace=True, drop=True)\n\t\tname = Counter(name).most_common(1)[0][0]\n\t\n\t\t#Clear the nan values from birth and death arrays\n\t\tbirth = [x for x in birth if str(x) != 'nan']\n\t\tdeath = [x for x in death if str(x) != 'nan']\n\t\tif len(birth) > 0:\n\t\t\tbirth = Counter(birth).most_common(1)[0][0]\n\t\telse:\n\t\t\tbirth = None\n\t\tif len(death) > 0:\n\t\t\tdeath = Counter(death).most_common(1)[0][0]\n\t\telse:\n\t\t\tdeath = None\n\t\n\t\t#Print results for the current artist\n\t\tprint(\"Deduplication resuls:\")\n\t\tprint(\"Name:\", name)\n\t\tprint(\"Birth:\", birth)\n\t\tprint(\"Death:\", death)\n\t\tprint(\"Count:\", count)\n\n\t\t#Insert into database the deduplicated entry\n\t\tres = CleanedArtist.insert({'artist':name, 'birth': birth, 'death': death, 'count': count}).execute()\n\t\tif res is not None and res > 0:\n\t\t\tprint(\"Record for\", name, \"inserted to CleanedArtist\")\n\t\tprint('')\n\t\t#index does not need to be incremented because we delete the current row in line 158, so we will just look at row 0 untill all rows are deleted\n\t\t#index += 1\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6932, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 19, "usage_type": "call"}, {"api_name": "models.sqlite_db.init", "line_number": 20, "usage_type": "call"}, {"api_name": "models.data_path", "line_number": 20, "usage_type": "argument"}, {"api_name": "models.sqlite_db", "line_number": 20, "usage_type": "name"}, {"api_name": "models.sqlite_db.connect", "line_number": 21, "usage_type": "call"}, {"api_name": "models.sqlite_db", "line_number": 21, "usage_type": "name"}, {"api_name": "models.sqlite_db.drop_tables", "line_number": 24, "usage_type": "call"}, {"api_name": "models.sqlite_db", "line_number": 24, "usage_type": "name"}, {"api_name": "models.SourceArtist", "line_number": 24, "usage_type": "name"}, {"api_name": "models.CleanedArtist", "line_number": 24, "usage_type": "name"}, {"api_name": "models.sqlite_db.create_tables", "line_number": 25, "usage_type": "call"}, {"api_name": "models.sqlite_db", "line_number": 25, "usage_type": "name"}, {"api_name": "models.SourceArtist", "line_number": 25, "usage_type": "name"}, {"api_name": "models.CleanedArtist", "line_number": 25, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 30, "usage_type": "call"}, {"api_name": "models.SourceArtist.insert_many", "line_number": 39, "usage_type": "call"}, {"api_name": "models.SourceArtist", "line_number": 39, "usage_type": "name"}, {"api_name": "models.SourceArtist.select", "line_number": 45, "usage_type": "call"}, {"api_name": "models.SourceArtist", "line_number": 45, "usage_type": "name"}, {"api_name": "models.SourceArtist.artist", "line_number": 45, "usage_type": "attribute"}, {"api_name": "models.SourceArtist.birth", "line_number": 45, "usage_type": "attribute"}, {"api_name": "models.SourceArtist.death", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 46, "usage_type": "call"}, {"api_name": "os.getpid", "line_number": 58, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 60, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 73, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 98, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 100, "usage_type": "call"}, {"api_name": "difflib.SequenceMatcher", "line_number": 119, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 161, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 178, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 180, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 186, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 190, "usage_type": "call"}, {"api_name": "models.CleanedArtist.insert", "line_number": 202, "usage_type": "call"}, {"api_name": "models.CleanedArtist", "line_number": 202, "usage_type": "name"}]} +{"seq_id": "170908468", "text": "import os\r\nimport sys\r\nimport torch\r\nimport torch.autograd as autograd\r\nimport torch.nn.functional as F\r\n\r\ndef save(model, save_dir, save_prefix, steps):\r\n if not os.path.isdir(save_dir):\r\n os.makedirs(save_dir)\r\n save_prefix = os.path.join(save_dir,save_prefix)\r\n save_path = '{}_steps_{}.pt'.format(save_prefix,steps)\r\n torch.save(model.state_dict(),save_path)\r\n\r\n\r\ndef train(train_iter, dev_iter, model, args):\r\n '''\r\n your code here.\r\n \r\n training process using backpropagation.\r\n print training loss and accuracy at args.log_interval.\r\n print evaluation loss and accuray at args.test_interval.\r\n Save the best model.\r\n \r\n Hint: view the size of data from train_iter before using them.\r\n Optional: Implement early stopping/dropout/L2 penalty.\r\n '''\r\n optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\r\n steps = 0\r\n best_acc = 0\r\n last_step = 0\r\n model.train()\r\n for epoch in range(1, args.epochs+1):\r\n for batch in train_iter:\r\n feature, target = batch.text, batch.label\r\n #print(type(feature.data))\r\n #print(feature.data.shape)\r\n f = torch.t(feature.data) \r\n optimizer.zero_grad()\r\n logit = model(f)\r\n target.data = target.data-1\r\n loss = F.cross_entropy(logit, target)\r\n loss.backward()\r\n optimizer.step()\r\n steps += 1\r\n if steps % args.log_interval == 0:\r\n result = torch.max(logit,1)[1].view(target.size())\r\n corrects = (result.data == target.data).sum()\r\n accuracy = corrects*100.0/batch.batch_size\r\n sys.stdout.write('\\rBatch[{}] - loss: {:.6f} acc: {:.4f}%({}/{})'.format(steps,\r\n loss.data.item(),\r\n accuracy,\r\n corrects,\r\n batch.batch_size))\r\n if steps % args.log_interval == 0:\r\n dev_acc = eval(dev_iter, model, args)\r\n if dev_acc > best_acc:\r\n best_acc = dev_acc\r\n last_step = steps\r\n if args.save_best:\r\n save(model,args.save_dir,'best',steps)\r\n else:\r\n if steps - last_step >= args.early_stop:\r\n print('early stop by {} steps.'.format(args.early_stop))\r\n elif steps % args.save_interval == 0:\r\n save(model,args.save_dir,args.snapshot,steps)\r\n\r\n\r\ndef eval(dev_iter, model, args):\r\n '''\r\n your code here.\r\n evaluation of the model.\r\n \r\n Hint: To save the best model and do earily stopping,\r\n you need to return the evaluation accuracy to train function.\r\n '''\r\n model.eval()\r\n corrects, avg_loss = 0,0\r\n for batch in dev_iter:\r\n feature, target = batch.text, batch.label\r\n f = torch.t(feature.data) \r\n logit = model(f)\r\n target.data = target.data-1\r\n loss = F.cross_entropy(logit,target)\r\n avg_loss += loss.data.item()\r\n result = torch.max(logit,1)[1]\r\n corrects += (result.view(target.size()).data == target.data).sum()\r\n size = len(dev_iter.dataset)\r\n avg_loss /= size \r\n accuracy = 100.0 * corrects/size\r\n print('\\nEvaluation - loss: {:.6f} acc: {:.4f}%({}/{}) \\n'.format(avg_loss,accuracy,corrects,size))\r\n \r\n return accuracy\r\n\r\n\r\n", "sub_path": "homework5/YulinChen/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 3697, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.isdir", "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.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 27, "usage_type": "attribute"}, {"api_name": "torch.t", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.max", "line_number": 46, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 49, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 49, "usage_type": "attribute"}, {"api_name": "torch.t", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn.functional.cross_entropy", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.max", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "87807998", "text": "import torch\nimport numpy as np\nimport os\nimport argparse\nimport datetime\nfrom tensorboardX import SummaryWriter\nfrom data.load_radio_ml import get_radio_ml_loader as get_loader\nfrom data.data_utils import iq2spiketrain as to_spike_train\nimport matplotlib.pyplot as plt\nimport tqdm\nfrom mpl_toolkits import mplot3d\n\n\nif __name__ == '__main__':\n classes = ['32PSK', '16APSK', '32QAM', 'FM', 'GMSK', '32APSK', 'OQPSK', '8ASK', 'BPSK', '8PSK', 'AM-SSB-SC', '4ASK',\n '16PSK', '64APSK', '128QAM', '128APSK', 'AM-DSB-SC', 'AM-SSB-WC', '64QAM', 'QPSK', '256QAM', 'AM-DSB-WC',\n 'OOK', '16QAM']\n\n modulation_idx = classes.index('32PSK') # OOK 32PSK 64QAM\n\n torch.manual_seed(123)\n np.random.seed(123)\n\n get_loader_kwargs = {}\n to_st_train_kwargs = {}\n\n # Set \"get loader\" kwargs\n get_loader_kwargs['data_dir'] = '/mnt/013c8c34-4de2-4dab-9e29-16618f093336/playground/RFSNN/2018.01'\n get_loader_kwargs['min_snr'] = 6\n get_loader_kwargs['max_snr'] = 6\n get_loader_kwargs['per_h5_frac'] = 0.25\n get_loader_kwargs['train_frac'] = 0.9\n get_loader_kwargs['per_sample_frac'] = 1.0\n get_loader_kwargs['normalize'] = True\n get_loader_kwargs['fake_height'] = False\n get_loader_kwargs['skip_1'] = False\n get_loader_kwargs['classes'] = 24\n # Set \"to spike train\" kwargs\n\n wh = 16\n to_st_train_kwargs['out_w'] = wh #args.I_resolution\n to_st_train_kwargs['out_h'] = wh #args.Q_resolution\n\n train_data = get_loader(24, train=True, **get_loader_kwargs)\n gen_train = iter(train_data)\n\n fig, ax = plt.subplots(2, 1)\n plt.ion()\n plt.show()\n for step in range(10):\n try:\n input, labels = next(gen_train)\n except StopIteration:\n gen_train = iter(train_data)\n input, labels = next(gen_train)\n\n input_spikes = to_spike_train(input, **to_st_train_kwargs)\n\n for idx in range(24):\n if labels[idx] == modulation_idx:\n img = None\n im3d = np.zeros((1024, wh, wh), dtype=np.uint8)\n for i in range(1024):\n\n im3d[i] = input_spikes[idx, i, 0, :, :]\n if False:\n ax[0].clear()\n ax[1].clear()\n if img is None:\n img = input_spikes[idx, i, 0, :, :]\n else:\n img += input_spikes[idx, i, 0, :, :]\n ax[0].imshow(img)\n ax[0].title.set_text(classes[labels[idx]])\n xx = input[idx, 0, i] * 2 - 1\n yy = input[idx, 1, i] * 2 - 1\n ax[1].scatter(xx, -1 * yy)\n ax[1].set_xlim(-1, 1)\n ax[1].set_ylim(-1, 1)\n plt.pause(0.0001)\n print(\"done\")\n pos = np.where(im3d == 1)\n fig2 = plt.axes(projection='3d')\n fig2.scatter3D(pos[0], pos[1], pos[2], c=pos[0])\n ys = np.arange(0,512)\n plt.figure()\n plt.plot(np.array(input[idx,0])[0:512],ys, 'g')\n plt.plot(np.array(input[idx,1])[0:512],ys, 'b')\n plt.pause(100)\n", "sub_path": "rf2/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 3271, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.manual_seed", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 22, "usage_type": "attribute"}, {"api_name": "data.load_radio_ml.get_radio_ml_loader", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ion", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "data.data_utils.iq2spiketrain", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 62, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.axes", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 85, "usage_type": "call"}, {"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": "numpy.array", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pause", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}]} +{"seq_id": "440379418", "text": "import pandas as pd\r\nimport numpy as np\r\nfrom sklearn.model_selection import KFold\r\nfrom sklearn.linear_model import LogisticRegression\r\n\r\nfeature_selected = ['Danceability', \r\n 'Energy', \r\n 'Speechiness', \r\n 'Acousticness', \r\n 'Instrumentalness', \r\n 'Liveness',\r\n 'Valence',\r\n 'Loudness',\r\n 'Tempo',\r\n 'Artist_Score']\r\n\r\nData = pd.read_excel('E:/Desktop/Data/feature_complete_normalized_1990_2019.xlsx')\r\nkf = KFold(n_splits=10, shuffle=True)\r\nkf.get_n_splits(Data)\r\n\r\nstore_train = []\r\nstore_test =[]\r\nstore_train_p = []\r\nstore_test_p =[]\r\nstore_train_r = []\r\nstore_test_r =[]\r\nfor train_index, test_index in kf.split(Data):\r\n train_set = Data.loc[train_index,]\r\n Xtrain = np.array(train_set[feature_selected])\r\n Ytrain = np.array(train_set['label'], dtype=float)\r\n test_set = Data.loc[test_index,]\r\n Xtest = np.array(test_set[feature_selected])\r\n Ytest = np.array(test_set['label'], dtype=float)\r\n #Unpenalized Logistic Regression \r\n clf = LogisticRegression(solver='lbfgs', C=np.inf) \r\n clf.fit(Xtrain, Ytrain)\r\n train_predict = clf.predict(Xtrain)\r\n train_accuracy = (train_predict==Ytrain).mean()\r\n test_predict = clf.predict(Xtest)\r\n test_accuracy = (test_predict==Ytest).mean()\r\n store_train.append(train_accuracy)\r\n store_test.append(test_accuracy)\r\n tp1 = sum(((train_predict == 1) & (Ytrain == 1)) * 1)\r\n fp1 = sum(((train_predict == 0) & (Ytrain == 1)) * 1)\r\n tn1 = sum(((train_predict == 1) & (Ytrain == 0)) * 1)\r\n fn1 = sum(((train_predict == 0) & (Ytrain == 0)) * 1)\r\n train_precision = tp1 / (tp1 + fp1)\r\n train_recall = tp1 / (tp1 + fn1)\r\n store_train_p.append(train_precision)\r\n store_train_r.append(train_recall)\r\n tp2 = sum(((test_predict == 1) & (Ytest == 1)) * 1)\r\n fp2 = sum(((test_predict == 0) & (Ytest == 1)) * 1)\r\n tn2 = sum(((test_predict == 1) & (Ytest == 0)) * 1)\r\n fn2 = sum(((test_predict == 0) & (Ytest == 0)) * 1)\r\n test_precision = tp2 / (tp2 + fp2)\r\n test_recall = tp2 / (tp2 + fn2)\r\n store_test_p.append(test_precision)\r\n store_test_r.append(test_recall)\r\n\r\naverge_train_accuracy = np.mean(store_train)\r\nprint('Train:',averge_train_accuracy)\r\naverge_test_accuracy = np.mean(store_test)\r\nprint('Test:',averge_test_accuracy)\r\naverge_train_precision = np.mean(store_train_p)\r\nprint(\"Train precision:\",averge_train_precision)\r\naverge_test_precision = np.mean(store_test_p)\r\nprint(\"Test precision:\",averge_test_precision)\r\naverge_train_recall = np.mean(store_train_r)\r\nprint(\"Train recall:\",averge_train_recall)\r\naverge_test_recall = np.mean(store_test_r)\r\nprint(\"Test recall:\",averge_test_recall)\r\n\r\n", "sub_path": "logistic.py", "file_name": "logistic.py", "file_ext": "py", "file_size_in_byte": 2771, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_excel", "line_number": 17, "usage_type": "call"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "334360576", "text": "from absl import app, flags, logging\nfrom absl.flags import FLAGS\nimport tensorflow as tf\nimport numpy as np\nimport os, shutil\nfrom tensorflow.keras.callbacks import (\n ReduceLROnPlateau,\n EarlyStopping,\n ModelCheckpoint,\n TensorBoard\n)\nfrom yolov3_tf2.models import (\n YoloV3, YoloV3Tiny, YoloLoss,\n yolo_anchors, yolo_anchor_masks,\n yolo_tiny_anchors, yolo_tiny_anchor_masks\n)\nfrom yolov3_tf2.utils import freeze_all\nimport yolov3_tf2.dataset as dataset\n\nflags.DEFINE_string('dataset', '', 'path to dataset')\nflags.DEFINE_boolean('tiny', False, 'yolov3 or yolov3-tiny')\nflags.DEFINE_string('weights', './checkpoints/yolov3.tf',\n 'path to weights file')\nflags.DEFINE_string('classes', './data/coco.names', 'path to classes file')\nflags.DEFINE_string('name', '', 'output file name to save')\nflags.DEFINE_string('gpu', '', 'name of gpu to use')\nflags.DEFINE_enum('mode', 'fit', ['fit', 'eager_fit', 'eager_tf'],\n 'fit: model.fit, '\n 'eager_fit: model.fit(run_eagerly=True), '\n 'eager_tf: custom GradientTape')\nflags.DEFINE_enum('transfer', 'none',\n ['none', 'darknet', 'no_output', 'frozen', 'fine_tune'],\n 'none: Training from scratch, '\n 'darknet: Transfer darknet, '\n 'no_output: Transfer all but output, '\n 'frozen: Transfer and freeze all, '\n 'fine_tune: Transfer all and freeze darknet only')\nflags.DEFINE_integer('size', 416, 'image size')\nflags.DEFINE_integer('epochs', 2, 'number of epochs')\nflags.DEFINE_integer('batch_size', 8, 'batch size')\nflags.DEFINE_float('learning_rate', 1e-3, 'learning rate')\nflags.DEFINE_integer('num_classes', 80, 'number of classes in the model')\n\n\ndef get_free_gpu():\n \"\"\"Selects the gpu with the most free memory\n \"\"\"\n import subprocess\n import numpy as np\n\n output = subprocess.Popen('nvidia-smi -q -d Memory |grep -A4 GPU|grep Free', stdout=subprocess.PIPE,\n shell=True).communicate()[0]\n output = output.decode(\"ascii\")\n # assumes that it is on the popiah server and the last gpu is not used\n memory_available = [int(x.split()[2]) for x in output.split(\"\\n\")[:-2]]\n if not memory_available:\n return\n print(\"Setting GPU to use to PID {}\".format(np.argmax(memory_available)))\n return np.argmax(memory_available)\n\n\ndef set_one_gpu():\n\n gpu = FLAGS.gpu\n if not gpu:\n gpu = str(get_free_gpu())\n\n if not gpu:\n return\n\n print(\"Using GPU: %s\" % gpu)\n os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\" # see issue #152\n os.environ['CUDA_VISIBLE_DEVICES'] = gpu\n\n\ndef main(_argv):\n set_one_gpu()\n\n if FLAGS.tiny:\n model = YoloV3Tiny(FLAGS.size, training=True,\n classes=FLAGS.num_classes)\n anchors = yolo_tiny_anchors\n anchor_masks = yolo_tiny_anchor_masks\n else:\n model = YoloV3(FLAGS.size, training=True, classes=FLAGS.num_classes)\n anchors = yolo_anchors\n anchor_masks = yolo_anchor_masks\n\n # train_dataset = dataset.load_fake_dataset()\n dataset_name = 'data/' + FLAGS.dataset + '.train.record'\n val_dataset_name = 'data/' + FLAGS.dataset + '.val.record'\n\n train_dataset = dataset.load_tfrecord_dataset(\n dataset_name, FLAGS.classes)\n train_dataset = train_dataset.shuffle(buffer_size=1024) # TODO: not 1024\n train_dataset = train_dataset.batch(FLAGS.batch_size)\n train_dataset = train_dataset.map(lambda x, y: (\n dataset.transform_images(x, FLAGS.size),\n dataset.transform_targets(y, anchors, anchor_masks, 80)))\n train_dataset = train_dataset.prefetch(\n buffer_size=tf.data.experimental.AUTOTUNE)\n\n tf_name = FLAGS.name\n if not tf_name:\n tf_name = 'train' + FLAGS.gpu\n best_tf_name = \"checkpoints/%s_best.tf\" % tf_name\n last_tf_name = \"checkpoints/%s_last.tf\" % tf_name\n\n # val_dataset = dataset.load_fake_dataset()\n val_dataset = dataset.load_tfrecord_dataset(\n val_dataset_name, FLAGS.classes)\n val_dataset = val_dataset.batch(FLAGS.batch_size)\n val_dataset = val_dataset.map(lambda x, y: (\n dataset.transform_images(x, FLAGS.size),\n dataset.transform_targets(y, anchors, anchor_masks, 80)))\n\n if FLAGS.transfer != 'none':\n model.load_weights(FLAGS.weights)\n if FLAGS.transfer == 'fine_tune':\n # freeze darknet\n darknet = model.get_layer('yolo_darknet')\n freeze_all(darknet)\n elif FLAGS.transfer == 'frozen':\n # freeze everything\n freeze_all(model)\n else:\n # reset top layers\n if FLAGS.tiny: # get initial weights\n init_model = YoloV3Tiny(\n FLAGS.size, training=True, classes=FLAGS.num_classes)\n else:\n init_model = YoloV3(\n FLAGS.size, training=True, classes=FLAGS.num_classes)\n\n if FLAGS.transfer == 'darknet':\n for l in model.layers:\n if l.name != 'yolo_darknet' and l.name.startswith('yolo_'):\n l.set_weights(init_model.get_layer(\n l.name).get_weights())\n else:\n freeze_all(l)\n elif FLAGS.transfer == 'no_output':\n for l in model.layers:\n if l.name.startswith('yolo_output'):\n l.set_weights(init_model.get_layer(\n l.name).get_weights())\n else:\n freeze_all(l)\n\n optimizer = tf.keras.optimizers.Adam(lr=FLAGS.learning_rate)\n loss = [YoloLoss(anchors[mask], classes=FLAGS.num_classes)\n for mask in anchor_masks]\n best_val_loss = 0\n history = None\n\n if FLAGS.mode == 'eager_tf':\n # Eager mode is great for debugging\n # Non eager graph mode is recommended for real training\n avg_loss = tf.keras.metrics.Mean('loss', dtype=tf.float32)\n avg_val_loss = tf.keras.metrics.Mean('val_loss', dtype=tf.float32)\n\n for epoch in range(1, FLAGS.epochs + 1):\n for batch, (images, labels) in enumerate(train_dataset):\n with tf.GradientTape() as tape:\n outputs = model(images, training=True)\n regularization_loss = tf.reduce_sum(model.losses)\n pred_loss = []\n for output, label, loss_fn in zip(outputs, labels, loss):\n pred_loss.append(loss_fn(label, output))\n total_loss = tf.reduce_sum(pred_loss) + regularization_loss\n\n grads = tape.gradient(total_loss, model.trainable_variables)\n optimizer.apply_gradients(\n zip(grads, model.trainable_variables))\n\n # logging.info(\"{}_train_{}, {}, {}\".format(\n # epoch, batch, total_loss.numpy(),\n # list(map(lambda x: np.sum(x.numpy()), pred_loss))))\n avg_loss.update_state(total_loss)\n\n for batch, (images, labels) in enumerate(val_dataset):\n outputs = model(images)\n regularization_loss = tf.reduce_sum(model.losses)\n pred_loss = []\n for output, label, loss_fn in zip(outputs, labels, loss):\n pred_loss.append(loss_fn(label, output))\n total_loss = tf.reduce_sum(pred_loss) + regularization_loss\n\n # logging.info(\"{}_val_{}, {}, {}\".format(\n # epoch, batch, total_loss.numpy(),\n # list(map(lambda x: np.sum(x.numpy()), pred_loss))))\n avg_val_loss.update_state(total_loss)\n\n val_lost = avg_val_loss.result().numpy()\n logging.info(\"{}, train: {}, val: {}\".format(\n epoch,\n avg_loss.result().numpy(),\n val_lost))\n\n avg_loss.reset_states()\n avg_val_loss.reset_states()\n model.save_weights(last_tf_name)\n if best_val_loss == 0 or best_val_loss > val_lost:\n best_val_loss = val_lost\n logging.info(\"saving best val loss: %s\" % best_tf_name)\n model.save_weights(best_tf_name)\n else:\n model.compile(optimizer=optimizer, loss=loss,\n run_eagerly=(FLAGS.mode == 'eager_fit'))\n\n callbacks = [\n ReduceLROnPlateau(verbose=1),\n EarlyStopping(patience=3, verbose=1),\n ModelCheckpoint(best_tf_name,\n verbose=1, save_weights_only=True),\n TensorBoard(log_dir='logs')\n ]\n\n history = model.fit(train_dataset,\n epochs=FLAGS.epochs,\n callbacks=callbacks,\n validation_data=val_dataset)\n\n if history is not None:\n print(history.history['val_loss'])\n best_val_loss = min(history.history['val_loss'])\n model.save_weights(best_tf_name)\n\n print(\"Best weights are saved as %s\" % best_tf_name)\n tiny = 'tiny_' if FLAGS.tiny else ''\n out_name = \"%s_d%s_%sm%s_bs%d_s%s_e%d_val%d\" % \\\n (tf_name, FLAGS.dataset, tiny, FLAGS.transfer, FLAGS.batch_size, FLAGS.size, FLAGS.epochs, best_val_loss)\n mfn = \"data/model/%s/\" % out_name\n\n final_tf_name = \"%s.tf\" % out_name\n copy_tf(\"%s_best.tf\" % tf_name, final_tf_name)\n print(\"Final checkpoint file saved as: %s\" % final_tf_name)\n model.load_weights(best_tf_name)\n tf.saved_model.save(model, mfn)\n print(\"Model file saved to: %s\" % mfn)\n\n\ndef copy_tf(ifn, ofn):\n for fn in os.listdir('checkpoints'):\n if not fn.startswith(ifn):\n continue\n out = fn.replace(ifn, ofn)\n shutil.copyfile('checkpoints/' + fn, 'checkpoints/' + out)\n\nif __name__ == '__main__':\n try:\n app.run(main)\n except SystemExit:\n pass\n", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 9984, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "absl.flags.DEFINE_string", "line_number": 20, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 20, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_boolean", "line_number": 21, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 21, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_string", "line_number": 22, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 22, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_string", "line_number": 24, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 24, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_string", "line_number": 25, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 25, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_string", "line_number": 26, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 26, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_enum", "line_number": 27, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 27, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_enum", "line_number": 31, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 31, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 38, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 38, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 39, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 39, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 40, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 40, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_float", "line_number": 41, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 41, "usage_type": "name"}, {"api_name": "absl.flags.DEFINE_integer", "line_number": 42, "usage_type": "call"}, {"api_name": "absl.flags", "line_number": 42, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 51, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 51, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 59, "usage_type": "call"}, {"api_name": "absl.flags.FLAGS.gpu", "line_number": 64, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 64, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 73, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS.tiny", "line_number": 79, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 79, "usage_type": "name"}, {"api_name": "yolov3_tf2.models.YoloV3Tiny", "line_number": 80, "usage_type": "call"}, {"api_name": "absl.flags.FLAGS.size", "line_number": 80, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 80, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.num_classes", "line_number": 81, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 81, "usage_type": "name"}, {"api_name": "yolov3_tf2.models.yolo_tiny_anchors", "line_number": 82, "usage_type": "name"}, {"api_name": "yolov3_tf2.models.yolo_tiny_anchor_masks", "line_number": 83, "usage_type": "name"}, {"api_name": "yolov3_tf2.models.YoloV3", "line_number": 85, "usage_type": "call"}, {"api_name": "absl.flags.FLAGS.size", "line_number": 85, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 85, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.num_classes", "line_number": 85, "usage_type": "attribute"}, {"api_name": "yolov3_tf2.models.yolo_anchors", "line_number": 86, "usage_type": "name"}, {"api_name": "yolov3_tf2.models.yolo_anchor_masks", "line_number": 87, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.dataset", "line_number": 90, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 90, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.dataset", "line_number": 91, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 91, "usage_type": "name"}, {"api_name": "yolov3_tf2.dataset.load_tfrecord_dataset", "line_number": 93, "usage_type": "call"}, {"api_name": "yolov3_tf2.dataset", "line_number": 93, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.classes", "line_number": 94, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 94, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.batch_size", "line_number": 96, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 96, "usage_type": "name"}, {"api_name": "yolov3_tf2.dataset.transform_images", "line_number": 98, "usage_type": "call"}, {"api_name": "yolov3_tf2.dataset", "line_number": 98, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.size", "line_number": 98, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 98, "usage_type": "name"}, {"api_name": "yolov3_tf2.dataset.transform_targets", "line_number": 99, "usage_type": "call"}, {"api_name": "yolov3_tf2.dataset", "line_number": 99, "usage_type": "name"}, {"api_name": "tensorflow.data", "line_number": 101, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS.name", "line_number": 103, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 103, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.gpu", "line_number": 105, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 105, "usage_type": "name"}, {"api_name": "yolov3_tf2.dataset.load_tfrecord_dataset", "line_number": 110, "usage_type": "call"}, {"api_name": "yolov3_tf2.dataset", "line_number": 110, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.classes", "line_number": 111, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 111, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.batch_size", "line_number": 112, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 112, "usage_type": "name"}, {"api_name": "yolov3_tf2.dataset.transform_images", "line_number": 114, "usage_type": "call"}, {"api_name": "yolov3_tf2.dataset", "line_number": 114, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.size", "line_number": 114, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 114, "usage_type": "name"}, {"api_name": "yolov3_tf2.dataset.transform_targets", "line_number": 115, "usage_type": "call"}, {"api_name": "yolov3_tf2.dataset", "line_number": 115, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.transfer", "line_number": 117, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 117, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.weights", "line_number": 118, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 118, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.transfer", "line_number": 119, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 119, "usage_type": "name"}, {"api_name": "yolov3_tf2.utils.freeze_all", "line_number": 122, "usage_type": "call"}, {"api_name": "absl.flags.FLAGS.transfer", "line_number": 123, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 123, "usage_type": "name"}, {"api_name": "yolov3_tf2.utils.freeze_all", "line_number": 125, "usage_type": "call"}, {"api_name": "absl.flags.FLAGS.tiny", "line_number": 128, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 128, "usage_type": "name"}, {"api_name": "yolov3_tf2.models.YoloV3Tiny", "line_number": 129, "usage_type": "call"}, {"api_name": "absl.flags.FLAGS.size", "line_number": 130, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 130, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.num_classes", "line_number": 130, "usage_type": "attribute"}, {"api_name": "yolov3_tf2.models.YoloV3", "line_number": 132, "usage_type": "call"}, {"api_name": "absl.flags.FLAGS.size", "line_number": 133, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 133, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.num_classes", "line_number": 133, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS.transfer", "line_number": 135, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 135, "usage_type": "name"}, {"api_name": "yolov3_tf2.utils.freeze_all", "line_number": 141, "usage_type": "call"}, {"api_name": "absl.flags.FLAGS.transfer", "line_number": 142, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 142, "usage_type": "name"}, {"api_name": "yolov3_tf2.utils.freeze_all", "line_number": 148, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 150, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 150, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS.learning_rate", "line_number": 150, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 150, "usage_type": "name"}, {"api_name": "yolov3_tf2.models.YoloLoss", "line_number": 151, "usage_type": "call"}, {"api_name": "absl.flags.FLAGS.num_classes", "line_number": 151, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 151, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.mode", "line_number": 156, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 156, "usage_type": "name"}, {"api_name": "tensorflow.keras.metrics.Mean", "line_number": 159, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 159, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 159, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.metrics.Mean", "line_number": 160, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 160, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 160, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS.epochs", "line_number": 162, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 162, "usage_type": "name"}, {"api_name": "tensorflow.GradientTape", "line_number": 164, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 166, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 170, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 183, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 187, "usage_type": "call"}, {"api_name": "absl.logging.info", "line_number": 195, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 195, "usage_type": "name"}, {"api_name": "absl.logging.info", "line_number": 205, "usage_type": "call"}, {"api_name": "absl.logging", "line_number": 205, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.mode", "line_number": 209, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 209, "usage_type": "name"}, {"api_name": "tensorflow.keras.callbacks.ReduceLROnPlateau", "line_number": 212, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks.EarlyStopping", "line_number": 213, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks.ModelCheckpoint", "line_number": 214, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks.TensorBoard", "line_number": 216, "usage_type": "call"}, {"api_name": "absl.flags.FLAGS.epochs", "line_number": 220, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 220, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.tiny", "line_number": 230, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 230, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.dataset", "line_number": 232, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS", "line_number": 232, "usage_type": "name"}, {"api_name": "absl.flags.FLAGS.transfer", "line_number": 232, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS.batch_size", "line_number": 232, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS.size", "line_number": 232, "usage_type": "attribute"}, {"api_name": "absl.flags.FLAGS.epochs", "line_number": 232, "usage_type": "attribute"}, {"api_name": "tensorflow.saved_model.save", "line_number": 239, "usage_type": "call"}, {"api_name": "tensorflow.saved_model", "line_number": 239, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 244, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 248, "usage_type": "call"}, {"api_name": "absl.app.run", "line_number": 252, "usage_type": "call"}, {"api_name": "absl.app", "line_number": 252, "usage_type": "name"}]} +{"seq_id": "504271677", "text": "# -*- encoding: utf-8 -*-\n'''\nCreated on 2017/7/1 13:49\nCopyright (c) 2017/7/1, 海牛学院版权所有.\n@author: 青牛\n'''\nimport sys\n\nsys.path.append('/home/qingniu/hainiu_crawler')\n\nfrom commons.util.db_util import DBUtil\nfrom commons.util.log_util import LogUtil\nfrom commons.util.file_util import FileUtil\nfrom commons.util.time_util import TimeUtil\nfrom hdfs.client import Client\nfrom configs import config\nimport time, sys, redis\n\n\ndef xpath_config_file():\n select_xpath_rule_sql = \"\"\"select host,xpath,type from stream_extract_xpath_rule where host='%s' and status=0\"\"\"\n rl = LogUtil().get_base_logger()\n try:\n # _HAINIU_DB = {'HOST': '192.168.137.190', 'USER': 'hainiu', 'PASSWD': '12345678', 'DB': 'hainiucrawler',\n # 'CHARSET': 'utf8', 'PORT': 3306}\n d = DBUtil(config._HAINIU_DB)\n #d = DBUtil(_HAINIU_DB)\n r = redis.Redis('nn1.hadoop', 6379, db=6)\n #r = redis.Redis('redis.hadoop', 6379, db=6)\n f = FileUtil()\n t = TimeUtil()\n c = Client(\"http://nn1.hadoop:50070\")\n\n time_str = t.now_time(format='%Y%m%d%H%M%S')\n #local_xpath_file_path = '/Users/leohe/Data/input/xpath_cache_file/xpath_file' + time_str\n local_xpath_file_path = '/home/qingniu/xpath_cache_file/xpath_file' + time_str\n\n start_cursor = 0\n is_finish = True\n starttime = time.clock()\n host_set = set()\n\n while is_finish:\n values = set()\n limit = r.scan(start_cursor,'total:*',10)\n if limit[0] == 0:\n is_finish = False\n start_cursor = limit[0]\n for h in limit[1]:\n host = h.split(\":\")[1]\n total_key = h\n txpath_key = 'txpath:%s' % host\n fxpath_key = 'fxpath:%s' % host\n total = r.get(total_key)\n\n txpath = r.zrevrange(txpath_key, 0, 1)\n row_format = \"%s\\t%s\\t%s\\t%s\"\n if txpath:\n # print 'txpath:%s' % txpath\n txpath_num = int(r.zscore(txpath_key, txpath[0]))\n if txpath.__len__() == 2:\n txpath_num_1 = int(r.zscore(txpath_key, txpath[1]))\n txpath_num_1 = txpath_num_1 if txpath_num_1 is not None else 0\n\n # print 'txpath_max_num:%s' % txpath_num\n if txpath_num / float(total) >= 0.8:\n values.add(row_format % (host, txpath[0], 'true', '0'))\n host_set.add(host)\n else:\n if txpath_num >= 1:\n values.add(row_format % (host, txpath[0], 'true', '0'))\n host_set.add(host)\n if txpath_num_1 is not None and txpath_num_1 >= 1:\n values.add(row_format % (host, txpath[1], 'true', '0'))\n host_set.add(host)\n\n fxpath = r.smembers(fxpath_key)\n if fxpath:\n # print 'fxpath:%s' % fxpath\n for fx in fxpath:\n values.add(row_format % (host, fx, 'false', '0'))\n host_set.add(host)\n\n sql = select_xpath_rule_sql % host\n list_rule = d.read_tuple(sql)\n for rule in list_rule:\n type = rule[2]\n if type == 0:\n values.add(row_format % (rule[0], rule[1], 'true', '2'))\n host_set.add(host)\n elif type == 1:\n values.add(row_format % (rule[0], rule[1], 'false', '3'))\n host_set.add(host)\n\n f.write_file_line_pattern(local_xpath_file_path, values, \"a\")\n #上传到HDFS的XPATH配置文件目录\n c.upload(\"/user/qingniu/xpath_cache_file/\", local_xpath_file_path)\n endtime = time.clock()\n worksec = int(round((endtime - starttime)))\n rl.info('total host %s,action time %s\\'s' % (host_set.__len__(), worksec))\n except:\n rl.exception()\n d.rollback()\n finally:\n d.close()\n\n\nif __name__ == '__main__':\n reload(sys)\n sys.setdefaultencoding('utf-8')\n xpath_config_file()\n", "sub_path": "src/main/resources/xpath_config.py", "file_name": "xpath_config.py", "file_ext": "py", "file_size_in_byte": 4285, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.path.append", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "commons.util.log_util.LogUtil", "line_number": 22, "usage_type": "call"}, {"api_name": "commons.util.db_util.DBUtil", "line_number": 26, "usage_type": "call"}, {"api_name": "configs.config._HAINIU_DB", "line_number": 26, "usage_type": "attribute"}, {"api_name": "configs.config", "line_number": 26, "usage_type": "name"}, {"api_name": "redis.Redis", "line_number": 28, "usage_type": "call"}, {"api_name": "commons.util.file_util.FileUtil", "line_number": 30, "usage_type": "call"}, {"api_name": "commons.util.time_util.TimeUtil", "line_number": 31, "usage_type": "call"}, {"api_name": "hdfs.client.Client", "line_number": 32, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 40, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 98, "usage_type": "call"}, {"api_name": "sys.setdefaultencoding", "line_number": 110, "usage_type": "call"}]} +{"seq_id": "548417195", "text": "import pandas as pd\nimport os\nimport simplejson as json\nimport csv\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom collections import defaultdict\nfrom sklearn.cluster import KMeans\nfrom sklearn.neighbors import NearestNeighbors\nimport sklearn\nfrom sklearn import preprocessing\nfrom numpy import inf\nfrom sklearn.decomposition import PCA\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras.layers import LSTM\nfrom tensorflow.keras.layers import Dropout\nimport time\n\n# linear regression for multioutput regression\nfrom sklearn.datasets import make_regression\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.linear_model import Ridge \n\n## Calculating Average. value of a list\ndef Average_val(lst):\n return sum(lst) / len(lst)\n\n## Normalizing feature vectors\ndef Normalize_feature(feat_df, PCA_option): \n if PCA_option == 'No':\n x = feat_df #.values \n else:\n x = feat_df \n min_max_scaler = preprocessing.MinMaxScaler()\n x_scaled = min_max_scaler.fit_transform(x)\n res = pd.DataFrame(x_scaled)\n \n return res\n\n### Check about empty files of a directory \ndef Check_Empty_Files(dirName):\n \n # Create a List \n listOfEmptyDirs = list()\n\n # Iterate over the directory tree and check if directory is empty.\n for (dirpath, dirnames, filenames) in os.walk(dirName):\n if len(dirnames) == 0 and len(filenames) == 0 :\n listOfEmptyDirs.append(dirpath)\n\n### Get Meta-Features matrix for the training and testing \ndef Get_Meta_Features (meta_feat_dir, data_list, data_type):\n \n df_meta_feat = pd.DataFrame()\n \n if data_type == 'Uni-var':\n \n for file_name in data_list:\n print(file_name)\n df = pd.read_csv(meta_feat_dir + file_name +'.csv', error_bad_lines=False, header = None)\n last_row = df.tail(1)\n df_meta_feat = df_meta_feat.append(last_row, ignore_index=True)\n df_meta_feat = df_meta_feat.fillna(0)\n \n \n return df_meta_feat\n\n '''\n else: \n for file_name in all_datasets_list:\n cnt = 0\n results_vec = []\n if '.DS_Store' not in file_name:\n cnt += 1\n all_models_list = os.listdir(win_res_dir_name + '/' + dir_name + '/' + file_name) \n \n \n for model_name in all_models_list:\n df = pd.read_csv(win_res_dir_name + '/' + dir_name+'/'+file_name+'/'+model_name, error_bad_lines=False, header = None)\n results_vec.append(df.iloc[0,-1])\n \n best_model_dataset_index = results_vec.index(min(results_vec))\n print(file_name + ':' + arr_models[best_model_dataset_index + 1])\n \n ## Append Best Model for each dataset variable to the vector\n a.append(best_model_dataset_index + 1)\n \n results_vec.insert(0,dir_name+'_'+file_name)\n \n numpy_perf_vec = np.array(results_vec).reshape((1, len(all_models_list)+1)) \n numpy_perf_list = numpy_perf_vec.tolist()\n \n wtr_perf.writerow (numpy_perf_list)\n \n return a \n''' \n \n### Get the best model for specific dataset (Used for ALgros baseline)\ndef Get_All_Model_Dataset(data_name, win_res_dir_name, data_type):\n \n arr_models = os.listdir(win_res_dir_name + '/' + data_name)\n \n all_models_list = os.listdir(win_res_dir_name + '/' + data_name) \n \n results_vec_mse = []\n results_vec_mape = []\n results_vec_smape = []\n for model_name in all_models_list:\n df = pd.read_csv(win_res_dir_name + '/' + data_name+'/'+ model_name, error_bad_lines=False, header = None)\n results_vec_mse.append(df.iloc[0,-1])\n results_vec_mape.append(df.iloc[1,-1])\n results_vec_smape.append(df.iloc[2,-1]) \n \n \n return results_vec_mse, results_vec_mape, results_vec_smape, arr_models\n \n### (a) Gloal Best Implementation\ndef Global_Best (dir_list, data_list, data_type):\n Models_Array_mse = [] \n Models_Array_mape = []\n Models_Array_smape = [] \n for data_name in data_list:\n for win_ind in os.listdir(dir_list):\n if '.DS_Store' not in win_ind:\n print(data_name)\n ##### Get Performance Matrix and Best Model Array for both Multi-variate and Uni-variate Datasets with a specific window\n if data_type == 'Uni-var':\n results_vec_mse = []\n results_vec_mape = []\n results_vec_smape = []\n \n all_models_list = os.listdir(dir_list + win_ind + '/' + data_name) \n for model_name in all_models_list:\n df = pd.read_csv(dir_list + win_ind + '/' + data_name + '/' + model_name, error_bad_lines=False, header = None)\n results_vec_mse.append(df.iloc[0,-1])\n results_vec_mape.append(df.iloc[1,-1])\n results_vec_smape.append(df.iloc[2,-1])\n \n best_model_dataset_index_mse = results_vec_mse.index(min(results_vec_mse))\n best_model_dataset_index_mape = results_vec_mape.index(min(results_vec_mape))\n best_model_dataset_index_smape = results_vec_smape.index(min(results_vec_smape))\n \n \n ## Append Best Model for each dataset variable to the vector\n Models_Array_mse.append(best_model_dataset_index_mse)\n Models_Array_mape.append(best_model_dataset_index_mape)\n Models_Array_smape.append(best_model_dataset_index_smape)\n\n \n d_mse = defaultdict(int)\n for i in Models_Array_mse:\n d_mse[i] += 1\n result_mse = max(d_mse.items(), key=lambda x: x[1])\n \n d_mape = defaultdict(int)\n for i in Models_Array_mape:\n d_mape[i] += 1\n result_mape = max(d_mape.items(), key=lambda x: x[1])\n \n d_smape = defaultdict(int)\n for i in Models_Array_smape:\n d_smape[i] += 1\n result_smape = max(d_smape.items(), key=lambda x: x[1])\n \n return all_models_list[result_mse[0]], all_models_list[result_mape[0]], all_models_list[result_smape[0]]\n \n### Get Average Performance of Models across dataset cluster (Used for ISAC baseline)\ndef Get_Best_Avg_Model_Dataset(data_cluster, win_res_dir_name, data_type):\n \n results_vec_mse_avg = [0] * 322\n results_vec_mape_avg = [0] * 322\n results_vec_smape_avg = [0] * 322\n \n for data_name in data_cluster:\n \n results_vec_mse = []\n results_vec_mape = []\n results_vec_smape = []\n \n arr_models = os.listdir(win_res_dir_name + '/' + data_name)\n all_models_list = os.listdir(win_res_dir_name + '/' + data_name) \n \n for model_name in all_models_list:\n df = pd.read_csv(win_res_dir_name + '/' + data_name+'/'+ model_name, error_bad_lines=False, header = None)\n \n results_vec_mse.append(df.iloc[0,-1])\n results_vec_mape.append(df.iloc[1,-1])\n results_vec_smape.append(df.iloc[2,-1]) \n \n results_vec_mse_avg += results_vec_mse\n results_vec_mape_avg += results_vec_mape\n results_vec_smape_avg += results_vec_smape \n \n best_model_dataset_index_mse_avg = results_vec_mse_avg.index(min(results_vec_mse_avg))\n best_model_dataset_index_mape_avg = results_vec_mape_avg.index(min(results_vec_mape_avg))\n best_model_dataset_index_smape_avg = results_vec_smape_avg.index(min(results_vec_smape_avg))\n \n \n return arr_models[best_model_dataset_index_mse_avg], arr_models[best_model_dataset_index_mape_avg], arr_models[best_model_dataset_index_smape_avg] \n\n### Get the best model for specific dataset (Used for ALgros baseline)\ndef Get_Best_Model_Dataset(data_name, win_res_dir_name, data_type):\n \n arr_models = os.listdir(win_res_dir_name + '/' + data_name)\n arr_models.insert(0,'Dataset')\n \n all_models_list = os.listdir(win_res_dir_name + '/' + data_name) \n \n \n results_vec_mse = []\n results_vec_mape = []\n results_vec_smape = []\n for model_name in all_models_list:\n df = pd.read_csv(win_res_dir_name + '/' + data_name+'/'+ model_name, error_bad_lines=False, header = None)\n results_vec_mse.append(df.iloc[0,-1])\n results_vec_mape.append(df.iloc[1,-1])\n results_vec_smape.append(df.iloc[2,-1]) \n \n \n best_model_dataset_index_mse = results_vec_mse.index(min(results_vec_mse))\n best_model_dataset_index_mape = results_vec_mape.index(min(results_vec_mape))\n best_model_dataset_index_smape = results_vec_smape.index(min(results_vec_smape))\n \n \n return arr_models[best_model_dataset_index_mse + 1], arr_models[best_model_dataset_index_mape + 1], arr_models[best_model_dataset_index_smape + 1]\n \n#### Get Model Files list from a dataset directory\ndef Get_Model_Files_List(win_res_dir_name, data_type):\n \n dir_datasets_name = os.listdir(win_res_dir_name) \n for dir_name in dir_datasets_name:\n \n if '.DS_Store' in dir_name:\n continue\n \n if data_type == 'Uni-var':\n all_models_list = os.listdir(win_res_dir_name+'/'+dir_name)\n \n else:\n all_datasets_list = os.listdir(win_res_dir_name+'/'+dir_name) \n \n for file_name in all_datasets_list:\n if '.DS_Store' not in file_name:\n all_models_list = os.listdir(win_res_dir_name+'/'+dir_name+'/'+file_name) \n \n \n return all_models_list \n\n#### Draw and Save Histogram for the best models\ndef Histogram_plot_save(window_best_models_arr):\n \n _ = plt.hist(window_best_models_arr, bins= 'auto', density = True) # arguments are passed to np.histogram\n plt.title(\"Histogram of Best Models for Training Dataset\", fontsize=12)\n plt.xlabel(\"Forecasting Model Index\", fontsize=12)\n plt.ylabel(\"Probability of being Best Model\", fontsize=12)\n plt.xticks(fontsize=12)\n plt.yticks(fontsize=12)\n plt.tick_params(axis='both', which='minor', labelsize=12)\n plt.savefig('hist_best_models.eps', format='eps')\n plt.show()\n \ndef chunks(l, n):\n n = max(1, n)\n return [l[i:i+n] for i in range(0, len(l), n)]\n\n### Get Difference between two lists\ndef Diff(li1, li2): \n return (list(set(li1) - set(li2)))\n\n#### Divide the datasets into 5 folds for training and testing \ndef Divide_Dataset_Folds(n_folds, datasets_dir_list):\n \n train_folds_list = []\n test_folds_list = []\n\n length = int(len(datasets_dir_list)/ n_folds) #length of each fold\n folds = []\n for i in range(n_folds-1):\n folds += [datasets_dir_list[i*length:(i+1)*length]]\n folds += [datasets_dir_list[4*length:len(datasets_dir_list)]]\n \n print(folds)\n \n for fold in folds: \n train_folds_list.append(Diff(datasets_dir_list, fold))\n test_folds_list.append(fold)\n\n\n return train_folds_list, test_folds_list \n \n\nif __name__ == '__main__':\n \n n_folds = 5\n PCA_option = 'Yes' #Yes\n n_components = 3 ## If PCA_option is Yes, the n_components\n \n ## Folds for MV datasets\n mv_dir = 'results_all_mv/Multi-variate_first_win/'\n dir_mv_list = os.listdir(mv_dir)\n dir_mv_list.remove('.DS_Store')\n train_folds_list_mv, test_folds_list_mv = Divide_Dataset_Folds(n_folds, dir_mv_list) ## Division of folds by dataset names\n \n ''' \n ## Folds for UV datasets\n uv_dir_1 = 'results_all_mv/Multi-variate_first_win/'\n uv_dir_2 = 'results_all_mv/Multi-variate_second_win'\n uv_dir_3 = 'results_all_mv/Multi-variate_third_win'\n dir_uv_list = os.listdir(uv_dir_1)\n dir_uv_list.remove('.DS_Store')\n train_folds_list_uv, test_folds_list_uv = Divide_Dataset_Folds(n_folds, dir_uv_list) ## Division of folds by dataset names\n \n \n #### Go across the folds and evaluate the different baselines and then average\n meta_feat_dir_1 = 'Meta-Features/Meta-Feat_first_win/Multi_Variate_Real_first_win/'\n meta_feat_dir_2 = 'Meta-Features/Meta-Feat_sec_win/Multi_Variate_Real_sec_win/'\n meta_feat_dir_3 = 'Meta-Features/Meta-Feat_third_win/Multi_Variate_Real_third_win/'\n \n '''\n \n #### Directories for perfromances results for all other windows \n uv_dir_1 = 'results_all_uv/Uni-variate_first_win/' \n uv_dir_2 = 'results_all_uv/Uni-variate_second_win'\n uv_dir_3 = 'results_all_uv/Uni-variate_third_win'\n dir_uv_list = os.listdir(uv_dir_1)\n dir_uv_list.remove('.DS_Store')\n train_folds_list_uv, test_folds_list_uv = Divide_Dataset_Folds(n_folds, dir_uv_list) ## Division of folds by dataset names\n \n #print('UV<<<<') #print(train_folds_list_uv) #print(test_folds_list_uv)\n \n \n #### Go across the folds and evaluate the different baselines and then average\n meta_feat_dir_1 = 'Meta-Features/Meta-Feat_first_win/Uni-Variate_first_win/'\n meta_feat_dir_2 = 'Meta-Features/Meta-Feat_sec_win/Uni-Variate_sec_win/'\n meta_feat_dir_3 = 'Meta-Features/Meta-Feat_third_win/Uni-Variate_third_win/'\n \n \n final_vec_all_folds = []\n cnt_k_all = 0\n train_time_vec = []\n \n for i in range(0, n_folds):\n \n \n train_X_mse = pd.DataFrame()\n test_X_mse = pd.DataFrame()\n train_Y_mse = pd.DataFrame()\n test_Y_mse = pd.DataFrame()\n predicted_mse = pd.DataFrame()\n \n train_Y_mape = []; train_Y_smape = []\n \n ### Extract Meta-Features\n ## (1) Train Meta-features\n meta_features_train_orig_1 = Get_Meta_Features (meta_feat_dir_1, train_folds_list_uv[i], 'Uni-var')\n meta_features_train_orig_2 = Get_Meta_Features (meta_feat_dir_2, train_folds_list_uv[i], 'Uni-var')\n meta_features_train_orig_3 = Get_Meta_Features (meta_feat_dir_3, train_folds_list_uv[i], 'Uni-var')\n \n meta_features_train_orig_par = meta_features_train_orig_1.append(meta_features_train_orig_2, ignore_index=True)\n meta_features_train_orig = meta_features_train_orig_par.append(meta_features_train_orig_3, ignore_index=True)\n \n ## Perform PCA for Training\n pca = PCA(n_components)\n pca.fit(meta_features_train_orig)\n meta_features_train_pca = pca.transform(meta_features_train_orig)\n #print(pca.explained_variance_ratio_)\n \n #print(meta_features_train_orig)\n #print(meta_features_train_pca)\n \n ## Normalize Features\n if PCA_option == 'No':\n meta_features_train = Normalize_feature(meta_features_train_orig, PCA_option)\n else: \n meta_features_train = Normalize_feature(meta_features_train_pca, PCA_option)\n \n print('Normalized Train Features')\n print(meta_features_train)\n \n ## (1) Test Meta-features\n meta_features_test_orig_1 = Get_Meta_Features (meta_feat_dir_1, test_folds_list_uv[i], 'Uni-var')\n meta_features_test_orig_2 = Get_Meta_Features (meta_feat_dir_2, test_folds_list_uv[i], 'Uni-var')\n meta_features_test_orig_3 = Get_Meta_Features (meta_feat_dir_3, test_folds_list_uv[i], 'Uni-var')\n \n meta_features_test_orig_par = meta_features_test_orig_1.append(meta_features_test_orig_2, ignore_index=True)\n meta_features_test_orig = meta_features_test_orig_par.append(meta_features_test_orig_3, ignore_index=True)\n \n ## Perform PCA for Testing\n pca = PCA(n_components)\n pca.fit(meta_features_test_orig)\n meta_features_test_pca = pca.transform(meta_features_test_orig)\n #print(pca.explained_variance_ratio_)\n \n ## Normalize Features\n if PCA_option == 'No':\n meta_features_test = Normalize_feature(meta_features_test_orig, PCA_option)\n else: \n meta_features_test = Normalize_feature(meta_features_test_pca, PCA_option)\n \n print('Normalized Test Features') \n print(meta_features_test)\n \n \n ## (B) Time-Series Meta-learner \n j = 0\n for train_data_name in train_folds_list_uv[i]: ## Append all of the first window performances \n \n train_X_mse_1 = [];train_X_mse_2 = [];train_X_mse_3 = []; \n train_Y_mse_1 = []; train_Y_mse_2 = [];train_Y_mse_3 = [];\n \n \n ## Collect Performances for that dataset\n results_vec_mse_1, results_vec_mape_1, results_vec_smape_1, arr_models = Get_All_Model_Dataset(train_data_name, uv_dir_1, 'Uni-var')\n results_vec_mse_2, results_vec_mape_2, results_vec_smape_2, arr_models = Get_All_Model_Dataset(train_data_name, uv_dir_2, 'Uni-var')\n results_vec_mse_3, results_vec_mape_3, results_vec_smape_3, arr_models = Get_All_Model_Dataset(train_data_name, uv_dir_3, 'Uni-var') \n \n \n ## First Window History features and performances\n train_X_mse_1.extend(meta_features_train.iloc[j,:])\n train_X_mse_1.extend([0] * 322)\n if PCA_option == 'No':\n train_X_mse_1.extend([0] * meta_features_train_orig_2.shape[1])\n else:\n train_X_mse_1.extend([0] * n_components)\n train_X_mse_1.extend([0] * 322)\n print('TRAIN_X After first window')\n #print(len(train_X_mse_1))\n \n a_series = pd.Series(train_X_mse_1)\n train_X_mse = train_X_mse.append(a_series, ignore_index=True)\n #print(train_X_mse)\n \n print('TRAIN_Y After first window')\n train_Y_mse_1.extend(results_vec_mse_1)\n #print(len(train_Y_mse_1))\n \n ## Appending the first raw to the train_Y dataframe \n a_series = pd.Series(train_Y_mse_1)\n train_Y_mse = train_Y_mse.append(a_series, ignore_index=True)\n #print(train_Y_mse)\n \n \n ## Second Window History features and performances\n train_X_mse_2.extend(meta_features_train.iloc[j,:])\n train_X_mse_2.extend(results_vec_mse_1)\n if PCA_option == 'No':\n train_X_mse_2.extend([0] * meta_features_train_orig_2.shape[1])\n else:\n train_X_mse_2.extend([0] * n_components)\n train_X_mse_2.extend([0] * 322) \n print('TRAIN_X After second window')\n #print(len(train_X_mse_2))\n \n a_series = pd.Series(train_X_mse_2)\n train_X_mse = train_X_mse.append(a_series, ignore_index=True)\n #print(train_X_mse)\n \n ## Appending the second raw to the train_X dataframe \n train_Y_mse_2.extend(results_vec_mse_2)\n print('TRAIN_Y After second window')\n #print(len(train_Y_mse_2))\n \n a_series = pd.Series(train_Y_mse_2)\n train_Y_mse = train_Y_mse.append(a_series, ignore_index=True)\n #print(train_Y_mse)\n \n \n ## Third Window History features and performances\n train_X_mse_3.extend(meta_features_train.iloc[j,:])\n train_X_mse_3.extend(results_vec_mse_1)\n train_X_mse_3.extend(meta_features_train.iloc[j+len(train_folds_list_uv[i]),:])\n train_X_mse_3.extend(results_vec_mse_2)\n print('TRAIN_X After third window')\n #print(len(train_X_mse_3))\n \n ## Appending the third raw to the train_X dataframe \n a_series = pd.Series(train_X_mse_3)\n train_X_mse = train_X_mse.append(a_series, ignore_index=True)\n print(train_X_mse)\n \n \n ## Appending the third raw to the train_Y dataframe \n train_Y_mse_3.extend(results_vec_mse_3)\n print('TRAIN_Y After third window')\n #print(len(train_Y_mse_3))\n \n a_series = pd.Series(train_Y_mse_3)\n train_Y_mse = train_Y_mse.append(a_series, ignore_index=True)\n print(train_Y_mse)\n \n j += 1\n \n \n print(train_X_mse) \n print(train_Y_mse) \n \n # Fit Model for MSE Performance Metric \n #model_ts_mse = LinearRegression(positive = True) ## normalize = True \n #model_ts_mse = Ridge(alpha = 1.0)\n \n ## Start Time for Training\n start_time = time.time()\n \n \n ## Creating RNN to model TS model\n train_X_mse = np.array(train_X_mse)\n train_X_mse = np.reshape(train_X_mse, (train_X_mse.shape[0], train_X_mse.shape[1], 1)) \n \n \n model_ts_mse = Sequential()\n print(train_X_mse.shape[1])\n model_ts_mse.add(LSTM(units = 50, return_sequences = True, input_shape = (train_X_mse.shape[1], 1)))\n model_ts_mse.add(Dropout(0.2)) ## Drop Out Regularization\n ## Adding Three More LSTM Layers\n for p in [True, True, False]: # 2 layers\n model_ts_mse.add(LSTM(units = 50, return_sequences = p))\n model_ts_mse.add(Dropout(0.2))\n \n ## Adding the output layer\n print(train_Y_mse.shape[1]) \n model_ts_mse.add(Dense(units = train_Y_mse.shape[1]))\n \n ## Compiling the RNN \n model_ts_mse.compile(optimizer = 'adam', loss = 'mean_squared_error')\n \n ## Fitting the RNN \n model_ts_mse.fit(train_X_mse, train_Y_mse, epochs = 40, batch_size = 50)\n \n \n AFY_train_time_seconds = time.time() - start_time \n print('AF-Y Train Time: '+ str(AFY_train_time_seconds))\n \n train_time_vec.append(AFY_train_time_seconds) \n \n print('Train_Time_Vector_After_Fold ' + str(i))\n print(train_time_vec)\n \n #model_ts_mse.fit(train_X_mse, train_Y_mse)\n #print(model_ts_mse.coef_)\n #print(model_ts_mse.score(train_X_mse, train_Y_mse))\n \n \n ## Testing of Time-Series Meta-learner \n j = 0\n for test_data_name in test_folds_list_uv[i]: ## Append all of the first window performances \n \n test_X_mse_1 = [];test_X_mse_2 = [];test_X_mse_3 = []; \n test_Y_mse_1 = [];test_Y_mse_2 = [];test_Y_mse_3 = []; ## Actual Performances (Ground Truth)\n \n \n ## Collect Performances for that dataset\n results_vec_mse_1, results_vec_mape_1, results_vec_smape_1, arr_models = Get_All_Model_Dataset(test_data_name, uv_dir_1, 'Uni-var')\n results_vec_mse_2, results_vec_mape_2, results_vec_smape_2, arr_models = Get_All_Model_Dataset(test_data_name, uv_dir_2, 'Uni-var')\n results_vec_mse_3, results_vec_mape_3, results_vec_smape_3, arr_models = Get_All_Model_Dataset(test_data_name, uv_dir_3, 'Uni-var') \n \n \n ## First Window History features and performances\n test_X_mse_1.extend(meta_features_test.iloc[j,:])\n test_X_mse_1.extend([0] * 322)\n if PCA_option == 'No':\n test_X_mse_1.extend([0] * meta_features_test_orig_2.shape[1])\n else:\n test_X_mse_1.extend([0] * n_components)\n \n test_X_mse_1.extend([0] * 322)\n \n a_series = pd.Series(test_X_mse_1)\n test_X_mse = test_X_mse.append(a_series, ignore_index=True)\n \n ## Ground Truth of the first window\n test_Y_mse_1.extend(results_vec_mse_1)\n a_series = pd.Series(test_Y_mse_1)\n test_Y_mse = test_Y_mse.append(a_series, ignore_index=True)\n \n ## Predict the First window performances\n #print(test_X_mse_1)\n #test_X_mse_1 = np.array(test_X_mse_1)\n #test_X_mse_1 = np.reshape(test_X_mse_1, (test_X_mse_1.shape[0], test_X_mse_1.shape[1], 1))\n \n #predicted_win_1 = model_ts_mse.predict([test_X_mse_1])\n\n #a_series = pd.Series(predicted_win_1[0])\n #predicted_mse = predicted_mse.append(a_series, ignore_index=True)\n \n ## Second Window History features and performances\n test_X_mse_2.extend(meta_features_test.iloc[j,:])\n #test_X_mse_2.extend(predicted_win_1[0])\n test_X_mse_2.extend(results_vec_mse_1)\n if PCA_option == 'No':\n test_X_mse_2.extend([0] * meta_features_test_orig_2.shape[1])\n else:\n test_X_mse_2.extend([0] * n_components)\n test_X_mse_2.extend([0] * 322) \n \n ## Append to test_X dataframe\n a_series = pd.Series(test_X_mse_2)\n test_X_mse = test_X_mse.append(a_series, ignore_index=True)\n \n\n test_Y_mse_2.extend(results_vec_mse_2)\n \n a_series = pd.Series(test_Y_mse_2)\n test_Y_mse = test_Y_mse.append(a_series, ignore_index=True)\n \n ## Predict the second window performances\n print(test_X_mse_2)\n #test_X_mse_2 = np.array(test_X_mse_2)\n #test_X_mse_2 = np.reshape(test_X_mse_2, (test_X_mse_2.shape[0], test_X_mse_2.shape[1], 1)) \n \n #predicted_win_2 = model_ts_mse.predict([test_X_mse_2])\n #a_series = pd.Series(predicted_win_2[0])\n #predicted_mse = predicted_mse.append(a_series, ignore_index=True)\n \n \n ## Third Window History features and performances\n test_X_mse_3.extend(meta_features_test.iloc[j,:])\n #test_X_mse_3.extend(predicted_win_1[0]) Predict everything from scratch\n test_X_mse_3.extend(results_vec_mse_1)\n test_X_mse_3.extend(meta_features_test.iloc[j+len(test_folds_list_uv[i]),:])\n test_X_mse_3.extend(results_vec_mse_2)\n #test_X_mse_3.extend(predicted_win_2[0])\n \n ## Appending the third raw to the test_X dataframe \n a_series = pd.Series(test_X_mse_3)\n test_X_mse = test_X_mse.append(a_series, ignore_index=True)\n print(test_X_mse)\n \n \n ## Predicting Third window performances using time-series regression model\n #test_X_mse_3 = np.array(test_X_mse_3)\n #test_X_mse_3 = np.reshape(test_X_mse_3, (test_X_mse_3.shape[0], test_X_mse_3.shape[1], 1))\n \n #predicted_win_3 = model_ts_mse.predict([test_X_mse_3])\n #a_series = pd.Series(predicted_win_3[0])\n #predicted_mse = predicted_mse.append(a_series, ignore_index=True)\n \n \n \n ## Appending the third raw to the test_Y dataframe \n test_Y_mse_3.extend(results_vec_mse_3)\n \n a_series = pd.Series(test_Y_mse_3)\n test_Y_mse = test_Y_mse.append(a_series, ignore_index=True)\n print(test_Y_mse)\n \n #print('Test Features')\n #print(test_X_mse)\n \n test_X_mse = np.array(test_X_mse)\n test_X_mse = np.reshape(test_X_mse, (test_X_mse.shape[0], test_X_mse.shape[1], 1))\n \n ## Start Time for Inference\n start_time = time.time()\n \n predicted_win = model_ts_mse.predict(test_X_mse)\n print(predicted_win)\n print(predicted_win.shape)\n print(predicted_win[0])\n a_series = pd.Series(predicted_win[0])\n predicted_mse = predicted_mse.append(a_series, ignore_index=True)\n print(predicted_mse)\n \n \n \n ## Get the best model index from Autoforecast time-series meta-learner \n predicted_mse['MinColumnID']= predicted_mse.idxmin(axis=1)\n test_Y_mse['MinColumnID']= test_Y_mse.idxmin(axis=1)\n \n \n \n print('Predicted ........')\n print(predicted_mse)\n #predicted_mse.to_csv('Predicted_MSE_Fold_No_' + str(i) + '.csv') \n \n print('Actual..........')\n print(test_Y_mse)\n #test_Y_mse.to_csv('Actual_MSE_Fold_No_' + str(i) + '.csv') \n\n \n ## Inference by getting the actual performance of the chosen model index by the time-series regression (i.e., the one with the least predicted output) \n K = 1\n a_vec = []\n cnt = 0\n for j in range(0, len(test_Y_mse)):\n res = sorted(range(len(test_Y_mse.iloc[j,:-1])), key = lambda sub: test_Y_mse.iloc[j,:-1][sub])[:K]\n #print(test_Y_mse.iloc[j,-1])\n print(res)\n print(np.argmin(predicted_win[j]))\n if np.argmin(predicted_win[j]) in res: #np.argmin(predicted_win[j]):\n cnt += 1\n #print(cnt)\n final_vec_all_folds.append(test_Y_mse.iloc[j,np.argmin(predicted_win[j])]) #predicted_mse.iloc[j,-1]])\n print(cnt)\n \n print('Fold No. ' + str(i))\n \n cnt_k_all += cnt\n print('Count-k: ' + str(cnt_k_all))\n print('Rank-k-Acc: ' + str((cnt_k_all / (len(test_Y_mse) * n_folds))))\n \n #break \n #print(final_vec_all_folds) \n \n ## Estimate Inference Time\n #AF_best_inference_run_time_seconds = time.time() - start_time \n #print('AutoForecast Inference Time: '+ str(AF_best_inference_run_time_seconds))\n \n \n #print('Performance Vector after all folds for time-series regression')\n \n print(final_vec_all_folds)\n print(Average_val(final_vec_all_folds)) \n \n \n ''' \n # (A) Define general meta-learning model\n \n #print(train_folds_list_uv[i]) \n \n \n \n train_X = meta_features_train\n train_Y_mse = []; train_Y_mape = []; train_Y_smape = []\n\n #df_meta_feat = df_meta_feat.fillna(0)\n \n ## Get the output vector for each dataset in that fold \n for train_data_name in train_folds_list_uv[i]: ## Append all of the first window performances \n results_vec_mse_1, results_vec_mape_1, results_vec_smape_1, arr_models = Get_All_Model_Dataset(train_data_name, uv_dir_1, 'Uni-var')\n train_Y_mse.append(results_vec_mse_1); train_Y_mape.append(results_vec_mape_1); train_Y_smape.append(results_vec_smape_1)\n \n for train_data_name in train_folds_list_uv[i]: ## Append all of the second window performances \n results_vec_mse_2, results_vec_mape_2, results_vec_smape_2, arr_models = Get_All_Model_Dataset(train_data_name, uv_dir_2, 'Uni-var')\n train_Y_mse.append(results_vec_mse_2); train_Y_mape.append(results_vec_mape_2); train_Y_smape.append(results_vec_smape_2)\n \n for train_data_name in train_folds_list_uv[i]: ## Append all of the third window performances \n results_vec_mse_3, results_vec_mape_3, results_vec_smape_3, arr_models = Get_All_Model_Dataset(train_data_name, uv_dir_3, 'Uni-var')\n train_Y_mse.append(results_vec_mse_3); train_Y_mape.append(results_vec_mape_3); train_Y_smape.append(results_vec_smape_3)\n \n ## Repeating the performance matrix to the multiple time windows\n #train_Y_mse = np.repeat(train_Y_mse, 3, axis=0)\n #train_Y_mape = np.repeat(train_Y_mape, 3, axis=0)\n #train_Y_smape = np.repeat(train_Y_smape, 3, axis=0)\n \n print (np.array(train_Y_mape).shape)\n \n \n # Fit Model for MSE Performance Metric \n model_mse = LinearRegression()\n model_mse.fit(train_X, train_Y_mse)\n print(model_mse.coef_)\n print(model_mse.score(train_X, train_Y_mse))\n \n \n # Fit Model for MAPE Performance Metric \n train_Y_mape = np.array(train_Y_mape)\n train_Y_mape[np.isinf(train_Y_mape)] = 100 ## Replacing infinity values with high number\n train_Y_mape[np.isnan(train_Y_mape)] = 100 ## Replacing NAN values with high number \n\n model_mape = LinearRegression()\n model_mape.fit(train_X, train_Y_mape)\n print(model_mape.coef_)\n print(model_mape.score(train_X, train_Y_mape))\n \n # Fit Model for sMAPE Performance Metric \n train_Y_smape = np.array(train_Y_smape)\n train_Y_smape[np.isinf(train_Y_smape)] = 100 ## Replacing infinity values with high number\n train_Y_smape[np.isnan(train_Y_smape)] = 100 ## Replacing NAN values with high number\n \n model_smape = LinearRegression()\n model_smape.fit(train_X, train_Y_smape)\n print(model_smape.coef_)\n print(model_smape.score(train_X, train_Y_smape))\n \n # Make a Prediction\n test_X = meta_features_test\n yhat_mse = model_mse.predict(test_X)\n yhat_mape = model_mape.predict(test_X) \n yhat_smape = model_smape.predict(test_X)\n \n # summarize prediction\n print(yhat_mse[0])\n print(yhat_mse)\n print(np.array(yhat_mse).shape)\n \n for test_idx in range(len(test_folds_list_uv[i])):\n \n ## Get the best model index from original data\n results_vec_mse, results_vec_mape, results_vec_smape, arr_models = Get_All_Model_Dataset(test_folds_list_uv[i][test_idx], uv_dir, 'Uni-var') \n \n best_model_dataset_index_mse_orig = results_vec_mse.index(min(results_vec_mse))\n best_model_dataset_index_mape_orig = results_vec_mape.index(min(results_vec_mape))\n best_model_dataset_index_smape_orig = results_vec_smape.index(min(results_vec_smape))\n \n \n ## Get the best model index from Autoforecast general meta-learner \n minimum_mse = np.min(yhat_mse[test_idx])\n best_model_dataset_index_mse = np.where(yhat_mse[test_idx] == minimum_mse)\n print('Autoforecast: ' + arr_models[best_model_dataset_index_mse[0][0]] + '(' + str(results_vec_mse[best_model_dataset_index_mse[0][0]]) + ')' + ' orig: ' + arr_models[best_model_dataset_index_mse_orig] + '(' + str(results_vec_mse[best_model_dataset_index_mse_orig]) + ')' )\n \n minimum_mape = np.min(yhat_mape[test_idx])\n best_model_dataset_index_mape = np.where(yhat_mape[test_idx] == minimum_mape)\n \n minimum_smape = np.min(yhat_smape[test_idx])\n best_model_dataset_index_smape = np.where(yhat_smape[test_idx] == minimum_smape)\n \n \n \n print('Fold Finished ............') \n \n #break\n '''\n\n \n\n\n\n", "sub_path": "src/Meta-Learner/Autoforecast_Train_timeseries_RNN.py", "file_name": "Autoforecast_Train_timeseries_RNN.py", "file_ext": "py", "file_size_in_byte": 35271, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 37, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 37, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 39, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 50, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 57, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 63, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 104, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 106, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 112, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 126, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 135, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 137, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 153, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 158, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 163, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 183, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 184, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 187, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 207, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 210, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 217, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 233, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 240, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 243, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 247, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 255, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 255, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 256, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 256, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 257, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 257, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 258, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 258, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 259, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 259, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 260, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 260, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 261, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 261, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 262, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 262, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 263, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 263, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 303, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 328, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 348, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 349, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 350, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 351, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 352, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 366, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 392, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 432, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 441, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 457, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 466, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 480, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 490, "usage_type": "call"}, {"api_name": "time.time", "line_number": 505, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 509, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 510, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 513, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.LSTM", "line_number": 515, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 516, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.LSTM", "line_number": 519, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 520, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 524, "usage_type": "call"}, {"api_name": "time.time", "line_number": 533, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 570, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 575, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 599, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 605, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 627, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 645, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 652, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 653, "usage_type": "call"}, {"api_name": "time.time", "line_number": 656, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 662, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 691, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 692, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 695, "usage_type": "call"}]} +{"seq_id": "382037113", "text": "#!/usr/bin/env python2\n\"\"\"\nThis module was made to wrap the hostapd\n\"\"\"\n\nimport os\nimport threading\nimport collections\nimport ctypes\nimport re\nimport roguehostapd.hostapd_constants as hostapd_constants\n\n\nclass KarmaData(ctypes.Structure):\n \"\"\"\n Handle the hostapd return mac/ssid data\n \"\"\"\n pass\n\n\nKarmaData._fields_ = [\n (\"is_assoc\", ctypes.c_ubyte),\n (\"ssid_len\", ctypes.c_size_t),\n (\"ssid\", ctypes.c_ubyte * 32),\n (\"mac_addr\", ctypes.c_ubyte * 6),\n (\"next_data\", ctypes.POINTER(KarmaData))]\n\n\nclass HostapdConfig(object):\n \"\"\"\n Handle the Hostapd configuration\n \"\"\"\n\n def __init__(self):\n \"\"\"\n Setup the class with all the given arguments\n\n :param self: A HostapdConfig object\n :type self: HostapdConfig\n :return: None\n :rtype: None\n \"\"\"\n\n # configurations for hostapd.conf\n self.configuration_dict = collections.defaultdict()\n # initialize the hostapd configuration\n self.initialize_hostapd_config()\n\n # configuration for hostapd command line options\n self.options = {\n 'debug_level': None,\n 'key_data': None,\n 'timestamp': None,\n 'version': None,\n 'mute': None,\n # Disable the eloop terminate in hostapd and control by\n # wifiphisher\n 'eloop_term_disable': None,\n }\n\n # custom action and relies on transformation by roguehostapd\n self.custom_action = {\n # the deny mac addresses\n 'deny_macs': self.update_black_macs,\n }\n # hostapd debug level\n self.debug_level = hostapd_constants.HOSTAPD_DEBUG_OFF\n\n def initialize_hostapd_config(self):\n \"\"\"\n Parse the hostapd.conf file in the hostapd source code and\n update to the attribute configuation_dict\n\n :param self: A HostapdConfig object\n :type self: HostapdConfig\n :return: None\n :rtype: None\n \"\"\"\n\n work_dir = os.path.dirname(os.path.abspath(__file__))\n hostapd_config = os.path.join(work_dir,\n hostapd_constants.HOSTAPD_DIR,\n 'hostapd.conf')\n # initialize the hostapd configuration dictionary\n with open(hostapd_config, 'r') as filep:\n for line in filep:\n m_obj = re.match(r'#([\\S-]+)=[\\S-].*$', line)\n if m_obj:\n key = m_obj.group(1)\n self.configuration_dict[key] = ''\n # initialize the basic information\n self.configuration_dict['ssid'] = hostapd_constants.SSID\n self.configuration_dict['channel'] = hostapd_constants.CHANNEL\n self.configuration_dict['beacon_int'] = hostapd_constants.BEACON_INT\n self.configuration_dict['hw_mode'] = hostapd_constants.HW_MODE\n self.configuration_dict['interface'] = hostapd_constants.INTERFACE\n self.configuration_dict['karma_enable'] = hostapd_constants.KARMA_ENABLE\n self.configuration_dict['deny_macs'] = []\n\n def update_black_macs(self, output_fp):\n \"\"\"\n Update the black mac addresses for hostapd\n\n :param self: A HostapdConfig object\n :param output_fp: Output file pointer\n :type self: HostapdConfig\n :type output_fp: file\n :return: None\n :rtype: None\n \"\"\"\n\n output_fp.write('macaddr_acl=0\\n')\n output_fp.write('deny_mac_file='+hostapd_constants.DENY_MACS_PATH+'\\n')\n with open(hostapd_constants.DENY_MACS_PATH, 'w') as writer:\n for mac_addr in self.configuration_dict['deny_macs']:\n writer.write(mac_addr+'\\n')\n\n def update_wps_configuration(self):\n \"\"\"\n Update the WPS configuration for hostapd\n\n :param self: A HostapdConfig object\n :type self: HostapdConfig\n :return: None\n :rtype: None\n \"\"\"\n\n # enable WPS\n self.configuration_dict['wps_state'] = '2'\n self.configuration_dict['ap_setup_locked'] = '1'\n self.configuration_dict['uuid'] = '12345678-9abc-def0-1234-56789abcdef0'\n self.configuration_dict['device_name'] = 'Wireless AP'\n self.configuration_dict['manufacturer'] = 'Company'\n self.configuration_dict['model_name'] = 'WAP'\n self.configuration_dict['model_number'] = '123'\n self.configuration_dict['serial_number'] = '12345'\n self.configuration_dict['device_type'] = '6-0050F204-1'\n self.configuration_dict['os_version'] = '01020300'\n self.configuration_dict['config_methods'] =\\\n 'label virtual_display virtual_push_button keypad'\n self.configuration_dict['eap_server'] = '1'\n\n def update_security_info(self, config_dict):\n \"\"\"\n Update the security configuration if passphrase is specified\n\n :param self: A HostapdConfig object\n :param config_dict: hostapd configuration dictionary\n :type self: HostapdConfig\n :type config_dict: dict\n :return: None\n :rtype: None\n \"\"\"\n\n # update WPS information\n self.update_wps_configuration()\n\n if 'wpa_passphrase' in config_dict and config_dict['wpa_passphrase']:\n self.configuration_dict['wpa_key_mgmt'] = \"WPA-PSK\"\n self.configuration_dict['wpa_pairwise'] = \"TKIP CCMP\"\n self.configuration_dict['wpa'] = '3'\n\n def update_configs(self, config_dict):\n \"\"\"\n Update the attributes based on the configuration dictionary\n\n :param self: A HostapdConfig object\n :param config_dict: hostapd configuration dictionary\n :type self: HostapdConfig\n :type config_dict: dict\n :return: None\n :rtype: None\n \"\"\"\n\n for key, value in config_dict.iteritems():\n if (key in self.configuration_dict) and value:\n self.configuration_dict[key] = value\n elif key not in self.configuration_dict:\n raise KeyError('Unsupported hostapd configuation!')\n\n self.update_security_info(config_dict)\n\n def _update_debug_level(self, options):\n \"\"\"\n Update the debug level from options dictionary\n\n :param self: A HostapdConfig object\n :type self: HostapdConfig\n :param options: configurations for command line options\n :type options: dict\n :return: None\n :rtype: None\n \"\"\"\n self.debug_level = options['debug_level']\n if self.debug_level == hostapd_constants.HOSTAPD_DEBUG_VERBOSE:\n self.options['debug_level'] = tuple(['-ddd'])\n\n def update_options(self, options):\n \"\"\"\n Update the comand line options\n\n :param self: A HostapdConfig object\n :type self: HostapdConfig\n :param options: configurations for command line options\n :type options: dict\n :return: None\n :rtype: None\n ..note: update the command line options\n \"\"\"\n\n for key in options:\n if key in self.options and options[key]:\n if key == 'debug_level':\n self._update_debug_level(options)\n elif key == 'key_data':\n self.options[key] = tuple(['-K'])\n elif key == 'timestamp':\n self.options[key] = tuple(['-t'])\n elif key == 'version':\n self.options[key] = tuple(['-v'])\n elif key == 'mute':\n self.options[key] = tuple(['-s'])\n elif key == 'eloop_term_disable':\n self.options[key] = tuple(['-E'])\n\n def write_configs(self, config_dict, options):\n \"\"\"\n Write the configurations to the file\n\n :param self: A HostapdConfig object\n :type self: HostapdConfig\n :param config_dict: configurations for hostapd.conf\n :type config_dict: dict\n :param options: hostapd command line options\n :type options: dict\n :return: None\n :rtype: None\n ..note: write the configuration file in the path /tmp/hostapd.conf\n \"\"\"\n\n self.update_options(options)\n self.update_configs(config_dict)\n with open(hostapd_constants.HOSTAPD_CONF_PATH, 'w') as conf:\n for key, value in self.configuration_dict.iteritems():\n if value:\n if key not in self.custom_action:\n conf.write(key + '=' + str(value) + '\\n')\n else:\n # callback for the custom action\n self.custom_action[key](conf)\n\n @classmethod\n def is_ssid_valid(cls, ssid):\n \"\"\"\n Check if the specified ssid is valid\n\n :param cls: A HostapdConfig class\n :param ssid: The service set identifier\n :type cls: HostapdConfig class\n :type ssid: str\n :return: True if the ssid is valid\n :rtype: bool\n \"\"\"\n\n return bool(len(ssid) < 33)\n\n\nclass Hostapd(object):\n \"\"\"\n Hostapd wrapper class\n \"\"\"\n\n def __init__(self):\n \"\"\"\n Contruct the class\n\n :param self: A Hostapd object\n :type self: Hostapd\n :return: None\n :rtype: None\n \"\"\"\n\n self.config_obj = None\n self.hostapd_thread = None\n self.hostapd_lib = None\n\n @staticmethod\n def _parse_karma_data(karma_data):\n \"\"\"\n get the associated clients' mac address and essid\n\n :param self: A Hostapd object\n :type self: Hostapd\n :param karma_data: A KarmaData object\n :type karma_data: KarmaData\n\n :return: A list of tuple of essid and mac address tuple\n :rtype: list\n \"\"\"\n\n ret = []\n if karma_data:\n current = karma_data\n while current:\n if current.contents.is_assoc:\n # convert ssid_len to integer\n ssid_len = int(current.contents.ssid_len)\n # convert mac address to string\n mac_addr = current.contents.mac_addr\n mac_l = [format(mac_addr[i], 'x') for i in range(6)]\n mac_str = ':'.join(mac_l)\n\n # convert ssid to string\n ssid_buf = current.contents.ssid\n ssid_list = [ssid_buf[i] for i in range(ssid_len)]\n ssid = ''.join(map(chr, ssid_list))\n ret.append((mac_str, ssid))\n current = current.contents.next_data\n return ret\n\n def get_karma_data(self):\n \"\"\"\n get the data for the KARMA attack victims from hostapd\n\n :param self: A Hostapd object\n :type self: Hostapd\n\n :return: A list of tuple of essid and mac address tuple\n :rtype: list\n \"\"\"\n\n karma_data = self.hostapd_lib.get_assoc_karma_data()\n mac_ssid_pairs = self._parse_karma_data(karma_data)\n return mac_ssid_pairs\n\n def is_alive(self):\n \"\"\"\n API for check if the hostapd thread is running\n :param self: A Hostapd object\n :type self: Hostapd\n :return: True if the hostapd is running else False\n :rtype: bool\n \"\"\"\n return self.hostapd_thread.is_alive()\n\n def start(self, hostapd_config, options):\n \"\"\"\n Start the hostapd process\n\n :param self: A Hostapd object\n :type self: Hostapd\n :param hostapd_config: Hostapd configuration for hostapd.conf\n :type hostapd_config: dict\n :param options: Hostapd command line options\n :type options: dict\n :return: None\n :rtype: None\n ..note: the start function uses ctypes to load the shared library\n of hostapd and use it to call the main function to lunch the AP\n \"\"\"\n\n self.config_obj = HostapdConfig()\n # update the hostapd configuration based on user input\n self.config_obj.write_configs(hostapd_config, options)\n\n work_dir = os.path.dirname(os.path.abspath(__file__))\n exe_path = os.path.join(work_dir, hostapd_constants.HOSTAPD_EXE_PATH)\n shared_lib_path = os.path.join(\n work_dir, hostapd_constants.HOSTAPD_SHARED_LIB_PATH)\n\n config_path = hostapd_constants.HOSTAPD_CONF_PATH\n\n # get the hostapd command to lunch the hostapd\n hostapd_cmd = [exe_path, config_path]\n for key in self.config_obj.options:\n if self.config_obj.options[key]:\n hostapd_cmd += self.config_obj.options[key]\n num_of_args = len(hostapd_cmd)\n str_arr_type = ctypes.c_char_p * num_of_args\n hostapd_cmd = str_arr_type(*hostapd_cmd)\n\n # get the hostapd shared library\n self.hostapd_lib = ctypes.cdll.LoadLibrary(shared_lib_path)\n\n # init hostapd lib info\n self.hostapd_lib.get_assoc_karma_data.restype = ctypes.POINTER(\n KarmaData)\n\n # start the hostapd thread\n self.hostapd_thread = threading.Thread(\n target=self.hostapd_lib.main, args=(len(hostapd_cmd), hostapd_cmd))\n self.hostapd_thread.start()\n\n def stop(self):\n \"\"\"\n Stop the hostapd\n\n :param self: A Hostapd object\n :type self: Hostapd\n :return: None\n :rtype: None\n ..note: the stop function uses the eloop_terminate function in hostapd\n shared library to stop AP.\n \"\"\"\n self.hostapd_lib.eloop_terminate()\n if self.hostapd_thread.is_alive():\n self.hostapd_thread.join(5)\n\n if os.path.isfile(hostapd_constants.HOSTAPD_CONF_PATH):\n os.remove(hostapd_constants.HOSTAPD_CONF_PATH)\n if os.path.isfile(hostapd_constants.DENY_MACS_PATH):\n os.remove(hostapd_constants.DENY_MACS_PATH)\n\nif __name__ == '__main__':\n\n HOSTAPD_CONFIG_DICT = {\n 'ssid': 'test',\n 'interface': 'wlan0',\n 'karma_enable': 1,\n 'deny_macs': ['00:00:00:11:22:33']\n }\n\n HOSTAPD_OPTION_DICT = {\n 'debug_level': hostapd_constants.HOSTAPD_DEBUG_OFF,\n 'key_data': True,\n 'timestamp': False,\n 'version': False,\n 'mute': True,\n 'eloop_term_disable': True}\n HOSTAPD_OBJ = Hostapd()\n HOSTAPD_OBJ.start(HOSTAPD_CONFIG_DICT, HOSTAPD_OPTION_DICT)\n import time\n while True:\n try:\n time.sleep(1)\n except KeyboardInterrupt:\n HOSTAPD_OBJ.stop()\n break\n", "sub_path": "roguehostapd/hostapd_controller.py", "file_name": "hostapd_controller.py", "file_ext": "py", "file_size_in_byte": 14468, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "ctypes.Structure", "line_number": 14, "usage_type": "attribute"}, {"api_name": "ctypes.c_ubyte", "line_number": 22, "usage_type": "attribute"}, {"api_name": "ctypes.c_size_t", "line_number": 23, "usage_type": "attribute"}, {"api_name": "ctypes.c_ubyte", "line_number": 24, "usage_type": "attribute"}, {"api_name": "ctypes.c_ubyte", "line_number": 25, "usage_type": "attribute"}, {"api_name": "ctypes.POINTER", "line_number": 26, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 45, "usage_type": "call"}, {"api_name": "roguehostapd.hostapd_constants.HOSTAPD_DEBUG_OFF", "line_number": 67, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 67, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 80, "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": "roguehostapd.hostapd_constants.HOSTAPD_DIR", "line_number": 82, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 82, "usage_type": "name"}, {"api_name": "re.match", "line_number": 87, "usage_type": "call"}, {"api_name": "roguehostapd.hostapd_constants.SSID", "line_number": 92, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 92, "usage_type": "name"}, {"api_name": "roguehostapd.hostapd_constants.CHANNEL", "line_number": 93, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 93, "usage_type": "name"}, {"api_name": "roguehostapd.hostapd_constants.BEACON_INT", "line_number": 94, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 94, "usage_type": "name"}, {"api_name": "roguehostapd.hostapd_constants.HW_MODE", "line_number": 95, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 95, "usage_type": "name"}, {"api_name": "roguehostapd.hostapd_constants.INTERFACE", "line_number": 96, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 96, "usage_type": "name"}, {"api_name": "roguehostapd.hostapd_constants.KARMA_ENABLE", "line_number": 97, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 97, "usage_type": "name"}, {"api_name": "roguehostapd.hostapd_constants.DENY_MACS_PATH", "line_number": 113, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 113, "usage_type": "name"}, {"api_name": "roguehostapd.hostapd_constants.DENY_MACS_PATH", "line_number": 114, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 114, "usage_type": "name"}, {"api_name": "roguehostapd.hostapd_constants.HOSTAPD_DEBUG_VERBOSE", "line_number": 195, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 195, "usage_type": "name"}, {"api_name": "roguehostapd.hostapd_constants.HOSTAPD_CONF_PATH", "line_number": 243, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 243, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 366, "usage_type": "call"}, {"api_name": "os.path", "line_number": 366, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 366, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 367, "usage_type": "call"}, {"api_name": "os.path", "line_number": 367, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants.HOSTAPD_EXE_PATH", "line_number": 367, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 367, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 368, "usage_type": "call"}, {"api_name": "os.path", "line_number": 368, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants.HOSTAPD_SHARED_LIB_PATH", "line_number": 369, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 369, "usage_type": "name"}, {"api_name": "roguehostapd.hostapd_constants.HOSTAPD_CONF_PATH", "line_number": 371, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 371, "usage_type": "name"}, {"api_name": "ctypes.c_char_p", "line_number": 379, "usage_type": "attribute"}, {"api_name": "ctypes.cdll.LoadLibrary", "line_number": 383, "usage_type": "call"}, {"api_name": "ctypes.cdll", "line_number": 383, "usage_type": "attribute"}, {"api_name": "ctypes.POINTER", "line_number": 386, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 390, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 409, "usage_type": "call"}, {"api_name": "os.path", "line_number": 409, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants.HOSTAPD_CONF_PATH", "line_number": 409, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 409, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 410, "usage_type": "call"}, {"api_name": "roguehostapd.hostapd_constants.HOSTAPD_CONF_PATH", "line_number": 410, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 410, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 411, "usage_type": "call"}, {"api_name": "os.path", "line_number": 411, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants.DENY_MACS_PATH", "line_number": 411, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 411, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 412, "usage_type": "call"}, {"api_name": "roguehostapd.hostapd_constants.DENY_MACS_PATH", "line_number": 412, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 412, "usage_type": "name"}, {"api_name": "roguehostapd.hostapd_constants.HOSTAPD_DEBUG_OFF", "line_number": 424, "usage_type": "attribute"}, {"api_name": "roguehostapd.hostapd_constants", "line_number": 424, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 435, "usage_type": "call"}]} +{"seq_id": "413580606", "text": "#!/usr/bin/env python\n\n\"\"\"\nCreated by: Lee Bergstrand (2017)\n\nDescription: The genome property class.\n\"\"\"\nfrom modules.database_reference import parse_database_references\nfrom modules.literature_reference import parse_literature_references\nfrom modules.step import parse_steps\n\n\nclass GenomeProperty(object):\n \"\"\"\n Represents a EBI Interpro genome property. Each represents specific capabilities of an\n organism as proven by the presence of genes found in its genome.\n \"\"\"\n\n def __init__(self, accession_id, name, property_type, threshold=0,\n parent=None, references=None, databases=None, steps=None,\n public=False, description=None, private_notes=None):\n \"\"\"\n Creates a new GenomeProperty object.\n :param accession_id: The genome property accession (i.e. \"GenProp00286\").\n :param name: The name of the genome property.\n :param property_type: The type of genome property (ex. \"METAPATH\").\n :param threshold: Is a threshold that the number of required steps must exceed.\n :param parent: The parent genome property of the current genome property (parent accession or direct link).\n :param references: A list of reference objects which help support the existence of the property.\n :param databases: A list of database objects which represent database entries related to the property.\n :param steps: A list of step objects that are part of the property.\n :param public: Boolean detailing if the genome property should be public.\n :param description: A detailed description of the genome property.\n :param private_notes: Private notes about the property a potential problems with it.\n \"\"\"\n if steps is None:\n steps = []\n if databases is None:\n databases = []\n if references is None:\n references = []\n\n self.id = accession_id\n self.name = name\n self.type = property_type\n self.threshold = threshold\n self.references = references\n self.databases = databases\n self.parent = parent\n self.steps = steps\n self.public = public\n self.description = description\n self.private_notes = private_notes\n\n def __repr__(self):\n has_references = False\n has_steps = False\n has_databases = False\n\n if self.references:\n has_references = True\n\n if self.steps:\n has_steps = True\n\n if self.databases:\n has_databases = True\n\n repr_data = [str(self.id),\n 'Type: ' + str(self.type),\n 'Name: ' + str(self.name),\n 'Thresh: ' + str(self.threshold),\n 'References: ' + str(has_references),\n 'Databases: ' + str(self.type),\n 'Steps: ' + str(has_steps),\n 'Parent: ' + str(has_databases),\n 'Public: ' + str(self.public)]\n\n return ', '.join(repr_data)\n\n\ndef parse_genome_property(genome_property_record):\n \"\"\"\n Parses a single genome property from a genome property record.\n :param genome_property_record: A list of marker, content tuples representing genome property flat file lines.\n :return: A single genome property object.\n \"\"\"\n # A list of record markers related to the genome property.\n core_genome_property_markers = ('AC', 'DE', 'TP', 'TH', 'PN', 'CC', '**')\n gathered_core_genome_property_markers = {}\n\n reference_index = False\n database_index = False\n step_index = False\n\n current_index = 0\n for marker, content in genome_property_record:\n if marker == 'RN':\n if not reference_index:\n reference_index = current_index\n elif marker == 'DC':\n if not database_index:\n database_index = current_index\n elif marker == '--':\n step_index = current_index + 1\n break # If we have reach steps we have covered all core_genome_property_markers and can leave the loop.\n elif marker in core_genome_property_markers:\n if marker == 'TH':\n content = int(content)\n gathered_core_genome_property_markers[marker] = content\n\n current_index = current_index + 1\n\n if reference_index:\n if database_index:\n reference_rows = genome_property_record[reference_index:database_index]\n else:\n reference_rows = genome_property_record[reference_index:]\n\n references = parse_literature_references(reference_rows)\n else:\n references = []\n\n if database_index:\n if step_index:\n database_rows = genome_property_record[database_index:step_index - 1]\n else:\n database_rows = genome_property_record[database_index:]\n\n databases = parse_database_references(database_rows)\n else:\n databases = []\n\n if step_index:\n step_rows = genome_property_record[step_index:]\n steps = parse_steps(step_rows)\n else:\n steps = []\n\n new_genome_property = GenomeProperty(accession_id=gathered_core_genome_property_markers.get('AC'),\n name=gathered_core_genome_property_markers.get('DE'),\n property_type=gathered_core_genome_property_markers.get('TP'),\n threshold=gathered_core_genome_property_markers.get('TH'),\n parent=gathered_core_genome_property_markers.get('PN'),\n description=gathered_core_genome_property_markers.get('CC'),\n private_notes=gathered_core_genome_property_markers.get('**'),\n references=references,\n databases=databases,\n steps=steps)\n return new_genome_property\n", "sub_path": "modules/genome_property.py", "file_name": "genome_property.py", "file_ext": "py", "file_size_in_byte": 6009, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "modules.literature_reference.parse_literature_references", "line_number": 120, "usage_type": "call"}, {"api_name": "modules.database_reference.parse_database_references", "line_number": 130, "usage_type": "call"}, {"api_name": "modules.step.parse_steps", "line_number": 136, "usage_type": "call"}]} +{"seq_id": "482362116", "text": "from django.conf.urls import url\n\nfrom . import views\n\nurlpatterns = [\n url(r'^$', views.main_app, name='main_app'),\n url(r'orders/$', views.orders, name='orders'),\n url(r'dicts/$', views.dicts, name='dicts'),\n url(r'dicts/services/$', views.dict_services_list, name='dict_services_list'),\n url(r'dicts/clients/$', views.dict_clients_list, name='dict_clients_list'),\n url(r'dicts/clients/(?P[0-9]+)/$', views.dict_clients_detail, name='dict_clients_detail'),\n]", "sub_path": "barbershopoffice/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 489, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"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"}]} +{"seq_id": "229714193", "text": "import nltk\nimport sys\nfrom nltk import load_parser\nimport subprocess\n\n#sentence = 'Angus gives a bone to every dog'.split()\n\n#sentence = 'You are imagining things'.split()\n#sentence = 'There is a price on my head'.split()\nsentence = 'your big opportunity is flying out of here'.split()\n\nsentence2 = str(sys.argv[1]).split()\n\ncp = load_parser('cfgrammar.fcfg', trace=0)\n\n\nfor tree in cp.parse(sentence2):\n print(tree.label()['SEM'])\n #for Java\n file = open(\"../../Java/Traduttore/src/fol.txt\", \"w\")\n file.write(str(tree.label()['SEM']))\n file.close()\n\n #for Python\n file = open(\"./src/fol.txt\", \"w\")\n file.write(str(tree.label()['SEM']))\n file.close()\n break\n\n\nsubprocess.call(['java', '-jar', '../../Java/Traduttore/Traduttore.jar'])\n\n\n\n", "sub_path": "Python/TLN-Mazzei/translator.py", "file_name": "translator.py", "file_ext": "py", "file_size_in_byte": 768, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "nltk.load_parser", "line_number": 14, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "21979772", "text": "from bson.binary import Binary, USER_DEFINED_SUBTYPE\nimport pickle\nfrom pymongo import MongoClient\n\nfrom database.entities.datasetentities import DatasetImage, DatasetLabel, DatasetEntity\nfrom common.logger import get_logger\n\nmy_logger = get_logger(__name__)\n\nSERVER = \"localhost\"\nDB = \"dataset\"\nCOL_IMG_CLASS = \"classimages\"\nCOL_LABEL = \"labels\"\nCOL_LABEL_NAMES = \"labelnames\"\n\n\nclass DatasetDB(object):\n\n def __init__(self, datasetcollections):\n my_logger.info(\"Starting up Dataset Database.\")\n client = MongoClient(SERVER, 27017)\n db = client[DB]\n self.class_image_collection = db[COL_IMG_CLASS]\n self.label_collection = db[COL_LABEL]\n self.label_names = db[COL_LABEL_NAMES]\n my_logger.info(\"Dataset Database has been initiated.\")\n\n def insert_image_class(self, image_data): # image must be RGB\n my_logger.info(\"Attempting to insert image with image_id \" + str(image_data.get_image_id()))\n image_data = image_data.__dict__\n image_data['image'] = Binary(pickle.dumps(image_data['image']), subtype=USER_DEFINED_SUBTYPE)\n self.class_image_collection.insert_one(image_data)\n my_logger.info(\"Image has been inserted for image_id \" + str(image_data['image_id']))\n\n def insert_label_data(self, label_data):\n my_logger.info(\"Attempting to insert label with image_id \" + str(label_data.get_image_id()))\n label_data = label_data.__dict__\n self.label_collection.insert_one(label_data)\n my_logger.info(\"Label has been inserted for image_id \" + str(label_data['image_id']))\n\n def insert_label_names(self, label_name):\n my_logger.info(\"Attempting to insert label name \" + label_name.get_name())\n label_name = label_name.__dict__\n self.label_names.insert_one(label_name)\n my_logger.info(\"Label names has been inserted with name\" + label_name['name'])\n\n def read_image_class_data(self, start_id, end_id):\n my_logger.info(\"Attempting to read image with start_id \" + str(start_id) + \" and end_id \" + str(end_id))\n results = self.class_image_collection.find({\"image_id\": {\"$gte\": start_id, \"$lte\": end_id}})\n if results is None:\n return None\n dataset = list()\n for data in list(results):\n dataset.append(DatasetImage(data['_id'], data['filename'], pickle.loads(data['image'])))\n my_logger.info(\"Images count found: \" + str(len(dataset)))\n return dataset\n\n def read_label_data(self, start_id, end_id):\n my_logger.info(\"Attempting to label with start_id \" + str(start_id) + \" and end_id \" + str(end_id))\n results = self.label_collection.find({\"image_id\": {\"$gte\": start_id, \"$lte\": end_id}})\n #results = self.label_collection.find({\"type\": type_of_data, \"image_id\": {\"$gte\": start_id, \"$lte\": end_id}})\n if results is None:\n return None\n dataset = list()\n for data in list(results):\n dataset.append(DatasetLabel(data['image_id'], data['source'], data['type'], data['label']))\n my_logger.info(\"Labels count found: \" + str(len(dataset)))\n return dataset\n\n def read_image_class_and_label_data(self, start_id, end_id, type_of_data):\n images = self.read_image_class_data(start_id, end_id)\n labels = self.read_label_data(start_id, end_id)\n return DatasetEntity(images, labels)\n", "sub_path": "database/datasetdb.py", "file_name": "datasetdb.py", "file_ext": "py", "file_size_in_byte": 3385, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "common.logger.get_logger", "line_number": 8, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 21, "usage_type": "call"}, {"api_name": "bson.binary.Binary", "line_number": 31, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 31, "usage_type": "call"}, {"api_name": "bson.binary.USER_DEFINED_SUBTYPE", "line_number": 31, "usage_type": "name"}, {"api_name": "database.entities.datasetentities.DatasetImage", "line_number": 54, "usage_type": "call"}, {"api_name": "pickle.loads", "line_number": 54, "usage_type": "call"}, {"api_name": "database.entities.datasetentities.DatasetLabel", "line_number": 66, "usage_type": "call"}, {"api_name": "database.entities.datasetentities.DatasetEntity", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "77009557", "text": "import pylab as plt\nfrom sklearn.datasets import fetch_mldata\nimport numpy as np\n\n\ndef save(image, name):\n fig = plt.figure()\n ax = fig.add_subplot(1, 1, 1)\n imgplot = ax.imshow(image, cmap=plt.cm.Greys)\n imgplot.set_interpolation('nearest')\n ax.xaxis.set_ticks_position('top')\n ax.yaxis.set_ticks_position('left')\n plt.imsave(name)\n # plt.savefig(name)\n\nmnist = fetch_mldata('MNIST original', data_home=\".\")\ny = mnist.target\nX = - mnist.data.reshape(len(y), 28, 28) + 255\n\ncounter = np.zeros(10)\nfrom itertools import izip\nfor image, label in izip(X, y):\n label = int(label)\n plt.imsave(\"%d_%d.png\" % (label, counter[label]), image, cmap=plt.cm.gray)\n counter[label] += 1\n", "sub_path": "download_mnist.py", "file_name": "download_mnist.py", "file_ext": "py", "file_size_in_byte": 706, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pylab.figure", "line_number": 7, "usage_type": "call"}, {"api_name": "pylab.cm", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pylab.imsave", "line_number": 13, "usage_type": "call"}, {"api_name": "sklearn.datasets.fetch_mldata", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 20, "usage_type": "call"}, {"api_name": "itertools.izip", "line_number": 22, "usage_type": "call"}, {"api_name": "pylab.imsave", "line_number": 24, "usage_type": "call"}, {"api_name": "pylab.cm", "line_number": 24, "usage_type": "attribute"}]} +{"seq_id": "124065022", "text": "from django.test import TestCase, Client\nfrom django.test.client import RequestFactory\nfrom .views import *\nfrom .models import Movie\nfrom django.core.urlresolvers import reverse\nfrom datetime import datetime\n\nclass Views_Test(TestCase):\n\n def setUp(self):\n self.movies = [\n Movie.objects.create(title = \"Film\", genere = \"komedia\", time = 120, text = \"nannanaa\"),\n Movie.objects.create(title = \"Siema\", genere = \"akcja\", time = 110, text = \"nana\"),\n Movie.objects.create(title = \"Komedia\", genere = \"komedia\", time = 130, text = \"nanna\")\n ]\n self.movie = Movie.objects.create(title = \"Fajny\", genere = \"Komedia\", time = 100, text = \"annaan\")\n\n self.room = Room.objects.create(seat_count = 20, room_number = 2)\n self.showing = Showing.objects.create(\n movie = self.movies[0],\n # Do zmiany - niech bę∂zie godzina seansu zamiast now()\n time = datetime.now(),\n room = self.room\n )\n \n self.client = Client()\n\n def test_movie_list(self):\n url = reverse('movie_list')\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, 'cinema/movie_list.html')\n\n def test_movie_list_dos(self):\n url = reverse('movie_list')\n rf = RequestFactory()\n request = rf.get(url)\n\n movies = Movie.objects.order_by('title')\n expected_response = render(request, 'cinema/movie_list.html', {'movies': movies})\n\n response = movie_list(request)\n self.assertEqual(response.status_code, 200)\n self.assertEqual(response.content, expected_response.content)\n \n def test_reservation_form(self):\n url = reverse('reservation_new', kwargs={\"pk\": self.showing.pk})\n\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, 'cinema/reservation_new.html')\n\n\n def test_reservation_form_showing_not_exist(self):\n url = reverse('reservation_new', kwargs={\"pk\": 0})\n\n response = self.client.get(url)\n self.assertEqual(response.status_code, 404)\n\n def test_showing_form(self):\n url = reverse('showing_new', kwargs={\"pk\": self.movies[0].pk})\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, 'cinema/showing_new.html')\n\n def test_movie_detail(self):\n url = reverse('movie_detail', kwargs={\"pk\": self.movie.pk})\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, 'cinema/movie_detail.html')\n\n def test_showing_delete(self):\n url = reverse('showing_delete', kwargs={\"pk\": self.showing.pk})\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, 'cinema/showing_delete_success.html')\n\n def test_showing_confirm_delete(self):\n url = reverse('showing_confirm_delete', kwargs={\"pk\": self.showing.pk})\n response = self.client.get(url)\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, 'cinema/showing_remove.html')\n\n def test_reservation_new_form_post(self):\n url = reverse('reservation_new', kwargs={\"pk\": self.showing.pk})\n response = self.client.post(url, {'seat_count': 2, 'email': 'katarzyna@op.pl'})\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, 'cinema/reservation_success.html')\n\n def test_showing_new_form_post(self):\n url = reverse('showing_new', kwargs={\"pk\": self.movies[0].pk})\n response = self.client.post(url, {'time': '2018-12-12 10:00', 'room': self.room.pk})\n self.assertEqual(response.status_code, 302)\n self.assertRedirects(response, reverse('movie_list'))\n\n def test_reservation_new_form_empty_email(self):\n url = reverse('reservation_new', kwargs={\"pk\": self.showing.pk})\n response = self.client.post(url, {})\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, 'cinema/reservation_new.html')\n\n", "sub_path": "cinema/test_views.py", "file_name": "test_views.py", "file_ext": "py", "file_size_in_byte": 4248, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.test.TestCase", "line_number": 8, "usage_type": "name"}, {"api_name": "models.Movie.objects.create", "line_number": 12, "usage_type": "call"}, {"api_name": "models.Movie.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "models.Movie", "line_number": 12, "usage_type": "name"}, {"api_name": "models.Movie.objects.create", "line_number": 13, "usage_type": "call"}, {"api_name": "models.Movie.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "models.Movie", "line_number": 13, "usage_type": "name"}, {"api_name": "models.Movie.objects.create", "line_number": 14, "usage_type": "call"}, {"api_name": "models.Movie.objects", "line_number": 14, "usage_type": "attribute"}, {"api_name": "models.Movie", "line_number": 14, "usage_type": "name"}, {"api_name": "models.Movie.objects.create", "line_number": 16, "usage_type": "call"}, {"api_name": "models.Movie.objects", "line_number": 16, "usage_type": "attribute"}, {"api_name": "models.Movie", "line_number": 16, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "name"}, {"api_name": "django.test.Client", "line_number": 26, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 29, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 35, "usage_type": "call"}, {"api_name": "django.test.client.RequestFactory", "line_number": 36, "usage_type": "call"}, {"api_name": "models.Movie.objects.order_by", "line_number": 39, "usage_type": "call"}, {"api_name": "models.Movie.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "models.Movie", "line_number": 39, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 47, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 55, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 61, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 67, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 73, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 79, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 85, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 91, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 94, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 97, "usage_type": "call"}]} +{"seq_id": "614030158", "text": "import numpy as np\n\nimport chainer\nimport chainer.links as L\nimport chainer.functions as F\nimport threading, random\n\nfrom myML.rl.model import PolicyValueModel, DisCreteSoftMaxPolicyValueModel\nfrom myML.rl.learner import AsyncLearner\n\n\nclass PPOLearner(AsyncLearner):\n def __init__(self, model: PolicyValueModel, optimizer: chainer.Optimizer, *, batch_size=32,\n num_train_per_episode=15, eps=0.2):\n super().__init__(model, optimizer, batch_size=batch_size)\n self.old_model = self.copy_model()\n self.num_train_per_episode = num_train_per_episode\n self.eps = eps\n\n def clear_buffer(self):\n self.train_buffer = []\n\n def push_train_buffer(self, state, action, reward):\n self.train_buffer.append((state, action, reward))\n\n def get_data_from_train_buffer(self):\n batch_size = self.batch_size if len(self.train_buffer) > self.batch_size else len(self.train_buffer)\n\n datas_index = random.sample(range(len(self.train_buffer)), batch_size)\n\n # rearange dates\n states = []\n actions = []\n advantages = []\n for index in datas_index:\n state, action, advantage = self.train_buffer[index]\n states.append(state)\n actions.append(action)\n advantages.append(advantage)\n\n states = np.array(states).astype(np.float32)\n actions = np.array(actions).astype(np.int32)\n advantages = np.array(advantages).astype(np.float32)\n return states, actions, advantages\n\n def update_model(self):\n # start minibatch learning\n for t in range(self.num_train_per_episode):\n # get learning data\n with self.lock:\n states, actions, advantages = self.get_data_from_train_buffer()\n # get policy and value\n policies, values = self.model(states)\n old_policies, _ = self.old_model(states)\n\n # calculate loss\n loss_v = F.squared_error(values, np.array(advantages).astype(np.float32))\n loss_ent = -policies.entropy()\n\n r = (policies.get_prob(actions) + 1.0e-10) / (old_policies.get_prob(actions) + 1.0e-10)\n loss_clip = (advantages - values.data) * F.minimum(r, F.clip(r, 1.0 - self.eps, 1.0 + self.eps))\n\n loss = F.mean(-loss_clip + loss_v * 0.2 + 0.01 * loss_ent)\n\n self.model.cleargrads()\n loss.backward()\n self.optimizer.update()\n # update old model\n self.old_model = self.copy_model()\n self.clear_buffer()\n\n\nclass PPO:\n def __init__(self, model: PolicyValueModel, make_env_func=None, *,\n lr=1e-3, batch_size=32, gamma=0.99, lam=0.95, t_max=8, clip_eps=0.2, num_episode=200,\n num_steps_per_episode=200,\n eps_start=0.4, eps_end=0.15, eps_steps=75000, num_train_per_episode=15):\n if make_env_func is None:\n raise Exception(\"set make_env_func:Callable\")\n self.make_env_func = make_env_func\n\n # share learner\n self.learner = PPOLearner(model, chainer.optimizers.RMSprop(lr=lr), batch_size=batch_size,\n num_train_per_episode=num_train_per_episode, eps=clip_eps)\n\n # setting\n self.eps_start = eps_start\n self.eps_end = eps_end\n self.eps_steps = eps_steps\n self.t_max = t_max\n self.gamma = gamma\n self.lam = lam\n self.num_episode = num_episode\n self.num_steps_per_episode = num_steps_per_episode\n\n # flag\n self.on_explore = False\n\n def async_explore(self, learner: PPOLearner, explorer_event: threading.Event, learner_event: threading.Event):\n # make env\n env = self.make_env_func()\n\n # individual model\n model = learner.copy_model()\n # wait updating model\n explorer_event.wait()\n explorer_event.clear()\n while self.on_explore:\n episode_reward = 0.0\n state = env.reset()\n sar_que = []\n for t in range(1, self.num_steps_per_episode):\n eps = max(self.eps_start - (self.eps_start - self.eps_end) * learner.step / self.eps_steps,\n self.eps_end)\n\n action = model.get_eps_greedy_action(state, eps)\n\n next_state, reward, done, _ = env.step(action)\n\n sar_que.append([state, action, reward])\n learner.step += 1\n episode_reward += reward\n\n if t % self.t_max == 0 or done:\n # calculate generalize advantage estimation\n # A_t=sum_{i=0}^{T-t}(gamma*lambda)^i * delta_{t+i}\n # delta_{t}:=gamma * V(s_{t+1}) + r_t - V(s_t)\n # lambda : [0,1]\n # in PPO, lambda=0.95\n\n # here calculate A_t + V(s_t)\n R = 0.0\n if not done:\n R += self.gamma * model.get_value([state]).data[0][0]\n\n for s, a, r in reversed(sar_que):\n R += r\n v = model.get_value([s])\n with learner.lock:\n learner.push_train_buffer(s, a, [R])\n R *= self.gamma * self.lam\n R += self.gamma * (1 - self.lam) * v.data[0][0]\n\n sar_que = []\n\n # start updateding model\n learner_event.set()\n\n # wait updating model\n explorer_event.wait()\n if self.on_explore:\n explorer_event.clear()\n # sync model\n model = learner.copy_model()\n\n if done:\n break\n state = next_state\n\n def start(self, num_explorer: int):\n # event to controll thread\n explorer_event = threading.Event()\n learner_events = [threading.Event() for i in range(num_explorer)]\n\n # evaluate env\n eval_env = self.make_env_func()\n explorers = [threading.Thread(target=self.async_explore, args=(self.learner, explorer_event, learner_events[i]))\n for i in range(num_explorer)]\n\n self.on_explore = True\n for explore in explorers:\n explore.start()\n\n for episode in range(self.num_episode):\n # restart explorer\n explorer_event.set()\n\n # wait explorer process\n for learner_event in learner_events:\n learner_event.wait()\n learner_event.clear()\n # start learner process\n self.learner.update_model()\n\n # evalurate\n model = self.learner.get_model()\n episode_reward = 0.0\n for e in range(2):\n s = eval_env.reset()\n for t in range(200):\n a = model.get_action(s)\n s, r, d, _ = eval_env.step(a)\n episode_reward += r\n if d:\n break\n episode_reward /= 2\n print(\"episode={}:score={}\".format(episode, episode_reward))\n\n self.on_explore = False\n explorer_event.set()\n for explore in explorers:\n explore.join()\n\n\nif __name__ == '__main__':\n import gym\n\n\n class myPVModel(DisCreteSoftMaxPolicyValueModel):\n def __init__(self, num_hidden: int, num_action: int):\n super().__init__(num_hidden, num_action)\n with self.init_scope():\n self.l1 = L.Linear(4, num_hidden)\n self.l2 = L.Linear(num_hidden, num_hidden)\n\n def __call__(self, x):\n x = np.array(x).astype(np.float32)\n h = F.relu(self.l1(x))\n h = F.relu(self.l2(h))\n return super().__call__(h)\n\n\n def make_env():\n return gym.make(\"CartPole-v0\")\n\n\n a = PPO(myPVModel(10, 2), make_env, lr=5e-3)\n a.start(8)\n", "sub_path": "myML/rl/ppo.py", "file_name": "ppo.py", "file_ext": "py", "file_size_in_byte": 8012, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "myML.rl.learner.AsyncLearner", "line_number": 12, "usage_type": "name"}, {"api_name": "myML.rl.model.PolicyValueModel", "line_number": 13, "usage_type": "name"}, {"api_name": "chainer.Optimizer", "line_number": 13, "usage_type": "attribute"}, {"api_name": "random.sample", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 43, "usage_type": "attribute"}, {"api_name": "chainer.functions.squared_error", "line_number": 57, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 57, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 57, "usage_type": "attribute"}, {"api_name": "chainer.functions.minimum", "line_number": 61, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 61, "usage_type": "name"}, {"api_name": "chainer.functions.clip", "line_number": 61, "usage_type": "call"}, {"api_name": "chainer.functions.mean", "line_number": 63, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 63, "usage_type": "name"}, {"api_name": "myML.rl.model.PolicyValueModel", "line_number": 74, "usage_type": "name"}, {"api_name": "chainer.optimizers.RMSprop", "line_number": 83, "usage_type": "call"}, {"api_name": "chainer.optimizers", "line_number": 83, "usage_type": "attribute"}, {"api_name": "threading.Event", "line_number": 99, "usage_type": "attribute"}, {"api_name": "threading.Event", "line_number": 162, "usage_type": "call"}, {"api_name": "threading.Event", "line_number": 163, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 167, "usage_type": "call"}, {"api_name": "myML.rl.model.DisCreteSoftMaxPolicyValueModel", "line_number": 209, "usage_type": "name"}, {"api_name": "chainer.links.Linear", "line_number": 213, "usage_type": "call"}, {"api_name": "chainer.links", "line_number": 213, "usage_type": "name"}, {"api_name": "chainer.links.Linear", "line_number": 214, "usage_type": "call"}, {"api_name": "chainer.links", "line_number": 214, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 217, "usage_type": "attribute"}, {"api_name": "chainer.functions.relu", "line_number": 218, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 218, "usage_type": "name"}, {"api_name": "chainer.functions.relu", "line_number": 219, "usage_type": "call"}, {"api_name": "chainer.functions", "line_number": 219, "usage_type": "name"}, {"api_name": "gym.make", "line_number": 224, "usage_type": "call"}]} +{"seq_id": "366050943", "text": "\"\"\"\nEmpirically evaluate the asymptotic error behavior of\ndifferent numerical methods of solving diff eqs.\n\"\"\"\n\nimport math\n\nfrom scipy.integrate import odeint\n\nfrom diffeq.numerical import (euler_step, rk2_step, rk3_step, third_order_step, fourth_order_step,\n numerical_solve)\n\n\nSTEP_FNS = {\n 'euler': euler_step,\n 'rk2': rk2_step,\n 'rk3': rk3_step,\n '3rd': third_order_step,\n '4th': fourth_order_step,\n}\n\n\ndef main():\n print('Evaluating single steps...')\n evaluate_steps()\n print('Evaluating full solutions...')\n evaluate_solutions()\n\n\ndef evaluate_steps():\n fns = [lambda x, y: y ** 2 - x,\n lambda x, y: x ** 2 - y,\n lambda x, y: math.sin(x) * math.cos(y)]\n for i, fn in enumerate(fns):\n print('Equation %d:' % i)\n solution1 = odeint(fn, 0, [1, 1.15], tfirst=True)[-1][0]\n solution2 = odeint(fn, 0, [1, 1.3], tfirst=True)[-1][0]\n for name, step_fn in STEP_FNS.items():\n _, approx1 = step_fn(fn, 1, 0, 0.15)\n _, approx2 = step_fn(fn, 1, 0, 0.3)\n error1 = abs(approx1 - solution1)\n error2 = abs(approx2 - solution2)\n print(' - %s: halving factor %f (error=%e)' % (name, error2 / error1, error1))\n\n\ndef evaluate_solutions():\n fns = [lambda x, y: y ** 2 - x,\n lambda x, y: x ** 2 - y,\n lambda x, y: math.sin(x) * math.cos(y)]\n for i, fn in enumerate(fns):\n print('Equation %d:' % i)\n solution = odeint(fn, 0, [1, 2], tfirst=True)[-1][0]\n for name, step_fn in STEP_FNS.items():\n solution1 = last(numerical_solve(fn, 1, 0, 2, h=0.1, step_fn=step_fn))[1]\n solution2 = last(numerical_solve(fn, 1, 0, 2, h=0.05, step_fn=step_fn))[1]\n error1 = abs(solution - solution1)\n error2 = abs(solution - solution2)\n print(' - %s: halving factor %f (error=%e)' % (name, error1 / error2, error2))\n\n\ndef last(iterator):\n y = None\n for x in iterator:\n y = x\n return y\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "eval_numerical.py", "file_name": "eval_numerical.py", "file_ext": "py", "file_size_in_byte": 2071, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "diffeq.numerical.euler_step", "line_number": 15, "usage_type": "name"}, {"api_name": "diffeq.numerical.rk2_step", "line_number": 16, "usage_type": "name"}, {"api_name": "diffeq.numerical.rk3_step", "line_number": 17, "usage_type": "name"}, {"api_name": "diffeq.numerical.third_order_step", "line_number": 18, "usage_type": "name"}, {"api_name": "diffeq.numerical.fourth_order_step", "line_number": 19, "usage_type": "name"}, {"api_name": "math.sin", "line_number": 33, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 33, "usage_type": "call"}, {"api_name": "scipy.integrate.odeint", "line_number": 36, "usage_type": "call"}, {"api_name": "scipy.integrate.odeint", "line_number": 37, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 49, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 49, "usage_type": "call"}, {"api_name": "scipy.integrate.odeint", "line_number": 52, "usage_type": "call"}, {"api_name": "diffeq.numerical.numerical_solve", "line_number": 54, "usage_type": "call"}, {"api_name": "diffeq.numerical.numerical_solve", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "71843687", "text": "import plot\nimport simulation\nimport json\nimport os\n\n\ndef simulations(directory):\n\n\thistories = []\n\tgraphs = []\n\tfail = []\n\n\tenv = json.loads(open(\"presets/{}.json\".format(directory), \"r\").read())\n\n\tfor i in range(10):\n\t\th, g, failures = simulation.simulate(env)\n\t\thistories.append(h)\n\t\tgraphs.append(g)\n\t\tfail.append(failures)\n\n\tif not os.path.exists(\"plots/{}\".format(directory)):\n\t\tos.mkdir(\"plots/{}\".format(directory))\n\tplot.plot_multiple_histories(histories, directory)\n\tplot.plot_wealth_distribution(graphs, directory)\n\tplot.plot_wealth_distribution_in(graphs, directory)\n\n\tplot.plot_path_length(graphs[0], directory)\n\n\tundirected = []\n\n\tfor gs in graphs:\n\t\tundirected.append(gs.to_undirected())\n\n\tplot.plot_robustness_random(undirected, 10, directory)\n\tplot.plot_robustness_coordinated(undirected, 7, directory)\n\n\tprint(fail)\n\n\nif __name__ == \"__main__\":\n\tsimulations(\"wealth_paste\")\n", "sub_path": "src/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 892, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "json.loads", "line_number": 13, "usage_type": "call"}, {"api_name": "simulation.simulate", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 22, "usage_type": "call"}, {"api_name": "plot.plot_multiple_histories", "line_number": 23, "usage_type": "call"}, {"api_name": "plot.plot_wealth_distribution", "line_number": 24, "usage_type": "call"}, {"api_name": "plot.plot_wealth_distribution_in", "line_number": 25, "usage_type": "call"}, {"api_name": "plot.plot_path_length", "line_number": 27, "usage_type": "call"}, {"api_name": "plot.plot_robustness_random", "line_number": 34, "usage_type": "call"}, {"api_name": "plot.plot_robustness_coordinated", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "537092005", "text": "from __future__ import annotations\n\nfrom collections import OrderedDict\nfrom collections.abc import Coroutine\nfrom dataclasses import dataclass, field\nfrom datetime import datetime, timezone\nfrom traceback import format_exc\nfrom typing import Any, Callable, List\n\nfrom anyio import create_task_group\nfrom anyio.abc import TaskGroup\nfrom apscheduler.abc import EventHub, Executor, Job\nfrom apscheduler.eventhubs.local import LocalEventHub\nfrom apscheduler.events import (\n Event, JobAdded, JobDeadlineMissed, JobFailed, JobSuccessful, JobUpdated)\n\n\n@dataclass\nclass LocalExecutor(Executor):\n \"\"\"Runs jobs locally in a task group.\"\"\"\n\n _event_hub: EventHub = field(init=False, default_factory=LocalEventHub)\n _task_group: TaskGroup = field(init=False)\n _queued_jobs: OrderedDict[Job, None] = field(init=False, default_factory=OrderedDict)\n _running_jobs: OrderedDict[Job, None] = field(init=False, default_factory=OrderedDict)\n\n async def __aenter__(self) -> LocalExecutor:\n self._task_group = create_task_group()\n await self._task_group.__aenter__()\n return self\n\n async def __aexit__(self, exc_type, exc_val, exc_tb) -> None:\n await self._task_group.__aexit__(exc_type, exc_val, exc_tb)\n\n async def _run_job(self) -> None:\n job = self._queued_jobs.popitem(last=False)[0]\n\n # Check if the job started before the deadline\n if job.start_deadline:\n tz = job.scheduled_start_time.tzinfo\n start_time = datetime.now(tz)\n if start_time >= job.start_deadline:\n event = JobDeadlineMissed(\n start_time, job_id=job.id, task_id=job.task_id, schedule_id=job.schedule_id,\n scheduled_start_time=job.scheduled_start_time,\n start_deadline=job.start_deadline\n )\n await self._event_hub.publish(event)\n return\n else:\n tz = timezone.utc\n start_time = datetime.now(tz)\n\n # Set the job as running and publish a job update event\n self._running_jobs[job] = None\n job.started_at = start_time\n event = JobUpdated(\n timestamp=datetime.now(tz), job_id=job.id, task_id=job.task_id,\n schedule_id=job.schedule_id, scheduled_start_time=job.scheduled_start_time\n )\n await self._event_hub.publish(event)\n\n try:\n return_value = job.func(*job.args, **job.kwargs)\n if isinstance(return_value, Coroutine):\n return_value = await return_value\n except Exception as exc:\n event = JobFailed(\n timestamp=datetime.now(tz), job_id=job.id, task_id=job.task_id,\n schedule_id=job.schedule_id, scheduled_start_time=job.scheduled_start_time,\n start_time=start_time, start_deadline=job.start_deadline,\n formatted_traceback=format_exc(), exception=exc)\n else:\n event = JobSuccessful(\n timestamp=datetime.now(tz), job_id=job.id, task_id=job.task_id,\n schedule_id=job.schedule_id, scheduled_start_time=job.scheduled_start_time,\n start_time=start_time, start_deadline=job.start_deadline, return_value=return_value\n )\n\n del self._running_jobs[job]\n await self._event_hub.publish(event)\n\n async def submit_job(self, job: Job) -> None:\n self._queued_jobs[job] = None\n await self._task_group.spawn(self._run_job)\n\n event = JobAdded(datetime.now(timezone.utc), job.id, job.task_id, job.schedule_id,\n job.scheduled_start_time)\n await self._event_hub.publish(event)\n\n async def get_jobs(self) -> List[Job]:\n return list(self._queued_jobs)\n\n async def subscribe(self, callback: Callable[[Event], Any]) -> None:\n await self._event_hub.subscribe(callback)\n", "sub_path": "apscheduler/workers/local.py", "file_name": "local.py", "file_ext": "py", "file_size_in_byte": 3892, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "apscheduler.abc.Executor", "line_number": 19, "usage_type": "name"}, {"api_name": "apscheduler.abc.EventHub", "line_number": 22, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 22, "usage_type": "call"}, {"api_name": "apscheduler.eventhubs.local.LocalEventHub", "line_number": 22, "usage_type": "name"}, {"api_name": "anyio.abc.TaskGroup", "line_number": 23, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 23, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 24, "usage_type": "name"}, {"api_name": "apscheduler.abc.Job", "line_number": 24, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 24, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 25, "usage_type": "name"}, {"api_name": "apscheduler.abc.Job", "line_number": 25, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 25, "usage_type": "call"}, {"api_name": "anyio.create_task_group", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 41, "usage_type": "name"}, {"api_name": "apscheduler.events.JobDeadlineMissed", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.timezone.utc", "line_number": 51, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 51, "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": "apscheduler.events.JobUpdated", "line_number": 57, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 58, "usage_type": "name"}, {"api_name": "collections.abc.Coroutine", "line_number": 65, "usage_type": "argument"}, {"api_name": "apscheduler.events.JobFailed", "line_number": 68, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 69, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 69, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 72, "usage_type": "call"}, {"api_name": "apscheduler.events.JobSuccessful", "line_number": 74, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 75, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 75, "usage_type": "name"}, {"api_name": "apscheduler.abc.Job", "line_number": 83, "usage_type": "name"}, {"api_name": "apscheduler.events.JobAdded", "line_number": 87, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 87, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 87, "usage_type": "name"}, {"api_name": "datetime.timezone.utc", "line_number": 87, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 87, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 91, "usage_type": "name"}, {"api_name": "apscheduler.abc.Job", "line_number": 91, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 94, "usage_type": "name"}, {"api_name": "apscheduler.events.Event", "line_number": 94, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 94, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 18, "usage_type": "name"}]} +{"seq_id": "636869580", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Created by mengqingyun on 14-5-22.\n\"\"\"\ncommon handler,webhandler,apihandler\n要获得torngas的中间件等特性需继承这些handler\n\"\"\"\nimport json\nimport tornado.locale\nfrom tornado.web import RequestHandler, HTTPError\nfrom logger.client import general_logger\nfrom torngas.settings_manager import settings\nfrom torngas.mixins.exception import UncaughtExceptionMixin\n\n\nclass CommonHandler(RequestHandler):\n _url_kwargs = {}\n\n def __init__(self, application, request, **kwargs):\n if kwargs:\n self._url_kwargs.update(kwargs)\n kwargs.clear()\n super(CommonHandler, self).__init__(application, request, **kwargs)\n\n def prepare(self):\n self.application.middleware_manager.run_request_hooks(self)\n return self.on_prepare()\n\n def on_prepare(self):\n pass\n\n def render_string(self, template_name, **kwargs):\n self.application.middleware_manager.run_render_hooks(self, template_name, **kwargs)\n return super(CommonHandler, self).render_string(template_name, **kwargs)\n\n def finish(self, chunk=None):\n # finish之前可能执行过多次write，反而chunk可能为None\n # 真正的chunk数据在self._write_buffer中，包含历次write的数据\n # 这里将chunk数据write进_write_buffer中，然后将chunk置空\n if chunk:\n self.write(chunk)\n chunk = None\n self.application.middleware_manager.run_response_hooks(self, self._write_buffer)\n super(CommonHandler, self).finish(chunk)\n\n def write(self, chunk, status=None):\n if status:\n self.set_status(status)\n super(CommonHandler, self).write(chunk)\n\n def log_exception(self, typ, value, tb):\n \"\"\"重写404请求的异常处理\n \"\"\"\n if isinstance(value, HTTPError):\n if value.log_message:\n format = \"%d %s: \" + value.log_message\n args = ([value.status_code, self._request_summary()] +\n list(value.args))\n general_logger.warning(format, *args)\n else:\n general_logger.error(\"Uncaught exception %s\\n%r\", self._request_summary(),\n self.request, exc_info=(typ, value, tb))\n\n def on_finish(self):\n self.application.middleware_manager.run_endcall_hooks(self)\n self.complete_finish()\n\n def complete_finish(self):\n pass\n\n def get_user_locale(self):\n if settings.TRANSLATIONS_CONF.use_accept_language:\n return None\n\n return tornado.locale.get(settings.TRANSLATIONS_CONF.locale_default)\n\n\nclass WebHandler(UncaughtExceptionMixin, CommonHandler):\n def create_template_loader(self, template_path):\n loader = self.application.tmpl\n if loader is None:\n return super(CommonHandler, self).create_template_loader(template_path)\n else:\n return loader(template_path)\n\n\nclass ApiHandler(CommonHandler):\n def get_format(self):\n format = self.get_argument('format', None)\n if not format:\n accept = self.request.headers.get('Accept')\n if accept:\n if 'javascript' in accept:\n format = 'jsonp'\n else:\n format = 'json'\n return format or 'json'\n\n def write_api(self, obj=None, nofail=False):\n if not obj:\n obj = {}\n format = self.get_format()\n if format == 'json':\n self.set_header(\"Content-Type\", \"application/json; charset=UTF-8\")\n self.write(json.dumps(obj))\n elif format == 'jsonp':\n self.set_header(\"Content-Type\", \"application/javascript\")\n callback = self.get_argument('callback', 'callback')\n self.write('%s(%s);' % (callback, json.dumps(obj)))\n elif nofail:\n self.write(json.dumps(obj))\n else:\n raise HTTPError(400, 'Unknown response format requested: %s' % format)\n\n\nclass ErrorHandler(UncaughtExceptionMixin, CommonHandler):\n def prepare(self):\n super(ErrorHandler, self).prepare()\n self.set_status(404)\n raise HTTPError(404)", "sub_path": "torngas/handler.py", "file_name": "handler.py", "file_ext": "py", "file_size_in_byte": 4203, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "tornado.web.RequestHandler", "line_number": 16, "usage_type": "name"}, {"api_name": "tornado.web.HTTPError", "line_number": 54, "usage_type": "argument"}, {"api_name": "logger.client.general_logger.warning", "line_number": 59, "usage_type": "call"}, {"api_name": "logger.client.general_logger", "line_number": 59, "usage_type": "name"}, {"api_name": "logger.client.general_logger.error", "line_number": 61, "usage_type": "call"}, {"api_name": "logger.client.general_logger", "line_number": 61, "usage_type": "name"}, {"api_name": "torngas.settings_manager.settings.TRANSLATIONS_CONF", "line_number": 72, "usage_type": "attribute"}, {"api_name": "torngas.settings_manager.settings", "line_number": 72, "usage_type": "name"}, {"api_name": "tornado.locale.locale.get", "line_number": 75, "usage_type": "call"}, {"api_name": "tornado.locale.locale", "line_number": 75, "usage_type": "attribute"}, {"api_name": "tornado.locale", "line_number": 75, "usage_type": "name"}, {"api_name": "torngas.settings_manager.settings.TRANSLATIONS_CONF", "line_number": 75, "usage_type": "attribute"}, {"api_name": "torngas.settings_manager.settings", "line_number": 75, "usage_type": "name"}, {"api_name": "torngas.mixins.exception.UncaughtExceptionMixin", "line_number": 78, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 105, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 109, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 111, "usage_type": "call"}, {"api_name": "tornado.web.HTTPError", "line_number": 113, "usage_type": "call"}, {"api_name": "torngas.mixins.exception.UncaughtExceptionMixin", "line_number": 116, "usage_type": "name"}, {"api_name": "tornado.web.HTTPError", "line_number": 120, "usage_type": "call"}]} +{"seq_id": "74452864", "text": "from flask_restful import Resource\nfrom flask import request\nfrom schema.user_schema import UserSchema,registerSchema\nfrom unitity.unitity import Tool,Model\nfrom database.user import UserModel,RevokedTokenModel\nfrom passlib.hash import pbkdf2_sha256 as sha256\nfrom flask_jwt_extended import (create_access_token, create_refresh_token, jwt_required, jwt_refresh_token_required, get_jwt_identity, get_raw_jwt)\n\nuser_schema = UserSchema(many=False)\nregister_schema = registerSchema(many=False)\nencrypt = Model()\ngetdata = Tool()\n\nclass TokenRefresh(Resource):\n @jwt_refresh_token_required\n def post(self):\n current_user = get_jwt_identity()\n access_token = create_access_token(identity = current_user)\n return {'access_token': access_token}\n\nclass Userlogin(Resource):\n\n def post(self,username):\n\n result = user_schema.load(getdata.get_param())\n user = UserModel.get_user(result['username'])\n if user == None:\n return {\n 'message': 'username not exist!'\n }, 403\n else:\n if encrypt.verify_hash(result['password'],user.password):\n access_token = create_access_token(identity = result['username'])\n refresh_token = create_refresh_token(identity = result['username'])\n return {\n 'message': '',\n 'user': user_schema.dump(user),\n 'access_token': access_token,\n 'refresh_token': refresh_token\n }\n else:\n return {\n 'message': 'password not match'\n }\n\n def put(self):\n\n result = user_schema.load(getdata.get_param())\n user = UserModel.get_user(result['username'])\n\n if user != None:\n user = UserModel(result['username'], result['email'], result ['password'])\n user.update_user()\n return {\n 'message': 'Update user success',\n 'user': user_schema.dump(user),\n }\n else:\n return {\n 'message': 'Can not update!',\n 'user': UserModel.username\n }\n\nclass UserRegistration(Resource):\n\n def post(self):\n\n result = register_schema.load(getdata.get_param())\n user = UserModel.get_user(result['username'])\n if user != None:\n return {\n 'message': 'username {0} is exist!'.format(result['username'])\n }, 403\n else:\n try:\n user = UserModel(result['username'],result['email'],encrypt.generate_hash(result['password']))\n user.add_user()\n access_token = create_access_token(identity = result['username'])\n refresh_token = create_refresh_token(identity = result['username'])\n return {\n 'message': 'Registration success',\n 'access_token': access_token,\n 'refresh_token': refresh_token\n }\n except:\n return {\n 'message': 'database insert error',\n }\n\n return {\n 'message': '',\n 'user': user_schema.dump(user)\n }\n\nclass UserLogoutAccess(Resource):\n @jwt_required\n def post(self):\n jti = get_raw_jwt()['jti']\n try:\n revoked_token = RevokedTokenModel(jti = jti)\n revoked_token.add()\n return {'message': 'Access token has been revoked'}\n except:\n return {'message': 'Something went wrong'}, 500\n\n\nclass UserLogoutRefresh(Resource):\n @jwt_refresh_token_required\n def post(self):\n jti = get_raw_jwt()['jti']\n try:\n revoked_token = RevokedTokenModel(jti = jti)\n revoked_token.add()\n return {'message': 'Refresh token has been revoked'}\n except:\n return {'message': 'Something went wrong'}, 500\n\n#class Users(Resource):\n# def get(self):\n# return {\n# 'message': '',\n# 'users': user_schema.dump(UserModel.get_all_user())\n# }\n\n ", "sub_path": "resources/user.py", "file_name": "user.py", "file_ext": "py", "file_size_in_byte": 4157, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "schema.user_schema.UserSchema", "line_number": 9, "usage_type": "call"}, {"api_name": "schema.user_schema.registerSchema", "line_number": 10, "usage_type": "call"}, {"api_name": "unitity.unitity.Model", "line_number": 11, "usage_type": "call"}, {"api_name": "unitity.unitity.Tool", "line_number": 12, "usage_type": "call"}, {"api_name": "flask_restful.Resource", "line_number": 14, "usage_type": "name"}, {"api_name": "flask_jwt_extended.get_jwt_identity", "line_number": 17, "usage_type": "call"}, {"api_name": "flask_jwt_extended.create_access_token", "line_number": 18, "usage_type": "call"}, {"api_name": "flask_jwt_extended.jwt_refresh_token_required", "line_number": 15, "usage_type": "name"}, {"api_name": "flask_restful.Resource", "line_number": 21, "usage_type": "name"}, {"api_name": "database.user.UserModel.get_user", "line_number": 26, "usage_type": "call"}, {"api_name": "database.user.UserModel", "line_number": 26, "usage_type": "name"}, {"api_name": "flask_jwt_extended.create_access_token", "line_number": 33, "usage_type": "call"}, {"api_name": "flask_jwt_extended.create_refresh_token", "line_number": 34, "usage_type": "call"}, {"api_name": "database.user.UserModel.get_user", "line_number": 49, "usage_type": "call"}, {"api_name": "database.user.UserModel", "line_number": 49, "usage_type": "name"}, {"api_name": "database.user.UserModel", "line_number": 52, "usage_type": "call"}, {"api_name": "database.user.UserModel.username", "line_number": 61, "usage_type": "attribute"}, {"api_name": "database.user.UserModel", "line_number": 61, "usage_type": "name"}, {"api_name": "flask_restful.Resource", "line_number": 64, "usage_type": "name"}, {"api_name": "database.user.UserModel.get_user", "line_number": 69, "usage_type": "call"}, {"api_name": "database.user.UserModel", "line_number": 69, "usage_type": "name"}, {"api_name": "database.user.UserModel", "line_number": 76, "usage_type": "call"}, {"api_name": "flask_jwt_extended.create_access_token", "line_number": 78, "usage_type": "call"}, {"api_name": "flask_jwt_extended.create_refresh_token", "line_number": 79, "usage_type": "call"}, {"api_name": "flask_restful.Resource", "line_number": 95, "usage_type": "name"}, {"api_name": "flask_jwt_extended.get_raw_jwt", "line_number": 98, "usage_type": "call"}, {"api_name": "database.user.RevokedTokenModel", "line_number": 100, "usage_type": "call"}, {"api_name": "flask_jwt_extended.jwt_required", "line_number": 96, "usage_type": "name"}, {"api_name": "flask_restful.Resource", "line_number": 107, "usage_type": "name"}, {"api_name": "flask_jwt_extended.get_raw_jwt", "line_number": 110, "usage_type": "call"}, {"api_name": "database.user.RevokedTokenModel", "line_number": 112, "usage_type": "call"}, {"api_name": "flask_jwt_extended.jwt_refresh_token_required", "line_number": 108, "usage_type": "name"}]} +{"seq_id": "519366780", "text": "import numpy as np\nfrom sklearn import preprocessing\n\n\n# Averages selected time intervals in each out_features[i][j]\n# that have been specified using param.intervals.\n# Returns 2D feature vectors n_of_examples x (number_of_time_windows x number_of_channels)\ndef windowed_means(out_features, param):\n # in all feature vectors\n output_features = []\n for i in range(out_features.shape[0]):\n feature = []\n # for all EEG channels\n for j in range(out_features.shape[1]):\n time_course = out_features[i][j]\n for k in range(param.intervals.shape[0]):\n borders = param.intervals[k] * param.sampling_fq\n feature.append(np.average(time_course[int(borders[0] - 1):int(borders[1] - 1)]))\n output_features.append(feature)\n# print(np.shape(output_features))\n return preprocessing.scale(np.array(output_features), axis=1)\n\n\n# Add a singleton dimension to enable CNN Keras classification\ndef cnn_reshape(out_features):\n # reshape the data to add a singleton dimension\n out_features = out_features.reshape(out_features.shape[0], out_features.shape[1], out_features.shape[2], 1)\n return out_features\n\n\n# From out_features, remove all epochs with any channel\n# exceeding ampl_threshold in the absolute value.\ndef reject_amplitude(out_features, out_labels, param):\n # in all feature vectors\n output_features = []\n retain_targets = []\n for i in range(out_features.shape[0]):\n feature = []\n reject = False\n # for all EEG channels\n for j in range(out_features.shape[1]):\n if np.max(np.absolute(out_features[i][j])) > param.rej_threshold:\n reject = True\n if not reject:\n output_features.append(out_features[i])\n retain_targets.append(not reject)\n output_features = np.array(output_features)\n if param.verbose:\n print('Rejected: ', (1 - output_features.shape[0] / out_features.shape[0]) * 100, ' %.')\n return output_features, out_labels[retain_targets, :]\n\n\n# Averages every N trials in EEG data structure\n# out_features - EEG feature vectors\n# averaging - N - number of trials to average together\ndef neighbor_average_all(out_features, out_labels, averaging_factor):\n if averaging_factor <= 1:\n return [out_features, out_labels]\n\n # separate only targets/non-target features\n out_t_features = out_features[out_labels[:, 0] == 1, :]\n out_n_features = out_features[out_labels[:, 1] == 1, :]\n\n # ensemble average targets and non-targets features\n out__t_features_avg = average(out_t_features, averaging_factor)\n out__n_features_avg = average(out_n_features, averaging_factor)\n\n # create corresponding labels\n out_t_labels = np.tile(np.array([1, 0]), (out__t_features_avg.shape[0], 1))\n out_n_labels = np.tile(np.array([0, 1]), (out__n_features_avg.shape[0], 1))\n\n # connect target/non-target features/labels\n out_labels = np.vstack((out_t_labels, out_n_labels))\n out_features = np.concatenate((out__t_features_avg, out__n_features_avg), axis=0)\n\n return [out_features, out_labels]\n\n\n# Average features only by a certain factor\ndef average(out_features, averaging_factor):\n out_eeg_data = []\n for trial in range(0, out_features.shape[0] - 1, averaging_factor):\n avg_fv = np.average(out_features[trial:(trial + averaging_factor), :], axis=0)\n out_eeg_data.append(avg_fv)\n return np.array(out_eeg_data)", "sub_path": "main/pre_processing.py", "file_name": "pre_processing.py", "file_ext": "py", "file_size_in_byte": 3458, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.average", "line_number": 18, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.scale", "line_number": 21, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 21, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "650315182", "text": "import os\nprint(os.__file__)\n\nimport numpy as np\n\nfrom matplotlib import pyplot as plt\nfrom matplotlib.collections import PatchCollection\nfrom matplotlib.patches import Circle\nimport matplotlib.animation as animation\nimport time\n\n\nfrom sklearn.datasets import make_circles\n\n# In[2]:\n\n\n# PROBLEM\nn = 500\np = 2\n\nX, Y = make_circles(n_samples=n, factor=0.5, noise=0.05)\nY = Y[:, np.newaxis]\n\ndef plot_circle():\n plt.scatter(X[Y[:, 0] == 0, 0], X[Y[:, 0] == 0, 1], c=\"skyblue\")\n plt.scatter(X[Y[:, 0] == 1, 0], X[Y[:, 0] == 1, 1], c=\"salmon\")\n plt.axis(\"equal\")\n plt.show()\n \n#plot_circle()\n\n\n# In[3]:\n\n\n# FUNCIONES DE ACTIVACION\nsigm = (lambda x: 1 / (1 + np.e ** (-x)),\n lambda x: x * (1 - x))\n\nrelu = lambda x: np.maximum(0, x)\n\nl2_cost = (lambda Yp, Yr: np.mean((Yp - Yr) ** 2),\n lambda Yp, Yr: (Yp - Yr))\n\ndef plot_activation_function():\n _x = np.linspace(-5, 5, 100)\n plt.plot(_x, relu(_x))\n\n\n# In[4]:\n\n\n# CAPA DE LA RN\nclass NNLayer:\n num_connections = 0\n num_neurons = 0\n activation_function = sigm\n\n def __init__(self, num_conections, num_neurons, activation_function):\n self.num_neurons = num_neurons\n self.num_connections = num_conections\n self.activation_function = activation_function\n\n self.bias = np.random.rand(1, num_neurons) * 2 - 1\n self.weights = np.random.rand(num_conections, num_neurons) * 2 - 1\n\n def info(self, l):\n print('Capa {} - N:{} C:{}'.format(l, self.num_neurons, self.num_connections))\n\n def val(self,n):\n return self.bias[0][n]\n\n# In[5]:\n\n# RED N\nclass NN():\n\n def __init__(self):\n self.layer = []\n self.layers = 0\n self.topology = []\n self.hidden_layers = 0\n self.out = [(None, X)]\n self.artist = {}\n self.loss = 0\n self.trains = 0\n\n def from_topology(self, topology: object, act_f: object) -> object:\n self.topology = topology\n self.layers = len(topology)\n self.hidden_layers = self.layers - 2\n\n # add layers\n for l, t in enumerate(topology[:-1]):\n print('Add layer {2:d}: con({0:2d}) neurons({1:2d})'.format(topology[l], topology[l + 1], l))\n self.layer.append(NNLayer(topology[l], topology[l + 1], act_f))\n\n #todo: add last layer\n self.layer.append(NNLayer(topology[-1], 1, act_f))\n\n def from_layers(self, num_inputs: int = 0, layers: object = []) -> object:\n self.layers = len(layers)\n self.hidden_layers = self.layers - 1\n self.topology = [num_inputs]\n\n for n, l in enumerate(layers):\n print('Add layer {2:d}: con({0:2d}) neurons({1:2d})'.format(l.num_connections, l.num_neurons, n))\n self.layer.append(l)\n self.topology.append(l.num_neurons)\n\n def fit(self,X):\n self.out = [(None, X)]\n\n # Forward pass\n for layer in nn.layer:\n z = self.out[-1][1] @ layer.weights + layer.bias\n a = layer.activation_function[0](z)\n\n self.out.append((z, a))\n\n return self.out[-1][1]\n\n def train(self,X,Y,lr=0.5):\n # Backward pass\n self.trains += 1\n deltas = []\n self.fit(X)\n for l in reversed(range(0, len(self.layer))):\n z = self.out[l + 1][0]\n a = self.out[l + 1][1]\n\n layer = self.layer[l]\n\n if l == len(self.layer) - 1:\n deltas.insert(0, l2_cost[1](a, Y) * layer.activation_function[1](a))\n else:\n deltas.insert(0, deltas[0] @ _W.T * layer.activation_function[1](a))\n\n _W = layer.weights\n\n # Gradient descent\n layer.bias = layer.bias - np.mean(deltas[0], axis=0, keepdims=True) * lr\n layer.weights = layer.weights - self.out[l][1].T @ deltas[0] * lr\n\n #self.artist[0, 0, 1].set_text(\"{0:.3f}\".format(X))\n #self.artist[0, 1, 1].set_text(\"{0:.3f}\".format(Y))\n # update loss\n self.loss = l2_cost[0](self.out[-1][1], Y)\n return self.out[-1][1]\n\n def closs(self,Y):\n self.loss = l2_cost[0](self.out[-1][1],Y)\n return self.loss\n\n def draw(self, ax, left, right, bottom, top):\n ims = []\n '''\n Draw a neural network cartoon using matplotilb.\n\n :usage:\n >>> fig = plt.figure(figsize=(12, 12))\n >>> draw_neural_net(fig.gca(), .1, .9, .1, .9, [4, 7, 2])\n\n :parameters:\n - ax : matplotlib.axes.AxesSubplot\n The axes on which to plot the cartoon (get e.g. by plt.gca())\n - left : float\n The center of the leftmost node(s) will be placed here\n - right : float\n The center of the rightmost node(s) will be placed here\n - bottom : float\n The center of the bottommost node(s) will be placed here\n - top : float\n The center of the topmost node(s) will be placed here\n - layer_sizes : list of int\n List of layer sizes, including input and output dimensionality\n '''\n v_spacing = (top - bottom) / float(max(self.topology))\n h_spacing = (right - left) / float(len(self.topology) - 1)\n # Nodes\n for n, layer_size in enumerate(self.topology):\n layer_top = v_spacing * (layer_size - 1) / 2. + (top + bottom) / 2.\n for m in range(layer_size):\n x, y = (n * h_spacing + left, layer_top - m * v_spacing)\n print(\"c\",n,\" n\",m)\n color=\"w\"\n val = 0\n\n if n > 0:\n val = self.layer[n-1].val(m)\n if val > 0:\n color=\"lime\"\n else:\n color=\"tomato\"\n\n circle = plt.Circle((x,y), v_spacing / 4., color=color, ec='k', zorder=4)\n ax.add_artist(circle)\n label = plt.text(x,y,\"{2:.3f}\".format(n,m,val),fontsize=8, ha=\"center\", zorder=5)\n ax.add_artist(label)\n\n self.artist[n, m, 0] = circle\n self.artist[n, m, 1] = label\n\n x, y = (1,1)\n label = plt.text(x, y, \"Loss {0:.5f} - Iter {1:4d}\".format(0,0), fontsize=8, ha=\"center\", zorder=5,color=\"green\")\n ax.add_artist(label)\n self.artist[n+1,m+1,1] = label\n\n # Edges\n for n, (layer_size_a, layer_size_b) in enumerate(zip(self.topology[:-1], self.topology[1:])):\n layer_top = v_spacing * (layer_size - 1) / 2. + (top + bottom) / 2.\n layer_top_a = v_spacing * (layer_size_a - 1) / 2. + (top + bottom) / 2.\n layer_top_b = v_spacing * (layer_size_b - 1) / 2. + (top + bottom) / 2.\n for m in range(layer_size_a):\n x, y = (n * h_spacing + left, layer_top - m * v_spacing)\n for o in range(layer_size_b):\n line = plt.Line2D([n * h_spacing + left, (n + 1) * h_spacing + left],\n [layer_top_a - m * v_spacing, layer_top_b - o * v_spacing], c='silver')\n ax.add_artist(line)\n self.artist[m,n,2,o] = plt.text(n * h_spacing + left, (n + 1) * layer_top_b - o * v_spacing,\"{0:.3f}\".format(0),color=\"blue\",fontsize=6)\n\n\n\n def update_draw(self):\n for n, layer_size in enumerate(self.topology):\n for m in range(layer_size):\n color = \"seashell\"\n val = 0\n if n > 0:\n val = self.layer[n - 1].val(m)\n if val > 0:\n color = \"lime\"\n else:\n color = \"tomato\"\n self.artist[n, m, 0].set_color(color)\n self.artist[n, m, 1].set_text(\"{0:.3f}\".format(val))\n self.artist[n+1,m+1,1].set_text(\"Loss {0:.5f} - Iter {1:4d}\".format(self.loss,self.trains))\n for n, (layer_size_a, layer_size_b) in enumerate(zip(self.topology[:-1], self.topology[1:])):\n for m in range(layer_size_a):\n for o in range(layer_size_b):\n self.artist[m,n,2,o].set_text(\"{0:.3f}\".format(self.layer[n].weights[1][o]))\n\n\n# In[16]:\n\n\n# FUNCION DE ENTRENAMIENTO\n\ntopology = [p, 16, 8, 1]\n\n\n\ndef train(nn, X, Y, l2_cost, lr=0.25, train=True):\n out = [(None, X)]\n\n # Forward pass\n for layer in nn.layer[:-1]:\n z = out[-1][1] @ layer.weights + layer.bias\n a = layer.activation_function[0](z)\n\n out.append((z, a))\n\n if train:\n\n # Backward pass\n deltas = []\n\n for l in reversed(range(0, nn.layers - 1)):\n z = out[l + 1][0]\n a = out[l + 1][1]\n\n if l == nn.layers - 2:\n deltas.insert(0, l2_cost[1](a, Y) * nn.layer[l].activation_function[1](a))\n else:\n deltas.insert(0, deltas[0] @ _W.T * nn.layer[l].activation_function[1](a))\n\n _W = nn.layer[l].weights\n\n # Gradient descent\n nn.layer[l].bias = nn.layer[l].bias - np.mean(deltas[0], axis=0, keepdims=True) * lr\n nn.layer[l].weights = nn.layer[l].weights - out[l][1].T @ deltas[0] * lr\n\n return out[-1][1]\n\n\nfrom IPython.display import clear_output\ndef visual_plot():\n # VISUALIZACIÓN Y TEST\n\n import time\n \n\n loss = []\n\n for i in range(1000):\n\n # Entrenemos a la red!\n pY = train(nn, X, Y, l2_cost, lr=0.05)\n\n if i % 25 == 0:\n\n print(pY)\n\n loss.append(l2_cost[0](pY, Y))\n\n res = 100\n\n _x0 = np.linspace(-1.5, 1.5, res)\n _x1 = np.linspace(-1.5, 1.5, res)\n\n _Y = np.zeros((res, res))\n\n for i0, x0 in enumerate(_x0):\n for i1, x1 in enumerate(_x1):\n _Y[i0, i1] = train(nn, np.array([[x0, x1]]), Y, l2_cost, train=False)[0][0]\n\n plt.pcolormesh(_x0, _x1, _Y, cmap=\"coolwarm\")\n plt.axis(\"equal\")\n\n plt.scatter(X[Y[:, 0] == 0, 0], X[Y[:, 0] == 0, 1], c=\"skyblue\")\n plt.scatter(X[Y[:, 0] == 1, 0], X[Y[:, 0] == 1, 1], c=\"salmon\")\n\n clear_output(wait=True)\n plt.show()\n\n plt.plot(range(len(loss)), loss)\n\n time.sleep(0.1)\n \n plt.show()\n # print(pY)\n\n\nfrom IPython.display import clear_output\n\n\ndef visual_plot2():\n # VISUALIZACIÓN Y TEST\n\n import time\n\n loss = []\n\n for i in range(1000):\n\n # Entrenemos a la red!\n pY = nn.train(X, Y, lr=0.5)\n\n if i % 25 == 0:\n\n #print(pY)\n\n cost = l2_cost[0](pY, Y)\n loss.append(nn.closs(Y))\n print(i,cost)\n\n res = 100\n\n _x0 = np.linspace(-1.5, 1.5, res)\n _x1 = np.linspace(-1.5, 1.5, res)\n\n _Y = np.zeros((res, res))\n\n for i0, x0 in enumerate(_x0):\n for i1, x1 in enumerate(_x1):\n _Y[i0, i1] = nn.fit(np.array([[x0, x1]]))[0][0]\n\n plt.pcolormesh(_x0, _x1, _Y, cmap=\"coolwarm\")\n plt.axis(\"equal\")\n\n plt.scatter(X[Y[:, 0] == 0, 0], X[Y[:, 0] == 0, 1], c=\"skyblue\")\n plt.scatter(X[Y[:, 0] == 1, 0], X[Y[:, 0] == 1, 1], c=\"salmon\")\n\n clear_output(wait=True)\n plt.show()\n\n plt.plot(range(len(loss)), loss)\n\n time.sleep(0.1)\n plt.show()\n\nloss = []\n\ndef vis_train(n):\n print(n)\n img = []\n # Entrenemos a la red!\n pY = nn.train(X, Y, lr=0.5)\n loss.append(nn.loss)\n nn.update_draw()\n #.plot(range(len(loss)),loss)\n return img\n\nzz = NN()\nzz.from_topology([p, 4, 8, 1], sigm)\n\nl1 = NNLayer(2, 4, sigm)\nl2 = NNLayer(4, 8, sigm)\nl3 = NNLayer(8, 1, sigm)\n\nnn = NN()\nnn.from_layers(2, [l1, l2, l3])\nprint(nn.topology)\n#from Draw_NN import draw_neural_net\n\n\nfig = plt.figure(figsize=(12, 12))\nax = fig.gca()\nax.axis('off')\n\nnn.draw(ax, .1, .9, .1, .9)\nani = animation.FuncAnimation(fig, vis_train, frames=100, interval=4, blit=True)\n#visual_plot2()\nplt.show()\n\n", "sub_path": "Red_Neuronal2.py", "file_name": "Red_Neuronal2.py", "file_ext": "py", "file_size_in_byte": 11997, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.__file__", "line_number": 2, "usage_type": "attribute"}, {"api_name": "sklearn.datasets.make_circles", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 23, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "numpy.e", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.maximum", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.random.rand", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 65, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 66, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.Circle", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.Line2D", "line_number": 218, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 218, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 221, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 221, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 309, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 310, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 316, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pcolormesh", "line_number": 318, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 318, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 319, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 319, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 321, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 321, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 322, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 322, "usage_type": "name"}, {"api_name": "IPython.display.clear_output", "line_number": 324, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 325, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 325, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 327, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 327, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 329, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 331, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 331, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 360, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 361, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 363, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 367, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pcolormesh", "line_number": 369, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 369, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 370, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 370, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 372, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 372, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 373, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 373, "usage_type": "name"}, {"api_name": "IPython.display.clear_output", "line_number": 375, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 376, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 376, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 378, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 378, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 380, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 381, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 381, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 408, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 408, "usage_type": "name"}, {"api_name": "matplotlib.animation.FuncAnimation", "line_number": 413, "usage_type": "call"}, {"api_name": "matplotlib.animation", "line_number": 413, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 415, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 415, "usage_type": "name"}]} +{"seq_id": "341581856", "text": "import argparse\nimport sys_path\nsys_path.insert_sys_path()\nfrom report.lib_delta_report.phocr_mem_peak_reporter import PHOcrMemoryPeakReporter\n\n\ndef parse_argument():\n parser = argparse.ArgumentParser(\n description='Run test to check memory peak for all nominated test case.')\n parser.add_argument('-t', '--test-folder', required=True,\n help='Folder contain test set')\n parser.add_argument('-r', '--test-file', required=True,\n help=\"Test result file which is generated from run_all.py\")\n parser.add_argument('-c', '--combined-file', required=True,\n help=\"Combine result file which is generated from \"\n \"combine_all_mem_peak.py\")\n\n return parser.parse_args()\n\n\ndef main():\n args = parse_argument()\n\n memory_peak_reporter = PHOcrMemoryPeakReporter(test_folder=args.test_folder,\n test_file=args.test_file,\n combine_file=args.combined_file)\n memory_peak_reporter.do_work()\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "Run_PHocr_test/Mekong/utilities/utilities/report/report_mem_peak_infomation.py", "file_name": "report_mem_peak_infomation.py", "file_ext": "py", "file_size_in_byte": 1137, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys_path.insert_sys_path", "line_number": 3, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 8, "usage_type": "call"}, {"api_name": "report.lib_delta_report.phocr_mem_peak_reporter.PHOcrMemoryPeakReporter", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "226013840", "text": "# coding: utf-8\n\n\"\"\"\n Feed API\n\n

The Feed API lets sellers upload input files, download reports and files including their status, filter reports using URI parameters, and retrieve customer service metrics task details.

# noqa: E501\n\n OpenAPI spec version: v1.3.1\n \n Generated by: https://github.com/swagger-api/swagger-codegen.git\n\"\"\"\n\nimport pprint\nimport re # noqa: F401\n\nimport six\n\nclass CustomerServiceMetricTaskCollection(object):\n \"\"\"NOTE: This class is auto generated by the swagger code generator program.\n\n Do not edit the class manually.\n \"\"\"\n \"\"\"\n Attributes:\n swagger_types (dict): The key is attribute name\n and the value is attribute type.\n attribute_map (dict): The key is attribute name\n and the value is json key in definition.\n \"\"\"\n swagger_types = {\n 'href': 'str',\n 'limit': 'int',\n 'next': 'str',\n 'offset': 'int',\n 'prev': 'str',\n 'tasks': 'list[ServiceMetricsTask]',\n 'total': 'int'\n }\n\n attribute_map = {\n 'href': 'href',\n 'limit': 'limit',\n 'next': 'next',\n 'offset': 'offset',\n 'prev': 'prev',\n 'tasks': 'tasks',\n 'total': 'total'\n }\n\n def __init__(self, href=None, limit=None, next=None, offset=None, prev=None, tasks=None, total=None): # noqa: E501\n \"\"\"CustomerServiceMetricTaskCollection - a model defined in Swagger\"\"\" # noqa: E501\n self._href = None\n self._limit = None\n self._next = None\n self._offset = None\n self._prev = None\n self._tasks = None\n self._total = None\n self.discriminator = None\n if href is not None:\n self.href = href\n if limit is not None:\n self.limit = limit\n if next is not None:\n self.next = next\n if offset is not None:\n self.offset = offset\n if prev is not None:\n self.prev = prev\n if tasks is not None:\n self.tasks = tasks\n if total is not None:\n self.total = total\n\n @property\n def href(self):\n \"\"\"Gets the href of this CustomerServiceMetricTaskCollection. # noqa: E501\n\n The URI of the current page of results. # noqa: E501\n\n :return: The href of this CustomerServiceMetricTaskCollection. # noqa: E501\n :rtype: str\n \"\"\"\n return self._href\n\n @href.setter\n def href(self, href):\n \"\"\"Sets the href of this CustomerServiceMetricTaskCollection.\n\n The URI of the current page of results. # noqa: E501\n\n :param href: The href of this CustomerServiceMetricTaskCollection. # noqa: E501\n :type: str\n \"\"\"\n\n self._href = href\n\n @property\n def limit(self):\n \"\"\"Gets the limit of this CustomerServiceMetricTaskCollection. # noqa: E501\n\n The value of the limit parameter submitted in the request, which is the maximum number of tasks to return per page, from the result set. A result set is the complete set of tasks returned by the method. Note: Even though this parameter is not required to be submitted in the request, the parameter defaults to 10 if omitted. Note: If this is the last or only page of the result set, the page may contain fewer tasks than the limit value. To determine the number of pages in a result set, divide the total value (total number of tasks matching input criteria) by this limit value, and then round up to the next integer. For example, if the total value was 120 (120 total tasks) and the limit value was 50 (show 50 tasks per page), the total number of pages in the result set is three, so the seller would have to make three separate getCustomerServiceMetricTasks calls to view all tasks matching the input criteria. # noqa: E501\n\n :return: The limit of this CustomerServiceMetricTaskCollection. # noqa: E501\n :rtype: int\n \"\"\"\n return self._limit\n\n @limit.setter\n def limit(self, limit):\n \"\"\"Sets the limit of this CustomerServiceMetricTaskCollection.\n\n The value of the limit parameter submitted in the request, which is the maximum number of tasks to return per page, from the result set. A result set is the complete set of tasks returned by the method. Note: Even though this parameter is not required to be submitted in the request, the parameter defaults to 10 if omitted. Note: If this is the last or only page of the result set, the page may contain fewer tasks than the limit value. To determine the number of pages in a result set, divide the total value (total number of tasks matching input criteria) by this limit value, and then round up to the next integer. For example, if the total value was 120 (120 total tasks) and the limit value was 50 (show 50 tasks per page), the total number of pages in the result set is three, so the seller would have to make three separate getCustomerServiceMetricTasks calls to view all tasks matching the input criteria. # noqa: E501\n\n :param limit: The limit of this CustomerServiceMetricTaskCollection. # noqa: E501\n :type: int\n \"\"\"\n\n self._limit = limit\n\n @property\n def next(self):\n \"\"\"Gets the next of this CustomerServiceMetricTaskCollection. # noqa: E501\n\n The relative path to the call URI for the next page of results. This value is returned if there is an additional page of results to return from the result set. # noqa: E501\n\n :return: The next of this CustomerServiceMetricTaskCollection. # noqa: E501\n :rtype: str\n \"\"\"\n return self._next\n\n @next.setter\n def next(self, next):\n \"\"\"Sets the next of this CustomerServiceMetricTaskCollection.\n\n The relative path to the call URI for the next page of results. This value is returned if there is an additional page of results to return from the result set. # noqa: E501\n\n :param next: The next of this CustomerServiceMetricTaskCollection. # noqa: E501\n :type: str\n \"\"\"\n\n self._next = next\n\n @property\n def offset(self):\n \"\"\"Gets the offset of this CustomerServiceMetricTaskCollection. # noqa: E501\n\n The number of results skipped in the result set before returning the first result. This value can be set in the request with the offset query parameter. Note: The items in a paginated result set use a zero-based list where the first item in the list has an offset of 0. # noqa: E501\n\n :return: The offset of this CustomerServiceMetricTaskCollection. # noqa: E501\n :rtype: int\n \"\"\"\n return self._offset\n\n @offset.setter\n def offset(self, offset):\n \"\"\"Sets the offset of this CustomerServiceMetricTaskCollection.\n\n The number of results skipped in the result set before returning the first result. This value can be set in the request with the offset query parameter. Note: The items in a paginated result set use a zero-based list where the first item in the list has an offset of 0. # noqa: E501\n\n :param offset: The offset of this CustomerServiceMetricTaskCollection. # noqa: E501\n :type: int\n \"\"\"\n\n self._offset = offset\n\n @property\n def prev(self):\n \"\"\"Gets the prev of this CustomerServiceMetricTaskCollection. # noqa: E501\n\n The URI for the previous page of results. This parameter is returned if a previous page of results from the result set exists. # noqa: E501\n\n :return: The prev of this CustomerServiceMetricTaskCollection. # noqa: E501\n :rtype: str\n \"\"\"\n return self._prev\n\n @prev.setter\n def prev(self, prev):\n \"\"\"Sets the prev of this CustomerServiceMetricTaskCollection.\n\n The URI for the previous page of results. This parameter is returned if a previous page of results from the result set exists. # noqa: E501\n\n :param prev: The prev of this CustomerServiceMetricTaskCollection. # noqa: E501\n :type: str\n \"\"\"\n\n self._prev = prev\n\n @property\n def tasks(self):\n \"\"\"Gets the tasks of this CustomerServiceMetricTaskCollection. # noqa: E501\n\n An array of the customer service tasks on this page. The tasks are sorted by creation date. An empty array is returned if the filter criteria excludes all tasks. # noqa: E501\n\n :return: The tasks of this CustomerServiceMetricTaskCollection. # noqa: E501\n :rtype: list[ServiceMetricsTask]\n \"\"\"\n return self._tasks\n\n @tasks.setter\n def tasks(self, tasks):\n \"\"\"Sets the tasks of this CustomerServiceMetricTaskCollection.\n\n An array of the customer service tasks on this page. The tasks are sorted by creation date. An empty array is returned if the filter criteria excludes all tasks. # noqa: E501\n\n :param tasks: The tasks of this CustomerServiceMetricTaskCollection. # noqa: E501\n :type: list[ServiceMetricsTask]\n \"\"\"\n\n self._tasks = tasks\n\n @property\n def total(self):\n \"\"\"Gets the total of this CustomerServiceMetricTaskCollection. # noqa: E501\n\n The total number of tasks that match the criteria. # noqa: E501\n\n :return: The total of this CustomerServiceMetricTaskCollection. # noqa: E501\n :rtype: int\n \"\"\"\n return self._total\n\n @total.setter\n def total(self, total):\n \"\"\"Sets the total of this CustomerServiceMetricTaskCollection.\n\n The total number of tasks that match the criteria. # noqa: E501\n\n :param total: The total of this CustomerServiceMetricTaskCollection. # noqa: E501\n :type: int\n \"\"\"\n\n self._total = total\n\n def to_dict(self):\n \"\"\"Returns the model properties as a dict\"\"\"\n result = {}\n\n for attr, _ in six.iteritems(self.swagger_types):\n value = getattr(self, attr)\n if isinstance(value, list):\n result[attr] = list(map(\n lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n value\n ))\n elif hasattr(value, \"to_dict\"):\n result[attr] = value.to_dict()\n elif isinstance(value, dict):\n result[attr] = dict(map(\n lambda item: (item[0], item[1].to_dict())\n if hasattr(item[1], \"to_dict\") else item,\n value.items()\n ))\n else:\n result[attr] = value\n if issubclass(CustomerServiceMetricTaskCollection, dict):\n for key, value in self.items():\n result[key] = value\n\n return result\n\n def to_str(self):\n \"\"\"Returns the string representation of the model\"\"\"\n return pprint.pformat(self.to_dict())\n\n def __repr__(self):\n \"\"\"For `print` and `pprint`\"\"\"\n return self.to_str()\n\n def __eq__(self, other):\n \"\"\"Returns true if both objects are equal\"\"\"\n if not isinstance(other, CustomerServiceMetricTaskCollection):\n return False\n\n return self.__dict__ == other.__dict__\n\n def __ne__(self, other):\n \"\"\"Returns true if both objects are not equal\"\"\"\n return not self == other\n", "sub_path": "src/ebay_rest/api/sell_feed/models/customer_service_metric_task_collection.py", "file_name": "customer_service_metric_task_collection.py", "file_ext": "py", "file_size_in_byte": 11241, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "six.iteritems", "line_number": 240, "usage_type": "call"}, {"api_name": "pprint.pformat", "line_number": 265, "usage_type": "call"}]} +{"seq_id": "429243428", "text": "import requests\r\n\r\n\r\n\r\ndef request_nlu(text, project_dir, port):\r\n url = 'http://localhost:' + port + '/parse'\r\n print(url)\r\n data = {\r\n \"q\": text,\r\n \"project\": project_dir,\r\n \"model\": \"nlu\"\r\n }\r\n try:\r\n response = requests.post(url, json=data)\r\n except Exception as e:\r\n print(e)\r\n return None\r\n \r\n return response.json()\r\n\r\n\r\nif __name__ == '__main__':\r\n print(request_nlu('Li Fu', 'name_server', '5061'))", "sub_path": "request.py", "file_name": "request.py", "file_ext": "py", "file_size_in_byte": 476, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.post", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "427555842", "text": "import os\nimport pathlib\nimport json\nimport base64\nimport datetime\nimport requests\nimport pathlib\nimport math\nimport pandas as pd\nimport flask\n\nimport plotly.graph_objs as go\nfrom plotly import tools\n\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom dash.dependencies import Input, Output, State\nimport dash_table\nimport dash_daq as daq\nimport ccxt\nimport crypto_stream\nfrom dash.exceptions import PreventUpdate\nimport models\nimport time\nimport backtesting\nimport dash_table.FormatTemplate as FormatTemplate\ncrypto_stream.init_connection()\n\nserver = flask.Flask(__name__)\n\napp = dash.Dash(\n __name__,\n server=server,\n meta_tags=[{\"name\": \"viewport\", \"content\": \"width=device-width, initial-scale=1\"}],\n)\n\napp.config[\"suppress_callback_exceptions\"] = True\n\nAPP_PATH = str(pathlib.Path(__file__).parent.resolve())\nSTREAM_TABLE = dict(id='stream-table',data=[{'close':0.0, \n \"balance\": 10000, \n \"shares\": 0, \n 'status':''}\n ],\n columns=[{'id':'close', 'name':'Close'},\n {'id':'balance', 'name':'Balance'},\n {'id':'shares', 'name':'Shares'},\n {'id':'status', 'name':'Status'}])\ncolumns=[\n 'Stock', \n 'Entry Date', \n 'Exit Date', \n 'Shares', \n 'Entry Share Price', \n 'Exit Share Price', \n 'Entry Portfolio Holding', \n 'Exit Portfolio Holding', \n 'Profit/Loss']\n#dict(id='trade-metric-table',data=[],columns=[{'id':col, 'name':col} for col in columns])\nTRADE_METRIC_TABLE = dict(id='trade-metric-table', data=[], \n columns=[{'id':'Stock', 'name':'Stock'},\n {'id':'Entry Date', 'name':'Entry Date', 'type': 'datetime'},\n {'id':'Exit Date', 'name':'Exit Date', 'type': 'datetime'},\n {'id':'Shares', 'name':'Shares'},\n {'id':'Entry Share Price', 'name':'Entry Share Price', 'type':'numeric','format': FormatTemplate.money(2)},\n {'id':'Exit Share Price', 'name':'Exit Share Price','type':'numeric','format': FormatTemplate.money(2)},\n {'id':'Entry Portfolio Holding', 'name':'Entry Portfolio Holding', 'type':'numeric','format': FormatTemplate.money(2)},\n {'id':'Exit Portfolio Holding', 'name':'Exit Portfolio Holding', 'type':'numeric','format': FormatTemplate.money(2)},\n {'id':'Profit/Loss', 'name':'Profit/Loss', 'type':'numeric','format': FormatTemplate.money(2)}])\n\n\n# API Requests for news div\nnews_requests = requests.get(\n \"https://newsapi.org/v2/top-headlines?sources=bbc-news&apiKey=da8e2e705b914f9f86ed2e9692e66012\"\n)\n\n# API Call to update news\ndef update_news():\n json_data = news_requests.json()[\"articles\"]\n df = pd.DataFrame(json_data)\n df = pd.DataFrame(df[[\"title\", \"url\"]])\n max_rows = 10\n return html.Div(\n children=[\n html.P(className=\"p-news\", children=\"Headlines\"),\n html.P(\n className=\"p-news float-right\",\n children=\"Last update : \"\n + datetime.datetime.now().strftime(\"%H:%M:%S\"),\n ),\n html.Table(\n className=\"table-news\",\n children=[\n html.Tr(\n children=[\n html.Td(\n children=[\n html.A(\n className=\"td-link\",\n children=df.iloc[i][\"title\"],\n href=df.iloc[i][\"url\"],\n target=\"_blank\",\n )\n ]\n )\n ]\n )\n for i in range(min(len(df), max_rows))\n ],\n ),\n ]\n )\n\n# MAIN CHART TRACES (STYLE tab)\ndef line_trace(df, y_col, color='rgb(244, 212, 77)'):\n trace = go.Scatter(\n x=df.index, \n y=df[y_col], \n mode=\"lines\", \n showlegend=False, \n name=y_col,\n line=dict(color=color)\n )\n return trace\n\ndef marker_trace(x_data, y_data, symbol, color, name, marker_size=15):\n trace = go.Scatter(\n x=x_data, \n y=y_data, \n mode=\"markers\", \n showlegend=False, \n marker_size=marker_size,\n marker_symbol=symbol,\n marker_color=color,\n name=name\n )\n return trace\n\ndef bar_trace(df, y_col):\n return go.Ohlc(\n x=df.index,\n open=df[y_col],\n increasing=dict(line=dict(color=\"#888888\")),\n decreasing=dict(line=dict(color=\"#888888\")),\n showlegend=False,\n name=\"bar\",\n )\ndef colored_bar_trace(df):\n return go.Ohlc(\n x=df.index,\n open=df[\"open\"],\n high=df[\"high\"],\n low=df[\"low\"],\n close=df[\"close\"],\n showlegend=False,\n name=\"colored bar\",\n )\n\ndef candlestick_trace(df, col):\n return go.Candlestick(\n x=df.index,\n open=df[\"open\"],\n high=df[\"high\"],\n low=df[\"low\"],\n close=df[\"close\"],\n increasing=dict(line=dict(color=\"#00ff00\")),\n decreasing=dict(line=dict(color=\"white\")),\n showlegend=False,\n name=\"candlestick\",\n )\n\ndef get_fig_layout(tickformat=\"%H:%M:%S\"):\n layout = dict(margin=dict(t=40),\n hovermode=\"closest\",\n #uirevision=True,\n height=350,\n paper_bgcolor=\"rgba(0,0,0,0)\",\n plot_bgcolor=\"rgba(0,0,0,0)\",\n legend={\"font\": {\"color\": \"darkgray\"}, \"orientation\": \"h\", \"x\": 0, \"y\": 1.1},\n font={\"color\": \"darkgray\"},\n showlegend=True,\n xaxis={\n \"zeroline\": False,\n \"showgrid\": False,\n \"title\": \"Closing Price\",\n \"showline\": False,\n #\"domain\": [0, 0.8],\n \"tickformat\" : tickformat,\n \"titlefont\": {\"color\": \"darkgray\"},\n },\n yaxis={\n \"title\": 'Time',\n \"showgrid\": False,\n \"showline\": False,\n \"zeroline\": False,\n \"autorange\": True,\n \"titlefont\": {\"color\": \"darkgray\"},\n },xaxis2={\n \"title\": \"Time\",\n #\"domain\": [0.8, 1], # 70 to 100 % of width\n \"titlefont\": {\"color\": \"darkgray\"},\n \"showgrid\": False,\n },\n yaxis2={\n \"anchor\": \"free\",\n \"overlaying\": \"y\",\n \"side\": \"right\",\n \"showticklabels\": False,\n \"titlefont\": {\"color\": \"darkgray\"},\n },\n )\n return layout\n \ndef generate_section_banner(title):\n return html.Div(className=\"section-banner\", children=title)\n\ndef get_close_fig(df):\n # Add main trace (style) to figure\n '''fig = make_subplots(\n rows=1,\n shared_xaxes=True,\n shared_yaxes=True,\n cols=1,\n print_grid=False,\n vertical_spacing=0.12,\n )\n fig.append_trace(line_trace(df), 1, 1)\n fig.append_trace(bar_trace(df), 2, 1)'''\n fig = go.Figure()\n fig.add_traces([line_trace(df, 'close')])\n fig[\"layout\"] = get_fig_layout()\n return fig\n \n\ndef get_sma_fig(df):\n fig = go.Figure()\n entry_df = df.loc[df[\"entry/exit\"] == 1.0]\n exit_df = df.loc[df[\"entry/exit\"] == -1.0]\n entry_marker = marker_trace(entry_df.index, \n entry_df.sma10, \n 'triangle-up', '#0efa0a', 'buy')\n exit_marker = marker_trace(exit_df.index, \n exit_df.sma10, \n 'triangle-down', '#FF0000', 'sell')\n fig.add_traces([line_trace(df, 'sma10', '#fa760a'), \n line_trace(df, 'sma20', '#0af7f7'), \n entry_marker, \n exit_marker])\n fig[\"layout\"] = get_fig_layout()\n return fig\n\n\ndef get_trade_fig(df):\n fig = go.Figure()\n fig.add_traces([line_trace(df, 'entry/exit')])\n fig[\"layout\"] = get_fig_layout()\n return fig\n\ndef get_backtest_fig(df, timeframe):\n fig = go.Figure()\n tickformat = {}\n if (timeframe in ['30m','1h','1d','1w']):\n tickformat = {'tickformat':'%Y-%m-%d'}\n entry_df = df.loc[df[\"Entry/Exit\"] == 1.0]\n exit_df = df.loc[df[\"Entry/Exit\"] == -1.0]\n entry_marker = marker_trace(entry_df.index, \n entry_df['Portfolio Total'], \n 'circle', '#15ed24', 'buy', 10)\n exit_marker = marker_trace(exit_df.index, \n exit_df['Portfolio Total'], \n 'circle', '#ed1f3f', 'sell', 8)\n fig.add_traces([line_trace(df, 'Portfolio Total', '#b2c2c0'), \n entry_marker, \n exit_marker])\n fig[\"layout\"] = get_fig_layout(**tickformat)\n return fig\n\n\n\n'''\nCallbacks starts\n'''\n\n#app.config.suppress_callback_exceptions = True\n@app.callback([Output('crypto-2-symbol', 'data'),\n Output('two-sec-interval', 'disabled'),\n Output('five-sec-interval', 'disabled')],\n [Input('trade-btn', 'n_clicks')],\n [State('crypto-2-select-dropdown', 'value'),\n State('trade-model-select-dropdown', 'value')])\ndef reinitalize_crypto(n_clicks, crypto, model):\n if(crypto==None or crypto==''):\n raise PreventUpdate\n crypto_stream.init_connection()\n #data = [{'close':0.0, \"balance\": 10000, \"shares\": 0, 'status':''}]\n return crypto, False, False\n\n@app.callback(Output('live-crypto-graph', 'figure'),\n [Input('two-sec-interval', 'n_intervals')],\n [State('crypto-2-symbol', 'data')])\ndef update_close_scatter(n, crypto):\n df = crypto_stream.fetch_data(crypto)\n return get_close_fig(df)\n\n\n@app.callback([Output('stream-table', 'data'),\n Output('entry-exit-dict', 'data'),\n Output('live-trade-graph', 'figure'),\n Output('live-signal-graph', 'figure')],\n [Input('five-sec-interval', 'n_intervals')],\n [State('stream-table', 'data'),\n State('entry-exit-dict', 'data'),\n State('trade-model-select-dropdown', 'value')])\ndef execute_trade(n_intervals, buy_sell_data, entry_exit_df, model):\n if entry_exit_df:\n entry_exit_df = pd.DataFrame.from_dict(entry_exit_df)\n is_sma = (model=='SMA10')\n entry_exit_df = crypto_stream.generate_signals(crypto_stream.get_data_from_table())\n if not is_sma and len(entry_exit_df)>20:\n entry_exit_df = models.predict(entry_exit_df, model, 20)\n if len(entry_exit_df)<10: \n raise PreventUpdate\n else:\n account= buy_sell_data[-1]\n account = crypto_stream.execute_trade_strategy(entry_exit_df, account)\n print(account)\n if account:\n buy_sell_data.append(account)\n return buy_sell_data, entry_exit_df.to_dict('series'), get_trade_fig(entry_exit_df), get_sma_fig(entry_exit_df)\n\n@app.callback([Output(\"loading-output-1\", \"children\"),\n Output('backtesting-results-container', 'style'),\n Output('crypto-1-symbol', 'data'),\n Output('trade-metric-table', 'data'),\n Output('backtesting-graph', 'figure'),\n Output('eval_metric_table', 'data')],\n [Input('backtest-btn', 'n_clicks')],\n [State('crypto-1-select-dropdown', 'value'),\n State('model-select-dropdown', 'value'),\n State('timeframe-select-dropdown', 'value'),\n State('initial-capital-input', 'value'),\n State('no-of-shares-input', 'value')])\ndef reinitalize_model(n_clicks, crypto, model_name, timeframe, initial_capital, no_of_shares):\n portfolio_metrics, trade_metrics, portfolio_evaluation = backtesting.main(crypto, model_name, timeframe, initial_capital, no_of_shares)\n return '', {'display':'block'},crypto, trade_metrics.to_dict(\"rows\"), get_backtest_fig(portfolio_metrics, timeframe), portfolio_evaluation.reset_index().to_dict(\"rows\")\n\n\n'''\nCallbacks ends\n'''\n\ndef get_data_table(table_info):\n return dash_table.DataTable(\n id=table_info['id'],\n style_header={\"fontWeight\": \"bold\", \"color\": \"inherit\"},\n style_as_list_view=True,\n fill_width=True,\n style_cell={\n \"backgroundColor\": \"#1e2130\",\n \"fontFamily\": \"Open Sans\",\n \"padding\": \"0 2rem\",\n \"color\": \"darkgray\",\n \"border\": \"none\",\n },\n css=[\n {\"selector\": \"tr:hover td\", \n \"rule\": \"color: #91dfd2 !important;\"},\n {\"selector\": \"tr:last-child\", \n \"rule\": \"display:none !important;\"},\n {\"selector\": \"td\", \n \"rule\": \"border: none !important;\"},\n {\"selector\": \".dash-cell.focused\",\"rule\": \n \"background-color: #1e2130 !important;\",\n },\n {\"selector\": \"table\", \n \"rule\": \"--accent: #1e2130;\"},\n {\"selector\": \"tr\", \n \"rule\": \"background-color: transparent\"},\n ],\n data=table_info['data'],\n columns=table_info['columns'])\n\ndef get_evaluation_metrics_table(data=[]):\n return dash_table.DataTable(\n id='eval_metric_table',\n style_header={\"fontWeight\": \"bold\", \"color\": \"inherit\"},\n style_as_list_view=True,\n fill_width=True,\n style_cell_conditional=[\n {\"if\": {\"column_id\": \"Specs\"}, \"textAlign\": \"left\"}\n ],\n style_cell={\n \"backgroundColor\": \"#1e2130\",\n \"fontFamily\": \"Open Sans\",\n \"padding\": \"0 2rem\",\n \"color\": \"darkgray\",\n \"border\": \"none\",\n },\n css=[\n {\"selector\": \"tr:hover td\", \"rule\": \"color: #91dfd2 !important;\"},\n {\"selector\": \"td\", \"rule\": \"border: none !important;\"},\n {\n \"selector\": \".dash-cell.focused\",\n \"rule\": \"background-color: #1e2130 !important;\",\n },\n {\"selector\": \"table\", \"rule\": \"--accent: #1e2130;\"},\n {\"selector\": \"tr\", \"rule\": \"background-color: transparent\"},\n ],\n data=data,#new_df.to_dict(\"rows\"),\n columns=[{\"id\": c, \"name\": c} for c in [\"Metrics\", \"Backtest\"]],\n )\n\ndef get_btn_div(id_btn, btn_name):\n return html.Div(\n children=[html.Button(\n btn_name,\n id=f\"{id_btn}-btn\",\n n_clicks=0\n )])\ndef get_dropdown(id_name, data_list, value, title):\n return html.Div(\n id=f\"{id_name}-select-menu\",\n # className='five columns',\n children=[\n html.Label(id=f\"{id_name}-select-title\", children=f\"{title}\"),\n dcc.Dropdown(\n id=f\"{id_name}-select-dropdown\",\n options=list(\n {\"label\": data, \"value\": data} for data in data_list\n ),\n value=value,\n )])\n\ndef get_numeric_input(id_name, value, title):\n return html.Div(\n id=f\"{id_name}-menu\",\n # className='five columns',\n children=[\n html.Label(id=f\"{id_name}-title\", children=title),\n daq.NumericInput(\n id=f\"{id_name}-input\", className=\"setting-input\", value=value, size=200, max=9999999\n)])\n \ndef build_trade_panel():\n return html.Div(\n id=\"top-section-container\",\n className=\"row\",\n children=[\n dcc.Store(id='crypto-2-symbol', storage_type='local', data=crypto_stream.SYMBOL),\n dcc.Store(id='entry-exit-dict'),\n # Metrics summary\n html.Div(\n id=\"live-data-streaming\",\n className=\"eight columns\",\n children=[\n generate_section_banner(\"Closing Price\"),\n dcc.Graph(id='live-crypto-graph'),\n generate_section_banner(\"Signals\"),\n dcc.Graph(id='live-signal-graph'),\n generate_section_banner(\"Trade\"),\n dcc.Graph(id='live-trade-graph', figure={'layout':get_fig_layout()})\n ],\n ),\n # Piechart\n html.Div(\n id=\"trade-table\",\n className=\"four columns\",\n children=[\n html.Br(),\n get_dropdown('crypto-2', crypto_stream.get_crypto_symbols(), '', 'Crypto'),\n html.Br(),\n get_dropdown('trade-model', models.MODEL_LIST , models.MODEL_LIST[0], 'Model'),\n html.Br(),\n get_btn_div('trade', 'Trade'),\n html.Br(),\n #get_crypto_dropdown('crypto-2'),\n generate_section_banner(\"Trade Data\"),\n get_data_table(STREAM_TABLE)\n ],\n ),\n ],\n )\n\n\ndef build_backtesting_panel():\n return html.Div([\n # Manually select metrics\n html.Div(\n id=\"set-specs-intro-container\",\n # className='twelve columns',\n children=html.P(\n \"Use Backtesting, to evaluate the effectiveness of a AI model by running the strategy against historical data \"\n )\n ),\n html.Div(\n id=\"settings-menu\",\n children=[\n dcc.Store(id='crypto-1-symbol', storage_type='local', data=crypto_stream.SYMBOL),\n html.Div(\n id=\"backtesting-settings\",\n className=\"five columns\",\n children=[\n html.Div(\n className=\"six columns\",\n children=[\n html.Br(),\n get_dropdown('crypto-1', crypto_stream.get_crypto_symbols(), crypto_stream.SYMBOL, 'Crypto'),\n #get_crypto_dropdown('crypto-1'),\n html.Br(),\n get_dropdown('model', backtesting.model_list(), backtesting.model_list()[0], 'Model'),\n html.Br(),\n get_numeric_input('no-of-shares', 10, 'No of Shares')\n ]\n ),\n html.Div(\n className=\"six columns\",\n children=[\n html.Br(),\n get_dropdown('timeframe', ['1m', '5m', '30m', '1h', '1d','1w'], '1m', 'Interval'),\n html.Br(),\n get_numeric_input('initial-capital', 100000.0, 'Initial Capital'),\n html.Br(),\n html.Br(),\n get_btn_div('backtest', 'Backtest'),\n html.Br(),\n \n ]\n )\n ]),\n html.Div(\n id='loading-div',\n className=\"one columns\",\n children=[\n html.Br(),\n html.Br(),\n html.Div(\n className='ten rows',\n children=[dcc.Loading(\n id=\"loading-1\",\n type=\"default\",\n children=html.Div(id=\"loading-output-1\")\n )\n ]\n ),\n html.Br(),\n ]\n ),\n html.Div(\n id=\"backtesting-metrics\",\n className=\"six columns\",\n children=[\n generate_section_banner(\"Portfolio Evaluation Metrics\"),\n html.Br(),\n get_evaluation_metrics_table()\n ]\n )\n ]\n\n ),\n html.Div(\n id=\"backtesting-results-container\",\n style={\"display\": \"none\"},\n className='twelve columns',\n children=[\n html.Br(),\n generate_section_banner(\" Trading Strategy vs. Backtest Results\"),\n dcc.Graph(id='backtesting-graph'),\n html.Br(),\n generate_section_banner(\"Trade Evaluation Metrics\"),\n html.Br(),\n html.Div(id=\"portfolio-metric-panel\", children=[get_data_table(TRADE_METRIC_TABLE),\n ],\n ),\n ])])\n\n\ndef build_tabs():\n return html.Div(\n id=\"tabs\",\n className=\"tabs\",\n children=[\n dcc.Tabs(\n id=\"app-tabs\",\n value=\"tab1\",\n className=\"custom-tabs\",\n children=[\n dcc.Tab(\n id=\"Specs-tab\",\n label=\"Model Backtesting\",\n value=\"tab1\",\n className=\"custom-tab\",\n selected_className=\"custom-tab--selected\",\n children=build_backtesting_panel()\n ),\n dcc.Tab(\n id=\"Control-chart-tab\",\n label=\"Control Charts Dashboard\",\n value=\"tab2\",\n className=\"custom-tab\",\n selected_className=\"custom-tab--selected\",\n children=build_trade_panel()\n ),\n ],\n )\n ],\n )\n\n\ndef build_banner():\n return html.Div(\n id=\"banner\",\n className=\"banner\",\n children=[\n html.Div(\n id=\"banner-text\",\n children=[\n html.H5(\"Mind Bot\"),\n html.H6(\"An Automated program that buy and sell cryptocurrencies at the right time\"),\n ],\n ),\n html.Div(\n id=\"banner-logo\",\n children=[\n #html.Button(id=\"learn-more-button\", children=\"LEARN MORE\", n_clicks=0),\n html.Img(id=\"logo\", src=app.get_asset_url(\"dash-new-logo.png\")),\n ],\n ),\n ],\n )\n\n\n\n\napp.layout = html.Div(\n id=\"big-app-container\",\n children=[\n build_banner(),\n # Interval component for live clock\n dcc.Interval(id=\"two-sec-interval-sma\", disabled=True, interval=1 * 1000, n_intervals=0),\n dcc.Interval(id=\"two-sec-interval\", disabled=True, interval=1 * 1000, n_intervals=0),\n dcc.Interval(id=\"five-sec-interval\", disabled=True, interval=1 * 1000, n_intervals=0),\n dcc.Interval(\n id=\"interval-component\",\n interval=2 * 1000, # in milliseconds\n n_intervals=50, # start at batch 50\n disabled=True,\n ),\n html.Div(\n id=\"app-container\",\n children=[\n build_tabs(),\n # Main app\n html.Div(id=\"app-content\"),\n ],\n )\n ],\n)\n\n\n\n# Running the server\nif __name__ == \"__main__\":\n #app.run_server(debug=True, port=8050)\n app.run_server()\n ", "sub_path": ".ipynb_checkpoints/app-checkpoint.py", "file_name": "app-checkpoint.py", "file_ext": "py", "file_size_in_byte": 23632, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "crypto_stream.init_connection", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 30, "usage_type": "call"}, {"api_name": "dash.Dash", "line_number": 32, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 40, "usage_type": "call"}, {"api_name": "dash_table.FormatTemplate.money", "line_number": 66, "usage_type": "call"}, {"api_name": "dash_table.FormatTemplate", "line_number": 66, "usage_type": "name"}, {"api_name": "dash_table.FormatTemplate.money", "line_number": 67, "usage_type": "call"}, {"api_name": "dash_table.FormatTemplate", "line_number": 67, "usage_type": "name"}, {"api_name": "dash_table.FormatTemplate.money", "line_number": 68, "usage_type": "call"}, {"api_name": "dash_table.FormatTemplate", "line_number": 68, "usage_type": "name"}, {"api_name": "dash_table.FormatTemplate.money", "line_number": 69, "usage_type": "call"}, {"api_name": "dash_table.FormatTemplate", "line_number": 69, "usage_type": "name"}, {"api_name": "dash_table.FormatTemplate.money", "line_number": 70, "usage_type": "call"}, {"api_name": "dash_table.FormatTemplate", "line_number": 70, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 74, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 81, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 82, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 84, "usage_type": "call"}, {"api_name": "dash_html_components.P", "line_number": 86, "usage_type": "call"}, {"api_name": "dash_html_components.P", "line_number": 87, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 90, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 90, "usage_type": "attribute"}, {"api_name": "dash_html_components.Table", "line_number": 92, "usage_type": "call"}, {"api_name": "dash_html_components.Tr", "line_number": 95, "usage_type": "call"}, {"api_name": "dash_html_components.Td", "line_number": 97, "usage_type": "call"}, {"api_name": "dash_html_components.A", "line_number": 99, "usage_type": "call"}, {"api_name": "plotly.graph_objs.Scatter", "line_number": 117, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 117, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Scatter", "line_number": 128, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 128, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Ohlc", "line_number": 141, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 141, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Ohlc", "line_number": 150, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 150, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Candlestick", "line_number": 161, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 161, "usage_type": "name"}, {"api_name": "dash_html_components.Div", "line_number": 216, "usage_type": "call"}, {"api_name": "plotly.graph_objs.Figure", "line_number": 230, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 230, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Figure", "line_number": 237, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 237, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Figure", "line_number": 255, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 255, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Figure", "line_number": 261, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 261, "usage_type": "name"}, {"api_name": "dash.exceptions.PreventUpdate", "line_number": 294, "usage_type": "name"}, {"api_name": "crypto_stream.init_connection", "line_number": 295, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 286, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 287, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 288, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 289, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 290, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 291, "usage_type": "call"}, {"api_name": "crypto_stream.fetch_data", "line_number": 303, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 299, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 300, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 301, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_dict", "line_number": 317, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 317, "usage_type": "attribute"}, {"api_name": "crypto_stream.generate_signals", "line_number": 319, "usage_type": "call"}, {"api_name": "crypto_stream.get_data_from_table", "line_number": 319, "usage_type": "call"}, {"api_name": "models.predict", "line_number": 321, "usage_type": "call"}, {"api_name": "dash.exceptions.PreventUpdate", "line_number": 323, "usage_type": "name"}, {"api_name": "crypto_stream.execute_trade_strategy", "line_number": 326, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 307, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 308, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 309, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 310, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 311, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 312, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 313, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 314, "usage_type": "call"}, {"api_name": "backtesting.main", "line_number": 345, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 332, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 333, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 334, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 335, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 336, "usage_type": "call"}, {"api_name": "dash.dependencies.Output", "line_number": 337, "usage_type": "call"}, {"api_name": "dash.dependencies.Input", "line_number": 338, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 339, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 340, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 341, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 342, "usage_type": "call"}, {"api_name": "dash.dependencies.State", "line_number": 343, "usage_type": "call"}, {"api_name": "dash_table.DataTable", "line_number": 354, "usage_type": "call"}, {"api_name": "dash_table.DataTable", "line_number": 385, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 415, "usage_type": "call"}, {"api_name": "dash_html_components.Button", "line_number": 416, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 422, "usage_type": "call"}, {"api_name": "dash_html_components.Label", "line_number": 426, "usage_type": "call"}, {"api_name": "dash_core_components.Dropdown", "line_number": 427, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 436, "usage_type": "call"}, {"api_name": "dash_html_components.Label", "line_number": 440, "usage_type": "call"}, {"api_name": "dash_daq.NumericInput", "line_number": 441, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 446, "usage_type": "call"}, {"api_name": "dash_core_components.Store", "line_number": 450, "usage_type": "call"}, {"api_name": "crypto_stream.SYMBOL", "line_number": 450, "usage_type": "attribute"}, {"api_name": "dash_core_components.Store", "line_number": 451, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 453, "usage_type": "call"}, {"api_name": "dash_core_components.Graph", "line_number": 458, "usage_type": "call"}, {"api_name": "dash_core_components.Graph", "line_number": 460, "usage_type": "call"}, {"api_name": "dash_core_components.Graph", "line_number": 462, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 466, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 470, "usage_type": "call"}, {"api_name": "crypto_stream.get_crypto_symbols", "line_number": 471, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 472, "usage_type": "call"}, {"api_name": "models.MODEL_LIST", "line_number": 473, "usage_type": "attribute"}, {"api_name": "dash_html_components.Br", "line_number": 474, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 476, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 487, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 489, "usage_type": "call"}, {"api_name": "dash_html_components.P", "line_number": 492, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 496, "usage_type": "call"}, {"api_name": "dash_core_components.Store", "line_number": 499, "usage_type": "call"}, {"api_name": "crypto_stream.SYMBOL", "line_number": 499, "usage_type": "attribute"}, {"api_name": "dash_html_components.Div", "line_number": 500, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 504, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 507, "usage_type": "call"}, {"api_name": "crypto_stream.get_crypto_symbols", "line_number": 508, "usage_type": "call"}, {"api_name": "crypto_stream.SYMBOL", "line_number": 508, "usage_type": "attribute"}, {"api_name": "dash_html_components.Br", "line_number": 510, "usage_type": "call"}, {"api_name": "backtesting.model_list", "line_number": 511, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 512, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 516, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 519, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 521, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 523, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 524, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 526, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 531, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 535, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 536, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 537, "usage_type": "call"}, {"api_name": "dash_core_components.Loading", "line_number": 539, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 542, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 546, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 549, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 554, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 561, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 566, "usage_type": "call"}, {"api_name": "dash_core_components.Graph", "line_number": 568, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 569, "usage_type": "call"}, {"api_name": "dash_html_components.Br", "line_number": 571, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 572, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 579, "usage_type": "call"}, {"api_name": "dash_core_components.Tabs", "line_number": 583, "usage_type": "call"}, {"api_name": "dash_core_components.Tab", "line_number": 588, "usage_type": "call"}, {"api_name": "dash_core_components.Tab", "line_number": 596, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 611, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 615, "usage_type": "call"}, {"api_name": "dash_html_components.H5", "line_number": 618, "usage_type": "call"}, {"api_name": "dash_html_components.H6", "line_number": 619, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 622, "usage_type": "call"}, {"api_name": "dash_html_components.Img", "line_number": 626, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 635, "usage_type": "call"}, {"api_name": "dash_core_components.Interval", "line_number": 640, "usage_type": "call"}, {"api_name": "dash_core_components.Interval", "line_number": 641, "usage_type": "call"}, {"api_name": "dash_core_components.Interval", "line_number": 642, "usage_type": "call"}, {"api_name": "dash_core_components.Interval", "line_number": 643, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 649, "usage_type": "call"}, {"api_name": "dash_html_components.Div", "line_number": 654, "usage_type": "call"}]} +{"seq_id": "423182324", "text": "import logging\nfrom struct import pack\nfrom zope.interface import implements\nfrom twisted.internet.protocol import ProcessProtocol\nfrom twisted.internet import reactor, interfaces\n\nfrom constants import VERBOSE\nfrom fcs import pppfcs16\nfrom utils import hexdump\n\n\nFLAG_SEQUENCE = b'\\x7e'\nCONTROL_ESCAPE = b'\\x7d'\n\nclass PPPDProtocol(ProcessProtocol):\n implements(interfaces.IPushProducer)\n\n frameBuffer = bytearray()\n paused = False\n\n def writeFrame(self, frame):\n fcs = pppfcs16(frame)\n buffer = bytearray(FLAG_SEQUENCE)\n for byte in frame:\n if ord(byte) < 0x20 or byte in (FLAG_SEQUENCE, CONTROL_ESCAPE):\n buffer.append(CONTROL_ESCAPE)\n buffer.append(ord(byte) ^ 0x20)\n else:\n buffer.append(byte)\n\n buffer.extend(pack('!H', fcs))\n buffer.append(FLAG_SEQUENCE)\n self.transport.write(str(buffer))\n\n\n def outReceived(self, data):\n logging.log(VERBOSE, \"Raw data: %s\", hexdump(data))\n escaped = False\n for byte in data:\n if escaped:\n escaped = False\n self.frameBuffer.append(ord(byte) ^ 0x20)\n elif byte == CONTROL_ESCAPE:\n escaped = True\n elif byte == FLAG_SEQUENCE:\n if not self.frameBuffer:\n continue\n if len(self.frameBuffer) < 4:\n logging.warning(\"Invalid PPP frame received from pppd. (%s)\",\n hexdump(self.frameBuffer))\n elif self.frameBuffer:\n del self.frameBuffer[-2:] # Remove FCS field\n self.pppFrameReceived(self.frameBuffer)\n self.frameBuffer = bytearray()\n else:\n self.frameBuffer.append(byte)\n\n\n def pppFrameReceived(self, frame):\n if self.paused:\n logging.debug('Drop a PPP frame.')\n return\n\n if frame.startswith('\\xff\\x03'):\n protocol = frame[2:4]\n else:\n protocol = frame[:2]\n\n if protocol[0] in (0x80, 0x82, 0xc0, 0xc2, 0xc4):\n self.sstp.writePPPControlFrame(frame)\n else:\n self.sstp.writePPPDataFrame(frame)\n\n\n def errReceived(self, data):\n logging.warn('Received errors from pppd.')\n logging.warn(data)\n\n\n def outConnectionLost(self):\n logging.debug('pppd stdout lost.')\n self.sstp.transport.loseConnection()\n\n\n def processEnded(self, reason):\n logging.info('pppd stopped.')\n self.sstp.pppStoped()\n\n\n def stopProducing(self):\n self.paused = True\n self.transport.loseConnection()\n\n\n def pauseProducing(self):\n logging.debug('Pause producting')\n self.paused = True\n\n\n def resumeProducing(self):\n logging.debug('Resume producing')\n self.paused = False\n\n", "sub_path": "sstpd/ppp.py", "file_name": "ppp.py", "file_ext": "py", "file_size_in_byte": 2895, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "twisted.internet.protocol.ProcessProtocol", "line_number": 15, "usage_type": "name"}, {"api_name": "zope.interface.implements", "line_number": 16, "usage_type": "call"}, {"api_name": "twisted.internet.interfaces.IPushProducer", "line_number": 16, "usage_type": "attribute"}, {"api_name": "twisted.internet.interfaces", "line_number": 16, "usage_type": "name"}, {"api_name": "fcs.pppfcs16", "line_number": 22, "usage_type": "call"}, {"api_name": "struct.pack", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.log", "line_number": 37, "usage_type": "call"}, {"api_name": "constants.VERBOSE", "line_number": 37, "usage_type": "argument"}, {"api_name": "utils.hexdump", "line_number": 37, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 49, "usage_type": "call"}, {"api_name": "utils.hexdump", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 61, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 76, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 77, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 81, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 86, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 96, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "219001182", "text": "import discord\nfrom discord.ext import commands\nimport json\nimport asyncio\nimport aiohttp\nimport async_timeout\nimport time\n\nsettings_json = open('settings.json', 'r')\nsettings_json = settings_json.read().strip()\nsettings_json = json.loads(settings_json)\n\nnasa_key = settings_json['nasa_api_key']\n\n\nasync def fetchGet(urlIn):\n async with aiohttp.ClientSession() as session:\n with async_timeout.timeout(10):\n async with session.get(urlIn) as response:\n return await response.text()\n\ngotToday = \"\"\n\n\nclass Apod():\n def __init__(self, bot):\n self.bot = bot\n\n @commands.command()\n async def apod(self, ctx):\n channel = ctx.message.channel\n global gotToday\n global apod_em\n today = time.strftime(\"%Y-%m-%d\")\n if today != gotToday:\n apod_json = await fetchGet('https://api.nasa.gov/planetary/apod?date={0}&api_key={1}'.format(today, nasa_key))\n apod_json=json.loads(apod_json)\n apod_pic_url=apod_json['hdurl']\n gotToday=today\n apod_em=discord.Embed(\n title = 'Nasa - Astronomy Picture Of The Day', colour = 0xD3D92)\n apod_em.set_image(url = '{0}'.format(apod_pic_url))\n await channel.send(embed = apod_em)\n else:\n await channel.send(embed = apod_em)\n\ndef setup(bot):\n bot.add_cog(Apod(bot))\n", "sub_path": "nasabot-apod.py", "file_name": "nasabot-apod.py", "file_ext": "py", "file_size_in_byte": 1384, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "json.loads", "line_number": 11, "usage_type": "call"}, {"api_name": "aiohttp.ClientSession", "line_number": 17, "usage_type": "call"}, {"api_name": "async_timeout.timeout", "line_number": 18, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 34, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 37, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 40, "usage_type": "call"}, {"api_name": "discord.ext.commands.command", "line_number": 29, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 29, "usage_type": "name"}]} +{"seq_id": "388218182", "text": "from flask import render_template, redirect, url_for\nfrom . import usuario\nfrom .forms import CadastroUsuarioForm, EditUsuarioForm\nfrom ..models import Usuario\nfrom .. import db\nfrom flask_login import login_required\n\n\n@usuario.route('/usuarios')\n@login_required\ndef index():\n users = Usuario.query.all()\n return render_template('usuario/lista.html', users=users)\n\n\n@usuario.route('/usuario/cadastro', methods=['GET', 'POST'])\n@login_required\ndef cadastro():\n usuarioForm = CadastroUsuarioForm()\n if usuarioForm.validate_on_submit():\n user = Usuario()\n user.nome = usuarioForm.nome.data\n user.sobrenome = usuarioForm.sobrenome.data\n user.email = usuarioForm.email.data\n db.session.add(user)\n db.session.commit()\n return redirect(url_for('usuario.index'))\n return render_template('usuario/cadastro.html', form=usuarioForm)\n\n\n@usuario.route('/usuario/delete/')\n@login_required\ndef delete(id):\n user = Usuario.query.get_or_404(id)\n db.session.delete(user)\n db.session.commit()\n return redirect(url_for('usuario.index'))\n\n\n@usuario.route('/usuario/editar/', methods=['GET', 'POST'])\n@login_required\ndef edit(id):\n user = Usuario.query.get_or_404(id)\n usuarioForm = EditUsuarioForm(user=user)\n if usuarioForm.validate_on_submit():\n user.nome = usuarioForm.nome.data\n user.sobrenome = usuarioForm.sobrenome.data\n db.session.add(user)\n db.session.commit()\n return redirect(url_for('usuario.index'))\n usuarioForm.nome.data = user.nome\n usuarioForm.sobrenome.data = user.sobrenome\n return render_template('usuario/editar.html', form=usuarioForm)\n", "sub_path": "app/usuario/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1682, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "models.Usuario.query.all", "line_number": 12, "usage_type": "call"}, {"api_name": "models.Usuario.query", "line_number": 12, "usage_type": "attribute"}, {"api_name": "models.Usuario", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 13, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 10, "usage_type": "name"}, {"api_name": "forms.CadastroUsuarioForm", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Usuario", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 28, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 17, "usage_type": "name"}, {"api_name": "models.Usuario.query.get_or_404", "line_number": 34, "usage_type": "call"}, {"api_name": "models.Usuario.query", "line_number": 34, "usage_type": "attribute"}, {"api_name": "models.Usuario", "line_number": 34, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 37, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 32, "usage_type": "name"}, {"api_name": "models.Usuario.query.get_or_404", "line_number": 43, "usage_type": "call"}, {"api_name": "models.Usuario.query", "line_number": 43, "usage_type": "attribute"}, {"api_name": "models.Usuario", "line_number": 43, "usage_type": "name"}, {"api_name": "forms.EditUsuarioForm", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 53, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 41, "usage_type": "name"}]} +{"seq_id": "149792675", "text": "#coding:utf-8\n\nimport db_config\nfrom functions import exchange_code\nimport logging\nimport datetime\nimport csv\nimport mysql.connector\nimport sys\nimport slackweb\nimport configparser\n\n\nBASE_DIR = '/usr/local/script/'\naccount = BASE_DIR + 'config/account.ini'\naccount_config = configparser.ConfigParser()\naccount_config.read(account, 'UTF-8')\n\npath = BASE_DIR + 'config/path.ini'\npath_config = configparser.ConfigParser()\npath_config.read(path, 'UTF-8')\n\n# Constants\nDB_USER = account_config.get('db', 'DB_USER')\nDB_PASSWORD = account_config.get('db', 'DB_PASSWORD')\nDB_HOST = account_config.get('db', 'DB_HOST')\nDB_DATABASE = account_config.get('db', 'DB_DATABASE')\nTABLE = 'japan_all_stock_prices'\nCSV_FILE_DIR = path_config.get('csv_path', 'data_base') + path_config.get('csv_path', 'japan_all_stock_prices')\n\nargs = sys.argv\n\nif __name__ == '__main__':\n # モジュール名でロガーを生成する(メインモジュールは 名前が '__main__' になる)\n log = logging.getLogger(__name__)\n # Slack Incoming webhook設定\n slack_log_url = account_config.get('slack', 'slack_log_url')\n slack = slackweb.Slack(url=slack_log_url)\n\n log.info('日本株全銘柄テーブルインポート処理 : 開始')\n\n # 対象の日付を設定（引数でYYYYMMDD形式で日付を入れるとその日付のファイルを対象とする）\n if len(args) < 2:\n TODAY = datetime.date.today()\n else:\n TARGET_DAY = args[1]\n TODAY = datetime.datetime(int(TARGET_DAY[:4]), int(TARGET_DAY[4:6]), int(TARGET_DAY[-2:]))\n\n # Target File Name\n file_name_date_part = str(TODAY.year) + '{:0=2}'.format(TODAY.month) + '{:0=2}'.format(TODAY.day)\n file_name = 'japan-all-stock-prices_' + file_name_date_part + '.csv'\n\n log.info('テーブル名:%s 対象ファイル:%s' % (TABLE, file_name))\n\n with open (CSV_FILE_DIR + file_name) as csvfile:\n reader = csv.reader(csvfile)\n # headerと日経225、TOPIXをスキップする\n for i in range(3):\n next(reader, None)\n\n # MariaDB connect\n try:\n conn = mysql.connector.connect(user=DB_USER, password=DB_PASSWORD, host=DB_HOST, database=DB_DATABASE)\n cursor = conn.cursor()\n\n for row in reader:\n security_code = row[0] if row[0] != '-' else 'null'\n dt = file_name_date_part\n company_name = row[1] if row[1] != '-' else 'null'\n stock_exchange_code = exchange_code.get_stock_exchange_code(row[2])\n industry_type = exchange_code.get_industry_type(row[3])\n opening_price = row[9] if row[9] != '-' else 'null'\n closing_price = row[5] if row[5] != '-' else 'null'\n high_price = row[10] if row[10] != '-' else 'null'\n low_price = row[11] if row[11] != '-' else 'null'\n day_before_ratio = row[6] if row[6] != '-' else 'null'\n day_before_ratio_percentage = row[7] if row[7] != '-' else 'null'\n last_day_closing_price = row[8] if row[8] != '-' else 'null'\n volume = row[12] if row[12] != '-' else 'null'\n trading_value = row[13] if row[13] != '-' else 'null'\n market_capitalization = row[14] if row[14] != '-' else 'null'\n price_range_lower_limit = row[15] if row[15] != '-' else'null'\n price_range_upper_limit = row[16] if row[16] != '-' else 'null'\n cursor.execute('''INSERT INTO %s.%s (security_code, dt, company_name,\n stock_exchange_code, industry_type, opening_price, closing_price, high_price, low_price,\n day_before_ratio, day_before_ratio_percentage, last_day_closing_price, volume,\n trading_value, market_capitalization, price_range_lower_limit, price_range_upper_limit)\n VALUES(%s, '%s', \"%s\", %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s);\n ''' % (DB_DATABASE, TABLE, security_code, dt, company_name, stock_exchange_code, industry_type, opening_price,\n closing_price, high_price, low_price, day_before_ratio, day_before_ratio_percentage,\n last_day_closing_price, volume, trading_value, market_capitalization,\n price_range_lower_limit, price_range_upper_limit))\n except mysql.connector.Error as e:\n log.error(e)\n conn.close()\n\n conn.commit()\n conn.close()\n\n # テーブルINSERT件数確認\n try:\n conn = mysql.connector.connect(user=DB_USER, password=DB_PASSWORD, host=DB_HOST, database=DB_DATABASE)\n cursor = conn.cursor()\n count_query = \"\"\"SELECT dt, COUNT(*) FROM %s.%s WHERE dt = '%s' GROUP BY dt\"\"\" % (DB_DATABASE, TABLE, TODAY)\n cursor.execute(count_query)\n result = cursor.fetchone()\n result_word = \"処理日時：%s 取得件数：%s\" % (result[0], result[1])\n slack.notify(text=\"日本株全銘柄テーブルインポート\\n\" + result_word)\n except mysql.connector.Error as e:\n log.error(e)\n conn.close()\n\n log.info('日本株全銘柄テーブルインポート処理 : 終了')\n conn.close()\n", "sub_path": "script/db/import_japan_all_stock_prices.py", "file_name": "import_japan_all_stock_prices.py", "file_ext": "py", "file_size_in_byte": 5393, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "configparser.ConfigParser", "line_number": 16, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 31, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 35, "usage_type": "call"}, {"api_name": "slackweb.Slack", "line_number": 38, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 44, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 47, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 56, "usage_type": "call"}, {"api_name": "mysql.connector.connector.connect", "line_number": 63, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 63, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 63, "usage_type": "name"}, {"api_name": "functions.exchange_code.get_stock_exchange_code", "line_number": 70, "usage_type": "call"}, {"api_name": "functions.exchange_code", "line_number": 70, "usage_type": "name"}, {"api_name": "functions.exchange_code.get_industry_type", "line_number": 71, "usage_type": "call"}, {"api_name": "functions.exchange_code", "line_number": 71, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 93, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 93, "usage_type": "name"}, {"api_name": "mysql.connector.connector.connect", "line_number": 102, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 102, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 102, "usage_type": "name"}, {"api_name": "mysql.connector.connector", "line_number": 109, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 109, "usage_type": "name"}]} +{"seq_id": "620608909", "text": "from django.urls import path\nfrom . import views\n\napp_name = \"board\"\nurlpatterns=[\n path('', views.index, name=\"index\"),\n path('create', views.create, name=\"create\"),\n path('detail/', views.detail, name=\"detail\"),\n path('delete/', views.delete, name=\"delete\"),\n path('update/', views.update, name=\"update\"),\n path('up//', views.up, name=\"up\"),\n path('create_reply/', views.create_reply, name=\"create_reply\"),\n path('agree//', views.agree, name=\"agree\")\n\n]", "sub_path": "board/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 524, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "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"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "613495349", "text": "# Create your views here.\nfrom django.template import RequestContext\nfrom history.models import Item, Skill\nfrom django.shortcuts import get_object_or_404\nfrom lib.overrides import render_response\nfrom django.core.paginator import Paginator, InvalidPage, EmptyPage\nimport time\nfrom calendar import month_name\nimport datetime\nfrom django.db.models import Q\n\ndef get_date_list():\n\t\"\"\"Get a list of years and months for the posts\"\"\"\n\n\tif not Item.objects.count(): return []\n\n\tyear, month = time.localtime()[:2]\n\tfirst = Item.objects.order_by(\"start_date\")[0]\n\tfyear = first.start_date.year\n\tdates = []\n\n\t# Loop over the years and months\n\tfor y in xrange(year, fyear - 1, -1):\n\t\tdates.append(y)\n\n\treturn dates\n\ndef paginate_items(request, items):\n paginator = Paginator(items,10)\n\n try: page = int(request.GET.get(\"page\",'1'))\n except ValueError: page = 1\n\n try: items = paginator.page(page)\n except (InvalidPage,EmptyPage):\n items = paginator.page(paginator.num_pages)\n\n return items\n\ndef history(request):\n\titems = Item.objects.filter(published=True).order_by(\"-end_date\")\n\titems = paginate_items(request, items)\n\tdates = get_date_list()\n\treturn render_response(request, 'history/index.html', {\n\t\t'skills': Skill.objects.all(),\n\t\t'items': items,\n\t\t'archive_list': dates\n\t\t})\n\ndef view_item(request, slug):\n\tdates = get_date_list()\n\treturn render_response(request, 'history/view_item.html', {\n\t\t'skills': Skill.objects.all(),\n\t\t'item': get_object_or_404(Item, slug=slug),\n\t\t'archive_list': dates\n\t\t})\n\ndef view_skill(request, slug):\n\tskill = get_object_or_404(Skill, slug=slug)\n\titems = Item.objects.filter(published=True, skills=skill).order_by(\"-end_date\")\n\titems = paginate_items(request, items)\n\tdates = get_date_list()\n\n\treturn render_response(request, 'history/skill.html', {\n\t\t'skills': Skill.objects.all(),\n\t\t'items': items,\n\t\t'skill': skill,\n\t\t'archive_list': dates\n\t\t})\n\ndef archive(request, year):\n\titems = \\\n\t Item.objects.filter(published = True,\n\t\t\t\t\t\t start_date__lte = datetime.date(eval(year),12,31),\n\t\t\t\t\t\t end_date__gte = \\\n\t\t\t\t\t\t datetime.date(eval(year),1,1)).order_by(\"-end_date\")\n\titems = paginate_items(request, items)\n\tdates = get_date_list()\n\n\treturn render_response(request, 'history/archive.html', {\n\t\t'skills': Skill.objects.all(),\n\t\t'items': items,\n\t\t'year': year,\n\t\t'archive_list': dates\n\t\t})\n", "sub_path": "history/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2350, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "history.models.Item.objects.count", "line_number": 15, "usage_type": "call"}, {"api_name": "history.models.Item.objects", "line_number": 15, "usage_type": "attribute"}, {"api_name": "history.models.Item", "line_number": 15, "usage_type": "name"}, {"api_name": "time.localtime", "line_number": 17, "usage_type": "call"}, {"api_name": "history.models.Item.objects.order_by", "line_number": 18, "usage_type": "call"}, {"api_name": "history.models.Item.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "history.models.Item", "line_number": 18, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 29, "usage_type": "call"}, {"api_name": "django.core.paginator.InvalidPage", "line_number": 35, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 35, "usage_type": "name"}, {"api_name": "history.models.Item.objects.filter", "line_number": 41, "usage_type": "call"}, {"api_name": "history.models.Item.objects", "line_number": 41, "usage_type": "attribute"}, {"api_name": "history.models.Item", "line_number": 41, "usage_type": "name"}, {"api_name": "lib.overrides.render_response", "line_number": 44, "usage_type": "call"}, {"api_name": "history.models.Skill.objects.all", "line_number": 45, "usage_type": "call"}, {"api_name": "history.models.Skill.objects", "line_number": 45, "usage_type": "attribute"}, {"api_name": "history.models.Skill", "line_number": 45, "usage_type": "name"}, {"api_name": "lib.overrides.render_response", "line_number": 52, "usage_type": "call"}, {"api_name": "history.models.Skill.objects.all", "line_number": 53, "usage_type": "call"}, {"api_name": "history.models.Skill.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "history.models.Skill", "line_number": 53, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 54, "usage_type": "call"}, {"api_name": "history.models.Item", "line_number": 54, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 59, "usage_type": "call"}, {"api_name": "history.models.Skill", "line_number": 59, "usage_type": "argument"}, {"api_name": "history.models.Item.objects.filter", "line_number": 60, "usage_type": "call"}, {"api_name": "history.models.Item.objects", "line_number": 60, "usage_type": "attribute"}, {"api_name": "history.models.Item", "line_number": 60, "usage_type": "name"}, {"api_name": "lib.overrides.render_response", "line_number": 64, "usage_type": "call"}, {"api_name": "history.models.Skill.objects.all", "line_number": 65, "usage_type": "call"}, {"api_name": "history.models.Skill.objects", "line_number": 65, "usage_type": "attribute"}, {"api_name": "history.models.Skill", "line_number": 65, "usage_type": "name"}, {"api_name": "history.models.Item.objects.filter", "line_number": 73, "usage_type": "call"}, {"api_name": "history.models.Item.objects", "line_number": 73, "usage_type": "attribute"}, {"api_name": "history.models.Item", "line_number": 73, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 74, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 76, "usage_type": "call"}, {"api_name": "lib.overrides.render_response", "line_number": 80, "usage_type": "call"}, {"api_name": "history.models.Skill.objects.all", "line_number": 81, "usage_type": "call"}, {"api_name": "history.models.Skill.objects", "line_number": 81, "usage_type": "attribute"}, {"api_name": "history.models.Skill", "line_number": 81, "usage_type": "name"}]} +{"seq_id": "45522638", "text": "\"\"\"SensorGrid and Sensor Schema\"\"\"\nfrom pydantic import Field, constr, validator\nfrom typing import List\nfrom enum import Enum\nfrom ..geometry import Mesh3D\nfrom .._base import NoExtraBaseModel\nfrom ._base import IDdRadianceBaseModel\n\nimport re\n\n\nclass _RadianceAsset(IDdRadianceBaseModel):\n \"\"\"Hidden base class for all Radiance Assets.\"\"\"\n\n display_name: str = Field(\n default=None,\n description='Text string for a unique display name, used to set the default '\n 'file name that the radiance asset is written to within a radiance folder. '\n 'Must not contain spaces or special characters.'\n )\n\n @validator('display_name')\n def valid_rad_string_display_name(cls, value):\n \"\"\"Check that a string is valid for Radiance.\n\n This method is modified from the honeybee-core.typing.valid_rad_string method.\n \"\"\"\n if value is not None:\n try:\n illegal_match = re.search(r'[^.A-Za-z0-9_-]', value)\n except TypeError:\n raise TypeError('display_name must be a text string. Got {}: {}.'.format(\n type(value), value))\n assert illegal_match is None, \\\n 'Illegal character \"{}\" found in display_name'.format(illegal_match.group(0))\n assert len(value) > 0, \\\n 'Input display_name \"{}\" contains no characters.'.format(value)\n return value\n\n room_identifier: str = Field(\n None,\n regex=r'[A-Za-z0-9_-]',\n min_length=1,\n max_length=100,\n description='Optional text string for the Room identifier to which this '\n 'object belongs. This will be used to narrow down the number of '\n 'aperture groups that have to be run with this sensor grid. If None, '\n 'the grid will be run with all aperture groups in the model.'\n )\n\n light_path: List[List[str]] = Field(\n None,\n description='Get or set a list of lists for the light path from the object to '\n 'the sky. Each sub-list contains identifiers of aperture groups through which '\n 'light passes. (eg. [[\"SouthWindow1\"], [\"static_apertures\", \"NorthWindow2\"]]).'\n 'Setting this property will override any auto-calculation of the light '\n 'path from the model and room_identifier upon export to the simulation.'\n )\n\n\nclass Sensor(NoExtraBaseModel):\n \"\"\"A single Radiance of sensors.\"\"\"\n\n type: constr(regex='^Sensor$') = 'Sensor'\n\n pos: List[float] = Field(\n ...,\n description=\"Position of sensor in space as an array of (x, y, z) values.\",\n min_items=3,\n max_items=3\n )\n\n dir: List[float] = Field(\n ...,\n description=\"Direction of sensor as an array of (x, y, z) values.\",\n min_items=3,\n max_items=3\n )\n\n\nclass SensorGrid(_RadianceAsset):\n \"\"\"A grid of sensors.\"\"\"\n\n type: constr(regex='^SensorGrid$') = 'SensorGrid'\n\n sensors: List[Sensor] = Field(\n ...,\n description='A list of sensors that belong to the grid.'\n )\n\n mesh: Mesh3D = Field(\n None,\n description='An optional Mesh3D that aligns with the sensors and can be '\n 'used for visualization of the grid. Note that the number of sensors in '\n 'the grid must match the number of faces or the number vertices within '\n 'the Mesh3D.'\n )\n\n\nclass ViewType(str, Enum):\n \"\"\"A single character for the view type (-vt).\"\"\"\n perspective = 'v'\n hemispherical_fisheye = 'h'\n parallel = 'l'\n cylindrical_panorama = 'c'\n angular_fisheye = 'a'\n planisphere = 's'\n\n\nclass View(_RadianceAsset):\n \"\"\"A single Radiance of sensors.\"\"\"\n\n type: constr(regex='^View$') = 'View'\n\n position: List[float] = Field(\n ...,\n description='The view position (-vp) as an array of (x, y, z) values.'\n 'This is the focal point of a perspective view or the center of a '\n 'parallel projection.',\n min_items=3,\n max_items=3\n )\n\n direction: List[float] = Field(\n ...,\n description='The view direction (-vd) as an array of (x, y, z) values.'\n 'The length of this vector indicates the focal distance as needed by '\n 'the pixel depth of field (-pd) in rpict.',\n min_items=3,\n max_items=3\n )\n\n up_vector: List[float] = Field(\n ...,\n description='The view up (-vu) vector as an array of (x, y, z) values.',\n min_items=3,\n max_items=3\n )\n\n view_type: ViewType = ViewType.perspective\n\n h_size: float = Field(\n 60,\n description='A number for the horizontal field of view in degrees (for '\n 'all perspective projections including fisheye). For a parallel '\n 'projection, this is the view width in world coordinates.'\n )\n\n v_size: float = Field(\n 60,\n description='A number for the vertical field of view in degrees (for '\n 'all perspective projections including fisheye). For a parallel '\n 'projection, this is the view width in world coordinates.'\n )\n\n shift: float = Field(\n None,\n description='The view shift (-vs). This is the amount the actual '\n 'image will be shifted to the right of the specified view. This '\n 'option is useful for generating skewed perspectives or rendering '\n 'an image a piece at a time. A value of 1 means that the rendered '\n 'image starts just to the right of the normal view. A value of -1 '\n 'would be to the left. Larger or fractional values are permitted '\n 'as well.'\n )\n\n lift: float = Field(\n None,\n description='The view lift (-vl). This is the amount the actual '\n 'image will be lifted up from the specified view. This '\n 'option is useful for generating skewed perspectives or rendering '\n 'an image a piece at a time. A value of 1 means that the rendered '\n 'image starts just to the right of the normal view. A value of -1 '\n 'would be to the left. Larger or fractional values are permitted '\n 'as well.'\n )\n\n fore_clip: float = Field(\n None,\n description='View fore clip (-vo) at a distance from the view point.'\n 'The plane will be perpendicular to the view direction for perspective '\n 'and parallel view types. For fisheye view types, the clipping plane is '\n 'actually a clipping sphere, centered on the view point with fore_clip radius. '\n 'Objects in front of this imaginary surface will not be visible.'\n )\n\n aft_clip: float = Field(\n None,\n description='View aft clip (-va) at a distance from the view point.'\n 'Like the view fore plane, it will be perpendicular to the view '\n 'direction for perspective and parallel view types. For fisheye '\n 'view types, the clipping plane is actually a clipping sphere, '\n 'centered on the view point with radius val.'\n )\n", "sub_path": "honeybee_schema/radiance/asset.py", "file_name": "asset.py", "file_ext": "py", "file_size_in_byte": 6946, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "_base.IDdRadianceBaseModel", "line_number": 12, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 15, "usage_type": "call"}, {"api_name": "re.search", "line_number": 30, "usage_type": "call"}, {"api_name": "pydantic.validator", "line_number": 22, "usage_type": "call"}, {"api_name": "pydantic.Field", "line_number": 40, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 51, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 51, "usage_type": "call"}, {"api_name": "_base.NoExtraBaseModel", "line_number": 61, "usage_type": "name"}, {"api_name": "pydantic.constr", "line_number": 64, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 66, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 66, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 73, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 73, "usage_type": "call"}, {"api_name": "pydantic.constr", "line_number": 84, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 86, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 86, "usage_type": "call"}, {"api_name": "geometry.Mesh3D", "line_number": 91, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 91, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 100, "usage_type": "name"}, {"api_name": "pydantic.constr", "line_number": 113, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 115, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 115, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 124, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 124, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 133, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 133, "usage_type": "call"}, {"api_name": "pydantic.Field", "line_number": 142, "usage_type": "call"}, {"api_name": "pydantic.Field", "line_number": 149, "usage_type": "call"}, {"api_name": "pydantic.Field", "line_number": 156, "usage_type": "call"}, {"api_name": "pydantic.Field", "line_number": 167, "usage_type": "call"}, {"api_name": "pydantic.Field", "line_number": 178, "usage_type": "call"}, {"api_name": "pydantic.Field", "line_number": 187, "usage_type": "call"}]} +{"seq_id": "331283559", "text": "import argparse\nimport os\nimport distutils.util\nimport sys\n\nfrom collections import defaultdict\n\n# import matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\n\nfrom matplotlib import transforms\n\nfrom __plot_utils import create_scaled_canvases, load_results, process_cli_args\n\nsys.path.append(os.path.normpath(os.path.join(__file__, \"../../../\"))) # noqa\nfrom experimentarium.utils import make_iter\n\nsns.set()\n\n\nDEFAULT_RESULTS_ROOT = os.path.normpath(\n os.path.join(os.path.dirname(__file__), \"../../../merged_results\")\n)\n\nDEFAULT_OUT_ROOT = os.path.normpath(\n os.path.join(os.path.dirname(__file__), \"../../../plots\")\n)\n\n\nif __name__ == \"__main__\":\n # ======================================================================\n # Parser setting up.\n # ======================================================================\n\n parser = argparse.ArgumentParser(\n \"Plotter\", formatter_class=argparse.ArgumentDefaultsHelpFormatter\n )\n parser.add_argument(\n \"--results-root\",\n type=str,\n help=\"Path to merged results or directory with files to merge\",\n )\n parser.add_argument(\"--out-root\", type=str, help=\"Directory to save plots\")\n parser.add_argument(\n \"--last\",\n type=distutils.util.strtobool,\n help=\"Whether to take the last created file if --results-root is directory\",\n )\n parser.add_argument(\"--extention\", type=str, help=\"Extention of saved plots\")\n parser.add_argument(\"--metrics\", type=str, nargs=\"+\", help=\"Metrics to plot\")\n parser.add_argument(\n \"--hard-tresholding\",\n type=distutils.util.strtobool,\n help=\"Whether not to display models with scores less than threshold\",\n )\n parser.add_argument(\n \"--threshold\",\n type=float,\n help=\"Threshold to set for soft and hard thresholding\",\n )\n parser.add_argument(\n \"--progress-bar\",\n type=distutils.util.strtobool,\n help=\"Whether to show progress bar over processed benchmarks\",\n )\n parser.add_argument(\n \"--benchmarks\", type=str, nargs=\"*\", help=\"Which benchmarks to plot\",\n )\n parser.add_argument(\n \"--joint-plots\",\n type=distutils.util.strtobool,\n help=\"Whether to plot joint plots. False means plotting only sl/ssl plots\",\n )\n parser.add_argument(\n \"--max-diff-display\",\n type=float,\n help=\"Maximum absolute value on difference score plots\",\n )\n\n parser.set_defaults(\n results_root=DEFAULT_RESULTS_ROOT,\n out_root=DEFAULT_OUT_ROOT,\n last=\"True\",\n extention=\"png\",\n metrics=[\"accuracy\", \"f1\"],\n hard_thresholding=\"True\",\n threshold=0.5,\n progress_bar=\"True\",\n benchmarks=[\"all\"],\n joint_plots=\"True\",\n max_diff_display=0.04,\n )\n args = parser.parse_args()\n process_cli_args(args)\n\n # ======================================================================\n # Plotting things.\n # ======================================================================\n df = load_results(args)\n args.benchmarks.intersection_update(pd.unique(df[\"benchmark\"]))\n df = df[df[\"benchmark\"].isin(args.benchmarks)]\n\n if not args.benchmarks:\n raise ValueError(f\"None of provided benchmarks is found in loaded data.\")\n\n metrics = list(set(df.columns).intersection(set(args.metrics)))\n if not metrics:\n raise ValueError(\"No given metric found in merged dataframe.\")\n\n models = tuple(pd.unique(df[\"model\"]))\n # markers = tuple(\n # marker\n # for marker in matplotlib.markers.MarkerStyle.markers\n # if marker not in {\",\", \"\", \" \", \"None\", None}\n # )\n markers = (\"s\", \"o\", \"v\", \"X\")\n colors = sns.color_palette(\"muted\")\n\n model2marker = dict(zip(models, markers))\n model2color = dict(zip(models, colors))\n\n linestyle2ssl = {True: \"-\", False: \"-.\"}\n marker2ssl = {True: \"P\", False: \"o\"}\n\n # Lsize/Ratio -> Metrics -> Benchmark -> Model -> Score\n diffs = defaultdict(lambda: defaultdict(lambda: defaultdict(dict)))\n\n # ======================================================================\n # Plotting joint results.\n # ======================================================================\n\n for benchmark, benchmark_df in make_iter(\n df.groupby(\"benchmark\"),\n args.progress_bar,\n desc=\"#Benchmarks processed/joint plots plotted\",\n ):\n if args.joint_plots:\n canvases = create_scaled_canvases(\n metrics, benchmark, [\"Accuracy\", \"F1 score\"]\n )\n\n for model, model_df in benchmark_df.groupby(\"model\"):\n ratios = pd.unique(model_df[\"ratio\"])\n lsizes = pd.unique(model_df[\"lsize\"])\n scale = lsizes[0] / ratios[0]\n\n # Some model may not have a baseline, but to build difference plot there are\n # needed both.\n add_to_diff = len(pd.unique(model_df[\"is_ssl\"])) == 2\n is_ssl_groupby = model_df.groupby(\"is_ssl\")\n\n if add_to_diff:\n diff_scores = np.subtract(\n *[\n np.maximum(_df[metrics].to_numpy(), 0.5)\n for _, _df in is_ssl_groupby\n ]\n )\n\n for is_ssl, is_ssl_df in is_ssl_groupby:\n\n for i, metric in enumerate(metrics):\n\n scores = np.maximum(is_ssl_df[metric].to_numpy(), 0.5)\n mask = scores > args.threshold\n if args.hard_tresholding and np.any(mask == False): # noqa\n mask = np.zeros_like(mask, dtype=np.bool)\n\n if np.any(mask == True): # noqa\n masked_ratios = ratios[mask]\n masked_scores = scores[mask]\n\n if args.joint_plots:\n canvases[metric].ax.plot(\n masked_ratios,\n masked_scores,\n label=\"{}, {}\".format(\n model.upper(), \"SSL\" if is_ssl else \"Baseline\"\n ),\n color=model2color[model],\n marker=marker2ssl[is_ssl],\n linestyle=linestyle2ssl[is_ssl],\n )\n canvases[metric].rescale(scale)\n\n if add_to_diff:\n for ratio, score, diff_score in zip(\n masked_ratios, masked_scores, diff_scores[mask, i]\n ):\n diffs[ratio][metric][benchmark][model] = diff_score\n\n if args.joint_plots:\n benchmark_root = os.path.join(args.out_root, \"joint_plots\", benchmark)\n try:\n os.makedirs(benchmark_root)\n except FileExistsError:\n pass\n\n for metric in metrics:\n canvases[metric].fig.savefig(\n os.path.join(benchmark_root, f\"{metric}.{args.extention}\")\n )\n plt.close(\"all\")\n\n # ======================================================================\n # Plotting score difference.\n # ======================================================================\n diff_out_root = os.path.join(args.out_root, \"score_difference\")\n max_diff_display = args.max_diff_display\n\n # Lsize/Ratio -> Metrics -> Benchmark -> Model -> Score\n def set_label(labelled_models: set, model: str) -> dict:\n if model in labelled_models:\n return dict()\n return dict(label=model)\n\n def sign2color(score: float) -> str:\n if score == 0:\n return \"black\"\n elif score > 0:\n return \"green\"\n return \"red\"\n\n # https://stackoverflow.com/a/43130355\n def offset(p):\n return transforms.ScaledTranslation(p / 72.0, 0, plt.gcf().dpi_scale_trans)\n\n handles = dict()\n marker_size = 60\n variance = 10\n figsize = (15, 15)\n\n for lsize, mapping in make_iter(\n diffs.items(), args.progress_bar, desc=\"#Difference plots plotted\"\n ):\n lsize_root = os.path.join(\n diff_out_root, f\"lsize_{str(lsize).replace('.', '_')}\"\n )\n try:\n os.makedirs(lsize_root)\n except FileExistsError:\n pass\n for metric, mapping_ in mapping.items():\n labelled_models = set()\n fig, ax = plt.subplots(figsize=figsize)\n trans_data = plt.gca().transData\n ax.tick_params(axis=\"x\", labelrotation=45)\n ax.set_title(f\"{metric} difference at ratio {lsize}\")\n ax.set_ylabel(f\"Difference\")\n for benchmark, mapping__ in mapping_.items():\n for idx, (model, score) in enumerate(mapping__.items()):\n if score <= -max_diff_display:\n score = -max_diff_display - 1e-3\n elif score >= max_diff_display:\n score = max_diff_display + 1e-3\n ax.scatter(\n benchmark,\n score,\n marker=model2marker[model],\n color=sign2color(score),\n edgecolor=\"black\",\n **set_label(labelled_models, model),\n s=marker_size,\n alpha=0.75,\n transform=trans_data\n + offset(variance * ((idx + 1) // 2) * (-1) ** idx),\n )\n if not handles.get(model):\n handles[model] = plt.scatter(\n [],\n [],\n marker=model2marker[model],\n color=\"None\",\n edgecolor=\"black\",\n label=model,\n )\n labelled_models.add(model)\n ax.axhline(max_diff_display, linestyle=\"--\")\n ax.axhline(-max_diff_display, linestyle=\"--\")\n ax.legend(\n handles=list(handles.values()),\n numpoints=1,\n bbox_to_anchor=(1.1, 0.5),\n borderaxespad=0,\n )\n fig.savefig(os.path.join(lsize_root, f\"{metric}.{args.extention}\"))\n plt.close(\"all\")\n", "sub_path": "experimentarium/tools/plot_results.py", "file_name": "plot_results.py", "file_ext": "py", "file_size_in_byte": 10526, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.path.append", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.normpath", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "seaborn.set", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.normpath", "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.dirname", "line_number": 29, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 38, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 39, "usage_type": "attribute"}, {"api_name": "distutils.util.util", "line_number": 49, "usage_type": "attribute"}, {"api_name": "distutils.util", "line_number": 49, "usage_type": "name"}, {"api_name": "distutils.util.util", "line_number": 56, "usage_type": "attribute"}, {"api_name": "distutils.util", "line_number": 56, "usage_type": "name"}, {"api_name": "distutils.util.util", "line_number": 66, "usage_type": "attribute"}, {"api_name": "distutils.util", "line_number": 66, "usage_type": "name"}, {"api_name": "distutils.util.util", "line_number": 74, "usage_type": "attribute"}, {"api_name": "distutils.util", "line_number": 74, "usage_type": "name"}, {"api_name": "__plot_utils.process_cli_args", "line_number": 97, "usage_type": "call"}, {"api_name": "__plot_utils.load_results", "line_number": 102, "usage_type": "call"}, {"api_name": "pandas.unique", "line_number": 103, "usage_type": "call"}, {"api_name": "pandas.unique", "line_number": 113, "usage_type": "call"}, {"api_name": "seaborn.color_palette", "line_number": 120, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 129, "usage_type": "call"}, {"api_name": "experimentarium.utils.make_iter", "line_number": 135, "usage_type": "call"}, {"api_name": "__plot_utils.create_scaled_canvases", "line_number": 141, "usage_type": "call"}, {"api_name": "pandas.unique", "line_number": 146, "usage_type": "call"}, {"api_name": "pandas.unique", "line_number": 147, "usage_type": "call"}, {"api_name": "pandas.unique", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 170, "usage_type": "attribute"}, {"api_name": "numpy.any", "line_number": 172, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 196, "usage_type": "call"}, {"api_name": "os.path", "line_number": 196, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 198, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 204, "usage_type": "call"}, {"api_name": "os.path", "line_number": 204, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 211, "usage_type": "call"}, {"api_name": "os.path", "line_number": 211, "usage_type": "attribute"}, {"api_name": "matplotlib.transforms.ScaledTranslation", "line_number": 229, "usage_type": "call"}, {"api_name": "matplotlib.transforms", "line_number": 229, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 229, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 229, "usage_type": "name"}, {"api_name": "experimentarium.utils.make_iter", "line_number": 236, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 239, "usage_type": "call"}, {"api_name": "os.path", "line_number": 239, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 243, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 248, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 248, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 249, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 249, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 272, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 272, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 289, "usage_type": "call"}, {"api_name": "os.path", "line_number": 289, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.close", "line_number": 290, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 290, "usage_type": "name"}]} +{"seq_id": "94290217", "text": "from django.conf.urls import url\nfrom .views import DetailView, CategoryView, TimelineView\napp_name = 'blog'\nurlpatterns = [\n # 文章详情页面\n url(r'^article/(?P.*?)/$', DetailView.as_view(), name='article'),\n # 分类页面\n url(r'^category/(?P[\\w-]+)/$',\n CategoryView.as_view(), name='category'),\n url(r'^category/(?P[\\w-]+)/hot/$', CategoryView.as_view(), {'sort': 'v'},\n name='category_hot'),\n # timeline页面\n url(r'^timeline/$', TimelineView.as_view(), name='timeline'),\n]\n", "sub_path": "apps/blog/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 539, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "views.DetailView.as_view", "line_number": 6, "usage_type": "call"}, {"api_name": "views.DetailView", "line_number": 6, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "views.CategoryView.as_view", "line_number": 9, "usage_type": "call"}, {"api_name": "views.CategoryView", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "views.CategoryView.as_view", "line_number": 10, "usage_type": "call"}, {"api_name": "views.CategoryView", "line_number": 10, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "views.TimelineView.as_view", "line_number": 13, "usage_type": "call"}, {"api_name": "views.TimelineView", "line_number": 13, "usage_type": "name"}]} +{"seq_id": "474462063", "text": "from __future__ import print_function\n\nimport sys\n\n\"\"\"\n===========================\nDBF and DF file reader\n===========================\nFile history and credits:\nC. Miller script development 10.08.14\nJ. A. Fonseca adaptation for CEA tool 25.05.16\n\n\"\"\"\n\nimport pysal\nimport numpy as np\nimport pandas as pd\nimport os\n\n\ndef dataframe_to_dbf(df, dbf_path, specs=None):\n if specs is None:\n type2spec = {int: ('N', 20, 0),\n np.int64: ('N', 20, 0),\n float: ('N', 36, 15),\n np.float64: ('N', 36, 15),\n unicode: ('C', 25, 0),\n str: ('C', 25, 0)\n }\n types = [type(df[i].iloc[0]) for i in df.columns]\n specs = [type2spec[t] for t in types]\n dbf = pysal.open(dbf_path, 'w', 'dbf')\n dbf.header = list(df.columns)\n dbf.field_spec = specs\n df_transpose = df.T\n length = len(df_transpose.columns)\n for row in range(length):\n dbf.write(df_transpose[row])\n dbf.close()\n return dbf_path\n\n\ndef dbf_to_dataframe(dbf_path, index=None, cols=False, include_index=False):\n db = pysal.open(dbf_path)\n if cols:\n if include_index:\n cols.append(index)\n vars_to_read = cols\n else:\n vars_to_read = db.header\n data = dict([(var, db.by_col(var)) for var in vars_to_read])\n if index:\n index = db.by_col(index)\n db.close()\n return pd.DataFrame(data, index=index)\n else:\n db.close()\n return pd.DataFrame(data)\n\n\ndef xls_to_dbf(input_path, output_path):\n if not input_path.endswith('.xls'):\n raise ValueError('Excel input file should have *.xls extension')\n\n if not os.path.exists(input_path):\n raise ValueError('Excel input file does not exist')\n\n if not output_path.endswith('.dbf'):\n raise ValueError('DBF output file should have *.dbf extension')\n\n df = pd.read_excel(input_path)\n dataframe_to_dbf(df, output_path)\n\n\ndef dbf_to_xls(input_path, output_path):\n if not input_path.endswith('.dbf'): # check if the extension of the input is dbf\n raise ValueError('DBF input file should have *.dbf extension')\n\n if not os.path.exists(input_path):\n raise ValueError('DBF input file does not exist')\n\n if not output_path.endswith('.xls'): # check if the extension of the input is xls\n raise ValueError('Excel output file should have *.xls extension')\n\n df = dbf_to_dataframe(input_path)\n df.to_excel(output_path)\n\n\ndef run_as_script(input_path, output_path):\n if input_path.endswith('.dbf'):\n dbf_to_xls(input_path=input_path, output_path=output_path)\n elif input_path.endswith('.xls'):\n xls_to_dbf(input_path=input_path, output_path=output_path)\n else:\n print('input file type not supported')\n\n\nif __name__ == '__main__':\n import argparse\n\n parser = argparse.ArgumentParser()\n parser.add_argument('--input-path')\n parser.add_argument('--output-path')\n args = parser.parse_args()\n run_as_script(input_path=args.input_path, output_path=args.output_path)\n", "sub_path": "cea/utilities/dbfreader.py", "file_name": "dbfreader.py", "file_ext": "py", "file_size_in_byte": 3111, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.int64", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 26, "usage_type": "attribute"}, {"api_name": "pysal.open", "line_number": 32, "usage_type": "call"}, {"api_name": "pysal.open", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 55, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "278951573", "text": "'''\r\nCreated on Aug 21, 2018\r\n\r\n@author: QDoan\r\n'''\r\nimport logging, os, time\r\nfrom selenium import webdriver\r\nfrom mtmf.pages.instructor_home_page import InstructorHomePage\r\nfrom mtmf.pages.instructor_dashboard import InstructorDashboard\r\nfrom mtmf.pages.assignment_editor_page import AssignmentEditor \r\nfrom mtmf.pages import login_page\r\nfrom myutils import utils_files_io\r\n\r\ndef set_homework_quantity():\r\n ''' Function to extract the amount number for homework exercise.\r\n This function works for quizzes too. ''' \r\n \r\n ''' Read list of assignments '''\r\n assignments_list = utils_files_io.read_list_from_file('data/assignments_list.txt')\r\n \r\n for assignment_name in assignments_list:\r\n ''' 1. Go to assignment via dropdown menu, and click on Edit Assignment '''\r\n msg = 'Go to assignment \"{}\" to edit...'.format(assignment_name)\r\n print(msg)\r\n logging.info(' ' + msg)\r\n dashboard = InstructorDashboard(driver)\r\n dashboard.select_assignment_from_dropdown(assignment_name)\r\n dashboard.click_edit_assignment(); time.sleep(1)\r\n \r\n editor = AssignmentEditor(driver)\r\n \r\n ''' 2. Get number of LO's '''\r\n learning_objectives = editor.get_learning_objectives()\r\n print('There are {} LOs'.format(len(learning_objectives)))\r\n \r\n for lo_number in range(1, len(learning_objectives) + 1):\r\n ''' 2a. Click to expand Exercise '''\r\n lo_name = editor.get_lo_name(lo_number)\r\n print(lo_name)\r\n editor.click_on_lo(lo_number); time.sleep(1)\r\n \r\n # Click on Exercise Set to display detail\r\n editor.click_on_exercise_set(); time.sleep(3)\r\n \r\n # Extract number of exercises\r\n exercises = editor.get_number_of_exercises(); time.sleep(1)\r\n print('There are {} exercises'.format(len(exercises)))\r\n \r\n ''' 2b. Set homework amount to 1 '''\r\n need_saving = False\r\n for exercise_number in range(1, len(exercises) + 1):\r\n amount = editor.extract_exercise_amount(exercise_number)\r\n if amount == \"0\":\r\n need_saving = True\r\n msg = 'Amount: {}. Set it to 1.'.format(amount)\r\n print(msg)\r\n logging.info(' ' + msg)\r\n editor.set_exercise_amount_to_one(exercise_number)\r\n \r\n if need_saving:\r\n editor.click_save_changes()\r\n else:\r\n # Click on Learning Objective again to hide its detail\r\n editor.click_on_lo(lo_number)\r\n \r\n msg = 'Closing assignment editor page'\r\n print(msg)\r\n logging.info(' ' + msg)\r\n time.sleep(1)\r\n driver.refresh()\r\n \r\n\r\nif __name__ == '__main__':\r\n logfile = 'C:/Workspace/Sandbox/log.txt'\r\n if os.path.isfile(logfile): os.remove(logfile)\r\n logging.basicConfig(filename=logfile, level=logging.INFO)\r\n \r\n driver = webdriver.Chrome('C:/Workspace/Tools/drivers/chromedriver.exe')\r\n driver.set_window_size(1650, 1080)\r\n \r\n ''' Login into Mindtap Math Foundation '''\r\n msg = 'Logging into MTMF...'\r\n print(msg)\r\n logging.info(' ' + msg)\r\n login_page.login_mindtap_prod(driver)\r\n \r\n ''' Launch course '''\r\n msg = 'Launching course \"[CLONE] CustomCourse_Master_7-20-2018\" ...'\r\n print(msg)\r\n logging.info(' ' + msg)\r\n home_page = InstructorHomePage(driver)\r\n home_page.launch_course('DEPNSXBPTPG5'); time.sleep(3)\r\n \r\n ''' Extract homework quantity setting '''\r\n set_homework_quantity()\r\n ", "sub_path": "DevMathPython/mtmf/course_editor/set_homework_quantity.py", "file_name": "set_homework_quantity.py", "file_ext": "py", "file_size_in_byte": 3698, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "myutils.utils_files_io.read_list_from_file", "line_number": 19, "usage_type": "call"}, {"api_name": "myutils.utils_files_io", "line_number": 19, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 25, "usage_type": "call"}, {"api_name": "mtmf.pages.instructor_dashboard.InstructorDashboard", "line_number": 26, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 28, "usage_type": "call"}, {"api_name": "mtmf.pages.assignment_editor_page.AssignmentEditor", "line_number": 30, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 40, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 43, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 46, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 57, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 68, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 75, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 76, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 76, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 78, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 78, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 84, "usage_type": "call"}, {"api_name": "mtmf.pages.login_page.login_mindtap_prod", "line_number": 85, "usage_type": "call"}, {"api_name": "mtmf.pages.login_page", "line_number": 85, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 90, "usage_type": "call"}, {"api_name": "mtmf.pages.instructor_home_page.InstructorHomePage", "line_number": 91, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "609366549", "text": "import cv2\r\nimport numpy as np\r\ndef detectImage(filename):\r\n thres = 0.45 # Threshold to detect object\r\n img = cv2.imread(filename)\r\n\r\n\r\n classNames= []\r\n classFile = 'coco.names'\r\n with open(classFile,'rt') as f:\r\n classNames = f.read().rstrip('\\n').split('\\n')\r\n\r\n configPath = 'ssd_mobilenet_v3_large_coco_2020_01_14.pbtxt'\r\n weightsPath = 'frozen_inference_graph.pb'\r\n\r\n net = cv2.dnn_DetectionModel(weightsPath,configPath)\r\n net.setInputSize(320,320)\r\n net.setInputScale(1.0/ 127.5)\r\n net.setInputMean((127.5, 127.5, 127.5))\r\n net.setInputSwapRB(True)\r\n\r\n #while True:\r\n #success,img = cap.read()\r\n classIds, confs, bbox = net.detect(img,confThreshold=thres)\r\n print(classIds,bbox)\r\n\r\n COLORS = np.random.uniform(0, 255, size=(len(classNames), 3))\r\n #if len(classIds) != 0:\r\n for classId, confidence,box in zip(classIds.flatten(),confs.flatten(),bbox):\r\n # cv2.rectangle(img,box,color=(0,255,0),thickness=2)\r\n # cv2.putText(img,classNames[classId-1],(box[0],box[1]+30),\r\n # cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)\r\n # cv2.putText(img,str(round(confidence*100,2)),(box[0],box[1]-10),\r\n # cv2.FONT_HERSHEY_COMPLEX,1,(0,255,0),2)\r\n\r\n\r\n (startX, startY, endX, endY) = box.astype(\"int\")\r\n # display the prediction\r\n label = \"{}: {:.2f}%\".format(classNames[classId-1], confidence * 100)\r\n cv2.rectangle(img,box ,COLORS[classId-1], 2)\r\n y = startY - 15 if startY - 15 > 15 else startY + 15\r\n cv2.putText(img, label, (startX, y),\r\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[classId-1], 2)\r\n\r\n cv2.imwrite('ImgToSend/img0.jpg', img)\r\n #cv2.imshow(\"Output\",img)\r\n #cv2.waitKey(0)", "sub_path": "ImageDetection.py", "file_name": "ImageDetection.py", "file_ext": "py", "file_size_in_byte": 1783, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cv2.imread", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.dnn_DetectionModel", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 27, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 43, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "619365289", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*\n'''\n项目名称: JD-Script / jd_jxcfd_100hb\n活动名称: 财富岛-100元红包-兑换\nAuthor: SheYu09\ncron: 0 0,10 * * * jd_jxcfd_100hb.py\nnew Env('京喜 -*- 财富岛100元红包')\n'''\nimport requests, json, os, sys\nsys.path.append('../repo/SheYu09_jd_scripts_master/')\nimport jdCookie, HEADERS, h5st, posturl\n\ndef ExchangeState(header, name):\n global aNum\n try:\n url = 'https://m.jingxi.com/jxbfd/user/ExchangeState?strZone=jxbfd&dwType=2&_stk=dwType,strZone&sceneval=2&h5st='\n url += h5st.start(url, '10032')\n r = requests.get(url=url, headers=header).text\n data = json.loads(r)\n hongbaopool = data[\"hongbaopool\"]\n hongbao = data[\"hongbao\"]\n for i in hongbao:\n if i[\"strPrizeName\"] == '100元':\n ddwPaperMoney = i['ddwPaperMoney']\n dwLvl = i['dwLvl']\n break\n return hongbaopool, ddwPaperMoney, dwLvl\n except Exception as e:\n if aNum < 5:\n aNum += 1\n return ExchangeState(header, name)\n else:\n aNum = 0\n print(f'========== 【京东账号】{name} 已被Jd拉黑 ==========')\n print()\n return 0, 0, 0\n\ndef ExchangePrize(header, strPoolName, ddwPaperMoney, dwLvl):\n url = f'https://m.jingxi.com/jxbfd/user/ExchangePrize?strZone=jxbfd&dwType=3&dwLvl={dwLvl}&ddwPaperMoney={ddwPaperMoney}&strPoolName={strPoolName}&_stk=ddwPaperMoney,dwLvl,dwType,strPoolName,strZone&sceneval=2&h5st='\n url += h5st.start(url, '10032')\n r = requests.get(url=url, headers=header).text\n data = json.loads(r)\n print(data)\n if data['iRet'] == 0:\n print(f'{data[\"strAwardDetail\"][\"strName\"]}')\n else:\n print(f'{data[\"sErrMsg\"]}')\n print()\n\ndef start():\n print(' ******* 财富岛-100元红包-兑换 *******')\n print()\n cookiesList, pinNameList = jdCookie.start()\n for ckname in jdCookie.Name():\n try:\n ckNum = pinNameList.index(ckname)\n except:\n print(f\"请检查被助力账号【{ckname}】名称是否正确？提示：助力名字可填pt_pin的值、也可以填账号名。\")\n print()\n continue\n print(f\"*******开始【京东账号】{pinNameList[int(ckNum)]} *******\")\n print()\n hongbaopool, ddwPaperMoney, dwLvl = ExchangeState(HEADERS.jd_jxcfd(cookiesList[ckNum]), pinNameList[int(ckNum)])\n if hongbaopool == 0:\n continue\n ExchangePrize(HEADERS.jd_jxcfd(cookiesList[ckNum]), hongbaopool, ddwPaperMoney, dwLvl)\n\naNum = 0\nif __name__ == '__main__':\n start()\n", "sub_path": "jd_jxcfd_100hb.py", "file_name": "jd_jxcfd_100hb.py", "file_ext": "py", "file_size_in_byte": 2654, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.path.append", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "h5st.start", "line_number": 18, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 19, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 20, "usage_type": "call"}, {"api_name": "h5st.start", "line_number": 41, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 42, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 43, "usage_type": "call"}, {"api_name": "jdCookie.start", "line_number": 54, "usage_type": "call"}, {"api_name": "jdCookie.Name", "line_number": 55, "usage_type": "call"}, {"api_name": "HEADERS.jd_jxcfd", "line_number": 64, "usage_type": "call"}, {"api_name": "HEADERS.jd_jxcfd", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "31442450", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jun 7 21:19:57 2021\n\n@author: 11200\n\"\"\"\n\n#使用Dataset 和 DataLoader\n\nimport torch\nimport numpy as np\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DataLoader\n\nclass DiabetesDataset(Dataset):\n def __init__(self,filepath):\n xy=np.loadtxt(filepath,delimiter=',',dtype=np.float32)\n #print(xy.shape) #(759, 9)\n #获得数据的行数\n self.len=xy.shape[0]\n self.x_data=torch.from_numpy(xy[:,:-1])\n self.y_data=torch.from_numpy(xy[:,[-1]])\n \n def __getitem__(self, index):\n return self.x_data[index],self.y_data[index]\n \n def __len__(self):\n return self.len\n\n\n\nclass Model(torch.nn.Module):\n def __init__(self):\n super(Model,self).__init__()\n self.linear1=torch.nn.Linear(8, 4)\n self.linear2=torch.nn.Linear(4, 2)\n self.linear3=torch.nn.Linear(2, 1)\n self.sigmoid=torch.nn.Sigmoid()\n def forward(self,x):\n x=self.sigmoid(self.linear1(x))\n x=self.sigmoid(self.linear2(x))\n x=self.sigmoid(self.linear3(x))\n return x\n\nif __name__ == '__main__':\n filepath=\"diabetes.csv.gz\"\n \n dataset=DiabetesDataset(filepath)\n \n\n train_loader=DataLoader(dataset=dataset,\n batch_size=32,\n shuffle=True,\n num_workers=2)\n \n model=Model()\n criterion=torch.nn.BCELoss(size_average=True)\n optimizer=torch.optim.SGD(model.parameters(), lr=0.01)\n \n Epoch=100\n for epoch in range(Epoch):\n cost=0.0\n for idx,data in enumerate(train_loader):\n inputs,targets=data\n\n outputs=model(inputs)\n \n loss=criterion(outputs,targets)\n \n \n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n cost+=loss.item()\n \n print(epoch,cost)\n \n\n \n \n \n \n \n \n \n \n \n \n \n \n", "sub_path": "test/09DatasetAndDataLoader_01.py", "file_name": "09DatasetAndDataLoader_01.py", "file_ext": "py", "file_size_in_byte": 2013, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.utils.data.Dataset", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.loadtxt", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "attribute"}, {"api_name": "torch.nn.Sigmoid", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn.BCELoss", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "attribute"}, {"api_name": "torch.optim.SGD", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 58, "usage_type": "attribute"}]} +{"seq_id": "533829111", "text": "import asyncio\nimport io\nimport logging\nimport os\nimport sys\nimport traceback\nfrom collections import namedtuple\nfrom concurrent.futures import CancelledError\nfrom datetime import datetime\nfrom logging.handlers import TimedRotatingFileHandler\n\nimport discord\nimport pytz\nimport sentry_sdk\nfrom aiohttp import ClientOSError, ServerDisconnectedError\nfrom discord import ConnectionClosed\nfrom discord.ext import commands\n\nfrom Bot import TheRealGearBot\nfrom Util import Configuration, Utils, MessageUtils\n\nLOGGER = logging.getLogger('gearbot')\nDISCORD_LOGGER = logging.getLogger('discord')\n\nBOT_LOG_CHANNEL: discord.TextChannel = None\nSTARTUP_ERRORS = []\nBOT: commands.AutoShardedBot = None\nLOG_PUMP = None\nLOG_ERRORS = 0\n\nlog_type = namedtuple(\"Log_type\", \"category emoji\")\nLOG_TYPES = {\n \"raid_new\": log_type('RAID_LOGS', 'BAD_USER'),\n \"raid_terminated\": log_type(\"RAID_LOGS\", 'INNOCENT'),\n 'censored_message': log_type('CENSORED_MESSAGES', 'WARNING'),\n 'censor_message_failed': log_type('CENSORED_MESSAGES', 'WARNING'),\n 'censored_invite': log_type('CENSORED_MESSAGES', 'WARNING'),\n 'invite_censor_fail': log_type('CENSORED_MESSAGES', 'WARNING'),\n 'invite_censor_forbidden': log_type('CENSORED_MESSAGES', 'WARNING'),\n 'automod_ban_failed': log_type('MOD_ACTIONS', 'WARNING'),\n 'warning_added_modlog': log_type('MOD_ACTIONS', 'WARNING'),\n 'warning_could_not_dm': log_type('MOD_ACTIONS', 'WARNING'),\n 'inf_delete_log': log_type('MOD_ACTIONS', 'DELETE'),\n 'ban_log': log_type('MOD_ACTIONS', 'BAN'),\n 'kick_log': log_type('MOD_ACTIONS', 'BOOT'),\n 'mute_role_already_removed': log_type('MOD_ACTIONS', 'WARNING'),\n 'unmute_missing_perms': log_type('MOD_ACTIONS', 'WARNING'),\n 'unmute_unknown_error': log_type('MOD_ACTIONS', 'WARNING'),\n 'unmuted': log_type('MOD_ACTIONS', 'INNOCENT'),\n 'tempban_expired_missing_perms': log_type('MOD_ACTIONS', 'WARNING'),\n 'tempban_already_lifted': log_type('MOD_ACTIONS', 'WARNING'),\n 'tempban_lifted': log_type('MOD_ACTIONS', 'INNOCENT'),\n 'softban_log': log_type('MOD_ACTIONS', 'BAN'),\n 'forceban_log': log_type('MOD_ACTIONS', 'BAN'),\n 'mute_log': log_type('MOD_ACTIONS', 'MUTE'),\n 'mute_duration_extended_log': log_type('MOD_ACTIONS', 'MUTE'),\n 'mute_duration_added_log': log_type('MOD_ACTIONS', 'MUTE'),\n 'mute_duration_overwritten_log': log_type('MOD_ACTIONS', 'MUTE'),\n 'mute_reapply_log': log_type('MOD_ACTIONS', 'BAD_USER'),\n 'mute_reapply_failed_log': log_type('MOD_ACTIONS', 'WARNING'),\n 'tempban_log': log_type('MOD_ACTIONS', 'BAN'),\n 'unban_log': log_type('MOD_ACTIONS', 'INNOCENT'),\n 'unmute_modlog': log_type('MOD_ACTIONS', 'INNOCENT'),\n 'channel_update_simple': log_type('CHANNEL_CHANGES', 'ALTER'),\n 'channel_update_simple_by': log_type('CHANNEL_CHANGES', 'ALTER'),\n 'role_update_simple': log_type('ROLE_CHANGES', 'ALTER'),\n 'role_update_simple_by': log_type('ROLE_CHANGES', 'ALTER'),\n 'command_used': log_type('COMMAND_EXECUTED', 'WRENCH'),\n 'channel_created_by': log_type('ROLE_CHANGES', 'CREATE'),\n 'channel_created': log_type('ROLE_CHANGES', 'CREATE'),\n 'channel_deleted_by': log_type('channel_deleted_by', 'DELETE'),\n 'channel_deleted': log_type('channel_deleted_by', 'DELETE'),\n 'permission_override_update': log_type('CHANNEL_CHANGES', 'ALTER'),\n 'permission_override_update_by': log_type('CHANNEL_CHANGES', 'ALTER'),\n 'permission_override_update_role': log_type('CHANNEL_CHANGES', 'ALTER'),\n 'permission_override_update_role_by': log_type('CHANNEL_CHANGES', 'ALTER'),\n 'permission_override_removed': log_type('CHANNEL_CHANGES', 'ALTER'),\n 'permission_override_removed_by': log_type('CHANNEL_CHANGES', 'ALTER'),\n 'permission_override_removed_role': log_type('CHANNEL_CHANGES', 'ALTER'),\n 'permission_override_removed_role_by': log_type('CHANNEL_CHANGES', 'ALTER'),\n 'permission_override_added': log_type('CHANNEL_CHANGES', 'ALTER'),\n 'permission_override_added_by': log_type('CHANNEL_CHANGES', 'ALTER'),\n 'permission_override_added_role': log_type('CHANNEL_CHANGES', 'ALTER'),\n 'permission_override_added_role_by': log_type('CHANNEL_CHANGES', 'ALTER'),\n 'role_created_by': log_type('ROLE_CHANGES', 'CREATE'),\n 'role_created': log_type('ROLE_CHANGES', 'CREATE'),\n 'role_deleted': log_type('ROLE_CHANGES', 'DELETE'),\n 'role_update_perm_added': log_type('ROLE_CHANGES', 'ALTER'),\n 'role_update_perm_added_by': log_type('ROLE_CHANGES', 'DELETE'),\n 'role_update_perm_revoked': log_type('ROLE_CHANGES', 'DELETE'),\n 'role_update_perm_revoked_by': log_type('ROLE_CHANGES', 'DELETE'),\n 'manual_ban_log': log_type('MOD_ACTIONS', 'BAN'),\n 'join_logging': log_type('JOIN_LOGS', 'JOIN'),\n 'join_logging_new': log_type('JOIN_LOGS', 'JOIN'),\n 'leave_logging': log_type('JOIN_LOGS', 'LEAVE'),\n 'manual_unban_log': log_type('MOD_ACTIONS', 'INNOCENT'),\n 'own_nickname_changed': log_type('NAME_CHANGES', 'NICKTAG'),\n 'unknown_nickname_changed': log_type('NAME_CHANGES', 'NICKTAG'),\n 'mod_nickname_changed': log_type('NAME_CHANGES', 'NICKTAG'),\n 'unknown_nickname_added': log_type('NAME_CHANGES', 'NICKTAG'),\n 'own_nickname_added': log_type('NAME_CHANGES', 'NICKTAG'),\n 'mod_nickname_added': log_type('NAME_CHANGES', 'NICKTAG'),\n 'own_nickname_removed': log_type('NAME_CHANGES', 'NICKTAG'),\n 'mod_nickname_removed': log_type('NAME_CHANGES', 'NICKTAG'),\n 'role_removed_by': log_type('ROLE_CHANGES', 'ROLE_REMOVE'),\n 'role_added_by': log_type('ROLE_CHANGES', 'ROLE_ADD'),\n 'role_removed': log_type('ROLE_CHANGES', 'ROLE_REMOVE'),\n 'role_added': log_type('ROLE_CHANGES', 'ROLE_ADD'),\n 'message_removed': log_type('EDIT_LOGS', 'TRASH'),\n 'edit_logging': log_type('EDIT_LOGS', 'EDIT'),\n 'username_changed': log_type('NAME_CHANGES', 'NAMETAG'),\n 'voice_change_deaf_true': log_type('VOICE_CHANGES_DETAILED', 'VOICE'),\n 'voice_change_deaf_false': log_type('VOICE_CHANGES_DETAILED', 'VOICE'),\n 'voice_change_mute_true': log_type('VOICE_CHANGES_DETAILED', 'VOICE'),\n 'voice_change_mute_false': log_type('VOICE_CHANGES_DETAILED', 'VOICE'),\n 'voice_change_self_mute_true': log_type('VOICE_CHANGES_DETAILED', 'VOICE'),\n 'voice_change_self_mute_false': log_type('VOICE_CHANGES_DETAILED', 'VOICE'),\n 'voice_change_self_deaf_true': log_type('VOICE_CHANGES_DETAILED', 'VOICE'),\n 'voice_change_self_deaf_false': log_type('VOICE_CHANGES_DETAILED', 'VOICE'),\n 'voice_change_afk_true': log_type('VOICE_CHANGES_DETAILED', 'VOICE'),\n 'voice_change_afk_false': log_type('VOICE_CHANGES_DETAILED', 'VOICE'),\n 'connected_to_voice': log_type('VOICE_CHANGES', 'VOICE'),\n 'disconnected_voice': log_type('VOICE_CHANGES', 'VOICE'),\n 'moved_voice': log_type('VOICE_CHANGES', 'VOICE'),\n 'purged_log': log_type('EDIT_LOGS', 'DELETE'),\n 'raid_mute_failed_no_role': log_type('RAID_LOGS', 'BAD_USER'),\n 'raid_message_failed': log_type('RAID_LOGS', 'BAD_USER'),\n 'raid_notification_failed': log_type('RAID_LOGS', 'BAD_USER'),\n 'raid_notification_forbidden': log_type('RAID_LOGS', 'BAD_USER'),\n 'raid_shield_triggered': log_type('RAID_LOGS', 'BAD_USER'),\n 'raid_shield_terminated': log_type('RAID_LOGS', 'INNOCENT'),\n 'unknown_nickname_removed': log_type('NAME_CHANGES', 'NICKTAG'),\n 'message_pinned': log_type('EDIT_LOGS', 'PIN'),\n 'message_pinned_by': log_type('EDIT_LOGS', 'PIN'),\n 'message_unpinned': log_type('EDIT_LOGS', 'PIN'),\n 'raid_message_failed_missing_channel': log_type('RAID_LOGS', 'WARNING'),\n 'raid_message_failed_channel': log_type('RAID_LOGS', 'WARNING'),\n 'raid_message_failed_channel_unknown_error': log_type('RAID_LOGS', 'WARNING'),\n 'raid_message_user_not_found': log_type('RAID_LOGS', 'WARNING'),\n 'raid_message_user_forbidden': log_type('RAID_LOGS', 'WARNING'),\n 'raid_mute_forbidden': log_type('RAID_LOGS', 'WARNING'),\n 'raid_mute_unknown_error': log_type('RAID_LOGS', 'WARNING'),\n 'raid_kick_forbidden': log_type('RAID_LOGS', 'WARNING'),\n 'raid_kick_unknown_error': log_type('RAID_LOGS', 'WARNING'),\n 'raid_ban_forbidden': log_type('RAID_LOGS', 'WARNING'),\n 'raid_ban_unknown_error': log_type('RAID_LOGS', 'WARNING'),\n 'shield_time_limit_reached': log_type('RAID_LOGS', 'WARNING'),\n 'slowmode_log': log_type('CHANNEL_CHANGES', 'ALTER'),\n 'spam_violate': log_type('SPAM_VIOLATION', 'BAD_USER'),\n \"config_change\": log_type('CONFIG_CHANGES', 'WRENCH'),\n \"config_change_role_removed\": log_type('CONFIG_CHANGES', 'WRENCH'),\n \"config_change_role_added\": log_type('CONFIG_CHANGES', 'WRENCH'),\n \"config_mute_role_disabled\": log_type('CONFIG_CHANGES', 'WRENCH'),\n \"config_mute_role_changed\": log_type('CONFIG_CHANGES', 'WRENCH'),\n \"config_mute_role_set\": log_type('CONFIG_CHANGES', 'WRENCH'),\n \"config_mute_setup_triggered\": log_type('CONFIG_CHANGES', 'WRENCH'),\n \"config_mute_setup_complete\": log_type('CONFIG_CHANGES', 'WRENCH'),\n \"config_mute_setup_failed\": log_type('CONFIG_CHANGES', 'WRENCH'),\n \"config_mute_cleanup_triggered\": log_type('CONFIG_CHANGES', 'WRENCH'),\n \"config_mute_cleanup_complete\": log_type('CONFIG_CHANGES', 'WRENCH'),\n \"config_mute_cleanup_failed\": log_type('CONFIG_CHANGES', 'WRENCH'),\n \"config_dash_security_change\": log_type('CONFIG_CHANGES', 'WRENCH'),\n \"verification_log\": log_type('MOD_ACTIONS', 'WRENCH')\n\n\n\n\n}\n\ndef before_send(event, hint):\n if event['level'] == \"error\" and 'logger' in event.keys() and event['logger'] == 'gearbot':\n return None # we send errors manually, in a much cleaner way\n if 'exc_info' in hint:\n exc_type, exc_value, tb = hint['exc_info']\n for t in [ConnectionClosed, ClientOSError, ServerDisconnectedError]:\n if isinstance(exc_value, t):\n return\n event['fingerprint'] = ['database-unavailable']\n return event\n\n\ndef init_logger():\n # track commits to make sentry versions\n dsn = Configuration.get_master_var('SENTRY_DSN', '')\n if dsn != '':\n sentry_sdk.init(dsn, before_send=before_send)\n\n LOGGER.setLevel(logging.DEBUG)\n\n DISCORD_LOGGER.setLevel(logging.DEBUG)\n\n formatter = logging.Formatter('%(asctime)s:%(levelname)s:%(name)s: %(message)s')\n\n handler = logging.StreamHandler(stream=sys.stdout)\n handler.setLevel(logging.INFO)\n handler.setFormatter(formatter)\n LOGGER.addHandler(handler)\n DISCORD_LOGGER.addHandler(handler)\n\n if not os.path.isdir(\"logs\"):\n os.mkdir(\"logs\")\n handler = TimedRotatingFileHandler(filename='logs/gearbot.log', encoding='utf-8', when=\"midnight\", backupCount=30)\n handler.setFormatter(formatter)\n handler.setLevel(logging.INFO)\n DISCORD_LOGGER.addHandler(handler)\n LOGGER.addHandler(handler)\n\n # handler = TimedRotatingFileHandler(filename='logs/discord.log', encoding='utf-8', when=\"h\", interval=1, backupCount=24)\n\n # DISCORD_LOGGER.addHandler(handler)\n\n\nasync def initialize(bot: commands.Bot, channelID):\n global BOT_LOG_CHANNEL, BOT, STARTUP_ERRORS, LOG_PUMP\n BOT = bot\n BOT_LOG_CHANNEL = bot.get_channel(int(channelID))\n if BOT_LOG_CHANNEL is None:\n LOGGER.error(\n \"==========================Logging channel is misconfigured, aborting startup!==========================\")\n await bot.logout()\n\n if len(STARTUP_ERRORS) > 0:\n await bot_log(\n f\":rotating_light: Caught {len(STARTUP_ERRORS)} {'exceptions' if len(STARTUP_ERRORS) > 1 else 'exception'} during startup.\")\n for e in STARTUP_ERRORS:\n await e\n STARTUP_ERRORS = []\n\n\ndef initialize_pump(bot):\n global LOG_PUMP\n LOG_PUMP = LogPump(bot)\n bot.loop.create_task(LOG_PUMP.pump())\n\n\ndef debug(message):\n LOGGER.debug(message)\n\n\ndef info(message):\n LOGGER.info(message)\n\n\ndef warn(message):\n LOGGER.warning(message)\n\n\ndef error(message):\n LOGGER.error(message)\n\n\ndef exception(message, error):\n LOGGER.error(message)\n trace = \"\"\n LOGGER.error(str(error))\n for line in traceback.format_tb(error.__traceback__):\n trace = f\"{trace}\\n{line}\"\n LOGGER.error(trace)\n\n\nasync def bot_log(message=None, embed=None):\n global BOT_LOG_CHANNEL, STARTUP_ERRORS\n if BOT_LOG_CHANNEL is not None:\n return await BOT_LOG_CHANNEL.send(content=message, embed=embed)\n else:\n STARTUP_ERRORS.append(bot_log(message, embed))\n\n\ndef log_raw(guild_id, location, message=None, embed=None, file=None):\n if isinstance(embed, int):\n raise ValueError(\"WTH IS SPAMMING MY LOGS?\")\n channels = Configuration.get_var(guild_id, \"LOG_CHANNELS\")\n if message is None and embed is None and file is None:\n raise ValueError(\"What the heck is trying to log nothing?\")\n if message is not None:\n message = Utils.trim_message(message, 1998)\n for cid, info in channels.items():\n if location in info:\n LOG_PUMP.receive(cid, (message, embed, file))\n\n\ndef log_to(guild_id, key, embed=None, file=None, can_stamp=True, tag_on=None, **kwargs):\n if isinstance(embed, int):\n raise ValueError(\"WTH IS SPAMMING MY LOGS?\")\n info = LOG_TYPES[key]\n remaining = None\n if key is None and embed is None and file is None:\n raise ValueError(\"What the heck is trying to log nothing?\")\n stamp = f\"[``{datetime.strftime(datetime.now().astimezone(pytz.timezone(Configuration.get_var(guild_id, 'GENERAL', 'TIMEZONE'))), '%H:%M:%S')}``] \" if can_stamp and Configuration.get_var(guild_id, 'GENERAL', \"TIMESTAMPS\") else \"\"\n m = MessageUtils.assemble(guild_id, info.emoji, key, **kwargs).replace('@', '@\\u200b')\n message = f\"{stamp}{Utils.trim_message(m, 1984)}\".replace(\"None\", \"\", 1)\n if tag_on is not None:\n tag_on = tag_on.replace('@', '@\\u200b')\n if message is None:\n message = tag_on\n else:\n if len(message) + len(tag_on) <= 1998:\n message = f\"{message} {tag_on}\"\n else:\n remaining = tag_on\n if message is not None:\n message = Utils.trim_message(message, 1998)\n channels = Configuration.get_var(guild_id, \"LOG_CHANNELS\")\n\n for cid, logging_keys in channels.items():\n if info.category in logging_keys:\n f = None\n if file is not None:\n buffer = file[0]\n name = file[1]\n buffer.seek(0)\n b2 = io.BytesIO()\n for line in buffer.readlines():\n b2.write(line)\n b2.seek(0)\n f = discord.File(b2, name)\n if remaining is None:\n LOG_PUMP.receive(cid, (message, embed, f))\n else:\n LOG_PUMP.receive(cid, (message, None, None))\n LOG_PUMP.receive(cid, (tag_on, embed, f))\n\n\nasync def message_owner(bot, message):\n if bot.owner_id is None:\n app = await bot.application_info()\n bot.owner_id = app.owner.id\n owner = bot.get_user(bot.owner_id)\n await owner.send(message)\n\n\nclass LogPump:\n\n def __init__(self, bot):\n self.todo = dict()\n self.running = True\n self.bot = bot\n self.NUKED = False\n info(\"Starting log pump\")\n\n async def pump(self):\n info(\"Log pump engaged\")\n empty = []\n embed = file = cid = todo = to_send = None\n while (self.running or len(self.todo) > 0) and not self.NUKED:\n try:\n empty = []\n senders = []\n embed = file = None\n for cid, todo in self.todo.items():\n channel = BOT.get_channel(int(cid))\n if channel is not None and len(todo) > 0:\n permissions = channel.permissions_for(channel.guild.me)\n to_send = \"\"\n while len(todo) > 0:\n message, embed, file = todo[0]\n if message is None or message.strip() == \"\":\n message = \"\"\n if (not permissions.send_messages) or (\n embed is not None and not permissions.embed_links) or (\n file is not None and not permissions.attach_files):\n todo.pop(0)\n continue\n elif len(to_send) + len(message) <= 1999:\n to_send += f\"{message}\\n\"\n todo.pop(0)\n else:\n break\n if embed is not None or file is not None:\n break\n try:\n senders.append(channel.send(to_send if to_send != \"\" else None, embed=embed, file=file))\n except CancelledError:\n return\n except Exception as e:\n await TheRealGearBot.handle_exception(\"LOG PUMP\", BOT, e,\n cid=cid, todo=todo, to_send=to_send,\n LOG_CACHE=self.todo, embed=embed,\n file=file, empty=empty)\n else:\n empty.append(cid)\n for e in empty:\n del self.todo[e]\n for s in senders:\n try:\n await s\n except discord.Forbidden:\n pass\n except CancelledError:\n return\n except Exception as e:\n await log_error()\n await TheRealGearBot.handle_exception(\"LOG PUMP\", BOT, e,\n cid=cid, todo=todo, to_send=to_send,\n LOG_CACHE=self.todo, embed=embed, file=file,\n empty=empty)\n await asyncio.sleep(0.1)\n except CancelledError:\n return # we're shutting down\n except Exception as e:\n await log_error()\n await TheRealGearBot.handle_exception(\"LOG PUMP\", BOT, e,\n cid=cid, todo=todo, to_send=to_send,\n LOG_CACHE=self.todo, embed=embed, file=file,\n empty=empty)\n info(\"Log pump terminated\")\n\n def receive(self, cid, data):\n if cid not in self.todo:\n self.todo[cid] = []\n self.todo[cid].append(data)\n\n\nasync def log_error():\n global LOG_ERRORS, LOG_PUMP\n LOG_ERRORS += 1\n if LOG_ERRORS >= 10:\n LOG_ERRORS = 0\n error(\"=========Log pump error limit reached, deploying nuke to unclog the system=========\")\n LOG_PUMP.NUKED = True\n initialize_pump(BOT)\n await bot_log(\"Log pump got clogged, nuked and restarted, moving on\")\n", "sub_path": "GearBot/Util/GearbotLogging.py", "file_name": "GearbotLogging.py", "file_ext": "py", "file_size_in_byte": 19169, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 23, "usage_type": "call"}, {"api_name": "discord.TextChannel", "line_number": 25, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.AutoShardedBot", "line_number": 27, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 27, "usage_type": "name"}, {"api_name": "collections.namedtuple", "line_number": 31, "usage_type": "call"}, {"api_name": "discord.ConnectionClosed", "line_number": 175, "usage_type": "name"}, {"api_name": "aiohttp.ClientOSError", "line_number": 175, "usage_type": "name"}, {"api_name": "aiohttp.ServerDisconnectedError", "line_number": 175, "usage_type": "name"}, {"api_name": "Util.Configuration.get_master_var", "line_number": 184, "usage_type": "call"}, {"api_name": "Util.Configuration", "line_number": 184, "usage_type": "name"}, {"api_name": "sentry_sdk.init", "line_number": 186, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 188, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 190, "usage_type": "attribute"}, {"api_name": "logging.Formatter", "line_number": 192, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 194, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 194, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 195, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 200, "usage_type": "call"}, {"api_name": "os.path", "line_number": 200, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 201, "usage_type": "call"}, {"api_name": "logging.handlers.TimedRotatingFileHandler", "line_number": 202, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 204, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.Bot", "line_number": 213, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 213, "usage_type": "name"}, {"api_name": "traceback.format_tb", "line_number": 256, "usage_type": "call"}, {"api_name": "Util.Configuration.get_var", "line_number": 272, "usage_type": "call"}, {"api_name": "Util.Configuration", "line_number": 272, "usage_type": "name"}, {"api_name": "Util.Utils.trim_message", "line_number": 276, "usage_type": "call"}, {"api_name": "Util.Utils", "line_number": 276, "usage_type": "name"}, {"api_name": "Util.Configuration.get_var", "line_number": 289, "usage_type": "call"}, {"api_name": "Util.Configuration", "line_number": 289, "usage_type": "name"}, {"api_name": "datetime.datetime.strftime", "line_number": 289, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 289, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 289, "usage_type": "call"}, {"api_name": "pytz.timezone", "line_number": 289, "usage_type": "call"}, {"api_name": "Util.MessageUtils.assemble", "line_number": 290, "usage_type": "call"}, {"api_name": "Util.MessageUtils", "line_number": 290, "usage_type": "name"}, {"api_name": "Util.Utils.trim_message", "line_number": 291, "usage_type": "call"}, {"api_name": "Util.Utils", "line_number": 291, "usage_type": "name"}, {"api_name": "Util.Utils.trim_message", "line_number": 302, "usage_type": "call"}, {"api_name": "Util.Utils", "line_number": 302, "usage_type": "name"}, {"api_name": "Util.Configuration.get_var", "line_number": 303, "usage_type": "call"}, {"api_name": "Util.Configuration", "line_number": 303, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 312, "usage_type": "call"}, {"api_name": "discord.File", "line_number": 316, "usage_type": "call"}, {"api_name": "concurrent.futures.CancelledError", "line_number": 373, "usage_type": "name"}, {"api_name": "Bot.TheRealGearBot.handle_exception", "line_number": 376, "usage_type": "call"}, {"api_name": "Bot.TheRealGearBot", "line_number": 376, "usage_type": "name"}, {"api_name": "discord.Forbidden", "line_number": 387, "usage_type": "attribute"}, {"api_name": "concurrent.futures.CancelledError", "line_number": 389, "usage_type": "name"}, {"api_name": "Bot.TheRealGearBot.handle_exception", "line_number": 393, "usage_type": "call"}, {"api_name": "Bot.TheRealGearBot", "line_number": 393, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 397, "usage_type": "call"}, {"api_name": "concurrent.futures.CancelledError", "line_number": 398, "usage_type": "name"}, {"api_name": "Bot.TheRealGearBot.handle_exception", "line_number": 402, "usage_type": "call"}, {"api_name": "Bot.TheRealGearBot", "line_number": 402, "usage_type": "name"}]} +{"seq_id": "80311746", "text": "from tools import timed\nimport codecs\n\nfrom graph import Node, Edge\nfrom mongo_dal import MongoDAL\n\nMAX_DEPTH = 3\n\nclass GraphWriter(object):\n def __init__(self, max_depth=MAX_DEPTH):\n self.max_depth = max_depth\n self.dal = MongoDAL()\n self.nodes_dict = self.dal.get_nodes_dictionary()\n self.titles_blacklist = ['List of ']\n\n @timed\n def generate_graph_files(self, topic):\n src_id = self.nodes_dict[topic]\n \n self.nodes_file = codecs.open('nodes.csv', 'w', encoding='UTF-8')\n self.edges_file = codecs.open('edges.csv', 'w', encoding='UTF-8')\n\n self.edges_file.write(\"Source,Target,Weight\\n\")\n self.nodes_file.write(\"Id, Label\\n\")\n\n self.recurse_write_node(topic)\n\n self.nodes_file.close()\n self.edges_file.close()\n\n def recurse_write_node(self, topic, depth=0):\n if depth >= self.max_depth:\n return\n\n node = self.dal.get_node(topic)\n self.nodes_file.write('%s, \"%s\"\\n' % (node['page_id'], node['title']))\n\n for link in node['links']:\n if not self.nodes_dict.has_key(link):\n continue\n if link in self.titles_blacklist:\n continue\n dest_id = self.nodes_dict[link]\n weight = self.get_edge_weight(node, link)\n edge = Edge(node['page_id'], dest_id, weight)\n self.edges_file.write(str(edge))\n\n self.recurse_write_node(link, depth+1)\n\n def get_edge_weight(self, node, link):\n # Count number of occurrences of link title in node body\n return 1 + node['content'].lower().count(link.lower())\n\n", "sub_path": "graph_utils.py", "file_name": "graph_utils.py", "file_ext": "py", "file_size_in_byte": 1648, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "mongo_dal.MongoDAL", "line_number": 12, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 20, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 21, "usage_type": "call"}, {"api_name": "tools.timed", "line_number": 16, "usage_type": "name"}, {"api_name": "graph.Edge", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "470633724", "text": "import re\r\nimport os\r\nimport sys\r\nimport json\r\nimport numpy as np\r\nfrom sklearn.preprocessing import LabelBinarizer, MultiLabelBinarizer\r\nimport keras\r\nimport tensorflow as tf\r\nfrom keras.layers import Layer\r\nfrom keras.preprocessing.text import Tokenizer\r\nfrom keras.preprocessing.sequence import pad_sequences\r\nfrom keras.layers import Dense, Input, Reshape, Concatenate, Flatten\r\nfrom keras.layers import Conv1D, GlobalMaxPooling1D, Embedding, Dropout, LSTM\r\nfrom keras.models import Model, load_model\r\nfrom keras import backend as K\r\nfrom keras.engine import Layer, InputSpec\r\nfrom keras import initializers, regularizers, constraints\r\nfrom keras.callbacks import Callback\r\nfrom keras.backend import manual_variable_initialization\r\nimport pickle\r\n\r\nclass SentenceClassifier:\r\n def __init__(self):\r\n self.MAX_SEQUENCE_LENGTH = 55\r\n self.EMBEDDING_DIM = 100\r\n self.LABEL_COUNT = 0\r\n self.WORD_INDEX = dict()\r\n self.LABEL_ENCODER = None\r\n\r\n def clean_str(self, string):\r\n \"\"\"\r\n Cleans each string and convert to lower case.\r\n \"\"\"\r\n string = re.sub(r\"\\'s\", \"\", string)\r\n string = re.sub(r\"\\'ve\", \"\", string)\r\n string = re.sub(r\"n\\'t\", \"n not\", string)\r\n string = re.sub(r\"\\'re\", \"\", string)\r\n string = re.sub(r\"\\'d\", \"\", string)\r\n string = re.sub(r\"\\'ll\", \"\", string)\r\n string = re.sub(r\"\\\\n\", \"\", string)\r\n string = re.sub(r\"[^A-Za-z0-9]\", \" \", string)\r\n string = re.sub(r\"\\s{2,}\", \" \", string)\r\n return string.strip().lower()\r\n\r\n def loader_encoder(self, table, type=\"json\"):\r\n \"\"\"\r\n Load and encode data from dataset.\r\n\r\n type = \"sql\" means get data from MySQL database.\r\n type = \"json\" means get data from .json file.\r\n \"\"\"\r\n\r\n # if type == \"sql\":\r\n # mydb, cursor = self.connect_to_db()\r\n #\r\n # cursor.execute(\"select question from \" + table) # load questions from db\r\n # questions = list(str(x[0]) for x in cursor.fetchall())\r\n #\r\n # cursor.execute(\"select tags from \" + table)\r\n # tags = list(re.split(',\\s*', tag[0]) for tag in cursor.fetchall())\r\n #\r\n # del (mydb)\r\n # del (cursor)\r\n\r\n if type == \"json\":\r\n with open('./data/' + table + '.json', 'r', encoding='utf8') as f:\r\n datastore = json.load(f)\r\n questions = []\r\n tags = []\r\n for row in datastore:\r\n questions.append(self.clean_str(row['question']))\r\n tags.append(row['tags'].split(','))\r\n\r\n if table.lower()=='trec' and os.path.exists('./saved/trec_tokenizer.pkl'):\r\n\r\n with open('./saved/trec_tokenizer.pkl', 'rb') as f:\r\n tokenizer = pickle.load(f)\r\n self.WORD_INDEX = tokenizer.word_index\r\n else:\r\n tokenizer = Tokenizer(lower=True, char_level=False)\r\n tokenizer.fit_on_texts(questions)\r\n self.WORD_INDEX = tokenizer.word_index\r\n\r\n questions_encoded = tokenizer.texts_to_sequences(questions)\r\n questions_encoded_padded = pad_sequences(questions_encoded, maxlen=self.MAX_SEQUENCE_LENGTH, padding='post')\r\n\r\n\r\n for i, ele in enumerate(tags):\r\n for j, tag in enumerate(ele):\r\n if len(tag) == 0 or tag == ',':\r\n del tags[i][j]\r\n\r\n if table.lower()=='trec' and os.path.exists('./saved/trec_label_encoder.pkl'):\r\n with open('./saved/trec_label_encoder.pkl', 'rb') as f:\r\n self.LABEL_ENCODER = pickle.load(f)\r\n self.LABEL_COUNT = 6\r\n encoder = self.LABEL_ENCODER\r\n tags_encoded = encoder.fit_transform(tags)\r\n else:\r\n encoder = MultiLabelBinarizer()\r\n encoder.fit(tags)\r\n self.LABEL_ENCODER = encoder\r\n tags_encoded = encoder.fit_transform(tags)\r\n self.LABEL_COUNT = len(tags_encoded[0]) # No. of labels\r\n print(\"\\tUnique Tokens in Training Data: \", len(self.WORD_INDEX))\r\n print(\"\\nNumber of labels: \", self.LABEL_COUNT)\r\n return questions_encoded_padded, tags_encoded\r\n\r\n def load_embeddings(self, EMBED_PATH='./embeddings/glove.6B.100d.txt'):\r\n \"\"\"\r\n Load pre-trained embeddings into memory.\r\n \"\"\"\r\n embeddings_index = {}\r\n try:\r\n \tf = open(EMBED_PATH, encoding='utf-8')\r\n except FileNotFoundError:\r\n \tprint(\"Embeddings missing.\")\r\n \tsys.exit()\r\n for line in f:\r\n values = line.rstrip().rsplit(' ')\r\n word = values[0]\r\n vec = np.asarray(values[1:], dtype='float32')\r\n embeddings_index[word] = vec\r\n f.close()\r\n print(\"\\tNumber of tokens in embeddings file: \", len(embeddings_index))\r\n return embeddings_index\r\n\r\n def create_embedding_matrix(self, embeddings_index):\r\n \"\"\"\r\n Creates an embedding matrix for all the words(vocab) in the training data with shape (vocab, EMBEDDING_DIM).\r\n Out-of-vocab words will be randomly initialized to values between +0.25 and -0.25.\r\n \"\"\"\r\n words_not_found = []\r\n vocab = len(self.WORD_INDEX) + 1\r\n embedding_matrix = np.random.uniform(-0.25, 0.25, size=(vocab, self.EMBEDDING_DIM))\r\n for word, i in self.WORD_INDEX.items():\r\n if i >= vocab:\r\n continue\r\n embedding_vector = embeddings_index.get(word)\r\n if (embedding_vector is not None) and len(embedding_vector) > 0:\r\n embedding_matrix[i] = embedding_vector\r\n else:\r\n words_not_found.append(word)\r\n\r\n print(\"\\tShape of embedding matrix: \", str(embedding_matrix.shape))\r\n print(\"\\tNo. of words not found in pre-trained embeddings: \", len(words_not_found))\r\n return embedding_matrix\r\n\r\n def sentence_classifier(self, embedding_matrix, x, y, table, load_saved=1):\r\n \"\"\"\r\n Makes uses of Keras functional API for constructing the model.\r\n\r\n If load_saved=1, THEN load old model, ELSE train new model\r\n \"\"\"\r\n\r\n model_name = table + \".model.h5\"\r\n if load_saved == 1 and os.path.exists('./saved/' + model_name):\r\n\r\n print(\"\\nLoading saved model:\" + model_name )\r\n model = load_model('./saved/' + model_name)\r\n print(\"Model Summary\")\r\n print(model.summary())\r\n\r\n else:\r\n print(\"\\nTraining model...\")\r\n inputs = Input(shape=(self.MAX_SEQUENCE_LENGTH,), dtype='int32')\r\n embedding = Embedding(input_dim=(len(self.WORD_INDEX) + 1), output_dim=self.EMBEDDING_DIM,\r\n weights=[embedding_matrix],\r\n input_length=self.MAX_SEQUENCE_LENGTH, trainable=False)(inputs)\r\n\r\n X = keras.layers.SpatialDropout1D(0.2)(embedding)\r\n\r\n\r\n output = Dense(units=self.LABEL_COUNT, activation='sigmoid')(X)\r\n\r\n model = Model(inputs=inputs, outputs=output, name='question_classifier')\r\n print(\"Model Summary\")\r\n print(model.summary())\r\n\r\n # cbk = OutputObserver(model, self, table)\r\n adam = keras.optimizers.Adam(lr=1e-5, decay=1e-6, epsilon=1e-7)\r\n\r\n model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\r\n model.fit(x, y,\r\n batch_size=200,\r\n epochs=800,\r\n verbose=2,\r\n callbacks=[]) # callbacks=[cbk] to test model on sample sentences at the end of every epoch.\r\n\r\n return model\r\n\r\n def tag_question(self, model, question, graph=None):\r\n question = self.clean_str(question)\r\n print(question)\r\n question_encoded = [[self.WORD_INDEX[w] for w in question.split(' ') if w in self.WORD_INDEX]]\r\n question_encoded_padded = pad_sequences(question_encoded, maxlen=self.MAX_SEQUENCE_LENGTH, padding='post')\r\n predictions = model.predict(question_encoded_padded)\r\n\r\n tags_list = list()\r\n possible_tags = dict()\r\n for i, probability in enumerate(predictions[0]):\r\n if probability >= 0.1:\r\n tags_list.append([self.LABEL_ENCODER.classes_[i], probability])\r\n\r\n tags_list.sort(key=lambda x: int(x[1]), reverse=True)\r\n tags_list = tags_list[:10]\r\n\r\n for ele in tags_list:\r\n possible_tags[ele[0].capitalize()] = str(ele[1])[:4]\r\n\r\n\r\n print(possible_tags)\r\n return possible_tags\r\n\r\n def setup_classifier(self, table=\"trec\", load_saved=1):\r\n keras.backend.clear_session()\r\n print(\"Loading Data Set...\")\r\n x, y = self.loader_encoder(table)\r\n\r\n embeddings_index = self.load_embeddings()\r\n\r\n print(\"\\nGenerating embedding matrix...\")\r\n embedding_matrix = self.create_embedding_matrix(embeddings_index)\r\n\r\n # Loading / Training model\r\n model = self.sentence_classifier(embedding_matrix, x, y, table, load_saved=load_saved)\r\n\r\n return model, embeddings_index\r\n\r\n # def connect_to_db(self):\r\n # mydb = mysql.connector.connect(host=\"localhost\", user=\"root\", passwd=\"root\", database=\"questiondb\")\r\n # cursor = mydb.cursor()\r\n # return mydb, cursor\r\n\r\n\r\nclass OutputObserver(Callback):\r\n \"\"\"\r\n Used to test model with sample sentences after every epoch, if [cbk] passed as arg to callbacks, in model.fit function.\r\n \"\"\"\r\n\r\n def __init__(self, model, classifier, table):\r\n self.model = model\r\n self.classifier = classifier\r\n self.table = table\r\n\r\n def on_epoch_end(self, epoch, logs={}):\r\n if self.table=='test':\r\n self.classifier.tag_question(self.model, \"Is this sensor ghosting, or something else?\")\r\n self.classifier.tag_question(self.model, \"The reason for my pale colored / bad contrast film images?\")\r\n self.classifier.tag_question(self.model, \"Cameras using mirrors instead of lenses to get coloured images?\")\r\n\r\n if self.table=='trec':\r\n self.classifier.tag_question(self.model, \"Who was the king of the Chinese ?\")\r\n self.classifier.tag_question(self.model, \"How much do fruit cost there in china ?\")\r\n self.classifier.tag_question(self.model, \"Who was the king of the Chinese ? How much do fruit cost in China ?\")\r\n self.classifier.tag_question(self.model, \"Who was the king of the Chinese and how much do fruits cost there in china ?\")\r\n self.classifier.tag_question(self.model, \"How To download images from Internet and what's the term for chinese fruits ?\")\r\n self.classifier.tag_question(self.model, \"Where is India located?\")\r\n\r\nif __name__ == '__main__':\r\n classifier = SentenceClassifier()\r\n model, embeddings_index = classifier.setup_classifier('trec') # Setup classifier with trec as default dataset.\r\n", "sub_path": "Server/glove_classifier.py", "file_name": "glove_classifier.py", "file_ext": "py", "file_size_in_byte": 11010, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "re.sub", "line_number": 34, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 35, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 36, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 37, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 38, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 39, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 40, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 41, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 42, "usage_type": "call"}, {"api_name": "json.load", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.preprocessing.text.Tokenizer", "line_number": 80, "usage_type": "call"}, {"api_name": "keras.preprocessing.sequence.pad_sequences", "line_number": 85, "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": "pickle.load", "line_number": 95, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MultiLabelBinarizer", "line_number": 100, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 135, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 157, "usage_type": "call"}, {"api_name": "os.path", "line_number": 157, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 160, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 166, "usage_type": "call"}, {"api_name": "keras.layers.Embedding", "line_number": 167, "usage_type": "call"}, {"api_name": "keras.layers.SpatialDropout1D", "line_number": 171, "usage_type": "call"}, {"api_name": "keras.layers", "line_number": 171, "usage_type": "attribute"}, {"api_name": "keras.layers.Dense", "line_number": 174, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 176, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 181, "usage_type": "call"}, {"api_name": "keras.optimizers", "line_number": 181, "usage_type": "attribute"}, {"api_name": "keras.preprocessing.sequence.pad_sequences", "line_number": 196, "usage_type": "call"}, {"api_name": "keras.backend.clear_session", "line_number": 216, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 216, "usage_type": "attribute"}, {"api_name": "keras.callbacks.Callback", "line_number": 236, "usage_type": "name"}]} +{"seq_id": "135613835", "text": "from flask import Flask\nimport json\n\napp = Flask(__name__)\n\ndogs = {\n \"Bowie\" : {\n \"owner\": \"Austin\",\n \"breed\": \"cutey\",\n \"language\": \"go-fetch\"\n },\n \"Clyde\": {\n \"owner\": \"Allie\",\n \"breed\": \"sweety\",\n \"language\": \"pawthon\"\n },\n \"Rowdy\": {\n \"owner\": \"David\",\n \"breed\": \"Dalmatian\",\n \"language\": \"catlin\"\n },\n \"Keno\": {\n \"owner\": \"Penghao\",\n \"breed\": \"Miniature Schnauzer\",\n \"language\": \"cloud-fur-mation\"\n }\n}\n\n@app.route('/')\ndef get_dogs():\n return json.dumps(dogs)\n\nif __name__ == '__main__':\n app.run(debug=True,host='0.0.0.0', port=80)\n", "sub_path": "app02/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 654, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "343317279", "text": "# -*- coding: utf-8 -*-\n# Licensed under a 3-clause BSD style license - see LICENSE.rst\n\nfrom __future__ import (absolute_import, division, print_function,\n unicode_literals)\n\nimport os\n\nimport six\n\nfrom . import Command\nfrom .run import Run\n\nfrom ..console import log, truncate_left, color_print\nfrom ..repo import get_repo\nfrom .. import results\nfrom .. import util\n\n\nclass Continuous(Command):\n @classmethod\n def setup_arguments(cls, subparsers):\n parser = subparsers.add_parser(\n \"continuous\", help=\n \"Run a side-by-side comparison of two commits for continuous \"\n \"integration.\")\n\n parser.add_argument(\n 'branch', nargs=1, default='master',\n help=\"\"\"The HEAD branch to test. This commit and its\n parent commit will be used as the two commits for\n comparison.\"\"\")\n parser.add_argument(\n '--factor', \"-f\", nargs='?', type=float, default=2.0,\n help=\"\"\"The factor above or below which a result is\n considered problematic. For example, with a factor of 2,\n if a benchmark gets twice as slow or twice as fast, it\n will be displayed in the results list.\"\"\")\n parser.add_argument(\n \"--bench\", \"-b\", type=str, nargs=\"*\",\n help=\"\"\"Regular expression(s) for benchmark to run. When\n not provided, all benchmarks are run.\"\"\")\n parser.add_argument(\n \"--machine-defaults\", action=\"store_true\",\n help=\"\"\"Use autogenerated defaults for the machine information,\n instead of using the .asv-machine.json file\"\"\")\n\n parser.set_defaults(func=cls.run_from_args)\n\n return parser\n\n @classmethod\n def run_from_conf_args(cls, conf, args):\n return cls.run(\n conf=conf, branch=args.branch[0], factor=args.factor,\n bench=args.bench, machine_defaults=args.machine_defaults\n )\n\n @classmethod\n def run(cls, conf, branch=\"master\", factor=2.0, bench=None,\n machine_defaults=False):\n repo = get_repo(conf)\n\n repo.checkout_remote_branch('origin', branch)\n head = repo.get_hash_from_head()\n\n repo.checkout_parent()\n parent = repo.get_hash_from_head()\n\n commit_hashes = [head, parent]\n run_objs = {}\n\n result = Run.run(\n conf, range_spec=commit_hashes, bench=bench,\n machine_defaults=machine_defaults, _returns=run_objs)\n if result:\n return result\n\n tabulated = []\n for commit_hash in commit_hashes:\n subtab = {}\n totals = {}\n for benchmark in run_objs['benchmarks']:\n subtab[benchmark] = 0.0\n totals[benchmark] = 0\n\n for env in run_objs['environments']:\n filename = results.get_filename(\n run_objs['machine_params']['machine'], commit_hash, env)\n filename = os.path.join(conf.results_dir, filename)\n result = results.Results.load(filename)\n\n for benchmark in run_objs['benchmarks']:\n timing = results.results.get(benchmark, None)\n if timing is not None:\n subtab[benchmark] += timing\n totals[benchmark] += 1\n\n for benchmark in run_objs['benchmarks']:\n subtab[benchmark] /= totals.get(benchmark, 1)\n\n tabulated.append(subtab)\n\n after, before = tabulated\n\n table = []\n slowed_down = False\n for name, benchmark in six.iteritems(run_objs['benchmarks']):\n change = after[name] / before[name]\n if change > factor or change < 1.0 / factor:\n table.append(\n (change, before[name], after[name], name, benchmark))\n if change > factor:\n slowed_down = True\n\n print()\n\n if not len(table):\n color_print(\"BENCHMARKS NOT SIGNIFICANTLY CHANGED.\\n\", 'green')\n return 0\n\n table.sort(reverse=True)\n\n color_print(\"SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.\\n\", 'red')\n print()\n color_print(\n \"{0:40s} {1:>8} {2:>8} {3:>8}\\n\".format(\"BENCHMARK\", \"BEFORE\", \"AFTER\", \"FACTOR\"),\n 'blue')\n for change, before, after, name, benchmark in table:\n before_display = util.human_value(before, benchmark['unit'])\n after_display = util.human_value(after, benchmark['unit'])\n\n print(\"{0:40s} {1:>8} {2:>8} {3:.8f}x\".format(\n truncate_left(name, 40),\n before_display, after_display, change))\n\n color_print(\n \"SOME BENCHMARKS HAVE CHANGED SIGNIFICANTLY.\\n\", 'red')\n\n return slowed_down\n", "sub_path": "asv/commands/continuous.py", "file_name": "continuous.py", "file_ext": "py", "file_size_in_byte": 4839, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "repo.get_repo", "line_number": 62, "usage_type": "call"}, {"api_name": "repo.checkout_remote_branch", "line_number": 64, "usage_type": "call"}, {"api_name": "repo.get_hash_from_head", "line_number": 65, "usage_type": "call"}, {"api_name": "repo.checkout_parent", "line_number": 67, "usage_type": "call"}, {"api_name": "repo.get_hash_from_head", "line_number": 68, "usage_type": "call"}, {"api_name": "run.Run.run", "line_number": 73, "usage_type": "call"}, {"api_name": "run.Run", "line_number": 73, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "six.iteritems", "line_number": 108, "usage_type": "call"}, {"api_name": "console.color_print", "line_number": 119, "usage_type": "call"}, {"api_name": "console.color_print", "line_number": 124, "usage_type": "call"}, {"api_name": "console.color_print", "line_number": 126, "usage_type": "call"}, {"api_name": "console.truncate_left", "line_number": 134, "usage_type": "call"}, {"api_name": "console.color_print", "line_number": 137, "usage_type": "call"}]} +{"seq_id": "35247525", "text": "import os\nimport sys\nimport InitSetUp \nimport OutputManager\n\nimport datetime\nimport timeit\nimport time\n\nfrom Driver import Driver\nimport EvolutionaryLearner\n\n\n# Importing needed python modules from the $SUMO_HOME/tools directory\nif 'SUMO_HOME' in os.environ:\n tools = os.path.join(os.environ['SUMO_HOME'], 'tools')\n sys.path.append(tools)\nelse:\n sys.exit(\"please declare environment variable 'SUMO_HOME'\")\n\n\nfrom sumolib import checkBinary # Checks for the binary in environ vars\nimport traci\n\nif __name__ == \"__main__\":\n print(\"Working...\")\n # --- TRAINING OPTIONS ---\n gui = False\n totalGenerations = 20\n individualRunsPerGen = 1 # Min number of training runs an individual gets per generation\n # ----------------------\n \n # --- USER-DEFINED RULES TOGGLE ---\n maxGreenAndYellowPhaseTime_UDRule = True\n maxRedPhaseTime_UDRule = False\n assignGreenPhaseToSingleWaitingPhase_UDRule = True\n # ----------------------\n\n # Attributes of the simulation\n numOfConfigFiles = 10\n resultsSuffixOptions = [\"UDLearned\", \"NoUD\", \"UDAfter\"]\n resultsSuffix = resultsSuffixOptions[0] # Used to run multiple instances and create different files\n sumoNetworkName = \"simpleNetwork.net.xml\"\n maxGreenPhaseTime = 225\n maxYellowPhaseTime = 5\n maxSimulationTime = 10000\n runTimeSet = []\n\n\n # setting the cmd mode or the visual mode\n if gui == False:\n sumoBinary = checkBinary('sumo')\n else:\n sumoBinary = checkBinary('sumo-gui')\n\n # initializations\n #sumoCmd = [sumoBinary, \"-c\", \"intersection/tlcs_config_train.sumocfg\", \"--no-step-log\", \"true\", \"--waiting-time-memory\", str(max_steps)]\n sumoCmd = [sumoBinary, \"-c\", \"config_file.sumocfg\", \"--waiting-time-memory\", \"5\", \"--time-to-teleport\", \"-1\"]\n generationRuntimes = []\n generations = 1\n resultsFileName = \"TestResults_\" + resultsSuffix + \".txt\"\n f = open(resultsFileName, \"w\")\n f.close()\n i = 0\n\nfor i in range(numOfConfigFiles):\n if numOfConfigFiles == 1:\n configFile = \"config_file.sumocfg\"\n else:\n configFile = \"config_file_\" + str(i+1) + \".sumocfg\"\n\n while generations <= totalGenerations:\n print(\"----- Start time:\", datetime.datetime.now())\n setUpTuple = InitSetUp.run(sumoNetworkName, individualRunsPerGen)\n genStart = datetime.datetime.now()\n\n for tl in setUpTuple[1]:\n tl.setMaxRedPhaseTime(maxGreenPhaseTime, maxYellowPhaseTime)\n tl.initPhaseTimeSpentInRedArray()\n\n simulationStartTime = datetime.datetime.now()\n\n # Evolutionary learning loop \n print(\"This simulation began at:\", simulationStartTime)\n genStart = datetime.datetime.now()\n startTime = time.time()\n \n sumoCmd = [sumoBinary, \"-c\", configFile, \"--waiting-time-memory\", \"5\", \"--time-to-teleport\", \"-1\"]\n simRunner = Driver(sumoCmd, setUpTuple, maxGreenPhaseTime, maxYellowPhaseTime, maxSimulationTime, maxGreenAndYellowPhaseTime_UDRule, maxRedPhaseTime_UDRule, assignGreenPhaseToSingleWaitingPhase_UDRule)\n\n print(\"Generation start time:\", genStart)\n print(\"Gener\")\n start = timeit.default_timer()\n simRuntime = simRunner.run() # run the simulation\n stop = timeit.default_timer()\n print('Time: ', round(stop - start, 1))\n\n sys.stdout.flush() \n\n print(\"Start time:\", simulationStartTime, \"----- End time:\", datetime.datetime.now())\n print(\"This simulation began at:\", simulationStartTime)\n generationRuntimes.append(simRuntime)\n if generations <= totalGenerations: \n generations += 1\n \n # Do something to save session stats here \n f = open(resultsFileName, \"a\")\n f.write(\"Altered Flow \" + str(i+1) + \": \" + str(sum(generationRuntimes)/totalGenerations) + \"s\\n\")\n f.close()\n\n generations = 0\n generationRuntimes = []\n print(generationRuntimes)\n print(\"Average simulation time of\", configFile, \"is\", sum(generationRuntimes)/totalGenerations)\n\n", "sub_path": "Project Development/Testing/main_old.py", "file_name": "main_old.py", "file_ext": "py", "file_size_in_byte": 4055, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.environ", "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.environ", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 19, "usage_type": "call"}, {"api_name": "sumolib.checkBinary", "line_number": 52, "usage_type": "call"}, {"api_name": "sumolib.checkBinary", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 73, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 73, "usage_type": "attribute"}, {"api_name": "InitSetUp.run", "line_number": 74, "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": "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": 85, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 85, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 86, "usage_type": "call"}, {"api_name": "Driver.Driver", "line_number": 89, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 93, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 95, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 98, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 98, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 100, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 100, "usage_type": "attribute"}]} +{"seq_id": "601621215", "text": "import os, sys, re, json\n\ndef CheckFilePattern(file, pattern):\n\tif file is None or not os.path.exists(file):\n\t\tprint(\"File wasn't specified or doesn't exist\")\n\t\tsys.exit(1)\n\tif pattern is None:\n\t\tprint(\"Pattern name wasn't specified\")\n\t\tsys.exit(1)\n\ndef CommentVariable(file, pattern):\n\tCheckFilePattern(file, pattern)\n\twith open(file, \"r+\") as kagamibuild:\n\t\ttext = kagamibuild.read().strip()\n\t\tp = re.search(\"# %s:(.*)\" % pattern, text)\n\t\tif p:\n\t\t\tp = p.group(0)\n\t\t\tp = p.replace(\"# %s:\" % pattern, \"\")\n\t\t\tp = p.lstrip()\n\t\t\tprint(p)\n\ndef JsonVariable(file, pattern, pattern2):\n\tCheckFilePattern(file, pattern)\n\twith open(file, \"r+\") as jsonfile:\n\t\tdata = json.load(jsonfile)\n\t\tif data:\n\t\t\tif data[\"%s\" % pattern]:\n\t\t\t\tp = data[\"%s\" % pattern]\n\t\t\t\tp = p[\"%s\" % pattern2]\n\t\t\t\tprint(p)\n", "sub_path": "src/python/parse.py", "file_name": "parse.py", "file_ext": "py", "file_size_in_byte": 785, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.exists", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 9, "usage_type": "call"}, {"api_name": "re.search", "line_number": 15, "usage_type": "call"}, {"api_name": "json.load", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "55258882", "text": "# import needed libraries\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import WeightedRandomSampler, DataLoader, TensorDataset\nfrom sklearn.model_selection import train_test_split\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.metrics import precision_recall_curve\nfrom sklearn.metrics import auc\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.metrics import roc_curve\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import confusion_matrix\nfrom hyper_params import HyperParams\nfrom tqdm import tqdm\n\n# create params object\nparams = HyperParams()\n# set PyTorch device\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\n\n# Create Loaders\ndef create_loaders(dataset, num_y, batch_size, balance = True):\n # split the data into train, validation, and test sets\n if num_y == 1:\n x_train, x_test, y_train, y_test = train_test_split(dataset[:,:-num_y], dataset[:,-num_y], test_size=params.test_set_fraction)\n else:\n x_train, x_test, y_train, y_test = train_test_split(dataset[:,:-num_y], dataset[:,-num_y:], test_size=params.test_set_fraction)\n x_train, x_val, y_train, y_val = train_test_split(\n x_train, y_train, test_size=params.validation_set_fraction / (params.validation_set_fraction + params.train_set_fraction))\n \n # convert the NumPy arrays into Pytorch tensors\n x_train = torch.from_numpy(x_train).type(torch.float)\n x_val = torch.from_numpy(x_val).type(torch.float)\n x_test = torch.from_numpy(x_test).type(torch.float)\n y_train = torch.from_numpy(y_train).type(torch.float)\n y_val = torch.from_numpy(y_val).type(torch.float)\n y_test = torch.from_numpy(y_test).type(torch.float)\n \n # Create datasets from the tensors\n train_dataset = TensorDataset(x_train, y_train)\n val_dataset = TensorDataset(x_val, y_val)\n test_dataset = TensorDataset(x_test, y_test)\n \n if balance:\n class_sample_count = np.unique(y_train, return_counts=True)[1]\n weight = 1. / class_sample_count\n samples_weight = weight[y_train.type(torch.int8)]\n\n samples_weight = torch.from_numpy(samples_weight)\n sampler = WeightedRandomSampler(samples_weight, len(samples_weight))\n\n train_loader = DataLoader(train_dataset, batch_size, sampler=sampler)\n else:\n train_loader = DataLoader(train_dataset, batch_size, shuffle=True)\n \n val_loader = DataLoader(val_dataset, batch_size)\n test_loader = DataLoader(test_dataset, batch_size)\n \n return train_loader, val_loader, test_loader\n\n# create MLP model class with 2 hidden layers, relu activation, and sigmoid output activation\nclass MLP(nn.Module):\n def __init__(self, nodes, p, num_in, num_out, multilabel):\n super(MLP, self).__init__()\n self.nodes = nodes\n self.p = p\n self.input_nodes = num_in\n self.output_nodes = num_out\n self.multilabel = multilabel\n if len(self.nodes) == 2:\n self.fc1 = nn.Linear(self.input_nodes, self.nodes[0])\n self.dropout1 = nn.Dropout(p = self.p)\n self.bn1 = nn.BatchNorm1d(self.nodes[0])\n self.fc2 = nn.Linear(self.nodes[0], self.nodes[1])\n self.dropout2 = nn.Dropout(p = self.p)\n self.bn2 = nn.BatchNorm1d(self.nodes[1])\n self.fc3 = nn.Linear(self.nodes[1], self.output_nodes)\n if self.multilabel:\n self.out = nn.Sigmoid()\n elif len(self.nodes) == 4:\n self.fc1 = nn.Linear(self.input_nodes, self.nodes[0])\n self.dropout1 = nn.Dropout(p = self.p)\n self.bn1 = nn.BatchNorm1d(self.nodes[0])\n self.fc2 = nn.Linear(self.nodes[0], self.nodes[1])\n self.dropout2 = nn.Dropout(p = self.p)\n self.bn2 = nn.BatchNorm1d(self.nodes[1])\n self.fc3 = nn.Linear(self.nodes[1], self.nodes[2])\n self.dropout3 = nn.Dropout(p = self.p)\n self.bn3 = nn.BatchNorm1d(self.nodes[2])\n self.fc4 = nn.Linear(self.nodes[2], self.nodes[3])\n self.dropout4 = nn.Dropout(p = self.p)\n self.bn4 = nn.BatchNorm1d(self.nodes[3])\n self.fc5 = nn.Linear(self.nodes[3], self.output_nodes)\n if self.multilabel:\n self.out = nn.Sigmoid()\n elif len(self.nodes) == 6:\n self.fc1 = nn.Linear(self.input_nodes, self.nodes[0])\n self.dropout1 = nn.Dropout(p = self.p)\n self.bn1 = nn.BatchNorm1d(self.nodes[0])\n self.fc2 = nn.Linear(self.nodes[0], self.nodes[1])\n self.dropout2 = nn.Dropout(p = self.p)\n self.bn2 = nn.BatchNorm1d(self.nodes[1])\n self.fc3 = nn.Linear(self.nodes[1], self.nodes[2])\n self.dropout3 = nn.Dropout(p = self.p)\n self.bn3 = nn.BatchNorm1d(self.nodes[2])\n self.fc4 = nn.Linear(self.nodes[2], self.nodes[3])\n self.dropout4 = nn.Dropout(p = self.p)\n self.bn4 = nn.BatchNorm1d(self.nodes[3])\n self.fc5 = nn.Linear(self.nodes[3], self.nodes[4])\n self.dropout5 = nn.Dropout(p = self.p)\n self.bn5 = nn.BatchNorm1d(self.nodes[4])\n self.fc6 = nn.Linear(self.nodes[4], self.nodes[5])\n self.dropout6 = nn.Dropout(p = self.p)\n self.bn6 = nn.BatchNorm1d(self.nodes[5])\n self.fc7 = nn.Linear(self.nodes[5], self.output_nodes)\n if self.multilabel:\n self.out = nn.Sigmoid()\n else:\n raise\n\n def forward(self, x):\n if len(self.nodes) == 2:\n x = self.fc1(x)\n x = self.dropout1(x)\n x = nn.functional.elu(x)\n x = self.bn1(x)\n x = self.fc2(x)\n x = self.dropout2(x)\n x = nn.functional.elu(x)\n x = self.bn2(x)\n x = self.fc3(x)\n if self.multilabel:\n x = self.out(x)\n return x\n elif len(self.nodes) == 4:\n x = self.fc1(x)\n x = self.dropout1(x)\n x = nn.functional.elu(x)\n x = self.bn1(x)\n x = self.fc2(x)\n x = self.dropout2(x)\n x = nn.functional.elu(x)\n x = self.bn2(x)\n x = self.fc3(x)\n x = self.dropout3(x)\n x = nn.functional.elu(x)\n x = self.bn3(x)\n x = self.fc4(x)\n x = self.dropout4(x)\n x = nn.functional.elu(x)\n x = self.bn4(x)\n x = self.fc5(x)\n if self.multilabel:\n x = self.out(x)\n return x\n elif len(self.nodes) == 6:\n x = self.fc1(x)\n x = self.dropout1(x)\n x = nn.functional.elu(x)\n x = self.bn1(x)\n x = self.fc2(x)\n x = self.dropout2(x)\n x = nn.functional.elu(x)\n x = self.bn2(x)\n x = self.fc3(x)\n x = self.dropout3(x)\n x = nn.functional.elu(x)\n x = self.bn3(x)\n x = self.fc4(x)\n x = self.dropout4(x)\n x = nn.functional.elu(x)\n x = self.bn4(x)\n x = self.fc5(x)\n x = self.dropout5(x)\n x = nn.functional.elu(x)\n x = self.bn5(x)\n x = self.fc6(x)\n x = self.dropout6(x)\n x = nn.functional.elu(x)\n x = self.bn6(x)\n x = self.fc7(x)\n if self.multilabel:\n x = self.out(x)\n return x\n else:\n raise\n\ndef plot_loss(n_epochs, train_loss, val_loss, upper_y_lim):\n plt.plot(list(range(1, n_epochs+1)), train_loss, color='blue')\n plt.plot(list(range(1, n_epochs+1)), val_loss, color='orange')\n plt.ylim((0.0, upper_y_lim))\n plt.xlabel('Epoch')\n plt.ylabel('Loss')\n plt.title('Training (Blue) and Validation (Orange) Loss')\n plt.show()\n return\n\ndef classify(model, loader, multilabel):\n model.eval()\n y_true = torch.LongTensor()\n y_pred = torch.LongTensor()\n for data in loader:\n x, y = data[0].to(device), data[1].to(device)\n y_hat = model(x)\n if multilabel:\n num_pos_labels = y_hat.shape[1] // 2\n y_hat = torch.max(y_hat[:,-num_pos_labels:], 1).values\n y = torch.max(y[:,-num_pos_labels:], 1).values\n else:\n y_hat = torch.sigmoid(y_hat)\n y_hat = y_hat.view(y_hat.shape[0])\n y_hat = torch.where(y_hat >= 0.5, torch.ones_like(y_hat), torch.zeros_like(y_hat))\n y_true = torch.cat((y_true, y.to('cpu').long()), dim=0)\n y_pred = torch.cat((y_pred, y_hat.to('cpu').long()), dim=0)\n return y_true, y_pred\n\ndef predict(model, loader, multilabel):\n model.eval()\n y_true = torch.FloatTensor()\n y_pred = torch.FloatTensor()\n for data in loader:\n x, y = data[0].to(device), data[1].to(device)\n y_hat = model(x)\n if multilabel:\n num_pos_labels = y_hat.shape[1] // 2\n y_hat = torch.max(y_hat[:,-num_pos_labels:], 1).values\n y = torch.max(y[:,-num_pos_labels:], 1).values\n else:\n y_hat = torch.sigmoid(y_hat)\n y_hat = y_hat.view(y_hat.shape[0])\n y_true = torch.cat((y_true, y.to('cpu').float()), dim=0)\n y_pred = torch.cat((y_pred, y_hat.to('cpu').float()), dim=0)\n return y_true, y_pred\n\n# ROC_AUC curve\ndef plt_roc_auc_curve(model, loader, model_name, multilabel):\n # predict probabilities\n y_test, model_probs = predict(model, loader, multilabel)\n # convert from Torch to Numpy\n y_test, model_probs = y_test.detach().numpy(), model_probs.detach().numpy()\n # generate a no skill prediction (majority class)\n ns_probs = [0 for _ in range(len(y_test))]\n # calculate scores\n ns_auc = roc_auc_score(y_test, ns_probs)\n model_auc = roc_auc_score(y_test, model_probs)\n # summarize scores\n print('No Skill: ROC AUC=%.3f' % (ns_auc))\n print(model_name + ': ROC AUC=%.3f' % (model_auc))\n # calculate roc curves\n ns_fpr, ns_tpr, _ = roc_curve(y_test, ns_probs)\n model_fpr, model_tpr, _ = roc_curve(y_test, model_probs)\n # plot the roc curve for the model\n plt.plot(ns_fpr, ns_tpr, linestyle='--', label='No Skill')\n plt.plot(model_fpr, model_tpr, marker='.', label=model_name)\n # axis labels\n plt.xlabel('False Positive Rate')\n plt.ylabel('True Positive Rate')\n # show the legend\n plt.legend()\n # show the plot\n plt.show()\n\ndef plt_precision_recall_curve(model, loader, model_name, multilabel):\n # predict probabilities\n y_test, model_probs = predict(model, loader, multilabel)\n # convert from Torch to Numpy\n y_test, model_probs = y_test.detach().numpy(), model_probs.detach().numpy()\n # predict class values\n _, y_pred = classify(model, loader, multilabel)\n # convert from Torch to Numpy\n y_pred = y_pred.detach().numpy()\n model_precision, model_recall, _ = precision_recall_curve(y_test, model_probs)\n model_f1, model_auc = f1_score(y_test, y_pred), auc(model_recall, model_precision)\n # summarize scores\n print(model_name + ': f1=%.3f auc=%.3f' % (model_f1, model_auc))\n # plot the precision-recall curves\n no_skill = len(y_test[y_test==1]) / len(y_test)\n plt.plot([0, 1], [no_skill, no_skill], linestyle='--', label='No Skill')\n plt.plot(model_recall, model_precision, marker='.', label=model_name)\n # axis labels\n plt.xlabel('Recall')\n plt.ylabel('Precision')\n # show the legend\n plt.legend()\n # show the plot\n plt.show()\n\ndef evaluate(model, test_loader, multilabel):\n # Prediction\n y_true, y_pred = classify(model, test_loader, multilabel)\n\n # Classification report (recall, preccision, f-score, accuracy)\n print(classification_report(y_true, y_pred, digits=4))\n print()\n tn, fp, fn, tp = confusion_matrix(y_true=y_true, y_pred=y_pred).ravel()\n print('TN:',tn, 'FP:',fp, 'FN:',fn, 'TP:',tp )\n\n # ROC_AUC curve\n model_name='MLP'\n print()\n plt_roc_auc_curve(model, test_loader, model_name, multilabel)\n # Precision_Recall curve\n print()\n plt_precision_recall_curve(model, test_loader, model_name, multilabel)\n return\n\n#-------------------------------------Binary-------------------------------------\ndef binary(dataset, n_epochs, nodes, batch_size = 32, upper_y_lim = 1, p = 0.5):\n train_loader, val_loader, test_loader = create_loaders(dataset, 1, batch_size, balance = True)\n\n # create a training function that will output the model and its metrics for given nodes\n def train(dataset, n_epochs, nodes, p):\n num_in = dataset.shape[1] - 1\n num_out = 1\n model = MLP(nodes, p, num_in, num_out, multilabel=False).to(device)\n criterion = nn.BCEWithLogitsLoss()\n optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n train_loss = []\n val_loss = []\n for epoch in tqdm(range(n_epochs)):\n train_loss_epoch = 0\n val_loss_epoch = 0\n model.train()\n for data in train_loader:\n x, y = data[0].to(device), data[1].to(device)\n optimizer.zero_grad()\n y_hat = model(x)\n y_hat = y_hat.view(y_hat.shape[0])\n loss = criterion(y_hat, y)\n loss.backward()\n optimizer.step()\n train_loss_epoch += loss.item()\n model.eval()\n for data in val_loader:\n x, y = data[0].to(device), data[1].to(device)\n y_hat = model(x)\n y_hat = y_hat.view(y_hat.shape[0])\n loss = criterion(y_hat, y)\n val_loss_epoch += loss.item()\n train_loss.append(train_loss_epoch / len(train_loader))\n val_loss.append(val_loss_epoch / len(val_loader))\n return model, train_loss, val_loss\n\n model, train_loss, val_loss = train(dataset, n_epochs, nodes, p)\n plot_loss(n_epochs, train_loss, val_loss, upper_y_lim)\n evaluate(model, test_loader, multilabel = False)\n \n return model\n\n#-----------------------------------Mulitlabel-----------------------------------\ndef multilabel(dataset, num_y, n_epochs, nodes, batch_size = 32, upper_y_lim = 1, p = 0.5):\n train_loader, val_loader, test_loader = create_loaders(dataset, num_y, batch_size, balance = False)\n \n # create a training function that will output the model and its metrics for given nodes\n def train(dataset, num_y, n_epochs, nodes, p):\n num_in = dataset.shape[1] - num_y\n num_out = num_y\n model = MLP(nodes, p, num_in, num_out, multilabel=True).to(device)\n criterion = nn.BCELoss()\n optimizer = torch.optim.Adam(model.parameters(), lr=0.001)\n train_loss = []\n val_loss = []\n for epoch in tqdm(range(n_epochs)):\n train_loss_epoch = 0\n val_loss_epoch = 0\n model.train()\n for data in train_loader:\n x, y = data[0].to(device), data[1].to(device)\n optimizer.zero_grad()\n y_hat = model(x)\n loss = criterion(y_hat, y)\n loss.backward()\n optimizer.step()\n train_loss_epoch += loss.item()\n model.eval()\n for data in val_loader:\n x, y = data[0].to(device), data[1].to(device)\n y_hat = model(x)\n loss = criterion(y_hat, y)\n val_loss_epoch += loss.item()\n train_loss.append(train_loss_epoch / len(train_loader))\n val_loss.append(val_loss_epoch / len(val_loader))\n return model, train_loss, val_loss\n \n model, train_loss, val_loss = train(dataset, num_y, n_epochs, nodes, p)\n plot_loss(n_epochs, train_loss, val_loss, upper_y_lim)\n evaluate(model, test_loader, multilabel = True)\n \n return model", "sub_path": "MLP.py", "file_name": "MLP.py", "file_ext": "py", "file_size_in_byte": 15932, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "hyper_params.HyperParams", "line_number": 21, "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": "sklearn.model_selection.train_test_split", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 31, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 36, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 37, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 38, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 40, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.float", "line_number": 41, "usage_type": "attribute"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.utils.data.TensorDataset", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.int8", "line_number": 51, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.utils.data.WeightedRandomSampler", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "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.BatchNorm1d", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 87, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 88, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 89, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 90, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 93, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 94, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 95, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 96, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 97, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 99, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 101, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 102, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 104, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 105, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 106, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 107, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 108, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 109, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 110, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 111, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 112, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 113, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 114, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 115, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 116, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 117, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 118, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 119, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 121, "usage_type": "name"}, {"api_name": "torch.nn.functional.elu", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 129, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 129, "usage_type": "name"}, {"api_name": "torch.nn.functional.elu", "line_number": 133, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 133, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 133, "usage_type": "name"}, {"api_name": "torch.nn.functional.elu", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 142, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 142, "usage_type": "name"}, {"api_name": "torch.nn.functional.elu", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 146, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 146, "usage_type": "name"}, {"api_name": "torch.nn.functional.elu", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 150, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 150, "usage_type": "name"}, {"api_name": "torch.nn.functional.elu", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 154, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 154, "usage_type": "name"}, {"api_name": "torch.nn.functional.elu", "line_number": 163, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 163, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 163, "usage_type": "name"}, {"api_name": "torch.nn.functional.elu", "line_number": 167, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 167, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 167, "usage_type": "name"}, {"api_name": "torch.nn.functional.elu", "line_number": 171, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 171, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 171, "usage_type": "name"}, {"api_name": "torch.nn.functional.elu", "line_number": 175, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 175, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 175, "usage_type": "name"}, {"api_name": "torch.nn.functional.elu", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 179, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 179, "usage_type": "name"}, {"api_name": "torch.nn.functional.elu", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 183, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 193, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "torch.LongTensor", "line_number": 204, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 205, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 211, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 212, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 214, "usage_type": "call"}, {"api_name": "torch.where", "line_number": 216, "usage_type": "call"}, {"api_name": "torch.ones_like", "line_number": 216, "usage_type": "call"}, {"api_name": "torch.zeros_like", "line_number": 216, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 217, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 218, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 223, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 224, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 230, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 231, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 233, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 235, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 236, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 248, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 249, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 254, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 255, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 257, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 257, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 258, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 258, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 260, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 260, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 261, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 261, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 263, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 263, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 265, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 265, "usage_type": "name"}, {"api_name": "sklearn.metrics.precision_recall_curve", "line_number": 276, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 277, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 277, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 282, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 282, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 283, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 283, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 285, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 285, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 286, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 286, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 288, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 288, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 290, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 290, "usage_type": "name"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 297, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 299, "usage_type": "call"}, {"api_name": "torch.nn.BCEWithLogitsLoss", "line_number": 320, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 320, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 321, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 321, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 324, "usage_type": "call"}, {"api_name": "torch.nn.BCELoss", "line_number": 363, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 363, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 364, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 364, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 367, "usage_type": "call"}]} +{"seq_id": "389008708", "text": "from hail.utils.java import Env\nfrom hail.ir import *\nfrom hail.expr.types import *\nfrom hail.expr.expressions import *\nfrom hail.typecheck import *\n\n@typecheck(f=anytype, param_types=HailType)\ndef define_function(f, *param_types):\n mname = Env.get_uid()\n param_names = [Env.get_uid() for _ in param_types]\n body = f(*(construct_expr(Ref(pn), pt) for pn, pt in zip(param_names, param_types)))\n ret_type = body.dtype\n\n r = Renderer(stop_at_jir=True)\n code = r(body._ir)\n jbody = body._ir.parse(code, ref_map=dict(zip(param_names, param_types)), ir_map=r.jirs)\n\n Env.hail().expr.ir.functions.IRFunctionRegistry.pyRegisterIR(\n mname, param_names, [pt._parsable_string() for pt in param_types], ret_type._parsable_string(),\n jbody)\n register_function(mname, param_types, ret_type)\n\n @typecheck(args=expr_any)\n def f(*args):\n indices, aggregations = unify_all(*args)\n return construct_expr(Apply(mname, *(a._ir for a in args)), ret_type, indices, aggregations)\n \n return f\n", "sub_path": "hail/python/hail/experimental/function.py", "file_name": "function.py", "file_ext": "py", "file_size_in_byte": 1039, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "hail.utils.java.Env.get_uid", "line_number": 9, "usage_type": "call"}, {"api_name": "hail.utils.java.Env", "line_number": 9, "usage_type": "name"}, {"api_name": "hail.utils.java.Env.get_uid", "line_number": 10, "usage_type": "call"}, {"api_name": "hail.utils.java.Env", "line_number": 10, "usage_type": "name"}, {"api_name": "hail.utils.java.Env.hail", "line_number": 18, "usage_type": "call"}, {"api_name": "hail.utils.java.Env", "line_number": 18, "usage_type": "name"}]} +{"seq_id": "343330147", "text": "\"\"\"\nTEST_EM.PY: Unit tests for estimate_model()\n\"\"\"\nimport pytest\nimport numpy as np\n\nfrom rcrbounds import estimate_model\n\n\n# Basic functionality\ndef test_em_realdata(moment_vector, true_result):\n \"\"\"estimate parameters and gradient with real data\"\"\"\n rc_range = np.array([0.0, 1.0])\n true_em = np.array([12.31059909, 8.16970996, 28.93548917,\n 5.13504376, 5.20150257])\n test_result = estimate_model(moment_vector, rc_range)[0]\n # Check parameter estimates\n assert test_result[:, 0] == pytest.approx(true_em)\n # Check parameter estimates and gradient\n assert test_result == pytest.approx(true_result, rel=1e-04, abs=1e-04)\n\n\n# Special cases for rc_range\ndef test_em_rcpoint(moment_vector):\n \"\"\"estimate when rc_range is a single point\"\"\"\n lr0 = np.array([0, 0])\n true_em = np.array([12.31059909, 8.16970996, 28.93548917,\n 5.20150257, 5.20150257])\n test_result = estimate_model(moment_vector, lr0)[0]\n assert test_result[:, 0] == pytest.approx(true_em)\n # need to check gradient too\n\n\ndef test_em_norclow(moment_vector):\n \"\"\"estimate when rc_range has no lower bound\"\"\"\n lr0 = np.array([-np.inf, 1])\n true_em = np.array([12.31059909, 8.16970996, 28.93548917,\n 5.13504376, 8.16970996])\n with pytest.warns(UserWarning, match=\"Inaccurate SE\"):\n test_result = estimate_model(moment_vector, lr0)[0]\n assert test_result[:, 0] == pytest.approx(true_em)\n # need to check gradient too\n\n\ndef test_em_norchigh(moment_vector):\n \"\"\"estimate when rc_range has no upper bound\"\"\"\n lr0 = np.array([0, np.inf])\n true_em = np.array([12.31059909, 8.16970996, 28.93548917,\n -np.inf, np.inf])\n test_result = estimate_model(moment_vector, lr0)[0]\n assert test_result[:, 0] == pytest.approx(true_em)\n assert np.all(test_result[3:4, 1:] == 0.0)\n # need to check gradient too\n\n\n# Special cases for moments\ndef test_em_nearrct():\n \"\"\"estimate for near-perfect RCT: cov(z,x) almost zero\"\"\"\n mv1 = np.array([0, 0, 0, 1, 0.5, 0.000001, 1, 0.5, 1.0])\n lr1 = np.array([0.0, 1.0])\n test_result = estimate_model(mv1, lr1)[0]\n assert np.all(test_result[0:3, 0] > 1000)\n assert test_result[3:4, 0] == pytest.approx(0.5, rel=1e-04)\n\n\ndef test_em_rct():\n \"\"\"estimate for perfect RCT: cov(z,x)=0\"\"\"\n mv1 = np.array([0, 0, 0, 1, 0.5, 0.0, 1, 0.5, 1.0])\n lr1 = np.array([0.0, 1.0])\n # This test currently fails with an UnboundLocalError\n try:\n test_result = estimate_model(mv1, lr1)[0]\n except UnboundLocalError:\n pass\n else:\n assert np.all(test_result[0:3, 0] > 1000)\n assert test_result[3:4, 0] == pytest.approx(0.5, rel=1e-04)\n\n\ndef test_em_invalid():\n \"\"\"estimate for invalid moment vector\"\"\"\n mv1 = np.array([0, 0, 0, 1, 0.5, 0.5, 1, 0.5, 0])\n lr1 = np.array([0.0, 1.0])\n with pytest.warns(UserWarning, match=\"Invalid data:\"):\n test_result = estimate_model(mv1, lr1)\n assert np.all(np.isnan(test_result))\n\n\ndef test_em_nonid():\n \"\"\"estimate for unidentifying moment vector\"\"\"\n mv1 = np.array([0, 0, 0, 1, 0., 0.5, 1, 0.5, 1.0])\n lr1 = np.array([0.0, 1.0])\n with pytest.warns(UserWarning, match=\"Model not identified:\"):\n test_result = estimate_model(mv1, lr1)\n assert np.all(np.isnan(test_result))\n", "sub_path": "python/testing/test_em.py", "file_name": "test_em.py", "file_ext": "py", "file_size_in_byte": 3359, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 14, "usage_type": "call"}, {"api_name": "rcrbounds.estimate_model", "line_number": 16, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 18, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "rcrbounds.estimate_model", "line_number": 29, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 36, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 37, "usage_type": "call"}, {"api_name": "pytest.warns", "line_number": 39, "usage_type": "call"}, {"api_name": "rcrbounds.estimate_model", "line_number": 40, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 49, "usage_type": "attribute"}, {"api_name": "rcrbounds.estimate_model", "line_number": 50, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 60, "usage_type": "call"}, {"api_name": "rcrbounds.estimate_model", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 62, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 69, "usage_type": "call"}, {"api_name": "rcrbounds.estimate_model", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 76, "usage_type": "call"}, {"api_name": "pytest.approx", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 83, "usage_type": "call"}, {"api_name": "pytest.warns", "line_number": 84, "usage_type": "call"}, {"api_name": "rcrbounds.estimate_model", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 92, "usage_type": "call"}, {"api_name": "pytest.warns", "line_number": 93, "usage_type": "call"}, {"api_name": "rcrbounds.estimate_model", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 95, "usage_type": "call"}]} +{"seq_id": "521548904", "text": "import sqlite3,cgi\r\nprint(\"Content-type:text/html \\r\\n\\r\\n\")\r\n\r\nconn=sqlite3.connect(\"election.db\")\r\nc=conn.cursor()\r\nsql=\"create table electers(id integer primary key autoincrement,party_name text unique,count integer)\"\r\nc.execute(sql)\r\nconn.commit()\r\nprint(\"success\")\r\nc.close()\r\nconn.close()", "sub_path": "cgi-bin/db.py", "file_name": "db.py", "file_ext": "py", "file_size_in_byte": 294, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sqlite3.connect", "line_number": 4, "usage_type": "call"}]} +{"seq_id": "10519666", "text": "from djongo import models\nfrom django import forms\nfrom django.utils.translation import ugettext_lazy as _\nfrom audit_log.models.fields import CreatingUserField, LastUserField\nfrom django.contrib.auth.models import User, Group\nfrom django.db.models.deletion import CASCADE\nfrom random import choices\nfrom master.models import (State,City,ClinicalSetting, HospitalMaster, ClinicalSetting, CaseCategory)\nfrom ckeditor.fields import RichTextField\nfrom django import forms\nfrom dal import autocomplete\n\n\n'''FINALIZED MODELS STARTS''' \n#Address Additional Profile using as embedded field in AdditionalProfile Model\nclass AddressAdditionalProfile(models.Model):\n address_line_1 = models.TextField(blank=True, null=True,verbose_name=_(\"Address Line 1\")) \n address_line_2 = models.TextField(blank=True, null=True,verbose_name=_(\"Address Line 2\"))\n pincode = models.BigIntegerField(blank=True, null=True, verbose_name=_(\"Pin Code\"))\n state = models.ForeignKey(State, blank=True, null=True, on_delete=models.CASCADE, verbose_name=_(\"State\"))\n city = models.ForeignKey(City, blank=True, null=True, on_delete=models.CASCADE, verbose_name=_(\"City\"))\n \n def __str__(self):\n return str(self.address_line_1)\n \n class Meta:\n abstract = True\n \nclass AddressAdditionalProfileForm(forms.ModelForm):\n address_line_1 = forms.CharField(label=_(\"Address Line1\"), max_length=300, widget=forms.Textarea(attrs={'class':'form-control','rows':'3', 'cols':'25', 'placeholder':_('Address Line1')})) \n address_line_2 = forms.CharField(label=_(\"Address Line2\"), max_length=300, widget=forms.Textarea(attrs={'class':'form-control','rows':'3', 'cols':'25', 'placeholder':_('Address Line2')})) \n pincode = forms.CharField(label=_('Pincode'),max_length='6', widget=forms.TextInput(attrs={'class':'form-control pincode', 'placeholder':_('Pincode')}))\n state=forms.ModelChoiceField(label=_(\"State\"),\n queryset=State.objects.all(),\n widget=autocomplete.ModelSelect2(url='user_profile:state-autocomplete' ,attrs={'class':'form-control', 'data-placeholder': 'State', 'data-minimum-input-length': 2})\n )\n city=forms.ModelChoiceField(label=_(\"City\"),\n queryset=City.objects.all(),\n widget=autocomplete.ModelSelect2(url='user_profile:city-autocomplete' ,attrs={'class':'form-control', 'data-placeholder': 'City', 'data-minimum-input-length': 2})\n )\n class Meta:\n model = AddressAdditionalProfile\n fields = (\n 'address_line_1', 'address_line_2','pincode','state','city'\n )\n\n#Profile Info Additional Profile using as embedded field in AdditionalProfile Model\nclass ProfileInfoAdditionalProfile(models.Model):\n Profile_dis_CHOICE = ( \n (u'0', u'No'),\n (u'1', u'Opt for Disable'),\n )\n profile_approved_datetime = models.DateTimeField(blank=True, null=True, verbose_name=(\"Profile Approved Date Time\"))\n profile_approved_remarks = models.TextField(blank=True, null=True,verbose_name=_(\"Profile Approved Remarks\")) \n profile_dis_opt_by_status = models.CharField(max_length=1, default='0',choices=Profile_dis_CHOICE, verbose_name=_(\"Profile Disabled Opt By Status\"))\n profile_dis_opt_by_remarks = models.TextField(blank=True, null=True,verbose_name=_(\"Profile Disabled Opt By Remarks\")) \n profile_dis_opt_by_datetime = models.DateTimeField(blank=True, null=True, verbose_name=(\" Profile Disabled Opt By Date Time\"))\n profile_dis_by_remarks = models.TextField(blank=True, null=True,verbose_name=_(\"Profile Disabled By Remarks\")) \n profile_dis_by_datetime = models.DateTimeField(blank=True, null=True, verbose_name=(\" Profile Disabled By Date Time\"))\n profile_approved_by = models.ForeignKey(User, blank=True, null=True, on_delete=models.CASCADE, verbose_name=_(\"Profile Approved By\"), related_name = \"ProfileApprovedBy\")\n profile_dis_by= models.ForeignKey(User, blank=True, null=True, on_delete=models.CASCADE, verbose_name=_(\"Profile Disabled By\"), related_name = \"ProfiledisBy\")\n\n def __str__(self):\n return str(self.profile_approved_remarks)\n \n class Meta:\n abstract = True\n \nclass ProfileInfoAdditionalProfileForm(forms.ModelForm):\n class Meta:\n model = ProfileInfoAdditionalProfile\n fields = (\n 'profile_approved_datetime', 'profile_approved_remarks','profile_dis_opt_by_status',\n 'profile_dis_opt_by_remarks','profile_dis_opt_by_datetime','profile_dis_by_remarks',\n 'profile_dis_by_datetime','profile_approved_by','profile_dis_by'\n )\n \n#Additional Profile Model\nclass AdditionalProfile(models.Model):\n _id = models.ObjectIdField()\n user = models.ForeignKey(User, on_delete=models.CASCADE, verbose_name=_(\"User\"))\n photo = models.ImageField(upload_to='Profile_image/', default='', blank=True, null=True, verbose_name=\"Profile Photo\")\n mobile_no = models.BigIntegerField( verbose_name=_(\"Mobile Number\"))\n addt_mobile_no = models.BigIntegerField(blank=True, null=True, verbose_name=_(\"Additional Mobile Number\"))\n address = models.EmbeddedField(\n model_container=AddressAdditionalProfile,\n model_form_class=AddressAdditionalProfileForm,\n )\n profile_info = models.EmbeddedField(\n model_container=ProfileInfoAdditionalProfile,\n model_form_class=ProfileInfoAdditionalProfileForm,\n )\n profile_status = models.CharField(max_length=100, blank=True, null=True, verbose_name=_(\" Profile Status\"))\n \n def __str__(self):\n return self.user.username+' '+self.user.email\n \n class Meta:\n verbose_name = \"Additional Profile\"\n verbose_name_plural = \"Additional Profile\"\n db_table = 'ccrh_user_addtional_profile'\n#Additional Profile Table Ends here\n\n#Creating model for Practical Details Table Starts here\n#when saving the upload path with the name starts here\ndef registartion_document_path_name(instance, filename):\n dir_name = instance.user.username \n return 'Certification Upload/%s/%s' % (dir_name, filename)\n\n#DocumentUploadPractDetails using as Array Field in PractDetails Model\nclass DocumentUploadPractDetails(models.Model):\n document_name = models.CharField(max_length=100, null=True, blank=True, verbose_name=_(\"Document Name\"))\n document_path = models.FileField(upload_to=registartion_document_path_name, null=True, blank=True)\n \n def __str__(self):\n return self.document_name\n \n class Meta:\n abstract = True\n\nclass DocumentUploadPractDetailsForm(forms.ModelForm):\n document_name = forms.CharField(label=_(\"Document Name\"), max_length=300, widget=forms.TextInput(attrs={'class':'form-control', 'placeholder':_('Document Name')})) \n document_path = forms.FileField(label=_(\"Registration Certificate\"), widget=forms.FileInput(attrs={'class':'form-control'})) \n\n class Meta:\n model = DocumentUploadPractDetails \n fields = (\n 'document_name','document_path'\n )\n \n#CsPractDetails using as Array Field in PractDetails Model\nclass CsPractDetails(models.Model):\n cs = models.ForeignKey(ClinicalSetting, on_delete=models.CASCADE, verbose_name=_(\"Type Of Clinical Setting\"))\n clinic_name = models.CharField(max_length=250, verbose_name=_(\"Clinical name\"))\n clinic_id = models.CharField(max_length=250, verbose_name=_(\"Clinical id\"))\n clinic_address_1 = models.TextField(verbose_name=_(\"Clinical Address 1\"))\n clinic_address_2 = models.TextField(verbose_name=_(\"Clinical Address 2\"))\n city = models.ForeignKey(City, on_delete=models.CASCADE, verbose_name=_(\"City\"))\n state = models.ForeignKey(State, on_delete=models.CASCADE, verbose_name=_(\"State\"))\n pincode = models.BigIntegerField(blank=True, null=True, verbose_name=_(\"Pin Code\"))\n affiliation = models.CharField(blank=True, null=True, max_length=100, verbose_name=_(\"Affiliation\"))\n \n def __str__(self):\n return self.clinic_name\n \n class Meta:\n abstract = True\n\n\nclass CsPractDetailsForm(forms.ModelForm):\n cs = forms.ModelChoiceField(required=True,queryset=ClinicalSetting.objects.all(), empty_label=\"Select Type Of Clinical\", label=_(\"Type Of Clinical Settings\"), widget=forms.Select(attrs={'class':'form-control type_of_clinical'}))\n clinic_name = forms.CharField(required=True, label=_(\"Clinic / Hospital Name\"), widget=forms.TextInput(attrs={'class':'form-control clinical_name','placeholder':_('Please enter 2 or more characters')}))\n clinic_id = forms.CharField(required=True, label=\"Clinical ID\", max_length=300, widget=forms.TextInput(attrs={'class':'form-control', 'placeholder':_('Clinical ID')})) \n clinic_address_1 = forms.CharField(required=True, label=\"Clinical/Hospital Address 1\", max_length=300, widget=forms.Textarea(attrs={'class':'form-control address_1','rows':'3', 'cols':'25', 'placeholder':_('Clinical/Hospital Address 1')})) \n clinic_address_2 = forms.CharField(required=True, label=\"Clinical/Hospital Address 2\", max_length=300, widget=forms.Textarea(attrs={'class':'form-control adress_2','rows':'3', 'cols':'25', 'placeholder':_('Clinical/Hospital Address 2')})) \n state = forms.ModelChoiceField(required=True, queryset=State.objects.all(), empty_label=\"Select State\", label=_(\"State\"), widget=forms.Select(attrs={'class':'form-control state','id':'state_id'}))\n city = forms.ModelChoiceField(required=True, queryset=City.objects.all(), empty_label=\"Select City\", label=_(\"City\"), widget=forms.Select(attrs={'class':'form-control city'}))\n pincode = forms.CharField(required=True, label='Pincode',max_length='6', widget=forms.TextInput(attrs={'class':'form-control pincode'}))\n affiliation = forms.CharField(required=False, label='Affiliation',max_length='100', widget=forms.TextInput(attrs={'class':'form-control affiliation', 'placeholder':_('Affiliation')}))\n\n class Meta:\n model = CsPractDetails\n fields = (\n 'cs','clinic_name','clinic_id','clinic_address_1' ,'clinic_address_2','city','state','pincode','affiliation',\n )\n \nclass PractDetails(models.Model):\n _id = models.ObjectIdField()\n user = models.ForeignKey(User, on_delete=models.CASCADE, verbose_name=_(\"User\"))\n pract_regis_body = models.CharField(max_length=10, verbose_name=_(\"Registration Body\"))\n pract_reg_no = models.CharField(max_length=50, verbose_name=_(\"Registration Number\"))\n pract_state = models.ForeignKey(State, blank=True, null=True, on_delete=models.CASCADE, verbose_name=_(\"State\"))\n document_name = models.CharField(max_length=100, null=True, blank=True, verbose_name=_(\"Document Name\"))\n document_path = models.FileField(upload_to=registartion_document_path_name, verbose_name=_(\"Registration Document\"), null=True, blank=True)\n\n clinical_setting = models.ArrayField(\n model_container=CsPractDetails,\n model_form_class=CsPractDetailsForm,\n )\n tnc = models.BooleanField(default=False, verbose_name=_(\"Terms & Conditions\"))\n objects = models.DjongoManager()\n\n \n def __str__(self):\n return self.user.username\n \n class Meta:\n verbose_name = \"Practitioner Details\"\n verbose_name_plural = \"Practitioner Details\"\n db_table = 'ccrh_pract_details'\n#Creating model for Practical Details Table Ends here\n\n#Panel User Group Mapping Model Starts here\nclass Category(models.Model):\n category = models.ForeignKey(CaseCategory, on_delete=models.CASCADE, verbose_name=_(\"Category\"))\n\n def __str__(self):\n return self.category.category_name\n \n class Meta:\n abstract = True\n\nclass SupervisorPool(models.Model):\n supervisor = models.ForeignKey(User, on_delete=models.CASCADE, verbose_name=_(\"Supervisor\"))\n \n def __str__(self):\n return self.supervisor.username\n \n class Meta:\n abstract = True\n \nclass ReviewerPool(models.Model):\n reviewer = models.ForeignKey(User, on_delete=models.CASCADE, verbose_name=_(\"Reviewer\"))\n \n def __str__(self):\n return self.reviewer.username\n \n class Meta:\n abstract = True\n \nclass PanelUserGroupMapping(models.Model):\n _id = models.ObjectIdField()\n panel_name = models.CharField(max_length=100, verbose_name=_(\"Panel Name\"))\n category = models.ArrayField(\n model_container=Category,\n )\n supervisor_pool = models.ArrayField(\n model_container=SupervisorPool,\n )\n reviewer_pool = models.ArrayField(\n model_container=ReviewerPool,\n )\n\n def __str__(self):\n return self.panel_name\n \n class Meta:\n verbose_name = \"Panel User Group Mapping\"\n verbose_name_plural = \"Panel User Group Mapping\"\n db_table = 'ccrh_panel_user_group_mapping'\n#Panel User Group Mapping Model Ends here \n\n# #Visitor History Model Starts here\n# class VisitorHistory(models.Model):\n# _id = models.ObjectIdField()\n# IS_ACCESSED_CHOICE = (\n# (u'0', u'No'),\n# (u'1', u'Yes'),\n# )\n# visitor_name = models.ForeignKey(User, on_delete=models.CASCADE, verbose_name=_(\"Visitor Name\"))\n# visitor_email = models.CharField(max_length=100, verbose_name=_(\"Visitor Email\"))\n# visitor_mobile = models.BigIntegerField(verbose_name=_(\"Visitor Mobile Number \")) \n# visitor_datetime = models.DateTimeField( verbose_name=_(\"Visitor Date Time\"))\n# visitor_link_unique_code = models.CharField(max_length=100, verbose_name=_(\"Visitor Link Unique Code\"))\n# visitor_link_expiry_datetime = models.DateTimeField( verbose_name=_(\"Visitor Link Expiry Date Time\"))\n# is_accessed = models.CharField(max_length=1, default='0',choices=IS_ACCESSED_CHOICE, verbose_name=_(\"Is Accessed\"))\n# accessed_datetime = models.DateTimeField( verbose_name=_(\"Accessed Date Time\"))\n# \n# class Meta:\n# verbose_name = \"Visitor History\"\n# verbose_name_plural = \"Visitor History\"\n# db_table = 'ccrh_vistor_history'\n# #Visitor History Model Ends here\n'''FINALIZED MODELS ENDS''' ", "sub_path": "user_profile/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 14049, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "djongo.models.Model", "line_number": 16, "usage_type": "attribute"}, {"api_name": "djongo.models", "line_number": 16, "usage_type": "name"}, {"api_name": "djongo.models.TextField", "line_number": 17, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 17, "usage_type": "call"}, {"api_name": "djongo.models.TextField", "line_number": 18, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 18, "usage_type": "call"}, {"api_name": "djongo.models.BigIntegerField", "line_number": 19, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 19, "usage_type": "call"}, {"api_name": "djongo.models.ForeignKey", "line_number": 20, "usage_type": "call"}, {"api_name": "master.models.State", "line_number": 20, "usage_type": "argument"}, {"api_name": "djongo.models", "line_number": 20, "usage_type": "name"}, {"api_name": "djongo.models.CASCADE", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 20, "usage_type": "call"}, {"api_name": "djongo.models.ForeignKey", "line_number": 21, "usage_type": "call"}, {"api_name": "master.models.City", "line_number": 21, "usage_type": "argument"}, {"api_name": "djongo.models", "line_number": 21, "usage_type": "name"}, {"api_name": "djongo.models.CASCADE", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 21, "usage_type": "call"}, {"api_name": "django.forms.ModelForm", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 29, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 30, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 30, "usage_type": "call"}, {"api_name": "django.forms.Textarea", "line_number": 30, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 31, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 31, "usage_type": "call"}, {"api_name": "django.forms.Textarea", "line_number": 31, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 32, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 32, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 32, "usage_type": "call"}, {"api_name": "django.forms.ModelChoiceField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 33, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 33, "usage_type": "call"}, {"api_name": "master.models.State.objects.all", "line_number": 34, "usage_type": "call"}, {"api_name": "master.models.State.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "master.models.State", "line_number": 34, "usage_type": "name"}, {"api_name": "dal.autocomplete.ModelSelect2", "line_number": 35, "usage_type": "call"}, {"api_name": "dal.autocomplete", "line_number": 35, "usage_type": "name"}, {"api_name": "django.forms.ModelChoiceField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 37, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 37, "usage_type": "call"}, {"api_name": "master.models.City.objects.all", "line_number": 38, "usage_type": "call"}, {"api_name": "master.models.City.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "master.models.City", "line_number": 38, "usage_type": "name"}, {"api_name": "dal.autocomplete.ModelSelect2", "line_number": 39, "usage_type": "call"}, {"api_name": "dal.autocomplete", "line_number": 39, "usage_type": "name"}, {"api_name": "djongo.models.Model", "line_number": 48, "usage_type": "attribute"}, {"api_name": "djongo.models", "line_number": 48, "usage_type": "name"}, {"api_name": "djongo.models.DateTimeField", "line_number": 53, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 53, "usage_type": "name"}, {"api_name": "djongo.models.TextField", "line_number": 54, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 54, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 54, "usage_type": "call"}, {"api_name": "djongo.models.CharField", "line_number": 55, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 55, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 55, "usage_type": "call"}, {"api_name": "djongo.models.TextField", "line_number": 56, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 56, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 56, "usage_type": "call"}, {"api_name": "djongo.models.DateTimeField", "line_number": 57, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 57, "usage_type": "name"}, {"api_name": "djongo.models.TextField", "line_number": 58, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 58, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 58, "usage_type": "call"}, {"api_name": "djongo.models.DateTimeField", "line_number": 59, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 59, "usage_type": "name"}, {"api_name": "djongo.models.ForeignKey", "line_number": 60, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 60, "usage_type": "argument"}, {"api_name": "djongo.models", "line_number": 60, "usage_type": "name"}, {"api_name": "djongo.models.CASCADE", "line_number": 60, "usage_type": "attribute"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 60, "usage_type": "call"}, {"api_name": "djongo.models.ForeignKey", "line_number": 61, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 61, "usage_type": "argument"}, {"api_name": "djongo.models", "line_number": 61, "usage_type": "name"}, {"api_name": "djongo.models.CASCADE", "line_number": 61, "usage_type": "attribute"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 61, "usage_type": "call"}, {"api_name": "django.forms.ModelForm", "line_number": 69, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 69, "usage_type": "name"}, {"api_name": "djongo.models.Model", "line_number": 79, "usage_type": "attribute"}, {"api_name": "djongo.models", "line_number": 79, "usage_type": "name"}, {"api_name": "djongo.models.ObjectIdField", "line_number": 80, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 80, "usage_type": "name"}, {"api_name": "djongo.models.ForeignKey", "line_number": 81, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 81, "usage_type": "argument"}, {"api_name": "djongo.models", "line_number": 81, "usage_type": "name"}, {"api_name": "djongo.models.CASCADE", "line_number": 81, "usage_type": "attribute"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 81, "usage_type": "call"}, {"api_name": "djongo.models.ImageField", "line_number": 82, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 82, "usage_type": "name"}, {"api_name": "djongo.models.BigIntegerField", "line_number": 83, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 83, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 83, "usage_type": "call"}, {"api_name": "djongo.models.BigIntegerField", "line_number": 84, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 84, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 84, "usage_type": "call"}, {"api_name": "djongo.models.EmbeddedField", "line_number": 85, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 85, "usage_type": "name"}, {"api_name": "djongo.models.EmbeddedField", "line_number": 89, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 89, "usage_type": "name"}, {"api_name": "djongo.models.CharField", "line_number": 93, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 93, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 93, "usage_type": "call"}, {"api_name": "djongo.models.Model", "line_number": 111, "usage_type": "attribute"}, {"api_name": "djongo.models", "line_number": 111, "usage_type": "name"}, {"api_name": "djongo.models.CharField", "line_number": 112, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 112, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 112, "usage_type": "call"}, {"api_name": "djongo.models.FileField", "line_number": 113, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 113, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 121, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 121, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 122, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 122, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 122, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 122, "usage_type": "call"}, {"api_name": "django.forms.FileField", "line_number": 123, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 123, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 123, "usage_type": "call"}, {"api_name": "django.forms.FileInput", "line_number": 123, "usage_type": "call"}, {"api_name": "djongo.models.Model", "line_number": 132, "usage_type": "attribute"}, {"api_name": "djongo.models", "line_number": 132, "usage_type": "name"}, {"api_name": "djongo.models.ForeignKey", "line_number": 133, "usage_type": "call"}, {"api_name": "master.models.ClinicalSetting", "line_number": 133, "usage_type": "argument"}, {"api_name": "djongo.models", "line_number": 133, "usage_type": "name"}, {"api_name": "djongo.models.CASCADE", "line_number": 133, "usage_type": "attribute"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 133, "usage_type": "call"}, {"api_name": "djongo.models.CharField", "line_number": 134, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 134, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 134, "usage_type": "call"}, {"api_name": "djongo.models.CharField", "line_number": 135, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 135, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 135, "usage_type": "call"}, {"api_name": "djongo.models.TextField", "line_number": 136, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 136, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 136, "usage_type": "call"}, {"api_name": "djongo.models.TextField", "line_number": 137, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 137, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 137, "usage_type": "call"}, {"api_name": "djongo.models.ForeignKey", "line_number": 138, "usage_type": "call"}, {"api_name": "master.models.City", "line_number": 138, "usage_type": "argument"}, {"api_name": "djongo.models", "line_number": 138, "usage_type": "name"}, {"api_name": "djongo.models.CASCADE", "line_number": 138, "usage_type": "attribute"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 138, "usage_type": "call"}, {"api_name": "djongo.models.ForeignKey", "line_number": 139, "usage_type": "call"}, {"api_name": "master.models.State", "line_number": 139, "usage_type": "argument"}, {"api_name": "djongo.models", "line_number": 139, "usage_type": "name"}, {"api_name": "djongo.models.CASCADE", "line_number": 139, "usage_type": "attribute"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 139, "usage_type": "call"}, {"api_name": "djongo.models.BigIntegerField", "line_number": 140, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 140, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 140, "usage_type": "call"}, {"api_name": "djongo.models.CharField", "line_number": 141, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 141, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 141, "usage_type": "call"}, {"api_name": "django.forms.ModelForm", "line_number": 150, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 150, "usage_type": "name"}, {"api_name": "django.forms.ModelChoiceField", "line_number": 151, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 151, "usage_type": "name"}, {"api_name": "master.models.ClinicalSetting.objects.all", "line_number": 151, "usage_type": "call"}, {"api_name": "master.models.ClinicalSetting.objects", "line_number": 151, "usage_type": "attribute"}, {"api_name": "master.models.ClinicalSetting", "line_number": 151, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 151, "usage_type": "call"}, {"api_name": "django.forms.Select", "line_number": 151, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 152, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 152, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 152, "usage_type": "call"}, {"api_name": "django.forms.TextInput", "line_number": 152, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 153, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 153, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 153, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 153, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 154, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 154, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 154, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 154, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 155, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 155, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 155, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 155, "usage_type": "call"}, {"api_name": "django.forms.ModelChoiceField", "line_number": 156, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 156, "usage_type": "name"}, {"api_name": "master.models.State.objects.all", "line_number": 156, "usage_type": "call"}, {"api_name": "master.models.State.objects", "line_number": 156, "usage_type": "attribute"}, {"api_name": "master.models.State", "line_number": 156, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 156, "usage_type": "call"}, {"api_name": "django.forms.Select", "line_number": 156, "usage_type": "call"}, {"api_name": "django.forms.ModelChoiceField", "line_number": 157, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 157, "usage_type": "name"}, {"api_name": "master.models.City.objects.all", "line_number": 157, "usage_type": "call"}, {"api_name": "master.models.City.objects", "line_number": 157, "usage_type": "attribute"}, {"api_name": "master.models.City", "line_number": 157, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 157, "usage_type": "call"}, {"api_name": "django.forms.Select", "line_number": 157, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 158, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 158, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 158, "usage_type": "call"}, {"api_name": "django.forms.CharField", "line_number": 159, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 159, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 159, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 159, "usage_type": "call"}, {"api_name": "djongo.models.Model", "line_number": 167, "usage_type": "attribute"}, {"api_name": "djongo.models", "line_number": 167, "usage_type": "name"}, {"api_name": "djongo.models.ObjectIdField", "line_number": 168, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 168, "usage_type": "name"}, {"api_name": "djongo.models.ForeignKey", "line_number": 169, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 169, "usage_type": "argument"}, {"api_name": "djongo.models", "line_number": 169, "usage_type": "name"}, {"api_name": "djongo.models.CASCADE", "line_number": 169, "usage_type": "attribute"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 169, "usage_type": "call"}, {"api_name": "djongo.models.CharField", "line_number": 170, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 170, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 170, "usage_type": "call"}, {"api_name": "djongo.models.CharField", "line_number": 171, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 171, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 171, "usage_type": "call"}, {"api_name": "djongo.models.ForeignKey", "line_number": 172, "usage_type": "call"}, {"api_name": "master.models.State", "line_number": 172, "usage_type": "argument"}, {"api_name": "djongo.models", "line_number": 172, "usage_type": "name"}, {"api_name": "djongo.models.CASCADE", "line_number": 172, "usage_type": "attribute"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 172, "usage_type": "call"}, {"api_name": "djongo.models.CharField", "line_number": 173, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 173, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 173, "usage_type": "call"}, {"api_name": "djongo.models.FileField", "line_number": 174, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 174, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 174, "usage_type": "call"}, {"api_name": "djongo.models.ArrayField", "line_number": 176, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 176, "usage_type": "name"}, {"api_name": "djongo.models.BooleanField", "line_number": 180, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 180, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 180, "usage_type": "call"}, {"api_name": "djongo.models.DjongoManager", "line_number": 181, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 181, "usage_type": "name"}, {"api_name": "djongo.models.Model", "line_number": 194, "usage_type": "attribute"}, {"api_name": "djongo.models", "line_number": 194, "usage_type": "name"}, {"api_name": "djongo.models.ForeignKey", "line_number": 195, "usage_type": "call"}, {"api_name": "master.models.CaseCategory", "line_number": 195, "usage_type": "argument"}, {"api_name": "djongo.models", "line_number": 195, "usage_type": "name"}, {"api_name": "djongo.models.CASCADE", "line_number": 195, "usage_type": "attribute"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 195, "usage_type": "call"}, {"api_name": "djongo.models.Model", "line_number": 203, "usage_type": "attribute"}, {"api_name": "djongo.models", "line_number": 203, "usage_type": "name"}, {"api_name": "djongo.models.ForeignKey", "line_number": 204, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 204, "usage_type": "argument"}, {"api_name": "djongo.models", "line_number": 204, "usage_type": "name"}, {"api_name": "djongo.models.CASCADE", "line_number": 204, "usage_type": "attribute"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 204, "usage_type": "call"}, {"api_name": "djongo.models.Model", "line_number": 212, "usage_type": "attribute"}, {"api_name": "djongo.models", "line_number": 212, "usage_type": "name"}, {"api_name": "djongo.models.ForeignKey", "line_number": 213, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 213, "usage_type": "argument"}, {"api_name": "djongo.models", "line_number": 213, "usage_type": "name"}, {"api_name": "djongo.models.CASCADE", "line_number": 213, "usage_type": "attribute"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 213, "usage_type": "call"}, {"api_name": "djongo.models.Model", "line_number": 221, "usage_type": "attribute"}, {"api_name": "djongo.models", "line_number": 221, "usage_type": "name"}, {"api_name": "djongo.models.ObjectIdField", "line_number": 222, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 222, "usage_type": "name"}, {"api_name": "djongo.models.CharField", "line_number": 223, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 223, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 223, "usage_type": "call"}, {"api_name": "djongo.models.ArrayField", "line_number": 224, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 224, "usage_type": "name"}, {"api_name": "djongo.models.ArrayField", "line_number": 227, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 227, "usage_type": "name"}, {"api_name": "djongo.models.ArrayField", "line_number": 230, "usage_type": "call"}, {"api_name": "djongo.models", "line_number": 230, "usage_type": "name"}]} +{"seq_id": "63325523", "text": "# -*- coding: utf-8 -*-\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport scipy.special \r\nfrom simOrtogonalh09 import simOrtogonal\r\n#qfunc = lambda x: 0.5-0.5*scipy.special.erf(x/np.sqrt(2))\r\n\r\nvtEbNoSim = np.arange(0,15)\r\nvtEbNoTeo = np.arange(-1,15,0.1)\r\nvtnMCSamples =[10, 100, 5000, 100000]\r\nvtMarkers = ('s','o','d','*','<')\r\nchLegend = []\r\nplt.figure(1,[10,7])\r\nfor ij in range(0,len(vtnMCSamples)):\r\n vtSimError=np.zeros(len(vtEbNoSim))\r\n nMCSamples = vtnMCSamples[ij];\r\n for ik in range(0,len(vtEbNoSim)):\r\n vtSimError[ik]= simOrtogonal(vtEbNoSim[ik], nMCSamples);\r\n hand1 = plt.semilogy(vtEbNoSim,vtSimError, vtMarkers[ij]);\r\n del vtSimError\r\n plt.hold \r\n plt.setp(hand1, linewidth= 2, markersize=14)\r\n chLegend.append('BER simulada com {} amostras'.format(nMCSamples))\r\nvtSNR = 10**(vtEbNoTeo/10);\r\nvtTeoError = 0.5-0.5*scipy.special.erf(np.sqrt(vtSNR)/np.sqrt(2))\r\nchLegend.append('Teórico - Pe')\r\nhand1 = plt.semilogy(vtEbNoTeo,vtTeoError);\r\nplt.setp(hand1, linewidth= 2)\r\nplt.legend(chLegend);\r\nplt.xlabel('SNR');\r\nplt.ylabel('BER ou P_e');\r\n", "sub_path": "HandsOn09/HandsOn09pt5.py", "file_name": "HandsOn09pt5.py", "file_ext": "py", "file_size_in_byte": 1099, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.arange", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 15, "usage_type": "call"}, {"api_name": "simOrtogonalh09.simOrtogonal", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.semilogy", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hold", "line_number": 21, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "scipy.special.special.erf", "line_number": 25, "usage_type": "call"}, {"api_name": "scipy.special.special", "line_number": 25, "usage_type": "attribute"}, {"api_name": "scipy.special", "line_number": 25, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.semilogy", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.setp", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}]} +{"seq_id": "292933394", "text": "# -*- coding:utf-8 -*-\nimport os\nimport shutil\nimport utils\nimport json\n\n__author__ = 'chengchao'\n\n\ndef mkdirs(relative_path):\n \"\"\"\n 1. 创建目录（递归创建）\n :param relative_path: 启动路径\n :return: None\n \"\"\"\n abs_path = utils.get_abs_path(relative_path)\n if not os.path.exists(abs_path):\n os.makedirs(abs_path)\n\n\ndef read_file(filepath):\n return utils.read_str_file(filepath)\n\n\ndef write_file(filename, content='', is_json_obj=True):\n \"\"\"\n 2. 创建文件（如果所在目录不存在一同创建），并写入数据（text）\n :param is_json_obj: content是否为对象\n :param filename: 相对文件路径，包括文件名 ，如：case/test/a.json\n :param content: 需要写入的内容\n :return: None\n \"\"\"\n abs_path = utils.get_abs_path(filename)\n dir_name = os.path.dirname(abs_path)\n if not os.path.exists(dir_name):\n os.makedirs(dir_name)\n stream = None\n try:\n stream = open(abs_path, 'w')\n stream.write(json.dumps(content) if is_json_obj else content)\n stream.flush()\n finally:\n if stream is not None:\n stream.close()\n\n\ndef rmdirs(path):\n \"\"\"\n 3. 删除目录\n :param path:目录的相对位置\n :return: None\n \"\"\"\n if utils.is_empty_str(path) or path in ['/', '//', '\\\\', '\\\\\\\\']:\n return\n abs_path = utils.get_abs_path(path)\n if not os.path.exists(abs_path) or not os.path.isdir(abs_path):\n return\n shutil.rmtree(abs_path)\n\n\ndef remove(filename):\n \"\"\"\n 4. 删除文件\n :param filename: 文件相对路径\n :return:\n \"\"\"\n abs_path = utils.get_abs_path(filename)\n if not os.path.exists(abs_path) or not os.path.isfile(abs_path):\n return\n os.remove(abs_path)\n\n\ndef rename(old, new):\n \"\"\"\n 5. 修改文件/目录名称\n :param old: old name\n :param new: new name\n :return: None\n \"\"\"\n abs_old_path = utils.get_abs_path(old)\n if not os.path.exists(abs_old_path):\n return\n abs_new_path = utils.get_abs_path(new)\n if os.path.exists(abs_new_path):\n return\n os.renames(abs_old_path, abs_new_path)\n\n\ndef move(path, new_path):\n \"\"\"\n 6. 移动目录/文件\n :param path: 需要移动的文件（目录）相对路径\n :param new_path: 移动到的路径\n :return: None\n \"\"\"\n abs_path = utils.get_abs_path(path)\n if not os.path.exists(abs_path):\n return\n mkdirs(new_path)\n abs_new_path = utils.get_abs_path(new_path)\n shutil.move(abs_path, abs_new_path)\n\n\ndef listfiles(path, only_visible=True):\n \"\"\"\n 7. 目录文件列表\n :param path: 相对目录\n :return:\n \"\"\"\n if utils.is_empty_str(path):\n return None\n abs_path = utils.get_abs_path(path)\n if not os.path.isdir(abs_path):\n return None\n result = os.listdir(abs_path)\n return [ret for ret in result if not only_visible or not ret.startswith('.')]\n\n\ndef is_dir(path):\n if utils.is_empty_str(path):\n return False\n abs_path = utils.get_abs_path(path)\n return os.path.isdir(abs_path)\n\n\ndef is_file(path):\n if utils.is_empty_str(path):\n return False\n abs_path = utils.get_abs_path(path)\n return os.path.isfile(abs_path)\n", "sub_path": "manager/files.py", "file_name": "files.py", "file_ext": "py", "file_size_in_byte": 3236, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "utils.get_abs_path", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 18, "usage_type": "call"}, {"api_name": "utils.read_str_file", "line_number": 22, "usage_type": "call"}, {"api_name": "utils.get_abs_path", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 36, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 40, "usage_type": "call"}, {"api_name": "utils.is_empty_str", "line_number": 53, "usage_type": "call"}, {"api_name": "utils.get_abs_path", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 56, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 58, "usage_type": "call"}, {"api_name": "utils.get_abs_path", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 68, "usage_type": "call"}, {"api_name": "os.path", "line_number": 68, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 68, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 70, "usage_type": "call"}, {"api_name": "utils.get_abs_path", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "utils.get_abs_path", "line_number": 83, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.renames", "line_number": 86, "usage_type": "call"}, {"api_name": "utils.get_abs_path", "line_number": 96, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "utils.get_abs_path", "line_number": 100, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 101, "usage_type": "call"}, {"api_name": "utils.is_empty_str", "line_number": 110, "usage_type": "call"}, {"api_name": "utils.get_abs_path", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 115, "usage_type": "call"}, {"api_name": "utils.is_empty_str", "line_number": 120, "usage_type": "call"}, {"api_name": "utils.get_abs_path", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path", "line_number": 123, "usage_type": "attribute"}, {"api_name": "utils.is_empty_str", "line_number": 127, "usage_type": "call"}, {"api_name": "utils.get_abs_path", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path", "line_number": 130, "usage_type": "attribute"}]} +{"seq_id": "541386662", "text": "import datetime\nfrom django.db.models.signals import post_save\nfrom django.contrib.auth.models import User\nfrom django.dispatch import receiver\nfrom .models import Profile, Timesheet, Day, Week\n\nimport logging\nlogger = logging.getLogger(__name__)\n\n@receiver(post_save, sender=User)\ndef create_profile(sender, instance, created, **kwargs):\n if created:\n Profile.objects.create(user=instance)\n\n@receiver(post_save, sender=User)\ndef save_profile(sender, instance, **kwargs):\n instance.profile.save()\n\n@receiver(post_save, sender=Timesheet)\ndef create_weeks(sender, instance, created, **kwargs):\n \"\"\"Creates day and week objects for timesheet. Week belongs to timesheet in\n which week ends. Usually has overlap into previous timesheet.\n First we create the week, then if that week overlaps with the previous\n timesheet we go back to the days from that timesheet and assign them to\n the week we just created. Then create additional weeks inside this timesheet\n along with days assigned to those weeks until the end of the week falls\n outside of the timesheet. After this, if there are days left over which fall\n outside of the last week but prior to the end of the timesheet, we create\n those days without a week assigned to them. Those days will = previous_days\n for the next timesheet and we'll assign those days to the first week of\n that timesheet.\"\"\"\n if created:\n start = instance.pay_period_start - datetime.timedelta(days=\n instance.pay_period_start.weekday())\n end = start + datetime.timedelta(days=6)\n current_day = instance.pay_period_start\n previous_days = Day.objects.filter(day__range=(start, current_day), \n user=instance.user)\n while end <= instance.pay_period_end:\n week = Week.objects.create(start_date=start, end_date=end,\n timesheet=instance, user=instance.user)\n if start < instance.pay_period_start:\n for day in previous_days:\n day.week = week\n day.save()\n while current_day <= end:\n Day.objects.create(day=current_day, week=week,\n timesheet=instance, user=instance.user)\n current_day += datetime.timedelta(days=1)\n start += datetime.timedelta(days=7)\n end += datetime.timedelta(days=7)\n while current_day <= instance.pay_period_end:\n Day.objects.create(day=current_day, timesheet=instance,\n user=instance.user)\n current_day += datetime.timedelta(days=1)\n\n# @receiver(post_save, sender=Timesheet)\n# def create_days(sender, instance, created, **kwargs):\n# \"\"\"Creates day objects for each day of the timesheet.\"\"\"\n# if created:\n# day = instance.pay_period_start\n# while day <= instance.pay_period_end:\n# #self.clockpunches.update({self.pay_period_start: []})\n# Day.objects.create(day=day, timesheet=instance, user=instance.user)\n# day += datetime.timedelta(days=1)\n\n''' Do we need a save signal too?\nThis signal below creates a second set of day objects when\ntimesheet is created. We don't want that. What about when it's updated?\nWill that ever really happen?\n '''\n\n# @receiver(post_save, sender=Timesheet)\n# def save_days(sender, instance, **kwargs):\n# day = instance.pay_period_start\n# while day <= instance.pay_period_end:\n# #self.clockpunches.update({self.pay_period_start: []})\n# current_day = Day(day=day, timesheet=instance)\n# current_day.save()\n# day += datetime.timedelta(days=1)\n", "sub_path": "timesheets/signals.py", "file_name": "signals.py", "file_ext": "py", "file_size_in_byte": 3736, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "models.Profile.objects.create", "line_number": 13, "usage_type": "call"}, {"api_name": "models.Profile.objects", "line_number": 13, "usage_type": "attribute"}, {"api_name": "models.Profile", "line_number": 13, "usage_type": "name"}, {"api_name": "django.dispatch.receiver", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 10, "usage_type": "argument"}, {"api_name": "django.contrib.auth.models.User", "line_number": 10, "usage_type": "name"}, {"api_name": "django.dispatch.receiver", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 15, "usage_type": "argument"}, {"api_name": "django.contrib.auth.models.User", "line_number": 15, "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": "models.Day.objects.filter", "line_number": 37, "usage_type": "call"}, {"api_name": "models.Day.objects", "line_number": 37, "usage_type": "attribute"}, {"api_name": "models.Day", "line_number": 37, "usage_type": "name"}, {"api_name": "models.Week.objects.create", "line_number": 40, "usage_type": "call"}, {"api_name": "models.Week.objects", "line_number": 40, "usage_type": "attribute"}, {"api_name": "models.Week", "line_number": 40, "usage_type": "name"}, {"api_name": "models.Day.objects.create", "line_number": 47, "usage_type": "call"}, {"api_name": "models.Day.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "models.Day", "line_number": 47, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 49, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 51, "usage_type": "call"}, {"api_name": "models.Day.objects.create", "line_number": 53, "usage_type": "call"}, {"api_name": "models.Day.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "models.Day", "line_number": 53, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 55, "usage_type": "call"}, {"api_name": "django.dispatch.receiver", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models.signals.post_save", "line_number": 19, "usage_type": "argument"}, {"api_name": "models.Timesheet", "line_number": 19, "usage_type": "name"}]} +{"seq_id": "239602199", "text": "# -*- coding: utf8 -*-\n#!/usr/bin/python\n\nfrom datetime import datetime\nimport os\nimport sys\nsys.path.append('../')\npath = '../external/'\n\ndef GetIP(fileName): \n address = open(fileName)\n tempList = address.readlines()\n addressList = [x.replace('\\n', '') for x in tempList]\n return addressList\n\ndef CreateFiles(city, addressList): \n currentTime = str(datetime.now())[:10] + '-' + str(datetime.now())[11:19]\n currentTime = currentTime.replace(':', '-')\n if not os.path.exists(path + city):\n os.mkdir(path + city)\n \n os.mkdir(path + city + '/' + currentTime)\n fileList = [path + city + '/' + currentTime + '/' + x for x in addressList]\n return fileList \n \ndef WriteToFile(fileName, log):\n fileDescriptor = open(fileName, 'a')\n for i in log:\n fileDescriptor.write(i+'\\n');\n fileDescriptor.close()\n\n", "sub_path": "PythonTestProject/external/external_utilities.py", "file_name": "external_utilities.py", "file_ext": "py", "file_size_in_byte": 857, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.path.append", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 20, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "585045383", "text": "from pystream.algorithms.base import VFDT, SVFDT, SVFDT_II\nfrom pystream.utils import read_arff_meta, instance_gen\nfrom pystream.evaluation import EvaluatePrequential\nimport logging\nfrom pathlib import Path\n\n\ndef run():\n DEBUG = True\n logging.basicConfig(format=\"%(levelname)s:%(message)s\", level=logging.INFO)\n methods = [\"entropy\", \"bgst_entropy\", \"budget_entropy\"]\n log_dir = Path(\"log/\") # Change this if you want to save the logs somewhere else\n log_dir.mkdir(parents=True, exist_ok=True)\n\n z_values = [0.1, 0.2, 0.5, 0.9]\n z_val_str = z_values\n\n # Make sure all these files are in the datasets folder\n commands = [\n (\"datasets/elecNormNew.csv\"),\n (\"datasets/hyper.csv\"),\n (\"datasets/sea.csv\"),\n (\"datasets/usenet.csv\"),\n (\"datasets/random_rbf_500k.csv\"),\n (\"datasets/covTypeNorm.csv\"),\n (\"datasets/led24_0_1kk.csv\"),\n (\"datasets/led24_10_1kk.csv\"),\n (\"datasets/led24_20_1kk.csv\"),\n (\"datasets/random_rbf_1kk.csv\"),\n (\"datasets/random_rbf_250k(50).csv\"),\n (\"datasets/poker-lsn.csv\"),\n (\"datasets/airlines_parsed.csv\"),\n (\"datasets/CTU_1.csv\"),\n (\"datasets/CTU_2.csv\"),\n (\"datasets/CTU_3.csv\"),\n (\"datasets/CTU_4.csv\"),\n (\"datasets/CTU_5.csv\"),\n (\"datasets/CTU_6.csv\"),\n (\"datasets/CTU_7.csv\"),\n (\"datasets/CTU_8.csv\"),\n (\"datasets/CTU_9.csv\"),\n (\"datasets/CTU_10.csv\"),\n (\"datasets/CTU_11.csv\"),\n (\"datasets/CTU_12.csv\"),\n (\"datasets/CTU_13.csv\"),\n ]\n for fname in commands:\n dataset_name = fname.split(\"/\")[-1].split(\".csv\")[0]\n meta_file = f\"datasets/metas/{dataset_name}.meta\"\n dtype, types, classes = read_arff_meta(meta_file)\n n_classes = len(classes)\n only_binary_splits = False\n base_learners_n_args = [\n (\n \"vfdt\",\n VFDT,\n {\n \"gp\": 100,\n \"split_criterion\": \"infogain\",\n \"tiebreaker\": 0.05,\n \"only_binary_splits\": only_binary_splits,\n },\n ),\n (\n \"svfdt\",\n SVFDT,\n {\n \"gp\": 100,\n \"split_criterion\": \"infogain\",\n \"tiebreaker\": 0.05,\n \"only_binary_splits\": only_binary_splits,\n },\n ),\n (\n \"svfdt_ii\",\n SVFDT_II,\n {\n \"gp\": 400,\n \"split_criterion\": \"infogain\",\n \"tiebreaker\": 0.05,\n \"only_binary_splits\": only_binary_splits,\n },\n ),\n ]\n for method in methods:\n for name, base_learner, kwargs in base_learners_n_args:\n csv_results = []\n for z, confidence in zip(z_values, z_val_str):\n algorithm = base_learner(types, n_classes, **kwargs)\n log_file = log_dir / f\"{dataset_name}_{confidence}.csv\"\n evaluator = EvaluatePrequential(\n n_classes, algorithm, algorithm_type=\"tree\"\n )\n stream = instance_gen(fname, dtype) # chunksize=500000\n\n evaluator.train_test_prequential_entropy(\n stream,\n DEBUG,\n 10,\n log_file=log_file,\n active=True,\n z=z,\n method=method,\n )\n\n csv_results.append(\n {\n \"Z Value\": str(confidence),\n \"Accuracy\": evaluator.stats.accuracy,\n \"Hits\": evaluator.stats[\"hits\"],\n \"Miss\": evaluator.stats[\"miss\"],\n \"Queried\": evaluator.stats[\"train_truelabel\"],\n }\n )\n\n with open(log_dir / f\"{method}_{dataset_name}_{name}.csv\", \"w\") as f:\n f.write(\"Z Value,Accuracy,Queries\\n\")\n for result in csv_results:\n f.write(\n (\n f\"{result['Z Value']},\"\n f\"{result['Accuracy']},\"\n f\"{result['Queried']}\\n\"\n )\n )\n\n\nif __name__ == \"__main__\":\n run()\n", "sub_path": "tests/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 4623, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.basicConfig", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 12, "usage_type": "call"}, {"api_name": "pystream.utils.read_arff_meta", "line_number": 50, "usage_type": "call"}, {"api_name": "pystream.algorithms.base.VFDT", "line_number": 56, "usage_type": "name"}, {"api_name": "pystream.algorithms.base.SVFDT", "line_number": 66, "usage_type": "name"}, {"api_name": "pystream.algorithms.base.SVFDT_II", "line_number": 76, "usage_type": "name"}, {"api_name": "pystream.evaluation.EvaluatePrequential", "line_number": 91, "usage_type": "call"}, {"api_name": "pystream.utils.instance_gen", "line_number": 94, "usage_type": "call"}]} +{"seq_id": "169491383", "text": "# -*- coding: utf-8 -*-\n\n#from __future__ import print_function\n#from keras.models import Sequential\nimport keras.backend as K\nfrom keras.callbacks import LearningRateScheduler\nimport shutil\nfrom keras.layers.core import Dense, Activation, Dropout\nfrom keras.layers.recurrent import LSTM\nfrom keras.layers import Input, Embedding, merge\nfrom keras.utils import np_utils\nfrom keras.models import Model\nimport pandas as pd\nimport numpy as np\nfrom keras.optimizers import Adam\nfrom keras.regularizers import l2\nimport csv\n#import random \nimport os\nimport matplotlib.pyplot as plt\nfrom matplotlib.pylab import mpl\nfrom matplotlib.font_manager import _rebuild\n#from sklearn import preprocessing\n#如需进行数据归一化则写入下一行代码\nfrom sklearn.preprocessing import MinMaxScaler\n#=====================================================================\nPARAMS = [[0.001,50,10,1],[0.001,50,10,8] ,[0.001,50,10,32],\n [0.001,10,10,16],[0.001,100,10,16],[0.001,500,10,16]]\nfor PARAM_NUM in range(3,8):\n #PARAM_NUM = 3 #在这里输入参数号\n NAME = u\"无照经营\"\n FILE_NAME = NAME+\"-所有街道数据.csv\"\n F_NAME = u\"../图表/LSTM图表/分站点预测/参数\"+str(PARAM_NUM)+\"/\"+NAME+\"/\"\n #=====================================================================\n #将下面这个参数从1开始跑，\n #比如num_site=1跑一次，num_site=2跑一次，num_site=3跑一次。。。以此类推，直到系统提示“本文件所有站点预测完毕”为止\n\n #num_site=12\n\n\n CURRENT_PARAMS = PARAMS[PARAM_NUM-2]\n #=====================================================================\n #my_lr=0.001 #初始学习率\n my_lr=CURRENT_PARAMS[0]\n #my_node=50 #LSTM层的节点数\n my_node = CURRENT_PARAMS[1]\n #delay=10 #根据前delay个数据预测下一个\n delay=CURRENT_PARAMS[2]\n #my_batch_size=1 #batch_size\n my_batch_size=CURRENT_PARAMS[3]\n\n #=====================================================================\n os.makedirs(F_NAME)\n os.makedirs(u\"../LSTM分站点输出预测数据文件/参数\" + str(PARAM_NUM) + \"/\" + NAME + \"/\")\n os.makedirs(u'../LSTM分站点结果分析/参数'+str(PARAM_NUM)+u'极端数据图表/')\n\n df=pd.read_csv(FILE_NAME,encoding='gbk')\n #df=pd.read_csv(FILE_NAME)\n #=================================常量的定义或声明====================================\n NUM_ROW = df.shape[0] #数据行数=表格行数-1（减表头）\n ROW=48 #如果报错尝试这个 将288变为相应的行数\n DATA_SIZE=48 #数据量 每个街道有DATA_SIZE个月的数据\n SITE_NUM=int(NUM_ROW/DATA_SIZE)#站点个数\n SITE_SIZE=1#分站点预测，所以只能等于1，这是最简便的更改方式\n #if(num_site>SITE_NUM):\n # print(\"本文件所有站点预测完毕\")\n # exit(0)\n for num_site in range(1,SITE_NUM+1):\n\n hourlyData=df.values[DATA_SIZE*(num_site-1):DATA_SIZE*num_site,3]\n\n\n hourlyData=hourlyData.astype('float32')#写成科学计数法（float32）\n Mon=df.values[:,2]#获得表格所有月份Mon\n\n\n X=[] #输入 根据delay个数据成一组作为X，得到输出y （一个X有delay个数据，有多组X，所以是二维的,再加上多个街道，变成三维的了）\n y=[] #输出\n pre=1 #不知道这是干啥的\n\n #设置绘图时的中文显示（需安装黑体字体）\n _rebuild()\n mpl.rcParams['font.sans-serif']=[u'SimHei']\n mpl.rcParams['axes.unicode_minus']=False\n #=================================================================================\n\n #输出一下得到的案件量个数\n print('aqi data length:', len(hourlyData))\n\n #立案量，转换成SITE_SIZE行DATA_SIZE列，每行代表不同的街道，每个街道有DATA_SIZE个月的数据\n hourlyData = hourlyData.reshape(SITE_SIZE,DATA_SIZE)\n\n #转置 现在每一列代表不同的街道了\n hourlyData = hourlyData.T\n\n #====================================以下为对训练样本的预处理 归一化、打乱、分训练测试组等================================\n #在进行运算之前可以对数据进行归一化，进而降低loss\n scaler = MinMaxScaler(feature_range=(0.01, 1))#这个归一化也有影响 比如要是（0，1）就无法拟合\n #scaler = MinMaxScaler()#这种有的就无法拟合\n hourlyData = scaler.fit_transform(hourlyData)\n\n #此处应该是将时间序列转换为x,y的监督学习问题\n for d in range(delay,len(hourlyData)-pre+1):\n X_one=hourlyData[d-delay:d,:]#二维\n X_one=X_one.reshape((1,X_one.shape[0],X_one.shape[1]))#转三维 1页1行10列\n y_one=hourlyData[d,:]\n X.append(X_one)\n y.append(y_one)\n X=np.array(X).reshape((len(X),delay,SITE_SIZE)) #reshape页、行、列 三维\n y=np.array(y) #二维\n\n\n '''\n print(\"X\")\n print(X)\n print(\"y\")\n print(y)\n '''\n '''\n #shuffle data\n #随机排列x,y,Mon，但一一对应\n random.seed(10)\n random.shuffle(X)\n random.seed(10)\n random.shuffle(y)\n random.seed(10)\n random.shuffle(Mon)\n \n #split dataset\n #将数据分成训练组和测试组 前80%的数据作为训练，后20%的数据作为测试\n trLen=int(0.8*X.shape[0])\n train_set_x=X[:trLen,:]\n train_set_y=y[:trLen]\n test_set_x = X[trLen:,:]\n test_set_y=y[trLen:]\n '''\n\n\n Mon = np.linspace(delay+1,DATA_SIZE,DATA_SIZE-delay)\n #Mon = np.linspace(1,DATA_SIZE,DATA_SIZE)\n #shuffle data\n\n '''\n np.random.seed(10)\n np.random.shuffle(X)\n np.random.seed(10)\n np.random.shuffle(y)\n np.random.seed(10)\n np.random.shuffle(Mon)\n '''\n #shuffle data\n index=np.arange(DATA_SIZE-delay)\n np.random.shuffle(index)\n X=X[index,:,:]\n y=y[index]\n Mon=Mon[index]\n\n\n #split dataset\n trLen=int(0.8*X.shape[0])\n train_set_x=X[:trLen,:]\n train_set_y=y[:trLen]\n test_set_x = X[trLen:,:]\n test_set_y=y[trLen:]\n\n\n\n '''\n print(\"train_set_x\")\n print(train_set_x)\n print(\"train_set_y\")\n print(train_set_y)\n print(\"test_set_x\")\n print(test_set_x)\n print(\"test_set_y\")\n print(test_set_y)\n '''\n\n #====================================================================\n\n #==========================================本模块采用LSTM建模================================================\n # build the model: 2 stacked LSTM\n print('Build model...')\n input_shape = (delay,SITE_SIZE) #每delay个数据预测一个 输入格式为delay行SITE_SIZE列的矩阵\n main_input = Input(shape=input_shape, name='main_input')\n\n rnn_out = LSTM(my_node, return_sequences=True,consume_less = 'gpu')(main_input)\n x = LSTM(my_node,consume_less = 'gpu')(rnn_out)\n\n #4、在后面连接一个隐层，输入为rnn输出和时间信息，采用sigmoid激活\n x = Dense(500, activation='relu')(x)\n #5、添加一个dropout层防止过拟合\n x = Dropout(0.5)(x)\n #6、后面添加一个隐层，采用relu作为激活函数，根据relu的特性，可以直接输出实数\n #x = Dense(100, activation='relu')(x)\n #7、继续使用relu输出最终预测值\n loss = Dense(SITE_SIZE, activation='relu', name='main_output')(x)\n\n #使用刚才创建的图生成模型\n model = Model(input=[main_input], output=[loss])\n\n solver = Adam(lr=my_lr) #学习率为0.001 一条直线的有可能是学习率过大的缘故\n model.compile(optimizer=solver,\n loss={'main_output': 'mape'} ) #optimizer优化器选择Adam 回头可以再尝试一下RMSprop\n #损失函数loss用的mape?\n\n #=============================================================================================\n\n\n\n #定义精度计算公式\n def cal_acc(pre,real):\n pre = scaler.inverse_transform(pre)\n real = scaler.inverse_transform(real)\n [m,n]=pre.shape\n pre=pre.reshape(m*n,1)\n real=real.reshape(m*n,1)\n acc=np.zeros((4,1))\n acc[0]=np.sqrt(((pre-real)**2).mean())\n acc[1]=(abs(pre-real)).mean()\n acc[2]=(abs(pre-real)/real).mean()\n acc[3]=1-sum((pre-real)**2)/sum((abs(pre-real.mean())+abs(real-real.mean()))**2)\n return acc.transpose()\n\n #把模型写入jason文件中，权重记录在.hdf5中？因为每次的权中事随机的\n model_json = model.to_json()\n model_path = '$8.json'\n model_weight_path = '$8_weights.hdf5'\n with open(model_path, \"w\") as json_file:\n json_file.write(model_json)\n\n #迭代次数为100次\n epoches = 80\n #生成epoches行4列的零矩阵\n acc_tr=np.zeros((epoches,4))\n acc_t=np.zeros((epoches,4))\n history = []\n\n #生成epoches行2列的零矩阵\n #msemae_tr = np.zeros((epoches,2))\n #msemae_t = np.zeros((epoches,2))\n\n #================================================================训练LSTM=====================================\n '''\n def scheduler(epoch):\n # 每隔50个epoch，学习率减小为原来的1/10\n if epoch % 50 == 0 and epoch != 0:\n lr = K.get_value(model.optimizer.lr)\n K.set_value(model.optimizer.lr, lr * 0.1)\n print(\"lr changed to {}\".format(lr * 0.1))\n return K.get_value(model.optimizer.lr)\n '''\n #开始迭代\n for epoch in range(epoches):\n print()\n print('-' * 50)\n print('epoch', epoch)\n #reduce_lr = LearningRateScheduler(scheduler)\n\n if epoch==50:\n solver = Adam(lr=my_lr/10)#降低学习率\n model.compile(optimizer=solver,\n loss={'main_output': 'mape'} )\n '''\n hist = model.fit({'main_input': train_set_x},\n {'main_output': train_set_y},validation_data=(\n {'main_input': test_set_x, },\n {'main_output': test_set_y}\n ),verbose = 1,\n nb_epoch=10, batch_size=16,callbacks=[reduce_lr]) #batch_size待确定 nb_epoch是训练数据遍历的次数\n '''\n hist = model.fit({'main_input': train_set_x},\n {'main_output': train_set_y},validation_data=(\n {'main_input': test_set_x, },\n {'main_output': test_set_y}\n ),verbose = 1,\n nb_epoch=10, batch_size=my_batch_size)\n acc_tr[epoch,:]=cal_acc(model.predict([train_set_x]),train_set_y)\n acc_t[epoch,:]=cal_acc(model.predict([test_set_x]),test_set_y)\n #msemae_tr[epoch,:] = cal_msemae_tr()\n #msemae_t[epoch,:] = cal_msemae_t()\n history.extend(hist.history.values())\n history = np.array(history).reshape((-1,1))\n if model_weight_path:\n if os.path.exists(model_weight_path):\n os.remove(model_weight_path)\n model.save_weights(model_weight_path) # eg: model_weight.h5\n #========================================================================================================\n\n #输出精度acc\n a=[acc_tr[:,3],acc_t[:,3]]\n a=np.array(a)\n a=a.T\n\n #定义预测值\n trainPredict = model.predict(train_set_x)\n testPredict = model.predict(test_set_x)\n\n #反归一化：如在开始时进行了归一化则取消以下代码的注释\n hourlyData = scaler.inverse_transform(hourlyData)\n trainPredict = scaler.inverse_transform(trainPredict)\n train_set_y = scaler.inverse_transform(train_set_y)\n testPredict = scaler.inverse_transform(testPredict)\n test_set_y = scaler.inverse_transform(test_set_y)\n\n #将数据按站点分为SITE_SIZE组\n site_names=[] #站点数据列表\n site_cnames=[] #站点名字列表\n #site_cnames.append(df.at[(num_site-1)*DATA_SIZE, u'事发街道'])\n site_cnames.append(\"站点\"+str(num_site))#隐藏站点名称\n site_names.append(hourlyData[:,0])\n\n\n #作图：立案量vs时间\n plt.figure(figsize=(16,9))\n layout_num = 0\n for i in range(0,SITE_SIZE):\n if(layout_num==1):\n layout_num=0\n plt.figure(figsize=(16, 9))\n plt.suptitle(u'各地点按月立案数量')\n subplot = plt.subplot(1, 1, layout_num + 1)\n site = site_names[i]\n plt.plot(site_names[i])\n plt.xlabel(u'时间')\n plt.ylabel(u'立案量')\n plt.legend(labels=[u'立案量'],loc = 'best')\n subplot.set_title(str(NAME)+'-'+site_cnames[i]+u\"-当前打乱的原始数据\")\n plt.tight_layout()\n layout_num = layout_num + 1\n plt.savefig(F_NAME+NAME+'-'+str(site_cnames[0])+\"-当前打乱的原始数据.png\")\n\n #作图：精度vs迭代次数\n plt.figure(figsize=(16,9))\n #x2 = np.linspace(1,100,len(a[:,0]))\n #x21 = np.linspace(1,100,len(a[:,1]))\n plt.plot(a[:,0])\n plt.plot(a[:,1])\n plt.xlabel(u'迭代次数')\n plt.ylabel(u'精度')\n plt.title(str(NAME)+'-'+str(site_cnames[0])+u'-测试/训练精度与时间对比')\n plt.legend(labels = [u'训练精度',u'测试精度'],loc ='best')\n plt.savefig(F_NAME+NAME+'-'+str(site_cnames[0])+\"-精度.png\")\n\n #作图：真实值&预测值vs时间\n #训练数据组\n\n plt.figure(figsize=(16,9))\n #plt.suptitle(u'分站点预测/实际值对比（训练数据）')\n layout_num = 0\n for i in range(0,SITE_SIZE):\n if(layout_num==6):\n layout_num=0\n plt.figure(figsize=(16, 9))\n plt.suptitle(u'分站点预测/实际值对比（训练数据）')\n subplot = plt.subplot(1, 1, layout_num + 1)\n #site = site_names[i]\n plt.plot(trainPredict[:, i])\n plt.plot(train_set_y[:, i])\n plt.xlabel(u'数据编号')\n plt.ylabel(u'数值')\n plt.legend(labels=[u'预测数据',u'实际数据'])\n subplot.set_title(str(NAME)+'-'+site_cnames[i]+u'-预测/实际值对比（训练数据）')\n plt.tight_layout()\n layout_num = layout_num + 1\n plt.savefig(F_NAME+NAME+'-'+str(site_cnames[0])+\"-训练预测.png\")\n\n #测试数据组\n plt.figure(figsize=(16,9))\n #plt.suptitle(u'分站点预测/实际值对比（测试数据）')\n layout_num = 0\n for i in range(0,SITE_SIZE):\n if(layout_num==6):\n layout_num=0\n plt.figure(figsize=(16, 9))\n plt.suptitle(u'分站点预测/实际值对比（测试数据）')\n subplot = plt.subplot(1, 1, layout_num + 1)\n #site = site_names[i]\n plt.plot(testPredict[:, i])\n plt.plot(test_set_y[:, i])\n plt.xlabel(u'数据编号')\n plt.ylabel(u'数值')\n plt.legend(labels=[u'预测数据',u'实际数据'])\n subplot.set_title(str(NAME)+'-'+site_cnames[i]+u'-预测/实际��对比（测试数据）')\n plt.tight_layout()\n layout_num=layout_num+1\n plt.savefig(F_NAME+NAME+'-'+str(site_cnames[0])+\"-测试预测.png\")\n #作图：MSE与MAE MAPE\n plt.figure(figsize=(16, 9))\n mseTrain = plt.subplot(321)\n maeTrain = plt.subplot(322)\n mseTest = plt.subplot(323)\n maeTest = plt.subplot(324)\n\n mapeTrain = plt.subplot(325)\n mapeTest = plt.subplot(326)\n\n\n mse=[acc_tr[:,0],acc_t[:,0]]\n mse=np.array(mse)\n mse=mse.T\n\n mae=[acc_tr[:,1],acc_t[:,1]]\n mae = np.array(mae)\n mae = mae.T\n\n mape=[acc_tr[:,2],acc_t[:,2]]\n mape = np.array(mape)\n mape = mape.T\n\n if mape[-1,-1]<=0.2:\n shutil.copyfile(F_NAME+NAME+u'-站点'+str(num_site)+'-当前打乱的原始数据.png', u'../LSTM分站点结果分析/参数'+str(PARAM_NUM)+u'极端数据图表/'+'【准】'+\n NAME+'-站点'+str(num_site)+'-当前打乱的原始数据.png')\n f = open(u'../LSTM分站点结果分析/参数'+str(PARAM_NUM)+'-分站点结果分析.txt','a')\n f.write('\\n站点'+str(num_site)+'准')\n elif mape[-1,-1]>=0.5:\n shutil.copyfile(F_NAME+NAME+u'-站点'+str(num_site)+'-当前打乱的原始数据.png', u'../LSTM分站点结果分析/参数'+str(PARAM_NUM)+u'极端数据图表/'+'【不准】'+\n NAME+'-站点'+str(num_site)+'-当前打乱的原始数据.png')\n f = open(u'../LSTM分站点结果分析/参数'+str(PARAM_NUM)+'-分站点结果分析.txt','a')\n f.write('\\n站点' + str(num_site) + '不准')\n\n msemaeList = [mseTrain,maeTrain,mseTest,maeTest,mapeTrain,mapeTest]\n msemaeData = [mse[:,0],mae[:,0],mse[:,1],mae[:,1],mape[:,0],mape[:,1]]\n msemaeLabels = [u'训练MSE',u'训练MAE',u'测试MSE',u'测试MAE','训练MAPE','测试MAPE']\n def plot4(i):\n plt.plot(msemaeData[i])\n plt.xlabel(u'迭代次数')\n plt.ylabel(u'误差值')\n for i in range(0,6):\n plt.sca(msemaeList[i])\n plot4(i)\n plt.legend(labels = [msemaeLabels[i]],loc = 'best')\n msemaeList[i].set_title(str(NAME)+'-'+str(site_cnames[0])+msemaeLabels[i])\n plt.suptitle(str(NAME)+'-'+str(site_cnames[0])+u'-训练与测试MSE/MAE/MAPE对比')\n plt.tight_layout()\n\n plt.savefig(F_NAME+NAME+'-'+str(site_cnames[0])+\"-误差.png\")\n #plt.show()\n\n\n '''\n #将预测数据输出为csv文件\n months = np.linspace(delay+1,DATA_SIZE,DATA_SIZE-10)\n months = np.array(months)\n with open(u\"输出文件：\"+FILE_NAME, \"w\", newline=\"\",encoding=\"utf-8-sig\") as datacsv:\n csvwriter = csv.writer(datacsv, dialect=(\"excel\"))\n first_row = [u'月份/地点']\n for i in range(0,SITE_SIZE):\n first_row.append(site_cnames[i]+u'预测')\n first_row.append(site_cnames[i]+u'实际')\n csvwriter.writerow(first_row)\n for i in range(0, trLen):\n train_row = [months[i]]\n for j in range(0, SITE_SIZE):\n train_row.append(trainPredict[i, j])\n train_row.append(train_set_y[i, j])\n csvwriter.writerow(train_row)\n for i in range(0, DATA_SIZE-trLen-10):\n test_row = [months[i + trLen]]\n for j in range(0,SITE_SIZE):\n test_row.append(testPredict[i,j])\n test_row.append(test_set_y[i,j])\n csvwriter.writerow(test_row)\n '''\n\n\n #将预测数据输出为csv文件\n #months = np.linspace(delay+1,num_Data,num_Data-10)\n months = Mon\n months = np.array(months)\n\n with open(u\"../LSTM分站点输出预测数据文件/参数\"+str(PARAM_NUM)+\"/\"+NAME+\"/预测输出：\"+NAME+\"-站点\"+str(num_site)+'.csv', \"w\", newline=\"\",encoding=\"utf-8-sig\") as datacsv:\n csvwriter = csv.writer(datacsv, dialect=(\"excel\"))\n first_row = [u'月份/地点']\n for i in range(0,SITE_SIZE):\n first_row.append(site_cnames[i]+u'预测')\n first_row.append(site_cnames[i]+u'实际')\n csvwriter.writerow(first_row)\n for i in range(0, trLen):\n train_row = [months[i]]\n for j in range(0, SITE_SIZE):\n train_row.append(trainPredict[i, j])\n train_row.append(train_set_y[i, j])\n csvwriter.writerow(train_row)\n for i in range(0, DATA_SIZE-trLen-delay):\n test_row = [months[i + trLen]]\n for j in range(0,SITE_SIZE):\n test_row.append(testPredict[i,j])\n test_row.append(test_set_y[i,j])\n csvwriter.writerow(test_row)\n\n\n\n", "sub_path": "code/LSTM(分站点预测-自动步骤1).py", "file_name": "LSTM(分站点预测-自动步骤1).py", "file_ext": "py", "file_size_in_byte": 20689, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.makedirs", "line_number": 53, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 54, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 55, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.font_manager._rebuild", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pylab.mpl.rcParams", "line_number": 83, "usage_type": "attribute"}, {"api_name": "matplotlib.pylab.mpl", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pylab.mpl.rcParams", "line_number": 84, "usage_type": "attribute"}, {"api_name": "matplotlib.pylab.mpl", "line_number": 84, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 98, "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.linspace", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 153, "usage_type": "attribute"}, {"api_name": "keras.layers.Input", "line_number": 185, "usage_type": "call"}, {"api_name": "keras.layers.recurrent.LSTM", "line_number": 187, "usage_type": "call"}, {"api_name": "keras.layers.recurrent.LSTM", "line_number": 188, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 191, "usage_type": "call"}, {"api_name": "keras.layers.core.Dropout", "line_number": 193, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 197, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 200, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 236, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 283, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 285, "usage_type": "call"}, {"api_name": "os.path", "line_number": 285, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 292, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 315, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 315, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 320, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 320, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 321, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 321, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 322, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 322, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 324, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 324, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 325, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 325, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 326, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 326, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 327, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 327, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 329, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 329, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 331, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 331, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 334, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 334, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 337, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 337, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 338, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 338, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 339, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 339, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 340, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 340, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 341, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 341, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 342, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 342, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 343, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 343, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 348, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 348, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 354, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 354, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 355, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 355, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 356, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 356, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 358, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 358, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 359, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 359, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 360, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 360, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 361, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 361, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 362, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 362, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 364, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 364, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 366, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 366, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 369, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 369, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 375, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 375, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 376, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 376, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 377, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 377, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 379, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 379, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 380, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 380, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 381, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 381, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 382, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 382, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 383, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 383, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 385, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 385, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 387, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 387, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 389, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 389, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 390, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 390, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 391, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 391, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 392, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 392, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 393, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 393, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 395, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 395, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 396, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 396, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 400, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 404, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 408, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 412, "usage_type": "call"}, {"api_name": "shutil.copyfile", "line_number": 417, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 426, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 426, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 427, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 427, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 428, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 428, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.sca", "line_number": 430, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 430, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 432, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 432, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 434, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 434, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 435, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 435, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 437, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 437, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 470, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 473, "usage_type": "call"}]} +{"seq_id": "613954083", "text": "# author:　zhang_ren@bentley.edu\n# library\nimport requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\n\n# webpage contain all the data\nurl = \"http://pokemondb.net/pokedex/all\"\n\n# get the web content for pokemon stats\npage = BeautifulSoup(requests.get(url).text, 'html.parser')\n\n# parse the data and store in a pandas DataFrame\ndata_table = page.findAll(\"tr\")\ndata = []\nfor row in data_table[1:]:\n\tnew_row = []\n\tfor data_cell in row.findAll(\"td\"):\n\t\t# this logic here is to try to hanle the situation when a pokemon has two type\n\t\t# I am adding a space in between the two type names\n\t\tif len(data_cell) > 1 and data_cell.find(\"a\"):\n\t\t\tnew_row.append(\" \".join([type_cell.text.strip() for type_cell in data_cell]))\n\t\telse: \n\t\t\tnew_row.append(data_cell.text.strip())\n\tdata.append(new_row)\ndata = pd.DataFrame(data)\n\n# rename columns\ndata.columns = [\"Id\", \"Name\", \"Type\", \"Total\", \"HP\", \"Attack\", \"Defense\", \"Sp..Atk\", \"Sp..Def\", \"Speed\"]\n\n# sepearte out the two types and drop the type\ndata[\"Type1\"] = data[\"Type\"].map(lambda x: x.split()[0])\ndata[\"Type2\"] = data[\"Type\"].map(lambda x: x.split()[1] if len(x.split()) > 1 else \"\")\ndata.drop(labels = \"Type\", axis = 1, inplace = True)\n\n# take care of data types\ndata = data.convert_objects(convert_numeric = True)\ndata[\"Name\"] = data[\"Name\"].map(lambda x: x.encode(\"ascii\", errors = \"ignore\")).astype(str)\ndata[\"Type1\"] = data[\"Type1\"].map(lambda x: x.encode(\"ascii\", errors = \"ignore\")).astype(str)\ndata[\"Type2\"] = data[\"Type2\"].map(lambda x: x.encode(\"ascii\", errors = \"ignore\")).astype(str)\n\n# add generation info\n# generate generations, noticed that there is clear boundary in id for different generation\n# see http://pokemondb.net/pokedex/national for the boundary reference\n# consider yourself a expert in python comprehension method if you understand the following two lines\ngeneration = [i for i in range(1,7) for _ in [151, 251-151, 386-251, 494-386, 650-494, 721-650]]\ngeneration = { (i+1): generation[i] for i in range(len(generation))}\ndata[\"Generation\"] = data[\"Id\"].map(generation)\n\n# save to csv file\ndata.to_csv(\"Pokemon.csv\", index = False)\n\n# for lengendary, I added the value manually, see http://pokemondb.net/pokebase/44682/what-defines-a-legendary-pokemon", "sub_path": "MA710/kohonen_map_SOM/python_scrape.py", "file_name": "python_scrape.py", "file_ext": "py", "file_size_in_byte": 2228, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 11, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "487456822", "text": "from django.db import models\n\n\nfrom django.db.models import Q\nfrom user.models import User\n\nclass Swiped(models.Model):\n '''滑动记录'''\n FLAGS = (\n ('superlike', '超级喜欢'),\n ('like', '喜欢'),\n ('dislike', '不喜欢'),\n )\n\n uid = models.IntegerField(verbose_name='滑动者的 UID')\n sid = models.IntegerField(verbose_name='被滑动者的 UID')\n flag = models.CharField(max_length=16, choices=FLAGS)\n time = models.DateTimeField(auto_now_add=True)\n\n @classmethod\n def mark(cls, uid, sid, flag):\n '''标记一次滑动'''\n if flag in ['superlike', 'like', 'dislike']:\n defaults = {'flag': flag}\n cls.objects.update_or_create(uid=uid, sid=sid, defaults=defaults)\n obj = cls.objects.update_or_create(uid=uid, sid=sid, defaults=defaults)\n return obj\n\n @classmethod\n def is_liked(cls, uid, sid):\n '''检查是否喜欢过某人'''\n return cls.objects.filter(uid=uid, sid=sid,\n flag__in=['like', 'superlike']).exists()\n @classmethod\n def like_me(cls,uid):\n return cls.objects.filter(sid=uid,flag__in=['like', 'superlike'])\n\n\nclass Friend(models.Model):\n '''好友'''\n uid1 = models.IntegerField()\n uid2 = models.IntegerField()\n\n @classmethod\n def be_friends(cls, uid1, uid2):\n '''成为好友'''\n uid1, uid2 = (uid1, uid2) if uid1 < uid2 else (uid2, uid1)\n cls.objects.get_or_create(uid1=uid1, uid2=uid2)\n\n @classmethod\n def is_friend(cls, uid1, uid2):\n '''检查是否是好友关系'''\n condition = Q(uid1=uid1, uid2=uid2) | Q(uid1=uid2, uid2=uid1)\n return cls.objects.filter(condition).exists()\n\n @classmethod\n def break_off(cls, uid1, uid2):\n '''绝交'''\n uid1, uid2 = (uid1, uid2) if uid1 < uid2 else (uid2, uid1)\n try:\n cls.objects.filter(uid1=uid1, uid2=uid2).delete()\n except cls.DoesNotExist:\n pass\n\n @classmethod\n def friends(cls, uid):\n condition = Q(uid1=uid) | Q(uid2=uid)\n relations = cls.objects.filter(condition) # 过滤出我的好友关系\n\n friend_id_list = []\n for r in relations:\n friend_id = r.uid2 if r.uid1 == uid else r.uid1\n friend_id_list.append(friend_id)\n\n return User.objects.filter(id__in=friend_id_list)\n", "sub_path": "social/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2396, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.db.models.Model", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "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.DateTimeField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 39, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 42, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 42, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 53, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 67, "usage_type": "call"}, {"api_name": "user.models.User.objects.filter", "line_number": 75, "usage_type": "call"}, {"api_name": "user.models.User.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "user.models.User", "line_number": 75, "usage_type": "name"}]} +{"seq_id": "270937804", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ('membership', '0006_auto_20161028_1121'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='SubscriptionBuddy',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('paid_until', models.DateTimeField(null=True, blank=True)),\n ('level_override', models.ForeignKey(blank=True, to='membership.Level', null=True)),\n ],\n ),\n migrations.AlterField(\n model_name='subscription',\n name='amount',\n field=models.DecimalField(default=0, help_text=b'If zero, this membership will always be active until deleted.', max_digits=30, decimal_places=2),\n ),\n migrations.AddField(\n model_name='subscriptionbuddy',\n name='subscription',\n field=models.ForeignKey(to='membership.Subscription'),\n ),\n migrations.AddField(\n model_name='subscriptionbuddy',\n name='user',\n field=models.ForeignKey(to=settings.AUTH_USER_MODEL),\n ),\n ]\n", "sub_path": "membership/migrations/0007_auto_20161122_1326.py", "file_name": "0007_auto_20161122_1326.py", "file_ext": "py", "file_size_in_byte": 1388, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "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.swappable_dependency", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 11, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 34, "usage_type": "name"}, {"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.conf.settings.AUTH_USER_MODEL", "line_number": 37, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 37, "usage_type": "name"}]} +{"seq_id": "525834708", "text": "from setuptools import setup\nimport os\n\nconfig = {\n 'name': 'askgeo-consumer',\n 'description': 'AskGeo Consumer',\n 'long_description': os.path.join(os.path.dirname(__file__), 'README.md'),\n 'author': 'Harun Yasar',\n 'url': 'https://github.com/harunyasar/askgeo-consumer',\n 'author_email': 'harunyasar@mail.com',\n 'version': '0.1.0',\n 'install_requires': [],\n 'packages': [],\n 'scripts': []\n}\n\nsetup(**config)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 439, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "352164510", "text": "import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\nimport os\r\nimport fnmatch\r\nfrom sklearn.compose import ColumnTransformer\r\nfrom scipy.stats import yeojohnson\r\nfrom tensorflow.python.keras.optimizer_v2.rmsprop import RMSProp\r\nfrom math import sqrt\r\nfrom numpy import concatenate\r\nfrom matplotlib import pyplot\r\nfrom pandas import read_csv, DataFrame, concat\r\nfrom sklearn.preprocessing import MinMaxScaler, LabelEncoder, PowerTransformer\r\nfrom sklearn.metrics import mean_squared_error\r\nfrom keras.models import Sequential, load_model\r\nfrom keras.layers import Dense, RepeatVector, LSTM, Input, TimeDistributed, Activation, Dropout\r\nfrom keras.optimizers import SGD\r\nfrom sklearn.compose import ColumnTransformer\r\nnp.set_printoptions(suppress=True)\r\n\r\n#variables\r\npowhr_start = 5\r\npowhr_end = 20\r\nshift_days = 3\r\nhoursteps = powhr_end-powhr_start+1 #(16)\r\ntimesteps = shift_days*hoursteps #hours step\r\ndata_dim = 7\r\nout_dim = 1\r\nn_model = 10\r\n\r\ndata_dir = '../Data'\r\nseason_mod = 'all_1102_f7'\r\ndate_start = '10190901'\r\ndate_end = '30191201'\r\nerr_date_list = ['20190912', '20191122', '20191130', '20191028', '20191107', '20191108', '20191109', '20191110', '20191111', '20191112', '20200214', '20200307', '20200308', '20200309', '20200310', '20200328', '20200329', '20200625', '20200809']\r\n\r\n#############################################\r\n# 종관기상관측\r\n#############################################\r\ndef get_weather():\r\n # pow 파일 load\r\n file_list = os.listdir(data_dir)\r\n print(len(file_list))\r\n for filename in os.listdir(data_dir):\r\n if fnmatch.fnmatch(filename, 'OBS_ASOS_TIM_*.csv'):\r\n print(filename)\r\n\r\n # load csv data\r\n dataset = read_csv(data_dir+'/'+filename, encoding='CP949')\r\n dataset.drop(['지점','지점명'], axis=1, inplace=True)\r\n dataset.drop(['기온 QC플래그','강수량 QC플래그','풍속 QC플래그','풍향 QC플래그','습도 QC플래그'], axis=1, inplace=True)\r\n dataset.drop(['현지기압 QC플래그','해면기압 QC플래그','일조 QC플래그','지면온도 QC플래그'], axis=1, inplace=True)\r\n dataset.drop(['5cm 지중온도(°C)','10cm 지중온도(°C)','20cm 지중온도(°C)','30cm 지중온도(°C)'], axis=1, inplace=True)\r\n dataset.drop(['3시간신적설(cm)','일사(MJ/m2)','운형(운형약어)','지면상태(지면상태코드)','현상번호(국내식)'], axis=1, inplace=True)\r\n\r\n # set column name\r\n dataset.columns = ['ymdhms', 'temprt', 'rain', 'wnd_spd', 'wnd_dir', 'humdt','steampressr', 'dewpnt', 'pressr','seapressr','sunshine','snow','cloud','cloud2','mincloud','visiblt','grd_temprt']\r\n\r\n # prioirty sort (피어슨상관계수)\r\n dataset = dataset[['ymdhms','sunshine','humdt','wnd_spd','visiblt','cloud2', 'cloud','grd_temprt','wnd_dir','dewpnt','steampressr','temprt','mincloud','rain','pressr','seapressr','snow']]\r\n\r\n # set NA data (관측값 0이 누적되어 결측된 경우. 0으로 세팅)\r\n dataset['rain'].fillna(0, inplace=True) #강수량\r\n dataset['sunshine'].fillna(0, inplace=True) #일조\r\n dataset['snow'].fillna(0, inplace=True) #적설량\r\n\r\n #일시 패턴 변환(2019-08-20 5:00 -> 2019082005)\r\n dataset['ymdhms'] = dataset['ymdhms'].str[0:4]+dataset['ymdhms'].str[5:7]+dataset['ymdhms'].str[8:10]+dataset['ymdhms'].str[11:13]\r\n # pow측정값 중 결측값 많은 일자 제거\r\n dataset = dataset[(dataset['ymdhms'].str[0:8]>=date_start) & (dataset['ymdhms'].str[0:8]=str(powhr_start).rjust(2, '0')) &(dataset['ymdhms'].str[-2:]<=str(powhr_end))]\r\n dataset = dataset.interpolate(method='linear')# 결측값 보간\r\n \r\n # save file (test용)\r\n dataset.to_csv(data_dir+\"/weather.csv\",mode='w',index=False)\r\n\r\n # normalization\r\n dataset.drop(['ymdhms'], axis=1, inplace=True)\r\n dataset = dataset.astype('float32')\r\n dataset = dataset.interpolate(method='linear')\r\n \r\n #YEO-JOHNSON transform\r\n yeo_df = yeo_johnson_transform(dataset)\r\n \r\n #insert feature (test)\r\n yeo_df.insert(2, 'temp_press', yeo_df['temprt']-yeo_df['steampressr'], True)\r\n yeo_df.insert(2, 'sunshine_humdt', abs(yeo_df['sunshine'])-(yeo_df['humdt']*(2.1)), True)#0.35\r\n \r\n sc = MinMaxScaler(feature_range = (0, 1))#scale\r\n scaled_weather = sc.fit_transform(yeo_df.values)\r\n weather = pd.DataFrame(scaled_weather, columns=yeo_df.columns, index=list(yeo_df.index.values))\r\n print(\"before : \", weather.shape)\r\n weather = weather.iloc[:, 0:data_dim] #feature size 조절\r\n print(\"after : \", weather.shape)\r\n \r\n return weather\r\n\r\n#############################################\r\n# 태양광 전력\r\n#############################################\r\ndef get_pow():\r\n\r\n # pow 파일 load\r\n dir_path = data_dir+\"/pow_24/UR00000126_csv\"\r\n file_list = os.listdir(dir_path)\r\n print(len(file_list))\r\n hrPow = [] \r\n\r\n # pow측정값 에러가 큰 일자 제거\r\n for filename in file_list:\r\n if (filename[:-4] not in err_date_list):\r\n if ((filename[:-4]>=date_start) & (filename\", \"\")\n\n\nclass SpotbugsParser:\n def __init__(self, spotbugs_xml, test):\n self.spotbugs_xml = spotbugs_xml\n\n dupes = dict()\n find_date = None\n\n logging.debug(\"Spotbugs parser initialization\")\n\n bugs_details = self.extract_bugs_details()\n\n context = etree.iterparse(self.spotbugs_xml, events=('end',), tag='BugInstance')\n\n for _, item in context:\n title = item.findtext('ShortMessage')\n description = item.findtext('LongMessage')\n category = item.get('category')\n issue_type = item.get('type')\n severity = item.get('priority')\n classname = item.find('Class').get('classname')\n filename = item.find('Class').find('SourceLine').get('sourcefile')\n file_path = item.find('Class').find('SourceLine').get('sourcepath')\n line = item.find('Class').find('SourceLine').findtext('Message')\n steps_to_reproduce = '\\n'*2\n\n # TODO: rewrite this to avoid errors\n for i, element in enumerate(item.findall('Method')):\n steps_to_reproduce += f\"Classname: {classname}\\t{element.findtext('Message')}\\t\"\n try:\n steps_to_reproduce += f\"{sanitize(item.findall('SourceLine')[i].findtext('Message'))}\"\n except IndexError:\n pass\n\n details = bugs_details.get(issue_type)\n\n if details:\n description += f'\\n\\n Details: {md(details)}'\n\n severity_level = SEVERITY_TYPE.get(int(severity), \"\")\n\n dupe_key = hashlib.md5(f'{title} {issue_type} {category}'.encode('utf-8')).hexdigest()\n\n if file_path:\n dupe_key += f' {file_path}'\n\n if filename:\n title += f' in {filename}'\n\n if dupe_key not in dupes:\n dupes[dupe_key] = Finding(title=title, tool=category.lower().replace(\" \", \"_\"),\n active=False, verified=False, description=description,\n severity=severity_level, numerical_severity=severity,\n mitigation=False, impact=False, references=False,\n file_path=file_path, line=line,\n url='N/A', date=find_date,\n steps_to_reproduce=f'
{issue_type} issue {steps_to_reproduce}
',\n static_finding=True)\n else:\n dupes[dupe_key].finding['steps_to_reproduce'].append(f\"
{steps_to_reproduce}
\")\n\n item.clear()\n while item.getprevious() is not None:\n del item.getparent()[0]\n\n del context\n\n self.items = dupes.values()\n\n logging.debug(\"Spotbugs output parsing done\")\n\n def extract_bugs_details(self):\n context = etree.iterparse(self.spotbugs_xml, events=('end',), tag='BugPattern')\n\n details = dict()\n for _, item in context:\n details[item.get(\"type\")] = item.findtext(\"Details\")\n item.clear()\n while item.getprevious() is not None:\n del item.getparent()[0]\n\n del context\n return details\n", "sub_path": "dusty/data_model/spotbugs/parser.py", "file_name": "parser.py", "file_ext": "py", "file_size_in_byte": 4308, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "xml.sax.saxutils.unescape", "line_number": 27, "usage_type": "call"}, {"api_name": "xml.sax.saxutils", "line_number": 27, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 37, "usage_type": "call"}, {"api_name": "lxml.etree.iterparse", "line_number": 41, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 41, "usage_type": "name"}, {"api_name": "markdownify.markdownify", "line_number": 66, "usage_type": "call"}, {"api_name": "dusty.constants.SEVERITY_TYPE.get", "line_number": 68, "usage_type": "call"}, {"api_name": "dusty.constants.SEVERITY_TYPE", "line_number": 68, "usage_type": "name"}, {"api_name": "hashlib.md5", "line_number": 70, "usage_type": "call"}, {"api_name": "dusty.data_model.canonical_model.DefaultModel", "line_number": 79, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 98, "usage_type": "call"}, {"api_name": "lxml.etree.iterparse", "line_number": 101, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 101, "usage_type": "name"}]} +{"seq_id": "88582222", "text": "\"\"\"\nhttp://datahacker.rs/010-how-to-align-faces-with-opencv-in-python/\n\"\"\"\n\nfrom PIL import Image\nimport numpy as np\nimport torch\nimport imutils\nimport cv2\n\n\ndef align_face(frame, face_coords, landmarks):\n landmarks = np.array(landmarks)\n \n roi = { \n 'nose': slice(27, 31),\n 'nose_point': slice(30, 31),\n 'nostril': slice(31, 36),\n 'eye1': slice(36, 42),\n 'eye2': slice(42, 48)\n }\n\n def get_roi_mid_point(roi):\n x, y, w, h = cv2.boundingRect(landmarks[roi])\n mid_x = x + w // 2\n mid_y = y + h // 2\n return mid_x, mid_y\n\n left_eye = get_roi_mid_point(roi['eye1'])\n right_eye = get_roi_mid_point(roi['eye2'])\n\n left_eye_x, left_eye_y = left_eye\n right_eye_x, right_eye_y = right_eye\n\n delta_x = right_eye_x - left_eye_x\n delta_y = right_eye_y - left_eye_y\n\n try:\n angle = np.arctan(delta_y/delta_x)\n angle = (angle * 180) / np.pi\n except ZeroDivisionError:\n angle = 0\n \n x1, y1, x2, y2 = face_coords\n img = frame[:, y1: y2, x1: x2].permute(1, 2, 0).numpy()\n\n # Width and height of the image\n h, w = img.shape[:2]\n # Calculating a center point of the image\n # Integer division \"//\"\" ensures that we receive whole numbers\n center = (w // 2, h // 2)\n # Defining a matrix M and calling\n # cv2.getRotationMatrix2D method\n M = cv2.getRotationMatrix2D(center, (angle), 1.0)\n # Applying the rotation to our image using the\n # cv2.warpAffine method\n rotated = cv2.warpAffine(img, M, (w, h))\n\n return rotated\n \n # nose = landmarks[roi['nose_point']]\n # if left_eye_y > right_eye_y:\n # A = (right_eye_x, left_eye_y)\n # # Integer -1 indicates that the image will rotate in the clockwise direction\n # direction = -1 \n # else:\n # A = (left_eye_x, right_eye_y)\n # # Integer 1 indicates that image will rotate in the counter clockwise \n # # direction\n # direction = 1 \n\n # # cv2_imshow(rotated)\n # # cv2.imshow('rotated', rotated)\n # # center_pred = x1 + ((x2 - x1) // 2), y1 + ((y2 - y1) // 2)\n # # length_line1 = distance(center_of_forehead, nose)\n # # length_line2 = distance(center_pred, nose)\n # # length_line3 = distance(center_pred, center_of_forehead)\n # # cos_a = cosine_formula(length_line1, length_line2, length_line3)\n # # angle = np.arccos(cos_a)\n # # rotated_point = rotate_point(nose, center_of_forehead, angle)\n # # rotated_point = (int(rotated_point[0]), int(rotated_point[1]))\n # # if is_between(nose, center_of_forehead, center_pred, rotated_point):\n # # angle = np.degrees(-angle)\n # # else:\n # # angle = np.degrees(angle)\n\n # # face = frame[:, y1: y2, x1: x2] \n # # img = frame.permute(1, 2, 0).numpy().astype(dtype=np.uint8)\n # # img = Image.fromarray(img)\n # # img = np.array(img.rotate(-angle))\n # # cv2.circle(img, (int(x), int(y)), 2, (0,255,0), -1)\n # for x, y in landmarks[roi['eye1']]:\n # cv2.circle(img, (int(x), int(y)), 2, (0,255,0), -1)\n # cv2.imshow('img', img)\n # cv2.waitKey(0)\n # print()\n # exit(0)\n\n\ndef align_and_crop_face(frame, face_coords, landmarks):\n face = align_face(frame, face_coords, landmarks) \n \n return torch.from_numpy(face).permute(2, 0, 1)\n", "sub_path": "datasets/face_utils.py", "file_name": "face_utils.py", "file_ext": "py", "file_size_in_byte": 3341, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.arctan", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 40, "usage_type": "attribute"}, {"api_name": "cv2.getRotationMatrix2D", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.warpAffine", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 103, "usage_type": "call"}]} +{"seq_id": "146358408", "text": "#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport numpy as np\nimport pandas as pd\nimport pickle\n\n\n# In[2]:\n\n\ndf = pd.read_csv('/Users/shashankshivam/Desktop/finalmarks.csv')\n\n\n# In[3]:\n\n\ndf.shape\n\n\n# In[4]:\n\n\ndf.head()\n\n\n# In[5]:\n\n\ndf.groupby('field').count()\n\n\n# In[6]:\n\n\ndf = df.drop('Unnamed: 0',axis=1)\n\n\n# In[7]:\n\n\ndf.head()\n\n\n# In[8]:\n\n\nX=df.drop(['field'],1)\nX=X.values\ny=df['field']\ny=y.values\n\n\n# In[9]:\n\n\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n\n\n# In[10]:\n\n\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.metrics import accuracy_score\nfrom sklearn import metrics\nmodel = GaussianNB()\nmodel.fit(X_train,y_train)\nprediction = model.predict(X_test)\n\nprint(metrics.classification_report(y_test, prediction))\nprint(metrics.confusion_matrix(y_test,prediction))\nprint(accuracy_score(y_test, prediction))\naccuracy_score(y_test,prediction)*100\n\n\n# In[14]:\n\n\nt=np.array(np.random.randint(40,101,24))\nfrom sklearn.naive_bayes import GaussianNB\nmodel = GaussianNB()\nmodel.fit(X_train,y_train)\n#prediction = model.predict(t.reshape(1,-1))\n#prediction[0]\npickle.dump(model, open('finalbackend.pkl','wb'))\n\n# Loading model to compare the results\nmodel = pickle.load(open('finalbackend.pkl','rb'))\nprediction=model.predict(t.reshape(1,-1))\nprint(prediction[0])\nprint(t)\n# In[ ]:\n", "sub_path": "dissertation project/finalbackend.py", "file_name": "finalbackend.py", "file_ext": "py", "file_size_in_byte": 1394, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 61, "usage_type": "call"}, {"api_name": "sklearn.naive_bayes.GaussianNB", "line_number": 70, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 74, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 74, "usage_type": "name"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 75, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 75, "usage_type": "name"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 76, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 83, "usage_type": "attribute"}, {"api_name": "sklearn.naive_bayes.GaussianNB", "line_number": 85, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 89, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "134700958", "text": "import matplotlib\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport pandas as pd\nmatplotlib.use('Qt5Agg')\nfrom nilearn import plotting\nfrom matplotlib import pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport seaborn as sns\nimport helper_functions.process_properties as prop\n\n\ndef plot_connectivity(X_conn):\n regions = ['LF','LC','LP','LO','LT','RF','RC','RP','RO','RT']\n conn_matrix = np.zeros((len(regions), len(regions)))\n coords = np.loadtxt('helper_functions/coordinates.txt')\n\n for t in range(len(X_conn)):\n tmp = X_conn\n conn_tmp = pd.DataFrame(np.zeros((len(regions), len(regions))))\n conn_tmp.columns = regions\n conn_tmp.index = regions\n\n for i in regions:\n for a in regions:\n try:\n conn_tmp.loc[i, a] = tmp[i + '_' + a]\n except:\n conn_tmp.loc[i, a] = tmp[a + '_' + i]\n\n conn_matrix = np.array(conn_tmp)\n\n colormap = matplotlib.cm.get_cmap('OrRd')\n norm = matplotlib.colors.Normalize(vmin=0, vmax=0.3)\n\n fig=plotting.plot_connectome(conn_matrix, node_coords=coords, edge_vmin=0, edge_vmax=0.3,\n edge_cmap=colormap, colorbar=True, edge_threshold=None,\n node_color=colormap(norm(conn_matrix.diagonal())),\n display_mode='lzr')\n return fig\n\ndef plot_pca_results(pdf,X3,Y_out):\n fig = plt.figure(figsize=(6, 6))\n ax = Axes3D(fig)\n n = np.where(Y_out == 0)\n ax.scatter(X3[n, 0], X3[n, 1], X3[n, 2], color='blue', label=\"Non-Recovered Patients\")\n n = np.where(Y_out == 1)\n ax.scatter(X3[n, 0], X3[n, 1], X3[n, 2], color='green', label=\"Non-Recovered CMD\")\n n = np.where(Y_out == 2)\n ax.scatter(X3[n, 0], X3[n, 1], X3[n, 2], color='red', label=\"Recovered Patients\")\n n = np.where(Y_out == 3)\n ax.scatter(X3[n, 0], X3[n, 1], X3[n, 2], color='orange', label=\"Healthy Control\")\n plt.title('PCA_allPart_wholeBrain_alpha')\n plt.legend(loc='lower right')\n pdf.savefig(fig)\n plt.close()\n\n fig, ax = plt.subplots(1, 3, figsize=(12, 6))\n fig.suptitle('PCA_allPart_wholeBrain_alpha', size=16)\n\n ax[0].set_title('PC 0 and 1')\n n = np.where(Y_out == 0)\n ax[0].scatter(X3[n, 0], X3[n, 1], color='blue', label=\"Non-Recovered Patients\")\n n = np.where(Y_out == 1)\n ax[0].scatter(X3[n, 0], X3[n, 1], color='green', label=\"Non-Recovered CMD\")\n n = np.where(Y_out == 2)\n ax[0].scatter(X3[n, 0], X3[n, 1], color='red', label=\"Recovered Patients\")\n n = np.where(Y_out == 3)\n ax[0].scatter(X3[n, 0], X3[n, 1], color='orange', label=\"Healthy Control\")\n\n ax[1].set_title('PC 1 and 2')\n n = np.where(Y_out == 0)\n ax[1].scatter(X3[n, 1], X3[n, 2], color='blue', label=\"Non-Recovered Patients\")\n n = np.where(Y_out == 1)\n ax[1].scatter(X3[n, 1], X3[n, 2], color='green', label=\"Non-Recovered CMD\")\n n = np.where(Y_out == 2)\n ax[1].scatter(X3[n, 1], X3[n, 2], color='red', label=\"Recovered Patients\")\n n = np.where(Y_out == 3)\n ax[1].scatter(X3[n, 1], X3[n, 2], color='orange', label=\"Healthy Control\")\n\n ax[2].set_title('PC 0 and 2')\n n = np.where(Y_out == 0)\n ax[2].scatter(X3[n, 0], X3[n, 2], color='blue', label=\"Non-Recovered Patients\")\n n = np.where(Y_out == 1)\n ax[2].scatter(X3[n, 0], X3[n, 2], color='green', label=\"Non-Recovered CMD\")\n n = np.where(Y_out == 2)\n ax[2].scatter(X3[n, 0], X3[n, 2], color='red', label=\"Recovered Patients\")\n n = np.where(Y_out == 3)\n ax[2].scatter(X3[n, 0], X3[n, 2], color='orange', label=\"Healthy Control\")\n\n plt.legend(loc='lower right')\n pdf.savefig(fig)\n plt.close()\n\n\ndef plot_clustered_pca(pdf,X3,Y_out,P,k):\n # visualize in 3D\n fig = plt.figure(figsize=(6,6))\n ax = Axes3D(fig)\n n = np.where(Y_out==0)[0]\n ax.scatter(X3[n, 0], X3[n, 1],X3[n, 2],marker='o',c=P[n],label=\"Non-Recovered Patients\")\n n= np.where(Y_out==1)[0]\n ax.scatter(X3[n, 0], X3[n, 1],X3[n, 2],marker='x',c=P[n],label=\"Non-Recovered CMD \")\n n = np.where(Y_out == 2)[0]\n ax.scatter(X3[n, 0], X3[n, 1], X3[n, 2],marker='.', c=P[n], label=\"Recovered Patients\")\n n = np.where(Y_out == 3)[0]\n ax.scatter(X3[n, 0], X3[n, 1], X3[n, 2],marker='v', c=P[n], label=\"Healthy controls\")\n plt.title('{}_Clusters_allPart_wholeBrain_alpha'.format(str(k)))\n plt.legend(loc='lower right')\n pdf.savefig(fig)\n plt.close()\n\n fig, ax = plt.subplots(1, 3, figsize=(12, 6))\n fig.suptitle('{}_Clusters_allPart_wholeBrain_alpha'.format(str(k)), size=16)\n\n ax[0].set_title('PC 0 and 1')\n n = np.where(Y_out == 0)[0]\n ax[0].scatter(X3[n, 0], X3[n, 1], marker='o', c=P[n], label=\"Non-Recovered Patients\")\n n = np.where(Y_out == 1)[0]\n ax[0].scatter(X3[n, 0], X3[n, 1], marker='x', c=P[n], label=\"Non-Recovered CMD \")\n n = np.where(Y_out == 2)[0]\n ax[0].scatter(X3[n, 0], X3[n, 1], marker='.', c=P[n], label=\"Recovered Patients\")\n n = np.where(Y_out == 3)[0]\n ax[0].scatter(X3[n, 0], X3[n, 1], marker='v', c=P[n], label=\"Healthy controls\")\n\n ax[1].set_title('PC 1 and 2')\n n = np.where(Y_out==0)[0]\n ax[1].scatter(X3[n, 1],X3[n, 2],marker='o',c=P[n],label=\"Non-Recovered Patients\")\n n= np.where(Y_out==1)[0]\n ax[1].scatter(X3[n, 1],X3[n, 2],marker='x',c=P[n],label=\"Non-Recovered CMD \")\n n = np.where(Y_out == 2)[0]\n ax[1].scatter(X3[n, 1], X3[n, 2],marker='.', c=P[n], label=\"Recovered Patients\")\n n = np.where(Y_out == 3)[0]\n ax[1].scatter(X3[n, 1], X3[n, 2],marker='v', c=P[n], label=\"Healthy controls\")\n\n ax[2].set_title('PC 0 and 2')\n n = np.where(Y_out==0)[0]\n ax[2].scatter(X3[n, 0], X3[n, 2], marker='o',c=P[n],label=\"Non-Recovered Patients\")\n n= np.where(Y_out==1)[0]\n ax[2].scatter(X3[n, 0], X3[n, 2], marker='x',c=P[n],label=\"Non-Recovered CMD \")\n n = np.where(Y_out == 2)[0]\n ax[2].scatter(X3[n, 0], X3[n, 2], marker='.', c=P[n], label=\"Recovered Patients\")\n n = np.where(Y_out == 3)[0]\n ax[2].scatter(X3[n, 0], X3[n, 2], marker='v', c=P[n], label=\"Healthy controls\")\n\n plt.legend(loc='lower right')\n pdf.savefig(fig)\n plt.close()\n\n\n\n\ndef plot_explained_variance(pdf,pca):\n # PLot explained Variance\n fig = plt.figure()\n plt.plot(np.cumsum(pca.explained_variance_ratio_))\n plt.xlabel('number of components')\n plt.ylabel('cumulative explained variance')\n plt.title('Explained_Variance_allPart_wholeBrain_alpha')\n pdf.savefig(fig)\n plt.close()\n\n\ndef plot_pie_and_distribution(pdf,part,part_cluster,k):\n fig, ax = plt.subplots(1, 2, figsize=(12, 6))\n fig.suptitle('Part {}; {}_Clusters_wholeBrain_alpha'.format(part, k), size=16)\n\n ax[0].plot(part_cluster)\n ax[0].set_ylim(0, k - 1)\n ax[0].set_title('Part {}; {}_Clusters_wholeBrain_alpha'.format(part, k))\n ax[0].set_ylabel('cluaster_Number')\n ax[0].set_xlabel('time')\n\n piedata = []\n clusternames = []\n for i in range(k):\n piedata.append(list(part_cluster).count(i))\n clusternames.append('cluster ' + str(i))\n\n ax[1].pie(piedata, labels=clusternames, autopct='%1.1f%%', startangle=90)\n pdf.savefig(fig)\n plt.close()\n\ndef plot_group_TPM(P, Y_out, k, pdf):\n P_nonr = P[Y_out == 0]\n P_ncmd = P[Y_out == 1]\n P_reco = P[Y_out == 2]\n P_heal = P[Y_out == 3]\n\n\n TPM_nonr = prop.get_transition_matrix(P_nonr,k)\n TPM_ncmd = prop.get_transition_matrix(P_ncmd,k)\n TPM_reco = prop.get_transition_matrix(P_reco,k)\n TPM_heal = prop.get_transition_matrix(P_heal,k)\n\n f, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize=(12,3))\n g1 = sns.heatmap(TPM_heal, annot=True,cbar=False, ax = ax1, fmt='.1g')\n g1.set_title('Healthy')\n g2 = sns.heatmap(TPM_reco, annot=True,cbar=False, ax = ax2, fmt='.1g')\n g2.set_title('Recovered')\n g3 = sns.heatmap(TPM_nonr, annot=True,cbar=False, ax= ax3, fmt='.1g')\n g3.set_title('Non recovered')\n g4 = sns.heatmap(TPM_ncmd, annot=True,cbar=False, ax= ax4, fmt='.1g')\n g4.set_title('Non recovered CMD')\n pdf.savefig(f)\n plt.close()\n\ndef plot_group_averaged_TPM(AllPart, P, Y_out, k, pdf, data):\n\n P_nonr = np.empty((len(AllPart[\"Part_nonr\"]),k,k))\n P_ncmd = np.empty((len(AllPart[\"Part_ncmd\"]),k,k))\n P_reco = np.empty((len(AllPart[\"Part_reco\"]),k,k))\n P_heal = np.empty((len(AllPart[\"Part_heal\"]),k,k))\n\n for c,part in enumerate(AllPart[\"Part_heal\"]):\n part_cluster = P[data['ID'] == part]\n P_heal[c,:,:] = prop.get_transition_matrix(part_cluster, k)\n\n for c,part in enumerate(AllPart[\"Part_reco\"]):\n part_cluster = P[data['ID'] == part]\n P_reco[c,:,:] = prop.get_transition_matrix(part_cluster, k)\n\n for c,part in enumerate(AllPart[\"Part_nonr\"]):\n part_cluster = P[data['ID'] == part]\n P_nonr[c,:,:] = prop.get_transition_matrix(part_cluster, k)\n\n for c,part in enumerate(AllPart[\"Part_ncmd\"]):\n part_cluster = P[data['ID'] == part]\n P_ncmd[c,:,:] = prop.get_transition_matrix(part_cluster, k)\n\n TPM_heal = np.mean(P_heal,axis=0)\n TPM_reco = np.mean(P_reco,axis=0)\n TPM_nonr = np.mean(P_nonr,axis=0)\n TPM_ncmd = np.mean(P_ncmd,axis=0)\n\n f, (ax1, ax2, ax3, ax4) = plt.subplots(1, 4, figsize=(12,3))\n g1 = sns.heatmap(TPM_heal, annot=True,cbar=False, ax = ax1, fmt='.1g')\n g1.set_title('Healthy')\n g2 = sns.heatmap(TPM_reco, annot=True,cbar=False, ax = ax2, fmt='.1g')\n g2.set_title('Recovered')\n g3 = sns.heatmap(TPM_nonr, annot=True,cbar=False, ax= ax3, fmt='.1g')\n g3.set_title('Non recovered')\n g4 = sns.heatmap(TPM_ncmd, annot=True,cbar=False, ax= ax4, fmt='.1g')\n g4.set_title('Non recovered CMD')\n pdf.savefig(f)\n plt.close()\n", "sub_path": "FC_Clustering_DOC/helper_functions/visualize.py", "file_name": "visualize.py", "file_ext": "py", "file_size_in_byte": 9651, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.use", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.cm.get_cmap", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.cm", "line_number": 33, "usage_type": "attribute"}, {"api_name": "matplotlib.colors.Normalize", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 34, "usage_type": "attribute"}, {"api_name": "nilearn.plotting.plot_connectome", "line_number": 36, "usage_type": "call"}, {"api_name": "nilearn.plotting", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "mpl_toolkits.mplot3d.Axes3D", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "mpl_toolkits.mplot3d.Axes3D", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 104, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "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.close", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"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.plot", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "numpy.cumsum", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 165, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "helper_functions.process_properties.get_transition_matrix", "line_number": 191, "usage_type": "call"}, {"api_name": "helper_functions.process_properties", "line_number": 191, "usage_type": "name"}, {"api_name": "helper_functions.process_properties.get_transition_matrix", "line_number": 192, "usage_type": "call"}, {"api_name": "helper_functions.process_properties", "line_number": 192, "usage_type": "name"}, {"api_name": "helper_functions.process_properties.get_transition_matrix", "line_number": 193, "usage_type": "call"}, {"api_name": "helper_functions.process_properties", "line_number": 193, "usage_type": "name"}, {"api_name": "helper_functions.process_properties.get_transition_matrix", "line_number": 194, "usage_type": "call"}, {"api_name": "helper_functions.process_properties", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 197, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 199, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 201, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 213, "usage_type": "call"}, {"api_name": "helper_functions.process_properties.get_transition_matrix", "line_number": 217, "usage_type": "call"}, {"api_name": "helper_functions.process_properties", "line_number": 217, "usage_type": "name"}, {"api_name": "helper_functions.process_properties.get_transition_matrix", "line_number": 221, "usage_type": "call"}, {"api_name": "helper_functions.process_properties", "line_number": 221, "usage_type": "name"}, {"api_name": "helper_functions.process_properties.get_transition_matrix", "line_number": 225, "usage_type": "call"}, {"api_name": "helper_functions.process_properties", "line_number": 225, "usage_type": "name"}, {"api_name": "helper_functions.process_properties.get_transition_matrix", "line_number": 229, "usage_type": "call"}, {"api_name": "helper_functions.process_properties", "line_number": 229, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 233, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 234, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 236, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 237, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 239, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 241, "usage_type": "call"}, {"api_name": "seaborn.heatmap", "line_number": 243, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 246, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 246, "usage_type": "name"}]} +{"seq_id": "484696246", "text": "import logging\nimport socket\nimport sys, ast\n\nlogger = logging.getLogger()\n\ndef main(host='chgoyang.iptime.org', port=9999):\n sock = socket.socket(socket.AF_INET, # Internet\n socket.SOCK_DGRAM) # UDP\n sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n sock.sendto(b'00000', (host, port))\n \n '''\n data, addr = sock.recvfrom(1024)\n data = ast.literal_eval(data.decode())\n print(type(data), data)\n sock = socket.socket(socket.AF_INET, # Internet\n socket.SOCK_DGRAM) # UDP\n sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n sock.bind((\"0.0.0.0\", data))\n sock.sendto(b'', (host, port))\n '''\n \n print(sock)\n\n while True:\n print(\"대기\")\n data, addr = sock.recvfrom(1024)\n print('서버에게 받음 : {} {}'.format(addr, data))\n data = ast.literal_eval(data.decode())\n if type(data) == type(list()):\n addr = data\n for i in addr:\n print(type(i), i)\n sock.sendto(b'44444', (\"0.0.0.0\", i[1]))\n data, addr = sock.recvfrom(4096)\n data = data.decode()\n print(data)\n\n else:\n \n print(type(addr),addr)\n sock.sendto(str(\"잘 받44444\").encode(), addr)\n \n\n\nif __name__ == '__main__':\n logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s')\n main()\n", "sub_path": "p2p test/444444444.py", "file_name": "444444444.py", "file_ext": "py", "file_size_in_byte": 1457, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 5, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 8, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 8, "usage_type": "attribute"}, {"api_name": "socket.SOCK_DGRAM", "line_number": 9, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 10, "usage_type": "attribute"}, {"api_name": "socket.SO_REUSEADDR", "line_number": 10, "usage_type": "attribute"}, {"api_name": "ast.literal_eval", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 48, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 48, "usage_type": "attribute"}]} +{"seq_id": "322149680", "text": "import pygame\r\nimport sys\r\nimport random\r\nimport os\r\n\r\nimport time\r\n\r\n\r\nfrom HighScore import HighScore\r\n\r\n\r\n\r\nclass GamePlay():\r\n def __init__(self,args):\r\n\r\n self.count = 0\r\n self.selected = args\r\n # Initialized resource path for cross platform compatibility\r\n self.BASE_DIR = os.path.dirname(__file__)\r\n self.RESOURCE_PATH = os.path.join(self.BASE_DIR, 'res')\r\n self.IMAGE_PATH = os.path.join(self.RESOURCE_PATH, 'Images')\r\n\r\n # Initializing pygame.\r\n\r\n pygame.init()\r\n # Initializing the width and height of the window to be 680 and 1250 pixels respectively.\r\n\r\n self.display_height = 680\r\n self.display_width = 1250\r\n\r\n # Setting the title and dimensions of the window\r\n\r\n pygame.display.set_caption('Train BRAIN')\r\n self.gameDisplay = pygame.display.set_mode((self.display_width, self.display_height))\r\n\r\n pygame.mixer.music.load(os.path.join(self.RESOURCE_PATH, 'Songs', 'bgScore.wav'))\r\n pygame.mixer.music.play(-1)\r\n\r\n # Defining the clock to set the FPS rates\r\n self.clock = pygame.time.Clock()\r\n\r\n # Relatively small sized images to be placed in the main menu\r\n self.witch_image = pygame.image.load(os.path.join(self.IMAGE_PATH, 'ghost.png'))\r\n\r\n self.basket_image = pygame.image.load(os.path.join(self.IMAGE_PATH, 'basket.png'))\r\n\r\n if self.selected is \"Fruits\":\r\n\r\n self.object_images_raw = ['apple.png', 'bananas.png', 'grapes.png', 'orange.png', 'pineapple.png', 'strawberry.png',\r\n 'watermelon.png', 'chilli.png', 'mango.png', 'blueberry.png', 'cherries.png',\r\n 'halloween.png','avocado.png']\r\n elif self.selected is \"Vegetables\":\r\n self.object_images_raw = ['tomato.png', 'beet.png', 'carrot.png', 'cucumber.png', 'eggplant.png',\r\n 'potato.png',\r\n 'spinach.png', 'chilli.png', 'garlic.png', 'halloween.png', 'cauliflower.png',\r\n 'corn.png','okra.png']\r\n elif self.selected is \"Animals\":\r\n self.object_images_raw = ['cow.png', 'sheep.png', 'dog.png', 'lion.png', 'giraffe.png',\r\n 'elephant.png',\r\n 'horse.png', 'chicken.png', 'pig.png', 'tiger.png', 'zebra.png',\r\n 'cat.png', 'okra.png']\r\n self.object_images = [os.path.join(self.IMAGE_PATH, i) for i in self.object_images_raw]\r\n\r\n self.bomb_image = pygame.image.load(self.object_images[7])\r\n\r\n self.basket = pygame.image.load(os.path.join(self.IMAGE_PATH, 'basket.png'))\r\n\r\n self.minion = pygame.image.load(self.object_images[11])\r\n\r\n self.explosion = pygame.image.load(os.path.join(self.IMAGE_PATH, 'ghost.png'))\r\n\r\n # Initializing the speed variable which denotes the speed with which the images move.\r\n\r\n self.image_speed = 3\r\n\r\n # Intialiaing the score variable.\r\n\r\n self.score = 0\r\n\r\n # Pixels being moved by the basket under keystrokes (10 px)\r\n\r\n self.unit = 4\r\n\r\n self.user_clicked = False\r\n\r\n # Setting white background to the game window\r\n self.gameDisplay.fill((255, 255, 255))\r\n\r\n # X and Y co-ordinates of the basket.\r\n self.basket_x = self.display_width / 2 - 200\r\n\r\n self.basket_y = self.display_height - 270\r\n\r\n # For changing the X co-ordinate of the basket when appropriate keys are pressed.\r\n self.x_change = 0\r\n\r\n # Random positions for fruits\r\n\r\n self.x = random.randint(0, self.display_width - 250)\r\n self.y = -150\r\n\r\n # Randomly loading fruit images\r\n\r\n self.random_images = self.object_images[random.randint(0, 12)]\r\n\r\n self.random_fruits = pygame.image.load(self.random_images)\r\n\r\n\r\n def setText(self,text, font_size, position, foreground_color, background_color=None, font_family=\"Times New Roman\"):\r\n # Setting the font and related font features as per the parameters supplied.\r\n\r\n font = pygame.font.SysFont(font_family, font_size)\r\n text_to_display = font.render(text, 1, foreground_color, background_color)\r\n self.gameDisplay.blit(text_to_display, position)\r\n pygame.display.update()\r\n\r\n # Defining functions for file operations.\r\n\r\n\r\n def file_open_write(self,high_score):\r\n\r\n f = open(os.path.join(self.RESOURCE_PATH, 'File', 'high_score.txt'), 'w')\r\n f.write(high_score)\r\n return f\r\n\r\n\r\n def file_open_read(self):\r\n\r\n f = open(os.path.join(self.RESOURCE_PATH, 'File', 'high_score.txt'), 'r')\r\n best_score = f.read()\r\n return f, best_score\r\n\r\n\r\n def file_close(self,file):\r\n\r\n file.close()\r\n\r\n def play(self):\r\n self.play_clicked = True\r\n while self.play_clicked:\r\n\r\n # New game window is created on clicking the play button with the same dimensions.\r\n self.play_window = pygame.display.set_mode((1250, 680))\r\n\r\n pygame.display.set_caption('Play')\r\n\r\n self.play_window.fill((255, 255, 255))\r\n\r\n self.play_clock = pygame.time.Clock()\r\n\r\n self.play_window.blit(self.random_fruits, (self.x, self.y))\r\n\r\n\r\n\r\n # Horizontal line carrying the basket.\r\n\r\n pygame.draw.line(self.play_window, (175, 115, 0), (0, self.display_height - 20), (self.display_width, self.display_height - 20))\r\n\r\n # Color beneath the line.\r\n\r\n pygame.draw.rect(self.play_window, (243, 128, 12), (0, self.display_height - 18, self.display_width, self.display_height))\r\n\r\n for self.event in pygame.event.get():\r\n\r\n # Event handling\r\n if self.event.type == pygame.QUIT:\r\n\r\n pygame.quit()\r\n sys.exit()\r\n\r\n elif self.event.type == pygame.KEYDOWN:\r\n\r\n if self.event.key == pygame.K_RIGHT:\r\n\r\n self.x_change = self.unit\r\n\r\n elif self.event.key == pygame.K_LEFT:\r\n\r\n self.x_change = -self.unit\r\n\r\n elif self.event.key == pygame.K_DOWN:\r\n\r\n self.x_change = 0\r\n\r\n self.basket_x += self.x_change\r\n\r\n self.y += self.image_speed\r\n\r\n # Placing the Basket in position\r\n\r\n self.play_window.blit(self.basket, (self.basket_x, self.basket_y))\r\n\r\n\r\n\r\n # Placing score on the game window.\r\n\r\n self.setText(\"You collected \"+str(self.count)+\" items and Your Score is \" + str(self.score), 40, (0, 0), (107, 20, 99), (128, 255, 255))\r\n\r\n # Checking fruit and basket crossover.\r\n\r\n if self.y + 80 >= self.basket_y and self.y + 80 <= self.basket_y + 15:\r\n\r\n if self.x >= self.basket_x - 40 and self.x + 100 <= self.basket_x + self.display_width / 2 - 240:\r\n\r\n # Checks collision with bomb and minion image\r\n\r\n if self.random_images == self.minion or self.random_images == self.object_images[7]:\r\n\r\n self.score -= 5\r\n self.setText(\"Your Score:\" + str(self.score), 40, (0, 0), (107, 20, 99), (128, 255, 255))\r\n\r\n # Checking whether the current score is greater than the best score.\r\n\r\n self.file, self.current_best_score = self.file_open_read()\r\n\r\n self.file_close(self.file)\r\n\r\n #if self.score > int(self.current_best_score):\r\n #self.file = self.file_open_write(str(self.score))\r\n #self.file_close(self.file)\r\n\r\n self.setText(\"Crashed\", 150, (self.display_width / 2 - 240, 35), (0, 0, 0))\r\n self.setText(None, 40, (0, 0), (255, 255, 255))\r\n self.play_window.blit(self.explosion, (self.basket_x, self.basket_y - 80))\r\n pygame.display.update()\r\n\r\n time.sleep(3)\r\n\r\n for k in range(0, self.display_width + 1, 5):\r\n self.setText(\"Your Score:\" + str(self.score), 40, (k, 0), (107, 20, 99), (128, 255, 255))\r\n pygame.time.wait(20)\r\n\r\n time.sleep(2)\r\n\r\n pygame.quit()\r\n highScore = HighScore(self.score,self.count)\r\n break\r\n\r\n #game_over = True\r\n #play_clicked = False\r\n\r\n\r\n # Makes the fruit disappear !\r\n\r\n self.y = self.display_height\r\n\r\n # Incrementing the score appropriately.\r\n\r\n if self.random_images == self.object_images[6]:\r\n\r\n self.count += 1\r\n self.score += 1\r\n\r\n elif self.random_images == self.object_images[0] or self.random_images == self.object_images[1] or self.random_images == self.object_images[\r\n 3]:\r\n self.count += 1\r\n self.score += 3\r\n\r\n elif self.random_images == self.object_images[4] or self.random_images == self.object_images[5] or self.random_images == self.object_images[\r\n 8]:\r\n self.count += 1\r\n self.score += 5\r\n\r\n elif self.random_images == self.object_images[2]:\r\n self.count += 1\r\n self.score += 10\r\n\r\n # Checking whether the fruit image had crossed the floor.\r\n\r\n if self.y >= self.display_height + 200:\r\n\r\n # Random positions for fruits\r\n\r\n self.x = random.randint(0, self.display_width - 250)\r\n self.y = -150\r\n\r\n # Randomly loading fruit images\r\n\r\n self.random_images = self.object_images[random.randint(0, 9)]\r\n\r\n self.random_fruits = pygame.image.load(self.random_images)\r\n\r\n # Increasing the speed in which the basket moves both the directions.\r\n\r\n if self.score > 75:\r\n self.unit += 1\r\n\r\n # Increasing the speed in which the images moves down.\r\n\r\n if self.score > 75:\r\n self.image_speed += 0.5\r\n\r\n # Restricting the basket within the width of the Game window\r\n\r\n if self.basket_x <= 0:\r\n\r\n self.basket_x = 0\r\n\r\n elif self.basket_x >= self.display_width - 300:\r\n\r\n self.basket_x = self.display_width - 300\r\n\r\n pygame.display.update()\r\n self.play_clock.tick(60)\r\n def setEvent(self,gestureEvent):\r\n self.event = gestureEvent\r\n", "sub_path": "GamePage.py", "file_name": "GamePage.py", "file_ext": "py", "file_size_in_byte": 10940, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.dirname", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "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": "pygame.init", "line_number": 25, "usage_type": "call"}, {"api_name": "pygame.display.set_caption", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 34, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 36, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 36, "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": "pygame.mixer.music.play", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 45, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 45, "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.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 64, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 64, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 66, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 66, "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": "pygame.image.load", "line_number": 68, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 68, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 70, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 70, "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": "random.randint", "line_number": 99, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 104, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 106, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 106, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 112, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 112, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 115, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 115, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 129, "usage_type": "call"}, {"api_name": "os.path", "line_number": 129, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 143, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 143, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 145, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 145, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 149, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 149, "usage_type": "attribute"}, {"api_name": "pygame.draw.line", "line_number": 157, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 157, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 161, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 161, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 163, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 163, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 166, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 168, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 169, "usage_type": "call"}, {"api_name": "pygame.KEYDOWN", "line_number": 171, "usage_type": "attribute"}, {"api_name": "pygame.K_RIGHT", "line_number": 173, "usage_type": "attribute"}, {"api_name": "pygame.K_LEFT", "line_number": 177, "usage_type": "attribute"}, {"api_name": "pygame.K_DOWN", "line_number": 181, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 225, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 225, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 227, "usage_type": "call"}, {"api_name": "pygame.time.wait", "line_number": 231, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 231, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 233, "usage_type": "call"}, {"api_name": "pygame.quit", "line_number": 235, "usage_type": "call"}, {"api_name": "HighScore.HighScore", "line_number": 236, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 274, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 279, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 281, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 281, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 303, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 303, "usage_type": "attribute"}]} +{"seq_id": "118778792", "text": "from img import logo\r\nimport random\r\nimport data\r\nimport img\r\n\r\ndisplay = []\r\n\r\nchosen_word = random.choice(data.word_list)\r\n\r\nword_length = len(chosen_word)\r\n\r\nfor _ in range(word_length):\r\n display.append(\"_\")\r\n\r\nlives = 6\r\n\r\nprint(logo)\r\n\r\nend_of_game = False\r\n\r\nwhile not end_of_game:\r\n guess = input(\"Guess a letter: \").lower()\r\n\r\n if guess in display:\r\n print(f\"You've already guessed {guess}\")\r\n\r\n for position in range(word_length):\r\n\r\n letter = chosen_word[position]\r\n\r\n if letter == guess:\r\n display[position] = letter\r\n if guess not in chosen_word:\r\n print(f\"You guessed {guess}, thats not in the word. You lose a life\")\r\n lives -= 1\r\n if lives == 0:\r\n end_of_game = True\r\n print(\"You lose.\")\r\n print(display)\r\n\r\n if \"_\" not in display:\r\n end_of_game = True\r\n print(\"You win\")\r\n\r\n print(img.stages[lives])\r\n", "sub_path": "hangman.py", "file_name": "hangman.py", "file_ext": "py", "file_size_in_byte": 931, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "random.choice", "line_number": 8, "usage_type": "call"}, {"api_name": "data.word_list", "line_number": 8, "usage_type": "attribute"}, {"api_name": "img.logo", "line_number": 17, "usage_type": "argument"}, {"api_name": "img.stages", "line_number": 45, "usage_type": "attribute"}]} +{"seq_id": "483713682", "text": "import requests\r\n\r\nimport re\r\n\r\nimport time\r\n\r\nimport random\r\n\r\nimport telnetlib\r\n\r\n\r\n\r\nkeys = [\r\n\r\n 'Mozilla/5.0 (Linux; Android 4.1.1; Nexus 7 Build/JRO03D) AppleWebKit/535.19 (KHTML, like Gecko) Chrome/18.0.1025.166 Safari/535.19',\r\n\r\n 'Mozilla/5.0 (Linux; U; Android 4.0.4; en-gb; GT-I9300 Build/IMM76D) AppleWebKit/534.30 (KHTML, like Gecko) Version/4.0 Mobile Safari/534.30',\r\n\r\n 'Mozilla/5.0 (Linux; U; Android 2.2; en-gb; GT-P1000 Build/FROYO) AppleWebKit/533.1 (KHTML, like Gecko) Version/4.0 Mobile Safari/533.1',\r\n\r\n 'Mozilla/5.0 (Windows NT 6.2; WOW64; rv:21.0) Gecko/20100101 Firefox/21.0',\r\n\r\n 'Mozilla/5.0 (Android; Mobile; rv:14.0) Gecko/14.0 Firefox/14.0',\r\n\r\n 'Mozilla/5.0 (Windows NT 6.2; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/27.0.1453.94 Safari/537.36',\r\n\r\n 'Mozilla/5.0 (Linux; Android 4.0.4; Galaxy Nexus Build/IMM76B) AppleWebKit/535.19 (KHTML, like Gecko) Chrome/18.0.1025.133 Mobile Safari/535.19',\r\n\r\n 'Mozilla/5.0 (iPad; CPU OS 5_0 like Mac OS X) AppleWebKit/534.46 (KHTML, like Gecko) Version/5.1 Mobile/9A334 Safari/7534.48.3',\r\n\r\n 'Mozilla/5.0 (iPod; U; CPU like Mac OS X; en) AppleWebKit/420.1 (KHTML, like Gecko) Version/3.0 Mobile/3A101a Safari/419.3'\r\n\r\n]\r\n\r\n\r\n\r\n# 伪装浏览器\r\n\r\nheaders = {\r\n\r\n 'User-Agent': keys[random.randint(0, len(keys) - 1)]\r\n\r\n}\r\n\r\n\r\n\r\n# 批量获取高匿代理ip\r\n\r\ndef getXCProxyIp(max_page_number):\r\n\r\n for i in range(1, max_page_number + 1):\r\n\r\n page_number = i\r\n\r\n init_url = 'http://www.xicidaili.com/nn/' + str(i)\r\n\r\n req = requests.get(init_url, headers=headers)\r\n\r\n # 获取代理ip\r\n\r\n agency_ip_re = re.compile(r'\\b(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\.){3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\b' ,re.S)\r\n\r\n agency_ip = agency_ip_re.findall(req.text)\r\n\r\n # 获取代理ip的端口号\r\n\r\n agency_port_re = re.compile('([0-9]{2,5})', re.S)\r\n\r\n agency_port = agency_port_re.findall(req.text)\r\n\r\n # 高匿代理ip页面中所列出的ip数量\r\n\r\n ip_number = len(agency_ip)\r\n\r\n print('正在获取第 %d 页代理中（请耐心等候）......' % page_number)\r\n\r\n for i in range(ip_number):\r\n\r\n total_ip = agency_ip[i] + ':' + agency_port[i]\r\n\r\n print(total_ip)\r\n\r\n verifyProxyIP(agency_ip[i], agency_port[i])\r\n\r\n print('第 %d 页代理获取完毕！' % page_number)\r\n\r\n print('------------------------------------')\r\n\r\n\r\n\r\n\r\n# 验证获取到的代理IP是否可用\r\n\r\ndef verifyProxyIP(verify_ip, verify_ip_port):\r\n\r\n print('正在验证此代理IP是否可用......')\r\n\r\n try:\r\n\r\n telnetlib.Telnet(verify_ip, verify_ip_port, timeout=3)\r\n\r\n except:\r\n\r\n print('此代理IP不可用')\r\n\r\n print('-------------------------')\r\n\r\n else:\r\n\r\n print('此代理IP可用')\r\n\r\n print('-------------------------')\r\n\r\n available_ip = verify_ip + ':' + verify_ip_port\r\n\r\n saveProxyIP(available_ip)\r\n saveProxyIP('................')\r\n\r\n\r\n\r\n# 将可用的代理IP保存到本地\r\n\r\ndef saveProxyIP(available_ip):\r\n\r\n with open('/Users/zhuji/Desktop/Python/ip.txt', 'a') as f:\r\n\r\n f.write(available_ip + '\\n')\r\n \r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n print('---------- 高匿代理ip获取 ----------')\r\n\r\n page= int(input('请输入您想获取的页数: '))\r\n\r\n getXCProxyIp(page)", "sub_path": "pachong/test/huoqu.py", "file_name": "huoqu.py", "file_ext": "py", "file_size_in_byte": 3441, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "random.randint", "line_number": 41, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 57, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 61, "usage_type": "call"}, {"api_name": "re.S", "line_number": 61, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 67, "usage_type": "call"}, {"api_name": "re.S", "line_number": 67, "usage_type": "attribute"}, {"api_name": "telnetlib.Telnet", "line_number": 100, "usage_type": "call"}]} +{"seq_id": "158085", "text": "import sys\r\nimport json\r\nimport codecs\r\n\r\nfrom nltk.corpus import WordNetCorpusReader\r\nfrom ruwordnet.ruwordnet_reader import RuWordnet\r\nfrom predict_models import BaselineModel, HCHModel, RankedModel, LRModel\r\n\r\n\r\ndef load_config():\r\n if len(sys.argv) < 2:\r\n raise Exception(\"Please specify path to config file\")\r\n with open(sys.argv[1], 'r', encoding='utf-8')as j:\r\n params = json.load(j)\r\n return params\r\n\r\n\r\ndef generate_taxonomy_fns(params, model):\r\n # for English WordNet\r\n if params['language'] == 'en':\r\n wn = WordNetCorpusReader(params[\"wordnet_path\"], None)\r\n return lambda x: [hypernym.name() for hypernym in wn.synset(x).hypernyms()\r\n if hypernym.name() in model.w2v_synsets.vocab], \\\r\n lambda x: [hyponym.name() for hyponym in wn.synset(x).hyponyms() if hyponym.name()\r\n in model.w2v_synsets.vocab], \\\r\n lambda x: x.split(\".\")[0].replace(\"_\", \" \")\r\n # for RuWordNet\r\n elif params['language'] == 'ru':\r\n ruwordnet = RuWordnet(db_path=params[\"db_path\"], ruwordnet_path=params[\"wordnet_path\"])\r\n return lambda x: ruwordnet.get_hypernyms_by_id(x), lambda x: ruwordnet.get_hyponyms_by_id(x), \\\r\n lambda x: ruwordnet.get_name_by_id(x)\r\n else:\r\n raise Exception(\"task / language is not supported\")\r\n\r\n\r\ndef save_to_file(words_with_hypernyms, output_path, _params):\r\n with codecs.open(output_path, 'w', encoding='utf-8') as f:\r\n for word, hypernyms in words_with_hypernyms.items():\r\n for hypernym in hypernyms:\r\n f.write(f\"{word}\\t{hypernym}\\n\")\r\n\r\n\r\ndef main():\r\n models = {\"simple\": BaselineModel, \"baseline\": HCHModel, \"ranked\": RankedModel, \"lr\": LRModel}\r\n params = load_config()\r\n\r\n with open(params['test_path'], 'r', encoding='utf-8') as f:\r\n test_data = f.read().split(\"\\n\")[:-1]\r\n\r\n model = models[params[\"model\"]](params)\r\n print(\"Model loaded\")\r\n\r\n topn = params[\"topn\"] if \"topn\" in params else 10\r\n results = model.predict_hypernyms(list(test_data), *generate_taxonomy_fns(params, model), topn)\r\n save_to_file(results, params['output_path'], params)\r\n\r\n\r\nif __name__ == '__main__':\r\n main()\r\n", "sub_path": "code/baselines/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2246, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 14, "usage_type": "call"}, {"api_name": "nltk.corpus.WordNetCorpusReader", "line_number": 21, "usage_type": "call"}, {"api_name": "ruwordnet.ruwordnet_reader", "line_number": 29, "usage_type": "name"}, {"api_name": "ruwordnet.ruwordnet_reader.RuWordnet", "line_number": 29, "usage_type": "call"}, {"api_name": "ruwordnet.ruwordnet_reader.get_hypernyms_by_id", "line_number": 30, "usage_type": "call"}, {"api_name": "ruwordnet.ruwordnet_reader", "line_number": 30, "usage_type": "name"}, {"api_name": "ruwordnet.ruwordnet_reader.get_hyponyms_by_id", "line_number": 30, "usage_type": "call"}, {"api_name": "ruwordnet.ruwordnet_reader.get_name_by_id", "line_number": 31, "usage_type": "call"}, {"api_name": "ruwordnet.ruwordnet_reader", "line_number": 31, "usage_type": "name"}, {"api_name": "codecs.open", "line_number": 37, "usage_type": "call"}, {"api_name": "predict_models.BaselineModel", "line_number": 44, "usage_type": "name"}, {"api_name": "predict_models.HCHModel", "line_number": 44, "usage_type": "name"}, {"api_name": "predict_models.RankedModel", "line_number": 44, "usage_type": "name"}, {"api_name": "predict_models.LRModel", "line_number": 44, "usage_type": "name"}]} +{"seq_id": "518917417", "text": "#!/usr/bin/env python3\n# Parse Movie web page.\n# Save result in _list.txt file if possible.\n\ndef ParseMoviePage(select_page):\n\t# return value\n\tmovie=[]\n\t\n\t# # Web page's URL:\n\t# print('请选择要解析的页面：1.豆瓣电影 2.BT天堂')\n\t# select_page=input()\n\tif select_page=='1':\n\t\ttag='db'\n\telif select_page=='2':\n\t\ttag='tt'\n\n\tfrom datetime import datetime\n\ttime_now=datetime.now()\n\n\t# 待解析源文件（.htm）\n\t# filename=time_now.strftime('%y-%m-%d_%H-%M-%S_')+tag+'.htm'#alpha edition\n\tfilename=time_now.strftime('%y-%m-%d_')+tag+'_home.htm'#beta edition\n\ttry:\n\t\tsourceFile=open(filename,encoding='utf-8')#parameter 'encoding' is important!\n\texcept FileNotFoundError as e:\n\t\tprint('文件不存在，请先下载网页（执行SaveMoviePage.py）:'+filename)\n\t\treturn# 必须有源文件，否则直接退出\n\n\t# 用于保存解析结果的问题\n\tresultFile=time_now.strftime('%y-%m-%d_')+tag+'_list.txt'#beta edition\n\ttry:\n\t\tresFile=open(resultFile,'x',encoding='utf-8')\n\texcept FileExistsError as e:\n\t\tprint('同名结果文件已存在:'+resultFile)\n\t\t# 可以不写结果文件\n\n\thtml=sourceFile.read().encode('utf-8')\n\n\tfrom bs4 import BeautifulSoup\n\tsoup = BeautifulSoup(html,\"html.parser\")\n\n\tif select_page=='1':# 豆瓣电影\n\t\tprint('豆瓣电影 正在热映：')\n\t\tdiv_hot = soup.find_all('li',class_='poster')\n\t\tfor i in div_hot:\n\t\t\tmovie_title = i.img.get('alt')\n\t\t\ttry:\n\t\t\t\tprint(movie_title)\n\t\t\t\tresFile.write(movie_title+'\\n')\n\t\t\texcept:\n\t\t\t\tprint('some error')\n\telif select_page=='2':# BT天堂\n\t\tprint('BT天堂 最新电影')\n\n\t\tmovie_title=[]\n\t\ttitle_hot = soup.find_all('div',class_='title')\n\t\tsize_t = len(title_hot)\n\n\t\tmovie_score=[]\n\t\tscore_hot = soup.find_all('p',class_='rt')\n\t\tsize_s = len(score_hot)\n\n\t\tmovie_href=[]\n\n\t\tif size_s==size_t:\n\t\t\timport re\n\t\t\tp = re.compile(\"\\d.?\\d\")\n\t\t\tfor i in range(0,size_t):\n\t\t\t\ttitle=title_hot[i].a.get_text()\n\t\t\t\tscore=p.search(score_hot[i].get_text()).group(0)\n\t\t\t\thref='http://www.bttiantang.com'+title_hot[i].a.get('href')\n\t\t\t\tmovie_title.append(title)\n\t\t\t\tmovie_score.append(score)\n\t\t\t\tmovie_href.append(href)\n\t\t\t\tmovie.append((title,score,href))\n\t\t\t\ttry:\n\t\t\t\t\tprint('评分'+score+'\\t片名：'+title+'\\t'+href)\n\t\t\t\texcept:\n\t\t\t\t\tprint('some error 1')\n\t\t\t\t# try:\n\t\t\t\t# \tresFile.write('评分'+movie_score[i]+'\\t片名：'+movie_title[i]+'\\n')\n\t\t\t\t# except:\n\t\t\t\t# \tprint('some error 2')\n\t\t\t# 按评分由高到低排序\n\t\t\tmovie.sort(key=lambda x:x[1],reverse=True)\n\t\t\tfor i in range(0,size_t):\n\t\t\t\ttry:\n\t\t\t\t\tresFile.write('评分'+movie[i][1]+'\\t片名：'+movie[i][0]+'\\t'+movie[i][2]+'\\n')\n\t\t\t\texcept:\n\t\t\t\t\t# print('some error 2')\n\t\t\t\t\tpass\n\ttry:\n\t\tresFile.close()\n\texcept:\n\t\t# print('some error 3')\n\t\tpass\n\treturn movie\n\n# print('\\n请按回车键退出')\n# input()", "sub_path": "movie_v1/ParseMoviePage1.py", "file_name": "ParseMoviePage1.py", "file_ext": "py", "file_size_in_byte": 2753, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datetime.datetime.now", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 40, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "11775527", "text": "import os\nfrom fake_useragent import UserAgent\nimport requests\nfrom bs4 import BeautifulSoup\nfrom urllib.request import urlretrieve\nfrom concurrent.futures import ThreadPoolExecutor,wait\nimport time\n \ndef one_book_url(url):#获取根目录所有本子信息\n headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Safari/537.36 OPR/70.0.3728.106'}\n r = requests.get(url,headers=headers)\n r.encoding = 'utf-8'\n soup = BeautifulSoup(r.text,'lxml')\n\n website = []\n the_begin = 'https://zh.nyahentai.site' ##修改此处\n content = soup.find_all('div',class_ = 'gallery') ##大的div\n for i in content:\n nexturl = i.find('a')\n one_url = nexturl.get('href')\n all_url = the_begin + one_url\n website.append(all_url)\n return website\n\ndef geturl_pic(url,download_path):#获取单个本子所有链接并创建文件夹\n headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Safari/537.36 OPR/70.0.3728.106'}\n r = requests.get(url,headers=headers)\n r.encoding = 'utf-8'\n soup = BeautifulSoup(r.text,'lxml')\n\n book_title = soup.find('h1').get_text()\n download_path2 = download_path+\"/\"+book_title\n if not os.path.exists(download_path2):\n os.makedirs(download_path2)\n \n website = []\n the_begin = 'https://zh.nyahentai.site'\n content = soup.find_all('div',class_ = 'thumb-container') ##大的div\n for i in content:\n nexturl = i.find('a')\n one_url = nexturl.get('href')\n all_url = the_begin + one_url\n website.append(all_url)\n return website,download_path2\n\ndef download_pic(url,download_path2):#下载图片\n headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Safari/537.36 OPR/70.0.3728.106'}\n r = requests.get(url,headers=headers)\n r.encoding = 'utf-8'\n soup = BeautifulSoup(r.text,'lxml')\n\n link = soup.find(class_ = \"fit-horizontal full-height\").find('img').get('src')\n name = soup.find(class_ = \"current\").get_text()\n html = requests.get(link)\n with open(f'{download_path2}/{name}.jpg','wb') as f:\n f.write(html.content)\n print(f'{download_path2}图片{name}下载完成')\n\n\n\ndef main():\n authors = ['hal'] #输入作者名---------------------------------------------------\n page_num = [2] #输入作者中文界面页数-----------------------------------------\n\n first_urls = 'https://zh.nyahentai.site/artist/' ##修改此处源地址\n end_urls = '/chinese'\n for author,page in zip(authors,page_num):\n website2 = []\n root_url = first_urls + author +end_urls\n start_urls = [root_url]\n for i in range(2,page+1):\n start_urls.append(f'{root_url}/page/{i}') #设置该作者的所有网页\n\n headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Safari/537.36 OPR/70.0.3728.106'}\n r = requests.get(root_url,headers=headers)\n r.encoding = 'utf-8'\n soup = BeautifulSoup(r.text,'lxml')#基础设置\n\n author_name_from = soup.find('div',id='content')\n author_name = author_name_from.find('span',class_=None).get_text() #获取作者名且创建文件夹\n download_path = \"./\"+author_name\n if not os.path.exists(download_path):\n os.makedirs(download_path)\n for url in start_urls:\n website = one_book_url(url)\n for url in website:\n website2.append(url)\n print(f\"{author_name}'s page was completed!\") #获取根目录本子链接至website2\n print(\"目前已下载几本?\")\n x = int(input())\n if x == 0:\n website2 = website2\n else:\n for i in range(1,x+1):\n del website2[0]\n print(website2)\n\n\n for url in website2:\n website3,download_path2 = geturl_pic(url,download_path)\n print('文件夹已更新，休息3s')\n time.sleep(3)\n print('休息结束')\n \n with ThreadPoolExecutor(max_workers=20) as executor: #开启多线程\n for url in website3:\n executor.submit(download_pic,url,download_path2)\n \n\nif __name__ == '__main__':\n main()\n", "sub_path": "download_series.py", "file_name": "download_series.py", "file_ext": "py", "file_size_in_byte": 4627, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.get", "line_number": 11, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 13, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 27, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 29, "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": "requests.get", "line_number": 48, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 50, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 54, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 75, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 83, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 102, "usage_type": "call"}, {"api_name": "concurrent.futures.ThreadPoolExecutor", "line_number": 105, "usage_type": "call"}]} +{"seq_id": "279354351", "text": "from unittest import TestCase\nfrom unittest.mock import patch\nimport unittest.mock\nimport sud\nimport io\n\n# May Chau\n# A01080616\n# 2019-03-11\n\n\nclass TestCheckLoadGame(TestCase):\n @patch('builtins.input', return_value=\"L\")\n def test_check_load_game_load(self, mock_input):\n self.assertTrue(sud.check_load_game())\n\n @patch('builtins.input', return_value=\"N\")\n def test_check_load_game_new(self, mock_input):\n self.assertFalse(sud.check_load_game())\n\n @unittest.mock.patch(\"sys.stdout\", new_callable=io.StringIO)\n @patch('builtins.input', side_effect=[\"\", \"L\"])\n def test_check_load_game_empty_input(self, mock_input, mock_output):\n sud.check_load_game()\n expected_output = \"Please retry\\n\"\n self.assertEqual(mock_output.getvalue(), expected_output)\n\n @unittest.mock.patch(\"sys.stdout\", new_callable=io.StringIO)\n @patch('builtins.input', side_effect=[\"abc\", \"L\"])\n def test_check_load_game_invalid_input(self, mock_input, mock_output):\n sud.check_load_game()\n expected_output = \"Please retry\\n\"\n self.assertEqual(mock_output.getvalue(), expected_output)", "sub_path": "SUD - RuPaul Drag Race/test_check_load_game.py", "file_name": "test_check_load_game.py", "file_ext": "py", "file_size_in_byte": 1137, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "unittest.TestCase", "line_number": 12, "usage_type": "name"}, {"api_name": "sud.check_load_game", "line_number": 15, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 13, "usage_type": "call"}, {"api_name": "sud.check_load_game", "line_number": 19, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 17, "usage_type": "call"}, {"api_name": "sud.check_load_game", "line_number": 24, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 21, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 21, "usage_type": "attribute"}, {"api_name": "io.StringIO", "line_number": 21, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch", "line_number": 22, "usage_type": "call"}, {"api_name": "sud.check_load_game", "line_number": 31, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 28, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 28, "usage_type": "attribute"}, {"api_name": "io.StringIO", "line_number": 28, "usage_type": "attribute"}, {"api_name": "unittest.mock.patch", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "369773773", "text": "# -*- coding: utf8 -*-\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport csv\nimport os\nimport re\nfrom pprint import pprint as print\nfrom scrapy.http import Request\n\nfrom alascrapy import items\nfrom alascrapy.spiders.base_spiders import ala_spider\n\n\nPATH_TO_PRODUCTS_FILE = '/tmp/valuechecker_feedin/products.csv'\nPATH_TO_PRODUCT_IDS_FILE = '/tmp/valuechecker_feedin/product_ids.csv'\n\n\nfiles = {'products': PATH_TO_PRODUCTS_FILE,\n 'product_ids': PATH_TO_PRODUCT_IDS_FILE}\n\n# magic prefix for source_id to show that this source is used for valuechecker\nMAGIC_NUMBER = '777'\n\n# table of original source_ids for sources in vinnie\n# '3140' - 'tweakers_id', # tweakers.net\n# '44000092' - 'idealo_id', # idealo.co.uk\n# '4904' - 'pricerunner_id_de', # pricerunner.de\n# '263852' - 'pricerunner_id_uk', # pricerunner.co.uk\n# '4780' - 'prisjakt_id', # prisjakt.no\n# '263853' - 'pricerunner_id_se', # pricerunner.se\n# '4622' - 'prisjakt_id', # prisjakt.nu\n# '3213' - 'tweakers_id', # tweakers.net (Belgium)\n# '3320' - 'idealo_fr_id', # idealo.fr\n# '49065' - 'idealo_de_id', # idealo.de\n# '4504' - 'pricerunner_id_dk', # pricerunner.dk\n# '44000089' - 'prisjakt_id', # pricespy.co.uk\n# '10000283' - 'bestbuy_ud', # bestbuy.com\n# '32000013' - 'kieskeurig_id', # kieskeurig.be\n# '300004' - 'kieskeurig_id', # kieskeurig.nl\n#\n\n# sources in valuechecker database with already added magic_number\n# Obtained on the VC database from table `valuechecker.sources`\n# '777 + source_id': 'id_kind',\nSOURCE_KINDS_TABLE = {\n '7771001' : 'bestbuy_id', # bestbuy.com\n '7771002' : 'gsmarena_id', # gsmarena.com\n '77710000': 'vc_alatest_catalog', # alatest_catalog\n '77731001': 'tweakers_id', # tweakers.net\n '77731002': 'kieskeurig_id', # kieskeurig.nl\n '77732001': 'tweakers_id', # tweakers.be (Belgium)\n '77732002': 'kieskeurig_id', # kieskeurig.be\n '77733001': 'idealo_id', # idealo.fr\n '77733003': 'prisjakt_id', # ledenicheur.fr\n '77734001': 'idealo_id', # idealo.es\n '77744001': 'pricerunner_id', # pricerunner.co.uk\n '77744002': 'prisjakt_id', # pricespy.co.uk\n '77744003': 'idealo_id', # idealo.co.uk\n '77744005': 'geizhals_id', # skinflint.co.uk\n '77745001': 'pricerunner_id', # pricerunner.dk\n '77745002': 'prisjakt_id', # prisjagd.dk\n '77746001': 'pricerunner_id', # pricerunner.se\n '77746002': 'prisjakt_id', # prisjakt.nu\n '77747001': 'prisjakt_id', # prisjakt.no\n '77749001': 'pricerunner_id', # pricerunner.de\n '77749002': 'idealo_id', # idealo.de\n '77749004': 'geizhals_id', # geizhals.de\n}\n\n\nclass FeedFileParser(object):\n \"\"\"\n object for parsing csv files from valuechecker feed\n \"\"\"\n COLUMNS = []\n\n def __init__(self, filepath):\n self._filepath = filepath\n\n def parse(self):\n \"\"\"\n :rtype None:\n \"\"\"\n with open(self._filepath) as f:\n # potential problem for python3 version. don't want to fix it now\n delimiter = '‖'.encode('utf-8')\n # read one line just to skip header of the file\n f.readline()\n for row in f:\n # hate it, but still. splitlines to remove \\n character,\n # sptil by delimiter and zip with column names, to get\n # dict with all product data\n row_to_dict = {x[0]: x[1] for x in zip(self.COLUMNS,\n row.splitlines()[0].split(delimiter))}\n\n yield row_to_dict\n\n\nclass ProductsFileParser(FeedFileParser):\n \"\"\"Override header for products file\"\"\"\n COLUMNS = ['sid', 'source_internal_id', 'pid', 'productname', 'category', 'manufacturer', 'url', 'pic_url' ]\n\n def __init__(self, filepath):\n super(ProductsFileParser, self).__init__(filepath)\n\n\nclass ProductIDsFileParser(FeedFileParser):\n \"\"\"Override header for product_ids file\"\"\"\n COLUMNS = ['pid', 'id_kind', 'id_value']\n\n def __init__(self, filepath):\n super(ProductIDsFileParser, self).__init__(filepath)\n\n\nclass CacheForProductIDs(object):\n \"\"\"\n object, that will be used to store product_id : id_kind info\n maybe structure will be improved in future\n {\n 'product_id' : {\n 'kind_id' : 'kind_id_value',\n 'product_name' : 'name',\n 'source_internal_id' : 'id',\n 'source_id' : id\n }\n }\n \"\"\"\n\n def __init__(self):\n self._products = {}\n\n def __getitem__(self, key):\n \"\"\"Method for debugging, for get element like cache[index].\"\"\"\n key = self._products.keys()[key]\n print({key: self._products[key]})\n return\n\n def _get_kind_id(self, source_id):\n \"\"\"\n :param source_id: already changed source_id with MAGIC_NUMBER at the\n beginning\n \"\"\"\n # TODO(mdovgal): need to add try / except block\n # and just log message. 'cause it's non trivial error\n return SOURCE_KINDS_TABLE[source_id]\n\n def add(self, product_id, **kwargs):\n \"\"\"\n :param product_id:\n :param kwargs: source_id\n product_name\n source_internal_id\n :rtype: None\n \"\"\"\n # TODO(mdovgal): need to figure out how to deal with the situation\n # when we already have this product id in cache\n kwargs['source_id'] = MAGIC_NUMBER + kwargs['source_id']\n\n if 'source_id' in kwargs:\n kwargs['kind_id'] = self._get_kind_id(kwargs['source_id'])\n\n # change from original valuechecher source id to our with MN before\n\n self._products[product_id] = dict(**kwargs)\n\n def get(self, product_id):\n \"\"\"\n :param product_id: valuechecker id of a product\n :return: None or dict with kind_id and product name\n info\n \"\"\"\n try:\n return self._products[product_id]\n except KeyError as e:\n # if we don't have this product\n # very awkward situation, but still real\n # log it and reraise\n\n # now I skip products for source_id = 10000. so return None if\n # we can't find product\n return None\n # raise\n\n def count(self):\n \"\"\":return: amount of all cached products.\"\"\"\n return len(self._products.keys())\n\n def print_all(self):\n \"\"\"pprint all products that were cached.\"\"\"\n print(self._products)\n\n\nclass ValueCheckerSpider(ala_spider.AlaSpider):\n \"\"\"\n main class for dealing with valuechecker feed in files\n \"\"\"\n name = 'valuechecker_net'\n products_file_url = \"https://us-east4-consummate-fold-158813.cloudfunctions.net/vc-alatest-feed-pipeline?filename=products.csv&api_key=M4UxWBb6Gf9gmUhHFfY9Mc4t7jM7zDcb\"\n product_ids_file_url = \"https://us-east4-consummate-fold-158813.cloudfunctions.net/vc-alatest-feed-pipeline?filename=product_ids.csv&api_key=M4UxWBb6Gf9gmUhHFfY9Mc4t7jM7zDcb\"\n # start url contains link to gcp cloud function on vc side that\n # will return real function to products file.\n # also we have one starting url for products file. product_ids file\n # need to be process after products one. That's why to be sure that\n # it will be executed after Request will be triggered at the end of\n # parse_product_file function\n start_urls = {products_file_url,\n product_ids_file_url}\n\n def __init__(self, *args, **kwargs):\n super(ValueCheckerSpider, self).__init__(*args, **kwargs)\n self.cache = CacheForProductIDs()\n self.process_file_callbacks = {'products': self.process_products_file}\n # create folder for temp files if it is not there for some reason\n try:\n os.makedirs('/tmp/valuechecker_feedin')\n except OSError as e:\n pass\n\n\n def parse(self, response):\n \"\"\"\n :param response: standart scrapy response object\n For this spider this function will accept different links as\n parameters and after make another call to get real file.\n Cloud function intergration on VC side is used.\n \"\"\"\n if response.request.url in self.start_urls:\n yield Request(url=response.body, callback=self.parse_vc_file)\n\n def parse_vc_file(self, response):\n \"\"\"save file recieved from GCS bucket on drive to be able to use\n old spiders code, not to change idea of processing local files.\"\"\"\n # here file name can be only one, but not to make if/else statements\n # to check if we get something\n filenames = re.findall(r'vc2-feeds-bucket/(.*?).csv', response.url)\n for filename in filenames:\n with open(files[filename], 'w') as f:\n f.write(response.body)\n\n for item in self.process_file_callbacks[filename]():\n yield item\n\n def process_products_file(self):\n \"\"\"\n process product files with saving categories, products and\n product ids (for real kind id and for valuechecker_pid)\n \"\"\"\n products = ProductsFileParser(filepath=PATH_TO_PRODUCTS_FILE)\n for product in products.parse():\n # get kind_id from source_kinds_table. will be None if we don't\n # know this source.\n kind_id = SOURCE_KINDS_TABLE.get(MAGIC_NUMBER + product['sid'], None)\n\n if kind_id is None:\n continue\n\n self.cache.add(product_id=product['pid'],\n source_id=product['sid'],\n product_name=product['productname'],\n source_internal_id=product['source_internal_id'])\n\n item = items.ProductItem()\n item['ProductName'] = product['productname']\n item['source_internal_id'] = product['source_internal_id']\n item['OriginalCategoryName'] = product['category']\n item['TestUrl'] = product['url']\n item['ProductManufacturer'] = product['manufacturer']\n item['PicURL'] = product['pic_url']\n item['source_id'] = MAGIC_NUMBER + product['sid']\n yield item\n\n # save product for original kind (`local_kind` like `geizhals_de_id`. See story #169011934)\n prod_id_item = items.ProductIdItem()\n prod_id_item['ID_kind'] = kind_id\n prod_id_item['ID_value'] = product['source_internal_id']\n prod_id_item['source_internal_id'] = product['source_internal_id']\n prod_id_item['source_id'] = MAGIC_NUMBER + product['sid']\n prod_id_item['ProductName'] = product['productname']\n yield prod_id_item\n\n # save product item for valuechecker_pid kind\n prod_id_item = items.ProductIdItem()\n prod_id_item['ID_kind'] = 'valuechecker_pid'\n prod_id_item['ID_value'] = product['pid']\n prod_id_item['source_internal_id'] = product['source_internal_id']\n prod_id_item['source_id'] = MAGIC_NUMBER + product['sid']\n prod_id_item['ProductName'] = product['productname']\n yield prod_id_item\n\n # here will be yield category\n category = items.CategoryItem()\n category['category_path'] = product['category']\n category['source_id'] = MAGIC_NUMBER + product['sid']\n yield category\n\n allowed_kind_ids = ['first_publish_date', 'EAN', 'screen_size', 'MPN']\n\n product_ids = ProductIDsFileParser(filepath=PATH_TO_PRODUCT_IDS_FILE)\n for product_id in product_ids.parse():\n # we skip all product ids whose kind id is not in the list\n if product_id['id_kind'] not in allowed_kind_ids:\n continue\n # get product from cache. we add them during parsing products file\n cached_product = self.cache.get(product_id['pid'])\n # check if product exists in our cache. skip if not\n if not cached_product:\n continue\n\n item = items.ProductIdItem()\n\n item['ID_kind'] = product_id['id_kind']\n item['ID_value'] = product_id['id_value']\n item['source_internal_id'] = cached_product['source_internal_id']\n item['ProductName'] = cached_product['product_name']\n item['source_id'] = cached_product['source_id']\n yield item", "sub_path": "alascrapy/spiders/valuechecker_net.py", "file_name": "valuechecker_net.py", "file_ext": "py", "file_size_in_byte": 12505, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pprint.pprint", "line_number": 136, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 191, "usage_type": "call"}, {"api_name": "alascrapy.spiders.base_spiders.ala_spider.AlaSpider", "line_number": 194, "usage_type": "attribute"}, {"api_name": "alascrapy.spiders.base_spiders.ala_spider", "line_number": 194, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 216, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 229, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 236, "usage_type": "call"}, {"api_name": "alascrapy.items.ProductItem", "line_number": 263, "usage_type": "call"}, {"api_name": "alascrapy.items", "line_number": 263, "usage_type": "name"}, {"api_name": "alascrapy.items.ProductIdItem", "line_number": 274, "usage_type": "call"}, {"api_name": "alascrapy.items", "line_number": 274, "usage_type": "name"}, {"api_name": "alascrapy.items.ProductIdItem", "line_number": 283, "usage_type": "call"}, {"api_name": "alascrapy.items", "line_number": 283, "usage_type": "name"}, {"api_name": "alascrapy.items.CategoryItem", "line_number": 292, "usage_type": "call"}, {"api_name": "alascrapy.items", "line_number": 292, "usage_type": "name"}, {"api_name": "alascrapy.items.ProductIdItem", "line_number": 310, "usage_type": "call"}, {"api_name": "alascrapy.items", "line_number": 310, "usage_type": "name"}]} +{"seq_id": "100941791", "text": "#!/usr/bin/env python\n\n## ========================================================================= ##\n## ##\n## Filename: fileHelper.py ##\n## ##\n## ##\n## Author: Fraunhofer Institut fuer Graphische Datenverarbeitung (IGD) ##\n## Competence Center Interactive Engineering Technologies ##\n## Fraunhoferstr. 5 ##\n## 64283 Darmstadt, Germany ##\n## ##\n## Rights: Copyright (c) 2018 by Fraunhofer IGD. ##\n## All rights reserved. ##\n## Fraunhofer IGD provides this product without warranty of any kind ##\n## and shall not be liable for any damages caused by the use ##\n## of this product. ##\n## ##\n## ========================================================================= ##\n\nimport xmltodict\nimport dict2xml\nimport os.path\n\n\ndef parseXML(file):\n result = {}\n if (len(file) == 0):\n return result\n if not os.path.isfile(file):\n return result\n with open(file, 'r') as fd:\n result = xmltodict.parse(fd.read())\n return result\n\n\ndef writeXML(file, dict):\n result = {}\n if (len(dict) == 0):\n return\n with open(file, 'w') as fd:\n result = \"\"\n result = result + dict2xml.dict2xml(dict, wrap=\"\", indent=\" \")\n fd.write(result)\n", "sub_path": "code_examples/Python/app_generic_gui/PythonTools/fileHelper.py", "file_name": "fileHelper.py", "file_ext": "py", "file_size_in_byte": 1922, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.path.isfile", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 30, "usage_type": "name"}, {"api_name": "xmltodict.parse", "line_number": 33, "usage_type": "call"}, {"api_name": "dict2xml.dict2xml", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "90069727", "text": "# -*- coding: utf-8 -*-\nimport logging\n_logger = logging.getLogger(__name__)\nfrom datetime import datetime\nimport time\nfrom openerp.osv import osv\nfrom openerp.tools.translate import _\n\nfrom ads_data import ads_data\nfrom tools import convert_date\n\nclass ads_sales_order(ads_data):\n \"\"\"\n Handles the importation and exportation of a sales order's delivery order\n \"\"\"\n\n file_name_prefix = ['CMDE', 'CREX']\n xml_root = 'orders'\n\n def extract(self, picking_out):\n \"\"\"\n Takes a stock.picking browse_record and extracts the\n appropriate data into self.data\n\n @param picking_out: browse_record(stock.picking)\n \"\"\"\n picking = picking_out.pool['stock.picking'].browse(picking_out._cr, 1, picking_out.id)\n shipping_partner = picking_out.sale_id.partner_shipping_id\n invoice_partner = picking_out.sale_id.partner_invoice_id\n carrier_name = picking_out.sale_id.carrier_id and picking_out.sale_id.carrier_id.ads_ref or ''\n\n # Delivery method can also be added as a move line, so find all move lines whose products\n # are the delivery products of a delivery method and save IDS and ads ref for later\n carrier_move_ids = []\n if not carrier_name:\n carrier_obj = picking_out.pool['delivery.carrier']\n product_obj = picking_out.pool['product.product']\n\n product_ids = [move.product_id.id for move in picking_out.move_lines if move.product_id]\n carrier_map = product_obj.is_delivery_method(picking_out._cr, 1, product_ids)\n\n carrier_product_ids = [k for k, v in carrier_map.iteritems() if v]\n carrier_move_ids = [move.id for move in picking.move_lines if move.product_id and move.product_id.id in carrier_product_ids]\n\n for move in picking_out.move_lines:\n if move.id in carrier_move_ids:\n carrier = carrier_obj.browse(picking_out._cr, 1, carrier_map[move.product_id.id][0])\n carrier_name = carrier.ads_ref or ''\n\n so_data = {\n # general\n 'NUM_CMDE': picking.ads_send_number and picking_out.sale_id.name + '-' + str(picking.ads_send_number) or picking_out.sale_id.name,\n 'NUM_FACTURE_BL': picking_out.name,\n 'DATE_EDITION': convert_date(picking_out.date),\n 'MONTANT_TOTAL_TTC': picking_out.sale_id.amount_total,\n 'DATE_ECHEANCE': convert_date(picking_out.min_date),\n 'TYPE_ENVOI': carrier_name,\n\n # invoice_partner address and contact\n 'SOCIETE_FAC': invoice_partner.is_company and invoice_partner.name or '',\n 'NOM_CLIENT_FAC': invoice_partner.name or '',\n 'ADR1_FAC': invoice_partner.street or '',\n 'ADR2_FAC': invoice_partner.street2 or '',\n 'CP_FAC': invoice_partner.zip or '',\n 'VILLE_FAC': invoice_partner.city or '',\n 'ETAT_FAC': invoice_partner.state_id and invoice_partner.state_id.name or '',\n 'PAYS_FAC': invoice_partner.country_id and invoice_partner.country_id.name or '',\n 'CODE_ISO_FAC': invoice_partner.country_id and invoice_partner.country_id.code or '',\n\n # delivery address and contact\n 'SOCIETE_LIV': shipping_partner.is_company and shipping_partner.name or '',\n 'NOM_CLIENT_LIV': shipping_partner.name or '',\n 'ADR1_LIV': shipping_partner.street or '',\n 'ADR2_LIV': shipping_partner.street2 or '',\n 'CP_LIV': shipping_partner.zip or '',\n 'VILLE_LIV': shipping_partner.city or '',\n 'ETAT_LIV': shipping_partner.state_id and shipping_partner.state_id.name or '',\n 'PAYS_LIV': shipping_partner.country_id and shipping_partner.country_id.name or '',\n 'CODE_ISO_LIV': shipping_partner.country_id and shipping_partner.country_id.code or '',\n 'TELEPHONE_LIV': shipping_partner.phone or u'no_phone',\n 'EMAIL_LIV': shipping_partner.email or u'noemail@incontinence-protection.com',\n }\n\n # asserts for required data\n required_data = {\n 'NUM_CMDE': 'The picking was not created by a sales order',\n 'NUM_FACTURE_BL': 'This should never happen - please contact OpenERP',\n 'NOM_CLIENT_FAC': 'Invoice partner name',\n 'ADR1_FAC': 'Invoice partner address line 1',\n 'CP_FAC': 'Invoice partner zip',\n 'VILLE_FAC': 'Invoice partner city',\n 'CODE_ISO_FAC': 'Invoice partner country',\n 'NOM_CLIENT_LIV': 'Shipping partner name',\n 'ADR1_LIV': 'Shipping partner address line 1',\n 'CP_LIV': 'Shipping partner zip',\n 'VILLE_LIV': 'Shipping partner city',\n 'CODE_ISO_LIV': 'Shipping partner country',\n 'TELEPHONE_LIV': 'Shipping partner phone',\n 'MONTANT_TOTAL_TTC': 'This should never happen - please contact OpenERP',\n }\n\n missing_data = {}\n for field in required_data:\n if not so_data[field]:\n missing_data[field] = required_data[field]\n\n if missing_data:\n message = _('While processing sales order %s and picking_out %s there was some data missing for the following required fields:' \\\n % (so_data['NUM_CMDE'], so_data['NUM_FACTURE_BL'])) + '\\n\\n' \\\n + \"\\n\".join(sorted(['- ' + _(missing_data[data]) for data in missing_data]))\\\n + '\\n\\n' + _('These fields must be filled before we can continue')\n raise osv.except_osv(_('Missing Required Data'), message)\n\n self.insert_data('order', so_data)\n\n line_seq = 1\n for move in picking_out.move_lines:\n\n # skip lines that are cancelled, or don't have a product, or have a discount, delivery method or service product\n if move.state == 'cancel' \\\n or not move.product_id \\\n or move.id in carrier_move_ids \\\n or move.product_id.discount \\\n or move.product_id.type == 'service':\n continue\n\n # Raise error if missing x_new_ref\n if not move.product_id.x_new_ref:\n raise osv.except_osv(_('Missing Reference'), _('Product \"%s\" on picking_out \"%s\" is missing an IP Reference. One must be entered before we can continue.') % (move.product_id.name, picking_out.name) )\n\n line = {\n 'NUM_FACTURE_BL': picking_out.name,\n 'CODE_ART': move.product_id.x_new_ref,\n 'LIBELLE_ART': move.product_id.name or '',\n 'QTE': move.product_qty,\n 'OBLIGATOIRE': '1',\n }\n self.insert_data('order.articles.line', line)\n line_seq += 1\n\n return self\n\n def upload(self, cr, ads_manager):\n \"\"\"\n Only upload BL's with article lines. Otherwise, all articles are non-uploadable (service,\n discount, delivery product), so return False so the BL can be automatically closed at sale_order.py level.\n\n Save uploaded file name to ads_file_name field.\n \"\"\"\n if self.data['order']['articles']:\n res = super(ads_sales_order, self).upload(cr, ads_manager)\n if self.browse_record and self.file_name:\n self.browse_record.write({'ads_file_name': self.file_name})\n return res\n else:\n return False\n\n def _find_picking(self, cr, picking_out_obj, picking_name):\n \"\"\" Finds pickings by name. If name >= 30, use wildcard at end due to length limitations of ADS \"\"\"\n if len(picking_name) < 30:\n return picking_out_obj.search(cr, 1, [('name', '=', picking_name)])\n else:\n return picking_out_obj.search(cr, 1, [('name', 'ilike', picking_name)])\n\n def process(self, pool, cr, expedition):\n \"\"\"\n Update picking tracking numbers / cancel picking orders\n @param pool: OpenERP object pool\n @param cr: OpenERP database cursor\n @param AutoVivification expedition: Data from ADS describing the expedition of the SO\n \"\"\"\n # extract information\n assert 'NUM_FACTURE_BL' in expedition, 'An expedition has been skipped because it was missing a NUM_FACTURE_BL'\n\n picking_name = expedition['NUM_FACTURE_BL']\n status = 'STATUT' in expedition and expedition['STATUT'] or ''\n tracking_number = 'NUM_TRACKING' in expedition and expedition['NUM_TRACKING'] or ''\n\n # find original picking\n picking_out_obj = pool.get('stock.picking')\n picking_ids = self._find_picking(cr, picking_out_obj, picking_name)\n\n assert len(picking_ids) == 1, 'Found %s pickings with name %s. Should have found 1' % (len(picking_ids), picking_name)\n picking_id, = picking_ids\n picking_out = picking_out_obj.browse(cr, 1, picking_id)\n\n # set / append tracking number on picking\n if tracking_number:\n try:\n if picking_out.carrier_tracking_ref:\n existing_tracking_number = picking_out.carrier_tracking_ref.split(',')\n if str(tracking_number) not in existing_tracking_number:\n existing_tracking_number.append(tracking_number)\n tracking_number = ','.join(map(str, existing_tracking_number))\n except:\n pass\n picking_out_obj.write(cr, 1, picking_id, {'carrier_tracking_ref': tracking_number})\n\n # if status is R, order has been cancelled by ADS because of lack of stock. We then need to\n # upload the same BL with a new name and new SO name. We handle this by cancelling BL,\n # duplicating it, confirming it then fixing the SO state from shipping_except\n if status == 'R':\n\n assert picking_out.state in ['assigned', 'confirmed'], \\\n _(\"The picking %s was not in state assigned or confirmed, and therefore cannot be cancelled\") % picking_name\n\n picking_obj = pool['stock.picking']\n picking_out_obj = pool['stock.picking']\n sale_order_obj = pool['sale.order']\n\n # get stock.picking version of stock.picking for access to send number field\n picking = picking_obj.browse(cr, 1, picking_out.id)\n sale = picking.sale_id\n\n # value for new picking's ads_send_number\n send_number = picking.ads_send_number + 1 or 1\n\n # Cancel original picking, then duplicate and confirm it\n picking_out_obj.action_cancel(cr, 1, [picking_id])\n\n # specify a name for the new BL otherwise stock module will delete the origin from it's values\n defaults = {\n 'ads_send_number': send_number,\n 'name': pool['ir.sequence'].get(cr, 1, 'stock.picking')\n }\n\n picking_id = picking_obj.copy(cr, 1, picking_id, defaults)\n picking_obj.signal_button_confirm(cr, 1, [picking_id])\n\n # fix sale order state from shipping_except to in progress\n sale_values = {}\n if sale.state == 'shipping_except':\n sale_values['state'] = 'progress'\n sale_values['shipped'] = False\n\n if (sale.order_policy == 'manual'):\n for line in sale.order_line:\n if (not line.invoiced) and (line.state not in ('cancel', 'draft')):\n sale_values['state'] = 'manual'\n break\n if sale_values:\n sale_order_obj.write(cr, 1, sale.id, sale_values)\n return True\n", "sub_path": "alpha_direct_services/ads_sales_order.py", "file_name": "ads_sales_order.py", "file_ext": "py", "file_size_in_byte": 11630, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 3, "usage_type": "call"}, {"api_name": "ads_data.ads_data", "line_number": 12, "usage_type": "name"}, {"api_name": "tools.convert_date", "line_number": 54, "usage_type": "call"}, {"api_name": "tools.convert_date", "line_number": 56, "usage_type": "call"}, {"api_name": "openerp.tools.translate._", "line_number": 108, "usage_type": "call"}, {"api_name": "openerp.tools.translate._", "line_number": 110, "usage_type": "call"}, {"api_name": "openerp.tools.translate._", "line_number": 111, "usage_type": "call"}, {"api_name": "openerp.osv.osv.except_osv", "line_number": 112, "usage_type": "call"}, {"api_name": "openerp.osv.osv", "line_number": 112, "usage_type": "name"}, {"api_name": "openerp.tools.translate._", "line_number": 112, "usage_type": "call"}, {"api_name": "openerp.osv.osv.except_osv", "line_number": 129, "usage_type": "call"}, {"api_name": "openerp.osv.osv", "line_number": 129, "usage_type": "name"}, {"api_name": "openerp.tools.translate._", "line_number": 129, "usage_type": "call"}, {"api_name": "openerp.tools.translate._", "line_number": 205, "usage_type": "call"}]} +{"seq_id": "586576358", "text": "import cv2 as cv\nimport numpy as np\nwebcam = False\ncThr = [100, 100]\ncap = cv.VideoCapture(0)\ncap.set(50, 160)\ncap.set(3, 1920)\ncap.set(4, 1080)\nimg = cv.imread('human_900x1200.jpeg')\nimgBlur = cv.GaussianBlur(img, (5, 5), 1)\nimgray = cv.cvtColor(imgBlur, cv.COLOR_BGR2GRAY)\nimgCanny = cv.Canny(imgray, cThr[0], cThr[1])\nret, threshold = cv.threshold(imgray, 127, 255, 0)\ncontours, hierarchy = cv.findContours(threshold, cv.RETR_TREE, cv.CHAIN_APPROX_NONE)\nprint(len(str(contours)))\nif webcam:\n success, img = cap.read()\ncv.drawContours(img, contours, -1, (0, 255, 0), 3)\ncv.imshow('Image', img)\ncv.imshow('BlUR', imgBlur)\ncv.imshow('Gray', imgray)\ncv.imshow('Canny', imgCanny)\ncv.waitKey(0)\n\n# while True:\n\n# ret, thresh = cv.threshold(imgray, 127, 255, 0)\n# im2, contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)\n# print(imgray)\n", "sub_path": "measurementapp/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 868, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cv2.VideoCapture", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "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.Canny", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 14, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_NONE", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.drawContours", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 19, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "93547286", "text": "\nfrom flask import Flask\nfrom flask_script import Manager\n\nfrom app.views import blue, login_manager\nfrom app.modles import db\n\napp = Flask(__name__)\n\napp.register_blueprint(blueprint=blue, url_prefix='/app')\n\n# 数据库配置\napp.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+pymysql://root:1234@127.0.0.1:3306/f_login_db'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n\napp.config['SECRET_KEY'] = 'secret_key'\n\n# 没有登录跳转地址\nlogin_manager.login_view = 'app.login'\n\n# 绑定\ndb.init_app(app)\n\nlogin_manager.init_app(app)\n\nmanage = Manager(app)\n\nif __name__ == '__main__':\n manage.run()", "sub_path": "5.flask/day05/manage.py", "file_name": "manage.py", "file_ext": "py", "file_size_in_byte": 606, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "app.views", "line_number": 8, "usage_type": "name"}, {"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "app.views.register_blueprint", "line_number": 10, "usage_type": "call"}, {"api_name": "app.views", "line_number": 10, "usage_type": "name"}, {"api_name": "app.views.blue", "line_number": 10, "usage_type": "name"}, {"api_name": "app.views.config", "line_number": 13, "usage_type": "attribute"}, {"api_name": "app.views", "line_number": 13, "usage_type": "name"}, {"api_name": "app.views.config", "line_number": 14, "usage_type": "attribute"}, {"api_name": "app.views", "line_number": 14, "usage_type": "name"}, {"api_name": "app.views.config", "line_number": 16, "usage_type": "attribute"}, {"api_name": "app.views", "line_number": 16, "usage_type": "name"}, {"api_name": "app.views.login_manager.login_view", "line_number": 19, "usage_type": "attribute"}, {"api_name": "app.views.login_manager", "line_number": 19, "usage_type": "name"}, {"api_name": "app.modles.db.init_app", "line_number": 22, "usage_type": "call"}, {"api_name": "app.views", "line_number": 22, "usage_type": "argument"}, {"api_name": "app.modles.db", "line_number": 22, "usage_type": "name"}, {"api_name": "app.views.login_manager.init_app", "line_number": 24, "usage_type": "call"}, {"api_name": "app.views", "line_number": 24, "usage_type": "argument"}, {"api_name": "app.views.login_manager", "line_number": 24, "usage_type": "name"}, {"api_name": "flask_script.Manager", "line_number": 26, "usage_type": "call"}, {"api_name": "app.views", "line_number": 26, "usage_type": "argument"}]} +{"seq_id": "321417130", "text": "# coding: UTF-8\nfrom django import forms\nfrom django.core.urlresolvers import reverse\nfrom django.forms.widgets import HiddenInput\n\nfrom crispy_forms.layout import HTML, Div, Field, Fieldset, Layout, Row\nfrom parsley.decorators import parsleyfy\n\nfrom e_travel.apps.main.models import Evaluation\nfrom e_travel.common.forms.forms import (\n CommonForm, CommonFormMixin, CommonFormMsgMixin, CommonModelForm\n)\n\nORDER_TOUR_DESC_PLACEHOLDER = \\\n u'Опишите тип тура который Вы желаете (страна, курорт, тип поездки), ' \\\n u'желаемые даты путевки и ценовой диапозон...'\n\n\n@parsleyfy\nclass QuestionFormMixin(CommonFormMsgMixin, CommonForm):\n \"\"\" Форма - \"Вопрос\"\n \"\"\"\n name = forms.CharField(max_length=20, required=True, label=u'Имя')\n email = forms.EmailField(required=True, label=u'E-mail')\n message = forms.CharField(max_length=200, required=True, label=u'Сообщение', widget=forms.Textarea)\n\n def __init__(self, *args, **kwargs):\n super(QuestionFormMixin, self).__init__(*args, **kwargs)\n self.add_form_content(\n Layout(\n Row(\n Fieldset(None, 'name', css_class='col-xs-6'),\n Fieldset(None, 'email', css_class='col-xs-6'),\n ),\n 'message'\n )\n )\n\n\n@parsleyfy\nclass OrderTourFormMixin(CommonFormMsgMixin, CommonForm):\n \"\"\" Форма заказа тура\n \"\"\"\n name = forms.CharField(max_length=20, required=True, label=u'Имя')\n email = forms.EmailField(required=True, label=u'E-mail')\n phone = forms.CharField(max_length=20, required=True, label=u'Телефон')\n description = forms.CharField(max_length=200, required=True, label=u'Описание', widget=forms.Textarea)\n\n def __init__(self, *args, **kwargs):\n super(OrderTourFormMixin, self).__init__(*args, **kwargs)\n self.add_form_content(\n Layout(\n Row(\n Fieldset(\n None,\n Field(\n 'description',\n placeholder=ORDER_TOUR_DESC_PLACEHOLDER\n ),\n css_class='col-xs-6 col-sm-5 col-md-6'\n ),\n Fieldset(\n None, 'name', 'phone', 'email',\n HTML(u\"Онлайн поиск\"),\n css_class='col-xs-6 col-sm-4'\n ),\n Fieldset(\n None,\n HTML(u\"Онлайн поиск\"),\n css_class='col-xs-12 col-sm-3 col-md-2 hidden-xs center-content'\n )\n )\n )\n )\n\n\n@parsleyfy\nclass EvaluationFormMixin(CommonFormMixin, CommonModelForm):\n \"\"\"\n Форма для отзывов\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super(EvaluationFormMixin, self).__init__(*args, **kwargs)\n self.helper.form_action = reverse('main_evaluation_form')\n self.add_form_content(Layout(\n Row(\n Fieldset(None, 'name', css_class='col-md-4'),\n Fieldset(None, 'email', css_class='col-md-4'),\n Fieldset(None, 'resting_place', css_class='col-md-4'),\n ),\n 'general_evaluation',\n 'manager_evaluation',\n 'rating',\n Div(\n css_class='rating-stars',\n template='crispy_forms/form_includes/evaluation_rating.html'\n )\n ))\n\n class Meta:\n model = Evaluation\n fields = \"__all__\"\n widgets = {\n 'rating': HiddenInput,\n 'general_evaluation': forms.Textarea,\n 'manager_evaluation': forms.Textarea\n }\n", "sub_path": "e_travel/apps/main/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 4028, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "e_travel.common.forms.forms.CommonFormMsgMixin", "line_number": 20, "usage_type": "name"}, {"api_name": "e_travel.common.forms.forms.CommonForm", "line_number": 20, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 23, "usage_type": "name"}, {"api_name": "django.forms.EmailField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 24, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 25, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 25, "usage_type": "attribute"}, {"api_name": "crispy_forms.layout.Layout", "line_number": 30, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Row", "line_number": 31, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Fieldset", "line_number": 32, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Fieldset", "line_number": 33, "usage_type": "call"}, {"api_name": "parsley.decorators.parsleyfy", "line_number": 19, "usage_type": "name"}, {"api_name": "e_travel.common.forms.forms.CommonFormMsgMixin", "line_number": 41, "usage_type": "name"}, {"api_name": "e_travel.common.forms.forms.CommonForm", "line_number": 41, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 44, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 44, "usage_type": "name"}, {"api_name": "django.forms.EmailField", "line_number": 45, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 45, "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.forms.CharField", "line_number": 47, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 47, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 47, "usage_type": "attribute"}, {"api_name": "crispy_forms.layout.Layout", "line_number": 52, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Row", "line_number": 53, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Fieldset", "line_number": 54, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Field", "line_number": 56, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Fieldset", "line_number": 62, "usage_type": "call"}, {"api_name": "crispy_forms.layout.HTML", "line_number": 64, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Fieldset", "line_number": 67, "usage_type": "call"}, {"api_name": "crispy_forms.layout.HTML", "line_number": 69, "usage_type": "call"}, {"api_name": "parsley.decorators.parsleyfy", "line_number": 40, "usage_type": "name"}, {"api_name": "e_travel.common.forms.forms.CommonFormMixin", "line_number": 78, "usage_type": "name"}, {"api_name": "e_travel.common.forms.forms.CommonModelForm", "line_number": 78, "usage_type": "name"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 85, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Layout", "line_number": 86, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Row", "line_number": 87, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Fieldset", "line_number": 88, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Fieldset", "line_number": 89, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Fieldset", "line_number": 90, "usage_type": "call"}, {"api_name": "crispy_forms.layout.Div", "line_number": 95, "usage_type": "call"}, {"api_name": "e_travel.apps.main.models.Evaluation", "line_number": 102, "usage_type": "name"}, {"api_name": "django.forms.widgets.HiddenInput", "line_number": 105, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 106, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 106, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 107, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 107, "usage_type": "name"}, {"api_name": "parsley.decorators.parsleyfy", "line_number": 77, "usage_type": "name"}]} +{"seq_id": "71121734", "text": "from tabulate import tabulate\nimport vcf_apps\nimport time \nimport sequence_modification\n\n\n\n#ENSG00000177535\n\n\n\ndef seq_check(snp, seq_pos, seq):\n \"slouzi ke kontorole, ze usek DNA ktery podleha mutaci koresponduje s referencni sekvenci\"\n snp_len = len(snp)\n if snp != seq[seq_pos:seq_pos + snp_len].upper():\n print('NESEDI SEKVENCE, ZVAZTE ZMENU SOUBORU S REFERENCNIMI SEKVENCEMI!')\n else:\n pass\n return\n\n \ndef mutation_count(changes, transcripts_id, log, chromosome, id):\n \n log_report = \"{}\\t{}\\t{}\\t{}\\t{}\\t{}\\t{}\\n\"\n for count, transcript_id in enumerate(transcripts_id):\n ins = 0\n dels = 0\n subs = 0\n if len(changes[count]) == 0:\n log.write(log_report.format(chromosome, id, transcripts_id, 0, 0, 0, \"\"))\n return\n for pos, allels in changes[count].items():\n size = allels[0]\n for allel in allels[1]:\n if size == len(allel):\n subs += 1\n elif size < len(allel):\n ins += 1\n else:\n dels += 1\n \n log.write(log_report.format(chromosome, id, transcript_id, subs, dels, ins, \"\"))\n return\n\ndef snp_on_dna_minus(transcripts_seqs, snps, info):\n changes_list = [] #list ktery ma jako prvky slovniky, ktere slouzi k zaznamenani zmen na jednotlivych transkriptech\n \"funkce prirazuje snp na pozice ve spravnych cds, vystupem je dict, jenz ma jako klice tuple (cislo refseq, pozice na refsequ) a jako hodnotu vsechny zmeny z vcf na dane pozici\"\n for count, transcript in enumerate(info[5:]): #iterace skrze transkripty\n seqs = transcripts_seqs[count] #prirazeni sekvenci k transkriptu\n cds_i = len(transcript) - 1 #kvuli - strandu se zacina odzadu\n cds_i_min = 3 #index prniho cds\n seq_i = len(seqs) - 1\n snp_i = 0\n snp_i_max = len(snps)\n flag = True\n changes = {} \n\n while snp_i < snp_i_max:\n #cyklus prohledava snp a prirazuje je spravnym cds\n while True:\n if snp_i == snp_i_max:\n flag = False\n break\n snp = snps[snp_i]\n pos = int(snp[2])\n cds_start = transcript[cds_i][0]\n cds_end = transcript[cds_i][1]\n if pos >= cds_start and pos <= cds_end: #pozice snp je v rozpeti daneho genu, opusten while cyklus a snp prirazen do vystupniho tuplu\n break\n elif pos > cds_end and cds_i > cds_i_min: #pozice snp je vetsi nez nejvetsi pozice cds, a jsou jeste dalsi cds, takze pokracujeme s dalsim cds a seq\n cds_i -= 1\n seq_i -= 1\n continue\n elif pos < cds_start and snp_i < snp_i_max: #pozice snp je mensi nez zacatek cds, snp nepatri do cds, muze byt preskocen\n snp_i += 1\n continue\n else: #uz jsme dosahli konce cds, ostatni snp nejsou v genu, ukoncujeme cykly diky flagu\n flag = False\n break\n if flag:\n seq = seqs[seq_i] #sekvence dana k cds\n \n seq_pos = pos - cds_start #nalezeni indexu pozice snp v cds\n #print('{} = {} -> {}'.format(seq[seq_pos], snp[2], snp[3])) \n if snp[3] != 'N':\n seq_check(snp[3], seq_pos, seq)\n \n changes[(seq_i, seq_pos)] = (len(snp[3]), snp[4].split(','))\n snp_i += len(snp[3])\n else:\n break\n changes_list.append(changes)\n\n return changes_list\n\ndef snp_on_dna_plus(transcript_seqs, snps, info):\n \n \n \"funkce prirazuje snp na pozice ve spravnych cds, vystupem je dict, jenz ma jako klice tuple (cislo refseq, pozice na refsequ) a jako hodnotu vsechny zmeny z vcf na dane pozici\"\n \n changes_list = [] #list ktery ma jako prvky slovniky, ktere slouzi k zaznamenani zmen na jednotlivych transkriptech\n \"funkce prirazuje snp na pozice ve spravnych cds, vystupem je dict, jenz ma jako klice tuple (cislo refseq, pozice na refsequ) a jako hodnotu vsechny zmeny z vcf na dane pozici\"\n for count, transcript in enumerate(info[5:]): #iterace skrze transkripty\n seqs = transcript_seqs[count]\n \n changes = {}\n\n cds_i = 3 #index prvniho cds\n cds_i_max = len(transcript)\n seq_i = 0\n snp_i = 0\n snp_i_max = len(snps)\n \n flag = True\n\n while snp_i < snp_i_max:\n #cyklus pro vyhledani spravneho CDS\n while True:\n if snp_i == snp_i_max:\n flag = False\n break\n snp = snps[snp_i]\n pos = int(snp[2])\n cds_start = transcript[cds_i][0]\n cds_end = transcript[cds_i][1]\n if pos >= cds_start and pos <= cds_end:\n break\n elif pos > cds_end and cds_i < cds_i_max - 1:\n cds_i += 1\n seq_i += 1\n continue\n elif pos < cds_start and snp_i < snp_i_max:\n snp_i += 1\n continue\n else:\n flag = False\n \n break\n if flag:\n seq = seqs[seq_i] #sekvence dana k cds\n \n seq_pos = pos - cds_start #nalezeni indexu pozice snp v cds\n #print('{} = {} -> {}'.format(seq[seq_pos], snp[2], snp[3])) \n \n if snp[3] != 'N':\n seq_check(snp[3], seq_pos, seq)\n\n changes[(seq_i, seq_pos)] = (len(snp[3]), snp[4].split(','))\n snp_i += 1\n else:\n break\n changes_list.append(changes)\n \n return changes_list\n\ndef write_to_fasta(file, seq, info = '' ):\n \"zapis do fasty, file je vystupovy soubor, seq je vstupni text, info je volitelne zahlavi\"\n file.write('>' + info + '\\n')\n rest = seq\n while rest != '':\n line = rest[:80]\n rest = rest[80:]\n file.write(line + '\\n')\n\ndef make_comparison_string(indexes, seq_snp, lens):\n comp_string = \"\"\n \n for i in range(len(indexes)):\n for j in range(len(indexes[i])):\n if lens[i][j] == 1:\n ind = indexes[i][j]\n if len(seq_snp[i][j][ind]) == 1:\n \n if isinstance(seq_snp[i][j], str):\n comp_string += 'N'\n else:\n comp_string += 'S'\n else:\n comp_string += 'N' + 'I' + 'X' * (len(seq_snp[i][j][ind]) - 2)\n else:\n l = lens[i][j]\n ind = indexes[i][j]\n l_snp = len(seq_snp[i][j][ind])\n if l == l_snp:\n comp_string += (l_snp - 1) * 'N' + 'I'\n elif l > l_snp:\n comp_string += 'D' + 'X' * (l_snp - 1)\n else:\n comp_string += 'N' * (l-1) + 'I' + 'X' * (l_snp - (l - 1) - 1)\n return comp_string\n\n\n\n\ndef aminoacid_comparison(ref_amk, new_amk, seq_snp, indexes, lens, strand):\n \"porovnani\"\n comp_string = make_comparison_string(indexes, seq_snp, lens)\n if strand == '-':\n comp_string = comp_string[::-1]\n \n if len(ref_amk) < len(new_amk):\n l = len(ref_amk)\n else:\n l = len(new_amk)\n #dels = 0\n #ins = 0\n #subs = 0\n dels = comp_string.count('D')\n ins = comp_string.count('I')\n subs = comp_string.count('S')\n #for i in range(l):\n # if ref_amk[i] != new_amk[i]:\n # kodon = comp_string[i*3: i*3+3]\n # dels += kodon.count('D')\n # ins += kodon.count('I')\n # subs += kodon.count('S')\n\n return(subs, dels, ins)\n\n\ndef get_seq_and_indexes(new_seq):\n \"vstupem je seznam dvojici, na prnim miste je nova sekvence, na druhem miste je pozice diky kterym vznikla ze sekvence, kde jsou baze jako seznamy\"\n whole_seq = \"\"\n indexes = []\n for seq, index in new_seq:\n whole_seq += seq\n indexes.append(index)\n return (whole_seq, indexes)\n\ndef analyse_newseq(newseq, strand, ref_seq, seq_snp, lens, log, gene_id, chromosome, seq_number, transcript_id):\n \"\"\"analyza vysledne sekvence, kontrola start a stop kodonu, zjisteni snp, deleci, inzerci\"\"\"\n log_report = \"{}\\t{}\\t{}\\t{}\\t{}\\t{}\\t{}\\n\"\n whole_seq, indexes = get_seq_and_indexes(newseq)\n if strand == '-':\n ref_aminoacids = sequence_modification.dna_to_aminoacids(sequence_modification.minus_strand(ref_seq))\n new_aminoacids = sequence_modification.dna_to_aminoacids(sequence_modification.minus_strand(whole_seq))\n else:\n ref_aminoacids = sequence_modification.dna_to_aminoacids(sequence_modification.plus_strand(ref_seq))\n new_aminoacids = sequence_modification.dna_to_aminoacids(sequence_modification.plus_strand(whole_seq))\n \n if gene_id == 'CPUR_00026':\n print() \n\n\n stop_ref = ref_aminoacids.find('*')\n stop_new = new_aminoacids.find('*')\n if stop_new != -1:\n new_aminoacids = new_aminoacids[:stop_new+1]\n ref_aminoacids = ref_aminoacids[:stop_ref+1]\n \n triplet_number = len(new_aminoacids)\n new_whole_seq = whole_seq[:triplet_number + 1]\n subs, dels, ins = aminoacid_comparison(ref_aminoacids, new_aminoacids, seq_snp, indexes, lens, strand)\n \n \n\n if stop_new == -1:\n #print(\"poruseni stop kodonu, nenasel se jiny, translace neni ukoncena\")\n info = \"destrukce STOP\"\n log.write(log_report.format(chromosome, gene_id + '.' + str(seq_number), transcript_id, subs,dels,ins,info))\n return \"\", info\n\n if new_aminoacids[0] != 'M':\n #print(\"poruseni start kodonu, translace neprobehne\")\n info = \"destrukce START\"\n log.write(log_report.format(chromosome, gene_id + '.' + str(seq_number), transcript_id, subs,dels,ins, info))\n return \"\", info\n\n \n\n \n if ref_aminoacids == new_aminoacids:\n #print(\"snp nevyvovaly zmenu v sekvenci amk\")\n info = \"synonymni SNP\"\n log.write(log_report.format(chromosome, gene_id + '.' + str(seq_number), transcript_id, subs,dels,ins, info))\n return whole_seq, info\n log.write(log_report.format(chromosome, gene_id + '.' + str(seq_number), transcript_id, subs,dels,ins, \"novy protein \" ))\n return whole_seq, \"novy protein\"\n \n \n\n\n \n\n\n\n\ndef iterative_newseq_maker(transcript_seqs, file, id, strand, snp_info, ref_seqs, seq_snp, lens, log, gene_id, chromosome, transcripts_id):\n \"Vystupem jsou vsechny sekvence poskladane ze vstupnich sekvenci\"\n out = open(file, 'a')\n \n for transcript_num, transcript in enumerate(transcript_seqs):\n index = 0\n indexes = [0] * len(transcript)\n act_seq = [0] * len(transcript)\n new_seq = []\n lengths = [0] * len(transcript)\n seq_number = 0\n while index != -1:\n if index == len(transcript): #poskladana od kazde sekvence jedna\n\n fasta_info = id + ' ' + str(seq_number) + ' transcript_id ' + transcripts_id[transcript_num] + ' strand ' + str(strand)\n seq, info = analyse_newseq(act_seq, strand, ref_seqs[transcript_num], seq_snp[transcript_num], lens[transcript_num], log, gene_id, chromosome, seq_number, transcripts_id[transcript_num])\n if id == 'CPUR_00564':\n print()\n if strand == '-':\n write_to_fasta(out, sequence_modification.plus_to_minus(\"\".join(seq)), fasta_info + ' ' + info)\n else:\n write_to_fasta(out, \"\".join(seq), fasta_info + ' ' + info)\n #print(seq_number)\n seq_number += 1\n \n index -= 1\n\n if indexes[index] == len(transcript[index]): #vyuzity vsechny moznosti pro danou sekvenci\n\n indexes[index] = 0\n index -= 1\n continue\n\n act_seq[index] = transcript[index][indexes[index]]\n length = len(transcript[index][indexes[index]])\n indexes[index] += 1\n lengths[index] = length\n index += 1\n\n out.close()\n return new_seq\n\n#CCE34763\n\ndef iterative_seq_maker(seq):\n \"vystupem vsechny mozne sekvence vznikle zmenami\"\n indexes = [0] * len(seq)\n lens = [0] * len(seq)\n act_seq = len(seq) * [0]\n index = 0\n new_seq = []\n \n while index != -1:\n \n if indexes[index] == len(seq[index]):\n act_seq[index] = []\n indexes[index] = 0\n index -= 1\n continue\n\n act_seq[index] = seq[index][indexes[index]]\n indexes[index] += 1\n \n \n\n if index == len(seq) - 1:\n new_seq.append((\"\".join(act_seq), indexes))\n else: index += 1 \n\n \n #if indexes[index] == len(seq[index]) :\n # if index != len(seq) - 1:\n # act_seq = act_seq[:-lens[index]]\n # indexes[index] = 0\n # index -= 1\n \n # continue\n \n #if indexes[index] != 0:\n # act_seq = act_seq[:-lens[index]]\n #act_seq += seq[index][indexes[index]]\n \n\n #lens[index] = len(seq[index][indexes[index]])\n #indexes[index] += 1\n \n #if index == len(seq) - 1:\n # new_seq.append(erase_deletions(act_seq))\n # act_seq = act_seq[:-lens[index]]\n \n\n #else:\n # index += 1\n return new_seq\n\ndef erase_deletions(seq):\n i = 0\n new_seq = \"\"\n for base in seq:\n if base != '*':\n new_seq += base\n return new_seq\n\n\n\ndef dna_change_seq(transcript_seqs, changes_list):\n \"vystupem je seznam new_seqs, ktery obsahuje prvky zmenenych sekvenci kvuli mutacim, tyto prvky jsou formou seznamu\"\n new_transcripts_seqs = []\n new_transcripts_lengths = []\n #delky pro pozdejsi zjisteni, kolik ref bazi se menilo\n for transcript_num,seqs in enumerate(transcript_seqs):\n changes = changes_list[transcript_num]\n lens = [] \n new_seqs = []\n for i in range(len(seqs)):\n new_seq = []\n length = []\n \n j = 0\n #for j in range(len(seqs[i])):\n while j < len(seqs[i]):\n \n try:\n base = changes[(i,j)]\n new_seq.append(base[1])\n length.append(base[0])\n j += base[0]\n \n except KeyError:\n base = seqs[i][j]\n new_seq.append(base)\n j += 1\n length.append(1)\n except ValueError:\n #print('bez <*>', base[1], base[0])\n new_seq.append(base[1])\n length.append(base[0])\n j += base[0]\n\n new_seqs.append(new_seq)\n lens.append(length)\n new_transcripts_seqs.append(new_seqs)\n new_transcripts_lengths.append(lens)\n\n return new_transcripts_seqs, new_transcripts_lengths\n\ndef dna_change(seqs, changes):\n \"vystupem seznam triplets, ktery obsahuje puvodni triplety, a new_triplets obsahujici triplety vznikle mutacemi, triplety jsou triprvkove seznamy\"\n \n triplets = []\n new_triplets = []\n triplet = []\n new_triplet = []\n for i in range(len(seqs)):\n for j in range(len(seqs[i])):\n \n try:\n base = changes[(i,j)]\n new_triplet.append(base)\n base = seqs[i][j]\n triplet.append(base)\n except KeyError:\n base = seqs[i][j]\n new_triplet.append(base)\n triplet.append(base)\n finally:\n if len(triplet) == 3:\n triplets.append(triplet)\n triplet = []\n new_triplets.append(new_triplet)\n new_triplet = []\n\n return (triplets, new_triplets)\n\n\ndef multi_seqs(transcript_seqs):\n \"vystupem jsou vsechny sekvence, ktere mohou vzniknout vlivem snp\"\n new_transcript_seqs = []\n if isinstance(transcript_seqs[0], list):\n for transcript in transcript_seqs:\n new_seqs = []\n for seq in transcript:\n \n new_seq = iterative_seq_maker(seq)\n new_seqs.append(new_seq)\n new_transcript_seqs.append(new_seqs)\n\n else:\n return iterative_seq_maker(transcript_seqs)\n \n \n\n return new_transcript_seqs\n\n\n\ndef show_sequence(table, seqs):\n \n\n while True:\n try:\n \n print(tabulate(table, stralign='center'))\n \n \n index = input(\n \"\"\"\n \\tZadejte: \n \\tčíslo CDS pro výpis DNA sekvence konkrétního CDS\n \\tc pro celou sekvenci DNA \n \\tk pro navrácení do předchozího menu\n \"\"\")\n if index == 'k':\n return\n else:\n strand = input('Zadejte - pro prohlidnuti reverse strand a + pro forward strand: ')\n \n if strand == '-':\n if index == 'c':\n print(sequence_modification.minus_strand(seqs))\n else:\n print(sequence_modification.minus_strand(seqs[int(index) - 1]))\n\n elif strand == '+':\n if index == 'c':\n print(sequence_modification.plus_strand(seqs))\n else:\n print(sequence_modification.plus_strand(seqs[int(index) - 1]))\n else:\n print('Zadali jste spatny strand')\n continue\n\n except IndexError:\n print('Zadali jste spatne cislo CDS, zkuste to znovu.')\n continue\n except ValueError:\n print('Zadali jste spatny znak, zkuste to znovu.')\n continue\n\ndef show_amk(table, transcript_seqs,strand):\n for seqs in transcript_seqs:\n if strand == '-':\n print(sequence_modification.dna_to_aminoacids(sequence_modification.minus_strand(seqs)))\n elif strand == '+':\n print(sequence_modification.dna_to_aminoacids(sequence_modification.plus_strand(seqs)))\n else:\n print(\"spatne precteny strand\")\n\n\ndef amk_changes(seqs, new_seqs, strand):\n \"\"\"funkce pro urceni zmen ktere zpusobi snp na amk, vysledkem jsou dva stejne dlouhe seznamy, \n jeden obsahuje puvodni triplety a druhy vznikle snp, pokud jich je vic, obsahuje seznam techto tripletu\"\"\"\n\n\n\"\"\" CHROM ID, GENE_ID, POCET SUBSTITUCI, POCET DELECI, POCET INZERCI, INFO\"\"\"\n\n\ndef show_snp(table, changes, seqs, id, vcf_header, strand, log, gene_id, chromosome,transcripts_id, full_genome = False, no_fasta = False ):\n \n while True:\n\n try:\n if full_genome:\n \n if not no_fasta:\n new_seq, lens = dna_change_seq(seqs, changes)\n new_seqs = multi_seqs(new_seq)\n fname = \"./fasta/\" +'full_genome' + '.fa'\n iterative_newseq_maker(new_seqs, fname, id, strand, changes, seqs, new_seq, lens, log, gene_id, chromosome, transcripts_id )\n else:\n \n mutation_count(changes,transcripts_id, log, chromosome, id)\n \n\n \n break\n print(tabulate(table, stralign='center'))\n index = input(\"\"\"\n Zadejte:\n cislo CDS pro zjištění vlivu SNP na určitý CDS\n u pro uložení všech sekvencí vytvořených vlivem SNP\n k pro navraceni do předchozícho menu \n \"\"\")\n if index == 'k':\n return\n if index == 'u':\n new_seq, lens = dna_change_seq(seqs, changes)\n new_seqs = multi_seqs(new_seq)\n fname = \"./fasta/\" +input('Zadejte nazev souboru, do ktereho chcete ulozit zmeny') + '.fa'\n iterative_newseq_maker(new_seqs, fname, id, strand, changes, seqs, new_seq, lens, log, gene_id, chromosome, transcripts_id )\n continue\n else:\n index = int(index)\n start = time.clock()\n #(triplets, changed_triplets) = dna_change(seqs, changes)\n #print_changes(triplets, changed_triplets)\n #print(\"AAAAAAAAAAAAAAA\")\n print(changes)\n #print(\"AAAAAAAAAAAAAAA\")\n new_seq, lens = dna_change_seq(seqs, changes)\n print(multi_seqs(new_seq[0][index])[0][0])\n #print(seqs[index])\n #new_seqs.append()\n end = time.clock()\n continue\n except IndexError:\n print('Zadali jste spatne cislo CDS, zkuste to znovu.')\n continue\n except ValueError:\n print('Zadali jste spatny znak, zkuste to znovu.')\n continue\n\ndef get_transcripts_id(gene):\n return [transcripts[0] for transcripts in gene[5:]]\n\ndef snp_print(table, info, snps, seqs, vcf_header, gtf_header, log, full_genome = False, no_fasta = False):\n \"zakladni funkce pro rozhodovani se nad moznostmi\"\n snp_table = [('pozice', 'zmena bazi', 'zmena aminokyseliny')] #prvni radek v tabulce\n\n \n start = info[1]\n end = info[2]\n chrom = info[3]\n strand = info[4]\n new_seqs = []\n gene_id = info[0]\n transcripts_id = get_transcripts_id(info)\n if len(snps) == 0:\n print('V genu nejsou zadne SNP, jsem uvnitr snp_print')\n return\n\n changes = snp_on_dna_plus(seqs, snps, info)\n #if strand == '-': \n\n # changes = snp_on_dna_minus(seqs, snps, info)\n #else:\n # changes = snp_on_dna_plus(seqs, snps, info)\n \n \n while True:\n try:\n if full_genome:\n show_snp(table, changes, seqs, gene_id, vcf_header,strand, log, info[0], info[3],transcripts_id, full_genome, no_fasta)\n break\n index = input(\"\"\"\n Zadejte:\n c pro prohlížení DNA sekvencí jednotlivých CDS\n s pro prohlížení vlivu SNP \n a pro prohlížení vzniklých aminokyselin z CDS\n k pro návrat do předchozího menu: \n \"\"\")\n if index == 'c':\n show_sequence(table, seqs)\n elif index == 's':\n show_snp(table, changes, seqs, gene_id, vcf_header,strand, log, info[0], info[3],transcripts_id, full_genome)\n elif index == 'a':\n show_amk(table, seqs, strand, )\n elif index == 'k':\n break\n else:\n print('Nezadal jste spravny znak')\n continue\n \n \n except IndexError:\n print('\\n Zadal jste spatne cislo CDS \\n')\n continue\n except ValueError:\n print('\\n Nezadal jste cislo \\n')\n continue\n\n \n return (len(changes[0]))\n \n", "sub_path": "snp_apps.py", "file_name": "snp_apps.py", "file_ext": "py", "file_size_in_byte": 23344, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sequence_modification.dna_to_aminoacids", "line_number": 235, "usage_type": "call"}, {"api_name": "sequence_modification.minus_strand", "line_number": 235, "usage_type": "call"}, {"api_name": "sequence_modification.dna_to_aminoacids", "line_number": 236, "usage_type": "call"}, {"api_name": "sequence_modification.minus_strand", "line_number": 236, "usage_type": "call"}, {"api_name": "sequence_modification.dna_to_aminoacids", "line_number": 238, "usage_type": "call"}, {"api_name": "sequence_modification.plus_strand", "line_number": 238, "usage_type": "call"}, {"api_name": "sequence_modification.dna_to_aminoacids", "line_number": 239, "usage_type": "call"}, {"api_name": "sequence_modification.plus_strand", "line_number": 239, "usage_type": "call"}, {"api_name": "sequence_modification.plus_to_minus", "line_number": 307, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 490, "usage_type": "call"}, {"api_name": "sequence_modification.minus_strand", "line_number": 507, "usage_type": "call"}, {"api_name": "sequence_modification.minus_strand", "line_number": 509, "usage_type": "call"}, {"api_name": "sequence_modification.plus_strand", "line_number": 513, "usage_type": "call"}, {"api_name": "sequence_modification.plus_strand", "line_number": 515, "usage_type": "call"}, {"api_name": "sequence_modification.dna_to_aminoacids", "line_number": 530, "usage_type": "call"}, {"api_name": "sequence_modification.minus_strand", "line_number": 530, "usage_type": "call"}, {"api_name": "sequence_modification.dna_to_aminoacids", "line_number": 532, "usage_type": "call"}, {"api_name": "sequence_modification.plus_strand", "line_number": 532, "usage_type": "call"}, {"api_name": "tabulate.tabulate", "line_number": 564, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 581, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 591, "usage_type": "call"}]} +{"seq_id": "328707770", "text": "# Copyright 2019 Huawei Technologies Co., Ltd\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ============================================================================\n\"\"\"\nrelu6\nf(x) = min(max(0,x), 6)\n\"\"\"\nfrom functools import reduce as reduce_ins\n\nimport te.lang.cce\nimport topi\nfrom te import platform as tbe_platform\nfrom te import tvm\nfrom te.platform.fusion_manager import fusion_manager\nfrom te.utils import op_utils\nfrom topi.cce import util\n\n\n# pylint: disable=locally-disabled,too-many-arguments,unused-argument\n@fusion_manager.register(\"relu6\")\ndef relu6_compute(input_x, output_y, kernel_name=\"relu6\"):\n \"\"\"\n compute of relu6\n\n Parameters\n ----------\n input_data: TVM tensor\n the placeholder of first input data\n output_y: dict\n shape and dtype of output,should be same shape and type as input\n kernel_name: str\n cce kernel name, default value is \"relu6\"\n\n Returns\n -------\n compute result of relu6\n \"\"\"\n tmp_res = te.lang.cce.vmaxs(input_x, tvm.const(0, input_x.dtype))\n final_res = te.lang.cce.vmins(tmp_res, tvm.const(6, input_x.dtype))\n\n return final_res\n\n\n@op_utils.check_op_params(op_utils.REQUIRED_INPUT, op_utils.REQUIRED_OUTPUT,\n op_utils.KERNEL_NAME)\ndef relu6(input_x, output_y, kernel_name=\"relu6\"):\n \"\"\"\n f(x)= 6(x >= 6)\n f(x)= 0(x <= 0)\n f(x)= x(0X 2

\")\n\n pixmap = self.draw_text(text)\n icon = QIcon(pixmap)\n self.btn_superscript.setIcon(icon)\n self.btn_superscript.setIconSize(pixmap.rect().size() / self.devicePixelRatioF())\n\n # Subscript button text\n text = QTextDocument()\n text.setHtml(\"

X 2

\")\n\n pixmap = self.draw_text(text)\n icon = QIcon(pixmap)\n self.btn_subscript.setIcon(icon)\n self.btn_subscript.setIconSize(pixmap.rect().size() / self.devicePixelRatioF())\n\n # Default color icons\n self.change_highlight_button_icon(self.act_clear_highlight)\n self.change_text_color_button_icon(self.act_black_text)\n self.change_list_button_icon(self.act_no_list)\n\n # Set visible components and event filter\n self.icon_frame.setVisible(False)\n self.txt_title.installEventFilter(self)\n self.txt_body.installEventFilter(self)\n self.txt_description.setVisible(False)\n\n def init_connection(self):\n self.btn_bold.clicked.connect(self.format_bold)\n self.btn_italic.clicked.connect(self.format_italic)\n self.btn_underline.clicked.connect(self.format_underline)\n self.btn_strikethrough.clicked.connect(self.format_strikethrough)\n self.btn_superscript.clicked.connect(self.format_superscript)\n self.btn_subscript.clicked.connect(self.format_subscript)\n self.highlight_menu.triggered.connect(self.change_highlight_button_icon)\n self.color_menu.triggered.connect(self.change_text_color_button_icon)\n self.list_menu.triggered.connect(self.change_list_button_icon)\n self.list_menu.triggered.connect(self.format_list)\n self.color_menu.triggered.connect(self.format_text_color)\n self.highlight_menu.triggered.connect(self.format_highlight)\n self.style_menu.triggered.connect(self.format_style)\n self.txt_body.cursorPositionChanged.connect(self.update_button)\n self.txt_width.textEdited.connect(self.update_height)\n self.txt_height.textEdited.connect(self.update_width)\n self.txt_width.editingFinished.connect(self.update_image_size)\n self.btn_left.clicked.connect(self.format_align_left)\n self.btn_right.clicked.connect(self.format_align_right)\n self.btn_center.clicked.connect(self.format_align_center)\n self.btn_justify.clicked.connect(self.format_align_justify)\n\n def update_height(self):\n \"\"\" Update height value when the width is changed \"\"\"\n if self.txt_width.text():\n self.txt_height.setText(\"{:.0f}\".format(int(self.txt_width.text()) * 1/self.width_height_ratio))\n else:\n self.txt_height.setText(\"0\")\n\n def update_width(self):\n \"\"\" Update width value when the height is changed \"\"\"\n if self.txt_height.text():\n self.txt_width.setText(\"{:.0f}\".format(int(self.txt_height.text()) * self.width_height_ratio))\n else:\n self.txt_width.setText(\"0\")\n\n def update_image_size(self):\n cursor = self.txt_body.textCursor()\n\n if not cursor.hasSelection():\n cursor.setPosition(self.txt_body.textCursor().position() - 1)\n cursor.setPosition(self.txt_body.textCursor().position(), QTextCursor.KeepAnchor)\n\n fmt = cursor.charFormat().toImageFormat()\n fmt.setWidth(int(self.txt_width.text()))\n fmt.setHeight(int(self.txt_height.text()))\n cursor.setCharFormat(fmt)\n self.txt_body.setTextCursor(cursor)\n\n def eventFilter(self, object, event):\n if event.type() == QEvent.FocusIn:\n if object == self.txt_title:\n self.edit_title()\n if object == self.txt_body:\n self.edit_body()\n return QWidget.eventFilter(self, object, event)\n\n def edit_title(self):\n \"\"\" Show the interface element required to edit title \"\"\"\n self.icon_frame.setVisible(False)\n self.txt_description.setVisible(True)\n self.txt_key.setVisible(True)\n\n def edit_body(self):\n \"\"\" Show the interface element required to edit the body \"\"\"\n self.icon_frame.setVisible(True)\n self.txt_description.setVisible(False)\n self.txt_key.setVisible(False)\n\n def change_list_button_icon(self, action):\n \"\"\"Change the list button icon to the selected list format\n\n :param action: Selected action.\n :type action: QAction\n \"\"\"\n\n # Ignore the indent change action when changing the icon type\n if not (action == self.act_increase_indent or action == self.act_decrease_indent):\n if action == self.act_bullet_list:\n icon = self.draw_list([\"•\", \"•\", \"•\"])\n elif action == self.act_numbered_list:\n icon = self.draw_list([\"1.\", \"2.\", \"3.\"])\n elif action == self.act_roman_list:\n icon = self.draw_list([\"I.\", \"II.\", \"III.\"])\n elif action == self.act_uppercase_list:\n icon = self.draw_list([\"A.\", \"B.\", \"C.\"])\n else:\n icon = self.draw_list([\"a.\", \"b.\", \"c.\"])\n self.btn_list.setIcon(icon)\n\n # Do no check the button is no list is selected\n if not (action == self.act_no_list):\n self.btn_list.setChecked(True)\n elif action == self.act_no_list:\n self.btn_list.setChecked(False)\n\n def draw_list(self, separator_list):\n \"\"\"Draw the list icons for the icon menu\n\n .. note::\n This function handle HiDPI as well a regular screen\n\n :param separator_list: List of the bullets\n :type separator_list: List[str]\n :returns: QPixmap -- Icon pixmap\n \"\"\"\n # Create a base pixmap\n # Set the pixmap pixel ratio so that the image looks good in normal as well as HiDPI screens\n dpr = self.devicePixelRatioF()\n pixmap = QPixmap(16 * dpr, 16 * dpr)\n pixmap.setDevicePixelRatio(dpr)\n pixmap.fill(Qt.transparent) # Required to create a transparent background\n\n # Paint the elements of the icon\n painter = QPainter(pixmap)\n painter.setFont(QFont(self.font().family(), 5, 50))\n pen = QPen(QColor(72, 72, 72), 1)\n painter.setPen(pen)\n painter.drawLine(7, 3, 15, 3)\n painter.drawText(0, 0, 32, 22, Qt.AlignLeft, separator_list[0])\n painter.drawLine(7, 8, 15, 8)\n painter.drawText(0, 5, 32, 22, Qt.AlignLeft, separator_list[1])\n painter.drawLine(7, 13, 15, 13)\n painter.drawText(0, 10, 32, 22, Qt.AlignLeft, separator_list[2])\n painter.end()\n\n return QIcon(pixmap)\n\n def draw_left(self):\n \"\"\" Draw the icon for the align left button \"\"\"\n dpr = self.devicePixelRatioF()\n pixmap = QPixmap(16 * dpr, 16 * dpr)\n pixmap.setDevicePixelRatio(dpr)\n pixmap.fill(Qt.transparent) # Required to create a transparent background\n\n # Paint the elements of the icon\n painter = QPainter(pixmap)\n pen = QPen(QColor(72, 72, 72), 1)\n painter.setPen(pen)\n painter.drawLine(2, 3, 15, 3)\n painter.drawLine(2, 6, 11, 6)\n painter.drawLine(2, 9, 15, 9)\n painter.drawLine(2, 12, 13, 12)\n painter.end()\n\n return QIcon(pixmap)\n\n def draw_center(self):\n \"\"\" Draw the icon for the align left button \"\"\"\n dpr = self.devicePixelRatioF()\n pixmap = QPixmap(16 * dpr, 16 * dpr)\n pixmap.setDevicePixelRatio(dpr)\n pixmap.fill(Qt.transparent) # Required to create a transparent background\n\n # Paint the elements of the icon\n painter = QPainter(pixmap)\n pen = QPen(QColor(72, 72, 72), 1)\n painter.setPen(pen)\n painter.drawLine(2, 3, 15, 3)\n painter.drawLine(5, 6, 11, 6)\n painter.drawLine(2, 9, 15, 9)\n painter.drawLine(4, 12, 13, 12)\n painter.end()\n\n return QIcon(pixmap)\n\n def draw_right(self):\n \"\"\" Draw the icon for the align left button \"\"\"\n dpr = self.devicePixelRatioF()\n pixmap = QPixmap(16 * dpr, 16 * dpr)\n pixmap.setDevicePixelRatio(dpr)\n pixmap.fill(Qt.transparent) # Required to create a transparent background\n\n # Paint the elements of the icon\n painter = QPainter(pixmap)\n pen = QPen(QColor(72, 72, 72), 1)\n painter.setPen(pen)\n painter.drawLine(3, 3, 15, 3)\n painter.drawLine(6, 6, 15, 6)\n painter.drawLine(2, 9, 15, 9)\n painter.drawLine(4, 12, 15, 12)\n painter.end()\n\n return QIcon(pixmap)\n\n def draw_justify(self):\n \"\"\" Draw the icon for the align left button \"\"\"\n dpr = self.devicePixelRatioF()\n pixmap = QPixmap(16 * dpr, 16 * dpr)\n pixmap.setDevicePixelRatio(dpr)\n pixmap.fill(Qt.transparent) # Required to create a transparent background\n\n # Paint the elements of the icon\n painter = QPainter(pixmap)\n pen = QPen(QColor(72, 72, 72), 1)\n painter.setPen(pen)\n painter.drawLine(2, 3, 15, 3)\n painter.drawLine(2, 6, 15, 6)\n painter.drawLine(2, 9, 15, 9)\n painter.drawLine(2, 12, 15, 12)\n painter.end()\n\n return QIcon(pixmap)\n\n def change_text_color_button_icon(self, action):\n \"\"\"Change the text color button icon to the selected color\n\n :param action: Selected action.\n :type action: QAction\n \"\"\"\n if action == self.act_gray_text:\n icon = self.draw_color(common.TEXT_COLOR['gray'].color, common.TEXT_COLOR['gray'].dark_shade)\n elif action == self.act_red_text:\n icon = self.draw_color(common.TEXT_COLOR['red'].color, common.TEXT_COLOR['red'].dark_shade)\n elif action == self.act_orange_text:\n icon = self.draw_color(common.TEXT_COLOR['orange'].color, common.TEXT_COLOR['orange'].dark_shade)\n elif action == self.act_yellow_text:\n icon = self.draw_color(common.TEXT_COLOR['yellow'].color, common.TEXT_COLOR['yellow'].dark_shade)\n elif action == self.act_green_text:\n icon = self.draw_color(common.TEXT_COLOR['green'].color, common.TEXT_COLOR['green'].dark_shade)\n elif action == self.act_blue_text:\n icon = self.draw_color(common.TEXT_COLOR['blue'].color, common.TEXT_COLOR['blue'].dark_shade)\n elif action == self.act_purple_text:\n icon = self.draw_color(common.TEXT_COLOR['purple'].color, common.TEXT_COLOR['purple'].dark_shade)\n else:\n icon = self.draw_color(common.TEXT_COLOR['black'].color, common.TEXT_COLOR['black'].dark_shade)\n self.btn_color.setIcon(icon)\n\n def change_highlight_button_icon(self, action):\n \"\"\"Change the highlight button icon to the selected color\n\n :param action: Selected action.\n :type action: QAction\n \"\"\"\n if action == self.act_red_highlight:\n icon = self.draw_color(common.HIGHLIGHT_COLOR['red'].color, common.HIGHLIGHT_COLOR['red'].dark_shade)\n elif action == self.act_orange_highlight:\n icon = self.draw_color(common.HIGHLIGHT_COLOR['orange'].color, common.HIGHLIGHT_COLOR['orange'].dark_shade)\n elif action == self.act_yellow_highlight:\n icon = self.draw_color(common.HIGHLIGHT_COLOR['yellow'].color, common.HIGHLIGHT_COLOR['yellow'].dark_shade)\n elif action == self.act_green_highlight:\n icon = self.draw_color(common.HIGHLIGHT_COLOR['green'].color, common.HIGHLIGHT_COLOR['green'].dark_shade)\n elif action == self.act_blue_highlight:\n icon = self.draw_color(common.HIGHLIGHT_COLOR['blue'].color, common.HIGHLIGHT_COLOR['blue'].dark_shade)\n elif action == self.act_purple_highlight:\n icon = self.draw_color(common.HIGHLIGHT_COLOR['purple'].color, common.HIGHLIGHT_COLOR['purple'].dark_shade)\n elif action == self.act_gray_highlight:\n icon = self.draw_color(common.HIGHLIGHT_COLOR['gray'].color, common.HIGHLIGHT_COLOR['gray'].dark_shade)\n else:\n icon = self.draw_color(common.HIGHLIGHT_COLOR['clear'].color, common.HIGHLIGHT_COLOR['clear'].dark_shade)\n self.btn_highlight.setIcon(icon)\n\n def draw_color(self, fill, border):\n \"\"\"Draw the color icons for the highlight and the text color menu\n\n :param fill: Fill color.\n :type fill: QColor\n :param border: Border color.\n :type border: QColor\n :returns: QPixmap -- Icon pixmap\n \"\"\"\n # Create a base pixmap\n # Set the pixmap pixel ratio so that the image looks good in normal as well as HiDPI screens\n dpr = self.devicePixelRatioF()\n pixmap = QPixmap(16 * dpr, 16 * dpr)\n pixmap.setDevicePixelRatio(dpr)\n pixmap.fill(Qt.transparent)\n\n # Paint the elements of the icon\n painter = QPainter(pixmap)\n painter.setRenderHint(QPainter.Antialiasing)\n path = QPainterPath()\n path.addRoundedRect(QRectF(2, 2, 12, 12), 2, 2)\n\n pen = QPen(border, 1)\n painter.setPen(pen)\n painter.fillPath(path, fill)\n painter.drawPath(path)\n painter.end()\n\n return QIcon(pixmap)\n\n def draw_text(self, text):\n \"\"\"Draw an icon from a html text and return it as a pixmap.\n\n .. note::\n This function handle HiDPI as well a regular screen.\n\n :param text: QTextDocument with HTML code for the icon.\n :type text: QTextDocument\n :returns: QPixmap -- Returns pixmap that can be used to create the icon\n\n .. note::\n Unlike the other drawing function, this function return a pixel map. As of now, this is required to create\n a good sized icon. This should therefore not be changed unless the the output icon size is right (it is\n currently too small).\n \"\"\"\n # Create a base pixmap\n # Set the pixmap pixel ratio so that the image looks good in normal as well as HiDPI screens\n dpr = self.devicePixelRatioF()\n pixmap = QPixmap(text.size().width() * dpr, text.size().height() * dpr)\n pixmap.setDevicePixelRatio(dpr)\n pixmap.fill(Qt.transparent)\n\n # Paint the elements of the icon\n painter = QPainter(pixmap)\n text.drawContents(painter, QRectF(pixmap.rect()))\n painter.end()\n\n return pixmap\n\n def merge_format_on_word_or_selection(self, fmt):\n \"\"\" Change the caracter format when a format button is pressed.\n\n The font is changed for the selection or from the cursor position.\n :param fmt: Text format\n \"\"\"\n cursor = self.txt_body.textCursor()\n cursor.mergeCharFormat(fmt)\n self.txt_body.mergeCurrentCharFormat(fmt)\n\n def format_bold(self):\n \"\"\" Set text format to bold. \"\"\"\n fmt = QTextCharFormat()\n if self.btn_bold.isChecked():\n fmt.setFontWeight(QFont.Bold)\n else:\n fmt.setFontWeight(QFont.Normal)\n self.merge_format_on_word_or_selection(fmt=fmt)\n\n def format_italic(self):\n \"\"\" Set text format to italic. \"\"\"\n fmt = QTextCharFormat()\n if self.btn_italic.isChecked():\n fmt.setFontItalic(True)\n else:\n fmt.setFontItalic(False)\n self.merge_format_on_word_or_selection(fmt=fmt)\n\n def format_underline(self):\n \"\"\" Set text format to underline. \"\"\"\n fmt = QTextCharFormat()\n if self.btn_underline.isChecked():\n fmt.setFontUnderline(True)\n else:\n fmt.setFontUnderline(False)\n self.merge_format_on_word_or_selection(fmt=fmt)\n\n def format_strikethrough(self):\n \"\"\" Set text format to strikethrough. \"\"\"\n fmt = QTextCharFormat()\n if self.btn_strikethrough.isChecked():\n fmt.setFontStrikeOut(True)\n else:\n fmt.setFontStrikeOut(False)\n self.merge_format_on_word_or_selection(fmt=fmt)\n\n def format_superscript(self):\n \"\"\" Set text vertical alignment to superscript. \"\"\"\n fmt = QTextCharFormat()\n if self.btn_superscript.isChecked():\n fmt.setVerticalAlignment(QTextCharFormat.AlignSuperScript)\n else:\n fmt.setVerticalAlignment(QTextCharFormat.AlignNormal)\n self.merge_format_on_word_or_selection(fmt=fmt)\n\n def format_subscript(self):\n \"\"\" Set text vertical alignment to subscript. \"\"\"\n fmt = QTextCharFormat()\n if self.btn_subscript.isChecked():\n fmt.setVerticalAlignment(QTextCharFormat.AlignSubScript)\n else:\n fmt.setVerticalAlignment(QTextCharFormat.AlignNormal)\n self.merge_format_on_word_or_selection(fmt=fmt)\n\n def format_align_left(self):\n \"\"\" Set text format to bold. \"\"\"\n if self.btn_left.isChecked():\n self.txt_body.setAlignment(Qt.AlignLeft)\n else:\n self.txt_body.setAlignment(Qt.AlignLeft)\n self.update_button()\n\n def format_align_right(self):\n \"\"\" Set text format to bold. \"\"\"\n if self.btn_left.isChecked():\n self.txt_body.setAlignment(Qt.AlignRight)\n else:\n self.txt_body.setAlignment(Qt.AlignLeft)\n self.update_button()\n\n def format_align_center(self):\n \"\"\" Set text format to bold. \"\"\"\n if self.btn_left.isChecked():\n self.txt_body.setAlignment(Qt.AlignCenter)\n else:\n self.txt_body.setAlignment(Qt.AlignLeft)\n self.update_button()\n\n def format_align_justify(self):\n \"\"\" Set text format to bold. \"\"\"\n if self.btn_left.isChecked():\n self.txt_body.setAlignment(Qt.AlignJustify)\n else:\n self.txt_body.setAlignment(Qt.AlignLeft)\n self.update_button()\n\n def format_list(self, action):\n \"\"\" set list format according to selected format\n\n :param action: Selected action.\n :type action: QAction\n \"\"\"\n\n # Create a new list\n if not (action == self.act_increase_indent or action == self.act_decrease_indent or action == self.act_no_list):\n # Set the list type format\n fmt = QTextListFormat()\n if action == self.act_bullet_list:\n fmt.setStyle(QTextListFormat.ListDisc)\n elif action == self.act_numbered_list:\n fmt.setStyle(QTextListFormat.ListDecimal)\n elif action == self.act_roman_list:\n fmt.setStyle(QTextListFormat.ListUpperRoman)\n elif action == self.act_uppercase_list:\n fmt.setStyle(QTextListFormat.ListUpperAlpha)\n else:\n fmt.setStyle(QTextListFormat.ListLowerAlpha)\n\n # Add the list to the the text edit\n cursor = self.txt_body.textCursor()\n cursor.createList(fmt)\n # Delete an existing list\n elif action == self.act_no_list:\n # Get the current list\n cursor = self.txt_body.textCursor()\n current_list = cursor.currentList()\n current_block = cursor.block()\n\n # Remove the list\n current_list.remove(current_block)\n\n # Restore indent\n fmt = cursor.blockFormat()\n fmt.setIndent(0)\n cursor.setBlockFormat(fmt)\n # Change the indent\n else:\n cursor = self.txt_body.textCursor()\n current_format = cursor.currentList().format()\n current_indent = current_format.indent()\n\n if action == self.act_increase_indent:\n new_indent = current_indent + 1\n else:\n new_indent = current_indent - 1\n\n new_format = current_format\n new_format.setIndent(new_indent)\n cursor.createList(new_format)\n\n def format_text_color(self, action):\n \"\"\" Set the text color\n\n :param action: Selected action.\n :type action: QAction\n \"\"\"\n if action == self.act_gray_text:\n text_color = QColor(117, 117, 117)\n elif action == self.act_red_text:\n text_color = QColor(150, 16, 16)\n elif action == self.act_orange_text:\n text_color = QColor(211, 116, 0)\n elif action == self.act_yellow_text:\n text_color = QColor(229, 221, 0)\n elif action == self.act_green_text:\n text_color = QColor(34, 139, 34)\n elif action == self.act_blue_text:\n text_color = QColor(18, 18, 130)\n elif action == self.act_purple_text:\n text_color = QColor(117, 21, 117)\n else:\n text_color = Qt.black\n\n fmt = QTextCharFormat()\n fmt.setForeground(QBrush(text_color))\n self.merge_format_on_word_or_selection(fmt=fmt)\n\n def format_highlight(self, action):\n \"\"\" Set the highlight color\n\n .. note::\n The highlight color alpha channel is set to 128 so the color are semi-transparent. This prevent the colors\n to be too harsh.\n\n :param action: Selected action.\n :type action: QAction\n \"\"\"\n fmt = QTextCharFormat()\n\n # Set the selected color to background\n if not action == self.act_clear_highlight:\n if action == self.act_red_highlight:\n highlight_color = QColor(242, 41, 74, 128)\n elif action == self.act_orange_highlight:\n highlight_color = QColor(252, 116, 42, 128)\n elif action == self.act_yellow_highlight:\n highlight_color = QColor(255, 251, 45, 128)\n elif action == self.act_green_highlight:\n highlight_color = QColor(0, 250, 154, 128)\n elif action == self.act_blue_highlight:\n highlight_color = QColor(49, 170, 226, 128)\n elif action == self.act_purple_highlight:\n highlight_color = QColor(155, 71, 229, 128)\n else:\n highlight_color = QColor(196, 196, 196, 128)\n\n fmt.setBackground(QBrush(highlight_color))\n # Remove the background\n else:\n fmt.setBackground(QBrush(Qt.white))\n self.merge_format_on_word_or_selection(fmt=fmt)\n\n def format_style(self, action):\n \"\"\" Set a predefined format on the selected text\n\n :param action: Selected action (format).\n :type action: QAction\n \"\"\"\n fmt = QTextCharFormat()\n\n # Define the format according to the selected style\n if action == self.act_part:\n fmt.setFontWeight(75)\n fmt.setFontPointSize(20)\n elif action == self.act_section:\n fmt.setFontWeight(75)\n fmt.setFontPointSize(16)\n elif action == self.act_subsection:\n fmt.setFontWeight(75)\n fmt.setFontPointSize(14)\n elif action == self.act_subsubsection:\n fmt.setFontWeight(75)\n fmt.setFontPointSize(13)\n elif action == self.act_body:\n fmt.setFontWeight(50)\n fmt.setFontPointSize(13)\n elif action == self.act_note:\n fmt.setFontWeight(50)\n fmt.setFontPointSize(10)\n\n # Define the format common to every style\n fmt.setForeground(QBrush(Qt.black))\n fmt.setBackground(QBrush(Qt.white))\n fmt.setFontItalic(False)\n fmt.setFontUnderline(False)\n fmt.setFontStrikeOut(False)\n fmt.setVerticalAlignment(QTextCharFormat.AlignNormal)\n\n self.merge_format_on_word_or_selection(fmt=fmt)\n\n def update_button(self):\n \"\"\" Set the button states to match the selected text format \"\"\"\n\n # Get text format\n cfmt = self.txt_body.textCursor().charFormat()\n\n # Bold button\n if cfmt.fontWeight() == 75:\n self.btn_bold.setChecked(True)\n else:\n self.btn_bold.setChecked(False)\n\n # Italic button\n if cfmt.fontItalic():\n self.btn_italic.setChecked(True)\n else:\n self.btn_italic.setChecked(False)\n\n # Underline button\n if cfmt.fontUnderline():\n self.btn_underline.setChecked(True)\n else:\n self.btn_underline.setChecked(False)\n\n # Strikethrough button\n if cfmt.fontStrikeOut():\n self.btn_strikethrough.setChecked(True)\n else:\n self.btn_strikethrough.setChecked(False)\n\n # Superscript button\n if cfmt.verticalAlignment() == QTextCharFormat.AlignSuperScript:\n self.btn_superscript.setChecked(True)\n else:\n self.btn_superscript.setChecked(False)\n\n # Subscript button\n if cfmt.verticalAlignment() == QTextCharFormat.AlignSubScript:\n self.btn_subscript.setChecked(True)\n else:\n self.btn_subscript.setChecked(False)\n\n # Get color format\n # Background color\n background_color = cfmt.background().color()\n if background_color.rgb() == common.HIGHLIGHT_COLOR['red'].color.rgb():\n self.change_highlight_button_icon(self.act_red_highlight)\n elif background_color.rgb() == common.HIGHLIGHT_COLOR['orange'].color.rgb():\n self.change_highlight_button_icon(self.act_orange_highlight)\n elif background_color.rgb() == common.HIGHLIGHT_COLOR['yellow'].color.rgb():\n self.change_highlight_button_icon(self.act_yellow_highlight)\n elif background_color.rgb() == common.HIGHLIGHT_COLOR['green'].color.rgb():\n self.change_highlight_button_icon(self.act_green_highlight)\n elif background_color.rgb() == common.HIGHLIGHT_COLOR['blue'].color.rgb():\n self.change_highlight_button_icon(self.act_blue_highlight)\n elif background_color.rgb() == common.HIGHLIGHT_COLOR['purple'].color.rgb():\n self.change_highlight_button_icon(self.act_purple_highlight)\n elif background_color.rgb() == common.HIGHLIGHT_COLOR['gray'].color.rgb():\n self.change_highlight_button_icon(self.act_gray_highlight)\n else:\n self.change_highlight_button_icon(self.act_clear_highlight)\n\n # Text color\n text_color = cfmt.foreground().color()\n\n if text_color == common.TEXT_COLOR['gray'].color:\n self.change_text_color_button_icon(self.act_gray_text)\n elif text_color == common.TEXT_COLOR['red'].color:\n self.change_text_color_button_icon(self.act_red_text)\n elif text_color == common.TEXT_COLOR['orange'].color:\n self.change_text_color_button_icon(self.act_orange_text)\n elif text_color == common.TEXT_COLOR['yellow'].color:\n self.change_text_color_button_icon(self.act_yellow_text)\n elif text_color == common.TEXT_COLOR['gray'].color:\n self.change_text_color_button_icon(self.act_gray_text)\n elif text_color == common.TEXT_COLOR['green'].color:\n self.change_text_color_button_icon(self.act_green_text)\n elif text_color == common.TEXT_COLOR['blue'].color:\n self.change_text_color_button_icon(self.act_blue_text)\n elif text_color == common.TEXT_COLOR['purple'].color:\n self.change_text_color_button_icon(self.act_purple_text)\n else:\n self.change_text_color_button_icon(self.act_black_text)\n\n # Get list format\n if self.txt_body.textCursor().currentList():\n self.btn_list.setChecked(True)\n else:\n self.btn_list.setChecked(False)\n\n if self.txt_body.is_image():\n fmt = self.txt_body.textCursor().charFormat().toImageFormat()\n self.txt_height.setText(\"{:d}\".format(int(fmt.height())))\n self.txt_width.setText(\"{:d}\".format(int(fmt.width())))\n self.width_height_ratio = fmt.width() / fmt.height()\n self.txt_width.setEnabled(True)\n self.txt_height.setEnabled(True)\n else:\n self.txt_width.setEnabled(False)\n self.txt_height.setEnabled(False)\n\n # Get align format\n if self.txt_body.alignment() == Qt.AlignLeft:\n self.btn_left.setChecked(True)\n else:\n self.btn_left.setChecked(False)\n\n if self.txt_body.alignment() == Qt.AlignCenter:\n self.btn_center.setChecked(True)\n else:\n self.btn_center.setChecked(False)\n\n if self.txt_body.alignment() == Qt.AlignRight:\n self.btn_right.setChecked(True)\n else:\n self.btn_right.setChecked(False)\n\n if self.txt_body.alignment() == Qt.AlignJustify:\n self.btn_justify.setChecked(True)\n else:\n self.btn_justify.setChecked(False)\n\n\nclass ProtocolTextEditor(TextEditor):\n def __init__(self, editor_type, tag_list, reference_list):\n super(ProtocolTextEditor, self).__init__(editor_type=editor_type, tag_list=tag_list, reference_list=reference_list)\n self.txt_key.setPlaceholderText(\"Protocol key\")\n self.txt_description.setPlaceholderText(\"Description of the protocol\")\n self.txt_title.setPlaceholderText(\"Untitled protocol\")\n\n\nclass ExperimentTextEditor(TextEditor):\n def __init__(self, tag_list, reference_list, dataset_list, protocol_list, key_list):\n super(ExperimentTextEditor, self).__init__(common.TYPE_EXPERIMENT, tag_list=tag_list,\n reference_list=reference_list,\n dataset_list=dataset_list, protocol_list=protocol_list)\n\n completer = QCompleter(key_list)\n self.txt_key.setCompleter(completer)\n\n # Remove the save button\n self.btn_save.deleteLater()\n sip.delete(self.save_layout)\n", "sub_path": "labnote/interface/widget/widget.py", "file_name": "widget.py", "file_ext": "py", "file_size_in_byte": 64452, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "labnote.core.common.TYPE_ARTICLE", "line_number": 28, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 28, "usage_type": "name"}, {"api_name": "labnote.core.common.TYPE_BOOK", "line_number": 29, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 29, "usage_type": "name"}, {"api_name": "labnote.core.common.TYPE_CHAPTER", "line_number": 30, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 30, "usage_type": "name"}, {"api_name": "labnote.core.common.TYPE_LIBRARY", "line_number": 33, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 33, "usage_type": "name"}, {"api_name": "labnote.core.common.TYPE_PROTOCOL", "line_number": 34, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 34, "usage_type": "name"}, {"api_name": "labnote.core.common.QT_LevelRole", "line_number": 37, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 37, "usage_type": "name"}, {"api_name": "labnote.core.common.QT_StateRole", "line_number": 38, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 38, "usage_type": "name"}, {"api_name": "labnote.core.common.LEVEL_CATEGORY", "line_number": 41, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 41, "usage_type": "name"}, {"api_name": "labnote.core.common.LEVEL_SUBCATEGORY", "line_number": 42, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 42, "usage_type": "name"}, {"api_name": "labnote.core.common.LEVEL_ENTRY", "line_number": 43, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 43, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 46, "usage_type": "name"}, {"api_name": "labnote.core.stylesheet.set_style_sheet", "line_number": 52, "usage_type": "call"}, {"api_name": "labnote.core.stylesheet", "line_number": 52, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 55, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 56, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignCenter", "line_number": 57, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 57, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.KeepAspectRatio", "line_number": 59, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 59, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 61, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.AlignCenter", "line_number": 62, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 62, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 65, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 70, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 74, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 75, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 76, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QModelIndex", "line_number": 76, "usage_type": "argument"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 77, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 82, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 99, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 103, "usage_type": "call"}, {"api_name": "labnote.interface.widget.view.DragDropTreeView", "line_number": 114, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 141, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 143, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 174, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMenu", "line_number": 207, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 208, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QFont.Normal", "line_number": 208, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 209, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 212, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 216, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 221, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 224, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 228, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAction", "line_number": 238, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QAbstractItemView.SingleSelection", "line_number": 246, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QAbstractItemView", "line_number": 246, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAbstractItemView.SelectRows", "line_number": 247, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QAbstractItemView", "line_number": 247, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAbstractItemView.NoEditTriggers", "line_number": 248, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QAbstractItemView", "line_number": 248, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QAbstractItemView.InternalMove", "line_number": 249, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QAbstractItemView", "line_number": 249, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 252, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.DisplayRole", "line_number": 264, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 264, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.UserRole", "line_number": 265, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 265, "usage_type": "name"}, {"api_name": "labnote.interface.dialog.category.Category", "line_number": 268, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.WindowModal", "line_number": 270, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 270, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Sheet", "line_number": 271, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 271, "usage_type": "name"}, {"api_name": "labnote.interface.dialog.category.Category", "line_number": 277, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.WindowModal", "line_number": 279, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 279, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Sheet", "line_number": 280, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 280, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.UserRole", "line_number": 291, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 291, "usage_type": "name"}, {"api_name": "labnote.utils.database.delete_category", "line_number": 294, "usage_type": "call"}, {"api_name": "labnote.utils.database", "line_number": 294, "usage_type": "name"}, {"api_name": "sqlite3.Error", "line_number": 295, "usage_type": "attribute"}, {"api_name": "labnote.core.sqlite_error.sqlite_err_handler", 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{"api_name": "labnote.core.common", "line_number": 1089, "usage_type": "name"}, {"api_name": "labnote.core.common.TEXT_COLOR", "line_number": 1091, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1091, "usage_type": "name"}, {"api_name": "labnote.core.common.TEXT_COLOR", "line_number": 1093, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1093, "usage_type": "name"}, {"api_name": "labnote.core.common.TEXT_COLOR", "line_number": 1095, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1095, "usage_type": "name"}, {"api_name": "labnote.core.common.TEXT_COLOR", "line_number": 1097, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1097, "usage_type": "name"}, {"api_name": "labnote.core.common.TEXT_COLOR", "line_number": 1099, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1099, "usage_type": "name"}, {"api_name": "labnote.core.common.HIGHLIGHT_COLOR", "line_number": 1109, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1109, "usage_type": "name"}, {"api_name": "labnote.core.common.HIGHLIGHT_COLOR", "line_number": 1111, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1111, "usage_type": "name"}, {"api_name": "labnote.core.common.HIGHLIGHT_COLOR", "line_number": 1113, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1113, "usage_type": "name"}, {"api_name": "labnote.core.common.HIGHLIGHT_COLOR", "line_number": 1115, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1115, "usage_type": "name"}, {"api_name": "labnote.core.common.HIGHLIGHT_COLOR", "line_number": 1117, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1117, "usage_type": "name"}, {"api_name": "labnote.core.common.HIGHLIGHT_COLOR", "line_number": 1119, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1119, "usage_type": "name"}, {"api_name": "labnote.core.common.HIGHLIGHT_COLOR", "line_number": 1121, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1121, "usage_type": "name"}, {"api_name": "labnote.core.common.HIGHLIGHT_COLOR", "line_number": 1123, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1123, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 1138, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.transparent", "line_number": 1140, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 1140, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPainter", "line_number": 1143, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPainter.Antialiasing", "line_number": 1144, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QPainter", "line_number": 1144, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPainterPath", "line_number": 1145, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QRectF", "line_number": 1146, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPen", "line_number": 1148, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QIcon", "line_number": 1154, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QPixmap", "line_number": 1174, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.transparent", "line_number": 1176, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 1176, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QPainter", "line_number": 1179, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QRectF", "line_number": 1180, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QTextCharFormat", "line_number": 1197, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QFont.Bold", "line_number": 1199, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 1199, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont.Normal", "line_number": 1201, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 1201, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QTextCharFormat", "line_number": 1206, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QTextCharFormat", "line_number": 1215, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QTextCharFormat", "line_number": 1224, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QTextCharFormat", "line_number": 1233, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QTextCharFormat.AlignSuperScript", "line_number": 1235, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QTextCharFormat", "line_number": 1235, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QTextCharFormat.AlignNormal", "line_number": 1237, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QTextCharFormat", "line_number": 1237, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QTextCharFormat", "line_number": 1242, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QTextCharFormat.AlignSubScript", "line_number": 1244, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QTextCharFormat", "line_number": 1244, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QTextCharFormat.AlignNormal", "line_number": 1246, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QTextCharFormat", "line_number": 1246, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignLeft", "line_number": 1252, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 1252, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignLeft", "line_number": 1254, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 1254, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignRight", "line_number": 1260, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 1260, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignLeft", "line_number": 1262, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 1262, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignCenter", "line_number": 1268, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 1268, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignLeft", "line_number": 1270, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 1270, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignJustify", "line_number": 1276, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 1276, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignLeft", "line_number": 1278, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 1278, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QTextListFormat", "line_number": 1291, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QTextListFormat.ListDisc", "line_number": 1293, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QTextListFormat", "line_number": 1293, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QTextListFormat.ListDecimal", "line_number": 1295, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QTextListFormat", "line_number": 1295, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QTextListFormat.ListUpperRoman", "line_number": 1297, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QTextListFormat", "line_number": 1297, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QTextListFormat.ListUpperAlpha", "line_number": 1299, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QTextListFormat", "line_number": 1299, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QTextListFormat.ListLowerAlpha", "line_number": 1301, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QTextListFormat", "line_number": 1301, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 1342, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 1344, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 1346, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 1348, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 1350, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 1352, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 1354, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.black", "line_number": 1356, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 1356, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QTextCharFormat", "line_number": 1358, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QBrush", "line_number": 1359, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QTextCharFormat", "line_number": 1372, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 1377, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 1379, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 1381, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 1383, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 1385, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 1387, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QColor", "line_number": 1389, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QBrush", "line_number": 1391, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QBrush", "line_number": 1394, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.white", "line_number": 1394, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 1394, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QTextCharFormat", "line_number": 1403, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QBrush", "line_number": 1426, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.black", "line_number": 1426, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 1426, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QBrush", "line_number": 1427, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.white", "line_number": 1427, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 1427, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QTextCharFormat.AlignNormal", "line_number": 1431, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QTextCharFormat", "line_number": 1431, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QTextCharFormat.AlignSuperScript", "line_number": 1466, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QTextCharFormat", "line_number": 1466, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QTextCharFormat.AlignSubScript", "line_number": 1472, "usage_type": "attribute"}, {"api_name": "PyQt5.QtGui.QTextCharFormat", "line_number": 1472, "usage_type": "name"}, {"api_name": "labnote.core.common.HIGHLIGHT_COLOR", "line_number": 1480, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1480, "usage_type": "name"}, {"api_name": "labnote.core.common.HIGHLIGHT_COLOR", "line_number": 1482, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1482, "usage_type": "name"}, {"api_name": "labnote.core.common.HIGHLIGHT_COLOR", "line_number": 1484, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1484, "usage_type": "name"}, {"api_name": "labnote.core.common.HIGHLIGHT_COLOR", "line_number": 1486, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1486, "usage_type": "name"}, {"api_name": "labnote.core.common.HIGHLIGHT_COLOR", "line_number": 1488, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1488, "usage_type": "name"}, {"api_name": "labnote.core.common.HIGHLIGHT_COLOR", "line_number": 1490, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1490, "usage_type": "name"}, {"api_name": "labnote.core.common.HIGHLIGHT_COLOR", "line_number": 1492, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1492, "usage_type": "name"}, {"api_name": "labnote.core.common.TEXT_COLOR", "line_number": 1500, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1500, "usage_type": "name"}, {"api_name": "labnote.core.common.TEXT_COLOR", "line_number": 1502, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1502, "usage_type": "name"}, {"api_name": "labnote.core.common.TEXT_COLOR", "line_number": 1504, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1504, "usage_type": "name"}, {"api_name": "labnote.core.common.TEXT_COLOR", "line_number": 1506, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1506, "usage_type": "name"}, {"api_name": "labnote.core.common.TEXT_COLOR", "line_number": 1508, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1508, "usage_type": "name"}, {"api_name": "labnote.core.common.TEXT_COLOR", "line_number": 1510, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1510, "usage_type": "name"}, {"api_name": "labnote.core.common.TEXT_COLOR", "line_number": 1512, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1512, "usage_type": "name"}, {"api_name": "labnote.core.common.TEXT_COLOR", "line_number": 1514, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1514, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignLeft", "line_number": 1537, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 1537, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignCenter", "line_number": 1542, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 1542, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignRight", "line_number": 1547, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 1547, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.AlignJustify", "line_number": 1552, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 1552, "usage_type": "name"}, {"api_name": "labnote.core.common.TYPE_EXPERIMENT", "line_number": 1568, "usage_type": "attribute"}, {"api_name": "labnote.core.common", "line_number": 1568, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QCompleter", "line_number": 1572, "usage_type": "call"}, {"api_name": "sip.delete", "line_number": 1577, "usage_type": "call"}]} +{"seq_id": "244683699", "text": "import json\nfrom flask.ext.cors import CORS, cross_origin\nfrom app.models.model import Brand\nfrom app import app\n\nbrandModel = Brand()\n\n# get all brands\n@app.route('/brand_graph/api/v1.0/brands',methods=['GET'])\n@cross_origin(origin='*', methods=['GET', 'POST', 'OPTIONS'], headers=['X-Requested-With', 'Content-Type', 'Origin'])\ndef get_brands():\n brands = brandModel.get_all()\n return json.dumps(brands)\n\n# get the brand with specified name\n@app.route('/brand_graph/api/v1.0/brands/',methods=['GET'])\n@cross_origin(origin='*', methods=['GET', 'POST', 'OPTIONS'], headers=['X-Requested-With', 'Content-Type', 'Origin'])\ndef get_brand(brandname):\n brand = brandModel.get_one(brandname)\n return json.dumps(brand)\n\n# get the category with specified name\n@app.route('/brand_graph/api/v1.0/category/',methods=['GET'])\n@cross_origin(origin='*', methods=['GET', 'POST', 'OPTIONS'], headers=['X-Requested-With', 'Content-Type', 'Origin'])\ndef get_category(categoryname):\n category = brandModel.get_category(categoryname)\n return json.dumps(category)\n\n\n\n", "sub_path": "server/app/routes/brands.py", "file_name": "brands.py", "file_ext": "py", "file_size_in_byte": 1103, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "app.models.model.Brand", "line_number": 6, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 13, "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": "flask.ext.cors.cross_origin", "line_number": 10, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 20, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 16, "usage_type": "call"}, {"api_name": "app.app", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.ext.cors.cross_origin", "line_number": 17, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 27, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 23, "usage_type": "call"}, {"api_name": "app.app", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.ext.cors.cross_origin", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "513527957", "text": "import panel as pn\nimport xarray as xr\nfrom .sigslot import SigSlot\nfrom .display import Display\nfrom .describe import Describe\nfrom .fields import Fields\n\n\nclass Control(SigSlot):\n \"\"\"\n This section allows the user to control the other subsections,\n such as displayer, fields.\n\n Parameters\n ----------\n data: `xarray` instance: `DataSet` or `DataArray`\n datset is used to initialize.\n\n Attributes\n ----------\n panel: Displays the generated template.\n displayer: Provides access to `Display` sub-section.\n describer: Provides access to `Describe` sub-section.\n fields: Provides access to `Fields` sub-section.\n kwargs: Provides access to kwargs selected in different subsections.\n \"\"\"\n\n def __init__(self, data):\n super().__init__()\n self.data = data\n self.displayer = Display(self.data)\n self.describer = Describe(self.data)\n self.fields = Fields(self.data)\n\n self.displayer.connect(\"variable_selected\", self.describer.setup)\n self.displayer.connect(\"variable_selected\", self.fields.setup)\n\n self.panel = pn.Column(\n pn.Row(self.displayer.panel,\n self.describer.panel),\n pn.Tabs(self.fields.panel,\n background=(230, 230, 230), width=1160))\n\n @property\n def kwargs(self):\n out = self.displayer.kwargs\n out.update(self.fields.kwargs)\n return out\n", "sub_path": "xrviz/control.py", "file_name": "control.py", "file_ext": "py", "file_size_in_byte": 1518, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sigslot.SigSlot", "line_number": 9, "usage_type": "name"}, {"api_name": "display.Display", "line_number": 31, "usage_type": "call"}, {"api_name": "describe.Describe", "line_number": 32, "usage_type": "call"}, {"api_name": "fields.Fields", "line_number": 33, "usage_type": "call"}, {"api_name": "panel.Column", "line_number": 38, "usage_type": "call"}, {"api_name": "panel.Row", "line_number": 39, "usage_type": "call"}, {"api_name": "panel.Tabs", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "186939134", "text": "import os\nimport glob\nimport tqdm\nimport logging\nfrom indra.sources import eidos, hume, cwms, sofia\nfrom indra.statements import Influence, Event\nfrom indra.tools import assemble_corpus as ac\nfrom indra.ontology.world.ontology import WorldOntology\nfrom indra.pipeline import register_pipeline, AssemblyPipeline\nfrom indra_world.assembly.operations import *\nfrom indra_world.sources.dart import process_reader_outputs\nfrom indra_world.corpus import Corpus\nfrom indra.statements import stmts_to_json_file\n\n\nreader_versions = {'flat':\n {'cwms': '2020.08.28',\n 'hume': 'r2020_08_19_4',\n 'sofia': '1.1',\n 'eidos': '1.0.3'},\n 'compositional':\n {'cwms': '2020.09.03',\n 'hume': 'r2020_09_28_4',\n 'sofia': '1.1',\n 'eidos': '1.0.4'}}\n\n\nont_url = 'https://github.com/WorldModelers/Ontologies/blob/'\\\n '25690a258d02fdf1f35ce9140f7cd54145e2b30c/'\\\n 'CompositionalOntology_v2.1_metadata.yml'\n\nlogger = logging.getLogger('wm_compositional.assembly')\n\n\ndef concept_matches_compositional(concept):\n wm = concept.db_refs.get('WM')\n if not wm:\n return concept.name\n wm_top = tuple(entry[0] if entry else None for entry in wm[0])\n return wm_top\n\n\ndef matches_compositional(stmt):\n if isinstance(stmt, Influence):\n key = (stmt.__class__.__name__,\n concept_matches_compositional(stmt.subj.concept),\n concept_matches_compositional(stmt.obj.concept),\n stmt.polarity_count(),\n stmt.overall_polarity()\n )\n elif isinstance(stmt, Event):\n key = (stmt.__class__.__name__,\n concept_matches_compositional(stmt.concept),\n stmt.delta.polarity)\n return str(key)\n\n\n@register_pipeline\ndef print_grounding_stats(statements):\n logger.info('-----------------------------------------')\n logger.info('Number of Influences: %s' % len([s for s in statements if\n isinstance(s, Influence)]))\n grs = []\n gr_combos = []\n evidences = 0\n evidence_by_reader = defaultdict(int)\n for stmt in statements:\n if isinstance(stmt, Influence):\n for concept in [stmt.subj.concept, stmt.obj.concept]:\n grs.append(concept.get_grounding())\n gr_combos.append((stmt.subj.concept.get_grounding(),\n stmt.obj.concept.get_grounding()))\n evidences += len(stmt.evidence)\n for ev in stmt.evidence:\n evidence_by_reader[ev.source_api] += 1\n logger.info('Unique groundings: %d' % len(set(grs)))\n logger.info('Unique combinations: %d' % len(set(gr_combos)))\n logger.info('Number of evidences: %d' % evidences)\n logger.info('Number of evidences by reader: %s' %\n str(dict(evidence_by_reader)))\n logger.info('-----------------------------------------')\n return statements\n\n\nif __name__ == '__main__':\n readers = ['sofia', 'eidos', 'hume', 'cwms']\n grounding = 'compositional'\n do_upload = False\n stmts = []\n for reader in readers:\n version = reader_versions[grounding][reader]\n pattern = '*' if reader != 'sofia' \\\n else ('*_new' if grounding == 'compositional' else '*_old')\n fnames = glob.glob('/Users/ben/data/dart/%s/%s/%s' % (reader, version,\n pattern))\n print('Found %d files for %s' % (len(fnames), reader))\n for fname in tqdm.tqdm(fnames):\n if reader == 'eidos':\n pp = eidos.process_json_file(fname, grounding_mode=grounding)\n elif reader == 'hume':\n pp = hume.process_jsonld_file(fname, grounding_mode=grounding)\n elif reader == 'cwms':\n pp = cwms.process_ekb_file(fname, grounding_mode=grounding)\n elif reader == 'sofia':\n pp = sofia.process_json_file(fname, grounding_mode=grounding)\n doc_id = os.path.basename(fname)[:32]\n for stmt in pp.statements:\n for ev in stmt.evidence:\n if 'provenance' not in ev.annotations:\n ev.annotations['provenance'] = [\n {'document': {'@id': doc_id}}]\n else:\n prov = ev.annotations['provenance'][0]['document']\n prov['@id'] = doc_id\n stmts += pp.statements\n if grounding == 'compositional':\n validate_grounding_format(stmts)\n\n ap = AssemblyPipeline.from_json_file('assembly_%s.json' % grounding)\n assembled_stmts = ap.run(stmts)\n\n if do_upload:\n corpus_id = 'compositional_v4'\n stmts_to_json_file(assembled_stmts, '%s.json' % corpus_id)\n\n meta_data = {\n 'corpus_id': corpus_id,\n 'description': ('Assembly of 4 reader outputs with the '\n 'compositional ontology (%s).' % ont_url),\n 'display_name': 'Compositional ontology assembly v3',\n 'readers': readers,\n 'assembly': {\n 'level': 'grounding',\n 'grounding_threshold': 0.6,\n },\n 'num_statements': len(assembled_stmts),\n 'num_documents': 382\n }\n corpus = Corpus(corpus_id, statements=assembled_stmts,\n raw_statements=stmts,\n meta_data=meta_data)\n corpus.s3_put()\n", "sub_path": "wm_compositional/assembly.py", "file_name": "assembly.py", "file_ext": "py", "file_size_in_byte": 5614, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 32, "usage_type": "call"}, {"api_name": "indra.statements.Influence", "line_number": 44, "usage_type": "argument"}, {"api_name": "indra.statements.Event", "line_number": 51, "usage_type": "argument"}, {"api_name": "indra.statements.Influence", "line_number": 62, "usage_type": "argument"}, {"api_name": "indra.statements.Influence", "line_number": 68, "usage_type": "argument"}, {"api_name": "indra.pipeline.register_pipeline", "line_number": 58, "usage_type": "name"}, {"api_name": "glob.glob", "line_number": 94, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 97, "usage_type": "call"}, {"api_name": "indra.sources.eidos.process_json_file", "line_number": 99, "usage_type": "call"}, {"api_name": "indra.sources.eidos", "line_number": 99, "usage_type": "name"}, {"api_name": "indra.sources.hume.process_jsonld_file", "line_number": 101, "usage_type": "call"}, {"api_name": "indra.sources.hume", "line_number": 101, "usage_type": "name"}, {"api_name": "indra.sources.cwms.process_ekb_file", "line_number": 103, "usage_type": "call"}, {"api_name": "indra.sources.cwms", "line_number": 103, "usage_type": "name"}, {"api_name": "indra.sources.sofia.process_json_file", "line_number": 105, "usage_type": "call"}, {"api_name": "indra.sources.sofia", "line_number": 105, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path", "line_number": 106, "usage_type": "attribute"}, {"api_name": "indra.pipeline.AssemblyPipeline.from_json_file", "line_number": 119, "usage_type": "call"}, {"api_name": "indra.pipeline.AssemblyPipeline", "line_number": 119, "usage_type": "name"}, {"api_name": "indra.statements.stmts_to_json_file", "line_number": 124, "usage_type": "call"}, {"api_name": "indra_world.corpus.Corpus", "line_number": 139, "usage_type": "call"}]} +{"seq_id": "308473016", "text": "# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n#\n#\n\nimport copy\n\nfrom mox3.mox import IsA # noqa\n\nfrom django.core.urlresolvers import reverse\nfrom django import http\n\nfrom openstack_dashboard.test import helpers as test\n\nfrom afloclient.v1.tickets import Ticket\nfrom afloclient.v1.tickettemplates import Tickettemplate\n\nfrom nec_portal import api\nfrom nec_portal.dashboards.project.ticket_list import panel # noqa\nfrom nec_portal.dashboards.project.ticket_templates \\\n import fixture_20160627 as fixture\nfrom nec_portal.dashboards.project.ticket_templates import panel # noqa\nfrom nec_portal.test import aflo_helpers as aflo_test\n\n\nclass WorkflowEngineTicketDetailTest(aflo_test.BaseAdminViewTests):\n \"\"\"Workflow engine ticket detail view test class\"\"\"\n\n @test.create_stubs({api.ticket: ('ticket_get_detailed',\n 'tickettemplates_get',)})\n def test_view_filled_data(self):\n \"\"\"Test 'Ticket detail view display' to successfully run\n In the filled data\n \"\"\"\n\n ticket_detail_data = self._get_ticket_detail_data(0)\n\n template_data = self._get_ticket_template_data(0)\n\n self._ticket_create_successfully_action(\n ticket_detail_data, template_data)\n\n def _get_ticket_detail_data(self, index):\n return copy.deepcopy(fixture.TICKET_DATA_LIST[index])\n\n def _get_ticket_template_data(self, index):\n return copy.deepcopy(fixture.TICKET_TEMPLATE_DATA_LIST[index])\n\n def _ticket_create_successfully_action(self, ticket_detail_data,\n template_data):\n\n ticket_detail_id = ticket_detail_data['id']\n\n api.ticket.ticket_get_detailed(\n IsA(http.HttpRequest), ticket_detail_id).AndReturn(\n Ticket(self, ticket_detail_data, loaded=True))\n\n template_id = template_data['id']\n\n api.ticket.tickettemplates_get(\n IsA(http.HttpRequest), template_id).AndReturn(\n Tickettemplate(self, template_data, loaded=True))\n\n self.mox.ReplayAll()\n\n url = reverse(\n 'horizon:project:ticket_list:wf_engine_detail:index',\n args=[ticket_detail_id])\n\n res = self.client.get(url)\n\n self.assertNoFormErrors(res)\n self.assertEqual(res.status_code, 200)\n self.assertTemplateUsed(\n res, 'project/ticket_templates/wf_engine/detail/detail.html')\n", "sub_path": "nec_portal/dashboards/project/ticket_list/wf_engine/detail/tests.py", "file_name": "tests.py", "file_ext": "py", "file_size_in_byte": 2926, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "nec_portal.test.aflo_helpers.BaseAdminViewTests", "line_number": 35, "usage_type": "attribute"}, {"api_name": "nec_portal.test.aflo_helpers", "line_number": 35, "usage_type": "name"}, {"api_name": "openstack_dashboard.test.helpers.create_stubs", "line_number": 38, "usage_type": "call"}, {"api_name": "openstack_dashboard.test.helpers", "line_number": 38, "usage_type": "name"}, {"api_name": "nec_portal.api.ticket", "line_number": 38, "usage_type": "attribute"}, {"api_name": "nec_portal.api", "line_number": 38, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 53, "usage_type": "call"}, {"api_name": "nec_portal.dashboards.project.ticket_templates.fixture_20160627.TICKET_DATA_LIST", "line_number": 53, "usage_type": "attribute"}, {"api_name": "nec_portal.dashboards.project.ticket_templates.fixture_20160627", "line_number": 53, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 56, "usage_type": "call"}, {"api_name": "nec_portal.dashboards.project.ticket_templates.fixture_20160627.TICKET_TEMPLATE_DATA_LIST", "line_number": 56, "usage_type": "attribute"}, {"api_name": "nec_portal.dashboards.project.ticket_templates.fixture_20160627", "line_number": 56, "usage_type": "name"}, {"api_name": "nec_portal.api.ticket.ticket_get_detailed", "line_number": 63, "usage_type": "call"}, {"api_name": "nec_portal.api.ticket", "line_number": 63, "usage_type": "attribute"}, {"api_name": "nec_portal.api", "line_number": 63, "usage_type": "name"}, {"api_name": "mox3.mox.IsA", "line_number": 64, "usage_type": "call"}, {"api_name": "django.http.HttpRequest", "line_number": 64, "usage_type": "attribute"}, {"api_name": "django.http", "line_number": 64, "usage_type": "name"}, {"api_name": "afloclient.v1.tickets.Ticket", "line_number": 65, "usage_type": "call"}, {"api_name": "nec_portal.api.ticket.tickettemplates_get", "line_number": 69, "usage_type": "call"}, {"api_name": "nec_portal.api.ticket", "line_number": 69, "usage_type": "attribute"}, {"api_name": "nec_portal.api", "line_number": 69, "usage_type": "name"}, {"api_name": "mox3.mox.IsA", "line_number": 70, "usage_type": "call"}, {"api_name": "django.http.HttpRequest", "line_number": 70, "usage_type": "attribute"}, {"api_name": "django.http", "line_number": 70, "usage_type": "name"}, {"api_name": "afloclient.v1.tickettemplates.Tickettemplate", "line_number": 71, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "556378156", "text": "import torch.nn as nn\nimport torch\n# from utils.math import *\n\n\nclass CNNPolicy(nn.Module):\n \"\"\"\n Convolutional Policy from Nature DQN.\n \"\"\"\n def __init__(self, state_dim, action_num):\n super().__init__()\n self.is_disc_action = True\n self.activation = torch.relu\n\n self.conv_layers = nn.Sequential(\n nn.Conv2d(4, 32, kernel_size=8, stride=4),\n nn.ReLU(),\n nn.Conv2d(32,64, kernel_size=4, stride=2),\n nn.ReLU(),\n nn.Conv2d(64,64, kernel_size=3, stride=1)\n )\n\n # 3136 = 56 * 56, figure out why\n self.decoder = nn.Sequential(\n nn.Linear(3136, 512),\n nn.Linear(512, action_num)\n )\n\n def forward(self, x):\n if not isinstance(x.size, int):\n x = x.view(x.size(0), 4, 84, 84)\n x = self.conv_layers(x)\n\n x = x.view(x.size(0), -1)\n x = self.decoder(x)\n\n action_prob = torch.softmax(x, dim=1)\n return action_prob\n\n def select_action(self, x):\n action_prob = self.forward(x)\n action = action_prob.multinomial(1)\n return action\n\n def get_kl(self, x):\n action_prob1 = self.forward(x)\n action_prob0 = action_prob1.detach()\n kl = action_prob0 * (torch.log(action_prob0) - torch.log(action_prob1))\n return kl.sum(1, keepdim=True)\n\n def get_log_prob(self, x, actions):\n action_prob = self.forward(x)\n return torch.log(action_prob.gather(1, actions.long().unsqueeze(1)))\n\n def get_fim(self, x):\n action_prob = self.forward(x)\n M = action_prob.pow(-1).view(-1).detach()\n return M, action_prob, {}\n\n", "sub_path": "models/cnn_policy.py", "file_name": "cnn_policy.py", "file_ext": "py", "file_size_in_byte": 1675, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "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.relu", "line_number": 13, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "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.ReLU", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "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": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.softmax", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "474347231", "text": "from itertools import permutations\ndef checkIfPan(string):\n digits = len(string)\n if digits > 10:\n return False\n for i in range(0,digits):\n print(i,string)\n if str(i) not in string:\n return False\n return True\n\nprimeList = [2,3,5,7,11,13,17]\ndef bizareDivi(stringPan):\n track = False\n for k in range(1,8):\n numb = stringPan[k:k+3]\n #print(numb, stringPan)\n if int(numb) % primeList[k-1] == 0:\n track = True\n else:\n return False\n return track\nsum=0\nfor i in permutations('0123456789'):\n if i[0] == '0':\n continue\n n = ''.join(list(i))\n if bizareDivi(n):\n sum += int(n)\n print(\"====\",n,sum)\n\n", "sub_path": "subStringDivisibilityPE43.py", "file_name": "subStringDivisibilityPE43.py", "file_ext": "py", "file_size_in_byte": 723, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "itertools.permutations", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "366093954", "text": "\"\"\"\nTransformer\n=================================\n\nThis example shows how to implement the Transformer model with Gluon NLP Toolkit.\n\n@inproceedings{vaswani2017attention,\n title={Attention is all you need},\n author={Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones,\n Llion and Gomez, Aidan N and Kaiser, Lukasz and Polosukhin, Illia},\n booktitle={Advances in Neural Information Processing Systems},\n pages={6000--6010},\n year={2017}\n}\n\"\"\"\n\n# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements. See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership. The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied. See the License for the\n# specific language governing permissions and limitations\n# under the License.\n# pylint:disable=redefined-outer-name,logging-format-interpolation\n\nimport argparse\nimport time\nimport random\nimport os\nimport zipfile\nimport logging\nimport numpy as np\nimport mxnet as mx\nfrom mxnet import gluon\nfrom mxnet.gluon.utils import download, check_sha1\nimport gluonnlp as nlp\n\nfrom gluonnlp.loss import MaskedSoftmaxCELoss\nfrom gluonnlp.model.translation import NMTModel\nfrom gluonnlp.model.transformer import get_transformer_encoder_decoder\nfrom translation import BeamSearchTranslator\nfrom utils import logging_config\nfrom bleu import _bpe_to_words, compute_bleu\nimport dataprocessor\n\nnp.random.seed(100)\nrandom.seed(100)\nmx.random.seed(10000)\n\nnlp.utils.check_version('0.7.0')\n\nparser = argparse.ArgumentParser(description='Neural Machine Translation Example.'\n 'We use this script only for transformer inference.')\nparser.add_argument('--dataset', type=str, default='WMT2014BPE', help='Dataset to use.')\nparser.add_argument('--src_lang', type=str, default='en', help='Source language')\nparser.add_argument('--tgt_lang', type=str, default='de', help='Target language')\nparser.add_argument('--num_units', type=int, default=512, help='Dimension of the embedding '\n 'vectors and states.')\nparser.add_argument('--hidden_size', type=int, default=2048,\n help='Dimension of the hidden state in position-wise feed-forward networks.')\nparser.add_argument('--dropout', type=float, default=0.1,\n help='dropout applied to layers (0 = no dropout)')\nparser.add_argument('--num_layers', type=int, default=6,\n help='number of layers in the encoder and decoder')\nparser.add_argument('--num_heads', type=int, default=8,\n help='number of heads in multi-head attention')\nparser.add_argument('--scaled', action='store_true', help='Turn on to use scale in attention')\nparser.add_argument('--batch_size', type=int, default=1024,\n help='Batch size. Number of tokens in a minibatch')\nparser.add_argument('--beam_size', type=int, default=4, help='Beam size')\nparser.add_argument('--lp_alpha', type=float, default=0.6,\n help='Alpha used in calculating the length penalty')\nparser.add_argument('--lp_k', type=int, default=5, help='K used in calculating the length penalty')\nparser.add_argument('--test_batch_size', type=int, default=256, help='Test batch size')\nparser.add_argument('--num_buckets', type=int, default=10, help='Bucket number')\nparser.add_argument('--bucket_scheme', type=str, default='constant',\n help='Strategy for generating bucket keys. It supports: '\n '\"constant\": all the buckets have the same width; '\n '\"linear\": the width of bucket increases linearly; '\n '\"exp\": the width of bucket increases exponentially')\nparser.add_argument('--bucket_ratio', type=float, default=0.0, help='Ratio for increasing the '\n 'throughput of the bucketing')\nparser.add_argument('--src_max_len', type=int, default=-1, help='Maximum length of the source '\n 'sentence, -1 means no clipping')\nparser.add_argument('--tgt_max_len', type=int, default=-1, help='Maximum length of the target '\n 'sentence, -1 means no clipping')\nparser.add_argument('--full', action='store_true',\n help='In default, we use the test dataset in'\n ' http://statmt.org/wmt14/test-filtered.tgz.'\n ' When the option full is turned on, we use the test dataset in'\n ' http://statmt.org/wmt14/test-full.tgz')\nparser.add_argument('--bleu', type=str, default='tweaked',\n help='Schemes for computing bleu score. It can be: '\n '\"tweaked\": it uses similar steps in get_ende_bleu.sh in tensor2tensor '\n 'repository, where compound words are put in ATAT format; '\n '\"13a\": This uses official WMT tokenization and produces the same results'\n ' as official script (mteval-v13a.pl) used by WMT; '\n '\"intl\": This use international tokenization in mteval-v14a.pl')\nparser.add_argument('--log_interval', type=int, default=100, metavar='N',\n help='report interval')\nparser.add_argument('--save_dir', type=str, default='transformer_out',\n help='directory path to save the final model and training log')\nparser.add_argument('--gpu', type=int,\n help='gpu id, e.g. 0 or 1. Unspecified means using cpu.')\nparser.add_argument('--model_parameter', type=str, default=' ', required=True,\n help='model parameter for inference, must be provided.')\n\nargs = parser.parse_args()\nlogging_config(args.save_dir)\nlogging.info(args)\n\n# data process\ndata_train, data_val, data_test, val_tgt_sentences, test_tgt_sentences, src_vocab, tgt_vocab \\\n = dataprocessor.load_translation_data(dataset=args.dataset, bleu=args.bleu, args=args)\n\ndataprocessor.write_sentences(test_tgt_sentences, os.path.join(args.save_dir, 'test_gt.txt'))\n\ndata_train = data_train.transform(lambda src, tgt: (src, tgt, len(src), len(tgt)), lazy=False)\ndata_val = gluon.data.SimpleDataset([(ele[0], ele[1], len(ele[0]), len(ele[1]), i)\n for i, ele in enumerate(data_val)])\ndata_test = gluon.data.SimpleDataset([(ele[0], ele[1], len(ele[0]), len(ele[1]), i)\n for i, ele in enumerate(data_test)])\n\ndata_train_lengths, data_val_lengths, data_test_lengths = [dataprocessor.get_data_lengths(x)\n for x in\n [data_train, data_val, data_test]]\n\ndetokenizer = nlp.data.SacreMosesDetokenizer()\n\n# model prepare\nctx = [mx.cpu()] if args.gpu is None else [mx.gpu(args.gpu)]\n\nif args.src_max_len <= 0 or args.tgt_max_len <= 0:\n max_len = np.max(\n [np.max(data_train_lengths, axis=0), np.max(data_val_lengths, axis=0),\n np.max(data_test_lengths, axis=0)],\n axis=0)\n\nif args.src_max_len > 0:\n src_max_len = args.src_max_len\nelse:\n src_max_len = max_len[0]\nif args.tgt_max_len > 0:\n tgt_max_len = args.tgt_max_len\nelse:\n tgt_max_len = max_len[1]\n\nencoder, decoder, one_step_ahead_decoder = get_transformer_encoder_decoder(\n units=args.num_units, hidden_size=args.hidden_size, dropout=args.dropout,\n num_layers=args.num_layers, num_heads=args.num_heads, max_src_length=max(src_max_len, 500),\n max_tgt_length=max(tgt_max_len, 500), scaled=args.scaled)\nmodel = NMTModel(src_vocab=src_vocab, tgt_vocab=tgt_vocab, encoder=encoder, decoder=decoder,\n one_step_ahead_decoder=one_step_ahead_decoder, share_embed=args.dataset != 'TOY',\n embed_size=args.num_units, tie_weights=args.dataset != 'TOY',\n embed_initializer=None, prefix='transformer_')\n\nparam_name = args.model_parameter\nif (not os.path.exists(param_name)):\n archive_param_url = 'http://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/{}'\n archive_file_hash = ('transformer_en_de_512_WMT2014-e25287c5.zip',\n '5193b469e0e2dfdda3c834f9212420758a0d1d71')\n param_file_hash = ('transformer_en_de_512_WMT2014-e25287c5.params',\n 'e25287c5a924b7025e08d626f02626d5fa3af2d1')\n archive_file, archive_hash = archive_file_hash\n param_file, param_hash = param_file_hash\n logging.warning('The provided param file {} does not exist, start to download it from {}...'\n .format(param_name, archive_param_url.format(archive_file)))\n\n root_dir = os.path.dirname(__file__)\n archive_file_path = '{}/{}'.format(root_dir, archive_file)\n param_name = '{}/{}'.format(root_dir, param_file)\n if (not os.path.exists(param_name) or not check_sha1(param_name, param_hash)):\n download(archive_param_url.format(archive_file),\n path=archive_file_path,\n sha1_hash=archive_hash)\n with zipfile.ZipFile(archive_file_path) as zf:\n zf.extractall(root_dir)\n\nmodel.load_parameters(param_name, ctx)\n\nstatic_alloc = True\nmodel.hybridize(static_alloc=static_alloc)\nlogging.info(model)\n\n# translator prepare\ntranslator = BeamSearchTranslator(model=model, beam_size=args.beam_size,\n scorer=nlp.model.BeamSearchScorer(alpha=args.lp_alpha,\n K=args.lp_k),\n max_length=200)\nlogging.info('Use beam_size={}, alpha={}, K={}'.format(args.beam_size, args.lp_alpha, args.lp_k))\n\ntest_loss_function = MaskedSoftmaxCELoss()\ntest_loss_function.hybridize(static_alloc=static_alloc)\n\ndef inference():\n \"\"\"inference function.\"\"\"\n logging.info('Inference on test_dataset!')\n\n # data prepare\n test_data_loader = dataprocessor.get_dataloader(data_test, args,\n dataset_type='test',\n use_average_length=True)\n\n if args.bleu == 'tweaked':\n bpe = bool(args.dataset != 'IWSLT2015' and args.dataset != 'TOY')\n split_compound_word = bpe\n tokenized = True\n elif args.bleu == '13a' or args.bleu == 'intl':\n bpe = False\n split_compound_word = False\n tokenized = False\n else:\n raise NotImplementedError\n\n translation_out = []\n all_inst_ids = []\n total_wc = 0\n total_time = 0\n batch_total_blue = 0\n\n for batch_id, (src_seq, tgt_seq, src_test_length, tgt_test_length, inst_ids) \\\n in enumerate(test_data_loader):\n\n total_wc += src_test_length.sum().asscalar() + tgt_test_length.sum().asscalar()\n\n src_seq = src_seq.as_in_context(ctx[0])\n tgt_seq = tgt_seq.as_in_context(ctx[0])\n src_test_length = src_test_length.as_in_context(ctx[0])\n tgt_test_length = tgt_test_length.as_in_context(ctx[0])\n all_inst_ids.extend(inst_ids.asnumpy().astype(np.int32).tolist())\n\n start = time.time()\n # Translate to get a bleu score\n samples, _, sample_test_length = \\\n translator.translate(src_seq=src_seq, src_valid_length=src_test_length)\n total_time += (time.time() - start)\n\n # generator the translator result for each batch\n max_score_sample = samples[:, 0, :].asnumpy()\n sample_test_length = sample_test_length[:, 0].asnumpy()\n translation_tmp = []\n translation_tmp_sentences = []\n for i in range(max_score_sample.shape[0]):\n translation_tmp.append([tgt_vocab.idx_to_token[ele] for ele in \\\n max_score_sample[i][1:(sample_test_length[i] - 1)]])\n\n # detokenizer each translator result\n for _, sentence in enumerate(translation_tmp):\n if args.bleu == 'tweaked':\n translation_tmp_sentences.append(sentence)\n translation_out.append(sentence)\n elif args.bleu == '13a' or args.bleu == 'intl':\n translation_tmp_sentences.append(detokenizer(_bpe_to_words(sentence)))\n translation_out.append(detokenizer(_bpe_to_words(sentence)))\n else:\n raise NotImplementedError\n\n # generate tgt_sentence for bleu calculation of each batch\n tgt_sen_tmp = [test_tgt_sentences[index] for \\\n _, index in enumerate(inst_ids.asnumpy().astype(np.int32).tolist())]\n batch_test_bleu_score, _, _, _, _ = compute_bleu([tgt_sen_tmp], translation_tmp_sentences,\n tokenized=tokenized, tokenizer=args.bleu,\n split_compound_word=split_compound_word,\n bpe=bpe)\n batch_total_blue += batch_test_bleu_score\n\n # log for every ten batchs\n if batch_id % 10 == 0 and batch_id != 0:\n batch_ave_bleu = batch_total_blue / 10\n batch_total_blue = 0\n logging.info('batch id={:d}, batch_bleu={:.4f}'\n .format(batch_id, batch_ave_bleu * 100))\n\n # reorg translation sentences by inst_ids\n real_translation_out = [None for _ in range(len(all_inst_ids))]\n for ind, sentence in zip(all_inst_ids, translation_out):\n real_translation_out[ind] = sentence\n\n # get bleu score, n-gram precisions, brevity penalty, reference length, and translation length\n test_bleu_score, _, _, _, _ = compute_bleu([test_tgt_sentences], real_translation_out,\n tokenized=tokenized, tokenizer=args.bleu,\n split_compound_word=split_compound_word,\n bpe=bpe)\n\n logging.info('Inference at test dataset. \\\n inference bleu={:.4f}, throughput={:.4f}K wps'\n .format(test_bleu_score * 100, total_wc / total_time / 1000))\n\n\nif __name__ == '__main__':\n inference()\n", "sub_path": "scripts/machine_translation/inference_transformer.py", "file_name": "inference_transformer.py", "file_ext": "py", "file_size_in_byte": 14629, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.random.seed", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 55, "usage_type": "attribute"}, {"api_name": "random.seed", "line_number": 56, "usage_type": "call"}, {"api_name": "mxnet.random.seed", "line_number": 57, "usage_type": "call"}, {"api_name": "mxnet.random", "line_number": 57, "usage_type": "attribute"}, {"api_name": "gluonnlp.utils.check_version", "line_number": 59, "usage_type": "call"}, {"api_name": "gluonnlp.utils", "line_number": 59, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 61, "usage_type": "call"}, {"api_name": "utils.logging_config", "line_number": 118, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 119, "usage_type": "call"}, {"api_name": "dataprocessor.load_translation_data", "line_number": 123, "usage_type": "call"}, {"api_name": "dataprocessor.write_sentences", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path", "line_number": 125, "usage_type": "attribute"}, {"api_name": "mxnet.gluon.data.SimpleDataset", "line_number": 128, "usage_type": "call"}, {"api_name": "mxnet.gluon.data", "line_number": 128, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 128, "usage_type": "name"}, {"api_name": "mxnet.gluon.data.SimpleDataset", "line_number": 130, "usage_type": "call"}, {"api_name": "mxnet.gluon.data", "line_number": 130, "usage_type": "attribute"}, {"api_name": "mxnet.gluon", "line_number": 130, "usage_type": "name"}, {"api_name": "dataprocessor.get_data_lengths", "line_number": 133, "usage_type": "call"}, {"api_name": "gluonnlp.data.SacreMosesDetokenizer", "line_number": 137, "usage_type": "call"}, {"api_name": "gluonnlp.data", "line_number": 137, "usage_type": "attribute"}, {"api_name": "mxnet.cpu", "line_number": 140, "usage_type": "call"}, {"api_name": "mxnet.gpu", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 145, "usage_type": "call"}, {"api_name": "gluonnlp.model.transformer.get_transformer_encoder_decoder", "line_number": 157, "usage_type": "call"}, {"api_name": "gluonnlp.model.translation.NMTModel", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 167, "usage_type": "call"}, {"api_name": "os.path", "line_number": 167, "usage_type": "attribute"}, {"api_name": "logging.warning", "line_number": 175, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path", "line_number": 181, "usage_type": "attribute"}, {"api_name": "mxnet.gluon.utils.check_sha1", "line_number": 181, "usage_type": "call"}, {"api_name": "mxnet.gluon.utils.download", "line_number": 182, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 185, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 192, "usage_type": "call"}, {"api_name": "translation.BeamSearchTranslator", "line_number": 195, "usage_type": "call"}, {"api_name": "gluonnlp.model.BeamSearchScorer", "line_number": 196, "usage_type": "call"}, {"api_name": "gluonnlp.model", "line_number": 196, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 199, "usage_type": "call"}, {"api_name": "gluonnlp.loss.MaskedSoftmaxCELoss", "line_number": 201, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 206, "usage_type": "call"}, {"api_name": "dataprocessor.get_dataloader", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 239, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 241, "usage_type": "call"}, {"api_name": "time.time", "line_number": 245, "usage_type": "call"}, {"api_name": "bleu._bpe_to_words", "line_number": 262, "usage_type": "call"}, {"api_name": "bleu._bpe_to_words", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 269, "usage_type": "attribute"}, {"api_name": "bleu.compute_bleu", "line_number": 270, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 280, "usage_type": "call"}, {"api_name": "bleu.compute_bleu", "line_number": 289, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 294, "usage_type": "call"}]} +{"seq_id": "227821990", "text": "# -*- coding:utf-8 -*-\n# @Time : 2019/8/2 9:39\n# @Author : naihai\n\nimport pandas as pd\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\n\nfrom factorization_machine import FMFast\n\n\ndef fm_fast_regression():\n data = pd.read_csv(\"E:\\Scala\\projects\\Recommend\\data\\house_price_train.txt\", sep='\\t', header=None)\n X = data[data.columns[1:]]\n y = data[0].values\n\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=12)\n print(X_train.shape)\n\n # 预处理\n scaler = StandardScaler()\n scaler.fit(X)\n X_train = scaler.transform(X_train)\n X_test = scaler.transform(X_test)\n\n model = FMFast(0, 0.000001, [0.1, 0.1, 0.1], 100)\n model.fit(X_train, y_train, 100, True)\n\n pres = model.predict(X_test)\n\n print(mean_squared_error(y_test, pres))\n\n\nif __name__ == '__main__':\n fm_fast_regression()\n", "sub_path": "factorization_machine/test2.py", "file_name": "test2.py", "file_ext": "py", "file_size_in_byte": 958, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_csv", "line_number": 14, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 18, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 22, "usage_type": "call"}, {"api_name": "factorization_machine.FMFast", "line_number": 27, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_squared_error", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "278347862", "text": "#! /usr/bin/env python\n\nimport numpy as np \nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport math \nimport rospy\nimport torch.nn.functional as F\nfrom torch.distributions import Categorical, Normal\nfrom std_msgs.msg import String, Int8\nfrom geometry_msgs.msg import Vector3\nimport vrep\nimport matplotlib.pyplot as plt\n\nfrom agent import Agent\nfrom Networks.network import Network\nfrom Networks.feudalNetwork import FeudalNetwork\nfrom Buffers.CounterFeudalBuffer import Memory\nfrom collections import namedtuple\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nTransition = namedtuple('Transition', ('states', 'actions', 'rewards', 'next_state', 'local','goals'))\n\n# Other considerations:\n# - Latent space for actor instead of manually reduced space. Use auxiliary reward prediction for feature extraction\n\n\nclass CounterFeudal(object):\n def __init__(self, params, name, task):\n self.name = name\n self.task = task\n self.vTrain = params['valTrain'] # Counterfactual network\n self.vPars = params['valPars']\n self.aTrain = params['actTrain'] # Local Actors\n self.aPars = params['actPars']\n self.m_params = params['m_pars'] # Manager\n self.m_train = params['m_train']\n self.local_vPars = params['local_pars'] # Local values\n self.local_vTrain = params['local_train']\n self.agents = params['agents'] # Agents\n\n self.pubs = {}\n for key in self.agents.keys():\n bot = self.agents[key]\n self.pubs[key] = rospy.Publisher(bot['pub'], Vector3, queue_size = 1)\n rospy.Subscriber(\"/finished\", Int8, self.receiveDone, queue_size = 1)\n\n self.tau = self.vPars['tau']\n self.trainMode = self.vPars['trainMode']\n self.batch_size = self.vTrain['batch']\n self.td_lambda = .8\n\n self.c = self.m_params['c']\n self.w_discount = self.vTrain['gamma']\n self.m_discount = self.m_train['gamma']\n self.prevState = None\n self.soft = nn.Softmax(dim=1)\n\n self.exp = Memory()\n self.valueLoss = []\n self.actorLoss = []\n self.temp = []\n self.goal_temp1 = None \n self.goal_temp2 = None\n self.iteration = 0\n self.totalSteps = 0\n\n self.counter_critic, self.counter_target = (Network(self.vPars, self.vTrain), Network(self.vPars, self.vTrain))\n self.manager = Network(self.m_params, self.m_train) # manager\n self.actor = Network(self.aPars, self.aTrain) # actor\n self.critic, self.target = (Network(self.local_vPars, self.local_vTrain), Network(self.local_vPars, self.local_vTrain))\n\n for target_param, param in zip(self.target.parameters(), self.critic.parameters()):\n target_param.data.copy_(param.data)\n for target_param, param in zip(self.counter_target.parameters(), self.counter_critic.parameters()):\n target_param.data.copy_(param.data) \n\n self.reset()\n\n task.initAgent(self)\n self.stop = False\n while(not self.stop):\n x = 1+1\n\n task.postTraining()\n\n def receiveDone(self, message):\n if message.data == 1: #all iterations are done. Check manager.py\n self.stop = True\n if message.data == 2: #timed out. Check manager.py\n self.task.restartProtocol(restart = 1)\n\n def get_action(self, s_true, s_split):\n if self.iteration % self.c == 0: \n self.goal = [self.manager(torch.FloatTensor(s)) for s in s_split]\n else: # Use goal transition \n self.goal = [torch.FloatTensor(self.prevState[i][:,:2]) + g - torch.FloatTensor(s_split[i][:,:2]) for i, g in enumerate(self.goal)]\n\n self.goal_temp2 = self.goal_temp1 \n self.goal_temp1 = self.goal\n\n policies = []\n for i, s in enumerate(s_split):\n inpt = torch.cat((s[:,:6], self.goal[i]), dim=1)\n policies.append(self.soft(self.actor(inpt))) \n act_indices = [self.choose(policy) for policy in policies]\n actions = [self.actionMap[index] for index in act_indices]\n\n self.prevState = s_split\n self.iteration += 1\n\n return act_indices\n\n def choose(self, policies):\n m = Categorical(policies)\n action = m.sample()\n action = action.data.cpu().numpy()\n if action.size == 1:\n return np.asscalar(action)\n return torch.Tensor(action).unsqueeze(1)\n \n def saveModel(self): \n pass\n\n def store(self, s, a, r, sprime, aprime, done, s_w):\n self.temp.append(Transition(s, a, r, sprime, s_w, self.goal_temp2))\n if self.iteration % self.c == 1 and self.iteration != 1: # remember, we push at 1 because we incremented in get_action\n self.exp.push(self.temp) # store into exp\n self.temp = []\n\n def reset(self):\n self.train(True)\n self.iteration = 0\n self.temp = []\n self.goal_temp1, self.goal_temp2 = (None, None)\n return \n\n def zipStack(self, data):\n data = zip(*data)\n data = [torch.stack(d).squeeze().to(device) for d in data]\n return data\n\n def train(self, episode_done = False): \n if len(self.exp) > self.batch_size:\n # UNPACK REPLAY\n groups = self.exp.sample(self.batch_size)\n \n # for each agent:\n # Extract manager samples by: taking first true state, taking first goal, sum rewards, last next state of each group\n # replace the goal: Sample new goals according to the paper \n # for each of the goals, get goal transitions using worker state_transitions. \n # Pass goal transitions concatenated with worker_states to get policy distribution\n # gather all actions according to the replay actions \n # multiply probabilities across time\n # choose the goal index that has highest probability of all and replace each of the groups with the new goal and transition\n \n \n # Train counterfactual\n # Same as continuous counterfactual learning. Use montecarlo for each of the manager transitions \n \n # Train manager \n # Same as continuous counterfactual learning \n \n # Train value\n # Same as normal value learning \n \n # Train worker\n # Use advantage actor critic but through experience replay\n \n # store the groupings back into the replay\n print('yes')\n return 1\n\n \n\n\n \n", "sub_path": "AN_Bridging/box_ws/src/multi_box/src/Algs/CounterFeudal.py", "file_name": "CounterFeudal.py", "file_ext": "py", "file_size_in_byte": 6857, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"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": "collections.namedtuple", "line_number": 23, "usage_type": "call"}, {"api_name": "rospy.Publisher", "line_number": 46, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.Vector3", "line_number": 46, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 47, "usage_type": "call"}, {"api_name": "std_msgs.msg.Int8", "line_number": 47, "usage_type": "argument"}, {"api_name": "torch.nn.Softmax", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "Buffers.CounterFeudalBuffer.Memory", "line_number": 60, "usage_type": "call"}, {"api_name": "Networks.network.Network", "line_number": 69, "usage_type": "call"}, {"api_name": "Networks.network.Network", "line_number": 70, "usage_type": "call"}, {"api_name": "Networks.network.Network", "line_number": 71, "usage_type": "call"}, {"api_name": "Networks.network.Network", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.distributions.Categorical", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.asscalar", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 141, "usage_type": "call"}]} +{"seq_id": "208083950", "text": "from janome.tokenizer import Tokenizer\nfrom gensim.models import KeyedVectors\nfrom torchtext.vocab import Vectors\nimport MeCab\nimport torchtext\nimport torch.nn.functional as F\nimport re\n\nj_t = Tokenizer()\nm_t = MeCab.Tagger('-Owakati')\n\ndef tokenizer_mecab(text):\n text = m_t.parse(text)\n return text.strip().split()\n\ndef tokenizer_janome(text):\n return[tok for tok in j_t.tokenize(text, wakati=True)]\n\n# 前処理として正規化する関数\ndef preprocessing_text(text):\n # 半角・全角の統一\n # 今回は無視\n\n # 英語の小文字化\n # 今回は無視\n # output = output.lower()\n\n # 改行、半角スペース、全角スペースを削除\n text = re.sub('\\r', '', text)\n text = re.sub('\\n', '', text)\n text = re.sub(' ', '', text)\n text = re.sub('　', '', text)\n\n # 数字文字の一律「0」化\n text = re.sub(r'[0-9 ０-９]', '0', text) # 数字\n\n # 記号と数字の除去\n # 今回は無視\n\n # 特定文字を正規表現で置換\n # 今回は無視\n\n return text\n\n# 前処理とJanomeの単語分割を合わせた関数を定義する\ndef tokenizer_with_preprocessing(text):\n text = preprocessing_text(text) # 文章の正規化\n ret = tokenizer_janome(text) # Janomeの単語分割\n\n return ret\n\nmax_length = 25\nTEXT = torchtext.data.Field(sequential=True, tokenize=tokenizer_with_preprocessing, use_vocab=True, lower=True, include_lengths=True,\n batch_first=True, fix_length=max_length)\nLABEL = torchtext.data.Field(sequential=False, use_vocab=False)\n\ntrain_ds, val_ds, test_ds = torchtext.data.TabularDataset.splits(path='./data/', train='text_train.tsv', validation='text_val.tsv',\n test='text_test.tsv', format='tsv', fields=[('Text', TEXT), ('Label', LABEL)])\n\n# entity_vector.model.binはそのままではtorchtextで扱えないため、gensimで読み込んで、torchtextで扱える形で保存しなおす。\n#model = KeyedVectors.load_word2vec_format('./data/entity_vector/entity_vector.model.bin', binary=True)\n#model.wv.save_word2vec_format('./data/japanese_word2vec_vectors.vec')\n\njapanese_word2vec_vectors = Vectors(name='./data/japanese_word2vec_vectors.vec')\n\n# 単語ベクトルの中身を確認\nprint('1単語を表現する次元数 : ', japanese_word2vec_vectors.dim)\nprint('単語数 : ', len(japanese_word2vec_vectors.itos))\n\n# ベクトル化したバージョンのボキャブラリを作成\nTEXT.build_vocab(train_ds, vectors=japanese_word2vec_vectors, min_freq=1)\n\n# ボキャブラリーのベクトルを確認\nprint(TEXT.vocab.vectors.shape) # 49個の単語が200次元のベクトルで表現されている\nprint(TEXT.vocab.vectors)\n\n# ボキャブラリーの順番を確認\nprint(TEXT.vocab.stoi)\n\n# 姫 - 女性 + 男性のベクトルがどれと似ているか確認\ntensor_calc = TEXT.vocab.vectors[41] - TEXT.vocab.vectors[38] + TEXT.vocab.vectors[46]\n\n# コサイン類似度を計算\nprint('女王', F.cosine_similarity(tensor_calc, TEXT.vocab.vectors[39], dim=0))\nprint('王', F.cosine_similarity(tensor_calc, TEXT.vocab.vectors[44], dim=0))\nprint('王子', F.cosine_similarity(tensor_calc, TEXT.vocab.vectors[45], dim=0))\nprint('機械学習', F.cosine_similarity(tensor_calc, TEXT.vocab.vectors[43], dim=0))", "sub_path": "Ogawa/ch7/7-4-1_vectorize_word2vec.py", "file_name": "7-4-1_vectorize_word2vec.py", "file_ext": "py", "file_size_in_byte": 3357, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "janome.tokenizer.Tokenizer", "line_number": 9, "usage_type": "call"}, {"api_name": "MeCab.Tagger", "line_number": 10, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 29, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 30, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 31, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 32, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 35, "usage_type": "call"}, {"api_name": "torchtext.data.Field", "line_number": 53, "usage_type": "call"}, {"api_name": "torchtext.data", "line_number": 53, "usage_type": "attribute"}, {"api_name": "torchtext.data.Field", "line_number": 55, "usage_type": "call"}, {"api_name": "torchtext.data", "line_number": 55, "usage_type": "attribute"}, {"api_name": "torchtext.data.TabularDataset.splits", "line_number": 57, "usage_type": "call"}, {"api_name": "torchtext.data", "line_number": 57, "usage_type": "attribute"}, {"api_name": "torchtext.vocab.Vectors", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn.functional.cosine_similarity", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 84, "usage_type": "name"}, {"api_name": "torch.nn.functional.cosine_similarity", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.nn.functional.cosine_similarity", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.nn.functional.cosine_similarity", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 87, "usage_type": "name"}]} +{"seq_id": "362382302", "text": "import responses\nfrom requests.exceptions import HTTPError\nfrom infoblox import infoblox\nfrom . import testcasefixture\n\n\nclass TestGetLease(testcasefixture.TestCaseWithFixture):\n fixture_name = 'lease_get'\n\n @classmethod\n def setUpClass(cls):\n super(TestGetLease, cls).setUpClass()\n cls.get_url = 'https://10.10.10.10/wapi/v1.6/lease'\n cls.query_params = {'address': '192.168.1.10'}\n\n @responses.activate\n def test_get_lease(self):\n responses.add(responses.GET, self.get_url, body=self.body, status=200)\n self.lease = self.iba_ipa.get_lease(query_params=self.query_params)\n self.assertEqual(self.lease[0]['address'], self.query_params['address'])\n\n @responses.activate\n def test_get_ip_by_hostnotfound(self):\n responses.add(responses.GET, self.get_url, body='[]', status=200)\n with self.assertRaises(infoblox.InfobloxNotFoundException):\n self.iba_ipa.get_lease(query_params=self.query_params)\n\n @responses.activate\n def test_get_ip_by_host_serverfail(self):\n responses.add(responses.GET, self.get_url, body='[]', status=500)\n with self.assertRaises(HTTPError):\n self.iba_ipa.get_lease(query_params=self.query_params)\n", "sub_path": "tests/test_getlease.py", "file_name": "test_getlease.py", "file_ext": "py", "file_size_in_byte": 1236, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "responses.add", "line_number": 18, "usage_type": "call"}, {"api_name": "responses.GET", "line_number": 18, "usage_type": "attribute"}, {"api_name": "responses.activate", "line_number": 16, "usage_type": "attribute"}, {"api_name": "responses.add", "line_number": 24, "usage_type": "call"}, {"api_name": "responses.GET", "line_number": 24, "usage_type": "attribute"}, {"api_name": "infoblox.infoblox.InfobloxNotFoundException", "line_number": 25, "usage_type": "attribute"}, {"api_name": "infoblox.infoblox", "line_number": 25, "usage_type": "name"}, {"api_name": "responses.activate", "line_number": 22, "usage_type": "attribute"}, {"api_name": "responses.add", "line_number": 30, "usage_type": "call"}, {"api_name": "responses.GET", "line_number": 30, "usage_type": "attribute"}, {"api_name": "requests.exceptions.HTTPError", "line_number": 31, "usage_type": "argument"}, {"api_name": "responses.activate", "line_number": 28, "usage_type": "attribute"}]} +{"seq_id": "199527319", "text": "import sys\nfrom gomoku import Gomoku\nfrom utils import get_row_idx\n\nINF = 1337\nMAX_DEPTH = 2\n\nclass EaxGomoku(Gomoku):\n def __init__(self):\n # super(EaxGomoku).__init__()\n pass\n\n # def get_point(self, state, player):\n # for l in range(15):\n # data_line = state[:(l+1)*15]\n # if data_line.find()\n\n def print_square(self, lst):\n sys.stdout.write('=' * 30)\n sys.stdout.write('\\n')\n for l in range(15):\n for c in range(15):\n i = l * 15 + c\n sys.stdout.write(' %02d ' % lst[i])\n sys.stdout.write('\\n')\n sys.stdout.write('=' * 30)\n sys.stdout.write('\\n')\n\n def generate_ignore(self, state):\n ignore_set = set()\n\n for l in range(15):\n for c in range(15):\n i = l * 15 + c\n if state[i]:\n continue\n \n i_up = (l - 1) * 15 + c if l > 0 else None\n i_down = (l + 1) * 15 + c if l < 15 - 1 else None\n i_left = l * 15 + c - 1 if c > 0 else None\n i_right = l * 15 + c + 1 if c < 15 - 1 else None\n i_upleft = (l - 1) * 15 + c - 1 if l > 0 and c > 0 else None\n i_upright = (l - 1) * 15 + c + 1 if l > 0 and c < 15 - 1 else None\n i_downleft = (l - 1) * 15 + c - 1 if l < 15 - 1 and c > 0 else None\n i_downright = (l - 1) * 15 + c + 1 if l < 15 - 1 and c < 15 - 1 else None\n\n to_check = [i_up, i_down, i_left, i_right, i_upleft, i_upright, i_downleft, i_downright]\n tmp_ignore = True\n for check in filter(lambda x: x is not None, to_check):\n if state[check] :\n tmp_ignore = False\n if tmp_ignore:\n ignore_set.add(i)\n return ignore_set\n\n def generate_dist_map(self, state):\n dist_map = [INF] * 225\n\n for l in range(15):\n for c in range(15):\n i = l * 15 + c\n if state[i]:\n dist_map[i] = 0\n\n did_modification = True\n while did_modification:\n # print(dist_map)\n did_modification = False\n for l in range(15):\n for c in range(15):\n i = l * 15 + c\n\n i_up = (l - 1) * 15 + c if l > 0 else None\n i_down = (l + 1) * 15 + c if l < 15 - 1 else None\n i_left = l * 15 + c - 1 if c > 0 else None\n i_right = l * 15 + c + 1 if c < 15 - 1 else None\n i_upleft = (l - 1) * 15 + c - 1 if l > 0 and c > 0 else None\n i_upright = (l - 1) * 15 + c + 1 if l > 0 and c < 15 - 1 else None\n i_downleft = (l - 1) * 15 + c - 1 if l < 15 - 1 and c > 0 else None\n i_downright = (l - 1) * 15 + c + 1 if l < 15 - 1 and c < 15 - 1 else None\n\n to_check = [i_up, i_down, i_left, i_right, i_upleft, i_upright, i_downleft, i_downright]\n min_around = INF\n for check in filter(lambda x: x is not None, to_check):\n if dist_map[check] < min_around:\n min_around = dist_map[check]\n if min_around < dist_map[i] - 1:\n # print('Changing %r to %r' % (dist_map[i], min_around + 1))\n dist_map[i] = min_around + 1\n did_modification = True\n \n\n return dist_map\n \n\n def get_winning_player(self, state):\n # TODO: diagonals\n w_l = self.get_winning_player_line(state)\n if w_l:\n return w_l\n return self.get_winning_player_col(state)\n \n def get_winning_player_col(self, state):\n for c in range(15):\n l = 0\n while l < 15:\n i = l * 15 + c\n if not state[i]:\n l += 1\n continue\n else:\n checking_player = state[i]\n from_idx = i\n cnt = 0\n while state[i] == checking_player:\n cnt += 1\n l += 1\n i = l * 15 + c\n if l == 15:\n break\n if cnt >= 5:\n return (checking_player, get_row_idx(0, 1, from_idx, i - 1))\n\n def get_winning_player_line(self, state):\n for l in range(15):\n c = 0\n while c < 15:\n i = l * 15 + c\n if not state[i]:\n c += 1\n continue\n else:\n checking_player = state[i]\n from_idx = i\n cnt = 0\n while state[i] == checking_player:\n cnt += 1\n c += 1\n i = l * 15 + c\n if c == 15:\n break\n if cnt >= 5:\n return (checking_player, get_row_idx(0, 1, from_idx, i - 1))\n\n def get_finishing_moves(self, state, player, moves=set([]), depth=0):\n w = e.get_winning_player(state)\n if w and w[0] == player:\n # print(state)\n return {frozenset(moves): [moves, depth, 1, w[1]]}\n \n if depth == MAX_DEPTH:\n return None\n winning_moves = {}\n # to_ignore = self.generate_ignore(state)\n dist_map = self.generate_dist_map(state)\n # self.print_square(dist_map)\n for i in range(15*15):\n # if i in to_ignore:\n # continue\n # if depth == 0:\n # print(i)\n if state[i]:\n continue\n if dist_map[i] > 2:\n continue\n newstate = state[:]\n newstate[i] = player\n ret = self.get_finishing_moves(newstate, player, moves | {i}, depth + 1)\n if not ret:\n ret = {}\n for k in ret:\n # if not winning_move:\n # winning_move = ret\n # print(type(k))\n # print(type(ret))\n if frozenset(ret[k][0]) not in winning_moves:\n winning_moves[frozenset(ret[k][0])] = ret[k]\n else:\n winning_moves[frozenset(ret[k][0])][2] += 1\n \n return winning_moves\n \n\n\n def get_weight_moves(self, moves):\n # weight_map = [0] * 225\n ordered_moves = []\n weight_idx = {}\n min_depth = INF\n max_depth = -INF\n\n for move in moves.values():\n if move[1] < min_depth:\n min_depth = move[1]\n if move[1] > max_depth:\n max_depth = move[1]\n for idx in move[3]:\n if idx not in weight_idx:\n weight_idx[idx] = 0\n weight_idx[idx] += 1\n\n for depth in range(min_depth, max_depth + 1):\n tmp_ordered = []\n for move in moves.values():\n if move[1] == depth:\n tmp_ordered.append(move)\n tmp_ordered = sorted(tmp_ordered, key=lambda x:sum(weight_idx[y] for y in x[0]), reverse=True)\n for move in tmp_ordered:\n move[0] = sorted([x for x in move[0]], key=lambda x: weight_idx[x], reverse=True)\n ordered_moves.append(move)\n print(weight_idx)\n return ordered_moves\n # for l in range(15):\n # for c in range(15):\n # i = l * 15 + c\n # if state[i] == player:\n # weight_map[i] = 1\n # for m in moves:\n \n # self.print_square(weight_map)\n \n def play_turn(self, state, player):\n other_player = 1 if player == -1 else -1\n other_nm = self.get_finishing_moves(state, other_player)\n nm = self.get_finishing_moves(state, player)\n\n print('Other: %r' % (other_nm,))\n print('nm: %r' % (nm,))\n\n # self.get_winning_map_weight(player, state, nm)\n print(self.get_weight_moves(nm))\n return nm\n\n\nif __name__ == '__main__':\n e = EaxGomoku()\n \n state = [0 for i in range(15*15)]\n\n state[0] = 1\n # state[2] = 1\n state[3] = 1\n\n state[9] = -1\n # state[10] = -1\n state[11] = -1\n\n # print(e.generate_dist_map(state))\n \n r = e.play_turn(state, -1)\n # print(r)\n", "sub_path": "eaxgomoku.py", "file_name": "eaxgomoku.py", "file_ext": "py", "file_size_in_byte": 8630, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "gomoku.Gomoku", "line_number": 8, "usage_type": "name"}, {"api_name": "sys.stdout.write", "line_number": 19, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 24, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 24, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 25, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 26, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 27, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 27, "usage_type": "attribute"}, {"api_name": "utils.get_row_idx", "line_number": 122, "usage_type": "call"}, {"api_name": "utils.get_row_idx", "line_number": 143, "usage_type": "call"}]} +{"seq_id": "204645626", "text": "import os\n\nimport json\nimport requests\n\n# Work out the public server address for the OpenShift REST API. Don't\n# know how to get this via the REST API client so do a raw request to\n# get it. Make sure request is done in a session so connection is closed\n# and later calls against REST API don't attempt to reuse it. This is\n# just to avoid potential for any problems with connection reuse.\n\nserver_url = 'https://openshift.default.svc.cluster.local'\napi_url = '%s/oapi' % server_url\n\nwith requests.Session() as session:\n response = session.get(api_url, verify=False)\n data = json.loads(response.content.decode('UTF-8'))\n address = data['serverAddressByClientCIDRs'][0]['serverAddress']\n\n# Enable the OpenShift authenticator. The OPENSHIFT_URL environment\n# variable must be set before importing the authenticator as it only\n# reads it when module is first imported.\n\nos.environ['OPENSHIFT_URL'] = 'https://%s' % address\n\nfrom oauthenticator.openshift import OpenShiftOAuthenticator\nc.JupyterHub.authenticator_class = OpenShiftOAuthenticator\n\n# Setup authenticator configuration using details from environment.\n\napplication_name = os.environ['APPLICATION_NAME']\n\nservice_account_name = '%s-hub' % application_name\nservice_account_path = '/var/run/secrets/kubernetes.io/serviceaccount'\n\nwith open(os.path.join(service_account_path, 'namespace')) as fp:\n namespace = fp.read().strip()\n\nclient_id = 'system:serviceaccount:%s:%s' % (namespace, service_account_name)\n\nc.OpenShiftOAuthenticator.client_id = client_id\n\nwith open(os.path.join(service_account_path, 'token')) as fp:\n client_secret = fp.read().strip()\n\nc.OpenShiftOAuthenticator.client_secret = client_secret\n\n# Work out hostname for the exposed route of the JupyterHub server. This\n# is tricky as we need to use the REST API to query it.\n\nimport openshift.client\nimport openshift.config\n\nopenshift.config.load_incluster_config()\n\napi_client = openshift.client.ApiClient()\noapi_client = openshift.client.OapiApi(api_client)\n\nroute_list = oapi_client.list_namespaced_route(namespace)\n\nhost = None\n\nfor route in route_list.items:\n if route.metadata.name == application_name:\n host = route.spec.host\n\nif not host:\n raise RuntimeError('Cannot calculate external host name for JupyterHub.')\n\nc.OpenShiftOAuthenticator.oauth_callback_url = 'https://%s/hub/oauth_callback' % host\n", "sub_path": "jupyterhub/.jupyter/jupyterhub_config.py", "file_name": "jupyterhub_config.py", "file_ext": "py", "file_size_in_byte": 2358, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.Session", "line_number": 15, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 17, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 24, "usage_type": "attribute"}, {"api_name": "oauthenticator.openshift.OpenShiftOAuthenticator", "line_number": 27, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 31, "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.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "openshift.client.config.load_incluster_config", "line_number": 54, "usage_type": "call"}, {"api_name": "openshift.client.config", "line_number": 54, "usage_type": "attribute"}, {"api_name": "openshift.client", "line_number": 54, "usage_type": "name"}, {"api_name": "openshift.client.client.ApiClient", "line_number": 56, "usage_type": "call"}, {"api_name": "openshift.client.client", "line_number": 56, "usage_type": "attribute"}, {"api_name": "openshift.client", "line_number": 56, "usage_type": "name"}, {"api_name": "openshift.client.client.OapiApi", "line_number": 57, "usage_type": "call"}, {"api_name": "openshift.client.client", "line_number": 57, "usage_type": "attribute"}, {"api_name": "openshift.client", "line_number": 57, "usage_type": "name"}]} +{"seq_id": "631333814", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jun 3 10:10:54 2020\n\n@author: Tobias\n\"\"\"\n\nimport time\nimport pybullet as p\nimport pybullet_data\nimport PID\nimport random as r\nimport numpy as np\nfrom scipy.spatial.transform import Rotation as rot\n\nimport RocketEngine as RE\n\n\n\n#Flags\nsyntheticCamera = 0\nRCSEnable = 1\nrealTime = 0\n\n#Paramters\ntimestep = 60\nthrustAtNozzle = [0,0,-0.15]\nlandedHeight = 1.5\n\n\n\nR = RE.RocketEngine\nRCS = RE.RocketEngine(100*9.81,150*9.81,10,0)\nrcs_right = 6\nrcs_left =7\nrcs_front = 8 \nrcs_rear = 9\n\n\nphysicsClient = p.connect(p.GUI) #or p.DIRECT for non-graphical version\np.setAdditionalSearchPath(pybullet_data.getDataPath()) #optionally\np.setTimeStep(1/timestep)\np.setRealTimeSimulation(realTime)\np.setGravity(0,0,-9.81)\nplaneId = p.loadURDF(\"plane.urdf\")\nrocketStartPos = [r.randrange(-20,20,1),0,20]\nrocketStartOrientation = p.getQuaternionFromEuler([0,0,0])\nrocket = p.loadURDF(\"rocketwithrcs.urdf\",rocketStartPos, rocketStartOrientation)\n \n\np.setRealTimeSimulation(0)\n\n\np.configureDebugVisualizer(p.COV_ENABLE_RGB_BUFFER_PREVIEW,syntheticCamera)\np.configureDebugVisualizer(p.COV_ENABLE_SEGMENTATION_MARK_PREVIEW,syntheticCamera)\np.configureDebugVisualizer(p.COV_ENABLE_DEPTH_BUFFER_PREVIEW,syntheticCamera)\n\n\n\n\nheightPID = PID.PID(0.5,0,3,0,1)\npitchPID = PID.PID(1,0.02,2,-0.2,0.2)\nXPID = PID.PID(0.01,0,0.02,-1,1)\n\n\n#Debug\ndrymass = p.getDynamicsInfo(rocket,-1)[0] + p.getDynamicsInfo(rocket,1)[0] + p.getDynamicsInfo(rocket,2)[0]*4\n\n\nthrottleInput = p.addUserDebugParameter(\"Throttle Position\",0.4,1,0.4)\nnozzleInput = p.addUserDebugParameter(\"Nozzle Position\",-0.2,0.2,0)\n\ntimestamp = time.time()\n\n\n\nfor i in range (100000):\n \n rocketPos, rocketOrn = p.getBasePositionAndOrientation(rocket)\n rocketYPR = p.getEulerFromQuaternion(rocketOrn)\n #print(rocketYPR)\n #print(rocketPos)\n rocketVel = p.getBaseVelocity(rocket)\n \n \n #Controller\n throttle = heightPID.control(rocketPos[2],rocketVel[0][2],1.5)\n pitchTgt = XPID.control(rocketPos[0],rocketVel[0][0],0)\n nozzleTgt = pitchPID.control(rocketYPR[1],rocketVel[1][1],pitchTgt)\n #print(nozzleTgt)\n \n \n #Debug\n \n if i < 1200:\n #Call Engine Model, apply thrust\n thrust,mdot = R.setThrottle(R,throttle)\n else:\n thrust,mdot = R.setThrottle(R,p.readUserDebugParameter(throttleInput))\n p.setJointMotorControl(rocket,1,p.POSITION_CONTROL,p.readUserDebugParameter(nozzleInput))\n \n \n thrust,mdot = R.setThrottle(R,throttle)\n \n \n p.setJointMotorControl(rocket,1,p.POSITION_CONTROL,(-nozzleTgt)*0.2)\n p.applyExternalForce(rocket,1,thrust,thrustAtNozzle,p.LINK_FRAME)\n \n \n \n \n \n \n #RCS\n if RCSEnable:\n RCSThrust,RCSmdot = RCS.setThrottle(nozzleTgt)\n mdot = mdot + RCSmdot\n if nozzleTgt < 0:\n p.applyExternalForce(rocket,rcs_front,RCSThrust,[0,0,0],p.LINK_FRAME)\n \n lineStuff = p.getLinkState(rocket, rcs_front)\n \n elif nozzleTgt > 0:\n p.applyExternalForce(rocket,rcs_rear,RCSThrust,[0,0,0],p.LINK_FRAME)\n \n lineStuff = p.getLinkState(rocket, rcs_rear)\n \n \n \n \n \n \n if (time.time()-timestamp) > 0.05:\n timestamp = time.time()\n \n thrustLineStuff = p.getLinkState(rocket, 1)\n thrustlineStart = list(thrustLineStuff)[0]\n thrustlineOrn = list(thrustLineStuff)[1]\n \n thrustlineEnd = np.dot(thrust,-0.01)\n thrustlineRot = rot.from_quat(thrustlineOrn)\n thrustlineEnd = thrustlineRot.apply(thrustlineEnd)\n \n \n lineStart = list(lineStuff)[0]\n lineOrn = list(lineStuff)[1]\n \n if(nozzleTgt) < 0:\n lineEnd = np.dot(RCSThrust,-1)\n else:\n lineEnd = RCSThrust\n lineRot = rot.from_quat(lineOrn)\n lineEnd = lineRot.apply(lineEnd)\n \n p.addUserDebugLine(thrustlineStart,thrustlineStart+thrustlineEnd,[1,0.5,0],5,1/20)\n p.addUserDebugLine(lineStart,lineStart+lineEnd,[1,0.5,0],2,1/20)\n \n \n #Update Fuel State\n fuelMass = p.getDynamicsInfo(rocket,0)\n fuelMass = fuelMass[0] - (mdot/timestep)\n p.changeDynamics(rocket, 0, mass=fuelMass)\n \n pos = -1+(fuelMass/10)\n p.setJointMotorControl(rocket,0,p.POSITION_CONTROL,pos)\n \n fuelLength = 2*fuelMass/10\n fuelInertia = [1/12*fuelMass*(3*0.2*0.2+fuelLength*fuelLength),\n 1/12*fuelMass*(3*0.2*0.2+fuelLength*fuelLength),\n 1/2*fuelMass*0.2*0.2]\n p.changeDynamics(rocket,0,localInertiaDiagonal=fuelInertia)\n \n \n \n #Simulate\n p.resetDebugVisualizerCamera(4,30,-30,rocketPos)\n p.stepSimulation()\n time.sleep(1./timestep)\n \nrocketPos, rocketOrn = p.getBasePositionAndOrientation(rocket)\nprint(rocketPos,rocketOrn)\np.disconnect()", "sub_path": "PyBullet/RocketSim2D.py", "file_name": "RocketSim2D.py", "file_ext": "py", "file_size_in_byte": 4871, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "RocketEngine.RocketEngine", "line_number": 32, "usage_type": "attribute"}, {"api_name": "RocketEngine.RocketEngine", "line_number": 33, "usage_type": "call"}, {"api_name": "pybullet.connect", "line_number": 40, "usage_type": "call"}, {"api_name": "pybullet.GUI", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pybullet.setAdditionalSearchPath", "line_number": 41, "usage_type": "call"}, {"api_name": "pybullet_data.getDataPath", "line_number": 41, "usage_type": "call"}, {"api_name": "pybullet.setTimeStep", "line_number": 42, "usage_type": "call"}, {"api_name": "pybullet.setRealTimeSimulation", "line_number": 43, "usage_type": "call"}, {"api_name": "pybullet.setGravity", "line_number": 44, "usage_type": "call"}, {"api_name": "pybullet.loadURDF", "line_number": 45, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 46, "usage_type": "call"}, {"api_name": "pybullet.getQuaternionFromEuler", "line_number": 47, "usage_type": "call"}, {"api_name": "pybullet.loadURDF", "line_number": 48, "usage_type": "call"}, {"api_name": "pybullet.setRealTimeSimulation", "line_number": 51, "usage_type": "call"}, {"api_name": "pybullet.configureDebugVisualizer", "line_number": 54, "usage_type": "call"}, {"api_name": "pybullet.COV_ENABLE_RGB_BUFFER_PREVIEW", "line_number": 54, "usage_type": "attribute"}, {"api_name": "pybullet.configureDebugVisualizer", "line_number": 55, "usage_type": "call"}, {"api_name": "pybullet.COV_ENABLE_SEGMENTATION_MARK_PREVIEW", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pybullet.configureDebugVisualizer", "line_number": 56, "usage_type": "call"}, {"api_name": "pybullet.COV_ENABLE_DEPTH_BUFFER_PREVIEW", "line_number": 56, "usage_type": "attribute"}, {"api_name": "PID.PID", "line_number": 61, "usage_type": "call"}, {"api_name": "PID.PID", "line_number": 62, "usage_type": "call"}, {"api_name": "PID.PID", "line_number": 63, "usage_type": "call"}, {"api_name": "pybullet.getDynamicsInfo", "line_number": 67, "usage_type": "call"}, {"api_name": "pybullet.addUserDebugParameter", "line_number": 70, "usage_type": "call"}, {"api_name": "pybullet.addUserDebugParameter", "line_number": 71, "usage_type": "call"}, {"api_name": "time.time", "line_number": 73, "usage_type": "call"}, {"api_name": "pybullet.getBasePositionAndOrientation", "line_number": 79, "usage_type": "call"}, {"api_name": "pybullet.getEulerFromQuaternion", "line_number": 80, "usage_type": "call"}, {"api_name": "pybullet.getBaseVelocity", "line_number": 83, "usage_type": "call"}, {"api_name": "pybullet.readUserDebugParameter", "line_number": 99, "usage_type": "call"}, {"api_name": "pybullet.setJointMotorControl", "line_number": 100, "usage_type": "call"}, {"api_name": "pybullet.POSITION_CONTROL", "line_number": 100, "usage_type": "attribute"}, {"api_name": "pybullet.readUserDebugParameter", "line_number": 100, "usage_type": "call"}, {"api_name": "pybullet.setJointMotorControl", "line_number": 106, "usage_type": "call"}, {"api_name": "pybullet.POSITION_CONTROL", "line_number": 106, "usage_type": "attribute"}, {"api_name": "pybullet.applyExternalForce", "line_number": 107, "usage_type": "call"}, {"api_name": "pybullet.LINK_FRAME", "line_number": 107, "usage_type": "attribute"}, {"api_name": "pybullet.applyExternalForce", "line_number": 119, "usage_type": "call"}, {"api_name": "pybullet.LINK_FRAME", "line_number": 119, "usage_type": "attribute"}, {"api_name": "pybullet.getLinkState", "line_number": 121, "usage_type": "call"}, {"api_name": "pybullet.applyExternalForce", "line_number": 124, "usage_type": "call"}, {"api_name": "pybullet.LINK_FRAME", "line_number": 124, "usage_type": "attribute"}, {"api_name": "pybullet.getLinkState", "line_number": 126, "usage_type": "call"}, {"api_name": "time.time", "line_number": 133, "usage_type": "call"}, {"api_name": "time.time", "line_number": 134, "usage_type": "call"}, {"api_name": "pybullet.getLinkState", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 140, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation.from_quat", "line_number": 141, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 141, "usage_type": "name"}, {"api_name": "numpy.dot", "line_number": 149, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation.from_quat", "line_number": 152, "usage_type": "call"}, {"api_name": "scipy.spatial.transform.Rotation", "line_number": 152, "usage_type": "name"}, {"api_name": "pybullet.addUserDebugLine", "line_number": 155, "usage_type": "call"}, {"api_name": "pybullet.addUserDebugLine", "line_number": 156, "usage_type": "call"}, {"api_name": "pybullet.getDynamicsInfo", "line_number": 160, "usage_type": "call"}, {"api_name": "pybullet.changeDynamics", "line_number": 162, "usage_type": "call"}, {"api_name": "pybullet.setJointMotorControl", "line_number": 165, "usage_type": "call"}, {"api_name": "pybullet.POSITION_CONTROL", "line_number": 165, "usage_type": "attribute"}, {"api_name": "pybullet.changeDynamics", "line_number": 171, "usage_type": "call"}, {"api_name": "pybullet.resetDebugVisualizerCamera", "line_number": 176, "usage_type": "call"}, {"api_name": "pybullet.stepSimulation", "line_number": 177, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 178, "usage_type": "call"}, {"api_name": "pybullet.getBasePositionAndOrientation", "line_number": 180, "usage_type": "call"}, {"api_name": "pybullet.disconnect", "line_number": 182, "usage_type": "call"}]} +{"seq_id": "361113334", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function\nimport gensim\nimport csv\nimport io\nimport sys\nimport numpy as np\nimport gzip\nimport os\nimport argparse\nimport logging\nfrom sklearn.manifold import TSNE\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nfrom gensim.models.poincare import PoincareModel, PoincareRelations\nfrom gensim.viz.poincare import poincare_2d_visualization\nfrom gensim.test.utils import datapath\nfrom data_loader import read_all_data, read_trial_data, read_input, compound_operator\nimport plotly.plotly as py\nfrom nltk.corpus import wordnet as wn\n#py.sign_in('RamiA', 'lAA8oTL51miiC79o3Hrz')\n\nfrom collections import Counter\n\n\nfrom sklearn import preprocessing\nimport numpy as np\nfrom sklearn.neighbors import LocalOutlierFactor, NearestNeighbors\nfrom sklearn.cluster import KMeans\nimport pandas\n\n\ndef compare_to_gold(gold, taxonomy, model, model_poincare = None, outliers = [], threshold_add = 0.4, new_nodes = [], log = \"\", write_file = \"\"):\n taxonomy_c = taxonomy.copy()\n global compound_operator\n removed_outliers = []\n for element in taxonomy_c:\n if (element[0].replace(' ', compound_operator), element[1].replace(' ', compound_operator)) in outliers:\n continue\n removed_outliers.append((element[0], element[1]))\n\n if new_nodes:\n for element in new_nodes:\n removed_outliers.append((element[0].replace(compound_operator, \" \"), element[1].replace(compound_operator, \" \")))\n\n removed_outliers = list(set(removed_outliers))\n\n correct = 0\n for element in removed_outliers:\n for ele_g in gold:\n if element[0] == ele_g[0] and element[1] == ele_g[1]:\n correct+=1\n break\n precision = correct / float(len(removed_outliers))\n recall = correct / float(len(gold))\n print(str(recall).replace(\".\", ',') +'\\t' + str(precision).replace(\".\", ',') + '\\t' + str(2*precision *recall / (precision + recall)).replace(\".\", ',') + '\\t' + str(len(new_nodes)))\n if log != None:\n path = os.path.join(os.path.dirname(os.path.abspath(__file__)), log)\n with open(path + \".txt\", 'w') as f:\n for element in outliers:\n f.write(element[0] + '\\t' + element[1] + '\\n')\n f.write(\"Elements Taxonomy:\" + str(float(len(removed_outliers))))\n f.write(str((float(len(gold)))) + '\\n')\n f.write(\"Correct: \" + str(correct) + '\\n')\n f.write(\"Precision: \" + str(precision) + '\\n')\n f.write(\"Recall: \" + str(recall) + '\\n')\n f.write(\"F1: \" + str(2*precision *recall / (precision + recall)) + '\\n')\n f.close()\n if write_file != None:\n path = os.path.join(os.path.dirname(os.path.abspath(__file__)), write_file + \".csv\")\n with open(path, 'w') as f:\n for i, element in enumerate(removed_outliers):\n f.write(str(i) + '\\t' + str(element[0]) + '\\t' + str(element[1]) + '\\n')\n f.close()\n\n return removed_outliers\n\n\ndef get_parent(relations,child):\n for relation in relations:\n if child == relation[1]:\n return relation[2]\n return None\n\ndef get_rank(entity1, entity2, model, threshhold):\n rank_inv = None\n similarities_rev = model.wv.similar_by_word(entity1, threshhold)\n similarities_rev = [entry[0] for entry in similarities_rev]\n for j in range(len(similarities_rev)):\n temp_rev = similarities_rev[j]\n if entity2 == temp_rev:\n rank_inv = j\n return rank_inv\n\n#do not need to check if words in vocab since outliers must be in vocab\n#TODO could happen that outlier would connect to new outlier, but is not regarded, so currently adding all but outlier, so order of replacing outliers is not irrelevant\ndef connect_new_nodes(gold, taxonomy, model, model_poincare, threshold, no_parents, no_co, wordnet = False):\n structure = {}\n new_nodes = set([])\n new_relationships = []\n gold_nodes = [relation[0] for relation in gold] + [relation[1] for relation in gold]\n taxonomy_nodes = (set([relation[0] for relation in taxonomy] + [relation[1] for relation in taxonomy]))\n results_parents = []\n results_substring = []\n pairs_parents = []\n results_co = []\n pairs_co = []\n for element in gold_nodes:\n if element not in taxonomy_nodes:\n new_nodes.add(element)\n count = 0\n count_p = 0\n for node in new_nodes:\n if node.replace(\" \", compound_operator) in model.wv:\n count+=1\n for node in new_nodes:\n if node.replace(\" \", compound_operator) +'.n.01' in model_poincare.kv:\n count_p +=1\n\n # print(count, \"in embedding\")\n # print(count_p, \"in poincare_embedding\")\n\n relations = taxonomy.copy()\n for i in range(len(relations)):\n relations[i] = (relations[i][0].replace(\" \", compound_operator), relations[i][1].replace(\" \", compound_operator))\n\n for parent in [relation[1] for relation in relations]:\n structure[parent] = [relation[0] for relation in relations if relation[1] == parent]\n\n for node in new_nodes:\n node = node.replace(\" \", compound_operator)\n result_co_min = 10000000\n pair_co_min = 0\n result_parent_min = 10000000\n pair_parent_min = 0\n for key in structure:\n #print(key)\n if structure[key] == []:\n print(\"no children: \" + key)\n continue\n cleaned_co_hyponyms = []\n if len(structure[key]) < 1:\n continue\n result_parent, pair_parent, result_co, pair_co = get_rank(node, key, structure[key], model, model_poincare, no_parents, no_co, compound = True, wordnet = wordnet)\n if result_parent < result_parent_min and result_parent != 0:\n result_parent_min = result_parent\n pair_parent_min = pair_parent\n if result_co < result_co_min and result_co != 0:\n result_co_min = result_co\n pair_co_min = pair_co\n\n if result_parent_min != 10000000:\n results_parents.append(result_parent_min)\n pairs_parents.append(pair_parent_min)\n if result_co_min != 10000000:\n results_co.append(result_co_min)\n pairs_co.append(pair_co_min)\n elif node.split('_')[0] in structure:\n results_substring.append((node, node.split('_')[0]))\n elif node.split('_')[-1] in structure:\n results_substring.append((node, node.split('_')[-1]))\n\n\n results_normalized1 = []\n results_normalized2 = []\n if not no_parents:\n results_normalized1= list(preprocessing.scale(results_parents))\n\n if not no_co:\n results_normalized2= list(preprocessing.scale(results_co))\n\n results_substring = set(results_substring)\n\n # results_normalized = results_normalized1 + results_normalized2\n #\n # pairs_all = []\n # results_all = []\n # for i, element in enumerate(pairs_parents):\n # if element in pairs_co:\n # results_all.append((results_normalized[i] + results_normalized[len(results_parents) + pairs_co.index(element)]) / 2)\n # pairs_all.append(element)\n # else:\n # results_all.append(results_normalized[i])\n # pairs_all.append(element)\n # for i, element in enumerate(pairs_co):\n # if element not in pairs_parents:\n # results_all.append(results_normalized[len(results_parents) + i])\n # pairs_all.append(element)\n #\n # new_relationships = list(find_outliers(results_all, pairs_all, threshold, mode = 'min'))\n #\n # outliers_parents = set([])\n # outliers_co = set([])\n # #POINCARE\n outliers_parents = find_outliers(results_normalized1, pairs_parents, threshold, mode = 'min')\n #print(results_substring)\n new_relationships = list(outliers_parents|results_substring)\n # #CO_OCCURENCE\n # outliers_co = find_outliers(results_normalized2, pairs_co, threshold, mode = 'min')\n #\n # #outliers = list(outliers_parents.intersection(outliers_co))\n # #outliers = list(outliers_parents | outliers_co)\n # new_relationships = list(outliers_co)\n\n return new_relationships\n\ndef get_rank(current_child, parent, children, model, model_poincare, no_parents, no_co, compound = True, wordnet = False):\n result_co = 0\n pair_co = 0\n result_parent = 0\n pair_parent = 0\n current_child2 = current_child.replace(compound_operator, \" \")\n parent2 = parent.replace(compound_operator, \" \")\n if not no_co:\n try:\n children = [chi for chi in children if chi != current_child]\n if children:\n most_similar_child = model.wv.most_similar_to_given(current_child, children)\n index_child = model.wv.rank(current_child, most_similar_child)\n\n result_co = index_child\n pair_co = (current_child,parent)\n else:\n index_child = 0\n except (KeyError,ZeroDivisionError) as e:\n index_child = 0\n if not no_parents:\n try:\n if wordnet:\n node_senses = [n_sense.name() for n_sense in wn.synsets(current_child) if current_child in n_sense.name()]\n parent_senses = [p_sense.name() for p_sense in wn.synsets(parent) if parent in p_sense.name()]\n index_parent = 1000000\n for parent_sense in parent_senses:\n for node_sense in node_senses:\n index_parent_c = model_poincare.kv.rank(node_sense, parent_sense)\n if index_parent_c < index_parent:\n index_parent = index_parent_c\n if index_parent == 1000000:\n index_parent = 0\n else:\n if compound:\n index_parent = model_poincare.kv.rank(current_child, parent)\n else:\n index_parent = model_poincare.kv.rank(current_child2,parent2)\n # hierarchy_distance = model_poincare.kv.difference_in_hierarchy(child2, parent2)\n # if hierarchy_distance >= 0:\n # index_parent = 0\n\n result_parent = index_parent\n pair_parent = (current_child,parent)\n\n\n except KeyError as e:\n index_parent = 0\n #print(result_parent)\n return [result_parent, pair_parent, result_co, pair_co]\n\n\n#create dictionary mit den begirffen wegen bindestrich\ndef calculate_outliers(relations_o, model, model_poincare = None, threshold = None, no_parents = False, no_co = True, compound = False, wordnet = False):\n outliers = []\n structure = {}\n results_parents = []\n pairs_parents = []\n results_co = []\n pairs_co = []\n relations = relations_o.copy()\n for i in range(len(relations)):\n relations[i] = (relations[i][0].replace(\" \", compound_operator), relations[i][1].replace(\" \", compound_operator))\n\n for parent in [relation[1] for relation in relations]:\n structure[parent] = [relation[0] for relation in relations if relation[1] == parent]\n\n for key in structure:\n #print(key)\n if structure[key] == []:\n print(\"no children: \" + key)\n continue\n elif not key in model.wv:\n continue\n cleaned_co_hyponyms = []\n for word in structure[key]:\n if word in model.wv:\n cleaned_co_hyponyms.append(word)\n if len(cleaned_co_hyponyms) < 1:\n continue\n\n\n cleaned_co_hyponyms_copy = cleaned_co_hyponyms.copy()\n for child in cleaned_co_hyponyms_copy:\n result_parent, pair_parent, result_co, pair_co = get_rank(child, key, cleaned_co_hyponyms, model, model_poincare, no_parents, no_co, compound, wordnet)\n if result_parent != 0 and child.split(\"_\")[0] != key and child.split(\"_\")[-1] != key:\n results_parents.append(result_parent)\n pairs_parents.append(pair_parent)\n if result_co != 0:\n results_co.append(result_co)\n pairs_co.append(pair_co)\n\n\n if not no_parents:\n results_normalized1= list(preprocessing.scale(results_parents))\n outliers_parents = find_outliers(results_normalized1, pairs_parents, threshold)\n outliers = list(outliers_parents)\n\n if not no_co:\n results_normalized2= list(preprocessing.scale(results_co))\n outliers_co = find_outliers(results_normalized2, pairs_co, threshold)\n outliers = list(outliers_parents)\n\n # results_normalized = results_normalized1 + results_normalized2\n # pairs_all = []\n # results_all = []\n # for i, element in enumerate(pairs_parents):\n # if element in pairs_co:\n # results_all.append((results_normalized[i] + results_normalized[len(results_parents) + pairs_co.index(element)]) / 2)\n # pairs_all.append(element)\n # else:\n # results_all.append(results_normalized[i])\n # pairs_all.append(element)\n # for i, element in enumerate(pairs_co):\n # if element not in pairs_parents:\n # results_all.append(results_normalized[len(results_parents) + i])\n # pairs_all.append(element)\n # outliers = find_outliers(results_all, pairs_all, threshold)\n\n\n if not no_co and not no_parents:\n outliers = list(outliers_parents.intersection(outliers_co))\n #outliers = list(outliers_parents | outliers_co)\n #print(outliers)\n return outliers\n\n\ndef find_outliers(results, pairs, threshold, mode = \"max\"):\n outliers = set([])\n num_clusters = threshold#15 wordnet #6 own_poincare\n results_all_s = np.asarray(results).reshape(-1,1)\n kmeans = KMeans(n_clusters=num_clusters, random_state = 0).fit(results_all_s) #3 for wordnet\n pred = kmeans.predict(results_all_s)\n pred_common = Counter(pred).most_common(2)\n main_cluster,_ = pred_common[0]\n indices = []\n remaining = results.copy()\n for j in range(num_clusters):\n if mode == \"max\":\n result_max = max(remaining)\n if mode == \"min\":\n result_max = min(remaining)\n cluster_max = kmeans.predict(np.asarray([result_max]).reshape(1, -1))[0]\n if cluster_max == main_cluster:\n if mode == 'max':\n break\n if mode == 'min':\n for i, element in enumerate(pred):\n if element == cluster_max:\n #print(results_all[i])\n indices.append(i)\n break\n for i, element in enumerate(pred):\n if element == cluster_max:\n #print(results_all[i])\n indices.append(i)\n remaining.remove(results[i])\n for index in indices:\n outliers.add(pairs[index])\n return outliers\n\n\ndef main():\n parser = argparse.ArgumentParser(description=\"Embeddings for Taxonomy\")\n parser.add_argument('-m', '--mode', type=str, default='preload', choices=[\"combined_embeddings_removal_and_new\", \"combined_embeddings_new_nodes\", \"combined_embeddings_removal\"], help=\"Mode of the system.\")\n parser.add_argument('-d', '--domain', type=str, default='science', choices=[\"science\", \"food\", \"environment\"], help=\"Domain\")\n parser.add_argument('-e', '--embedding', type=str, nargs='?', default=None, choices=[\"own_and_poincare\", \"poincare\", \"poincare_all\", \"fasttext\", \"wiki2M\", \"wiki1M_subword\", \"own_w2v\", \"quick\", \"none\"], help=\"Embedding to use\")\n parser.add_argument('-ep', '--exparent', action='store_true', help='Exclude \"parent\" relations')\n parser.add_argument('-ico', '--inco', action='store_true', help='Include \"co-hypernym relations')\n parser.add_argument('-com', '--compound', action='store_true', help='Includes compound word in outlier removal')\n parser.add_argument('-wn', '--wordnet', action ='store_true', help= 'Use Wordnet instead of own embeddings')\n parser.add_argument('--experiment_name', nargs='?', type=str, default=None, help=\"Name of the Experiment\")\n parser.add_argument('--log', action='store_true', help=\"Logs taxonomy and results\")\n args = parser.parse_args()\n print(\"Mode: \", args.mode)\n run(args.mode, args.domain, args.embedding, args.exparent, args.inco, args.compound, args.wordnet, args.experiment_name, args.log)\n\n\ndef run(mode, domain, embedding, exclude_parent = False, include_co = False, compound = False, wordnet = False, experiment_name = None, log = False):\n if embedding == \"fasttext\":\n #model = gensim.models.KeyedVectors.load_word2vec_format('wiki-news-300d-1M-subword.vec', binary=False)\n model = gensim.models.FastText.load_fasttext_format('wiki.en.bin')\n #model = gensim.models.FastText.load_fasttext_format('crawl-300d-2M.vec')\n elif embedding == \"wiki2M\":\n #model = gensim.models.FastText.load_fasttext_format('crawl-300d-2M.vec','vec')\n model = gensim.models.KeyedVectors.load_word2vec_format('embeddings/crawl-300d-2M.vec', binary=False)\n #model.save(\"crawl-300d-2M.bin\")\n elif embedding == \"wiki1M_subword\":\n model = gensim.models.KeyedVectors.load_word2vec_format('embeddings/wiki-news-300d-1M-subword.vec', binary=False)\n\n elif embedding == \"own_w2v\":\n model = gensim.models.KeyedVectors.load('embeddings/own_embeddings_w2v')\n\n elif embedding == \"quick\":\n model = gensim.models.KeyedVectors.load_word2vec_format('embeddings/crawl-300d-2M.vec', binary=False, limit = 50000)\n\n elif embedding == 'own_and_poincare':\n print(\"init\")\n model = gensim.models.KeyedVectors.load('embeddings/own_embeddings_w2v_all') #n2 #all\n #model_poincare = PoincareModel.load('embeddings/embeddings_' + domain +'_crawl_poincare_3_50')\n #model_poincare = PoincareModel.load('embeddings/embeddings_science_crawl_merge_poincare_10_3_50_02')\n\n model_poincare = PoincareModel.load('embeddings/poincare_common_domains02_5_3_50')\n #model_poincare = PoincareModel.load('embeddings/embeddings_poincare_wordnet')\n\n gold = []\n relations = []\n taxonomy = []\n outliers = []\n exclude_co = not include_co\n\n if mode =='combined_embeddings_removal':\n #thresholds = [2,4,6,8,10,12,14]poincare and co-hyper testrun\n thresholds = [6]\n for value in thresholds:\n gold, relations = read_all_data(domain)\n outliers = calculate_outliers(relations, model, threshold = value, model_poincare = model_poincare, compound = compound, no_parents = exclude_parent, no_co = exclude_co, wordnet = wordnet)\n compare_to_gold(gold = gold, taxonomy = relations, outliers = outliers, model = model, log = \"logs/\" + mode + \"_\" + embedding + \"_\" + str(value), write_file = \"out/\" + mode + \"_\" + embedding + \"_\" + str(value))\n\n\n elif mode == 'combined_embeddings_new_nodes':\n #thresholds = [2]\n thresholds = [2,4,6,8,10,12,14] #poincare testrun\n #thresholds = [12,14,18,20] #co-hyper testrun\n for value in thresholds:\n gold, relations = read_all_data(domain)\n new_nodes = connect_new_nodes(taxonomy = relations, gold = gold, model = model, model_poincare = model_poincare, threshold = value, no_parents = exclude_parent, no_co = exclude_co, wordnet = wordnet)\n compare_to_gold(gold = gold, taxonomy = relations, model = model, model_poincare = model_poincare, new_nodes = new_nodes)\n\n\n elif mode == 'combined_embeddings_removal_and_new':\n gold, relations = read_all_data(domain)\n new_nodes = connect_new_nodes(taxonomy = relations, gold = gold, model = model, model_poincare = model_poincare, threshold = 2, no_parents = exclude_parent, no_co = exclude_co, wordnet = wordnet)\n outliers = calculate_outliers(relations, model, threshold = 6, model_poincare = model_poincare, compound = compound, no_parents = exclude_parent, no_co = exclude_co, wordnet = wordnet)\n relations1 = compare_to_gold(gold = gold, taxonomy = relations, model = model, model_poincare = model_poincare, new_nodes = new_nodes)\n relations2 = compare_to_gold(gold = gold, taxonomy = relations, model = model, model_poincare = model_poincare, new_nodes = new_nodes, outliers = outliers)\n outliers = calculate_outliers(relations1, model, threshold = 6, model_poincare = model_poincare, compound = compound, no_parents = exclude_parent, no_co = exclude_co, wordnet = wordnet)\n compare_to_gold(gold = gold, taxonomy = relations1, outliers = outliers, new_nodes = new_nodes, model = model, model_poincare = model_poincare)\n\nif __name__ == '__main__':\n main()\n", "sub_path": "distributed_semantics/distributed_semantics.py", "file_name": "distributed_semantics.py", "file_ext": "py", "file_size_in_byte": 20607, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.use", "line_number": 16, "usage_type": "call"}, {"api_name": "data_loader.compound_operator", "line_number": 41, "usage_type": "argument"}, {"api_name": "data_loader.compound_operator", "line_number": 47, "usage_type": "argument"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 61, "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": "os.path.dirname", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 73, "usage_type": "call"}, {"api_name": "data_loader.compound_operator", "line_number": 117, "usage_type": "argument"}, {"api_name": "data_loader.compound_operator", "line_number": 120, "usage_type": "argument"}, {"api_name": "data_loader.compound_operator", "line_number": 128, "usage_type": "argument"}, {"api_name": "data_loader.compound_operator", "line_number": 134, "usage_type": "argument"}, {"api_name": "sklearn.preprocessing.scale", "line_number": 170, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 170, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.scale", "line_number": 173, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 173, "usage_type": "name"}, {"api_name": "data_loader.compound_operator", "line_number": 215, "usage_type": "argument"}, {"api_name": "data_loader.compound_operator", "line_number": 216, "usage_type": "argument"}, {"api_name": "nltk.corpus.wordnet.synsets", "line_number": 233, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet", "line_number": 233, "usage_type": "name"}, {"api_name": "nltk.corpus.wordnet.synsets", "line_number": 234, "usage_type": "call"}, {"api_name": "nltk.corpus.wordnet", "line_number": 234, "usage_type": "name"}, {"api_name": "data_loader.compound_operator", "line_number": 272, "usage_type": "argument"}, {"api_name": "sklearn.preprocessing.scale", "line_number": 304, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 304, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.scale", "line_number": 309, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 309, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 340, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 341, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 343, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 352, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 373, "usage_type": "call"}, {"api_name": "gensim.models.FastText.load_fasttext_format", "line_number": 391, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 391, "usage_type": "attribute"}, {"api_name": "gensim.models.KeyedVectors.load_word2vec_format", "line_number": 395, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 395, "usage_type": "attribute"}, {"api_name": "gensim.models.KeyedVectors.load_word2vec_format", "line_number": 398, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 398, "usage_type": "attribute"}, {"api_name": "gensim.models.KeyedVectors.load", "line_number": 401, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 401, "usage_type": "attribute"}, {"api_name": "gensim.models.KeyedVectors.load_word2vec_format", "line_number": 404, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 404, "usage_type": "attribute"}, {"api_name": "gensim.models.KeyedVectors.load", "line_number": 408, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 408, "usage_type": "attribute"}, {"api_name": "gensim.models.poincare.PoincareModel.load", "line_number": 412, "usage_type": "call"}, {"api_name": "gensim.models.poincare.PoincareModel", "line_number": 412, "usage_type": "name"}, {"api_name": "data_loader.read_all_data", "line_number": 425, "usage_type": "call"}, {"api_name": "data_loader.read_all_data", "line_number": 435, "usage_type": "call"}, {"api_name": "data_loader.read_all_data", "line_number": 441, "usage_type": "call"}]} +{"seq_id": "374475646", "text": "import argparse\nimport logging\nimport torch.utils.data\nimport helpers\n\nlogging.basicConfig(level=logging.INFO)\n\ndef parse_args():\n parser = argparse.ArgumentParser(description='Evaluate GG-CNN')\n parser.add_argument('--network', type=str, help='Path to saved network to evaluate')\n parser.add_argument('--use-depth', type=int, default=1, help='Use Depth image for evaluation (1/0)')\n parser.add_argument('--use-rgb', type=int, default=0, help='Use RGB image for evaluation (0/1)')\n parser.add_argument('--num-workers', type=int, default=8, help='Dataset workers')\n parser.add_argument('--n-grasps', type=int, default=1, help='Number of grasps to consider per image')\n parser.add_argument('--vis', action='store_true', help='Visualise the network output')\n return parser.parse_args()\n\nif __name__ == '__main__':\n args = parse_args()\n\n # Load example data\n\n #root_path = '/container/Data/CornellGraspDatasetSmall/'\n #example = 'pcd0100'\n #rgb_img, dep_img = get_images_from_cornell(root_path, example)\n\n root_path = '/container/Data/OCID-dataset/ARID10/floor/bottom/fruits/seq07/'\n example = 'result_2018-08-24-17-29-40'\n rgb_img, dep_img = helpers.get_images_from_ocid(root_path, example)\n\n # Load Network\n net = torch.load(args.network)\n device = torch.device(\"cuda:0\")\n\n x1 = helpers.numpy_to_torch(dep_img)\n x1 = x1.view(1, x1.shape[0], x1.shape[1], x1.shape[2])\n with torch.no_grad():\n xc1 = x1.to(device)\n pos_output, cos_output, sin_output, width_output = net.forward(xc1)\n q_img, ang_img, width_img = helpers.post_process_output(pos_output, cos_output, sin_output, width_output)\n\n if args.vis:\n helpers.plot_output(rgb_img, dep_img, q_img, ang_img, no_grasps=args.n_grasps, grasp_width_img=width_img)", "sub_path": "test_ggcnn.py", "file_name": "test_ggcnn.py", "file_ext": "py", "file_size_in_byte": 1803, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.basicConfig", "line_number": 6, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 6, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 9, "usage_type": "call"}, {"api_name": "helpers.get_images_from_ocid", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.utils.data.load", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.utils.data.device", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 33, "usage_type": "name"}, {"api_name": "helpers.numpy_to_torch", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.utils.data.no_grad", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 37, "usage_type": "name"}, {"api_name": "helpers.post_process_output", "line_number": 40, "usage_type": "call"}, {"api_name": "helpers.plot_output", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "85493208", "text": "#\n# Python bindings for the Cisco VIRL 2 Network Simulation Platform\n#\n# This file is part of VIRL 2\n#\n# Copyright 2020-2021 Cisco Systems Inc.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n#\n\nimport pytest\nimport time\nimport warnings\n\nfrom requests import HTTPError\nfrom unittest.mock import patch\nfrom urllib3.exceptions import InsecureRequestWarning\n\nfrom virl2_client import ClientLibrary\n\n\ndef pytest_addoption(parser):\n parser.addoption(\n \"--controller-url\",\n default=\"http://127.0.0.1:8001\",\n help=\"The URL of simple controller server\",\n )\n\n\n@pytest.fixture(scope=\"session\")\ndef controller_url(request):\n return request.config.getoption(\"--controller-url\")\n\n\n@pytest.fixture\ndef no_ssl_warnings():\n with warnings.catch_warnings():\n # We don't care about SSL connections to untrusted servers in tests:\n warnings.simplefilter(\"ignore\", InsecureRequestWarning)\n yield\n\n\ndef stop_wipe_and_remove_all_labs(client_library: ClientLibrary):\n lab_list = client_library.get_lab_list()\n for lab_id in lab_list:\n lab = client_library.join_existing_lab(lab_id)\n lab.stop()\n lab.wipe()\n client_library.remove_lab(lab_id)\n\n\ndef client_library_keep_labs_base(\n url, usr=\"cml2\", pwd=\"cml2cml2\", ssl_verify=False, allow_http=True\n):\n clientlibrary = ClientLibrary(\n url,\n username=usr,\n password=pwd,\n ssl_verify=ssl_verify,\n allow_http=allow_http,\n )\n for _ in range(5):\n try:\n clientlibrary.is_system_ready()\n except HTTPError as err:\n if err.errno == 504:\n # system still initialising, wait longer\n time.sleep(2)\n\n return clientlibrary\n\n\n@pytest.fixture\ndef client_library_keep_labs(no_ssl_warnings, controller_url: str) -> ClientLibrary:\n # for integration testing, the client library needs to connect to a mock simulator\n # running via HTTP on a non SSL servr / non-standard port. We therefore need to\n # set the allow_http to True. Otherwise the client library would enforce the HTTPS\n # scheme and the tests would fail. This should never be required in the wild.\n yield client_library_keep_labs_base(url=controller_url)\n\n\n@pytest.fixture(scope=\"session\")\ndef client_library_session(controller_url: str) -> ClientLibrary:\n \"\"\"This client library has session lifetime\"\"\"\n yield client_library_keep_labs_base(url=controller_url)\n\n\n@pytest.fixture\ndef client_library(client_library_keep_labs: ClientLibrary) -> ClientLibrary:\n clientlibrary = client_library_keep_labs\n stop_wipe_and_remove_all_labs(clientlibrary)\n # Reset \"current\" lab:\n clientlibrary.lab = None\n yield clientlibrary\n # tear down - delete labs from the tests\n # TODO: see if these need updating now remove_all_labs doesnt stop the lab\n stop_wipe_and_remove_all_labs(clientlibrary)\n", "sub_path": "conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 3390, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pytest.fixture", "line_number": 40, "usage_type": "call"}, {"api_name": "warnings.catch_warnings", "line_number": 47, "usage_type": "call"}, {"api_name": "warnings.simplefilter", "line_number": 49, "usage_type": "call"}, {"api_name": "urllib3.exceptions.InsecureRequestWarning", "line_number": 49, "usage_type": "argument"}, {"api_name": "pytest.fixture", "line_number": 45, "usage_type": "attribute"}, {"api_name": "virl2_client.ClientLibrary", "line_number": 53, "usage_type": "name"}, {"api_name": "virl2_client.ClientLibrary", "line_number": 65, "usage_type": "call"}, {"api_name": "requests.HTTPError", "line_number": 75, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 78, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 83, "usage_type": "attribute"}, {"api_name": "virl2_client.ClientLibrary", "line_number": 84, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 92, "usage_type": "call"}, {"api_name": "virl2_client.ClientLibrary", "line_number": 93, "usage_type": "name"}, {"api_name": "virl2_client.ClientLibrary", "line_number": 99, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 98, "usage_type": "attribute"}]} +{"seq_id": "408827598", "text": "import falcon\nimport log\nfrom sqlalchemy.orm.exc import NoResultFound\nfrom controller.base import BaseResource\nfrom util.hooks import authorization\nfrom util.authorization import encrypt_token, hash_password, uuid\nfrom model import User, Base\nfrom util.error.errors import NotValidParameterError, UserNotExistsError, AppError, OperationError\nfrom util.validators import validate_user_create, validate_money_transfer_create\n\nLOG = log.get_logger()\n\n\nclass Collection(BaseResource):\n \"\"\"\n /resev/v1/users\n \"\"\"\n @falcon.before(validate_user_create)\n def on_post(self, req, res):\n session = req.context['session']\n user_req = req.context['data']\n if user_req:\n user = User()\n user.username = user_req['username']\n user.email = user_req['email']\n user.password = hash_password(user_req['password']).decode('utf-8')\n user.details = user_req['details'] if 'info' in user_req else None\n user.balance = user_req['balance'] if 'balance'in user_req else 100.0\n uuid_id = uuid()\n user.uuid_id = uuid_id\n user.token = encrypt_token(uuid_id).decode('utf-8')\n session.add(user)\n user_db = session.query(User).filter_by(username=user_req['username']).one()\n self.on_success(res, user_db.to_dict())\n else:\n raise NotValidParameterError(req.context['data'])\n\n @falcon.before(authorization)\n def on_get(self, req, res):\n session = req.context['session']\n user_dbs = session.query(User).all()\n if user_dbs:\n obj = [user.to_dict() for user in user_dbs]\n self.on_success(res, obj)\n else:\n raise AppError()\n\n @falcon.before(authorization)\n def on_put(self, req, res):\n pass\n\n\nclass Item(BaseResource):\n \"\"\"\n /resev/v1/users/{user_id}\n \"\"\"\n @falcon.before(authorization)\n def on_get(self, req, res, user_id):\n session = req.context['session']\n try:\n user_db = User.find_one(session, user_id)\n self.on_success(res, user_db.to_dict())\n except NoResultFound:\n raise UserNotExistsError('user id: %s' % user_id)\n\n\nclass ItemTransfer(BaseResource):\n \"\"\"\n /resev/v1/users/{user_id}/transfer\n \"\"\"\n\n @falcon.before(authorization)\n @falcon.before(validate_money_transfer_create)\n def on_post(self, req, res, user_id):\n session = req.context['session']\n borrow_data = req.context['data']\n if borrow_data:\n try:\n user_lender = User.find_one(session, user_id)\n user_borrower = User.find_by_username(session, borrow_data['borrower'])\n quantity = borrow_data['quantity']\n except NoResultFound:\n raise UserNotExistsError('user id: %s' % user_id)\n try:\n if user_lender.balance < 0:\n raise OperationError()\n lender_quantity = user_lender.balance - quantity\n borrower_quantity = user_borrower.balance + quantity\n user_lender.find_update(session, user_lender.user_id, {User.balance: lender_quantity})\n user_borrower.find_update(session, user_borrower.user_id, {User.balance: borrower_quantity})\n session.commit()\n user_updated = User.find_one(session, user_id)\n except Exception as e:\n raise OperationError()\n self.on_success(res, user_updated.to_dict())\n\n else:\n raise NotValidParameterError(req.context['data'])\n", "sub_path": "app/controller/user.py", "file_name": "user.py", "file_ext": "py", "file_size_in_byte": 3616, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "log.get_logger", "line_number": 11, "usage_type": "call"}, {"api_name": "controller.base.BaseResource", "line_number": 14, "usage_type": "name"}, {"api_name": "model.User", "line_number": 23, "usage_type": "call"}, {"api_name": "util.authorization.hash_password", "line_number": 26, "usage_type": "call"}, {"api_name": "util.authorization.uuid", "line_number": 29, "usage_type": "call"}, {"api_name": "util.authorization.encrypt_token", "line_number": 31, "usage_type": "call"}, {"api_name": "model.User", "line_number": 33, "usage_type": "argument"}, {"api_name": "util.error.errors.NotValidParameterError", "line_number": 36, "usage_type": "call"}, {"api_name": "falcon.before", "line_number": 18, "usage_type": "call"}, {"api_name": "util.validators.validate_user_create", "line_number": 18, "usage_type": "argument"}, {"api_name": "model.User", "line_number": 41, "usage_type": "argument"}, {"api_name": "util.error.errors.AppError", "line_number": 46, "usage_type": "call"}, {"api_name": "falcon.before", "line_number": 38, "usage_type": "call"}, {"api_name": "util.hooks.authorization", "line_number": 38, "usage_type": "argument"}, {"api_name": "falcon.before", "line_number": 48, "usage_type": "call"}, {"api_name": "util.hooks.authorization", "line_number": 48, "usage_type": "argument"}, {"api_name": "controller.base.BaseResource", "line_number": 53, "usage_type": "name"}, {"api_name": "model.User.find_one", "line_number": 61, "usage_type": "call"}, {"api_name": "model.User", "line_number": 61, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.exc.NoResultFound", "line_number": 63, "usage_type": "name"}, {"api_name": "util.error.errors.UserNotExistsError", "line_number": 64, "usage_type": "call"}, {"api_name": "falcon.before", "line_number": 57, "usage_type": "call"}, {"api_name": "util.hooks.authorization", "line_number": 57, "usage_type": "argument"}, {"api_name": "controller.base.BaseResource", "line_number": 67, "usage_type": "name"}, {"api_name": "model.User.find_one", "line_number": 79, "usage_type": "call"}, {"api_name": "model.User", "line_number": 79, "usage_type": "name"}, {"api_name": "model.User.find_by_username", "line_number": 80, "usage_type": "call"}, {"api_name": "model.User", "line_number": 80, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.exc.NoResultFound", "line_number": 82, "usage_type": "name"}, {"api_name": "util.error.errors.UserNotExistsError", "line_number": 83, "usage_type": "call"}, {"api_name": "util.error.errors.OperationError", "line_number": 86, "usage_type": "call"}, {"api_name": "model.User.balance", "line_number": 89, "usage_type": "attribute"}, {"api_name": "model.User", "line_number": 89, "usage_type": "name"}, {"api_name": "model.User.balance", "line_number": 90, "usage_type": "attribute"}, {"api_name": "model.User", "line_number": 90, "usage_type": "name"}, {"api_name": "model.User.find_one", "line_number": 92, "usage_type": "call"}, {"api_name": "model.User", "line_number": 92, "usage_type": "name"}, {"api_name": "util.error.errors.OperationError", "line_number": 94, "usage_type": "call"}, {"api_name": "util.error.errors.NotValidParameterError", "line_number": 98, "usage_type": "call"}, {"api_name": "falcon.before", "line_number": 72, "usage_type": "call"}, {"api_name": "util.hooks.authorization", "line_number": 72, "usage_type": "argument"}, {"api_name": "falcon.before", "line_number": 73, "usage_type": "call"}, {"api_name": "util.validators.validate_money_transfer_create", "line_number": 73, "usage_type": "argument"}]} +{"seq_id": "623531910", "text": "from lib.midiv2 import EpianoDataset\r\nimport torch.nn as nn\r\nfrom torch.utils.data import DataLoader\r\nimport torch.optim as optim\r\nimport torch\r\nfrom lib.utils import Logger\r\nimport progressbar\r\nimport time\r\nimport torch.utils.tensorboard\r\n\r\nclass Discriminator(nn.Module):\r\n def __init__(self):\r\n super(Discriminator, self).__init__() # call __init__ of parent class of Discriminator (which is nn.Module)\r\n nFeatures = maxlength*388\r\n nOut = 1\r\n\r\n self.hiddenLayer1 = nn.Sequential( # a sequential container for modules\r\n nn.Linear(nFeatures, 1024), # y = xA^T + b: linear transformation\r\n # nn.BatchNorm1d(1024),\r\n nn.LeakyReLU(0.2), # leaky ReLU: paramter controls angle of negative slope\r\n nn.Dropout(0.3) # dropout layer: parameter controls probability p of zeroing\r\n )\r\n\r\n self.hiddenLayer2 = nn.Sequential(\r\n nn.Linear(1024, 512), # same as above but different in/out size\r\n # nn.BatchNorm1d(512),\r\n nn.LeakyReLU(0.2), # same as hiddenlayer1\r\n nn.Dropout(0.3) # same as hiddenlayer1\r\n )\r\n\r\n self.hiddenLayer3 = nn.Sequential(\r\n nn.Linear(512, 256), # same as above but different in/out size\r\n # nn.BatchNorm1d(256),\r\n nn.LeakyReLU(0.2), # same as hiddenlayer1\r\n nn.Dropout(0.3) # same as hiddenlayer1\r\n )\r\n\r\n self.out = nn.Sequential(\r\n nn.Linear(256, nOut),\r\n nn.Sigmoid()\r\n )\r\n\r\n def forward(self, x):\r\n x = self.hiddenLayer1(x)\r\n x = self.hiddenLayer2(x)\r\n x = self.hiddenLayer3(x)\r\n x = self.out(x)\r\n return x\r\n\r\n\r\nclass Generator(nn.Module):\r\n def __init__(self):\r\n super(Generator, self).__init__()\r\n n_features = 100\r\n n_out = maxlength*388\r\n\r\n self.hiddenlayer1 = nn.Sequential(\r\n nn.Linear(n_features, int(n_features * 2)),\r\n # nn.BatchNorm1d(int(n_features * 2)),\r\n nn.LeakyReLU(0.2),\r\n nn.Dropout(0.3)\r\n )\r\n\r\n self.hiddenlayer2 = nn.Sequential(\r\n nn.Linear(int(n_features * 2), int(n_features * 4)),\r\n # nn.BatchNorm1d(int(n_features * 4)),\r\n nn.LeakyReLU(0.2),\r\n nn.Dropout(0.3)\r\n )\r\n\r\n self.hiddenlayer3 = nn.Sequential(\r\n nn.Linear(int(n_features * 4), int(n_features * 8)),\r\n # nn.BatchNorm1d(int(n_features * 8)),\r\n nn.LeakyReLU(0.2),\r\n nn.Dropout(0.3)\r\n )\r\n\r\n self.out = nn.Sequential(\r\n nn.Linear(int(n_features * 8), n_out),\r\n nn.Sigmoid()\r\n )\r\n\r\n def forward(self, x):\r\n x = self.hiddenlayer1(x)\r\n x = self.hiddenlayer2(x)\r\n x = self.hiddenlayer3(x)\r\n x = self.out(x)\r\n return x\r\n\r\n\r\ndef noise(size):\r\n '''\r\n Generates 1-D vector of gaussian sampled rand values\r\n '''\r\n n = torch.randn(size, 100, requires_grad=True)\r\n return n\r\n\r\n\r\n\"\"\"\r\nBegin NN\r\n\"\"\"\r\n# Device\r\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\r\n\r\ndef to_device(data, device):\r\n \"\"\"Move tensor(s) to a chosen device\"\"\"\r\n if isinstance(data, (list,tuple)):\r\n return [to_device(x, device) for x in data]\r\n return data.to(device, non_blocking=True)\r\n\r\n\r\nclass DeviceDataLoader():\r\n \"\"\"Wrap a dataloader to move data to a device\"\"\"\r\n def __init__(self, dl, device):\r\n self.dl = dl\r\n self.device = device\r\n\r\n def __iter__(self):\r\n \"\"\"Yield a batch of data after moving it to device\"\"\"\r\n for b in self.dl:\r\n yield to_device(b, self.device)\r\n\r\n def __len__(self):\r\n \"\"\"Number of bathces\"\"\"\r\n return len(self.dl)\r\n\r\n\r\nmaxlength = 100\r\nBATCH_SIZE = 60\r\ntrainset = DataLoader(EpianoDataset(maxlength), BATCH_SIZE, shuffle=True)\r\ntrain_dl = DeviceDataLoader(trainset, device)\r\nnum_batches = len(trainset) # number of batches\r\n\r\ndiscriminator = Discriminator()\r\nto_device(discriminator, device)\r\ngenerator = Generator()\r\nto_device(generator, device)\r\n\r\nd_optimizer = optim.Adam(discriminator.parameters(), lr = 0.000002)\r\ng_optimizer = optim.Adam(generator.parameters(), lr = 0.00002)\r\n\r\n\r\nloss = nn.BCELoss()\r\n'''\r\nBinary Cross Entropy Loss:\r\nL = {l1,l2....lN)^T l(i) = -w(i) [ y(i) * log(v(i)) + (1 - y) * log(1 - v(i)) ] \r\nmean is calculated by computing sum(L) / N\r\nbecause we don't need weights, set w(i) = 1 for all i\r\n'''\r\n\r\n\r\ndef ones_target(size):\r\n return torch.ones(size, 1)\r\n\r\n\r\ndef zeros_target(size):\r\n return torch.zeros(size, 1)\r\n\r\n\r\ndef samples_to_vectors(samples):\r\n return samples.view(samples.size(0), maxlength*388)\r\n\r\n\r\ndef vectors_to_samples(vectors):\r\n return vectors.view(vectors.size(0), 1, -1, 388)\r\n\r\n\r\ndef train_discriminator(optimizer, realdata, fakedata):\r\n size = realdata.size(0)\r\n optimizer.zero_grad() # reset gradients\r\n '''\r\n Discriminator Loss: (1/m * sum for i=1 to i=m) (log D(x(i)) + log (1 - D(G(z(i)))))\r\n '''\r\n\r\n\r\n '''\r\n Train on real data (1st half of above loss equation)\r\n '''\r\n # D(x(i))\r\n pred_real = discriminator(realdata)\r\n err_real = loss(pred_real, to_device(ones_target(size), device)) # real data has 1 target\r\n err_real.backward()\r\n\r\n '''\r\n Train on fake data (2nd half of above loss equation)\r\n '''\r\n # D(G(z))\r\n pred_fake = discriminator(fakedata)\r\n err_fake = loss(pred_fake, to_device(zeros_target(size), device)) # fake data has 0 target\r\n err_fake.backward()\r\n\r\n '''\r\n Update weights with gradients\r\n '''\r\n optimizer.step()\r\n\r\n '''\r\n Return error and predictions for real and fake inputs\r\n '''\r\n return err_real + err_fake, pred_real, pred_fake\r\n\r\n\r\ndef train_generator(optimizer, fakedata):\r\n size = fakedata.size(0)\r\n optimizer.zero_grad() # reset gradients\r\n '''\r\n Generator Loss: (1/m * sum for i=1 to i=m) (log (1 - D(G(z(i)))))\r\n '''\r\n\r\n '''\r\n Train on fake data\r\n '''\r\n # D(G(z))\r\n prediction = discriminator(fakedata) # this fake data comes from generator outside of function\r\n\r\n '''\r\n Calculate error and backpropagate\r\n '''\r\n error = loss(prediction, to_device(ones_target(size), device)) # instead of minimizing log(1-D(G(z))), maximise log(D(gz)) for stronger gradients in early training\r\n error.backward()\r\n\r\n\r\n '''\r\n Update weights with gradients\r\n '''\r\n optimizer.step()\r\n return error\r\n\r\n\r\n# Testing\r\nnum_test_samples = 3\r\ntest_noise = to_device(noise(num_test_samples), device)\r\n\r\n# Create logger instance\r\nlogger = Logger(model_name='VGAN', data_name='EPianoDataset')\r\n\r\n# Printing model & optimizer state_dict\r\nprint(\"Discriminator state_dict:\")\r\nfor param_tensor in discriminator.state_dict():\r\n print(param_tensor, \"\\t\", discriminator.state_dict()[param_tensor].size())\r\n\r\nprint(\"Generator state_dict:\")\r\nfor param_tensor in generator.state_dict():\r\n print(param_tensor, \"\\t\", generator.state_dict()[param_tensor].size())\r\n\r\nprint(\"D_Optimizer state_dict:\")\r\nfor var_name in d_optimizer.state_dict():\r\n print(var_name, \"\\t\", d_optimizer.state_dict()[var_name])\r\n\r\nprint(\"G_Optimizer state_dict:\")\r\nfor var_name in g_optimizer.state_dict():\r\n print(var_name, \"\\t\", g_optimizer.state_dict()[var_name])\r\n\r\n\r\n# Training\r\nnum_epochs = 10\r\nfor epoch in range(num_epochs):\r\n for batch_num, (real_batch,_) in progressbar.progressbar(enumerate(train_dl)): # index is discarded\r\n N = real_batch.size(0)\r\n\r\n '''1. Train Discriminator'''\r\n real_data = samples_to_vectors(real_batch)\r\n\r\n # generate fake data and detach (so gradient not calculated for generator)\r\n fake_data = generator(to_device(noise(N), device)).detach()\r\n\r\n # train discriminator\r\n d_error, d_pred_real, d_pred_fake = train_discriminator(d_optimizer, real_data, fake_data)\r\n\r\n\r\n '''2. Train Generator'''\r\n # generate fake data (no detach this time because need graidents)\r\n fake_data = generator(to_device(noise(N), device))\r\n\r\n # train generator\r\n g_error = train_generator(g_optimizer, fake_data)\r\n\r\n\r\n ''' Log batch error '''\r\n logger.log(d_error, g_error, d_pred_real, d_pred_fake, epoch, batch_num, num_batches)\r\n\r\n\r\n ''' Display progress every few batches & save model checkpoint '''\r\n if (batch_num) % 100 == 0:\r\n generator.eval()\r\n test_samples = vectors_to_samples(generator(test_noise))\r\n test_samples = test_samples.data\r\n generator.train()\r\n\r\n logger.log_images(test_samples.cpu(), num_test_samples, epoch, batch_num, num_batches)\r\n\r\n # Display status logs\r\n logger.display_status(epoch, num_epochs, batch_num, num_batches, d_error, g_error, d_pred_real, d_pred_fake)\r\n\r\n # # Save model dictionaries (uses HEAPS OF MEMORY ~300-600MB per save)\r\n # torch.save(discriminator.state_dict(), './saves/D_epoch'+str(epoch)+'_batch'+str(batch_num)+'.pt')\r\n # torch.save(generator.state_dict(), './saves/G_epoch'+str(epoch)+'_batch'+str(batch_num)+'.pt')\r\n # torch.save(d_optimizer.state_dict(), './saves/Dopt_epoch'+str(epoch)+'_batch'+str(batch_num)+'.pt')\r\n # torch.save(g_optimizer.state_dict(), './saves/Gopt_epoch'+str(epoch)+'_batch'+str(batch_num)+'.pt')\r\n\r\n\r\n time.sleep(0.000001)", "sub_path": "PianoGAN/old_midi_and_miscellaneous/23rd_September_EpianoDataset - Copy.py", "file_name": "23rd_September_EpianoDataset - Copy.py", "file_ext": "py", "file_size_in_byte": 9436, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "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.Sequential", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "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.LeakyReLU", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "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.LeakyReLU", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "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.Sigmoid", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 40, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 51, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "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.LeakyReLU", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 60, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 67, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "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.Linear", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 103, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 130, "usage_type": "call"}, {"api_name": "lib.midiv2.EpianoDataset", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 139, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 140, "usage_type": "name"}, {"api_name": "torch.nn.BCELoss", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 143, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 157, "usage_type": "call"}, {"api_name": "lib.utils.Logger", "line_number": 235, "usage_type": "call"}, {"api_name": "progressbar.progressbar", "line_number": 258, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 302, "usage_type": "call"}]} +{"seq_id": "297626089", "text": "from django.shortcuts import render\r\nfrom reviewPage.models import reviewMovie,reviewGame,reviewTV\r\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\r\n# Create your views here.\r\ndef reviews(request,type,id,pid):\r\n if type == \"m\":\r\n cmmtList = reviewMovie.objects.filter(contentsId=id).order_by('-score')\r\n if type == \"g\":\r\n cmmtList = reviewGame.objects.filter(contentsId=id).order_by('-score')\r\n if type == \"t\":\r\n cmmtList = reviewTV.objects.filter(contentsId=id).order_by('-score')\r\n\r\n lenCmmtList = len(cmmtList)\r\n pageHeadIdx = (int(pid)-1)*20\r\n pageTailIdx = pageHeadIdx+20\r\n if pageTailIdx > lenCmmtList:\r\n pageTailIdx = lenCmmtList\r\n\r\n return render(request,'reviewPage/reviewPage_scorelow.html',{\"reviewList\":cmmtList[pageHeadIdx:pageTailIdx]})\r\n", "sub_path": "beekaveWebdev/reviewPage_scoreLow/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 829, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "reviewPage.models.reviewMovie.objects.filter", "line_number": 7, "usage_type": "call"}, {"api_name": "reviewPage.models.reviewMovie.objects", "line_number": 7, "usage_type": "attribute"}, {"api_name": "reviewPage.models.reviewMovie", "line_number": 7, "usage_type": "name"}, {"api_name": "reviewPage.models.reviewGame.objects.filter", "line_number": 9, "usage_type": "call"}, {"api_name": "reviewPage.models.reviewGame.objects", "line_number": 9, "usage_type": "attribute"}, {"api_name": "reviewPage.models.reviewGame", "line_number": 9, "usage_type": "name"}, {"api_name": "reviewPage.models.reviewTV.objects.filter", "line_number": 11, "usage_type": "call"}, {"api_name": "reviewPage.models.reviewTV.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "reviewPage.models.reviewTV", "line_number": 11, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "563378120", "text": "#Importing modules needed for game\nimport pygame, random, sys\nfrom pygame.locals import *\n\n#Window Dimensions\nWINDOWWIDTH = 600\nWINDOWHEIGHT = 600\n\n#Game-mechanic constants\nPLAYERMOVERATE = 8\nMAXBADDIEMOVERATE = 2\nFPS = 40\n \n#Boss level constants\nSPERAZZOMINSIZE = 10\nSPERAZZOMAXSIZE = 40\nSPERAZZOMINSPEED = 1\nSPERAZZOMAXSPEED = 3\nADDNEWSPERAZZORATE = 20\n\n#Colors\nTITLETEXTCOLOR = (0, 0, 0) #Sets color of text on title screen\nBACKGROUNDCOLOR = (0, 0, 0) #Sets background color for game\nINSTTEXTCOLOR = (255, 0, 255)\n\n#Functions\ndef terminate(): #Function to kill the program\n pygame.quit()\n sys.exit()\n \ndef insertNewTeacher():\n numOnBoard = 0\n while True:\n index = random.randint(0, len(areTeachersOn) - 1)\n if areTeachersOn[index] == False:\n areTeachersOn[index] = True\n break\n for b in areTeachersOn:\n if b == True:\n numOnBoard += 1\n return numOnBoard\n\ndef checkIfGameIsOver():\n for b in areTeachersOn:\n if b == False:\n return\n pygame.mixer.music.stop()\n return True\n\ndef gameOver():\n pygame.mixer.stop()\n numOnBoard = 0\n windowSurface.blit(gameOverBackground, gameOverRect)\n for b in range(0, len(areTeachersOn)):\n areTeachersOn[b] = False\n hasCriedOut[b] = False\n drawInstructions(\"Busted...\", gameOverFont, windowSurface, 200, 500)\n pygame.display.update()\n pygame.mixer.music.stop()\n pygame.mixer.music.load('death.wav')\n pygame.mixer.music.play(0)\n pygame.time.wait(3000)\n startGame()\n \ndef drawText(text, font, surface, x, y): #Function to simplify drawing text\n textobj = font.render(text, 1, TITLETEXTCOLOR)\n textrect = textobj.get_rect()\n textrect.topleft = (x, y)\n surface.blit(textobj, textrect)\n\ndef drawInstructions(text, font, surface, x, y): #Function to simplify drawing text\n textobj = font.render(text, 1, INSTTEXTCOLOR)\n textrect = textobj.get_rect()\n textrect.topleft = (x, y)\n surface.blit(textobj, textrect)\n \ndef instructions(): #Displays game instructions\n windowSurface.blit(instImage, instRect)\n pygame.display.update()\n\ndef credit():\n windowSurface.blit(creditImage, creditRect)\n pygame.display.update()\n\ndef playerHasHitTeacher(player, baddies):\n for b in baddies:\n if player['rect'].colliderect(b['rect']):\n return True\n return False\n\n#Setting up pygame and the game window\npygame.init() #Initializes all pygame modules automatically\nmainClock = pygame.time.Clock() #object to help keep track of time\nwindowSurface = pygame.display.set_mode((WINDOWWIDTH, WINDOWHEIGHT)) #Sets up game window\npygame.display.set_caption(\"GHS Teacher Dodge!\") #Sets caption for window\n\n#Setting up player\nplayerImage = pygame.image.load('Student\\student.jpg').convert()\nplayer = {'rect': playerImage.get_rect()\n }\n\n#Setting up teachers\nareTeachersOn = [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]\nhasCriedOut = [False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False, False]\nteachers = []\nteacherSpeeds = (random.randint(1, MAXBADDIEMOVERATE), random.randint(1, MAXBADDIEMOVERATE), random.randint(1, MAXBADDIEMOVERATE), random.randint(1, MAXBADDIEMOVERATE), random.randint(1, MAXBADDIEMOVERATE), random.randint(1, MAXBADDIEMOVERATE), random.randint(1, MAXBADDIEMOVERATE), random.randint(1, MAXBADDIEMOVERATE), random.randint(1, MAXBADDIEMOVERATE), random.randint(1, MAXBADDIEMOVERATE), random.randint(1, MAXBADDIEMOVERATE), random.randint(1, MAXBADDIEMOVERATE), random.randint(1, MAXBADDIEMOVERATE), random.randint(1, MAXBADDIEMOVERATE), random.randint(1, MAXBADDIEMOVERATE), random.randint(1, MAXBADDIEMOVERATE), random.randint(1, MAXBADDIEMOVERATE), random.randint(1, MAXBADDIEMOVERATE))\nnumOnBoard = 0\n\n#Teacher images\nantoneImage = pygame.image.load('antone.jpg').convert()\nbarksdaleImage = pygame.image.load('barksdale.jpg').convert()\nbeyrentImage = pygame.image.load('beyrent.jpg').convert()\nbrooksImage = pygame.image.load('brooks.jpg').convert()\ncampbellImage = pygame.image.load('campbell.jpg').convert()\ncloosImage = pygame.image.load('cloos.jpg').convert()\nconwayImage = pygame.image.load('conway.jpg').convert()\ndavisImage = pygame.image.load('davis.jpg').convert()\ndumaisImage = pygame.image.load('dumais.jpg').convert()\nfinchImage = pygame.image.load('finch.jpg').convert()\nhutchImage = pygame.image.load('hutch.jpg').convert()\njagueshImage = pygame.image.load('jaguesh.jpg').convert()\nnazerImage = pygame.image.load('nazer.jpg').convert()\noriordonImage = pygame.image.load('oriordon.jpg').convert()\nrouhanImage = pygame.image.load('rouhan.jpg').convert()\nsawyerImage = pygame.image.load('sawyer.jpg').convert()\nsperazzoImage = pygame.image.load('sperazzo.jpg').convert()\nstowellImage = pygame.image.load('stowell.jpg').convert()\nwilsonImage = pygame.image.load('wilson.jpg').convert()\nzulaufImage = pygame.image.load('zulauf.jpg').convert()\n\n#Antone\nantone = {'rect': antoneImage.get_rect(),\n 'speed': teacherSpeeds[0],\n 'cry': pygame.mixer.Sound('antoneCry.wav')\n }\nteachers.append(antone)\n#Barksdale\nbarksdale = {'rect': barksdaleImage.get_rect(),\n 'speed': teacherSpeeds[1],\n 'cry': pygame.mixer.Sound('barksdaleCry.wav')\n }\nteachers.append(barksdale)\n#Beyrent\nbeyrent = {'rect': beyrentImage.get_rect(),\n 'speed': teacherSpeeds[2],\n 'cry': pygame.mixer.Sound('beyrentCry.wav')\n }\nteachers.append(beyrent)\n#Brooks\nbrooks = {'rect': brooksImage.get_rect(),\n 'speed': teacherSpeeds[3],\n 'cry': pygame.mixer.Sound('brooksCry.wav')\n }\nteachers.append(brooks)\n#Campbell\ncampbell = {'rect': campbellImage.get_rect(),\n 'speed': teacherSpeeds[4],\n 'cry': pygame.mixer.Sound('campbellCry.wav')\n }\nteachers.append(campbell)\n#Cloos\ncloos = {'rect': cloosImage.get_rect(),\n 'speed': teacherSpeeds[5],\n 'cry': pygame.mixer.Sound('cloosCry.wav')\n }\nteachers.append(cloos)\n#Conway\nconway = {'rect': conwayImage.get_rect(),\n 'speed': teacherSpeeds[6],\n 'cry': pygame.mixer.Sound('conwayCry.wav')\n }\nteachers.append(conway)\n#Davis\ndavis = {'rect': davisImage.get_rect(),\n 'speed': teacherSpeeds[7],\n 'cry': pygame.mixer.Sound('davisCry.wav')\n }\nteachers.append(davis)\n#Dumais\ndumais = {'rect': dumaisImage.get_rect(),\n 'speed': teacherSpeeds[8],\n 'cry': pygame.mixer.Sound('dumaisCry.wav')\n }\nteachers.append(dumais)\n#Finch\nfinch = {'rect': finchImage.get_rect(),\n 'speed': teacherSpeeds[9],\n 'cry': pygame.mixer.Sound('finchCry.wav')\n }\nteachers.append(finch)\n#Hutch\nhutch = {'rect': hutchImage.get_rect(),\n 'speed': teacherSpeeds[10],\n 'cry': pygame.mixer.Sound('hutchCry.wav')\n }\nteachers.append(hutch)\n#Jaguesh\njaguesh = {'rect': jagueshImage.get_rect(),\n 'speed': teacherSpeeds[11],\n 'cry': pygame.mixer.Sound('jagueshCry.wav')\n }\nteachers.append(jaguesh)\n#Nazer\nnazer = {'rect': nazerImage.get_rect(),\n 'speed': teacherSpeeds[12],\n 'cry': pygame.mixer.Sound('nazerCry.wav')\n }\nteachers.append(nazer)\n#O'Riordon\noriordon = {'rect': oriordonImage.get_rect(),\n 'speed': teacherSpeeds[13],\n 'cry': pygame.mixer.Sound('oriordonCry.wav')\n }\nteachers.append(oriordon)\n#Rouhan\nrouhan = {'rect': rouhanImage.get_rect(),\n 'speed': teacherSpeeds[14],\n 'cry': pygame.mixer.Sound('rouhanCry.wav')\n }\nteachers.append(rouhan)\n#Stowell\nstowell = {'rect': stowellImage.get_rect(),\n 'speed': teacherSpeeds[15],\n 'cry': pygame.mixer.Sound('stowellCry.wav')\n }\nteachers.append(stowell)\n#Wilson\nwilson = {'rect': wilsonImage.get_rect(),\n 'speed': teacherSpeeds[16],\n 'cry': pygame.mixer.Sound('wilsonCry.wav')\n }\nteachers.append(wilson)\n#Zulauf\nzulauf = {'rect': zulaufImage.get_rect(),\n 'speed': teacherSpeeds[17],\n 'cry': pygame.mixer.Sound('zulaufCry.wav')\n }\nteachers.append(zulauf)\n#Sawyer\nsawyer = {'rect': sawyerImage.get_rect(),\n }\n\n#Setting up other game images\ntitleBackground = pygame.image.load('Images\\Title.jpg').convert()\ntitleRect = titleBackground.get_rect()\ninstImage = pygame.image.load('Images\\Instructions.jpg').convert()\ninstRect = instImage.get_rect()\nmainBackground = pygame.image.load('Images\\Main.jpg').convert()\nmainRect = mainBackground.get_rect()\nbossBackground = pygame.image.load('Images\\Final.jpg').convert()\nbossRect = bossBackground.get_rect()\ngameOverBackground = pygame.image.load('Images\\GameOver.jpg').convert()\ngameOverRect = gameOverBackground.get_rect()\nvictoryBackground = pygame.image.load('Images\\Victory.jpg').convert()\nvictoryRect = victoryBackground.get_rect()\ncreditImage = pygame.image.load('Images\\Credits.jpg').convert()\ncreditRect = creditImage.get_rect()\n\n# setting up fonts\ntitleFont = pygame.font.SysFont('Times New Roman', 40, False, False)\ninstructionFont = pygame.font.SysFont('Times New Roman', 30, False, False)\ngameInfoFont = pygame.font.SysFont('Times New Roman', 32, False, False)\ngameOverFont = pygame.font.SysFont('Times New Roman', 72, False, False)\nfont = pygame.font.SysFont('Times New Roman', 48, False, True)\n\n#Defining levels and title screen\ndef startGame():\n while True:\n #Sets up and displays title screen\n windowSurface.blit(titleBackground, titleRect)\n drawText(\"GHS Teacher Dodge!\", titleFont, windowSurface, 150, 440)\n drawText(\"Press a number...\", titleFont, windowSurface, 150, 480)\n drawText(\"1. Start Game\", titleFont, windowSurface, 50, 520)\n drawText(\"2. Instructions\", titleFont, windowSurface, 50, 555)\n drawText(\"3. Credits\", titleFont, windowSurface, 400, 520)\n drawText(\"4. Quit\", titleFont, windowSurface, 400, 555)\n pygame.display.flip() #Updates entire title screen to display whats coded above\n readyForBoss = False\n #Cycling through menu until game starts\n while True:\n for event in pygame.event.get():\n if event.type == KEYDOWN:\n if event.key == K_1 or event.key == K_RETURN:\n askDifficulty()\n if event.key == K_2:\n instructions()\n if event.key == K_3:\n credit()\n if event.key == K_4 or event.key == K_ESCAPE:\n terminate()\ndef askDifficulty():\n while True:\n windowSurface.blit(titleBackground, titleRect)\n drawText(\"1. Easy\", titleFont, windowSurface, 30, 500)\n drawText(\"2. Medium\", titleFont, windowSurface, 200, 500)\n drawText(\"3. Hard\", titleFont, windowSurface, 430, 500)\n pygame.display.flip()\n difficulty = 0\n while True:\n for event in pygame.event.get():\n if event.type == KEYDOWN:\n if event.key == K_1 or event.key == K_RETURN:\n levelOne(1)\n if event.key == K_2:\n levelOne(1.5)\n if event.key == K_3:\n levelOne(2)\n if event.key == K_ESCAPE:\n terminate()\n \ndef levelOne(difficulty):\n while True:\n cheatMode = False\n readyForBoss = False\n pygame.mixer.music.load('mainmusic.wav')\n numOnBoard = insertNewTeacher()\n timer = 250\n player['rect'].topleft = (300, 300)\n for t in teachers:\n t['rect'].bottomright = (-5, -5)\n moveLeft = moveRight = moveUp = moveDown = False\n pygame.mixer.music.play(-1)\n while True:\n windowSurface.blit(mainBackground, mainRect)\n timer -= 1\n if timer == 0:\n if checkIfGameIsOver() == True:\n readyForBoss = True\n break\n numOnBoard = insertNewTeacher()\n timer = 250\n #Player controls\n for event in pygame.event.get():\n if event.type == QUIT:\n terminate()\n if event.type == KEYDOWN:\n if event.key == K_LEFT:\n moveRight = False\n moveLeft = True\n if event.key == K_RIGHT:\n moveLeft = False\n moveRight = True\n if event.key == K_UP:\n moveDown = False\n moveUp = True\n if event.key == K_DOWN:\n moveUp = False\n moveDown = True\n if event.key == K_ESCAPE:\n terminate()\n if event.key == K_x:\n cheatMode = not cheatMode\n\n if event.type == KEYUP:\n if event.key == K_LEFT:\n moveLeft = False\n if event.key == K_RIGHT:\n moveRight = False\n if event.key == K_UP:\n moveUp = False\n if event.key == K_DOWN:\n moveDown = False\n\n if moveLeft and player['rect'].left > 0:\n player['rect'].move_ip(-1 * PLAYERMOVERATE, 0)\n if moveRight and player['rect'].right < WINDOWWIDTH:\n player['rect'].move_ip(PLAYERMOVERATE, 0)\n if moveUp and player['rect'].top > 0:\n player['rect'].move_ip(0, -1 * PLAYERMOVERATE)\n if moveDown and player['rect'].bottom < WINDOWHEIGHT:\n player['rect'].move_ip(0, PLAYERMOVERATE)\n\n #Teacher AI\n k = 0\n for i in teachers:\n if player['rect'].centerx < i['rect'].centerx and player['rect'].centery < i['rect'].centery and areTeachersOn[k] == True:\n i['rect'].move_ip(-1 * teacherSpeeds[k] * difficulty, -1 * teacherSpeeds[k] * difficulty)\n if player['rect'].centerx == i['rect'].centerx and player['rect'].centery < i['rect'].centery and areTeachersOn[k] == True:\n i['rect'].move_ip(0, -1 * teacherSpeeds[k] * difficulty)\n if player['rect'].centerx > i['rect'].centerx and player['rect'].centery < i['rect'].centery and areTeachersOn[k] == True:\n i['rect'].move_ip(1 * teacherSpeeds[k] * difficulty, -1 * teacherSpeeds[k] * difficulty)\n if player['rect'].centerx > i['rect'].centerx and player['rect'].centery == i['rect'].centery and areTeachersOn[k] == True:\n i['rect'].move_ip(1 * teacherSpeeds[k] * difficulty, 0)\n if player['rect'].centerx > i['rect'].centerx and player['rect'].centery > i['rect'].centery and areTeachersOn[k] == True:\n i['rect'].move_ip(1 * teacherSpeeds[k] * difficulty, 1 * teacherSpeeds[k] * difficulty)\n if player['rect'].centerx == i['rect'].centerx and player['rect'].centery > i['rect'].centery and areTeachersOn[k] == True:\n i['rect'].move_ip(0, 1 * teacherSpeeds[k] * difficulty)\n if player['rect'].centerx < i['rect'].centerx and player['rect'].centery > i['rect'].centery and areTeachersOn[k] == True:\n i['rect'].move_ip(-1 * teacherSpeeds[k] * difficulty, 1 * teacherSpeeds[k] * difficulty)\n if player['rect'].centerx < i['rect'].centerx and player['rect'].centery == i['rect'].centery and areTeachersOn[k] == True:\n i['rect'].move_ip(-1 * teacherSpeeds[k] * difficulty, 0)\n k = k + 1\n k = 0\n \n drawText('Next teacher in: %s' % (timer), gameInfoFont, windowSurface, 10, 0)\n drawText('%s / 18' % (numOnBoard), gameInfoFont, windowSurface, 510, 0)\n drawText('Cheat Mode: %s' % (cheatMode), gameInfoFont, windowSurface, 340, 560)\n windowSurface.blit(playerImage, player['rect'])\n for b in range(0, len(areTeachersOn)):\n if areTeachersOn[0] == True:\n windowSurface.blit(antoneImage, antone['rect'])\n if hasCriedOut[0] == False:\n pygame.mixer.Sound.play(antone['cry'])\n hasCriedOut[0] = True\n if areTeachersOn[1] == True:\n windowSurface.blit(barksdaleImage, barksdale['rect'])\n if hasCriedOut[1] == False:\n pygame.mixer.Sound.play(barksdale['cry'])\n hasCriedOut[1] = True\n if areTeachersOn[2] == True:\n windowSurface.blit(beyrentImage, beyrent['rect'])\n if hasCriedOut[2] == False:\n pygame.mixer.Sound.play(beyrent['cry'])\n hasCriedOut[2] = True \n if areTeachersOn[3] == True:\n windowSurface.blit(brooksImage, brooks['rect'])\n if hasCriedOut[3] == False:\n pygame.mixer.Sound.play(brooks['cry'])\n hasCriedOut[3] = True\n if areTeachersOn[4] == True:\n windowSurface.blit(campbellImage, campbell['rect'])\n if hasCriedOut[4] == False:\n pygame.mixer.Sound.play(campbell['cry'])\n hasCriedOut[4] = True\n if areTeachersOn[5] == True:\n windowSurface.blit(cloosImage, cloos['rect'])\n if hasCriedOut[5] == False:\n pygame.mixer.Sound.play(cloos['cry'])\n hasCriedOut[5] = True\n if areTeachersOn[6] == True:\n windowSurface.blit(conwayImage, conway['rect'])\n if hasCriedOut[6] == False:\n pygame.mixer.Sound.play(conway['cry'])\n hasCriedOut[6] = True\n if areTeachersOn[7] == True:\n windowSurface.blit(davisImage, davis['rect'])\n if hasCriedOut[7] == False:\n pygame.mixer.Sound.play(davis['cry'])\n hasCriedOut[7] = True\n if areTeachersOn[8] == True:\n windowSurface.blit(dumaisImage, dumais['rect'])\n if hasCriedOut[8] == False:\n pygame.mixer.Sound.play(dumais['cry'])\n hasCriedOut[8] = True\n if areTeachersOn[9] == True:\n windowSurface.blit(finchImage, finch['rect'])\n if hasCriedOut[9] == False:\n pygame.mixer.Sound.play(finch['cry'])\n hasCriedOut[9] = True\n if areTeachersOn[10] == True:\n windowSurface.blit(hutchImage, hutch['rect'])\n if hasCriedOut[10] == False:\n pygame.mixer.Sound.play(hutch['cry'])\n hasCriedOut[10] = True\n if areTeachersOn[11] == True:\n windowSurface.blit(jagueshImage, jaguesh['rect'])\n if hasCriedOut[11] == False:\n pygame.mixer.Sound.play(jaguesh['cry'])\n hasCriedOut[11] = True\n if areTeachersOn[12] == True:\n windowSurface.blit(nazerImage, nazer['rect'])\n if hasCriedOut[12] == False:\n pygame.mixer.Sound.play(nazer['cry'])\n hasCriedOut[12] = True\n if areTeachersOn[13] == True:\n windowSurface.blit(oriordonImage, oriordon['rect'])\n if hasCriedOut[13] == False:\n pygame.mixer.Sound.play(oriordon['cry'])\n hasCriedOut[13] = True\n if areTeachersOn[14] == True:\n windowSurface.blit(rouhanImage, rouhan['rect'])\n if hasCriedOut[14] == False:\n pygame.mixer.Sound.play(rouhan['cry'])\n hasCriedOut[14] = True\n if areTeachersOn[15] == True:\n windowSurface.blit(stowellImage, stowell['rect'])\n if hasCriedOut[15] == False:\n pygame.mixer.Sound.play(stowell['cry'])\n hasCriedOut[15] = True\n if areTeachersOn[16] == True:\n windowSurface.blit(wilsonImage, wilson['rect'])\n if hasCriedOut[16] == False:\n pygame.mixer.Sound.play(wilson['cry'])\n hasCriedOut[16] = True\n if areTeachersOn[17] == True:\n windowSurface.blit(zulaufImage, zulauf['rect'])\n if hasCriedOut[17] == False:\n pygame.mixer.Sound.play(zulauf['cry'])\n hasCriedOut[17] = True\n pygame.display.update()\n \n if playerHasHitTeacher(player, teachers) and cheatMode == False:\n break\n \n mainClock.tick(FPS)\n break\n \n if not readyForBoss:\n gameOver()\n else:\n bossLevel(difficulty, cheatMode)\n\ndef bossLevel(difficulty, cheatMode):\n pygame.mixer.stop()\n if difficulty == 1.5:\n difficulty = 2\n elif difficulty == 2:\n difficulty = 3\n youWin = False\n pygame.mixer.music.load('bossmusic.wav')\n bossImage = pygame.image.load('sperazzo.jpg')\n while True:\n if youWin:\n break\n timer = 3500\n sperazzos = []\n sawyer['rect'].topleft = (10, 20)\n moveLeft = moveRight = moveUp = moveDown = False\n sawyerMoveRight = False\n sperazzoAddCounter = 0\n pygame.mixer.music.play(-1)\n\n while True:\n windowSurface.blit(bossBackground, bossRect)\n timer -= 1\n if timer == 0:\n youWin = True\n break\n for event in pygame.event.get():\n if event.type == QUIT:\n terminate()\n\n if event.type == KEYDOWN:\n if event.key == K_LEFT:\n moveRight = False\n moveLeft = True\n if event.key == K_RIGHT:\n moveLeft = False\n moveRight = True\n if event.key == K_UP:\n moveDown = False\n moveUp = True\n if event.key == K_DOWN:\n moveUp = False\n moveDown = True\n if event.key == K_x:\n cheatMode = not cheatMode\n \n if event.type == KEYUP:\n if event.key == K_ESCAPE:\n terminate()\n if event.key == K_LEFT:\n moveLeft = False\n if event.key == K_RIGHT:\n moveRight = False\n if event.key == K_UP:\n moveUp = False\n if event.key == K_DOWN:\n moveDown = False\n\n sperazzoAddCounter += 1\n \n if sperazzoAddCounter == int(ADDNEWSPERAZZORATE / difficulty):\n sperazzoAddCounter = 0\n sperazzoSize = random.randint(SPERAZZOMINSIZE, SPERAZZOMAXSIZE)\n newSperazzo = {'rect': pygame.Rect(random.randint(0, WINDOWWIDTH-sperazzoSize), 0 - sperazzoSize, sperazzoSize, sperazzoSize),\n 'speed': random.randint(SPERAZZOMINSPEED, SPERAZZOMAXSPEED * difficulty),\n 'surface': pygame.transform.scale(bossImage, (sperazzoSize, sperazzoSize)),\n }\n sperazzos.append(newSperazzo)\n if moveLeft and player['rect'].left > 0:\n player['rect'].move_ip(-1 * PLAYERMOVERATE, 0)\n if moveRight and player['rect'].right < WINDOWWIDTH:\n player['rect'].move_ip(PLAYERMOVERATE, 0)\n if moveUp and player['rect'].top > 0:\n player['rect'].move_ip(0, -1 * PLAYERMOVERATE)\n if moveDown and player['rect'].bottom < WINDOWHEIGHT:\n player['rect'].move_ip(0, PLAYERMOVERATE)\n\n if sawyer['rect'].centerx < 100:\n sawyerMoveRight = True\n if sawyer['rect'].centerx > 500:\n sawyerMoveRight = False\n\n if sawyerMoveRight == True:\n sawyer['rect'].move_ip(10, 0)\n else:\n sawyer['rect'].move_ip(-10, 0)\n windowSurface.blit(sawyerImage, sawyer['rect'])\n \n for s in sperazzos:\n s['rect'].move_ip(0, s['speed'])\n\n for s in sperazzos[:]:\n if s['rect'].top > WINDOWHEIGHT:\n sperazzos.remove(s)\n drawInstructions('Time to Victory: %s' % (timer), gameInfoFont, windowSurface, 10, 0)\n drawInstructions('BOSS LEVEL', gameInfoFont, windowSurface, 405, 0)\n drawInstructions('Cheat Mode: %s' % (cheatMode), gameInfoFont, windowSurface, 340, 560)\n windowSurface.blit(playerImage, player['rect'])\n\n # Draw each baddie\n for s in sperazzos:\n windowSurface.blit(s['surface'], s['rect'])\n pygame.display.update()\n \n # Check if any of the baddies have hit the player.\n if playerHasHitTeacher(player, sperazzos) and cheatMode == False:\n gameOver()\n if player['rect'].colliderect(sawyer['rect']) and cheatMode == False:\n gameOver()\n mainClock.tick(FPS)\n \n pygame.mixer.music.stop()\n victory()\n\ndef victory():\n pygame.mixer.music.load('victory.wav')\n pygame.mixer.music.play(0)\n windowSurface.blit(victoryBackground, victoryRect)\n drawText('Congratulations!', gameOverFont, windowSurface, 60, 100)\n drawText('Press ESC to quit', gameOverFont, windowSurface, 50, 500)\n pygame.display.update()\n while True:\n for event in pygame.event.get():\n if event.type == KEYDOWN:\n if event.key == K_ESCAPE:\n terminate()\n\nstartGame()\n", "sub_path": "Material/Source Code.py", "file_name": "Source Code.py", "file_ext": "py", "file_size_in_byte": 26631, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pygame.quit", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 29, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 34, "usage_type": "call"}, {"api_name": "pygame.mixer.music.stop", "line_number": 47, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pygame.mixer.stop", "line_number": 51, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 58, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.stop", "line_number": 59, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 59, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 60, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pygame.time.wait", "line_number": 62, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 79, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 83, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 83, "usage_type": "attribute"}, {"api_name": "pygame.init", "line_number": 92, "usage_type": "call"}, {"api_name": "pygame.time.Clock", "line_number": 93, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pygame.display.set_mode", "line_number": 94, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 94, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 95, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 98, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 98, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 106, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 110, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 110, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 111, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 111, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 112, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 112, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 113, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 113, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 114, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 114, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 115, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 115, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 116, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 116, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 117, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 117, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 118, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 118, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 119, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 119, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 120, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 120, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 121, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 121, "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": 123, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 123, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 124, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 124, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 125, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 125, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 126, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 127, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 127, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 128, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 129, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 129, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 134, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 134, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 140, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 140, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 146, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 146, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 152, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 152, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 158, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 158, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 164, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 164, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 170, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 170, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 176, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 176, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 182, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 182, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 188, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 188, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 194, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 194, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 200, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 200, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 206, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 206, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 212, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 212, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 218, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 218, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 224, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 224, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 230, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 230, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound", "line_number": 236, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 236, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 244, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 244, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 246, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 246, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 248, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 248, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 250, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 250, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 252, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 252, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 254, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 254, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 256, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 256, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 260, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 260, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 261, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 261, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 262, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 262, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 263, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 263, "usage_type": "attribute"}, {"api_name": "pygame.font.SysFont", "line_number": 264, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 264, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 277, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 277, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 281, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 281, "usage_type": "attribute"}, {"api_name": "pygame.display.flip", "line_number": 297, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 297, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 300, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 300, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 315, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 315, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 322, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 322, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 333, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 333, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound.play", "line_number": 403, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 403, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound.play", "line_number": 408, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 408, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound.play", "line_number": 413, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 413, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound.play", "line_number": 418, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 418, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound.play", "line_number": 423, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 423, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound.play", "line_number": 428, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 428, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound.play", "line_number": 433, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 433, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound.play", "line_number": 438, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 438, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound.play", "line_number": 443, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 443, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound.play", "line_number": 448, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 448, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound.play", "line_number": 453, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 453, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound.play", "line_number": 458, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 458, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound.play", "line_number": 463, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 463, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound.play", "line_number": 468, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 468, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound.play", "line_number": 473, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 473, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound.play", "line_number": 478, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 478, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound.play", "line_number": 483, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 483, "usage_type": "attribute"}, {"api_name": "pygame.mixer.Sound.play", "line_number": 488, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 488, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 490, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 490, "usage_type": "attribute"}, {"api_name": "pygame.mixer.stop", "line_number": 504, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 504, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 510, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 510, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 511, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 511, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 521, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 521, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 529, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 529, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 565, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 566, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 566, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 567, "usage_type": "call"}, {"api_name": "pygame.transform.scale", "line_number": 568, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 568, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 605, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 605, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.stop", "line_number": 614, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 614, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.load", "line_number": 618, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 618, "usage_type": "attribute"}, {"api_name": "pygame.mixer.music.play", "line_number": 619, "usage_type": "call"}, {"api_name": "pygame.mixer", "line_number": 619, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 623, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 623, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 625, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 625, "usage_type": "attribute"}]} +{"seq_id": "316547116", "text": "import requests\nimport time\nimport sys\ndef write_excel(path_target,data,sheetname='Sheet1'):\n import xlwt\n # 创建一个workbook 设置编码\n workbook = xlwt.Workbook(encoding='utf-8')\n # 创建一个worksheet\n worksheet = workbook.add_sheet(sheetname)\n # 写入excel\n # 参数对应 行, 列, 值\n rows,cols = len(data),len(data[0])\n for i in range(rows):\n for j in range(cols):\n worksheet.write(i, j, label=str(data[i][j]))\n # 保存\n workbook.save(path_target)\ndef main(url,path_data,tails=''):\n t0 = time.time()\n #url = url0+port+\"/api/gen\"\n #path_data = 'data/test_text.txt'\n with open(path_data,'r') as f:\n s = f.read().strip().split('\\n')\n R = []\n for str in s:\n data = {\"input\":str+tails}\n res = requests.post(url=url,json=data)\n #if res.json()['message']!='success':\n #print(res.json())\n R.append(res.json())\n t1 = time.time()\n #print('number of samles:{},total time:{}s, QPS:{}'.format(len(s),'%0.4f'%(t1-t0),'%0.4f'%(len(s)/(t1-t0))))\n return R\ndef demo0():\n path_data='data/test_headlove.txt'\n url = 'http://10.141.104.42:5000/api/gen'\n blackwords = '白头'\n tails = '#lv'\n R = main(url,path_data,tails)\n maxnb = 0\n res = []\n for r in R:\n words = r['input'][:-len(tails)]\n T = r['result']\n T = [t for t in T if '(诗)' in t]\n T = [t.replace('(诗)','') for t in T]\n if maxnb\" +\\\n\t\"\" +\\\n\t\" \" +\\\n\t\"\" +\\\n\t\"

Common

\" +\\\n\t\t\"
\" +\\\n\t\t\"

\" +\\\n\t\t\"
\" +\\\n\t\"

Forum

\" +\\\n\t\"

Thread

\" +\\\n\t\"

Post

\" +\\\n\t\"\" +\\\n\t\"\"\n\treturn response\n\n@app.route(\"/db/api/clear\", methods=[\"POST\"])\ndef clear():\n\tcursor = mysql.get_db().cursor()\n\t\"\"\"\n\tcode = cursor.execute(\"SHOW TABLES IN forums LIKE 'forum'\")\n\tif (code == 1): #forum exists\n\t\tcursor.execute(\"ALTER TABLE forums.forum DROP FOREIGN KEY forum_ibfk_1\")\n\n\tcode = cursor.execute(\"SHOW TABLES IN forums LIKE 'thread'\")\n\tif (code == 1): #forum exists\n\t\tcursor.execute(\"ALTER TABLE forums.thread DROP FOREIGN KEY thread_ibfk_1\")\n\t\tcursor.execute(\"ALTER TABLE forums.thread DROP FOREIGN KEY thread_ibfk_2\")\n\n\tcode = cursor.execute(\"SHOW TABLES IN forums LIKE 'post'\")\n\tif (code == 1): #forum exists\n\t\tcursor.execute(\"ALTER TABLE forums.post DROP FOREIGN KEY post_ibfk_1\")\n\t\tcursor.execute(\"ALTER TABLE forums.post DROP FOREIGN KEY post_ibfk_2\")\n\n\tcode = cursor.execute(\"SHOW TABLES IN forums LIKE 'followers'\")\n\tif (code == 1): #forum exists\n\t\tcursor.execute(\"ALTER TABLE forums.followers DROP FOREIGN KEY followers_ibfk_1\")\n\t\tcursor.execute(\"ALTER TABLE forums.followers DROP FOREIGN KEY followers_ibfk_2\")\n\n\tcode = cursor.execute(\"SHOW TABLES IN forums LIKE 'subscribers'\")\n\tif (code == 1): #forum exists\n\t\tcursor.execute(\"ALTER TABLE forums.subscribers DROP FOREIGN KEY subscribers_ibfk_1\")\n\t\tcursor.execute(\"ALTER TABLE forums.subscribers DROP FOREIGN KEY subscribers_ibfk_2\")\n\t\"\"\"\n\tcursor.execute(\"SET FORREIGN_KEY_CHECK = 0\")\n\tcursor.execute(\"TRUNCATE TABLE IF EXISTS \"+\\\n\t\t\"forums.user, \"+\\\n\t\t\"forums.post, \"+\\\n\t\t\"forums.thread, \"+\\\n\t\t\"forums.forum, \"+\\\n\t\t\"forums.followers, \"+\\\n\t\t\"forums.subscribers\")\\\n\t\n\tcursor.close()\n\t\n\tcode = 0#code0 + code1 + code2\n\tif (code == 0):\n\t\tmessage = 'OK'\n\telse:\n\t\tmessage = 'NOT OK'\n\treturn jsonify({'code': code, 'message': message})\n\nif __name__ == \"__main__\":\n\tapp.run(debug=True)\n", "sub_path": "hello.py", "file_name": "hello.py", "file_ext": "py", "file_size_in_byte": 2437, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flaskext.mysql.MySQL", "line_number": 5, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 75, "usage_type": "call"}]} +{"seq_id": "616289821", "text": "import sqlparse\n\nskip_token_type = [\n sqlparse.tokens.Comment.Single,\n sqlparse.tokens.Comment.Multi,\n sqlparse.tokens.Whitespace,\n sqlparse.tokens.Newline,\n]\n\n\ndef get_statement_ranges(query):\n statements = sqlparse.parse(query)\n statement_ranges = []\n start_index = 0\n\n for statement in statements:\n statement_str = statement.value\n statement_len = len(statement_str)\n\n if get_sanitized_statement(statement_str) != \"\":\n statement_start = start_index\n statement_end = start_index\n found_start = False\n\n for token in statement.flatten():\n token_type = getattr(token, \"ttype\")\n if not found_start: # Skipping for start\n if token_type in skip_token_type:\n statement_start += len(token.value)\n else:\n found_start = True\n statement_end = statement_start\n # Don't change this to else:, since token from not found start\n # might be used here\n if found_start: # Looking for end ;\n if token_type != sqlparse.tokens.Punctuation or token.value != \";\":\n statement_end += len(token.value)\n else:\n break\n\n statement_range = (statement_start, statement_end)\n statement_ranges.append(statement_range)\n start_index += statement_len\n\n return statement_ranges\n\n\ndef get_statements(query):\n statement_ranges = get_statement_ranges(query)\n return [\n get_sanitized_statement(query[start:end]) for start, end in statement_ranges\n ]\n\n\ndef get_sanitized_statement(statement):\n return sqlparse.format(statement, strip_comments=True).strip(\" \\n\\r\\t;\")\n", "sub_path": "querybook/server/lib/query_analysis/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1834, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sqlparse.tokens", "line_number": 4, "usage_type": "attribute"}, {"api_name": "sqlparse.tokens", "line_number": 5, "usage_type": "attribute"}, {"api_name": "sqlparse.tokens", "line_number": 6, "usage_type": "attribute"}, {"api_name": "sqlparse.tokens", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sqlparse.parse", "line_number": 12, "usage_type": "call"}, {"api_name": "sqlparse.tokens", "line_number": 36, "usage_type": "attribute"}, {"api_name": "sqlparse.format", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "95404009", "text": "import numpy as np\nimport torch\nimport torch.nn.functional as F\nimport torch.nn as nn\nimport sys\nfrom torch.autograd import Variable\nimport math\n\ndef flip(x, dim):\n\txsize = x.size()\n\tdim = x.dim() + dim if dim < 0 else dim\n\tx = x.contiguous()\n\tx = x.view(-1, *xsize[dim:])\n\tx = x.view(x.size(0), x.size(1), -1)[:, getattr(torch.arange(x.size(1)-1, \n\t\t\t\t\t\t\t\t\t\t-1, -1), ('cpu','cuda')[x.is_cuda])().long(), :]\n\treturn x.view(xsize)\n\n\ndef sinc(band,t_right):\n\ty_right= torch.sin(2*math.pi*band*t_right)/(2*math.pi*band*t_right)\n\ty_left= flip(y_right,0)\n\n\ty=torch.cat([y_left,Variable(torch.ones(1)).cuda(),y_right])\n\n\treturn y\n\t\t\n\nclass SincConv_fast(nn.Module):\n\t\"\"\"Sinc-based convolution\n\tParameters\n\t----------\n\tin_channels : `int`\n\t\t\tNumber of input channels. Must be 1.\n\tout_channels : `int`\n\t\t\tNumber of filters.\n\tkernel_size : `int`\n\t\t\tFilter length.\n\tsample_rate : `int`, optional\n\t\t\tSample rate. Defaults to 16000.\n\tUsage\n\t-----\n\tSee `torch.nn.Conv1d`\n\tReference\n\t---------\n\tMirco Ravanelli, Yoshua Bengio,\n\t\"Speaker Recognition from raw waveform with SincNet\".\n\thttps://arxiv.org/abs/1808.00158\n\t\"\"\"\n\n\t@staticmethod\n\tdef to_mel(hz):\n\t\treturn 2595 * np.log10(1 + hz / 700)\n\n\t@staticmethod\n\tdef to_hz(mel):\n\t\treturn 700 * (10 ** (mel / 2595) - 1)\n\n\tdef __init__(self, out_channels, kernel_size, sample_rate, in_channels=1,\n\t\t\t\t\t\t\t stride=1, padding=0, dilation=1, bias=False, groups=1, min_low_hz=50, min_band_hz=50):\n\n\t\tsuper(SincConv_fast,self).__init__()\n\n\t\tif in_channels != 1:\n\t\t\t#msg = (f'SincConv only support one input channel '\n\t\t\t# f'(here, in_channels = {in_channels:d}).')\n\t\t\tmsg = \"SincConv only support one input channel (here, in_channels = {%i})\" % (in_channels)\n\t\t\traise ValueError(msg)\n\n\t\tself.out_channels = out_channels\n\t\tself.kernel_size = kernel_size\n\t\t\n\t\t# Forcing the filters to be odd (i.e, perfectly symmetrics)\n\t\tif kernel_size%2==0:\n\t\t\tself.kernel_size=self.kernel_size+1\n\t\t\t\t\n\t\tself.stride = stride\n\t\tself.padding = padding\n\t\tself.dilation = dilation\n\n\t\tif bias:\n\t\t\traise ValueError('SincConv does not support bias.')\n\t\tif groups > 1:\n\t\t\traise ValueError('SincConv does not support groups.')\n\n\t\tself.sample_rate = sample_rate\n\t\tself.min_low_hz = min_low_hz\n\t\tself.min_band_hz = min_band_hz\n\n\t\t# initialize filterbanks such that they are equally spaced in Mel scale\n\t\tlow_hz = 30\n\t\thigh_hz = self.sample_rate / 2 - (self.min_low_hz + self.min_band_hz)\n\n\t\tmel = np.linspace(self.to_mel(low_hz),\n\t\t\t\t\t\t\t\t\t\t\tself.to_mel(high_hz),\n\t\t\t\t\t\t\t\t\t\t\tself.out_channels + 1)\n\t\thz = self.to_hz(mel)\n\t\t\n\n\t\t# filter lower frequency (out_channels, 1)\n\t\tself.low_hz_ = nn.Parameter(torch.Tensor(hz[:-1]).view(-1, 1))\n\n\t\t# filter frequency band (out_channels, 1)\n\t\tself.band_hz_ = nn.Parameter(torch.Tensor(np.diff(hz)).view(-1, 1))\n\n\t\t# Hamming window\n\t\t#self.window_ = torch.hamming_window(self.kernel_size)\n\t\tn_lin=torch.linspace(0, (self.kernel_size/2)-1, steps=int((self.kernel_size/2))) # computing only half of the window\n\t\tself.window_=0.54-0.46*torch.cos(2*math.pi*n_lin/self.kernel_size);\n\n\n\t\t# (kernel_size, 1)\n\t\tn = (self.kernel_size - 1) / 2.0\n\t\tself.n_ = 2*math.pi*torch.arange(-n, 0).view(1, -1) / self.sample_rate # Due to symmetry, I only need half of the time axes\n\n\n\n\n\tdef forward(self, waveforms):\n\t\t\"\"\"\n\t\tParameters\n\t\t----------\n\t\twaveforms : `torch.Tensor` (batch_size, 1, n_samples)\n\t\t\t\tBatch of waveforms.\n\t\tReturns\n\t\t-------\n\t\tfeatures : `torch.Tensor` (batch_size, out_channels, n_samples_out)\n\t\t\t\tBatch of sinc filters activations.\n\t\t\"\"\"\n\n\t\tself.n_ = self.n_.to(waveforms.device)\n\n\t\tself.window_ = self.window_.to(waveforms.device)\n\n\t\tlow = self.min_low_hz + torch.abs(self.low_hz_)\n\t\t\n\t\thigh = torch.clamp(low + self.min_band_hz + torch.abs(self.band_hz_),self.min_low_hz,self.sample_rate/2)\n\t\tband=(high-low)[:,0]\n\t\t\n\t\tf_times_t_low = torch.matmul(low, self.n_)\n\t\tf_times_t_high = torch.matmul(high, self.n_)\n\n\t\tband_pass_left=((torch.sin(f_times_t_high)-torch.sin(f_times_t_low))/(self.n_/2))*self.window_ # Equivalent of Eq.4 of the reference paper (SPEAKER RECOGNITION FROM RAW WAVEFORM WITH SINCNET). I just have expanded the sinc and simplified the terms. This way I avoid several useless computations. \n\t\tband_pass_center = 2*band.view(-1,1)\n\t\tband_pass_right= torch.flip(band_pass_left,dims=[1])\n\t\t\n\t\t\n\t\tband_pass=torch.cat([band_pass_left,band_pass_center,band_pass_right],dim=1)\n\n\t\t\n\t\tband_pass = band_pass / (2*band[:,None])\n\t\t\n\n\t\tself.filters = (band_pass).view(\n\t\t\t\tself.out_channels, 1, self.kernel_size)\n\n\t\treturn F.conv1d(waveforms, self.filters, stride=self.stride,\n\t\t\t\t\t\t\t\t\t\tpadding=self.padding, dilation=self.dilation,\n\t\t\t\t\t\t\t\t\t\t bias=None, groups=1) \n\n\n\t\t\t\t\n\t\t\t\t\nclass sinc_conv(nn.Module):\n\n\tdef __init__(self, N_filt,Filt_dim,fs):\n\t\tsuper(sinc_conv,self).__init__()\n\n\t\t# Mel Initialization of the filterbanks\n\t\tlow_freq_mel = 80\n\t\thigh_freq_mel = (2595 * np.log10(1 + (fs / 2) / 700)) # Convert Hz to Mel\n\t\tmel_points = np.linspace(low_freq_mel, high_freq_mel, N_filt) # Equally spaced in Mel scale\n\t\tf_cos = (700 * (10**(mel_points / 2595) - 1)) # Convert Mel to Hz\n\t\tb1=np.roll(f_cos,1)\n\t\tb2=np.roll(f_cos,-1)\n\t\tb1[0]=30\n\t\tb2[-1]=(fs/2)-100\n\t\t\t\t\t\t\n\t\tself.freq_scale=fs*1.0\n\t\tself.filt_b1 = nn.Parameter(torch.from_numpy(b1/self.freq_scale))\n\t\tself.filt_band = nn.Parameter(torch.from_numpy((b2-b1)/self.freq_scale))\n\n\t\t\n\t\tself.N_filt=N_filt\n\t\tself.Filt_dim=Filt_dim\n\t\tself.fs=fs\n\t\t\t\t\n\n\tdef forward(self, x):\n\t\t\t\n\t\tfilters=Variable(torch.zeros((self.N_filt,self.Filt_dim))).cuda()\n\t\tN=self.Filt_dim\n\t\tt_right=Variable(torch.linspace(1, (N-1)/2, steps=int((N-1)/2))/self.fs).cuda()\n\t\t\n\t\t\n\t\tmin_freq=50.0;\n\t\tmin_band=50.0;\n\t\t\n\t\tfilt_beg_freq=torch.abs(self.filt_b1)+min_freq/self.freq_scale\n\t\tfilt_end_freq=filt_beg_freq+(torch.abs(self.filt_band)+min_band/self.freq_scale)\n\t \n\t\tn=torch.linspace(0, N, steps=N)\n\n\t\t# Filter window (hamming)\n\t\twindow=0.54-0.46*torch.cos(2*math.pi*n/N);\n\t\twindow=Variable(window.float().cuda())\n\n\t\t\n\t\tfor i in range(self.N_filt):\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\tlow_pass1 = 2*filt_beg_freq[i].float()*sinc(filt_beg_freq[i].float()*self.freq_scale,t_right)\n\t\t\t\tlow_pass2 = 2*filt_end_freq[i].float()*sinc(filt_end_freq[i].float()*self.freq_scale,t_right)\n\t\t\t\tband_pass=(low_pass2-low_pass1)\n\n\t\t\t\tband_pass=band_pass/torch.max(band_pass)\n\n\t\t\t\tfilters[i,:]=band_pass.cuda()*window\n\n\t\tout=F.conv1d(x, filters.view(self.N_filt,1,self.Filt_dim))\n\n\t\treturn out\n\t\t\n\ndef act_fun(act_type):\n\tif act_type==\"softplus\":\n\t\treturn nn.Softplus()\n\n\tif act_type==\"relu\":\n\t\treturn nn.ReLU()\n\t\t\t\t\t\t\n\tif act_type==\"tanh\":\n\t\treturn nn.Tanh()\n\t\t\t\t\t\t\n\tif act_type==\"sigmoid\":\n\t\treturn nn.Sigmoid()\n\t\t\t\t\t \n\tif act_type==\"leaky_relu\":\n\t\treturn nn.LeakyReLU(0.2)\n\t\t\t\t\t\t\n\tif act_type==\"elu\":\n\t\treturn nn.ELU()\n\t\t\t\t\t\t\t\t\t\t \n\tif act_type==\"softmax\":\n\t\treturn nn.LogSoftmax(dim=1)\n\t\t\t\t\n\tif act_type==\"linear\":\n\t\treturn nn.LeakyReLU(1) # initializzed like this, but not used in forward!\n\t\t\t\t\t\t\n\t\t\t\t\t\t\nclass LayerNorm(nn.Module):\n\tdef __init__(self, features, eps=1e-6):\n\t\tsuper(LayerNorm,self).__init__()\n\t\tself.gamma = nn.Parameter(torch.ones(features))\n\t\tself.beta = nn.Parameter(torch.zeros(features))\n\t\tself.eps = eps\n\n\tdef forward(self, x):\n\t\tmean = x.mean(-1, keepdim=True)\n\t\tstd = x.std(-1, keepdim=True)\n\t\treturn self.gamma * (x - mean) / (std + self.eps) + self.beta\n\n\nclass MLP(nn.Module):\n\tdef __init__(self, options):\n\t\tsuper(MLP, self).__init__()\n\t\t\n\t\tself.input_dim=int(options['input_dim'])\n\t\tself.fc_lay=options['fc_lay']\n\t\tself.fc_drop=options['fc_drop']\n\t\tself.fc_use_batchnorm=options['fc_use_batchnorm']\n\t\tself.fc_use_laynorm=options['fc_use_laynorm']\n\t\tself.fc_use_laynorm_inp=options['fc_use_laynorm_inp']\n\t\tself.fc_use_batchnorm_inp=options['fc_use_batchnorm_inp']\n\t\tself.fc_act=options['fc_act']\n\t\t\n\t \n\t\tself.wx = nn.ModuleList([])\n\t\tself.bn = nn.ModuleList([])\n\t\tself.ln = nn.ModuleList([])\n\t\tself.act = nn.ModuleList([])\n\t\tself.drop = nn.ModuleList([])\n\t \n\n\t \n\t\t# input layer normalization\n\t\tif self.fc_use_laynorm_inp:\n\t\t\tself.ln0=LayerNorm(self.input_dim)\n\t\t\t\n\t\t# input batch normalization \n\t\tif self.fc_use_batchnorm_inp:\n\t\t\tself.bn0=nn.BatchNorm1d([self.input_dim],momentum=0.05)\n\t\t\t \n\t\t\t \n\t\tself.N_fc_lay=len(self.fc_lay)\n\t\t\t\t \n\t\tcurrent_input=self.input_dim\n\t\t\n\t\t# Initialization of hidden layers\n\t\t\n\t\tfor i in range(self.N_fc_lay):\n\t\t\t# dropout\n\t\t\tself.drop.append(nn.Dropout(p=self.fc_drop[i]))\n\t\t\n\t\t\t# activation\n\t\t\tself.act.append(act_fun(self.fc_act[i]))\n\t\t\t \n\t\t\t \n\t\t\tadd_bias=True\n\t\t\t \n\t\t\t# layer norm initialization\n\t\t\tself.ln.append(LayerNorm(self.fc_lay[i]))\n\t\t\tself.bn.append(nn.BatchNorm1d(self.fc_lay[i],momentum=0.05))\n\t\t\t \n\t\t\tif self.fc_use_laynorm[i] or self.fc_use_batchnorm[i]:\n\t\t\t\tadd_bias=False\n\t\t\t \n\t\t\t\t\t\t\n\t\t\t# Linear operations\n\t\t\tself.wx.append(nn.Linear(current_input, self.fc_lay[i],bias=add_bias))\n\t\t\t \n\t\t\t# weight initialization\n\t\t\tself.wx[i].weight = torch.nn.Parameter(torch.Tensor(self.fc_lay[i],current_input).uniform_(-np.sqrt(0.01/(current_input+self.fc_lay[i])),np.sqrt(0.01/(current_input+self.fc_lay[i]))))\n\t\t\tself.wx[i].bias = torch.nn.Parameter(torch.zeros(self.fc_lay[i]))\n\t\t\t \n\t\t\tcurrent_input=self.fc_lay[i]\n\t\t\t \n\t\t\t \n\tdef forward(self, x):\n\n\t\t\t\n\t\t# Applying Layer/Batch Norm\n\t\tif bool(self.fc_use_laynorm_inp):\n\t\t\tx=self.ln0((x))\n\t\t\t\n\t\tif bool(self.fc_use_batchnorm_inp):\n\t\t\tx=self.bn0((x))\n\t\t\t\n\t\tfor i in range(self.N_fc_lay):\n\n\t\t\tif self.fc_act[i]!='linear':\n\t\t\t\t\t\n\t\t\t\tif self.fc_use_laynorm[i]:\n\t\t\t\t\tx = self.drop[i](self.act[i](self.ln[i](self.wx[i](x))))\n\t\t\t\t\n\t\t\t\tif self.fc_use_batchnorm[i]:\n\t\t\t\t\tx = self.drop[i](self.act[i](self.bn[i](self.wx[i](x))))\n\t\t\t\t\n\t\t\t\tif self.fc_use_batchnorm[i]==False and self.fc_use_laynorm[i]==False:\n\t\t\t\t\tx = self.drop[i](self.act[i](self.wx[i](x)))\n\t\t\t\t \n\t\t\telse:\n\t\t\t\tif self.fc_use_laynorm[i]:\n\t\t\t\t\tx = self.drop[i](self.ln[i](self.wx[i](x)))\n\t\t\t\t\n\t\t\t\tif self.fc_use_batchnorm[i]:\n\t\t\t\t\tx = self.drop[i](self.bn[i](self.wx[i](x)))\n\t\t\t\t\n\t\t\t\tif self.fc_use_batchnorm[i]==False and self.fc_use_laynorm[i]==False:\n\t\t\t\t\tx = self.drop[i](self.wx[i](x)) \n\t\t\t\t\n\t\treturn x\n\n\nclass MLP_for_me(nn.Module):\n\tdef __init__(self, options):\n\t\tsuper(MLP_for_me, self).__init__()\n\t\t\n\t\tself.input_dim=int(options['input_dim'])\n\t\tself.fc_lay=options['fc_lay']\n\t\tself.fc_drop=options['fc_drop']\n\t\tself.fc_use_batchnorm=options['fc_use_batchnorm']\n\t\tself.fc_use_laynorm=options['fc_use_laynorm']\n\t\tself.fc_use_laynorm_inp=options['fc_use_laynorm_inp']\n\t\tself.fc_use_batchnorm_inp=options['fc_use_batchnorm_inp']\n\t\tself.fc_act=options['fc_act']\n\t\t\n\t \n\t\tself.wx = nn.ModuleList([])\n\t\tself.bn = nn.ModuleList([])\n\t\tself.ln = nn.ModuleList([])\n\t\tself.act = nn.ModuleList([])\n\t\tself.drop = nn.ModuleList([])\n\t \n\n\t \n\t\t# input layer normalization\n\t\tif self.fc_use_laynorm_inp:\n\t\t\tself.ln0=LayerNorm(self.input_dim)\n\t\t\t\n\t\t# input batch normalization \n\t\tif self.fc_use_batchnorm_inp:\n\t\t\tself.bn0=nn.BatchNorm1d(self.input_dim,momentum=0.05)\n\t\t\t \n\t\t\t \n\t\tself.N_fc_lay=len(self.fc_lay)\n\t\t\t\t \n\t\tcurrent_input=self.input_dim\n\t\t\n\t\t# Initialization of hidden layers\n\t\t\n\t\tfor i in range(self.N_fc_lay):\n\t\t\t# dropout\n\t\t\tself.drop.append(nn.Dropout(p=self.fc_drop[i]))\n\t\t\n\t\t\t# activation\n\t\t\tself.act.append(act_fun(self.fc_act[i]))\n\n\t\t\tadd_bias=True\n\t\t\t \n\t\t\t# layer norm initialization\n\t\t\tself.ln.append(LayerNorm(self.fc_lay[i]))\n\t\t\tself.bn.append(nn.BatchNorm1d(self.fc_lay[i],momentum=0.05))\n\t\t\t \n\t\t\tif self.fc_use_laynorm[i] or self.fc_use_batchnorm[i]:\n\t\t\t\tadd_bias=False\n\t\t\t \n\t\t\t\t\t\t\n\t\t\t# Linear operations\n\t\t\tself.wx.append(nn.Linear(current_input, self.fc_lay[i],bias=add_bias))\n\t\t\t \n\t\t\t# weight initialization\n\t\t\tself.wx[i].weight = torch.nn.Parameter(torch.Tensor(self.fc_lay[i],current_input).uniform_(-np.sqrt(0.01/(current_input+self.fc_lay[i])),np.sqrt(0.01/(current_input+self.fc_lay[i]))))\n\t\t\tself.wx[i].bias = torch.nn.Parameter(torch.zeros(self.fc_lay[i]))\n\t\t\t \n\t\t\tcurrent_input=self.fc_lay[i]\n\t\t\t \n\t\t\t \n\tdef forward(self, x):\t\n\t\t# Applying Layer/Batch Norm\n\t\tif bool(self.fc_use_laynorm_inp):\n\t\t\tx=self.ln0((x))\n\t\t\t\n\t\tif bool(self.fc_use_batchnorm_inp):\n\t\t\tx=self.bn0(x.transpose(-1, -2)).transpose(-1, -2)\n\t\t\n\t\tfor i in range(self.N_fc_lay):\n\n\t\t\tif self.fc_act[i]!='linear':\n\t\t\t\tif self.fc_use_laynorm[i]:\n\t\t\t\t\tx = self.drop[i](self.act[i](self.ln[i](self.wx[i](x))))\n\t\t\t\t\n\t\t\t\telif self.fc_use_batchnorm[i]:\n\t\t\t\t\tx = self.drop[i](self.act[i](self.bn[i](self.wx[i](x).transpose(-1, -2)).transpose(-1, -2)))\n\n\t\t\t\telif self.fc_use_batchnorm[i]==False and self.fc_use_laynorm[i]==False:\n\t\t\t\t\tx = self.drop[i](self.act[i](self.wx[i](x)))\n\t\t\t\t \n\t\t\telse:\n\t\t\t\tif self.fc_use_laynorm[i]:\n\t\t\t\t\tx = self.drop[i](self.ln[i](self.wx[i](x)))\n\t\t\t\t\n\t\t\t\tif self.fc_use_batchnorm[i]:\n\t\t\t\t\tx = self.drop[i](self.bn[i](self.wx[i](x).transpose(-1, -2)).transpose(-1, -2))\n\t\t\t\t\n\t\t\t\tif self.fc_use_batchnorm[i]==False and self.fc_use_laynorm[i]==False:\n\t\t\t\t\tx = self.drop[i](self.wx[i](x)) \n\t\t\n\t\treturn x\n\n\nclass SincNet(nn.Module):\n\t\t\n\tdef __init__(self,options):\n\t\tsuper(SincNet,self).__init__()\n\t\n\t\tself.cnn_N_filt=options['cnn_N_filt']\n\t\tself.cnn_len_filt=options['cnn_len_filt']\n\t\tself.cnn_max_pool_len=options['cnn_max_pool_len']\n\t\t \n\t\t \n\t\tself.cnn_act=options['cnn_act']\n\t\tself.cnn_drop=options['cnn_drop']\n\t\t \n\t\tself.cnn_use_laynorm=options['cnn_use_laynorm']\n\t\tself.cnn_use_batchnorm=options['cnn_use_batchnorm']\n\t\tself.cnn_use_laynorm_inp=options['cnn_use_laynorm_inp']\n\t\tself.cnn_use_batchnorm_inp=options['cnn_use_batchnorm_inp']\n\t\t \n\t\tself.input_dim=int(options['input_dim'])\n\t\t \n\t\tself.fs=options['fs']\n\t\t \n\t\tself.N_cnn_lay=len(options['cnn_N_filt'])\n\t\tself.conv = nn.ModuleList([])\n\t\tself.bn = nn.ModuleList([])\n\t\tself.ln = nn.ModuleList([])\n\t\tself.act = nn.ModuleList([])\n\t\tself.drop = nn.ModuleList([])\n\t\t \n\t\t\t\t\t \n\t\tif self.cnn_use_laynorm_inp:\n\t\t\tself.ln0=LayerNorm(self.input_dim)\n\t\t\t\t \n\t\tif self.cnn_use_batchnorm_inp:\n\t\t\tself.bn0=nn.BatchNorm1d([self.input_dim],momentum=0.05)\n\t\t\t\t \n\t\tcurrent_input=self.input_dim \n\t\t \n\t\tfor i in range(self.N_cnn_lay):\n\t\t\t \n\t\t\tN_filt=int(self.cnn_N_filt[i])\n\t\t\tlen_filt=int(self.cnn_len_filt[i])\n\t\t\t \n\t\t\t# dropout\n\t\t\tself.drop.append(nn.Dropout(p=self.cnn_drop[i]))\n\t\t\t \n\t\t\t# activation\n\t\t\tself.act.append(act_fun(self.cnn_act[i]))\n\t\t\t\t\t\t\t\t\t\n\t\t\t# layer norm initialization \n\t\t\tself.ln.append(LayerNorm([N_filt,int((current_input-self.cnn_len_filt[i]+1)/self.cnn_max_pool_len[i])]))\n\n\t\t\tself.bn.append(nn.BatchNorm1d(N_filt,int((current_input-self.cnn_len_filt[i]+1)/self.cnn_max_pool_len[i]),momentum=0.05))\n\t\t\t\t\t\n\n\t\t\tif i==0:\n\t\t\t\tself.conv.append(SincConv_fast(self.cnn_N_filt[0],self.cnn_len_filt[0],self.fs))\n\t\t\t\t\t\t\n\t\t\telse:\n\t\t\t\tself.conv.append(nn.Conv1d(self.cnn_N_filt[i-1], self.cnn_N_filt[i], self.cnn_len_filt[i]))\n\t\t\t\t\n\t\t\tcurrent_input=int((current_input-self.cnn_len_filt[i]+1)/self.cnn_max_pool_len[i])\n\n\t\t\t \n\t\tself.out_dim=current_input*N_filt\n\n\n\n\tdef forward(self, x):\n\t\tbatch=x.shape[0]\n\t\tseq_len=x.shape[1]\n\t\t \n\t\tif bool(self.cnn_use_laynorm_inp):\n\t\t\tx=self.ln0((x))\n\t\t\t\n\t\tif bool(self.cnn_use_batchnorm_inp):\n\t\t\tx=self.bn0((x))\n\t\t\t\n\t\tx=x.view(batch,1,seq_len)\n\n\t\t \n\t\tfor i in range(self.N_cnn_lay):\n\t\t\t\t \n\t\t\tif self.cnn_use_laynorm[i]:\n\t\t\t\tif i==0:\n\t\t\t\t\tx = self.drop[i](self.act[i](self.ln[i](F.max_pool1d(torch.abs(self.conv[i](x)), self.cnn_max_pool_len[i])))) \n\t\t\t\telse:\n\t\t\t\t\tx = self.drop[i](self.act[i](self.ln[i](F.max_pool1d(self.conv[i](x), self.cnn_max_pool_len[i])))) \n\t\t\t\t\n\t\t\tif self.cnn_use_batchnorm[i]:\n\t\t\t\tx = self.drop[i](self.act[i](self.bn[i](F.max_pool1d(self.conv[i](x), self.cnn_max_pool_len[i]))))\n\n\t\t\tif self.cnn_use_batchnorm[i]==False and self.cnn_use_laynorm[i]==False:\n\t\t\t\tx = self.drop[i](self.act[i](F.max_pool1d(self.conv[i](x), self.cnn_max_pool_len[i])))\n\n\t\t \n\t\tx = x.view(batch,-1)\n\n\t\treturn x\n\nclass ConvNet(nn.Module):\n\t\t\n\tdef __init__(self,options):\n\t\tsuper(ConvNet,self).__init__()\n\t\n\t\tself.cnn_N_filt=options['cnn_N_filt']\n\t\tself.cnn_len_filt=options['cnn_len_filt']\n\t\tself.cnn_max_pool_len=options['cnn_max_pool_len']\n\t\t \n\t\t \n\t\tself.cnn_act=options['cnn_act']\n\t\tself.cnn_drop=options['cnn_drop']\n\t\t \n\t\tself.cnn_use_laynorm=options['cnn_use_laynorm']\n\t\tself.cnn_use_batchnorm=options['cnn_use_batchnorm']\n\t\tself.cnn_use_laynorm_inp=options['cnn_use_laynorm_inp']\n\t\tself.cnn_use_batchnorm_inp=options['cnn_use_batchnorm_inp']\n\t\t \n\t\tself.input_dim=int(options['input_dim'])\n\t\t \n\t\tself.fs=options['fs']\n\t\t \n\t\tself.N_cnn_lay=len(options['cnn_N_filt'])\n\t\tself.conv = nn.ModuleList([])\n\t\tself.bn = nn.ModuleList([])\n\t\tself.ln = nn.ModuleList([])\n\t\tself.act = nn.ModuleList([])\n\t\tself.drop = nn.ModuleList([])\n\t\t \n\t\t\t\t\t \n\t\tif self.cnn_use_laynorm_inp:\n\t\t\tself.ln0=LayerNorm(self.input_dim)\n\t\t\t\t \n\t\tif self.cnn_use_batchnorm_inp:\n\t\t\tself.bn0=nn.BatchNorm1d([self.input_dim],momentum=0.05)\n\t\t\t\t \n\t\tcurrent_input=self.input_dim \n\t\t \n\t\tfor i in range(self.N_cnn_lay):\n\t\t\t \n\t\t\tN_filt=int(self.cnn_N_filt[i])\n\t\t\tlen_filt=int(self.cnn_len_filt[i])\n\t\t\t \n\t\t\t# dropout\n\t\t\tself.drop.append(nn.Dropout(p=self.cnn_drop[i]))\n\t\t\t \n\t\t\t# activation\n\t\t\tself.act.append(act_fun(self.cnn_act[i]))\n\t\t\t\t\t\t\t\t\t\n\t\t\t# layer norm initialization \n\t\t\tself.ln.append(LayerNorm([N_filt,int((current_input-self.cnn_len_filt[i]+1)/self.cnn_max_pool_len[i])]))\n\n\t\t\tself.bn.append(nn.BatchNorm1d(N_filt,int((current_input-self.cnn_len_filt[i]+1)/self.cnn_max_pool_len[i]),momentum=0.05))\n\t\t\t\t\t\n\n\t\t\tif i==0:\n\t\t\t\tself.conv.append(nn.Conv1d(8, self.cnn_N_filt[i], self.cnn_len_filt[i]))\n\t\t\t\t\t\t\n\t\t\telse:\n\t\t\t\tself.conv.append(nn.Conv1d(self.cnn_N_filt[i-1], self.cnn_N_filt[i], self.cnn_len_filt[i]))\n\t\t\t\t\n\t\t\tcurrent_input=int((current_input-self.cnn_len_filt[i]+1)/self.cnn_max_pool_len[i])\n\n\t\t\t \n\t\tself.out_dim=current_input*N_filt\n\n\n\n\tdef forward(self, x):\n\t\tx = x.transpose(1, 2)\n\t\tbatch=x.shape[0]\n\t\tseq_len=x.shape[1]\n\t\t \n\t\tif bool(self.cnn_use_laynorm_inp):\n\t\t\tx=self.ln0((x))\n\t\t\t\n\t\tif bool(self.cnn_use_batchnorm_inp):\n\t\t\tx=self.bn0((x))\n\t\t\n\t\tx=x.view(batch,1,seq_len)\n\t\t \n\t\tfor i in range(self.N_cnn_lay):\n\t\t\t\t \n\t\t\tif self.cnn_use_laynorm[i]:\n\t\t\t\tx = self.drop[i](self.act[i](self.ln[i](F.max_pool1d(self.conv[i](x), self.cnn_max_pool_len[i])))) \n\t\t\t\t\n\t\t\tif self.cnn_use_batchnorm[i]:\n\t\t\t\tx = self.drop[i](self.act[i](self.bn[i](F.max_pool1d(self.conv[i](x), self.cnn_max_pool_len[i]))))\n\n\t\t\tif self.cnn_use_batchnorm[i]==False and self.cnn_use_laynorm[i]==False:\n\t\t\t\tx = self.drop[i](self.act[i](F.max_pool1d(self.conv[i](x), self.cnn_max_pool_len[i])))\n\n\t\t \n\t\tx = x.view(batch,-1)\n\n\t\treturn x\n\n\n\nclass LSTM(nn.Module):\n\tdef __init__(self,\n\t\t\t\t embed_dim=8,\n\t\t\t\t hidden_size=128,\n\t\t\t\t num_layers=4,\n\t\t\t\t bidirectional=True,\n\t\t\t\t dropout_in=0.25,\n\t\t\t\t dropout_out=0.25):\n\n\t\tsuper(LSTM, self).__init__()\n\n\t\tself.dropout_in = dropout_in\n\t\tself.dropout_out = dropout_out\n\t\tself.bidirectional = bidirectional\n\t\tself.hidden_size = hidden_size\n\t\tself.out_dim = 2 * hidden_size if bidirectional else hidden_size\n\n\t\t\n\t\tdropout_lstm = dropout_out if num_layers > 1 else 0.\n\t\tself.lstm = nn.LSTM(input_size=embed_dim,\n\t\t\t\t\t\t\t\t\t\t\t\thidden_size=hidden_size,\n\t\t\t\t\t\t\t\t\t\t\t\tnum_layers=num_layers,\n\t\t\t\t\t\t\t\t\t\t\t\tdropout=dropout_lstm,\n\t\t\t\t\t\t\t\t\t\t\t\tbidirectional=bidirectional)\n\n\tdef forward(self, src_embeddings):\n\t\t\"\"\" Performs a single forward pass through the instantiated encoder sub-network. \"\"\"\n\t\t# Embed tokens and apply dropout\n\t\tbatch_size, src_time_steps, embed_dim = src_embeddings.size()\n\t\tsrc_lengths = [src_time_steps] * batch_size\n\t\t_src_embeddings = F.dropout(src_embeddings, p=self.dropout_in, training=self.training)\n\n\t\t# Transpose batch: [batch_size, src_time_steps, num_features] -> [src_time_steps, batch_size, num_features]\n\t\tsrc_embeddings = _src_embeddings.transpose(0, 1)\n\n\t\t# Pack embedded tokens into a PackedSequence\n\t\tpacked_source_embeddings = nn.utils.rnn.pack_padded_sequence(src_embeddings, src_lengths)\n\n\t\t# Pass source input through the recurrent layer(s)\n\t\tpacked_outputs, (final_hidden_states, final_cell_states) = self.lstm(packed_source_embeddings)\n\n\t\t# Unpack LSTM outputs and optionally apply dropout (dropout currently disabled)\n\t\tlstm_output, _ = nn.utils.rnn.pad_packed_sequence(packed_outputs, padding_value=0.)\n\t\tlstm_output = F.dropout(lstm_output, p=self.dropout_out, training=self.training)\n\t\tassert list(lstm_output.size()) == [src_time_steps, batch_size, self.out_dim] # sanity check\n\t\t\n\t\tlstm_output = lstm_output.transpose(0, 1)\n\n\t\treturn lstm_output\n\n\n\ndef make_positions(tensor, padding_idx, left_pad):\n\t\"\"\"Replace non-padding symbols with their position numbers.\n\tPosition numbers begin at padding_idx+1.\n\tPadding symbols are ignored, but it is necessary to specify whether padding\n\tis added on the left side (left_pad=True) or right side (left_pad=False).\n\t\"\"\"\n\tmax_pos = padding_idx + 1 + tensor.size(1)\n\tif not hasattr(make_positions, 'range_buf'):\n\t\tmake_positions.range_buf = tensor.new()\n\tmake_positions.range_buf = make_positions.range_buf.type_as(tensor)\n\tif make_positions.range_buf.numel() < max_pos:\n\t\ttorch.arange(padding_idx + 1, max_pos, out=make_positions.range_buf)\n\tmask = tensor.ne(padding_idx)\n\tpositions = make_positions.range_buf[:tensor.size(1)].expand_as(tensor)\n\tif left_pad:\n\t\tpositions = positions - mask.size(1) + mask.long().sum(dim=1).unsqueeze(1)\n\treturn tensor.clone().masked_scatter_(mask, positions[mask])\n\nclass LearnedPositionalEmbedding(nn.Embedding):\n\t\"\"\"This module learns positional embeddings up to a fixed maximum size.\n\tPadding symbols are ignored, but it is necessary to specify whether padding\n\tis added on the left side (left_pad=True) or right side (left_pad=False).\n\t\"\"\"\n\n\tdef __init__(self, num_embeddings, embedding_dim, padding_idx, left_pad):\n\t\tsuper().__init__(num_embeddings, embedding_dim, padding_idx)\n\t\tself.left_pad = left_pad\n\n\tdef forward(self, input, incremental_state=None):\n\t\t\"\"\"Input is expected to be of size [bsz x seqlen].\"\"\"\n\t\tif incremental_state is not None:\n\t\t\t# positions is the same for every token when decoding a single step\n\t\t\tpositions = input.data.new(1, 1).fill_(self.padding_idx + input.size(1))\n\t\telse:\n\t\t\tpositions = make_positions(input.data, self.padding_idx, self.left_pad)\n\t\treturn super().forward(Variable(positions))\n\n\tdef max_positions(self):\n\t\t\"\"\"Maximum number of supported positions.\"\"\"\n\t\treturn self.num_embeddings - self.padding_idx - 1\n\n\nclass SinusoidalPositionalEmbedding(nn.Module):\n\t\"\"\"This module produces sinusoidal positional embeddings of any length.\n\tPadding symbols are ignored, but it is necessary to specify whether padding\n\tis added on the left side (left_pad=True) or right side (left_pad=False).\n\t\"\"\"\n\n\tdef __init__(self, embedding_dim, padding_idx, left_pad, init_size=1024):\n\t\tsuper().__init__()\n\t\tself.embedding_dim = embedding_dim\n\t\tself.padding_idx = padding_idx\n\t\tself.left_pad = left_pad\n\t\tself.register_buffer(\n\t\t\t'weights',\n\t\t\tSinusoidalPositionalEmbedding.get_embedding(\n\t\t\t\tinit_size,\n\t\t\t\tembedding_dim,\n\t\t\t\tpadding_idx,\n\t\t\t),\n\t\t)\n\n\t@staticmethod\n\tdef get_embedding(num_embeddings, embedding_dim, padding_idx=None):\n\t\t\"\"\"Build sinusoidal embeddings.\n\t\tThis matches the implementation in tensor2tensor, but differs slightly\n\t\tfrom the description in Section 3.5 of \"Attention Is All You Need\".\n\t\t\"\"\"\n\t\thalf_dim = embedding_dim // 2\n\t\temb = math.log(10000) / (half_dim - 1)\n\t\temb = torch.exp(torch.arange(half_dim) * -emb)\n\t\temb = torch.arange(num_embeddings).unsqueeze(1) * emb.unsqueeze(0)\n\t\temb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1).view(num_embeddings, -1)\n\t\tif embedding_dim % 2 == 1:\n\t\t\t# zero pad\n\t\t\temb = torch.cat([emb, torch.zeros(num_embeddings, 1)], dim=1)\n\t\tif padding_idx is not None:\n\t\t\temb[padding_idx, :] = 0\n\t\treturn emb\n\n\tdef forward(self, input, incremental_state=None):\n\t\t\"\"\"Input is expected to be of size [bsz x seqlen].\"\"\"\n\t\t# recompute/expand embeddings if needed\n\t\tbsz, seq_len = input.size()\n\t\tmax_pos = self.padding_idx + 1 + seq_len\n\t\tif max_pos > self.weights.size(0):\n\t\t\tself.weights = SinusoidalPositionalEmbedding.get_embedding(\n\t\t\t\tmax_pos,\n\t\t\t\tself.embedding_dim,\n\t\t\t\tself.padding_idx,\n\t\t\t).type_as(self.weights)\n\t\tweights = Variable(self.weights)\n\n\t\tif incremental_state is not None:\n\t\t\t# positions is the same for every token when decoding a single step\n\t\t\treturn weights[self.padding_idx + seq_len, :].expand(bsz, 1, -1)\n\n\t\tpositions = Variable(make_positions(input.data, self.padding_idx, self.left_pad))\n\t\treturn weights.index_select(0, positions.view(-1)).view(bsz, seq_len, -1)\n\n\tdef max_positions(self):\n\t\t\"\"\"Maximum number of supported positions.\"\"\"\n\t\treturn int(1e5) # an arbitrary large number\n\n\nclass LayerNormalization(nn.Module):\n\t\"\"\"Layer normalization for module\"\"\"\n\n\tdef __init__(self, hidden_size, eps=1e-6, affine=True):\n\t\tsuper(LayerNormalization, self).__init__()\n\n\t\tself.affine = affine\n\t\tself.eps = eps\n\t\tif self.affine:\n\t\t\tself.gamma = nn.Parameter(torch.ones(hidden_size))\n\t\t\tself.beta = nn.Parameter(torch.zeros(hidden_size))\n\n\tdef forward(self, x):\n\t\tmean = x.mean(-1, keepdim=True)\n\t\tstd = x.std(-1, keepdim=True)\n\t\treturn self.gamma * (x - mean) / (std + self.eps) + self.beta\n\n\ndef PositionalEmbedding(num_embeddings, embedding_dim, padding_idx, left_pad):\n\tm = LearnedPositionalEmbedding(num_embeddings, embedding_dim, padding_idx, left_pad)\n\tm.weight.data.normal_(0, 0.1)\n\treturn m\n\ndef residual(x, y, dropout, training):\n\t\"\"\"Residual connection\"\"\"\n\ty = F.dropout(y, p=dropout, training=training)\n\treturn x + y\n\ndef Linear(in_features, out_features, bias=True, dropout=0):\n\t\"\"\"Weight-normalized Linear layer (input: N x T x C)\"\"\"\n\tm = nn.Linear(in_features, out_features, bias=bias)\n\tm.weight.data.uniform_(-0.1, 0.1)\n\tif bias:\n\t\tm.bias.data.uniform_(-0.1, 0.1)\n\treturn m\n\ndef split_heads(x, num_heads):\n\t\"\"\"split x into multi heads\n\tArgs:\n\t\tx: [batch_size, length, depth]\n\tReturns:\n\t\ty: [[batch_size, length, depth / num_heads] x heads]\n\t\"\"\"\n\tsz = x.size()\n\t# x -> [batch_size, length, heads, depth / num_heads]\n\tx = x.view(sz[0], sz[1], num_heads, sz[2] // num_heads)\n\t# [batch_size, length, 1, depth // num_heads] * \n\theads = torch.chunk(x, num_heads, 2)\n\tx = []\n\tfor i in range(num_heads):\n\t\tx.append(torch.squeeze(heads[i], 2))\n\treturn x\n\ndef combine_heads(x):\n\t\"\"\"combine multi heads\n\tArgs:\n\t\tx: [batch_size, length, depth / num_heads] x heads\n\tReturns:\n\t\tx: [batch_size, length, depth]\n\t\"\"\"\n\treturn torch.cat(x, 2)\n\n\ndef dot_product_attention(q, k, v, bias, dropout, to_weights=False):\n\t\"\"\"dot product for query-key-value\n\tArgs:\n\t\tq: query antecedent, [batch, length, depth]\n\t\tk: key antecedent, [batch, length, depth]\n\t\tv: value antecedent, [batch, length, depth]\n\t\tbias: masked matrix\n\t\tdropout: dropout rate\n\t\tto_weights: whether to print weights\n\t\"\"\"\n\t# [batch, length, depth] x [batch, depth, length] -> [batch, length, length]\n\tlogits = torch.bmm(q, k.transpose(1, 2).contiguous())\n\tif bias is not None:\n\t\tlogits += bias\n\tsize = logits.size()\n\tweights = F.softmax(logits.view(size[0] * size[1], size[2]), dim=1)\n\tweights = weights.view(size)\n\tif to_weights:\n\t\treturn torch.bmm(weights, v), weights\n\telse:\n\t\treturn torch.bmm(weights, v)\n\n\nclass FeedForwardNetwork(nn.Module):\n\tdef __init__(self, hidden_size, filter_size, dropout):\n\t\tsuper(FeedForwardNetwork, self).__init__()\n\t\tself.fc1 = Linear(hidden_size, filter_size, bias=False)\n\t\tself.fc2 = Linear(filter_size, hidden_size, bias=False)\n\t\tself.dropout = dropout\n\n\tdef forward(self, x):\n\t\tx = F.relu(self.fc1(x))\n\t\tx = F.dropout(x, p=self.dropout, training=self.training)\n\t\tx = self.fc2(x)\n\t\treturn x\n\n\nclass MultiheadAttention(nn.Module):\n\t\"\"\"Multi-head attention mechanism\"\"\"\n\tdef __init__(self, \n\t\t\t\t key_depth, value_depth, output_depth,\n\t\t\t\t num_heads, dropout=0.1):\n\t\tsuper(MultiheadAttention, self).__init__()\n\n\t\tself._query = Linear(key_depth, key_depth, bias=False)\n\t\tself._key = Linear(key_depth, key_depth, bias=False)\n\t\tself._value = Linear(value_depth, value_depth, bias=False)\n\t\tself.output_perform = Linear(value_depth, output_depth, bias=False)\n\n\t\tself.num_heads = num_heads\n\t\tself.key_depth_per_head = key_depth // num_heads\n\t\tself.dropout = dropout\n\t\t\n\tdef forward(self, query_antecedent, memory_antecedent, bias, to_weights=False):\n\t\tif memory_antecedent is None:\n\t\t\tmemory_antecedent = query_antecedent\n\t\tq = self._query(query_antecedent)\n\t\tk = self._key(memory_antecedent)\n\t\tv = self._value(memory_antecedent)\n\t\tq *= self.key_depth_per_head ** -0.5\n\t\t\n\t\t# split heads\n\t\tq = split_heads(q, self.num_heads)\n\t\tk = split_heads(k, self.num_heads)\n\t\tv = split_heads(v, self.num_heads)\n\n\t\tx = []\n\t\tavg_attn_scores = None\n\t\tfor i in range(self.num_heads):\n\t\t\tresults = dot_product_attention(q[i], k[i], v[i],\n\t\t\t\t\t\t\t\t\t\t\tbias,\n\t\t\t\t\t\t\t\t\t\t\tself.dropout,\n\t\t\t\t\t\t\t\t\t\t\tto_weights)\n\t\t\tif to_weights:\n\t\t\t\ty, attn_scores = results\n\t\t\t\tif avg_attn_scores is None:\n\t\t\t\t\tavg_attn_scores = attn_scores\n\t\t\t\telse:\n\t\t\t\t\tavg_attn_scores.add_(attn_scores)\n\t\t\telse:\n\t\t\t\ty = results\n\t\t\tx.append(y)\n\t\tx = combine_heads(x)\n\t\tx = self.output_perform(x)\n\t\tif to_weights:\n\t\t\treturn x, avg_attn_scores / self.num_heads\n\t\telse:\n\t\t\treturn x\n\ndef attention_bias_ignore_padding(src_tokens, padding_idx):\n\t\"\"\"Calculate the padding mask based on which embedding are zero\n\tArgs:\n\t\tsrc_tokens: [batch_size, length]\n\tReturns:\n\t\tbias: [batch_size, length]\n\t\"\"\"\n\treturn src_tokens.eq(padding_idx).unsqueeze(1)\n\ndef encoder_attention_bias(bias):\n\tbatch_size, _, length = bias.size()\n\treturn bias.expand(batch_size, length, length).float() * -1e9\n\n\nclass TransformerEncoder(nn.Module):\n\t\"\"\"Transformer encoder.\"\"\"\n\tdef __init__(self, embed_dim=256, max_positions=1024, pos=\"learned\",\n\t\t\t\t num_layers=4, num_heads=8,\n\t\t\t\t filter_size=256, hidden_size=256,\n\t\t\t\t dropout=0.1, attention_dropout=0.1, relu_dropout=0.1, cuda=True):\n\t\tsuper(TransformerEncoder, self).__init__()\n\t\tassert pos == \"learned\" or pos == \"timing\" or pos == \"nopos\"\n\n\t\tself.cuda = cuda\n\n\t\tself.dropout = dropout\n\t\tself.attention_dropout = attention_dropout\n\t\tself.relu_dropout = relu_dropout\n\t\tself.pos = pos\n\n\t\tpadding_idx = 0\n\t\tif self.pos == \"learned\":\n\t\t\tself.embed_positions = PositionalEmbedding(max_positions, embed_dim, padding_idx,\n\t\t\t\t\t\t\t\t\t\t\t\t\t left_pad=False)\n\t\tif self.pos == \"timing\":\n\t\t\tself.embed_positions = SinusoidalPositionalEmbedding(embed_dim, padding_idx,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t left_pad=False)\n\n\t\tself.layers = num_layers\n\n\t\tself.self_attention_blocks = nn.ModuleList()\n\t\tself.ffn_blocks = nn.ModuleList()\n\t\tself.norm1_blocks = nn.ModuleList()\n\t\tself.norm2_blocks = nn.ModuleList()\n\t\tfor i in range(num_layers):\n\t\t\tself.self_attention_blocks.append(MultiheadAttention(hidden_size,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t hidden_size,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t hidden_size,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t num_heads))\n\t\t\tself.ffn_blocks.append(FeedForwardNetwork(hidden_size, filter_size, relu_dropout))\n\t\t\tself.norm1_blocks.append(LayerNormalization(hidden_size))\n\t\t\tself.norm2_blocks.append(LayerNormalization(hidden_size))\n\t\tself.out_norm = LayerNormalization(hidden_size)\n\n\tdef forward(self, encoder_input):\n\t\t# embed tokens plus positions\n\t\tbatch_size, src_time_steps, embed_dim = encoder_input.size()\n\t\tsrc_lengths = [src_time_steps] * batch_size\n\t\tsrc_tokens = encoder_input[:, :, 0]\n\t\tpadding_idx = 0\n\t\tinput_to_padding = attention_bias_ignore_padding(src_tokens, padding_idx)\n\t\tencoder_self_attention_bias = encoder_attention_bias(input_to_padding)\n\t\tif self.pos != \"nopos\":\n\t\t\tif self.cuda:\n\t\t\t\tencoder_input += self.embed_positions(src_tokens.type(torch.cuda.LongTensor))\n\t\t\telse:\n\t\t\t\tencoder_input += self.embed_positions(src_tokens.type(torch.LongTensor))\n\n\t\tx = F.dropout(encoder_input, p=self.dropout, training=self.training)\n\t\tfor self_attention, ffn, norm1, norm2 in zip(self.self_attention_blocks,\n\t\t\t\t\t\t\t\t\t\t\t\t\t self.ffn_blocks,\n\t\t\t\t\t\t\t\t\t\t\t\t\t self.norm1_blocks,\n\t\t\t\t\t\t\t\t\t\t\t\t\t self.norm2_blocks):\n\t\t\ty = self_attention(norm1(x), None, encoder_self_attention_bias)\n\t\t\tx = residual(x, y, self.dropout, self.training)\n\t\t\ty = ffn(norm2(x))\n\t\t\tx = residual(x, y, self.dropout, self.training)\n\t\tx = self.out_norm(x)\n\t\treturn x\n\n\tdef max_positions(self):\n\t\t\"\"\"Maximum input length supported by the encoder.\"\"\"\n\t\tif self.pos == \"learned\":\n\t\t\treturn self.embed_positions.max_positions()\n\t\telse:\n\t\t\treturn 1024\n\t\t \n\nclass FunTimesCNN(nn.Module):\n\n\tdef __init__(self, MLP_before_arch, MLP_after_arch, CNN_arch, use_sinc_net):\n\t\tsuper(FunTimes, self).__init__()\n\t\tif MLP_before_arch != None:\n\t\t\tself.embed_dim_projection = MLP_for_me(MLP_before_arch)\n\t\telse:\n\t\t\tself.embed_dim_projection = None\n\n\t\tif use_sinc_net:\n\t\t\tself.CNN_net = SincNet(CNN_arch)\n\t\telse:\n\t\t\tself.CNN_net = ConvNet(CNN_arch)\n\t\tself.result_projection = MLP_for_me(MLP_after_arch)\n\t\t\n\tdef forward(self, x):\n\t\tif self.embed_dim_projection:\n\t\t\tx = self.embed_dim_projection(x)\n\t\treturn self.result_projection(self.CNN_net(x))\n\nclass FunTimesLSTM(nn.Module):\n\n\tdef __init__(self, MLP_before_arch, MLP_after_arch, lstm_embed_dim, lstm_hidden_size, lstm_num_layers, lstm_bidirectional, lstm_dropout_in, lstm_dropout_out, raw=False):\n\t\tsuper(FunTimesLSTM, self).__init__()\n\n\t\tif MLP_before_arch != None:\n\t\t\tself.embed_dim_projection = MLP_for_me(MLP_before_arch)\n\t\telse:\n\t\t\tself.embed_dim_projection = None\n\t\tself.LSTM = LSTM(lstm_embed_dim, lstm_hidden_size, lstm_num_layers, lstm_bidirectional, lstm_dropout_in, lstm_dropout_out)\n\t\tself.result_projection = MLP_for_me(MLP_after_arch)\n\t\tself.raw = raw\n\t\t\n\tdef forward(self, x):\n\t\tif self.raw:\n\t\t\tx = x.unsqueeze(-1)\n\t\tif self.embed_dim_projection:\n\t\t\tx = self.embed_dim_projection(x)\n\t\tx = self.LSTM(x)\n\t\tx = self.result_projection(x)\n\t\treturn x.squeeze(-1)\n\n\nclass FunTimesTransformer(nn.Module):\n\n\tdef __init__(self, MLP_before_arch, MLP_after_arch, tr_embed_dim, tr_max_positions, tr_pos, tr_num_layers,\n\t\ttr_num_heads, tr_filter_size, tr_hidden_size, tr_dropout, \n\t\ttr_attention_dropout, tr_relu_dropout, cuda):\n\n\t\tsuper(FunTimesTransformer, self).__init__()\n\n\t\tif MLP_before_arch != None:\n\t\t\tself.embed_dim_projection = MLP_for_me(MLP_before_arch)\n\t\telse:\n\t\t\tself.embed_dim_projection = None\n\n\t\tself.transformer = TransformerEncoder(\n\t\t\ttr_embed_dim, tr_max_positions, tr_pos, tr_num_layers,\n\t\t\ttr_num_heads, tr_filter_size, tr_hidden_size, tr_dropout, \n\t\t\ttr_attention_dropout, tr_relu_dropout, cuda)\n\n\t\tself.result_projection = MLP_for_me(MLP_after_arch)\n\t\t\n\tdef forward(self, x):\n\t\tif self.embed_dim_projection:\n\t\t\tx = self.embed_dim_projection(x)\n\t\tx = self.transformer(x)\n\t\tx = self.result_projection(x)\n\t\treturn x.squeeze(-1)\n\n\nclass YeetZ_MLP(nn.Module):\n\tdef __init__(self):\n\t\tsuper(YeetZ_MLP, self).__init__()\n\t\tself.layers = nn.Sequential(\n\t\t\tnn.Linear(60, 10),\n\t\t\tnn.ReLU(),\n\t\t\tnn.Linear(10, 1),\n\t\t\tnn.Softplus()\n\t\t)\n\t\t\n\tdef forward(self, x):\n\t\tx = self.layers(x)\n\t\treturn x.squeeze(-1)\n\nclass EZConv(nn.Module):\n\tdef __init__(self):\n\t\tsuper(EZConv, self).__init__()\n\t\tself.conv = nn.Conv1d(8, 80, 25)\n\t\tself.conv2 = nn.Conv1d(80, 60, 5)\n\t\tself.conv3 = nn.Conv1d(60, 60, 5)\n\t\tself.act = nn.LeakyReLU(0.2)\n\t\tself.mlp = YeetZ_MLP()\n\n\tdef forward(self, x):\n\t\tx = x.transpose(1, 2)\n\t\tx = self.conv(x)\n\t\tx = F.max_pool1d(x, 3)\n\t\tx = self.act(x)\n\n\t\tx = self.conv2(x)\n\t\tx = F.max_pool1d(x, 3)\n\t\tx = self.act(x)\n\n\t\tx = self.conv3(x)\n\t\tx = F.max_pool1d(x, 3)\n\t\tx = self.act(x)\n\n\t\tx = x.transpose(1, 2)\n\t\tx = self.mlp(x)\n\t\treturn x", "sub_path": "dnn_models.py", "file_name": "dnn_models.py", "file_ext": "py", "file_size_in_byte": 34799, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.arange", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.sin", "line_number": 20, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "numpy.log10", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 100, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.linspace", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.cos", "line_number": 108, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 108, "usage_type": "attribute"}, {"api_name": "math.pi", "line_number": 113, "usage_type": "attribute"}, {"api_name": "torch.arange", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.clamp", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.matmul", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.matmul", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.sin", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.flip", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.nn.functional.conv1d", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 156, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 163, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 163, "usage_type": "name"}, {"api_name": "numpy.log10", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 179, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 180, "usage_type": "name"}, {"api_name": "torch.from_numpy", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 190, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 190, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.linspace", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 198, "usage_type": "call"}, {"api_name": "torch.abs", "line_number": 199, "usage_type": "call"}, {"api_name": "torch.linspace", "line_number": 201, "usage_type": "call"}, {"api_name": "torch.cos", "line_number": 204, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 204, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 205, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 214, "usage_type": "call"}, {"api_name": "torch.nn.functional.conv1d", "line_number": 218, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 218, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 225, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 228, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 228, "usage_type": "name"}, {"api_name": "torch.nn.Tanh", "line_number": 231, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 231, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 234, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 234, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 237, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 237, "usage_type": "name"}, {"api_name": "torch.nn.ELU", "line_number": 240, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 240, "usage_type": "name"}, {"api_name": "torch.nn.LogSoftmax", "line_number": 243, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 243, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 246, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 246, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 249, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 249, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 252, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 252, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 252, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 253, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 253, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 253, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 262, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 262, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 276, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 276, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 277, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 277, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 278, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 278, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 279, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 279, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 280, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 280, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 290, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 290, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 301, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 301, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 311, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 311, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 318, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 318, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 321, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 321, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 321, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 321, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 322, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 322, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 322, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 363, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 363, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 377, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 377, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 378, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 378, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 379, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 379, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 380, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 380, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 381, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 381, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 391, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 391, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 402, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 402, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 411, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 411, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 418, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 418, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 421, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 421, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 421, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 421, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 422, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 422, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 422, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 460, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 460, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 483, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 483, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 484, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 484, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 485, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 485, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 486, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 486, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 487, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 487, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 494, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 494, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 504, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 504, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 512, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 512, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 519, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 519, "usage_type": "name"}, {"api_name": "torch.nn.functional.max_pool1d", "line_number": 545, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 545, "usage_type": "name"}, {"api_name": "torch.abs", "line_number": 545, "usage_type": "call"}, {"api_name": "torch.nn.functional.max_pool1d", "line_number": 547, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 547, "usage_type": "name"}, {"api_name": "torch.nn.functional.max_pool1d", "line_number": 550, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 550, "usage_type": "name"}, {"api_name": "torch.nn.functional.max_pool1d", "line_number": 553, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 553, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 560, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 560, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 583, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 583, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 584, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 584, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 585, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 585, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 586, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 586, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 587, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 587, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 594, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 594, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 604, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 604, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 612, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 612, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 616, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 616, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 619, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 619, "usage_type": "name"}, {"api_name": "torch.nn.functional.max_pool1d", "line_number": 644, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 644, "usage_type": "name"}, {"api_name": "torch.nn.functional.max_pool1d", "line_number": 647, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 647, "usage_type": "name"}, {"api_name": "torch.nn.functional.max_pool1d", "line_number": 650, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 650, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 659, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 659, "usage_type": "name"}, {"api_name": "torch.nn.LSTM", "line_number": 678, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 678, "usage_type": "name"}, {"api_name": "torch.nn.functional.dropout", "line_number": 689, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 689, "usage_type": "name"}, {"api_name": "torch.nn.utils.rnn.pack_padded_sequence", "line_number": 695, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 695, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 695, "usage_type": "name"}, {"api_name": "torch.nn.utils.rnn.pad_packed_sequence", "line_number": 701, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 701, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 701, "usage_type": "name"}, {"api_name": "torch.nn.functional.dropout", "line_number": 702, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 702, "usage_type": "name"}, {"api_name": "torch.arange", "line_number": 722, "usage_type": "call"}, {"api_name": "torch.nn.Embedding", "line_number": 729, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 729, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 746, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 753, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 753, "usage_type": "name"}, {"api_name": "math.log", "line_number": 780, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 781, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 781, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 782, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 783, "usage_type": "call"}, {"api_name": "torch.sin", "line_number": 783, "usage_type": "call"}, {"api_name": "torch.cos", "line_number": 783, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 786, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 786, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 802, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 808, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 816, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 816, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 825, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 825, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 825, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 826, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 826, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 826, "usage_type": "call"}, {"api_name": "torch.nn.functional.dropout", "line_number": 841, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 841, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 846, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 846, "usage_type": "name"}, {"api_name": "torch.chunk", "line_number": 863, "usage_type": "call"}, {"api_name": "torch.squeeze", "line_number": 866, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 876, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 890, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 894, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 894, "usage_type": "name"}, {"api_name": "torch.bmm", "line_number": 897, "usage_type": "call"}, {"api_name": "torch.bmm", "line_number": 899, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 902, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 902, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 910, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 910, "usage_type": "name"}, {"api_name": "torch.nn.functional.dropout", "line_number": 911, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 911, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 916, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 916, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 982, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 982, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 1008, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 1008, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 1009, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 1009, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 1010, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 1010, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 1011, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 1011, "usage_type": "name"}, {"api_name": "torch.cuda", "line_number": 1032, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 1034, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.dropout", "line_number": 1036, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 1036, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 1056, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 1056, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 1076, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 1076, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 1099, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 1099, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 1127, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 1127, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 1130, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 1130, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 1131, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 1131, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 1132, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 1132, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 1133, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 1133, "usage_type": "name"}, {"api_name": "torch.nn.Softplus", "line_number": 1134, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 1134, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 1141, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 1141, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 1144, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 1144, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 1145, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 1145, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 1146, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 1146, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 1147, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 1147, "usage_type": "name"}, {"api_name": "torch.nn.functional.max_pool1d", "line_number": 1153, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 1153, "usage_type": "name"}, {"api_name": "torch.nn.functional.max_pool1d", "line_number": 1157, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 1157, "usage_type": "name"}, {"api_name": "torch.nn.functional.max_pool1d", "line_number": 1161, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 1161, "usage_type": "name"}]} +{"seq_id": "448604534", "text": "import sys\nimport pandas as pd\nimport sklearn.model_selection\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.svm import SVC\nfrom warnings import filterwarnings\nfilterwarnings('ignore')\n\n# print version\nprint('python: {}'.format(sys.version))\nprint('sklearn: {}'.format(sklearn.__version__))\nprint('pandas: {}'.format(pd.__version__))\n\n\ndef species_to_int(species: str) -> int:\n if species == \"versicolor\": return 0\n elif species == \"setosa\": return 1\n elif species == \"virginica\": return 2\n\n\ndef species_to_string(species: int) -> str:\n if species == 0: return \"versicolor\"\n elif species == 1: return \"setosa\"\n elif species == 2: return \"virginica\"\n\n\nsum_custom = 0\nlist_custom = []\nsum_SVM = 0\nlist_SVM = []\nsum_KNN = 0\nlist_KNN = []\n# loopsize = 30\n\ndata: pd.DataFrame = pd.read_csv(\"modified_irisData.csv\", names = [\"sepal length\", \"sepal width\", \"petal length\", \"petal width\", \"species\"])\ndata[\"species\"]: pd.Series = data[\"species\"].apply(species_to_int)\n\n# split into train and test\ntrain: pd.DataFrame\ntest: pd.DataFrame\ntrain, test = train_test_split(data, test_size = 0.1) # 1-0.x training, 0.x testing\n\n# training info\nX: pd.DataFrame = train.iloc[:, 0:-1]\ny: pd.Series = train.iloc[:, -1]\ny = pd.to_numeric(y)\n\n# test info\ny_test: pd.Series = pd.to_numeric(test.iloc[:, -1])\n\n# create custom model\nmodel = MLPClassifier(activation='tanh', solver='adam', hidden_layer_sizes=(15, 15), max_iter = 200)\nmodel.fit(X, y)\npredictions_custom = model.predict(test.iloc[:, :-1]) # predict\n\n# print custom model results\nprint('\\nCUSTOM MODEL PREDICTION RESULTS:')\ncomparison = pd.DataFrame({'test_data': y_test, 'prediction': predictions_custom})\ncomparison['test_data'] = comparison['test_data'].apply(species_to_string)\ncomparison['prediction'] = comparison['prediction'].apply(species_to_string)\ncomparison['correct'] = comparison['test_data'] == comparison['prediction']\nprint(comparison.to_string())\nacc = accuracy_score(y_test, predictions_custom)\nprint('\\nCUSTOM ACCURACY SCORE: ', acc)\nlist_custom.append(acc)\nsum_custom += acc\n\n# apply SVC(SVM model)\nclf = SVC()\nclf.fit(X, y) # train\npredictions_SVC = clf.predict(test.iloc[:, :-1]) # predict\n\n# print SVC results\nprint('\\nSVC PREDICTION RESULTS:')\ncomparison = pd.DataFrame({'test_data': y_test, 'prediction': predictions_SVC})\ncomparison['test_data'] = comparison['test_data'].apply(species_to_string)\ncomparison['prediction'] = comparison['prediction'].apply(species_to_string)\ncomparison['correct'] = comparison['test_data'] == comparison['prediction']\nprint(comparison.to_string())\nacc = accuracy_score(y_test, predictions_SVC)\nprint('\\nSVC ACCURACY SCORE: ', acc)\nlist_SVM.append(acc)\nsum_SVM += acc\n\n# apply KNN\nKNN = KNeighborsClassifier()\nKNN.fit(X, y) # train\npredictions_KNN = KNN.predict(test.iloc[:, :-1]) # predict\n\n# print KNN results\nprint('\\nKNN PREDICTION RESULTS:')\ncomparison = pd.DataFrame({'test_data': y_test, 'prediction': predictions_KNN})\ncomparison['test_data'] = comparison['test_data'].apply(species_to_string)\ncomparison['prediction'] = comparison['prediction'].apply(species_to_string)\ncomparison['correct'] = comparison['test_data'] == comparison['prediction']\nprint(comparison.to_string())\nacc = accuracy_score(y_test, predictions_KNN)\nprint('\\nKNN ACCURACY SCORE: ', acc)\nlist_KNN.append(acc)\nsum_KNN += acc\n\n# compare custom to others\n# for i in range(loopsize):\n# print('{:4.3f}\\t{:4.3f}\\t{:4.3f}'.format(list_custom[i], list_SVM[i], list_KNN[i]))\n# print('{:4.3f}\\t{:4.3f}\\t{:4.3f}'.format(sum_custom/loopsize, sum_SVM/loopsize, sum_KNN/loopsize))", "sub_path": "IrisClassifier.py", "file_name": "IrisClassifier.py", "file_ext": "py", "file_size_in_byte": 3728, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "warnings.filterwarnings", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.version", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.__version__", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection", "line_number": 14, "usage_type": "name"}, {"api_name": "pandas.__version__", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 38, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 39, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 43, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 44, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 47, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pandas.to_numeric", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pandas.to_numeric", "line_number": 52, "usage_type": "call"}, {"api_name": "sklearn.neural_network.MLPClassifier", "line_number": 55, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 61, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 66, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 72, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 78, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 83, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 89, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 95, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 100, "usage_type": "call"}]} +{"seq_id": "615479637", "text": "from django.core.management.base import BaseCommand, CommandError\nfrom django.contrib.contenttypes.models import ContentType\n\n\nclass Command(BaseCommand):\n\n def handle(self, *args, **options):\n all_objects = []\n for ct in ContentType.objects.all():\n model = ct.model_class()\n all_objects.append(\"%s(%d)\" % (model.__name__,\n model._default_manager.count()))\n\n self.stdout.write(str(all_objects))\n self.stderr.write('error:' + str(all_objects))\n", "sub_path": "django_hello_world/hello/management/commands/all_models.py", "file_name": "all_models.py", "file_ext": "py", "file_size_in_byte": 526, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.core.management.base.BaseCommand", "line_number": 5, "usage_type": "name"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects.all", "line_number": 9, "usage_type": "call"}, {"api_name": "django.contrib.contenttypes.models.ContentType.objects", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.contrib.contenttypes.models.ContentType", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "127414054", "text": "#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n \nimport tkinter as tk\nimport wikipedia as wiki\n\n \nclass TestUI(object):\n def __init__(self, master):\n self.root = master\n self.create_frame()\n\n def create_frame(self):\n '''\n create frame,left and right\n '''\n self.frm_left = tk.LabelFrame(self.root)\n self.frm_right = tk.LabelFrame(self.root)\n \n self.frm_left.grid(row=0, column=0, sticky=\"wesn\")\n self.frm_right.grid(row=0, column=5, sticky=\"wesn\")\n \n self.create_frm_left()\n #self.create_frm_right()\n \n def create_frm_left(self):\n self.search_btn = tk.Button(self.frm_left, text=\"搜索\", command=self.showButton)\n self.search_Entry = tk.Entry(self.frm_left)\n\n self.brief_lable = tk.Label(self.frm_left, text=\"简介\")\n self.brief_listbox = tk.Text(self.frm_left, height=30, width=30)\n\n self.search_words_lable = tk.Label(self.frm_left, text=\"相关搜索词\")\n self.search_words_listbox = tk.Text(self.frm_left, height=30, width=30)\n\n self.link_lable = tk.Label(self.frm_left, text=\"介绍链接\")\n self.link_listbox = tk.Text(self.frm_left, height=30, width=30)\n\n self.recommend_lable = tk.Label(self.frm_left, text=\"相关推荐\")\n self.recommend_listbox = tk.Text(self.frm_left, height=30, width=30)\n\n\n self.search_btn.grid(row=0, column=2, padx=5, pady=5, sticky=\"wesn\")\n self.search_Entry.grid(row=0, column=1, padx=5, pady=5, sticky=\"wesn\")\n\n self.brief_lable.grid(row=1, column=0, padx=5, pady=5, sticky=\"w\")\n self.brief_listbox.grid(row=2, column=0, padx=5, pady=5, sticky=\"wesn\")\n\n self.search_words_lable.grid(row=1, column=1, padx=5, pady=5, sticky=\"w\")\n self.search_words_listbox.grid(row=2, column=1, padx=5, pady=5, sticky=\"wesn\")\n\n self.link_lable.grid(row=1, column=2, padx=5, pady=5, sticky=\"w\")\n self.link_listbox.grid(row=2, column=2, padx=5, pady=5, sticky=\"wesn\")\n\n self.recommend_lable.grid(row=1, column=3, padx=5, pady=5, sticky=\"w\")\n self.recommend_listbox.grid(row=2, column=3, padx=5, pady=5, sticky=\"wesn\")\n\n\n\n\n def showButton(self):\n self.brief_listbox.insert(\"end\", wiki.summary(self.search_Entry.get()))\n self.search_words_listbox.insert(\"end\", wiki.search(self.search_Entry.get()))\n self.link_listbox.insert(\"end\", wiki.page(self.search_Entry.get()).url)\n self.recommend_listbox.insert(\"end\", wiki.page(self.search_Entry.get()).links)\n\n \n def create_frm_right(self):\n self.frm_right_canvas = tk.Canvas(self.frm_right, bg=\"white\")\n \n self.frm_right_canvas.grid(row=0, column=0, padx=5, pady=5, sticky=\"wesn\")\n\n\n\nif __name__ == '__main__':\n '''\n main loop\n '''\n root = tk.Tk()\n root.title(\"知识图谱作业\")\n\n TestUI(master=root)\n\n root.resizable(False, False)\n root.mainloop()\n\n\n", "sub_path": "knowledge_engineering.py", "file_name": "knowledge_engineering.py", "file_ext": "py", "file_size_in_byte": 2911, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "tkinter.LabelFrame", "line_number": 17, "usage_type": "call"}, {"api_name": "tkinter.LabelFrame", "line_number": 18, "usage_type": "call"}, {"api_name": "tkinter.Button", "line_number": 27, "usage_type": "call"}, {"api_name": "tkinter.Entry", "line_number": 28, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 30, "usage_type": "call"}, {"api_name": "tkinter.Text", "line_number": 31, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 33, "usage_type": "call"}, {"api_name": "tkinter.Text", "line_number": 34, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 36, "usage_type": "call"}, {"api_name": "tkinter.Text", "line_number": 37, "usage_type": "call"}, {"api_name": "tkinter.Label", "line_number": 39, "usage_type": "call"}, {"api_name": "tkinter.Text", "line_number": 40, "usage_type": "call"}, {"api_name": "wikipedia.summary", "line_number": 62, "usage_type": "call"}, {"api_name": "wikipedia.search", "line_number": 63, "usage_type": "call"}, {"api_name": "wikipedia.page", "line_number": 64, "usage_type": "call"}, {"api_name": "wikipedia.page", "line_number": 65, "usage_type": "call"}, {"api_name": "tkinter.Canvas", "line_number": 69, "usage_type": "call"}, {"api_name": "tkinter.Tk", "line_number": 79, "usage_type": "call"}]} +{"seq_id": "632467984", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Dec 18 13:20:12 2018\r\n\r\n@author: kasy\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport os\r\nimport imageio\r\nfrom skimage import transform, color, measure\r\nfrom tqdm import tqdm\r\nimport glob\r\nimport pickle\r\n\r\ndir_path = './data/prepare/'\r\nlabel_path = './data/prepare/label/'\r\ntmp_path = './data/tmp/'\r\nfig_path_list = glob.glob(dir_path+'*.jpg')\r\nlabel_count = 6\r\nif not os.path.exists(label_path):\r\n os.makedirs(label_path)\r\nif not os.path.exists(tmp_path):\r\n os.makedirs(tmp_path) \r\n#\r\n#for fig_name in fig_path_list[2:]:\r\n# fig = imageio.imread(fig_name)#[2000:3000, 2000:3000, :]\r\n# h, w, _ = fig.shape\r\n# \r\n# green_fig = np.zeros_like(fig[..., 0])\r\n# for i in tqdm(range(h)):\r\n# for j in range(w):\r\n# pixel = fig[i, j, :]\r\n# green_fig[i, j] = (int(pixel[1]) - int(pixel[2])) > 40\r\n# # green 40, blue 50 , 90(liquid)\r\n# green_fig *= 1\r\n# #imageio.imsave('./data/3.jpg', fig)\r\n# imageio.imsave(label_path+os.path.basename(fig_name), 255*green_fig) \r\n\r\nlabel_path_list = glob.glob(label_path+'*.jpg')\r\n\r\nfor label_name in label_path_list:\r\n fig_label = imageio.imread(label_name)\r\n\r\n contours = measure.find_contours(fig_label, 0.5)\r\n \r\n #fig, (ax0,ax1) = plt.subplots(1,2,figsize=(20,20))\r\n #ax0.imshow(fig_label,plt.cm.gray)\r\n #ax1.imshow(fig_label,plt.cm.gray)\r\n count = 0\r\n container_point_list = []\r\n task = 'label'\r\n cell_area = 175\r\n predict_area = 512\r\n for contour in contours:\r\n h_min = np.min(contour, axis=0)[0]\r\n h_max = np.max(contour, axis=0)[0]\r\n \r\n col_list = contour[:, 1]\r\n w_min = np.min(col_list)\r\n w_max = np.max(col_list)\r\n # cell 180, predict 512\r\n if task == 'cell':\r\n if ((h_max-h_min)*(w_max-w_min)) > cell_area : \r\n count += 1\r\n # container_point_list.append([int((h_min+h_max)/2), int((w_min+w_max)/2)])\r\n if ((h_max-h_min)*(w_max-w_min)) > cell_area*2 and ((h_max-h_min)*(w_max-w_min)) predict_area : \r\n count += 1 \r\n container_point_list.append([int((h_min+h_max)/2), int((w_min+w_max)/2)]) \r\n point_list = container_point_list\r\n count = 0\r\n fig_data = imageio.imread(dir_path+os.path.basename(label_name))\r\n for i in point_list:\r\n #print('1')\r\n h, w = int(i[0]), int(i[1])\r\n crop_fig = fig_data[(h-16):(h+16), (w-16):(w+16), :]\r\n crop_fig = transform.resize(crop_fig, [64, 64])\r\n imageio.imsave(tmp_path+'{0}_{1}.jpg'.format(label_count,count), crop_fig)\r\n count += 1 \r\n label_count += 1\r\n\r\n ", "sub_path": "build_tmp_figs.py", "file_name": "build_tmp_figs.py", "file_ext": "py", "file_size_in_byte": 2840, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "glob.glob", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 24, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 40, "usage_type": "call"}, {"api_name": "imageio.imread", "line_number": 43, "usage_type": "call"}, {"api_name": "skimage.measure.find_contours", "line_number": 45, "usage_type": "call"}, {"api_name": "skimage.measure", "line_number": 45, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 61, "usage_type": "call"}, {"api_name": "imageio.imread", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "skimage.transform.resize", "line_number": 80, "usage_type": "call"}, {"api_name": "skimage.transform", "line_number": 80, "usage_type": "name"}, {"api_name": "imageio.imsave", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "448974411", "text": "#Spambase Dataset\nimport numpy as np\nimport csv\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix\n\n#Global data declaration\nfileLocation = \"spambase/spambase.data\"\ndataArr, train, test = [],[],[]\nfeaturesTrain, labelsTrain, featuresTest, labelsTest = [],[],[],[]\nprior0=prior1=0\nmean0, mean1, std0, std1, prob0, prob1, finalprob = [], [], [], [], [], [], []\ns, t = 0,0\n\n#Read from file - spambase.data\ndef readFile():\n with open(fileLocation) as file:\n reader = csv.reader(file)\n for data in reader:\n dataArr.append(data)\n file.close()\n np.random.shuffle(dataArr)\n\n#Spliting the dataset in two parts of : Spam and not spam\ndef Split_SpamNotSpam():\n global train, test, featuresTrain, labelsTrain, featuresTest, labelsTest\n\n Spam, NotSpam = [], []\n for x in dataArr:\n if x[-1] == '1':\n Spam.append(x)\n else:\n NotSpam.append(x)\n\n NotSpamHalf, SpamHalf = np.array_split(NotSpam,2), np.array_split(Spam,2)\n train = np.concatenate((NotSpamHalf[0], SpamHalf[0]),axis=0)\n test = np.concatenate((NotSpamHalf[1], SpamHalf[1]),axis=0)\n\n#Shuffling the training set and testing set\ndef shuffle():\n global featuresTrain, labelsTrain, featuresTest, labelsTest\n for i in [train, test]:\n np.random.shuffle(i)\n\n #Currently, train and test are shuffled\n featuresTrain = np.array([x[:-1] for x in train]).astype(np.float)\n labelsTrain = np.array([x[-1] for x in train]).astype(np.float)\n featuresTest = np.array([x[:-1] for x in test]).astype(np.float)\n labelsTest = np.array([x[-1] for x in test]).astype(np.float)\n\n# Find Prior Probability\ndef computeProbability():\n global prior0, prior1\n prior0 = (np.count_nonzero(labelsTrain == 0.0)) / len(labelsTrain)\n prior1 = (np.count_nonzero(labelsTrain == 1.0)) / len(labelsTrain)\n print(prior0)\n print(prior1)\n\n#Checking if standard deviation is positive or not.\n# If not, put the standard deviation as 0.0001\ndef putStd(a):\n return a if (a > 0) else 0.0001\n\n# Compute the mean and standard deviation for 57 features for class0 and class1\ndef TrainNaiveBayes():\n for col in featuresTrain.T:\n NotSpam, Spam = [], []\n for row in range(len(featuresTrain)):\n if(labelsTrain[row]==0):\n NotSpam.append(col[row])\n else:\n Spam.append(col[row])\n\n mean0.append(np.mean(NotSpam))\n mean1.append(np.mean(Spam))\n std0.append(putStd(np.std(NotSpam)))\n std1.append(putStd(np.std(Spam)))\n\n#Writing a gauss function for testing\ndef gaussFunction(x, mean, std):\n return (1 / (np.sqrt(2*np.pi) * std)) * np.exp((-1) * (((x-mean)**2) / (2*(std**2))))\n\n\n#3. Use the Gaussian Naïve Bayes algorithm to classify the instances in your test set, using the Gauss function\ndef TestNaiveBayes():\n vectorGauss = np.vectorize(gaussFunction)\n for i in featuresTest:\n prob0.append(vectorGauss(i, mean0, std0))\n prob1.append(vectorGauss(i, mean1, std1))\n\n #Finding the probability of class0 and class1 for all features\n for rowSpam, rowNonSpam in zip(prob0, prob1):\n class0 = np.log(prior0) + np.sum(np.log(rowSpam))\n class1 = np.log(prior1) + np.sum(np.log(rowNonSpam))\n\n #Finding argmax\n finalprob.append(float(np.argmax([class0, class1])))\n\n#Printing accuracy, precision, recall and confusion matrix\ndef printScores():\n print(\"Accuracy Score: \", accuracy_score(labelsTest, finalprob))\n print(\"Precision Score: \", precision_score(labelsTest, finalprob))\n print(\"Recall: \", recall_score(labelsTest, finalprob))\n print(\"Confusion Matrix: \\n\", confusion_matrix(labelsTest, finalprob))\n\n\n#Main function\ndef main():\n readFile()\n Split_SpamNotSpam()\n shuffle()\n computeProbability()\n TrainNaiveBayes()\n TestNaiveBayes()\n printScores()\n\n#Calling main\nif __name__ == \"__main__\":\n main()\n", "sub_path": "SpamNaiveBayes.py", "file_name": "SpamNaiveBayes.py", "file_ext": "py", "file_size_in_byte": 3942, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "csv.reader", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.array_split", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.count_nonzero", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.count_nonzero", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.vectorize", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 96, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 100, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 101, "usage_type": "call"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 102, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 103, "usage_type": "call"}]} +{"seq_id": "421310388", "text": "import numpy as np\nfrom os import listdir\nimport operator\nimport cv2\nfrom PIL import Image\nimport math\nimport matplotlib.pyplot as plt\n\n# 开始程序\n# input： none\n# output yourNumber：最后得到的结果\ndef loadproject():\n while(1):\n print(\"是否开始识别（Y/N）：\")\n initial = input()\n if(initial == 'N'):\n break\n else:\n #getOnePicture()\n #imageToGray()\n #exponentiation()\n # grayToSmaller()\n # canny()\n imgToCanny()\n #binaryToStandard()\n #grayToBinary()\n\n # trainData, labelVec, testData, testLabelVec = dataSetClassfication()\n # precisionRateTest(trainData, labelVec, testData, testLabelVec)\n # classfication(testData, trainData, labelVec, num = 25)\n\n# 从视频流中截取一帧图片\n# input：视频流\n# output：yourImage 需要处理的图片\ndef getOnePicture():\n cameraCapture = cv2.VideoCapture(0) # 不知道摄像头设备索引\n fps = 30 #不知道帧率\n size = (int(cameraCapture.get(cv2.CAP_PROP_FRAME_WIDTH)),\n int(cameraCapture.get(cv2.CAP_PROP_FRAME_HEIGHT)))\n videoWriter = cv2.VideoWriter(\n 'C:/Users/18139/Desktop/getcamera/first.avi', cv2.VideoWriter_fourcc('I', '4', '2', '0'),\n fps, size\n ) # 定义文件放置位置 cv2.VideoWriter_fourcc('I', '4', '2', '0')这里的编码格式也要确定\n success, frame = cameraCapture.read() # 判断是否取得了有效值 frame应该是某一帧\n numFramesRemaining = 10\n while success and numFramesRemaining > 0:\n cv2.imwrite('C:/Users/18139/Desktop/getcamera/initPicture.jpg', frame)\n videoWriter.write(frame) # 将读取到的帧写入视频\n success, frame = cameraCapture.read()\n numFramesRemaining -= 1\n cameraCapture.release()\n img1 = cv2.imread('C:/Users/18139/Desktop/getcamera/initPicture.jpg') # 需要改图片\n cv2.namedWindow(\"Image\") # 可加可不加，加上的话一般和imshow之间会定义一些窗口事件处理用函数\n cv2.imshow('Image', img1) # 显示图片\n if cv2.waitKey(1000) == 27 :\n cv2.destroyAllWindows() # 释放所有窗口\n\n\n# 数字图像处理1 转为灰度图\n# input：yourImage 从视频流中截取的图片\n# output：txt 处理后的灰度化图片\ndef imageToGray():\n img2 = Image.open('C:/Users/18139/Desktop/getcamera/initPicture.jpg').convert(\"L\")\n img2.save('C:/Users/18139/Desktop/getcamera/grayPicture.bmp')\n img2.show()\n img_array = np.array(img2)\n w, h = img_array.shape\n print(w,h)\n fp = open('C:/Users/18139/Desktop/getcamera/array.txt', 'w')\n for i in img_array:\n fp.write(str(i))\n fp.close()\n\n# 数字图像处理2 利用插值法降低像素 暂时用后面的替代\n# input: 灰度化图片\n# output：降低后像素的图片\ndef grayToSmaller():\n image2 = Image.open('C:/Users/18139/Desktop/getcamera/grayPicture.bmp')\n img_array = np.array(image2)\n a = img_array.shape\n img_array2 = cv2.resize(img_array, (int(a[1] / 1.5), int(a[0] / 1.5)), interpolation=cv2.INTER_AREA) # 可更改数据调整\n image3 = Image.fromarray(img_array2)\n image3.save('C:/Users/18139/Desktop/getcamera/picture3.bmp')\n\n\n# 数字图像处理3 利用canny算子实现边缘检测 高斯滤波 计算梯度值与方向 非极大值抑制（NMS） 双阀值选取（这样精度更高）边缘连接 **************\n# input: 降低像素后的图片\n# output：边缘检测后的图片\ndef imgToCanny():\n image = Image.open('C:/Users/18139/Desktop/getcamera/grayPicture.bmp')\n img_array = np.array(image)\n c = 1\n r = 2\n a = c * ((img_array / 255) ** r * 255)\n # image2 = Image.fromarray(a.astype(np.int))\n # image = Image.open('C:/Users/18139/Desktop/getcamera/grayPicture.bmp')\n # img_array = np.array(image)\n img_array2 = cv2.Canny(a, 50, 150)\n image2 = Image.fromarray(img_array2)\n image2.save('C:/Users/18139/Desktop/getcamera/cannyPicture.bmp')\n image2.show()\n\n# 数字图像处理4 A4纸矫正 **********\n# input：顶点提取后的图片\n# output：矫正后的图片Y\n\n# 数字图像处理5 插值法将图像化为标准大小 (需要注意的是，插值法会将二值化图片变成非二值化，要在二值化之前进行）\n# input：单个图片\n# output：标准图并储存\ndef binaryToStandard():\n image4 = Image.open('C:/Users/18139/Desktop/getcamera/grayPicture.bmp')\n img_array = np.array(image4)\n a = img_array.shape\n b = a[0]/32\n c = a[1]/32\n img_array2 = cv2.resize(img_array, (int(a[1] / c), int(a[0] / b)), interpolation=cv2.INTER_AREA) # 可更改数据调整\n fp = open('C:/Users/18139/Desktop/getcamera/array2.txt', 'w')\n for i in img_array2:\n fp.write(str(i))\n fp.close()\n image5 = Image.fromarray(img_array2)\n image5.save('C:/Users/18139/Desktop/getcamera/standSizePicture.bmp')\n\n# 数字图像处理6 图像二值化 全阈值（这个简单）\n# input：提取后的图像\n# output； 二值化图片\ndef grayToBinary():\n image3 = Image.open('C:/Users/18139/Desktop/getcamera/standSizePicture.bmp')\n img_array = np.array(image3)\n img_array2 = img_array\n for i in range(img_array.shape[0]-1):\n for j in range(img_array.shape[1]-1):\n if (img_array[i][j] >= 100):\n img_array2[i][j] = 255\n else:\n img_array2[i][j] = 0\n fp = open('C:/Users/18139/Desktop/getcamera/array3.txt', 'w')\n for i in img_array2:\n fp.write(str(i))\n fp.close()\n image4 = Image.fromarray(img_array2)\n image4.save('C:/Users/18139/Desktop/getcamera/binaryPicture.bmp')\n\n# 数字图像处理7 垂直方向分割后水平方向分割（统计方面） ***\n# input：二值化的灰度图\n# output：分割后的子图（前期处理会使数字断裂）\n\n# 数字图像处理8 子图进行断裂字符修复（滤波器原理） ***\n# input：分割后子图\n# output：修复后的子图\n\n# 数字图像处理9 连通域标记法从左到右分割数字 ***\n# input：切割后无断点的子图\n# output：多个数字切割后的框图\n\n# 数字图像处理10 切割后的每个数字，分离并进行储存为所需格式 ***\n# input：切割后带框图的数字\n# output：单个带标签（不是数字标签，是位置标签）的表\n\n\n# 图像数据矩阵变换为向量\n# input：imageFileName 处理后二值化的图片； height 图片高度； weight 图片宽度\n# output：imageVec 转化后的行向量\ndef Mat2Vec(imageFileName, height, weight):\n imageVec = np.zeros((1, height*weight))\n fileread = open(imageFileName)\n for i in range(height):\n linestr = fileread.readline()\n for j in range(weight):\n imageVec[0, 32*i+j] = int(linestr(j))\n return imageVec\n\n# 数据可视化 *****************\n# input：trainData 用于训练的数据 ；testData 用于测试的数据； labelVec 数据标签\n# output：输出图像\ndef viewTheData():\n return 0\n\n# 分类并处理标准数据集\n# input：filename 数据集地址\n# output：trainData 用于训练的数据 ；testData 用于测试的数据； labelVec 数据标签; testLabelVec 测试集标签\ndef dataSetClassfication():\n height = 32\n weight = 32\n pixels = height*weight # 这里1024需要换为具体我们数据处理得到的像素点的个数\n print(\"enter the path to the trainSet:\")\n trainSetFileName = input() # 加入文件名\n print(\"enter the path to the testSet:\")\n testSetFileName = input()\n trainDatalist = listdir(trainSetFileName)\n dataNumber = len(trainDatalist)\n trainData = np.zeros((dataNumber, pixels))\n labelVec = []\n for i in range(dataNumber):\n fileHeadName = trainDatalist[i]\n classNumber = int(fileHeadName.split('_')[0]) # 因为在储存的数据时，文件名第一个字符是具体哪个数字\n labelVec.append(classNumber)\n trainData[i, :] = Mat2Vec(trainSetFileName+'/'+fileHeadName, height, weight)\n testDataList = listdir(testSetFileName)\n testNumber = len(testDataList)\n testData = np.zeros((testNumber, pixels))\n testLabelVec = []\n for i in range(testNumber):\n fileHeadName = testDataList[i]\n classNumber = int(fileHeadName.split('_')[0])\n testLabelVec.append(classNumber)\n testData[1, :] = Mat2Vec(testSetFileName+'/'+fileHeadName, height, weight)\n return trainData, labelVec, testData, testLabelVec\n\n# 准确率测试\n# input：trainData 用于训练的数据 ；testData 用于测试的数据； labelVec 数据标签; testLabelVec 测试集标签\n# output：precisionRate 准确率\ndef precisionRateTest(trainData, labelVec, testData, testLabelVec):\n num = 25 # 附近数据个数\n errorCount = 0\n testNumber = len(testLabelVec)\n for i in range(testNumber):\n yourNumber = classfication(testData[i], trainData, labelVec, num)\n print(\"your number :%d true number :%d\" % (yourNumber, testLabelVec[i]))\n if (yourNumber != testLabelVec[i]):\n errorCount += 1\n print(\"precisionRata: %f%%\" %(errorCount/testNumber))\n\n# KNN分类器模型\n# input：trainData 训练集；testData 测试集/ yourData 需要分辨的数据； labelVec 数据标签； num 附近数据个数\n# output：yourNumber 分类得到的数据\ndef classfication(testData, trainData, labelVec, num):\n disMat = np.tile(testData, (trainData.shape[0], 1))-trainData\n disMat2 = disMat**2\n disMat3 = disMat2.sum(axis=1)\n disMat4 = disMat3**0.5\n sortDistant = disMat4.argsort() # 得到索引值\n classCount = {}\n for i in range(num):\n labels = labelVec[sortDistant[i]]\n classCount[labels] = classCount.get(labels, 0) + 1\n sortClaccCount = sorted(classCount.items(), key=operator.itemgetter(1), reverse=True)\n yourNumber = sortClaccCount[0][0]\n return yourNumber\n\nloadproject()", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 10053, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cv2.VideoCapture", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 38, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 39, "usage_type": "attribute"}, {"api_name": "cv2.VideoWriter", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 41, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.namedWindow", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 54, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 56, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 63, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 63, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 66, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 78, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 78, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 79, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 81, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 82, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 82, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 90, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 90, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 91, "usage_type": "call"}, {"api_name": "cv2.Canny", "line_number": 98, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 99, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 99, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 111, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 111, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 112, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 116, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 116, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 121, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 121, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 128, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 128, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 129, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 141, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 141, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 165, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 190, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 192, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 228, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 237, "usage_type": "call"}]} +{"seq_id": "451966692", "text": "from pygame import Vector2\nimport random as rand\nimport pygame\nimport pygame.gfxdraw\nimport math\n\n\nclass Colors:\n colors = (\n (66, 135, 245),\n (245, 66, 135),\n (44, 230, 109),\n (255, 158, 74),\n (127, 3, 252)\n )\n\n\nclass Bubble:\n def __init__(self, pos, radius, color, surface, velocity):\n self.pos, self.velocity = pos, velocity\n self.radius, self.color = radius, color\n self.__surface = surface\n self.__born_time = pygame.time.get_ticks()\n self.__chain = []\n self.__deploy = False\n self.blocked = False\n\n def __bubbles_collision(self, bubbles):\n def collision(ball):\n return (self.pos - ball.pos).length() <= self.radius + ball.radius\n\n def line_equation():\n p1, p2 = self.pos, self.pos + self.velocity\n return 1 / (p2.x - p1.x), 1 / (p1.y - p2.y), p1.y / (p2.y - p1.y) - p1.x / (p2.x - p1.x)\n\n def collision_point(direction, bubble):\n a, b, c = direction\n pos = bubble.pos\n inter = Vector2(\n (b * (b * pos.x - a * pos.y) - a * c) / (a ** 2 + b ** 2),\n (a * (-b * pos.x + a * pos.y) - b * c) / (a ** 2 + b ** 2)\n )\n return inter - self.velocity.normalize() * math.sqrt(\n (self.radius + bubble.radius) ** 2 - (pos - inter).length() ** 2)\n\n direction = line_equation()\n position, collide = self.pos, False\n for bubble in bubbles:\n if bubble is not self and bubble.blocked and collision(bubble):\n point = collision_point(direction, bubble)\n if point.y > position.y:\n position = point\n collide = True\n if collide:\n for bubble in bubbles:\n if bubble is not self and bubble.blocked and self.color == bubble.color\\\n and (position - bubble.pos).length() < 125:\n self.__chain.append(bubble)\n bubble.__chain.append(self)\n self.pos, self.__deploy, self.blocked = position, True, True\n\n def update_position(self, delta_time, bubbles):\n if not self.blocked:\n self.pos += delta_time * self.velocity\n if self.pos.y <= self.radius:\n self.pos.y = self.radius\n self.blocked = self.__deploy = True\n if self.pos.x <= 0 or self.pos.x >= 800:\n self.velocity.x *= -1\n self.pos.x = max(0, min(800, self.pos.x))\n self.__bubbles_collision(bubbles)\n\n def deploy(self):\n if not self.__deploy: return\n queue, destroy = [self], set()\n while queue:\n node = queue.pop(0)\n destroy.add(node)\n select = [bubble for bubble in node.__chain if bubble not in destroy]\n queue.extend(select)\n self.__deploy = False\n return None if len(destroy) < 4 else destroy\n\n def render(self):\n def render_particle(color):\n for bubble in self.__chain:\n mid = (self.pos + bubble.pos) / 2\n x, y = (rand.random() - 0.5) * 25 + mid[0], (rand.random() - 0.5) * 25 + mid[1]\n pygame.gfxdraw.filled_circle(self.__surface, int(x), int(y), 5, color)\n\n light = (math.sin((self.__born_time + pygame.time.get_ticks()) / 500) + 1) / 8 + 0.2\n color = self.color + (int(255 * light),)\n pygame.gfxdraw.aacircle(self.__surface, int(self.pos.x), int(self.pos.y), self.radius, color)\n pygame.gfxdraw.filled_circle(self.__surface, int(self.pos.x), int(self.pos.y), self.radius, color)\n render_particle(color)\n\n\nclass Cannon:\n sensitivity = 0.005\n\n def __init__(self, pos, color, surface):\n self.pos, self.color = pos, color\n self.surface, self.original_texture = surface, pygame.image.load('Resources/cannon.png')\n self.unit, self.angle = Vector2(0, -1), -math.pi / 2\n self.next_color = rand.choice(Colors.colors) + (100, )\n self.texture = self.original_texture\n self.original_ray = pygame.Surface((80, 1000)).convert_alpha()\n self.original_ray.fill(self.next_color[0:3] + (50, ))\n self.ray = self.original_ray\n\n def throw_bubble(self):\n bubble = Bubble(self.unit * 120 + self.pos, 50, self.next_color[0:3], self.surface, 100 * self.unit)\n self.next_color = rand.choice(Colors.colors)\n self.original_ray.fill(self.next_color + (50, ))\n self.next_color += (100, )\n return bubble\n\n def rotate(self, angle):\n self.angle = min(max(-math.pi + 0.25, angle), -0.25)\n self.unit = Vector2(math.cos(self.angle), math.sin(self.angle))\n self.texture = pygame.transform.rotozoom(self.original_texture, math.degrees(-self.angle - math.pi / 2), 1.0)\n self.ray = pygame.transform.rotozoom(self.original_ray, math.degrees(-self.angle - math.pi / 2), 1.0)\n\n def follow_mouse(self):\n self.rotate(self.angle + pygame.mouse.get_rel()[0] * self.sensitivity)\n\n def render(self):\n bubble_pos = self.unit * 120 + self.pos\n bubble_pos = (int(bubble_pos[0]), int(bubble_pos[1]))\n self.surface.blit(self.ray, self.ray.get_rect(center=self.pos))\n pygame.gfxdraw.aacircle(self.surface, bubble_pos[0], bubble_pos[1], 40, self.next_color)\n pygame.gfxdraw.filled_circle(self.surface, bubble_pos[0], bubble_pos[1], 40, self.next_color)\n self.surface.blit(self.texture, self.texture.get_rect(center=self.pos))\n", "sub_path": "src/Entites.py", "file_name": "Entites.py", "file_ext": "py", "file_size_in_byte": 5558, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pygame.time.get_ticks", "line_number": 23, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pygame.Vector2", "line_number": 39, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 43, "usage_type": "call"}, {"api_name": "random.random", "line_number": 88, "usage_type": "call"}, {"api_name": "pygame.gfxdraw.filled_circle", "line_number": 89, "usage_type": "call"}, {"api_name": "pygame.gfxdraw", "line_number": 89, "usage_type": "attribute"}, {"api_name": "math.sin", "line_number": 91, "usage_type": "call"}, {"api_name": "pygame.time.get_ticks", "line_number": 91, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 91, "usage_type": "attribute"}, {"api_name": "pygame.gfxdraw.aacircle", "line_number": 93, "usage_type": "call"}, {"api_name": "pygame.gfxdraw", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pygame.gfxdraw.filled_circle", "line_number": 94, "usage_type": "call"}, {"api_name": "pygame.gfxdraw", "line_number": 94, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 103, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pygame.Vector2", "line_number": 104, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 104, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 105, "usage_type": "call"}, {"api_name": "pygame.Surface", "line_number": 107, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 113, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 119, "usage_type": "attribute"}, {"api_name": "pygame.Vector2", "line_number": 120, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 120, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 120, "usage_type": "call"}, {"api_name": "pygame.transform.rotozoom", "line_number": 121, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 121, "usage_type": "attribute"}, {"api_name": "math.degrees", "line_number": 121, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 121, "usage_type": "attribute"}, {"api_name": "pygame.transform.rotozoom", "line_number": 122, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 122, "usage_type": "attribute"}, {"api_name": "math.degrees", "line_number": 122, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 122, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_rel", "line_number": 125, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 125, "usage_type": "attribute"}, {"api_name": "pygame.gfxdraw.aacircle", "line_number": 131, "usage_type": "call"}, {"api_name": "pygame.gfxdraw", "line_number": 131, "usage_type": "attribute"}, {"api_name": "pygame.gfxdraw.filled_circle", "line_number": 132, "usage_type": "call"}, {"api_name": "pygame.gfxdraw", "line_number": 132, "usage_type": "attribute"}]} +{"seq_id": "433277755", "text": "#!/usr/bin/env python\n# coding: utf-8\n\nimport torch\nimport torch.nn as nn\n\nimport torch.nn.functional as F\nfrom torchvision import transforms\nfrom torch.utils.data import Dataset, DataLoader\n\nfrom PIL import Image\nimport numpy as np\nimport pandas as pd\nfrom sklearn.manifold import TSNE\nfrom sklearn.decomposition import PCA, TruncatedSVD\nfrom tqdm import tqdm\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom lime.lime_image import LimeImageExplainer\nfrom skimage.segmentation import mark_boundaries\nimport cv2 as cv\n\n\nCLASSES = {\n 1: 'Species-1',\n 2: 'Species-2',\n 3: 'Species-3',\n 4: 'Species-4',\n 5: 'Species-5',\n 6: 'Species-6',\n 7: 'Species-7',\n 8: 'Species-8',\n 9: 'Species-9'\n}\n\n\n# ## ResNet 모델 만들기\n\nclass BasicBlock(nn.Module):\n def __init__(self, in_planes, planes, stride=1):\n super(BasicBlock, self).__init__()\n self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3,\n stride=stride, padding=1, bias=False)\n self.bn1 = nn.BatchNorm2d(planes)\n self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,\n stride=1, padding=1, bias=False)\n self.bn2 = nn.BatchNorm2d(planes)\n\n self.shortcut = nn.Sequential()\n if stride != 1 or in_planes != planes:\n self.shortcut = nn.Sequential(\n nn.Conv2d(in_planes, planes,\n kernel_size=1, stride=stride, bias=False),\n nn.BatchNorm2d(planes)\n )\n\n def forward(self, x):\n out = F.relu(self.bn1(self.conv1(x)))\n out = self.bn2(self.conv2(out))\n out += self.shortcut(x)\n out = F.relu(out)\n return out\n\nclass ResNet(nn.Module):\n def __init__(self, num_classes=8):\n super(ResNet, self).__init__()\n self.in_planes = 16\n\n self.conv1 = nn.Conv2d(3, 16, kernel_size=3,\n stride=1, padding=1, bias=False)\n self.bn1 = nn.BatchNorm2d(16)\n self.layer1 = self._make_layer(16, 2, stride=1)\n self.layer2 = self._make_layer(32, 2, stride=2)\n self.layer3 = self._make_layer(64, 2, stride=2)\n self.linear = nn.Linear(12544, num_classes) # 64\n\n def _make_layer(self, planes, num_blocks, stride):\n strides = [stride] + [1] * (num_blocks - 1)\n layers = []\n for stride in strides:\n layers.append(BasicBlock(self.in_planes, planes, stride))\n self.in_planes = planes\n return nn.Sequential(*layers)\n\n def forward(self, x):\n out = F.relu(self.bn1(self.conv1(x)))\n out = self.layer1(out)\n out = self.layer2(out)\n out = self.layer3(out)\n out = F.avg_pool2d(out, 8)\n out = out.reshape(out.shape[0], -1)\n out = self.linear(out)\n return out\n\n\n\nclass SeedDataset(Dataset):\n def __init__(self, dataframe, root='data/img'):\n super(SeedDataset, self).__init__()\n self.dataframe = dataframe\n self.root = root\n self.compose = transforms.Compose([transforms.Resize((448, 448))])\n\n def __len__(self):\n return len(self.dataframe)\n\n def __getitem__(self, i):\n file_name, label = self.dataframe.loc[i]['name'], self.dataframe.loc[i]['label']\n img_dir = self.root + file_name +'.JPG'\n r_im = Image.open(img_dir)\n r_im = self.compose(r_im)\n r_im = np.array(r_im)\n data = torch.FloatTensor(r_im)\n return data, label\n\ndef draw_clustering(model, test_loader):\n model.eval()\n dimension = 2\n tsne = TSNE(n_components=dimension)\n # pca = PCA(n_components=dimension)\n # svd = TruncatedSVD(n_components=dimension)\n first = True\n base_data = None\n labels = []\n\n for x, label in tqdm(test_loader):\n x = np.array(x) / 255.0\n x = x.transpose((0, 3, 1, 2))\n test_x = torch.FloatTensor(x).to(DEVICE)\n encoded_data = model(test_x)\n encoded_data = encoded_data.to(\"cpu\").detach().numpy()\n encoded_data = tsne.fit_transform(encoded_data)\n if first:\n first = False\n base_data = encoded_data\n else:\n base_data = np.concatenate((base_data, encoded_data), axis=0)\n labels += label.tolist()\n encoded_data = base_data\n colormap = ['red', 'orange', '#0077BB', 'green', 'blue', 'indigo', 'purple', '#EE3377', 'pink', 'saddlebrown']\n if dimension == 2:\n fig = plt.figure(figsize=(10, 8))\n ax = fig.add_subplot()\n X = encoded_data[:, 0]\n Y = encoded_data[:, 1]\n names = []\n\n for x, y, s in zip(X, Y, labels):\n name = CLASSES[s]\n color = colormap[s % 10]\n if name not in names:\n ax.scatter(x, y, label=name, c=color)\n names.append(name)\n else:\n ax.scatter(x, y, c=color)\n\n ax.set_xlim(X.min(), X.max())\n ax.set_ylim(Y.min(), Y.max())\n plt.legend()\n plt.savefig('save/classification_img/2d.png', dpi=300)\n plt.show()\n else: # dimension == 3\n fig = plt.figure(figsize=(10, 8))\n ax = Axes3D(fig)\n X = encoded_data[:, 0]\n Y = encoded_data[:, 1]\n Z = encoded_data[:, 2]\n names = []\n\n for x, y, z, s in zip(X, Y, Z, labels):\n name = CLASSES[s]\n color = colormap[s]\n if name not in names:\n ax.scatter(x, y, z, c=color, label=name)\n names.append(name)\n else:\n ax.scatter(x, y, c=color)\n\n ax.set_xlim(X.min(), X.max())\n ax.set_ylim(Y.min(), Y.max())\n ax.set_zlim(Z.min(), Z.max())\n plt.legend()\n plt.savefig('save/classification_img/3d.png', dpi=300)\n plt.show()\n\n# ## load camera data\nUSE_CUDA = torch.cuda.is_available()\nDEVICE = torch.device(\"cuda\" if USE_CUDA else \"cpu\")\n\n# load model\nmodel = ResNet().to(DEVICE)\nmodel.load_state_dict(torch.load('save/resnet_classification.pt'))\n\ndef predict_fn(test_xs):\n # data = np.expand_dims(test_xs, axis=0)\n data = test_xs.transpose((0, 3, 1, 2))\n data = torch.FloatTensor(data).to(DEVICE)\n output = model(data)\n return output\n\nif __name__ == '__main__':\n\n compose = transforms.Compose([transforms.Resize((448, 448))])\n\n file_name = 'data/img/26-4b.JPG'\n r_im = Image.open(file_name)\n r_im = compose(r_im)\n data_arr = np.array(r_im)\n data = np.expand_dims(data_arr, axis=0)\n data = data.transpose((0, 3, 1, 2))\n data = torch.FloatTensor(data).to(DEVICE)\n\n\n\n output = model(data)\n print(output)\n pred = output.max(1, keepdim=True)[1]\n print('prediction label is', pred.item()+1)\n\n\n # # to visualize test_set.csv\n # test_file_name = 'data/test_set.csv'\n # testset = pd.read_csv(test_file_name)\n # testset = SeedDataset(testset, root='data/img/')\n #\n # test_loader = DataLoader(\n # testset,\n # batch_size=8,\n # shuffle=True,\n # num_workers=4\n # )\n # draw_clustering(model, test_loader)\n explainer = LimeImageExplainer()\n explanation = explainer.explain_instance(data_arr, predict_fn, hide_color=0, top_labels=2, num_samples=1000)\n\n temp, mask = explanation.get_image_and_mask(0, positive_only=False, num_features=2, hide_rest=False)\n cv.imwrite('limeTest.jpg', mark_boundaries(temp / 2 + 0.5, mask).astype('uint8'))\n\n plt.imshow(mark_boundaries(temp / 2 + 0.5, mask).astype('uint8'))\n plt.show()\n\n\n\n\n", "sub_path": "classification_after_train.py", "file_name": "classification_after_train.py", "file_ext": "py", "file_size_in_byte": 7489, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.nn.Module", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 42, "usage_type": "name"}, {"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.Conv2d", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 61, "usage_type": "name"}, {"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.Conv2d", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.nn.functional.avg_pool2d", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 90, "usage_type": "name"}, {"api_name": "torch.utils.data.Dataset", "line_number": 97, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 102, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 102, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 102, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 110, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 110, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 113, "usage_type": "call"}, {"api_name": "sklearn.manifold.TSNE", "line_number": 119, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "mpl_toolkits.mplot3d.Axes3D", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 187, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 188, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 192, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 197, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 203, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 203, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 203, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 206, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 206, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 209, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 211, "usage_type": "call"}, {"api_name": "lime.lime_image.LimeImageExplainer", "line_number": 233, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 237, "usage_type": "call"}, {"api_name": "skimage.segmentation.mark_boundaries", "line_number": 237, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 239, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 239, "usage_type": "name"}, {"api_name": "skimage.segmentation.mark_boundaries", "line_number": 239, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 240, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 240, "usage_type": "name"}]} +{"seq_id": "97218718", "text": "from app import app\nfrom flask import render_template, url_for, request, session, jsonify\n# import lyricsgenius as genius\n\n@app.route('/')\ndef index():\n return render_template('index.html')\n \n#accepts input as JSON, in form:\n# {\n# \"song_name\": datafromsearchbarasstring\n# }\n# returns JSON:\n# {\n# \"genre\": resultgenre,\n# \"accuracy\": resultaccuracy\n# }\n@app.route('/searchGenius', methods=['GET', 'POST'])\ndef searchGenius():\n if request.method == \"POST\":\n print(reques)\n title = rjson['song_name']\n api = genius.Genius('4vPfh0l1I33h0qqY-Bzn1ITfTGl_MPnKBHRRk_8XgvKnX70r9bumFYPWRkJ2VNSl')\n testinput = {\"song_name\": \"God's Plan\"}\n lyric = api.search_song(title).lyrics\n return request.get_json()\n return request", "sub_path": "app/.~c9_invoke_zUyimE.py", "file_name": ".~c9_invoke_zUyimE.py", "file_ext": "py", "file_size_in_byte": 771, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.render_template", "line_number": 7, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 5, "usage_type": "call"}, {"api_name": "app.app", "line_number": 5, "usage_type": "name"}, {"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.get_json", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 18, "usage_type": "call"}, {"api_name": "app.app", "line_number": 18, "usage_type": "name"}]} +{"seq_id": "577954985", "text": "# -*- coding:utf-8 -*-\n\"\"\"\n Author : 'longguangbin'\n Contact : longguangbin@163.com\n Date : 2018/8/16\n Usage : \n\"\"\"\n\nimport os\nfrom string import Template\nfrom dateutil.parser import parse\nimport datetime\nimport pandas as pd\nimport numpy as np\nimport matplotlib\n\nmatplotlib.use('TkAgg') # 使用tk画图\n\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nfrom dateutil.rrule import rrule, DAILY\n\ndt = '2018-09-16'\npath = '/Users/longguangbin/Work/scripts/anta_offline/detail'\nsale_file = 'sale_active.tsv'\npre_file = 'pred_{0}.tsv'.format(dt)\n\n# sku_code = '11721360-7/8.5'\n# 问题 sku：15821204-3/2XL - KL00 | 15821185-3/M_K50N | 19827252-2_K50P\nsku_code = '__all__' # __all__ | 19827252-2\n# store_id = 'L611'\nstore_id = '__all__' # '__all__'\nmodels = ['combine'] # reg_single | hw | wma | combine\npre_len = 7\nbefore_day = 90\nshow_qty = True\n\n\ndef get_date_range1(date_start, pre_len):\n date_start_dt = parse(date_start)\n return [(date_start_dt + datetime.timedelta(x + 1)).strftime('%Y-%m-%d') for x in range(pre_len)]\n\n\ndef get_all_date(date_begin, date_end):\n date_begin_dt = parse(date_begin)\n date_end_dt = parse(date_end)\n date_len = (date_end_dt - date_begin_dt).days + 1\n return [(date_begin_dt + datetime.timedelta(x)).strftime('%Y-%m-%d') for x in range(date_len)]\n\n\ndef get_data(dt, path, sale_file, pre_file, sku_code, store_id, pre_len, models):\n pre_data = pd.read_table(path + os.sep + pre_file)\n real_data = pd.read_table(path + os.sep + sale_file)\n\n group_store = store_id.lower() == '__all__'\n group_sku = sku_code.lower() == '__all__'\n\n date_range = get_date_range1(dt, pre_len)\n\n if group_store and group_sku:\n tmp_real = real_data.groupby(['dt']).agg({'sale': 'sum'}).reset_index()\n else:\n tmp_real = real_data[(real_data['sku_code'].apply(lambda x: sku_code in x)) & (\n real_data['store_id'].apply(lambda x: True if group_store else store_id == x))]\n # tmp_real = tmp_real.groupby(['sku_code', 'dt']).agg({'qty': 'sum', 'sale': 'sum'}).reset_index()\n tmp_real = tmp_real.groupby(['sku_code', 'dt']).agg({'sale': 'sum'}).reset_index()\n # tmp_real = tmp_real.groupby(['dt']).agg({'qty': 'sum', 'sale': 'sum'}).reset_index()\n tmp_real = tmp_real.groupby(['dt']).agg({'sale': 'sum'}).reset_index()\n\n if len(tmp_real) < 1:\n raise Exception(''' tmp_real is empty : {0} '''.format(tmp_real))\n\n dt_min, dt_max = min([np.min(tmp_real['dt'])] + date_range), max([np.max(tmp_real['dt'])] + date_range)\n all_date_range = get_all_date(dt_min, dt_max)\n date_range_df = pd.DataFrame(all_date_range, columns=['dt'])\n\n pre_list = []\n for model in models:\n if group_store and group_sku:\n tmp_pre = pre_data[pre_data['sale_type'].apply(lambda x: model == x)]\n tmp_value = np.sum(map(lambda x: eval(x), tmp_pre['sale_list'].values), axis=0)\n else:\n tmp_pre = pre_data[(pre_data['sku_code'].apply(lambda x: sku_code in x)) & (\n pre_data['store_id'].apply(lambda x: True if group_store else store_id == x)) & (\n pre_data['sale_type'].apply(lambda x: model == x))]\n tmp_pre = tmp_pre.groupby(['sku_code']).agg(\n {'sale_list': lambda y: str(np.sum(map(lambda x: eval(x), y.values), axis=0).tolist())}).reset_index()\n tmp_value = tmp_pre['sale_list'].values\n if len(tmp_value) == 0:\n tmp_value = [0] * pre_len\n else:\n tmp_value = np.sum(map(lambda x: eval(x), tmp_value), axis=0).tolist()\n pre_values = tmp_value[:pre_len]\n pre_df = pd.DataFrame(pre_values, columns=['sale'])\n pre_df['dt'] = date_range\n pre_list.append(pre_df)\n\n # real_sale_df = date_range_df.merge(tmp_real.loc[:, ['sale', 'qty', 'dt']], on=['dt'], how='left').fillna(0)\n real_sale_df = date_range_df.merge(tmp_real.loc[:, ['sale', 'dt']], on=['dt'], how='left').fillna(0)\n\n return real_sale_df, pre_list, dt_min, dt_max\n\n\ndef get_weekend(start_date, end_date):\n this_day = parse(start_date).weekday()\n start_date = parse(start_date)\n end_date = parse(end_date)\n\n sat_delta = 6 if this_day > 5 else 5 - this_day\n sun_delta = 6 - this_day\n\n sat_day = start_date + datetime.timedelta(sat_delta)\n sun_day = start_date + datetime.timedelta(sun_delta)\n\n day_list = []\n while (sat_day < end_date) or (sun_day < end_date):\n if sat_day < end_date:\n day_list.append(sat_day)\n sat_day = sat_day + datetime.timedelta(7)\n if sun_day < end_date:\n day_list.append(sun_day)\n sun_day = sun_day + datetime.timedelta(7)\n week_list = sorted(map(lambda x: x.strftime('%Y-%m-%d'), day_list))\n return week_list\n\n\ndef get_date_list(start_date, day_len):\n date_list = map(lambda x: x.strftime('%Y-%m-%d'),\n list(rrule(freq=DAILY, count=day_len, dtstart=parse(start_date) + datetime.timedelta(1))))\n return date_list\n\n\ndef get_date_range(start_date, end_date):\n start_date_dt = parse(start_date)\n end_date_dt = parse(end_date)\n date_range = map(lambda x: (start_date_dt + datetime.timedelta(x)).strftime(\"%Y-%m-%d\"),\n range((end_date_dt - start_date_dt).days + 1))\n return date_range\n\n\ndef get_week_df(start_date, end_date):\n week_list = get_weekend(start_date, end_date)\n date_list = get_date_range(start_date, end_date)\n date_pd = pd.DataFrame(date_list, columns=['dt'])\n week_pd = pd.DataFrame(week_list, columns=['dt'])\n week_pd['week'] = '1'\n week_df = date_pd.merge(week_pd, on=['dt'], how='left').fillna('0')\n return week_df\n\n\ndef plot_func(start_date=None, before_day=30, real_sale=None, data_list=None, name_list=None, qty=True, dt_min=None,\n dt_max=None, sku_code=None, store_id=None):\n before_date = (parse(start_date) - datetime.timedelta(before_day)).strftime('%Y-%m-%d')\n dt_min_min = before_date\n week_df = get_week_df(dt_min_min, dt_max)\n tmp_sale = real_sale[real_sale['dt'] > dt_min_min]\n all_date_range = get_all_date(dt_min_min, dt_max)\n date_range_df = pd.DataFrame(all_date_range, columns=['dt'])\n # real_sale = date_range_df.merge(tmp_sale.loc[:, ['sale', 'qty', 'dt']], on=['dt'], how='left').fillna(0)\n real_sale = date_range_df.merge(tmp_sale.loc[:, ['sale', 'dt']], on=['dt'], how='left').fillna(0)\n # tmp_sale = sale_sum[sale_sum['dt'] > before_date]\n fig = plt.figure(figsize=(14, 8))\n ax = fig.add_subplot(111)\n if qty:\n ax.plot(real_sale['dt'], real_sale['qty'], label='qty', alpha=0.9)\n ax.plot(real_sale['dt'], real_sale['sale'], label='real')\n name_list = ['dev'] * len(data_list) if name_list is None else name_list\n have_pre = data_list is not None and len(data_list) > 0\n if have_pre:\n for i, each in enumerate(data_list):\n ax.plot(each['dt'], each['sale'], label=name_list[i])\n x_tick_labels = list(ax.get_xticklabels())\n tick_num = 10 # 刻度数目\n tick_spacing = int(np.ceil(len(x_tick_labels) * 1.0 / tick_num))\n # print x_labels, tick_spacing\n y_max = max([np.max(real_sale['sale'])] + map(lambda x: np.max(x['sale']), data_list)) if have_pre else np.max(\n real_sale['sale'])\n y_min = min([np.min(real_sale['sale'])] + map(lambda x: np.min(x['sale']), data_list)) if have_pre else np.min(\n real_sale['sale'])\n y_gap = y_max - y_min\n width = 1\n ax.bar(week_df['dt'], week_df['week'].apply(lambda x: y_max + y_gap * 0.2 if x == '1' else 0), width, color=\"red\",\n align='center', alpha=0.15)\n # ax.yaxis.grid(False)\n ax.set_ylim(y_min - y_gap * 0.03, y_max + y_gap * 0.05)\n ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))\n ax.legend(loc='upper left', bbox_to_anchor=(1.01, 0.55))\n ax.set_title('Sku_code : {2} Store_id : {3} \\nPredict date : {0} . History Windows : {1} days.'.\n format(start_date, before_day, sku_code, store_id),\n fontsize=15)\n plt.show()\n\n\nreal_sale_df, pre_list, dt_min, dt_max = get_data(dt, path, sale_file, pre_file, sku_code, store_id, pre_len, models)\n\nplot_func(start_date=dt, before_day=before_day, real_sale=real_sale_df, data_list=pre_list, name_list=models,\n qty=show_qty, dt_min=dt_min, dt_max=dt_max, sku_code=sku_code, store_id=store_id)\n\n\n# 衡量数据 稀疏度+连续度+不平衡度\n# 稀疏度：有销量天数 / base_day\n# 连续度：max 有销量的连续长度\n# 不平衡度：sale_sum\n\n# base_day：45\n\n# 均销量高：((sale_sum / 有销量天数) > 4 )\n\n# 数据稀疏：((有销量天数 / base_day) < 0.2 )\n\n# sku 角度：\n# sku + store ：总销量排序\n\n\ndef plotHistPer(plot_data, binsn=[], xlabeln='x', ylabeln='y', titlen='', save_path='', cum_True=True, size=(12, 8),\n detail=0, is_drop_zero=False, is_show=True, p_detail=2, sp_0=False):\n '''\n 画hist的百分比图，指定bins\n :param data: pd.DataFrame 单列数据\n :param binsn: numeric 指定的bins\n :param xlabeln: unicode x轴名称\n :param ylabeln: unicode y轴名称\n :param titlen: unicode 图的标题\n :param save_path: string 文件路径\n :param cum_True: boolean 是否添加累计概率线\n :param size: tuple 画图的size大小\n :param is_int: boolean 是否把标签变成整数\n :return: None 仅用于作图\n '''\n # plot_data=z_value_frame.z_value; binsn=[-np.inf, 0, 2, 4, 6, 8, 10, 12, 14, np.inf]\n # xlabeln = u'z值'; ylabeln = u'频数'; titlen = u\"Z值分布图\"; size=(12,8); intshu=True\n plot_data = list(plot_data)\n plt.style.use('seaborn-darkgrid')\n if binsn == []:\n ret = plt.hist(plot_data, label='Z', color='#0070C0', histtype='bar', rwidth=0.6)\n else:\n ret = plt.hist(plot_data, bins=binsn, label='Z', color='#0070C0', histtype='bar', rwidth=0.6)\n plt.close()\n counts, bins, patches = ret[0], ret[1], ret[2]\n detail_method = Template(\"[{0:.${detail}f},{1:.${detail}f})\")\n bins_name = [detail_method.substitute(detail=detail).format(bins[i], bins[i + 1]) for i in range(len(bins) - 1)]\n if sp_0:\n bins_name[0] = '(' + bins_name[0][1:]\n bins_name = ['[0]'] + bins_name\n zero_cnt = sum([1 if n_i == 0 else 0 for n_i in plot_data])\n counts = list(counts)\n counts[0] = counts[0] - zero_cnt\n counts = [zero_cnt] + counts\n counts = np.array(counts)\n if is_drop_zero:\n tmp_counts = []\n tmp_bins_name = []\n for i, each in enumerate(counts):\n if each != 0:\n tmp_counts.append(counts[i])\n tmp_bins_name.append(bins_name[i])\n counts = tmp_counts\n bins_name = tmp_bins_name\n ind = np.arange(len(counts))\n fig1, ax1 = plt.subplots(figsize=size)\n ax1.set_xlabel(xlabeln)\n ax1.set_ylabel(ylabeln)\n width = 0.5\n width2 = 0\n ax1.bar(ind + width2, counts, width, color=\"#0070C0\", tick_label=bins_name, align='center', alpha=0.8)\n counts_per = counts / np.sum(counts)\n counts_per_cum = np.cumsum(counts_per)\n i = 0\n ymin, ymax = plt.ylim()\n ax1.set_ylim(ymin - ymax * 0.05, ymax * 1.05)\n # ax1.set_xlim(-1, len(bins_name)+1)\n percent_detail = Template(\"{0:.${p_detail}f}%\").substitute(p_detail=p_detail)\n for x, y in zip(ind, counts):\n ax1.text(x + width2, y + 0.05, percent_detail.format(counts_per[i] * 100), ha='center', va='bottom')\n i += 1\n plt.title(titlen)\n if cum_True:\n ax2 = ax1.twinx()\n ax2.set_ylabel('Cumulative probability distribution')\n ax2.plot(ind + width2, counts_per_cum, '--', color=\"red\")\n ax2.yaxis.grid(False)\n ax2.set_ylim(-0.05, 1.05)\n ax2.set_xlim(-0.5, len(bins_name) - 0.5)\n if save_path != '':\n plt.savefig(save_path)\n if is_show:\n plt.show()\n return [fig1, ax1, ax2]\n\n\ndef plot_real_data(data, pre_list=None, name_list=None, start_date=None, before_day=None, sku_code=None, store_id=None):\n before_date = (parse(start_date) - datetime.timedelta(before_day)).strftime('%Y-%m-%d')\n dt_min_min = before_date\n week_df = get_week_df(dt_min_min, dt_max)\n all_date_range = get_all_date(dt_min_min, dt_max)\n date_range_df = pd.DataFrame(all_date_range, columns=['dt'])\n real_sale = date_range_df.merge(data.loc[:, ['sale', 'dt']], on=['dt'], how='left').fillna(0)\n # tmp_sale = sale_sum[sale_sum['dt'] > before_date]\n fig = plt.figure(figsize=(14, 8))\n ax = fig.add_subplot(111)\n ax.plot(real_sale['dt'], real_sale['sale'], label='real')\n have_pre = pre_list is not None and len(pre_list) > 0\n if have_pre:\n for i, each in enumerate(pre_list):\n ax.plot(each['dt'], each['sale'], label=name_list[i])\n x_tick_labels = list(ax.get_xticklabels())\n tick_num = 10 # 刻度数目\n tick_spacing = int(np.ceil(len(x_tick_labels) * 1.0 / tick_num))\n # print x_labels, tick_spacing\n y_max = np.max(real_sale['sale'])\n y_min = np.min(real_sale['sale'])\n y_gap = y_max - y_min\n width = 1\n ax.bar(week_df['dt'], week_df['week'].apply(lambda x: y_max + y_gap * 0.2 if x == '1' else 0), width, color=\"red\",\n align='center', alpha=0.15)\n # ax.yaxis.grid(False)\n ax.set_ylim(y_min - y_gap * 0.03, y_max + y_gap * 0.05)\n ax.xaxis.set_major_locator(ticker.MultipleLocator(tick_spacing))\n ax.legend(loc='upper left', bbox_to_anchor=(1.01, 0.55))\n ax.set_title('Sku_code : {2} Store_id : {3} \\nPredict date : {0} . History Windows : {1} days.'.\n format(start_date, before_day, sku_code, store_id),\n fontsize=15)\n plt.show()\n\n\npre_data = pd.read_table(path + os.sep + 'pred_2018-08-04.tsv')\nreal_data = pd.read_table(path + os.sep + 'sale_active.tsv')\ncate_data = pd.read_table(path + os.sep + 'cate_active.tsv')\nloc_data = pd.read_table(path + os.sep + 'loc_active.tsv')\n\nthis_date = '2018-08-04'\ndelay_days = 60\nstart_date = (parse(this_date) - datetime.timedelta(delay_days)).strftime('%Y-%m-%d')\ntmp_data = real_data.loc[(real_data['dt'] > start_date) & (real_data['dt'] < this_date),\n ['sku_code', 'store_id', 'sale']]\nsale_sum = tmp_data.groupby(['sku_code', 'store_id']).agg({'sale': 'sum'}).reset_index(). \\\n sort_values('sale', ascending=False)\nkeep_sum = sale_sum[sale_sum['sku_code'].apply(lambda x: not any(map(lambda y: y in x, ['XSTD', 'FSTD'])))]\n\n# pd.set_option('display.max_columns', 40)\n# pd.set_option('display.width', 180)\nkeep_sum.merge(cate_data.loc[:, ['sku_code', 'sku_name', 'spu_name']], on=['sku_code']). \\\n sort_values('sale', ascending=False)\n\n## 1、real_data 数据需要剔除塑料袋 XSTD | FSTD\n# mm = real_data.groupby(['sku_code']).agg({'sale': 'sum'}).reset_index(). \\\n# sort_values('sale', ascending=False)\n# mm.index = range(len(mm))\ncond_1 = real_data['sku_code'].apply(lambda x: not any(map(lambda y: y in x, ['XSTD', 'FSTD'])))\n## 2、只看 180 天至分析日以后的数据，分析日：this_date = '2018-08-20'\nthis_date = '2018-08-04'\ndelay_days = 180\nstart_date = (parse(this_date) - datetime.timedelta(delay_days)).strftime('%Y-%m-%d')\ncond_2 = (real_data['dt'] > start_date) & (real_data['dt'] < this_date)\nreal_keep = real_data[cond_1 & cond_2]\nreal_keep_no = real_data[cond_2]\nreal_keep_no['sku_code'].drop_duplicates()\nreal_keep_no['store_id'].drop_duplicates()\nreal_keep_no['cate1'].drop_duplicates()\nlen(real_keep_no['cate2'].drop_duplicates())\nreal_keep_no.loc[:, ['sku_code', 'store_id']].drop_duplicates()\n# cate1:5, 38\n\n## 查看 sales 分布情况\nsale_sum = real_keep.groupby(['sku_code', 'store_id']).agg({'sale': 'sum'}).reset_index(). \\\n sort_values('sale', ascending=False)\n# key : 199376 (23865 sku) '2018-08-01'\n# key : 188897 (22596 sku) '2018-08-20' - 180\nplotHistPer(sale_sum['sale'], binsn=[1, 2, 3, 5, 7, 9, 11, 15, 30, 50, 180, np.inf], xlabeln='sales',\n ylabeln='count number', titlen='( {0} ~ {1} ) Sales Distribution'.format(start_date, this_date),\n save_path='', cum_True=True, size=(12, 8), detail=0, is_drop_zero=False, is_show=True)\n\n## 查看 sales_days 分布情况\nsales_days = real_keep.groupby(['sku_code', 'store_id']).agg({'dt': 'count'}).reset_index(). \\\n sort_values('dt', ascending=False)\nplotHistPer(sales_days['dt'], binsn=[1, 2, 3, 5, 7, 9, 11, 15, 30, 50, 90, 180, np.inf], xlabeln='dt_cnt',\n ylabeln='count number', titlen='( {0} ~ {1} ) Sales Days Distribution'.format(start_date, this_date),\n save_path='', cum_True=True, size=(12, 8), detail=0, is_drop_zero=False, is_show=True, p_detail=3)\n\n## 查看个例 sku 的情况\ndt = '2018-08-04'\npath = '/Users/longguangbin/Work/scripts/anta_offline/detail'\nsale_file = 'sale_active.tsv'\npre_file = 'pred_{0}.tsv'.format(dt)\n# sale_sum[sale_sum['sale'] == 50] 19827252-2_K50P\nsku_code, store_id = '19827252-2', 'K50P'\n# sales低：15821783-3/L + K50M, 11827711-1/8 + L638\n# sales中等：19817361-2 + K515, 15821204-3/2XL + KL00\n# sales高：\n# 19746302-1_KLA4\n# store_id = 'L64C' # '__all__'\nmodels = ['reg_single', 'hw', 'wma', 'combine'] # reg_single | hw | wma | combine\npre_len = 7\nbefore_day = delay_days\nshow_qty = False\nsample_1 = real_keep[(real_keep['sku_code'] == sku_code) & (real_keep['store_id'] == store_id)]\nreal_sale_df, pre_list, dt_min, dt_max = get_data(dt, path, sale_file, pre_file, sku_code, store_id, pre_len, models)\ndt_max = (parse(dt) + datetime.timedelta(pre_len)).strftime('%Y-%m-%d')\npre_list = None\nplot_func(start_date=dt, before_day=before_day, real_sale=real_sale_df, data_list=pre_list, name_list=models,\n qty=show_qty, dt_min=dt_min, dt_max=dt_max, sku_code=sku_code, store_id=store_id)\n\n\ndef plot_for(sku_code=None, store_id=None):\n dt = '2018-08-04'\n path = '/Users/longguangbin/Work/scripts/anta_offline/detail'\n sale_file = 'sale_active.tsv'\n pre_file = 'pred_{0}.tsv'.format(dt)\n models = ['reg_single', 'hw', 'wma', 'combine'] # reg_single | hw | wma | combine\n pre_len = 7\n before_day = delay_days\n show_qty = False\n sample_1 = real_keep[(real_keep['sku_code'] == sku_code) & (real_keep['store_id'] == store_id)]\n real_sale_df, pre_list, dt_min, dt_max = get_data(dt, path, sale_file, pre_file, sku_code, store_id, pre_len,\n models)\n dt_max = (parse(dt) + datetime.timedelta(pre_len)).strftime('%Y-%m-%d')\n # pre_list = None\n plot_func(start_date=dt, before_day=before_day, real_sale=real_sale_df, data_list=pre_list, name_list=models,\n qty=show_qty, dt_min=dt_min, dt_max=dt_max, sku_code=sku_code, store_id=store_id)\n\n\nm_list = [['731300056', 'KL2A'], ['19827308-3', 'KL30'], ['19827303-3', 'KL5B'], ['19827308-3', 'KL5H'],\n ['19827306-6', 'KL3S'], ['15823504-2/XL', 'K50F'], ['15823504-2/L', 'K50F'], ['19827303-3', 'KL5H'],\n ['15821742-3/XL', 'K50W'], ['19827306-1', 'KL5B'], ['19827308-1', 'KL5B'], ['19825303-2', 'KL0C'],\n ['19827303-2', 'KL5B'], ['19827306-6', 'KL5A'], ['15821742-3/L', 'K50W'], ['731100105', 'KL54'],\n ['16827160-2/L', 'K507'], ['19827301-3', 'KL5B'], ['19827308-2', 'KL0H'], ['19827308-3', 'KL3A'],\n ['19827308-2', 'KL1G'], ['19817311-3', 'K501'], ['19825304-5', 'KL0C'], ['19825304-4', 'KL0C'],\n ['19825303-3', 'KL3D'], ['19817311-2', 'L64G'], ['19827301-3', 'KL5H'], ['15821742-3/XL', 'K55P']]\n\nfor v in m_list:\n sku_code = v[0]\n store_id = v[1]\n plot_for(sku_code=sku_code, store_id=store_id)\n\n## 查看 gap 的分布情况\ngap_df = pd.read_csv(path + os.sep + 'gap_sp2.csv', header=None)\ngap_df.columns = ['sku_code', 'sale', 'gap']\n\ngap_keep = gap_df[gap_df['sku_code'].apply(lambda x: not any(map(lambda y: y in x, ['XSTD', 'FSTD'])))]\n\nplotHistPer(gap_keep['gap'], binsn=[0, 1, 2, 3, 4, 5, 6, 8, 10, 15, 20, np.inf], xlabeln='gap',\n ylabeln='count number', titlen='( {0} ~ {1} ) Sales Gap Distribution'.format(start_date, this_date),\n save_path='', cum_True=True, size=(12, 8), detail=0, is_drop_zero=False, is_show=True, p_detail=3, sp_0=True)\n\n\n\ngap_keep[gap_keep['gap']>5].sort_values('gap', ascending=False)\n\n\nreal_data_tmp = real_data[(real_data['dt'] >= '2018-02-05') & (real_data['dt'] <= '2018-08-11')]\nreal_data_tmp = real_data_tmp.loc[:, ['sku_code', 'store_id']].drop_duplicates()\nreal_data_tmp = real_data_tmp[real_data_tmp['sku_code'].apply(lambda x: not any(map(lambda y: y in x, ['XSTD', 'FSTD'])))]\nreal_data_tmp.index = range(len(real_data_tmp))\n\nbe_data = real_data[(real_data['dt'] >= '2018-07-16') & (real_data['dt'] <= '2018-08-04')].groupby(['sku_code', 'store_id']).agg({'sale': 'sum'}).reset_index()\naf_data = real_data[(real_data['dt'] >= '2018-08-05') & (real_data['dt'] <= '2018-08-11')].groupby(['sku_code', 'store_id']).agg({'sale': 'sum'}).reset_index()\nbe_data = be_data[be_data['sku_code'].apply(lambda x: not any(map(lambda y: y in x, ['XSTD', 'FSTD'])))]\naf_data = af_data[af_data['sku_code'].apply(lambda x: not any(map(lambda y: y in x, ['XSTD', 'FSTD'])))]\n# join_data = be_data.merge(af_data, on=['sku_code', 'store_id'], how='left').fillna(0)\njoin_data = real_data_tmp.merge(be_data, on=['sku_code', 'store_id'], how='left').\\\n merge(af_data, on=['sku_code', 'store_id'], how='left').fillna(0)\n\n\n# 11827711-1/8 L638\n\ndef map_score(x):\n if x == 0:\n return '0'\n elif x <= 1 :\n return '1'\n elif x <= 2 :\n return '2'\n elif x <= 3 :\n return '3'\n elif x <= 4 :\n return '4'\n elif x <= 5 :\n return '5'\n else:\n return '5>'\n\njoin_data['be_flag'] = join_data['sale_x'].apply(lambda x: map_score(x))\njoin_data['af_flag'] = join_data['sale_y'].apply(lambda x: map_score(x))\n\nimport seaborn as sns\nfrom collections import Counter\n\njoin_data['cnt'] = join_data['be_flag'] + '|' + join_data['af_flag']\nmm = pd.DataFrame().from_dict(dict(Counter(join_data['cnt'])), orient='index').reset_index()\nmm.columns = ['name', 'cnt']\nmm['before_sale'] = mm['name'].apply(lambda x: x.split('|')[0])\nmm['after_sale'] = mm['name'].apply(lambda x: x.split('|')[1])\nsns_data = mm.pivot(\"before_sale\", \"after_sale\", \"cnt\").fillna(0.0)\n\nfig = plt.figure()\nax = fig.add_subplot(figsize=(14, 8))\n# sns.heatmap(mm.pivot(\"before_sale\", \"after_sale\", \"cnt\").fillna(0.0), annot=True, fmt=\"d\", linewidths=.5, cmap='YlGnBu', ax=ax)\nsns.heatmap(sns_data, annot=True, fmt=\".0f\", linewidths=.5, cmap='YlGnBu', ax=ax)\nplt.title('Sales migration')\nplt.show()\n\n\n# plot series of kpi\ndata = [[4 , 20641 , 1.2334488903751435,1.297406426703958,1.0169647169696603,1.0096910135587038,0.9972456983036421],\n[5 , 14333 , 1.1720888179507842,1.221004717039609,0.9603272515961072,0.9506811256430916,0.9958191284943905],\n[6 , 10329 , 1.1239744471047821,1.158306445718606,0.8956865855963708,0.8752276258584837,0.9953878142524097],\n[7 , 7638 ,1.0900513154525668,1.1137926737933692,0.8573045480732303,0.9534541063884264,0.9950518499551919],\n[9 , 4478 ,1.0469679130434786,1.058932461538462,0.8108711304347829,0.936437725752509,0.9930836120401337],\n[10 , 3540 ,1.0314996979569673,1.0421080737120754,0.7780938641145728,0.7793817314871376,0.991905925473427],\n[13 , 1979,1.0061014545196536,1.01060586774514,0.7160779409677874,0.724736274301121,0.988888888888889],\n[15 , 1000 ,0.9969654363636352,0.9992984218181815,0.6519841672727268,0.7261242618181817,0.988054545454546]]\nname = ['order' , 'cnt', 'wma_mape', 'hw_mape', 'reg_single_mape', 'reg_sum_mape', 'ma_mape']\nser_pd = pd.DataFrame(data, columns=name)\n\nser_pd.loc[:, ['order', 'cnt', 'reg_single_mape']]\n\nfig = plt.figure()\nax = fig.add_subplot(111)\nax.plot(ser_pd['order'], ser_pd['reg_single_mape'], label='reg_single_mape')\n# ax.plot(ser_pd['order'], ser_pd['reg_sum_mape'], label='reg_sum_mape')\nax.set_title('Reg_single Mape (cnt change)')\nax.set_xlabel('cnt')\nax.set_ylabel('reg_single_mape')\nplt.show()\n", "sub_path": "longgb/Scripts/PyCode/test_local_model/learn1/plot_analysis.py", "file_name": "plot_analysis.py", "file_ext": "py", "file_size_in_byte": 23889, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.use", "line_number": 17, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 41, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 45, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 48, "usage_type": "call"}, {"api_name": "pandas.read_table", "line_number": 52, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pandas.read_table", "line_number": 53, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 53, "usage_type": "attribute"}, {"api_name": "numpy.min", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 73, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 92, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 94, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 105, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 106, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 107, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 112, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 113, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 119, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 122, "usage_type": "call"}, {"api_name": "dateutil.rrule.rrule", "line_number": 129, "usage_type": "call"}, {"api_name": "dateutil.rrule.DAILY", "line_number": 129, "usage_type": "name"}, {"api_name": "dateutil.parser.parse", "line_number": 129, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 129, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 134, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 135, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 136, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 144, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 145, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 153, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 153, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "numpy.ceil", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.ticker", "line_number": 186, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.style.use", "line_number": 233, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 233, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 233, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 235, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 235, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 237, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 237, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 238, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 238, "usage_type": "name"}, {"api_name": "string.Template", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 259, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 260, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 260, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 266, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 267, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 269, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 269, "usage_type": "name"}, {"api_name": "string.Template", "line_number": 272, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 276, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 276, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 285, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 285, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 287, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 287, "usage_type": "name"}, {"api_name": "dateutil.parser.parse", "line_number": 292, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 292, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 296, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 299, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 299, "usage_type": "name"}, {"api_name": "numpy.ceil", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 310, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 311, "usage_type": "call"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 318, "usage_type": "call"}, {"api_name": "matplotlib.ticker", "line_number": 318, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 323, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 323, "usage_type": "name"}, {"api_name": "pandas.read_table", "line_number": 326, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 326, "usage_type": "attribute"}, {"api_name": "pandas.read_table", "line_number": 327, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 327, "usage_type": "attribute"}, {"api_name": "pandas.read_table", "line_number": 328, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 328, "usage_type": "attribute"}, {"api_name": "pandas.read_table", "line_number": 329, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 329, "usage_type": "attribute"}, {"api_name": "dateutil.parser.parse", "line_number": 333, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 333, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 353, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 353, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 369, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 376, "usage_type": "attribute"}, {"api_name": "dateutil.parser.parse", "line_number": 398, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 398, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 416, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 416, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 436, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 436, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 441, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 489, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 489, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 495, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 495, "usage_type": "name"}, {"api_name": "seaborn.heatmap", "line_number": 498, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 499, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 499, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 500, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 500, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 513, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 517, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 517, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 524, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 524, "usage_type": "name"}]} +{"seq_id": "102955146", "text": "#-*- coding: utf-8 -*-\nimport matplotlib.pyplot as plt # plt 用于显示图片\nimport matplotlib.image as mpimg # mpimg 用于读取图片\nimport numpy as np\n\ndef rgb2gray(rgb):\n return np.dot(rgb[...,:3], [0.299, 0.587, 0.144])\n\nlena = mpimg.imread('gz.jpg') # 读取和代码处于同一目录下的 lena.png\nout = np.zeros(lena.shape)\n\nfor c in range(0,lena.shape[2]):\n for x in range(0,lena.shape[0]):\n for y in range(0,lena.shape[1]):\n if x-1 >= 0 and x+1 < lena.shape[0] and y-1 >=0 and y+1 < lena.shape[1]:\n out[x,y,c]=abs(lena[x-1,y+1,c]+2*lena[x,y+1,c]+lena[x+1,y+1,c]-lena[x-1,y-1,c]-2*lena[x,y-1,c]-lena[x+1,y-1,c]) + abs(lena[x-1,y-1,c]+2*lena[x-1,y,c]+lena[x-1,y+1,c]-lena[x+1,y-1,c]-2*lena[x+1,y,c]-lena[x+1,y+1,c])\n if(out[x,y,c]>255):\n out[x,y,c]=255\n else:\n out[x,y,c]=lena[x,y,c]\n\nout = rgb2gray(out)\nprint(out.shape)\nplt.imshow(out, cmap=plt.cm.gray_r)# 显示图片\nplt.axis('off')\nplt.savefig('gz2.jpg')\nplt.show()\n", "sub_path": "Sobel算子/Sobel.py", "file_name": "Sobel.py", "file_ext": "py", "file_size_in_byte": 1036, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.dot", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.image.imread", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.image", "line_number": 9, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 24, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "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": "403358724", "text": "import numpy as np\nfrom sklearn.externals import joblib\nfrom image_util import show_image, crop_digit, resize, enlarge_image_and_highlight_features, find_sudoku_rectangle\nfrom sudoku_util import solve\n\ntry:\n import cv2\nexcept ImportError:\n print(\"Please install OpenCV\")\n\nmodel = joblib.load(\".\\\\model\\\\model.pkl\")\n\ndef image_to_feature(img):\n #img = cv2.imread(image_file, cv2.IMREAD_GRAYSCALE)\n img = cv2.resize(img, (28,28), interpolation = cv2.INTER_AREA)\n img = np.array(img) / 10\n img[img < 20] = 0\n img = img.astype(\"uint8\")\n return img.reshape(1, -1)[0]\n\ndef image_to_array(img, size):\n\t#show_image(img)\n\tside = img.shape[:1]\n\tside = side[0] / 9\n\tresult = [[0 for i in range(9)] for j in range(9)] \n\tfor i in range(9):\n\t\tfor j in range(9):\n\t\t\ttl = (i * side, j * side) # Top left corner\n\t\t\tbr = ((i + 1) * side, (j + 1) * side) # Bottom right corner\t\t\n\t\t\tdigit = crop_digit(img, (tl, br), size)\n\t\t\tif(digit is not None):\t\t\t\t\n\t\t\t\tdigit = resize(digit, size, 4)\n\t\t\t\t#show_image(digit)\n\t\t\t\tfeature = image_to_feature(digit)\n\t\t\t\tif len(feature) != 0:\t\t\t\t\t\t\t\t\n\t\t\t\t\tpredicted = model.predict([feature])[0]\n\t\t\t\t\t#print(predicted)\n\t\t\t\t\tresult[j][i] = int(predicted)\n\t\t\t\telse:\t\t\t\t\t\n\t\t\t\t\tresult[j][i] = int(0)\n\treturn result\n\ndef print2DArray(result):\n\tfor i in range(9):\n\t\tfor j in range(9):\t\n\t\t\tprint(result[i][j], end =\" , \")\n\t\tprint()\n\noriginal_image = cv2.imread('.\\\\image\\\\sudoku.png', cv2.IMREAD_GRAYSCALE)\n#show_image(original_image)\nsudoku_rectangle = find_sudoku_rectangle(original_image)\n# re-assuring if box is indeed the sudoku\nsudoku_rectangle = find_sudoku_rectangle(sudoku_rectangle)\n#show_image(sudoku_rectangle)\nprocessed_image = enlarge_image_and_highlight_features(sudoku_rectangle, 400)\n#show_image(processed_image)\nresult = image_to_array(processed_image, 28)\nprint('Before')\nprint2DArray(result)\nresult = solve(result)\nprint('After')\nprint2DArray(result)", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1901, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sklearn.externals.joblib.load", "line_number": 11, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 11, "usage_type": "name"}, {"api_name": "cv2.resize", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "image_util.crop_digit", "line_number": 30, "usage_type": "call"}, {"api_name": "image_util.resize", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.IMREAD_GRAYSCALE", "line_number": 49, "usage_type": "attribute"}, {"api_name": "image_util.find_sudoku_rectangle", "line_number": 51, "usage_type": "call"}, {"api_name": "image_util.find_sudoku_rectangle", "line_number": 53, "usage_type": "call"}, {"api_name": "image_util.enlarge_image_and_highlight_features", "line_number": 55, "usage_type": "call"}, {"api_name": "sudoku_util.solve", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "342696825", "text": "import os, logging, json, re\nimport pandas as pd\nimport numpy as np\nfrom translate_R_to_pandas import *\n\n\ndef data_prep_1(data_dir, FEBA_dir, debug_bool=False, meta_ix=7, cfg=None):\n \"\"\" The first phase of data preparation for the BarSeqR Computations\n Args:\n data_dir: (str) Path to directory which contains the \n following files: 'all.poolcount', 'genes',\n 'exps', 'pool' - all TSV files.\n Optionally contains the following files:\n strainusage.barcodes.json - json list\n strainusage.genes.json - json list\n strainusage.genes12.json - json list\n ignore_list.json - json list ( list of str \n with sample-index name to ignore )\n All these files are changed depending on the input.\n FEBA_dir: (str) Path to directory which contains the \n following files: 'desc_short_rules'\n debug_bool: Whether you'd like to print the dataframes\n as a test to the data_dir before running FEBA_Fit\n meta_ix (int): The number of meta column indeces in all.poolcount\n cfg (python dict): The default and config variables required:\n drop_exps (bool): Do we drop the 'Drop' experiments\n from the experiments dataframe\n already?\n okControls (bool): Are we defining controls by\n the method where it's written\n into the Experiments file?\n \n \n \n\n Returns:\n list\n \n exps_df (pandas DataFrame): Must contain cols: (Variable)\n \n all_df (pandas DataFrame): Must contain cols:\n \n genes_df (pandas DataFrame): Must contain cols:\n scaffold, begin\n\n strainsUsed_list (py list or None):\n\n genesUsed_list (py list or None):\n\n genesUsed12_list (py list or None):\n \n \n Description:\n Within data_prep1 we perform the following functions:\n getDataFrames:\n We import the tables genes, all, exps, rules using a dict to say which \n data type is in each column. The dataframes we get are called:\n genes_df, all_df, exps_df, rules_df\n Within exps_df:\n We optionally remove the rows who have 'Drop' set to True (if drop_exps==True).\n We strip (remove the spaces from) the values in 'Group', \n 'Condition_1', 'Condition_2'\n We check that the right column names exist in each of the tables.\n \n checkLocusIdEquality:\n We check all the locusIds in all_df are also present in genes_df\n If debugging we also print the number of unique locusIds in each.\n check_exps_df_against_all_df:\n We check that the index names in all.poolcount are equivalent to the \n 'SetName' + '.' + 'Index' in exps\n prepare_set_names: \n We replace the SetNames from their original version to a simplified standard one,\n remove the period in between SetName and Index in all.poolcount columns,\n and make the 'names' column in the experiments file and the all.poolcount columns\n have the same values. For example, we move column name from Keio_ML9_set2.IT004 to \n set2IT004, and rename the values in the Experiments file similarly.\n get_special_lists:\n We get the lists from the files in data_dir if they are there,\n otherwise we return their values as empty lists. The lists we\n look for are genesUsed, which should be a list of locusIds\n from this genome that we are using, and ignore_list, which is a list\n of experiment names to ignore (columns from all.poolcount).\n If debug_bool is set to true we print out resultant exps, all, genes to 'tmp' dir\n We return the following variables: \n 'exps_df' (The experiments dataframe)\n 'all_df' (The barcodes and locations dataframe)\n 'genes_df' (The total genes dataframe)\n 'genesUsed_list' (A python list of locusIds that we will use)\n 'ignore_list' (A python list of experiment names to ignore)\n \n \"\"\"\n\n genes_df, all_df, exps_df, rules_df = getDataFrames(data_dir, FEBA_dir, \n drop_exps=cfg['drop_exps'],\n okControls = cfg['okControls'],\n dbg_lvl=0)\n\n # Makes no changes to the variables\n checkLocusIdEquality(all_df, genes_df, debug_bool=debug_bool)\n\n # We check that SetNames and Indexes in experiments file match all.poolcount file\n check_exps_df_against_all_df(exps_df, all_df, meta_ix)\n\n # We make it so the names are cleaner and create 'names', 'num', 'short' in exps_df\n exps_df, all_df, replace_col_d = prepare_set_names(exps_df, all_df, rules_df, \n okControls=cfg['okControls'],\n meta_ix=meta_ix,\n debug_bool=debug_bool)\n\n genesUsed_list, ignore_list = get_special_lists(data_dir, all_df,\n replace_col_d, debug_bool=debug_bool)\n\n if debug_bool:\n exps_df.to_csv(\"tmp/py_test1_exps_fp.tsv\", sep=\"\\t\")\n all_df.to_csv(\"tmp/py_test1_all_fp.tsv\", sep=\"\\t\")\n genes_df.to_csv(\"tmp/py_test1_genes_fp.tsv\", sep=\"\\t\")\n\n return [exps_df, all_df, genes_df, genesUsed_list, ignore_list]\n\n\ndef getDataFrames(data_dir, FEBA_dir, drop_exps=False, \n okControls=False, dbg_lvl=0):\n \"\"\"\n Args:\n data_dir: (str) Path to directory which contains the \n following files: 'all.poolcount', 'genes',\n 'exps' - all TSV files.\n Optionally contains the following files:\n strainusage.barcodes.json - json list\n strainusage.genes.json - json list\n strainusage.genes12.json - json list\n All these files are changed depending on the input.\n\n FEBA_dir: (str) Path to directory which contains the \n following files: 'desc_short_rules'\n drop_exps (bool): Should we drop all experiments that have Drop=True\n already?\n\n Returns:\n genes_df (pandas DataFrame): Contains columns:\n locusId, sysName, type, scaffoldId, begin, end, strand, name, desc, GC, nTA\n all_df (pandas DataFrame): Contains columns:\n barcode, rcbarcode, scaffold, strand, pos, locusId, f, setName1, ..., setNameN\n exps_df (pandas DataFrame): Must contains columns:\n Index (str)\n Date_pool_expt_started (str)\n Description (str)\n SetName (Str)\n Group (str)\n Drop (bool)\n [Condition_1]\n [Condition_2]\n \n rules_df (pandas DataFrame): Contains columns:\n V1 (str): Original string to replace\n V2 (str): String to replace V1 by\n\n Description:\n We import the tables using a dict to say which data type is in each column.\n In exps_df:\n We might remove the rows who have 'Drop' set to True (if drop_exps==True).\n We remove the spaces from the values in 'Group', 'Condition_1', 'Condition_2'\n We check that the right column names exist in each of the tables.\n\n To Do:\n Should we strip all of the column names when we import them?\n \"\"\"\n\n data_files = os.listdir(data_dir)\n for x in [\"all.poolcount\", \"genes\", \"exps\", \"pool\"]:\n if x not in data_files:\n raise Exception(\"Input data_dir to RunFEBA must include files:\\n\"\n \"all.poolcount, genes, exps, and pool.\"\n \" Currently missing: \" + x)\n\n\n all_fp = os.path.join(data_dir, \"all.poolcount\")\n genes_fp = os.path.join(data_dir, \"genes\")\n exps_fp = os.path.join(data_dir, \"exps\")\n short_rules_fp = os.path.join(FEBA_dir, \"desc_short_rules.tsv\")\n\n # Checking access permissions\n for x in [all_fp, genes_fp, exps_fp]:\n if not os.access(x, os.R_OK):\n raise Exception(\"To run, program requires read permission to file \" + x)\n\n # Read tsv files into dataframes, making sure columns locusId and scaffoldId read as stings\n genes_dtypes = {\n 'locusId': str,\n 'sysName': str,\n 'type': int,\n 'scaffoldId': str,\n 'begin': int,\n 'end': int,\n 'strand': str,\n 'name': str,\n 'desc': str,\n 'GC': float,\n 'nTA': int\n }\n genes_df = pd.read_table(genes_fp, dtype=genes_dtypes)\n #barcode\trcbarcode\tscaffold\tstrand\tpos\tlocusId\tf\n all_dtypes = {\n 'barcode': str,\n 'rcbarcode': str,\n 'scaffold': str,\n 'strand': str,\n 'pos': int,\n 'locusId': str,\n 'f': float\n }\n all_df = pd.read_table(all_fp, dtype=all_dtypes) \n \n exps_dtypes = {\n 'SetName': str,\n 'Index': str,\n 'Date_pool_expt_started': str,\n \"Description\": str,\n \"Group\": str,\n \"Drop\": str,\n \"Condition_1\": str,\n \"Condition_2\": str,\n \"control_group\": str,\n \"control_bool\": str\n }\n exps_df = pd.read_table(exps_fp, dtype=exps_dtypes)\n\n # We update the 'Drop' experiments\n if 'Drop' in exps_df:\n new_drops = []\n for ix, value in exps_df['Drop'].items():\n if not isinstance(value, str):\n if pd.isna(value):\n new_drops.append(False)\n else:\n raise Exception(f\"Value in 'Drop' not string: {value}\")\n elif str(value).strip().upper() == \"TRUE\":\n new_drops.append(True)\n elif value.strip().upper() == \"FALSE\":\n new_drops.append(False)\n else:\n raise Exception(f\"Cannot recognize Drop value in row {ix}:\"\n f\" {value}\")\n exps_df['Drop'] = new_drops\n else:\n exps_df['Drop'] = [False]*exps_df.shape[0]\n\n \"\"\"\n if drop_exps:\n # Removing Drop rows\n exps_df.drop(remove_indeces, axis=0, inplace=True)\n \"\"\"\n\n # Remove trailing spaces:\n for x in [\"Group\", \"Condition_1\", \"Condition_2\", \"control_bool\"]:\n if x in exps_df:\n # We take the entire column (pandas Series) and remove the spaces\n # from either end\n exps_df[x] = exps_df[x].str.strip()\n\n\n rules_dtypes = {\n \"V1\": str,\n \"V2\": str\n }\n rules_df = pd.read_table(short_rules_fp, keep_default_na=False, dtype=rules_dtypes)\n\n # Checking genes.GC\n for x in [\"scaffoldId\", \"locusId\", \"sysName\", \"desc\", \"begin\", \"end\"]:\n if x not in genes_df.columns:\n raise Exception(f\"Genes table must include header {x}\")\n # Checking exps table\n for x in [\"SetName\", \"Index\", \"Date_pool_expt_started\", \"Description\"]:\n if x not in exps_df.columns:\n raise Exception(f\"Experiments table must include header {x}\")\n if okControls:\n for x in [\"control_group\", \"control_bool\"]:\n if x not in exps_df.columns:\n raise Exception(\"If okControls is set To True, then \"\n f\"experiments table must include header {x}\")\n\n # Checking all_df\n for x in [\"scaffold\", \"locusId\", \"f\", \"pos\"]:\n if x not in all_df.columns:\n raise Exception(f\"All.PoolCount file must include header {x}\")\n\n if dbg_lvl > 1:\n print(genes_df)\n print(all_df)\n print(exps_df)\n print(rules_df)\n\n\n return [genes_df, all_df, exps_df, rules_df]\n\n\n\ndef checkLocusIdEquality(all_df, genes_df, debug_bool=False):\n \"\"\" We check all the locusIds in all_df are also present in genes_df\n\n \n Description:\n We check all the locusIds in all_df are also present in genes_df\n If debugging we also print the number of unique locusIds\n \"\"\"\n\n if debug_bool:\n logging.debug(\"Original locusId col\")\n logging.debug(all_df['locusId'])\n\n # below both are pandas series\n unique_all_locusIds = all_df['locusId'].dropna().unique()\n unique_genes_locusIds = genes_df['locusId'].dropna().unique()\n\n if debug_bool:\n # All\n logging.debug(\"Unique All Locus Ids: \")\n logging.debug(unique_all_locusIds)\n logging.debug(\"Number of Unique All Locus Ids: \")\n logging.debug(len(unique_all_locusIds))\n # Genes\n logging.debug(\"Unique Gene Locus Ids: \")\n logging.debug(unique_genes_locusIds)\n logging.debug(\"Number of Unique Gene Locus Ids: \")\n logging.debug(len(unique_genes_locusIds))\n \n\n # Checking if every locusId from all.poolcount also exists in genes\n not_found_locusIds = []\n for x in unique_all_locusIds:\n if x not in unique_genes_locusIds:\n not_found_locusIds.append(x)\n if len(not_found_locusIds) > 0:\n raise Exception(\"The following locusIds were not found in the genes file.\"\n \" (All locusIds from all.poolcount must also be in the genes\"\n \" file.)\"\n \"', '\".join(not_found_locusIds))\n\n\n\ndef check_exps_df_against_all_df(exps_df, all_df, meta_ix):\n \"\"\"\n We make sure that all the experiment names left in the all_df dataframe\n are the same as the experiment names in the rows of the experiments\n dataframe.\n \"\"\"\n\n experiment_names_test = [exps_df['SetName'].iat[i] + \".\" + exps_df['Index'].iat[i] for i in \\\n range(len(exps_df['SetName']))]\n index_names = list(all_df.head())[meta_ix:]\n\n # Number of rows:\n if len(index_names) != exps_df.shape[0]:\n raise Exception(f\"Number of data columns in {all_fp} does not match\"\n f\" number of rows in {exps_fp}\\n\"\n f\"{len(index_names)} != {exps_df.shape[0]}\")\n for i in range(len(index_names)):\n if index_names[i] not in experiment_names_test:\n raise Exception(f\"Column names in {all_fp} do not match names from\"\n f\"{exps_fp} at index {i}\")\n\n\n logging.debug(\"There are the same experiment names in all_df and exps_df.\")\n\n\n\n\ndef prepare_set_names(exps_df, all_df, rules_df, \n okControls=False, meta_ix=7, debug_bool=False):\n \"\"\"\n\n\n Description:\n We replace the SetNames from the complicated version to a simpler one,\n remove the period in between SetName and Index in all.poolcount columns,\n and make the 'names' column in the experiments file and the all.poolcount columns\n have the same values. For example, we move column name from Keio_ML9_set2.IT004 to \n set2IT004, and rename the values in the Experiments file similarly.\n We also add multiple new columns to exps_df:\n \"num\", \"short\", \"name\", \"t0set\"\n We also make sure that any experiment with its \"Group\" being \"Time0\" has\n its short as \"Time0\" as well.\n We initialize the 't0set' column as being the date + the set name (lane).\n\n \"\"\"\n\n # Below is a numpy array, not a series\n uniqueSetNames_nparray = exps_df['SetName'].unique()\n # shortSetNames is numpy ndarray, shortNamesTranslation_d is a dict which contains\n # conversions from original names to short names.\n shortSetNames, shortNamesTranslation_d = ShortSetNames(uniqueSetNames_nparray)\n\n if debug_bool:\n logging.debug(\"uniqueSetNames:\")\n logging.debug(uniqueSetNames_nparray)\n logging.debug(\"shortSetNames\")\n logging.debug(shortSetNames)\n logging.debug(\"Above 2 arrays should be the same length.\")\n\n\n # We concatenate the string of the set name and the index column\n # But first we need to find the original location of the set name\n # match_list is a list of indeces (int) for each element in the first list\n # where it is found in the second list.\n match_list = match_ix(list(exps_df['SetName']), list(uniqueSetNames_nparray)) \n # We apply the match list to shortSetNames_list to recreate the original SetName order\n # just with the newly created 'short' setNames.\n short_names_srs = shortSetNames[match_list]\n \n if debug_bool:\n logging.info(\"short_names_srs: (shortSetNames[match_list])\")\n logging.info(short_names_srs)\n logging.info(\"original set Names:\")\n logging.info(exps_df['SetName'])\n logging.info('match_list')\n logging.info(match_list)\n # If there are 3 unique set names and 100 items in exps_df['SetName'],\n # then match_list will contain 100 items with only 3 different values (0, 1, 2)\n\n # expNamesNew ends up being a list\n expNamesNew = [] \n for i in range(len(short_names_srs)):\n if not short_names_srs[i] in [None, np.nan]:\n expNamesNew.append(short_names_srs[i] + exps_df['Index'][i])\n else:\n expNamesNew.append(exps_df['Index'][i])\n\n if debug_bool:\n logging.info('expNamesNew:')\n logging.info(expNamesNew)\n\n exps_df['num'] = range(1, exps_df.shape[0] + 1)\n # We replace certain strings with others using the 'rules' table.\n exps_df['short'] = applyRules(rules_df, list(exps_df['Description']))\n\n if okControls:\n if not \"control_bool\" in exps_df.columns:\n raise Exception(\"Using manual control label but no column \"\n \"'control_bool' in Experiments file!\")\n else:\n for ix, val in exps_df[\"control_bool\"].iteritems():\n if val.strip().upper() == \"TRUE\":\n exps_df[\"short\"].loc[ix] = \"Time0\"\n else:\n # Should not be a Time0 short\n if exps_df[\"short\"].loc[ix].upper() == \"TIME0\":\n raise Exception(\"Description of experiment indicates Time0, but\"\n f\" value in control_bool is not 'True', instead '{val}'.\")\n \n\n if debug_bool:\n logging.info(\"exps_df of col 'short':\")\n logging.info(exps_df['short'])\n\n # We remove the \".\" in the names of the values. Just SetNameIndex now\n replace_col_d = {list(all_df.head())[meta_ix + i]: expNamesNew[i] for i in range(len(expNamesNew))}\n if debug_bool:\n logging.info('replace_col_d')\n logging.info(replace_col_d)\n logging.info('original all_df col names:')\n logging.info(list(all_df.columns))\n all_df = all_df.rename(columns=replace_col_d)\n if debug_bool:\n logging.info('after replacement all_df col names:')\n logging.info(list(all_df.columns))\n \n exps_df['name'] = expNamesNew\n\n\n\n # updating short to include Groups with Time0\n num_time_zero = 0\n for ix, val in exps_df['Group'].items():\n if val.strip().upper() == \"TIME0\":\n num_time_zero += 1\n exps_df.loc[ix, 'short'] = \"Time0\"\n\n # Updating column 't0sets' which refers to the date and SetName\n exps_df['t0set'] = [exps_df['Date_pool_expt_started'].iat[ix] + \" \" + \\\n val for ix, val in exps_df['SetName'].items()]\n\n if okControls:\n if not \"control_group\" in exps_df.columns:\n raise Exception(\"Using manual control label but no column \"\n \"'control_group' in Experiments file!\")\n else:\n for ix, val in exps_df[\"control_group\"].iteritems():\n exps_df['t0set'].loc[ix] = val\n\n if debug_bool:\n logging.info('exps_df short: ')\n logging.info(exps_df['short'])\n logging.info('exps_df t0set: ')\n logging.info(exps_df['t0set'])\n logging.info(f\"Total number of time zeros: {num_time_zero}\")\n\n return exps_df, all_df, replace_col_d\n\n\ndef ShortSetNames(set_names_nparray, dbg_lvl=0):\n \"\"\" Using a table with rules, shorten the names of these sets\n Args:\n set_names_nparray (numpy.ndarray): Array of string, unique set names from exps file\n Returns:\n set_names_nparray (numpy.ndarray): Edited set Names to be \n in the format setX* or testX*\n\n This might convert \n [ Keio_ML9_set2, Keio_ML9_set2, Keio_ML9_set2, ..., Keio_ML9_set3, Keio_ML9_set3,..., Keio_ML9_set3]\n to \n [ set2, set2, set2, ..., set3, set3, ..., set3]\n \"\"\"\n set_names_nparray = np.copy(set_names_nparray)\n\n # Below returns a TRUE/FALSE vector indicating which \n # elements of the character vector contain a match (i.o.w a simple name)\n simple = [bool(re.search(r\"(set|test)[0-9A-Z]+[0-9A-Z0-9]*$\", x)) for x in set_names_nparray]\n\n if dbg_lvl > 0:\n if len(simple) > 0:\n logging.debug(\"simple names: \\n\" + \",\".join(list([str(x) for x in simple])))\n else:\n logging.debug(\"No simple names found.\")\n\n \n # We edit the values of set_names_nparray who are true for simple\n # by removing anything before 'set' or 'test'\n # We count the number of values that were false\n nleft = 0\n simple_set_names = []\n for i in range(len(simple)):\n if simple[i]:\n new_set_name = re.sub(\"^.*(set|test)\", \"\\\\1\", set_names_nparray[i]) \n set_names_nparray[i] = new_set_name\n simple_set_names.append(new_set_name)\n else:\n nleft += 1\n\n if dbg_lvl > 0:\n logging.debug(\"fixed set_names:\\n\" + \",\".join(list(set_names_nparray)))\n \n candidates = []\n for x in \"A.B.C.D.E.F.G.H.I.J.K.L.M.N.O.P.Q.R.S.T.U.V.W.X.Y.Z\".split(\".\"):\n candidates.append(\"set\" + x)\n\n if dbg_lvl > 0:\n logging.debug(candidates)\n\n # get the elements in candidates that are not in set_names_nparray[simple]\n candidates = [x for x in candidates if x not in simple_set_names]\n if (nleft > len(candidates)):\n raise Exception(f\"Too many unexpected set names: {nleft}.\\n To fix this, contact developer \"\n \"and say to change the number of possible extensions in list candidates (A.B...Z).\")\n\n # Get the non-simple values from set_names_nparray\n oldComplex = [x for x in set_names_nparray if x not in simple_set_names]\n if dbg_lvl > 0:\n logging.debug(\"oldComplex:\\n\" + \",\".join(oldComplex))\n\n cnd_ix = 0 \n translation_dict = {}\n for i in range(len(simple)):\n if not simple[i]:\n logging.info(f\"Set {set_names_nparray[i]} simplified to {candidates[cnd_ix]}\")\n translation_dict[set_names_nparray[i]] = candidates[cnd_ix]\n set_names_nparray[i] = candidates[cnd_ix]\n cnd_ix += 1\n\n\n \n crnt_unq = list(pd.Series(set_names_nparray).unique())\n repeats = []\n for x in list(set_names_nparray):\n if x in crnt_unq:\n crnt_unq.remove(x)\n else:\n repeats.append(x)\n\n if not (len(repeats) == 0):\n raise Exception(\"Non-unique set names! :\\n\" + \\\n \", \".join(repeats))\n else:\n logging.debug(\"Finished running short set names\")\n if dbg_lvl > 0:\n logging.debug(\"Final set names list: \" + \", \".join(set_names_nparray))\n\n return set_names_nparray, translation_dict\n\n\n\n\ndef get_special_lists(data_dir, all_df, replace_col_d, debug_bool=False):\n \"\"\"\n Args:\n replace_col_d: Dict mapping original all_df experiment name to replacement name\n data_dir\n\n Returns:\n genesUsed_list list: LocusIds of genes to use\n ignore_list: List New names for the experiments we want to ignore.\n\n Description: We get the lists from the files in data_dir if they are there.\n Otherwise we return their values as empty lists. The lists we\n look for are genesUsed, which should be a list of locusIds\n from this genome that we are using, and ignore_list, which is a list\n of experiment names to ignore (columns from all.poolcount)\n \"\"\"\n\n genesUsed_list = []\n ignore_list = []\n # list of locusIds\n genesUsed_fp = os.path.join(data_dir, \"strainusage.genes.json\")\n # list of extra ignored experiments\n ignore_list_fp = os.path.join(data_dir, \"ignore_list.json\")\n\n if os.path.isfile(genesUsed_fp) and os.access(genesUsed_fp, os.R_OK):\n genesUsed_list = json.loads(open(GenesUsed_fp).read())\n logging.info(f\"Loaded {len(genesUsed_list)} genes to include in the \"\n \"analysis\\n\")\n\n if os.path.isfile(ignore_list_fp) and os.access(ignore_list_fp, os.R_OK):\n pre_ignore_list = json.loads(open(ignore_list_fp).read())\n for x in pre_ignore_list:\n if x in replace_col_d:\n ignore_list.append(x)\n else:\n raise Exception(f\"Avoid list contains experiment {x} but experiment name\"\n \" not found in all.poolcount.\"\n f\" Possible names: {', '.join(list(replace_col_d.keys()))}\")\n\n ignore_list = [replace_col_d[x] for x in ignore_list]\n\n\n return genesUsed_list, ignore_list\n\n\ndef applyRules(rules_df, desc_str_list):\n \"\"\"\n We replace str value in V1 with value in V2\n Args:\n rules_df: data frame with cols:\n V1, V2\n desc_str_list: list\n Returns:\n new_desc_list: list\n \"\"\"\n new_desc_list = []\n for j in range(len(desc_str_list)):\n new_desc_list.append(desc_str_list[j])\n for i in range(0, rules_df.shape[0]):\n new_desc_list[-1] = new_desc_list[-1].replace(rules_df[\"V1\"].iloc[i], \n rules_df[\"V2\"].iloc[i])\n return new_desc_list \n", "sub_path": "data_prep1.py", "file_name": "data_prep1.py", "file_ext": "py", "file_size_in_byte": 26570, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.listdir", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 178, "usage_type": "call"}, {"api_name": "os.path", "line_number": 178, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 179, "usage_type": "call"}, {"api_name": "os.path", "line_number": 179, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path", "line_number": 180, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 181, "usage_type": "call"}, {"api_name": "os.path", "line_number": 181, "usage_type": "attribute"}, {"api_name": "os.access", "line_number": 185, "usage_type": "call"}, {"api_name": "os.R_OK", "line_number": 185, "usage_type": "attribute"}, {"api_name": "pandas.read_table", "line_number": 202, "usage_type": "call"}, {"api_name": "pandas.read_table", "line_number": 213, "usage_type": "call"}, {"api_name": "pandas.read_table", "line_number": 227, "usage_type": "call"}, {"api_name": "pandas.isna", "line_number": 234, "usage_type": "call"}, {"api_name": "pandas.read_table", "line_number": 267, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 309, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 310, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 318, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 319, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 320, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 321, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 323, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 324, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 325, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 326, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 364, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 395, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 396, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 397, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 398, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 399, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 412, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 413, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 414, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 415, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 416, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 417, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 424, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 430, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 431, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 453, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 454, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 459, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 460, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 461, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 462, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 465, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 466, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 492, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 493, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 494, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 495, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 496, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 514, "usage_type": "call"}, {"api_name": "re.search", "line_number": 518, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 522, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 524, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 534, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 541, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 548, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 559, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 565, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 572, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 584, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 586, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 613, "usage_type": "call"}, {"api_name": "os.path", "line_number": 613, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 615, "usage_type": "call"}, {"api_name": "os.path", "line_number": 615, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 617, "usage_type": "call"}, {"api_name": "os.path", "line_number": 617, "usage_type": "attribute"}, {"api_name": "os.access", "line_number": 617, "usage_type": "call"}, {"api_name": "os.R_OK", "line_number": 617, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 618, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 619, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 622, "usage_type": "call"}, {"api_name": "os.path", "line_number": 622, "usage_type": "attribute"}, {"api_name": "os.access", "line_number": 622, "usage_type": "call"}, {"api_name": "os.R_OK", "line_number": 622, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 623, "usage_type": "call"}]} +{"seq_id": "299692170", "text": "from __future__ import print_function\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport torch.optim as optim\r\nfrom torchvision import datasets, transforms\r\n\r\n# Getting to know Data\r\n\r\n\r\ndef stat():\r\n data = datasets.MNIST(\r\n './data', train=True, transform=transforms.Compose([transforms.ToTensor(), ]), download=True)\r\n\r\n exp = data.data\r\n exp = data.transform(exp.numpy())\r\n\r\n print('Train Statistics')\r\n print(' - Numpy Shape:', data.data.cpu().numpy().shape)\r\n print(' - Tensor Shape:', data.data.size())\r\n print(' - min:', torch.min(exp))\r\n print(' - max:', torch.max(exp))\r\n print(' - mean:', torch.mean(exp))\r\n print(' - std:', torch.std(exp))\r\n\r\n# Transforming Data (Normalizing to mean=1, std= 0)\r\n\r\n\r\ndef transform(mean, std, rot):\r\n if rot != 0.0:\r\n train_transform = transforms.Compose([\r\n transforms.RandomRotation((-rot, rot), fill=(1,)),\r\n transforms.ToTensor(),\r\n transforms.Normalize((mean,), (std,))\r\n ]\r\n )\r\n elif rot == 0.0:\r\n train_transform = transforms.Compose([\r\n transforms.ToTensor(),\r\n transforms.Normalize((mean,), (std,))\r\n ]\r\n )\r\n\r\n test_transform = transforms.Compose([\r\n transforms.ToTensor(),\r\n transforms.Normalize((mean,), (std,))\r\n ])\r\n\r\n return train_transform, test_transform\r\n\r\n# Getting Train and Test Data\r\n\r\n\r\ndef split(mean=0.1311, std=0.3081, rot=0.0):\r\n train_transform, test_transform = transform(mean, std, rot)\r\n train = datasets.MNIST('./data', train=True,\r\n transform=train_transform, download=True)\r\n test = datasets.MNIST('./data', train=False,\r\n transform=test_transform, download=True)\r\n\r\n return train, test\r\n\r\n\r\ndef load(mean=0.1311, std=0.3081, rot=0.0):\r\n seed = 1\r\n\r\n train, test = split(mean, std, rot)\r\n\r\n # CUDA Availability\r\n cuda = torch.cuda.is_available()\r\n print(\"CUDA Available?\", cuda)\r\n\r\n # For Reproducibility\r\n torch.manual_seed(seed)\r\n\r\n if cuda:\r\n torch.cuda.manual_seed(seed)\r\n\r\n dataloader_args = dict(shuffle=True, batch_size=64, num_workers=4,\r\n pin_memory=True) if cuda else dict(shuffle=True, batch_size=64)\r\n\r\n # Train Dataloader\r\n train_loader = torch.utils.data.DataLoader(train, **dataloader_args)\r\n\r\n # Test Dataloader\r\n test_loader = torch.utils.data.DataLoader(test, **dataloader_args)\r\n\r\n return train_loader, test_loader\r\n", "sub_path": "06_L1L2Reg_GhostBN/data.py", "file_name": "data.py", "file_ext": "py", "file_size_in_byte": 2640, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torchvision.datasets.MNIST", "line_number": 12, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 12, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 13, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 13, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.min", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.std", "line_number": 24, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 31, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 31, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomRotation", "line_number": 32, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 32, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 33, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 33, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 34, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 34, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 38, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 38, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 39, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 39, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 40, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 40, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 44, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 44, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 45, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 45, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 46, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 46, "usage_type": "name"}, {"api_name": "torchvision.datasets.MNIST", "line_number": 56, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 56, "usage_type": "name"}, {"api_name": "torchvision.datasets.MNIST", "line_number": 58, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 70, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 77, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 83, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 86, "usage_type": "attribute"}]} +{"seq_id": "628792056", "text": "import gym\r\nimport math\r\nimport random\r\nimport numpy as np\r\nimport matplotlib\r\nimport matplotlib.pyplot as plt\r\nfrom collections import namedtuple\r\nfrom itertools import count\r\nfrom PIL import Image\r\n\r\n# pytorch libraries\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.optim as optim\r\nimport torch.nn.functional as F\r\nimport torchvision.transforms as T\r\n\r\n# System libraries\r\nimport glob\r\nimport os\r\nimport time\r\n\r\n# Project libraries\r\nfrom model import DQN\r\nfrom config import Config\r\nfrom replay_memory import ReplayMemory\r\nfrom replay_memory import Transition\r\nfrom PER import Memory\r\n\r\nclass Agent:\r\n\t\"\"\"\r\n\tThe intelligent agent of the simulation. Set the model of the neural network used and general parameters.\r\n\tIt is responsible to select the actions, optimize the neural network and manage the models.\r\n\t\"\"\"\r\n\r\n\tdef __init__(self, action_set, train=True, load_path=None):\r\n\t\t#1. Initialize agent params\r\n\t\tself.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\r\n\t\tself.action_set = action_set\r\n\t\tself.action_number = len(action_set)\r\n\t\tself.steps_done = 0\r\n\t\tself.epsilon = Config.EPS_START\r\n\t\tself.episode_durations = []\r\n\r\n\t\tprint('LOAD PATH\t-- agent.init:', load_path)\r\n\r\n\t\t#2. Build networks\r\n\t\tself.policy_net = DQN().to(self.device)\r\n\t\tself.target_net = DQN().to(self.device)\r\n\t\t\r\n\t\tself.optimizer = optim.RMSprop(self.policy_net.parameters(), lr=Config.LEARNING_RATE)\r\n\r\n\t\tif not train:\r\n\t\t\tprint('entrou no not train')\t\t\r\n\t\t\tself.optimizer = optim.RMSprop(self.policy_net.parameters(), lr=0)\t\r\n\t\t\tself.policy_net.load(load_path, optimizer=self.optimizer)\r\n\t\t\tself.policy_net.eval()\r\n\r\n\t\tself.target_net.load_state_dict(self.policy_net.state_dict())\r\n\t\tself.target_net.eval()\r\n\r\n\t\t#3. Create Prioritized Experience Replay Memory\r\n\t\tself.memory = Memory(Config.MEMORY_SIZE)\r\n\r\n\r\n\t \r\n\tdef append_sample(self, state, action, next_state, reward):\r\n\t\t\"\"\"\r\n\t\tsave sample (error,) to the replay memory\r\n\t\t\"\"\"\r\n\r\n\t\t# Define if is the end of the simulation\r\n\t\tdone = True if next_state is None else False\r\n\r\n\t\t# Compute Q(s_t, a) - the model computes Q(s_t), then we select the columns of actions taken\r\n\t\tstate_action_values = self.policy_net(state).gather(1, action)\r\n\t\t\r\n\t\tif not done:\r\n\t\t\t# Compute argmax Q(s', a; θ)\t\t\r\n\t\t\tnext_state_actions = self.policy_net(next_state).max(1)[1].detach().unsqueeze(1)\r\n\r\n\t\t\t# Compute Q(s', argmax Q(s', a; θ), θ-)\r\n\t\t\tnext_state_values = self.target_net(next_state).gather(1, next_state_actions).squeeze(1).detach()\r\n\r\n\t\t\t# Compute the expected Q values\r\n\t\t\texpected_state_action_values = (next_state_values * Config.GAMMA) + reward\r\n\t\telse:\r\n\t\t\texpected_state_action_values = reward\r\n\r\n\r\n\t\terror = abs(state_action_values - expected_state_action_values).data.cpu().numpy()\r\n\r\n\r\n\t\tself.memory.add(error, state, action, next_state, reward)\r\n\r\n\r\n\tdef select_action(self, state, train=True):\r\n\t\t\"\"\"\r\n\t\tSelet the best action according to the Q-values outputed from the neural network\r\n\r\n\t\tParameters\r\n\t\t----------\r\n\t\t\tstate: float ndarray\r\n\t\t\t\tThe current state on the simulation\r\n\t\t\ttrain: bool\r\n\t\t\t\tDefine if we are evaluating or trainning the model\r\n\t\tReturns\r\n\t\t-------\r\n\t\t\ta.max(1)[1]: int\r\n\t\t\t\tThe action with the highest Q-value\r\n\t\t\ta.max(0): float\r\n\t\t\t\tThe Q-value of the action taken\r\n\t\t\"\"\"\r\n\t\tglobal steps_done\r\n\t\tsample = random.random()\r\n\t\t#1. Perform a epsilon-greedy algorithm\r\n\t\t#a. set the value for epsilon\r\n\t\tself.epsilon = Config.EPS_END + (Config.EPS_START - Config.EPS_END) * \\\r\n\t\t\tmath.exp(-1. * self.steps_done / Config.EPS_DECAY)\r\n\t\t\t\r\n\t\tself.steps_done += 1\r\n\r\n\t\t#b. make the decision for selecting a random action or selecting an action from the neural network\r\n\t\tif sample > self.epsilon or (not train):\r\n\t\t\t# select an action from the neural network\r\n\t\t\twith torch.no_grad():\r\n\t\t\t\t# a <- argmax Q(s, theta)\r\n\t\t\t\ta = self.policy_net(state)\r\n\t\t\t\treturn a.max(1)[1].view(1, 1), a.max(0)\r\n\t\telse:\r\n\t\t\t# select a random action\r\n\t\t\tprint('random action')\r\n\t\t\treturn torch.tensor([[random.randrange(2)]], device=self.device, dtype=torch.long), None\r\n\r\n\tdef optimize_model(self):\r\n\t\t\"\"\"\r\n\t\tPerform one step of optimization on the neural network\r\n\t\t\"\"\"\r\n\r\n\t\tif self.memory.tree.n_entries < Config.BATCH_SIZE:\r\n\t\t\treturn\r\n\t\ttransitions, idxs, is_weights = self.memory.sample(Config.BATCH_SIZE)\r\n\r\n\t\t# Transpose the batch (see http://stackoverflow.com/a/19343/3343043 for detailed explanation).\r\n\t\tbatch = Transition(*zip(*transitions))\r\n\r\n\t\t# Compute a mask of non-final states and concatenate the batch elements\r\n\t\tnon_final_mask = torch.tensor(tuple(map(lambda s: s is not None,\r\n\t\t\t\t\t\t\t\t\t\t\t batch.next_state)), device=self.device, dtype=torch.uint8)\r\n\t\tnon_final_next_states = torch.cat([s for s in batch.next_state\r\n\t\t\t\t\t\t\t\t\t\t\t\t\tif s is not None])\r\n\t\t\r\n\t\tstate_batch = torch.cat(batch.state)\r\n\t\taction_batch = torch.cat(batch.action)\r\n\t\treward_batch = torch.cat(batch.reward)\r\n\t\t\r\n\t\t# Compute Q(s_t, a) - the model computes Q(s_t), then we select the columns of actions taken\r\n\t\tstate_action_values = self.policy_net(state_batch).gather(1, action_batch)\r\n\t\t\r\n\t\r\n\t\t# Compute argmax Q(s', a; θ)\t\t\r\n\t\tnext_state_actions = self.policy_net(non_final_next_states).max(1)[1].detach().unsqueeze(1)\r\n\r\n\t\t# Compute Q(s', argmax Q(s', a; θ), θ-)\r\n\t\tnext_state_values = torch.zeros(Config.BATCH_SIZE, device=self.device)\r\n\t\tnext_state_values[non_final_mask] = self.target_net(non_final_next_states).gather(1, next_state_actions).squeeze(1).detach()\r\n\r\n\t\t# Compute the expected Q values\r\n\t\texpected_state_action_values = (next_state_values * Config.GAMMA) + reward_batch\r\n\r\n\t\t# Update priorities\r\n\t\terrors = torch.abs(state_action_values.squeeze() - expected_state_action_values).data.cpu().numpy()\r\n\t\t\r\n\t\t# update priority\r\n\t\tfor i in range(Config.BATCH_SIZE):\r\n\t\t\tidx = idxs[i]\r\n\t\t\tself.memory.update(idx, errors[i])\r\n\r\n\r\n\t\t# Compute Huber loss\r\n\t\tloss = F.smooth_l1_loss(state_action_values, expected_state_action_values.unsqueeze(1))\r\n\t\t\r\n\t\t# Optimize the model\r\n\t\tself.optimizer.zero_grad()\r\n\t\tloss.backward()\r\n\t\tfor param in self.policy_net.parameters():\r\n\t\t\tparam.grad.data.clamp_(-1, 1)\r\n\t\tself.optimizer.step()\r\n\r\n\tdef save(self, step, logs_path, label):\r\n\t\t\"\"\"\r\n\t\tSave the model on hard disc\r\n\r\n\t\tParameters\r\n\t\t----------\r\n\t\t\tstep: int\r\n\t\t\t\tcurrent step on the simulation\r\n\t\t\tlogs_path: string\r\n\t\t\t\tpath to where we will store the model\r\n\t\t\tlabel: string\r\n\t\t\t\tlabel that will be used to store the model\r\n\t\t\"\"\"\r\n\r\n\t\tos.makedirs(logs_path + label, exist_ok=True)\r\n\r\n\t\tfull_label = label + str(step) + '.pth'\r\n\t\tlogs_path = os.path.join(logs_path, label, full_label)\r\n\r\n\t\tself.policy_net.save(logs_path, step=step, optimizer=self.optimizer)\r\n\t\r\n\tdef restore(self, logs_path):\r\n\t\t\"\"\"\r\n\t\tLoad the model from hard disc\r\n\r\n\t\tParameters\r\n\t\t----------\r\n\t\t\tlogs_path: string\r\n\t\t\t\tpath to where we will store the model\r\n\t\t\"\"\"\r\n\t\tself.policy_net.load(logs_path)\r\n\t\tself.target_net.load(logs_path)\r\n", "sub_path": "PER_DDQN/agent.py", "file_name": "agent.py", "file_ext": "py", "file_size_in_byte": 6891, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.device", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 38, "usage_type": "attribute"}, {"api_name": "config.Config.EPS_START", "line_number": 42, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 42, "usage_type": "name"}, {"api_name": "model.DQN", "line_number": 48, "usage_type": "call"}, {"api_name": "model.DQN", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.optim.RMSprop", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 51, "usage_type": "name"}, {"api_name": "config.Config.LEARNING_RATE", "line_number": 51, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 51, "usage_type": "name"}, {"api_name": "torch.optim.RMSprop", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 55, "usage_type": "name"}, {"api_name": "PER.Memory", "line_number": 63, "usage_type": "call"}, {"api_name": "config.Config.MEMORY_SIZE", "line_number": 63, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 63, "usage_type": "name"}, {"api_name": "config.Config.GAMMA", "line_number": 86, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 86, "usage_type": "name"}, {"api_name": "random.random", "line_number": 115, "usage_type": "call"}, {"api_name": "config.Config.EPS_END", "line_number": 118, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 118, "usage_type": "name"}, {"api_name": "config.Config.EPS_START", "line_number": 118, "usage_type": "attribute"}, {"api_name": "math.exp", "line_number": 119, "usage_type": "call"}, {"api_name": "config.Config.EPS_DECAY", "line_number": 119, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 119, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 133, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 133, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 133, "usage_type": "attribute"}, {"api_name": "config.Config.BATCH_SIZE", "line_number": 140, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 140, "usage_type": "name"}, {"api_name": "config.Config.BATCH_SIZE", "line_number": 142, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 142, "usage_type": "name"}, {"api_name": "replay_memory.Transition", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 148, "usage_type": "call"}, {"api_name": "torch.uint8", "line_number": 149, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 165, "usage_type": "call"}, {"api_name": "config.Config.BATCH_SIZE", "line_number": 165, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 165, "usage_type": "name"}, {"api_name": "config.Config.GAMMA", "line_number": 169, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 169, "usage_type": "name"}, {"api_name": "torch.abs", "line_number": 172, "usage_type": "call"}, {"api_name": "config.Config.BATCH_SIZE", "line_number": 175, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 175, "usage_type": "name"}, {"api_name": "torch.nn.functional.smooth_l1_loss", "line_number": 181, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 181, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 204, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 207, "usage_type": "call"}, {"api_name": "os.path", "line_number": 207, "usage_type": "attribute"}]} +{"seq_id": "98741337", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Jan 23 13:43:35 2019\r\n\r\n@author: jordan\r\n\"\"\"\r\nimport requests\r\n\r\nurl = 'http://...:5000/deviceprofile'\r\n\r\ndata = {'uid':'X7TWVZHT','file':open('test.txt','rb'),'submit':'Upload'}\r\n\r\nfiles = [('file', open('test.txt', 'rb'))]\r\n\r\nr = requests.post(url, data=data, files = files)\r\n\r\nprint(r.content)\r\n", "sub_path": "testSaveDeviceProfile.py", "file_name": "testSaveDeviceProfile.py", "file_ext": "py", "file_size_in_byte": 343, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.post", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "603736088", "text": "import base64\nimport email\nimport imaplib\nimport imaplib_connect\nfrom email.parser import HeaderParser\nfrom imaplib_list_parse import parse_list_response\nfrom email.header import decode_header\n# https://docs.python.org/3/library/email.header.html\nprefix = '=?UTF-8?'\nsuffix = '?='\n\nM = imaplib_connect.open_connection()\n\nM.select()\ntyp, data = M.search(None, 'ALL')\n\nmessages = []\nmessageInfo = {}\n\n# http://www.tutorialspoint.com/python/string_find.htm\n\n\n\nfor num in data[0].split():\n typ, data = M.fetch(num, '(RFC822)')\n \n message = data[0][1]\n\n \n ### TO\n # message = message.replace('Delivered-To:', '') # so the next find isn't confused\n\n # messageToPos = message.find('To: ')\n # endPos = message.find('\\n', messageToPos)\n # messageTo = message[messageToPos + 3:endPos].strip() \n \n # if messageTo.find(' 0:\n # \tmessageTo = messageTo.split('', '')\n\n # messageInfo['to'] = messageTo\n\n # https://www.sitekickr.com/snippets/python/retrieve-messages-imap-account\n # http://stackoverflow.com/questions/5259601/how-convert-email-subject-from-utf-8-to-readable-string\n #SUBJECT\n # messageSubjectPos = message.find(b'Subject: ')\n \n # endPos = message.find('\\n', messageSubjectPos)\n # endPos = message.find(b'\\n', messageSubjectPos)\n \n # http://stackoverflow.com/questions/14773732/python-email-parser-extract-header-from-email\n # messageSubject = message[messageSubjectPos + 9:endPos].strip() \n raw_message=message.decode(\"utf-8\")\n msg = email.message_from_string(raw_message)\n print(msg['Subject'])\n messageInfo['subject'] = msg['Subject']\t \n # print(num, messageSubject)\n print()\n print(num.decode('ascii'), )\n \n \n if (\"=?UTF-8?\" in messageInfo['subject'] ):\n # messageSubject=messageSubject.decode('GBK')\n # messageSubject=messageSubject.decode('ascii')\n \n \n to_decode = decode_header( messageInfo['subject'] )\n # print(to_decode)\n desired_subject=\"\"\n for x in to_decode:\n # print( \" \",x)\n # print( \" ---\",x[0])\n # print( \" ---\",x[1])\n if (x[1] == None):\n # print(x[0].decode(\"ascii\"))\n desired_subject += x[0].decode(\"ascii\")\n else:\n # print(x[0].decode(x[1]))\n desired_subject += x[0].decode(x[1])\n print(desired_subject)\n \n ", "sub_path": "test-b02.py", "file_name": "test-b02.py", "file_ext": "py", "file_size_in_byte": 2429, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "imaplib_connect.open_connection", "line_number": 12, "usage_type": "call"}, {"api_name": "email.message_from_string", "line_number": 53, "usage_type": "call"}, {"api_name": "email.header.decode_header", "line_number": 66, "usage_type": "call"}]} +{"seq_id": "598698167", "text": "# /bin/env python3\n# ==============================================================================\n# Copyright (c) Moises Martinez by Fictizia. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom sklearn.cluster import KMeans\nfrom joblib import load\nfrom google.cloud import storage\n\nimport os\n\nTRAINED_MODEL_PATH = '../../models'\nTRAINED_MODEL_NAME = 'iris-trained'\nDOWNLOADED_MODEL_NAME = 'iris-loaded'\nBLOB_FOLDER = 'kmeans_models'\n\nPATH = os.path.join(os.getcwd())\n\n\nclass GCSHandler:\n\n def __init__(self, credentials_file):\n self.__client = storage.Client.from_service_account_json(credentials_file)\n self.__bucket_name_loaded = None\n self.__bucket = None\n\n def load_bucket(self, name):\n if name != self.__bucket_name_loaded:\n self.__bucket_name_loaded == name\n self.__bucket = self.__client.get_bucket(name)\n\n def get_bucket(self):\n return self.__bucket\n\n def download_file(self, local_path, blob_path):\n blob = self.__bucket.get_blob(blob_path)\n return blob.download_to_filename(local_path)\n\n def upload_file(self, local_path, blob_path):\n blob = self.__bucket.get_blob(blob_path)\n return blob.upload_from_filename(local_path)\n\n\nremote_file_name = TRAINED_MODEL_NAME + '.joblib'\nlocal_file_name = DOWNLOADED_MODEL_NAME + '.joblib'\nfile_local_path = os.path.join(TRAINED_MODEL_PATH, local_file_name)\nfile_remote_path = os.path.join(BLOB_FOLDER, remote_file_name)\n\n\nif os.path.exists(file_local_path):\n os.remove(file_local_path)\n\ngcs_handler = GCSHandler('../../credentials/credentials.json')\ngcs_handler.load_bucket('fictizia')\ngcs_handler.download_file(file_local_path, file_remote_path)\n\n\ndef predict(field_1: float, field_2: float, field_3: float, field_4: float):\n\n input = [[field_1, field_2, field_3, field_4]]\n output = dict()\n\n model: KMeans = load(file_local_path)\n\n try:\n output['class'] = int(model.predict(input)[0])\n return output, 200\n except Exception as e:\n return str(e), 300\n", "sub_path": "capitulo_8/recursos/ejercicio_1/api/src/functions.py", "file_name": "functions.py", "file_ext": "py", "file_size_in_byte": 2751, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 32, "usage_type": "call"}, {"api_name": "google.cloud.storage.Client.from_service_account_json", "line_number": 38, "usage_type": "call"}, {"api_name": "google.cloud.storage.Client", "line_number": 38, "usage_type": "attribute"}, {"api_name": "google.cloud.storage", "line_number": 38, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "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": "os.path.exists", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 66, "usage_type": "call"}, {"api_name": "sklearn.cluster.KMeans", "line_number": 78, "usage_type": "name"}, {"api_name": "joblib.load", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "310077373", "text": "import numpy as np\nimport networkx as nx\nfrom matplotlib import pyplot as plt\nfrom collections import deque\n#from dataset import GraphFactory\nfrom utils import seed_everything\nclass BFS:\n def run(self, graph, root=0):\n '''\n Parameters\n ----------\n graph: NetworkX Graph instance\n The graph on which the algorithm should be run\n root: index of the node that should be used as root for the DFS\n Returns:\n --------\n The history of x (states) when executing the DFS algorithm, and the DFS\n output\n '''\n\n E = nx.to_numpy_matrix(graph)\n E=np.array(E)\n \n x = self.initialize_x(graph, root)\n history = [x.copy()]\n\n queue = deque()\n queue.append(root)\n memory = set()\n terminate=False\n while len(queue) > 0 and np.sum(x) < len(x):\n second_queue = deque()\n while len(queue) > 0 and np.sum(x) < len(x):\n cur = queue.popleft()\n #print(\"cur\",cur)\n memory.add(cur)\n neighbours = np.where(E[cur] > 0)[0]\n #print(E[cur])\n for n in neighbours:\n #print(\"n\",n)\n if n not in memory:\n #print(\"added\")\n second_queue.append(n)\n x[n] = 1\n if (x == history[-1]).all():\n terminate = True\n break\n history.append(x.copy())\n queue = second_queue\n if terminate:\n break\n return np.asarray(history)\n\n \n\n def initialize_x(self, graph, root=0):\n '''\n Parameters\n ----------\n graph: NetworkX Graph instance\n The graph on which the algorithm should be run\n root: index of the node that should be used as a root for the DFS\n Returns:\n --------\n Initialized numpy representation of the graph, as used by our DFS implementation\n '''\n\n nb_nodes = graph.number_of_nodes()\n x = np.zeros((nb_nodes))\n x[root] = 1\n\n return x\n\nif __name__ == '__main__':\n seed=17\n seed_everything(seed)\n graph = GraphFactory.get_graph(7, 'erdos_renyi')\n E = nx.to_numpy_matrix(graph)\n E=np.array(E)\n\n bfs = BFS()\n hist = bfs.run(graph)\n for arr in hist:\n print(arr)\n \n print(E)\n", "sub_path": "bfs.py", "file_name": "bfs.py", "file_ext": "py", "file_size_in_byte": 2314, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "networkx.to_numpy_matrix", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 22, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 31, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 69, "usage_type": "call"}, {"api_name": "utils.seed_everything", "line_number": 76, "usage_type": "call"}, {"api_name": "networkx.to_numpy_matrix", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 79, "usage_type": "call"}]} +{"seq_id": "343378946", "text": "import simplejson\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.contrib.auth.models import User\nfrom django.core.context_processors import csrf\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import render_to_response\nfrom netaddr import valid_ipv4, valid_ipv6\n\n\nfrom dbapi.mysql_connector import get_database_connection\nfrom dbapi.queries import find_ip_list_type, del_source_with_ip\nfrom dbapi.queries import del_ip_from_list, get_sources_data_by_id\nfrom dbapi.queries import update_sources_data, get_sources\nfrom dbapi.queries import select_top_oldest_IP, add_ip_to_mywhitelist\nfrom dbapi.queries import select_table_statistic, add_feed\nfrom dbapi.statistics import select_IP_by_country, black_ip_by_cit\nfrom dbapi.statistics import count_all_IP_by_country, country_for_info\nfrom decorators import add_user_info_and_menu\nfrom forms import *\nfrom project_settings import DBAPI_CONFIG_PATH, JSON_PATH, FEEDS_OUTPUT_PATH\nfrom django.utils.translation import ugettext as _\nfrom django.template import RequestContext\n\n\ndef login_page(request):\n '''Login user to the site.'''\n if request.user.is_authenticated():\n return HttpResponseRedirect('/index/')\n else:\n form = LoginForm()\n dic = {'form': form}\n dic.update(csrf(request))\n if request.method == 'POST':\n form = LoginForm(data=request.POST)\n if form.is_valid():\n form.clean()\n user = form.get_user()\n login(request, user)\n if not request.POST.get('remember_me') is None:\n request.session.set_expiry(60 * 60 * 24 * 7 * 2)\n return HttpResponseRedirect('/index/')\n else:\n dic['error'] = form.errors\n dic['errors'] = form.non_field_errors\n return render_to_response('login_page.html', dic)\n\n\ndef user_logout(request):\n '''User logout.'''\n if request.user.is_authenticated():\n logout(request)\n return HttpResponseRedirect('/login/')\n\n\ndef registration_page(request):\n '''Register a new user.'''\n form = RegisterForm()\n dic = {'form': form}\n dic.update(csrf(request))\n if request.method == 'POST':\n data = {'first_name': request.POST['first_name'],\n 'last_name': request.POST['last_name'],\n 'email': request.POST['email'],\n 'username': request.POST['username']}\n form = RegisterForm(data)\n dic['form'] = form\n form = RegisterForm(request.POST)\n dic['error'] = form.errors\n if form.is_valid():\n dic['user'] = form.cleaned_data['username']\n dic['pas'] = form.cleaned_data['password1']\n dic['fname'] = form.cleaned_data['first_name']\n dic['lname'] = form.cleaned_data['last_name']\n dic['email'] = form.cleaned_data['email']\n user = User.objects.create_user(\n username=dic['user'],\n password=dic['pas'],\n first_name=dic['fname'],\n last_name=dic['lname'],\n email=dic['email']\n )\n user.save()\n user = authenticate(username=dic['user'], password=dic['pas'])\n if user is not None and user.is_active:\n login(request, user)\n return HttpResponseRedirect('/index/')\n return render_to_response('registration_page.html', dic)\n\n\n@add_user_info_and_menu\ndef welcome_page(request, *args):\n dic = args[0]\n dic['msg'] = 'Entered incorrect data!'\n return render_to_response('welcome_page.html', dic)\n\n\n@add_user_info_and_menu\ndef add_ip_to_wlist(request, *args):\n dic = args[0]\n form = AddIpToWlist()\n dic['form'] = form\n dic.update(csrf(request))\n ip = ''\n st = ''\n if request.method == 'POST':\n form = AddIpToWlist(request.POST)\n dic['error'] = [item[1] for item in form.errors.items()]\n if form.is_valid():\n ip = form.cleaned_data['ip_field']\n dic['items'] = form.cleaned_data['items']\n connection = get_database_connection(DBAPI_CONFIG_PATH)\n ip = ip.split()\n dic['info'] = ip[0]\n i = 0\n lis = []\n while i < len(ip):\n if valid_ipv4(ip[i]) or valid_ipv6(ip[i]):\n st += add_ip_to_mywhitelist(connection, ip[i]) + '\\n'\n #dic['info'] = add_ip_to_mywhitelist(connection, ip[i])\n else:\n lis.append(ip[i])\n i = i + 1\n if len(lis) > 0:\n message = 'Some IP addres was not added \\\n please correct your data. \\n' + st\n dic['info'] = message.split('\\n')\n lists = ' '.join(lis)\n data = {'ip_field': lists}\n form = AddIpToWlist(data)\n dic['form'] = form\n else:\n dic['info'] = st.split('\\n')\n return render_to_response('add_to_wlist.html', dic)\n\n\n@add_user_info_and_menu\ndef delete_feed(request, *args):\n dic = args[0]\n form = DeleteFeed()\n dic['form'] = form\n dic.update(csrf(request))\n feed = ''\n if request.method == 'POST':\n feed = request.POST['id']\n connection = get_database_connection(DBAPI_CONFIG_PATH, True)\n dic['info'] = del_source_with_ip(connection, feed)\n dic['data'] = get_sources(connection)\n return render_to_response('del_feed.html', dic)\n\n\n@add_user_info_and_menu\ndef update_feed(request, *args):\n dic = args[0]\n dic.update(csrf(request))\n connection = get_database_connection(DBAPI_CONFIG_PATH)\n dic['info'] = get_sources(connection)\n return render_to_response('update_feed.html', dic)\n\n\n@add_user_info_and_menu\ndef update_feed2(request, *args):\n dic = args[0]\n #if not request.method == 'GET' or not request.method == 'POST':\n # return HttpResponseRedirect('/update_feed')\n if request.method == 'POST':\n form = UpdateFeed(request.POST)\n if form.is_valid():\n ids = form.cleaned_data['ids']\n name = (form.cleaned_data['name']).strip()\n rank = form.cleaned_data['rank']\n connection = get_database_connection(DBAPI_CONFIG_PATH)\n update_sources_data(connection, name, rank, ids)\n return HttpResponseRedirect('/update_feed/')\n else:\n #dic['error'] = form.errors\n form = UpdateFeed(request.POST)\n dic['form'] = form\n dic.update(csrf(request))\n return render_to_response('update_feed2.html', dic)\n else:\n if request.method == 'GET':\n try:\n ids = request.GET['ids']\n connection = get_database_connection(DBAPI_CONFIG_PATH)\n result = get_sources_data_by_id(connection, ids)\n data = {\n 'ids': result[0],\n 'name': result[1],\n 'url': result[2],\n 'rank': result[5]\n }\n form = UpdateFeed(data)\n dic['form'] = form\n dic.update(csrf(request))\n except:\n return HttpResponseRedirect('/update_feed/')\n return render_to_response('update_feed2.html', dic)\n\n\n@add_user_info_and_menu\ndef check_ip_status(request, *args):\n\n \"\"\"View for checking whether ip in black or white list.\"\"\"\n context = args[0]\n context.update(csrf(request))\n if request.method == 'POST':\n submitted_form = CheckIPStatusForm(request.POST)\n if submitted_form.is_valid():\n address = submitted_form.cleaned_data['ip_address']\n context['address'] = address\n connection = get_database_connection(DBAPI_CONFIG_PATH)\n list_type = find_ip_list_type(connection, address)\n context['list_type'] = list_type\n connection.close()\n else:\n context['errors'] = submitted_form.errors.values()\n context['ip_status_form'] = CheckIPStatusForm()\n return render_to_response('check_ip_status.html', context)\n\n\n@add_user_info_and_menu\ndef list_downloads(request, *args):\n \"\"\"Export blacklist to specified formats.\"\"\"\n return render_to_response('list_downloads.html', args[0],\n context_instance=RequestContext(request))\n\n\n@add_user_info_and_menu\ndef country_charts(request, *args):\n return render_to_response('country_count.html', args[0],\n context_instance=RequestContext(request))\n\n\n@add_user_info_and_menu\ndef blacklist_map(request, *args):\n return render_to_response('country_map_blacklist.html', args[0],\n context_instance=RequestContext(request))\n\n\n@add_user_info_and_menu\ndef whitelist_map(request, *args):\n return render_to_response('country_map_whitelist.html', args[0],\n context_instance=RequestContext(request))\n\n\n@add_user_info_and_menu\ndef index_page(request, *args):\n \"Main page of our site\"\n context = args[0]\n connection = get_database_connection(DBAPI_CONFIG_PATH)\n context['wlist_cnt'] = select_table_statistic(connection, 'whitelist')\n context['blist_cnt'] = select_table_statistic(connection, 'blacklist')\n context['source_cnt'] = select_table_statistic(connection, 'sources')\n context['ipv4_cnt'] = select_table_statistic(connection, 'ipv4_addresses')\n context['ipv6_cnt'] = select_table_statistic(connection, 'ipv6_addresses')\n # Uncomment that functions to add more statistics to index page\n \"\"\"\n context['ipv4_wlist_cnt'] = select_table_statistic(connection, 'whitelist',\n 'v4_id_whitelist')\n context['ipv6_wlist_cnt'] = select_table_statistic(connection, 'whitelist',\n 'v6_id_whitelist')\n context['ipv4_blist_cnt'] = select_table_statistic(connection, 'blacklist',\n 'v4_id_blacklist')\n context['ipv6_blist_cnt'] = select_table_statistic(connection, 'blacklist',\n 'v6_id_blacklist')\n \"\"\"\n return render_to_response('index.html', context,\n context_instance=RequestContext(request))\n\n\n@add_user_info_and_menu\ndef delete_ipwl(request, *args):\n context = args[0]\n context.update(csrf(request))\n if request.method == 'POST':\n del_form = DeleteIps(request.POST)\n if del_form.is_valid():\n address = del_form.cleaned_data['ip_or_iprange']\n context['address'] = address\n connection = get_database_connection(DBAPI_CONFIG_PATH)\n context['list_type'] = find_ip_list_type(connection, address)\n if context['list_type'] == 'whitelist':\n del_ip_from_list(connection, address, context['list_type'])\n context['confirm_mssg'] = _('IP address \"%s\" succesfull \\\n deleted') % address\n else:\n context['errors'] = _('IP address \"%s\" is not in WL') % address\n connection.close()\n else:\n context['errors'] = _('Enter a valid IPv4 or IPv6 address.')\n context['ip_delete_form'] = DeleteIps()\n return render_to_response('delete_wlip.html', context)\n\n\n@add_user_info_and_menu\ndef top_black_ip_by_country(request, *args):\n context = args[0]\n context.update(csrf(request))\n context['form'] = SelectTopBlackIPByCountry()\n if request.method == 'POST':\n submitted_form = SelectTopBlackIPByCountry(request.POST)\n if not submitted_form.is_valid():\n context['error'] = '* Please, enter the country!'\n else:\n context['form'] = submitted_form\n country = submitted_form.cleaned_data['countryfield']\n context['country'] = country\n count = submitted_form.cleaned_data['count_IPs']\n context['info'] = select_IP_by_country(country, count)\n context['infocountIP'] = count_all_IP_by_country()\n context['infocountry'] = country_for_info()\n return render_to_response('top_black_IP.html', context)\n\n\n@add_user_info_and_menu\ndef top_old_ip(request, *args):\n context = args[0]\n form = SelectTopOldestIP()\n context['form'] = form\n context.update(csrf(request))\n connection = get_database_connection(DBAPI_CONFIG_PATH)\n if request.method == 'POST':\n form = SelectTopOldestIP(request.POST)\n context['form'] = form\n if form.is_valid():\n count = form.cleaned_data['count']\n context['info'] = select_top_oldest_IP(connection, int(count))\n return render_to_response('top_old_IP.html', context)\n\n\n@add_user_info_and_menu\ndef test(request, *args):\n return render_to_response('test.html', args[0],\n context_instance=RequestContext(request))\n\n\n@add_user_info_and_menu\ndef black_ip_by_city(request, *args):\n context = args[0]\n form = BlackIPByCity()\n context['form'] = form\n context.update(csrf(request))\n if request.method == 'POST':\n form = BlackIPByCity(request.POST)\n context['error'] = form.errors\n if form.is_valid():\n city = (form.cleaned_data['city']).strip()\n count = form.cleaned_data['count']\n with open('./static/json/cities.json') as cities:\n data = cities.read()\n if city.encode('utf-8') in data:\n ips = black_ip_by_cit(city, count)\n if len(ips) > 1:\n context['info'] = ips\n else:\n context['no_ip'] = 'No black ips from this country!'\n else:\n context['no_city'] = 'No such city in GeoIP!'\n return render_to_response('black_ip_by_city.html', context)\n\n\n@add_user_info_and_menu\ndef add_feed_page(request, *args):\n context = args[0]\n context['form'] = AddFeedForm()\n context.update(csrf(request))\n if request.method == 'POST':\n form = AddFeedForm(request.POST)\n if form.is_valid():\n feed_name = form.cleaned_data['feed_name']\n feed_url = form.cleaned_data['feed_url']\n feed_rank = form.cleaned_data['feed_rank']\n feed_dirname = form.cleaned_data['feed_dirname']\n feed_type = form.cleaned_data['feed_type']\n feed_adaptor = form.cleaned_data['feed_adaptor']\n feed_downloader = form.cleaned_data['download_type']\n conn = get_database_connection()\n add_feed(conn, feed_name, feed_url, feed_rank, feed_dirname,\n feed_type, feed_adaptor, feed_downloader)\n conn.close()\n return HttpResponseRedirect('/add-feed/')\n else:\n context['form'] = form\n return render_to_response('add_feed.html', context)\n return render_to_response('add_feed.html', context)\n\n\n@add_user_info_and_menu\ndef daemon_control(request, *args):\n context = args[0]\n json_path = os.path.join(JSON_PATH, 'loaderd.json')\n if os.path.isfile(json_path):\n initial_data = simplejson.load(open(json_path))\n else:\n initial_data = {\n u'loglevel': u'DEBUG',\n u'output_dir': FEEDS_OUTPUT_PATH,\n u'timeout': 25}\n context['form'] = DaemonControlsForm(initial=initial_data)\n return render_to_response('daemon-control.html', context,\n context_instance=RequestContext(request))\n", "sub_path": "web/ipconflux/ipmanager/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 15604, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.http.HttpResponseRedirect", "line_number": 28, "usage_type": "call"}, {"api_name": "django.core.context_processors.csrf", "line_number": 32, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 38, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 41, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 45, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 51, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 52, "usage_type": "call"}, {"api_name": "django.core.context_processors.csrf", "line_number": 59, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.create_user", "line_number": 75, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 75, "usage_type": "name"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 83, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 85, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 86, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 87, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 94, "usage_type": "call"}, {"api_name": "decorators.add_user_info_and_menu", "line_number": 90, "usage_type": "name"}, {"api_name": "django.core.context_processors.csrf", "line_number": 102, "usage_type": "call"}, {"api_name": "dbapi.mysql_connector.get_database_connection", "line_number": 111, "usage_type": "call"}, {"api_name": "project_settings.DBAPI_CONFIG_PATH", "line_number": 111, "usage_type": "argument"}, {"api_name": "netaddr.valid_ipv4", "line_number": 117, "usage_type": "call"}, {"api_name": "netaddr.valid_ipv6", "line_number": 117, "usage_type": "call"}, {"api_name": "dbapi.queries.add_ip_to_mywhitelist", "line_number": 118, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 133, "usage_type": "call"}, {"api_name": "decorators.add_user_info_and_menu", "line_number": 97, "usage_type": "name"}, {"api_name": "django.core.context_processors.csrf", "line_number": 141, "usage_type": "call"}, {"api_name": "dbapi.mysql_connector.get_database_connection", "line_number": 145, "usage_type": "call"}, {"api_name": "project_settings.DBAPI_CONFIG_PATH", "line_number": 145, "usage_type": "argument"}, {"api_name": "dbapi.queries.del_source_with_ip", "line_number": 146, "usage_type": "call"}, {"api_name": "dbapi.queries.get_sources", "line_number": 147, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 148, "usage_type": "call"}, {"api_name": "decorators.add_user_info_and_menu", "line_number": 136, "usage_type": "name"}, {"api_name": "django.core.context_processors.csrf", "line_number": 154, "usage_type": "call"}, {"api_name": "dbapi.mysql_connector.get_database_connection", "line_number": 155, "usage_type": "call"}, {"api_name": "project_settings.DBAPI_CONFIG_PATH", "line_number": 155, "usage_type": "argument"}, {"api_name": "dbapi.queries.get_sources", "line_number": 156, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 157, "usage_type": "call"}, {"api_name": "decorators.add_user_info_and_menu", "line_number": 151, "usage_type": "name"}, {"api_name": "dbapi.mysql_connector.get_database_connection", "line_number": 171, "usage_type": "call"}, {"api_name": "project_settings.DBAPI_CONFIG_PATH", "line_number": 171, "usage_type": "argument"}, {"api_name": "dbapi.queries.update_sources_data", "line_number": 172, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 173, "usage_type": "call"}, {"api_name": "django.core.context_processors.csrf", "line_number": 178, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 179, "usage_type": "call"}, {"api_name": "dbapi.mysql_connector.get_database_connection", "line_number": 184, "usage_type": "call"}, {"api_name": "project_settings.DBAPI_CONFIG_PATH", "line_number": 184, "usage_type": "argument"}, {"api_name": "dbapi.queries.get_sources_data_by_id", "line_number": 185, "usage_type": "call"}, {"api_name": "django.core.context_processors.csrf", "line_number": 194, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 196, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 197, "usage_type": "call"}, {"api_name": "decorators.add_user_info_and_menu", "line_number": 160, "usage_type": "name"}, {"api_name": "django.core.context_processors.csrf", "line_number": 205, "usage_type": "call"}, {"api_name": "dbapi.mysql_connector.get_database_connection", "line_number": 211, "usage_type": "call"}, {"api_name": "project_settings.DBAPI_CONFIG_PATH", "line_number": 211, "usage_type": "argument"}, {"api_name": "dbapi.queries.find_ip_list_type", "line_number": 212, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 218, "usage_type": "call"}, {"api_name": "decorators.add_user_info_and_menu", "line_number": 200, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 224, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 225, "usage_type": "call"}, {"api_name": "decorators.add_user_info_and_menu", "line_number": 221, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 230, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 231, "usage_type": "call"}, {"api_name": "decorators.add_user_info_and_menu", "line_number": 228, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 236, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 237, "usage_type": "call"}, {"api_name": "decorators.add_user_info_and_menu", "line_number": 234, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 242, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 243, "usage_type": "call"}, {"api_name": "decorators.add_user_info_and_menu", "line_number": 240, "usage_type": "name"}, {"api_name": "dbapi.mysql_connector.get_database_connection", "line_number": 250, "usage_type": "call"}, {"api_name": "project_settings.DBAPI_CONFIG_PATH", "line_number": 250, "usage_type": "argument"}, {"api_name": "dbapi.queries.select_table_statistic", "line_number": 251, "usage_type": "call"}, {"api_name": "dbapi.queries.select_table_statistic", "line_number": 252, "usage_type": "call"}, {"api_name": "dbapi.queries.select_table_statistic", "line_number": 253, "usage_type": "call"}, {"api_name": "dbapi.queries.select_table_statistic", "line_number": 254, "usage_type": "call"}, {"api_name": "dbapi.queries.select_table_statistic", "line_number": 255, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 267, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 268, "usage_type": "call"}, {"api_name": "decorators.add_user_info_and_menu", "line_number": 246, "usage_type": "name"}, {"api_name": "django.core.context_processors.csrf", "line_number": 274, "usage_type": "call"}, {"api_name": "dbapi.mysql_connector.get_database_connection", "line_number": 280, "usage_type": "call"}, {"api_name": "project_settings.DBAPI_CONFIG_PATH", "line_number": 280, "usage_type": "argument"}, {"api_name": "dbapi.queries.find_ip_list_type", "line_number": 281, "usage_type": "call"}, {"api_name": "dbapi.queries.del_ip_from_list", "line_number": 283, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 284, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 287, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext", "line_number": 290, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 292, "usage_type": "call"}, {"api_name": "decorators.add_user_info_and_menu", "line_number": 271, "usage_type": "name"}, {"api_name": "django.core.context_processors.csrf", "line_number": 298, "usage_type": "call"}, {"api_name": "dbapi.statistics.select_IP_by_country", "line_number": 309, "usage_type": "call"}, {"api_name": "dbapi.statistics.count_all_IP_by_country", "line_number": 310, "usage_type": "call"}, {"api_name": "dbapi.statistics.country_for_info", "line_number": 311, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 312, "usage_type": "call"}, {"api_name": "decorators.add_user_info_and_menu", "line_number": 295, "usage_type": "name"}, {"api_name": "django.core.context_processors.csrf", "line_number": 320, "usage_type": "call"}, {"api_name": "dbapi.mysql_connector.get_database_connection", "line_number": 321, "usage_type": "call"}, {"api_name": "project_settings.DBAPI_CONFIG_PATH", "line_number": 321, "usage_type": "argument"}, {"api_name": "dbapi.queries.select_top_oldest_IP", "line_number": 327, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 328, "usage_type": "call"}, {"api_name": "decorators.add_user_info_and_menu", "line_number": 315, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 333, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 334, "usage_type": "call"}, {"api_name": "decorators.add_user_info_and_menu", "line_number": 331, "usage_type": "name"}, {"api_name": "django.core.context_processors.csrf", "line_number": 342, "usage_type": "call"}, {"api_name": "dbapi.statistics.black_ip_by_cit", "line_number": 352, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 359, "usage_type": "call"}, {"api_name": "decorators.add_user_info_and_menu", "line_number": 337, "usage_type": "name"}, {"api_name": "django.core.context_processors.csrf", "line_number": 366, "usage_type": "call"}, {"api_name": "dbapi.mysql_connector.get_database_connection", "line_number": 377, "usage_type": "call"}, {"api_name": "dbapi.queries.add_feed", "line_number": 378, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 381, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 384, "usage_type": "call"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 385, "usage_type": "call"}, {"api_name": "decorators.add_user_info_and_menu", "line_number": 362, "usage_type": "name"}, {"api_name": "project_settings.JSON_PATH", "line_number": 391, "usage_type": "argument"}, {"api_name": "simplejson.load", "line_number": 393, "usage_type": "call"}, {"api_name": "project_settings.FEEDS_OUTPUT_PATH", "line_number": 397, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 400, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 401, "usage_type": "call"}, {"api_name": "decorators.add_user_info_and_menu", "line_number": 388, "usage_type": "name"}]} +{"seq_id": "502523286", "text": "# coding=utf-8\nfrom urllib import request\nfrom datetime import datetime\nfrom bs4 import BeautifulSoup as soup\nfrom elasticsearch import Elasticsearch\nimport requests\nfrom requests.auth import HTTPBasicAuth\nimport json , time\n\n#------------------------------------------------------------\n#Some Global Testing variables\n\n# List instagram users to monitor\n#usersToMonitor=[\"ronaldo\"]\nusersToMonitor=[\"maisontxell\",\"katiapanteli\"\n\t\t\t\t\"_triatlon\",\n\t\t\t\t\"keepgoing_es\",\n\t\t\t\t\"chaneladdict123\",\n\t\t\t\t\"katyackermann\",\n\t\t\t\t\"mariu666\",\n\t\t\t\t\"iglesiasgabriela\",\n \t\t\t\t\"msorannom\"]\n\n# Elasticsearch params\nusarElastic=True #False\nelasticUser=\"user1\"\nelasticPass=\"123456\"\nelasticIP=\"localhost\"\nelasticPort=\"9200\"\nelasticurl=\"http://\"+elasticIP+\":\"+elasticPort\nelasticIndex=\"insta_index\"\nmappings = {\n 'insta': {\n 'properties': {\n 'fecha': {'type': 'date'},\n 'usuario': {'type': 'string'},\n 'publicaciones': {'type': 'integer'},\n 'seguidores': {'type': 'integer', },\n 'seguidos': {'type': 'integer'},\n }\n }\n}\nbody = {'mappings': mappings}\n\n#end of globals\n#------------------------------------------------------------\n\n\n\n\ndef testElasticConnection():\n\n\t\"\"\" Test the conecction wie ES before do anything \"\"\"\n\t#verificamos que este ejecutandose\n\ttry:\n\t\tres = requests.get(elasticurl, auth=HTTPBasicAuth(elasticUser, elasticPass))\n\t\tif res.status_code == 200:\n\t\t\t#print(\"Elastic esta vivo.\")\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\t\t\t#print (\"Error de conexión:\")\n\t\t\t#print(res.content)\n\texcept:\n\t\t#print(\"ostionsss\")\n\t\treturn False\n\n\n\ndef getNextValueID(mydict):\n\n\t\"\"\"Return the nex value available for insert a new document.\"\"\"\n\n #Nos quedamos solo con los valores de los IDs para \n #meterlos en un lista y determinar el mayor.\n\n\ttotalIDs=mydict['hits']['total']\n\tproximoID=int(totalIDs)+1\n\t#print(mydict)\n\t#listaIDs=[]\n\t# for k,v in mydict.items():\n\t# \tmyID=v['total'])\n\t# \tlistaIDs.append(int(myID))\n\n\t# print(listaIDs)\t\t\n\t# mayorEnLista=max(listaIDs)\n\t\n\treturn proximoID\n\t\ndef checkValues(valor):\n\n\t\"\"\" This function check for values like 1.6m , 110K , etc and transform them to real values. \"\"\"\n\tnewvalor=\"\"\n\t#print (\"me ha llegado: \", valor)\n\tletra = valor[-1]\n\tif letra == 'k':\n\t\t#print (\"estoy en la k\")\n\t\tfinddot = valor.find(\".\")\n\t\tif finddot >=0 :\n\t\t\t#print (\"estoy en el dot\", finddot)\n\t\t\tsolovalor = len(valor) - 1\n\t\t\tnewvalor = valor[0:solovalor] + \"00\"\n\t\t\tnewvalor = newvalor.replace('.' , '')\n\t\telse:\n\t\t\t#print (\"estoy fuera del dot\")\n\t\t\tsolovalor = len(valor) - 1\n\t\t\tnewvalor = valor[0:solovalor] + \"000\"\n\telif letra == 'm':\n\t\t#print (\"estoy en la m\")\n\t\tfinddot = valor.find(\".\")\n\t\tif finddot >=0 :\n\t\t\t#print (\"estoy en el dot\", finddot)\n\t\t\tsolovalor = len(valor) - 1\n\t\t\tnewvalor = valor[0:solovalor] + \"000000\"\n\t\t\tnewvalor = newvalor.replace('.' , '')\n\t\telse:\n\t\t\t#print (\"estoy fuera del dot\")\n\t\t\tsolovalor = len(valor) - 1\n\t\t\tnewvalor = valor[0:solovalor] + \"000000\"\n\telse:\n\t\tnewvalor = valor\n\n\treturn int(newvalor)\n\t\ndef main_noElastic():\n\t\"\"\" Main course \"\"\"\n\n\t# Comienza el trabajo por usuarios a monitorizar\n\ti = 0\n\tfor usuario in usersToMonitor:\n\t\tprint('-----usuario: ' + usuario)\n\t\ttime.sleep(5)\n\t\ti += 1\n\t\turlpage = \"https://www.instagram.com/\" + usuario + \"/\"\n\t\tuCliente = request.urlopen(urlpage)\n\t\thtml_urlpage = uCliente.read()\n\t\tuCliente.close()\n\t\tsoup_urlpage = soup(html_urlpage, \"html.parser\", )\n\n\t\t#---------\n\t\t# Recogemos Followers\n\t\t#---------\n\t\t#Esto recoge la seccion del script numero 2\n\t\tdata_string = soup_urlpage.findAll('script')[1].string.encode('utf8')\n\t\t#Esto separa la mierdaca del verdadero jason\n\t\tpre_followers = str(data_string).split('\"followed_by\": {\"count\":')[1]\n\t\tpost_followers = str(pre_followers).split('}, \"followed_by_viewer')[0]\n\t\tprint('followers: ' + post_followers)\n\n\t\t#---------\n\t\t# Recogemos followed by y posts\n\t\t#---------\n\t\tmeta_data = soup_urlpage.find(\"meta\", property=\"og:description\")\n\t\tpre_folloed_by2 = str(meta_data).split('Followers,')[1]\n\t\tpost_folloed_by = str(pre_folloed_by2).split('Following,')[0]\n\t\tprint('Followed_by: ' + post_folloed_by)\n\t\tpre_posts = str(meta_data).split('Following,')[1]\n\t\tpre_posts2 = str(pre_posts).split('Posts')[0]\n\t\tprint('Posts: ' + pre_posts2)\n\n\ndef main():\n\n\t\"\"\" Main course \"\"\"\n\n\trecienCreadoIndex=False\n\telasticConnect=\"CONNKO\"\n\telasticIndexCreated=\"newIndex\"\n\tpublicaciones=0\n\tseguidores=0\n\tseguidos=0\n\tusuario=\"nadie\"\n\tnextID=1\n\tnow=datetime.now()\n\n\tif testElasticConnection():\n\t\telasticConnect=\"CONNOK\"\n\t\t#Conectamos con elastic y creamos el indice si no existe.\n\t\tes = Elasticsearch([{'host': elasticIP, 'port': elasticPort}], http_auth=(elasticUser, elasticPass))\n\t\tif es.indices.exists(index=elasticIndex):\n\t\t\t#print(\"El indice existe.\")\n\t\t\telasticIndexCreated=\"IndexExist\"\n\t\telse:\n\t\t\t#print(\"El indice NO existe, lo creamos.\")\n\t\t\telasticIndexCreated=\"newIndex\"\n\t\t\tcreaIndex = es.indices.create(index=elasticIndex, ignore=400, body=body)\n\t\t\trecienCreadoIndex=True\n\t\t\t#creaIndex = es.indices.create(index=elasticIndex, ignore=400)\n\n\n\t\t#Comienza el trabajo por usuarios a monitorizar\n\t\ti=0\n\t\tfor usuario in usersToMonitor:\n\t\t\ttime.sleep(5)\n\t\t\ti += 1 \n\t\t\turlpage = \"https://www.instagram.com/\"+usuario+\"/\"\n\t\t\tuCliente = request.urlopen(urlpage)\n\t\t\thtml_urlpage = uCliente.read()\n\t\t\tuCliente.close()\n\t\t\tsoup_urlpage = soup(html_urlpage, \"html.parser\", )\n\n\t\t\t# dataCompleta = soup_urlpage.find_all(\"span\", {\"class\" : \"_bkw5z\"})\n\n\t\t\t# # The followers filed use 'title' to hide the real value\n\t\t\t# # here we extract this field.\n\t\t\t# for item in dataCompleta:\n\t\t\t# \tif str(item).find('title') >= 0:\n\t\t\t# \t\t#print(item['title'])\n\t\t\t# \t\tfollowersTittle = item['title']\n\t\t\t# \telse:\n\t\t\t# \t\tpass\n\n\t\t\t \n\t\t\t# #print(now)\n\t\t\t# # ------ remove dots and commas\n\n\t\t\t# # - working publicaciones\n\t\t\t# publicaciones=dataCompleta[0].text.replace(',' , '')\n\n\t\t\t# # - working followers\n\t\t\t# #seguidores=dataCompleta[1].text.replace(',' , '')\n\t\t\t# seguidores_v1=followersTittle.replace(',' , '')\n\t\t\t# seguidores_v2=seguidores_v1.replace('.' , '')\n\t\t\t# seguidores=seguidores_v2\n\n\t\t\t# # - working follow\n\t\t\t# seguidos=dataCompleta[2].text.replace(',' , '')\n\t\t\t# # --------------------------------------------------\n\n\n\t\t\t#---------\n\t\t\t# Recogemos Followers\n\t\t\t#---------\n\t\t\t#Esto recoge la seccion del script numero 2\n\t\t\tdata_string = soup_urlpage.findAll('script')[1].string.encode('utf8')\n\t\t\t#Esto separa la mierdaca del verdadero jason\n\t\t\tpre_followers = str(data_string).split('\"followed_by\": {\"count\":')[1]\n\t\t\tpost_followers = str(pre_followers).split('}, \"followed_by_viewer')[0]\n\t\t\t#print('followers: ' + post_followers)\n\n\t\t\t#---------\n\t\t\t# Recogemos seguidos y posts\n\t\t\t#---------\n\t\t\tmeta_data = soup_urlpage.find(\"meta\", property=\"og:description\")\n\t\t\tpre_seguidos = str(meta_data).split('Followers,')[1]\n\t\t\tpost_seguidos = str(pre_seguidos).split('Following,')[0]\n\t\t\t#print('Followed_by: ' + post_seguidos)\n\t\t\tpre_posts = str(meta_data).split('Following,')[1]\n\t\t\tpre_posts2 = str(pre_posts).split('Posts')[0]\n\t\t\t#print('Posts: ' + pre_posts2)\n\n\t\t\t# --\n\t\t\t# quitamos las comas de los valores\n\t\t\tpre_posts2_v1=pre_posts2.replace(',' , '')\n\t\t\tpost_followers_v1=post_followers.replace(',' , '')\n\t\t\tpost_seguidos_v1=post_seguidos.replace(',' , '')\n\n\t\t\t# ------ check for special characters\n\t\t\t#Vamos a tratar de averiguar si vienen cosas raras en los valores\n\t\t\t#como 1.6m (1.600.000) o 130k (130.000)\n\t\t\tpublicaciones = checkValues(str(pre_posts2_v1))\n\t\t\tseguidores = checkValues(str(post_followers_v1))\n\t\t\tseguidos = checkValues(str(post_seguidos_v1))\n\t\t\t# ----------------------------------------------------\n\n\n\t\t\t# -- Filling the document\n\t\t\tdoc = {\n\t\t\t 'fecha': now,\n\t\t\t 'usuario': usuario,\n\t\t\t 'publicaciones': publicaciones,\n\t\t\t 'seguidores': seguidores,\n\t\t\t 'seguidos': seguidos\n\t\t\t}\n\n\t\t\t# -- Insertion wheter is a new creation index or not.\n\t\t\tif recienCreadoIndex:\n\t\t\t\t#si el indice esta recien creado insertamos directamente con id=1\n\t\t\t\tes.create(index=elasticIndex, doc_type='instsa', id=nextID, body=doc)\n\t\t\t\tprint(\"insertado \", elasticIndexCreated, elasticConnect, nextID, \"fecha:\",now, \"usuario:\",usuario, \"publicaciones:\",publicaciones,\"seguidores:\", seguidores,\"seguidos:\", seguidos)\n\t\t\t\trecienCreadoIndex=False\n\t\t\t\telasticIndexCreated=\"IndexExist\"\n\t\t\telse:\n\t\t\t\t#dictIDs = es.search(index=elasticIndex, filter_path=['hits.hits._id'])\n\t\t\t\tdictIDs = es.search(index=elasticIndex, filter_path=['hits.total'])\n\t\t\t\t#print(dictIDs.items())\n\t\t\t\tnextID=getNextValueID(dictIDs)\n\t\t\t\tes.create(index=elasticIndex, doc_type='instsa', id=nextID, body=doc)\n\t\t\t\tprint(\"insertado \", elasticIndexCreated, elasticConnect, nextID, \"fecha:\",now, \"usuario:\",usuario, \"publicaciones:\",publicaciones,\"seguidores:\", seguidores,\"seguidos:\", seguidos)\n\n\t\t\t\t#break\n\telse:\n\t\tprint(\"Error \", elasticIndexCreated, elasticConnect, nextID, \"fecha:\",now, \"usuario:\",usuario, \"publicaciones:\",publicaciones,\"seguidores:\", seguidores,\"seguidos:\", seguidos)\n\nif __name__ == \"__main__\":\n\t#print(\"----start----\")\n\t#print(datetime.now())\n\t#print(\"....is being run directly\")\n\tif usarElastic:\n\t\tmain()\n\telse:\n\t\tmain_noElastic()\n\t#print(\"----end----\")\nelse:\n print(\"....is being imported into another module\")\n", "sub_path": "beautifullsoup/insta_beautifullSoup.py", "file_name": "insta_beautifullSoup.py", "file_ext": "py", "file_size_in_byte": 9169, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.get", "line_number": 56, "usage_type": "call"}, {"api_name": "requests.auth.HTTPBasicAuth", "line_number": 56, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 132, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 135, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 135, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 138, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 174, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 174, "usage_type": "name"}, {"api_name": "elasticsearch.Elasticsearch", "line_number": 179, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 194, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 197, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 197, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 200, "usage_type": "call"}]} +{"seq_id": "442116477", "text": "# -*- coding: utf-8 -*-\nimport scrapy\nfrom jd.items import JdItem\nfrom scrapy.http import Request\nimport json\nimport re\n\n\nclass AutojdSpider(scrapy.Spider):\n name = 'autojd'\n allowed_domains = ['jd.com']\n start_urls = ['https://search.jd.com/Search?keyword=%E7%AC%94%E8%AE%B0%E6%9C%AC%E7%94%B5%E8%84%91&enc=utf-8&wq=%E5%A5%B3%E5%AD%A9%E8%B6%85%E7%9F%AD%E8%A3%A4&pvid=c1352e6c483c424ab70e5802fb43d07f']\n\n def parse(self, response):\n item=JdItem() \n item['link']=response.xpath(\"//div[@class='p-name p-name-type-2']/a/@href\").extract()\n # print(item['link'])\n for url in item['link']:\n yield Request('https://'+url,headers={'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.109 Safari/537.36'},callback=self.parselink)\n\n for i in range(1,2):\n url1='https://search.jd.com/Search?keyword=%E7%AC%94%E8%AE%B0%E6%9C%AC%E7%94%B5%E8%84%91&enc=utf-8&qrst=1&rt=1&stop=1&vt=2&wq=%E5%A5%B3%E5%AD%A9%E8%B6%85%E7%9F%AD%E8%A3%A4&page='+str(i)+'&s=61&click=0'\n yield Request(url1,headers={'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.109 Safari/537.36'},callback=self.parse) \n\n def parselink(self,response):\n urlmo1=re.compile(r'desc: \\'(\\S+)\\'')\n item1=JdItem() \n item1['scr']=response.xpath(\"/html/head/script[@charset='gbk']\").extract()[0]\n b=urlmo1.findall(str(item1['scr']))\n yield Request('https://'+b[0],headers={'User-Agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.109 Safari/537.36'},callback=self.parsePic) \n\n def parsePic(self,response):\n item2=JdItem() \n # print(response.body)\n urlmo2=re.compile(r'data-lazyload=\\\\\\\\\"(\\S+)\\\\\\\\\"')\n item2['pic']=urlmo2.findall(str(response.body))\n yield item2\n # print(c)\n\n", "sub_path": "jd/jd/spiders/autojd.py", "file_name": "autojd.py", "file_ext": "py", "file_size_in_byte": 1958, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "scrapy.Spider", "line_number": 9, "usage_type": "attribute"}, {"api_name": "jd.items.JdItem", "line_number": 15, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 19, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 23, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 26, "usage_type": "call"}, {"api_name": "jd.items.JdItem", "line_number": 27, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 30, "usage_type": "call"}, {"api_name": "jd.items.JdItem", "line_number": 33, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "399774922", "text": "from socketserver import ThreadingMixIn\nimport threading\nimport time\nfrom http.server import BaseHTTPRequestHandler, HTTPServer, SimpleHTTPRequestHandler\nimport os\nimport sys\nimport json\nimport logging\n\n\n# The logger is used to log the results to a file in a thread safe environment.\n# Use the log() function to log anything\n# When wrting to file is needed use the `print_to_file` parameter\nLOGGER = logging.getLogger(\"Server\")\nFILE_OUT_LOGGER = logging.getLogger(\"File\")\nstreamformatter = logging.Formatter(fmt= \"\\033[33m%(asctime)s:\\033[32m%(name)s:\\033[34;1m%(levelname)s \\033[0m%(message)s\")\nfileformatter = logging.Formatter(fmt= \"%(message)s\")\nstreamHandler = logging.StreamHandler(sys.stdout)\nstreamHandler.setFormatter(streamformatter)\nstreamHandler.setLevel(logging.INFO)\nfileHandler = logging.FileHandler(\"results.out\")\nfileHandler.setFormatter(fileformatter)\nfileHandler.setLevel(logging.INFO)\nLOGGER.handlers = []\nFILE_OUT_LOGGER.handlers = []\nLOGGER.addHandler(streamHandler)\nFILE_OUT_LOGGER.addHandler(fileHandler)\nLOGGER.setLevel(logging.DEBUG)\nFILE_OUT_LOGGER.setLevel(logging.DEBUG)\n\ndef log(message, level=logging.INFO, print_to_file = False):\n LOGGER.log(level, message)\n if print_to_file:\n FILE_OUT_LOGGER.log(level, message)\n\n\ntry:\n import torch\n have_smart_player = True\n have_torch = True\n from models.deep_learning_feed_forward import Model\n from models.deep_learning_feed_forward import convert_state_to_relative\n log(\"pytorch is available\")\nexcept ImportError:\n have_smart_player = False\n have_torch = False\n log(\"pytorch not available, not loading smart_player\")\n\nif have_smart_player:\n MODEL = Model()\n try:\n model_state = torch.load(\"models/model_RL.out\")\n MODEL.load_state_dict(model_state['model'])\n except FileNotFoundError:\n log(\"The model has not been trined, execute deep_learning_feed_forward.py script\")\n log(\"Launching without smart_player\")\n have_smart_player = False\n\n\n\nclass Handler(SimpleHTTPRequestHandler):\n \n def do_GET(self):\n # This section was copied from the SimpleHTTPRequestHandler source of cpython\n if \"smart_player.html\" in self.path and not have_smart_player:\n self.send_response(200)\n self.send_header(\"Content-type\", \"html\")\n self.end_headers()\n if not have_torch:\n message = \"You don't have pytorch installed go to pytorch.org for instructions on how to install pytorch.\"\n else:\n message = \"You don't have a trained model. Execute the deep_learning_feed_forward.py script.\"\n self.wfile.write(message.encode('utf-8'))\n self.wfile.write('\\n'.encode('utf-8'))\n return\n f = self.send_head()\n if f:\n try:\n self.copyfile(f, self.wfile)\n finally:\n f.close()\n else:\n # handle other requests here \n self.send_response(200)\n self.end_headers()\n message = \"Oopts, what are you trying to do mate!\"\n self.wfile.write(message.encode('utf-8'))\n self.wfile.write('\\n'.encode('utf-8'))\n\n def do_POST(self):\n if self.path.endswith(\"register_result\"):\n content_length = int(self.headers['Content-Length'])\n post_data = json.loads(self.rfile.read(content_length))\n log(\"Recieved data:\")\n log(\",\".join([str(i) for i in post_data['grid']+[post_data['winner']]]), print_to_file=False)\n self.send_response(200)\n self.end_headers()\n \n \n elif self.path.endswith(\"get_move\"):\n if not have_smart_player:\n self.send_response(200)\n self.send_header(\"Content-type\", \"html\")\n self.end_headers()\n if not have_torch:\n message = \"You don't have pytorch installed go to pytorch.org for instructions on how to install pytorch.\"\n else:\n message = \"You don't have a trained model. Execute the deep_learning_feed_forward.py script.\"\n self.wfile.write(message.encode('utf-8'))\n self.wfile.write('\\n'.encode('utf-8'))\n return\n content_length = int(self.headers['Content-Length'])\n post_data = json.loads(self.rfile.read(content_length))\n\n #currentPlayer = post_data[\"currentPlayer\"]\n result = self._get_result_from_model(post_data['grid'],\n post_data[\"currentPlayer\"])\n \n log(\"giving move: Recived: \".format(post_data['grid']))\n log(\"returning : {}\".format(result))\n self.send_response(200)\n self.send_header('Content-type', 'application/json')\n self.end_headers()\n message = json.dumps({\"idx\":result})\n self.wfile.write(message.encode('utf-8'))\n\n\n def _get_result_from_model(self, grid, currentPlayer):\n grid = convert_state_to_relative(torch.Tensor(grid), currentPlayer)\n grid_for_current_player = torch.autograd.Variable(grid).view(1,1,9)\n grid_score = MODEL(grid_for_current_player)\n\n sorted_grid_scores, sorted_grid_index = torch.sort(grid_score, dim = 2,descending = True)\n sorted_grid_index = sorted_grid_index.squeeze()\n sorted_grid_scores = sorted_grid_scores.squeeze()\n\n updated_grid_for_current_player = grid_for_current_player.clone()\n\n for state in range(9):\n if grid_for_current_player.squeeze()[sorted_grid_index[state]] != 0:\n continue\n candidate_grid = grid_score == sorted_grid_scores[state]\n updated_grid_for_current_player[candidate_grid] = 2\n break\n result = (updated_grid_for_current_player != grid_for_current_player).squeeze().nonzero().item()\n return result\n \n # This was copied from the SimpleHTTPRequestHandler source code of cpython\n def send_head(self):\n \"\"\"Common code for GET and HEAD commands.\n This sends the response code and MIME headers.\n Return value is either a file object (which has to be copied\n to the outputfile by the caller unless the command was HEAD,\n and must be closed by the caller under all circumstances), or\n None, in which case the caller has nothing further to do.\n \"\"\"\n path = self.translate_path(self.path)\n f = None\n if os.path.isdir(path):\n if not self.path.endswith('/'):\n # redirect browser - doing basically what apache does\n self.send_response(301)\n self.send_header(\"Location\", self.path + \"/\")\n self.end_headers()\n return None\n for index in \"index.html\", \"index.htm\":\n index = os.path.join(path, index)\n if os.path.exists(index):\n path = index\n break\n else:\n return self.list_directory(path)\n ctype = self.guess_type(path)\n try:\n f = open(path, 'rb')\n except IOError:\n #instead of raising an exception, return None\n return None\n try:\n self.send_response(200)\n self.send_header(\"Content-type\", ctype)\n fs = os.fstat(f.fileno())\n self.send_header(\"Content-Length\", str(fs[6]))\n self.send_header(\"Last-Modified\", self.date_time_string(fs.st_mtime))\n self.end_headers()\n return f\n except:\n f.close()\n raise\n\n\nclass ThreadedHTTPServer(ThreadingMixIn, HTTPServer):\n \"\"\"Handle requests in a separate thread.\"\"\"\n\nif __name__ == '__main__':\n server = ThreadedHTTPServer(('localhost', 8080), Handler)\n log('Starting server, use to stop')\n log('Server running on: localhost:8080')\n server.serve_forever()\n \n", "sub_path": "simple_server.py", "file_name": "simple_server.py", "file_ext": "py", "file_size_in_byte": 8002, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 18, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 18, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 20, "usage_type": "attribute"}, {"api_name": "logging.FileHandler", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 23, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 28, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 29, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 31, "usage_type": "attribute"}, {"api_name": "models.deep_learning_feed_forward.Model", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 52, "usage_type": "call"}, {"api_name": "http.server.SimpleHTTPRequestHandler", "line_number": 61, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 93, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 113, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 124, "usage_type": "call"}, {"api_name": "models.deep_learning_feed_forward.convert_state_to_relative", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 130, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 130, "usage_type": "attribute"}, {"api_name": "torch.sort", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 159, "usage_type": "call"}, {"api_name": "os.path", "line_number": 159, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 167, "usage_type": "call"}, {"api_name": "os.path", "line_number": 167, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 168, "usage_type": "call"}, {"api_name": "os.path", "line_number": 168, "usage_type": "attribute"}, {"api_name": "os.fstat", "line_number": 182, "usage_type": "call"}, {"api_name": "socketserver.ThreadingMixIn", "line_number": 192, "usage_type": "name"}, {"api_name": "http.server.HTTPServer", "line_number": 192, "usage_type": "name"}]} +{"seq_id": "533640001", "text": "from os.path import basename\nimport xml.etree.ElementTree as ET\nfrom xml.etree.ElementTree import SubElement as sub\n\nfrom dateutil.parser import parse as dateparse\n\nfrom . import geo\n\n\ndef prep_subtype(p):\n return p._get_raw_doc()['meta']['subtype']\n\ndef eld(tagname, attrs={}, text=None, children=[]):\n ret = {\"tagname\": tagname}\n if attrs:\n ret['attrs'] = attrs\n if text:\n ret['text'] = text\n if children:\n ret['children'] = children\n return ret\n\ndef hier_sub(parent, tagname, attrs={}, text=None, children=[]):\n el = sub(parent, tagname, attrs)\n if text:\n el.text = text\n ret = [el]\n for kwargs in children:\n ret.append(hier_sub(el, **kwargs))\n return ret\n\n\ndef very_last(nested_list):\n last = nested_list[-1]\n while hasattr(last, \"__iter__\"):\n last = last[-1]\n return last\n\n\ndef spuid(obj):\n return eld(\"SPUID\", attrs={\"spuid_namespace\": \"hmp2\"}, text=obj.id)\n\n\ndef flatten_list(l):\n def _flat(ls):\n for item in ls:\n if hasattr(item, \"__iter__\"):\n for subitem in _flat(item):\n yield subitem\n else:\n yield item\n return list(_flat(l))\n\n\n# Thanks, http://stackoverflow.com/a/4590052\ndef indent(elem, level=0):\n i = \"\\n\" + level*\" \"\n if len(elem):\n if not elem.text or not elem.text.strip():\n elem.text = i + \" \"\n if not elem.tail or not elem.tail.strip():\n elem.tail = i\n for elem in elem:\n indent(elem, level+1)\n if not elem.tail or not elem.tail.strip():\n elem.tail = i\n else:\n if level and (not elem.tail or not elem.tail.strip()):\n elem.tail = i\n\ndef reg_text(t):\n return u\" \".join(t.split())\n\ndef reg_sample(s):\n s.mixs['lat_lon'] = \" \".join(geo.cardinal(s.mixs['lat_lon']))\n return s\n\n\ndef _add_description(root, st, release_date=None):\n children = [ \n eld(\"Comment\", text=\"iHMP project \"+st.name),\n eld(\"Organization\", attrs={\"role\":\"owner\", \"type\":\"institute\"},\n children=[\n eld(\"Name\", text=\"iHMP DCC\"),\n eld(\"Contact\", attrs={\"email\":\"schwager@hsph.harvard.edu\"},\n children=[ eld(\"Name\", children=[\n eld(\"First\", text=\"Randall\"),\n eld(\"Last\", text=\"Schwager\")\n ])\n ])\n ]\n )\n ]\n if release_date:\n d = dateparse(release_date).strftime(\"%Y-%m-%d\")\n children.append( eld(\"Hold\", attrs={\"release_date\": d}) )\n hier_sub(root, \"Description\", children=children)\n return root\n\n\ndef _add_bioproject(root, st, bioproject_id=None):\n if bioproject_id:\n return root\n ret = hier_sub(root, \"Action\", children=[\n eld(\"AddData\", attrs={\"target_db\":\"BioProject\"}, children=[\n eld(\"Data\", attrs={\"content_type\":\"xml\"}, children=[\n eld(\"XmlContent\", children=[\n eld(\"Project\", attrs={\"schema_version\":\"2.0\"})\n ])\n ]),\n eld(\"Identifier\", children=[spuid(st)]),\n ])\n ])\n prj = flatten_list(ret)[-3]\n hier_sub(prj, \"ProjectID\", children=[spuid(st)])\n hier_sub(prj, \"Descriptor\", children=[\n eld(\"Title\", text=\"iHMP \"+st.name),\n eld(\"Description\", text=reg_text(st.description)),\n eld(\"Relevance\", children=[ eld(\"Medical\", text=\"Yes\") ])\n ])\n pts_attrs = {\"sample_scope\":\"eEnvironment\"}\n hier_sub(prj, \"ProjectType\", children=[\n eld(\"ProjectTypeSubmission\", attrs=pts_attrs, children=[\n eld(\"IntendedDataTypeSet\", children=[\n eld(\"DataType\", text=\"metagenome\")\n ])\n ])\n ])\n return root\n\n\ndef _add_biosample(root, st, sample, prep, release_date=None, \n bioproject_id=None):\n sample = reg_sample(sample)\n ret = hier_sub(root, \"Action\", children=[\n eld(\"AddData\", attrs={\"target_db\":\"BioSample\"}, children=[\n eld(\"Data\", attrs={\"content_type\":\"xml\"}, children=[\n eld(\"XmlContent\", children=[\n eld(\"BioSample\", attrs={\"schema_version\":\"2.0\"})\n ])\n ]),\n eld(\"Identifier\", children=[spuid(sample)])\n ])\n ])\n bs_node = flatten_list(ret)[-3]\n hier_sub(bs_node, \"SampleId\", children=[spuid(sample)])\n hier_sub(bs_node, \"Descriptor\", children=[\n eld(\"Title\", text=sample.name),\n ])\n if bioproject_id:\n hier_sub(bs_node, \"BioProject\", \n children=[eld(\"PrimaryId\", attrs={\"name\": \"BioProject\"}, \n text=bioproject_id)])\n else:\n hier_sub(bs_node, \"BioProject\", children=[spuid(st)])\n hier_sub(bs_node, \"Organism\",\n attrs={\"taxonomy_id\": prep.ncbi_taxon_id},\n children=[eld(\"OrganismName\", text=\"Metagenome\")])\n hier_sub(bs_node, \"Package\", text=\"MIMS.me.human-associated.4.0\")\n kv = lambda k, v: eld(\"Attribute\", attrs={\"attribute_name\": k}, text=v)\n get = lambda v: sample.mixs.get(v, \"missing\").strip() or \"missing\"\n hier_sub(bs_node, \"Attributes\", children=[\n kv(\"env_biome\", get(\"biome\")),\n kv(\"collection_date\", get(\"collection_date\")),\n kv(\"env_feature\", get(\"feature\")),\n kv(\"env_material\", get(\"material\")),\n kv(\"geo_loc_name\", get(\"geo_loc_name\")),\n kv(\"host\", \"Homo sapiens\"),\n kv(\"lat_lon\", get(\"lat_lon\"))\n ]+[kv(k, get(k)) for k in (\"rel_to_oxygen\", \"samp_collect_device\",\n \"samp_mat_process\", \"samp_size\")\n if bool(sample.mixs.get(k, None))]\n )\n return root\n\n\ndef _add_sra(root, st, sample, prep, seq, files_sizes, \n bioproject_id=None):\n kv = lambda k, v: eld(\"Attribute\", attrs={\"name\": k}, text=v)\n strategy = \"AMPLICON\" if prep_subtype(prep) == \"16s\" else \"WGS\"\n mims_or_mimarks = prep.mimarks if prep_subtype(prep) == \"16s\" else prep.mims\n file_nodes = [ \n eld(\"File\", attrs={\"file_path\":basename(name)},\n children=[eld(\"DataType\", text=\"sra-run-fastq\")])\n for name, _ in files_sizes\n ]\n if not file_nodes and seq.size == 0:\n return root\n if bioproject_id:\n st_spuid = eld(\"PrimaryId\", attrs={\"db\": \"BioProject\"}, \n text=bioproject_id)\n else:\n st_spuid = spuid(st)\n hier_sub(root, \"Action\", children=[\n eld(\"AddFiles\", attrs={\"target_db\": \"SRA\"}, children=file_nodes+[\n kv(\"instrument_model\",seq.seq_model),\n kv(\"library_strategy\",strategy),\n kv(\"library_source\", \"GENOMIC\"),\n kv(\"library_selection\", prep.lib_selection.upper()),\n kv(\"library_layout\", \"FRAGMENT\"),\n kv(\"library_construction_protocol\",\n reg_text(mims_or_mimarks['lib_const_meth'])),\n eld(\"AttributeRefId\", attrs={\"name\": \"BioProject\"}, children=[\n eld(\"RefId\", children=[st_spuid])\n ]),\n eld(\"AttributeRefId\", attrs={\"name\": \"BioSample\"}, children=[\n eld(\"RefId\", children=[spuid(sample)])\n ]),\n eld(\"Identifier\", children=[spuid(seq)])\n ])\n ])\n return root\n\n\ndef to_xml(st, samples, tardict, release_date=None, bioproject_id=None):\n root = ET.Element('Submission')\n root = _add_description(root, st, release_date)\n root = _add_bioproject(root, st, bioproject_id)\n sample_cache = set()\n for sample in samples:\n if not sample.prepseqs:\n continue\n for prep, seq in sample.prepseqs:\n if sample.sample.id not in sample_cache:\n root = _add_biosample(root, st, sample.sample, prep, \n bioproject_id=bioproject_id)\n sample_cache.add(sample.sample.id)\n is_16s = seq._get_raw_doc()['node_type'].startswith(\"16s\") \n seqtype = \"16s\" if is_16s else \"wgs\"\n tarkey = (basename(seq.urls[0]), seqtype)\n root = _add_sra(root, st, sample.sample, prep, seq, tardict[tarkey],\n bioproject_id)\n\n return root\n\n", "sub_path": "dcc_sra/serialize.py", "file_name": "serialize.py", "file_ext": "py", "file_size_in_byte": 8174, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "xml.etree.ElementTree.SubElement", "line_number": 24, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 183, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 216, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 216, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 230, "usage_type": "call"}]} +{"seq_id": "418663794", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# Definition for a binary tree node.\nimport queue\nfrom typing import List\n\n\nclass TreeNode:\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\n\nclass Solution:\n def levelOrder(self, root: TreeNode) -> List[int]:\n if root is None : return []\n result = []\n q = queue.Queue()\n q.put(root)\n while not q.empty():\n item = q.get()\n result.append(item.val)\n if item.left is not None: q.put(item.left)\n if item.right is not None: q.put(item.right)\n return result\n\n\nif __name__ == '__main__':\n A = TreeNode(1)\n B = TreeNode(2)\n C = TreeNode(3)\n\n A.left = B\n A.right = C\n\n s = Solution()\n result = s.levelOrder(A)\n print(result)\n", "sub_path": "algorithm/sword/剑指 Offer 32 - I. 从上到下打印二叉树.py", "file_name": "剑指 Offer 32 - I. 从上到下打印二叉树.py", "file_ext": "py", "file_size_in_byte": 827, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "queue.Queue", "line_number": 20, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "629293215", "text": "import numpy as np\nimport theano\nimport theano.tensor as T\n\nfrom MLP import MLP\n\n\"\"\"\nTrain a fully connected multilayer NNet\nTradition Back prop, without pre-training\nwith: \n\t1. L1 and L2 regularization\n\t2. adjustable mini-batch SGD\n\t3. momentum\n\"\"\"\n\n\n\"\"\"\nTODO: \n1. momentumGradientUpdate\n2. \n\"\"\"\ndef MLPtrainer(net ,\n\t learning_rate = 0.1,\n \t momentum = 0.9 ,\n \t L1 = 0.0 ,\n \t L2 = 0.0 ):\n\n input = T.matrix('input') # shape(n_instances , n_features)\n label = T.matrix('label') # shape(n_instances , n_labels)\n \n # cost = net.squareError(x=input,y=label)\n # cost = net.crossEntropyError(x=input , y=label)\n cost = costFntWithL1L2(net,L1,L2,input,label)\n\n # updates = fixedLearningRateUpdate(cost , net.params , learning_rate)\n\n updates = momentumGradientUpdate(cost , net.params , learning_rate , momentum)\n \n train_model = theano.function(\n inputs = [input,label],\n outputs = cost ,\n updates = updates\n \t)\n return train_model\n \n\ndef costFntWithL1L2(net , L1 , L2 ,x,y):\n regL1 = 0\n for layer in net.layers:\n \tregL1 += abs(layer.W).mean()\n\n regL2 = 0\n for layer in net.layers:\n \tregL2 += (layer.W ** 2).mean()\n\n cost = (\n net.rationalError(x,y)\n + L1 * regL1\n + L2 * regL2\n )\n return cost\n\ndef fixedLearningRateUpdate(cost , params , learning_rate):\n gparams = [T.grad(cost, param) for param in params]\n\n updates = [\n (param, param - learning_rate * gparam)\n for param, gparam in zip(params, gparams)\n ]\n return updates\n\n\n\n\ndef momentumGradientUpdate(cost, params, learning_rate, momentum):\n '''\n Compute updates for gradient descent with momentum\n \n :parameters:\n - cost : theano.tensor.var.TensorVariable\n Theano cost function to minimize\n - params : list of theano.tensor.var.TensorVariable\n Parameters to compute gradient against\n - learning_rate : float\n Gradient descent learning rate\n - momentum : float\n Momentum parameter, should be at least 0 (standard gradient descent) and less than 1\n \n :returns:\n updates : list\n List of updates, one for each parameter\n '''\n # Make sure momentum is a sane value\n assert momentum < 1 and momentum >= 0\n # List of update steps for each parameter\n updates = []\n # Just gradient descent on cost\n for param in params:\n \t# For each parameter, we'll create a param_update shared variable.\n # This variable will keep track of the parameter's update step across iterations.\n # We initialize it to 0\n param_update = theano.shared(param.get_value()*0., broadcastable=param.broadcastable)\n # Each parameter is updated by taking a step in the direction of the gradient.\n # However, we also \"mix in\" the previous step according to the given momentum value.\n # Note that when updating param_update, we are using its old value and also the new gradient step.\n updates.append((param, param - learning_rate*param_update))\n # Note that we don't need to derive backpropagation to compute updates - just use T.grad!\n updates.append((param_update, momentum*param_update + (1. - momentum)*T.grad(cost, param)))\n return updates\n\n\n# def updateModel(net,x,y):", "sub_path": "2015_MLDS/HW1/MLPtrainer.py", "file_name": "MLPtrainer.py", "file_ext": "py", "file_size_in_byte": 3261, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "theano.tensor.matrix", "line_number": 28, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 28, "usage_type": "name"}, {"api_name": "theano.tensor.matrix", "line_number": 29, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 29, "usage_type": "name"}, {"api_name": "theano.function", "line_number": 39, "usage_type": "call"}, {"api_name": "theano.tensor.grad", "line_number": 64, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 64, "usage_type": "name"}, {"api_name": "theano.shared", "line_number": 102, "usage_type": "call"}, {"api_name": "theano.tensor.grad", "line_number": 108, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 108, "usage_type": "name"}]} +{"seq_id": "581982415", "text": "from django.db import models, Error\nfrom django.contrib.auth.models import User\nfrom django.core.validators import MinValueValidator\n\n\nclass Product(models.Model):\n\n name = models.CharField(max_length=100, blank=False, null=False)\n brand = models.CharField(max_length=100, blank=False, null=False)\n price = models.DecimalField(decimal_places=2, max_digits=8)\n in_stock_quantity = models.IntegerField(\n default=0, validators=[MinValueValidator(0)]\n )\n\n def __str__(self):\n return self.name\n\n\nclass WeddingList(models.Model):\n user = models.ForeignKey(User, on_delete=models.CASCADE)\n product = models.ForeignKey(Product, on_delete=models.CASCADE)\n quantity = models.IntegerField(default=0, validators=[MinValueValidator(0)])\n purchased = models.IntegerField(default=0, validators=[MinValueValidator(0)])\n\n class Meta:\n constraints = [\n models.UniqueConstraint(fields=[\"user\", \"product\"], name=\"unique_product\")\n ]\n", "sub_path": "weddingshop/wedding_list/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 985, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "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.CharField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 8, "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.DecimalField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 11, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.core.validators.MinValueValidator", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 20, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 20, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.db.models.IntegerField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.core.validators.MinValueValidator", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models.IntegerField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.core.validators.MinValueValidator", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models.UniqueConstraint", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "62433747", "text": "# Define here the models for your scraped items\n#\n# See documentation in:\n# https://docs.scrapy.org/en/latest/topics/items.html\n\nimport scrapy\n\n\nclass QtpjtItem(scrapy.Item):\n # define the fields for your item here like:\n # name = scrapy.Field()\n # 建立picurl存储图片额网址\n picurl = scrapy.Field()\n # 建立picid存储图片网址中的图片名字，以方便构造本地文件名\n picid = scrapy.Field()", "sub_path": "qtpjt/qtpjt/items.py", "file_name": "items.py", "file_ext": "py", "file_size_in_byte": 433, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "scrapy.Item", "line_number": 9, "usage_type": "attribute"}, {"api_name": "scrapy.Field", "line_number": 13, "usage_type": "call"}, {"api_name": "scrapy.Field", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "141485788", "text": "import os, sys, json, codecs\nfrom pydash import py_ as _\nfrom pymongo import MongoClient, ReadPreference\nfrom bson.objectid import ObjectId\nfrom bson.timestamp import Timestamp\n\n\ng_db = None\n\n\ndef init_db():\n global g_db\n if g_db is None:\n g_db = MongoClient(host='192.168.1.2', port=27017)\n\n\ndef test():\n p = r'C:\\Users\\xiejun\\Desktop\\Content1.json'\n o = None\n with codecs.open(p, 'r', 'utf-8-sig') as f:\n o = json.loads(f.read(), encoding='utf-8')\n print(o)\n\ndef import_json():\n DIR = r'K:/msys64/home/XIEJUN/work'\n line_path = os.path.join(DIR, 'line.json')\n towers_path = os.path.join(DIR, 'tower1.json')\n insulator_path = os.path.join(DIR, 'insulator.json')\n line = None\n towers = None\n insulator = None\n with codecs.open(line_path, 'r', 'utf-8-sig') as f:\n line = json.loads(f.read())\n print('insert line start...')\n insert_line(line)\n print('insert line end')\n with codecs.open(towers_path, 'r', 'utf-8-sig') as f:\n towers = json.loads(f.read())\n with codecs.open(insulator_path, 'r', 'utf-8-sig') as f:\n insulator = json.loads(f.read())\n print('insert tower start...')\n insert_tower(towers, insulator)\n print('insert tower end')\n\ndef insert_line(line):\n global g_db\n init_db()\n if g_db['dali'].get_collection('line'):\n g_db['dali'].drop_collection('line')\n g_db['dali'].create_collection('line')\n def check_valid_float(field):\n return isinstance( field, str) and len(field) and not field == '无'\n def convert(n):\n for k in [\n 'line_length',\n 'overhead_length',\n 'cable_length',\n 'common_tower_length'\n ]:\n if check_valid_float( n[k]):\n n[k] = float(n[k])\n else:\n n[k] = 0\n\n del n['num']\n del n['id']\n if check_valid_float(n['voltage']):\n if not 'kV' in n['voltage']:\n n['voltage'] = n['voltage'] + 'kV'\n return n\n line = list(map(convert, line))\n g_db['dali']['line'].insert_many(line)\n\ndef insert_tower(tower, insulator):\n global g_db\n init_db()\n if g_db['dali'].get_collection('tower'):\n g_db['dali'].drop_collection('tower')\n g_db['tower'].create_collection('tower')\n\n def dms2dd(degrees, minutes, seconds, ):\n dd = float(degrees) + float(minutes) / 60 + float(seconds) / (60 * 60)\n # if direction == 'E' or direction == 'N':\n # dd *= -1\n return dd\n\n def check_valid_float(field):\n return isinstance( field, str) and len(field) and not field == '无'\n\n def convert1(n):\n for k in [\n 'strand_number',\n 'creepage_distance',\n 'tolerance_voltage_dry',\n 'tolerance_voltage_wet',\n 'rated_stretch_load',\n 'tolerance_voltage_lighting',\n 'insulator_weight',\n 'height',\n 'dry_arc_distance',\n 'max_bending_moment',\n ]:\n if check_valid_float( n[k]):\n # if isinstance(n[k], str):\n # print(n['id'])\n # print(n[k])\n n[k] = float(n[k])\n if k in [\n 'strand_number',\n 'tolerance_voltage_dry',\n 'tolerance_voltage_wet',\n 'rated_stretch_load',\n 'tolerance_voltage_lighting',\n 'dry_arc_distance',\n 'max_bending_moment',\n 'creepage_distance',\n ]:\n n[k] = int(n[k])\n else:\n n[k] = 0\n del n['num']\n n['identifier'] = n['id']\n del n['id']\n return n\n\n def convert(n):\n for k in [\n 'loop_num_avialable',\n 'loop_num_done',\n 'nominal_height',\n 'total_height',\n 'weight_tower',\n 'weight_arm',\n 'ice_thickness',\n 'ground_resistance',\n 'front_root',\n 'side_root',\n ]:\n if check_valid_float( n[k]):\n # if isinstance(n[k], str):\n # print(n['id'])\n # print(n[k])\n n[k] = float(n[k])\n if k in [\n 'loop_num_avialable',\n 'loop_num_done',\n ]:\n n[k] = int(n[k])\n else:\n n[k] = 0\n\n\n lng = dms2dd(n['lng_d'], n['lng_m'], n['lng_s'])\n lat = dms2dd(n['lat_d'], n['lat_m'], n['lat_s'])\n del n['num']\n n['identifier'] = n['id']\n del n['id']\n del n['lng_d']\n del n['lng_m']\n del n['lng_s']\n del n['lat_d']\n del n['lat_m']\n del n['lat_s']\n o = {}\n o['type'] = 'Feature'\n o['geometry']={'type':'Point', 'coordinates':[lng, lat]}\n o['properties'] = n\n l = _.filter_(insulator, {'line_name':n['line_name'], 'tower_number':n['tower_number']})\n l = list(map(convert1, l))\n # print(l)\n o['properties']['insulator'] = l\n # print(o['properties']['line_name'] + o['properties']['tower_number'])\n # print(len(l))\n return o\n\n towers = list(map(convert, tower))\n # g_db['dali']['tower'].insert_many(list(tower))\n cnt = 0\n for i in towers:\n cnt += len(i['properties']['insulator'])\n print('len(insulator)={}'.format(len(insulator)))\n print('sum={}'.format(cnt))\n # print(json.dumps(towers, ensure_ascii=False, indent=4))\n g_db['dali']['tower'].insert_many(towers)\nif __name__ == '__main__':\n import_json()\n", "sub_path": "test/test_json.py", "file_name": "test_json.py", "file_ext": "py", "file_size_in_byte": 5694, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pymongo.MongoClient", "line_number": 14, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 20, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 21, "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": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "codecs.open", "line_number": 32, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 33, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 37, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 38, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 39, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 40, "usage_type": "call"}, {"api_name": "pydash.py_.filter_", "line_number": 168, "usage_type": "call"}, {"api_name": "pydash.py_", "line_number": 168, "usage_type": "name"}]} +{"seq_id": "628586782", "text": "from datetime import datetime\nfrom flask_marshmallow import Marshmallow\nfrom flask_marshmallow.fields import fields\nfrom src.database import db\n\nma = Marshmallow()\n\nclass HistoryModel(db.Model):\n __tablename__ = 'history'\n __table_args__ = {'extend_existing': True}\n \n room_id = db.Column(db.Integer, primary_key=True)\n item_id = db.Column(db.Integer, primary_key=True)\n user_id = db.Column(db.Integer, primary_key=True)\n amount = db.Column(db.Integer, defaul=0)\n created_at = db.Column(db.DateTime, nullable=False, default=datetime.now)\n updated_at = db.Column(db.DateTime, nullable=False, default=datetime.now, onupdate=datetime.now)\n deleted_at = db.Column(db.DateTime, nullable=False, default=datetime.now, onupdate=datetime.now)\n\n def __repr__(self):\n return ''.format(\n room_id=self.room_id,\n item_id=self.item_id,\n user_id=self.user_id,\n amount=self.amount\n )\n\nclass HistorySchema(ma.ModelSchema):\n class Meta:\n model = HistoryModel", "sub_path": "src/models/history.py", "file_name": "history.py", "file_ext": "py", "file_size_in_byte": 1064, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask_marshmallow.Marshmallow", "line_number": 6, "usage_type": "call"}, {"api_name": "src.database.db.Model", "line_number": 8, "usage_type": "attribute"}, {"api_name": "src.database.db", "line_number": 8, "usage_type": "name"}, {"api_name": "src.database.db.Column", "line_number": 12, "usage_type": "call"}, {"api_name": "src.database.db", "line_number": 12, "usage_type": "name"}, {"api_name": "src.database.db.Integer", "line_number": 12, "usage_type": "attribute"}, {"api_name": "src.database.db.Column", "line_number": 13, "usage_type": "call"}, {"api_name": "src.database.db", "line_number": 13, "usage_type": "name"}, {"api_name": "src.database.db.Integer", "line_number": 13, "usage_type": "attribute"}, {"api_name": "src.database.db.Column", "line_number": 14, "usage_type": "call"}, {"api_name": "src.database.db", "line_number": 14, "usage_type": "name"}, {"api_name": "src.database.db.Integer", "line_number": 14, "usage_type": "attribute"}, {"api_name": "src.database.db.Column", "line_number": 15, "usage_type": "call"}, {"api_name": "src.database.db", "line_number": 15, "usage_type": "name"}, {"api_name": "src.database.db.Integer", "line_number": 15, "usage_type": "attribute"}, {"api_name": "src.database.db.Column", "line_number": 16, "usage_type": "call"}, {"api_name": "src.database.db", "line_number": 16, "usage_type": "name"}, {"api_name": "src.database.db.DateTime", "line_number": 16, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 16, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 16, "usage_type": "name"}, {"api_name": "src.database.db.Column", "line_number": 17, "usage_type": "call"}, {"api_name": "src.database.db", "line_number": 17, "usage_type": "name"}, {"api_name": "src.database.db.DateTime", "line_number": 17, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 17, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "name"}, {"api_name": "src.database.db.Column", "line_number": 18, "usage_type": "call"}, {"api_name": "src.database.db", "line_number": 18, "usage_type": "name"}, {"api_name": "src.database.db.DateTime", "line_number": 18, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 18, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "name"}]} +{"seq_id": "187617871", "text": "\"\"\"Classes for forms and form fields.\"\"\"\n\nfrom django.contrib.postgres.forms import SimpleArrayField as BaseSimpleArrayField\nfrom django.forms import DateTimeField\n\nfrom modularhistory.widgets.historic_date_widget import HistoricDateWidget\n\n\nclass SimpleArrayField(BaseSimpleArrayField):\n \"\"\"Array field.\"\"\"\n\n def widget_attrs(self, widget):\n \"\"\"Return the attributes to apply to the field widget.\"\"\"\n attrs = super().widget_attrs(widget)\n class_attr = 'vTextField'\n additional_classes = attrs.get('class')\n if additional_classes:\n class_attr = f'{class_attr} {additional_classes}'\n attrs['class'] = class_attr\n return attrs\n\n\nclass HistoricDateFormField(DateTimeField):\n \"\"\"Form field for historic datetimes (potentially BCE, with variable specificity.\"\"\"\n\n widget = HistoricDateWidget\n", "sub_path": "modularhistory/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 859, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.contrib.postgres.forms.SimpleArrayField", "line_number": 9, "usage_type": "name"}, {"api_name": "django.forms.DateTimeField", "line_number": 23, "usage_type": "name"}, {"api_name": "modularhistory.widgets.historic_date_widget.HistoricDateWidget", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "427795573", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport psycopg2\nimport os\nimport parameters as gl\nimport datetime\nimport base64\n\ndef attach_file(file_path):\n f = open(file_path, 'rb')\n binary = f.read()\n c = attachment_info(file_path)\n c['file_size'] = len(binary)\n conn = psycopg2.connect(gl.conn_string)\n curs = conn.cursor()\n curs.execute(\"INSERT into att_unit (att_data) values (%s)\", (psycopg2.Binary(binary),))\n conn.commit()\n curs.close()\n conn.close()\n\n\ndef attachment_info(file_path):\n if os.path.exists(file_path):\n ext = file_path[file_path.rfind('.') +1 :]\n file_name = file_path[file_path.rfind('/') +1 :]\n file_name = file_name[: file_name.rfind('.')]\n return {'ext':ext,'file_name':file_name, 'file_size':0}\n else:\n print('file not found')\n return {'ext':'','file_name':'', 'file_size': -1}\n\n\n", "sub_path": "att_unit.py", "file_name": "att_unit.py", "file_ext": "py", "file_size_in_byte": 884, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "psycopg2.connect", "line_number": 14, "usage_type": "call"}, {"api_name": "parameters.conn_string", "line_number": 14, "usage_type": "attribute"}, {"api_name": "psycopg2.Binary", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}]} +{"seq_id": "282696663", "text": "''' \nN: cantidad de individuos en la población.\nEl area mide 100 m x 100 m, y cada persona \"ocupa\" 0.5 m x 0.5 m\n---> El espacio tiene 200 celdas de largo y 200 de ancho. (40 000 celdas)\n\nInicialmente el 2% de los N individuos esta infectado.\nHay tres tipos de individuos:\n\t-A: (70%) se desplaza 1 celda en 1 instante de t.\n\t-B: (25%) se desplaza 1 celda en 2 instantes de t.\n\t-C: (5%) se desplaza 1 celda en 4 instantes de t.\n\nLa probabilidad de transmitir la enfermedad es:\n\t-0.5 cuando la dist es entre 6 o menos celdas y mas de 3\n\t-0.7 cuando la dist es entre 3 o menos celdas\n\nSupongo que los individuos unicamente se mueven hacia los costados o arriba y abajo. No en diagonal.\nSe modelan los espacios en blanco como _, los espacios con infectados como X, y los sanos como O.\n'''\nimport matplotlib.pyplot as plt\nimport random\nn = 500 #cantidad de individuos en la poblacion\nNCELDASXFILA = 100\nINSTANTES = 1000\nALPHA = 15\nBETA = 0.3\n\ndef generar_espacio_vacio():\n\tespacio = []\n\tfor i in range(NCELDASXFILA):\n\t\tfila = []\n\t\tfor j in range(NCELDASXFILA):\n\t\t\tfila.append(\"_\")\n\t\tespacio.append(fila)\n\treturn espacio\n\ndef obtener_estado(personas):\n\tespacio = generar_espacio_vacio()\n\tfor v in personas.values():\n\t\tespacio[v[0]][v[1]] = v[2]\n\treturn espacio\n\ndef imprimir_estado(personas):\n\tprint(obtener_estado(personas))\n\tprint(\"\")\n\ndef hay_movimiento(tipo):\n\tif (tipo == \"A\"):\n\t\treturn True\n\tprobMovimiento = random.random()\n\tif (tipo == \"B\" and probMovimiento <= 0.5):\n\t\treturn True\n\tif (tipo == \"C\" and probMovimiento <= 0.25):\n\t\treturn True\n\treturn False\n\ndef verificar_rango(i,j,dist):\n\tif (i + dist < 0 or i + dist >= NCELDASXFILA):\n\t\treturn False\n\tif (j + dist < 0 or j + dist >= NCELDASXFILA):\n\t\treturn False\n\treturn True\n\ndef verificar_si_se_infecta(espacio,i,j):\n\tfor dist in range(-3,3):\n\t\tif (verificar_rango(i,j,dist) == False):\n\t\t\tcontinue\n\t\tif (espacio[i][j+dist] == \"X\" or espacio[i+dist][j] == \"X\"):\n\t\t\tprobContagio = random.random()\n\t\t\tif (probContagio <= 0.7):\n\t\t\t\treturn True\n\n\tfor dist in range(-6,-4):\n\t\tif (verificar_rango(i,j,dist) == False):\n\t\t\tcontinue\n\t\tif (espacio[i][j+dist] == \"X\" or espacio[i+dist][j] == \"X\"):\n\t\t\tprobContagio = random.random()\n\t\t\tif (probContagio <= 0.5):\n\t\t\t\treturn True\n\n\tfor dist in range(4,6):\n\t\tif (verificar_rango(i,j,dist) == False):\n\t\t\tcontinue\n\t\tif (espacio[i][j+dist] == \"X\" or espacio[i+dist][j] == \"X\"):\n\t\t\tprobContagio = random.random()\n\t\t\tif (probContagio <= 0.5):\n\t\t\t\treturn True\n\treturn False\n\ndef random_walking(personas, contagiados):\n\tdias_enfermedad = {}\n\tfor i in range(n):\n\t\tif (personas[i][2] == \"X\"):\n\t\t\tdias_enfermedad[i] = 1\n\t\telse:\n\t\t\tdias_enfermedad[i] = 0\n\n\tlista_contagios_por_t = []\n\tlista_sanos_por_t = []\n\tlista_contagios_por_t.append(contagiados)\n\tlista_sanos_por_t.append(n-contagiados)\n\tfor instante in range(INSTANTES):\n\t\tfor k,v in personas.items():\n\t\t\tif (hay_movimiento(v[3]) == False):\n\t\t\t\tcontinue\n\t\t\testado = obtener_estado(personas)\n\t\t\tmovimiento = random.random()\n\t\t\tif (movimiento <= 0.25): #sentido del movimiento\n\t\t\t\tif (v[1]+1 == NCELDASXFILA): #que no se salga de rango\n\t\t\t\t\tcontinue\n\t\t\t\tif estado[v[0]][v[1]+1] == '_':#que no este ocupada\n\t\t\t\t\tpersonas[k] = (v[0],v[1]+1,v[2],v[3])\n\t\t\t\t\n\t\t\telif (movimiento <= 0.5): #sentido del movimiento\n\t\t\t\tif (v[1]-1 < 0): #que no se salga de rango\n\t\t\t\t\tcontinue\n\t\t\t\tif estado[v[0]][v[1]-1] == '_':#que no este ocupada\n\t\t\t\t\tpersonas[k] = (v[0],v[1]-1,v[2],v[3])\n\n\t\t\telif (movimiento <= 0.75): #sentido del movimiento\n\t\t\t\tif (v[0]+1 == NCELDASXFILA): #que no se salga de rango\n\t\t\t\t\tcontinue\n\t\t\t\tif estado[v[0]+1][v[1]] == '_':#que no este ocupada\n\t\t\t\t\tpersonas[k] = (v[0]+1,v[1],v[2],v[3])\n\n\t\t\telse: #sentido del movimiento\n\t\t\t\tif (v[0]-1 < 0): #que no se salga de rango\n\t\t\t\t\tcontinue\n\t\t\t\tif estado[v[0]-1][v[1]] == '_':#que no este ocupada\n\t\t\t\t\tpersonas[k] = (v[0]-1,v[1],v[2],v[3])\n\n\n\t\tfor i in range(NCELDASXFILA):\n\t\t\tfor j in range(NCELDASXFILA):\n\t\t\t\tif (estado[i][j] == '_'):\n\t\t\t\t\tcontinue\n\t\t\t\tp = 0\n\t\t\t\tfor k,v in personas.items():\n\t\t\t\t\t\tif (v[0] == i and v[1] == j):\n\t\t\t\t\t\t\tp = k\n\t\t\t\t\t\t\tbreak\n\t\t\t\tif (estado[i][j] == \"O\"):\n\t\t\t\t\tif (verificar_si_se_infecta(estado,i,j) == True):\n\t\t\t\t\t\testado[i][j] = \"X\"\n\t\t\t\t\t\tpersonas[p] = (i,j,\"X\",v[3])\n\t\t\t\t\t\tdias_enfermedad[p] = 1\n\t\t\t\telse:\n\t\t\t\t\tdias_enfermedad[p] += 1\n\t\t\t\t\tif (dias_enfermedad[p] >= ALPHA):\n\t\t\t\t\t\tprobCurarse = random.random()\n\t\t\t\t\t\tif (probCurarse <= BETA): #CURADO\n\t\t\t\t\t\t\testado[i][j] = \"O\"\n\t\t\t\t\t\t\tpersonas[p] = (i,j,\"O\",v[3])\n\t\t\t\t\t\t\tdias_enfermedad[p] = 0\n\n\t\tcontagiados_al_dia = 0\n\t\tsanos_al_dia = 0\n\t\tfor k in dias_enfermedad.keys():\n\t\t\tif (dias_enfermedad[k] == 0):\n\t\t\t\tsanos_al_dia += 1\n\t\t\telse:\n\t\t\t\tcontagiados_al_dia += 1\n\t\tlista_contagios_por_t.append(contagiados_al_dia)\n\t\tlista_sanos_por_t.append(sanos_al_dia)\n\t\timprimir_estado(personas)\t\n\treturn lista_contagios_por_t,lista_sanos_por_t\t\t\t\t\n\ndef estado_inicial():\t\n\t#Ubico a las N personas de forma aleatoria. El 2% estan infectados.\n\t#Se decide el \"tipo\" de persona\n\tpersonas = {} #clave: persona. valor: x,y,infectado o no, tipo de persona\n\tespacio = generar_espacio_vacio()\n\tcontagiados = 0\n\tfor i in range(n):\n\t\tx = random.randrange(0,NCELDASXFILA-1)\n\t\ty = random.randrange(0,NCELDASXFILA-1)\n\t\twhile (espacio[x][y] != \"_\"):\n\t\t\tx = random.randrange(0,NCELDASXFILA-1)\n\t\t\ty = random.randrange(0,NCELDASXFILA-1)\n\n\t\ttipo = \"\"\n\t\tprobTipo = random.random()\n\t\tif (probTipo <= 0.70):\n\t\t\ttipo = \"A\"\n\t\telif (probTipo <= 0.95):\n\t\t\ttipo = \"B\"\n\t\telse:\n\t\t\ttipo = \"C\"\n\n\t\tprobInfectado = random.random()\n\t\tif (probInfectado <= 0.02):\n\t\t\tpersonas[i] = (x,y,\"X\",tipo)\n\t\t\tespacio[x][y]= \"X\"\n\t\t\tcontagiados += 1\n\t\telse:\n\t\t\tpersonas[i] = (x,y,\"O\",tipo)\n\t\t\tespacio[x][y] = \"O\"\n\treturn personas, contagiados\n\ndef main():\t\n\t#Genero el espacio inicial\n\tpersonas, contagiados = estado_inicial()\n\tif (contagiados != 0):\n\t\t#LOS PACIENTES RECUPERADOS PUEDEN VOLVER A CONTAGIARSE\n\t\tlista_contagiados, lista_sanos = random_walking(personas,contagiados)\n\n\t\tplt.plot(lista_contagiados);\n\t\tplt.show()\n\n\t\tplt.plot(lista_sanos);\n\t\tplt.show()\n#\tPara pruebas, ir variando N, ALPHA, BETA Y INSTANTES DE T\n\n\nmain()", "sub_path": "Ej9.1.py", "file_name": "Ej9.1.py", "file_ext": "py", "file_size_in_byte": 6038, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "random.random", "line_number": 49, "usage_type": "call"}, {"api_name": "random.random", "line_number": 68, "usage_type": "call"}, {"api_name": "random.random", "line_number": 76, "usage_type": "call"}, {"api_name": "random.random", "line_number": 84, "usage_type": "call"}, {"api_name": "random.random", "line_number": 106, "usage_type": "call"}, {"api_name": "random.random", "line_number": 149, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 174, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 175, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 177, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 178, "usage_type": "call"}, {"api_name": "random.random", "line_number": 181, "usage_type": "call"}, {"api_name": "random.random", "line_number": 189, "usage_type": "call"}, {"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.show", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 207, "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.show", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 210, "usage_type": "name"}]} +{"seq_id": "637941939", "text": "from .db import get_db\nfrom datetime import datetime, timedelta\n\n\n# Insert a new ride request in the database\ndef insert_ride(ride):\n db = get_db()\n sql = 'INSERT INTO RideRequest (ride_id, user_id, from_lat, from_lon, to_lat, to_lon, last_lat, last_lon, '\n sql = sql + 'request_time, update_time, state) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) '\n sql = sql + 'ON CONFLICT(ride_id) DO UPDATE SET user_id = ?, from_lat = ?, from_lon = ?, to_lat = ?, to_lon = ?, '\n sql = sql + 'last_lat = ?, last_lon = ?, request_time = ?, update_time = ?, state = ?'\n values = [ride['ride_id'], ride['user_id'], ride['from_location']['lat'], ride['from_location']['lon'],\n ride['to_location']['lat'], ride['to_location']['lon'], ride['from_location']['lat'],\n ride['from_location']['lon'], datetime.strptime(ride['request_time'], '%Y-%m-%d %H:%M:%S.%f'),\n datetime.strptime(ride['request_time'], '%Y-%m-%d %H:%M:%S.%f'), ride['state'],\n ride['user_id'], ride['from_location']['lat'], ride['from_location']['lon'],\n ride['to_location']['lat'], ride['to_location']['lon'], ride['from_location']['lat'],\n ride['from_location']['lon'], datetime.strptime(ride['request_time'], '%Y-%m-%d %H:%M:%S.%f'),\n datetime.strptime(ride['request_time'], '%Y-%m-%d %H:%M:%S.%f'), ride['state']]\n cursor = db.execute(sql, values)\n db.commit()\n return cursor.lastrowid\n\n\n# Get list of ride requests\ndef get_all_rides():\n res = []\n db = get_db()\n sql = 'SELECT * FROM RideRequest'\n cursor = db.execute(sql)\n for ride in cursor:\n from_location = {'lat': ride['from_lat'], 'lon': ride['from_lon']}\n to_location = {'lat': ride['to_lat'], 'lon': ride['to_lon']}\n last_location = {'lat': ride['last_lat'], 'lon': ride['last_lon']}\n r = {'ride_id': ride['ride_id'], 'user_id': ride['user_id'], 'driver_id': ride['driver_id'],\n 'from_location': from_location, 'to_location': to_location, 'last_location': last_location,\n 'request_time': ride['request_time'].strftime('%Y-%m-%d %H:%M:%S.%f'),\n 'allocation_time': ride['allocation_time'].strftime('%Y-%m-%d %H:%M:%S.%f'),\n 'update_time': ride['update_time'].strftime('%Y-%m-%d %H:%M:%S.%f'),\n 'state': ride['state'], 'event_type': ride['event_type'], 'evaluated':ride['evaluated']}\n res.append(r)\n return res\n\n\n# Get rides by state:\ndef get_rides_by_state(state):\n res = []\n db = get_db()\n sql = 'SELECT * FROM RideRequest WHERE state = ?'\n values = [state]\n cursor = db.execute(sql, values)\n for ride in cursor:\n from_location = {'lat': ride['from_lat'], 'lon': ride['from_lon']}\n to_location = {'lat': ride['to_lat'], 'lon': ride['to_lon']}\n last_location = {'lat': ride['last_lat'], 'lon': ride['last_lon']}\n r = {'ride_id': ride['ride_id'], 'user_id': ride['user_id'], 'driver_id': ride['driver_id'],\n 'from_location': from_location, 'to_location': to_location, 'last_location': last_location,\n 'request_time': ride['request_time'].strftime('%Y-%m-%d %H:%M:%S.%f'),\n 'allocation_time': ride['allocation_time'],\n 'update_time': ride['update_time'].strftime('%Y-%m-%d %H:%M:%S.%f'),\n 'state': ride['state'], 'event_type': ride['event_type']}\n res.append(r)\n return res\n\n\n# Update ride request status\ndef update_ride(ride_id, info):\n db = get_db()\n sql = ''\n values = []\n for key, value in info.items():\n sql = sql + ' ' + key + ' = ?,'\n values.append(value)\n sql = sql[:-1]\n sql = 'UPDATE RideRequest SET' + sql + ' WHERE ride_id = ?'\n values.append(ride_id)\n cursor = db.execute(sql, values)\n db.commit()\n if cursor.rowcount > 0:\n return ride_id\n else:\n return None\n\n\n# Get ride wait times\ndef get_ride_wait_times():\n db = get_db()\n sql = 'SELECT * FROM RideRequest WHERE state = ? AND evaluated is NULL'\n values = ['ENROUTE']\n cursor = db.execute(sql, values)\n return cursor\n", "sub_path": "ride_allocation/soa_app_min/flaskr/data/ride.py", "file_name": "ride.py", "file_ext": "py", "file_size_in_byte": 4108, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "db.get_db", "line_number": 7, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 19, "usage_type": "name"}, {"api_name": "db.execute", "line_number": 20, "usage_type": "call"}, {"api_name": "db.commit", "line_number": 21, "usage_type": "call"}, {"api_name": "db.get_db", "line_number": 28, "usage_type": "call"}, {"api_name": "db.execute", "line_number": 30, "usage_type": "call"}, {"api_name": "db.get_db", "line_number": 48, "usage_type": "call"}, {"api_name": "db.execute", "line_number": 51, "usage_type": "call"}, {"api_name": "db.get_db", "line_number": 68, "usage_type": "call"}, {"api_name": "db.execute", "line_number": 77, "usage_type": "call"}, {"api_name": "db.commit", "line_number": 78, "usage_type": "call"}, {"api_name": "db.get_db", "line_number": 87, "usage_type": "call"}, {"api_name": "db.execute", "line_number": 90, "usage_type": "call"}]} +{"seq_id": "553764853", "text": "\"\"\"\nThe MIT License (MIT)\n\nCopyright (c) 2013 ibrahimyilmaz\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.\n\n\"\"\"\nfrom django.conf.urls import patterns, include, url,handler404 as handler404 ,handler500 as handler500\n\n# Uncomment the next two lines to enable the admin:\n# from django.contrib import admin\n# admin.autodiscover()\nfrom tastypie.api import Api\nfrom main.api import PostResource\nfrom main.views import error as error_page\n\nv1_api = Api(api_name='v1')\nv1_api.register(PostResource())\n\nhandler404= 'main.views.error'\nhandler500 ='main.views.error'\nurlpatterns = patterns('',\n # Examples:\n url(r'^$', 'main.views.home', name='home'),\n url(r'^posts/new/','main.views.new_post',name='new_post'),\n url(r'^login/','main.views.login_user',name='login_user'),\n url(r'^posts/$','main.views.post_list',name='posts'),\n url(r'^posts/(?P\\d{4})/(?P\\d{2})/(?P\\d+)/(?P[-\\w\\d]+)/$','main.views.post',name='post'),\n url(r'^posts/edit/(?P\\d{4})/(?P\\d{2})/(?P\\d+)/(?P[-\\w\\d]+)/$','main.views.post_edit',name='post_edit'),\n url(r'^search/','main.views.search_post',name='search_post'),\n url(r'^api/', include(v1_api.urls)),\n\n # url(r'^iblog/', include('iblog.foo.urls')),\n\n # Uncomment the admin/doc line below to enable admin documentation:\n # url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n # Uncomment the next line to enable the admin:\n # url(r'^admin/', include(admin.site.urls)),\n)\n", "sub_path": "iblog/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2469, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "tastypie.api.Api", "line_number": 34, "usage_type": "call"}, {"api_name": "main.api.PostResource", "line_number": 35, "usage_type": "call"}, {"api_name": "django.conf.urls.handler404", "line_number": 37, "usage_type": "name"}, {"api_name": "django.conf.urls.handler500", "line_number": 38, "usage_type": "name"}, {"api_name": "django.conf.urls.patterns", "line_number": 39, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 41, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 42, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 43, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 44, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 45, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 46, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 47, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 48, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "513884733", "text": "import sys\nfrom enum import Enum, unique\nfrom functools import partial\n\nfrom PyQt5.QtCore import Qt, pyqtSignal\nfrom PyQt5.QtWidgets import QDialog, QSpinBox, QCheckBox, QVBoxLayout, QLabel, QApplication, QDialogButtonBox, \\\n QHBoxLayout, QLineEdit\n\nimport Imageplay\nfrom CommonUtils import AppSettings\nfrom CustomUI import QHLine\n\n\n@unique\nclass SettingsKeys(Enum):\n image_delay = \"image_delay\"\n animation_speed = \"animation_speed\"\n animation_by_frame = \"gif_by_frame\"\n animation_loop = \"animation_loop\"\n recurse_subdirs = \"recurse_subdirs\"\n shuffle = \"shuffle\"\n loop = \"loop\"\n image_scaled = \"image_scaled\"\n external_app = \"external_app\"\n\n\n_settings = AppSettings(\n \"ImagePlay\",\n {}\n)\n\n\ndef _set_setting(setting, value):\n Imageplay.logger.info(f\"{setting} -> {value}\")\n _settings.apply_setting(setting, value)\n\n\ndef _get_setting(setting, default=None):\n return _settings.get_setting(setting, default)\n\n\ndef get_recurse():\n return _settings.get_setting(SettingsKeys.recurse_subdirs, False)\n\n\ndef get_animation_by_frame():\n return _settings.get_setting(SettingsKeys.recurse_subdirs, False)\n\n\ndef get_image_delay():\n return _settings.get_setting(SettingsKeys.image_delay, 10)\n\n\ndef get_loop():\n return _settings.get_setting(SettingsKeys.loop, True)\n\n\ndef set_loop(value):\n _set_setting(SettingsKeys.loop, value)\n\n\ndef get_shuffle():\n return _settings.get_setting(SettingsKeys.shuffle, False)\n\n\ndef set_shuffle(value):\n _set_setting(SettingsKeys.shuffle, value)\n\n\ndef get_animation_loop():\n return _settings.get_setting(SettingsKeys.animation_loop, 5)\n\n\ndef get_animation_speed():\n return _settings.get_setting(SettingsKeys.animation_speed, 50)\n\n\ndef get_image_scaled():\n return _settings.get_setting(SettingsKeys.image_scaled, False)\n\n\ndef set_image_scaled(value):\n _set_setting(SettingsKeys.image_scaled, value)\n\n\ndef get_external_app():\n return _settings.get_setting(SettingsKeys.external_app, None)\n\n\nclass ImagePlaySettings(QDialog):\n\n setting_changed_event = pyqtSignal(SettingsKeys, 'PyQt_PyObject')\n\n def __init__(self):\n super().__init__()\n self.image_spinner = QSpinBox()\n self.image_spinner.setMinimum(1)\n\n self.animation_spinner = QSpinBox()\n self.animation_spinner.setMinimum(1)\n self.animation_spinner.setMaximum(100)\n\n self.animation_loop = QSpinBox()\n self.animation_loop.setMinimum(-1)\n\n self.recurse = QCheckBox(\"Scan all child directories for images\")\n self.animation_by_frame = QCheckBox(\"View animation files one frame at a time\")\n\n self.external_app = QLineEdit()\n self.external_app.setPlaceholderText(\"External Application to open image in\")\n external_app = _get_setting(SettingsKeys.external_app)\n if external_app:\n self.external_app.setText(external_app)\n\n self.init_ui()\n self.load_settings_and_hooks()\n\n def init_ui(self):\n layout = QVBoxLayout()\n layout.addWidget(self.recurse)\n layout.addLayout(self.create_spinner_layout(QLabel(\"Load next image in (seconds)\"), self.image_spinner))\n layout.addWidget(QHLine())\n layout.addWidget(self.animation_by_frame)\n layout.addLayout(self.create_spinner_layout(QLabel(\"Animation speed (%)\"), self.animation_spinner))\n layout.addLayout(self.create_spinner_layout(QLabel(\"Times to loop (-1 is infinite)\"), self.animation_loop))\n layout.addWidget(QHLine())\n layout.addWidget(self.external_app)\n\n buttons = QDialogButtonBox(QDialogButtonBox.Ok, Qt.Horizontal, self)\n buttons.clicked.connect(self.close)\n layout.addWidget(buttons)\n\n self.setLayout(layout)\n self.setWindowTitle(\"Configure Imageplay\")\n self.setWindowModality(Qt.ApplicationModal)\n\n def load_settings_and_hooks(self):\n self.recurse.setChecked(get_recurse())\n self.recurse.stateChanged.connect(partial(self.hook, SettingsKeys.recurse_subdirs))\n\n self.animation_by_frame.setChecked(get_animation_by_frame())\n self.animation_by_frame.stateChanged.connect(partial(self.hook, SettingsKeys.animation_by_frame))\n\n self.animation_spinner.setMinimum(1)\n self.animation_spinner.setValue(get_animation_speed())\n self.animation_spinner.valueChanged.connect(partial(self.hook, SettingsKeys.animation_speed))\n self.animation_spinner.setEnabled(self.animation_by_frame.isChecked())\n\n self.image_spinner.setMinimum(1)\n self.image_spinner.setValue(get_image_delay())\n self.image_spinner.valueChanged.connect(partial(self.hook, SettingsKeys.image_delay))\n self.external_app.textEdited.connect(partial(self.hook, SettingsKeys.external_app))\n\n @staticmethod\n def create_spinner_layout(label, spinbox):\n layout = QHBoxLayout()\n layout.addWidget(label)\n layout.addStretch(1)\n layout.addWidget(spinbox)\n return layout\n\n def hook(self, setting, value=None):\n if setting == SettingsKeys.animation_by_frame:\n self.animation_spinner.setEnabled(self.animation_by_frame.isChecked())\n _set_setting(setting, value)\n self.setting_changed_event.emit(setting, value)\n \n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n ex = ImagePlaySettings()\n ex.show()\n sys.exit(app.exec_())", "sub_path": "Imageplay/ui/Settings.py", "file_name": "Settings.py", "file_ext": "py", "file_size_in_byte": 5372, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "enum.Enum", "line_number": 15, "usage_type": "name"}, {"api_name": "enum.unique", "line_number": 14, "usage_type": "name"}, {"api_name": "CommonUtils.AppSettings", "line_number": 27, "usage_type": "call"}, {"api_name": "Imageplay.logger.info", "line_number": 34, "usage_type": "call"}, {"api_name": "Imageplay.logger", "line_number": 34, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QDialog", "line_number": 90, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 92, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QSpinBox", "line_number": 96, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QSpinBox", "line_number": 99, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QSpinBox", "line_number": 103, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 106, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 107, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 109, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 119, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 121, "usage_type": "call"}, {"api_name": "CustomUI.QHLine", "line_number": 122, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 124, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 125, "usage_type": "call"}, {"api_name": "CustomUI.QHLine", "line_number": 126, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QDialogButtonBox", "line_number": 129, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QDialogButtonBox.Ok", "line_number": 129, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 129, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 129, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.ApplicationModal", "line_number": 135, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 135, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 139, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 142, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 146, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 151, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 152, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 156, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 170, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 170, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 173, "usage_type": "call"}]} +{"seq_id": "183458977", "text": "import argparse\n\n# TODO why isn't it in constants?\nDEFAULT_SETTINGS_FILE = \"settings/basic.yml\"\n\n# Help messages:\nSETTINGS_HELP_MSG = \"load settings from yaml files. \" \\\n \"If multiple files are specified, overlapping settings \" \\\n \"will be overwritten according to order of appearance \" \\\n \"(e.g. settings from file #1 will be overwritten by file #2).\"\nQ_HELP_MSG = \"use n-step qlearning instead of a3c\"\n\n\ndef _create_train_parser(description):\n parser = argparse.ArgumentParser(description=description,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument(\"--settings\", \"-s\",\n dest=\"settings_yml\",\n metavar='YAML_FILE',\n nargs=\"*\",\n type=str,\n default=[DEFAULT_SETTINGS_FILE],\n help=SETTINGS_HELP_MSG)\n parser.add_argument(\"--run_tag\",\n \"-rt\",\n dest=\"run_tag\",\n metavar=\"RUN_TAG\",\n help=\"Prefix added to tensorboard summaries\",\n default=None)\n parser.add_argument(\"--frameskip\", \"-fs\",\n dest=\"frameskip\",\n metavar='FRAMESKIP',\n type=int,\n default=None,\n help=\"Override frameskip setting.\")\n return parser\n\n\ndef _create_test_parser(description):\n parser = argparse.ArgumentParser(description=description,\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n parser.add_argument(dest=\"model\",\n metavar=\"MODEL_PATH\",\n type=str,\n help=\"Path to trained model directory.\"\n )\n parser.add_argument(\"--episodes\", \"-e\",\n dest=\"episodes_num\",\n metavar=\"EPISODES_NUM\",\n type=int,\n default=10,\n help=\"Number of episodes to test.\"\n )\n parser.add_argument(\"--hide-window\", \"-ps\",\n dest=\"print_settings\",\n action=\"store_const\",\n default=False,\n const=True,\n help=\"Hide window.\"\n )\n parser.add_argument(\"--print-settings\", \"-hw\",\n dest=\"hide_window\",\n action=\"store_const\",\n default=False,\n const=True,\n help=\"Print settings upon loading.\"\n )\n parser.add_argument(\"--fps\", \"-fps\",\n dest=\"fps\",\n metavar=\"FRAMERATE\",\n default=35,\n help=\"If window is visible, tests will be run with given framerate.\"\n )\n parser.add_argument(\"--agent-view\",\n \"-av\",\n dest=\"agent_view\",\n action=\"store_const\",\n default=False,\n const=True,\n help=\"If True, window will display exactly what agent sees(with frameskip), \"\n \"not the smoothed out version.\"\n )\n parser.add_argument(\"--seed\", \"-seed\",\n dest=\"seed\",\n metavar=\"SEED\",\n default=None,\n type=int,\n help=\"Seed for ViZDoom.\"\n )\n parser.add_argument(\"-o\", \"--output\",\n dest=\"output\",\n metavar=\"STATS_OUTPUT_FILE\",\n default=None,\n help=\"File for output of stats\"\n )\n parser.add_argument(\"--deterministic\", \"-d\",\n dest=\"deterministic\",\n metavar=\"DETERMINISTIC\",\n default=1,\n type=int,\n choices=[0, 1],\n help=\"If 1 dtests will be deterministic.\"\n )\n\n return parser\n\n\ndef parse_train_a3c_args():\n parser = _create_train_parser(description='A3C: training script for ViZDoom.')\n\n return parser.parse_args()\n\n\ndef parse_train_adqn_args():\n parser = _create_train_parser(description='Asynchronous n-step DQN: training script for ViZDoom.')\n\n return parser.parse_args()\n\n\ndef parse_train_dqn_args():\n parser = _create_train_parser(description='DQN: training script for ViZDoom')\n\n return parser.parse_args()\n\n\ndef parse_test_dqn_args():\n parser = _create_test_parser(description='DQN: testing script for ViZDoom')\n\n return parser.parse_args()\n\n\ndef parse_test_adqn_args():\n parser = _create_test_parser(description='Asynchronous n-step DQN: testing script for ViZDoom')\n\n return parser.parse_args()\n\n\ndef parse_test_a3c_args():\n parser = _create_test_parser(description='A3C: testing script for ViZDoom')\n\n return parser.parse_args()\n", "sub_path": "util/parsers.py", "file_name": "parsers.py", "file_ext": "py", "file_size_in_byte": 5275, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 15, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 16, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 40, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 41, "usage_type": "attribute"}]} +{"seq_id": "119002872", "text": "# %load q02_data_cleaning_all/build.py\n# Default Imports\nimport sys, os\n# sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom greyatomlib.logistic_regression_project.q01_outlier_removal.build import outlier_removal\nfrom sklearn.preprocessing import Imputer\n\nloan_data = pd.read_csv('data/loan_prediction_uncleaned.csv')\nloan_data = loan_data.drop('Loan_ID', 1)\nloan_data = outlier_removal(loan_data)\n\n\ndef data_cleaning(data):\n X = data.iloc[:,:-1]\n y = data.iloc[:,-1]\n np.random.seed=9\n X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)\n mean_imputer = Imputer(strategy='mean')\n mode_imputer = Imputer(strategy='most_frequent')\n for i in range(len(X_train.columns)):\n col = X_train.columns[i]\n if X_train.iloc[:,i].dtype != np.object:\n X_train.iloc[:,i] = mean_imputer.fit_transform(X_train[[col]])\n else:\n X_train.iloc[:,i].fillna(X_train.iloc[:,i].mode(), inplace=True)\n for i in range(len(X_test.columns)):\n col = X_test.columns[i]\n if X_test.iloc[:,i].dtype != np.object:\n X_test.iloc[:,i] = mean_imputer.fit_transform(X_test[[col]])\n else:\n X_test.iloc[:,i].fillna(X_test.iloc[:,i].mode(), inplace=True)\n return X, y, X_train, X_test, y_train, y_test\n\n\n", "sub_path": "q02_data_cleaning_all/build.py", "file_name": "build.py", "file_ext": "py", "file_size_in_byte": 1419, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "greyatomlib.logistic_regression_project.q01_outlier_removal.build.outlier_removal", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 20, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.Imputer", "line_number": 21, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.Imputer", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.object", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.object", "line_number": 31, "usage_type": "attribute"}]} +{"seq_id": "219435732", "text": "import pymysql\n\ndb = None\n\ntry:\n # pymysql.connect() 함수를 사용하면 DB 서버에 접속할 수 있습니다.\n # connect() 함수의 인자는 다음과 같습니다.\n # DB 호스트 정보에 맞게 입력\n db = pymysql.connect(\n\n # 데이터 베이스 서버가 존재하는 호스트 주소\n host='localhost',\n\n # 데이터베이스 로그인 유저\n user='root',\n\n # 데이터베이스 로그인 패스워드\n passwd='joker77&',\n\n # 데이터베이스 명\n db='k_digital',\n\n # 데이터베이스에서 사용할 charset 인코딩\n charset='utf8'\n )\n print(\"DB 연결 성공 \")\n\n # 테이블 삽입 sql 정의 -- ①\n sql = '''\n CREATE TABLE user(\n # 컬럼명 id는 기본 키, 자동 증가, null 일 수 없는 제약 조건을 갖는다.\n id int primary key auto_increment not null,\n \n # 컬럼명 name은 32자 내외의 가변 길이의 문자열을 받는 제약 조건\n name varchar(32),\n \n # 칼럼명 age은 정수를 받는 제약 조건\n age int,\n \n # 칼럼명 address은 32자 내외의 가변 길이의 문자열을 받는 제약 조건\n address varchar(32)\n \n # DB 테이블을 생성할 때 사용되는 기본 설정\n ) ENGINE = InnoDB DEFAULT CHARSET=utf8\n '''\n\n # 테이블 생성 -- ②\n # 연결한 DB와 상호 작용하려면 cursor 객체가 필요합니다.\n # cursor 객체는 우리가 임의로 생성할 수 없으며 반드시 DB 호스트에 연결된\n # 객체(db)의 cursor() 함수로 cursor 객체를 받아와야 합니다.\n with db.cursor() as cursor:\n\n # cursor 객체의 execute() 함수로 SQL 구문을 실행합니다.\n # with 구문 내에서 cursor 객체를 사용하기 때문에\n # 사용 후에는 자동으로 메모리에서 해제됩니다.\n cursor.execute(sql)\n\nexcept Exception as e:\n # DB 연결 실패 시 오류 내용 출력\n print(e)\n\nfinally:\n # DB 가 연결된 경우에만 접속 닫기 시도\n if db is not None:\n\n # 데이터베이스 서버 닫기\n db.close()\n print('table 생성 완료')\n print(\"DB 연결 닫기 성공\")", "sub_path": "create_table.py", "file_name": "create_table.py", "file_ext": "py", "file_size_in_byte": 2284, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pymysql.connect", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "229587166", "text": "#import needed classes\r\nimport keras\r\nfrom keras.datasets import cifar10\r\nfrom keras.layers import Dense,Conv2D,MaxPooling2D,Flatten,AveragePooling2D,Dropout,BatchNormalization,Activation\r\nfrom keras.models import Model,Input\r\nfrom keras.optimizers import Adam\r\nfrom keras.callbacks import LearningRateScheduler\r\nfrom keras.callbacks import ModelCheckpoint\r\nimport os\r\n\r\n#load the mnist dataset\r\n(train_x, train_y) , (test_x, test_y) = cifar10.load_data()\r\n\r\n#normalize the data\r\ntrain_x = train_x.astype('float32') / 255\r\ntest_x = test_x.astype('float32') / 255\r\n\r\n#print the shapes of the data arrays\r\nprint(\"Train Images: \",train_x.shape)\r\nprint(\"Train Labels: \",train_y.shape)\r\nprint(\"Test Images: \",test_x.shape)\r\nprint(\"Test Labels: \",test_y.shape)\r\n\r\n\r\n\r\n#Encode the labels to vectors\r\ntrain_y = keras.utils.to_categorical(train_y,10)\r\ntest_y = keras.utils.to_categorical(test_y,10)\r\n\r\n#define a common unit\r\ndef Unit(x,filters):\r\n out = BatchNormalization()(x)\r\n out = Activation(\"relu\")(out)\r\n out = Conv2D(filters=filters, kernel_size=[3, 3], strides=[1, 1], padding=\"same\")(out)\r\n\r\n return out\r\n\r\n#Define the model\r\n\r\n\r\ndef MiniModel(input_shape):\r\n images = Input(input_shape)\r\n\r\n net = Unit(images,64)\r\n net = Unit(net,64)\r\n net = Unit(net,64)\r\n net = MaxPooling2D(pool_size=(2,2))(net)\r\n\r\n net = Unit(net,128)\r\n net = Unit(net,128)\r\n net = Unit(net,128)\r\n net = Unit(net, 128)\r\n net = Unit(net, 128)\r\n net = Unit(net, 128)\r\n net = MaxPooling2D(pool_size=(2, 2))(net)\r\n\r\n net = Unit(net,256)\r\n net = Unit(net,256)\r\n net = Unit(net,256)\r\n net = Unit(net, 256)\r\n net = Unit(net, 256)\r\n net = Unit(net, 256)\r\n\r\n net = Dropout(0.25)(net)\r\n net = AveragePooling2D(pool_size=(2,2))(net)\r\n net = Flatten()(net)\r\n net = Dense(units=10,activation=\"softmax\")(net)\r\n\r\n model = Model(inputs=images,outputs=net)\r\n\r\n return model\r\n\r\ninput_shape = (32,32,3)\r\nmodel = MiniModel(input_shape)\r\n\r\n#Print a Summary of the model\r\n\r\nmodel.summary()\r\n\r\n#Define the Learning rate schedule function\r\n\r\ndef lr_schedule(epoch):\r\n\r\n lr = 0.001\r\n\r\n if epoch > 15:\r\n lr = lr / 100\r\n elif epoch > 10:\r\n lr = lr / 10\r\n elif epoch > 5:\r\n lr = lr / 5\r\n\r\n print(\"Learning Rate: \",lr)\r\n\r\n return lr\r\n\r\n#Pass teh scheduler function to the Learning Rate Scheduler class\r\nlr_scheduler = LearningRateScheduler(lr_schedule)\r\n\r\n#Directory in which to create models\r\nsave_direc = os.path.join(os.getcwd(), 'cifar10savedmodels')\r\n\r\n#Name of model files\r\nmodel_name = 'cifar10model.{epoch:03d}.h5'\r\n\r\n#Create Directory if it doesn't exist\r\nif not os.path.isdir(save_direc):\r\n os.makedirs(save_direc)\r\n\r\n#Join the directory with the model file\r\nmodelpath = os.path.join(save_direc, model_name)\r\n\r\ncheckpoint = ModelCheckpoint(filepath=modelpath,\r\n monitor='val_acc',\r\n verbose=1,\r\n save_best_only=True,\r\n period=1)\r\n\r\n#Specify the training components\r\nmodel.compile(optimizer=Adam(lr_schedule(0)),loss=\"categorical_crossentropy\",metrics=[\"accuracy\"])\r\n\r\n#Fit the model\r\nmodel.fit(train_x,train_y,batch_size=128,epochs=20,shuffle=True,validation_split=0.1,verbose=1,callbacks=[checkpoint,lr_scheduler])\r\n\r\n#Evaluate the accuracy of the test dataset\r\naccuracy = model.evaluate(x=test_x,y=test_y,batch_size=128)\r\n\r\nprint(\"Accuracy: \",accuracy[1])\r\n\r\n\r\n\r\n", "sub_path": "basics/cifardepth.py", "file_name": "cifardepth.py", "file_ext": "py", "file_size_in_byte": 3456, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "keras.datasets.cifar10.load_data", "line_number": 12, "usage_type": "call"}, {"api_name": "keras.datasets.cifar10", "line_number": 12, "usage_type": "name"}, {"api_name": "keras.utils.to_categorical", "line_number": 27, "usage_type": "call"}, {"api_name": "keras.utils", "line_number": 27, "usage_type": "attribute"}, {"api_name": "keras.utils.to_categorical", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.utils", "line_number": 28, "usage_type": "attribute"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 32, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 33, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 34, "usage_type": "call"}, {"api_name": "keras.models.Input", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.layers.AveragePooling2D", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 66, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.callbacks.LearningRateScheduler", "line_number": 98, "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.getcwd", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 113, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 120, "usage_type": "call"}]} +{"seq_id": "378691920", "text": "# -*- coding: utf-8 -*-\n\n# Copyright 2018, IBM.\n#\n# This source code is licensed under the Apache License, Version 2.0 found in\n# the LICENSE.txt file in the root directory of this source tree.\n\n# pylint: disable=invalid-name,unused-import\n\n\"\"\"Tests for checking qiskit interfaces to simulators.\"\"\"\n\nimport unittest\nimport qiskit\nimport qiskit.extensions.simulator\nfrom qiskit.tools.qi.qi import state_fidelity\nfrom qiskit.wrapper import available_backends, register, execute, get_backend\nfrom qiskit.backends.local import QasmSimulatorPy, QasmSimulatorCpp\nfrom .common import requires_qe_access, QiskitTestCase\n\n\n# Cpp backend required\ntry:\n cpp_backend = QasmSimulatorCpp()\nexcept FileNotFoundError:\n _skip_class = True\nelse:\n _skip_class = False\n\n\n@unittest.skipIf(_skip_class, 'C++ simulators unavailable')\nclass TestCrossSimulation(QiskitTestCase):\n \"\"\"Test output consistency across simulators.\n \"\"\"\n _desired_fidelity = 0.99\n\n def test_statevector(self):\n \"\"\"statevector from a bell state\"\"\"\n q = qiskit.QuantumRegister(2)\n circ = qiskit.QuantumCircuit(q)\n circ.h(q[0])\n circ.cx(q[0], q[1])\n\n sim_cpp = 'local_statevector_simulator_cpp'\n sim_py = 'local_statevector_simulator_py'\n result_cpp = execute(circ, sim_cpp).result()\n result_py = execute(circ, sim_py).result()\n statevector_cpp = result_cpp.get_statevector()\n statevector_py = result_py.get_statevector()\n fidelity = state_fidelity(statevector_cpp, statevector_py)\n self.assertGreater(\n fidelity, self._desired_fidelity,\n \"cpp vs. py statevector has low fidelity{0:.2g}.\".format(fidelity))\n\n @requires_qe_access\n def test_qasm(self, QE_TOKEN, QE_URL, hub=None, group=None, project=None):\n \"\"\"counts from a GHZ state\"\"\"\n register(QE_TOKEN, QE_URL, hub, group, project)\n q = qiskit.QuantumRegister(3)\n c = qiskit.ClassicalRegister(3)\n circ = qiskit.QuantumCircuit(q, c)\n circ.h(q[0])\n circ.cx(q[0], q[1])\n circ.cx(q[1], q[2])\n circ.measure(q, c)\n\n sim_cpp = 'local_qasm_simulator_cpp'\n sim_py = 'local_qasm_simulator_py'\n sim_ibmq = 'ibmq_qasm_simulator'\n shots = 2000\n result_cpp = execute(circ, sim_cpp, shots=shots).result()\n result_py = execute(circ, sim_py, shots=shots).result()\n result_ibmq = execute(circ, sim_ibmq, shots=shots).result()\n counts_cpp = result_cpp.get_counts()\n counts_py = result_py.get_counts()\n counts_ibmq = result_ibmq.get_counts()\n self.assertDictAlmostEqual(counts_cpp, counts_py, shots*0.05)\n self.assertDictAlmostEqual(counts_py, counts_ibmq, shots*0.05)\n\n def test_qasm_snapshot(self):\n \"\"\"snapshot a circuit at multiple places\"\"\"\n q = qiskit.QuantumRegister(3)\n c = qiskit.ClassicalRegister(3)\n circ = qiskit.QuantumCircuit(q, c)\n circ.h(q[0])\n circ.cx(q[0], q[1])\n circ.snapshot(1)\n circ.ccx(q[0], q[1], q[2])\n circ.snapshot(2)\n circ.reset(q)\n circ.snapshot(3)\n\n sim_cpp = 'local_qasm_simulator_cpp'\n sim_py = 'local_qasm_simulator_py'\n result_cpp = execute(circ, sim_cpp, shots=2).result()\n result_py = execute(circ, sim_py, shots=2).result()\n snapshots_cpp = result_cpp.get_snapshots()\n snapshots_py = result_py.get_snapshots()\n self.assertEqual(snapshots_cpp.keys(), snapshots_py.keys())\n snapshot_cpp_1 = result_cpp.get_snapshot(slot='1')\n snapshot_py_1 = result_py.get_snapshot(slot='1')\n self.assertEqual(len(snapshot_cpp_1), len(snapshot_py_1))\n fidelity = state_fidelity(snapshot_cpp_1[0], snapshot_py_1[0])\n self.assertGreater(fidelity, self._desired_fidelity)\n\n @requires_qe_access\n def test_qasm_reset_measure(self, QE_TOKEN, QE_URL, hub=None, group=None, project=None):\n \"\"\"counts from a qasm program with measure and reset in the middle\"\"\"\n register(QE_TOKEN, QE_URL, hub, group, project)\n q = qiskit.QuantumRegister(3)\n c = qiskit.ClassicalRegister(3)\n circ = qiskit.QuantumCircuit(q, c)\n circ.h(q[0])\n circ.cx(q[0], q[1])\n circ.reset(q[0])\n circ.cx(q[1], q[2])\n circ.t(q)\n circ.measure(q[1], c[1])\n circ.h(q[2])\n circ.measure(q[2], c[2])\n\n # TODO: bring back online simulator tests when reset/measure doesn't\n # get rejected by the api\n sim_cpp = 'local_qasm_simulator_cpp'\n sim_py = 'local_qasm_simulator_py'\n # sim_ibmq = 'ibmq_qasm_simulator'\n shots = 1000\n result_cpp = execute(circ, sim_cpp, shots=shots, seed=1).result()\n result_py = execute(circ, sim_py, shots=shots, seed=1).result()\n # result_ibmq = execute(circ, sim_ibmq, {'shots': shots}).result()\n counts_cpp = result_cpp.get_counts()\n counts_py = result_py.get_counts()\n # counts_ibmq = result_ibmq.get_counts()\n self.assertDictAlmostEqual(counts_cpp, counts_py, shots * 0.04)\n # self.assertDictAlmostEqual(counts_py, counts_ibmq, shots*0.04)\n\n\nclass TestSimulatorNames(QiskitTestCase):\n \"\"\"Test aliased and deprecated names.\n \"\"\"\n\n def test_alias(self):\n \"\"\"test short alias names work\"\"\"\n compact_name = \"local_qasm_simulator\"\n backend = get_backend(compact_name)\n if not _skip_class:\n self.assertIsInstance(backend, QasmSimulatorCpp)\n else:\n self.assertIsInstance(backend, QasmSimulatorPy)\n\n def test_compact(self):\n \"\"\"test the compact flag for available_backends works\"\"\"\n compact_names = available_backends()\n expanded_names = available_backends(compact=False)\n self.assertIn('local_qasm_simulator', compact_names)\n self.assertIn('local_statevector_simulator', compact_names)\n self.assertIn('local_unitary_simulator', compact_names)\n self.assertIn('local_qasm_simulator_py', expanded_names)\n self.assertIn('local_statevector_simulator_py', expanded_names)\n\n def test_deprecated(self):\n \"\"\"test deprecated backends are resolved correctly\"\"\"\n old_name = \"local_qiskit_simulator\"\n if not _skip_class:\n new_backend = get_backend(old_name)\n self.assertIsInstance(new_backend, QasmSimulatorCpp)\n\n\nif __name__ == '__main__':\n unittest.main(verbosity=2)\n", "sub_path": "test/python/test_simulator_interfaces.py", "file_name": "test_simulator_interfaces.py", "file_ext": "py", "file_size_in_byte": 6450, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "qiskit.backends.local.QasmSimulatorCpp", "line_number": 23, "usage_type": "call"}, {"api_name": "common.QiskitTestCase", "line_number": 31, "usage_type": "name"}, {"api_name": "qiskit.QuantumRegister", "line_number": 38, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 39, "usage_type": "call"}, {"api_name": "qiskit.wrapper.execute", "line_number": 45, "usage_type": "call"}, {"api_name": "qiskit.wrapper.execute", "line_number": 46, "usage_type": "call"}, {"api_name": "qiskit.tools.qi.qi.state_fidelity", "line_number": 49, "usage_type": "call"}, {"api_name": "qiskit.wrapper.register", "line_number": 57, "usage_type": "call"}, {"api_name": "qiskit.QuantumRegister", "line_number": 58, "usage_type": "call"}, {"api_name": "qiskit.ClassicalRegister", "line_number": 59, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 60, "usage_type": "call"}, {"api_name": "qiskit.wrapper.execute", "line_number": 70, "usage_type": "call"}, {"api_name": "qiskit.wrapper.execute", "line_number": 71, "usage_type": "call"}, {"api_name": "qiskit.wrapper.execute", "line_number": 72, "usage_type": "call"}, {"api_name": "common.requires_qe_access", "line_number": 54, "usage_type": "name"}, {"api_name": "qiskit.QuantumRegister", "line_number": 81, "usage_type": "call"}, {"api_name": "qiskit.ClassicalRegister", "line_number": 82, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 83, "usage_type": "call"}, {"api_name": "qiskit.wrapper.execute", "line_number": 94, "usage_type": "call"}, {"api_name": "qiskit.wrapper.execute", "line_number": 95, "usage_type": "call"}, {"api_name": "qiskit.tools.qi.qi.state_fidelity", "line_number": 102, "usage_type": "call"}, {"api_name": "qiskit.wrapper.register", "line_number": 108, "usage_type": "call"}, {"api_name": "qiskit.QuantumRegister", "line_number": 109, "usage_type": "call"}, {"api_name": "qiskit.ClassicalRegister", "line_number": 110, "usage_type": "call"}, {"api_name": "qiskit.QuantumCircuit", "line_number": 111, "usage_type": "call"}, {"api_name": "qiskit.wrapper.execute", "line_number": 127, "usage_type": "call"}, {"api_name": "qiskit.wrapper.execute", "line_number": 128, "usage_type": "call"}, {"api_name": "common.requires_qe_access", "line_number": 105, "usage_type": "name"}, {"api_name": "unittest.skipIf", "line_number": 30, "usage_type": "call"}, {"api_name": "common.QiskitTestCase", "line_number": 137, "usage_type": "name"}, {"api_name": "qiskit.wrapper.get_backend", "line_number": 144, "usage_type": "call"}, {"api_name": "qiskit.backends.local.QasmSimulatorCpp", "line_number": 146, "usage_type": "argument"}, {"api_name": "qiskit.backends.local.QasmSimulatorPy", "line_number": 148, "usage_type": "argument"}, {"api_name": "qiskit.wrapper.available_backends", "line_number": 152, "usage_type": "call"}, {"api_name": "qiskit.wrapper.available_backends", "line_number": 153, "usage_type": "call"}, {"api_name": "qiskit.wrapper.get_backend", "line_number": 164, "usage_type": "call"}, {"api_name": "qiskit.backends.local.QasmSimulatorCpp", "line_number": 165, "usage_type": "argument"}, {"api_name": "unittest.main", "line_number": 169, "usage_type": "call"}]} +{"seq_id": "497974778", "text": "import numpy as np\nfrom numpy.linalg import inv\nfrom tensor import Tensor\nfrom scipy.spatial.distance import pdist, squareform\nfrom scipy.stats import multivariate_normal\nfrom scipy.linalg import cholesky, cho_solve\nfrom typing import Tuple, Any, Iterable\n\n\nimport matplotlib.pyplot as plt\n\n\nclass Kernel:\n def __init__(self, X: Tensor, Y: Tensor, beta: float = 0.1) -> None:\n self.X = X\n self.Y = Y\n self.beta = beta\n self.K = self.fit(X)\n self.N, self.D = X.shape\n \n @classmethod\n def from_dataset(cls: Any, data: Tensor, beta: float = 0.1, **kwargs: float) -> Any:\n X, Y = data[:,:-1], data[:,-1:]\n return cls(X, Y, beta, **kwargs)\n\n def fit(self, X: Tensor, noise: bool = True) -> Tensor:\n \"\"\"Build kernel matrix from a dataset.\"\"\"\n raise NotImplementedError\n\n def _get_params(self, point: Tensor) -> Tuple[Tensor, Tensor]:\n \"\"\"Calculate necessary parameters to augment the kernel matrix K.\"\"\"\n raise NotImplementedError\n\n def _expand_kernel_matrix(self, K: Tensor, k: Tensor, c: Tensor) -> Tensor:\n \"\"\"\n Expand base kernel matrix K right with k, then bottom with [k.T; c].\n dim(K) = (N,N)\n dim(k) = (N,n)\n dim(c) = (n,n)\n \"\"\"\n print(K.shape, k.shape, c.shape)\n K = np.concatenate((K,k), axis=1)\n k_ = np.concatenate((k.T, c), axis=1)\n K = np.concatenate((K, k_), axis=0)\n return K\n\n def augment(self, new_data: Tensor) -> None:\n \"\"\"Augment base kernel matrix with new noisy points.\"\"\"\n new_x, new_y = new_data[:,:-1], new_data[:,-1:]\n k, c = self._get_params(new_x)\n c += (1/self.beta) * np.eye(c.shape[0])\n self.K = self._expand_kernel_matrix(self.K, k, c) \n self.N = self.K.shape[0]\n self.X = np.concatenate((self.X, new_x), axis=0)\n self.Y = np.concatenate((self.Y, new_y), axis=0)\n\n def predict(self, x_space: Tensor, return_std: bool = False, return_cov: bool = False) -> Iterable[Tensor]:\n \"\"\"Predict values of new points under the current model, and their variance.\"\"\"\n k, c = self._get_params(x_space)\n L = cholesky(self.K, lower=True)\n alpha = cho_solve((L, True), self.Y)\n predicted_mean = (k.T @ alpha)[:,0]\n\n if return_std and return_cov:\n raise RuntimeError(\n \"Not returning standard deviation of predictions when \"\n \"returning full covariance.\")\n \n if return_cov or return_std:\n v = cho_solve((L, True), k)\n predicted_cov = c - k.T @ v\n\n if return_cov: return (predicted_mean, predicted_cov)\n \n elif return_std: return (predicted_mean, np.sqrt(np.diag(predicted_cov)))\n\n return predicted_mean\n \n def draw_samples(self, x_space: Tensor, n_samples: int = 5) -> Tensor:\n \"\"\"Draw samples from the GP given its covariance matrix.\"\"\"\n gauss_params = self.predict(x_space, return_cov=True)\n #print(gauss_params[1])\n #print(multivariate_normal(*gauss_params).rvs(n_samples).shape)\n return multivariate_normal(*gauss_params).rvs(n_samples)\n\n k, c = self._get_params(x_space)\n \n K = self._expand_kernel_matrix(self.K, k, c)\n # compute conditional on the training set\n nk = self.N \n A = inv(K)\n A_bb = A[nk:,nk:]\n A_ba = A[nk:,:nk]\n A_bb_inv = inv(A_bb)\n m_b = -A_bb_inv @ A_ba @ self.Y\n # sample from it\n print(A_bb_inv[:4,:4])\n A_bb_inv += 0.1*np.eye(A_bb_inv.shape[0])\n samples = multivariate_normal(m_b.T[0,:], A_bb_inv).rvs(n_samples)\n return samples\n\n\nclass GaussianKernel(Kernel):\n def __init__(self, X: Tensor, Y: Tensor, beta: float = 10, sigma: float = 1) -> None:\n self.sigma = sigma\n super().__init__(X, Y, beta)\n\n def fit(self, X: Tensor, noise: bool = True) -> Tensor:\n \"\"\"Build kernel matrix from a dataset.\"\"\"\n pairwise_dists = squareform(pdist(X, 'euclidean'))\n K = np.exp(-pairwise_dists ** 2 / (2*self.sigma ** 2))\n if noise:\n return K + (1/self.beta) * np.eye(X.shape[0])\n return K\n\n def _get_params(self, points: Tensor) -> Tuple[Tensor, Tensor]:\n \"\"\"Calculate necessary parameters to augment the kernel matrix K.\"\"\"\n k = np.square(points[:,:,None] - self.X.T[None,:,:]).T\n k = np.sum(k, axis=1)\n k = np.exp(-k / (2*self.sigma ** 2))\n c = self.fit(points, noise=False)\n return k, c\n\n\nN, D = 10, 1\nx = np.linspace(1,10, N) + 0.1*(2*np.random.random(N)-1)\nx = x[:,None]\ny = np.cos(x) + (2*np.random.random((N, 1))-1)\n\ndata = np.concatenate((x,y), axis=1)\n\n\nkernel = GaussianKernel.from_dataset(data, sigma=1, beta=100)\n\n\n\n\n\nn_test = 10\nz = np.linspace(1,10,n_test)\nz = z[:,None]\n\n# draw_sample test\np = kernel.draw_samples(z, 5)\nprint(p.shape)\n\n# fit test\n\npred_mean, pred_std = kernel.predict(z, return_std=True)\n\nplt.figure()\nz = z[:,0]\nplt.plot(z, pred_mean, label=r'$Predicted \\pm 1\\sigma$')\nplt.plot(x, y, 'o', label='Training Points')\nplt.plot(z, np.cos(z), label='True Distribution')\nplt.fill_between(z, pred_mean+pred_std, pred_mean-pred_std, alpha=.2)\nfor sample in p:\n plt.plot(z, sample, 'k--')\n\nplt.legend(loc=0)\nplt.show()\n", "sub_path": "scripts/GaussianProcessesLib/gps/InheritanceTest.py", "file_name": "InheritanceTest.py", "file_ext": "py", "file_size_in_byte": 5320, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "tensor.Tensor", "line_number": 14, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 22, "usage_type": "name"}, {"api_name": "tensor.Tensor", "line_number": 22, "usage_type": "name"}, {"api_name": "tensor.Tensor", "line_number": 26, "usage_type": "name"}, {"api_name": "tensor.Tensor", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 30, "usage_type": "name"}, {"api_name": "tensor.Tensor", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 44, "usage_type": "call"}, {"api_name": "tensor.Tensor", "line_number": 47, "usage_type": "name"}, {"api_name": "numpy.eye", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 55, "usage_type": "call"}, {"api_name": "tensor.Tensor", "line_number": 57, "usage_type": "name"}, {"api_name": "scipy.linalg.cholesky", "line_number": 60, "usage_type": "call"}, {"api_name": "scipy.linalg.cho_solve", "line_number": 61, "usage_type": "call"}, {"api_name": "scipy.linalg.cho_solve", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 75, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 57, "usage_type": "name"}, {"api_name": "tensor.Tensor", "line_number": 79, "usage_type": "name"}, {"api_name": "scipy.stats.multivariate_normal", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 98, "usage_type": "call"}, {"api_name": "scipy.stats.multivariate_normal", "line_number": 99, "usage_type": "call"}, {"api_name": "tensor.Tensor", "line_number": 104, "usage_type": "name"}, {"api_name": "tensor.Tensor", "line_number": 108, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.squareform", "line_number": 110, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.pdist", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 113, "usage_type": "call"}, {"api_name": "tensor.Tensor", "line_number": 116, "usage_type": "name"}, {"api_name": "numpy.square", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 120, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 116, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 126, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 128, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "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.plot", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "numpy.cos", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 156, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 156, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}]} +{"seq_id": "509579255", "text": "import requests\nfrom bs4 import BeautifulSoup\n\nresponse = requests.get('http://askdjango.github.io/lv1/')\n\n#응답 요청 OK한 코드를 텍스트로 변환해서 html에 저장\nhtmltotext = response.text\n\n#응답받은 코드를 BS에 사용하기 위해 인스턴스 지정\nbs = BeautifulSoup(htmltotext, 'html.parser')\n\n#원하는 태그만 골라뽑기\nfor tag in bs.select('li[class=course]'):\n print(tag.text)", "sub_path": "excercise.py", "file_name": "excercise.py", "file_ext": "py", "file_size_in_byte": 419, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.get", "line_number": 4, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "290846795", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import integrate\nfrom scipy.optimize import curve_fit\nimport AnaUtils as au\n\ndef extRun(fname,nbase,winS,winF,cut=4,pmt=1,trigM=100,qbins=1000,ret=False,plot=False):\n waves = au.ReadDDC10_BinWave(fname)\n waves[0],base = au.Subtract_Baseline(waves[0],nBase=nbase)\n #require baseline has no pulse. i.e. integral over baseline less than cut*rms\n bmask = np.absolute(integrate.simps(waves[0][:,pmt,:nbase]))trigM #trigger pulse must have amplitude > trigM\n \n Trigshift = np.average(PromptPeak[1]-TrigPeaks[1],weights=tmask*PromptPeak[2])\n wStart = TrigPeaks[1]+Trigshift-winS\n wFin = TrigPeaks[1]+Trigshift+winF\n evmask = bmask*tmask\n\n Qhist = au.winQHist(waves,ch=pmt,init=wStart.astype(int),end=wFin.astype(int),nBins=qbins,evMask=evmask[...,np.newaxis])\n if ret:\n Qhist['waves'] = waves[0][:,pmt]\n Qhist['trgT'] = TrigPeaks[1]\n Qhist['evMask'] = evmask\n Qhist['baserms'] = base[1][:,pmt]\n return Qhist,waves[1]\n else:\n return Qhist['qHist']\n\ndef fitQ(Qhist,P,bounds=(-np.inf,np.inf),doErr=False):\n def gauss(x, x0, y0, sigma):\n p = [x0, y0, sigma]\n return p[1]* np.exp(-((x-p[0])/p[2])**2)\n def gauss2(x,x0,y0,s0,x1,y1,s1):\n p0 = gauss(x,x0,y0,s0)\n p1 = gauss(x,x1,y1,s1)\n return p0+p1\n def gauss3(x,x0,y0,s0,x1,y1,s1,x2,y2,s2):\n p0 = gauss(x,x0,y0,s0)\n p1 = gauss(x,x1,y1,s1)\n g2 = 2*x1 - x0 +x2\n p2 = gauss(x,g2,y2,s2)\n return p0+p1+p2\n ng = len(P)/3\n mx = Qhist[1]\n my = Qhist[0]\n merr = None\n abSig = None\n if doErr:\n args = Qhist[3]\n mx = mx[args]\n my = my[args]\n merr = np.sqrt(Qhist[2][args])\n abSig = True\n if ng==3:\n fit,tmp = curve_fit(gauss3,mx,my,p0=P,bounds=bounds,sigma=merr,absolute_sigma=abSig,maxfev=5000,ftol=1e-7,gtol=1e-7)\n if ng==2:\n fit,tmp = curve_fit(gauss2,mx,my,p0=P,bounds=bounds,sigma=merr,absolute_sigma=abSig,maxfev=5000,ftol=1e-7,gtol=1e-7)\n else:\n fit,tmp = curve_fit(gauss,mx,my,p0=P,bounds=bounds,sigma=merr,absolute_sigma=abSig,maxfev=5000,ftol=1e-7,gtol=1e-7)\n return fit,tmp\n", "sub_path": "Triggered.py", "file_name": "Triggered.py", "file_ext": "py", "file_size_in_byte": 2428, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "AnaUtils.ReadDDC10_BinWave", "line_number": 8, "usage_type": "call"}, {"api_name": "AnaUtils.Subtract_Baseline", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.absolute", "line_number": 11, "usage_type": "call"}, {"api_name": "scipy.integrate.simps", "line_number": 11, "usage_type": "call"}, {"api_name": "scipy.integrate", "line_number": 11, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 11, "usage_type": "call"}, {"api_name": "AnaUtils.peakHist", "line_number": 13, "usage_type": "call"}, {"api_name": "AnaUtils.peakHist", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 18, "usage_type": "call"}, {"api_name": "AnaUtils.winQHist", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 56, "usage_type": "call"}, {"api_name": "scipy.optimize.curve_fit", "line_number": 59, "usage_type": "call"}, {"api_name": "scipy.optimize.curve_fit", "line_number": 61, "usage_type": "call"}, {"api_name": "scipy.optimize.curve_fit", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "420063431", "text": "#! /usr/bin/env python\n# -*- coding:utf-8 -*-\n\nfrom django.http import HttpResponse\nfrom django.shortcuts import render\nfrom django import forms\nimport json\nfrom django.core.mail import send_mail\nfrom forms import *\nfrom models import Record\nfrom time import strftime,localtime\n\ndef rest(request):\n\tadmins_email = ['flowliu@sina.com']\n\tcontent_to_sender = u\"Dear %s,\\n\\nWe have received your application.Please do NOT reply this email directly.\\n\\nThanks,\\nTech Team\"\n\tcontent_to_admin = u\"Received an application from %s %s at %s\"\n\treceive_time_str = strftime(\"%H:%M:%S %Y-%m-%d\", localtime())\n\tif request.META.has_key('CONTENT_TYPE'):\n\t\tinfo = request.META['CONTENT_TYPE'] \n\t\tif info == 'application/json' and request.method == 'POST':\n\t\t\tdata = json.loads(request.body) \n\t\t\tform = RecordForm(data)\n\n\t\t\tif form.is_valid():\n\t\t\t\tr = Record(email = form.cleaned_data['email'],\n\t\t\t\t\t\t\tfirst_name = form.cleaned_data['first_name'],\n\t\t\t\t\t\t\tlast_name = form.cleaned_data['last_name'],\n\t\t\t\t\t\t\tcontact_number = form.cleaned_data['contact_number'],\n\t\t\t\t\t\t\ttitle = form.cleaned_data['title'],\n\t\t\t\t\t\t\tcontent = form.cleaned_data['content'],\n\t\t\t\t\t\t\tlink = form.cleaned_data['link'])\n\t\t\t\tr.save()\n\n\t\t\t\tsend_mail(u'Thanks for your application',\n\t\t\t\t\t\t\tcontent_to_sender % data['last_name'],\n\t\t\t\t\t\t\t'flowjacky@gmail.com',\n\t\t\t\t\t\t\t[data['email']])\n\t\t\t\tsend_mail(u'Application Received from %s' % data['email'],\n\t\t\t\t\t\t\tcontent_to_admin % (data['last_name'],data['first_name'],receive_time_str),\n\t\t\t\t\t\t\t'flowjacky@gmail.com',\n\t\t\t\t\t\t\tadmins_email)\n\t\t\t\treturn HttpResponse('save successful')\n\t\treturn HttpResponse('data invalid')\t\n\telse:\n\t\treturn HttpResponse('Please verify Header info')\n", "sub_path": "views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1669, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "time.strftime", "line_number": 17, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 17, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Record", "line_number": 25, "usage_type": "call"}, {"api_name": "django.core.mail.send_mail", "line_number": 34, "usage_type": "call"}, {"api_name": "django.core.mail.send_mail", "line_number": 38, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 42, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 43, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "390078954", "text": "import os\nfrom setuptools import setup\n\n# Utility function to read the README file.\n# Used for the long_description. It's nice, because now 1) we have a top level\n# README file and 2) it's easier to type in the README file than to put a raw\n# string in below ...\ndef read(fname):\n return open(os.path.join(os.path.dirname(__file__), fname)).read()\n\nsetup(\n name = \"lca_algebraic\",\n version = read(\"VERSION\").strip(),\n author = \"OIE - Mines ParisTech\",\n author_email = \"raphael.jolivet@mines-paristech.fr\",\n description = (\"This library provides a layer above brightway2 for defining parametric models and running super fast LCA for monte carlo analysis.\"),\n license = \"BSD\",\n keywords = \"LCA brightway2 monte-carlo parametric\",\n url = \"https://github.com/oie-mines-paristech/lca_algebraic/\",\n packages=['lca_algebraic'],\n long_description=read('README.md'),\n long_description_content_type='text/markdown',\n classifiers=[],\n install_requires=[\n 'tabulate',\n 'ipywidgets',\n 'pandas',\n 'seaborn',\n 'sympy',\n 'matplotlib',\n 'brightway2>=2.3',\n 'SALib']\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1150, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "610506692", "text": "import tensorflow.keras as tfk\nimport tensorflow.keras.layers as tfkl\n\nfrom functools import partial\n\nfrom ..layers import Fire\n\n\ndef squeezenet(num_output, weight_decay=0.0001):\n fire = partial(Fire, weight_decay=weight_decay)\n pool = partial(tfkl.MaxPool2D, pool_size=3, strides=2, padding='same')\n\n conv = partial(\n tfkl.Conv2D, kernel_size=3, padding='same',\n kernel_initializer=tfk.initializers.TruncatedNormal(\n stddev=0.001),\n kernel_regularizer=tfk.regularizers.l2(weight_decay))\n\n return tfk.Sequential([\n conv(name='conv1', filters=64, strides=2, activation='relu',\n trainable=False),\n pool(name='pool1'),\n\n fire(name='fire2', s1x1=16, e1x1=64, e3x3=64),\n fire(name='fire3', s1x1=16, e1x1=64, e3x3=64),\n pool(name='pool3'),\n\n fire(name='fire4', s1x1=32, e1x1=128, e3x3=128),\n fire(name='fire5', s1x1=32, e1x1=128, e3x3=128),\n pool(name='pool5'),\n\n fire(name='fire6', s1x1=48, e1x1=192, e3x3=192),\n fire(name='fire7', s1x1=48, e1x1=192, e3x3=192),\n\n fire(name='fire8', s1x1=64, e1x1=256, e3x3=256),\n fire(name='fire9', s1x1=64, e1x1=256, e3x3=256),\n\n fire(name='fire10', s1x1=96, e1x1=384, e3x3=384),\n fire(name='fire11', s1x1=96, e1x1=384, e3x3=384),\n tfkl.Dropout(name='drop11', rate=0.5),\n\n conv(name='conv12', filters=num_output, strides=1)\n ])\n", "sub_path": "squeezedet/models/squeezenet.py", "file_name": "squeezenet.py", "file_ext": "py", "file_size_in_byte": 1429, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "functools.partial", "line_number": 10, "usage_type": "call"}, {"api_name": "layers.Fire", "line_number": 10, "usage_type": "argument"}, {"api_name": "functools.partial", "line_number": 11, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.MaxPool2D", "line_number": 11, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers", "line_number": 11, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 13, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Conv2D", "line_number": 14, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.layers", "line_number": 14, "usage_type": "name"}, {"api_name": "tensorflow.keras.initializers.TruncatedNormal", "line_number": 15, "usage_type": "call"}, {"api_name": "tensorflow.keras.initializers", "line_number": 15, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 15, "usage_type": "name"}, {"api_name": "tensorflow.keras.regularizers.l2", "line_number": 17, "usage_type": "call"}, {"api_name": "tensorflow.keras.regularizers", "line_number": 17, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 17, "usage_type": "name"}, {"api_name": "tensorflow.keras.Sequential", "line_number": 19, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 19, "usage_type": "name"}, {"api_name": "tensorflow.keras.layers.Dropout", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "206196857", "text": "from typing import Optional\r\nfrom typing_extensions import Literal\r\n\r\nfrom pydantic import BaseModel, PositiveInt, PositiveFloat, Field\r\nfrom enum import Enum\r\n\r\nfrom ..constants import ClassOfSupply\r\n\r\n\r\nclass ResourceType(str, Enum):\r\n discrete = \"discrete\"\r\n continuous = \"continuous\"\r\n\r\n class Config:\r\n title: \"Resource Type\"\r\n\r\n\r\nclass Resource(BaseModel):\r\n name: str = Field(..., title=\"Name\", description=\"Resource name\")\r\n cos: ClassOfSupply = Field(\r\n ..., title=\"Class of Supply\", description=\"Class of supply number\"\r\n )\r\n units: str = Field(default=\"kg\", title=\"Units\")\r\n description: Optional[str] = Field(\r\n default=None, title=\"Description\", description=\"Short description\"\r\n )\r\n\r\n class Config:\r\n title = \"Resource Data\"\r\n\r\n\r\nclass DiscreteResource(Resource):\r\n type: Literal[ResourceType.discrete] = Field(\r\n ..., title=\"Type\", description=\"Resource type\"\r\n )\r\n unit_mass: PositiveInt = Field(..., title=\"Unit Mass\", description=\"Resource mass\")\r\n unit_volume: PositiveInt = Field(\r\n ..., title=\"Unit Volume\", description=\"Resource volume\"\r\n )\r\n\r\n class Config:\r\n title = \"Discrete Resource\"\r\n\r\n\r\nclass ContinuousResource(Resource):\r\n type: Literal[ResourceType.continuous] = Field(\r\n ..., title=\"Type\", description=\"Resource type\"\r\n )\r\n unit_mass: PositiveFloat = Field(\r\n ..., title=\"Unit Mass\", description=\"Resource mass\"\r\n )\r\n unit_volume: PositiveFloat = Field(\r\n ..., title=\"Unit Volume\", description=\"Resource volume\"\r\n )\r\n\r\n class Config:\r\n title = \"Continuous Resource\"\r\n", "sub_path": "spacenet/schemas/resource.py", "file_name": "resource.py", "file_ext": "py", "file_size_in_byte": 1646, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "enum.Enum", "line_number": 10, "usage_type": "name"}, {"api_name": "pydantic.BaseModel", "line_number": 18, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 19, "usage_type": "call"}, {"api_name": "constants.ClassOfSupply", "line_number": 20, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 20, "usage_type": "call"}, {"api_name": "pydantic.Field", "line_number": 23, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 24, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 24, "usage_type": "call"}, {"api_name": "typing_extensions.Literal", "line_number": 33, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 33, "usage_type": "call"}, {"api_name": "pydantic.PositiveInt", "line_number": 36, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 36, "usage_type": "call"}, {"api_name": "pydantic.PositiveInt", "line_number": 37, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 37, "usage_type": "call"}, {"api_name": "typing_extensions.Literal", "line_number": 46, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 46, "usage_type": "call"}, {"api_name": "pydantic.PositiveFloat", "line_number": 49, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 49, "usage_type": "call"}, {"api_name": "pydantic.PositiveFloat", "line_number": 52, "usage_type": "name"}, {"api_name": "pydantic.Field", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "14969862", "text": "from statistics import mean\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import style\n\nstyle.use('fivethirtyeight')\n\n# Convert the array to np.array, but type is changed to float64\nxs = np.array([1,2,3,4,5,6,7], dtype = np.float64)\nys = np.array([5,4,6,5,6,7,8], dtype = np.float64)\n\nplt.scatter(xs, ys)\nplt.show()\n\ndef squared_error(ys_orig, ys_line):\n return sum((ys_line - ys_orig) **2)\n\ndef best_fit_slope_and_intercept(xs, ys):\n\n m = (((mean(xs) * mean(ys)) - mean(xs*ys)) /\n (mean(xs)**2 - mean(xs**2)))\n\n b = mean(ys) - m *mean(xs)\n\n return m, b\n\nm, b = best_fit_slope_and_intercept(xs, ys)\n\n\n# Regression point it calculated one by one\n# [(m * x) + b for x in xs]\n\nregression_line = [(m * x) + b for x in xs]\n\n\nplt.scatter(xs, ys)\nplt.plot(xs, regression_line)\nplt.show()\nprint (m, b)", "sub_path": "MachineLearning/python_tutorial/LinearRegressionFromScratch.py", "file_name": "LinearRegressionFromScratch.py", "file_ext": "py", "file_size_in_byte": 834, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.style.use", "line_number": 6, "usage_type": "call"}, {"api_name": "matplotlib.style", "line_number": 6, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 9, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 10, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.scatter", "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": "statistics.mean", "line_number": 20, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 21, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}]} +{"seq_id": "309837000", "text": "\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy\n\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.linear_model import LinearRegression\n\nfrom helpers import unison_shuffled_copies\n\nfrom sklearn.model_selection import train_test_split\n\n\n\ndef safe_ln(x):\n\treturn np.log(x+0.0001)\n\n\n\nclass BaseEnsemble(object):\n\t\"\"\"Base Object for Ensembles\n\tMostly there to give the plotting utility to all it's children\"\"\"\n \n\tdef plot_residuals(self,X_train,y_train,X_test,y_test):\n\t\tplt.scatter(self.predict(X_train).ravel(),self.predict(X_train).ravel()-y_train.ravel(),c='b',s=40,alpha=.5,label='train data prediction')\n\t\tplt.scatter(self.predict(X_test).ravel(),self.predict(X_test).ravel()-y_test.ravel(),c='g',s=40,alpha=.5,label='test data prediction')\n\n\t\tplt.hlines(y=0,xmin=0,xmax=50)\n\t\tplt.legend()\n\t\tplt.show()\n\tdef scatterplot(self,X_test,X=None,y=None,title=None):\n\t\tif y is not None and X is not None:\n\t\t\tplt.scatter(X,y,s=20, edgecolor=\"black\",\n\t\t\t\tc=\"darkorange\", label=\"data\")\n\t\ty_hat, std = self.predict(X_test,std=True)\n\t\tplt.plot(X_test,y_hat,label = 'predictive Mean')\n\n\t\t#plt.plot(X,std)\n\t\tvar = y_hat + std\n\t\tvar2 = y_hat - std\n\t\tassert(np.shape(var)==np.shape(y_hat))\n\n\t\tplt.fill_between(X_test.ravel(), y_hat.ravel(), var, alpha=.3, color='b',\n\t\t\t\t\t\t label='uncertainty')\n\n\t\tplt.fill_between(X_test.ravel(), y_hat.ravel(), var2, alpha=.3, color='b')\n\t\t#plt.scatter(X_test,y_hat,s=20, edgecolor=\"black\",\n\t\t# c=\"darkorange\", label=\"prediction\")\n\t\tplt.xlabel(\"data\")\n\t\tplt.ylabel(\"target\")\n\t\tif title ==None:\n\t\t\tplt.title(\"Ensemble\")\n\t\telse:\n\t\t\tplt.title(title)\n\t\tplt.legend()\n\t\tplt.show()\n\n\tdef mutli_dimenstional_scatterplot(self,X_test,y_test,X=None,y=None,figsize=(20,50)):\n \n\t\ty_hat,std = self.predict(X_test,std=True)\n\n\t\t#plt.rcParams[\"figure.figsize\"] = (20,20)\n\t\tplt.figure(figsize=figsize)\n\t\t#plt.scatter(X[:,5],y)\n\n\t\tnum_features = len(X_test.T)\n\t\tfor i,feature in enumerate(X_test.T):\n\t\t\t#sort the arrays\n\t\t\ts = np.argsort(feature)\n\t\t\tvar = y_hat[s]-std[s]\n\t\t\tvar2 = y_hat[s] +std[s]\n\n\n\t\t\tplt.subplot(num_features,1,i+1)\n\t\t\tplt.plot(feature[s],y_hat[s],label = 'predictive Mean',)\n\t\t\tplt.fill_between(feature[s].ravel(),y_hat[s].ravel(),var,alpha=.3, color='b',label='uncertainty')\n\t\t\tplt.fill_between(feature[s].ravel(),y_hat[s].ravel(),var2,alpha=.3, color='b')\n\t\t\tplt.scatter(feature[s],y_test[s],label='data',s=20, edgecolor=\"black\",\n\t\t\t\tc=\"darkorange\")\n\t\t\tplt.xlabel(\"data\")\n\t\t\tplt.ylabel(\"target\")\n\t\t\tplt.title(\"Ensemble\")\n\t\t\tplt.legend() \n\t\tplt.show()\n\n \n #evaluation\n\t\n\tdef nlpd(self,X,y):\n\t\ty_hat,std = self.predict(X,std=True)\n\t\t\n\t\treturn -1/2 *np.mean( safe_ln(std) + ((y_hat - y)**2/(std+0.0001)))\n\t\n\tdef normalised_nlpd(self,X,y):\n\t\tpass\n\t\t\n \n \n\tdef coverage_probability(self,X, y):\n\n\t\ty_hat,std = self.predict(X,std=True) \n\t\t#print(y_hat.shape,std.shape,y.shape)\n\n\t\tCP = 0\n\t\tfor pred, s, target in zip(y_hat, std, y):\n\t\t\t#print(len(pred))\n\t\t\t#print(len(s))\n\t\t\t#print(len(target))\n\t\t\tif pred + s > target > pred - s:\n\t\t\t\tCP += 1\n\t\treturn CP / len(y)\n \n\tdef error_uncertainty_correlation(self,X,y):\n\t\tprediction,variance = self.predict(X,std=True)\n\n\t\terror = (prediction - y)**2\n\t\tcorrelation = scipy.stats.pearsonr(error.flatten(),variance.flatten())\n\n\t\t#np.correlate(error.flatten(),variance.flatten())\n\t\treturn correlation\n\n\tdef y_predicts_uncertainty(self,X,y):\n\t\tprediction = self.predict(X,std=False)\n\n\t\tcorrelation = scipy.stats.pearsonr(prediction.flatten(),y.flatten())\n\t\treturn correlation\n\n\n\tdef y_predicts_error(self,X,y):\n\t\tprediction = self.predict(X,std=False)\n\n\t\tcorrelation = scipy.stats.pearsonr(prediction.flatten(),y.flatten())\n\t\treturn correlation\n\n\n\tdef error_target_normalcy(self,X,y):\n\t\tscipy.stats.normaltest\n\n\n\t\tprediction,variance = self.predict(X,std=True)\n\n\t\terror = (prediction - y)**2\n\t\tnormalcy = scipy.stats.normaltest(error.flatten())\n\n\t\t#np.correlate(error.flatten(),variance.flatten())\n\t\treturn normalcy\n\n\tdef compute_rsme(self,X,y):\n\t\ty_hat = self.predict(X,False)\n\t\treturn np.sqrt(np.mean((y_hat - y)**2))\n\n\n #eval meta\n\tdef self_evaluate(self,X,y):\n \n\t\trsme = self.compute_rsme(X,y)\n\n\t\tcov_prob = self.coverage_probability(X,y)\n\t\t#print('coverage Probability is: {}'.format(cov_prob))\n\t\terr_var_corr = self.error_uncertainty_correlation(X,y)[0]\n\t\t#print('correlation of error and uncertainty is: {}'.format(err_var_corr)) #0 is the coefficient\n\t\ty_uncertainty_pred = self.y_predicts_uncertainty(X,y)[0]\n\t\t#print('correlation of target value and uncertainty is: {}'.format(y_uncertainty_pred)) #0 is the coefficient\n\t\ty_predicts_error = self.y_predicts_error(X,y)[0]\n\t\t#print('correlation of target value and error is: {}'.format(y_uncertainty_pred)) #0 is the coefficient\n\t\ttarget_error_normalcy = self.error_target_normalcy(X,y)[0]\n\t\t#print('error-target normalcy is {}'.format(target_error_normalicy))\n\t\tnlpd = self.nlpd(X,y)\n\n\t\treturn {'rsme':rsme,\n\t\t\t\t'coverage probability':cov_prob,\n\t\t\t 'correlation between error and variance':err_var_corr,\n\t\t\t\t'NLPD':nlpd,\n\t\t\t #'predictive power of y on the uncertainty':y_uncertainty_pred,\n\t\t\t #'predictive power of y on the error': y_predicts_error,\n\t\t\t #'error normalcy':target_error_normalcy\n\t\t\t }\n\n\n\n\n\n \n \n\nclass RegressionEnsemble(BaseEnsemble):\n def __init__(self,\n num_models=None,\n model_type=None,\n seed = None):\n self.num_models = num_models or 10\n self.model_type = model_type or DecisionTreeRegressor\n self.seed = seed or 42\n self.regressor_list = []\n \n def fit(self, X_train,y_train):\n for i in range(self.num_models):\n try:\n new_regressor = self.model_type(random_state=self.seed + i)#random_state=self.seed+i)\n except:\n np.random.seed(self.seed+i)\n\n new_regressor = self.model_type()#random_state=self.seed+i)\n\n \n new_regressor.fit(X_train,y_train)\n self.regressor_list.append(new_regressor)\n return 'ensemble of {} {}s is hired and at the ready'.format(self.num_models,self.model_type.__name__)\n \n \n def predict(self,y_test,std=False):\n prediction_list = []\n for regressor in self.regressor_list:\n prediction_list.append(regressor.predict(y_test))\n \n predictive_means = np.mean(prediction_list,0)\n if not std:\n return predictive_means\n \n predictive_stds = np.std(prediction_list,0)\n return predictive_means, predictive_stds\n \n \n \nclass SubspaceEnsemble(RegressionEnsemble,BaseEnsemble):\n def __init__(self,\n num_models=None,\n model_type = None,\n seed=None,\n num_drop_dimensions=None,):\n \n super().__init__(num_models=num_models,\n model_type=model_type,\n seed = seed)\n self.num_drop_dimensions = num_drop_dimensions or 1\n \n def fit(self,X_train,y_train):\n \n for i in range(self.num_models):\n idx = np.random.choice(X_train.shape[0], X_train.shape[1]-self.num_drop_dimensions, replace=False)\n\n X_new = X_train[idx]\n y_new = y_train[idx]\n \n new_regressor = self.model_type()\n new_regressor.fit(X_new,y_new)\n self.regressor_list.append(new_regressor)\n\n\n\n\n \nclass BootstrapEnsemble(RegressionEnsemble, BaseEnsemble):\n \"\"\"essentially a regression ensemble, except during the fitting part,\n sub-datasets are created\n Currently still shuffles :/\n Currently no putting data back into the drawer :/\"\"\"\n def __init__(self,\n num_models=None,\n model_type=None,\n seed = None,\n keep_p = None):\n \n super().__init__(num_models=num_models,\n model_type=model_type,\n seed = seed)\n\n self.keep_p = keep_p or 0.7\n \n def fit(self,X_train,y_train):\n #print(X_train.size,y_train.size)\n for i in range(self.num_models):\n new_regressor = self.model_type()\n X_new, throwaway1, y_new ,throwaway2 = train_test_split(X_train, y_train, test_size=self.keep_p, random_state=self.seed+i,shuffle=True)\n new_regressor.fit(X_new,y_new)\n self.regressor_list.append(new_regressor)\n return 'ensemble of {} {}s is hired and at the ready'.format(self.num_models,self.model_type.__name__)\n \n\n\nclass ShuffleEnsemble(BootstrapEnsemble, BaseEnsemble):\n \"\"\"Essentially a Bootstrapensemble with keep-probability of 1, \n so the data only get's shuffled differently for each model\"\"\"\n def __init__(self,\n num_models=None,\n model_type=None,\n seed = None):\n \n super().__init__(num_models=num_models,\n model_type=model_type,\n seed = seed,\n keep_p = 1) #only change: no data is thrown away\n \n\n \n \nclass MixedRegressionEnsemble(BaseEnsemble):\n def __init__(self,\n models = [],\n\t\t\t\tseed = None ):\n self.models = models or [DecisionTreeRegressor(),LinearRegression()]\n self.seed = seed or 42\n #self.model_type.__name__\n \n def fit(self,X_train,y_train):\n np.random.seed(self.seed)\n for model in self.models:\n model.fit(X_train,y_train)\n \n def predict(self,X_test,std=False):\n prediction_list = []\n for model in self.models:\n prediction_list.append(model.predict(X_test))\n predictive_means = np.mean(prediction_list,0)\n if not std:\n return predictive_means\n \n predictive_stds = np.std(prediction_list,0)\n return predictive_means, predictive_stds\n \n ", "sub_path": ".ipynb_checkpoints/ensemble-checkpoint.py", "file_name": "ensemble-checkpoint.py", "file_ext": "py", "file_size_in_byte": 9802, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.log", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hlines", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"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.scatter", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "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": "numpy.shape", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "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.title", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"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.legend", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "numpy.argsort", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "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.mean", "line_number": 92, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 117, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 117, "usage_type": "attribute"}, {"api_name": "scipy.stats.pearsonr", "line_number": 125, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 125, "usage_type": "attribute"}, {"api_name": "scipy.stats.pearsonr", "line_number": 132, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 132, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 137, "usage_type": "attribute"}, {"api_name": "scipy.stats.normaltest", "line_number": 143, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 143, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 150, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeRegressor", "line_number": 192, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 201, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 240, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 274, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeRegressor", "line_number": 301, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LinearRegression", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 306, "usage_type": "attribute"}, {"api_name": "numpy.mean", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 318, "usage_type": "call"}]} +{"seq_id": "73448793", "text": "import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib\n\nfont = {'size':20}\nmatplotlib.rc('font', **font)\n\nlw = 3 # line width\ntemp_300_V_lower_lim = 1.2\ntemp_300_V_upper_lim = 1.8\n\ndata = np.load('../../data/PSIIRC_photocell_MRT_current_statistics_data_no_mode.npz')\ncurrent = data['current']\nF2 = data['F2']\nvoltage = data['voltage']\n\ndata_single_mode = np.load('../../data/PSIIRC_photocell_MRT_current_statistics_data_single_mode.npz')\ncurrent_single_mode = data_single_mode['current']\nF2_single_mode = data_single_mode['F2']\nvoltage_single_mode = data_single_mode['voltage']\n\ndata_modes = np.load('../../data/PSIIRC_photocell_MRT_current_statistics_data_full_spectral_density.npz')\ncurrent_modes = data_modes['current']\nF2_modes = data_modes['F2']\nvoltage_modes = data_modes['voltage']\n\ndata_modes_slow_CT = np.load('../../data/PSIIRC_photocell_MRT_current_statistics_data_full_spectral_density_slow_secondary_CT_rate.npz')\ncurrent_modes_slow_CT = data_modes_slow_CT['current']\nF2_modes_slow_CT = data_modes_slow_CT['F2']\nvoltage_modes_slow_CT = data_modes_slow_CT['voltage']\n\n#plt.figure(num=1, figsize=(12,12), dpi=100)\n\nf, ((ax1, ax2), (ax3, ax4), (ax5, ax6)) = plt.subplots(3, 2, figsize=(10,10), dpi=100, sharex='col', sharey='row')\n\n#ax1 = plt.subplot(411)\nax1.axhline(1, ls='--', color='grey')\n# ax1.plot(voltage_modes, F2_modes, linewidth=lw, color='k')\n# ax1.plot(voltage_modes_slow_CT, F2_modes_slow_CT, ls='--', linewidth=lw, color='k')\nax1.axhline(F2_modes[360], linewidth=lw, color='k')\nax1.axhline(F2_modes_slow_CT[360], ls='--', linewidth=lw, color='k')\nax1.set_xlim(0, 0.1)\nax1.set_ylim(0.5, 1.15)\nax1.set_xticks([])\nax1.set_yticks([0.5,0.6,0.7,0.8,0.9,1.0])\nax1_pos = ax1.get_position()\nax1.set_position([ax1_pos.x0, ax1_pos.y0-0.094, ax1_pos.width*0.3, ax1_pos.height])\nax1.set_ylabel(r'F$^{(2)}$(0)')\nax1.spines['right'].set_visible(False)\nax1.yaxis.tick_left()\n\n#ax2 = plt.subplot(412)\nax2.axhline(1, ls='--', color='grey')\nax2.plot(voltage_modes, F2_modes, linewidth=lw, color='k')\nax2.plot(voltage_modes_slow_CT, F2_modes_slow_CT, ls='--', linewidth=lw, color='k')\nax2.set_xlim(1.27, 1.8)\nax2.set_ylim(0.5, 1.15)\nax2.set_xticks([])\n#ax2.set_yticks([0.5,0.6,0.7,0.8,0.9,1.0])\nax2_pos = ax2.get_position()\nax2.set_position([ax2_pos.x0-0.3, ax2_pos.y0-0.094, ax2_pos.width*1.8, ax2_pos.height])\n#ax2.set_ylabel(r'F$^{(2)}$(0)')\nax2.spines['left'].set_visible(False)\nax2.tick_params(axis='y', which='both', left='off')\n\nax2.text(1.79, 1.1, 'Full spectral density', horizontalalignment='right', verticalalignment='top', fontsize=14)\n\nv = 0.02\nh = 0.008\noffset = 0.024\nkwargs = dict(transform=ax2.transAxes, color='k', clip_on=False)\n#ax2.plot((-h,h), (-v,v), **kwargs)\nax2.plot((-h,h), (1-v,1+v), **kwargs)\n#ax2.plot((-h-offset,h-offset), (-v,v), **kwargs)\nax2.plot((-h-offset,h-offset), (1-v,1+v), **kwargs)\n\nax3.axhline(1, ls='--', color='grey')\n# ax1.plot(voltage_modes, F2_modes, linewidth=lw, color='k')\n# ax1.plot(voltage_modes_slow_CT, F2_modes_slow_CT, ls='--', linewidth=lw, color='k')\nax3.axhline(F2_single_mode[360], linewidth=lw, color='k')\nax3.set_xlim(0, 0.1)\nax3.set_ylim(0.5, 1.15)\nax3.set_xticks([])\nax3.set_yticks([0.5,0.6,0.7,0.8,0.9,1.0])\nax3_pos = ax3.get_position()\nax3.set_position([ax3_pos.x0, ax3_pos.y0-0.047, ax3_pos.width*0.3, ax3_pos.height])\nax3.set_ylabel(r'F$^{(2)}$(0)')\nax3.spines['right'].set_visible(False)\nax3.yaxis.tick_left()\n\n#ax2 = plt.subplot(412)\nax4.axhline(1, ls='--', color='grey')\nax4.plot(voltage_single_mode, F2_single_mode, linewidth=lw, color='k')\nax4.set_xlim(1.27, 1.8)\nax4.set_ylim(0.5, 1.15)\nax4.set_xticks([])\n#ax2.set_yticks([0.5,0.6,0.7,0.8,0.9,1.0])\nax4_pos = ax4.get_position()\nax4.set_position([ax4_pos.x0-0.3, ax4_pos.y0-0.047, ax4_pos.width*1.8, ax4_pos.height])\n#ax2.set_ylabel(r'F$^{(2)}$(0)')\nax4.spines['left'].set_visible(False)\nax4.tick_params(axis='y', which='both', left='off')\n\nax4.text(1.79, 1.1, 'Drude + mode', horizontalalignment='right', verticalalignment='top', fontsize=14)\n\nv = 0.02\nh = 0.008\noffset = 0.024\nkwargs = dict(transform=ax4.transAxes, color='k', clip_on=False)\n#ax2.plot((-h,h), (-v,v), **kwargs)\nax4.plot((-h,h), (1-v,1+v), **kwargs)\n#ax2.plot((-h-offset,h-offset), (-v,v), **kwargs)\nax4.plot((-h-offset,h-offset), (1-v,1+v), **kwargs)\n\nax5.axhline(1, ls='--', color='grey')\nax5.axhline(F2[350], linewidth=lw, color='k')\nax5.set_xlim(0, 0.1)\nax5.set_ylim(0.5, 1.15)\nax5.set_xticks([0])\nax5.set_yticks([0.5,0.6,0.7,0.8,0.9,1.0])\nax5_pos = ax5.get_position()\nax5.set_position([ax5_pos.x0, ax5_pos.y0, ax5_pos.width*0.3, ax5_pos.height])\nax5.set_ylabel(r'F$^{(2)}$(0)')\nax5.spines['right'].set_visible(False)\nax5.yaxis.tick_left()\n\n#ax4 = plt.subplot(421)\nax6.axhline(1, ls='--', color='grey')\nax6.axhline(1, ls='--', color='grey')\nax6.plot(voltage, F2, linewidth=lw, color='k')\nax6.set_xlim(1.27, 1.8)\nax6.set_ylim(0.5, 1.15)\nax6.set_xlabel('voltage (V)', x=0.4)\n#ax4.set_ylabel(r'F$^{(2)}$(0)')\nax6.set_xticks([1.3,1.4,1.5,1.6,1.7,1.8])\n#ax4.set_yticks([0.5,0.6,0.7,0.8,0.9,1.0])\nax6_pos = ax6.get_position()\nax6.set_position([ax6_pos.x0-0.3, ax6_pos.y0, ax6_pos.width*1.8, ax6_pos.height])\nax6.spines['left'].set_visible(False)\nax6.tick_params(axis='y', which='both', left='off')\n\nax6.text(1.79, 1.1, 'Drude', horizontalalignment='right', verticalalignment='top', fontsize=14)\n\nv = 0.02\nh = 0.008\noffset = 0.024\nkwargs = dict(transform=ax6.transAxes, color='k', clip_on=False)\nax6.plot((-h,h), (-v,v), **kwargs)\nax6.plot((-h,h), (1-v,1+v), **kwargs)\nax6.plot((-h-offset,h-offset), (-v,v), **kwargs)\nax6.plot((-h-offset,h-offset), (1-v,1+v), **kwargs)\n\n# kwargs.update(transform=ax3.transAxes)\n# ax3.plot((1-h,1+h), (-v,v), **kwargs)\n# ax3.plot((1-h,1+h), (1-v,1+v), **kwargs)\n\nplt.show()\n\n", "sub_path": "PSIIRC_photocell_counting_statistics/plots/scripts/MRT_current_statistics_broken_axis.py", "file_name": "MRT_current_statistics_broken_axis.py", "file_ext": "py", "file_size_in_byte": 5716, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.rc", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}]} +{"seq_id": "588793812", "text": "import torchvision\nimport torchvision.transforms as transforms\nimport torch\nfrom LeNet5 import LeNet5\nimport os\n\n\ndef load_dataset(batch_size):\n mnist_train = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST',\n train=True, download=True,\n transform=transforms.ToTensor())\n mnist_test = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST',\n train=False, download=True,\n transform=transforms.ToTensor())\n train_iter = torch.utils.data.DataLoader(mnist_train,\n batch_size=batch_size,\n shuffle=True,\n num_workers=1)\n test_iter = torch.utils.data.DataLoader(mnist_test,\n batch_size=batch_size,\n shuffle=True,\n num_workers=1)\n return train_iter, test_iter\n\n\ndef inference(data_iter, device, model_save_dir='./MODEL'):\n net = LeNet5() # 初始化现有模型的权重参数\n net.to(device)\n model_save_path = os.path.join(model_save_dir, 'model.pt')\n if os.path.exists(model_save_path):\n loaded_paras = torch.load(model_save_path)\n net.load_state_dict(loaded_paras) # 用本地已有模型来重新初始化网络权重参数\n net.eval()\n with torch.no_grad():\n acc_sum, n = 0.0, 0\n for x, y in data_iter:\n x, y = x.to(device), y.to(device)\n logits = net(x)\n acc_sum += (logits.argmax(1) == y).float().sum().item()\n n += len(y)\n print(\"Accuracy in test data is :\", acc_sum / n)\n\n\nif __name__ == '__main__':\n train_iter, test_iter = load_dataset(64)\n device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n inference(test_iter, device)\n", "sub_path": "04_ModelPersistence/02_LoadForInference/inference.py", "file_name": "inference.py", "file_ext": "py", "file_size_in_byte": 2061, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torchvision.datasets.FashionMNIST", "line_number": 9, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 9, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 11, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 11, "usage_type": "name"}, {"api_name": "torchvision.datasets.FashionMNIST", "line_number": 12, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 14, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 15, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 19, "usage_type": "attribute"}, {"api_name": "LeNet5.LeNet5", "line_number": 27, "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.path.exists", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 46, "usage_type": "attribute"}]} +{"seq_id": "366231718", "text": "# route between two nodes\n\n# find if there exists a route between two nodes\n\n# normal graph traversal\n# breadth first search\n\n# assume there's no cycles\n\n# from the starting node, add it's children\n# once you add all its children to the queue, pop hte queue and repeat until\n# there is no more children to check\n# if there is no more children to check and then return false\n# if the node found does in fact have a route and one of the children is equal return true\n\n\n\n\nfrom collections import defaultdict \n \n#This class represents a directed graph using adjacency list representation \nclass Graph: \n \n def __init__(self,vertices): \n self.V= vertices #No. of vertices \n self.graph = defaultdict(list) # default dictionary to store graph \n \n def addEdge(self,u,v): \n self.graph[u].append(v) \n \n def isReachable(self, s, d):\n visited_nodes = set()\n visited_nodes.add(s)\n queue = []\n queue.append(s)\n\n while queue:\n pop_value = queue.pop()\n for vert in self.graph[pop_value]:\n if d == vert:\n return True\n if vert not in visited_nodes:\n visited_nodes.add(vert)\n queue.append(vert)\n\n return False\n\n\ng = Graph(4) \ng.addEdge(0, 1) \ng.addEdge(0, 2) \ng.addEdge(1, 2) \ng.addEdge(2, 0) \ng.addEdge(2, 3) \ng.addEdge(3, 3)\n\n\nprint(g.isReachable(3,1))\n", "sub_path": "ctci_chapter_4_route_between_nodes/solution.py", "file_name": "solution.py", "file_ext": "py", "file_size_in_byte": 1429, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "collections.defaultdict", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "147390866", "text": "#%%\nfrom numpy.lib.arraypad import pad\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n#%%\ndef create_plots(data, elements):\n Treatment = data.Treatment.unique().tolist()\n Tissue = data.Tissue.unique().tolist()\n fig_num = 0\n for tis in Tissue:\n Line = data.Line.unique().tolist()\n for lin in Line:\n for elem in elements:\n elem_avg = elem + ' AVG'\n elem_se = elem + ' SE'\n plt.figure(fig_num)\n shapes = ['o', '^', 's', 'd']\n leg_label = []\n tick_label = []\n i = 0\n max_avg = 0\n Tis_max = data[np.equal.outer(data.to_numpy(copy=False), [tis]).any(axis=1).all(axis=1)]\n Tis_max = Tis_max[elem_avg].max()\n if Tis_max > max_avg:\n max_avg = Tis_max\n for trt in Treatment:\n Line = data[np.equal.outer(data.to_numpy(copy=False), [tis, lin, trt]).any(axis=1).all(axis=1)]\n Line = Line[['Tissue','Line', 'Harvest', 'Treatment', elem_avg, elem_se]]\n yerr = [Line[elem_se].values.tolist(), Line[elem_se].values.tolist()]\n plt.errorbar('Harvest', elem_avg, data=Line, yerr=yerr, marker=shapes[i], markersize=10, linewidth=3, solid_capstyle='projecting', elinewidth=1, ecolor='black', capsize=2, capthick=1)\n handles, labels = plt.gca().get_legend_handles_labels()\n leg_label.append(trt.split('_')[1])\n i += 1\n plt.title(lin.split('_')[1] + ' ' + elem + ' Concentrations (' + tis.split('_')[1] + ')')\n plt.legend(handles, leg_label, bbox_to_anchor=(1.05, 1), loc='upper left', markerscale=.65)\n plt.ylim(0, max_avg * 2)\n plt.ylabel(elem + ' Conc. (ppm)')\n plt.xlabel('Harvest')\n plt.xlim(-0.15, 3.15)\n ax = plt.gca()\n plt.draw()\n x_ticks = ax.get_xticklabels()\n for lab in x_ticks:\n tick_label.append(lab.get_text().split('_')[1])\n ax.set_xticklabels(tick_label)\n plt.savefig(\"Mike's Plots/\" + tis + '/' + lin + '/' + lin.split('_')[1] + ' ' + elem + ' Concentrations (' + tis.split('_')[1] + ')' + '.jpg', bbox_inches='tight', pad_inches=1.1)\n plt.close()\n plt.show()\n fig_num += 1\n\n#%%\ndata = pd.read_csv(\"Full data set_felix_rice_Updated root and peduncle values.csv\")\n#%%\nelements = ['Mg', 'P', 'S', 'Ca', 'Mn', 'Fe', 'Co', 'Ni', 'Cu', 'Zn', 'Cd']\n\n#%%\nHarvest = data.Harvest.unique().tolist()\n# %%\n\ndata['Harvest'] = pd.Categorical(data['Harvest'], categories=data['Harvest'].unique())\ndata = data.groupby(['Tissue', 'Line', 'Harvest', 'Treatment'], as_index=False).first()\n# %%\ncreate_plots(data, elements)\n# %%\nprint(data)\n# %%\n", "sub_path": "Mike's_Data.py", "file_name": "Mike's_Data.py", "file_ext": "py", "file_size_in_byte": 2958, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "numpy.equal.outer", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.equal", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.equal.outer", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.equal", "line_number": 29, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "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.xlabel", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 54, "usage_type": "call"}, {"api_name": "pandas.Categorical", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "113767443", "text": "'''\nThis file contains util methods that we can use\n'''\nfrom slackclient import SlackClient\nimport config\n\nslack_client = SlackClient(config.apiT)\nslack_web_client = SlackClient(config.oauthT)\ndef grab_user(use:str) ->str:\n \"\"\"\n converts an id to usernamae\n :param the user id to convert\n \"\"\"\n api = slack_client.api_call('users.list')\n if (api.get('ok')):\n users = api.get('members')\n for user in users:\n if 'name' in user and user.get('id') == use:\n return user['name']\n\n\ndef username_to_id(username:str) -> str:\n \"\"\"\n converts the username to an id\n :param the username\n \"\"\"\n api = slack_client.api_call('users.list')\n if api.get('ok'):\n users = api.get('members')\n for user in users:\n if 'id' in user and user['name'] == username:\n return user['id']\ndef message(channelid:str, message:str) -> None:\n \"\"\"\n Util Function to send a message\n \"\"\"\n slack_client.api_call(\"chat.postMessage\", channel=channelid,text=message, as_user=True)\n", "sub_path": "util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 1083, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "slackclient.SlackClient", "line_number": 7, "usage_type": "call"}, {"api_name": "config.apiT", "line_number": 7, "usage_type": "attribute"}, {"api_name": "slackclient.SlackClient", "line_number": 8, "usage_type": "call"}, {"api_name": "config.oauthT", "line_number": 8, "usage_type": "attribute"}]} +{"seq_id": "425091150", "text": "\n\nimport matplotlib\nimport numpy as np\nmatplotlib.use(\"Qt5Agg\")\nimport matplotlib.pyplot as plt\n\nfrom numpyflow.layers import *\nfrom numpyflow.models import *\nfrom numpyflow.datasets import *\nfrom numpyflow.loss_funcs import *\nfrom numpyflow.optimizers import *\nfrom numpyflow.activations import *\n\nnp.random.seed(1)\n\nlosses = []\n\nif __name__ == \"__main__\":\n\n\tlearning_rate = 1e-0\n\tEPOCHS = 2000 # 500000 # 2000\n\n\tX = np.zeros((32, 3, 10, 10))\n\ty = np.zeros((32, 1))\n\n\tmodel = Sequential([\n\t\tConv2D(filters=5, channels=3, kernel_size=3, name=\"conv.1\"),\n\t\tFlatten(name=\"flatten\"),\n\t\tDense(320, 10, activation=ReLU, name=\"dense.1\"),\n\t\tBatchNormalisation(10, name=\"bn\"),\n\t\tDense(10, 10, is_end_layer=True, name=\"dense.2\"),\n\t])\n\n\tmodel.compile(\n\t\tloss=softmax_loss,\n\t\toptimizer=SGD_With_Momentum(Nesterov=False),\n\t)\n\n\tloss = model.fit(X, y, epochs=EPOCHS, print_every=10)\n\n\tplt.plot(model.loss_history)\n\tplt.savefig(\"damn.png\")\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 945, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.use", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 15, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 25, "usage_type": "call"}, {"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.savefig", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "422794216", "text": "from copy import deepcopy\nfrom functools import reduce\nfrom .board_searcher import BoardSearcher\nfrom .board_initializer import BoardInitializer\nfrom prettytable import PrettyTable, NONE\nfrom colorama import Fore, Back, Style\n\n\nclass Board:\n\n\tdef __init__(self):\n\t\tself.player_turn = 1\n\t\tself.width = 4\n\t\tself.height = 8\n\t\tself.position_count = self.width * self.height\n\t\tself.rows_per_user_with_pieces = 3\n\t\tself.position_layout = {}\n\t\tself.piece_requiring_further_capture_moves = None\n\t\tself.previous_move_was_capture = False\n\t\tself.searcher = BoardSearcher()\n\t\tBoardInitializer(self).initialize()\n\n\tdef count_movable_player_pieces(self, player_number = 1):\n\t\treturn reduce((lambda count, piece: count + (1 if piece.is_movable() else 0)), self.searcher.get_pieces_by_player(player_number), 0)\n\n\tdef get_possible_moves(self):\n\t\tcapture_moves = self.get_possible_capture_moves()\n\n\t\treturn capture_moves if capture_moves else self.get_possible_positional_moves()\n\n\tdef get_possible_capture_moves(self):\n\t\treturn reduce((lambda moves, piece: moves + piece.get_possible_capture_moves()), self.searcher.get_pieces_in_play(), [])\n\n\tdef get_possible_positional_moves(self):\n\t\treturn reduce((lambda moves, piece: moves + piece.get_possible_positional_moves()), self.searcher.get_pieces_in_play(), [])\n\n\tdef position_is_open(self, position):\n\t\treturn not self.searcher.get_piece_by_position(position)\n\n\tdef create_new_board_from_move(self, move):\n\t\tnew_board = deepcopy(self)\n\n\t\tif move in self.get_possible_capture_moves():\n\t\t\tnew_board.perform_capture_move(move)\n\t\telse:\n\t\t\tnew_board.perform_positional_move(move)\n\n\t\treturn new_board\n\n\tdef perform_capture_move(self, move):\n\t\tself.previous_move_was_capture = True\n\t\tpiece = self.searcher.get_piece_by_position(move[0])\n\t\toriginally_was_king = piece.king\n\t\tenemy_piece = piece.capture_move_enemies[move[1]]\n\t\tenemy_piece.capture()\n\t\tself.move_piece(move)\n\t\tfurther_capture_moves_for_piece = [capture_move for capture_move in self.get_possible_capture_moves() if move[1] == capture_move[0]]\n\n\t\tif further_capture_moves_for_piece and (originally_was_king == piece.king):\n\t\t\tself.piece_requiring_further_capture_moves = self.searcher.get_piece_by_position(move[1])\n\t\telse:\n\t\t\tself.piece_requiring_further_capture_moves = None\n\t\t\tself.switch_turn()\n\n\tdef perform_positional_move(self, move):\n\t\tself.previous_move_was_capture = False\n\t\tself.move_piece(move)\n\t\tself.switch_turn()\n\n\tdef switch_turn(self):\n\t\tself.player_turn = 1 if self.player_turn == 2 else 2\n\n\tdef move_piece(self, move):\n\t\tself.searcher.get_piece_by_position(move[0]).move(move[1])\n\t\tself.pieces = sorted(self.pieces, key = lambda piece: piece.position if piece.position else 0)\n\n\tdef is_valid_row_and_column(self, row, column):\n\t\tif row < 0 or row >= self.height:\n\t\t\treturn False\n\n\t\tif column < 0 or column >= self.width:\n\t\t\treturn False\n\n\t\treturn True\n\n\tdef __setattr__(self, name, value):\n\t\tsuper(Board, self).__setattr__(name, value)\n\n\t\tif name == 'pieces':\n\t\t\t[piece.reset_for_new_board() for piece in self.pieces]\n\n\t\t\tself.searcher.build(self)\n\n\tdef __str__(self):\n\t\tx = PrettyTable()\n\t\tx.field_names = [\" \", \"a\", \"b\", \"c\", \"d\", \"e\", \"f\", \"g\", \"h\"]\n\t\tgreensq = Back.GREEN + \" \" + Back.RESET\n\t\twhitesq = Back.WHITE + \" \" + Back.RESET\n\t\tblackpc = Back.BLACK + \" \" + Back.RESET\n\t\tredpc = Back.RED + \" \" + Back.RESET\n\t\tblackkg = Back.BLACK + Fore.YELLOW + \" K \" + Back.RESET + Fore.RESET\n\t\tredkg = Back.RED + Fore.BLACK + \" K \" + Back.RESET + Fore.RESET\n\t\tbrd = [\"_\"] * 64\n\t\tfor i in range(64):\n\t\t\tif (i // 8) % 2 == 0:\n\t\t\t\tbrd[i] = whitesq if i % 2 == 0 else greensq\n\t\t\telse:\n\t\t\t\tbrd[i] = greensq if i % 2 == 0 else whitesq\n\n\t\tfor player in self.searcher.player_positions.items():\n\t\t\tfor piece in player[1]:\n\t\t\t\tif self.searcher.get_piece_by_position(piece).king:\n\t\t\t\t\tbrd[(piece - 1) * 2 + 1 - (((piece - 1) * 2) // 8) % 2] = blackkg if player[0] == 1 else redkg\n\t\t\t\telse:\n\t\t\t\t\tbrd[(piece - 1) * 2 + 1 - (((piece - 1) * 2) // 8) % 2] = redpc if player[0] == 1 else blackpc\n\n\t\tfor k in range(0, 57, 8):\n\t\t\tx.add_row([str(k // 8 + 1)] + brd[k:k+8])\n\t\tx.padding_width = 0\n\t\tx.vrules = NONE\n\t\treturn x.get_string()\n", "sub_path": "BFHScheckers/board.py", "file_name": "board.py", "file_ext": "py", "file_size_in_byte": 4129, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "board_searcher.BoardSearcher", "line_number": 20, "usage_type": "call"}, {"api_name": "board_initializer.BoardInitializer", "line_number": 21, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 24, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 32, "usage_type": "call"}, {"api_name": "functools.reduce", "line_number": 35, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 41, "usage_type": "call"}, {"api_name": "prettytable.PrettyTable", "line_number": 95, "usage_type": "call"}, {"api_name": "colorama.Back.GREEN", "line_number": 97, "usage_type": "attribute"}, {"api_name": "colorama.Back", "line_number": 97, "usage_type": "name"}, {"api_name": "colorama.Back.RESET", "line_number": 97, "usage_type": "attribute"}, {"api_name": "colorama.Back.WHITE", "line_number": 98, "usage_type": "attribute"}, {"api_name": "colorama.Back", "line_number": 98, "usage_type": "name"}, {"api_name": "colorama.Back.RESET", "line_number": 98, "usage_type": "attribute"}, {"api_name": "colorama.Back.BLACK", "line_number": 99, "usage_type": "attribute"}, {"api_name": "colorama.Back", "line_number": 99, "usage_type": "name"}, {"api_name": "colorama.Back.RESET", "line_number": 99, "usage_type": "attribute"}, {"api_name": "colorama.Back.RED", "line_number": 100, "usage_type": "attribute"}, {"api_name": "colorama.Back", "line_number": 100, "usage_type": "name"}, {"api_name": "colorama.Back.RESET", "line_number": 100, "usage_type": "attribute"}, {"api_name": "colorama.Back.BLACK", "line_number": 101, "usage_type": "attribute"}, {"api_name": "colorama.Back", "line_number": 101, "usage_type": "name"}, {"api_name": "colorama.Fore.YELLOW", "line_number": 101, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 101, "usage_type": "name"}, {"api_name": "colorama.Back.RESET", "line_number": 101, "usage_type": "attribute"}, {"api_name": "colorama.Fore.RESET", "line_number": 101, "usage_type": "attribute"}, {"api_name": "colorama.Back.RED", "line_number": 102, "usage_type": "attribute"}, {"api_name": "colorama.Back", "line_number": 102, "usage_type": "name"}, {"api_name": "colorama.Fore.BLACK", "line_number": 102, "usage_type": "attribute"}, {"api_name": "colorama.Fore", "line_number": 102, "usage_type": "name"}, {"api_name": "colorama.Back.RESET", "line_number": 102, "usage_type": "attribute"}, {"api_name": "colorama.Fore.RESET", "line_number": 102, "usage_type": "attribute"}, {"api_name": "prettytable.NONE", "line_number": 120, "usage_type": "name"}]} +{"seq_id": "539071264", "text": "# Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport argparse\nimport logging\nimport numpy as np\n# disable gpu training for this example\nimport os\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"\"\nimport paddle\nimport paddle.fluid as fluid\nlogging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')\nlogger = logging.getLogger(\"fluid\")\nlogger.setLevel(logging.INFO)\nnum_context_feature = 22\n\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"PaddlePaddle DeepFM example\")\n parser.add_argument(\n '--model_path',\n type=str,\n #required=True,\n default='models',\n help=\"The path of model parameters gz file\")\n parser.add_argument(\n '--data_path',\n type=str,\n required=False,\n help=\"The path of the dataset to infer\")\n parser.add_argument(\n '--embedding_size',\n type=int,\n default=16,\n help=\"The size for embedding layer (default:10)\")\n parser.add_argument(\n '--sparse_feature_dim',\n type=int,\n default=1000001,\n help=\"The size for embedding layer (default:1000001)\")\n parser.add_argument(\n '--batch_size',\n type=int,\n default=1000,\n help=\"The size of mini-batch (default:1000)\")\n\n return parser.parse_args()\n\n\ndef to_lodtensor(data, place):\n seq_lens = [len(seq) for seq in data]\n cur_len = 0\n lod = [cur_len]\n for l in seq_lens:\n cur_len += l\n lod.append(cur_len)\n flattened_data = np.concatenate(data, axis=0).astype(\"int64\")\n flattened_data = flattened_data.reshape([len(flattened_data), 1])\n res = fluid.LoDTensor()\n res.set(flattened_data, place)\n res.set_lod([lod])\n\n return res\n\n\ndef data2tensor(data, place):\n feed_dict = {}\n dense = data[0]\n sparse = data[1:-1]\n y = data[-1]\n #user_data = np.array([x[0] for x in data]).astype(\"float32\")\n #user_data = user_data.reshape([-1, 10])\n #feed_dict[\"user_profile\"] = user_data\n dense_data = np.array([x[0] for x in data]).astype(\"float32\")\n dense_data = dense_data.reshape([-1, 3])\n feed_dict[\"dense_feature\"] = dense_data\n for i in range(num_context_feature):\n sparse_data = to_lodtensor([x[1 + i] for x in data], place)\n feed_dict[\"context\" + str(i)] = sparse_data\n\n context_fm = to_lodtensor(\n np.array([x[-2] for x in data]).astype(\"float32\"), place)\n\n feed_dict[\"context_fm\"] = context_fm\n y_data = np.array([x[-1] for x in data]).astype(\"int64\")\n y_data = y_data.reshape([-1, 1])\n feed_dict[\"label\"] = y_data\n return feed_dict\n\n\ndef test():\n args = parse_args()\n\n place = fluid.CPUPlace()\n test_scope = fluid.core.Scope()\n\n # filelist = [\"%s/%s\" % (args.data_path, x) for x in os.listdir(args.data_path)]\n from map_reader import MapDataset\n map_dataset = MapDataset()\n map_dataset.setup(args.sparse_feature_dim)\n exe = fluid.Executor(place)\n\n whole_filelist = [\"./out/normed_test_session.txt\"]\n test_files = whole_filelist[int(0.0 * len(whole_filelist)):int(1.0 * len(\n whole_filelist))]\n\n epochs = 1\n\n for i in range(epochs):\n cur_model_path = os.path.join(args.model_path,\n \"epoch\" + str(1) + \".model\")\n with open(\"./testres/res\" + str(i), 'w') as r:\n with fluid.scope_guard(test_scope):\n [inference_program, feed_target_names, fetch_targets] = \\\n fluid.io.load_inference_model(cur_model_path, exe)\n\n test_reader = map_dataset.test_reader(test_files, 1000, 100000)\n k = 0\n for batch_id, data in enumerate(test_reader()):\n print(len(data[0]))\n feed_dict = data2tensor(data, place)\n loss_val, auc_val, accuracy, predict, _ = exe.run(\n inference_program,\n feed=feed_dict,\n fetch_list=fetch_targets,\n return_numpy=False)\n\n x = np.array(predict)\n for j in range(x.shape[0]):\n r.write(str(x[j][1]))\n r.write(\"\\n\")\n\n\nif __name__ == '__main__':\n test()\n", "sub_path": "PaddleRec/ctr/Paddle_baseline_KDD2019/generate_test.py", "file_name": "generate_test.py", "file_ext": "py", "file_size_in_byte": 4787, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.environ", "line_number": 21, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 26, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 69, "usage_type": "call"}, {"api_name": "paddle.fluid.LoDTensor", "line_number": 71, "usage_type": "call"}, {"api_name": "paddle.fluid", "line_number": 71, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 97, "usage_type": "call"}, {"api_name": "paddle.fluid.CPUPlace", "line_number": 106, "usage_type": "call"}, {"api_name": "paddle.fluid", "line_number": 106, "usage_type": "name"}, {"api_name": "paddle.fluid.core.Scope", "line_number": 107, "usage_type": "call"}, {"api_name": "paddle.fluid.core", "line_number": 107, "usage_type": "attribute"}, {"api_name": "paddle.fluid", "line_number": 107, "usage_type": "name"}, {"api_name": "map_reader.MapDataset", "line_number": 111, "usage_type": "call"}, {"api_name": "paddle.fluid.Executor", "line_number": 113, "usage_type": "call"}, {"api_name": "paddle.fluid", "line_number": 113, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 122, "usage_type": "call"}, {"api_name": "os.path", "line_number": 122, "usage_type": "attribute"}, {"api_name": "paddle.fluid.scope_guard", "line_number": 125, "usage_type": "call"}, {"api_name": "paddle.fluid", "line_number": 125, "usage_type": "name"}, {"api_name": "paddle.fluid.io.load_inference_model", "line_number": 127, "usage_type": "call"}, {"api_name": "paddle.fluid.io", "line_number": 127, "usage_type": "attribute"}, {"api_name": "paddle.fluid", "line_number": 127, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 140, "usage_type": "call"}]} +{"seq_id": "588167558", "text": "\r\nimport json, os, speech_recognition as sr\r\nfrom dotenv import load_dotenv\r\n\r\nload_dotenv()\r\nKEYDIR_PATH = os.getenv(key='KEYDIR_PATH')\r\n\r\n\r\ndef get_transcript(audio_path: str):\r\n \"\"\"\r\n Gets transcript of audio file.\r\n\r\n Args:\r\n content (bytes): Content of audio file as bytes.\r\n audio_path (str): Path or uri to audio file.\r\n\r\n Returns:\r\n object: Processed audio file for speech-to-text.\r\n \"\"\"\r\n # use the audio file as the audio source\r\n r = sr.Recognizer()\r\n with sr.AudioFile(filename_or_fileobject=audio_path) as audio_src:\r\n audio = r.record(source=audio_src)\r\n\r\n # recognize speech using Sphinx\r\n transcript = str()\r\n try:\r\n transcript = r.recognize_sphinx(audio_data=audio)\r\n print('Sphinx thinks you said:\\n' + transcript)\r\n except sr.UnknownValueError:\r\n print('Sphinx could not understand audio')\r\n except sr.RequestError as e:\r\n print(f'Sphinx error; {e}')\r\n\r\n return transcript\r\n\r\n# recognize speech using Google Cloud Speech\r\n# credentials_file = open(file=KEYDIR_PATH, mode='rb').read()\r\n# credentials = json.dumps(obj=json.loads(s=credentials_file))\r\n# print(credentials)\r\n\r\n# try:\r\n# print('Google Cloud Speech thinks you said:\\n' + r.recognize_google_cloud(audio_data=audio, credentials_json=credentials))\r\n# except sr.UnknownValueError:\r\n# print('Google Cloud Speech could not understand audio')\r\n# except sr.RequestError as e:\r\n# print(f'Could not request results from Google Cloud Speech service; {e}')\r\n\r\nif __name__ == '__main__':\r\n audio_path = './data/customer_support_sample_2.wav'\r\n transcript = get_transcript(audio_path=audio_path)\r\n\r\n", "sub_path": "backend/transcribe_audio_file.py", "file_name": "transcribe_audio_file.py", "file_ext": "py", "file_size_in_byte": 1681, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 5, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 6, "usage_type": "call"}, {"api_name": "speech_recognition.Recognizer", "line_number": 21, "usage_type": "call"}, {"api_name": "speech_recognition.AudioFile", "line_number": 22, "usage_type": "call"}, {"api_name": "speech_recognition.UnknownValueError", "line_number": 30, "usage_type": "attribute"}, {"api_name": "speech_recognition.RequestError", "line_number": 32, "usage_type": "attribute"}]} +{"seq_id": "365028290", "text": "# Copyrights 2010-2011 Pierre Chanial\n# All rights reserved\n#\ntry:\n import fftw3\nexcept:\n print('Warning: Library PyFFTW3 is not installed.')\n\nimport copy\nimport gc\nimport multiprocessing\nimport numpy as np\nimport scipy.signal\nimport scipy.sparse.linalg\nimport tamasisfortran as tmf\n\nfrom mpi4py import MPI\nfrom scipy.sparse.linalg.interface import LinearOperator\nfrom . import var\nfrom .datatypes import Map, Tod, combine_sliced_shape, flatten_sliced_shape, \\\n validate_sliced_shape\nfrom .numpyutils import _my_isscalar\nfrom .processing import interpolate_linear\nfrom .quantity import Quantity, UnitError, _divide_unit, _multiply_unit\nfrom .utils import diff, diffT, diffTdiff, shift\nfrom .mpiutils import split_shape, split_work\n\n__all__ = [\n 'AcquisitionModel',\n 'AcquisitionModelLinear',\n 'DistributionGlobal',\n 'DistributionLocal',\n 'CircularShift',\n 'Clip',\n 'CompressionAverage',\n 'Convolution',\n 'DdTdd',\n 'Diagonal',\n 'DiscreteDifference',\n 'DownSampling',\n 'Fft',\n 'FftHalfComplex',\n 'Identity',\n 'InterpolationLinear',\n 'InvNtt',\n 'Masking',\n 'Maximum',\n 'Minimum',\n 'Offset',\n 'Packing',\n 'Padding',\n 'Projection',\n 'Reshaping',\n 'ResponseTruncatedExponential',\n 'Rounding',\n 'Scalar',\n 'Shift',\n 'SqrtInvNtt',\n 'Unpacking',\n 'acquisitionmodel_factory',\n 'asacquisitionmodel',\n]\n\n\nclass ValidationError(Exception): pass\n\nclass AcquisitionModel(object):\n \"\"\"Abstract class representing an instrument acquisition model.\n\n The response y from an input signal x by an acquisition model M is given by\n y = M.direct(x) or y = M(x)\n where x and y can be multidimensional numpy.ndarray.\n An acquisition model can be the combination of several submodels\n describing various parts of the instrumental chain:\n M = M3 * M2 * M1 ...\n\n The direct method must not rely on input attributes (except Tod's nsamples\n if the acquisition model is unconstrained) since this method is supposed to\n work on bare ndarrays (in which case the acquisition model must be\n constrained: nsamples is extracted from the shapein property). Attribute\n handling must be dealt with in the AcquisitionModel's __init__ method via\n the attrin and attrout keywords.\n \"\"\"\n\n def __init__(self, direct=None, cache=False, dtype=None, description=None,\n attrin=None, attrout=None, shapein=None, shapeout=None, \n typein=None, typeout=None, unitin=None, unitout=None):\n\n if direct is not None:\n if not hasattr(direct, '__call__'):\n raise TypeError('The input direct method is not callable.')\n self.direct = direct\n self.dtype = dtype\n if description is None:\n description = self.__class__.__name__\n self.description = description\n self.attrin = {} if attrin is None else attrin\n self.attrout = attrout or self.attrin\n shapein = validate_sliced_shape(shapein)\n self.shapein = shapein\n shapeout = validate_sliced_shape(shapeout or shapein)\n self.shapeout = shapeout\n self.typein = typein\n self.typeout = typeout or typein\n self.unitin = Quantity(1., unitin )._unit\n self.unitout = Quantity(1., unitout or unitin)._unit\n\n self.cache = cache\n \n if isinstance(self, AcquisitionModelTranspose):\n return\n\n if cache:\n # store the input of the direct model. Its memory allocation\n # is re-used as the output of the transpose model\n self.cachein = None\n # store the input of the transpose model. Its memory allocation\n # is re-used as the output of the direct model\n self.cacheout = None\n else:\n if typein != (typeout or typein):\n raise ValueError('Inplace handling requires same input and ou' \\\n 'tput type (' + str(typein) + ',' + \\\n str(typeout or typein) + ').')\n if self.attrin != self.attrout:\n raise ValueError('Inplace handling requires same input and ou' \\\n 'tput attributes.')\n\n if shapein and type(shapein[-1]) is tuple:\n if not issubclass(typein, Tod):\n raise TypeError('The input type should be a Tod.')\n if shapeout and type(shapeout[-1]) is tuple:\n if not issubclass(typeout or typein, Tod):\n raise TypeError('The output type should be a Tod.')\n\n def __call__(self, input, inplace=False, cachein=False, cacheout=False):\n return self.direct(input, inplace, cachein, cacheout)\n\n def direct(self, input, inplace, cachein, cacheout):\n raise NotImplementedError()\n\n @property\n def shape(self):\n shape = (np.product(flatten_sliced_shape(self.shapeout)),\n np.product(flatten_sliced_shape(self.shapein)))\n if shape[0] is None or shape[1] is None:\n return None\n return shape\n\n @property\n def dtype(self):\n if self._dtype is not None:\n return self._dtype\n return var.FLOAT_DTYPE\n\n @dtype.setter\n def dtype(self, dtype):\n self._dtype = np.dtype(dtype)\n \n def validate_shapein(self, shapein):\n \"\"\"\n Validate input shape and return the output shape of the direct model\n \"\"\"\n selfshapein = self.shapein\n if shapein is None or shapein == selfshapein:\n return self.shapeout\n if selfshapein is None:\n return shapein\n if flatten_sliced_shape(shapein) == flatten_sliced_shape(selfshapein):\n return self.shapeout\n raise ValidationError('The input of ' + self.description + ' has an i' \\\n 'ncompatible shape ' + str(shapein) + '. Expected shape is ' + \\\n str(self.shapein) + '.')\n\n def validate_shapeout(self, shapeout):\n \"\"\"\n Validate input shape and return the output shape of the transpose model\n \"\"\"\n selfshapeout = self.shapeout\n if shapeout is None or shapeout == selfshapeout:\n return self.shapein\n if selfshapeout is None:\n return shapeout\n if flatten_sliced_shape(shapeout) == flatten_sliced_shape(selfshapeout):\n return self.shapein\n raise ValidationError(\"The input of '\" + self.description + \".T' has \" \\\n 'an incompatible shape ' + str(shapeout) + '. Expected shape is ' +\\\n str(self.shapeout) + '.')\n\n def validate_input_inplace(self, input, inplace):\n\n input = np.asanyarray(input)\n try:\n input = np.asanyarray(input, _get_dtype(self.dtype, input.dtype))\n except:\n raise TypeError(\"The input of '\" + self.description + \\\n \"' has a non-numeric type.\")\n\n shape = self.validate_shapein(validate_sliced_shape(input.shape, \n getattr(input, 'nsamples', None) or (self.shapein[-1] \\\n if self.shapein and type(self.shapein[-1]) is tuple else None)))\n if shape is None:\n raise ValidationError('The shape of the output of ' + \\\n self.description+' is not known.')\n\n typein = self.typein\n if shape and type(shape[-1]) is tuple and (typein is None or \\\n not issubclass(typein, Tod)):\n typein = Tod\n if typein is not None and not isinstance(input, typein):\n input = input.view(typein)\n if shape and type(shape[-1]) is tuple:\n input.nsamples = shape[-1]\n\n if not inplace:\n if var.verbose:\n print('Info: Allocating ' + input.dtype.type.__name__ + \\\n str(input.shape).replace(' ','') + ' = ' + \\\n str(input.dtype.itemsize * input.size / 2.**20) + \\\n ' MiB in ' + self.description + '.')\n try:\n input = input.copy()\n except MemoryError:\n gc.collect()\n input = input.copy()\n\n for k,v in self.attrin:\n setattr(input, k, v)\n\n return input\n\n def validate_input_direct(self, input, cachein, cacheout):\n\n def set_cachein(cache):\n self.cachein = cache\n def set_cacheout(cache):\n self.cacheout = cache\n\n return self.validate_input_cache(input, self.description,\n cachein, cacheout,\n self.attrin, self.attrout,\n self.cachein, self.cacheout,\n set_cachein, set_cacheout,\n self.shapein, self.shapeout,\n self.typein, self.typeout,\n self.unitin, self.unitout,\n lambda shape: self.validate_shapein(shape))\n\n def validate_input_transpose(self, input, cachein, cacheout):\n\n def set_cachein(cache):\n self.cachein = cache\n def set_cacheout(cache):\n self.cacheout = cache\n\n return self.validate_input_cache(input, self.description + '.T',\n cachein, cacheout,\n self.attrout, self.attrin,\n self.cacheout, self.cachein,\n set_cacheout, set_cachein,\n self.shapeout, self.shapein,\n self.typeout, self.typein,\n self.unitout, self.unitin,\n lambda shape: self.validate_shapeout(shape))\n\n def validate_input_cache(self, input, description,\n do_cachein, do_cacheout,\n attrin, attrout,\n cachein, cacheout,\n set_cachein, set_cacheout,\n shapein, shapeout,\n typein, typeout,\n unitin, unitout,\n validate_input_shape):\n\n input = np.array(input, ndmin=1, subok=True, copy=False)\n try:\n input = np.asanyarray(input, _get_dtype(self.dtype, input.dtype))\n except:\n raise TypeError(\"The input of '\" + description + \"' has a non-num\" \\\n 'eric type.')\n\n shapein = validate_sliced_shape(input.shape, getattr(input, 'nsamples',\\\n None) or (shapein[-1] if shapein and type(shapein[-1]) is tuple \\\n else None))\n shapeout = validate_input_shape(shapein)\n\n if type(shapein[-1]) is tuple and (typein is None or \\\n not issubclass(typein, Tod)):\n typein = Tod\n if typein is not None and not isinstance(input, typein):\n input = input.view(typein)\n if shapein and type(shapein[-1]) is tuple:\n input.nsamples = shapein[-1]\n\n if do_cachein and id(input) != id(cachein):\n # validate input before storing it (nsamples is not enforced)\n _validate_input_unit(input, unitin)\n for k,v in attrin.items():\n setattr(input, k, v)\n \n # store it\n set_cachein(input)\n\n # get output from the cache\n shapeout_flat = flatten_sliced_shape(shapeout)\n if do_cacheout and cacheout is not None and \\\n shapeout_flat == cacheout.shape and cacheout.dtype == input.dtype:\n output = cacheout\n\n else:\n\n # allocate output\n if var.verbose:\n reason = 'cache not requested' if not do_cacheout else \\\n 'empty cache' if cacheout is None else \\\n 'type mismatch' if cacheout.dtype != input.dtype else \\\n 'shape mismatch'\n print('Info: Allocating ' + self.dtype.type.__name__ + \\\n str(shapeout_flat).replace(' ','') + ' = ' + \\\n str(input.dtype.itemsize * np.product(shapeout_flat) \\\n / 2.**20) + ' MiB in ' + description + ' (' + reason + \\\n ').')\n if typeout is None:\n typeout = input.__class__\n if type(shapeout[-1]) is tuple and not issubclass(typeout, Tod):\n typeout = Tod\n if hasattr(typeout, 'empty'):\n try:\n output = typeout.empty(shapeout, dtype=self.dtype)\n except MemoryError:\n gc.collect()\n output = typeout.empty(shapeout, dtype=self.dtype)\n else:\n try:\n output = np.empty(shapeout_flat, self.dtype)\n except MemoryError:\n gc.collect()\n output = np.empty(shapeout_flat, self.dtype)\n\n # store output\n if do_cacheout:\n set_cacheout(output)\n\n if type(shapeout[-1]) is tuple:\n output = Tod(output, nsamples=shapeout[-1], copy=False)\n\n _propagate_attributes(input, output, unitin, unitout, attrout)\n\n return input, output\n\n def __mul__(self, other):\n if isinstance(other, np.ndarray):\n return self.matvec(other)\n return Composition([self, other])\n\n def __rmul__(self, other):\n if not _my_isscalar(other):\n raise NotImplementedError(\"It is not possible to multiply '\" + \\\n str(type(other)) + \"' with an AcquisitionModel.\")\n return Composition([other, self])\n\n def __imul__(self, other):\n _tocompositemodel(self, Composition, [copy.copy(self), other])\n return self\n\n def __add__(self, other):\n return Addition([self, other])\n\n def __radd__(self, other):\n return Addition([other, self])\n\n def __iadd__(self, other):\n _tocompositemodel(self, Addition, [copy.copy(self), other])\n return self\n\n def __sub__(self, other):\n return Addition([self, -other])\n\n def __rsub__(self, other):\n return Addition([other, -self])\n\n def __isub__(self, other):\n _tocompositemodel(self, Addition, [copy.copy(self), -other])\n return self\n\n def __neg__(self):\n return Scalar(-1.) * self\n\n def __str__(self):\n result = self.description\n if type(self) == Identity:\n result += ' (Identity)'\n if self.shapein is not None or self.shapeout is not None:\n result += ' [input:'\n if self.shapein is None:\n result += 'unconstrained'\n else:\n result += str(self.shapein).replace(' ','')\n result += ', output:'\n if self.shapeout is None:\n result += 'unconstrained'\n else:\n result += str(self.shapeout).replace(' ','')\n result += ']'\n return result\n\n\n#-------------------------------------------------------------------------------\n\n\nclass AcquisitionModelLinear(AcquisitionModel, LinearOperator):\n \"\"\"Abstract class representing a linear instrument acquisition model.\n\n The response y from an input signal x by an acquisition model M is given by\n y = M.direct(x) or y = M(x)\n where x and y can be multidimensional numpy.ndarray.\n The transpose of the acquisition model is\n x = M.transpose(y) or M.T(y)\n This class subclasses the LinearOperator class, so it also provides the\n methods matvec and rmatvec which operate on 1d ndarray.\n\n An acquisition model can be the combination of several submodels\n describing various parts of the instrumental chain:\n M = M3 * M2 * M1 ...\n \"\"\"\n def __init__(self, transpose=None, **keywords):\n AcquisitionModel.__init__(self, **keywords)\n if transpose is not None:\n if not hasattr(transpose, '__call__'):\n raise TypeError('The input transpose method is not callable.')\n self.transpose = transpose\n\n def transpose(self, input, inplace, cachein, cacheout):\n raise NotImplementedError()\n\n def matvec(self, v, inplace=False, cachein=False, cacheout=False):\n v = v.reshape(flatten_sliced_shape(self.shapein))\n return self.direct(v, inplace, cachein, cacheout).ravel()\n\n def rmatvec(self, v, inplace=False, cachein=False, cacheout=False):\n v = v.reshape(flatten_sliced_shape(self.shapeout))\n return self.transpose(v, inplace, cachein, cacheout).ravel()\n\n def dense(self):\n d = np.ndarray(self.shape, dtype=self.dtype)\n v = np.zeros(self.shape[1], dtype=var.FLOAT_DTYPE)\n for i in range(self.shape[1]):\n v[:] = 0\n v[i] = 1\n d[:,i] = self.matvec(v, inplace=True, cachein=True, cacheout=True)\n return d\n\n @property\n def T(self):\n return AcquisitionModelTranspose(self)\n\n\n#-------------------------------------------------------------------------------\n\n\nclass AcquisitionModelTranspose(AcquisitionModelLinear):\n\n def __init__(self, model):\n self.model = model\n AcquisitionModelLinear.__init__(self,\n direct=model.transpose,\n transpose=model.direct,\n cache=model.cache,\n dtype=model.dtype,\n description=model.description + '.T',\n attrin=model.attrout,\n attrout=model.attrin,\n shapein=model.shapeout,\n shapeout=model.shapein,\n typein=model.typeout,\n typeout=model.typein,\n unitin=model.unitout,\n unitout=model.unitin)\n\n @property\n def cachein(self):\n return self.model.cacheout\n\n @cachein.setter\n def cachein(self, cache):\n self.model.cacheout = cache\n \n @property\n def cacheout(self):\n return self.model.cachein\n\n @cacheout.setter\n def cacheout(self, cache):\n self.model.cachein = cache\n \n @property\n def T(self):\n return self.model\n\n def validate_shapein(self, shapein):\n return self.model.validate_shapeout(shapein)\n\n def validate_shapeout(self, shapeout):\n return self.model.validate_shapein(shapeout)\n\n\n#-------------------------------------------------------------------------------\n\n\nclass Composite(AcquisitionModel):\n \"\"\"\n Class for grouping acquisition models\n \"\"\"\n\n def __init__(self, models):\n\n models = [ asacquisitionmodel(m) for m in models ]\n self.blocks = []\n\n for model in models:\n if isinstance(model, self.__class__):\n self.blocks.extend(model.blocks)\n else:\n self.blocks.append(model)\n\n AcquisitionModel.__init__(self)\n\n if all([hasattr(m, 'matvec') for m in self.blocks]):\n self.matvec = \\\n lambda v, inplace=False, cachein=False, cacheout=False: \\\n self.direct(v.reshape(flatten_sliced_shape(self.shapein)),\n inplace, cachein, cacheout).ravel()\n def dense():\n d = np.ndarray(self.shape, dtype=self.dtype)\n v = np.zeros(self.shape[1], dtype=var.FLOAT_DTYPE)\n for i in range(self.shape[1]):\n v[:] = 0\n v[i] = 1\n d[:,i] = self.matvec(v, True, True, True)\n return d\n self.dense = dense\n\n if all([hasattr(m, 'rmatvec') for m in self.blocks]):\n self.rmatvec = \\\n lambda v, inplace=False, cachein=False, cacheout=False: \\\n self.transpose(v.reshape(flatten_sliced_shape(\n self.shapeout)), inplace, cachein, cacheout).ravel()\n\n @property\n def dtype(self):\n for block in self.blocks:\n if block.dtype.type in (np.complex64, np.complex128, np.complex256):\n return block.dtype\n return var.FLOAT_DTYPE\n\n @dtype.setter\n def dtype(self, dtype):\n pass\n\n @property\n def T(self):\n return AcquisitionModelTranspose(self)\n\n @property\n def typein(self):\n for model in reversed(self.blocks):\n if model.typein is not None:\n return model.typein\n return np.ndarray\n\n @typein.setter\n def typein(self, value):\n pass\n\n @property\n def typeout(self):\n for model in reversed(self.blocks):\n if model.typeout is not None:\n return model.typeout\n return np.ndarray\n\n @typeout.setter\n def typeout(self, value):\n pass\n\n @property\n def unitin(self):\n for model in reversed(self.blocks):\n if len(model.unitin) > 0:\n return model.unitin\n return {}\n\n @unitin.setter\n def unitin(self, value):\n pass\n\n @property\n def unitout(self):\n for model in self.blocks:\n if len(model.unitout) > 0:\n return model.unitout\n return {}\n\n @unitout.setter\n def unitout(self, value):\n pass\n\n def validate_input(self, input, shape):\n input = np.array(input, ndmin=1, subok=True, copy=False)\n if shape is not None and type(shape[-1]) is tuple:\n input = Tod(input, nsamples=shape[-1], copy=False)\n return input\n\n def __str__(self):\n result = AcquisitionModel.__str__(self) + ':'\n components = []\n for block in self.blocks:\n components.extend(str(block).split('\\n'))\n result += '\\n '+'\\n '.join(components)\n return result\n\n\n#-------------------------------------------------------------------------------\n\n\nclass Addition(Composite):\n \"\"\"\n Class for acquisition models addition\n\n If at least one of the input already is the result of an addition,\n a flattened list of operators is created by associativity, in order to\n benefit from the AcquisitionModel's caching mechanism.\n \"\"\"\n\n def direct(self, input, inplace, cachein, cacheout):\n input = self.validate_input(input, self.shapein)\n output = self.blocks[0].direct(input, False, False, False)\n for i, model in enumerate(self.blocks[1:]):\n last = i == len(self.blocks) - 2\n tmf.add_inplace(np.array(output, ndmin=1, copy=False).T,\n np.array(model.direct(input, inplace and last,\n cachein, cacheout), ndmin=1, copy=False).T)\n return output\n\n def transpose(self, input, inplace, cachein, cacheout):\n input = self.validate_input(input, self.shapeout)\n output = self.blocks[0].transpose(input, False, False, False)\n for i, model in enumerate(self.blocks[1:]):\n last = i == len(self.blocks) - 2\n tmf.add_inplace(np.array(output, ndmin=1, copy=False).T,\n np.array(model.transpose(input, inplace and last,\n cachein, cacheout), ndmin=1, copy=False).T)\n return output\n\n @property\n def shapein(self):\n shapein = None\n for model in self.blocks:\n shapein_ = model.shapein\n if shapein_ is None:\n continue\n if shapein is None or type(shapein_[-1]) is tuple:\n shapein = shapein_\n continue\n if flatten_sliced_shape(shapein) != flatten_sliced_shape(shapein_):\n raise ValidationError(\"Incompatible shape in operands: '\" + \\\n str(shapein) +\"' and '\" + str(shapein_) + \"'.\")\n return shapein\n\n @shapein.setter\n def shapein(self, value):\n pass\n\n @property\n def shapeout(self):\n shapeout = None\n for model in self.blocks:\n shapeout_ = model.shapeout\n if shapeout_ is None:\n continue\n if shapeout is None or type(shapeout_[-1]) is tuple:\n shapeout = shapeout_\n continue\n if flatten_sliced_shape(shapeout) != \\\n flatten_sliced_shape(shapeout_):\n raise ValidationError(\"Incompatible shape in operands: '\" + \\\n str(shapeout) +\"' and '\" + str(shapeout_) + \"'.\")\n return shapeout\n\n @shapeout.setter\n def shapeout(self, value):\n pass\n\n @property\n def T(self):\n return Addition([model.T for model in self.blocks])\n\n def __iadd__(self, other):\n oldblocks = self.blocks\n if isinstance(other, Addition):\n self.blocks.extend(other.blocks)\n else:\n self.blocks.append(asacquisitionmodel(other))\n try:\n shapein = self.shapein\n shapeout = self.shapeout\n except ValidationError as errmsg:\n self.blocks = oldblocks\n raise ValidationError(errmsg)\n return self\n\n def __isub__(self, other):\n return self.__iadd__(-other)\n\n\n#-------------------------------------------------------------------------------\n\n\nclass Composition(Composite):\n \"\"\"\n Class for acquisition models composition\n\n If at least one of the input already is the result of a composition,\n a flattened list of operators is created by associativity, in order to\n benefit from the AcquisitionModel's caching mechanism.\n \"\"\"\n\n def direct(self, input, inplace, cachein, cacheout):\n input = self.validate_input(input, self.shapein)\n caches = [m.cache for m in self.blocks]\n if any(caches):\n first_cache = caches.index(True)\n last_cache = len(self.blocks) - caches.index(True) - 1\n else:\n first_cache = len(self.blocks)\n last_cache = -1\n for i, model in enumerate(reversed(self.blocks)):\n input = model.direct(input, inplace or i != 0,\n cachein or i > first_cache,\n cacheout or i < last_cache)\n return input\n\n def transpose(self, input, inplace, cachein, cacheout):\n input = self.validate_input(input, self.shapeout)\n caches = [m.cache for m in self.blocks]\n first_cache = len(self.blocks) - caches.index(True) - 1\n last_cache = caches.index(True)\n for i, model in enumerate(self.blocks):\n input = model.transpose(input, inplace or i != 0,\n cachein or i > first_cache,\n cacheout or i < last_cache)\n return input\n\n @property\n def shapein(self):\n shapeout = None\n for model in self.blocks:\n shapeout = model.validate_shapeout(shapeout)\n return shapeout\n\n @shapein.setter\n def shapein(self, value):\n pass\n\n @property\n def shapeout(self):\n shapein = None\n for model in reversed(self.blocks):\n shapein = model.validate_shapein(shapein)\n return shapein\n\n @shapeout.setter\n def shapeout(self, value):\n pass\n\n @property\n def T(self):\n return Composition([model.T for model in reversed(self.blocks)])\n\n def __imul__(self, other):\n oldblocks = self.blocks\n self.blocks.append(asacquisitionmodel(other))\n try:\n shapein = self.shapein\n shapeout = self.shapeout\n except ValidationError as errmsg:\n self.blocks = oldblocks\n raise ValidationError(errmsg)\n return self\n\n\n#-------------------------------------------------------------------------------\n\n\nclass Square(object):\n \"\"\"\n Square operator\n \n The input and output must have the same shape\n This operator does not implement the cache mechanism, but operation on\n the input can be done inplace or on a copy.\n \"\"\"\n\n def __init__(self, shapein=None, **keywords):\n self.shapeout = shapein\n self.validate_shapeout = self.validate_shapein\n \n\n#-------------------------------------------------------------------------------\n\n\nclass Symmetric(AcquisitionModelLinear, Square):\n \"\"\"Symmetric operator\"\"\"\n\n def __init__(self, **keywords):\n AcquisitionModelLinear.__init__(self, **keywords)\n Square.__init__(self, **keywords)\n self.transpose = self.direct\n self.rmatvec = self.matvec\n\n @property\n def T(self):\n return self\n\n\n#-------------------------------------------------------------------------------\n\n\nclass Diagonal(Symmetric):\n \"\"\"\n Diagonal operator.\n\n Multiply by a diagonal matrix. The input of a Diagonal instance can be of\n rank greater than the specified diagonal array, in which case the latter\n is broadcast along the fast dimensions.\n \"\"\"\n\n def __init__(self, diagonal, **keywords):\n diagonal = np.array(diagonal, dtype=var.get_default_dtype(diagonal),\n order='c')\n Symmetric.__init__(self, dtype=diagonal.dtype, **keywords)\n self.isscalar = diagonal.ndim == 0\n self.data = np.array(diagonal, ndmin=1, copy=False)\n\n def direct(self, input, inplace, cachein, cacheout):\n output = self.validate_input_inplace(input, inplace)\n if self.dtype == var.FLOAT_DTYPE:\n tmf.multiply_inplace(output.T, self.data.T)\n else:\n output.T[:] *= self.data.T\n return output\n\n def validate_shapein(self, shapein):\n if shapein is None:\n return self.shapein\n if self.isscalar:\n return shapein\n if flatten_sliced_shape(shapein[0:self.data.ndim]) != self.data.shape:\n raise ValueError('The input has an incompatible shape ' + \\\n str(shapein) + '.')\n return shapein\n\n def matvec(self, v, inplace=False, cachein=False, cacheout=False):\n shape = list(self.data.shape)\n shape.append(-1)\n v = v.reshape(shape)\n return self.direct(v, inplace, cachein, cacheout).ravel()\n\n\nclass Offset(AcquisitionModel, Square):\n \"\"\"\n Offset operator.\n\n Add an offset to the input. The input of an Offset instance can be of rank\n greater than the specified diagonal array, in which case the latter is\n broadcast along the fast dimensions. This operator is not linear.\n \"\"\"\n def __init__(self, offset, **keywords):\n offset = np.array(offset, dtype=var.get_default_dtype(offset),\n order='c')\n AcquisitionModel.__init__(self, dtype=offset.dtype, **keywords)\n Square.__init__(self, **keywords)\n self.isscalar = offset.ndim == 0\n self.data = np.array(offset, ndmin=1, copy=False)\n\n def direct(self, input, inplace, cachein, cacheout):\n output = self.validate_input_inplace(input, inplace)\n if self.dtype == var.FLOAT_DTYPE:\n tmf.add_inplace(output.T, self.data.T)\n else:\n output.T[:] += self.data.T\n return output\n\n def validate_shapein(self, shapein):\n if shapein is None:\n return self.shapein\n if self.isscalar:\n return shapein\n if flatten_sliced_shape(shapein[0:self.data.ndim]) != self.data.shape:\n raise ValueError('The input has an incompatible shape ' + \\\n str(shapein) + '.')\n return shapein\n\n def matvec(self, v, inplace=False, cachein=False, cacheout=False):\n shape = list(self.data.shape)\n shape.append(-1)\n v = v.reshape(shape)\n return self.direct(v, inplace, cachein, cacheout).ravel()\n\n\n#-------------------------------------------------------------------------------\n\n\nclass Rounding(AcquisitionModel, Square):\n \"\"\"Rounding operator.\n \n The rounding method may be one of the following:\n - rtz : round towards zero (truncation)\n - rti : round towards infinity\n - rtmi : round towards minus infinity (floor)\n - rtpi : round towards positive infinity (ceil)\n - rhtz : round half towards zero\n - rhti : round half towards infinity (numpy's round, fortran's nint)\n - rhtmi : round half towards minus infinity\n - rhtpi : round half towards positive infinity\n - rhs : round half stochastically\n \"\"\"\n\n def __init__(self, method='rhti', **keywords):\n AcquisitionModel.__init__(self, **keywords)\n Square.__init__(self, **keywords)\n method = method.lower()\n table = {'rtz' : tmf.round_rtz,\n 'rti' : tmf.round_rti,\n 'rtmi' : tmf.round_rtmi,\n 'rtpi' : tmf.round_rtpi,\n 'rhtz' : tmf.round_rhtz,\n 'rhti' : tmf.round_rhti,\n 'rhtmi' : tmf.round_rhtmi,\n 'rhtpi' : tmf.round_rhtpi,\n 'rhs' : tmf.round_rhs}\n if method not in table:\n raise ValueError('The rounding method must be one of the following'\\\n ': ' + ','.join(\"'\" + k + \"'\" for k in table.keys()) + '.')\n self.round = table[method]\n\n def direct(self, input, inplace, cachein, cacheout):\n output = self.validate_input_inplace(input, inplace)\n self.round(output.T)\n return output\n\n\n#-------------------------------------------------------------------------------\n\n\nclass DiscreteDifference(AcquisitionModelLinear, Square):\n \"\"\"\n Discrete difference operator.\n\n Calculate the nth order discrete difference along given axis.\n \"\"\"\n\n def __init__(self, axis=0, n=1, comm=None, **keywords):\n AcquisitionModelLinear.__init__(self, **keywords)\n Square.__init__(self, **keywords)\n self.n = n\n self.axis = axis\n self.comm = comm or var.comm_map\n\n def direct(self, input, inplace, cachein, cacheout):\n output = self.validate_input_inplace(input, inplace)\n for i in range(self.n):\n diff(output, self.axis, comm=self.comm)\n return output\n\n def transpose(self, input, inplace, cachein, cacheout):\n output = self.validate_input_inplace(input, inplace)\n for i in range(self.n):\n diffT(output, self.axis, comm=self.comm)\n return output \n\n\n#-------------------------------------------------------------------------------\n\n\nclass DdTdd(Symmetric):\n \"\"\"Calculate operator dX.T dX along a given axis.\"\"\"\n\n def __init__(self, axis=0, scalar=1., description=None, comm=None,\n **keywords):\n if description is None and scalar != 1.:\n description = str(scalar) + ' DdTdd'\n Symmetric.__init__(self, **keywords)\n self.axis = axis\n self.scalar = scalar\n self.comm = comm or var.comm_map\n\n def direct(self, input, inplace, cachein, cacheout):\n output = self.validate_input_inplace(input, inplace)\n diffTdiff(output, self.axis, self.scalar, comm=self.comm)\n return output\n\n \n#-------------------------------------------------------------------------------\n\n\nclass Projection(AcquisitionModelLinear):\n \"\"\"\n This class handles operations by the pointing matrix\n\n The input observation has the following required attributes/methods:\n - nfinesamples\n - nsamples\n - ndetectors\n - get_pointing_matrix()\n - unit\n The instance has the following specific attributes:\n - header: the FITS header of the map\n - pmatrix: transparent view of the pointing matrix\n - _pmatrix: opaque representation of the pointing matrix\n - npixels_per_sample: maximum number of sky map pixels that can be\n intercepted by a detector\n \"\"\"\n\n def __init__(self, observation, method=None, header=None, resolution=None,\n npixels_per_sample=0, oversampling=True, comm_map=None,\n packed=False, description=None):\n\n self.comm_map = comm_map or var.comm_map\n self.comm_tod = observation.comm_tod\n\n self.method, pmatrix, self.header, self.ndetectors, nsamples, \\\n self.npixels_per_sample, (unitout, unitin), (duout, duin) = \\\n observation.get_pointing_matrix(header,\n resolution,\n npixels_per_sample,\n method=method,\n oversampling=oversampling)\n\n self.nsamples_tot = int(np.sum(nsamples))\n self._pmatrix = pmatrix\n if self.npixels_per_sample == 0:\n pmatrix = np.empty(0, dtype=np.int64)\n self.pmatrix = pmatrix.view([('weight', 'f4'), ('pixel', 'i4')]) \\\n .view(np.recarray)\n self.pmatrix.shape = (self.ndetectors, np.sum(nsamples),\n self.npixels_per_sample)\n\n shapein = tuple([self.header['NAXIS'+str(i+1)] for i in \\\n range(self.header['NAXIS'])])[::-1]\n mask = Map.empty(shapein, dtype=np.bool8, header=self.header)\n tmf.pointing_matrix_mask(self._pmatrix, mask.view(np.int8).T, \n self.npixels_per_sample, self.nsamples_tot, self.ndetectors)\n\n ismapdistributed = self.comm_map.Get_size() > 1\n istoddistributed = self.comm_tod.Get_size() > 1\n self.ispacked = packed or ismapdistributed\n if self.ispacked:\n tmf.pointing_matrix_pack(self._pmatrix, mask.view(np.int8).T,\n self.npixels_per_sample, self.nsamples_tot, self.ndetectors)\n shapein = (int(np.sum(~mask)))\n\n attrin = {'header' : self.header}\n if duin is not None:\n attrin['derived_units'] = duin\n attrout = {}\n if duout is not None:\n attrout['derived_units'] = duout\n shapeout = combine_sliced_shape(self.ndetectors, nsamples)\n AcquisitionModelLinear.__init__(self,\n cache=True,\n description=description,\n attrin=attrin,\n attrout=attrout,\n shapein=shapein,\n shapeout=shapeout,\n typein=Map,\n typeout=Tod,\n unitin=unitin,\n unitout=unitout)\n self.mask = mask\n if not self.ispacked and not istoddistributed:\n return\n\n if self.ispacked:\n if ismapdistributed:\n self *= DistributionLocal(self.mask)\n else:\n self *= Packing(self.mask)\n elif istoddistributed:\n self *= DistributionGlobal(self.shapein, share=True,\n comm=self.comm_tod)\n s = self.blocks[0]\n self.header = s.header\n self.mask = s.mask\n self.method = s.method\n self.ndetectors = s.ndetectors\n self.npixels_per_sample = s.npixels_per_sample\n self.nsamples_tot = s.nsamples_tot\n self.pmatrix = s.pmatrix\n\n def direct(self, input, inplace, cachein, cacheout):\n input, output = self.validate_input_direct(input, cachein, cacheout)\n tmf.pointing_matrix_direct(self._pmatrix, input.T, output.T,\n self.npixels_per_sample)\n return output\n\n def transpose(self, input, inplace, cachein, cacheout):\n input, output = self.validate_input_transpose(input, cachein, cacheout)\n tmf.pointing_matrix_transpose(self._pmatrix, input.T, output.T, \n self.npixels_per_sample)\n return output\n\n def get_ptp(self):\n npixels = np.product(self.shapein)\n return tmf.pointing_matrix_ptp(self._pmatrix, self.npixels_per_sample,\n self.nsamples_tot, self.ndetectors, npixels).T\n\n\n#-------------------------------------------------------------------------------\n\n\nclass DistributionGlobal(AcquisitionModelLinear):\n \"\"\"\n Distribute a global map to different MPI processes.\n By default, they are locally distributed, in the sense that an MPI process\n will only handle a subset of the global map.\n \"\"\"\n\n def __init__(self, shape, share=False, comm=None, **keywords):\n\n if comm is None:\n comm = var.comm_map\n self.comm = comm\n\n # if share is true, the maps are not distributed\n if share:\n def direct(input, inplace, cachein, cacheout):\n return self.validate_input_inplace(input, inplace)\n def transpose(input, inplace, cachein, cacheout):\n output = self.validate_input_inplace(input, inplace)\n if self.comm.Get_size() > 1:\n self.comm.Allreduce(MPI.IN_PLACE, [output, MPI.DOUBLE],\n op=MPI.SUM)\n return output\n AcquisitionModelLinear.__init__(self, shapein=shape, typein=Map,\n direct=direct, transpose=transpose,\n cache=False, **keywords)\n return\n\n shapeout = split_shape(shape, comm)\n self.counts = []\n self.offsets = [0]\n for rank in range(comm.Get_size()):\n s = split_work(shape[0], rank=rank, comm=comm)\n n = (s.stop - s.start) * np.product(shape[1:])\n self.counts.append(n)\n self.offsets.append(self.offsets[-1] + n)\n self.offsets.pop()\n attrin = { 'comm':MPI.COMM_SELF, 'shape_global':shape }\n attrout = { 'comm':self.comm, 'shape_global':shape }\n AcquisitionModelLinear.__init__(self, cache=True, shapein=shape,\n shapeout=shapeout, attrin=attrin, attrout=attrout, typein=Map,\n **keywords)\n\n def direct(self, input, inplace, cachein, cacheout):\n input, output = self.validate_input_direct(input, cachein, cacheout)\n s = split_work(self.shapein[0], comm=self.comm)\n n = s.stop - s.start\n output[0:n] = input[s.start:s.stop]\n output[n:] = 0\n return output\n\n def transpose(self, input, inplace, cachein, cacheout):\n input, output = self.validate_input_transpose(input, cachein, cacheout)\n s = split_work(self.shapein[0], comm=self.comm)\n n = s.stop - s.start\n self.comm.Allgatherv([input[0:n], MPI.DOUBLE], [output, (self.counts,\n self.offsets), MPI.DOUBLE])\n return output\n\n\n#-------------------------------------------------------------------------------\n\n\nclass DistributionLocal(AcquisitionModelLinear):\n \"\"\"\n Scatter a distributed map to different MPI processes under the control of a\n local non-distributed mask.\n \"\"\"\n\n def __init__(self, mask, operator=MPI.SUM, comm=None, **keywords):\n if comm is None:\n comm = var.comm_map\n shapeout = (int(np.sum(~mask)),)\n shapein = split_shape(mask.shape, comm)\n attrin = { 'comm':comm, 'shape_global':mask.shape }\n attrout = { 'comm':MPI.COMM_SELF, 'shape_global':shapeout}\n AcquisitionModelLinear.__init__(self, cache=True, typein=Map,\n shapein=shapein, shapeout=shapeout, attrin=attrin, **keywords)\n self.comm = comm\n self.mask = mask\n self.operator = operator\n\n def direct(self, input, inplace, cachein, cacheout):\n input, output = self.validate_input_direct(input, cachein, cacheout)\n status = tmf.mpi_allscatterlocal(input.T, self.mask.view(np.int8).T,\n output.T, self.comm.py2f())\n if status == 0: return output\n if status < 0:\n raise RuntimeError('Incompatible sizes.')\n raise MPI.Exception(status)\n\n def transpose(self, input, inplace, cachein, cacheout):\n input, output = self.validate_input_transpose(input, cachein, cacheout)\n status = tmf.mpi_allreducelocal(input.T, self.mask.view(np.int8).T,\n output.T, self.operator.py2f(), self.comm.py2f())\n if status == 0: return output\n if status < 0:\n raise RuntimeError('Incompatible mask.')\n raise MPI.Exception(status)\n\n\n#-------------------------------------------------------------------------------\n\n\nclass Compression(AcquisitionModelLinear):\n \"\"\"\n Abstract class for compressing the input signal.\n \"\"\"\n\n def __init__(self, compression_factor, shapein=None, **keywords):\n if _my_isscalar(compression_factor):\n compression_factor = (compression_factor,)\n self.factor = np.array(compression_factor, int)\n\n if np.all(self.factor == 1):\n def direct(input, inplace, cachein, cacheout):\n return self.validate_input_inplace(input, inplace)\n transpose = direct\n cache = False\n else:\n direct = None\n transpose = None\n cache = True\n shapeout = self.validate_shapein(shapein)\n AcquisitionModelLinear.__init__(self,\n direct=direct,\n transpose=transpose,\n cache=cache,\n shapein=shapein,\n shapeout=shapeout,\n typein=Tod,\n **keywords)\n\n def validate_shapein(self, shapein):\n if shapein is None:\n return None\n if np.any(np.array(shapein[-1]) % self.factor != 0):\n raise ValidationError('The input timeline size ('+str(shapein[-1])+\\\n ') is not an integer times the compression factor (' + \\\n str(self.factor)+').')\n return combine_sliced_shape(shapein[0:-1],\n np.array(shapein[-1]) / self.factor)\n\n def validate_shapeout(self, shapeout):\n if shapeout is None:\n return None\n if self.shapeout is not None and flatten_sliced_shape(shapeout) == \\\n flatten_sliced_shape(self.shapeout):\n return self.shapein\n return combine_sliced_shape(shapeout[0:-1],\n np.array(shapeout[-1]) * self.factor)\n\n def __str__(self):\n return super(Compression, self).__str__()+' (x'+str(self.factor)+')'\n \n\n#-------------------------------------------------------------------------------\n\n\nclass CompressionAverage(Compression):\n \"\"\"\n Compress the input signal by averaging blocks of specified size.\n \"\"\"\n\n def direct(self, input, inplace, cachein, cacheout):\n input, output = self.validate_input_direct(input, cachein, cacheout)\n tmf.compression_average_direct(input.T, output.T,\n np.array(input.nsamples, np.int32), self.factor.astype(np.int32))\n return output\n\n def transpose(self, input, inplace, cachein, cacheout):\n input, output = self.validate_input_transpose(input, cachein, cacheout)\n tmf.compression_average_transpose(input.T, output.T,\n np.array(input.nsamples, np.int32), self.factor.astype(np.int32))\n return output\n\n\n#-------------------------------------------------------------------------------\n\n\nclass DownSampling(Compression):\n \"\"\"\n Downsample the input signal by picking up one sample out of a number\n specified by the compression factor\n \"\"\"\n\n def direct(self, input, inplace, cachein, cacheout):\n input, output = self.validate_input_direct(input, cachein, cacheout)\n tmf.downsampling_direct(input.T, output.T,\n np.array(input.nsamples, np.int32), self.factor.astype(np.int32))\n return output\n\n def transpose(self, input, inplace, cachein, cacheout):\n input, output = self.validate_input_transpose(input, cachein, cacheout)\n tmf.downsampling_transpose(input.T, output.T,\n np.array(input.nsamples, np.int32), self.factor.astype(np.int32))\n return output\n \n\n#-------------------------------------------------------------------------------\n\n\nclass Identity(Symmetric):\n \"\"\"\n Identity class.\n \"\"\"\n\n def __init__(self, **keywords):\n Symmetric.__init__(self, **keywords)\n\n def direct(self, input, inplace, cachein, cacheout):\n return self.validate_input_inplace(input, inplace)\n \n\n#-------------------------------------------------------------------------------\n\n\nclass Scalar(Diagonal):\n \"\"\"\n Class for scalar multiplication\n \"\"\"\n\n def __init__(self, value, **keywords):\n if not np.iscomplex(value):\n value = np.real(value)\n Diagonal.__init__(self, value, **keywords)\n \n def __str__(self):\n return super(self.__class__, self).__str__()+' (' + \\\n str(self.data) + ')'\n \n\n#-------------------------------------------------------------------------------\n\n\nclass Masking(Symmetric):\n \"\"\"\n Mask operator.\n\n Sets to zero values whose mask is True (non-null). The input of a Masking\n instance can be of rank greater than the speficied mask, in which case the\n latter is broadcast along the fast dimensions.\n \"\"\"\n\n def __init__(self, mask, **keywords):\n Symmetric.__init__(self, dtype=var.FLOAT_DTYPE, **keywords)\n if mask is None:\n print('Warning: input mask is None.')\n mask = False\n mask = np.array(mask, order='c', dtype=np.bool8)\n self.isscalar = mask.ndim == 0\n self.mask = np.array(mask, ndmin=1, copy=False)\n\n def direct(self, input, inplace, cachein, cacheout):\n output = self.validate_input_inplace(input, inplace)\n status = tmf.masking(output.T, self.mask.view(np.int8).T)\n if status != 0: raise RuntimeError()\n return output\n\n def validate_shapein(self, shapein):\n if shapein is None:\n return self.shapein\n if self.isscalar:\n return shapein\n if flatten_sliced_shape(shapein[0:self.mask.ndim]) != self.mask.shape:\n raise ValueError('The input has shape ' + str(shapein) + ' incomp' \\\n 'atible with that of the mask ' + str(self.mask.shape) + '.')\n return shapein\n\n\n#-------------------------------------------------------------------------------\n\n\nclass Unpacking(AcquisitionModelLinear):\n \"\"\"\n Convert 1d map into an nd array, under the control of a mask.\n The elements for which the mask is True are equal to the field argument.\n \"\"\"\n\n def __init__(self, mask, field=0., **keywords):\n mask = np.array(mask, np.bool8)\n AcquisitionModelLinear.__init__(self,\n cache=True,\n shapein=np.sum(mask == 0),\n shapeout=mask.shape,\n **keywords)\n self.mask = mask\n self.field = field\n\n def direct(self, input, inplace, cachein, cacheout):\n input, output = self.validate_input_direct(input, cachein, cacheout)\n tmf.unpack_direct(input.T, self.mask.view(np.int8).T, output.T,\n self.field)\n return output\n\n def transpose(self, input, inplace, cachein, cacheout):\n input, output = self.validate_input_transpose(input, cachein, cacheout)\n tmf.unpack_transpose(input.T, self.mask.view(np.int8).T, output.T)\n return output\n\n\n#-------------------------------------------------------------------------------\n\n\nclass Packing(AcquisitionModelLinear):\n \"\"\"\n Convert an nd array in a 1d map, under the control of a mask.\n The elements for which the mask is True are equal to the field argument.\n \"\"\"\n\n def __init__(self, mask, field=0., **keywords):\n mask = np.array(mask, np.bool8)\n AcquisitionModelLinear.__init__(self,\n cache=True,\n shapein=mask.shape,\n shapeout=np.sum(mask == 0),\n **keywords)\n self.mask = mask\n self.field = field\n\n def direct(self, input, inplace, cachein, cacheout):\n input, output = self.validate_input_direct(input, cachein, cacheout)\n tmf.unpack_transpose(input.T, self.mask.view(np.int8).T, output.T)\n return output\n\n def transpose(self, input, inplace, cachein, cacheout):\n input, output = self.validate_input_transpose(input, cachein, cacheout)\n tmf.unpack_direct(input.T, self.mask.view(np.int8).T, output.T,\n self.field)\n return output\n\n\n#-------------------------------------------------------------------------------\n\n\nclass Reshaping(AcquisitionModelLinear):\n \"\"\"\n Reshape arrays\n \"\"\"\n\n def __init__(self, shapein, shapeout, **keywords):\n if shapein is None or shapeout is None:\n raise ValueError('The shapes are not defined.')\n if np.product(flatten_sliced_shape(shapein)) != \\\n np.product(flatten_sliced_shape(shapeout)):\n raise ValueError('The number of elements of the input and output o'\\\n 'f the Reshaping operator are incompatible.')\n AcquisitionModelLinear.__init__(self, shapein=shapein,\n shapeout=shapeout, **keywords)\n\n def direct(self, input, inplace, cachein, cacheout):\n output = self.validate_input_direct(input, inplace)\n output = _smart_reshape(output, self.shapeout)\n return output\n\n def transpose(self, input, inplace, cachein, cacheout):\n output = self.validate_input_transpose(input, inplace)\n output = _smart_reshape(output, self.shapein)\n return output\n\n def validate_input_direct(self, input, inplace):\n input = np.array(input, ndmin=1, copy=not inplace, subok=True)\n shapeout = self.validate_shapein(input.shape)\n return input\n\n def validate_input_transpose(self, input, inplace):\n input = np.array(input, ndmin=1, copy=not inplace, subok=True)\n shapeout = self.validate_shapeout(input.shape)\n return input\n\n\n#-------------------------------------------------------------------------------\n\n\nclass ResponseTruncatedExponential(AcquisitionModelLinear, Square):\n \"\"\"\n ResponseTruncatedExponential(tau)\n\n Apply a truncated exponential response to the signal\n\n Parameters\n ==========\n \n tau: number\n Time constant divided by the signal sampling period\n \"\"\"\n \n def __init__(self, tau, **keywords):\n \"\"\"\n \"\"\"\n AcquisitionModelLinear.__init__(self, typein=Tod, **keywords)\n Square.__init__(self, **keywords)\n if hasattr(tau, 'SI'):\n tau = tau.SI\n if tau.unit != '':\n raise ValueError('The time constant must be dimensionless.')\n self.tau = np.array(tau, dtype=var.FLOAT_DTYPE, ndmin=1)\n\n def direct(self, input, inplace, cachein, cacheout):\n output = self.validate_input_inplace(input, inplace)\n tmf.convolution_trexp_direct(output.T, np.array(output.nsamples),\n self.tau)\n return output\n\n def transpose(self, input, inplace, cachein, cacheout):\n output = self.validate_input_inplace(input, inplace)\n tmf.convolution_trexp_transpose(output.T, np.array(output.nsamples),\n self.tau)\n return output\n\n\n#-------------------------------------------------------------------------------\n\n\nclass Padding(AcquisitionModelLinear):\n \"Pads before and after a Tod.\"\n\n def __init__(self, left=0, right=0, value=0., shapein=None, **keywords):\n if shapein is not None:\n shapeout = self.validate_shapein(shapein)\n else:\n shapeout = None\n AcquisitionModelLinear.__init__(self,\n cache=True,\n shapein=shapein,\n shapeout=shapeout,\n typein=Tod,\n **keywords)\n left = np.array(left, ndmin=1, dtype=int)\n right = np.array(right, ndmin=1, dtype=int)\n if np.any(left < 0):\n raise ValueError('Left padding is not positive.')\n if np.any(right < 0):\n raise ValueError('Right padding is not positive.')\n if np.rank(left) != 1 or np.rank(right) != 1:\n raise ValueError('Padding must be scalar or a vector.')\n self.left = tuple(left)\n self.right = tuple(right)\n self.value = value\n \n def direct(self, input, inplace, cachein, cacheout):\n input, output = self.validate_input_direct(input, cachein, cacheout)\n dest = 0\n dest_padded = 0\n for islice in range(len(input.nsamples)):\n nsamples = input.nsamples[islice]\n left = self.left[islice if len(self.left) > 1 else 0]\n output[...,dest_padded:dest_padded+left] = self.value\n output[...,dest_padded+left:dest_padded+left+nsamples] = \\\n input[...,dest:dest+nsamples]\n output[...,dest_padded+left+nsamples:dest_padded+ \\\n output.nsamples[islice]] = self.value\n dest += nsamples\n dest_padded += output.nsamples[islice]\n return output\n \n def transpose(self, input, inplace, cachein, cacheout):\n input, output = self.validate_input_transpose(input, cachein, cacheout)\n dest = 0\n dest_padded = 0\n for islice in range(len(input.nsamples)):\n nsamples = output.nsamples[islice]\n left = self.left [islice if len(self.left) > 1 else 0]\n output[...,dest:dest+nsamples] = \\\n input[...,dest_padded+left:dest_padded+left+nsamples]\n dest += nsamples\n dest_padded += input.nsamples[islice]\n return output\n\n def validate_input_direct(self, input, cachein, cacheout):\n input, output = super(Padding, self).validate_input_direct(input,\n cachein, cacheout)\n if len(self.left) != 1 and len(self.left) != len(input.nsamples):\n raise ValueError(\"The input Tod has a number of slices '\" + \\\n str(len(input.nsamples)) + \\\n \"' incompatible with the specified padding.\")\n return input, output\n \n def validate_input_transpose(self, input, cachein, cacheout):\n input, output = super(Padding, self).validate_input_transpose(input,\n cachein, cacheout)\n if len(self.left) != 1 and len(self.left) != len(input.nsamples):\n raise ValueError(\"The input Tod has a number of slices '\" + \\\n str(len(input.nsamples)) +\n \"' incompatible with the specified padding.\")\n return input, output\n\n def validate_shapein(self, shapein):\n if shapein is None:\n return None\n return combine_sliced_shape(shapein[0:-1], np.array(shapein[-1]) + \\\n self.left + self.right)\n \n def validate_shapeout(self, shapeout):\n if shapeout is None:\n return None\n return combine_sliced_shape(shapeout[0:-1], np.array(shapeout[-1]) -\\\n self.left - self.right)\n\n\n#-------------------------------------------------------------------------------\n\n\nclass Shift(AcquisitionModelLinear, Square):\n\n def __init__(self, n, axis=None, **keywords):\n AcquisitionModelLinear.__init__(self, **keywords)\n Square.__init__(self, **keywords)\n if axis is None:\n if not isinstance(n, (list, tuple, np.ndarray)):\n n = (n,)\n axis = tuple(np.arange(-len(n), 0))\n elif not isinstance(axis, (list, tuple, np.ndarray)):\n n = (n,)\n axis = (axis,)\n elif not isinstance(n, (list, tuple, np.ndarray)) or \\\n len(n) != len(axis):\n n = len(axis) * (n,)\n self.n = [np.array(v, dtype=int) for v in n]\n self.axis = [int(v) for v in axis]\n\n def direct(self, input, inplace, cachein, cacheout):\n output = self.validate_input_inplace(input, inplace)\n for n, axis in zip(self.n, self.axis):\n shift(output, n, axis)\n return output\n\n def transpose(self, input, inplace, cachein, cacheout):\n output = self.validate_input_inplace(input, inplace)\n for n, axis in zip(self.n, self.axis):\n shift(output, -n, axis)\n return output \n\n\n#-------------------------------------------------------------------------------\n\n\nclass CircularShift(AcquisitionModelLinear, Square):\n \n def __init__(self, n, axis=None, **keywords):\n AcquisitionModelLinear.__init__(self, **keywords)\n Square.__init__(self, **keywords)\n if _my_isscalar(n):\n n = (n,)\n if axis is None:\n axis = tuple(np.arange(-len(n), 0))\n elif _my_isscalar(axis):\n axis = (axis,)\n self.n = tuple(map(int, n))\n self.axis = tuple(map(int, axis))\n\n def direct(self, input, inplace, cachein, cacheout):\n for axis, n in zip(self.axis, self.n):\n input = np.roll(input, -n, axis=axis)\n return input\n\n def transpose(self, input, inplace, cachein, cacheout):\n for axis, n in zip(self.axis, self.n):\n input = np.roll(input, n, axis=axis)\n return input\n\n\n#-------------------------------------------------------------------------------\n\n\nclass Fft(AcquisitionModelLinear, Square):\n \"\"\"\n Performs complex fft\n \"\"\"\n\n def __init__(self, shape, flags=['estimate'], **keywords):\n AcquisitionModelLinear.__init__(self, shapein=shape,\n dtype=var.COMPLEX_DTYPE, **keywords)\n Square.__init__(self, **keywords)\n if fftw3.planning.lib_threads is None:\n nthreads = 1\n else:\n nthreads = tmf.info_nthreads()\n self.n = np.product(shape)\n self._in = np.zeros(shape, dtype=var.COMPLEX_DTYPE)\n self._out = np.zeros(shape, dtype=var.COMPLEX_DTYPE)\n self.forward_plan = fftw3.Plan(self._in, self._out, direction='forward',\n flags=flags, nthreads=nthreads)\n self.backward_plan= fftw3.Plan(self._in, self._out,direction='backward',\n flags=flags, nthreads=nthreads)\n\n def direct(self, input, inplace, cachein, cacheout):\n self._in[:] = input\n fftw3.execute(self.forward_plan)\n return Map(self._out)\n\n def transpose(self, input, inplace, cachein, cacheout):\n self._in[:] = input\n fftw3.execute(self.backward_plan)\n return Map(self._out / self.n, copy=False)\n\n\n#-------------------------------------------------------------------------------\n\n\nclass FftHalfComplex(AcquisitionModelLinear, Square):\n \"\"\"\n Performs real-to-half-complex fft\n \"\"\"\n\n def __init__(self, nsamples, **keywords):\n AcquisitionModelLinear.__init__(self, typein=Tod, **keywords)\n Square.__init__(self, **keywords)\n self.nsamples = tuple(np.array(nsamples, ndmin=1, dtype=int))\n self.forward_plan = np.empty(len(self.nsamples), dtype=int)\n self.backward_plan = np.empty(len(self.nsamples), dtype=int)\n for i, n in enumerate(self.nsamples):\n tarray = np.empty(n, dtype=var.FLOAT_DTYPE)\n farray = np.empty(n, dtype=var.FLOAT_DTYPE)\n self.forward_plan[i] = \\\n fftw3.Plan(tarray, farray, direction='forward',\n flags=['measure'], realtypes=['halfcomplex r2c'],\n nthreads=1)._get_parameter()\n self.backward_plan[i] = \\\n fftw3.Plan(farray, tarray, direction='backward',\n flags=['measure'], realtypes=['halfcomplex c2r'],\n nthreads=1)._get_parameter()\n\n def direct(self, input, inplace, cachein, cacheout):\n output = self.validate_input_inplace(input, inplace)\n output_ = _smart_reshape(output, (np.product(input.shape[:-1]),\n input.shape[-1]))\n tmf.fft_plan(output_.T, np.array(self.nsamples), self.forward_plan)\n return output\n\n def transpose(self, input, inplace, cachein, cacheout):\n output = self.validate_input_inplace(input, inplace)\n output_ = _smart_reshape(output, (np.product(input.shape[:-1]), \n input.shape[-1]))\n tmf.fft_plan(output_.T, np.array(self.nsamples), self.backward_plan)\n dest = 0\n for n in self.nsamples:\n output_[:,dest:dest+n] /= n\n dest += n\n return output\n\n def validate_shapein(self, shape):\n if shape is None:\n return None\n nsamples = shape[-1]\n if nsamples != self.nsamples and nsamples != sum(self.nsamples):\n raise ValidationError(\"Invalid FFT size '\" + str(nsamples) + \\\n \"' instead of '\"+str(self.nsamples)+\"'.\")\n return combine_sliced_shape(shape[0:-1], self.nsamples)\n\n\n#-------------------------------------------------------------------------------\n\n\nclass Convolution(Symmetric):\n\n def __init__(self, kernel, **keywords):\n Symmetric.__init__(self, **keywords)\n self.kernel = np.asanyarray(kernel)\n\n def direct(self, input, inplace, cachein, cacheout):\n output = self.validate_input_inplace(input, inplace)\n output[:] = scipy.signal.fftconvolve(input, self.kernel, mode='same')\n return output\n\n\n#-------------------------------------------------------------------------------\n\n\nclass InvNtt(AcquisitionModelLinear):\n\n def __init__(self, obs, filename=None, **keywords):\n nsamples = obs.get_nsamples()\n length = np.asarray(2**np.ceil(np.log2(np.array(nsamples) + 200)), int)\n invntt = self._get_diagonal(length, obs.get_filter_uncorrelated(\n filename=filename, **keywords))\n fft = FftHalfComplex(length)\n padding = Padding(left=invntt.ncorrelations, right=length - nsamples - \\\n invntt.ncorrelations)\n _tocompositemodel(self, Composition,\n [ padding.T, fft.T, invntt, fft, padding ])\n\n def _get_diagonal(self, nsamples, filter, **keywords):\n nsamples = np.asarray(nsamples)\n ndetectors = filter.shape[-2]\n ncorrelations = filter.shape[-1] - 1\n nslices = nsamples.size\n if np.rank(filter) == 2:\n filter = np.resize(filter,(nslices, ndetectors, ncorrelations+1))\n tod_filter, status = \\\n tmf.fft_filter_uncorrelated(filter.T, np.asarray(nsamples, \n dtype=np.int32), np.sum(nsamples))\n if status != 0: raise RuntimeError()\n d = Diagonal(tod_filter.T, shapein=tod_filter.T.shape, **keywords)\n d = np.maximum(d, 0)\n d.data /= var.comm_tod.allreduce(np.max(d.data), op=MPI.MAX)\n d.ncorrelations = ncorrelations\n return d\n\n\n#-------------------------------------------------------------------------------\n\n\nclass SqrtInvNtt(InvNtt):\n def __init__(self, *args, **kw):\n invntt = InvNtt(*args, **kw)\n _tocompositemodel(self, Composition, invntt.blocks)\n data = self.blocks[2].data\n data[:] = np.sqrt(data)\n #np.sqrt(data, out=data) does not work with numpy 1.5\n\n\n#-------------------------------------------------------------------------------\n\n\nclass InterpolationLinear(AcquisitionModelLinear, Square):\n\n def __init__(self, mask, **keywords):\n AcquisitionModelLinear.__init__(self, attrin={'mask':mask}, **keywords)\n Square.__init__(self, **keywords)\n self.mask = mask\n\n def direct(self, input, inplace, cachein, cacheout):\n return interpolate_linear(output)\n\n def transpose(self, input, inplace, cachein, cacheout):\n raise NotImplementedError()\n\n\n#-------------------------------------------------------------------------------\n\n\ndef acquisitionmodel_factory(direct, transpose=None, description=None, **keywords):\n \"\"\"Creates an AcquisitionModel from a function\"\"\"\n description = description or direct.__name__\n if description == '':\n description = ''\n\n if transpose is None:\n a = AcquisitionModel(description=description, **keywords)\n else:\n a = AcquisitionModelLinear(description=description, **keywords)\n\n def d(input, inplace, cachein, cacheout):\n output = a.validate_input_inplace(input, inplace)\n return direct(output)\n a.direct = d\n if transpose is None:\n return a\n\n def t(input, inplace, cachein, cacheout):\n output = a.validate_input_inplace(input, inplace)\n return transpose(output)\n a.transpose = t\n return a\n\ndef Clip(vmin, vmax, description=None, **keywords):\n description = description or 'Clip'\n return acquisitionmodel_factory(lambda x: np.clip(x, vmin, vmax, out=x),\n description=description, **keywords)\n\ndef Maximum(value, description=None, **keywords):\n description = description or 'Maximum'\n return acquisitionmodel_factory(lambda x: np.maximum(x, value, x),\n description=description, **keywords)\n\ndef Minimum(value, description=None, **keywords):\n description = description or 'Minimum'\n return acquisitionmodel_factory(lambda x: np.minimum(x, value, x),\n description=description, **keywords)\n\n\n#-------------------------------------------------------------------------------\n\n\ndef asacquisitionmodel(operator, shapein=None, shapeout=None, description=None):\n if isinstance(operator, AcquisitionModel):\n if shapein and operator.shapein and shapein != operator.shapein:\n raise ValueError('The input shapein ' + str(shapein) + ' is incom' \\\n 'patible with that of the input ' + str(operator.shapein) + '.')\n if shapeout and operator.shapeout and shapeout != operator.shapeout:\n raise ValueError('The input shapeout ' + str(shapeout) + ' is inco'\\\n 'mpatible with that of the input ' + str(operator.shapeout) + \\\n '.')\n if shapein and not operator.shapein or \\\n shapeout and not operator.shapeout:\n operator = copy.copy(operator)\n operator.shapein = shapein\n operator.shapeout = shapeout\n return operator\n if _my_isscalar(operator):\n return Scalar(operator)\n if isinstance(operator, LinearOperator):\n direct = lambda input, inplace, cachein, cacheout: \\\n operator.matvec(input)\n transpose = lambda input, inplace, cachein, cacheout: \\\n operator.rmatvec(input)\n model = AcquisitionModelLinear(direct=direct,\n transpose=transpose,\n shapein=shapein or operator.shape[1],\n shapeout=shapeout or operator.shape[0],\n dtype=operator.dtype,\n description=description)\n return model\n return asacquisitionmodel(scipy.sparse.linalg.aslinearoperator(operator),\n description=description)\n\n\n#-------------------------------------------------------------------------------\n\n\ndef _tocompositemodel(model, cls, models):\n if model.__class__ == cls:\n return model\n model.__class__ = cls\n model.__init__(models)\n\n\n#-------------------------------------------------------------------------------\n\n\ndef _get_dtype(type1, type2):\n t1 = type1.type()\n t2 = type2.type()\n t = t1 * t2\n return t.dtype\n\n\n#-------------------------------------------------------------------------------\n\n\ndef _is_scientific_dtype(dtype):\n \"\"\"Return true if the data type is \"\"\"\n return issubclass(dtype.type, np.number) or dtype.type == np.bool8\n\n\n#-------------------------------------------------------------------------------\n\n\ndef _propagate_attributes(input, output, unitin, unitout, attrout):\n \"\"\"Copy over attributes from input to output\"\"\"\n\n # if the arguments do not have the same shape, only copy the units\n if input.shape != output.shape:\n try:\n setattr(output, '_unit', input._unit)\n except:\n pass\n try:\n setattr(output, '_derived_units', input._derived_units)\n except:\n pass\n\n elif hasattr(input, '__dict__'):\n\n # copy over input's attributes\n for k, v in input.__dict__.items():\n setattr(output, k, v)\n\n _validate_output_unit(input, output, unitin, unitout)\n\n # copy over operator's attributes\n for k,v in attrout.items():\n setattr(output, k, v)\n\n\n#-------------------------------------------------------------------------------\n\n\ndef _smart_reshape(input, shape):\n curr = input\n shape = flatten_sliced_shape(shape)\n while True:\n if curr.shape == shape:\n return curr\n base = curr.base\n if base is None or base.dtype != input.dtype or \\\n base.__class__ != input.__class__ or base.size != input.size:\n return curr.reshape(shape)\n curr = base\n\n\n#-------------------------------------------------------------------------------\n\n\ndef _validate_input_unit(input, expected):\n if len(expected) == 0 or not hasattr(input, '_unit') or \\\n len(input._unit) == 0:\n return\n for u,v in expected.items():\n if u not in input._unit or input._unit[u] != v:\n unit = Quantity(1, expected).unit\n raise ValidationError(\"The input unit '\" + input.unit + \"' is inco\"\\\n \"mpatible with the required unit '\" + unit + \"'.\")\n\n\n#-------------------------------------------------------------------------------\n\n\ndef _validate_output_unit(input, output, unitin, unitout):\n if not hasattr(output, '_unit'):\n return\n if len(unitout) == 0:\n return\n if len(output._unit) == 0:\n output._unit = unitout\n return\n output._unit = _divide_unit(output._unit, unitin)\n output._unit = _multiply_unit(output._unit, unitout)\n", "sub_path": "core/src/acquisitionmodels.py", "file_name": "acquisitionmodels.py", "file_ext": "py", "file_size_in_byte": 74418, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datatypes.validate_sliced_shape", "line_number": 99, "usage_type": "call"}, {"api_name": "datatypes.validate_sliced_shape", "line_number": 101, "usage_type": "call"}, {"api_name": "quantity.Quantity", "line_number": 105, "usage_type": "call"}, {"api_name": "quantity.Quantity", "line_number": 106, "usage_type": "call"}, {"api_name": "datatypes.Tod", "line_number": 130, "usage_type": "argument"}, {"api_name": "datatypes.Tod", "line_number": 133, "usage_type": "argument"}, {"api_name": "numpy.product", "line_number": 144, "usage_type": "call"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.product", "line_number": 145, "usage_type": "call"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 158, "usage_type": "call"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 169, "usage_type": "call"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.asanyarray", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.asanyarray", "line_number": 194, "usage_type": "call"}, {"api_name": "datatypes.validate_sliced_shape", "line_number": 199, "usage_type": "call"}, {"api_name": "datatypes.Tod", "line_number": 208, "usage_type": "argument"}, {"api_name": "datatypes.Tod", "line_number": 209, "usage_type": "name"}, {"api_name": "gc.collect", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 276, "usage_type": "call"}, {"api_name": "numpy.asanyarray", "line_number": 278, "usage_type": "call"}, {"api_name": "datatypes.validate_sliced_shape", "line_number": 283, "usage_type": "call"}, {"api_name": "datatypes.Tod", "line_number": 289, "usage_type": "argument"}, {"api_name": "datatypes.Tod", "line_number": 290, "usage_type": "name"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.product", "line_number": 321, "usage_type": "call"}, {"api_name": "datatypes.Tod", "line_number": 326, "usage_type": "argument"}, {"api_name": "datatypes.Tod", "line_number": 327, "usage_type": "name"}, {"api_name": "gc.collect", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 336, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 339, "usage_type": "call"}, {"api_name": "datatypes.Tod", "line_number": 346, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 353, "usage_type": "attribute"}, {"api_name": "numpyutils._my_isscalar", "line_number": 358, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 364, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 374, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 384, "usage_type": "call"}, {"api_name": "scipy.sparse.linalg.interface.LinearOperator", "line_number": 412, "usage_type": "name"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 438, "usage_type": "call"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 442, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 446, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 447, "usage_type": "call"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 532, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 535, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 536, "usage_type": "call"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 547, "usage_type": "call"}, {"api_name": "numpy.complex64", "line_number": 553, "usage_type": "attribute"}, {"api_name": "numpy.complex128", "line_number": 553, "usage_type": "attribute"}, {"api_name": "numpy.complex256", "line_number": 553, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 570, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 581, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 610, "usage_type": "call"}, {"api_name": "datatypes.Tod", "line_number": 612, "usage_type": "call"}, {"api_name": "tamasisfortran.add_inplace", "line_number": 641, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 641, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 642, "usage_type": "call"}, {"api_name": "tamasisfortran.add_inplace", "line_number": 651, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 651, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 652, "usage_type": "call"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 666, "usage_type": "call"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 685, "usage_type": "call"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 686, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 840, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 844, "usage_type": "call"}, {"api_name": "tamasisfortran.multiply_inplace", "line_number": 849, "usage_type": "call"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 859, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 880, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 885, "usage_type": "call"}, {"api_name": "tamasisfortran.add_inplace", "line_number": 890, "usage_type": "call"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 900, "usage_type": "call"}, {"api_name": "tamasisfortran.round_rtz", "line_number": 934, "usage_type": "attribute"}, {"api_name": "tamasisfortran.round_rti", "line_number": 935, "usage_type": "attribute"}, {"api_name": "tamasisfortran.round_rtmi", "line_number": 936, "usage_type": "attribute"}, {"api_name": "tamasisfortran.round_rtpi", "line_number": 937, "usage_type": "attribute"}, {"api_name": "tamasisfortran.round_rhtz", "line_number": 938, "usage_type": "attribute"}, {"api_name": "tamasisfortran.round_rhti", "line_number": 939, "usage_type": "attribute"}, {"api_name": "tamasisfortran.round_rhtmi", "line_number": 940, "usage_type": "attribute"}, {"api_name": "tamasisfortran.round_rhtpi", "line_number": 941, "usage_type": "attribute"}, {"api_name": "tamasisfortran.round_rhs", "line_number": 942, "usage_type": "attribute"}, {"api_name": "utils.diff", "line_number": 974, "usage_type": "call"}, {"api_name": "utils.diffT", "line_number": 980, "usage_type": "call"}, {"api_name": "utils.diffTdiff", "line_number": 1001, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 1041, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 1044, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 1044, "usage_type": "attribute"}, {"api_name": "numpy.recarray", "line_number": 1046, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 1047, "usage_type": "call"}, {"api_name": "datatypes.Map.empty", "line_number": 1052, "usage_type": "call"}, {"api_name": "datatypes.Map", "line_number": 1052, "usage_type": "name"}, {"api_name": "numpy.bool8", "line_number": 1052, "usage_type": "attribute"}, {"api_name": "tamasisfortran.pointing_matrix_mask", "line_number": 1053, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 1053, "usage_type": "attribute"}, {"api_name": "tamasisfortran.pointing_matrix_pack", "line_number": 1060, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 1060, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 1062, "usage_type": "call"}, {"api_name": "datatypes.combine_sliced_shape", "line_number": 1070, "usage_type": "call"}, {"api_name": "datatypes.Map", "line_number": 1078, "usage_type": "name"}, {"api_name": "datatypes.Tod", "line_number": 1079, "usage_type": "name"}, {"api_name": "tamasisfortran.pointing_matrix_direct", "line_number": 1105, "usage_type": "call"}, {"api_name": "tamasisfortran.pointing_matrix_transpose", "line_number": 1111, "usage_type": "call"}, {"api_name": "numpy.product", "line_number": 1116, "usage_type": "call"}, {"api_name": "tamasisfortran.pointing_matrix_ptp", "line_number": 1117, "usage_type": "call"}, {"api_name": "mpi4py.MPI.IN_PLACE", "line_number": 1144, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 1144, "usage_type": "name"}, {"api_name": "mpi4py.MPI.DOUBLE", "line_number": 1144, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI.SUM", "line_number": 1145, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 1145, "usage_type": "name"}, {"api_name": "datatypes.Map", "line_number": 1147, "usage_type": "name"}, {"api_name": "mpiutils.split_shape", "line_number": 1152, "usage_type": "call"}, {"api_name": "mpiutils.split_work", "line_number": 1156, "usage_type": "call"}, {"api_name": "numpy.product", "line_number": 1157, "usage_type": "call"}, {"api_name": "mpi4py.MPI.COMM_SELF", "line_number": 1161, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 1161, "usage_type": "name"}, {"api_name": "datatypes.Map", "line_number": 1164, "usage_type": "name"}, {"api_name": "mpiutils.split_work", "line_number": 1169, "usage_type": "call"}, {"api_name": "mpiutils.split_work", "line_number": 1177, "usage_type": "call"}, {"api_name": "mpi4py.MPI.DOUBLE", "line_number": 1179, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 1179, "usage_type": "name"}, {"api_name": "mpi4py.MPI.DOUBLE", "line_number": 1180, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 1180, "usage_type": "name"}, {"api_name": "mpi4py.MPI.SUM", "line_number": 1193, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 1193, "usage_type": "name"}, {"api_name": "numpy.sum", "line_number": 1196, "usage_type": "call"}, {"api_name": "mpiutils.split_shape", "line_number": 1197, "usage_type": "call"}, {"api_name": "mpi4py.MPI.COMM_SELF", "line_number": 1199, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 1199, "usage_type": "name"}, {"api_name": "datatypes.Map", "line_number": 1200, "usage_type": "name"}, {"api_name": "tamasisfortran.mpi_allscatterlocal", "line_number": 1208, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 1208, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI.Exception", "line_number": 1213, "usage_type": "call"}, {"api_name": "mpi4py.MPI", "line_number": 1213, "usage_type": "name"}, {"api_name": "tamasisfortran.mpi_allreducelocal", "line_number": 1217, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 1217, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI.Exception", "line_number": 1222, "usage_type": "call"}, {"api_name": "mpi4py.MPI", "line_number": 1222, "usage_type": "name"}, {"api_name": "numpyutils._my_isscalar", "line_number": 1234, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1236, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 1238, "usage_type": "call"}, {"api_name": "datatypes.Tod", "line_number": 1254, "usage_type": "name"}, {"api_name": "numpy.any", "line_number": 1260, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1260, "usage_type": "call"}, {"api_name": "datatypes.combine_sliced_shape", "line_number": 1264, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1265, "usage_type": "call"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 1270, "usage_type": "call"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 1271, "usage_type": "call"}, {"api_name": "datatypes.combine_sliced_shape", "line_number": 1273, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1274, "usage_type": "call"}, {"api_name": "tamasisfortran.compression_average_direct", "line_number": 1290, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1291, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 1291, "usage_type": "attribute"}, {"api_name": "tamasisfortran.compression_average_transpose", "line_number": 1296, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1297, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 1297, "usage_type": "attribute"}, {"api_name": "tamasisfortran.downsampling_direct", "line_number": 1312, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1313, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 1313, "usage_type": "attribute"}, {"api_name": "tamasisfortran.downsampling_transpose", "line_number": 1318, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1319, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 1319, "usage_type": "attribute"}, {"api_name": "numpy.iscomplex", "line_number": 1347, "usage_type": "call"}, {"api_name": "numpy.real", "line_number": 1348, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1373, "usage_type": "call"}, {"api_name": "numpy.bool8", "line_number": 1373, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 1375, "usage_type": "call"}, {"api_name": "tamasisfortran.masking", "line_number": 1379, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 1379, "usage_type": "attribute"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 1388, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1404, "usage_type": "call"}, {"api_name": "numpy.bool8", "line_number": 1404, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 1407, "usage_type": "call"}, {"api_name": "tamasisfortran.unpack_direct", "line_number": 1415, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 1415, "usage_type": "attribute"}, {"api_name": "tamasisfortran.unpack_transpose", "line_number": 1421, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 1421, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 1435, "usage_type": "call"}, {"api_name": "numpy.bool8", "line_number": 1435, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 1439, "usage_type": "call"}, {"api_name": "tamasisfortran.unpack_transpose", "line_number": 1446, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 1446, "usage_type": "attribute"}, {"api_name": "tamasisfortran.unpack_direct", "line_number": 1451, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 1451, "usage_type": "attribute"}, {"api_name": "numpy.product", "line_number": 1467, "usage_type": "call"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 1467, "usage_type": "call"}, {"api_name": "numpy.product", "line_number": 1468, "usage_type": "call"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 1468, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1485, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1490, "usage_type": "call"}, {"api_name": "datatypes.Tod", "line_number": 1514, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 1520, "usage_type": "call"}, {"api_name": "tamasisfortran.convolution_trexp_direct", "line_number": 1524, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1524, "usage_type": "call"}, {"api_name": "tamasisfortran.convolution_trexp_transpose", "line_number": 1530, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1530, "usage_type": "call"}, {"api_name": "datatypes.Tod", "line_number": 1550, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 1552, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1553, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 1554, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 1556, "usage_type": "call"}, {"api_name": "numpy.rank", "line_number": 1558, "usage_type": "call"}, {"api_name": "datatypes.combine_sliced_shape", "line_number": 1614, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1614, "usage_type": "call"}, {"api_name": "datatypes.combine_sliced_shape", "line_number": 1620, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1620, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 1633, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 1635, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 1636, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 1639, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 1642, "usage_type": "call"}, {"api_name": "utils.shift", "line_number": 1648, "usage_type": "call"}, {"api_name": "utils.shift", "line_number": 1654, "usage_type": "call"}, {"api_name": "numpyutils._my_isscalar", "line_number": 1666, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 1669, "usage_type": "call"}, {"api_name": "numpyutils._my_isscalar", "line_number": 1670, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 1677, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 1682, "usage_type": "call"}, {"api_name": "fftw3.planning", "line_number": 1698, "usage_type": "attribute"}, {"api_name": "tamasisfortran.info_nthreads", "line_number": 1701, "usage_type": "call"}, {"api_name": "numpy.product", "line_number": 1702, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 1703, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 1704, "usage_type": "call"}, {"api_name": "fftw3.Plan", "line_number": 1705, "usage_type": "call"}, {"api_name": "fftw3.Plan", "line_number": 1707, "usage_type": "call"}, {"api_name": "fftw3.execute", "line_number": 1712, "usage_type": "call"}, {"api_name": "datatypes.Map", "line_number": 1713, "usage_type": "call"}, {"api_name": "fftw3.execute", "line_number": 1717, "usage_type": "call"}, {"api_name": "datatypes.Map", "line_number": 1718, "usage_type": "call"}, {"api_name": "datatypes.Tod", "line_number": 1730, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 1732, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 1733, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 1734, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 1736, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 1737, "usage_type": "call"}, {"api_name": "fftw3.Plan", "line_number": 1739, "usage_type": "call"}, {"api_name": "fftw3.Plan", "line_number": 1743, "usage_type": "call"}, {"api_name": "numpy.product", "line_number": 1749, "usage_type": "call"}, {"api_name": "tamasisfortran.fft_plan", "line_number": 1751, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1751, "usage_type": "call"}, {"api_name": "numpy.product", "line_number": 1756, "usage_type": "call"}, {"api_name": "tamasisfortran.fft_plan", "line_number": 1758, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1758, "usage_type": "call"}, {"api_name": "datatypes.combine_sliced_shape", "line_number": 1772, "usage_type": "call"}, {"api_name": "numpy.asanyarray", "line_number": 1782, "usage_type": "call"}, {"api_name": "scipy.signal.signal.fftconvolve", "line_number": 1786, "usage_type": "call"}, {"api_name": "scipy.signal.signal", "line_number": 1786, "usage_type": "attribute"}, {"api_name": "scipy.signal", "line_number": 1786, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 1797, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 1797, "usage_type": "call"}, {"api_name": "numpy.log2", "line_number": 1797, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1797, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 1807, "usage_type": "call"}, {"api_name": "numpy.rank", "line_number": 1811, "usage_type": "call"}, {"api_name": "numpy.resize", "line_number": 1812, "usage_type": "call"}, {"api_name": "tamasisfortran.fft_filter_uncorrelated", "line_number": 1814, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 1814, "usage_type": "call"}, {"api_name": "numpy.int32", "line_number": 1815, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 1815, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 1818, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 1819, "usage_type": "call"}, {"api_name": "mpi4py.MPI.MAX", "line_number": 1819, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 1819, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 1832, "usage_type": "call"}, {"api_name": "processing.interpolate_linear", "line_number": 1847, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 1882, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 1887, "usage_type": "call"}, {"api_name": "numpy.minimum", "line_number": 1892, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 1910, "usage_type": "call"}, {"api_name": "numpyutils._my_isscalar", "line_number": 1914, "usage_type": "call"}, {"api_name": "scipy.sparse.linalg.interface.LinearOperator", "line_number": 1916, "usage_type": "argument"}, {"api_name": "scipy.signal.sparse.linalg.aslinearoperator", "line_number": 1928, "usage_type": "call"}, {"api_name": "scipy.signal.sparse", "line_number": 1928, "usage_type": "attribute"}, {"api_name": "scipy.signal", "line_number": 1928, "usage_type": "name"}, {"api_name": "numpy.number", "line_number": 1957, "usage_type": "attribute"}, {"api_name": "numpy.bool8", "line_number": 1957, "usage_type": "attribute"}, {"api_name": "datatypes.flatten_sliced_shape", "line_number": 1995, "usage_type": "call"}, {"api_name": "quantity.Quantity", "line_number": 2015, "usage_type": "call"}, {"api_name": "quantity._divide_unit", "line_number": 2031, "usage_type": "call"}, {"api_name": "quantity._multiply_unit", "line_number": 2032, "usage_type": "call"}]} +{"seq_id": "400840106", "text": "import itk\nfrom wslink import register as rpc\n\nimport helper\n\nclass Protocol(helper.ObjectProtocol):\n @rpc('median_filter')\n @helper.deferResults\n @helper.objdir_wrap\n def median_filter(self, image, radius):\n itk_image = helper.vtkjs_to_itk_image(image)\n \n median_filter = itk.MedianImageFilter[type(itk_image), type(itk_image)].New()\n median_filter.SetInput(itk_image)\n median_filter.SetRadius(radius)\n median_filter.Update()\n\n result = median_filter.GetOutput()\n\n # maybe auto-serialize in objdir_wrap?\n return helper.itk_to_vtkjs_image(\n result,\n 'Median filter of {}'.format(image['name']))\n\n @rpc('segment')\n @helper.objdir_wrap\n def segment(self, image, point):\n itk_image = helper.vtkjs_to_itk_image(image)\n\n print('segment at:', point)\n\n return helper.deferCall(lambda: self.segmentAtPoint(image, point))\n # returns ID\n return [\n {\n 'id': 1,\n 'points': [\n {'point': [0, 10, 10], 'radius': 10},\n {'point': [1, 11, 11], 'radius': 20},\n {'point': [1, 12, 12], 'radius': 30},\n {'point': [0, 13, 13], 'radius': 40},\n {'point': [0, 14, 14], 'radius': 50},\n ]\n }\n ]\n", "sub_path": "server/protocol.py", "file_name": "protocol.py", "file_ext": "py", "file_size_in_byte": 1385, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "helper.ObjectProtocol", "line_number": 6, "usage_type": "attribute"}, {"api_name": "helper.vtkjs_to_itk_image", "line_number": 11, "usage_type": "call"}, {"api_name": "itk.MedianImageFilter", "line_number": 13, "usage_type": "attribute"}, {"api_name": "helper.itk_to_vtkjs_image", "line_number": 21, "usage_type": "call"}, {"api_name": "wslink.register", "line_number": 7, "usage_type": "call"}, {"api_name": "helper.deferResults", "line_number": 8, "usage_type": "attribute"}, {"api_name": "helper.objdir_wrap", "line_number": 9, "usage_type": "attribute"}, {"api_name": "helper.vtkjs_to_itk_image", "line_number": 28, "usage_type": "call"}, {"api_name": "helper.deferCall", "line_number": 32, "usage_type": "call"}, {"api_name": "wslink.register", "line_number": 25, "usage_type": "call"}, {"api_name": "helper.objdir_wrap", "line_number": 26, "usage_type": "attribute"}]} +{"seq_id": "629237942", "text": "import click\r\nimport json\r\n\r\nfrom pathlib import Path\r\nfrom sketchy.sketchy import LineageIndex\r\n\r\n\r\n@click.command()\r\n@click.option(\r\n '--index', '-i', type=Path, required=True,\r\n help='Path to feature index input file'\r\n)\r\n@click.option(\r\n '--drop', '-d', type=str, required=False, default=None,\r\n help='Comma separated string of columns to drop'\r\n)\r\n@click.option(\r\n '--prefix', '-p', type=Path, required=False, default=\"index\",\r\n help='Prefix for prepared feature index output files'\r\n)\r\ndef prepare(index, drop, prefix):\r\n\r\n \"\"\" Prepare a feature index file for evaluation in Rust \"\"\"\r\n\r\n idx = LineageIndex(index_file=index)\r\n\r\n idx.write(file=Path(f\"{prefix}.reference.tsv\"), idx=False, header=True)\r\n\r\n if drop is not None:\r\n if ',' in drop:\r\n drop = drop.split(',')\r\n else:\r\n drop = [drop]\r\n\r\n idx.index = idx.index.drop(columns=drop)\r\n\r\n _, index_key = idx.prepare_columns(integers=True)\r\n\r\n idx.write(file=Path(f\"{prefix}.tsv\"), idx=False, header=False)\r\n\r\n with Path(f\"{prefix}.json\").open('w') as fout:\r\n json.dump(index_key, fout, sort_keys=False)\r\n", "sub_path": "sketchy/terminal/feature/prepare/commands.py", "file_name": "commands.py", "file_ext": "py", "file_size_in_byte": 1152, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sketchy.sketchy.LineageIndex", "line_number": 25, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 27, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 39, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 41, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 42, "usage_type": "call"}, {"api_name": "click.command", "line_number": 8, "usage_type": "call"}, {"api_name": "click.option", "line_number": 9, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 10, "usage_type": "name"}, {"api_name": "click.option", "line_number": 13, "usage_type": "call"}, {"api_name": "click.option", "line_number": 17, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 18, "usage_type": "name"}]} +{"seq_id": "338849949", "text": "\"\"\"\nRegister the list of OAuth2 scopes that can be requested by third parties. This populates the Postgres collection\nreferenced by CAS when responding to authorization grant requests. The database class is minimal; the exact\nspecification for what a scope contains lives in the python module from which this collection is drawn.\n\"\"\"\n\nimport sys\nimport logging\nimport argparse\nimport csv\nimport os\n\nimport django\nfrom django.db import transaction\n\ndjango.setup()\n\nfrom scripts import utils as script_utils\n\nfrom website.app import init_app\nfrom addons.metadata.models import ERadRecordSet\nfrom admin.rdm_metadata.erad import ERAD_COLUMNS, validate_record\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef do_populate(file):\n _, filename = os.path.split(file)\n code, _ = os.path.splitext(filename)\n\n recordset = ERadRecordSet.get_or_create(code=code)\n\n with open(file, encoding='utf-8-sig') as f:\n reader = csv.DictReader(f)\n for record_num, row in enumerate(reader):\n validate_record(record_num, row)\n kenkyusha_no = row['KENKYUSHA_NO']\n kadai_id = row['KADAI_ID']\n nendo = int(row['NENDO'])\n record = recordset.get_or_create_record(kenkyusha_no, kadai_id, nendo)\n for key in ERAD_COLUMNS:\n setattr(record, key.lower(), row[key])\n record.save()\n logger.info(f'Row inserted: {kenkyusha_no}, {kadai_id}')\n recordset.save()\n\n\ndef main(files, dry=True):\n init_app(routes=False)\n with transaction.atomic():\n for file in files:\n do_populate(file)\n if dry:\n raise Exception('Abort Transaction - Dry Run')\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-d', '--dry-run', action='store_true', help='Dry run')\nparser.add_argument('files', metavar='files', type=str, nargs='+',\n help='Path of the file containing the e-Rad data')\n\nif __name__ == '__main__':\n args = parser.parse_args()\n if not args.dry_run:\n script_utils.add_file_logger(logger, __file__)\n main(args.files, dry=args.dry_run)\n", "sub_path": "scripts/register_erad_metadata.py", "file_name": "register_erad_metadata.py", "file_ext": "py", "file_size_in_byte": 2104, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.setup", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "addons.metadata.models.ERadRecordSet.get_or_create", "line_number": 32, "usage_type": "call"}, {"api_name": "addons.metadata.models.ERadRecordSet", "line_number": 32, "usage_type": "name"}, {"api_name": "csv.DictReader", "line_number": 35, "usage_type": "call"}, {"api_name": "admin.rdm_metadata.erad.validate_record", "line_number": 37, "usage_type": "call"}, {"api_name": "admin.rdm_metadata.erad.ERAD_COLUMNS", "line_number": 42, "usage_type": "name"}, {"api_name": "website.app.init_app", "line_number": 50, "usage_type": "call"}, {"api_name": "django.db.transaction.atomic", "line_number": 51, "usage_type": "call"}, {"api_name": "django.db.transaction", "line_number": 51, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 58, "usage_type": "call"}, {"api_name": "scripts.utils.add_file_logger", "line_number": 66, "usage_type": "call"}, {"api_name": "scripts.utils", "line_number": 66, "usage_type": "name"}]} +{"seq_id": "370412313", "text": "import direct.directbase.DirectStart\nfrom direct.showbase.ShowBase import ShowBase\nfrom panda3d.core import Texture\nfrom panda3d.core import BillboardEffect\nfrom panda3d.core import Camera\nfrom panda3d.core import TextNode\nfrom direct.gui.OnscreenText import OnscreenText\nfrom direct.showbase.DirectObject import DirectObject\nfrom direct.task.Task import Task\nfrom pandac.PandaModules import *\nfrom direct.directbase import DirectStart\nimport sys\nfrom panda3d.core import loadPrcFileData\n\n#classes for procedural generation of shapes and textures\nfrom shapeGenerator import *\nfrom makeTextures import *\n\nclass Hallway(ShowBase):\n def __init__(self):\n base.setBackgroundColor(0, 0, 0) #Set the background color\n \n #Set up the keyboard input for movement\n #We use self.keyMap to maintain the state of each key. \n #This allows multiple movement keys to be pressed at the same time.\n self.keyMap = {\"w\":0, \"s\":0, \"a\":0, \"d\":0} \n self.accept('w', self.setKey, [\"w\",1])\n self.accept('s', self.setKey, [\"s\",1])\n self.accept('a', self.setKey, [\"a\",1])\n self.accept('d', self.setKey, [\"d\",1])\n self.accept('w-up', self.setKey, [\"w\",0])\n self.accept('s-up', self.setKey, [\"s\",0])\n self.accept('a-up', self.setKey, [\"a\",0])\n self.accept('d-up', self.setKey, [\"d\",0])\n \n #normal keys with no special hold down / release properties\n self.accept('escape', sys.exit) #Escape quits\n self.accept('r', self.triggerReward) #R gives a reward\n \n self.travelDirection = 0 #where the camera is currently pointing\n self.maxSpeed = 1000 #how fast we can move, pixels / second\n self.guardRails = True #Prevents the animal from leaving the arena\n \n #When we turn, we're still moving forward, just like a car.\n self.maxTurnRate = 40 #degrees per second\n \n #Set up a recurring task to process input and perform movement accordingly\n taskMgr.add(self.updateTask, \"update\")\n \n #add some lighting\n self.setUpLights()\n \n #generate the room and the goal\n self.roomLength=9000\n self.roomWidth=3000\n self.roomHeight=1000\n self.cubeRoom = CubeRoom(self.roomWidth,self.roomLength,self.roomHeight)\n self.cubeRoom.setPos(0,0,0)\n self.goal = Cube(self.roomWidth/3,self.roomWidth,self.roomHeight)\n self.goal.setPos(0,8500,0)\n \n #generate the textures\n makeTextures.makeGratingTexture(self.roomWidth,self.roomHeight,50,45,'c:/python-projects/panda3D/goalTexture.jpg')\n #makeTextures.makeDotTexture(850,500,25,50,'c:/python-projects/panda3D/wallTexture.jpg')\n \n #load the texture\n self.cubeRoomTexture = loader.loadTexture(\"textures-104.jpg\")\n self.cubeRoom.setTexture(self.cubeRoomTexture, 1) #Set the texture\n self.cubeRoom.reparentTo(render) #Parent to render\n self.goalTexture = loader.loadTexture(\"goalTexture.jpg\")\n self.goal.setTexture(self.goalTexture, 1) #Set the texture\n self.goal.reparentTo(render) #Parent to render\n \n #make a camera\n self.initCameraX = self.roomWidth/2\n self.initCameraY = 0\n self.initCameraZ = self.roomHeight/2\n self.initCamera()\n base.disableMouse() #necessary to allow programmatic camera control\n base.camLens.setFar(2147483647) #eliminate the problem of draw distance\n \n def initCamera(self):\n base.camera.setPos(self.initCameraX,self.initCameraY,self.initCameraZ)\n base.camera.setHpr(0,0,0)\n\n def setUpLights(self):\n # We're just using ambient light for now. \n # Lighting up particular surfaces might be interesting to explore at some point -- wonder what\n # that would do in cortex?\n parent = base.camera\n alight = AmbientLight('alight')\n alight.setColor(VBase4(0.9, 0.9, 0.9, 1))\n alnp = parent.attachNewNode(alight)\n render.setLight(alnp)\n\n def setKey(self, key, value):\n self.keyMap[key] = value\n\n def triggerReward(self):\n pass\n \n def fail(self):\n self.initCamera() #put the camera back into its start position\n \n def succeed(self):\n self.initCamera() #put the camera back into its start position\n \n def updateTask(self, task):\n timeDelta = globalClock.getDt() #how much time has passed since the last frame was rendered?\n travelDist = self.maxSpeed*timeDelta #move according to the time that has passed\n \n #check if we're headed off the track, fix it if guardrails are active, fail trial if not.\n movementDone = False\n if base.camera.getX() > self.roomWidth and self.guardRails: \n base.camera.setX(base.camera, -travelDist)\n movementDone = True\n if base.camera.getX() < 0 and self.guardRails: \n base.camera.setX(base.camera, travelDist)\n movementDone = True\n \n #Movement is done relative to the camera's current position. \n #Heading updates first, then movement is performed in the heading direction. \n if not movementDone:\n if self.keyMap['w']==1 and self.keyMap['a']==1:\n base.camera.setHpr(base.camera, self.maxTurnRate*timeDelta/2, 0, 0)\n base.camera.setY(base.camera, travelDist)\n elif self.keyMap['w']==1 and self.keyMap['d']==1:\n base.camera.setHpr(base.camera, -self.maxTurnRate*timeDelta/2, 0, 0)\n base.camera.setY(base.camera, travelDist)\n elif self.keyMap['w']==1:\n base.camera.setY(base.camera, travelDist)\n elif self.keyMap['s']==1 and self.keyMap['a']==1:\n base.camera.setHpr(base.camera, self.maxTurnRate*timeDelta/2, 0, 0)\n base.camera.setY(base.camera, -travelDist)\n elif self.keyMap['s']==1 and self.keyMap['d']==1:\n base.camera.setHpr(base.camera, -self.maxTurnRate*timeDelta/2, 0, 0)\n base.camera.setY(base.camera, -travelDist)\n elif self.keyMap['s']==1:\n base.camera.setY(base.camera, -travelDist)\n elif self.keyMap['a']==1:\n base.camera.setHpr(base.camera, self.maxTurnRate*timeDelta, 0, 0)\n base.camera.setY(base.camera, travelDist)\n elif self.keyMap['d']==1:\n base.camera.setHpr(base.camera, -self.maxTurnRate*timeDelta, 0, 0)\n base.camera.setY(base.camera, travelDist)\n \n #We also need to alter the texture of the goal so that its bars remain consistent size\n #as we move closer to them.\n self.goal.setTexOffset(TextureStage.getDefault(), base.camera.getY()/self.roomLength, base.camera.getY()/self.roomLength)\n self.goal.setTexScale(TextureStage.getDefault(), base.camera.getY()/self.roomLength, base.camera.getY()/self.roomLength)\n return Task.cont\n \n#you definitely want to run in fullscreen, otherwise you get some nasty frame drops.\nfullscreen = False\nif fullscreen:\n wp = WindowProperties()\n wp.setSize(1920, 1200)\n wp.setFullscreen(True) #will fail if setSize is the wrong size for the screen!\n\nw = Hallway() #Create an instance of our class\nrun() #Run the world", "sub_path": "cantrips/panda3D/hallway.py", "file_name": "hallway.py", "file_ext": "py", "file_size_in_byte": 6847, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "direct.showbase.ShowBase.ShowBase", "line_number": 19, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 37, "usage_type": "attribute"}, {"api_name": "makeTextures.makeGratingTexture", "line_number": 63, "usage_type": "call"}, {"api_name": "direct.task.Task.Task.cont", "line_number": 151, "usage_type": "attribute"}, {"api_name": "direct.task.Task.Task", "line_number": 151, "usage_type": "name"}]} +{"seq_id": "306126455", "text": "import collections\n\n\nclass Solution:\n def minReorder(self, n, cs):\n\n self.ans = 0\n self.visited = set([0])\n self.path = collections.defaultdict(list)\n self.rev = collections.defaultdict(list)\n\n for u, v in cs:\n self.path[u].append(v)\n self.rev[v].append(u)\n\n def dfs(cur):\n\n for adj in self.path[cur]:\n if adj not in self.visited:\n self.ans += 1\n self.visited.add(adj)\n dfs(adj)\n\n for adj in self.rev[cur]:\n if adj not in self.visited:\n self.visited.add(adj)\n dfs(adj)\n\n dfs(0)\n return self.ans\n", "sub_path": "Python/reorder-routes-to-make-all-paths-lead-to-the-city-zero.py", "file_name": "reorder-routes-to-make-all-paths-lead-to-the-city-zero.py", "file_ext": "py", "file_size_in_byte": 723, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "collections.defaultdict", "line_number": 9, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "156108869", "text": "'''\nCreated on Mar 21, 2017\n\n@author: Ford\n'''\nimport discord\nimport time\nimport asyncio\nimport re\nfrom urllib.parse import urlparse\nimport pytz\nimport twitter\n\nLOOKING_STRING = 'Type **!looking** to add yourself to the list.\\nThen, type **!found** to remove yourself from the list.\\n\\nThe following people are looking for {}:\\n{}'\n\nSTATUS_ORDER = [discord.Status.online, discord.Status.idle, discord.Status.dnd, discord.Status.invisible, discord.Status.offline]\n\nclass Region:\n def __init__(self, name, representative_timezones, central_timezone, populous_timezone, active):\n self.name = name\n self.representative_timezones = representative_timezones\n self.central_timezone = central_timezone\n self.populous_timezone = populous_timezone\n self.active = active\n \n def __eq__(self, other):\n return self.name == other.name\n \n def __ne__(self, other):\n return not self == other\n\nREGIONS = {'na': Region('na', [pytz.timezone('US/Pacific'), pytz.timezone('US/Eastern')], pytz.timezone('US/Central'), pytz.timezone('US/Eastern'), True)}\n #'eu': Region('eu', [pytz.timezone('Europe/London'), pytz.timezone('Europe/Rome')], pytz.timezone('Europe/London'), pytz.timezone('Europe/Rome'), True)}\n \nclass Command:\n \"\"\"\n \"\"\"\n \n def __init__(self, name=None, parameterString=None, helpString=None, exactArg=False, permissionLevel=0, function=None, channelName=None, privateMessage=False):\n self.name = name\n self.parameterString = parameterString\n self.helpString = helpString\n self.exactArg = exactArg\n self.permissionLevel = permissionLevel\n self.lock = 0\n self.function = function\n self.channelName = channelName\n self.privateMessage = privateMessage\n \n @property\n def nameAndParams(self):\n __ret = self.name\n if self.parameterString:\n __ret += ' ' + self.parameterString\n return __ret\n \n def check_channel(self, channel):\n return channel is not None and (channel.type == discord.ChannelType.private or (not self.privateMessage and (not self.channelName or channel.name == self.channelName)))\n \n def has_permission(self, user, channel):\n return channel is not None and get_user_permission_level(user) >= self.lock and get_user_permission_level(user) >= self.permissionLevel\n \n def get_symbol(self):\n if self.permissionLevel > 1:\n return '•'\n elif self.permissionLevel > 0:\n return '◦'\n else:\n return '·'\n \n def execute(self):\n ''\n \nclass LookingChannel:\n \"\"\"\n \"\"\"\n \n def __init__(self, channel, client, filename, specString, timeout):\n self.channel = channel\n self.client = client\n self.filename = filename\n self.specString = specString\n self.timeout = timeout\n self.playerDict = {}\n \n @classmethod\n async def load_channel(cls, client, server, channelName, filename, specString, timeout):\n tempChan = discord.utils.find(lambda m: m.name.lower() == channelName, server.channels)\n if not tempChan:\n return cls(await client.create_channel(server, channelName), client, filename, specString, timeout)\n else:\n __obj = cls(tempChan, client, filename, specString, timeout)\n await __obj.load_people()\n await __obj.reset_message()\n return __obj\n \n async def load_people(self):\n try:\n with open(self.filename, 'r', encoding='utf-8') as f:\n fileContents = f.read().strip()\n if fileContents:\n for line in fileContents.split('\\n'):\n # Split off the time only, leaving names with spaces in them\n vals = line.strip().rsplit(' ', 1)\n await self.add_person(vals[0], float(vals[1]) if not vals[1] == 'None' else None, shouldWrite=False)\n except IOError:\n open(self.filename, 'w', encoding='utf-8').close()\n \n async def reset_message(self):\n await self.remove_message()\n await self.send_message()\n \n async def remove_message(self):\n async for m in self.client.logs_from(self.channel):\n if m.author == self.client.user:\n await self.client.delete_message(m)\n break\n \n async def send_message(self):\n await self.client.send_message(self.channel, LOOKING_STRING.format(self.specString, '\\n'.join('**{}**'.format(p) for p in self.playerDict.keys())))\n \n async def clear(self):\n self.playerDict.clear()\n clear_file(self.filename)\n await self.reset_message()\n\n async def add_person(self, name, timeToRemove=None, shouldWrite=True):\n if name not in self.playerDict.keys():\n self.playerDict[name] = timeToRemove\n if timeToRemove is not None:\n self.client.loop.create_task(self.remove_person_at_time(name, timeToRemove))\n if shouldWrite:\n self.write_players()\n \n async def remove_person_at_time(self, name, timeToRemove):\n curTime = time.time()\n # Sleep until timeToRemove\n if timeToRemove - curTime > 0:\n await asyncio.sleep(timeToRemove - curTime)\n if not self.client.is_closed:\n if await self.remove_person(name):\n await self.remove_message()\n await self.client.send_message(self.channel, '{} You have been automatically timed-out of the list.'.format(get_user_by_name(self.channel.server, name).mention))\n await self.send_message()\n \n async def remove_person(self, name, shouldWrite=True):\n if name in self.playerDict.keys():\n del self.playerDict[name]\n if shouldWrite:\n self.write_players()\n return True\n return False\n \n def write_players(self):\n write_file(self.filename, '\\n'.join(k + ' ' + str(v) for k,v in self.playerDict.items()))\n \n def __str__(self):\n return self.channel.name if self.channel is not None else None\n \nclass GeneralTeamChannels:\n def __init__(self, client, server):\n self.client = client\n self.server = server\n self.channelSets = {}\n \n @classmethod\n async def load_channels(cls, client, server, *args):\n __obj = cls(client, server)\n for chan in server.channels:\n if chan.type == discord.ChannelType.voice:\n num = __obj._channel_num_from_name(chan.name)\n if num is not None:\n if num not in __obj.channelSets:\n __obj.channelSets[num] = []\n __obj.channelSets[num].append(chan)\n \n for num in args:\n if num not in __obj.channelSets:\n __obj.channelSets[num] = []\n await __obj.add_channel_to_set(num=num)\n else:\n await __obj.nerf_channel_set(num=num)\n await __obj.buff_channel_set(num=num)\n \n return __obj\n \n async def add_channel_to_set(self, name=None, num=None):\n if num is None:\n num = self._channel_num_from_name(name)\n if name is None:\n name = self._channel_name_from_num(num)\n \n if num is not None and name is not None:\n voiceChannels = sorted((chan for chan in self.server.channels if chan.type == discord.ChannelType.voice), key=lambda c: c.position)\n try:\n newPos = voiceChannels.index(max([chan for chan in voiceChannels if chan.name == name], default=None, key=lambda c: c.position)) + 1\n except ValueError:\n newPos = len(voiceChannels)\n tempChan = await self.client.create_channel(self.server, name, type=discord.ChannelType.voice)\n await self.client.edit_channel(tempChan, user_limit=num)\n await self.client.move_channel(tempChan, max(0, min(newPos, len([chan for chan in self.server.channels if chan.type == discord.ChannelType.voice]) - 1)))\n # Because channel objects upon creation do not contain a voice_member list\n self.channelSets[num].append(discord.utils.get(self.server.channels, id=tempChan.id))\n \n async def nerf_channel_set(self, name=None, num=None):\n if num is None:\n num = self._channel_num_from_name(name)\n if name is None:\n name = self._channel_name_from_num(num)\n \n if num is not None and name is not None:\n deletableChannels = [chan for chan in self.channelSets[num] if len(chan.voice_members) == 0]\n self.channelSets[num] = [chan for chan in self.channelSets[num] if len(chan.voice_members) > 0]\n if len(deletableChannels) > 0:\n self.channelSets[num].append(deletableChannels[0])\n deletableChannels = deletableChannels[1:]\n else:\n await self.add_channel_to_set(name=name)\n \n for chan in deletableChannels:\n await self.client.delete_channel(chan)\n \n async def buff_channel_set(self, name=None, num=None):\n if num is None:\n num = self._channel_num_from_name(name)\n if name is None:\n name = self._channel_name_from_num(num)\n \n if num is not None and name is not None:\n if all(len(chan.voice_members) > 0 for chan in self.channelSets[num]):\n await self.add_channel_to_set(name=name)\n \n @staticmethod\n def match_name(name):\n return re.match(r'\\d+v\\d+$', name)\n \n def _channel_num_from_name(self, name):\n nums = name.split('v')\n if len(nums) == 2 and nums[0] == nums[1]:\n try:\n num = int(nums[0])\n if num > 0 and num < 100:\n return num\n else:\n return None \n except ValueError:\n return None\n return None\n \n def _channel_name_from_num(self, num):\n if num and num > 0 and num < 100:\n return '{n}v{n}'.format(n=num)\n else:\n return None\n \nclass SmashLinkQueue:\n \"\"\"\n \"\"\"\n \n def __init__(self, client, server, filename, message=None):\n self.client = client\n self.server = server\n self.filename = filename\n self.message = message\n self.queue = []\n \n @classmethod\n async def load_queue(cls, client, server, filename):\n _obj = cls(client, server, filename)\n await _obj.load_people()\n return _obj\n \n async def load_people(self):\n try:\n with open(self.filename, 'r', encoding='utf-8') as f:\n fileContents = f.read().strip()\n if fileContents:\n for line in fileContents.split('\\n'):\n vals = line.strip().split(' ', 1)\n await self.add_person(vals[0], vals[1])\n except IOError:\n open(self.filename, 'w', encoding='utf-8').close()\n \n async def reset_message(self):\n if self.message is None:\n modChan = get_text_channel_by_name(self.server, 'supersecretclub')\n if modChan is None:\n await self.create_channel(self.server, 'supersecretclub', (self.server.default_role, discord.PermissionOverwrite(read_messages=False)))\n else:\n modChan = self.message.channel\n try:\n await self.client.delete_message(self.message)\n except:\n ''\n \n mess = None\n for discID, smashName in self.queue:\n user = get_user_by_id(self.server, discID)\n if user is not None:\n if mess is None:\n mess = ' {:>30} | {}\\n'.format('Discord Name/Nickname', 'Smash name')\n mess += 'CONFIRM OR DENY -> {:>30} | {}\\n'.format(user.nick if user.nick else user.name, smashName)\n else:\n mess += ' {:>30} | {}\\n'.format(user.nick if user.nick else user.name, smashName)\n else:\n if mess is None:\n mess = ' {:>30} | {}\\n'.format('Discord Name/Nickname', 'Smash name')\n mess += 'CONFIRM OR DENY -> {:>30} | {}\\n'.format('ERROR: COULD NOT FIND USER', smashName)\n else:\n mess += ' {:>30} | {}\\n'.format('ERROR: COULD NOT FIND USER', smashName)\n \n if mess is None:\n mess = 'There are no more people in the discord-smash link queue.'\n self.message = await self.client.send_message(modChan, '```{}```'.format(mess))\n \n async def add_person(self, discordID, smashName):\n self.queue.append((discordID, smashName))\n write_file(self.filename, '\\n'.join(tup[0] + ' ' + tup[1] for tup in self.queue))\n await self.reset_message()\n \n async def pop_person(self):\n if len(self.queue) > 0:\n ret = self.queue.pop(0)\n write_file(self.filename, '\\n'.join(tup[0] + ' ' + tup[1] for tup in self.queue))\n await self.reset_message()\n return ret\n else:\n return None\n \n def __len__(self):\n return len(self.queue)\n \n def __contains__(self, item):\n return item in [tup[1] for tup in self.queue]\n \ndef write_file(filename, contents):\n \"\"\"Write a string to a file.\n \n Removes all non-utf8 characters before write.\n \n Args:\n filename (string): Name of the file to write to.\n contents (string): String to write to the file.\n \"\"\"\n with open(filename, 'w', encoding='utf-8') as f:\n f.write(contents)\n \ndef clear_file(filename):\n \"\"\"Clear the contents of a file.\n \n Args:\n filename (string): Name of the file to clear.\n \"\"\"\n open(filename, 'w', encoding='utf-8').close()\n \ndef get_real_name(server, inputName):\n \"\"\"Determine the correctly capitalized name or nickname of a member given a case-\n insensitive name or nickname.\n \n In case of duplicate names/nicknames, the first match found is chosen.\n \n Args:\n inputName (string): The case-insensitive name or nickname of the member.\n \n Returns:\n string: Correctly capitalized name or nickname of the member. If the member\n has a nickname, the nickname is returned. If a member with the given name or\n nickname does not exist, an empty string is returned.\n \"\"\"\n for member in server.members:\n if inputName.lower() == member.name.lower() or (member.nick and inputName.lower() == member.nick.lower()):\n return member.nick if member.nick else member.name\n return ''\n \ndef get_user_by_name(server, name):\n \"\"\"Determine the member that matches the given name or nickname.\n \n In case of duplicate names/nicknames, the first match found is chosen.\n \n Args:\n name (string): Name or nickname of the member to find (case-insensitive).\n \n Returns:\n discord.Member: Member that matches the given name or nickname. If no member\n matches, None is returned.\n \"\"\"\n return discord.utils.find(lambda m: name.lower() == m.name.lower() or (m.nick and name.lower() == m.nick.lower()), server.members)\n \ndef get_user_by_id(server, id_):\n return discord.utils.get(server.members, id=id_)\n\ndef get_text_channel_by_name(server, name):\n \"\"\"Determine the channel that matches the given name.\n \n In case of duplicate names, the first match found is chosen.\n \n Args:\n server (Discord.Server): server to search\n name (string): Name of the channel to find (case-insensitive).\n \n Returns:\n discord.Channel: Channel that matches the given name.\n \"\"\"\n return discord.utils.find(lambda c: c.name == name and c.type == discord.ChannelType.text, server.channels)\n\ndef get_text_channel_by_id(server, id_):\n return discord.utils.get(server.channels, id=id_)\n \ndef num_string(num, st):\n \"\"\"Create a string that pluralizes the given word naively according to the given\n number.\n \n Args:\n num (int): The number to use.\n this function.\n st (string): The string to pluralize or not.\n \n Returns:\n string: The given number in bold, followed by given string, pluralized naively\n according to the number.\n \"\"\"\n return '**{}** {}'.format(num, st) + ('s' if num-1 else '')\n\ndef list_string(items):\n \"\"\"Create a string that lists elements in a list.\n \n Args:\n items (list): Python list of items.\n \n Returns:\n string: Grammatical list of items, separated by commas and a final 'and'.\n \"\"\"\n if len(items) == 0:\n return ''\n elif len(items) == 1:\n return items[0]\n elif len(items) == 2:\n return items[0] + ' and ' + items[1]\n else:\n return ', '.join(items[:-1]) + ', and ' + items[-1]\n \ndef write_prizepool(regions, prizeNamesList, prizepool):\n \"\"\"Write the prize pool to a file.\n \n The is separated because the names of prize pool entrants need to be recalculated\n each time. The first line of the file is always the list of prize names separated\n by spaces. Lines with just a region name indicate the region of the entrants below.\n Each other line begins with the entrant name, followed by the number of each prize\n they contributed.\n \"\"\"\n writeString = ' '.join(prizeNamesList)\n for region in regions:\n names = set(tup[1] for tup in prizepool if tup[0] == region) \n writeString += '\\n' + region\n for name in names:\n writeString += '\\n' + name + ' ' + ' '.join(str(prizepool[(region, name, prizeName)]) for prizeName in prizeNamesList)\n write_file('prizepool.txt', writeString)\n \ndef distribute_prizes(available, weights):\n \"\"\"Distribute list of items according to the given weights.\n \n Args:\n available (int): Prize amount.\n weights (int list): List of weights.\n \n Returns:\n int list: The distributed amounts.\n \"\"\"\n distributedAmounts = []\n totalWeights = sum(weights)\n for weight in weights:\n weight = float(weight)\n # Multiplier use to calculate a distributed amount. Avoids division by 0.\n p = (weight / totalWeights) if totalWeights else 0\n distributedAmount = round(p * available)\n distributedAmounts.append(distributedAmount)\n totalWeights -= weight\n available -= distributedAmount\n return distributedAmounts\n\ndef create_table_string(matrix):\n # Calculate max widths for each column, taking into account double truncation\n columnWidths = [max(len('{num:{formatter}}'.format(num=cell, formatter=('.3f' if type(cell) is float else ''))) for cell in column) for column in zip(*matrix)]\n # Create header with the first row and add a solid line under it\n header = ' │ '.join('{num:{width}}'.format(num=matrix[0][i], width=columnWidths[i]) for i in range(0, len(matrix[0]))) + '\\n'\n header += '———'.join('—' * width for width in columnWidths)\n # Create table string, truncating all doubles at 3 places\n return '```{}\\n{}```'.format(header, '\\n'.join(' │ '.join('{num:{width}{formatter}}'.format(num=row[i], width=columnWidths[i], formatter=('.3f' if type(row[i]) is float else '')) for i in range(0, len(row))) for row in matrix[1:]))\n\ndef num_urls(s):\n return len(re.findall(r'\\bhttps?://.*\\b', s))\n\ndef remove_url(s, count=0):\n return re.sub(r'\\bhttps?://.*\\b', '', s, count=count)\n \ndef get_region_time_string(timeObj, region, day=False, time=True, short=False):\n dayStr = ''\n if short:\n try: # assume not naive\n zonedTime = timeObj.astimezone(region.populous_timezone)\n except ValueError: # naive\n zonedTime = region.populous_timezone.localize(timeObj)\n if day:\n dayStr = '{ct:%A}, {ct:%m}/{ct:%d}'.format(ct=zonedTime)\n if time:\n dayStr += ' @ '\n if time:\n dayStr += get_time_string(zonedTime)\n else:\n try: # assume not naive\n zonedTimes = [timeObj.astimezone(zone) for zone in region.representative_timezones]\n except ValueError: # naive\n zonedTimes = [zone.localize(timeObj) for zone in region.representative_timezones]\n if day:\n dayStr = '{pt:%A}, {pt:%B} {pt:%d}'.format(pt=zonedTimes[len(zonedTimes) // 2])\n if time:\n dayStr += ' at '\n if time:\n dayStr += ' / '.join(get_time_string(time) for time in zonedTimes)\n return dayStr\n\ndef get_time_string(localized):\n return '{hour}:{lt:%M} {lt:%p} {lt:%Z}'.format(lt=localized, hour=((localized.hour - 1) % 12 + 1))\n \ndef is_media_site(urlString):\n \"\"\"Check if a link is to an accepted media website. This includes direct links to\n gfycat, imgur, youtube, and twitch clips.\n \n Args:\n urlstring (string): the url to check\n \n Returns:\n boolean: Whether the url is a link to an acceptable media site\n \"\"\"\n url = urlparse(urlString)\n return (url.hostname == 'gfycat.com' and re.match(r'/[^/]+$', url[2])) or (url.hostname == 'i.imgur.com' and re.match(r'/[^/]+.gifv$', url[2])) or (url.hostname == 'www.youtube.com' and re.match(r'/watch$', url[2])) or (url.hostname == 'clips.twitch.tv' and re.match(r'/[^/]+$', url[2]))\n \nasync def send_long_message(client, channel, message):\n # Split the return message into multiple messages if it's\n # longer than 2000 characters so that Discord can handle it\n\n subLines = message.split('\\n')\n substrs = []\n \n # Ensure no string in substrs is longer than 2000 characters\n temp = ''\n for line in subLines:\n if len(temp) + len(line) < 1994:\n temp = '\\n'.join([temp, line])\n else:\n if temp.count('```') % 2 == 1:\n temp += '```'\n substrs.append(temp)\n temp = '```' + line\n else:\n substrs.append(temp)\n temp = line\n \n if temp.count('```') % 2 == 1:\n temp += '```'\n substrs.append(temp)\n \n for s in substrs:\n await client.send_message(channel, s)\n\ndef compare_status(status1, status2):\n return STATUS_ORDER.index(status1) - STATUS_ORDER.index(status2)\n \ndef cmp_to_key(mycmp):\n 'Convert a cmp= function into a key= function'\n class K(object):\n def __init__(self, obj, *args):\n self.obj = obj\n def __lt__(self, other):\n return mycmp(self.obj, other.obj) < 0\n def __gt__(self, other):\n return mycmp(self.obj, other.obj) > 0\n def __eq__(self, other):\n return mycmp(self.obj, other.obj) == 0\n def __le__(self, other):\n return mycmp(self.obj, other.obj) <= 0 \n def __ge__(self, other):\n return mycmp(self.obj, other.obj) >= 0\n def __ne__(self, other):\n return mycmp(self.obj, other.obj) != 0\n return K\n\ndef get_user_permission_level(user):\n try:\n if user.top_role.permissions.administrator:\n return 2\n elif user.top_role.permissions.manage_messages:\n return 1\n else:\n return 0\n except AttributeError:\n return 0\n \ndef is_partner(user):\n try:\n for role in user.roles:\n if role.name.lower() == 'partner':\n return True\n return False\n except AttributeError:\n return False\n \ndef get_copy_string_from_embed(embed, half_suppress=False):\n ret = '\\\\*\\\\*\\\\*{}\\\\*\\\\*\\\\* Presents:'.format(embed.author.name)\n ret += '\\n\\n\\\\*\\\\*{}\\\\*\\\\*\\n{}'.format(embed.title, embed.description)\n for field in embed.fields:\n ret += '\\n\\n\\\\_\\\\_\\\\*\\\\*{}\\\\*\\\\*\\\\_\\\\_\\n'.format(field.name)\n pat = re.compile(r'\\[(.+?)\\]\\((.+?)\\)')\n if half_suppress:\n ret += pat.sub(r'\\*\\1\\*: \\<<\\2>', field.value)\n else:\n ret += pat.sub(r'\\*\\1\\*: <\\2>', field.value)\n return ret\n\ndef rreplace(s, old, new):\n return new.join(s.rsplit(old, 1))\n\ndef create_twitter_obj():\n keysDict = {}\n with open('twitter.ini', 'r', encoding='utf-8') as f:\n line = f.readline()\n while line:\n vals = [val.strip() for val in line.split('=', 1)]\n if len(vals) == 2 and len(vals[0]) > 0 and len(vals[1]) > 0:\n keysDict[vals[0]] = vals[1]\n line = f.readline()\n for attr in ('consumer_key', 'consumer_secret', 'access_token_key', 'access_token_secret'):\n if not attr in keysDict:\n raise Exception('The twitter.ini file does not contain the required fields (consumer_key, consumer_secret, access_token_key, access_token_secret).')\n \n return twitter.Api(**keysDict)\n\n", "sub_path": "src/hoopstournamentbot/bot.py", "file_name": "bot.py", "file_ext": "py", "file_size_in_byte": 25070, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "discord.Status", "line_number": 16, "usage_type": "attribute"}, {"api_name": "pytz.timezone", "line_number": 32, "usage_type": "call"}, {"api_name": "discord.ChannelType", "line_number": 58, "usage_type": "attribute"}, {"api_name": "discord.utils.find", "line_number": 88, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 88, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 136, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 139, "usage_type": "call"}, {"api_name": "discord.ChannelType", "line_number": 170, "usage_type": "attribute"}, {"api_name": "discord.ChannelType", "line_number": 194, "usage_type": "attribute"}, {"api_name": "discord.ChannelType", "line_number": 199, "usage_type": "attribute"}, {"api_name": "discord.ChannelType", "line_number": 201, "usage_type": "attribute"}, {"api_name": "discord.utils.get", "line_number": 203, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 203, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 235, "usage_type": "call"}, {"api_name": "discord.PermissionOverwrite", "line_number": 288, "usage_type": "call"}, {"api_name": "discord.utils.find", "line_number": 387, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 387, "usage_type": "attribute"}, {"api_name": "discord.utils.get", "line_number": 390, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 390, "usage_type": "attribute"}, {"api_name": "discord.utils.find", "line_number": 404, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 404, "usage_type": "attribute"}, {"api_name": "discord.ChannelType", "line_number": 404, "usage_type": "attribute"}, {"api_name": "discord.utils.get", "line_number": 407, "usage_type": "call"}, {"api_name": "discord.utils", "line_number": 407, "usage_type": "attribute"}, {"api_name": "re.findall", "line_number": 491, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 494, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 535, "usage_type": "call"}, {"api_name": "re.match", "line_number": 536, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 613, "usage_type": "call"}, {"api_name": "twitter.Api", "line_number": 636, "usage_type": "call"}]} +{"seq_id": "212084918", "text": "import argparse\nimport os\n\nimport keras.backend as K\nimport numpy as np\nimport pandas as pd\nfrom keras.callbacks import TensorBoard, ModelCheckpoint\nfrom keras.layers import Activation, Add, BatchNormalization, Conv1D, Dense, Dropout, Input, Lambda, \\\n RepeatVector, Permute, Multiply, Concatenate\nfrom keras.models import Model\nfrom keras.optimizers import Adam, RMSprop\nfrom keras.preprocessing.sequence import pad_sequences\nfrom sklearn.metrics import roc_auc_score\n\nfrom riken.protein_io import data_op\nfrom riken.protein_io import prot_features\nfrom riken.rnn.rnn_keras_with_psiblast import get_embeddings, transfer_model, get_all_features\n\n\"\"\"\n\"\"\"\nRANDOM_STATE = 42\nPARAMS = {\n 'depth': 8,\n 'n_filters': 25,\n 'kernel_size': 7,\n 'dropout_rate': 0.5,\n 'optim': Adam(),\n 'nb_epochs': 100,\n 'batch_size': 64,\n}\n\n\ndef residual_block(inp, dilatation, kernel_size, n_filters, dropout_rate, activation,\n kernel_initializer, do1conv=True):\n conv = inp\n for _ in range(2):\n conv = Conv1D(n_filters, kernel_size=kernel_size, dilation_rate=dilatation, padding='causal',\n activation=activation, kernel_initializer=kernel_initializer)(conv)\n # here do weight norm (later)\n # instead here use of batch norm because already implemented\n conv = BatchNormalization()(conv)\n conv = Activation(activation='relu')(conv)\n conv = Dropout(rate=dropout_rate)(conv)\n\n if do1conv:\n rescaled_input = Conv1D(n_filters, kernel_size=1)(inp)\n else:\n rescaled_input = inp\n\n last = Add()([conv, rescaled_input])\n return last\n\n\ndef tcn_model(n_classes, depth, n_filters, kernel_size, dropout_rate=0.0, optim=Adam(), \n maxlen=500, activation='relu', kernel_initializer='glorot_uniform',\n trainable_embeddings=False):\n aa_ind = Input(shape=(maxlen,), name='aa_indice')\n h = get_embeddings(aa_ind, trainable_embeddings=trainable_embeddings)\n\n psiblast_prop = Input(shape=(maxlen, 42), name='psiblast_prop', dtype=np.float32)\n\n h = Concatenate()([h, psiblast_prop])\n for it in range(depth):\n # 1conv done only first layer (elsewhere number of filters stays the same\n do1conv = (it == 0)\n h = residual_block(h, dilatation=2**it, n_filters=n_filters, kernel_size=kernel_size,\n dropout_rate=dropout_rate, activation=activation,\n kernel_initializer=kernel_initializer, do1conv=do1conv)\n attention = Dense(1)(h)\n attention = Lambda(lambda x: K.squeeze(x, axis=2))(attention)\n attention = Activation(activation='softmax')(attention)\n attention = RepeatVector(n_filters)(attention)\n attention = Permute((2, 1))(attention)\n last = Multiply()([attention, h])\n last = Lambda(lambda x: K.sum(x, axis=1), output_shape=(n_filters,))(last)\n\n h = Dense(n_classes, activation='softmax')(last)\n mdl = Model(inputs=[aa_ind, psiblast_prop], outputs=h)\n mdl.compile(loss='categorical_crossentropy',\n optimizer=optim,\n metrics=['accuracy'])\n\n return mdl\n\n\ndef parse_arguments():\n parser = argparse.ArgumentParser()\n parser.add_argument('-max_len', type=int, default=1000, help='max sequence lenght')\n parser.add_argument('-data_path', type=str, help='path to tsv data')\n parser.add_argument('-index_col', type=int, default=0,\n help='path to ckpt if doing transfer learning')\n parser.add_argument('-key_to_predict', type=str, help='key to predict (y)')\n parser.add_argument('-log_dir', type=str, help='path to save ckpt and summaries')\n parser.add_argument('-transfer_path', type=str, default=None,\n help='path to ckpt if doing transfer learning')\n parser.add_argument('-layer_name', type=str, default=None,\n help='Name of layer to use for transfer')\n parser.add_argument('-groups', type=str, default='NO', help='should we use groups')\n parser.add_argument('-pssm_format_file', type=str, help='path format of pssm files')\n return parser.parse_args()\n\n\ndef main():\n args = parse_arguments()\n groups_mode = args.groups if args.groups != 'NO' else None\n splitter = data_op.shuffle_indices if groups_mode is None else data_op.group_shuffle_indices\n nb_epochs = PARAMS.pop('nb_epochs')\n batch_size = PARAMS.pop('batch_size')\n\n df = pd.read_csv(args.data_path, sep='\\t', index_col=args.index_col).dropna()\n df = df.loc[df.seq_len >= 50, :]\n\n sequences, y = df['sequences'].values, df[args.key_to_predict]\n y = pd.get_dummies(y).values\n X = pad_sequences([[prot_features.safe_char_to_idx(char) for char in sequence]\n for sequence in sequences], maxlen=args.max_len)\n indices = df.index.values\n if groups_mode == 'predefined':\n train_inds, test_inds = np.where(df.is_train)[0], np.where(df.is_train == False)[0]\n else:\n groups = None if groups_mode is None else df[groups_mode].values\n train_inds, test_inds = splitter(sequences, y, groups)\n print('{} train examples and {} test examples'.format(len(train_inds), len(test_inds)))\n assert len(np.intersect1d(train_inds, test_inds)) == 0\n X, pssm, y = get_all_features(X, y, indices, pssm_format_fi=args.pssm_format_file)\n Xtrain, Xtest, ytrain, ytest = X[train_inds], X[test_inds], y[train_inds], y[test_inds]\n pssm_train, pssm_test = pssm[train_inds], pssm[test_inds]\n\n if args.transfer_path is None:\n model = tcn_model(n_classes=y.shape[1], **PARAMS)\n else:\n model = transfer_model(n_classes_new=y.shape[1], mdl_path=args.transfer_path,\n prev_model_output_layer='lambda_1')\n print(model.summary())\n\n tb = TensorBoard(log_dir=args.log_dir)\n ckpt = ModelCheckpoint(\n filepath=os.path.join(args.log_dir, 'weights.{epoch:02d}-{val_loss:.2f}.hdf5'),\n verbose=1, save_best_only=False, save_weights_only=False, mode='auto',\n period=1)\n model.fit([Xtrain, pssm_train], ytrain, # bf [Xtrain, features_train], ...\n batch_size=batch_size,\n epochs=nb_epochs,\n validation_data=([Xtest, pssm_test], ytest),\n callbacks=[tb, ckpt])\n print(roc_auc_score(ytest[:, 1], model.predict(Xtest)[:, 1]))\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "riken/tcn/tcn_keras.py", "file_name": "tcn_keras.py", "file_ext": "py", "file_size_in_byte": 6359, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "keras.optimizers.Adam", "line_number": 27, "usage_type": "call"}, {"api_name": "keras.layers.Conv1D", "line_number": 37, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 43, "usage_type": "call"}, {"api_name": "keras.layers.Conv1D", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.layers.Add", "line_number": 50, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 57, "usage_type": "call"}, {"api_name": "riken.rnn.rnn_keras_with_psiblast.get_embeddings", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 60, "usage_type": "attribute"}, {"api_name": "keras.layers.Concatenate", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.layers.Lambda", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.backend.squeeze", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 70, "usage_type": "name"}, {"api_name": "keras.layers.Activation", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.layers.RepeatVector", "line_number": 72, "usage_type": "call"}, {"api_name": "keras.layers.Permute", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.layers.Multiply", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.layers.Lambda", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.backend.sum", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 75, "usage_type": "name"}, {"api_name": "keras.layers.Dense", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 78, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 87, "usage_type": "call"}, {"api_name": "riken.protein_io.data_op.shuffle_indices", "line_number": 106, "usage_type": "attribute"}, {"api_name": "riken.protein_io.data_op", "line_number": 106, "usage_type": "name"}, {"api_name": "riken.protein_io.data_op.group_shuffle_indices", "line_number": 106, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 110, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 114, "usage_type": "call"}, {"api_name": "keras.preprocessing.sequence.pad_sequences", "line_number": 115, "usage_type": "call"}, {"api_name": "riken.protein_io.prot_features.safe_char_to_idx", "line_number": 115, "usage_type": "call"}, {"api_name": "riken.protein_io.prot_features", "line_number": 115, "usage_type": "name"}, {"api_name": "numpy.where", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.intersect1d", "line_number": 124, "usage_type": "call"}, {"api_name": "riken.rnn.rnn_keras_with_psiblast.get_all_features", "line_number": 125, "usage_type": "call"}, {"api_name": "riken.rnn.rnn_keras_with_psiblast.transfer_model", "line_number": 132, "usage_type": "call"}, {"api_name": "keras.callbacks.TensorBoard", "line_number": 136, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path", "line_number": 138, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 146, "usage_type": "call"}]} +{"seq_id": "444120432", "text": "from enum import Enum\nfrom typing import List, Union, Optional\nfrom dataclasses import dataclass\nimport regex as re\nfrom icontract import require, ensure, DBC\n\n\"\"\"\nOccurred when testing parse()\nFalsifying example: execute(\n kwargs={'expression': ':'},\n)\n---------\n File \"/home/lauren/Nextcloud/Documents/2020-2021/thesis/code/aocdbc/aocdbc/day_18_operation_order.py\", line 48, in \n @ensure(lambda result, expression: serialize(result) == expression)\n File \"/home/lauren/Nextcloud/Documents/2020-2021/thesis/code/aocdbc/aocdbc/day_18_operation_order.py\", line 83, in serialize\n if isinstance(node.head, int):\nAttributeError: 'NoneType' object has no attribute 'head'\n\"\"\"\n\n\nNUMBER_RE = re.compile(r'^(\\d+)')\n\n\nclass Operation(Enum):\n ADD = '+'\n MUL = '*'\n\n\n@dataclass\nclass Tail:\n op: Operation\n right: Union[int, 'Node']\n\n\n@dataclass\nclass Node:\n head: Union[int, 'Node']\n tail: Optional[List[Tail]]\n\n\n@require(lambda expr: expr.startswith('('))\n@require(lambda expr: expr.count('(') == expr.count(')'))\n@ensure(lambda expr, result: result in expr)\ndef extract_expression(expr: str) -> str:\n parenthesis_balance = 0\n result = ''\n\n for c in expr:\n if c == '(':\n parenthesis_balance += 1\n elif c == ')':\n parenthesis_balance -= 1\n\n if parenthesis_balance == 0:\n return result[1:]\n else:\n result += c\n raise Exception(\"I should never end up here!\")\n\n\n@ensure(lambda result, expression: serialize(result) == expression)\ndef parse(expression: str) -> Optional[Node]:\n if not expression:\n return None\n if expression.startswith('('):\n head_str = extract_expression(expression)\n head = parse(head_str)\n rest_expr = expression[len(head_str)+2:]\n elif NUMBER_RE.match(expression):\n head_str = NUMBER_RE.match(expression).group(1)\n head = int(head_str)\n rest_expr = expression[len(head_str):]\n else:\n raise Exception\n\n tails: List[Tail] = []\n\n while rest_expr:\n op = Operation(rest_expr[0])\n if rest_expr[1:].startswith('('):\n right_str = extract_expression(rest_expr[1:])\n right = parse(right_str)\n rest_expr = rest_expr[len(right_str)+3:]\n elif NUMBER_RE.match(rest_expr[1:]):\n right_str = NUMBER_RE.match(rest_expr[1:]).group(1)\n right = int(right_str)\n rest_expr = rest_expr[len(right_str)+1:]\n else:\n raise Exception\n tails.append(Tail(op=op, right=right))\n return Node(head=head, tail=tails)\n\n\ndef serialize(node: Node) -> str:\n result = ''\n if isinstance(node.head, int):\n result += str(node.head)\n else:\n result += \"({})\".format(serialize(node.head))\n\n if node.tail:\n for tail in node.tail:\n if isinstance(tail.right, int):\n result += \"{}{}\".format(tail.op.value, tail.right)\n else:\n result += \"{}({})\".format(tail.op.value, serialize(tail.right))\n\n return result\n\n\ndef compute(node: Node) -> int:\n if isinstance(node.head, int):\n result = node.head\n else:\n result = compute(node.head)\n\n for tail in node.tail:\n if isinstance(tail.right, int):\n right = tail.right\n else:\n right = compute(tail.right)\n\n if tail.op == Operation.ADD:\n result += right\n else:\n result *= right\n\n return result\n\n\nif __name__ == '__main__':\n e1 = '(1+2)+(3*4)' # 15\n e2 = '(1+(2*3))+4' # 11\n e3 = '1+2*3+4+6*7' # 133\n\n n1 = Node(head=Node(head=1, tail=[Tail(op=Operation.ADD, right=2)]), tail=[Tail(op=Operation.ADD, right=Node(head=3, tail=[Tail(op=Operation.MUL, right=4)]))])\n n2 = Node(head=Node(head=1, tail=[Tail(op=Operation.ADD, right=Node(head=2, tail=[Tail(op=Operation.MUL, right=3)]))]), tail=[Tail(op=Operation.ADD, right=4)])\n\n assert n1 == parse(e1)\n assert n2 == parse(e2)\n assert e1 == serialize(parse(e1))\n assert e2 == serialize(parse(e2))\n assert compute(parse(e1)) == 15\n assert compute(parse(e2)) == 11\n assert compute(parse(e3)) == 133\n", "sub_path": "recorded_failures/aoc2020/day_18_operation_order/empty_expressions.py", "file_name": "empty_expressions.py", "file_ext": "py", "file_size_in_byte": 4165, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "regex.compile", "line_number": 21, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 32, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 38, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 38, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 35, "usage_type": "name"}, {"api_name": "icontract.require", "line_number": 41, "usage_type": "call"}, {"api_name": "icontract.require", "line_number": 42, "usage_type": "call"}, {"api_name": "icontract.ensure", "line_number": 43, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 76, "usage_type": "name"}, {"api_name": "icontract.ensure", "line_number": 61, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 62, "usage_type": "name"}]} +{"seq_id": "535058432", "text": "import shutil\nimport os\nimport re\nimport json\n\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nfrom database_setup import Book, Base\n\nengine = create_engine('sqlite:///books.db')\n# Bind the engine to the metadata of the Base class so that the\n# declaratives can be accessed through a DBSession instance\nBase.metadata.bind = engine\n\nDBSession = sessionmaker(bind=engine)\n# A DBSession() instance establishes all conversations with the database\n# and represents a \"staging zone\" for all the objects loaded into the\n# database session object. Any change made against the objects in the\n# session won't be persisted into the database until you call\n# session.commit(). If you're not happy about the changes, you can\n# revert all of them back to the last commit by calling\n# session.rollback()\nsession = DBSession()\n\nfor root, dirs, files in os.walk(\"./books/\"):\n for tmpfile in files:\n if '.json' in tmpfile:\n book_file = tmpfile.rstrip('.json')\n book_file_decomp = book_file.split('.')\n file_postfix = book_file_decomp[-1]\n jsonfile = open(root+tmpfile,'r')\n jsontext = jsonfile.read()\n meta_info = json.loads(jsontext)\n jsonfile.close()\n \n # print(meta_info)\n tmpbook = Book(book_id=meta_info['book_id'], title=meta_info['title'], format=file_postfix)\n if 'authors' in meta_info:\n tmpbook.authors = meta_info['authors']\n if 'year' in meta_info:\n tmpbook.year = int(meta_info['year'])\n if 'labels' in meta_info:\n tmpbook.labels = \" and \".join(meta_info['labels'])\n \n session.add(tmpbook)\n session.commit()\n\nprint(\"books scanned!\")\n", "sub_path": "scan_library.py", "file_name": "scan_library.py", "file_ext": "py", "file_size_in_byte": 1789, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 11, "usage_type": "call"}, {"api_name": "database_setup.Base.metadata", "line_number": 14, "usage_type": "attribute"}, {"api_name": "database_setup.Base", "line_number": 14, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 16, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 26, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 34, "usage_type": "call"}, {"api_name": "database_setup.Book", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "297639036", "text": "import os\nimport json\nimport time\nimport numpy as np\nimport pandas as pd\nimport shappack\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA, KernelPCA\n\n## Parameters ###################################################\nFILE_PATH = \"./data/2021-08-18-argowf-chaos-b2qdj-user_pod-memory-hog_0.json\"\nPLOTS_NUM = 120\nTARGET_METRICS = [\"cpu_usage_seconds_total\",\n \"memory_working_set_bytes\",\n \"network_transmit_bytes_total\",\n \"network_receive_bytes_total\",\n \"fs_writes_total\",\n \"fs_reads_total\"]\nPARAMS = {\n \"n_components\": 0.8\n}\nANALYSIS_PERIOD = 20\nN_WORKERS = 1\nSEED = 123\nnp.random.seed(SEED)\n#################################################################\n\nclass ShapPCA(object):\n def __init__(self, train_data, model=PCA(n_components=0.80)):\n self.model = model.fit(train_data)\n\n def predict(self, data):\n input_data = np.asarray(data)\n output_data = self._reconstruct_data(input_data)\n errors = np.mean((input_data - output_data) ** 2, axis=1)\n return np.asarray(errors)\n\n def reconstruction_error(self, data):\n input_data = np.asarray(data)\n output_data = self._reconstruct_data(input_data)\n recon_error = (input_data - output_data) ** 2\n return recon_error\n\n def _reconstruct_data(self, data):\n transformed_data = self.model.transform(data)\n reconstructed_data = self.model.inverse_transform(transformed_data)\n return reconstructed_data\n\n\ndef read_file(file_path):\n with open(file_path) as f:\n raw_data = json.load(f)\n containers_data = raw_data[\"containers\"]\n data_df = pd.DataFrame()\n for con in containers_data:\n if con in [\"queue-master\", \"rabbitmq\", \"session-db\"]:\n continue\n for metric in containers_data[con]:\n container_name = metric[\"container_name\"]\n metric_name = metric[\"metric_name\"].replace(\"container_\", \"\")\n if metric_name not in TARGET_METRICS:\n continue\n column_name = \"{}_{}\".format(container_name, metric_name)\n data_df[column_name] = np.array(metric[\"values\"], dtype=np.float)[:, 1][-PLOTS_NUM:]\n data_df = data_df.round(4).fillna(data_df.mean())\n return data_df\n\n\ndef preprocessing(data_df):\n scaler = StandardScaler()\n data_std = scaler.fit_transform(data_df)\n return data_std\n\nif __name__ == '__main__':\n data_df = read_file(FILE_PATH)\n data_df = preprocessing(data_df)\n train_data, test_data = data_df[:-ANALYSIS_PERIOD], data_df[-ANALYSIS_PERIOD:]\n start = time.time()\n model = ShapPCA(train_data, model=PCA(n_components=PARAMS[\"n_components\"]))\n time_train = round(time.time() - start, 6)\n print(f\"Training: {time_train}\")\n start = time.time()\n explainer = shappack.KernelExplainer(model.predict, train_data)\n shap_value = explainer.shap_values(test_data, n_workers=N_WORKERS)\n time_shap = round(time.time() - start, 3)\n print(f\"SHAP: {time_shap}\")\n", "sub_path": "diagnosis_time.py", "file_name": "diagnosis_time.py", "file_ext": "py", "file_size_in_byte": 3066, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.random.seed", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 39, "usage_type": "call"}, {"api_name": "json.load", "line_number": 52, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 64, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 70, "usage_type": "call"}, {"api_name": "time.time", "line_number": 78, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 79, "usage_type": "call"}, {"api_name": "time.time", "line_number": 80, "usage_type": "call"}, {"api_name": "time.time", "line_number": 82, "usage_type": "call"}, {"api_name": "shappack.KernelExplainer", "line_number": 83, "usage_type": "call"}, {"api_name": "time.time", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "113888525", "text": "import plotly\nimport shutil\nimport unittest\n\nimport hardy.data_reporting.reporting as reporting\n\nimport pandas as pd\n\nfrom hardy import run_hardy as run\nfrom hardy.handling import handling\nfrom hardy.handling import pre_processing as preprocessing\nfrom hardy.handling import to_catalogue as catalogue\nfrom hardy.recognition import cnn\n\n# from hardy.handling import pre_processing as preprocessing\n\npath = './hardy/test/test_image/'\ndata_path = './hardy/test/test_data/'\ntform_config_path = data_path + 'tform_config.yaml'\nconfig_path = './hardy/test/'\nsplit = 0.25\n\n\nclass TestSimulationTools(unittest.TestCase):\n\n def test_summary_report_plots(self):\n\n run.hardy_main(\n data_path, tform_config_path, config_path,\n iterator_mode='arrays',\n num_test_files_class=1, classes=['noise', 'one'], split=0.25,\n classifier='tuner', batch_size=1, project_name='test_wrapper1')\n report_path = './hardy/test/test_data/test_wrapper1/'\n fig1, fig2 = reporting.summary_report_plots(\n report_path)\n\n assert isinstance(fig1, plotly.graph_objs._figure.Figure),\\\n 'The returned figure is not a plotly object'\n assert isinstance(fig2, plotly.graph_objs._figure.Figure),\\\n 'The returned figure is not a plotly object'\n\n # shutil.rmtree('./hardy/test/temp_report')\n # shutil.rmtree('./test_run')\n\n def test_summary_report_tables(self):\n report_path = './hardy/test/test_data/test_wrapper1/'\n\n summary, tform_rank = reporting.summary_report_tables(\n report_path)\n\n assert isinstance(summary, pd.DataFrame),\\\n 'The returned table is not a dataframe'\n assert isinstance(tform_rank, pd.DataFrame),\\\n 'The returned table is not a dataframe'\n\n shutil.rmtree('./hardy/test/test_data/test_wrapper1')\n # shutil.rmtree('./test_run')\n\n def test_model_analysis(self):\n\n num_files = 1\n data_tups = catalogue._data_tuples_from_fnames(input_path=data_path)\n data_storage = data_path + 'test_1.pkl'\n catalogue.rgb_list(data_tups, storage_location=data_storage)\n plot_tups = handling.pickled_data_loader(data_path, 'test_1')\n\n test_set_filenames = preprocessing.hold_out_test_set(\n data_path, number_of_files_per_class=num_files)\n\n test_set_list, learning_set_list = catalogue.data_set_split(\n plot_tups, test_set_filenames)\n train, val = catalogue.learning_set(image_list=learning_set_list,\n split=split,\n classes=['noise', 'one'],\n iterator_mode='arrays')\n testing_set = catalogue.test_set(image_list=test_set_list,\n classes=['noise', 'one'],\n iterator_mode='arrays')\n model, history = cnn.build_model(train, val,\n config_path='./hardy/test/')\n\n result = reporting.model_analysis(model, testing_set, test_set_list)\n\n assert isinstance(result, pd.DataFrame)\n", "sub_path": "hardy/test/test_reporting.py", "file_name": "test_reporting.py", "file_ext": "py", "file_size_in_byte": 3185, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "unittest.TestCase", "line_number": 24, "usage_type": "attribute"}, {"api_name": "hardy.run_hardy.hardy_main", "line_number": 28, "usage_type": "call"}, {"api_name": "hardy.run_hardy", "line_number": 28, "usage_type": "name"}, {"api_name": "hardy.data_reporting.reporting.summary_report_plots", "line_number": 34, "usage_type": "call"}, {"api_name": "hardy.data_reporting.reporting", "line_number": 34, "usage_type": "name"}, {"api_name": "plotly.graph_objs", "line_number": 37, "usage_type": "attribute"}, {"api_name": "plotly.graph_objs", "line_number": 39, "usage_type": "attribute"}, {"api_name": "hardy.data_reporting.reporting.summary_report_tables", "line_number": 48, "usage_type": "call"}, {"api_name": "hardy.data_reporting.reporting", "line_number": 48, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 53, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 56, "usage_type": "call"}, {"api_name": "hardy.handling.to_catalogue._data_tuples_from_fnames", "line_number": 62, "usage_type": "call"}, {"api_name": "hardy.handling.to_catalogue", "line_number": 62, "usage_type": "name"}, {"api_name": "hardy.handling.to_catalogue.rgb_list", "line_number": 64, "usage_type": "call"}, {"api_name": "hardy.handling.to_catalogue", "line_number": 64, "usage_type": "name"}, {"api_name": "hardy.handling.handling.pickled_data_loader", "line_number": 65, "usage_type": "call"}, {"api_name": "hardy.handling.handling", "line_number": 65, "usage_type": "name"}, {"api_name": "hardy.handling.pre_processing.hold_out_test_set", "line_number": 67, "usage_type": "call"}, {"api_name": "hardy.handling.pre_processing", "line_number": 67, "usage_type": "name"}, {"api_name": "hardy.handling.to_catalogue.data_set_split", "line_number": 70, "usage_type": "call"}, {"api_name": "hardy.handling.to_catalogue", "line_number": 70, "usage_type": "name"}, {"api_name": "hardy.handling.to_catalogue.learning_set", "line_number": 72, "usage_type": "call"}, {"api_name": "hardy.handling.to_catalogue", "line_number": 72, "usage_type": "name"}, {"api_name": "hardy.handling.to_catalogue.test_set", "line_number": 76, "usage_type": "call"}, {"api_name": "hardy.handling.to_catalogue", "line_number": 76, "usage_type": "name"}, {"api_name": "hardy.recognition.cnn.build_model", "line_number": 79, "usage_type": "call"}, {"api_name": "hardy.recognition.cnn", "line_number": 79, "usage_type": "name"}, {"api_name": "hardy.data_reporting.reporting.model_analysis", "line_number": 82, "usage_type": "call"}, {"api_name": "hardy.data_reporting.reporting", "line_number": 82, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 84, "usage_type": "attribute"}]} +{"seq_id": "16625713", "text": "from urllib.request import urlopen\nfrom bs4 import BeautifulSoup as bs\nfrom instituties.ivmiit import get_link_from_button\n\ndef get_name_link_of_cathedras_ecology(url):\n site = urlopen(url)\n soup = bs(site,'html.parser')\n\n div = soup.find('div',class_='visit_link')\n links = div.find_all('a')\n cathedras = list()\n for item in links:\n if item.text.startswith('Кафедра'):\n cathedras.append((item.text, item.get('href')))\n return cathedras\n\ndef get_name_link_of_teachers(url):\n site = urlopen(url)\n soup = bs(site,'html.parser')\n\n iframe = soup.find('iframe')\n src = iframe.get('src')\n\n sourse = urlopen(src)\n soup = bs(sourse, 'html.parser')\n\n stuff = []\n spans = soup.find_all('span', class_='fio')\n for item in spans:\n tag_a = item.find('a')\n if tag_a:\n stuff.append((tag_a.text, tag_a.get('href')))\n return stuff\n\n\ndef parse_ecology(url):\n info_button_url = get_link_from_button(url, 'Структура')\n cathedras = get_name_link_of_cathedras_ecology(info_button_url)\n\n res = {}\n\n for name, url in cathedras:\n stuff_url = get_link_from_button(url, 'Состав')\n res[name] = stuff_url\n\n for name, url in res.items():\n res[name] = len(get_name_link_of_teachers(url))\n\n return res\n\n# print(parse_ecology('https://kpfu.ru/ecology'))", "sub_path": "instituties/ecology.py", "file_name": "ecology.py", "file_ext": "py", "file_size_in_byte": 1376, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "urllib.request.urlopen", "line_number": 6, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 7, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 18, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 19, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 24, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 25, "usage_type": "call"}, {"api_name": "instituties.ivmiit.get_link_from_button", "line_number": 37, "usage_type": "call"}, {"api_name": "instituties.ivmiit.get_link_from_button", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "407548654", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 14 15:57:48 2021\n@author: juliasolhed\n\"\"\"\n\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nsns.set_theme(style=\"ticks\", color_codes=True)\n\nimport pandas as pd \nfrom matplotlib import pyplot as plt\ndataTrain = pd.read_csv(\"trainPattern.csv\")\ndataTest = pd.read_csv(\"testPattern.csv\")\n\n#sns.catplot(x=\"GNN\",y=\"Train_accuracy\",hue='A',kind=\"bar\", palette=\"pastel\", edgecolor=\".6\",data=da)\n#sns.catplot(x=\"GNN\", y=\"AA\", hue=\"A\", kind=\"bar\", data=da)\n\ncolors = [\"#3498DB\",\"#e74c3c\",\"#34495e\",\"#2ecc71\"]\nsns.set_palette(colors)\ng = sns.catplot(x=\"GNN\", y=\"Test_Acc\", hue=\"Agg\", kind=\"bar\", data=dataTest)\ng.set(ylim=(85, 86))\nplt.title(\"Test accuracy (PATTERN)\")\nplt.show(g)\n\ng = sns.catplot(x=\"GNN\", y=\"Train_Acc\", hue=\"Agg\", kind=\"bar\", data=dataTrain)\ng.set(ylim=(85.5, 87))\nplt.title(\"Test accuracy (PATTERN)\")\nplt.show(g)\n\n#g= sns.catplot(x=\"GNN\", y=\"Train\", hue=\"Agg\", kind=\"swarm\",aspect=1, data=da, palette=\"Spectral\")", "sub_path": "Plots/plots - kopia.py", "file_name": "plots - kopia.py", "file_ext": "py", "file_size_in_byte": 996, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "seaborn.set_theme", "line_number": 10, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "seaborn.set_palette", "line_number": 21, "usage_type": "call"}, {"api_name": "seaborn.catplot", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "seaborn.catplot", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}]} +{"seq_id": "50795942", "text": "import redis\nimport random, string\n\nr = redis.Redis(host='127.0.0.1',port=6379,db=0)\nchars = string.ascii_uppercase + string.digits\nactive_code=[]\nwhile len(active_code)!=200:\n\ttemp = ''\n\ts = random.sample(chars,20)\n\tfor j in range(20):\n\t\tif j%4 ==0 and j!=0:\n\t\t\ttemp=temp+'-'+s[j]\n\t\telse:\n\t\t\ttemp=temp+s[j]\n\tif temp not in active_code:\n\t\tactive_code.append(temp)\n\t\tr.lpush('code',temp)\n\n\n\n\n\n", "sub_path": "p3/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 392, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "redis.Redis", "line_number": 4, "usage_type": "call"}, {"api_name": "string.ascii_uppercase", "line_number": 5, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 5, "usage_type": "attribute"}, {"api_name": "random.sample", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "558824389", "text": "from django import forms\nfrom .choice_tuples_for_models import UNIVERSITIES\nfrom .models import *\n\nclass SearchForm(forms.Form):\n\t\"\"\"\n\tForm used to search for a professor.\n\t\"\"\"\n\tprofessor_name = forms.CharField(\n\t\tlabel=\"Enter a professor's name or name code\",\n\t\tmax_length=255,\n\t\twidget=forms.TextInput(attrs={\n\t\t\t'placeholder': 'Search for a professor',\n\t\t\t'required' : 'required',\n\t\t}),\n\t)\n\tuniversity = forms.ChoiceField(\n\t\tlabel=\"Select your university\",\n\t\tchoices=UNIVERSITIES,\n\t)\n\nclass ReviewForm(forms.ModelForm):\n\tclass Meta:\n\t\tmodel = Review\n\t\tfields = [\n\t\t\t'easiness', 'clarity', 'helpfulness',\n\t\t\t'grade', 'text', 'tags'\n\t\t]\n\t\n\tdef __init__(self, *args, **kwargs):\n\t\tsuper(ReviewForm, self).__init__(*args, **kwargs)\n\t\tself.fields['easiness'].help_text = \"
How easy this professor's course was. The higher the number, the easier the course
\"\n\t\tself.fields['clarity'].help_text = \"
How clear this professor was regarding the course materials.
\"\n\t\tself.fields['helpfulness'].help_text = \"
How helpful this professor was in helping you understand the course materials.
\"\n\t\tself.fields['grade'].help_text = \"
What grade did you get for this professor's course? If you have not received your results, please select N/A.
\"\n\t\tself.fields['text'].help_text = \"
Describe this professor. Be objective. Max: 600 characters.
\"\n\t\tself.fields['tags'].help_text = \"
Choose upto 3 tags which best describes this professor and his/her course. Hold down ctrl/cmnd button to select more.
\"", "sub_path": "myapp/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1531, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.forms.Form", "line_number": 5, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 5, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 9, "usage_type": "name"}, {"api_name": "django.forms.TextInput", "line_number": 12, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 12, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 17, "usage_type": "name"}, {"api_name": "choice_tuples_for_models.UNIVERSITIES", "line_number": 19, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "390147934", "text": "from django import forms\r\nimport logging\r\n\r\nclass AddClassroomForm(forms.Form):\r\n name = forms.CharField(max_length=255)\r\n location = forms.CharField(max_length=255)\r\n capacity = forms.IntegerField()\r\n\r\n# def __init__(self,post,schedules_number):\r\n# super(AddClassroomForm,self).__init__(post)\r\n# schedule_day = []\r\n# schedule_start_date = []\r\n# schedule_end_date = []\r\n# for x in range(0,schedules_number):\r\n# logger = logging.getLogger(__name__)\r\n# logger.error('x value: '+str(x))\r\n# schedule_day.append(forms.IntegerField())\r\n# schedule_start_date.append(forms.IntegerField())\r\n# schedule_end_date.append(forms.IntegerField())\r\n\r\nclass ScheduleForm(forms.Form):\r\n day = forms.IntegerField()\r\n start_hour = forms.TimeField()\r\n end_hour = forms.TimeField()\r\n\r\nclass InputsForm(forms.Form):\r\n name = forms.CharField()", "sub_path": "hcmfront/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 932, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.forms.Form", "line_number": 4, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 4, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 5, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 5, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 6, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 6, "usage_type": "name"}, {"api_name": "django.forms.IntegerField", "line_number": 7, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 7, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 21, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 21, "usage_type": "name"}, {"api_name": "django.forms.IntegerField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 22, "usage_type": "name"}, {"api_name": "django.forms.TimeField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 23, "usage_type": "name"}, {"api_name": "django.forms.TimeField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 24, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 26, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 26, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "155098901", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Sep 11 16:29:17 2019\n\n@author: zhangyan\n\"\"\"\n\n\nfrom matplotlib import pyplot as plt\n\n# print(plt.style.available)\n# plt.style.use('fivethirtyeight') # ggplot\nplt.xkcd() # xkcd comics style\n\nages_x = [1, 2, 3]\n\ndev_y = [4, 5, 6]\nplt.plot(\n ages_x,\n dev_y,\n color=\"#5a7d9a\",\n linestyle=\"--\",\n marker=\".\",\n linewidth=3,\n label=\"All Devs\",\n) # label is used to specify legend\n\npy_dev_y = [4.4, 5.5, 6.6]\nplt.plot(ages_x, py_dev_y, label=\"Python\")\n\n# plt.legend(['All Devs', 'Python']) # add a list of legends for each plot in the order that they are plotted\n\njs_dev_y = [10, 20, 30]\nplt.plot(ages_x, js_dev_y, label=\"JavaScript\")\n\nplt.xlabel(\"Ages\")\nplt.ylabel(\"Median Salary\")\nplt.title(\"Median Salary (USD) by Age\")\nplt.legend()\n\n# plt.grid(True)\nplt.tight_layout()\n\nplt.savefig(\"plot.png\") # save the image in a png file\nplt.show()\n\n# format strings: https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.plot.html\n", "sub_path": "Python_Plot_Tutorial/matplotlib_intro.py", "file_name": "matplotlib_intro.py", "file_ext": "py", "file_size_in_byte": 1010, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.pyplot.xkcd", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "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": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}]} +{"seq_id": "230374208", "text": "import json\nimport os\nimport sqlite3\nfrom datetime import datetime\n\nfrom flask import Flask, request, g, send_from_directory\n\napp = Flask(__name__)\napp.config.update({\n 'DATABASE': os.path.join(app.root_path, 'tests.db'),\n 'DEBUG': True,\n})\n\nDATABASE = 'database.db'\n\n\ndef connect_db():\n conn = sqlite3.connect(app.config['DATABASE'])\n conn.row_factory = sqlite3.Row\n return conn\n\n\ndef get_db():\n if not getattr(g, 'sqilte_db'):\n g.sqlite_db = connect_db()\n return g.sqilte_db\n\n\n@app.route('/index.html')\ndef index():\n return send_from_directory('', 'index.html')\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef test_results():\n\n conn = connect_db()\n\n if request.method == 'GET':\n rows = conn.execute('select * from test_reports').fetchall()\n res = [dict(row) for row in rows]\n return json.dumps(res)\n\n data = request.json\n\n finished = data['finished'].replace('T', ' ')\n\n conn.executemany(\n 'insert into test_reports ('\n 'run_finished,'\n 'result,'\n 'method_name,'\n 'method_doc,'\n 'traceback,'\n 'time'\n ') values (?, ?, ?, ?, ?, ?)',\n [\n (\n finished,\n d['result'],\n d['method'],\n d['doc'],\n d.get('traceback', None),\n datetime.now(),\n )\n for d in data['tests']\n ]\n )\n conn.commit()\n\n return 'ok'\n\n\nif __name__ == '__main__':\n\n app.run()\n", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1508, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 18, "usage_type": "call"}, {"api_name": "sqlite3.Row", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 24, "usage_type": "argument"}, {"api_name": "flask.g.sqlite_db", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.g.sqilte_db", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.send_from_directory", "line_number": 31, "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": "json.dumps", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 44, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 64, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 64, "usage_type": "name"}]} +{"seq_id": "232042878", "text": "from flask import Blueprint, request, jsonify, \\\n make_response\n\nfrom src.estimator import estimator\n\nfrom .api_helpers import method_not_allowed_405, \\\n estimates_xml_serializer, read_log_to_file\n\napi_v1 = Blueprint('api_v1', __name__, url_prefix='/api/v1')\n\n\n@api_v1.route('/on-covid-19/', methods=('GET', 'POST'))\n@api_v1.route('/on-covid-19/', methods=('GET', 'POST'))\ndef covid19_estimator(res_format='json'):\n \"\"\"Handles POST request to the covid19 estimator\n\n :param res_format: {str:string} - name of response format\n :return: a response formatted as per the res_format param\n \"\"\"\n\n if request.method == 'POST':\n if request.data:\n res_data = request.get_json()\n estimates = estimator(res_data)\n\n if res_format.lower() == 'json':\n return jsonify(estimates), 200\n\n elif res_format.lower() == 'xml':\n estimates_xml = estimates_xml_serializer(estimates)\n res = make_response(estimates_xml)\n res.mimetype = 'application/xml'\n res.headers[\"Content-Type\"] = \"application/xml; charset=utf-8\"\n return res\n\n return jsonify(\n estimates,\n message = f\"'{res_format}' response format not supported\"\n ), 400\n return jsonify(\n data={},\n message=\"Empty data was presented\"\n ), 400\n\n return method_not_allowed_405()\n\n\n@api_v1.route('/on-covid-19/logs', methods=('GET',))\ndef covid19_logs():\n \"\"\"Handles a GET request to access the API Logs\n\n :return: string response\n \"\"\"\n if request.method == 'GET':\n if read_log_to_file('endpoint_logs.txt'):\n logs = read_log_to_file('endpoint_logs.txt')\n log_text = ''\n for log in logs:\n log_text += f\"{log}\"\n res = make_response(log_text)\n res.mimetype = 'application/text'\n res.headers[\"Content-Type\"] = \"text/plain;\"\n return res\n else:\n res = make_response(\"logs are empty\")\n res.mimetype = 'application/text'\n res.headers[\"Content-Type\"] = \"text/plain;\"\n return res\n else:\n return method_not_allowed_405()\n", "sub_path": "covid19E_api/api_v1.py", "file_name": "api_v1.py", "file_ext": "py", "file_size_in_byte": 2274, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Blueprint", "line_number": 9, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.request.data", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 23, "usage_type": "name"}, {"api_name": "src.estimator.estimator", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 27, "usage_type": "call"}, {"api_name": "api_helpers.estimates_xml_serializer", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 40, "usage_type": "call"}, {"api_name": "api_helpers.method_not_allowed_405", "line_number": 45, "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": "api_helpers.read_log_to_file", "line_number": 55, "usage_type": "call"}, {"api_name": "api_helpers.read_log_to_file", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 65, "usage_type": "call"}, {"api_name": "api_helpers.method_not_allowed_405", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "347432438", "text": "import sys\nimport os\nimport cv2\nimport numpy as np\nimport math\nimport random\nimport copy\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtCore import *\nfrom functools import partial\nfrom numpy import unique\nfrom scipy.stats import entropy as scipy_entropy\nfrom math import log, e\n\nfrom imgproc import ImgProc\n\n\nclass SubImageControl(QObject) :\n _ToUpdate = pyqtSignal(int)\n def __init__(self, parent = None):\n super(SubImageControl,self).__init__(parent)\n self.classNum = []\n self.Images = []\n self.labels = []\n self.workingLabelIndexes = []\n self.qpixmaps = []\n self.positions = []\n self.selectedIdx = []\n self.indexs = []\n self.subEntropy = []\n self.originfilename = []\n\n def subImageMousePressEvent(self, i, event) :\n self.classNum[i] = 0\n for idx in range(len(self.selectedIdx)) :\n if self.selectedIdx[idx] == i:\n del self.selectedIdx[idx]\n break\n self._ToUpdate.emit(i)\n\n def setSubImage(self, Image, position, entropy, filelabel,__classNum) :\n self.classNum.append(__classNum)\n self.Images.append(Image)\n temp = ImgProc.CvImgToQPixmap(Image)\n temp = temp.scaled(64,64, Qt.KeepAspectRatio)\n self.qpixmaps.append(temp)\n self.positions.append(position)\n self.subEntropy.append(entropy)\n self.originfilename.append(filelabel)\n\n def makeWorkingList(self, size, entropy) :\n self.workingLabelIndexes = []\n for i in range(len(self.labels)) :\n if self.positions[i][2] == size :\n if self.subEntropy[i] > entropy :\n self.workingLabelIndexes.append(i)\n def saveClassImage(self, classNum) :\n for index in range(len(self.classNum)) :\n if self.classNum[index] == 0 :\n continue\n tempfilename = '%s_%d_%d_%d_%d_%d.png' % (self.originfilename[index], self.classNum[index], self.positions[index][0], self.positions[index][1],self.positions[index][2],self.positions[index][3])\n ImgProc.SaveSubImg(tempfilename, self.Images[index], self.classNum[index])\n\n\n def getImagePos(self) :\n pos = []\n for i in range(len(self.workingLabelIndexes)) :\n pos.append(self.positions[self.workingLabelIndexes[i]])\n return pos\n\n def getImagePosWithClass(self, _classNum) :\n pos = []\n for i in range(len(self.workingLabelIndexes)) :\n if self.classNum[self.workingLabelIndexes[i]]==_classNum :\n pos.append(self.positions[self.workingLabelIndexes[i]])\n return pos\n\n def getWorkingLabelsWithClass(self, _classNum) :\n labels = []\n for i in range(len(self.workingLabelIndexes)) :\n if self.classNum[self.workingLabelIndexes[i]]==_classNum :\n labels.append(self.labels[self.workingLabelIndexes[i]])\n return labels\n\n def getAllLabelsWithClass(self, _classNum) :\n labels = []\n for i in range(len(self.labels)) :\n if self.classNum[i]==_classNum :\n labels.append(self.labels[i])\n return labels\n\n def connectAllLabelsToLayoutWithClass(self, layout, _classNum, startIdx, endIdx) :\n labels = self.getAllLabelsWithClass(_classNum)\n index = startIdx\n for i in range(len(labels)) :\n if index >= len(labels) : break\n elif index >= endIdx : break\n else :\n layout.addWidget(labels[index])\n index+=1\n return len(labels)\n\n def genLabels(self) :\n self.labels = []\n for i in range(len(self.classNum)) :\n temp = QLabel()\n temp.setPixmap(self.qpixmaps[i])\n\n temp.mousePressEvent = partial(self.subImageMousePressEvent, i)\n self.labels.append(temp)\n\n def connectToLayout(self, layout, index) :\n if len(self.labels) < index or index < 0 :\n return\n else :\n if self.classNum[index] == 0 :\n layout.addWidget(self.labels[index])\n\n def connectSelectedLabelToLayout(self, layout, startIdx, endIdx) :\n if len(self.selectedIdx)==0 :\n NullImg64 = QPixmap(64,64)\n NullImg64.fill(QColor(240,240,240))\n temp = QLabel()\n temp.setPixmap(NullImg64)\n layout.addWidget(temp)\n else :\n for i in range(len(self.selectedIdx)) :\n if (i+startIdx) >= endIdx : break\n elif (i+startIdx) >= len(self.selectedIdx) : break\n self.connectToLayout(layout, self.selectedIdx[i+startIdx])\n\n def getSelectedIndexLength(self) :\n return len(self.selectedIdx)\n\n def addSelectedIdx(self, idx) :\n self.selectedIdx.append(idx)\n self.selectedIdx = list(set(self.selectedIdx))\n self.selectedIdx = sorted(self.selectedIdx)\n\n def removeSelectedIdx(self, idx) :\n for i in range(len(self.selectedIdx)) :\n if self.selectedIdx[i] == idx :\n del self.selectedIdx[i]\n break\n\n def deleteAllSelectedIdx(self) :\n self.selectedIdx=[]\n\n def selectIdxCheck(self, point) :\n indexlist = []\n #print('indexcheck start')\n for i in range(len(self.workingLabelIndexes)) :\n if self.classNum[self.workingLabelIndexes[i]] ==0 :\n x1 = self.positions[self.workingLabelIndexes[i]][0]\n x2 = self.positions[self.workingLabelIndexes[i]][0]+self.positions[self.workingLabelIndexes[i]][2]\n y1 = self.positions[self.workingLabelIndexes[i]][1]\n y2 = self.positions[self.workingLabelIndexes[i]][1]+self.positions[self.workingLabelIndexes[i]][3]\n\n if x1 <= point[0] and x2 >= point[0] :\n if y1 <= point[1] and y2 >= point[1] :\n indexlist.append(self.workingLabelIndexes[i])\n #print('[%d](%d,%d,%d,%d), point[%d,%d]' %(self.workingLabelIndexes[i], x1,x2,y1,y2, point[0], point[1]))\n #print('indexcheck done')\n\n return indexlist\n\n def classIdxCheck(self, point) :\n indexlist = []\n #print('indexcheck start')\n for i in range(len(self.workingLabelIndexes)) :\n if self.classNum[self.workingLabelIndexes[i]] !=0 :\n x1 = self.positions[self.workingLabelIndexes[i]][0]\n x2 = self.positions[self.workingLabelIndexes[i]][0]+self.positions[self.workingLabelIndexes[i]][2]\n y1 = self.positions[self.workingLabelIndexes[i]][1]\n y2 = self.positions[self.workingLabelIndexes[i]][1]+self.positions[self.workingLabelIndexes[i]][3]\n\n if x1 <= point[0] and x2 >= point[0] :\n if y1 <= point[1] and y2 >= point[1] :\n indexlist.append(self.workingLabelIndexes[i])\n #print('[%d](%d,%d,%d,%d), point[%d,%d]' %(self.workingLabelIndexes[i], x1,x2,y1,y2, point[0], point[1]))\n #print('indexcheck done')\n\n return indexlist\n\n def classifySelectedImage(self, _classNum) :\n for i in self.selectedIdx :\n self.classNum[i] = _classNum\n self.selectedIdx=[]\n\n def unclassImage(self, _classNum) :\n for i in range(len(self.classNum)) :\n if self.classNum[i] == _classNum :\n self.classNum[i] = 0\n", "sub_path": "SDDVR/imagelabeling/subimage_control.py", "file_name": "subimage_control.py", "file_ext": "py", "file_size_in_byte": 7428, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "imgproc.ImgProc.CvImgToQPixmap", "line_number": 45, "usage_type": "call"}, {"api_name": "imgproc.ImgProc", "line_number": 45, "usage_type": "name"}, {"api_name": "imgproc.ImgProc.SaveSubImg", "line_number": 63, "usage_type": "call"}, {"api_name": "imgproc.ImgProc", "line_number": 63, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 110, "usage_type": "call"}]} +{"seq_id": "298922118", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jun 21 10:45:04 2018\n\n@author: cemre\n\"\"\"\nimport pandas as ps\n\nfrom bs4 import BeautifulSoup\n\nimport requests\n\nimport string\nfrom nltk.corpus import stopwords\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.pyplot import rcParams\n\nfrom wordcloud import (WordCloud, get_single_color_func)\n\nfrom PIL import Image \n\nfrom googletrans import Translator\n\ntranslator = Translator()\n\n\nurl = \"http://www.akparti.org.tr/site/haberler/cumhurbaskani-erdogan-24-haziran-secimleri-manifestosunu-acikladi/100114#1\"\n\nrtemanif = requests.get(url) \nhtml = rtemanif.text \nmanifsoup = BeautifulSoup(html, 'html.parser')\nprint(manifsoup.find('div', {\"class\" : \"detail-text\"}).text)\n\nmaniftext = str(manifsoup.find('div', {\"class\" : \"detail-text\"}).text)\n\n#Removing the punctuation and making the words lowercase\ntranslator = str.maketrans('', '', string.punctuation)\n#print(text.translate(translator))\n\nmaniftext = maniftext.translate(translator).lower()\n\n# Creating a dictionary for the words in the text\n\ndat = list(maniftext.split())\ndict1 = {}\nfor i in range(len(dat)):\n word = dat[i]\n dict1[word] = dat.count(word)\n \n#Checking the listed stopwords in NLTK package\n#stop_words = stopwords.words('turkish')\n\n#Importing the extended stopwords list\n#Resource: https://github.com/ahmetax/trstop/blob/master/dosyalar/derlemtr2016-10000.txt\nstoplist = open(\"swNI.csv\", \"r\")\nstopwords = stoplist.readlines()\nstopwords = [i.replace('\"', '') for i in stopwords]\nstopwords = [i.replace('\\n', '') for i in stopwords]\n\n#print(stopwords)\n\n#Removing stopwords\nkeys = list(dict1)\nkeys = [i.replace('\"', '') for i in keys]\nkeys = [i.replace(\"'\", '') for i in keys]\nfiltered_words = [word for word in keys if word not in stopwords]\n#filtered_words = [i.replace('i̇', 'i') for i in filtered_words]\ndict2 = dict((k, dict1[k]) for k in filtered_words if k in filtered_words)\n\n\ndef SequenceSelection(dictionary, length, startindex = 0): \n \n lengthDict = len(dictionary)\n if length > lengthDict:\n return print(\"length is longer than dictionary length\");\n else:\n d = dictionary\n items = [(v, k) for k, v in d.items()]\n items.sort()\n items.reverse() \n itemsOut = [(k, v) for v, k in items]\n \n highest = itemsOut[startindex:startindex + length]\n dd = dict(highest)\n wanted_keys = dd.keys()\n dictshow = dict((k, d[k]) for k in wanted_keys if k in d)\n\n return dictshow;\n \ndictshow = SequenceSelection(dictionary = dict2, length = 8, startindex = 0)\n\n\n\n\n\n# Visualizing the frequent words in Turkish\nn = range(len(dictshow))\nplt.bar(n, dictshow.values(), align='center')\nplt.xticks(n, dictshow.keys(), rotation = 45)\nplt.title(\"Erdogan Manifesto\" + \" Most Frequent Words\")\nplt.tight_layout()\nplt.savefig(\"Erd Manif FrequentWords.png\", transparent = True, dpi=1000)\n\n#Translating most frequest words into English\nfrom googletrans import Translator\n\ntranslator = Translator()\n\n\neng_FQwords = []\n\nfor fqword in list(dictshow):\n trs = translator.translate(fqword, src = 'tr', dest = 'en')\n eng_FQwords.append(trs.text)\n \nprint(eng_FQwords)\n\n\n# Removing duplicates - happens due to differences in the language\ndef remove_duplicates(values):\n output = []\n seen = set()\n for value in values:\n # If value has not been encountered yet,\n # ... add it to both list and set.\n if value not in seen:\n output.append(value)\n seen.add(value)\n return output\n\neng_FQwords = remove_duplicates(eng_FQwords)\n\n\ncount = list(dictshow.values())\ndel count[2]\n\n# Visualising the most frequent words in English\nn = range(len(eng_FQwords))\nplt.bar(n, count, align='center')\nplt.xticks(n, eng_FQwords, rotation = 45)\nplt.title(\"Erdogan Election Manifesto - Most Frequent Words\")\nplt.tight_layout() \nplt.savefig(\"Erd Manif FrequentWords EN.png\", transparent=True, dpi=1000)\n\n\n# Creating the word cloud of filtered words in Turkish\n###### Face of erdogan as a mask IN PROGRESS ######\nimport numpy as np\nfrom os import path\nimport os\nroot_path = os.getcwd()\n\n\nrte_mask = np.array(Image.open(path.join(root_path, \"rte.png\")))\n\n\nfiltered_WC = ' '.join(filtered_words)\nfiltered_WC = filtered_WC.replace('i̇', 'i')\nfiltered_WC = filtered_WC.replace('\"', '')\nfiltered_WC = filtered_WC.replace(\"'\", '')\nwordcloud_FW = WordCloud(background_color='white', mask=rte_mask, mode='RGBA').generate(filtered_WC)\n\nplt.figure()\nplt.imshow(wordcloud_FW, interpolation='bilinear')\nplt.axis(\"off\")\nplt.imshow(rte_mask, cmap=plt.cm.gray, interpolation='bilinear', alpha =0.2)\n#plt.axis(\"off\")\n#plt.title(title + \" - \" + date_stamp)\nplt.savefig(\"Erd Manif_Wordcloud_TR.png\", transparent=True, dpi=1000)\nplt.show()\n\n\n# Creating the word cloud of filtered words in english\nfrom googletrans import Translator\n\ntranslator = Translator()\n\neng_fil_words = []\n\nfor filword in filtered_words:\n trs = translator.translate(filword, src = 'tr', dest = 'en')\n eng_fil_words.append(trs.text)\n# print(eng_fil_words)\n # eng_fil_words\n\nfiltered_WC_eng = ' '.join(eng_fil_words)\n#filtered_WC_eng = filtered_WC.replace('i̇', 'i')\nfiltered_WC_eng = filtered_WC_eng.replace('\"', '')\nfiltered_WC_eng = filtered_WC_eng.replace(\"'\", '')\nwordcloud_FW_eng = WordCloud(background_color='white', mask=rte_mask, mode='RGBA').generate(filtered_WC_eng)\n\nplt.figure()\nplt.imshow(wordcloud_FW_eng, interpolation='bilinear')\nplt.axis(\"off\")\nplt.imshow(rte_mask, cmap=plt.cm.gray, interpolation='bilinear', alpha=0.2)\nplt.title(\"Erdogan Election Manifesto - Word Cloud\")\nplt.savefig(\"Erdogan Election Manifesto - Word Cloud.png\", transparent=True, dpi=1000)\nplt.show()\n", "sub_path": "DEDA_Class_SS2018_DictionaryForTurkishSentiment/Erdogan_Manifesto.py", "file_name": "Erdogan_Manifesto.py", "file_ext": "py", "file_size_in_byte": 5685, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "googletrans.Translator", "line_number": 26, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 31, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 33, "usage_type": "call"}, {"api_name": "string.punctuation", "line_number": 39, "usage_type": "attribute"}, {"api_name": "nltk.corpus.stopwords", "line_number": 58, "usage_type": "name"}, {"api_name": "nltk.corpus.stopwords", "line_number": 59, "usage_type": "name"}, {"api_name": "nltk.corpus.stopwords", "line_number": 60, "usage_type": "name"}, {"api_name": "nltk.corpus.stopwords", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "googletrans.Translator", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 141, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 141, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "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.tight_layout", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 156, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 156, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 156, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path", "line_number": 156, "usage_type": "name"}, {"api_name": "wordcloud.WordCloud", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 165, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 166, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 168, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 168, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.savefig", "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": "googletrans.Translator", "line_number": 178, "usage_type": "call"}, {"api_name": "wordcloud.WordCloud", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "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.cm", "line_number": 197, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.title", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}]} +{"seq_id": "146788070", "text": "import os\nimport os.path\nfrom pathlib import Path\n\ndef find_vscode(filePath):\n if os.path.isfile(filePath):\n filePath, _ = os.path.split(filePath)\n filePathCp = filePath\n while filePath != str(Path.home()):\n for dir in os.listdir(filePath):\n if dir == '.vscode':\n return os.path.join(filePath, dir)\n filePath, _ = os.path.split(filePath)\n return _make_vscode_folder(filePathCp)\n\ndef find_workspace(filePath):\n if os.path.isfile(filePath):\n filePath, _ = os.path.split(filePath)\n while filePath != str(Path.home()):\n for dir in os.listdir(filePath):\n if dir == '.vscode':\n return filePath\n filePath, _ = os.path.split(filePath)\n return None \n\ndef _make_vscode_folder(filePath):\n vscodeDir = os.path.join(filePath, '.vscode')\n try:\n os.mkdir(vscodeDir)\n except FileExistsError:\n pass\n return vscodeDir\n", "sub_path": "rplugin/python3/palette/dot_vscode.py", "file_name": "dot_vscode.py", "file_ext": "py", "file_size_in_byte": 941, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.isfile", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "pathlib.Path.home", "line_number": 9, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 9, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 10, "usage_type": "call"}, {"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.split", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pathlib.Path.home", "line_number": 19, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 19, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "45648230", "text": "import socket, threading\r\nfrom multiprocessing import Value, Array\r\n\r\ndef recp_handler(soc, ip, port, point, board):\r\n while 1:\r\n mode=soc.recv(1024).decode()\r\n print(mode)\r\n if mode==\"i\":\r\n message = soc.recv(1024).decode()\r\n #print(\"cliant_name_is\", myname)\r\n print('Recv_message >> {}'.format(message))\r\n #from_name=myname\r\n my_name=\"\"\r\n send_message=\"\"\r\n f=1\r\n print(message)\r\n p=0\r\n while p < len(message):\r\n #print(i)\r\n if message[p] not in [\"[\", \"]\", \"'\"]:#list型を受け取るので、解析する。適宜宛先とメッセージに分解\r\n if message[p]==\",\":\r\n f=0\r\n p+=3\r\n if f:\r\n my_name+=message[p]\r\n else:\r\n send_message+=message[p]\r\n p+=1\r\n my_name=my_name.strip()\r\n send_message=send_message.strip()\r\n print(\"name=\",my_name)\r\n print(\"mess=\",send_message)\r\n tempp=+point.value\r\n for i,j in enumerate(my_name):\r\n print(i,j)\r\n #print(\"i=\", i)\r\n #print(type(board), type(board[i+point.value]), type(j.encode()))\r\n board[i+tempp]=j.encode()\r\n point.value+=1\r\n board[point.value]=\"\\n\".encode()\r\n point.value+=1\r\n tempp=+point.value\r\n for i,j in enumerate(send_message):\r\n print(i,j)\r\n board[i+tempp]=j.encode()\r\n point.value+=1\r\n board[point.value]=\"\\n\".encode()\r\n point.value+=1\r\n print(\"board=\")\r\n #tempb=board.decode()\r\n c=0\r\n for i in range(point.value):\r\n print(c, end=\"\")\r\n print(board[i].decode(), end=\"\")\r\n c+=1\r\n print()\r\n print()\r\n # 受信したデータをそのまま送り返す (エコー)\r\n # if not message:\r\n # break\r\n #soc.send(send_message.encode())\r\n #print(\"{0}:{1}にオウム返しシマシタ\".format(ip, port))\r\n print(\"finish\")\r\n elif mode==\"b\":\r\n sendb=\"\"\r\n for i in range(point.value):\r\n sendb+=board[i].decode()\r\n soc.send(sendb.encode())\r\n print(\"finishb\")\r\n elif mode==\"q\":\r\n soc.close()\r\n print('Bye-Bye: {0}:{1}'.format(ip, port))\r\n break\r\n\r\n\r\ndef main():\r\n count = Value('i', 0)\r\n name_array = Array('c', 1024)\r\n\r\n ssoc = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n ssoc.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, True)\r\n\r\n ip=\"localhost\"\r\n port = 50000\r\n ssoc.bind((ip, port))\r\n ssoc.listen(1)\r\n\r\n while 1:\r\n csoc, addr = ssoc.accept() # 要求が来るまでブロック\r\n print(\"Conneted by\"+str(addr)) #サーバ側の合図\r\n # 接続してきたクライアントを処理するスレッドを用意する\r\n client_thread = threading.Thread(target=recp_handler, args=(csoc,ip,port,count,name_array))\r\n # 親 (メイン) スレッドが死んだら子も道連れにする\r\n client_thread.daemon = True\r\n # スレッドを起動する\r\n client_thread.start()\r\n\r\nif __name__ == '__main__':\r\n main()\r\n", "sub_path": "ok0/server3000.py", "file_name": "server3000.py", "file_ext": "py", "file_size_in_byte": 3533, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "multiprocessing.Value", "line_number": 77, "usage_type": "call"}, {"api_name": "multiprocessing.Array", "line_number": 78, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 80, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 80, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 80, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 81, "usage_type": "attribute"}, {"api_name": "socket.SO_REUSEADDR", "line_number": 81, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "152126330", "text": "#!/usr/bin/env python\n\nimport os\nimport sys\nimport spice\nimport unittest\n\nclass TestHorizons(unittest.TestCase):\n def setUp(self):\n ### Load default kernels\n mydir = os.path.dirname(__file__)\n self.kernels = [ os.path.join( mydir,i.strip()) for i in\n \"\"\"\nkernels/naif0010.tls\nkernels/spk_drm239_WithBurn-full.bsp\n \"\"\".strip().split('\\n') ]\n for kernel in self.kernels: spice.furnsh( kernel )\n\n def tearDown(self):\n ### Unload any kernels\n for kernel in self.kernels: spice.unload( kernel )\n\n def test_horizons(self):\n import horizons\n\n target = 'C/2013 S1'\n target = 'C/2011 L4'\n\n spkFilename,spiceId,status = horizons.gomain(target)\n\n spice.furnsh( spkFilename )\n self.kernels += [spkFilename]\n\n target_ = '_'.join( target.split() )\n\n et0 = spice.utc2et( '2013-01-10T12:00:00' )\n\n ls2au = spice.convrt( spice.clight(), 'KM', 'AU' )\n dpr = spice.dpr()\n spd = spice.spd()\n\n deltatime = None\n\n while deltatime is None or abs(deltatime) > 5e-7:\n stS2I,lsS2I = spice.spkgeo( spiceId, et0, 'J2000', 10 )\n posn, veloc = stS2I[:3], stS2I[3:]\n deltatime = - spice.vdot( posn, veloc ) / spice.vdot( veloc, veloc )\n et0 += deltatime\n\n\n valarrs = [ ]\n print( (deltatime,spice.et2utc(et0,'ISOC',3),) )\n\n deltatime = 1.0\n sixmonths = spice.pi() * 1e7\n\n while deltatime < sixmonths:\n for pmdet in (-deltatime,deltatime):\n et = et0 + pmdet\n utc = spice.et2utc(et,'ISOC',1)\n\n stD2I,lsD2I = spice.spkgeo( spiceId, et, 'J2000', -140)\n stI2S,lsI2S = spice.spkgeo( 10, et, 'J2000', spiceId )\n stD2S,lsD2S = spice.spkgeo( 10, et, 'J2000', -140 )\n\n rD2I, rI2S = [ ls * ls2au for ls in [lsD2I,lsI2S] ]\n aDIS, aSDI = [ ang * dpr for ang in \n [ spice.vsep( spice.vminus(stD2I[:3]), stI2S[:-3] )\n , spice.vsep( stD2S[:3], stD2I[:-3] )\n ]\n ]\n valarrs += [ (et,pmdet,rD2I,rI2S,aDIS,aSDI,utc,) ]\n\n deltatime *= 1.2\n\n valarrs.sort()\n for valarr in valarrs:\n print( '%12.1f %9.3f %9.3f %7.2f %7.2f %s' % valarr[1:] )\n\n days = [i[1]/spd for i in valarrs]\n\n titles = [ i % (target_,) for i in \"\"\"\n Range, %s-DI, AU\n Range, %s-Sun, AU\n Phase, DI-%s-Sun, deg\n Elongation, Sun-DI-%s, deg\n \"\"\".strip().split('\\n')]\n\n try:\n ### Moved matplotlib import to here so test runs to here at least\n from matplotlib import pyplot as plt\n plt.figure(1)\n for idx in range(len(titles)):\n ordinate = [i[idx+2] for i in valarrs]\n plt.subplot( 221+idx )\n plt.plot( days, ordinate )\n plt.plot( days, ordinate, '.')\n plt.title( titles[idx] )\n plt.ylabel( titles[idx] )\n if idx>1: plt.xlabel( 'T-Tperi, d' )\n\n plt.show()\n\n except:\n print( \"Bypassed, or failed, matplotlib tests\" )\n\n\n\nif __name__==\"__main__\":\n unittest.main()\n", "sub_path": "tests/test_horizons2plot.py", "file_name": "test_horizons2plot.py", "file_ext": "py", "file_size_in_byte": 2945, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "unittest.TestCase", "line_number": 8, "usage_type": "attribute"}, {"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": "spice.furnsh", "line_number": 17, "usage_type": "call"}, {"api_name": "spice.unload", "line_number": 21, "usage_type": "call"}, {"api_name": "horizons.gomain", "line_number": 29, "usage_type": "call"}, {"api_name": "spice.furnsh", "line_number": 31, "usage_type": "call"}, {"api_name": "spice.utc2et", "line_number": 36, "usage_type": "call"}, {"api_name": "spice.convrt", "line_number": 38, "usage_type": "call"}, {"api_name": "spice.clight", "line_number": 38, "usage_type": "call"}, {"api_name": "spice.dpr", "line_number": 39, "usage_type": "call"}, {"api_name": "spice.spd", "line_number": 40, "usage_type": "call"}, {"api_name": "spice.spkgeo", "line_number": 45, "usage_type": "call"}, {"api_name": "spice.vdot", "line_number": 47, "usage_type": "call"}, {"api_name": "spice.et2utc", "line_number": 52, "usage_type": "call"}, {"api_name": "spice.pi", "line_number": 55, "usage_type": "call"}, {"api_name": "spice.et2utc", "line_number": 60, "usage_type": "call"}, {"api_name": "spice.spkgeo", "line_number": 62, "usage_type": "call"}, {"api_name": "spice.spkgeo", "line_number": 63, "usage_type": "call"}, {"api_name": "spice.spkgeo", "line_number": 64, "usage_type": "call"}, {"api_name": "spice.vsep", "line_number": 68, "usage_type": "call"}, {"api_name": "spice.vminus", "line_number": 68, "usage_type": "call"}, {"api_name": "spice.vsep", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 95, "usage_type": "call"}, {"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.title", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "unittest.main", "line_number": 110, "usage_type": "call"}]} +{"seq_id": "260458053", "text": "from os import path, remove\nfrom pyDANDIA import crossmatch\nfrom pyDANDIA import normalize_photometry\nfrom pyDANDIA import logs\nfrom astropy.table import Table, Column\nfrom astropy import units as u\nimport numpy as np\nfrom numpy.testing import assert_almost_equal\nimport matplotlib.pyplot as plt\n\ndef simulate_photometry(nstars, nimages):\n \"\"\"Simulated dataset, with a sigma=1.0 applied to all stars\"\"\"\n sigma = 1.0\n data = np.zeros((nstars, nimages,2))\n mean_mag = np.linspace(14.0, 22.0, nstars)\n dmag = np.resize(mean_mag, (nimages, len(mean_mag)))\n dmag = np.swapaxes(dmag,0,1)\n data[:,:,0] = sigma * np.random.randn(nstars, nimages) + dmag\n data[:,:,1].fill(1.0)\n\n return data, mean_mag\n\ndef test_calc_weighted_rms():\n \"\"\"This function expects to receive an array of photometry for\n all stars in a single image as a 2D array shaped (nstars, 2),\n with columns of magnitude and magnitude error.\n It also requires a single-column array with the mean magnitudes of\n all stars in the image.\n \"\"\"\n\n # Simulated dataset, with a sigma=1.0 applied to all stars\n nstars = 1000\n nimages = 100\n (data, mean_mag) = simulate_photometry(nstars, nimages)\n\n rms = normalize_photometry.calc_weighted_rms(data, mean_mag)\n\n fig = plt.figure(1,(10,10))\n plt.hist(rms)\n plt.savefig('test_distro.png')\n plt.close(1)\n\n drms = abs(rms - 1.0)\n assert (drms < 1.0).all()\n\ndef test_calc_weighted_mean():\n\n # Simulated dataset, with a sigma=1.0 applied to all stars\n nstars = 1000\n nimages = 100\n (data, mean_mag) = simulate_photometry(nstars, nimages)\n\n (wmean, wmean_error) = normalize_photometry.calc_weighted_mean(data)\n\n dmean = abs(wmean - mean_mag)\n assert (dmean < 1.0).all()\n\ndef test_find_constant_stars():\n\n # Simulated dataset, with a sigma=1.0 applied to all stars\n nstars = 1000\n nimages = 100\n (sim_phot, mean_mag) = simulate_photometry(nstars, nimages)\n\n # Replace default high-scatter lightcurves with a sample of constant stars\n constant_idx = np.arange(0,100,10)\n dmag = np.resize(mean_mag[constant_idx], (nimages, len(constant_idx)))\n dmag = np.swapaxes(dmag,0,1)\n sim_phot[constant_idx,:,0] = 0.01 * np.random.randn(len(constant_idx), nimages) \\\n + dmag\n sim_phot[constant_idx,:,1].fill(0.01)\n\n # Replace a few lightcurves with zero-scatter samples to simulate stars\n # with very few measurements and artifically low scatter.\n poor_data = np.arange(1,nstars,10)\n sim_phot[poor_data,::2,0] = 0.0\n sim_phot[poor_data,::2,1].fill(0.0)\n\n # Transfer the photometry to the full size array\n data = np.zeros((nstars, nimages, 25))\n data[:,:,23] = sim_phot[:,:,0]\n data[:,:,24] = sim_phot[:,:,1]\n\n # Simulate a xmatch table\n xmatch = crossmatch.CrossMatchTable()\n xmatch.datasets = Table([\n Column(name='dataset_code', data=['ROME-FIELD-01_lsc-doma-1m0-05-fa15_ip']),\n Column(name='dataset_red_dir', data=['/no/path/used']),\n Column(name='dataset_filter', data=['ip']),\n Column(name='primary_ref', data=[1]),\n Column(name='norm_a0', data=[1.0]),\n Column(name='norm_a1', data=[0.0]),\n Column(name='norm_covar_0', data=[0.0]),\n Column(name='norm_covar_1', data=[0.0]),\n Column(name='norm_covar_2', data=[0.0]),\n Column(name='norm_covar_3', data=[0.0]),\n ])\n xmatch.images = Table([\n Column(name='dataset_code', data=np.array(['ROME-FIELD-01_lsc-doma-1m0-05-fa15_ip']*nimages)),\n ])\n\n constant_stars = normalize_photometry.find_constant_stars(xmatch, data)\n\n assert (constant_stars == constant_idx).all()\n\ndef test_calc_phot_normalization():\n nstars = 1000\n factor = 0.005\n ref_phot = np.zeros((nstars,2))\n ref_phot[:,0] = np.linspace(14.0, 22.0, nstars)\n ref_phot[:,1].fill(0.005)\n dset_phot = np.zeros((nstars,2))\n dset_phot[:,0] = np.linspace(14.0, 22.0, nstars)\n dset_phot[:,0] += np.random.randn(nstars)*factor\n dset_phot[:,1].fill(0.01)\n constant_stars = np.arange(0,nstars,1)\n\n (fit, covar_fit) = normalize_photometry.calc_phot_normalization(ref_phot, dset_phot,\n constant_stars)\n assert (abs(fit[0]-1.0) < factor)\n assert (fit[1] 0.0) & (np.median(cal_phot[:,1]) < 0.1))\n\n logs.close_log(log)\n\ndef test_normalize_timeseries_photometry():\n\n log = logs.start_stage_log( '.', 'postproc_phot_norm' )\n\n nstars = 100\n nimages = 10\n mag_col = 23\n mag_err_col = 24\n norm_mag_col = 26\n norm_mag_err_col = 27\n phot_data = np.zeros((nstars,nimages,28))\n\n\n dmag = np.resize(np.linspace(14.0,22.0,nstars), (nimages, nstars))\n dmag = np.swapaxes(dmag,0,1)\n phot_data[:,:,23] = dmag\n phot_data[:,:,24].fill(0.005)\n\n image_index = np.arange(0,nimages,1)\n\n fit = np.array([1.0, 0.5])\n covar_fit = np.array([ [0.00016, -0.0028], [-0.0028, 0.05] ])\n\n phot_data = normalize_photometry.normalize_timeseries_photometry(phot_data, image_index,\n fit, covar_fit,\n mag_col, mag_err_col,\n norm_mag_col, norm_mag_err_col,\n log)\n\n np.testing.assert_array_almost_equal(phot_data[:,:,26], phot_data[:,:,23]+fit[1])\n assert ((np.median(phot_data[:,:,27]) > 0.0) & (np.median(phot_data[:,:,27]) < 0.1))\n\n logs.close_log(log)\n\nif __name__ == '__main__':\n #test_calc_weighted_rms()\n #test_calc_weighted_mean()\n #test_find_constant_stars()\n #test_calc_phot_normalization()\n #test_apply_phot_normalization_single_frame()\n test_normalize_timeseries_photometry()\n", "sub_path": "pyDANDIA/tests/test_normalize_photometry.py", "file_name": "test_normalize_photometry.py", "file_ext": "py", "file_size_in_byte": 6829, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.zeros", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.resize", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 17, "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": "pyDANDIA.normalize_photometry.calc_weighted_rms", "line_number": 36, "usage_type": "call"}, {"api_name": "pyDANDIA.normalize_photometry", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "pyDANDIA.normalize_photometry.calc_weighted_mean", "line_number": 53, "usage_type": "call"}, {"api_name": "pyDANDIA.normalize_photometry", "line_number": 53, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.resize", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 69, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 80, "usage_type": "call"}, {"api_name": "pyDANDIA.crossmatch.CrossMatchTable", "line_number": 85, "usage_type": "call"}, {"api_name": "pyDANDIA.crossmatch", "line_number": 85, "usage_type": "name"}, {"api_name": "astropy.table.Table", "line_number": 86, "usage_type": "call"}, {"api_name": "astropy.table.Column", "line_number": 87, "usage_type": "call"}, {"api_name": "astropy.table.Column", "line_number": 88, "usage_type": "call"}, {"api_name": "astropy.table.Column", "line_number": 89, "usage_type": "call"}, {"api_name": "astropy.table.Column", "line_number": 90, "usage_type": "call"}, {"api_name": "astropy.table.Column", "line_number": 91, "usage_type": "call"}, {"api_name": "astropy.table.Column", "line_number": 92, "usage_type": "call"}, {"api_name": "astropy.table.Column", "line_number": 93, "usage_type": "call"}, {"api_name": "astropy.table.Column", "line_number": 94, "usage_type": "call"}, {"api_name": "astropy.table.Column", "line_number": 95, "usage_type": "call"}, {"api_name": "astropy.table.Column", "line_number": 96, "usage_type": "call"}, {"api_name": "astropy.table.Table", "line_number": 98, "usage_type": "call"}, {"api_name": "astropy.table.Column", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "pyDANDIA.normalize_photometry.find_constant_stars", "line_number": 102, "usage_type": "call"}, {"api_name": "pyDANDIA.normalize_photometry", "line_number": 102, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 110, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 114, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 116, "usage_type": "call"}, {"api_name": "pyDANDIA.normalize_photometry.calc_phot_normalization", "line_number": 118, "usage_type": "call"}, {"api_name": "pyDANDIA.normalize_photometry", "line_number": 118, "usage_type": "name"}, {"api_name": "pyDANDIA.logs.start_stage_log", "line_number": 126, "usage_type": "call"}, {"api_name": "pyDANDIA.logs", "line_number": 126, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 131, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 135, "usage_type": "call"}, {"api_name": "pyDANDIA.normalize_photometry.apply_phot_normalization_single_frame", "line_number": 137, "usage_type": "call"}, {"api_name": "pyDANDIA.normalize_photometry", "line_number": 137, "usage_type": "name"}, {"api_name": "numpy.testing.assert_array_almost_equal", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 140, "usage_type": "attribute"}, {"api_name": "numpy.median", "line_number": 141, "usage_type": "call"}, {"api_name": "pyDANDIA.logs.close_log", "line_number": 143, "usage_type": "call"}, {"api_name": "pyDANDIA.logs", "line_number": 143, "usage_type": "name"}, {"api_name": "pyDANDIA.logs.start_stage_log", "line_number": 147, "usage_type": "call"}, {"api_name": "pyDANDIA.logs", "line_number": 147, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.resize", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 166, "usage_type": "call"}, {"api_name": "pyDANDIA.normalize_photometry.normalize_timeseries_photometry", "line_number": 168, "usage_type": "call"}, {"api_name": "pyDANDIA.normalize_photometry", "line_number": 168, "usage_type": "name"}, {"api_name": "numpy.testing.assert_array_almost_equal", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 174, "usage_type": "attribute"}, {"api_name": "numpy.median", "line_number": 175, "usage_type": "call"}, {"api_name": "pyDANDIA.logs.close_log", "line_number": 177, "usage_type": "call"}, {"api_name": "pyDANDIA.logs", "line_number": 177, "usage_type": "name"}]} +{"seq_id": "196864877", "text": "from flask import render_template, redirect, session, flash, url_for, request\nfrom flask_login import current_user, login_user\nfrom flask_login import logout_user\nfrom flask_login import login_required\nfrom datetime import datetime, timedelta\n\nfrom app import app\nfrom app import db\nfrom app.forms import LoginForm, OverviewForm, NewTaskForm, DeleteTaskForm, RegisterForm, FindTaskForm, EditTaskForm, ShareTaskForm, SetPriorityForm, CategorizeForm\n# Make sure to import all tables\nfrom app.models import User, Task\n\n@app.route('/register', methods=['GET', 'POST'])\ndef register():\n \"\"\"\n Registers new user by creating username and password\n \n Returns\n -------\n Render the register.html template.\n Redirect to the register page. \n Redirect to the login page.\n \"\"\"\n form = RegisterForm()\n if form.validate_on_submit():\n u = User.query.filter_by(username=form.username.data).first()\n if u is None:\n newuser = User(username=form.username.data, password=form.password.data)\n db.session.add(newuser)\n db.session.commit()\n flash('Success!')\n return redirect('/login')\n flash('User already exists')\n return redirect('/login')\n return render_template(\"register.html\", title = 'Register', form=form)\n\n@app.route(\"/login\", methods=['GET', 'POST'])\n@app.route(\"/\", methods=['GET', 'POST'])\ndef login():\n \"\"\"\n Logs in user with existing username and password.\n \n Returns\n -------\n Render the login.html template.\n Redirect to the overview page. \n Redirect to the login page.\n \"\"\"\n form = LoginForm()\n if form.validate_on_submit():\n user = User.query.filter_by(username=form.username.data).first()\n if user is None:\n flash('No account found')\n return redirect('/login')\n if not user.password == form.password.data:\n flash('Incorrect password')\n return redirect('/login')\n login_user(user)\n return redirect('/overview')\n \n return render_template(\"login.html\", title=\"Sign In\", form=form)\n\n@app.route('/overview', methods=['GET', 'POST'])\n@login_required\ndef overview():\n \"\"\"\n Create the account overview page.\n\n Display all existing tasks in a list. Provide options for creating, deleting, and\n editing tasks along with other options for interacting with tasks.\n \n Also displays the completed/incomplete tasks, and updates these lists if a task gets marked as complete.\n\n Returns\n -------\n Render the overview.html template.\n Redirect to the overview page. \n \"\"\"\n form = OverviewForm()\n if request.method == 'GET': \n taskList = []\n completedTasks = []\n uncompletedTasks = []\n format = \"%b-%d-%Y\"\n buttondisplay = {}\n if len(current_user.tasks) != 0:\n buttondisplay[\"show\"] = True\n for task in current_user.tasks:\n due_by = datetime.strptime(task.deadline, format) - datetime.now() + timedelta(days=1)\n\n if task.reminder == 1:\n if due_by.days < 0:\n overdue = due_by.days * -1\n taskList.append({\"Title\": task.title, \"Reminder\": True, \"Deadline\": task.deadline,\n \"Due_By\": f'OVERDUE BY {overdue} DAY(S)', \"ID\": task.id, \"Priority\": task.priority})\n else:\n taskList.append({\"Title\": task.title, \"Reminder\": True, \"Deadline\": task.deadline,\n \"Due_By\": f'DUE IN {due_by.days} DAY(S)', \"ID\": task.id, \"Priority\": task.priority})\n\n uncompletedTasks.append({\"Title\":task.title})\n else:\n taskList.append({\"Title\": task.title, \"Deadline\": task.deadline, \"ID\": task.id, \"Priority\": task.priority})\n uncompletedTasks.append({\"Title\":task.title})\n\n if task.description != \"\":\n for dict in taskList:\n if dict[\"Title\"] == task.title:\n dict[\"Description\"] = task.description\n\n if task.priority != 11:\n for dict in taskList:\n if dict[\"Title\"] == task.title:\n dict[\"PriorityExists\"] = dict[\"Priority\"]\n\n if task.category != \"\":\n for dict in taskList:\n if dict[\"Title\"] == task.title:\n dict[\"Category\"] = task.category\n\n if task.complete == 1:\n completedTasks.append({\"Title\":task.title})\n for dict in taskList:\n if dict[\"Title\"] == task.title:\n dict[\"Complete\"] = task.complete\n uncompletedTasks.remove({\"Title\":task.title})\n taskList.sort(key=lambda i: (i[\"Priority\"] is None, i[\"Priority\"])) \n return render_template('overview.html', title='Account Overview', form=form, list=taskList, \n completedTasks=completedTasks, uncompletedTasks=uncompletedTasks, buttondisplay=buttondisplay)\n \n elif request.method == 'POST':\n if request.form['submit'] == \"clear\":\n for task in current_user.tasks:\n db.session.delete(task)\n db.session.commit()\n if request.form['submit'] == \"save\":\n checks = request.form.getlist('check')\n for key in checks:\n t = Task.query.get(key)\n t.setCompleteStatus(1)\n t.priority = None\n db.session.commit()\n return redirect('/overview')\n\n@app.route('/logout', methods=['GET', 'POST'])\n@login_required\ndef logout():\n \"\"\"\n Log user out of account.\n\n User will be returned to the login page.\n \n Returns\n -------\n Redirect to the login page.\n \"\"\"\n logout_user()\n return redirect('/')\n \n\n@app.route('/createtask', methods = ['GET', 'POST'])\n@login_required\ndef createtask():\n \"\"\"\n Creates a new task.\n \n User will return to the overview page once finished creating task\n User remains on createtask page if all fields required are not filled out.\n Title and finish by date required\n\n Returns\n -------\n Redirect to the createtask page.\n Redirect to the overview page.\n Render the newtask.html template.\n \"\"\"\n form = NewTaskForm()\n if form.validate_on_submit():\n \n if form.title.data is None:\n flash('Please type in a title for new task')\n return redirect('/createtask')\n\n t = Task.query.filter_by(title=form.title.data).first()\n if t is not None:\n flash('Task already exists.')\n return redirect('/createtask')\n \n else:\n newtasks = Task(title=form.title.data, user_id=current_user.id, reminder = form.reminder.data)\n newtasks.setDeadline(form.date.data.strftime(\"%b-%d-%Y\"))\n\n if form.description.data is not None:\n newtasks.description = form.description.data\n \n if form.priority.data == 'None':\n newtasks.priority = 11\n else:\n newtasks.priority = form.priority.data\n \n if form.category.data == 'None':\n newtasks.category = None\n else:\n newtasks.category = form.category.data\n\n current_user.tasks.append(newtasks)\n db.session.add(newtasks)\n db.session.commit()\n\n return redirect('/overview')\n flash('New task created')\n return render_template('newtask.html', title='New Task', form=form)\n\n@app.route('/deletetask', methods = ['GET', 'POST'])\n@login_required\ndef deletetask():\n \"\"\"\n Deletes a task.\n\n User will return to the overview page once finsihed deleting a task.\n User remains on the deletetask if all the fields required are not filled out.\n\n Returns\n -------\n Redirect to the deletetask page.\n Redirect to the overview page.\n Render the deletetask.html template.\n \"\"\"\n form = DeleteTaskForm()\n if form.validate_on_submit():\n if form.title.data is None:\n flash('Please type in a title of task to delete')\n return redirect('/deletetask')\n elif form.title.data is not None:\n t = Task.query.filter_by(title=form.title.data).first()\n if t is None:\n flash(\"Task does not exist!\")\n return redirect('/deletetask')\n else:\n db.session.delete(t)\n db.session.commit()\n return redirect('/overview')\n flash('Task deleted')\n return render_template('deletetask.html', title='Delete Task', form=form)\n\n@app.route('/edittask', methods=['GET', 'POST'])\n@login_required\ndef editTask():\n \"\"\"\n Edits a task.\n\n User remains on the edittask if no title is entered for the task that you want to make edits for. \n User remains on the edittask if the title is already taken. \n User will return to the overview page once finished editing a task.\n \n Returns\n -------\n Redirect to the edittask page.\n Redirect to the overview page.\n Render the edittask.html template. \n \"\"\"\n \n form = EditTaskForm()\n task = session.get('task', None)\n tk = Task.query.filter_by(title=task).first()\n if form.validate_on_submit():\n if form.title.data is None:\n flash('Enter a title')\n return redirect('/edittask')\n t = Task.query.filter_by(title=form.title.data).first()\n if t is not None:\n flash('Title already taken.')\n return redirect('/edittask')\n tk.title = form.title.data\n tk.reminder = form.reminder.data\n tk.setDeadline(form.date.data.strftime(\"%b-%d-%Y\"))\n if form.description.data is not None:\n tk.description = form.description.data\n if form.category.data == 'None':\n tk.category = None\n else:\n tk.category = form.category.data\n db.session.commit()\n return redirect('/overview')\n return render_template('edittask.html', title='Edit Task', form=form)\n\n@app.route('/setpriority', methods=['GET', 'POST'])\n@login_required\ndef setPriority():\n \"\"\"\n Sets the priority of a given task.\n \n User remains on the setpriority if no title is entered for the task.\n User remains on the setpriority if a priority number is not specified. \n User remains on the setpriority if title of task entered does not exist. \n User will return to the overview page once finished setting priority for a task.\n\n Returns\n -------\n Redirect to the setpriority page.\n Redirect to the overview page.\n Render the setpriority.html template. \n \"\"\"\n\n form = SetPriorityForm()\n #task = session.get('task', None)\n tt = Task.query.filter_by(title=form.title.data).first()\n if form.validate_on_submit():\n if form.title.data is None:\n flash('Enter title to set priority')\n return redirect('/setpriority')\n elif form.title.data is not None:\n if tt is None:\n flash(\"Task does not exist!\")\n return redirect('/setpriority')\n elif form.priority.data == 'None':\n tt.priority = 11\n db.session.commit()\n return redirect('/overview')\n else:\n tt.priority = form.priority.data\n db.session.commit()\n return redirect('/overview')\n flash('Priority set!')\n return render_template('setpriority.html', title='Set Priority', form=form)\n#no flash messages pop up when testing\n\n@app.route('/setcategory', methods=['GET', 'POST'])\n@login_required\ndef setCategory():\n \"\"\"\n Sets a category for a given task.\n\n User remains on the setcategory if title of task entered does not exits..\n User will return to the overview page once finished setting category for a task.\n\n Returns\n -------\n Redirect to the setcategory page.\n Redirect to the overview page.\n Render the setcategory.html template.\n \"\"\"\n\n form = CategorizeForm()\n tt = Task.query.filter_by(title=form.title.data).first() \n if form.validate_on_submit():\n if tt is None:\n flash(\"Task does not exist!\")\n return redirect('/setcategory')\n if form.category.data == 'None':\n tt.category = None\n db.session.commit()\n return redirect('/overview')\n else:\n tt.category = form.category.data\n db.session.commit()\n return redirect('/overview')\n flash('Category set!')\n return render_template('setcategory.html', title='Set Category', form=form)\n\n\n@app.route('/findtask', methods=['GET', 'POST'])\n@login_required\ndef findTask():\n \"\"\"\n Finds a task.\n \n User will be able to search for a task by title. This is used for choosing a task to edit.\n \n Returns\n -------\n Redirect to Edit Task page.\n Redirect to Find Task page.\n Render the findtask.html template.\n \"\"\"\n form = FindTaskForm()\n if form.validate_on_submit():\n if form.title.data is None:\n flash('Enter a title')\n return redirect('/findtask')\n t = Task.query.filter_by(title=form.title.data).first()\n if t is None:\n flash(\"No task found\")\n return redirect('/findtask')\n session['task'] = t.title\n return redirect(url_for('editTask'))\n return render_template(\"/findtask.html\", title='Find Task', form=form)\n\n@app.route('/share', methods=['GET', 'POST'])\n@login_required\ndef shareTask():\n \"\"\"\n Shares a task with another user.\n \n Recipient user will also share editing and deleting capabilities over the task.\n \n Returns\n -------\n Redirect to the share task page.\n Redirect to the overview page.\n Renders the share.html template.\n \"\"\"\n form = ShareTaskForm()\n if form.validate_on_submit():\n if form.title.data is None:\n flash(\"Enter a task to share\")\n return redirect('/share')\n if form.username.data is None:\n flash(\"Enter a user to share with\")\n t = Task.query.filter_by(title=form.title.data).first()\n u = User.query.filter_by(username=form.username.data).first()\n if t is None:\n flash(\"Task does not exist\")\n return redirect('/share')\n if u is None:\n flash(\"User does not exist\")\n return redirect('/share')\n u.tasks.append(t)\n db.session.commit()\n flash(\"Successful share\")\n return redirect(\"/overview\")\n return render_template(\"/share.html\", title='Share Task', form=form)\n", "sub_path": "app/routes.py", "file_name": "routes.py", "file_ext": "py", "file_size_in_byte": 14688, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "app.forms.RegisterForm", "line_number": 24, "usage_type": "call"}, {"api_name": "app.models.User.query.filter_by", "line_number": 26, "usage_type": "call"}, {"api_name": "app.models.User.query", "line_number": 26, "usage_type": "attribute"}, {"api_name": "app.models.User", "line_number": 26, "usage_type": "name"}, {"api_name": "app.models.User", "line_number": 28, "usage_type": "call"}, {"api_name": "app.db.session.add", "line_number": 29, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 29, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 29, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 30, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 30, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 35, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 13, "usage_type": "call"}, {"api_name": "app.app", "line_number": 13, "usage_type": "name"}, {"api_name": "app.forms.LoginForm", "line_number": 49, "usage_type": "call"}, {"api_name": "app.models.User.query.filter_by", "line_number": 51, "usage_type": "call"}, {"api_name": "app.models.User.query", "line_number": 51, "usage_type": "attribute"}, {"api_name": "app.models.User", "line_number": 51, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 57, "usage_type": "call"}, {"api_name": "flask_login.login_user", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 61, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 37, "usage_type": "call"}, {"api_name": "app.app", "line_number": 37, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 38, "usage_type": "call"}, {"api_name": "app.app", "line_number": 38, "usage_type": "name"}, {"api_name": "app.forms.OverviewForm", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 80, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 80, "usage_type": "name"}, {"api_name": "flask_login.current_user.tasks", "line_number": 86, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 86, "usage_type": "name"}, {"api_name": "flask_login.current_user.tasks", "line_number": 88, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 88, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 89, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 89, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 89, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 127, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 130, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 130, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 131, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 131, "usage_type": "name"}, {"api_name": "flask_login.current_user.tasks", "line_number": 132, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 132, "usage_type": "name"}, {"api_name": "app.db.session.delete", "line_number": 133, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 133, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 133, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 134, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 134, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 134, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 135, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 135, "usage_type": "name"}, {"api_name": "flask.request.form.getlist", "line_number": 136, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 136, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 136, "usage_type": "name"}, {"api_name": "app.models.Task.query.get", "line_number": 138, "usage_type": "call"}, {"api_name": "app.models.Task.query", "line_number": 138, "usage_type": "attribute"}, {"api_name": "app.models.Task", "line_number": 138, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 141, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 141, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 141, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 142, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 63, "usage_type": "call"}, {"api_name": "app.app", "line_number": 63, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 64, "usage_type": "name"}, {"api_name": "flask_login.logout_user", "line_number": 156, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 157, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 144, "usage_type": "call"}, {"api_name": "app.app", "line_number": 144, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 145, "usage_type": "name"}, {"api_name": "app.forms.NewTaskForm", "line_number": 176, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 180, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 181, "usage_type": "call"}, {"api_name": "app.models.Task.query.filter_by", "line_number": 183, "usage_type": "call"}, {"api_name": "app.models.Task.query", "line_number": 183, "usage_type": "attribute"}, {"api_name": "app.models.Task", "line_number": 183, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 185, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 186, "usage_type": "call"}, {"api_name": "app.models.Task", "line_number": 189, "usage_type": "call"}, {"api_name": "flask_login.current_user.id", "line_number": 189, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 189, "usage_type": "name"}, {"api_name": "flask_login.current_user.tasks.append", "line_number": 205, "usage_type": "call"}, {"api_name": "flask_login.current_user.tasks", "line_number": 205, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 205, "usage_type": "name"}, {"api_name": "app.db.session.add", "line_number": 206, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 206, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 206, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 207, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 207, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 207, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 209, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 210, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 211, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 160, "usage_type": "call"}, {"api_name": "app.app", "line_number": 160, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 161, "usage_type": "name"}, {"api_name": "app.forms.DeleteTaskForm", "line_number": 228, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 231, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 232, "usage_type": "call"}, {"api_name": "app.models.Task.query.filter_by", "line_number": 234, "usage_type": "call"}, {"api_name": "app.models.Task.query", "line_number": 234, "usage_type": "attribute"}, {"api_name": "app.models.Task", "line_number": 234, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 236, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 237, "usage_type": "call"}, {"api_name": "app.db.session.delete", "line_number": 239, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 239, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 239, "usage_type": "name"}, {"api_name": "app.db.session.commit", "line_number": 240, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 240, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 240, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 241, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 242, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 243, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 213, "usage_type": "call"}, {"api_name": "app.app", "line_number": 213, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 214, "usage_type": "name"}, {"api_name": "app.forms.EditTaskForm", "line_number": 262, "usage_type": "call"}, {"api_name": "flask.session.get", "line_number": 263, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 263, "usage_type": "name"}, {"api_name": "app.models.Task.query.filter_by", "line_number": 264, "usage_type": "call"}, {"api_name": "app.models.Task.query", "line_number": 264, "usage_type": "attribute"}, {"api_name": "app.models.Task", "line_number": 264, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 267, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 268, "usage_type": "call"}, {"api_name": "app.models.Task.query.filter_by", "line_number": 269, "usage_type": "call"}, {"api_name": "app.models.Task.query", "line_number": 269, "usage_type": "attribute"}, {"api_name": "app.models.Task", "line_number": 269, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 271, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 272, "usage_type": "call"}, {"api_name": "app.db.session.commit", "line_number": 282, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 282, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 282, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 283, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 284, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 245, "usage_type": "call"}, {"api_name": "app.app", "line_number": 245, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 246, "usage_type": "name"}, {"api_name": "app.forms.SetPriorityForm", "line_number": 304, "usage_type": "call"}, {"api_name": "app.models.Task.query.filter_by", "line_number": 306, "usage_type": "call"}, {"api_name": "app.models.Task.query", "line_number": 306, "usage_type": "attribute"}, {"api_name": "app.models.Task", "line_number": 306, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 309, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 310, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 313, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 314, "usage_type": "call"}, {"api_name": "app.db.session.commit", "line_number": 317, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 317, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 317, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 318, "usage_type": "call"}, {"api_name": "app.db.session.commit", "line_number": 321, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 321, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 321, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 322, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 323, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 324, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 286, "usage_type": "call"}, {"api_name": "app.app", "line_number": 286, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 287, "usage_type": "name"}, {"api_name": "app.forms.CategorizeForm", "line_number": 343, "usage_type": "call"}, {"api_name": "app.models.Task.query.filter_by", "line_number": 344, "usage_type": "call"}, {"api_name": "app.models.Task.query", "line_number": 344, "usage_type": "attribute"}, {"api_name": "app.models.Task", "line_number": 344, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 347, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 348, "usage_type": "call"}, {"api_name": "app.db.session.commit", "line_number": 351, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 351, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 351, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 352, "usage_type": "call"}, {"api_name": "app.db.session.commit", "line_number": 355, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 355, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 355, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 356, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 357, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 358, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 327, "usage_type": "call"}, {"api_name": "app.app", "line_number": 327, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 328, "usage_type": "name"}, {"api_name": "app.forms.FindTaskForm", "line_number": 375, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 378, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 379, "usage_type": "call"}, {"api_name": "app.models.Task.query.filter_by", "line_number": 380, "usage_type": "call"}, {"api_name": "app.models.Task.query", "line_number": 380, "usage_type": "attribute"}, {"api_name": "app.models.Task", "line_number": 380, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 382, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 383, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 384, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 385, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 385, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 386, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 361, "usage_type": "call"}, {"api_name": "app.app", "line_number": 361, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 362, "usage_type": "name"}, {"api_name": "app.forms.ShareTaskForm", "line_number": 402, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 405, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 406, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 408, "usage_type": "call"}, {"api_name": "app.models.Task.query.filter_by", "line_number": 409, "usage_type": "call"}, {"api_name": "app.models.Task.query", "line_number": 409, "usage_type": "attribute"}, {"api_name": "app.models.Task", "line_number": 409, "usage_type": "name"}, {"api_name": "app.models.User.query.filter_by", "line_number": 410, "usage_type": "call"}, {"api_name": "app.models.User.query", "line_number": 410, "usage_type": "attribute"}, {"api_name": "app.models.User", "line_number": 410, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 412, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 413, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 415, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 416, "usage_type": "call"}, {"api_name": "app.db.session.commit", "line_number": 418, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 418, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 418, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 419, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 420, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 421, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 388, "usage_type": "call"}, {"api_name": "app.app", "line_number": 388, "usage_type": "name"}, {"api_name": "flask_login.login_required", "line_number": 389, "usage_type": "name"}]} +{"seq_id": "212064763", "text": "import pytz\nfrom datetime import datetime as dt\nimport boto.s3.connection\nimport catflap.settings as settings\nimport base64\nimport re\nimport math\n\nAWS_HEADERS = {\n \"Cache-Control\": \"public, max-age=86400\"\n}\n\nINSIDE = 1\nOUTSIDE = 0\nSCHRODINGER = 2\n\n\nclass ImgUrl(object):\n def __init__(self, key):\n self.filename = key.name\n self.time_taken = localise(\n dt.fromtimestamp(float(re.search(\".+_(\\d+_\\d+)[^\\d]+\", key.name).groups()[0].replace(\"_\", \".\"))))\n self.id = base64.urlsafe_b64encode((self.filename + settings.SALT).encode())\n self.size = key.size\n self.url = key.generate_url(expires_in = 0, query_auth = False, response_headers = AWS_HEADERS)\n self.httpurl = key.generate_url(expires_in = 0, query_auth = False, force_http = True)\n if \"-\" not in key.name:\n self.direction = SCHRODINGER\n else:\n if key.name.split(\"-\")[-1] == \"1.jpg\":\n self.direction = INSIDE\n else:\n self.direction = OUTSIDE\n\n\n @property\n def time_ago(self):\n return now() - self.time_taken\n\n @property\n def time_ago_str(self):\n timeago = self.time_ago\n days = timeago.days\n hours = math.floor(timeago.seconds / 3600)\n if hours > 1:\n hp = \"s\"\n else:\n hp = \"\"\n minutes = math.floor((timeago.seconds - (hours * 3600)) / 60)\n if minutes > 1:\n mp = \"s\"\n else:\n mp = \"\"\n\n if days < 1:\n return f\"{hours} hour{hp} and {minutes} minute{mp}\"\n else:\n return f\"{days} days, {hours} hour{hp}, and {minutes} minute{mp}\"\n\n @property\n def iscat(self):\n try:\n return \"not%20a%20cat\" not in self.url\n except:\n return True # always assume cat\n\n\nclass S3Conn(object):\n def __init__(self):\n self.client = boto.s3.connect_to_region(\"eu-west-2\", aws_access_key_id = settings.AWS_KEY, is_secure = False, aws_secret_access_key = settings.AWS_SECRET, calling_format = boto.s3.connection.OrdinaryCallingFormat())\n self.bucket = self.client.get_bucket(settings.IMAGE_BUCKET)\n\n @property\n def raw_keys(self):\n return list(reversed(\n sorted([k for k in self.bucket.get_all_keys() if k.name.endswith(\".jpg\")],\n key = lambda x: x.last_modified)))\n\n @property\n def custom_keys(self):\n return [ImgUrl(k) for k in self.raw_keys]\n\n @property\n def cats(self):\n return [k for k in self.custom_keys if k.iscat]\n\n @property\n def latest_cat(self):\n return next(k for k in self.custom_keys if k.iscat)\n\n def get_key(self, b64imgid):\n filename = decode_filename(b64imgid)\n return self.bucket.get_key(filename, validate = False)\n\n def set_not_cat(self, b64imgid):\n filename = decode_filename(b64imgid)\n new_key = \"not a cat/\" + filename\n self.bucket.copy_key(new_key, settings.IMAGE_BUCKET, filename)\n self.bucket.delete_key(filename)\n\n\ndef localise(t):\n london = pytz.timezone(\"Europe/London\")\n return london.localize(t)\n\n\ndef now():\n return localise(dt.now())\n\n\ndef decode_filename(b64imgid):\n bytesid = bytes(b64imgid, \"utf-8\")\n b64id = base64.urlsafe_b64decode(bytesid)\n decodeid = b64id.decode()\n filename = decodeid.replace(settings.SALT, \"\").split(\"?\")[0]\n return filename\n\n\nconn = S3Conn()\n", "sub_path": "catflapsite/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 3430, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datetime.datetime.fromtimestamp", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "name"}, {"api_name": "re.search", "line_number": 22, "usage_type": "call"}, {"api_name": "base64.urlsafe_b64encode", "line_number": 23, "usage_type": "call"}, {"api_name": "catflap.settings.SALT", "line_number": 23, "usage_type": "attribute"}, {"api_name": "catflap.settings", "line_number": 23, "usage_type": "name"}, {"api_name": "math.floor", "line_number": 44, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 49, "usage_type": "call"}, {"api_name": "boto.s3.connection.s3.connect_to_region", "line_number": 70, "usage_type": "call"}, {"api_name": "boto.s3.connection.s3", "line_number": 70, "usage_type": "attribute"}, {"api_name": "boto.s3.connection", "line_number": 70, "usage_type": "name"}, {"api_name": "catflap.settings.AWS_KEY", "line_number": 70, "usage_type": "attribute"}, {"api_name": "catflap.settings", "line_number": 70, "usage_type": "name"}, {"api_name": "catflap.settings.AWS_SECRET", "line_number": 70, "usage_type": "attribute"}, {"api_name": "boto.s3.connection.s3.connection.OrdinaryCallingFormat", "line_number": 70, "usage_type": "call"}, {"api_name": "catflap.settings.IMAGE_BUCKET", "line_number": 71, "usage_type": "attribute"}, {"api_name": "catflap.settings", "line_number": 71, "usage_type": "name"}, {"api_name": "catflap.settings.IMAGE_BUCKET", "line_number": 98, "usage_type": "attribute"}, {"api_name": "catflap.settings", "line_number": 98, "usage_type": "name"}, {"api_name": "pytz.timezone", "line_number": 103, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 108, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 108, "usage_type": "name"}, {"api_name": "base64.urlsafe_b64decode", "line_number": 113, "usage_type": "call"}, {"api_name": "catflap.settings.SALT", "line_number": 115, "usage_type": "attribute"}, {"api_name": "catflap.settings", "line_number": 115, "usage_type": "name"}]} +{"seq_id": "292359468", "text": "import torch\nfrom torch import nn as nn\nfrom torch.nn import functional as F\n\nfrom ..registry import NECKS\nfrom ..utils import ConvModule\n\n\ndef use_ceil_mode(xs, xl):\n ws, hs = xs.size()[2:4]\n wl, hl = xl.size()[2:4]\n return (wl / ws < 2) or (hl / hs < 2)\n\n\ndef get_groups(channels, ref=8):\n if channels == 1:\n return 1\n xs = filter(lambda x: channels % x == 0, range(2, channels + 1))\n c = min(filter(lambda x: x >= ref, xs), key=lambda x: x - ref)\n return channels // c\n\n\ndef get_gn_cfg(channels):\n return {\n \"type\": \"GN\",\n \"num_groups\": get_groups(channels)\n }\n\n\ndef fast_normalize(w, eps=1e-4, dim=0):\n w = torch.relu(w)\n w = w / (torch.sum(w, dim=dim, keepdim=True) + eps)\n return w\n\n\ndef dwconv(in_channels, out_channels, kernel_size=3):\n conv1 = ConvModule(\n in_channels, in_channels, kernel_size,\n padding=kernel_size // 2, groups=in_channels,\n norm_cfg=get_gn_cfg(in_channels), activation='relu',\n )\n conv2 = ConvModule(\n in_channels, out_channels, 1,\n norm_cfg=get_gn_cfg(out_channels), activation=None,\n )\n return nn.Sequential(\n conv1,\n conv2,\n )\n\n\nclass BottomUpFusion2(nn.Module):\n def __init__(self, f_channels):\n super().__init__()\n self.weight = nn.Parameter(torch.ones((2,)), requires_grad=True)\n self.conv = dwconv(f_channels, f_channels, kernel_size=3)\n\n def forward(self, p, pp):\n pp = F.max_pool2d(pp, kernel_size=2, ceil_mode=use_ceil_mode(p, pp))\n w = fast_normalize(self.weight)\n p = w[0] * p + w[1] * pp\n p = self.conv(p)\n return p\n\n\nclass TopDownFusion2(nn.Module):\n\n def __init__(self, f_channels):\n super().__init__()\n self.weight = nn.Parameter(torch.ones((2,)), requires_grad=True)\n self.conv = dwconv(f_channels, f_channels, kernel_size=3)\n\n def forward(self, p, pp):\n h, w = p.size()[2:4]\n pp = F.interpolate(pp, (h, w), mode='bilinear', align_corners=False)\n w = fast_normalize(self.weight)\n p = w[0] * p + w[1] * pp\n p = self.conv(p)\n return p\n\n\nclass BottomUpFusion3(nn.Module):\n\n def __init__(self, f_channels):\n super().__init__()\n self.weight = nn.Parameter(torch.ones((3,)), requires_grad=True)\n self.conv = dwconv(f_channels, f_channels, kernel_size=3)\n\n def forward(self, p1, p2, pp):\n pp = F.max_pool2d(pp, kernel_size=2, ceil_mode=use_ceil_mode(p1, pp))\n w = fast_normalize(self.weight)\n p = w[0] * p1 + w[1] * p2 + w[2] * pp\n p = self.conv(p)\n return p\n\n\nclass BiFPNLayer(nn.Module):\n\n def __init__(self, in_channels, out_channels):\n super().__init__()\n assert isinstance(in_channels, list)\n self.in_channels = in_channels\n n = len(in_channels)\n self.lats = nn.ModuleList([\n ConvModule(c, out_channels, kernel_size=1, norm_cfg=get_gn_cfg(out_channels))\n if c != out_channels else nn.Identity()\n for c in in_channels\n ])\n self.tds = nn.ModuleList([\n TopDownFusion2(out_channels)\n for _ in range(n - 1)\n ])\n self.bus = nn.ModuleList([\n BottomUpFusion3(out_channels)\n for _ in range(n - 2)\n ])\n self.bu = BottomUpFusion2(out_channels)\n\n def forward(self, ps):\n ps = [lat(p) for p, lat in zip(ps, self.lats)]\n\n ps2 = [ps[-1]]\n for p, td in zip(reversed(ps[:-1]), self.tds):\n ps2.append(td(p, ps2[-1]))\n ps3 = [ps2[-1]]\n ps2 = reversed(ps2[1:-1])\n\n for p1, p2, bu in zip(ps[1:-1], ps2, self.bus):\n ps3.append(bu(p1, p2, ps3[-1]))\n ps3.append(self.bu(ps[-1], ps3[-1]))\n\n return tuple(ps3)\n\n\n@NECKS.register_module\nclass BiFPN(nn.Module):\n\n def __init__(self, in_channels, out_channels, num_outs, num_layers):\n super().__init__()\n assert isinstance(in_channels, list)\n self.in_channels = in_channels\n\n self.extras = nn.ModuleList()\n extra_levels = num_outs - len(in_channels)\n\n self.fpns = nn.ModuleList([\n BiFPNLayer(in_channels + [out_channels] * extra_levels, out_channels)\n ])\n for _ in range(num_layers - 1):\n self.fpns.append(BiFPNLayer([out_channels] * num_outs, out_channels))\n\n if extra_levels >= 1:\n for i in range(extra_levels):\n if i == 0:\n extra_fpn_conv = ConvModule(\n self.in_channels[-1],\n out_channels,\n 3,\n stride=2,\n padding=1,\n norm_cfg=get_gn_cfg(out_channels),\n activation=None)\n else:\n extra_fpn_conv = ConvModule(\n out_channels,\n out_channels,\n 3,\n stride=2,\n padding=1,\n norm_cfg=get_gn_cfg(out_channels),\n activation='relu',\n inplace=False,\n order=('act', 'conv', 'norm')\n )\n self.extras.append(extra_fpn_conv)\n\n def forward(self, ps):\n assert isinstance(ps, (tuple, list))\n ps = list(ps)\n for extra in self.extras:\n ps.append(extra(ps[-1]))\n for fpn in self.fpns:\n ps = fpn(tuple(ps))\n return ps\n\n def init_weights(self):\n for m in self.modules():\n if isinstance(m, nn.Conv2d):\n nn.init.xavier_uniform_(m.weight)\n if m.bias is not None:\n nn.init.zeros_(m.bias)\n", "sub_path": "mmdet/models/necks/bifpn.py", "file_name": "bifpn.py", "file_ext": "py", "file_size_in_byte": 5780, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.relu", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 32, "usage_type": "call"}, {"api_name": "utils.ConvModule", "line_number": 37, "usage_type": "call"}, {"api_name": "utils.ConvModule", "line_number": 42, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 46, "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.Parameter", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 55, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn.functional.max_pool2d", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 66, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 82, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn.functional.max_pool2d", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 90, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 97, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 97, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 104, "usage_type": "name"}, {"api_name": "utils.ConvModule", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.nn.Identity", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 106, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 109, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 113, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 136, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 136, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 143, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 146, "usage_type": "name"}, {"api_name": "utils.ConvModule", "line_number": 155, "usage_type": "call"}, {"api_name": "utils.ConvModule", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.nn.Conv2d", "line_number": 188, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 188, "usage_type": "name"}, {"api_name": "torch.nn.init.xavier_uniform_", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 189, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 189, "usage_type": "name"}, {"api_name": "torch.nn.init.zeros_", "line_number": 191, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 191, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 191, "usage_type": "name"}, {"api_name": "registry.NECKS.register_module", "line_number": 135, "usage_type": "attribute"}, {"api_name": "registry.NECKS", "line_number": 135, "usage_type": "name"}]} +{"seq_id": "204698095", "text": "from image_utility import ImageUtility\nimport os\nfrom PIL import Image\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom skimage import transform\n\ndef print_image_arr(k, image, landmarks_x, landmarks_y):\n plt.figure()\n plt.imshow(image)\n implot = plt.imshow(image)\n\n plt.scatter(x=landmarks_x[:], y=landmarks_y[:], c='black', s=20)\n plt.scatter(x=landmarks_x[:], y=landmarks_y[:], c='white', s=15)\n plt.axis('off')\n plt.savefig('sss' + str(k) + '.png', bbox_inches='tight')\n # plt.show()\n plt.clf()\n\ndef __gaussian_k(x0, y0, sigma, width, height):\n \"\"\" Make a square gaussian kernel centered at (x0, y0) with sigma as SD.\n \"\"\"\n x = np.arange(0, width, 1, float)\n y = np.arange(0, height, 1, float)[:, np.newaxis]\n return np.exp(-((x - x0) ** 2 + (y - y0) ** 2) / (2 * sigma ** 2))\n\ndef generate_hm(height, width, landmarks, s=1.0, upsample=True):\n \"\"\" Generate a full Heap Map for every landmarks in an array\n Args:\n height : The height of Heat Map (the height of target output)\n width : The width of Heat Map (the width of target output)\n joints : [(x1,y1),(x2,y2)...] containing landmarks\n maxlenght : Lenght of the Bounding Box\n \"\"\"\n\n Nlandmarks = len(landmarks)\n hm = np.zeros((height, width, Nlandmarks // 2), dtype=np.float32)\n\n j = 0\n for i in range(0, Nlandmarks, 2):\n\n if upsample:\n x = (112 - float(landmarks[i]) * 224)\n y = (112 - float(landmarks[i + 1]) * 224)\n else:\n x = landmarks[i]\n y = landmarks[i + 1]\n\n x = int(x // 4)\n y = int(y // 4)\n\n hm[:, :, j] = __gaussian_k(x, y, s, height, width)\n j += 1\n return hm\n\ndef generate_hm_and_save():\n images_dir = IbugConf.images_dir\n npy_dir = IbugConf.lbls_dir\n\n for file in os.listdir(images_dir):\n if file.endswith(\".pts\"):\n points_arr = []\n file_name = os.path.join(images_dir, file)\n file_name_save = str(file)[:-3] + \"npy\"\n hm_f = npy_dir + file_name_save\n # imgpr.print_image_arr_heat(1, hm, print_single=False)\n\n np.save(hm_f, hm)\n with open(file_name) as fp:\n line = fp.readline()\n cnt = 1\n while line:\n if 3 < cnt < 72:\n x_y_pnt = line.strip()\n x = float(x_y_pnt.split(\" \")[0])\n y = float(x_y_pnt.split(\" \")[1])\n points_arr.append(x)\n points_arr.append(y)\n line = fp.readline()\n cnt += 1\n hm = generate_hm(56, 56, np.array(points_arr), 1.5, False)\n\n\nclass InputDataSize:\n image_input_size = 224\n landmark_len = 11\n landmark_face_len = 54\n landmark_nose_len = 18\n landmark_eys_len = 24\n landmark_mouth_len = 40\n pose_len = 3\n\nclass IbugConf:\n\n images_dir = '/media/ali/extradata/facial_landmark_ds/from_ibug/train_set/train_before_heatmap/'\n lbls_dir = '/media/ali/extradata/facial_landmark_ds/from_ibug/train_set/train_before_heatmap_npy/'\n\n\n tf_train_path = '/media/ali/extradata/facial_landmark_ds/from_ibug/train_set/train.tfrecords'\n tf_test_path = '/media/ali/extradata/facial_landmark_ds/from_ibug/train_set/test.tfrecords'\n tf_evaluation_path = '/media/ali/extradata/facial_landmark_ds/from_ibug/train_set/evaluation.tfrecords'\n\n tf_train_path_heatmap = '/home/soroush/PycharmProjects/Bodytracking/dataloader/LSP/lsp_dataset_original/heatmap/'\n tf_test_path_heatmap = '/media/ali/extradata/facial_landmark_ds/from_ibug/train_set/test_heatmap.tfrecords'\n tf_evaluation_path_heatmap = '/media/ali/extradata/facial_landmark_ds/from_ibug/train_set/evaluation_heatmap.tfrecords'\n\n # origin_number_of_all_sample = 3148 # afw, train_helen, train_lfpw\n # origin_number_of_train_sample = 2834 # 95 % for train\n # origin_number_of_evaluation_sample = 314 # 5% for evaluation\n\n # origin_number_of_all_sample = 1000 # afw, train_helen, train_lfpw\n origin_number_of_all_sample = 3987 # afw, train_helen, train_lfpw\n origin_number_of_train_sample = 3987 # 95 % for train\n origin_number_of_evaluation_sample = 0 # 5% for evaluation\n\n # augmentation_factor = 3 # create 100 image from 1\n augmentation_factor = 5 # create 100 image from 1\n # augmentation_factor_rotate = 20 # create 100 image from 1\n augmentation_factor_rotate = 13 # create 100 image from 1\n\n sum_of_train_samples = origin_number_of_train_sample * augmentation_factor\n sum_of_validation_samples = origin_number_of_evaluation_sample * augmentation_factor\n\n # img_path_prefix = '/home/soroush/PycharmProjects/Bodytracking/dataloader/LSP/lsp_dataset_original/images/'\n img_path_prefix = '/media/data/Soroush_data/body_tracking/FLIC/FLIC/images/'\n # img_path_prefix = '/home/soroush/PycharmProjects/Bodytracking/dataloader/FLIC/images/'\n pts_path_prefix = '/media/data/Soroush_data/body_tracking/FLIC/FLIC/imagesAnnotaion_train/'\n # pts_path_prefix = '/home/soroush/PycharmProjects/Bodytracking/dataloader/FLIC/imagesAnnotaion_train/'\n # pts_path_prefix = '/home/soroush/PycharmProjects/Bodytracking/dataloader/LSP/lsp_dataset_original/points/'\n\n rotated_img_path_prefix = '/media/data/Soroush_data/body_tracking/FLIC/FLIC/images_rotated/'\n # rotated_img_path_prefix = '/home/soroush/PycharmProjects/Bodytracking/dataloader/FLIC/images_rotated/'\n rotated_pts_path_prefix = '/media/data/Soroush_data/body_tracking/FLIC/FLIC/points_rotated/'\n # rotated_pts_path_prefix = '/home/soroush/PycharmProjects/Bodytracking/dataloader/FLIC/points_rotated/'\n\n # before_heatmap_img_path_prefix = '/home/soroush/PycharmProjects/Bodytracking/dataloader/LSP/lsp_dataset_original/heatmap/'\n # before_heatmap_img_path_prefix = '/home/soroush/PycharmProjects/Bodytracking/dataloader/FLIC/heatmap/'\n before_heatmap_img_path_prefix = '/media/data/Soroush_data/body_tracking/FLIC/FLIC/heatmap/'\n\n\n\nimage_utility = ImageUtility()\n\n\"\"\"\nimport random\npng_file_arr = []\npng_file_name = []\nfor file in sorted(os.listdir(IbugConf.pts_path_prefix)):\n # if file.endswith(\".jpg\") or file.endswith(\".png\"):\n if file.endswith(\".pts\"):\n png_file_arr.append(os.path.join(IbugConf.img_path_prefix, file[:-3] + \"jpg\"))\n png_file_name.append(file)\n\nnumber_of_samples = IbugConf.origin_number_of_all_sample\n# number_of_samples = 1000\n\n\n\n# rotate\nfor i in range(number_of_samples):\n img_file = png_file_arr[i]\n pts_file = os.path.join(IbugConf.pts_path_prefix, png_file_name[i])[:-3] + \"pts\"\n print( 'image: ' + img_file)\n points_arr = []\n points_x_arr = []\n points_y_arr = []\n with open(pts_file) as fp:\n line = fp.readline()\n cnt = 1\n while line:\n if 3 < cnt < 15:\n x_y_pnt = line.strip()\n x = float(x_y_pnt.split(\" \")[0])\n y = float(x_y_pnt.split(\" \")[1])\n points_arr.append(x)\n points_arr.append(y)\n points_x_arr.append(x)\n points_y_arr.append(y)\n line = fp.readline()\n cnt += 1\n\n img = Image.open(img_file)\n img = np.array(img)\n\n resized_img = img\n landmark_arr_xy = points_arr\n # print_image_arr(10000 * (i + 1) + 1, resized_img, points_x_arr, points_y_arr)\n\n # heatmap_lbl_img = np.zeros(shape=[resized_img.shape[0], resized_img.shape[1]]) # 2d is ok\n # for j in range(0, len(landmark_arr_xy), 2):\n # heatmap_lbl_img[int(landmark_arr_xy[j + 1]), int(landmark_arr_xy[j])] = 255\n\n for j in range(IbugConf.augmentation_factor_rotate):\n image_utility.random_rotate(resized_img, landmark_arr_xy,\n IbugConf.rotated_img_path_prefix + str(10000 * (i + 1) + j),\n IbugConf.rotated_pts_path_prefix + str(10000 * (i + 1) + j), str(10000 * (i + 1) + j))\n\n\n\n\n number_of_samples = IbugConf.origin_number_of_all_sample\n number_of_train = IbugConf.origin_number_of_train_sample\n number_of_evaluation = IbugConf.origin_number_of_evaluation_sample\n\n\"\"\"\npng_file_arr = []\npng_file_name = []\n\nfor file in sorted(os.listdir(IbugConf.rotated_img_path_prefix)):\n if file.endswith(\".jpg\") or file.endswith(\".png\"):\n png_file_arr.append(os.path.join(IbugConf.rotated_img_path_prefix, file))\n png_file_name.append(file)\n\nnumber_of_samples = IbugConf.origin_number_of_all_sample * IbugConf.augmentation_factor_rotate\n# number_of_samples = 1000\n\n\nnpy_dir = IbugConf.before_heatmap_img_path_prefix\n\nfor i in range(len(os.listdir(IbugConf.rotated_img_path_prefix))):\n print(i)\n img_file = png_file_arr[i]\n pts_file = os.path.join(IbugConf.rotated_pts_path_prefix, png_file_name[i])[:-3] + \"pts\"\n\n points_arr = []\n points_x_arr = []\n points_y_arr = []\n with open(pts_file) as fp:\n line = fp.readline()\n cnt = 1\n while line:\n if 3 < cnt < 15:\n x_y_pnt = line.strip()\n x = float(x_y_pnt.split(\" \")[0])\n y = float(x_y_pnt.split(\" \")[1])\n points_arr.append(x)\n points_arr.append(y)\n points_x_arr.append(x)\n points_y_arr.append(y)\n line = fp.readline()\n cnt += 1\n\n img = Image.open(img_file)\n\n '''normalize image'''\n resized_img = np.array(img) / 255.0\n\n #resized_img = transform.resize(resized_img, (224, 224))\n '''crop data: we add a small margin to the images'''\n landmark_arr_xy, landmark_arr_x, landmark_arr_y = image_utility.create_landmarks(landmarks=points_arr,\n scale_factor_x=1,\n scale_factor_y=1)\n\n '''augment the images, then normalize the landmarks based on the hyperface method'''\n for k in range(IbugConf.augmentation_factor):\n '''save the origin image as well'''\n #print(k)\n if k == 0:\n landmark_arr_flat_aug = landmark_arr_xy\n img_aug = resized_img\n\n else:\n '''save the augmented images'''\n if k % 2 == 0:\n #print(np.shape(resized_img))\n landmark_arr_flat_aug, img_aug = image_utility.random_augmentation(landmark_arr_xy, resized_img)\n else:\n landmark_arr_flat_aug, img_aug = image_utility.augment(resized_img, landmark_arr_xy)\n\n '''test '''\n #print_image_arr(k, img_aug, [], [])\n\n '''again resize image to 224*224 after augmentation'''\n resized_img_new = transform.resize(img_aug,\n (InputDataSize.image_input_size, InputDataSize.image_input_size, 3)\n , anti_aliasing=True)\n\n #print_image_arr(k, resized_img_new, [], [])\n\n dims = resized_img.shape\n height = dims[0]\n width = dims[1]\n scale_factor_y = InputDataSize.image_input_size / height\n scale_factor_x = InputDataSize.image_input_size / width\n\n '''retrieve and rescale landmarks in after augmentation'''\n landmark_arr_flat, landmark_arr_x, landmark_arr_y = image_utility.create_landmarks(landmarks=landmark_arr_flat_aug,\n scale_factor_x=scale_factor_x,\n scale_factor_y=scale_factor_y)\n\n # print_image_arr(k, resized_img_new, landmark_arr_x, landmark_arr_y)\n\n '''calculate pose'''\n resized_img_new_cp = np.array(resized_img_new)\n # yaw_predicted, pitch_predicted, roll_predicted = detect.detect(resized_img_new_cp, isFile=False,show=False)\n '''normalize pose -1 -> +1 '''\n # min_degree = -65\n # max_degree = 65\n # yaw_normalized = 2 * ((yaw_predicted - min_degree) / (max_degree - min_degree)) - 1\n # pitch_normalized = 2 * ((pitch_predicted - min_degree) / (max_degree - min_degree)) - 1\n # roll_normalized = 2 * ((roll_predicted - min_degree) / (max_degree - min_degree)) - 1\n # pose_array = np.array([yaw_normalized, pitch_normalized, roll_normalized])\n pose_array = np.array([1, 1, 1])\n\n '''normalize landmarks based on hyperface method'''\n width = len(resized_img_new[0])\n height = len(resized_img_new[1])\n x_center = width / 2\n y_center = height / 2\n landmark_arr_flat_normalized = []\n for p in range(0, len(landmark_arr_flat), 2):\n landmark_arr_flat_normalized.append((x_center - landmark_arr_flat[p]) / width)\n landmark_arr_flat_normalized.append((y_center - landmark_arr_flat[p + 1]) / height)\n\n '''test print after augmentation'''\n landmark_arr_flat_n, landmark_arr_x_n, landmark_arr_y_n = image_utility.create_landmarks_from_normalized(\n landmark_arr_flat_normalized, 224, 224, 112, 112)\n # print_image_arr((i*100)+(k+1), resized_img_new, landmark_arr_x_n, landmark_arr_y_n)\n\n\n heatmap_landmark = generate_hm(56, 56, landmark_arr_flat_normalized, s=3.0)\n heatmap_landmark_all = np.sum(heatmap_landmark, axis=2)\n #print_image_arr(2*k, heatmap_landmark_all, [], [])\n # save heatmap\n\n file_name_save = png_file_name[i][0:-4] + \"_\" + str(k) + \".npy\"\n hm_f = npy_dir + file_name_save\n # imgpr.print_image_arr_heat(1, hm, print_single=False)\n\n np.save(hm_f, heatmap_landmark)\n\n landmark_arr_flat_normalized = np.array(landmark_arr_flat_normalized)\n\n\n '''save image'''\n im = Image.fromarray((resized_img_new * 255).astype(np.uint8))\n file_name = IbugConf.before_heatmap_img_path_prefix + png_file_name[i][0:-4] + \"_\" + str(k)\n im.save(str(file_name) + '.jpg')\n\n pnt_file = open(str(file_name) + \".pts\", \"w\")\n pre_txt = [\"version: 1 \\n\", \"n_points: 14 \\n\", \"{ \\n\"]\n pnt_file.writelines(pre_txt)\n points_txt = \"\"\n for l in range(0, len(landmark_arr_flat_normalized), 2):\n points_txt += str(landmark_arr_flat_normalized[l]) + \" \" + str(landmark_arr_flat_normalized[l + 1]) + \"\\n\"\n\n pnt_file.writelines(points_txt)\n pnt_file.write(\"} \\n\")\n pnt_file.close()\n", "sub_path": "data_augment.py", "file_name": "data_augment.py", "file_ext": "py", "file_size_in_byte": 14360, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 13, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "image_utility.ImageUtility", "line_number": 142, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 209, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 211, "usage_type": "call"}, {"api_name": "os.path", "line_number": 211, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 220, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 223, "usage_type": "call"}, {"api_name": "os.path", "line_number": 223, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 243, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 243, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 246, "usage_type": "call"}, {"api_name": "image_utility.create_landmarks", "line_number": 250, "usage_type": "call"}, {"api_name": "image_utility.random_augmentation", "line_number": 266, "usage_type": "call"}, {"api_name": "image_utility.augment", "line_number": 268, "usage_type": "call"}, {"api_name": "skimage.transform.resize", "line_number": 274, "usage_type": "call"}, {"api_name": "skimage.transform", "line_number": 274, "usage_type": "name"}, {"api_name": "image_utility.create_landmarks", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 294, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 303, "usage_type": "call"}, {"api_name": "image_utility.create_landmarks_from_normalized", "line_number": 316, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 322, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 330, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 332, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 336, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 336, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 336, "usage_type": "attribute"}]} +{"seq_id": "430159632", "text": "import requests\nfrom bs4 import BeautifulSoup\nimport traceback\nimport re\n\ndef getHTML(url):\n try:\n r = requests.get(url,timeout=30)\n r.raise_for_status()\n r.encoding =r.apparent_encoding\n return r.text\n except:\n return 0\n\nif __name__ == '__main__':\n output_file = 'C:/Users/kai/Desktop/邮科院学习/爬虫/stock.txt'\n url = 'https://gupiao.baidu.com/stock/sz00000'\n\n url = url+'1'+'.html'\n html = getHTML(url)\n infoDict = {}\n soup = BeautifulSoup(html, 'html.parser')\n stockinfo = soup.find('div',attrs={'class':\"stock-bets\"})\n name = stockinfo.find_all(attrs={'class': 'bets-name'})[0]\n infoDict.update({'股票名称': name.text.split()[0]})\n keyList = stockinfo.find_all('dt')\n valueList = stockinfo.find_all('dd')\n\n for i in range(len(keyList)):\n key = keyList[i].text\n val = valueList[i].text\n infoDict[key] = val\n\n with open(output_file,'a',encoding='utf-8') as f:\n f.write(str(infoDict)+'\\n')\n", "sub_path": "爬虫学习/shares2.py", "file_name": "shares2.py", "file_ext": "py", "file_size_in_byte": 1011, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.get", "line_number": 8, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "562832408", "text": "#!/usr/bin/python3\n##############################################\n#\n# Name: U7_5_Firewall_Analyse.py\n#\n# Author: Peter Christen\n#\n# Version: 1.1\n#\n# Date: 26.09.2017\n# 27.09.2017: V1.1 Div. Anpassungen\n#\n# Purpose: Analysiert Firewall Log Daten\n#\n# Hinweis: Braucht die Module matplotlib und python-pptx\n#\n##############################################\n\n# Module\nimport sqlite3\nimport openpyxl\nfrom openpyxl import load_workbook\nfrom openpyxl.styles import PatternFill, Border, Side, Alignment, Protection, Font, Color, colors\nimport datetime\n\n# Variablen\nconnection = sqlite3.connect(':memory:')\ncursor = connection.cursor()\nExcelName = \"U7_5_Firewall_Log_Analyse.xlsx\"\nTitel = 'Firewall Analyse'\nnow = datetime.datetime.now()\ntoday = now.strftime(\"%d.%m.%Y\")\n\nsid = {\n '2501': 'DB1',\n '2502': 'DB2',\n '2511': 'DB3',\n '2512': 'DB4',\n '2513': 'DB5',\n '2514': 'DB6',\n '2525': 'DB7',\n '2526': 'DB8',\n '2527': 'DB9',\n '2528': 'DB10',\n '2529': 'DB11',\n '2530': 'DB12',\n '3024': 'DB13',\n '3016': 'DB14',\n '636': 'DB15'}\n\n################### Keine Aenderungen mehr nötig ab hier ################\n\n# Functions\n\n\ndef colsize(col):\n if col > 90:\n col = col - 26\n b = chr(col)\n col = 'A' + b\n else:\n col = str(chr(col))\n return col\n\n\ndef createdb():\n # Datenbank erstellen\n cursor.execute(\n 'create table if not exists firelog ( sourceip varchar(20), targetip varchar(20), port varchar(20), sid varchar(10), count int)')\n cursor.execute(\n 'CREATE INDEX if not exists firelog_ind on firelog (sourceip, targetip, port)')\n\n\ndef read_excel():\n # Firewall log einlesen\n\n # xlsx-File öffnen\n wb = load_workbook(\n filename='U7_5_Firewall_Log_Auszug.xlsx',\n read_only=True)\n sheet1 = wb.worksheets[0]\n worksheet = wb[sheet1.title]\n\n # xlsx-File einlesen\n r = 0\n w = []\n for row in worksheet.iter_rows():\n r += 1\n c = 0\n for cell in row:\n c += 1\n w.append(cell.value)\n\n if r > 2:\n po = str(w[2]).split(\".\")\n cursor.execute(\"replace into firelog values(?,?,?,?,?)\",\n (w[0], w[1], po[0], sid[po[0]], w[4]))\n del w[:]\n\n cursor.execute(\"delete from firelog where sourceip like 'Source IP'\")\n connection.commit()\n\n\ndef write_excel():\n # Erstelle Excel aus Firewall DB\n\n wb = openpyxl.Workbook()\n ws1 = wb.worksheets[0]\n ws1.title = 'Analyse'\n\n # Styles definieren\n # Font\n fontT = Font(bold=True, size=14, color=colors.BLACK)\n fontb = Font(bold=True, color=colors.BLACK)\n\n # Alignment\n alignC = Alignment(\n horizontal='center',\n vertical='top',\n text_rotation=0,\n shrink_to_fit=False,\n wrap_text=False)\n alignR = Alignment(\n horizontal='right',\n vertical='top',\n text_rotation=0,\n shrink_to_fit=False,\n wrap_text=False)\n alignL = Alignment(\n horizontal='left',\n vertical='top',\n text_rotation=0,\n shrink_to_fit=False,\n wrap_text=False)\n\n # Zellenfarben\n fillT = PatternFill(\n fill_type='solid',\n start_color='6EB7FF',\n end_color='6EB7FF')\n fillR = PatternFill(\n fill_type='solid',\n start_color='FF0000',\n end_color='FF0000')\n fillO = PatternFill(\n fill_type='solid',\n start_color='FCD020',\n end_color='FCD020')\n fillG = PatternFill(\n fill_type='solid',\n start_color='22B604',\n end_color='22B604')\n fillY = PatternFill(\n fill_type='solid',\n start_color='FFF251',\n end_color='FFF251')\n fillGh = PatternFill(\n fill_type='solid',\n start_color='e6ffcc',\n end_color='e6ffcc')\n fillOh = PatternFill(\n fill_type='solid',\n start_color='ffe6cc',\n end_color='ffe6cc')\n\n # Kollonengroesse definieren\n ws1.column_dimensions[\"A\"].width = 14.0\n ws1.column_dimensions[\"B\"].width = 14.0\n ws1.column_dimensions[\"C\"].width = 8.0\n ws1.column_dimensions[\"D\"].width = 7.0\n ws1.column_dimensions[\"E\"].width = 7.0\n\n ws1.merge_cells('A1:E1')\n ws1['A1'].font = fontT\n ws1['A1'].fill = fillT\n ws1['A1'].value = Titel\n ws1['A3'].font = fontb\n ws1['A3'].value = \"SourceIP\"\n ws1['B3'].font = fontb\n ws1['B3'].value = \"TargetIP\"\n ws1['C3'].font = fontb\n ws1['C3'].value = \"Port\"\n ws1['D3'].font = fontb\n ws1['D3'].value = \"SID\"\n ws1['E3'].font = fontb\n ws1['E3'].value = \"Hits\"\n\n # Daten aus der Datenbanken einfuegen\n coln = 65\n z = 4\n\n cursor.execute(\"select * from firelog\")\n\n for row in cursor:\n col = colsize(coln)\n ce = col + str(z)\n ws1[ce].value = row[0]\n col = colsize(coln + 1)\n ce = col + str(z)\n ws1[ce].value = row[1]\n col = colsize(coln + 2)\n ce = col + str(z)\n ws1[ce].value = row[2]\n col = colsize(coln + 3)\n ce = col + str(z)\n ws1[ce].value = row[3]\n col = colsize(coln + 4)\n ce = col + str(z)\n if row[4] > 500000:\n ws1[ce].fill = fillR\n elif row[4] > 100000:\n ws1[ce].fill = fillO\n else:\n ws1[ce].fill = fillG\n ws1[ce].value = row[4]\n z += 1\n\n # Summe der Hit Counts ausgeben\n dbnamen = []\n dbhits = []\n z = 3\n col = colsize(coln + 6)\n ce = col + str(z)\n ws1.merge_cells('G3:H3')\n ws1[ce].fill = fillT\n ws1[ce].font = fontb\n ws1[ce].value = \"Summe der Hits\"\n z += 1\n\n rows = cursor.execute(\n \"select sid,sum(count) from firelog group by sid order by sum(count) desc\")\n for row in rows:\n dbnamen.append(row[0])\n dbhits.append(row[1])\n col = colsize(coln + 6)\n ce = col + str(z)\n ws1[ce].value = row[0]\n col = colsize(coln + 7)\n ce = col + str(z)\n ws1[ce].value = row[1]\n z += 1\n\n wb.save(filename=ExcelName)\n create_grafik_pie(dbnamen, dbhits)\n create_presi(dbnamen, dbhits)\n\n\ndef create_grafik_pie(dbnamen, dbhits):\n # Module matplotlib\n import matplotlib.pyplot as plt\n\n fig1, ax1 = plt.subplots()\n ax1.pie(dbhits[0:10], labels=dbnamen[0:10],\n autopct='%1.1f%%', shadow=True, startangle=0)\n ax1.axis('equal')\n plt.title('Verteilung der Hits der 10 top Datenbank')\n\n fig1.savefig('DBHits.png')\n\n # plt.show()\n\n\ndef create_presi(dbnamen, dbhits):\n # Modul python-pptx\n from pptx import Presentation\n from pptx.util import Inches\n\n N = len(dbnamen)\n\n # Präsentation eröffnen\n prs = Presentation()\n\n # Slides definieren\n title_slide_layout = prs.slide_layouts[0]\n setting_slide_layout = prs.slide_layouts[1]\n\n # Slides hinzufügen\n slide0 = prs.slides.add_slide(title_slide_layout)\n slide1 = prs.slides.add_slide(setting_slide_layout)\n\n # Header Slide\n title = slide0.shapes.title\n subtitle = slide0.placeholders[1]\n title.text = \"Verteilung Netzwerk Traffic\"\n subtitle.text = \"auf die 10 Top Datenbanken\\n\" + today\n\n # Setting Slide\n shapes = slide1.shapes\n\n title_shape = shapes.title\n body_shape = shapes.placeholders[1]\n\n title_shape.text = 'Verteilung'\n\n tf = body_shape.text_frame\n beschreibung = 'Zehn von insgesamt ' + str(N) + ' Datenbanken'\n tf.text = beschreibung\n\n left = Inches(1.5)\n top = Inches(2.5)\n height = Inches(5)\n pic = slide1.shapes.add_picture('DBHits.png', left, top, height=height)\n\n # Präsentation sichern\n prs.save('U7_5_Firewall_Analyse_Presi.pptx')\n\n# End Functions\n\n\n# Main\ncreatedb()\nread_excel()\nwrite_excel()\n\n# End\ncursor.close()\n", "sub_path": "U7_5_Firewall_Analyse.py", "file_name": "U7_5_Firewall_Analyse.py", "file_ext": "py", "file_size_in_byte": 7692, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sqlite3.connect", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "attribute"}, {"api_name": "openpyxl.load_workbook", "line_number": 78, "usage_type": "call"}, {"api_name": "openpyxl.Workbook", "line_number": 107, "usage_type": "call"}, {"api_name": "openpyxl.styles.Font", "line_number": 113, "usage_type": "call"}, {"api_name": "openpyxl.styles.colors.BLACK", "line_number": 113, "usage_type": "attribute"}, {"api_name": "openpyxl.styles.colors", "line_number": 113, "usage_type": "name"}, {"api_name": "openpyxl.styles.Font", "line_number": 114, "usage_type": "call"}, {"api_name": "openpyxl.styles.colors.BLACK", "line_number": 114, "usage_type": "attribute"}, {"api_name": "openpyxl.styles.colors", "line_number": 114, "usage_type": "name"}, {"api_name": "openpyxl.styles.Alignment", "line_number": 117, "usage_type": "call"}, {"api_name": "openpyxl.styles.Alignment", "line_number": 123, "usage_type": "call"}, {"api_name": "openpyxl.styles.Alignment", "line_number": 129, "usage_type": "call"}, {"api_name": "openpyxl.styles.PatternFill", "line_number": 137, "usage_type": "call"}, {"api_name": "openpyxl.styles.PatternFill", "line_number": 141, "usage_type": "call"}, {"api_name": "openpyxl.styles.PatternFill", "line_number": 145, "usage_type": "call"}, {"api_name": "openpyxl.styles.PatternFill", "line_number": 149, "usage_type": "call"}, {"api_name": "openpyxl.styles.PatternFill", "line_number": 153, "usage_type": "call"}, {"api_name": "openpyxl.styles.PatternFill", "line_number": 157, "usage_type": "call"}, {"api_name": "openpyxl.styles.PatternFill", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 252, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 252, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 256, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 256, "usage_type": "name"}, {"api_name": "pptx.Presentation", "line_number": 271, "usage_type": "call"}, {"api_name": "pptx.util.Inches", "line_number": 299, "usage_type": "call"}, {"api_name": "pptx.util.Inches", "line_number": 300, "usage_type": "call"}, {"api_name": "pptx.util.Inches", "line_number": 301, "usage_type": "call"}]} +{"seq_id": "287726764", "text": "import sys\nimport json\n\ndef solve(data):\n result = []\n print(data)\n overweight = 0\n for ob in data:\n BMI = ob[\"WeightKg\"]/(ob[\"HeightCm\"]*ob[\"HeightCm\"]/10000)\n BMI = round(BMI, 2)\n countofoverweight = 0\n BMICategory = \"\"\n HealthRisk = \"\"\n if BMI <= 18.4:\n BMICategory = \"Under Weight\"\n BMIRisk = \"Malnutrition risk\"\n elif BMI >=18.5 and BMI<=24.9:\n BMICategory = \"Normal weight\"\n BMIRisk = \"Low risk\"\n elif BMI >=25 and BMI<=29.9:\n BMICategory = \"Overweight\"\n BMIRisk = \"Enhanced risk\"\n overweight +=1\n elif BMI >=30 and BMI<=34.9:\n BMICategory = \"Moderately obese\"\n BMIRisk = \"Medium risk\"\n elif BMI >=35 and BMI<=39.9:\n BMICategory = \"Severely obese\"\n BMIRisk = \"Very high risk\"\n else:\n BMICategory = \"Very severely obese\"\n BMIRisk = \"Malnutrition risk\"\n \n temp = ob\n temp.update({\"BMI\":BMI, \"BMICategory\":BMICategory, \"BMIRisk\":BMIRisk})\n result.append(temp)\n return overweight, result\n\n\n\n\nif __name__==\"__main__\":\n filepath = input(\"please provide the filepath contains jsondata\\n\")\n f = open(filepath,)\n data = json.load(f)\n overweight, res = solve(data)\n print(overweight, res)\n output = {}\n output['Overweight'] = overweight\n output['data'] = res\n with open('data.json', 'w') as outfile:\n json.dump(output, outfile, indent=4)", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1646, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "json.load", "line_number": 45, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "116304853", "text": "from statistics import mean\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import style\nimport random\n\nstyle.use('fivethirtyeight')\n\nxs = np.array([1, 2, 3, 4, 5, 6], dtype=np.float64)\nys = np.array([5, 4, 6, 5, 6, 7], dtype=np.float64)\n\n\ndef create_dataset(hm, variance):\n xs_local = []\n ys_local = []\n for i in range(0, hm, 1):\n xs_local.append(i)\n ys_local.append(random.randrange(0, variance))\n\n return np.array(xs_local), np.array(ys_local)\n\n\ndef best_fit_slope_and_intercept(xs, ys):\n m_local = ((xs.mean() * ys.mean() - (xs * ys).mean()) /\n (xs.mean() ** 2 - (xs ** 2).mean()))\n b_local = ys.mean() - m_local * xs.mean()\n return m_local, b_local\n\n\ndef calc_regression_line(xs):\n regression_line_local = []\n for x in xs:\n regression_line_local.append(m * x + b)\n return regression_line_local\n\n\ndef squared_error(ys_orig, ys_line):\n return sum((ys_line - ys_orig) ** 2)\n\n\ndef coefficient_of_determination(ys_orig, ys_line):\n SSEreg_line = squared_error(ys_orig, ys_line)\n SSEmean_y = squared_error(ys_orig, mean(ys_orig))\n return 1 - SSEreg_line / SSEmean_y\n\n\n# xs, ys = create_dataset(40, 10)\nm, b = best_fit_slope_and_intercept(xs, ys)\nregression_line = calc_regression_line(xs)\n\npredict_x = 8\npredict_y = m * predict_x + b\n\nprint ('Data set ', ys)\nprint ('Confidence is: ', coefficient_of_determination(ys, regression_line) * 100, '%')\n\nplt.scatter(xs, ys)\nplt.scatter(predict_x, predict_y, color='g')\nplt.plot(xs, regression_line)\nplt.show()\n", "sub_path": "LinearRegression/toy_regression_line.py", "file_name": "toy_regression_line.py", "file_ext": "py", "file_size_in_byte": 1548, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.style.use", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.style", "line_number": 7, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 9, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 10, "usage_type": "attribute"}, {"api_name": "random.randrange", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 20, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "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": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}]} +{"seq_id": "345054485", "text": "from pymongo import MongoClient\nclient = MongoClient()\n# 클래스 객체 할당\n\nclient = MongoClient('localhost', 27017)\ndb=client['seat']\ncollection=db['seat']\nresult=collection.find({'name':'제1강의실'})\nfor i in result:\n seat=i['seat']\nseat=list(seat)\nprint(seat)", "sub_path": "test_mongo.py", "file_name": "test_mongo.py", "file_ext": "py", "file_size_in_byte": 275, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pymongo.MongoClient", "line_number": 2, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "305153644", "text": "import datetime\nimport time\nimport csv\nimport os\nimport ast\nimport glob\nfrom math import log\nfrom sense_hat import SenseHat\nfrom weather import get_timestamp\nfrom sendEmail import *\nimport tablib\nimport pandas as pd\nimport json\n\n\ndef convert_epoch(epoch_time):\n converted_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(epoch_time))\n return converted_time\n\n\ndef epoch_to_day(epoch_time):\n converted_time = time.strftime('%A', time.localtime(epoch_time))\n return converted_time\n\n\ndef get_csv_data():\n \"\"\"Open the daily csv log and return the content\"\"\"\n csv_list = []\n day = get_timestamp().split()[0]\n # csv_path = os.path.join(os.path.dirname(__file__) + '/logs/', day + '.csv')\n csv_path = '/home/pi/Pi_Weather_Station/src/logs/' + day + '.csv'\n with open(csv_path, 'r') as csv_file:\n # content = f.read()\n csv_reader = csv.reader(csv_file, delimiter=',')\n for row in csv_reader:\n # print(row)\n csv_list.append(row)\n return csv_list\n\n\ndef get_dark_sky():\n \"\"\"Read the most recent dark sky log and return a list of the stats\"\"\"\n csv_content = get_csv_data()\n most_recent = csv_content[-1]\n dark_sky_string = most_recent[9]\n dark_sky_list = dark_sky_string.strip('][').split(', ')\n ds_temp = dark_sky_list[0]\n ds_cond = dark_sky_list[1].strip(\"'\")\n ds_fore = dark_sky_list[2].strip(\"'\")\n return [ds_temp, ds_cond, ds_fore]\n\n# print(get_dark_sky())\n\ndef get_gov_aqi():\n \"\"\"Read the most recent aqi log and return the stats\"\"\"\n csv_content = get_csv_data()\n most_recent = csv_content[-1]\n aqi_string = most_recent[10]\n aqi_list = aqi_string.strip('][').split(', ')\n aqi = aqi_list[0]\n air_cond = aqi_list[1].strip(\"'\")\n return [aqi, air_cond]\n\n# print(get_gov_aqi())\n\n\ndef get_timestamp():\n ts = time.time()\n st = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d %H:%M:%S')\n return st\n\n# test_list = ['boom', 'bam', 0]\n\ntest_dict = {'max' : '45', 'min' : '45', 'AQI' : 0}\n\ndef save_alert(result_dict):\n \"\"\"Take a list and save it as a csv\"\"\"\n # src_dir = os.path.dirname(os.path.realpath(__file__))\n # w_log = os.path.join(src_dir + '/logs/', day + '.csv')\n file_path = '/home/pi/Pi_Weather_Station/src/alerts.txt'\n with open(file_path, 'w') as output:\n output.write(str(result_dict))\n\n# save_alert(test_dict)\n\ndef read_alert():\n file_path = '/home/pi/Pi_Weather_Station/src/alerts.txt'\n with open(file_path, 'r') as input:\n s = input.read()\n whip = ast.literal_eval(s)\n return whip\n\n# print(read_alert())\n\ndef check_max():\n try: \n alert_cont = read_alert()\n maximum_temp = int(alert_cont['max_temp'])\n current_temp = get_dark_sky()[0]\n current_temp = float(current_temp)\n if current_temp >= maximum_temp:\n print('Current temperature exceeds maximum temperature threshhold set')\n print('Check https://pi.sisto.solutions/alerts')\n return True\n else:\n print('Temperature is within limit set')\n return False\n except:\n print('That did not work.')\n print('probably did not have a value set for maximum temp')\n\n\n# check_max()\n# if check_max() == True:\n# print('truth')\n# sendEmail('max temp exceeded', 'Pi max temp exceeded')\n\n\ndef check_min():\n try:\n alert_cont = read_alert()\n minimum_temp = int(alert_cont['min_temp'])\n current_temp = get_dark_sky()[0]\n current_temp = float(current_temp) \n if current_temp <= minimum_temp:\n print('Current temperature exceeds minimum temperature threshhold set')\n print('Check https://pi.sisto.solutions/alerts')\n return True\n else:\n print('Temperature is within limit set')\n return False\n except:\n print('That did not work.')\n print('probably did not have a value set for minimum temp')\n\n\n# if check_min() == True:\n# print('truth')\n\n\ndef check_air():\n try:\n alert_cont = read_alert()\n maximum_aqi = int(alert_cont['aqi_max'])\n current_aqi = get_gov_aqi()[0]\n current_aqi = float(current_aqi) \n if current_aqi >= maximum_aqi:\n print('Current AQI exceeds maximum threshhold set')\n print('Check https://pi.sisto.solutions/alerts')\n return True\n else:\n print('AQI is within limit set')\n return False\n except:\n print('That did not work.')\n print('probably did not have a value set for aqi')\n\n\n# check_air()\n\n\n# # csv_path = os.path.join(os.path.dirname(__file__) + '/logs/', day + '.csv')\n# csv_path = '/home/pi/Pi_Weather_Station/src/logs/' + day + '.csv'\n# with open(csv_path, 'r') as fh:\n# imported_data = tablib.Dataset().load(fh)\n# imported_data.headers = ['Log Time', 'Temp (C)', 'Temp (F)', 'Humidity', 'Pressure', 'DewPoint', 'X', 'Y', 'Z', 'Weather', 'AQI']\n# print(type(imported_data))\n# data = imported_data.export('csv')\n\n# print(type(data))\n# print(data)\n\ndef update_logs_html(): \n day = get_timestamp().split()[0]\n csv_path = '/home/pi/Pi_Weather_Station/src/logs/' + day + '.csv'\n columns = ['Log Time', 'Temp (C)', 'Temp (F)', 'Humidity', 'Pressure', 'DewPoint', 'X', 'Y', 'Z', 'Weather', 'AQI']\n df = pd.read_csv(csv_path, names=columns)\n with open('/home/pi/Pi_Weather_Station/src/templates/logs.html', 'w') as html_file:\n html_file.write(df.to_html())\n\n# print(df.to_html())\n\n# update_logs_html()\n\n# send_email('mailjet fix', 'mailjet has updated credentials')\n\n\nwith open('weather.json') as json_file:\n data = json.load(json_file)\ncurrent_cond = data['currently']['summary']\nchance_of_rain = data['currently']['precipProbability']\ncurrent_temp = data['currently']['temperature']\nfeels_like_temp = data['currently']['apparentTemperature']\ndew_point = data['currently']['dewPoint']\ncurrent_hum = data['currently']['humidity']\ncurrent_press = data['currently']['pressure']\ncurrent_wind = data['currently']['windSpeed']\nwind_bearing = data['currently']['windBearing']\ncurrent_uv = data['currently']['uvIndex']\ncurrent_vis = data['currently']['visibility']\n\nforecast = data['daily']['summary']\ntoday_sunrise = data['daily']['data'][0]['sunriseTime']\ntoday_sunset = data['daily']['data'][0]['sunsetTime']\ntoday_temp_hi = data['daily']['data'][0]['temperatureHigh']\ntoday_temp_lo = data['daily']['data'][0]['temperatureLow']\n\ntom_time = data['daily']['data'][1]['time']\ntomorrow = epoch_to_day(tom_time) # get day of week for tomorrow\ntom_summary = data['daily']['data'][1]['summary']\ntom_temp_hi = data['daily']['data'][1]['temperatureHigh']\ntom_temp_lo = data['daily']['data'][1]['temperatureLow']\ntom_chance_rain = data['daily']['data'][1]['precipProbability']\n\nd2_time = data['daily']['data'][2]['time']\nd2 = epoch_to_day(d2_time) # get day 2\nd2_summary = data['daily']['data'][2]['summary']\nd2_temp_hi = data['daily']['data'][2]['temperatureHigh']\nd2_temp_lo = data['daily']['data'][2]['temperatureLow']\nd2_chance_rain = data['daily']['data'][2]['precipProbability']\n\nd3_time = data['daily']['data'][3]['time']\nd3 = epoch_to_day(d3_time) # get day 2\nd3_summary = data['daily']['data'][3]['summary']\nd3_temp_hi = data['daily']['data'][3]['temperatureHigh']\nd3_temp_lo = data['daily']['data'][3]['temperatureLow']\nd3_chance_rain = data['daily']['data'][3]['precipProbability']\n\n# print(current_press)\n# print(convert_epoch(today_sunrise))\n# print(convert_epoch(today_sunset))\n# print()\n# print(tomorrow)\n# print(d2)\n# print(d3)\n\nsend_email('Are we online?', 'hoping this works')\n", "sub_path": "src/tester_weather.py", "file_name": "tester_weather.py", "file_ext": "py", "file_size_in_byte": 7648, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "time.strftime", "line_number": 17, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 17, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 22, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 22, "usage_type": "call"}, {"api_name": "weather.get_timestamp", "line_number": 29, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 34, "usage_type": "call"}, {"api_name": "time.time", "line_number": 68, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 69, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 69, "usage_type": "attribute"}, {"api_name": "ast.literal_eval", "line_number": 90, "usage_type": "call"}, {"api_name": "weather.get_timestamp", "line_number": 174, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 177, "usage_type": "call"}, {"api_name": "json.load", "line_number": 189, "usage_type": "call"}]} +{"seq_id": "241152188", "text": "from zope.interface import implements\nfrom lbrynet.interfaces import IMetadataHandler, IRequestCreator\nfrom lbrynet.core.client.ClientRequest import ClientRequest, ClientPaidRequest\nfrom lbrynet.core.Error import InsufficientFundsError, InvalidResponseError, RequestCanceledError\nfrom lbrynet.core.Error import NoResponseError, ConnectionClosedBeforeResponseError\nfrom ValuableBlobInfo import ValuableBlobInfo\nimport datetime\nimport logging\nimport random\nfrom twisted.internet import defer\nfrom twisted.python.failure import Failure\nfrom collections import defaultdict\n\n\nlog = logging.getLogger(__name__)\n\n\nclass BlindMetadataHandler(object):\n implements(IMetadataHandler, IRequestCreator)\n\n def __init__(self, info_manager, peers, peer_finder, approved_peers, payment_rate_manager, wallet,\n download_manager):\n self.info_manager = info_manager\n self.payment_rate_manager = payment_rate_manager\n self.wallet = wallet\n self.download_manager = download_manager\n self._peers = peers # {Peer: score}\n self.peer_finder = peer_finder\n self.approved_peers = approved_peers\n self._valuable_protocol_prices = {}\n self._info_protocol_prices = {}\n self._price_disagreements = [] # [Peer]\n self._incompatible_peers = [] # [Peer]\n self._last_blob_hashes_from_peers = {} # {Peer: (blob_hash, expire_time)}\n self._valuable_hashes = {} # {blob_hash: (peer score, reference, peer)}\n self._blob_infos = {} # {blob_hash: ValuableBlobInfo}\n self._peer_search_results = defaultdict(list) # {peer: [blob_hash]}\n\n ######### IMetadataHandler #########\n\n def get_initial_blobs(self):\n d = self.info_manager.get_all_blob_infos()\n return d\n\n def final_blob_num(self):\n return None\n\n ######### IRequestCreator #########\n\n def send_next_request(self, peer, protocol):\n # Basic idea:\n # If the peer has been sending us blob hashes to download recently (10 minutes?),\n # send back an example of one (the most recent?) so that it can\n # keep sending us more like it. Otherwise, just ask for\n # valuable blobs\n sent_request = False\n if self._should_send_request_to(peer):\n v_r = self._get_valuable_blob_request(peer)\n if v_r is not None:\n v_p_r = self._get_valuable_price_request(peer, protocol)\n reserved_points = self._reserve_points_valuable(peer, protocol, v_r.max_pay_units)\n if reserved_points is not None:\n d1 = protocol.add_request(v_r)\n d1.addCallback(self._handle_valuable_blob_response, peer, v_r)\n d1.addBoth(self._pay_or_cancel_payment, protocol, reserved_points,\n self._info_protocol_prices)\n d1.addErrback(self._request_failed, \"valuable blob request\", peer)\n sent_request = True\n if v_p_r is not None:\n d2 = protocol.add_request(v_p_r)\n d2.addCallback(self._handle_valuable_price_response, peer, v_p_r, protocol)\n d2.addErrback(self._request_failed, \"valuable price request\", peer)\n else:\n return defer.fail(InsufficientFundsError())\n i_r = self._get_info_request(peer)\n if i_r is not None:\n i_p_r = self._get_info_price_request(peer, protocol)\n reserved_points = self._reserve_points_info(peer, protocol, i_r.max_pay_units)\n if reserved_points is not None:\n d3 = protocol.add_request(i_r)\n d3.addCallback(self._handle_info_response, peer, i_r, protocol, reserved_points)\n d3.addBoth(self._pay_or_cancel_payment, protocol, reserved_points,\n self._valuable_protocol_prices)\n d3.addErrback(self._request_failed, \"info request\", peer, reserved_points)\n sent_request = True\n if i_p_r is not None:\n d4 = protocol.add_request(i_p_r)\n d4.addCallback(self._handle_info_price_response, peer, i_p_r, protocol)\n d4.addErrback(self._request_failed, \"info price request\", peer)\n else:\n return defer.fail(InsufficientFundsError())\n return defer.succeed(sent_request)\n\n def get_new_peers(self):\n peers = None\n if self._peer_search_results:\n peers = self._peer_search_results.keys()\n elif len(self.approved_peers) != 0:\n peers = random.sample(self.approved_peers, len(self.approved_peers))\n return defer.succeed(peers)\n\n ######### internal #########\n\n def _should_send_request_to(self, peer):\n if peer in self._incompatible_peers:\n return False\n if self._peers[peer] >= 0:\n return True\n return False\n\n def _get_valuable_blob_request(self, peer):\n blob_hash = None\n if peer in self._last_blob_hashes_from_peers:\n h, expire_time = self._last_blob_hashes_from_peers[peer]\n if datetime.datetime.now() > expire_time:\n del self._last_blob_hashes_from_peers[peer]\n else:\n blob_hash = h\n r_dict = {'valuable_blob_hashes': {'reference': blob_hash, 'max_blob_hashes': 20}}\n response_identifier = 'valuable_blob_hashes'\n request = ClientPaidRequest(r_dict, response_identifier, 20)\n return request\n\n def _get_valuable_price_request(self, peer, protocol):\n request = None\n if not protocol in self._valuable_protocol_prices:\n self._valuable_protocol_prices[protocol] = self.payment_rate_manager.get_rate_valuable_blob_hash(peer)\n request_dict = {'valuable_blob_payment_rate': self._valuable_protocol_prices[protocol]}\n request = ClientRequest(request_dict, 'valuable_blob_payment_rate')\n return request\n\n def _get_info_request(self, peer):\n if peer in self._peer_search_results:\n blob_hashes = self._peer_search_results[peer]\n del self._peer_search_results[peer]\n references = []\n for blob_hash in blob_hashes:\n if blob_hash in self._valuable_hashes:\n references.append(self._valuable_hashes[blob_hash][1])\n hashes_to_search = [h for h, (s, r, p) in self._valuable_hashes.iteritems() if r in references]\n if hashes_to_search:\n r_dict = {'blob_length': {'blob_hashes': hashes_to_search}}\n response_identifier = 'blob_length'\n request = ClientPaidRequest(r_dict, response_identifier, len(hashes_to_search))\n return request\n if not self._peer_search_results:\n self._search_for_peers()\n return None\n\n def _get_info_price_request(self, peer, protocol):\n request = None\n if not protocol in self._info_protocol_prices:\n self._info_protocol_prices[protocol] = self.payment_rate_manager.get_rate_valuable_blob_info(peer)\n request_dict = {'blob_length_payment_rate': self._info_protocol_prices[protocol]}\n request = ClientRequest(request_dict, 'blob_length_payment_rate')\n return request\n\n def _update_local_score(self, peer, amount):\n self._peers[peer] += amount\n\n def _reserve_points_valuable(self, peer, protocol, max_units):\n return self._reserve_points(peer, protocol, max_units, self._valuable_protocol_prices)\n\n def _reserve_points_info(self, peer, protocol, max_units):\n return self._reserve_points(peer, protocol, max_units, self._info_protocol_prices)\n\n def _reserve_points(self, peer, protocol, max_units, prices):\n assert protocol in prices\n points_to_reserve = 1.0 * max_units * prices[protocol] / 1000.0\n return self.wallet.reserve_points(peer, points_to_reserve)\n\n def _pay_or_cancel_payment(self, arg, protocol, reserved_points, protocol_prices):\n if isinstance(arg, Failure) or arg == 0:\n self._cancel_points(reserved_points)\n else:\n self._pay_peer(protocol, arg, reserved_points, protocol_prices)\n return arg\n\n def _pay_peer(self, protocol, num_units, reserved_points, prices):\n assert num_units != 0\n assert protocol in prices\n point_amount = 1.0 * num_units * prices[protocol] / 1000.0\n self.wallet.send_points(reserved_points, point_amount)\n\n def _cancel_points(self, reserved_points):\n self.wallet.cancel_point_reservation(reserved_points)\n\n def _handle_valuable_blob_response(self, response_dict, peer, request):\n if not request.response_identifier in response_dict:\n return InvalidResponseError(\"response identifier not in response\")\n response = response_dict[request.response_identifier]\n if 'error' in response:\n if response['error'] == \"RATE_UNSET\":\n return 0\n else:\n return InvalidResponseError(\"Got an unknown error from the peer: %s\" %\n (response['error'],))\n if not 'valuable_blob_hashes' in response:\n return InvalidResponseError(\"Missing the required field 'valuable_blob_hashes'\")\n hashes = response['valuable_blob_hashes']\n log.info(\"Handling %s valuable blob hashes from %s\", str(len(hashes)), str(peer))\n expire_time = datetime.datetime.now() + datetime.timedelta(minutes=10)\n reference = None\n unique_hashes = set()\n if 'reference' in response:\n reference = response['reference']\n for blob_hash, peer_score in hashes:\n if reference is None:\n reference = blob_hash\n self._last_blob_hashes_from_peers[peer] = (blob_hash, expire_time)\n if not (blob_hash in self._valuable_hashes or blob_hash in self._blob_infos):\n self._valuable_hashes[blob_hash] = (peer_score, reference, peer)\n unique_hashes.add(blob_hash)\n\n if len(unique_hashes):\n self._update_local_score(peer, len(unique_hashes))\n peer.update_stats('downloaded_valuable_blob_hashes', len(unique_hashes))\n peer.update_score(len(unique_hashes))\n else:\n self._update_local_score(peer, -.0001)\n return len(unique_hashes)\n\n def _handle_info_response(self, response_dict, peer, request):\n if not request.response_identifier in response_dict:\n return InvalidResponseError(\"response identifier not in response\")\n response = response_dict[request.response_identifier]\n if 'error' in response:\n if response['error'] == 'RATE_UNSET':\n return 0\n else:\n return InvalidResponseError(\"Got an unknown error from the peer: %s\" %\n (response['error'],))\n if not 'blob_lengths' in response:\n return InvalidResponseError(\"Missing the required field 'blob_lengths'\")\n raw_blob_lengths = response['blob_lengths']\n log.info(\"Handling %s blob lengths from %s\", str(len(raw_blob_lengths)), str(peer))\n log.debug(\"blobs: %s\", str(raw_blob_lengths))\n infos = []\n unique_hashes = set()\n for blob_hash, length in raw_blob_lengths:\n if blob_hash in self._valuable_hashes:\n peer_score, reference, peer = self._valuable_hashes[blob_hash]\n del self._valuable_hashes[blob_hash]\n infos.append(ValuableBlobInfo(blob_hash, length, reference, peer, peer_score))\n unique_hashes.add(blob_hash)\n elif blob_hash in request.request_dict['blob_length']['blob_hashes']:\n unique_hashes.add(blob_hash)\n d = self.info_manager.save_blob_infos(infos)\n d.addCallback(lambda _: self.download_manager.add_blobs_to_download(infos))\n\n def pay_or_penalize_peer():\n if len(unique_hashes):\n self._update_local_score(peer, len(unique_hashes))\n peer.update_stats('downloaded_valuable_blob_infos', len(unique_hashes))\n peer.update_score(len(unique_hashes))\n else:\n self._update_local_score(peer, -.0001)\n return len(unique_hashes)\n\n d.addCallback(lambda _: pay_or_penalize_peer())\n\n return d\n\n def _handle_valuable_price_response(self, response_dict, peer, request, protocol):\n if not request.response_identifier in response_dict:\n return InvalidResponseError(\"response identifier not in response\")\n assert protocol in self._valuable_protocol_prices\n response = response_dict[request.response_identifier]\n if response == \"RATE_ACCEPTED\":\n return True\n else:\n del self._valuable_protocol_prices[protocol]\n self._price_disagreements.append(peer)\n return True\n\n def _handle_info_price_response(self, response_dict, peer, request, protocol):\n if not request.response_identifier in response_dict:\n return InvalidResponseError(\"response identifier not in response\")\n assert protocol in self._info_protocol_prices\n response = response_dict[request.response_identifier]\n if response == \"RATE_ACCEPTED\":\n return True\n else:\n del self._info_protocol_prices[protocol]\n self._price_disagreements.append(peer)\n return True\n\n def _request_failed(self, reason, request_type, peer):\n if reason.check(RequestCanceledError):\n return\n if reason.check(NoResponseError):\n self._incompatible_peers.append(peer)\n log.warning(\"Valuable blob info requester: a request of type %s has failed. Reason: %s\",\n str(request_type), str(reason.getErrorMessage()))\n self._update_local_score(peer, -10.0)\n peer.update_score(-5.0)\n if reason.check(ConnectionClosedBeforeResponseError):\n return\n return reason\n\n def _search_for_peers(self):\n references_with_sources = set()\n for h_list in self._peer_search_results.itervalues():\n for h in h_list:\n if h in self._valuable_hashes:\n references_with_sources.add(self._valuable_hashes[h][1])\n hash_to_search = None\n used_references = []\n for h, (s, r, p) in self._valuable_hashes.iteritems():\n if not r in used_references:\n used_references.append(r)\n hash_to_search = h\n if not r in references_with_sources:\n break\n if hash_to_search:\n d = self.peer_finder.find_peers_for_blob(hash_to_search)\n d.addCallback(self._set_peer_search_results, hash_to_search)\n\n def _set_peer_search_results(self, peers, searched_hash):\n for peer in peers:\n self._peer_search_results[peer].append(searched_hash)", "sub_path": "lbrynet/lbrynet_console/plugins/BlindRepeater/BlindMetadataHandler.py", "file_name": "BlindMetadataHandler.py", "file_ext": "py", "file_size_in_byte": 15136, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "zope.interface.implements", "line_number": 19, "usage_type": "call"}, {"api_name": "lbrynet.interfaces.IMetadataHandler", "line_number": 19, "usage_type": "argument"}, {"api_name": "lbrynet.interfaces.IRequestCreator", "line_number": 19, "usage_type": "argument"}, {"api_name": "collections.defaultdict", "line_number": 37, "usage_type": "call"}, {"api_name": "twisted.internet.defer.fail", "line_number": 74, "usage_type": "call"}, {"api_name": "twisted.internet.defer", "line_number": 74, "usage_type": "name"}, {"api_name": "lbrynet.core.Error.InsufficientFundsError", "line_number": 74, "usage_type": "call"}, {"api_name": "twisted.internet.defer.fail", "line_number": 91, "usage_type": "call"}, {"api_name": "twisted.internet.defer", "line_number": 91, "usage_type": "name"}, {"api_name": "lbrynet.core.Error.InsufficientFundsError", "line_number": 91, "usage_type": "call"}, {"api_name": "twisted.internet.defer.succeed", "line_number": 92, "usage_type": "call"}, {"api_name": "twisted.internet.defer", "line_number": 92, "usage_type": "name"}, {"api_name": "random.sample", "line_number": 99, "usage_type": "call"}, {"api_name": "twisted.internet.defer.succeed", "line_number": 100, "usage_type": "call"}, {"api_name": "twisted.internet.defer", "line_number": 100, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 115, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 115, "usage_type": "attribute"}, {"api_name": "lbrynet.core.client.ClientRequest.ClientPaidRequest", "line_number": 121, "usage_type": "call"}, {"api_name": "lbrynet.core.client.ClientRequest.ClientRequest", "line_number": 129, "usage_type": "call"}, {"api_name": "lbrynet.core.client.ClientRequest.ClientPaidRequest", "line_number": 144, "usage_type": "call"}, {"api_name": "lbrynet.core.client.ClientRequest.ClientRequest", "line_number": 155, "usage_type": "call"}, {"api_name": "twisted.python.failure.Failure", "line_number": 173, "usage_type": "argument"}, {"api_name": "lbrynet.core.Error.InvalidResponseError", "line_number": 190, "usage_type": "call"}, {"api_name": "lbrynet.core.Error.InvalidResponseError", "line_number": 196, "usage_type": "call"}, {"api_name": "lbrynet.core.Error.InvalidResponseError", "line_number": 199, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 202, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 202, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 202, "usage_type": "call"}, {"api_name": "lbrynet.core.Error.InvalidResponseError", "line_number": 225, "usage_type": "call"}, {"api_name": "lbrynet.core.Error.InvalidResponseError", "line_number": 231, "usage_type": "call"}, {"api_name": "lbrynet.core.Error.InvalidResponseError", "line_number": 234, "usage_type": "call"}, {"api_name": "ValuableBlobInfo.ValuableBlobInfo", "line_number": 244, "usage_type": "call"}, {"api_name": "lbrynet.core.Error.InvalidResponseError", "line_number": 266, "usage_type": "call"}, {"api_name": "lbrynet.core.Error.InvalidResponseError", "line_number": 278, "usage_type": "call"}, {"api_name": "lbrynet.core.Error.RequestCanceledError", "line_number": 289, "usage_type": "argument"}, {"api_name": "lbrynet.core.Error.NoResponseError", "line_number": 291, "usage_type": "argument"}, {"api_name": "lbrynet.core.Error.ConnectionClosedBeforeResponseError", "line_number": 297, "usage_type": "argument"}]} +{"seq_id": "321685036", "text": "\"\"\"\nAuthor: Nick Baron\nDate: 3/22/2018\nDescription: This is a test class for a game controller using the inputs librbry. It gets and prints the max and min values of the controller that is being used.\n\"\"\"\n\nfrom inputs import get_gamepad\n\nx = [0,0]\ny = [0,0]\nz = [0, 0]\nrx = [0, 0]\nry = [0, 0]\nrz = [0, 0]\n\n\ntry:\n while True:\n try:\n events = get_gamepad()\n for event in events:\n if event.code == \"ABS_X\":\n x[0] = max(event.state, x[0])\n x[1] = min(event.state, x[1])\n elif event.code == \"ABS_Y\":\n y[0] = max(event.state, y[0])\n y[1] = min(event.state, y[1])\n elif event.code == \"ABS_Z\":\n z[0] = max(event.state, z[0])\n z[1] = min(event.state, z[1])\n\n elif event.code == \"ABS_RY\":\n ry[0] = max(event.state, ry[0])\n ry[1] = min(event.state, ry[1])\n elif event.code == \"ABS_RX\":\n rx[0] = max(event.state, rx[0])\n rx[1] = min(event.state, rx[1])\n elif event.code == \"ABS_RZ\":\n rz = max(event.state, rz)\n elif event.code == \"SYN_REPORT\":\n nothing = 0\n else:\n print(event.code, event.state)\n print(x,y,z,rx,ry,rz)\n except:\n print('disarm')\nfinally:\n print(\"large disarm\")", "sub_path": "RaspberryPi/old things/ControllerRangeTests.py", "file_name": "ControllerRangeTests.py", "file_ext": "py", "file_size_in_byte": 1499, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "inputs.get_gamepad", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "626550029", "text": "import re\n\nfrom utils.response_code import RET\nfrom .BaseHandler import BaseHandler\nfrom utils.captcha.captcha import captcha\n\nimport random\nimport logging\nimport constants\nfrom libs.yuntongxun.CCP import ccp\n\n\nclass ImageCodeHandler(BaseHandler):\n def get(self):\n code_id = self.get_argument(\"codeid\")\n pre_code_id = self.get_argument(\"pcodeid\")\n # 生成图片验证码\n name, text, image = captcha.generate_captcha()\n try:\n if pre_code_id:\n self.redis.delete(\"image_code_%s\" % pre_code_id)\n self.redis.setex(\"image_code_%s\" % pre_code_id, constants.PIC_CODE_EXPIRES_SECONDS, text)\n except Exception as e:\n logging.error(e)\n self.write(\"\")\n else:\n self.set_header(\"Content-Type\", \"image/jpg\")\n self.write(image)\n\n\nclass SMSCodeHandler(BaseHandler):\n def post(self):\n global real_image_code_text\n mobile = self.json_args.get(\"mobile\")\n image_code_id = self.json_args.get(\"image_code_id\")\n image_code_text = self.json_args.get(\"image_code_text\")\n if not all((mobile, image_code_id, image_code_text)):\n return self.write(dict(errcode=RET.PARAMERR, errmsg=\"参数不完整\"))\n if not re.match(r'1\\d{10}$', mobile):\n return self.write(dict(errcode=RET.PARAMERR, errmsg=\"手机号格式错误\"))\n\n # 判断图片验证码\n try:\n real_image_code_text = self.redis.get(\"image_code_%s\" % image_code_id).decode()\n except Exception as e:\n logging.error(e)\n self.write(dict(errcode=RET.DBERR, errmsg=\"查询验证码错误\"))\n\n if not real_image_code_text:\n return self.write(dict(errcode=RET.NODATA, errmsg=\"验证码已过期\"))\n\n if real_image_code_text.lower() != image_code_text.lower():\n return self.write(dict(errcode=RET.DATAERR, errmsg=\"验证码错误！\"))\n\n # 删除图片验证码\n try:\n self.redis.delete(\"image_code_%s\" % image_code_id)\n except Exception as e:\n logging.error(e)\n\n # 手机号是否存在\n sql = \"select count(*) counts from ih_user_profile where up_mobile=%s \"\n try:\n ret = self.db.get(sql, mobile)\n except Exception as e:\n logging.error(e)\n else:\n if 0 != ret[\"counts\"]:\n return self.write(dict(errcode=RET.DATAEXIST, errmsg=\"手机号已注册\"))\n\n # 验证成功\n sms_code = \"%06d\" % random.randint(0, 1000000)\n try:\n self.redis.setex(\"sms_code_%s\" % mobile, constants.SMS_CODE_EXPIRES_SECONDS, sms_code)\n except Exception as e:\n logging.error(e)\n self.write(dict(errcode=RET.DBERR, errmsg=\"生成短信验证码错误\"))\n\n # 发送验证码\n try:\n result = ccp.sendTemplateSMS(mobile, [sms_code, constants.SMS_CODE_EXPIRES_SECONDS/60], 1)\n except Exception as e:\n logging.error(e)\n return self.write(dict(errcode=RET.THIRDERR, errmsg=\"发送失败\"))\n if result:\n self.write(dict(errcode=RET.OK, errmsg=\"OK\"))\n else:\n return self.write(dict(errcode=RET.THIRDERR, errmsg=\"发送失败\"))\n", "sub_path": "ihome/handlers/VerifyCode.py", "file_name": "VerifyCode.py", "file_ext": "py", "file_size_in_byte": 3280, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "BaseHandler.BaseHandler", "line_number": 13, "usage_type": "name"}, {"api_name": "utils.captcha.captcha.captcha.generate_captcha", "line_number": 18, "usage_type": "call"}, {"api_name": "utils.captcha.captcha.captcha", "line_number": 18, "usage_type": "name"}, {"api_name": "constants.PIC_CODE_EXPIRES_SECONDS", "line_number": 22, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 24, "usage_type": "call"}, {"api_name": "BaseHandler.BaseHandler", "line_number": 31, "usage_type": "name"}, {"api_name": "utils.response_code.RET.PARAMERR", "line_number": 38, "usage_type": "attribute"}, {"api_name": "utils.response_code.RET", "line_number": 38, "usage_type": "name"}, {"api_name": "re.match", "line_number": 39, "usage_type": "call"}, {"api_name": "utils.response_code.RET.PARAMERR", "line_number": 40, "usage_type": "attribute"}, {"api_name": "utils.response_code.RET", "line_number": 40, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 46, "usage_type": "call"}, {"api_name": "utils.response_code.RET.DBERR", "line_number": 47, "usage_type": "attribute"}, {"api_name": "utils.response_code.RET", "line_number": 47, "usage_type": "name"}, {"api_name": "utils.response_code.RET.NODATA", "line_number": 50, "usage_type": "attribute"}, {"api_name": "utils.response_code.RET", "line_number": 50, "usage_type": "name"}, {"api_name": "utils.response_code.RET.DATAERR", "line_number": 53, "usage_type": "attribute"}, {"api_name": "utils.response_code.RET", "line_number": 53, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 59, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 66, "usage_type": "call"}, {"api_name": "utils.response_code.RET.DATAEXIST", "line_number": 69, "usage_type": "attribute"}, {"api_name": "utils.response_code.RET", "line_number": 69, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 72, "usage_type": "call"}, {"api_name": "constants.SMS_CODE_EXPIRES_SECONDS", "line_number": 74, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 76, "usage_type": "call"}, {"api_name": "utils.response_code.RET.DBERR", "line_number": 77, "usage_type": "attribute"}, {"api_name": "utils.response_code.RET", "line_number": 77, "usage_type": "name"}, {"api_name": "libs.yuntongxun.CCP.ccp.sendTemplateSMS", "line_number": 81, "usage_type": "call"}, {"api_name": "libs.yuntongxun.CCP.ccp", "line_number": 81, "usage_type": "name"}, {"api_name": "constants.SMS_CODE_EXPIRES_SECONDS", "line_number": 81, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 83, "usage_type": "call"}, {"api_name": "utils.response_code.RET.THIRDERR", "line_number": 84, "usage_type": "attribute"}, {"api_name": "utils.response_code.RET", "line_number": 84, "usage_type": "name"}, {"api_name": "utils.response_code.RET.OK", "line_number": 86, "usage_type": "attribute"}, {"api_name": "utils.response_code.RET", "line_number": 86, "usage_type": "name"}, {"api_name": "utils.response_code.RET.THIRDERR", "line_number": 88, "usage_type": "attribute"}, {"api_name": "utils.response_code.RET", "line_number": 88, "usage_type": "name"}]} +{"seq_id": "480443612", "text": "# -*- coding: utf-8 -*-\n# @Time : 2020/6/18 10:34\n# @Author : zhoujun\nimport math\nfrom PIL import Image, ImageDraw, ImageFont\n\n\ndef draw_ocr_box_txt(image, boxes, txts):\n h, w = image.height, image.width\n img_left = image.copy()\n img_right = Image.new('RGB', (w, h), (255, 255, 255))\n\n import random\n # 每次使用相同的随机种子 ，可以保证两次颜色一致\n random.seed(0)\n draw_left = ImageDraw.Draw(img_left)\n draw_right = ImageDraw.Draw(img_right)\n for (box, txt) in zip(boxes, txts):\n color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))\n draw_left.polygon(box, fill=color)\n draw_right.polygon([box[0][0], box[0][1],\n box[1][0], box[1][1],\n box[2][0], box[2][1],\n box[3][0], box[3][1]], outline=color)\n box_height = math.sqrt((box[0][0] - box[3][0]) ** 2 + (box[0][1] - box[3][1]) ** 2)\n box_width = math.sqrt((box[0][0] - box[1][0]) ** 2 + (box[0][1] - box[1][1]) ** 2)\n if box_height > 2 * box_width:\n font_size = max(int(box_width * 0.9), 10)\n font = ImageFont.truetype(\"./doc/田氏颜体大字库2.0.ttf\", font_size, encoding=\"utf-8\")\n cur_y = box[0][1]\n for c in txt:\n char_size = font.getsize(c)\n draw_right.text((box[0][0] + 3, cur_y), c, fill=(0, 0, 0), font=font)\n cur_y += char_size[1]\n else:\n font_size = max(int(box_height * 0.8), 10)\n font = ImageFont.truetype(\"./doc/田氏颜体大字库2.0.ttf\", font_size, encoding=\"utf-8\")\n draw_right.text([box[0][0], box[0][1]], txt, fill=(0, 0, 0), font=font)\n img_left = Image.blend(image, img_left, 0.5)\n img_show = Image.new('RGB', (w * 2, h), (255, 255, 255))\n img_show.paste(img_left, (0, 0, w, h))\n img_show.paste(img_right, (w, 0, w * 2, h))\n return np.array(img_show)\n", "sub_path": "torchocr/utils/vis.py", "file_name": "vis.py", "file_ext": "py", "file_size_in_byte": 1975, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "PIL.Image.new", "line_number": 11, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 11, "usage_type": "name"}, {"api_name": "random.seed", "line_number": 15, "usage_type": "call"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 16, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 16, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 17, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 17, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 19, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 25, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 26, "usage_type": "call"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 29, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 29, "usage_type": "name"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 37, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 37, "usage_type": "name"}, {"api_name": "PIL.Image.blend", "line_number": 39, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 39, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 40, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "476510552", "text": "\nfrom collections import OrderedDict\nfrom odoo import api, models\nfrom dateutil.relativedelta import relativedelta\nimport datetime\nimport logging\nimport pytz\n_logger = logging.getLogger(__name__)\n\n\nclass ReportPeriodicalSale(models.AbstractModel):\n _name = 'report.product_qty_inventory.report_product_qty_inventory'\n\n @api.model\n def _get_report_values(self, docids, data=None):\n date_from = data['form']['date_from']\n date_to = data['form']['date_to']\n pro = data['form']['product']\n warehouse_id = data['form']['warehouse_id']\n\n total_sale = 0.0\n period_value = ''\n domain = []\n if warehouse_id:\n domain.append(('warehouse_id', '=', warehouse_id))\n if pro :\n domain.append(('product_id','=',pro))\n stock_moves = self.env['stock.move'].search(domain)\n\n moves = []\n order_line = []\n ids=[]\n dates=[]\n if date_from:\n date_from=datetime.datetime.strptime(date_from, '%Y-%m-%d')\n if date_to:\n date_to=datetime.datetime.strptime(date_to, '%Y-%m-%d')\n\n old_timezone = pytz.timezone(\"UTC\")\n new_timezone = pytz.timezone(\"Africa/Cairo\")\n\n _logger.info('STOKE MOVE')\n note_sale = ''\n\n ids=[]\n old_timezone = pytz.timezone(\"UTC\")\n new_timezone = pytz.timezone(\"Africa/Cairo\")\n product_list=[]\n product=0\n for rec in stock_moves:\n if rec.product_id not in product_list:\n product_list.append(rec.product_id)\n if date_to or date_from:\n for rec in stock_moves:\n\n last_new_timezone = old_timezone.localize(rec.date).astimezone(new_timezone)\n last_new_timezone=last_new_timezone.strftime('%Y-%m-%d')\n last_new_timezone=datetime.datetime.strptime(last_new_timezone, '%Y-%m-%d')\n if date_to and date_from:\n if date_from<=last_new_timezone and date_to>=last_new_timezone:\n ids.append(rec.id)\n elif date_from:\n if date_from<=last_new_timezone:\n ids.append(rec.id)\n elif date_to:\n if date_to>=last_new_timezone :\n ids.append(rec.id)\n \n if ids:\n stock_moves=self.env[\"stock.move\"].search([('id','in',ids)])\n else:\n stock_moves=[]\n return_so,delivery_so,return_po,delivery_po,return_ma,delivery_ma,return_internal,delivery_internal=0,0,0,0,0,0,0,0\n value_list=[]\n i=0\n print(product_list)\n warehouse_id = self.env['stock.warehouse'].search([('id','=',warehouse_id)])\n for product in product_list:\n i+=1\n return_so, delivery_so, return_po, delivery_po, \\\n return_ma, delivery_ma, return_internal, delivery_internal ,onhand= 0,0, 0, 0, 0, 0, 0, 0, 0\n for rec in stock_moves:\n if rec.product_id.id == product.id:\n if rec.location_id.usage=='customer':\n return_so+=rec.product_uom_qty\n elif rec.location_id.usage=='internal':\n return_internal+=rec.product_uom_qty\n elif rec.location_id.usage == 'supplier':\n return_po += rec.product_uom_qty\n elif rec.location_id.usage=='production':\n return_ma+=rec.product_uom_qty\n if rec.location_dest_id.usage=='customer':\n delivery_so+=rec.product_uom_qty\n elif rec.location_dest_id.usage=='internal':\n delivery_internal+=rec.product_uom_qty\n elif rec.location_dest_id.usage == 'supplier':\n delivery_po += rec.product_uom_qty\n elif rec.location_dest_id.usage=='production':\n delivery_ma+=rec.product_uom_qty\n\n if warehouse_id:\n stock_qty = self.env['stock.quant'].search([('product_id', '=', product.id),\n ('on_hand', '=', True),\n ('location_id', '=', warehouse_id.lot_stock_id.id)])\n for line in stock_qty:\n onhand+=line.quantity\n else:\n onhand = product.qty_available\n value_list.append({'i':i,'product_id':product,'return_so':return_so,'delivery_so':delivery_so,'return_internal':return_internal,\n 'delivery_internal':delivery_internal,'return_po':return_po,\n 'delivery_po':delivery_po,'return_ma':return_ma,'delivery_ma':\n delivery_ma,'onhand':onhand})\n\n\n if date_from:\n date_from=date_from.strftime('%Y-%m-%d')\n if date_to:\n date_to=date_to.strftime('%Y-%m-%d')\n print(value_list)\n return {\n 'doc_ids': data['ids'],\n 'doc_model': data['model'],\n 'date_from': date_from,\n 'date_to': date_to,\n 'value_list': value_list,\n 'product_name': self.env['product.product'].search([('id', '=', pro)]).name,\n 'data_check': False,\n\n\n }\n\n", "sub_path": "hdl-addons/product_qty_inventory/report/report_product_qty_inventory.py", "file_name": "report_product_qty_inventory.py", "file_ext": "py", "file_size_in_byte": 5401, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 8, "usage_type": "call"}, {"api_name": "odoo.models.AbstractModel", "line_number": 11, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 11, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 37, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pytz.timezone", "line_number": 39, "usage_type": "call"}, {"api_name": "pytz.timezone", "line_number": 40, "usage_type": "call"}, {"api_name": "pytz.timezone", "line_number": 46, "usage_type": "call"}, {"api_name": "pytz.timezone", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 58, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 58, "usage_type": "attribute"}, {"api_name": "odoo.api.model", "line_number": 14, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 14, "usage_type": "name"}]} +{"seq_id": "289940467", "text": "# https://www.acmicpc.net/problem/1697\nimport sys\nimport collections\n\ndef bfs():\n queue = collections.deque([[n, 0]])\n d = [-1, 1, 2]\n visited = set([n])\n while queue:\n cur, sec = queue.popleft()\n if cur == k:\n return sec\n for i in range(3):\n if i != 2:\n next = cur + d[i]\n else:\n next = cur * d[i]\n if 0 <= next <= 100000 and next not in visited:\n queue.append([next, sec+1])\n visited.add(next)\n\nn, k = map(int, sys.stdin.readline().split())\nprint(bfs())", "sub_path": "Study2021/Graph/7576(숨바꼭질).py", "file_name": "7576(숨바꼭질).py", "file_ext": "py", "file_size_in_byte": 592, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "collections.deque", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.stdin.readline", "line_number": 22, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 22, "usage_type": "attribute"}]} +{"seq_id": "297540577", "text": "from picture import Picture\nimport os\nimport numpy\nimport cv2\nimport scipy.stats\n\n# brute force comparison (pixel by pixel comparison)\ndef sim_brute(image, base):\n\tcount = 0\n\tsize_x, size_y = base.width, base.height\n\tfor y in range(size_y):\n\t\tfor x in range(size_x):\n\t\t\tr, g, b = image.get_RGB_value((x,y))\n\t\t\trb, gb, bb = base.get_RGB_value((x,y))\n\t\t\trgb = [abs(rb-r),abs(gb-g),abs(bb-b)]\n\t\t\tif sum(rgb) <= 10:\n\t\t\t\tcount = count+1\n\treturn '%.2f' % (float(count) / (size_x * size_y))\n\n# make histograms for correlation test using OpenCV functions\ndef create_correlation_hist(image):\n\timage = numpy.array(image.rgb_img)\n\timage = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n\thist = cv2.calcHist([image], [0,1,2], None, [8,8,8],[0,256,0,256,0,256])\n\thist = cv2.normalize(hist).flatten()\n\treturn hist\n\n# compare histograms based on correlation\ndef compare_correlation(hist1, hist2):\n\treturn cv2.compareHist(hist1, hist2, cv2.cv.CV_COMP_CORREL)\n\ndef sim_correlation(image1, image2):\n\treturn '%.2f' % compare_correlation(create_correlation_hist(image1), create_correlation_hist(image2))\n\n# histogram for chi-square\n# normalize histogram\ndef normalize(arr):\n\ttotal = sum(arr)\n\tfor i in range(len(arr)):\n\t\tarr[i] = float(arr[i])/total\n\treturn arr\n\n# create color histogram from image object\ndef create_chi_hist(image):\n\tred = [0] * 256\n\tgreen = [0] * 256\n\tblue = [0] * 256\n\n\tsize_x, size_y = image.width, image.height\n\n\tfor y in range(size_y):\n\t\tfor x in range(size_x):\n\t\t\tr, g, b = image.get_RGB_value((x,y))\n\t\t\tred[r] = red[r] + 1\n\t\t\tgreen[g] = green[g] + 1\n\t\t\tblue[b] = blue[b] + 1\n\n\trgb = [normalize(red), normalize(green), normalize(blue)]\n\treturn rgb\n\n# calculate chi-squared value\ndef chi_square(v1, v2):\n\tif v2 != 0:\n\t\tval = (v1 - v2)*(v1 - v2)/(v2*v2)\n\telse:\n\t\tval = 0\n\treturn val\n\n# pass in two arrays, each histograms of 1 color channel\n# chi-square test\ndef chi_squared_comp(hist1, hist2):\n\tcomp1 = [0] * 256\n\tfor i in range(255):\n\t\tcomp1[i] = chi_square(hist1[i], hist2[i])/2\n\treturn sum(comp1)\n\ndef comp_color_histograms(hist1, hist2):\n\tred_comp = chi_squared_comp(hist1[0], hist2[0])\n\tgreen_comp = chi_squared_comp(hist1[1], hist2[1])\n\tblue_comp = chi_squared_comp(hist1[2], hist2[2])\n\treturn (red_comp, green_comp, blue_comp)\n\ndef get_p_value (r_comp, g_comp, b_comp):\n\tr_prob = scipy.stats.chi2.cdf(r_comp, 255)\n\tg_prob = scipy.stats.chi2.cdf(g_comp, 255)\n\tb_prob = scipy.stats.chi2.cdf(b_comp, 255)\n\treturn (r_prob, g_prob, b_prob)\n\ndef sim_chi(image1, image2):\n\tr, g, b = comp_color_histograms(create_chi_hist(image1), create_chi_hist(image2))\n\trp, gp, bp = get_p_value (r, g, b)\n\treturn '%.3e' % ((rp+gp+bp)/3.0)", "sub_path": "code/similarity.py", "file_name": "similarity.py", "file_ext": "py", "file_size_in_byte": 2620, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.array", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.calcHist", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.normalize", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.compareHist", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.cv", "line_number": 30, "usage_type": "attribute"}, {"api_name": "scipy.stats.stats.chi2.cdf", "line_number": 84, "usage_type": "call"}, {"api_name": "scipy.stats.stats", "line_number": 84, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 84, "usage_type": "name"}, {"api_name": "scipy.stats.stats.chi2.cdf", "line_number": 85, "usage_type": "call"}, {"api_name": "scipy.stats.stats", "line_number": 85, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 85, "usage_type": "name"}, {"api_name": "scipy.stats.stats.chi2.cdf", "line_number": 86, "usage_type": "call"}, {"api_name": "scipy.stats.stats", "line_number": 86, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 86, "usage_type": "name"}]} +{"seq_id": "151013511", "text": "import cv2\nimport numpy as np\n\n\nimg = cv2.imread(\"D:/bag3.png\")\nr=None\ng=None\nb=None\n\ngray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\ngray = cv2.medianBlur(gray, 7)\nret, gray = cv2.threshold(gray, 120, 255, cv2.THRESH_BINARY)\nXb, Yb = np.where(gray > 0)\n# thresh_copy[Xb, Yb] = 0\nb, g, r = cv2.split(img)\n\n\ndef getposgray(event, x, y, flags, param):\n if event==cv2.EVENT_LBUTTONDOWN:\n print(\"r\", r[y, x])\n\n\n# foreground = cv2.medianBlur(red_binary, 3)\ncv2.imshow(\"bag\", img)\nret, g = cv2.threshold(g, 100, 255, cv2.THRESH_BINARY)\nret, b = cv2.threshold(b, 100, 255, cv2.THRESH_BINARY)\n\nXb, Yb = np.where((b > 0)|(g > 0))\n\nr[Xb, Yb] = 0\n\n\n\nret, r = cv2.threshold(r, 120, 255, cv2.THRESH_BINARY)\nr = cv2.medianBlur(r, 3)\ncv2.imshow(\"b\",b)\ncv2.imshow(\"r\",r)\ncv2.imshow(\"g\",g)\n# ret, g = cv2.threshold(g, 160, 255, cv2.THRESH_BINARY)\n# cv2.imshow(\"g\",g)\n\n# ret, not_green = cv2.threshold(g, 160, 255, cv2.THRESH_BINARY_INV)\n# cv2.imshow(\"not_green\",not_green)\n\n# result = cv2.bitwise_and(r, r, mask=not_green)\n# result = cv2.medianBlur(r, 3)\n# cv2.imshow(\"b\",b)\n# cv2.imshow(\"result\",result)\ncv2.setMouseCallback(\"r\",getposgray)\n\n\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n", "sub_path": "难点积累/当绿色射灯照在红色物体上丢失红色目标问题.py", "file_name": "当绿色射灯照在红色物体上丢失红色目标问题.py", "file_ext": "py", "file_size_in_byte": 1170, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cv2.imread", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 10, "usage_type": "attribute"}, {"api_name": "cv2.medianBlur", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.split", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.EVENT_LBUTTONDOWN", "line_number": 19, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 25, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 34, "usage_type": "attribute"}, {"api_name": "cv2.medianBlur", "line_number": 35, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.setMouseCallback", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "219115819", "text": "from rest_framework import generics, permissions, status\nfrom rest_framework.response import Response\n\n# from knox.models import AuthToken\nfrom rest_framework_simplejwt.tokens import RefreshToken\nfrom rest_framework_simplejwt.exceptions import TokenError\nfrom .serializers import UserSerializer, RegisterSerializer, LoginSerializer\n\n\n# Register API\nclass RegisterAPI(generics.GenericAPIView):\n permission_classes = (permissions.AllowAny,)\n\n serializer_class = RegisterSerializer\n\n def post(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n user = serializer.save()\n user_serializer = UserSerializer(user, context=self.get_serializer_context())\n return Response({\"user\": user_serializer.data}, status=201,)\n\n\n# Login API\nclass LoginAPI(generics.GenericAPIView):\n permission_classes = (permissions.AllowAny,)\n\n serializer_class = LoginSerializer\n\n def post(self, request, *args, **kwargs):\n serializer = self.get_serializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n user = serializer.validated_data\n user_serializer = UserSerializer(user, context=self.get_serializer_context())\n try:\n refresh = RefreshToken.for_user(user)\n refresh.check_blacklist()\n # refresh.access_token.check_blacklist()\n pass\n except Exception as e:\n print(e)\n raise e\n return Response(\n status=status.HTTP_412_PRECONDITION_FAILED, exception=str(e)\n )\n\n return Response(\n {\n \"user\": user_serializer.data,\n \"access_token\": str(refresh.access_token),\n \"refresh_token\": str(refresh),\n },\n status=200,\n )\n\n\n# Get User API\nclass UserAPI(generics.RetrieveAPIView):\n permission_classes = [permissions.IsAuthenticated]\n\n serializer_class = UserSerializer\n\n def get_object(self):\n return self.request.user\n\n\nclass LogoutAndBlacklistRefreshTokenForUserView(generics.GenericAPIView):\n permission_classes = (permissions.AllowAny,)\n authentication_classes = ()\n\n def post(self, request):\n try:\n refresh_token = request.data[\"refresh_token\"]\n token = RefreshToken(refresh_token)\n token.blacklist()\n return Response(status=status.HTTP_205_RESET_CONTENT)\n except Exception as e:\n print(e)\n return Response(status=status.HTTP_417_EXPECTATION_FAILED, exception=str(e))\n", "sub_path": "leadsmanager/accounts/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 2617, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "rest_framework.generics.GenericAPIView", "line_number": 11, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 11, "usage_type": "name"}, {"api_name": "rest_framework.permissions.AllowAny", "line_number": 12, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 12, "usage_type": "name"}, {"api_name": "serializers.RegisterSerializer", "line_number": 14, "usage_type": "name"}, {"api_name": "serializers.UserSerializer", "line_number": 20, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 21, "usage_type": "call"}, {"api_name": "rest_framework.generics.GenericAPIView", "line_number": 25, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 25, "usage_type": "name"}, {"api_name": "rest_framework.permissions.AllowAny", "line_number": 26, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 26, "usage_type": "name"}, {"api_name": "serializers.LoginSerializer", "line_number": 28, "usage_type": "name"}, {"api_name": "serializers.UserSerializer", "line_number": 34, "usage_type": "call"}, {"api_name": "rest_framework_simplejwt.tokens.RefreshToken.for_user", "line_number": 36, "usage_type": "call"}, {"api_name": "rest_framework_simplejwt.tokens.RefreshToken", "line_number": 36, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 43, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_412_PRECONDITION_FAILED", "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": 47, "usage_type": "call"}, {"api_name": "rest_framework.generics.RetrieveAPIView", "line_number": 58, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 58, "usage_type": "name"}, {"api_name": "rest_framework.permissions.IsAuthenticated", "line_number": 59, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 59, "usage_type": "name"}, {"api_name": "serializers.UserSerializer", "line_number": 61, "usage_type": "name"}, {"api_name": "rest_framework.generics.GenericAPIView", "line_number": 67, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 67, "usage_type": "name"}, {"api_name": "rest_framework.permissions.AllowAny", "line_number": 68, "usage_type": "attribute"}, {"api_name": "rest_framework.permissions", "line_number": 68, "usage_type": "name"}, {"api_name": "rest_framework_simplejwt.tokens.RefreshToken", "line_number": 74, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 76, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_205_RESET_CONTENT", "line_number": 76, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 76, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 79, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_417_EXPECTATION_FAILED", "line_number": 79, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 79, "usage_type": "name"}]} +{"seq_id": "484268834", "text": "# -*- coding:utf-8 -*-\nimport binascii\nimport hashlib\nimport json\nimport urllib\nfrom pyDes import des, CBC, PAD_PKCS5\nfrom collections import OrderedDict\n\nkey = '83A6AEDE-3B5A-4D3E-B789-DC780421C1A1'\nsmac = 'C628C57D3F0FCB7652D9C5D64898DFFF'\n\ndef des_encrypt(s):\n \"\"\"\n DES 加密\n :param s: 原始字符串\n :return: 加密后字符串，16进制\n \"\"\"\n iv = key\n k = des(key, CBC, iv, pad=None, padmode=PAD_PKCS5)\n en = k.encrypt(s, padmode=PAD_PKCS5)\n return binascii.b2a_hex(en)\n\n\ndef des_descrypt(s):\n \"\"\"\n DES 解密\n :param s: 加密后的字符串，16进制\n :return: 解密后的字符串\n \"\"\"\n iv = key\n k = des(key, CBC, iv, pad=None, padmode=PAD_PKCS5)\n de = k.decrypt(binascii.a2b_hex(s), padmode=PAD_PKCS5)\n return de\n\n# 创建md5对象\nhl = hashlib.md5()\nhl.update(key.encode(encoding='utf-8'))\nkey = hl.hexdigest()\nkey = key[0:8].upper()\n\nprint(\"key:\" + key)\n\n# 定义出金数据\ndata = OrderedDict()\ndata[\"realName\"] = urllib.quote(\"实名02\")\ndata[\"coinId\"] = \"1\"\ndata[\"amount\"] = \"0.48\"\ndata[\"orderId\"] = \"2018071318275278925\"\ndata[\"idCard\"] = \"2222222222\"\ndata[\"notifyUrl\"] = \"https://www.domain.com/notify/withdraw/callback\"\ndata[\"returnUrl\"] = \"https://www.domain.com/return/withdraw/callback\"\ndata[\"sendTime\"] = \"2018-07-13 18:27:52\"\n\n# 定义加密数据\n# 签名出金数据\nhl2 = hashlib.md5();\nencodedData = json.dumps(data, separators=(',',':')) + smac\n\nhl2.update(encodedData.encode(encoding='utf-8'))\nsign=hl2.hexdigest()\n\nciphertext = OrderedDict()\nciphertext[\"data\"] = data\nciphertext['sign'] = sign.upper()\n\n#加密出金数据\nstr_en = des_encrypt(json.dumps(ciphertext, separators=(',',':'))).upper()\nprint(str_en)\n\n#解密出金数据\nstr_de = des_descrypt(str_en)\nprint(str_de)\n", "sub_path": "python/withdraw.py", "file_name": "withdraw.py", "file_ext": "py", "file_size_in_byte": 1770, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pyDes.des", "line_number": 19, "usage_type": "call"}, {"api_name": "pyDes.CBC", "line_number": 19, "usage_type": "argument"}, {"api_name": "pyDes.PAD_PKCS5", "line_number": 19, "usage_type": "name"}, {"api_name": "pyDes.PAD_PKCS5", "line_number": 20, "usage_type": "name"}, {"api_name": "binascii.b2a_hex", "line_number": 21, "usage_type": "call"}, {"api_name": "pyDes.des", "line_number": 31, "usage_type": "call"}, {"api_name": "pyDes.CBC", "line_number": 31, "usage_type": "argument"}, {"api_name": "pyDes.PAD_PKCS5", "line_number": 31, "usage_type": "name"}, {"api_name": "binascii.a2b_hex", "line_number": 32, "usage_type": "call"}, {"api_name": "pyDes.PAD_PKCS5", "line_number": 32, "usage_type": "name"}, {"api_name": "hashlib.md5", "line_number": 36, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 44, "usage_type": "call"}, {"api_name": "urllib.quote", "line_number": 45, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 56, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 57, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 62, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "109620626", "text": "from flask import Flask, render_template, request, redirect, url_for, session\nimport db\napp = Flask(__name__)\n\n\n@app.route('/')\ndef hello_world():\n return redirect(url_for('index'))\n\n@app.route('/index')\ndef index():\n return render_template(\"listIndex.html\", tasks=db.getTasks())\n\n@app.route('/task', methods=['POST','GET'])\ndef task():\n newtask=request.form.get['taskname']\n db.addTask(newtask)\n return render_template(\"insertTask_page.html\", description=newtask,urgency=db.getUrgency(newtask))\n\n@app.route('/delete', methods=['POST','GET'])\ndef delete():\n id=request.form.get('taskname')\n if id != None:\n db.deleteTask(id)\n return render_template(\"delete_page.html\")\n\nif __name__ == '__main__':\n app.run()\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 742, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 12, "usage_type": "call"}, {"api_name": "db.getTasks", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 16, "usage_type": "name"}, {"api_name": "db.addTask", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 18, "usage_type": "call"}, {"api_name": "db.getUrgency", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "db.deleteTask", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "642218065", "text": "import argparse\nimport os\nimport sys\nimport requests\nfrom lxml import html\n\n# Parser for arguments\nparser = argparse.ArgumentParser()\nparser.add_argument(\"url\", default=None, nargs='*')\nparser.add_argument(\"-f\", nargs=1, help='specify file', metavar='file')\nparser.add_argument(\"--nolinks\", action='store_true', help='no links')\nparser.add_argument(\"--nonewlines\", action='store_true',\n help='no newlines between links')\nargs = parser.parse_args()\n\n\ndef print_titles(urls):\n # Remove empty entries\n urls = [url.strip() for url in urls if url.strip() != \"\"]\n\n # Process each URL\n for index, url in enumerate(urls):\n # Attempt to retrieve and print the title\n try:\n r = requests.get(url)\n tree = html.fromstring(r.content)\n print(tree.findtext(\".//title\"))\n except Exception as err:\n print(\"Error:\", err)\n sys.exit(1)\n\n # Print URL\n if not args.nolinks:\n print(url)\n # Print newline when appropriate\n if not args.nonewlines and index != len(urls) - 1:\n print(\"\")\n\n\n# URL arguments\nif args.url:\n print_titles(args.url)\n\n# File argument\nif args.f:\n # Ensure that file exists\n if not os.path.isfile(args.f[0]):\n print(\"File does not exist\")\n sys.exit(1)\n\n # Read file and get each line\n try:\n f = open(args.f[0], 'r')\n filecontents = f.readlines()\n f.close()\n print_titles(filecontents)\n except Exception as err:\n print(\"Error:\", err)\n sys.exit(1)\n", "sub_path": "WTRetrieve.py", "file_name": "WTRetrieve.py", "file_ext": "py", "file_size_in_byte": 1574, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 8, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "lxml.html.fromstring", "line_number": 26, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 26, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 30, "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": "sys.exit", "line_number": 49, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 59, "usage_type": "call"}]} +{"seq_id": "219199449", "text": "import pandas as pd \nimport numpy as np\nimport requests \n\ndef stateNames(stateAbbreviation):\n states = {\n 'AK': 'Alaska',\n 'AL': 'Alabama',\n 'AR': 'Arkansas',\n 'AS': 'American Samoa',\n 'AZ': 'Arizona',\n 'CA': 'California',\n 'CO': 'Colorado',\n 'CT': 'Connecticut',\n 'DC': 'District of Columbia',\n 'DE': 'Delaware',\n 'FL': 'Florida',\n 'GA': 'Georgia',\n 'GU': 'Guam',\n 'HI': 'Hawaii',\n 'IA': 'Iowa',\n 'ID': 'Idaho',\n 'IL': 'Illinois',\n 'IN': 'Indiana',\n 'KS': 'Kansas',\n 'KY': 'Kentucky',\n 'LA': 'Louisiana',\n 'MA': 'Massachusetts',\n 'MD': 'Maryland',\n 'ME': 'Maine',\n 'MI': 'Michigan',\n 'MN': 'Minnesota',\n 'MO': 'Missouri',\n 'MP': 'Northern Mariana Islands',\n 'MS': 'Mississippi',\n 'MT': 'Montana',\n 'NA': 'National',\n 'NC': 'North Carolina',\n 'ND': 'North Dakota',\n 'NE': 'Nebraska',\n 'NH': 'New Hampshire',\n 'NJ': 'New Jersey',\n 'NM': 'New Mexico',\n 'NV': 'Nevada',\n 'NY': 'New York',\n 'OH': 'Ohio',\n 'OK': 'Oklahoma',\n 'OR': 'Oregon',\n 'PA': 'Pennsylvania',\n 'PR': 'Puerto Rico',\n 'RI': 'Rhode Island',\n 'SC': 'South Carolina',\n 'SD': 'South Dakota',\n 'TN': 'Tennessee',\n 'TX': 'Texas',\n 'UT': 'Utah',\n 'VA': 'Virginia',\n 'VI': 'Virgin Islands',\n 'VT': 'Vermont',\n 'WA': 'Washington',\n 'WI': 'Wisconsin',\n 'WV': 'West Virginia',\n 'WY': 'Wyoming'\n }\n if stateAbbreviation is not None:\n if stateAbbreviation in states:\n return states[stateAbbreviation]\n else:\n return None\n else:\n return None\n\n#Veteran Population Projection Estimates by County (Estimates span several decades; 2021 is selected out below)\nveterans = pd.read_csv('data_folder/VetPop2018_County_Data__9L.csv',dtype={'FIPS':'str'})\n\nveterans['Date'] = pd.to_datetime(veterans['Date'])\nveterans2021 = veterans[veterans['Date'] == '2021-09-30'].reset_index(drop=True)\nveterans2021['County'] = veterans2021['County, St'].str.split(',').str[0]\n#Remove unneeded columns as well as Gender and then sum to remove Gender split of data\nveterans2021 = veterans2021.drop(['County, St', 'Date', 'Gender'],axis=1)\nveterans2021 = (veterans2021.groupby(['FIPS','Age Group','County'], sort=False, as_index=False)\n .agg({'Veterans':'sum', 'State':'first'})\n .reindex(columns=veterans2021.columns)) \n#Remove non-CONUS areas, reorder columns, and change column names for consistency\nveterans2021.drop(veterans2021[veterans2021['State'] == 'Island Areas & Foreign'].index, inplace=True)\nveterans2021.drop(veterans2021[veterans2021['State'] == 'Puerto Rico'].index, inplace=True)\nveterans2021.loc[(veterans2021.FIPS == '11001'),'State'] = 'District of Columbia'\nveterans2021 = veterans2021[['FIPS','County','State','Age Group','Veterans']]\nveterans2021 = veterans2021.rename(columns={'County':'COUNTY','Age Group':'AGEGROUP','State':'STATE','Veterans':'VETS'})\n\n\n#Create a dataframe without age column to get total counts of veterans by county\nPOPveterans2021 = veterans2021.drop(['AGEGROUP'],axis=1)\nPOPveterans2021 = (POPveterans2021.groupby(['FIPS','COUNTY'], sort=False, as_index=False)\n .agg({'VETS':'sum', 'STATE':'first'})\n .reindex(columns=POPveterans2021.columns))\n\nAPI_KEY = 'eed39902208fc176e948f1dc4c8ecd60a81fd8d1'\nAGEPOP_API_URL = 'https://api.census.gov/data/2019/pep/charagegroups?get=NAME,POP,AGEGROUP&for=county:*&in=state:*&key={}'.format(API_KEY)\nPOP_API_URL = 'https://api.census.gov/data/2019/pep/charagegroups?get=NAME,POP&for=county:*&in=state:*&key={}'.format(API_KEY)\n\n#API Call for Census data by age groups \nresults = requests.get(AGEPOP_API_URL).json()\nagepop = pd.DataFrame(results[1:], columns=results[0])\n\n#Cleaning of Population by Age Group data\nagepop['FIPS'] = agepop.state + agepop.county\nagepop = agepop.astype(dtype={'NAME': 'str', 'POP':'float','AGEGROUP':'int64','state':'str','county':'str'})\nagepop = agepop.sort_values(by=['NAME','AGEGROUP'])\nagepop[['COUNTY','STATE']] = agepop['NAME'].str.split(', ', expand=True)\nagepop['COUNTY'] = agepop['COUNTY'].str.replace(' County', '')\nagepop['COUNTY'] = agepop['COUNTY'].str.replace(' Parish', '')\nagepop = agepop[['FIPS','COUNTY','STATE','AGEGROUP','POP']].reset_index(drop=True)\nagepop.drop(agepop[agepop['STATE'].str.contains('Puerto Rico')].index, inplace=True)\nagepop = agepop.sort_values(by=['FIPS','AGEGROUP']).reset_index(drop=True)\n\nAG1 = agepop[agepop['AGEGROUP'] == 30].rename(columns={'POP':'TOTPOP1'}).drop(['AGEGROUP'], axis=1).reset_index(drop=True)\nAG2 = agepop[agepop['AGEGROUP'] == 25].rename(columns={'POP':'TOTPOP2'}).drop(['AGEGROUP'], axis=1).reset_index(drop=True)\n\nAG3 = agepop[agepop['AGEGROUP'].isin([14,15,16,17])].rename(columns={'POP':'TOTPOP3'}).drop(['AGEGROUP'], axis=1).reset_index(drop=True)\nAG3 = (AG3.groupby(['FIPS','COUNTY','STATE'], sort=False, as_index=False).agg({'TOTPOP3':'sum'}))\n\nAG4 = agepop[agepop['AGEGROUP'] == 18].rename(columns={'POP':'TOTPOP4'}).drop(['AGEGROUP'], axis=1).reset_index(drop=True)\n\nAGKEY = ['FIPS','STATE']\n\nAG = AG1.merge(AG2,on=AGKEY,how='left',suffixes=('','_drop')).merge(AG3,on=AGKEY,how='left',suffixes=('','_drop')).merge(AG4,on=AGKEY,how='left',suffixes=('','_drop'))\nAG.drop([col for col in AG.columns if 'drop' in col],axis=1,inplace=True)\n\nAGVet1 = veterans2021[veterans2021['AGEGROUP'] == '17-44'].rename(columns={'VETS':'VETPOP1'}).drop(['AGEGROUP'],axis=1).reset_index(drop=True)\nAGVet2 = veterans2021[veterans2021['AGEGROUP'] == '45-64'].rename(columns={'VETS':'VETPOP2'}).drop(['AGEGROUP'],axis=1).reset_index(drop=True)\nAGVet3 = veterans2021[veterans2021['AGEGROUP'] == '65-84'].rename(columns={'VETS':'VETPOP3'}).drop(['AGEGROUP'],axis=1).reset_index(drop=True)\nAGVet4 = veterans2021[veterans2021['AGEGROUP'] == '85+'].rename(columns={'VETS':'VETPOP4'}).drop(['AGEGROUP'],axis=1).reset_index(drop=True)\n\nAGVet = AGVet1.merge(AGVet2,on=AGKEY,how='left',suffixes=('','_drop')).merge(AGVet3,on=AGKEY,how='left',suffixes=('','_drop')).merge(AGVet4,on=AGKEY,how='left',suffixes=('','_drop'))\nAGVet.drop([col for col in AGVet.columns if 'drop' in col],axis=1,inplace=True)\n\nAGProportions = AG.merge(AGVet,on=AGKEY,how='left',suffixes=('','_drop'))\nAGProportions.drop([col for col in AGProportions.columns if 'drop' in col],axis=1,inplace=True)\n\nAGProportions['Vet1Perc'] = AGProportions['VETPOP1']/AGProportions['TOTPOP1']\nAGProportions['Vet2Perc'] = AGProportions['VETPOP2']/AGProportions['TOTPOP2']\nAGProportions['Vet3Perc'] = AGProportions['VETPOP3']/AGProportions['TOTPOP3']\nAGProportions['Vet4Perc'] = AGProportions['VETPOP4']/AGProportions['TOTPOP4']\nAGProportions = AGProportions[['FIPS','COUNTY','STATE','Vet1Perc','Vet2Perc','Vet3Perc','Vet4Perc']]\n\nAGProportions.to_csv('data_folder/AGProportions.csv',index=False)\n\n#API Call for total population counts not split by age groups\nresults2 = requests.get(POP_API_URL).json()\npop = pd.DataFrame(results2[1:], columns=results2[0])\n\n#Cleaning of total population counts\npop['FIPS'] = pop.state + pop.county\npop = pop.astype(dtype={'NAME': 'str', 'POP':'float','state':'str','county':'str'})\npop[['COUNTY','STATE']] = pop['NAME'].str.split(', ', expand=True)\npop['COUNTY'] = pop['COUNTY'].str.replace(' County', '')\npop['COUNTY'] = pop['COUNTY'].str.replace(' Parish', '')\npop = pop[['FIPS','COUNTY','STATE','POP']].reset_index(drop=True)\npop.drop(pop[pop['STATE'].str.contains('Puerto Rico')].index, inplace=True)\npop = pop.sort_values(by='FIPS')\n\n#Start central frame with Vet count and Veteran Percentage of Total Population\ntotpop_withvet = pd.merge(pop,POPveterans2021, on=AGKEY, how='left',suffixes=('','_drop'))\ntotpop_withvet.drop([col for col in totpop_withvet.columns if 'drop' in col],axis=1,inplace=True)\ntotpop_withvet['VET_PERCENT'] = totpop_withvet['VETS']/totpop_withvet['POP']\ntotpop_withvet.to_csv('data_folder/totpop_withvet.csv',index=False)\n\n#CDC Megadata File\nCDCcovid = pd.read_csv('data_folder/CDCcovid.csv', dtype={'FIPS':'str','COUNTY':'str','STATE':'str','AGEGROUP':'str','CASES':'int'})\n\n#Processing the CDC data to obtain columns for cases by county by age group and total cases by county\nCases1 = CDCcovid[CDCcovid['AGEGROUP'] == '18 to 49 years'].rename(columns={'CASES':'AG1CASES'}).drop(['AGEGROUP'],axis=1).reset_index(drop=True)\nCases2 = CDCcovid[CDCcovid['AGEGROUP'] == '50 to 64 years'].rename(columns={'CASES':'AG2CASES'}).drop(['AGEGROUP'],axis=1).reset_index(drop=True)\nCases3 = CDCcovid[CDCcovid['AGEGROUP'] == '65+ years'].rename(columns={'CASES':'AG3CASES'}).drop(['AGEGROUP'],axis=1).reset_index(drop=True)\n\nCases3 = Cases3.merge(AG3,on=AGKEY,how='left',suffixes=('','_drop')).merge(AG4,on=AGKEY,how='left',suffixes=('','_drop'))\nCases3.drop([col for col in Cases3.columns if 'drop' in col],axis=1,inplace=True)\nCases3['AG3CASES'] = Cases3['AG3CASES'] * (Cases3['TOTPOP3'] / (Cases3['TOTPOP3'] +Cases3['TOTPOP4']))\nCases3 = Cases3.drop(['TOTPOP3','TOTPOP4'], axis=1)\n\nCases4 = CDCcovid[CDCcovid['AGEGROUP'] == '65+ years'].rename(columns={'CASES':'AG4CASES'}).drop(['AGEGROUP'],axis=1).reset_index(drop=True)\n\nCases4 = Cases4.merge(AG3,on=AGKEY,how='left',suffixes=('','_drop')).merge(AG4,on=AGKEY,how='left',suffixes=('','_drop'))\nCases4.drop([col for col in Cases4.columns if 'drop' in col],axis=1,inplace=True)\nCases4['AG4CASES'] = Cases4['AG4CASES'] * (Cases4['TOTPOP4'] / (Cases4['TOTPOP3'] +Cases4['TOTPOP4']))\nCases4 = Cases4.drop(['TOTPOP3','TOTPOP4'], axis=1)\n\nCDC = Cases1.merge(Cases2,on=AGKEY,how='left',suffixes=('','_drop')).merge(Cases3,on=AGKEY,how='left',suffixes=('','_drop')).merge(Cases4,on=AGKEY,how='left',suffixes=('','_drop'))\nCDC.drop([col for col in CDC.columns if 'drop' in col],axis=1,inplace=True)\n\nCDC['TOTALCASES'] = CDC['AG1CASES']+CDC['AG2CASES']+CDC['AG3CASES']+CDC['AG4CASES']\n\n#Processing of these values to create county-level age-group percentage of cases\nCDC['AG1AR'] = CDC['AG1CASES']/CDC['TOTALCASES']\nCDC['AG2AR'] = CDC['AG2CASES']/CDC['TOTALCASES']\nCDC['AG3AR'] = CDC['AG3CASES']/CDC['TOTALCASES']\nCDC['AG4AR'] = CDC['AG4CASES']/CDC['TOTALCASES']\n\nCDC.to_csv('data_folder/CDC.csv',index=False)\n\nVAMC = pd.read_csv('data_folder/VAMC.csv', usecols=['NAME','STATE','STATEFP','COUNTYFP','CountyName','VISN'], converters={'STATEFP': '{:0>2}'.format,'COUNTYFP': '{:0>3}'.format}) \\\n .dropna(subset=['VISN']) \\\n .astype(dtype={'NAME': 'str', 'VISN':'int','STATEFP':'str','COUNTYFP':'str'}) \n \nVAMC['STATE'] = VAMC.apply(lambda x: stateNames(x['STATE']), axis=1)\nVAMC['FIPS'] = VAMC.STATEFP + VAMC.COUNTYFP\nVAMC = VAMC[['VISN','NAME','FIPS','CountyName','STATE']].rename(columns={'CountyName':'COUNTY','NAME':'VAMC'}).sort_values(by=['VISN','VAMC'])\nVAMC.to_csv('data_folder/CleanVAMC.csv',index=False)\n", "sub_path": "Population.py", "file_name": "Population.py", "file_ext": "py", "file_size_in_byte": 11212, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_csv", "line_number": 74, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 76, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 103, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 104, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 150, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 151, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 164, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 170, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 202, "usage_type": "call"}]} +{"seq_id": "96739776", "text": "# 图片去噪\n\nimport cv2\nimport numpy as np\nimport random\nimport matplotlib.pyplot as plt\n\n\ndef sp_noise(image, prob):\n '''\n 添加椒盐噪声\n prob:噪声比例\n '''\n output = np.zeros(image.shape, np.uint8)\n thres = 1 - prob\n for i in range(image.shape[0]):\n for j in range(image.shape[1]):\n rdn = random.random()\n if rdn < prob:\n output[i][j] = 0\n elif rdn > thres:\n output[i][j] = 255\n else:\n output[i][j] = image[i][j]\n return output\n\n\ndef gasuss_noise(image, mean=0, var=0.005):\n '''\n 添加高斯噪声\n mean : 均值\n var : 方差\n '''\n image = np.array(image / 255, dtype=float)\n noise = np.random.normal(mean, var ** 0.5, image.shape)\n out = image + noise\n if out.min() < 0:\n low_clip = -1.\n else:\n low_clip = 0.\n out = np.clip(out, low_clip, 1.0)\n out = np.uint8(out * 255)\n # cv.imshow(\"gasuss\", out)\n return out\n\n\nimg = cv2.imread('img/shanghai.jpg')\nimg_spnoise = sp_noise(img, 0.05)\nimg_gsnoise = gasuss_noise(img)\n\nimg = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\nimg_spnoise = cv2.cvtColor(img_spnoise, cv2.COLOR_BGR2RGB)\nimg_gsnoise = cv2.cvtColor(img_gsnoise, cv2.COLOR_BGR2RGB)\nimg_meanfilter=cv2.blur(img_gsnoise,(5,5))\n\nplt.figure()\nplt.subplot(2, 2, 1), plt.imshow(img), plt.title('Original Image')\nplt.subplot(2, 2, 2), plt.imshow(img_spnoise), plt.title('S&P Noise Image')\nplt.subplot(2, 2, 3), plt.imshow(img_gsnoise), plt.title('Gasuss Noise Image')\nplt.subplot(2, 2, 4), plt.imshow(img_meanfilter), plt.title('Mean Filter Image')\nplt.show()\n", "sub_path": "Denoising.py", "file_name": "Denoising.py", "file_ext": "py", "file_size_in_byte": 1654, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.zeros", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 14, "usage_type": "attribute"}, {"api_name": "random.random", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 51, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 52, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 53, "usage_type": "attribute"}, {"api_name": "cv2.blur", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 58, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}]} +{"seq_id": "567689816", "text": "# -*- coding: utf-8 -*-\n__author__ = 'ffuentes'\n\nfrom datetime import datetime\nfrom django.conf import settings\nfrom django.contrib.auth.middleware import get_user\nfrom django.contrib.auth.models import User\nfrom django.contrib.sessions.models import Session\nfrom django.contrib.sessions.backends.base import UpdateError\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.shortcuts import redirect\nfrom django.utils.cache import patch_vary_headers\nfrom django.utils.functional import SimpleLazyObject\nfrom django.utils.http import cookie_date\nfrom graphql_jwt import signals\nfrom graphql_jwt.settings import jwt_settings\nfrom graphql_jwt.shortcuts import get_token, get_user_by_token\nfrom graphql_jwt.refresh_token.shortcuts import refresh_token_lazy\nfrom graphql_jwt.refresh_token.signals import refresh_token_rotated\nfrom graphql_jwt.utils import get_credentials, get_payload\nfrom graphql_jwt.exceptions import JSONWebTokenError, JSONWebTokenExpired\nfrom importlib import import_module\n\nimport time\nimport logging\n\nlogger = logging.getLogger(__name__)\n\ndef token_is_expired(token):\n ret = False\n\n try:\n get_payload(token)\n except JSONWebTokenError:\n ret = True\n except JSONWebTokenExpired:\n ret = True\n\n return ret\n\n\ndef get_user_from_session_key(session_key):\n session = Session.objects.get(session_key=session_key)\n session_data = session.get_decoded()\n uid = session_data.get('_auth_user_id')\n user = User.objects.get(id=uid)\n\n return user\n\n\ndef delete_jwt_cookie(request, response):\n max_age = request.session.get_expiry_age()\n anti_expires_time = cookie_date(time.time() - max_age)\n\n response.set_cookie(\n jwt_settings.JWT_COOKIE_NAME,\n '',\n domain=settings.COOKIE_DOMAIN,\n expires=anti_expires_time,\n secure=settings.JWT_COOKIE_SECURE or None,\n httponly=settings.JWT_COOKIE_HTTPONLY or None,\n samesite='Lax',\n )\n\n\nclass SRIJWTAuthMiddleware(object):\n def __init__(self, get_response):\n self.get_response = get_response\n\n def __call__(self, request):\n session_created = False\n has_token = False\n\n # add user\n request.user = SimpleLazyObject(lambda: get_user(request))\n token = get_credentials(request)\n\n if token is not None and token != '' and token != 'None' and \\\n not token_is_expired(token):\n user = get_user_by_token(token, request)\n request.user = user\n has_token = True\n\n # add session\n if not hasattr(request, 'session'):\n session_engine = import_module(settings.SESSION_ENGINE)\n session_key = request.COOKIES.get(settings.SESSION_COOKIE_NAME)\n\n # if the session cannot be saved, start with an empty session\n try:\n request.session = session_engine.SessionStore(session_key)\n request.session.save()\n session_created = True\n except UpdateError:\n response = redirect(request.get_full_path())\n response.delete_cookie(\n settings.SESSION_COOKIE_NAME,\n path=settings.SESSION_COOKIE_PATH,\n domain=settings.SESSION_COOKIE_DOMAIN,\n )\n response.delete_cookie(jwt_settings.JWT_COOKIE_NAME)\n patch_vary_headers(response, ('Cookie',))\n\n return response\n\n max_age = request.session.get_expiry_age()\n expires_time = time.time() + max_age\n anti_expires_time = cookie_date(time.time() - max_age)\n cookie_expires = cookie_date(expires_time)\n\n if request.session.get_expire_at_browser_close():\n max_age = None\n cookie_expires = None\n\n if token and token_is_expired(token):\n cookie_token = request.COOKIES.get(jwt_settings.JWT_COOKIE_NAME)\n session_key = request.COOKIES.get(settings.SESSION_COOKIE_NAME)\n\n if cookie_token and cookie_token != '\"\"':\n try:\n user = get_user_from_session_key(session_key)\n request.user = user\n refresh_token_lazy(request.user)\n token = get_token(request.user)\n refresh_token_rotated.send(\n sender=SRIJWTAuthMiddleware,\n request=request,\n refresh_token=self,\n )\n signals.token_issued.send(\n sender=SRIJWTAuthMiddleware, request=request, user=request.user)\n except ObjectDoesNotExist:\n ## fallback solution\n response = redirect(request.get_full_path())\n delete_jwt_cookie(request, response)\n patch_vary_headers(response, ('Cookie',))\n\n return response\n\n # process response with inner middleware\n response = self.get_response(request)\n\n if request.user.is_authenticated and not has_token:\n token = get_token(request.user)\n signals.token_issued.send(\n sender=SRIJWTAuthMiddleware, request=request, user=request.user)\n\n # if token is expired, refresh it\n if token_is_expired(token):\n refresh_token_lazy(request.user)\n token = get_token(request.user)\n refresh_token_rotated.send(\n sender=SRIJWTAuthMiddleware,\n request=request,\n refresh_token=self,\n )\n signals.token_issued.send(\n sender=SRIJWTAuthMiddleware, request=request, user=request.user)\n\n #expires = datetime.utcnow() + jwt_settings.JWT_EXPIRATION_DELTA\n response.set_cookie(\n jwt_settings.JWT_COOKIE_NAME,\n token,\n domain=settings.COOKIE_DOMAIN,\n max_age=max_age,\n expires=cookie_expires,\n secure=settings.JWT_COOKIE_SECURE or None,\n httponly=settings.JWT_COOKIE_HTTPONLY or None,\n samesite='Lax',\n )\n patch_vary_headers(response, ('Cookie',))\n\n accessed = request.session.accessed\n modified = request.session.modified\n empty = request.session.is_empty()\n\n # we'll force the session cookie creation if:\n # * we have a valid token but we didn't have a session for the user\n # * the session was not created because the user is logged in\n create_session_cookie = token and session_created \\\n or token and not request.user.is_authenticated\n\n if settings.SESSION_COOKIE_NAME in request.COOKIES and empty:\n response.delete_cookie(\n settings.SESSION_COOKIE_NAME,\n path=settings.SESSION_COOKIE_PATH,\n domain=settings.SESSION_COOKIE_DOMAIN,\n )\n response.delete_cookie(jwt_settings.JWT_COOKIE_NAME)\n patch_vary_headers(response, ('Cookie',))\n else:\n if accessed:\n patch_vary_headers(response, ('Cookie',))\n\n try:\n SESSION_SAVE_EVERY_REQUEST = settings.SESSION_SAVE_EVERY_REQUEST\n except AttributeError:\n SESSION_SAVE_EVERY_REQUEST = None\n\n if (modified or SESSION_SAVE_EVERY_REQUEST) and not empty or create_session_cookie:\n # Save the session data and refresh the client cookie.\n # Skip session save for 500 responses, refs #3881.\n if response.status_code != 500:\n try:\n request.session.save()\n except UpdateError:\n raise SuspiciousOperation(\n \"The request's session was deleted before the \"\n \"request completed. The user may have logged \"\n \"out in a concurrent request, for example.\"\n )\n response.set_cookie(\n settings.SESSION_COOKIE_NAME,\n request.session.session_key, max_age=max_age,\n expires=cookie_expires, domain=settings.SESSION_COOKIE_DOMAIN,\n path=settings.SESSION_COOKIE_PATH,\n secure=settings.SESSION_COOKIE_SECURE or None,\n httponly=settings.SESSION_COOKIE_HTTPONLY or None,\n samesite='Strict',\n )\n\n return response\n", "sub_path": "src/niweb/apps/noclook/middleware.py", "file_name": "middleware.py", "file_ext": "py", "file_size_in_byte": 8639, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 27, "usage_type": "call"}, {"api_name": "graphql_jwt.utils.get_payload", "line_number": 33, "usage_type": "call"}, {"api_name": "graphql_jwt.exceptions.JSONWebTokenError", "line_number": 34, "usage_type": "name"}, {"api_name": "graphql_jwt.exceptions.JSONWebTokenExpired", "line_number": 36, "usage_type": "name"}, {"api_name": "django.contrib.sessions.models.Session.objects.get", "line_number": 43, "usage_type": "call"}, {"api_name": "django.contrib.sessions.models.Session.objects", "line_number": 43, "usage_type": "attribute"}, {"api_name": "django.contrib.sessions.models.Session", "line_number": 43, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 46, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 46, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 46, "usage_type": "name"}, {"api_name": "django.utils.http.cookie_date", "line_number": 53, "usage_type": "call"}, {"api_name": "time.time", "line_number": 53, "usage_type": "call"}, {"api_name": "graphql_jwt.settings.jwt_settings.JWT_COOKIE_NAME", "line_number": 56, "usage_type": "attribute"}, {"api_name": "graphql_jwt.settings.jwt_settings", "line_number": 56, "usage_type": "name"}, {"api_name": "django.conf.settings.COOKIE_DOMAIN", "line_number": 58, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 58, "usage_type": "name"}, {"api_name": "django.conf.settings.JWT_COOKIE_SECURE", "line_number": 60, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 60, "usage_type": "name"}, {"api_name": "django.conf.settings.JWT_COOKIE_HTTPONLY", "line_number": 61, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 61, "usage_type": "name"}, {"api_name": "django.utils.functional.SimpleLazyObject", "line_number": 75, "usage_type": "call"}, {"api_name": "django.contrib.auth.middleware.get_user", "line_number": 75, "usage_type": "call"}, {"api_name": "graphql_jwt.utils.get_credentials", "line_number": 76, "usage_type": "call"}, {"api_name": "graphql_jwt.shortcuts.get_user_by_token", "line_number": 80, "usage_type": "call"}, {"api_name": "importlib.import_module", "line_number": 86, "usage_type": "call"}, {"api_name": "django.conf.settings.SESSION_ENGINE", "line_number": 86, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 86, "usage_type": "name"}, {"api_name": "django.conf.settings.SESSION_COOKIE_NAME", "line_number": 87, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 87, "usage_type": "name"}, {"api_name": "django.contrib.sessions.backends.base.UpdateError", "line_number": 94, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 95, "usage_type": "call"}, {"api_name": "django.conf.settings.SESSION_COOKIE_NAME", "line_number": 97, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 97, "usage_type": "name"}, {"api_name": "django.conf.settings.SESSION_COOKIE_PATH", "line_number": 98, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 98, "usage_type": "name"}, {"api_name": "django.conf.settings.SESSION_COOKIE_DOMAIN", "line_number": 99, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 99, "usage_type": "name"}, {"api_name": "graphql_jwt.settings.jwt_settings.JWT_COOKIE_NAME", "line_number": 101, "usage_type": "attribute"}, {"api_name": "graphql_jwt.settings.jwt_settings", "line_number": 101, "usage_type": "name"}, {"api_name": "django.utils.cache.patch_vary_headers", "line_number": 102, "usage_type": "call"}, {"api_name": "time.time", "line_number": 107, "usage_type": "call"}, {"api_name": "django.utils.http.cookie_date", "line_number": 108, "usage_type": "call"}, {"api_name": "time.time", "line_number": 108, "usage_type": "call"}, {"api_name": "django.utils.http.cookie_date", "line_number": 109, "usage_type": "call"}, {"api_name": "graphql_jwt.settings.jwt_settings.JWT_COOKIE_NAME", "line_number": 116, "usage_type": "attribute"}, {"api_name": "graphql_jwt.settings.jwt_settings", "line_number": 116, "usage_type": "name"}, {"api_name": "django.conf.settings.SESSION_COOKIE_NAME", "line_number": 117, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 117, "usage_type": "name"}, {"api_name": "graphql_jwt.refresh_token.shortcuts.refresh_token_lazy", "line_number": 123, "usage_type": "call"}, {"api_name": "graphql_jwt.shortcuts.get_token", "line_number": 124, "usage_type": "call"}, {"api_name": "graphql_jwt.refresh_token.signals.refresh_token_rotated.send", "line_number": 125, "usage_type": "call"}, {"api_name": "graphql_jwt.refresh_token.signals.refresh_token_rotated", "line_number": 125, "usage_type": "name"}, {"api_name": "graphql_jwt.signals.token_issued.send", "line_number": 130, "usage_type": "call"}, {"api_name": "graphql_jwt.signals.token_issued", "line_number": 130, "usage_type": "attribute"}, {"api_name": "graphql_jwt.signals", "line_number": 130, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 132, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 134, "usage_type": "call"}, {"api_name": "django.utils.cache.patch_vary_headers", "line_number": 136, "usage_type": "call"}, {"api_name": "graphql_jwt.shortcuts.get_token", "line_number": 144, "usage_type": "call"}, {"api_name": "graphql_jwt.signals.token_issued.send", "line_number": 145, "usage_type": "call"}, {"api_name": "graphql_jwt.signals.token_issued", "line_number": 145, "usage_type": "attribute"}, {"api_name": "graphql_jwt.signals", "line_number": 145, "usage_type": "name"}, {"api_name": "graphql_jwt.refresh_token.shortcuts.refresh_token_lazy", "line_number": 150, "usage_type": "call"}, {"api_name": "graphql_jwt.shortcuts.get_token", "line_number": 151, "usage_type": "call"}, {"api_name": "graphql_jwt.refresh_token.signals.refresh_token_rotated.send", "line_number": 152, "usage_type": "call"}, {"api_name": "graphql_jwt.refresh_token.signals.refresh_token_rotated", "line_number": 152, "usage_type": "name"}, {"api_name": "graphql_jwt.signals.token_issued.send", "line_number": 157, "usage_type": "call"}, {"api_name": "graphql_jwt.signals.token_issued", "line_number": 157, "usage_type": "attribute"}, {"api_name": "graphql_jwt.signals", "line_number": 157, "usage_type": "name"}, {"api_name": "graphql_jwt.settings.jwt_settings.JWT_COOKIE_NAME", "line_number": 162, "usage_type": "attribute"}, {"api_name": "graphql_jwt.settings.jwt_settings", "line_number": 162, "usage_type": "name"}, {"api_name": "django.conf.settings.COOKIE_DOMAIN", "line_number": 164, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 164, "usage_type": "name"}, {"api_name": "django.conf.settings.JWT_COOKIE_SECURE", "line_number": 167, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 167, "usage_type": "name"}, {"api_name": "django.conf.settings.JWT_COOKIE_HTTPONLY", "line_number": 168, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 168, "usage_type": "name"}, {"api_name": "django.utils.cache.patch_vary_headers", "line_number": 171, "usage_type": "call"}, {"api_name": "django.conf.settings.SESSION_COOKIE_NAME", "line_number": 183, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 183, "usage_type": "name"}, {"api_name": "django.conf.settings.SESSION_COOKIE_NAME", "line_number": 185, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 185, "usage_type": "name"}, {"api_name": "django.conf.settings.SESSION_COOKIE_PATH", "line_number": 186, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 186, "usage_type": "name"}, {"api_name": "django.conf.settings.SESSION_COOKIE_DOMAIN", "line_number": 187, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 187, "usage_type": "name"}, {"api_name": "graphql_jwt.settings.jwt_settings.JWT_COOKIE_NAME", "line_number": 189, "usage_type": "attribute"}, {"api_name": "graphql_jwt.settings.jwt_settings", "line_number": 189, "usage_type": "name"}, {"api_name": "django.utils.cache.patch_vary_headers", "line_number": 190, "usage_type": "call"}, {"api_name": "django.utils.cache.patch_vary_headers", "line_number": 193, "usage_type": "call"}, {"api_name": "django.conf.settings.SESSION_SAVE_EVERY_REQUEST", "line_number": 196, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 196, "usage_type": "name"}, {"api_name": "django.contrib.sessions.backends.base.UpdateError", "line_number": 206, "usage_type": "name"}, {"api_name": "django.conf.settings.SESSION_COOKIE_NAME", "line_number": 213, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 213, "usage_type": "name"}, {"api_name": "django.conf.settings.SESSION_COOKIE_DOMAIN", "line_number": 215, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 215, "usage_type": "name"}, {"api_name": "django.conf.settings.SESSION_COOKIE_PATH", "line_number": 216, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 216, "usage_type": "name"}, {"api_name": "django.conf.settings.SESSION_COOKIE_SECURE", "line_number": 217, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 217, "usage_type": "name"}, {"api_name": "django.conf.settings.SESSION_COOKIE_HTTPONLY", "line_number": 218, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 218, "usage_type": "name"}]} +{"seq_id": "44146900", "text": "import pandas as pd\nimport numpy as np\nimport tensorflow as tf\nimport time, sys\nimport re\nimport pickle\nimport argparse\nimport os\nimport copy\nfrom model import ManyEncodersTransformer\nfrom utils import *\nfrom scheduler import CustomSchedule\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\n\ndef create_model():\n transformer = ManyEncodersTransformer(\n opt.num_encoders,\n opt.num_layers,\n opt.d_model,\n opt.num_heads,\n opt.dff,\n encoder_vocab_size,\n decoder_vocab_size,\n pe_input=encoder_vocab_size,\n pe_target=decoder_vocab_size,\n )\n\n return transformer\n\n\ndef compute_loss(real, pred):\n mask = tf.math.logical_not(tf.math.equal(real, 0))\n loss_ = loss_object(real, pred)\n\n mask = tf.cast(mask, dtype=loss_.dtype)\n loss_ *= mask\n\n return (tf.reduce_sum(loss_) / tf.reduce_sum(mask)) / num_gpus\n\n\ndef train_step(inp, tar):\n tar_inp = tar[:, :-1]\n tar_real = tar[:, 1:]\n\n enc_padding_mask, combined_mask, dec_padding_mask = create_masks(inp, tar_inp)\n\n with tf.GradientTape() as tape:\n predictions, _ = transformer(\n inp, tar_inp,\n True,\n enc_padding_mask,\n combined_mask,\n dec_padding_mask\n )\n loss = compute_loss(tar_real, predictions)\n\n gradients = tape.gradient(loss, transformer.trainable_variables)\n optimizer.apply_gradients(zip(gradients, transformer.trainable_variables))\n\n train_accuracy.update_state(tar_real, predictions)\n\n return loss\n\n\n@tf.function\ndef distributed_train_step(inp_dis, tar_dis):\n per_replica_losses = strategy.run(train_step, args=(inp_dis, tar_dis,))\n return strategy.reduce(tf.distribute.ReduceOp.SUM, per_replica_losses,\n axis=None)\n\n\ndef increment_tokens(tar, step, step_increment_tar):\n tar = tar.numpy()\n tar[:, step * step_increment_tar:] = 0\n tar = tf.convert_to_tensor(tar, dtype=tf.int32)\n return tar\n\n\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser()\n parser.add_argument('-encoder_max_len', type=int, default=2000)\n parser.add_argument('-decoder_max_len', type=int, default=216)\n parser.add_argument('-batch_size', type=int, default=32)\n parser.add_argument('-num_layers', type=int, default=4)\n parser.add_argument('-d_model', type=int, default=128)\n parser.add_argument('-dff', type=int, default=2048)\n parser.add_argument('-num_heads', type=int, default=8)\n parser.add_argument('-encoder_max_vocab', type=int, default=100000)\n parser.add_argument('-decoder_max_vocab', type=int, default=100000)\n parser.add_argument('-num_encoders', type=int, default=4)\n parser.add_argument('-data_path', type=str, required=True)\n parser.add_argument('-checkpoint_path', type=str, required=True)\n parser.add_argument('-vocab_load_dir', type=str, required=True)\n parser.add_argument('-epoch_extra_training', type=int, default=10)\n parser.add_argument('-epoch_inter', type=int, default=8)\n parser.add_argument('-type_ft', type=int, default=1)\n\n opt = parser.parse_args()\n\n strategy = tf.distribute.MirroredStrategy()\n num_gpus = strategy.num_replicas_in_sync\n print('### Number of devices: {} ...'.format(num_gpus))\n\n oov_token = ''\n\n news = pd.read_excel(opt.data_path, dtype=str)\n news.drop(['id_articles'], axis=1, inplace=True)\n\n documents = news['articles']\n summaries = news['abstracts']\n summaries = summaries.apply(lambda x: ' ' + x + ' ')\n\n print('### Loading vocab ...')\n with open(os.path.join(opt.vocab_load_dir) + 'document_tokenizer_{}.pickle'.format(opt.encoder_max_vocab),\n 'rb') as fp:\n document_tokenizer = pickle.load(fp)\n\n with open(os.path.join(opt.vocab_load_dir) + 'summary_tokenizer_{}.pickle'.format(opt.decoder_max_vocab),\n 'rb') as fp:\n summary_tokenizer = pickle.load(fp)\n\n inputs = document_tokenizer.texts_to_sequences(documents)\n targets = summary_tokenizer.texts_to_sequences(summaries)\n\n if opt.encoder_max_vocab != -1:\n encoder_vocab_size = opt.encoder_max_vocab\n else:\n encoder_vocab_size = len(document_tokenizer.word_index) + 1\n\n if opt.decoder_max_vocab != -1:\n decoder_vocab_size = opt.decoder_max_vocab\n else:\n decoder_vocab_size = len(summary_tokenizer.word_index) + 1\n\n print(\"### Obtaining insights on lengths for defining maxlen...\"); sys.stdout.flush();\n document_lengths = pd.Series([len(x) for x in documents])\n summary_lengths = pd.Series([len(x) for x in summaries])\n BUFFER_SIZE = int(document_lengths.count())\n\n print(\"### Padding/Truncating sequences for identical sequence lengths...\"); sys.stdout.flush();\n inputs = tf.keras.preprocessing.sequence.pad_sequences(inputs, maxlen=opt.encoder_max_len, padding='post',\n truncating='post')\n targets = tf.keras.preprocessing.sequence.pad_sequences(targets, maxlen=opt.decoder_max_len, padding='post',\n truncating='post')\n\n print(\"### Creating dataset pipeline...\"); sys.stdout.flush();\n inputs = tf.cast(inputs, dtype=tf.int32)\n targets = tf.cast(targets, dtype=tf.int32)\n\n dataset_train = tf.data.Dataset.from_tensor_slices((inputs, targets)).shuffle(BUFFER_SIZE).batch(opt.batch_size)\n # train_dist_dataset = strategy.experimental_distribute_dataset(dataset_train)\n\n with strategy.scope():\n print(\"### Creating model...\"); sys.stdout.flush();\n transformer = create_model()\n print('### Defining losses and other metrics...'); sys.stdout.flush();\n learning_rate = CustomSchedule(opt.d_model)\n optimizer = tf.keras.optimizers.Adam(learning_rate, beta_1=0.9, beta_2=0.98, epsilon=1e-9)\n loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction='none')\n\n with strategy.scope():\n train_loss = tf.keras.metrics.Mean(name='train_loss')\n\n print(\"### Enter to restore the checkpoint...\"); sys.stdout.flush();\n ckpt_restore = tf.train.Checkpoint(transformer=transformer, optimizer=optimizer)\n ckpt_manager_restore = tf.train.CheckpointManager(ckpt_restore, opt.checkpoint_path, max_to_keep=5)\n if ckpt_manager_restore.latest_checkpoint:\n ckpt_restore.restore(ckpt_manager_restore.latest_checkpoint)\n print('Latest checkpoint restored!!', ' - ' * 10)\n\n # End-chunk training\n step_increment_tar = round(opt.decoder_max_len / opt.epoch_inter)\n step_increment_inp = round(opt.encoder_max_len / opt.epoch_inter)\n\n print(\"step_increment_tar: \", step_increment_tar)\n if opt.type_ft == 2:\n print(\"step_increment_inp: \", step_increment_inp)\n\n with strategy.scope():\n train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')\n\n print(\"### Training...\"); sys.stdout.flush();\n with strategy.scope():\n\n for epoch in range(opt.epoch_extra_training):\n\n total_loss_global = 0\n num_batches_global = 0\n\n for step in range(1, opt.epoch_inter + 1):\n\n total_loss = 0\n num_batches = 0\n\n for (batch, (inp, tar)) in enumerate(dataset_train):\n\n tar_np = tar\n tar_np = tar_np.numpy()\n\n tar_np[:, step * step_increment_tar:] = 0\n tar_ = tf.convert_to_tensor(tar_np, dtype=tf.int32)\n\n num_batches += 1\n\n if opt.type_ft == 1:\n total_loss += distributed_train_step(inp, tar_)\n\n if opt.type_ft == 2:\n inp_np = inp\n inp_np = inp_np.numpy()\n\n inp_np[:, step * step_increment_inp:] = 0\n inp_ = tf.convert_to_tensor(inp_np, dtype=tf.int32)\n\n total_loss += distributed_train_step(inp_, tar_)\n\n total_loss_global += total_loss\n num_batches_global += 1\n\n if batch % 100 == 0:\n template = \"Epoch {} Step {} Batch {}\"\n print(template.format(epoch + 1, step, batch + 1))\n pass\n\n train_loss = total_loss / num_batches\n template = \"Epoch {} , Step {} Batch {}, Loss: {}, Accuracy: {}\"\n print(template.format(epoch + 1, step, batch + 1, train_loss, train_accuracy.result() * 100))\n\n train_loss_global = total_loss_global / num_batches_global\n template = \"Epoch {} Loss: {} Accuracy: {}\"\n print('= ' * 15, template.format(epoch + 1, train_loss_global, train_accuracy.result() * 100))\n\n print('### Save the checkpoints ...')\n if epoch % 1 == 0:\n\n path_save_ckp = opt.checkpoint_path + 'epoch_' + str(epoch + 1) + '_FT_' + str(opt.type_ft)\n\n if not os.path.isdir(path_save_ckp):\n os.makedirs(path_save_ckp)\n ckpt_manager_restore = tf.train.CheckpointManager(ckpt_restore, path_save_ckp, max_to_keep=5)\n ckpt_save_path = ckpt_manager_restore.save()\n print('Saving checkpoint for epoch {} at {}'.format(epoch + 1, ckpt_save_path));\n sys.stdout.flush();", "sub_path": "extra_train_more_encoders.py", "file_name": "extra_train_more_encoders.py", "file_ext": "py", "file_size_in_byte": 9380, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.environ", "line_number": 14, "usage_type": "attribute"}, {"api_name": "model.ManyEncodersTransformer", "line_number": 18, "usage_type": "call"}, {"api_name": "tensorflow.math.logical_not", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 34, "usage_type": "attribute"}, {"api_name": "tensorflow.math.equal", "line_number": 34, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.GradientTape", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.distribute", "line_number": 70, "usage_type": "attribute"}, {"api_name": "tensorflow.function", "line_number": 67, "usage_type": "attribute"}, {"api_name": "tensorflow.convert_to_tensor", "line_number": 77, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 77, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 83, "usage_type": "call"}, {"api_name": "tensorflow.distribute.MirroredStrategy", "line_number": 103, "usage_type": "call"}, {"api_name": "tensorflow.distribute", "line_number": 103, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 109, "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": "pickle.load", "line_number": 119, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 121, "usage_type": "call"}, {"api_name": "os.path", "line_number": 121, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 123, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 138, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 138, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 139, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 140, "usage_type": "call"}, {"api_name": "sys.stdout.flush", "line_number": 143, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 143, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.preprocessing.sequence.pad_sequences", "line_number": 144, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 144, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.preprocessing.sequence.pad_sequences", "line_number": 146, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 146, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 149, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 149, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 150, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 150, "usage_type": "attribute"}, {"api_name": "tensorflow.cast", "line_number": 151, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 151, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 153, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 153, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 157, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 157, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 159, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 159, "usage_type": "attribute"}, {"api_name": "scheduler.CustomSchedule", "line_number": 160, "usage_type": "call"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 161, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 161, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.losses.SparseCategoricalCrossentropy", "line_number": 162, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 162, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.metrics.Mean", "line_number": 165, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 165, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 167, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 167, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Checkpoint", "line_number": 168, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 168, "usage_type": "attribute"}, {"api_name": "tensorflow.train.CheckpointManager", "line_number": 169, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 169, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.metrics.SparseCategoricalAccuracy", "line_number": 183, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 183, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 185, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 185, "usage_type": "attribute"}, {"api_name": "tensorflow.convert_to_tensor", "line_number": 204, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 204, "usage_type": "attribute"}, {"api_name": "tensorflow.convert_to_tensor", "line_number": 216, "usage_type": "call"}, {"api_name": "tensorflow.int32", "line_number": 216, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 241, "usage_type": "call"}, {"api_name": "os.path", "line_number": 241, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 242, "usage_type": "call"}, {"api_name": "tensorflow.train.CheckpointManager", "line_number": 243, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 243, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 246, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 246, "usage_type": "attribute"}]} +{"seq_id": "30761288", "text": "import numpy as np\nimport json\nimport requests\nfrom flask import request\nimport ast\nimport logging\nfrom app.my_model import my_model\n# from tensorflow.keras.preprocessing.image import load_img\n# from tensorflow.keras.preprocessing.image import img_to_array\nfrom flask import jsonify\nimport time\nimport io\nimport zlib\n\nfrom app import app\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\n\ndef compress_nparr(nparr):\n \"\"\"\n Returns the given numpy array as compressed bytestring,\n the uncompressed and the compressed byte size.\n \"\"\"\n bytestream = io.BytesIO()\n np.save(bytestream, nparr)\n uncompressed = bytestream.getvalue()\n compressed = zlib.compress(uncompressed)\n print(\"compress from %f to %f\" %(len(uncompressed), len(compressed)))\n return compressed\n\ndef uncompress_nparr(bytestring):\n \"\"\"\n \"\"\"\n return np.load(io.BytesIO(zlib.decompress(bytestring)))\n\ndef init(model_name, cut_point, next_cut_point, is_first=False, is_last=False, output_layer=-1, num_output=1):\n global model \n model = my_model(model_name, cut_point=cut_point, next_cut_point=next_cut_point, is_first=is_first, is_last=is_last)\n @app.route('/', methods=['POST', 'GET'])\n def post():\n start = time.time()\n input = request.get_data()\n input = uncompress_nparr(input)\n # print(\"Request from form time usage: %f\" %(time.time()-start))\n # input = ast.literal_eval(input)\n # print(\"literal_eval time usage: %f\" %(time.time()-start))\n # input = np.array(input)\n print(\"Data retrieve time usage: %f\" %(time.time()-start))\n output = model.predict(input)\n\n if not is_last:\n if len(next_cut_point) == 1:\n try:\n res = requests.post('http://'+model_name+'-'+str(next_cut_point[0]+1)+'.default.svc.cluster.local:5000', data=compress_nparr(output)).text\n except Exception as e:\n print(e)\n res = 'Network error'\n return res\n else:\n res = []\n for n in next_cut_point[1:]:\n print(n)\n try:\n res.append(requests.post('http://'+model_name+'-'+str(n)+'.default.svc.cluster.local:5000', data=compress_nparr(output)).text)\n print(res)\n except Exception as e:\n print(e)\n res.append('Network error')\n continue\n return '\\n'.join(str(r) for r in res)\n else:\n index = np.argmax(output)\n return str(np.argmax(output))\n\n @app.route('/info', methods=['GET'])\n def info():\n if not is_last:\n res = []\n res.append(model.get_layers())\n if len(next_cut_point) == 1:\n try:\n for l in json.loads(requests.get('http://'+model_name+'-'+str(next_cut_point[0]+1)+'.default.svc.cluster.local:5000/info').text):\n res.append(l)\n except Exception as e:\n print(e)\n return 'model not ready', 500\n else:\n for n in next_cut_point[1:]:\n print(n)\n try:\n for l in json.loads(requests.get('http://'+model_name+'-'+str(n)+'.default.svc.cluster.local:5000/info').text):\n res.append(l)\n except Exception as e:\n print(e)\n continue\n return jsonify(res)\n else:\n return jsonify([model.get_layers()])\n\n @app.route('/name', methods=['GET'])\n def name():\n return model_name\n\n @app.route('/cuttable', methods=['GET'])\n def cuttable():\n if not is_first:\n return None\n else:\n return jsonify(model.get_cuttable())\n\n @app.route('/time', methods=['GET'])\n def get_avg_time():\n if not is_last:\n res = []\n res.append(model.get_avg_time())\n if len(next_cut_point) == 1:\n try:\n for l in json.loads(requests.get('http://'+model_name+'-'+str(next_cut_point[0]+1)+'.default.svc.cluster.local:5000/time').text):\n res.append(l)\n except Exception as e:\n print(e)\n else:\n for n in next_cut_point[1:]:\n print(n)\n try:\n for l in json.loads(requests.get('http://'+model_name+'-'+str(n)+'.default.svc.cluster.local:5000/time').text):\n res.append(l)\n except Exception as e:\n print(e)\n continue\n return jsonify(res)\n else:\n return jsonify([model.get_avg_time()])\n\n @app.route('/layer', methods=['POST'])\n def change_layer():\n cut_point = int(request.form['cut_point'])\n next_cut_point = [int(n) for n in request.form['next_cut_point'].split(',')]\n # try:\n global model\n model = my_model(model_name, cut_point=cut_point, next_cut_point=next_cut_point, is_first=is_first, is_last=is_last)\n # except Exception as e:\n # print(e)\n \n return jsonify({'cut_point': cut_point, 'next_cut_point': next_cut_point})\n\n\n\n @app.route('/metrics', methods=['GET'])\n def get_metric():\n if not is_last:\n res = []\n res.append({\"time\": model.get_time(), \"cpu\": model.get_cpu(), \"memory\": model.get_memory()})\n if len(next_cut_point) == 1:\n try:\n for l in json.loads(requests.get('http://'+model_name+'-'+str(next_cut_point[0]+1)+'.default.svc.cluster.local:5000/metrics').text):\n res.append(l)\n except Exception as e:\n print(e)\n else:\n for n in next_cut_point[1:]:\n print(n)\n try:\n for l in json.loads(requests.get('http://'+model_name+'-'+str(n)+'.default.svc.cluster.local:5000/metrics').text):\n res.append(l)\n except Exception as e:\n print(e)\n continue\n return jsonify(res)\n else:\n return jsonify([{\"time\": model.get_time(), \"cpu\": model.get_cpu(), \"memory\": model.get_memory()}])\n", "sub_path": "app/route.py", "file_name": "route.py", "file_ext": "py", "file_size_in_byte": 6539, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 18, "usage_type": "attribute"}, {"api_name": "io.BytesIO", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 26, "usage_type": "call"}, {"api_name": "zlib.compress", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 35, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 35, "usage_type": "call"}, {"api_name": "zlib.decompress", "line_number": 35, "usage_type": "call"}, {"api_name": "app.my_model.my_model", "line_number": 39, "usage_type": "call"}, {"api_name": "time.time", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.request.get_data", "line_number": 43, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "time.time", "line_number": 49, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 55, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 74, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 40, "usage_type": "call"}, {"api_name": "app.app", "line_number": 40, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 83, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 83, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 92, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 97, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 99, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 76, "usage_type": "call"}, {"api_name": "app.app", "line_number": 76, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 101, "usage_type": "call"}, {"api_name": "app.app", "line_number": 101, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 110, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 105, "usage_type": "call"}, {"api_name": "app.app", "line_number": 105, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 119, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 119, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 127, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 127, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 132, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 134, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 112, "usage_type": "call"}, {"api_name": "app.app", "line_number": 112, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 138, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 138, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 139, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 139, "usage_type": "name"}, {"api_name": "app.my_model.my_model", "line_number": 142, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 146, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 136, "usage_type": "call"}, {"api_name": "app.app", "line_number": 136, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 157, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 157, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 165, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 165, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 170, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 172, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 150, "usage_type": "call"}, {"api_name": "app.app", "line_number": 150, "usage_type": "name"}]} +{"seq_id": "119660143", "text": "\"\"\"Game night!\"\"\"\nimport asyncio\nimport discord\nfrom discord.ext import commands\nfrom util.perms import or_check_perms\nfrom .cog import Cog\n\nclass GameNight(Cog):\n \"\"\"Now's your chance to have a quick and easy game night!\"\"\"\n def __init__(self, bot):\n self.games = {}\n super().__init__(bot)\n\n @commands.group(aliases=['game_night'], no_pm=True)\n async def gamenight(self, ctx):\n \"\"\"Game night!\n Usage: gamenight {stuff}\"\"\"\n if ctx.invoked_subcommand is None:\n await self.bot.send_cmd_help(ctx)\n\n @gamenight.command(aliases=['end', 'finish'])\n async def stop(self, ctx):\n \"\"\"Stop the current game night session.\n Usage: gamenight stop\"\"\"\n or_check_perms(ctx, ['manage_guild', 'manage_channels', 'manage_messages', 'manage_roles'])\n if ctx.channel.id in self.games:\n game = self.games[ctx.channel.id]\n if game['role']:\n try:\n await game['role'].delete(reason='Deleting game night session-specific role')\n except discord.Forbidden:\n pass\n del self.games[ctx.channel.id]\n await ctx.send('**Ended the current game night session at round ' + str(game['round']) + '.**')\n del game\n else:\n await ctx.send(ctx.mention + ' There\\'s no game night session active here!')\n\n @gamenight.command(aliases=['meme_war', 'meme-war', 'memes', 'meme', 'mwar', 'memwar'])\n async def memewar(self, ctx, *, topic: str):\n \"\"\"Start a meme war on a topic.\n Usage: gamenight memewar [topic]\"\"\"\n or_check_perms(ctx, ['manage_guild', 'manage_channels', 'manage_messages', 'manage_roles'])\n game = {\n 'active': False,\n 'topic': topic,\n 'duration': 1.5 * 60,\n 'players': {\n ctx.author: 0\n },\n 'recruiting': True,\n 'role': None,\n 'round': 1,\n 'round_active': False,\n 'r_mention': ''\n }\n if ctx.channel.id in self.games:\n await ctx.send(ctx.mention + ' There\\'s already a game night session here!')\n return\n self.games[ctx.channel.id] = game\n await ctx.send(f''':clap: Now hosting a **meme war** for `{topic}`! :clap:\nWe need at least 3 participants. ({ctx.mention} is already in.)\nEveryone, you have 1 minute to join! Just use `{ctx.prefix}gamenight join`.''')\n await asyncio.sleep(60, loop=self.loop)\n game['recruiting'] = False\n r_mention = ''\n if len(game['players']) < 3:\n await ctx.send('⚠ **Stopped due to insufficent number of participants.**')\n del self.games[ctx.channel.id]\n return\n try:\n role = await ctx.guild.create_role(name='Game Night Player', color=discord.Color.dark_teal(), mentionable=True,\n reason='Creating game night session-specific role')\n for player in game['players']:\n await player.add_roles(role, reason='Adding game night session-specific role for mentioning')\n r_mention = '<@&' + str(role.id) + '> '\n game['role'] = role\n except discord.Forbidden:\n await ctx.send('⚠ **I work best with the Manage Roles permission.**')\n game['r_mention'] = r_mention\n await ctx.send('''Starting the **meme war** in 30 seconds!\n{}Get your butts in here, and grab your dankest memes!'''.format(r_mention))\n await asyncio.sleep(28.6, loop=self.loop)\n game['active'] = True\n game['round_active'] = True\n await ctx.send(f'''{r_mention}The **meme war** is now starting for the topic `{topic}`!\nGet your memes in already! :clap::clap:\nLeaders: when you're ready, select a winner (and end the round) with `{ctx.prefix}gamenight winner`!''')\n\n @gamenight.command()\n async def topic(self, ctx, *, topic: str):\n \"\"\"Start the current round with a topic.\"\"\"\n or_check_perms(ctx, ['manage_guild', 'manage_channels', 'manage_messages', 'manage_roles'])\n if ctx.channel.id in self.games:\n try:\n await ctx.message.delete(reason='Deleting message sent to change the topic, so players don\\'t see and prepare before the round')\n except discord.Forbidden:\n await ctx.send('⚠ **I work best with the Manage Messages permission.**')\n game = self.games[ctx.channel.id]\n r_mention = game['r_mention']\n game['topic'] = topic\n await ctx.send('''Starting **round {}** in 30 seconds!\n{}Get your butts in here, and grab your dankest memes!'''.format(str(game['round']), r_mention))\n await asyncio.sleep(28.6, loop=self.loop)\n game['active'] = True\n game['round_active'] = True\n await ctx.send(f'''{r_mention}The **meme war** is now starting for the topic `{topic}`!\nGet your memes in already! :clap::clap:\nLeaders: when you're ready, select a winner (and end the round) with `{ctx.prefix}gamenight winner`!''')\n else:\n await ctx.send(ctx.mention + ' There isn\\'t a game night session in this channel!')\n\n @gamenight.command()\n async def winner(self, ctx, *, winner: discord.Member):\n \"\"\"Select a winner for a game night session.\n Usage: gamenight winner [winner]\"\"\"\n or_check_perms(ctx, ['manage_guild', 'manage_channels', 'manage_messages', 'manage_roles'])\n if ctx.channel.id in self.games:\n try:\n await ctx.message.delete(reason='Deleting message sent to select winner, so players don\\'t see the winner until after the drum roll')\n except discord.Forbidden:\n await ctx.send('⚠ **I work best with the Manage Messages permission.**')\n game = self.games[ctx.channel.id]\n if winner in game['players']:\n k = '.'\n key = '**...and the winner is'\n msg = await ctx.send(key + '**')\n for i in range(1, 4):\n await asyncio.sleep(0.96, loop=self.loop)\n await msg.edit(content=key + (k * i) + '**')\n await asyncio.sleep(0.97, loop=self.loop)\n await msg.edit(content=mkey + '...:drum:**')\n await asyncio.sleep(0.97, loop=self.loop)\n await msg.edit(content=key + '...:drum: ' + str(winner) + '!**')\n game['players'][winner] += 1\n game['round'] += 1\n game['round_active'] = False\n await asyncio.sleep(1.5, loop=self.loop)\n await ctx.send(f'Leaders: to set the topic for the next round, do `{ctx.prefix}gamenight topic [topic]`!')\n else:\n await ctx.send(ctx.mention + ' That person isn\\'t in this game night session!')\n else:\n await ctx.send(ctx.mention + ' There isn\\'t a game night session in this channel!')\n\n @gamenight.command()\n async def join(self, ctx):\n \"\"\"Join the current channel's game night session.\n Usage: gamenight join\"\"\"\n if ctx.channel.id in self.games:\n game = self.games[ctx.channel.id]\n if game['recruiting']:\n if ctx.author in game:\n await ctx.send(ctx.mention + ' You\\'re already in the game night session! **ALLOWING FOR DEV TESTING PURPOSES**')\n game['players'][ctx.author] = 0\n else:\n game['players'][ctx.author] = 0\n await ctx.send(ctx.mention + ' You\\'ve joined the game night session!')\n else:\n await ctx.send(ctx.mention + ' It\\'s too late to join this game night session!')\n else:\n await ctx.send(ctx.mention + ' There isn\\'t a game night session in this channel!')\n\n @gamenight.command()\n async def start(self, ctx):\n or_check_perms(ctx, ['manage_guild', 'manage_channels', 'manage_messages', 'manage_roles'])\n await ctx.send(f':clap: Use `{ctx.prefix}gamenight memewar [topic]` for now.')\n\ndef setup(bot):\n c = GameNight(bot)\n bot.add_cog(c)\n", "sub_path": "default_cogs/game_night.py", "file_name": "game_night.py", "file_ext": "py", "file_size_in_byte": 8189, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cog.Cog", "line_number": 8, "usage_type": "name"}, {"api_name": "discord.ext.commands.group", "line_number": 14, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 14, "usage_type": "name"}, {"api_name": "util.perms.or_check_perms", "line_number": 25, "usage_type": "call"}, {"api_name": "discord.Forbidden", "line_number": 31, "usage_type": "attribute"}, {"api_name": "util.perms.or_check_perms", "line_number": 43, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 64, "usage_type": "call"}, {"api_name": "discord.Color.dark_teal", "line_number": 72, "usage_type": "call"}, {"api_name": "discord.Color", "line_number": 72, "usage_type": "attribute"}, {"api_name": "discord.Forbidden", "line_number": 78, "usage_type": "attribute"}, {"api_name": "asyncio.sleep", "line_number": 83, "usage_type": "call"}, {"api_name": "util.perms.or_check_perms", "line_number": 93, "usage_type": "call"}, {"api_name": "discord.Forbidden", "line_number": 97, "usage_type": "attribute"}, {"api_name": "asyncio.sleep", "line_number": 104, "usage_type": "call"}, {"api_name": "discord.Member", "line_number": 114, "usage_type": "attribute"}, {"api_name": "util.perms.or_check_perms", "line_number": 117, "usage_type": "call"}, {"api_name": "discord.Forbidden", "line_number": 121, "usage_type": "attribute"}, {"api_name": "asyncio.sleep", "line_number": 129, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 131, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 133, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 138, "usage_type": "call"}, {"api_name": "util.perms.or_check_perms", "line_number": 165, "usage_type": "call"}]} +{"seq_id": "533988908", "text": "__author__ = 'boyd-st'\r\n\r\n### Main method ###\r\n### Point of Strart ###\r\n## 1. Verify you are partner-manager / launch tms\r\n## 2. Login. py / launch admin portal\r\n## 3. run role_setup.py / call role_UI_verification.py for Roles verification\r\n##### Verify Portlets layout.\r\n\r\nimport sys\r\nfrom selenium import webdriver\r\nfrom systems import systems_tab\r\n\r\nfrom pyvirtualdisplay import Display\r\ndisplay = Display(visible=0, size=(1024, 768))\r\ndisplay.start()\r\n\r\n\r\ndef driver():\r\n selenium_driver = webdriver.Firefox\r\n return selenium_driver()\r\n\r\ndef main():\r\n d = driver()\r\n\r\n syst = systems_tab()\r\n syst.logIn(d)\r\n syst.system_sub_menu_buttons(d)\r\n syst.systems_tab(d)\r\n syst.editSystem(d)\r\n\r\n syst.new_cloud_node(d)\r\n syst.cloudVerification(d)\r\n syst.cleanUpCloudNote(d)\r\n\r\n syst.cleanUpSystem(d)\r\n\r\n sys.stdout.close()\r\n display.stop()\r\n d.quit()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n main()", "sub_path": "TMS_PY/Local_scripts_TMS_PY/systems/main_systems.py", "file_name": "main_systems.py", "file_ext": "py", "file_size_in_byte": 934, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pyvirtualdisplay.Display", "line_number": 15, "usage_type": "call"}, {"api_name": "selenium.webdriver.Firefox", "line_number": 20, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 20, "usage_type": "name"}, {"api_name": "systems.systems_tab", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.stdout.close", "line_number": 38, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 38, "usage_type": "attribute"}]} +{"seq_id": "563917378", "text": "from __future__ import unicode_literals\n\nfrom django.db import models\nimport re\n\nEMAIL_REGXE = re.compile('^[a-zA-Z0-9.+_-]+@[a-zA-Z0-9._-]+\\.[a-zA-Z]+$')\n# Create your models here.\n\nclass UserManager(models.Manager):# ORM\n def registration(self,email): # we will call this in our views like this: Email.UserManager.registration(self,email)\n if EMAIL_REGXE.match(email):\n\n return True\n else:\n return False\n\n\n\nclass Email(models.Model):\n user_email = models.EmailField(max_length = 255)\n created_at = models.DateTimeField(auto_now_add=True)\n updated_at = models.DateTimeField(auto_now=True)\n userManager = UserManager() #our defined instance of the UserManager class called userManager to extend the functionality of our Email\n", "sub_path": "Python/Django/emailVal/apps/email_val/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 782, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "re.compile", "line_number": 6, "usage_type": "call"}, {"api_name": "django.db.models.Manager", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.EmailField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "325791362", "text": "# -*- coding: utf-8 -*-\nimport cv2 as cv\nimport math\nimport numpy as np\nnp.seterr(divide='ignore',invalid='ignore')\ndef roi_mask(img, corner_points):\n mask = np.zeros_like(img)\n cv.fillPoly(mask, corner_points, 255)\n masked_img = cv.bitwise_and(img, mask)\n return masked_img\ndef hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):\n lines = cv.HoughLinesP(img, rho, theta, threshold,minLineLength=min_line_len, maxLineGap=max_line_gap)\n return lines\ndef myhough(img):\n thetas = np.deg2rad(np.arange(-90.0, 90.0,1)).astype(int)\n cost = np.cos(thetas)\n sint = np.sin(thetas)\n row,column = np.nonzero(img)\n rhoss = []\n for i in range(len(thetas)):\n r = row[i]\n c = column[i]\n rho = r*cost[i]+c*sint[i]\n rho = rho.astype(int)\n rhoss.append(rho)\n return rhoss,thetas\n'''\n gaussian 引數\n'''\nblur_ksize = 199\n'''\n canny 檢測高低閥值\n'''\ncanny_lth = 43\ncanny_hth = 122\n'''\n hough 引數\n'''\nrho = 10\ntheta = np.pi / 180\nthreshold = 39\nmin_line_len = 130\nmax_line_gap = 29\ndef process_an_image(img):\n '''\n 一、將照片灰化，將照片做 gaussian 濾波，對照片做 canny 檢測\n '''\n gray = cv.cvtColor(img, cv.COLOR_RGB2GRAY)\n blur_gray = cv.GaussianBlur(gray, (blur_ksize, blur_ksize), 1)\n edges = cv.Canny(blur_gray, canny_lth, canny_hth)\n # [[[0 540], [460 325], [520 325], [960 540]]]\n points = np.array([[(0, edges.shape[0]), (460, 325), (520, 325), (edges.shape[1],edges.shape[0])]])\n roi_edges = roi_mask(edges, points)\n drawing = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)\n lines = hough_lines(roi_edges, rho, theta,threshold, min_line_len, max_line_gap)\n r,h = myhough(edges)\n arr = []\n global arra\n arra = []\n for z,w in zip(r,h):\n a = np.cos(w)\n b = np.sin(w)\n x0 = z * a\n y0 = z * b\n x1 = int(x0 + 1000 * (-b))\n if(x1<0):\n x1=abs(x1)\n y1 = int(y0 + 1000 * (a))\n x2 = int(x0 - 1000 * (b))\n if(x2<0):\n x2 = abs(x2)\n y2 = int(y0 - 1000 * (a))\n if(y2<0):\n y2 = abs(y2)\n s = ([x1,y1,x2,y2])\n arr.append(s)\n arra = np.array(arr)\n leftpart,rightpart = [],[]\n for a,b,c,d in arra:\n slope = (d-b)/(c-a)\n if slope <0:\n leftpart.append(arra)\n else:\n rightpart.append(arra)\n if(len(leftpart)<=0 or len(rightpart)<=0):\n return\n clean_lines(leftpart,0.1)\n clean_lines(rightpart,0.1)\n left_points = [(x1, y1) for arra in leftpart for x1, y1, x2, y2 in arra]\n left_points = left_points + [(x2, y2) for arra in leftpart for x1, y1, x2, y2 in arra]\n right_points = [(x1, y1) for arra in rightpart for x1, y1, x2, y2 in arra]\n right_points = right_points + [(x2, y2) for arra in rightpart for x1, y1, x2, y2 in arra]\n left_results = least_squares_fit(left_points, 325, img.shape[0])\n right_results = least_squares_fit(right_points, 325, img.shape[0])\n vtxs = np.array([[left_results[1], left_results[0], right_results[0], right_results[1]]])\n cv.fillPoly(img, vtxs, (0, 255, 0))\n draw_lanes(drawing, lines)\n result = cv.addWeighted(img, 0.9, drawing, 0.2, 0)\n return result\ndef draw_lanes(img, lines, color=[0,255, 0]):\n left_lines, right_lines = [],[]\n for line in lines:\n for x1, y1, x2, y2 in line:\n k = (y2 - y1) / (x2 - x1)\n if k < 0:\n left_lines.append(line)\n else:\n right_lines.append(line)\n if (len(left_lines) <= 0 or len(right_lines) <= 0):\n return\n '''\n 清理異常數據\n '''\n clean_lines(left_lines, 0.1)\n clean_lines(right_lines, 0.1)\n left_points = [(x1, y1) for line in left_lines for x1, y1, x2, y2 in line]\n left_points = left_points + [(x2, y2) for line in left_lines for x1, y1, x2, y2 in line]\n right_points = [(x1, y1) for line in right_lines for x1, y1, x2, y2 in line]\n right_points = right_points + [(x2, y2) for line in right_lines for x1, y1, x2, y2 in line]\n left_results = least_squares_fit(left_points, 325, img.shape[0])\n right_results = least_squares_fit(right_points, 325, img.shape[0])\n vtxs = np.array([[left_results[1], left_results[0], right_results[0], right_results[1]]])\n '''\n 填充車道區域\n '''\n cv.fillPoly(img, vtxs, (0,0,255))\n'''\n迭代計算斜率平均值，清理差異較大的數據\n'''\ndef clean_lines(lines, threshold):\n slope = [(y2 - y1) / (x2 - x1) for line in lines for x1, y1, x2, y2 in line]\n while len(lines) > 0:\n mean = np.mean(slope)\n diff = [abs(s - mean) for s in slope]\n idx = np.argmax(diff)\n if diff[idx] > threshold:\n slope.pop(idx)\n lines.pop(idx)\n else:\n break\n'''\n使用 least square 來 fit\n'''\ndef least_squares_fit(point_list, ymin, ymax):\n x = [p[0] for p in point_list]\n y = [p[1] for p in point_list]\n '''\n polyfit() 第三個引數為 fit 多項式的階數，所以一代表線性\n '''\n fit = np.polyfit(y, x, 1)\n fit_fn = np.poly1d(fit)\n xmin = int(fit_fn(ymin))\n xmax = int(fit_fn(ymax))\n return [(xmin, ymin), (xmax, ymax)]\nif __name__ == \"__main__\":\n img = cv.imread('2.jpg')\n img = cv.resize(img,(1000,500))\n result = process_an_image(img)\n cv.imshow(\"\", np.hstack((img, result)))\n cv.waitKey(0)\n", "sub_path": "hough.py", "file_name": "hough.py", "file_ext": "py", "file_size_in_byte": 5423, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.seterr", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 8, "usage_type": "call"}, {"api_name": "cv2.bitwise_and", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.HoughLinesP", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.deg2rad", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 40, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2GRAY", "line_number": 48, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.Canny", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 54, "usage_type": "attribute"}, {"api_name": "numpy.cos", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 95, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 96, "usage_type": "call"}, {"api_name": "cv2.addWeighted", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 122, "usage_type": "call"}, {"api_name": "cv2.fillPoly", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.polyfit", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.poly1d", "line_number": 151, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 156, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 157, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 159, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 160, "usage_type": "call"}]} +{"seq_id": "306138803", "text": "from sklearn.feature_extraction import DictVectorizer\n\n\"\"\"\n 字典特征抽取：\n 把字典中的一些类别数据（即字符串数据，比如'city':'上海'）转换为特征值('city=上海')，是上海就是1，不是上海就是0。\n\"\"\"\n\n\ndef dictvec():\n\n # 默认处理后返回的是sparse矩阵，如果设置sparse=False，则返回的是numpy中使用的矩阵(即ndarray，二位数组)\n vector = DictVectorizer(sparse=False)\n\n data = [{'city': '北京', 'temperature': 10},\n {'city': '上海', 'temperature': 24},\n {'city': '深圳', 'temperature': 30}]\n\n # 参数要么是字典，要么是包含字典的迭代器。\n # 返回的是sparse格式的矩阵。\n # sparse矩阵是scipy工具的矩阵格式，这种矩阵格式比较节约内存，方便读取处理。\n res = vector.fit_transform(data)\n\n print(vector.get_feature_names()) # 该方法获取特征值（即列索引）\n print(vector.inverse_transform(res)) # 该方法是传入转换后的数据，返回原来的数据\n\n print(res)\n return\n\n\nif __name__ == '__main__':\n dictvec()\n", "sub_path": "sklearn_practice/dict_vectorizer.py", "file_name": "dict_vectorizer.py", "file_ext": "py", "file_size_in_byte": 1126, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sklearn.feature_extraction.DictVectorizer", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "125960124", "text": "from flask import (\n Blueprint, flash, g, redirect, render_template, request, url_for, jsonify\n)\nfrom werkzeug.exceptions import abort\nimport json\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# from ml_viewer.auth import login_required\nbp = Blueprint('viewer', __name__)\n\n@bp.route('/')\ndef index():\n return render_template('viewer/index.html')\n\n@bp.route('/test')\ndef test():\n return render_template('viewer/test.html')\n\n@bp.route('/_gradient_descent', methods=['GET','POST'])\ndef _gradient_descent():\n data = request.json\n if(len(data['X']) <= 1):\n return jsonify({'error':'not enough data'});\n X = np.array([data['X']]).T\n y = np.array([data['Y']]).T\n theta_guess = np.array([data['theta']]).T\n alpha = data['alpha']\n num_iter = min(data['num_iter'],5000);\n\n tempX = X\n for i in range(1,data['poly']):\n temp = tempX;\n for j in range(i):\n temp = np.multiply(temp,tempX)\n X = np.hstack((X,temp))\n X, mu, sigma = feature_normalize(X)\n ones = np.ones((len(data['X']),1))\n X = np.hstack((ones,X))\n\n theta = gradient_descent(X,y,theta_guess,alpha,num_iter);\n return jsonify({'theta':theta.tolist(), 'sigma': sigma.tolist(), 'mu':mu.tolist()})\n\ndef gradient_descent(X, y, theta, alpha, num_iter):\n m = y.size\n for i in range(num_iter):\n h = X.dot(theta)\n loss = h - y\n gradient = np.dot(X.T, loss) / m\n theta = theta - alpha * gradient\n return theta\n\n@bp.route('/_log_regress', methods=['GET','POST'])\ndef _log_regress():\n data = request.json\n if(len(data['X']) <= 1):\n return jsonify({'error':'not enough data'})\n X = np.array([data['X'][0]]).T\n for i in range(1, len(data['X'])):\n feature = np.array([data['X'][i]]).T\n X = np.hstack((X,feature))\n y = np.array([data['Y']]).T\n alpha = data['alpha']\n num_iter = min(data['num_iter'],5000);\n\n for i in range(X.shape[0]):\n Xi = X[i]\n for j in range(1,data['poly']):\n for k in range(j):\n Xi2 = np.multiply(Xi,Xi)\n X = np.hstack((X,Xi2))\n X, mu, sigma = feature_normalize(X)\n X = mapFeature(X[:,0],X[:,1])\n # ones = np.ones((X.shape[0],1))\n # X = np.hstack((ones,X))\n theta_guess = np.ones((X.shape[1],1))\n\n theta = log_regress(X,y,theta_guess,alpha,num_iter);\n hypothesis = computeBoundary(X,y,theta,mu,sigma)\n\n return jsonify({'theta':theta.tolist(), 'sigma': sigma.tolist(), 'mu':mu.tolist(), 'hypothesis':hypothesis.tolist()})\n\ndef log_regress(X,y,theta,alpha,num_iter):\n m = y.size\n h = 1 / (1 + np.exp(-(X.dot(theta))))\n loss = h - y\n gradient = np.dot(X.T, loss) / m\n theta = theta - alpha*gradient\n\n l = 0.1\n for i in range(1, num_iter):\n h = 1 / (1 + np.exp(-(X.dot(theta))))\n loss = h-y\n gradient = np.dot(X.T, loss)\n theta = theta * (1-alpha*l/m) - alpha * gradient / m\n return theta\n\ndef computeBoundary(X,y,theta,mu,sigma):\n u = np.linspace(0,1000,50)\n v = np.linspace(0,1000,50)\n\n z = np.zeros((u.size, v.size))\n\n c = []\n test = mapFeature((u[5]-mu[0])/sigma[0],(v[5]-mu[1])/sigma[1]).dot(theta)\n print(test)\n for i in range(u.size):\n for j in range(v.size):\n z[i,j] = mapFeature((u[i]-mu[0])/sigma[0],(v[j]-mu[1])/sigma[1]).dot(theta)\n return z;\n # plt.contour(u,v,z,[0])\n # plt.show()\n # print(c)\n\ndef connectLine(c):\n for i in range(len(c)):\n minDistance = distance(c[i],c[(i+1) % len(c)])\n c[i]['next'] = (i+1) % len(c)\n for j in range(len(c)):\n if i != j:\n dist = distance(c[i],c[j])\n if(dist < minDistance):\n minDistance = dist\n c[i]['next'] = j\n return c\n\ndef distance(a,b):\n return ((b['x']-a['x'])**2+(b['y']-a['y'])**2)**0.5\n\ndef mapFeature(X1,X2):\n degree = 6\n out = [np.ones(X1.size)]\n for i in range(1, degree+1):\n for j in range(i+1):\n out.append(X1 ** (i-j) * X2 ** j)\n\n if np.isscalar(X1):\n return np.hstack(out) # if inputs are scalars, return a vector\n else:\n return np.column_stack(out)\n\ndef feature_normalize(X):\n X_norm = X;\n\n mu = np.mean(X, axis=0)\n sigma = np.std(X, axis=0)\n X_norm = (X_norm - mu) / sigma\n return (X_norm, mu, sigma)\n", "sub_path": "ml_viewer/viewer.py", "file_name": "viewer.py", "file_ext": "py", "file_size_in_byte": 4348, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Blueprint", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 14, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 24, "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": 27, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 55, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.isscalar", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 146, "usage_type": "call"}]} +{"seq_id": "195270000", "text": "\nfrom .views import *\nfrom django.conf.urls import patterns, url\n\nurlpatterns = patterns('',\n # login \n url(r'^login/', LoginView.as_view(), name='login'),\n url(r'^logout/',LogoutView.as_view(), name='logout'),\n url(r'^change-password/$',change_password,name='change_password'),\n url(r'^password-recover/$', recover, name='password_reset_recover'),\n # service requests\n url(r'^servicerequest/', ServiceRequestListView.as_view(), name='servicerequest'),\n url(r'^servicerequest-create/', ServiceRequesCreateView.as_view(), name='servicerequest_create'),\n url(r'^servicerequest-view/', ServiceRequestSingleView.as_view(), name='servicerequest_view'),\n url(r'^servicerequest-rating/', ServiceRequestRatingView.as_view(), name='service_request_rating'),\n url(r'^servicerequest-feedback/', ServiceRequestFeedbackView.as_view(), name='servicerequest_feedback'),\n # events\n url(r'^events/', EventListView.as_view(), name='events'),\n url(r'^my-events/', MyEventListView.as_view(), name='my_events'),\n url(r'^event-view/', EventSingleView.as_view(), name='event_view'),\n url(r'^event-details/', MyEventSingleView.as_view(), name='event_details'),\n url(r'^event-edit/', EventEditView.as_view(), name='event_edit'),\n url(r'^event-create/', EventCreateView.as_view(), name='event_create'),\n # documents\n url(r'^documents/', DocumentListView.as_view(), name='documents'),\n # albums\n url(r'^albums/$', AlbumListView.as_view(), name=\"list_album\"),\n # ads\n url(r'^ads/$', AdListView.as_view(), name='ad_list'),\n\turl(r'^ad-view/$', AdSingleView.as_view(), name='ad_view'),\n # polls\n url(r'^polls/$', PollListView.as_view(), name='polls'),\n\turl(r'^poll-vote/$', PollVoteView.as_view(), name='poll_vote'),\n # evote\n url(r'^evotes/$', VoteListView.as_view(), name='evotes'),\n\turl(r'^evote-vote/$', EvoteView.as_view(), name='evote_ vote'),\n # overview\n url(r'^overview/$', OverView.as_view(), name='overview'),\n # forum\n url(r'^forum/$', ForumListView.as_view(), name='forum'),\n\turl(r'^forum-comment/$', ForumCommentView.as_view(), name='forum_comment'),\n # key personal\n url(r'^keypersonnel/$', KeyPersonnelListView.as_view(), name='keypersonnel'),\n # house \n url(r'^my-account/$', MyAccountView.as_view(), name='my_account'),\n url(r'^house-view/$',OwnerHouseView.as_view(), name='owner_house_view'),\n url(r'^house-details-edit/$',HouseDetailEditView.as_view(), name='house_details_edit'),\n url(r'^house-owner-edit/$',HouseOwnerEditView.as_view(), name='house_owner_edit'),\n url(r'^house-tenant-edit/$',HouseTenantEditView.as_view(), name='house_tenant_edit'),\n # dues\n url(r'^dues-house-view/$',DuesHouseView.as_view(), name='dues_house_view'),\n url(r'^house-dues-account-statement/$',HouseDuesAccountStatementView.as_view(), name='house_dues_account_statement'),\n url(r'^house-advance-account-statement/$',HouseAdvanceAccountStatementView.as_view(), name='house_advance_account_statement'),\n url(r'^house-account-statement/$',HouseAccountStatementView.as_view(), name='house_account_statement'),\n url(r'^dues-receipt-list/$',DuesReceiptList.as_view(), name='dues_receipt_list'),\n url(r'^dues-receipt-view/$',DuesReceiptView.as_view(), name='dues_receipt'),\n url(r'^dues-statement-view/$',DuesStatementView.as_view(), name='dues_statement'),\n url(r'^dues-settle/$',DuesSettleView.as_view(),name='dues_settle'),\n url(r'^pay-advance-online-payment/$',PayAdvanceOnlinePayment.as_view(),name='pay_advance_online_payment'),\n # financial year\n url(r'^financial-year/$',FinancialYearView.as_view(), name='financial_year'),\n # site autocomplete\n url(r'^sites-autocomplete/$',SiteAutocomplete.as_view(),name='sites-autocomplete'),\n )", "sub_path": "api_app/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 3770, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 5, "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": 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": 18, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 21, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 23, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 29, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 30, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 32, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 33, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 35, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 36, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 38, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 40, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 41, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 43, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 45, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 46, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 47, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 48, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 49, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 51, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 52, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 53, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 54, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 55, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 56, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 57, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 58, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 59, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 61, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "60112393", "text": "from functools import wraps\nimport contextlib\nimport shutil\nimport tempfile\n\nfrom nose import SkipTest\n\n\ndef build_po_string(data):\n return (\n '#, fuzzy\\n'\n 'msgid \"\"\\n'\n 'msgstr \"\"\\n'\n '\"Project-Id-Version: foo\\\\n\"\\n'\n '\"POT-Creation-Date: 2013-06-05 14:16-0700\\\\n\"\\n'\n '\"PO-Revision-Date: 2010-04-26 18:00-0700\\\\n\"\\n'\n '\"Last-Translator: Automatically generated\\\\n\"\\n'\n '\"Language-Team: English\\\\n\"\\n'\n '\"Language: \\\\n\"\\n'\n '\"MIME-Version: 1.0\\\\n\"\\n'\n '\"Content-Type: text/plain; charset=UTF-8\\\\n\"\\n'\n '\"Content-Transfer-Encoding: 8bit\\\\n\"\\n'\n '\"X-Generator: Translate Toolkit 1.6.0\\\\n\"\\n\\n'\n + data)\n\n\n@contextlib.contextmanager\ndef tempdir():\n \"\"\"Builds a tempdir and cleans up afterwards\n\n Usage::\n\n with tempdir() as dir_:\n # blah blah blah\n\n \"\"\"\n dir_ = tempfile.mkdtemp()\n yield dir_\n shutil.rmtree(dir_)\n\n\ndef skip_if(testfun):\n def _skip_if(fun):\n @wraps(fun)\n def _skip_if_inner(*args, **kwargs):\n if testfun():\n raise SkipTest\n return fun(*args, **kwargs)\n return _skip_if_inner\n return _skip_if\n", "sub_path": "dennis/tests/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1209, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "tempfile.mkdtemp", "line_number": 37, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 39, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 27, "usage_type": "attribute"}, {"api_name": "nose.SkipTest", "line_number": 47, "usage_type": "name"}, {"api_name": "functools.wraps", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "575180672", "text": "#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jun 8 13:57:07 2018\n\n@author: rstyczynski\n\"\"\"\n\n#!/usr/bin/python\nimport jinja2\n\ntemplateLoader = jinja2.FileSystemLoader(searchpath=\"/Users/rstyczynski/github/umc/varia/jmeter/lib\")\ntemplateEnv = jinja2.Environment(loader=templateLoader)\nTEMPLATE_FILE = \"plantuml.jinja\"\ntemplate = templateEnv.get_template(TEMPLATE_FILE)\n\nFLOW = 'A\\nB\\nC'\n\noutputText = template.render(FLOW=FLOW, TESTID='ss') \n\nprint(outputText)\n", "sub_path": "varia/template.py", "file_name": "template.py", "file_ext": "py", "file_size_in_byte": 482, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "jinja2.FileSystemLoader", "line_number": 12, "usage_type": "call"}, {"api_name": "jinja2.Environment", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "109669022", "text": "from typing import Optional\n\nimport boto3\nfrom arrow import Arrow\n\nfrom src.model import Member, Room\n\ntable_name = \"plapo\"\n\n\nclass RoomRepository:\n def __init__(self):\n dynamodb = boto3.resource(\"dynamodb\")\n self.table = dynamodb.Table(table_name)\n\n def query_room(self, room_id: str) -> Optional[Room]:\n \"\"\"\n 指定したidの部屋が存在すれば返す\n :param room_id:\n :return: 指定したidの部屋が存在すれば返す\n \"\"\"\n res = self.table.get_item(Key={\"room_id\": room_id}).get(\"Item\")\n if not res:\n return None\n\n # 内包表記ver.\n members = [\n Member(\n member_id=key[4:],\n nickname=value[\"nickname\"],\n point=value.get(\"point\"),\n )\n for key, value in res.items()\n if key.startswith(\"mem_\")\n ]\n\n return Room(\n room_id=res[\"room_id\"], opened=res[\"opened\"], members=members\n )\n\n def initialize_room(self, room: Room, now: Arrow) -> Room:\n \"\"\"\n 部屋の情報を初期化する。次のバックログの見積もりを始める際に実施する。\n :param now:\n :param room: 部屋\n :return:\n \"\"\"\n item = {\n \"room_id\": room.room_id,\n \"opened\": False,\n \"ttl\": now.shift(days=1).int_timestamp,\n }\n for member in room.members:\n item[f\"mem_{member.member_id}\"] = {\"nickname\": member.nickname, \"point\": None}\n\n self.table.put_item(Item=item)\n\n new_room = self.query_room(room.room_id)\n if new_room:\n return new_room\n raise Exception\n\n def act_member(self, member: Member, room: Room) -> Room:\n \"\"\"\n 部屋に参加する / 見積もりポイントを登録する\n :return: セッションインスタンス\n \"\"\"\n\n if member.point:\n item = {\"nickname\": member.nickname, \"point\": member.point}\n else:\n item = {\"nickname\": member.nickname, \"point\": None}\n\n update_expression_str = f\"set mem_{member.member_id}=:m\"\n\n res = self.table.update_item(\n Key={'room_id': room.room_id},\n UpdateExpression=update_expression_str,\n ExpressionAttributeValues={\":m\": item},\n ReturnValues=\"ALL_NEW\"\n )\n\n new_room = res.get(\"Attributes\")\n\n if new_room:\n return new_room\n raise Exception\n", "sub_path": "src/repository.py", "file_name": "repository.py", "file_ext": "py", "file_size_in_byte": 2510, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "boto3.resource", "line_number": 13, "usage_type": "call"}, {"api_name": "src.model.Member", "line_number": 28, "usage_type": "call"}, {"api_name": "src.model.Room", "line_number": 37, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 16, "usage_type": "name"}, {"api_name": "src.model.Room", "line_number": 16, "usage_type": "name"}, {"api_name": "src.model.Room", "line_number": 41, "usage_type": "name"}, {"api_name": "arrow.Arrow", "line_number": 41, "usage_type": "name"}, {"api_name": "src.model.Member", "line_number": 63, "usage_type": "name"}, {"api_name": "src.model.Room", "line_number": 63, "usage_type": "name"}]} +{"seq_id": "282677902", "text": "import nltk\nimport numpy as np\nimport tflearn\nimport tensorflow as tf\nimport random\nimport json\nimport pickle\nfrom nltk.stem.lancaster import LancasterStemmer\n\nnltk.download('punkt')\n\nstemmer = LancasterStemmer()\n\n# Load data from json file to array\nwith open('intents.json') as json_data:\n intents = json.load(json_data)\n\n# ======================================================================\n# Json data to words, documents and classes\n# ======================================================================\n\nwords = []\nclasses = []\ndocuments = []\nignore_words = ['?']\n# Loop through all intents from our intens array\nfor intent in intents['intents']:\n for pattern in intent['patterns']:\n # Tokenize every word\n w = nltk.word_tokenize(pattern)\n # Add the word with his token to our words list\n words.extend(w)\n # Add the word with his token to our documents with the tag\n documents.append((w, intent['tag']))\n # Add tag to our classes\n if intent['tag'] not in classes:\n classes.append(intent['tag'])\n\n# Stem all words in our words array and remove useless words\nwords = [stemmer.stem(w.lower()) for w in words if w not in ignore_words and w[0] is not \"'\"]\n\n# Sort our words and classes\nwords = sorted(list(set(words)))\nclasses = sorted((list(set(classes))))\n\n# ======================================================================\n# Create more efficient training data\n# ======================================================================\n\ntraining = []\noutput = []\noutput_empty = [0] * len(classes)\n\nfor doc in documents:\n # Creates a new bag\n bag = []\n # Gets all words for a documentation (e.g. 'Hi', 'How are you', 'Hello', ... -> ['greeting'])\n pattern_words = doc[0]\n # Stemms all words\n pattern_words = [stemmer.stem(word.lower()) for word in pattern_words]\n for w in words:\n # if pattern_words contains word (e.g. 'hi') append 1 else 0\n bag.append(1) if w in pattern_words else bag.append(0)\n\n output_row = list(output_empty)\n # Creates an output where all classes are 1 if bag contains at least one 1\n output_row[classes.index(doc[1])] = 1\n # Training gets the bag and the ouput as one array\n training.append([bag, output_row])\n\n# Shuffle data to use some of it as test data\nrandom.shuffle(training)\ntraining = np.array(training)\n\ntrain_x = list(training[:, 0])\ntrain_y = list(training[:, 1])\n\n# ======================================================================\n# Build the neural network\n# ======================================================================\n\ntf.reset_default_graph()\n\n# Create the input layer with the length of the first element in train_x\nnet = tflearn.input_data(shape=[None, len(train_x[0])])\n# Create two hidden layers with 8 nodes (back-propagations)\nnet = tflearn.fully_connected(net, 8)\nnet = tflearn.fully_connected(net, 8)\n# Create the output layer which has the length of the first element in train_y and the action function softmax\nnet = tflearn.fully_connected(net, len(train_y[0]), activation='softmax')\nnet = tflearn.regression(net)\n\nmodel = tflearn.DNN(net, tensorboard_dir='tflearn_logs')\nmodel.fit(train_x, train_y, n_epoch=1000, batch_size=8, show_metric=True)\nmodel.save('model.tflearn')\n\npickle.dump({'words': words, 'classes': classes, 'train_x': train_x, 'train_y': train_y}, open('training_data', 'wb'))\n\n# ======================================================================\n# Load the build module (Could be extern)\n# ======================================================================\n\ndata = pickle.load(open('training_data', 'rb'))\nwords = data['words']\nclasses = data['classes']\ntrain_x = data['train_x']\ntrain_y = data['train_y']\n\nwith open('intents.json') as json_data:\n intents = json.load(json_data)\n\nmodel.load('./model.tflearn')\n\n\ndef clean_up_sentence(sentence):\n sentence_words = nltk.word_tokenize(sentence)\n sentence_words = [stemmer.stem(word.lower()) for word in sentence_words]\n return sentence_words\n\n\ndef bow(sentence, bow_words, show_details=False):\n sentence_words = clean_up_sentence(sentence)\n bow_bag = [0] * len(bow_words)\n for s in sentence_words:\n for i, word in enumerate(bow_words):\n if word == s:\n bow_bag[i] = 1\n if show_details:\n print('found in bag: %s' % word)\n return np.array(bow_bag)\n\n\nERROR_THRESHOLD = .25\n\n\ndef classify(sentence):\n results = model.predict([bow(sentence, words)])[0]\n results = [[i, r] for i, r in enumerate(results) if r > ERROR_THRESHOLD]\n results.sort(key=lambda x: x[1], reverse=True)\n return_list = []\n for r in results:\n return_list.append((classes[r[0]], r[1]))\n return return_list\n\n\ndef response(sentence, user_id='123', show_details=False):\n results = classify(sentence)\n if results:\n while results:\n for i in intents['intents']:\n if i['tag'] == results[0][0]:\n return print(random.choice(i['responses']))\n results.pop(0)\n\n\nresponse('what are your hours today?')\n", "sub_path": "trainer.py", "file_name": "trainer.py", "file_ext": "py", "file_size_in_byte": 5180, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "nltk.download", "line_number": 10, "usage_type": "call"}, {"api_name": "nltk.stem.lancaster.LancasterStemmer", "line_number": 12, "usage_type": "call"}, {"api_name": "json.load", "line_number": 16, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 30, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 82, "usage_type": "call"}, {"api_name": "tflearn.input_data", "line_number": 85, "usage_type": "call"}, {"api_name": "tflearn.fully_connected", "line_number": 87, "usage_type": "call"}, {"api_name": "tflearn.fully_connected", "line_number": 88, "usage_type": "call"}, {"api_name": "tflearn.fully_connected", "line_number": 90, "usage_type": "call"}, {"api_name": "tflearn.regression", "line_number": 91, "usage_type": "call"}, {"api_name": "tflearn.DNN", "line_number": 93, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 97, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 103, "usage_type": "call"}, {"api_name": "json.load", "line_number": 110, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 130, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 152, "usage_type": "call"}]} +{"seq_id": "392824053", "text": "from flask import Flask, request, jsonify\nfrom models import Base, Bet\nfrom sqlalchemy.orm import relationship, sessionmaker\nfrom sqlalchemy import create_engine\n\n''' Handling database '''\nengine = create_engine('sqlite:///bets.db')\n\nBase.metadata.bind = engine\nDBSession = sessionmaker(bind=engine)\nsession = DBSession()\n\napp = Flask(__name__)\n\n''' Auth '''\n\n\n''' Routes '''\n@app.route('/bets', methods=['GET', 'POST'])\ndef bets():\n if request.method == 'GET':\n bets = session.query(Bet).all()\n if bets:\n return jsonify(bets=[i.serialize for i in bets])\n\n elif request.method == 'POST':\n better_id = request.args.get('better_id')\n item_id = request.args.get('item_id')\n price = request.args.get('price')\n timer = request.args.get('timer')\n if better_id is not None and item_id is not None and price is not None and timer is not None:\n bet = Bet(item_id=item_id, better_id=better_id, price=price, timer=timer)\n session.add(bet)\n session.commit()\n return jsonify(bet=bet.serialize)\n else:\n return jsonify({\"error\": \"Some data is incorrect\"})\n\nif __name__ == '__main__':\n app.debug = True\n app.run(host='0.0.0.0', port=5000)\n", "sub_path": "auction.py", "file_name": "auction.py", "file_ext": "py", "file_size_in_byte": 1257, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 7, "usage_type": "call"}, {"api_name": "models.Base.metadata", "line_number": 9, "usage_type": "attribute"}, {"api_name": "models.Base", "line_number": 9, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 10, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 13, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "models.Bet", "line_number": 22, "usage_type": "argument"}, {"api_name": "flask.jsonify", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "models.Bet", "line_number": 32, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 35, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "585108209", "text": "# -*- coding: utf-8 -*-\nfrom classes.play.IPlay import IPlay\nfrom classes.content.Slide import Slide\nfrom omxplayer import OMXPlayer\nfrom dbus.exceptions import DBusException\nfrom time import sleep\nimport env_variables\nimport logging, logging.config\nlogging.config.dictConfig(env_variables.LOGGING)\n#logging.getLogger('omxplayer').setLevel(logging.CRITICAL)\n#logging.getLogger('dbus').setLevel(logging.CRITICAL)\nlogging.getLogger('omxplayer').setLevel(logging.DEBUG)\nlogging.getLogger('dbus').setLevel(logging.INFO)\n\nclass PlayMovie(IPlay):\n\n def play(self, play_thread, singleContentMovie):\n \"\"\"\n function qui lance une bande-annonce dans omx player\n \"\"\"\n try:\n player = OMXPlayer(singleContentMovie.filepath, args=['-o', 'hdmi', '-b', '--no-osd'], pause=False)\n sleep(5)\n logging.info(\"ba: %s, status: %s\" % (singleContentMovie.filepath, player.playback_status())) \n except Exception as e:\n logging.error(str(e), exc_info=1)\n player.quit()\n return\n\n # affichage d'un ecran noir pour ne pas voir l'ecran de la ba precedente\n # brievement avant le changement d'ecran\n background_slide = Slide(env_variables.background_image, 1, static=True)\n background_slide.play(play_thread)\n\n # tant que la ba n'est pas fini ou stoppee, on attend\n while True:\n try:\n if player.playback_status() == \"Playing\" \\\n and not play_thread.stoprequest.isSet() \\\n and not play_thread.previousrequest.isSet() \\\n and not play_thread.nextrequest.isSet():\n sleep(1)\n #logging.info(\"%s, %s, %s\" % (player.playback_status(),stop, time_status)) \n else:\n logging.info(\"player quit\")\n player.quit()\n # sortie boucle while\n break\n except DBusException:\n # on passe ici a la fin de la ba, sortie du while\n logging.debug(\"dbus exception, ba ended\")\n player.quit()\n break\n except Exception as e:\n logging.error(str(e), exc_info=1)\n break\n", "sub_path": "classes/play/PlayMovie.py", "file_name": "PlayMovie.py", "file_ext": "py", "file_size_in_byte": 2283, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.config.dictConfig", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.config", "line_number": 9, "usage_type": "attribute"}, {"api_name": "env_variables.LOGGING", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 12, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 13, "usage_type": "attribute"}, {"api_name": "classes.play.IPlay.IPlay", "line_number": 15, "usage_type": "name"}, {"api_name": "omxplayer.OMXPlayer", "line_number": 22, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 23, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 24, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 26, "usage_type": "call"}, {"api_name": "classes.content.Slide.Slide", "line_number": 32, "usage_type": "call"}, {"api_name": "env_variables.background_image", "line_number": 32, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 45, "usage_type": "call"}, {"api_name": "dbus.exceptions.DBusException", "line_number": 49, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 51, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "560463401", "text": "import numpy as np\nimport os\nimport datetime\nimport contextlib\nimport subprocess\nimport glob\nimport shutil\n\nimport ase\nfrom ase.calculators.dftb import Dftb\n \nfrom . import data, utils, units, potential\n\nclass Wrapper(potential.Potential):\n \"\"\"\n Wrappers for other codes that provide potential energies.\n \"\"\"\n def __init__(self):\n super(Wrapper, self).__init__() \n\n def connect(self):\n \"\"\"\n Create folder for running calculation.\n\n This function can be overwritten in inheriting classes as necessary.\n \"\"\"\n self.curr_dir = os.getcwd()\n dir_name = self.__class__.__name__.lower() + \"_\" + str(datetime.datetime.now()).replace(\" \", \"_\")\n os.mkdir(dir_name)\n self.working_dir = os.path.abspath(dir_name)\n self.job_count = 0\n\n @contextlib.contextmanager\n def run_in_job_dir(self):\n \"\"\"A simple context manager which switches into the specified job\n directory, run enclosed commands then switch back. Context managers\n ensure that whatever happens (exit, return, exceptions, etc), the\n working directory will be switched back\n\n This is to alleviate the pain caused by ASE writing input and output\n files directly in the current directory. Wrap any code that generate\n I/O with the following with block:\n\n with self.run_in_job_dir():\n ...\n \"\"\"\n try:\n self.job_count += 1\n self.job_dir = os.path.join(self.working_dir, \"job_%d\" % self.job_count)\n os.mkdir(self.job_dir)\n os.chdir(self.job_dir)\n yield\n finally:\n if self.job_count > 4:\n del_job_dir = os.path.join(self.working_dir, \"job_%d\" % (self.job_count - 4))\n shutil.rmtree(del_job_dir)\n os.chdir(self.curr_dir)\n\n def disconnect(self):\n \"\"\"\n Function called to cleanup for use with 'with' statement, can be \n overwritten if certain cleanup functionality is desired.\n \"\"\"\n return \n\n def __enter__(self):\n self.connect()\n return self\n\n def __exit__(self, exception_type, exception_value, traceback):\n self.disconnect()\n\nclass DFTBPlus(Wrapper):\n \"\"\"\n This is a wrapper of ASE DFTB+ (a wrapper of a wrapper...) to compute\n single point energies/gradients/forces.\n \"\"\"\n default_HD = {\n 'Br' : -0.0573, \n 'C' : -0.1492, \n 'Ca' : -0.0340, \n 'Cl' : -0.0697, \n 'F' : -0.1623, \n 'H' : -0.1857, \n 'I' : -0.0433, \n 'K' : -0.0339, \n 'Mg' : -0.0200,\n 'N' : -0.1535,\n 'Na' : -0.0454,\n 'O' : -0.1575,\n 'P' : -0.1400,\n 'S' : -0.1100,\n 'Zn' : -0.0300, \n }\n default_AM = {\n 'Br' : '\"d\"', \n 'C' : '\"p\"', \n 'Ca' : '\"p\"', \n 'Cl' : '\"d\"', \n 'F' : '\"p\"', \n 'H' : '\"s\"', \n 'I' : '\"d\"', \n 'K' : '\"p\"', \n 'Mg' : '\"p\"',\n 'N' : '\"p\"',\n 'Na' : '\"p\"',\n 'O' : '\"p\"',\n 'P' : '\"p\"',\n 'S' : '\"d\"',\n 'Zn' : '\"d\"',\n }\n def __init__(self, symbols, **dftb_opts):\n self.reset(symbols, **dftb_opts)\n super(DFTBPlus, self).__init__() \n\n def reset(self, symbols, **dftb_opts):\n \"\"\"\n Give the wrapper a new molecule and options.\n \"\"\"\n self.ase_mol = ase.Atoms(symbols=symbols, pbc=False)\n mol_atoms = set(symbols)\n thirdorder_opt = dftb_opts.get('Hamiltonian_ThirdOrderFull', None) or dftb_opts.get('Hamiltonian_ThirdOrder', None)\n for atom_sym in mol_atoms:\n dftb_opts['Hamiltonian_MaxAngularMomentum_' + atom_sym] = DFTBPlus.default_AM[atom_sym]\n if thirdorder_opt is \"Yes\" or thirdorder_opt is \"YES\":\n dftb_opts['Hamiltonian_HubbardDerivs_'] = ''\n if not ('Hamiltonian_HubbardDerivs_' + atom_sym) in dftb_opts:\n dftb_opts['Hamiltonian_HubbardDerivs_' + atom_sym] = DFTBPlus.default_HD[atom_sym]\n\n self.engine = Dftb(\n atoms=self.ase_mol,\n run_manyDftb_steps=False,\n Hamiltonian_MaxAngularMomentum_='',\n **dftb_opts\n )\n\n self.ase_mol.set_calculator(self.engine)\n self.dftb_opts = dftb_opts\n\n def update_charges(self):\n scc_opt = self.dftb_opts.get(\"Hamiltonian_SCC\", None)\n if scc_opt == \"YES\" or scc_opt == \"Yes\":\n charges = self.engine.results['charges']\n charge_str = \"\\n\\t\\t\".join([str(c) for c in charges])\n charge_dict = {'Hamiltonian_InitialCharges_AllAtomCharges_empty' : charge_str}\n charge_dict['Hamiltonian_InitialCharges_'] = ''\n charge_dict['Hamiltonian_InitialCharges_AllAtomCharges_'] = ''\n self.engine.set(**charge_dict)\n\n def compute_energy(self, X):\n with self.run_in_job_dir():\n # Positions must be set directly to Angstroms, ASE writes them in Bohr under the hood\n self.ase_mol.set_positions(X / units.ANGSTROM_TO_AU) \n # ASE converts DFTB+ Hartree to eV so we convert back to Hartree\n pe = self.ase_mol.get_potential_energy() / ase.units.Hartree\n self.update_charges()\n return pe\n\n def compute_force(self, X):\n with self.run_in_job_dir():\n # Positions must be set directly to Angstroms, ASE writes them in Bohr under the hood\n self.ase_mol.set_positions(X / units.ANGSTROM_TO_AU) \n # ASE converts DFTB+ Hartree to eV so we convert back to Hartree\n pe = self.ase_mol.get_potential_energy() / ase.units.Hartree\n # ASE converts DFTB+ Hartree/Bohr to eV/Angstrom so we convert back to Hartree/Bohr\n force = self.ase_mol.get_forces() / ase.units.Hartree * ase.units.Bohr\n self.update_charges()\n return pe, force\n\n def compute_gradient(self, X):\n pe, f = self.compute_force(X)\n return pe, -f\n\nclass ReaxFF(Wrapper):\n def __init__(self, \n symbols, \n lammps_exec, \n ffieldfile, \n inputfile=\"lammps.in\", \n datafile=\"lammps.data\", \n dumpfile=\"lammps.dump\", \n outputfile=\"lammps.out\", \n bondfile=\"lammps.bonds\",\n neb_coord_file=\"coord.initial\",\n etol=0.0,\n ftol=0.36,\n neb_iter=100000,\n cineb_iter=50000,\n dump_freq=50,\n ):\n \"\"\"\n Note: masses correspond to unique atoms and are mostly included for \n completeness when writing the data file. None will result in looking up \n the most frequent isotopic mass in AMU.\n\n Args:\n symbols: atomic symbols to be used\n lammps_exec: command to be subprocessed to compute the lammps forces\n ffieldfile: path to reaxFF forcefield parameter file\n masses: masses to use for given computation, included for completeness\n inputfile: name of lammps input\n datafile: name of lammps data file\n dumpfile: name of lammps dump file\n outputfile: name of lammps output file\n \"\"\"\n self.symbols = symbols\n self.unique_atoms = sorted(list(set(symbols)))\n self.masses = utils.symbol_to_mass(self.unique_atoms) / units.AMU_TO_AU\n self.inputfile = inputfile\n self.datafile = datafile\n self.ffieldfile = ffieldfile\n self.dumpfile = dumpfile\n self.outputfile = outputfile\n self.bondfile = bondfile\n self.neb_coord_file = neb_coord_file\n self.etol = etol\n self.ftol = ftol\n self.neb_iter = neb_iter\n self.cineb_iter = cineb_iter\n self.dump_freq = dump_freq\n self.lammps_exec = lammps_exec.split()\n self.result = {}\n super(ReaxFF, self).__init__() \n\n def get_bound_box(self, Xs):\n Xarray = np.array(Xs)\n return [\n np.min(Xarray[..., 0]), np.max(Xarray[..., 0]), \n np.min(Xarray[..., 1]), np.max(Xarray[..., 1]), \n np.min(Xarray[..., 2]), np.max(Xarray[..., 2]), \n ]\n\n def write_data(self, X, box=None, datafile=None):\n if datafile is None:\n datafile = self.datafile\n Xconv = X / units.ANGSTROM_TO_AU\n if box is None:\n box = self.get_bound_box(Xconv)\n \n with open(datafile, \"w\") as fout:\n fout.write(\"\\n#Units in Angstroms and grams/mole for masses\")\n fout.write(\"\\n%d atoms\\n%d atom types\\n\" % (X.shape[0], len(self.unique_atoms)))\n fout.write(\"\\n%.3f %.3f xlo xhi\" % (round(box[0]) - 1.0, round(box[1]) + 1.0))\n fout.write(\"\\n%.3f %.3f ylo yhi\" % (round(box[2]) - 1.0, round(box[3]) + 1.0))\n fout.write(\"\\n%.3f %.3f zlo zhi\\n\" % (round(box[4]) - 1.0, round(box[5]) + 1.0))\n fout.write(\"\\nMasses\\n\\n\")\n for i, ua in enumerate(self.unique_atoms):\n # \"real\" units use grams/mole, equivalent to AMU\n fout.write(\"%d\\t%.9f\\n\" % (i + 1, self.masses[i]))\n fout.write(\"\\nAtoms\\n\\n\")\n for a in range(X.shape[0]):\n fout.write(\"%3d %3d 0.0\\t % .11E\\t % .11E\\t % .11E\\n\" %\n (a+1, self.unique_atoms.index(self.symbols[a])+1, Xconv[a, 0], Xconv[a, 1], Xconv[a, 2]))\n\n def write_input(self):\n with open(self.inputfile, 'w') as fout:\n fout.write(\n \"# File auto-generated via ReaxFF wrapper code in mdprop python package\\n\"\n + \"units real\\n\" \n + \"atom_style charge\\n\"\n + \"boundary s s s\\n\"\n + \"read_data %s\\n\" % self.datafile\n + \"pair_style reax/c NULL safezone 10.0 mincap 1000\\n\" \n + \"pair_coeff * * %s %s\\n\" % (self.ffieldfile, \" \".join([a for a in self.unique_atoms]))\n + \"neighbor 2.0 nsq\\n\"\n + \"neigh_modify every 1 delay 0 check no\\n\"\n + \"compute reax all pair reax/c\\n\"\n + \"fix 10 all qeq/reax 1 0.0 10.0 1.0e-6 reax/c\\n\"\n + \"fix 97 all reax/c/bonds 1 %s\\n\" % self.bondfile\n + \"compute 2 all property/atom fx fy fz\\n\"\n + \"dump 15 all custom 100 %s fx fy fz\\n\" % self.dumpfile\n + 'dump_modify 15 format line \"%.11E %.11E %.11E\" sort id\\n'\n + \"run\t0\\n\"\n )\n\n def write_minimization_input(self):\n with open(self.inputfile, 'w') as fout:\n fout.write(\n \"# File auto-generated via ReaxFF wrapper code in mdprop python package\\n\"\n + \"units real\\n\" \n + \"atom_style charge\\n\"\n + \"boundary s s s\\n\"\n + \"read_data %s\\n\" % self.datafile\n + \"pair_style reax/c NULL safezone 10.0 mincap 1000\\n\" \n + \"pair_coeff * * %s %s\\n\" % (self.ffieldfile, \" \".join([a for a in self.unique_atoms]))\n + \"neighbor 2.0 nsq\\n\"\n + \"neigh_modify every 1 delay 0 check no\\n\"\n + \"compute reax all pair reax/c\\n\"\n + \"fix 10 all qeq/reax 1 0.0 10.0 1.0e-6 reax/c\\n\"\n + \"thermo 1\\n\"\n + \"dump 15 all custom 100 %s x y z fx fy fz\\n\" % self.dumpfile\n + \"dump_modify 15 format line \\\"%.11E %.11E %.11E %.11E %.11E %.11E\\\" sort id\\n\"\n + \"minimize 1.0e-6 1.0e-6 5000 10000\\n\"\n + \"run\t0\\n\"\n )\n\n def write_neb_input(self):\n with open(self.inputfile, 'w') as fout:\n fout.write(\n \"# File auto-generated via ReaxFF wrapper code in mdprop python package\\n\"\n + \"units real\\n\" \n + \"atom_style charge\\n\"\n + \"atom_modify map yes\\n\"\n + \"boundary s s s\\n\"\n + \"read_data %s\\n\" % self.datafile\n + \"pair_style reax/c NULL safezone 10.0 mincap 1000\\n\" \n + \"pair_coeff * * %s %s\\n\" % (self.ffieldfile, \" \".join([a for a in self.unique_atoms]))\n + \"neighbor 2.0 nsq\\n\"\n + \"neigh_modify every 1 delay 0 check no\\n\"\n + \"compute reax all pair reax/c\\n\"\n + \"variable i equal part\\n\"\n + \"fix 10 all qeq/reax 1 0.0 10.0 1.0e-6 reax/c\\n\"\n + \"thermo 1\\n\"\n + \"dump 15 all custom 100 %s.$i x y z fx fy fz\\n\" % self.dumpfile\n + \"dump_modify 15 format line \\\"%.11E %.11E %.11E %.11E %.11E %.11E\\\" sort id\\n\"\n + \"min_style quickmin\\n\"\n + \"fix 1 all neb 0.1\\n\"\n + \"neb %f %f %d %d %d each %s.$i\\n\" % (self.etol, self.ftol, self.neb_iter, self.cineb_iter, self.dump_freq, self.neb_coord_file)\n + \"run\t0\\n\"\n )\n\n def write_neb_coord(self, X, coordfile):\n Xconv = X / units.ANGSTROM_TO_AU\n with open(coordfile, \"w\") as fout:\n N = X.shape[0]\n fout.write(\"%d\\n\" % N)\n for a in range(N):\n fout.write(\"%3d\\t % .11E\\t % .11E\\t % .11E\\n\" %\n (a+1, Xconv[a, 0], Xconv[a, 1], Xconv[a, 2]))\n\n def write_neb_data(self, Xs):\n traj_box = self.get_bound_box(np.array(Xs) / units.ANGSTROM_TO_AU)\n self.write_data(Xs[0], box=traj_box, datafile=self.datafile)\n for i, X in enumerate(Xs):\n coordfile = self.neb_coord_file + \".%d\" % i\n self.write_neb_coord(X, coordfile)\n\n def parse_dump(self):\n with open(self.outputfile, 'r') as fin:\n lines = fin.readlines()\n pe = None\n for i, line in enumerate(lines):\n if line[:9] == \"Step Temp\":\n pe = float(lines[i+1].split()[2]) * units.KCAL_MOL_TO_AU\n break\n with open(self.dumpfile, 'r') as fin:\n lines = fin.readlines()\n output = np.array([l.split() for l in lines[9:]], dtype=np.float64)\n force = output * units.KCAL_MOL_TO_AU / units.ANGSTROM_TO_AU # Convert to a.u. from real\n return pe, force\n\n def parse_minimization_dump(self):\n with open(self.outputfile, 'r') as fin:\n lines = fin.readlines()\n pe = None\n begin_line = None\n end_line = None\n for i, line in enumerate(lines):\n if line[:9] == \"Step Temp\" and begin_line is None:\n begin_line = i+1\n elif line[:9] == \"Loop time\" and begin_line is not None:\n end_line = i\n break\n pe = np.array([lines[i].split()[2] for i in range(begin_line, end_line)], dtype=np.float64) * units.KCAL_MOL_TO_AU\n\n with open(self.dumpfile, 'r') as fin:\n lines = fin.readlines()\n nframes = len(pe)\n frame_len = 9 + len(self.symbols)\n output = []\n for i in range(nframes):\n output.append([l.split() for l in lines[frame_len*i + 9:frame_len*(i+1)]])\n output_np = np.array(output)\n Xs = output_np[:, :, :3] * units.ANGSTROM_TO_AU # Convert to a.u. from real\n force = output_np[:, :, 3:] * units.KCAL_MOL_TO_AU / units.ANGSTROM_TO_AU # Convert to a.u. from real\n return pe, Xs, forces\n\n def parse_neb_dump(self, dumpfiles=None):\n \"\"\"\n Strip dump files to collect the geometry and gradients of the final frames\n \"\"\"\n Natom = None\n Nframe = 0\n Xs = []\n Gs = []\n if dumpfiles is None:\n dumpfiles = glob.glob(self.dumpfile + \".*\")\n for curr_dump in dumpfiles:\n Nframe += 1\n with open(curr_dump, 'r') as fin:\n lines = fin.readlines()\n if Natom is None:\n Natom = int(lines[3])\n Xcurr = np.array([l.split() for l in lines[-Natom:]], dtype=np.float64)\n Xs.append(Xcurr[:, :3])\n Gs.append(Xcurr[:, 3:])\n Xs = np.array(Xs) * units.ANGSTROM_TO_AU \n Gs = -np.array(Gs) * units.KCAL_MOL_TO_AU / units.ANGSTROM_TO_AU\n return Xs, Gs\n\n def parse_bond_order(self):\n \"\"\"Parse the ReaxFF/C bond order information, saved in self.bondfile\"\"\"\n natom = len(self.symbols)\n bond_order_matrix = np.zeros((natom, natom))\n n_lone_pair = np.zeros(natom)\n with open(self.bondfile) as fin:\n for line in fin:\n if line.startswith('#'):\n continue\n data = line.split()\n iatom = int(data[0]) - 1\n nbond = int(data[2])\n n_lone_pair[iatom] = float(data[-2])\n for ibond in range(nbond):\n iatom2 = int(data[3 + ibond]) - 1\n bond_order = float(data[4 + nbond + ibond])\n bond_order_matrix[iatom, iatom2] = bond_order\n return bond_order_matrix, n_lone_pair\n\n def run_job(self, X):\n with self.run_in_job_dir():\n self.write_data(X)\n self.write_input()\n with open(self.inputfile, 'r') as fin, open(self.outputfile, 'w') as fout:\n subprocess.call(self.lammps_exec, stdin=fin, stdout=fout)\n self.result['potential_energy'], self.result['force'] = self.parse_dump()\n self.result['gradient'] = -self.result['force']\n self.result['bond_order'], self.result['lone_pairs'] = self.parse_bond_order()\n return self.result\n\n def run_neb(self, Xs):\n with self.run_in_job_dir():\n self.write_neb_data(Xs)\n self.write_neb_input()\n sub_command = (\"mpirun -np %d \" % len(Xs)).split() + self.lammps_exec + (\"-partition %dx1\" % len(Xs)).split()\n with open(self.inputfile, 'r') as fin, open(self.outputfile, 'w') as fout:\n subprocess.call(sub_command, stdin=fin, stdout=fout)\n\n def compute_energy(self, X):\n res = self.run_job(X)\n return res['potential_energy']\n\n def compute_force(self, X):\n res = self.run_job(X)\n return res['potential_energy'], res['force']\n\n def compute_gradient(self, X):\n pe, force = self.compute_force(X)\n return pe, -force\n\n def compute_bond_order(self, X):\n res = self.run_job(X)\n return res['bond_order']\n\n def compute_lone_pairs(self, X):\n res = self.run_job(X)\n return res['lone_pairs']\n\n def compute_all(self, X):\n results = self.run_job(X)\n return results\n", "sub_path": "mdprop/wrapper.py", "file_name": "wrapper.py", "file_ext": "py", "file_size_in_byte": 18846, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.getcwd", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime", "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.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 50, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 51, "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": "shutil.rmtree", "line_number": 56, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 57, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 33, "usage_type": "attribute"}, {"api_name": "ase.Atoms", "line_number": 120, "usage_type": "call"}, {"api_name": "ase.calculators.dftb.Dftb", "line_number": 130, "usage_type": "call"}, {"api_name": "ase.units", "line_number": 155, "usage_type": "attribute"}, {"api_name": "ase.units", "line_number": 164, "usage_type": "attribute"}, {"api_name": "ase.units", "line_number": 166, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 229, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 348, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 348, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 364, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 364, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 373, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 387, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 394, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 394, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 397, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 398, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 404, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 405, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 425, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 437, "usage_type": "call"}]} +{"seq_id": "136198261", "text": "#twitter bot that sends a tweet\nimport tweepy\n\n#authentication\nauth = tweepy.OAuthHandler('x', 'y', 'http://127.0.0.1:8080/callback/')\nauth.set_access_token('z', 'a')\n\napi = tweepy.API(auth)\n\n\nuser = api.get_user('BotRushil')\n\npublicTweets = api.home_timeline()\n\n#have to move authentication into the Tweeter class, basically a user instance, and then have username and password\n#pass through it. after that we can begin to start in on the functionalities which will eventually have to be linked\n#to the electron GUI\nclass Tweeter():\n #this works\n def sendTweet(self):\n self.tweet = input(\"Tweet: \")\n api.update_status(self.tweet)\n\n\n#you already know who the fuck it is baby figured that shit out real quick\ntweets = []\n[tweets.append(tweet.text) for tweet in publicTweets]\nprint(tweets)\n\n\n\n", "sub_path": "pyBot.py", "file_name": "pyBot.py", "file_ext": "py", "file_size_in_byte": 811, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "tweepy.OAuthHandler", "line_number": 5, "usage_type": "call"}, {"api_name": "tweepy.API", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "594890084", "text": "import cx_Oracle\nfrom python_fission.lib.atomiq.classes.security import get_security_details\nimport logging\nfrom python_fission.lib.atomiq.classes import constants\nfrom python_fission.lib.atomiq.classes.helper import get_dict\n\nclass DeleteQuery(Exception):\n pass\n\n# library helper method\ndef result_to_json(database_cursor):\n '''\n :param database_cursor: an open cursor after query execution\n :type database_cursor: cx_Oracle.Cursor\n :returns: a tuple of 2 objects:\n at first position containing the rows count (integer)\n at second position containing list of dictionaries\n containing column:row pairs\n :rtype: tuple, tuple[0] of type int, tuple[1] of type list\n '''\n # cursor.description is a tuple containing:\n # (name, type, display_size, internal_size, precision, scale, null_ok)\n col_list = list(x[0] for x in database_cursor.description)\n\n # fetch all() returns a list of tuples each tuple containing oen row data\n # expected a single row\n rows_list = database_cursor.fetchall()\n data_result = []\n\n if len(rows_list) > 0:\n for row in rows_list:\n # create a dictionary containing column-name:column-value\n row_as_dict = {col: str(val) for col, val in zip(col_list, row)}\n data_result.append(row_as_dict)\n\n return len(rows_list), data_result\n\n\ndef run_select_query(conn_add_prms, select_query):\n '''\n :param conn_add_prms: entiy_name or connection_deatils_dict to fetch database details from security atom\n :type conn_add_prms: str or dict\n :param select_query: query string to be executed\n :type select_query: str\n :returns: in case of success a tuple result_to_json(db_cursor)\n otherwise (failure) a string for error-details\n :rtype: tuple\n Note, db_connection will not be closed by this function\n '''\n error = None\n db_cursor = None\n logging.info(select_query)\n try:\n db_cursor = __create_connection(conn_add_prms).cursor()\n if select_query.lower().startswith(\"select\") and select_query.lower().find(\"delete from\") or select_query.lower().find(\"drop table\") == -1:\n db_cursor.execute(select_query)\n else:\n raise DeleteQuery\n except DeleteQuery:\n error = \"Delete statement used inside select query\"\n logging.error(error)\n except cx_Oracle.DatabaseError as ex:\n # error of type cx_Oracle._Error, available read-only attributes:\n # code, offset, message, context, is recoverable\n error = ex.args[0].message\n logging.error(error)\n\n return __return_response(error, db_cursor, True)\n\n\ndef run_dml_query(conn_add_prms, dml_query):\n '''\n :param conn_add_prms: entiy_name or connection_deatils_dict to fetch database details from security atom\n :type conn_add_prms: str or dict\n :param dml_query: query string to be executed\n :type dml_query: str\n :returns: in case of success a tuple result_to_json(db_cursor)\n otherwise (failure) a string for error-details\n :rtype: tuple\n Note, db_connection will not be closed by this function\n '''\n try:\n error = None\n db_connection = __create_connection(conn_add_prms)\n logging.info(dml_query)\n db_cursor = db_connection.cursor()\n if dml_query.lower().find(\"delete from\") or dml_query.lower().find(\"drop table\")== -1:\n db_cursor.execute(dml_query)\n db_connection.commit()\n return __return_response(error, db_cursor)\n else:\n raise DeleteQuery\n except DeleteQuery:\n error = \"Delete statement used in query\"\n logging.error(error)\n except cx_Oracle.DatabaseError as ex:\n # error of type cx_Oracle._Error, available read-only attributes:\n # code, offset, message, context, is recoverable\n error = ex.args[0].message\n logging.error(error)\n\n return __return_response(error, \"\")\n\n\n# library helper method\ndef db_connect(connection_dict):\n '''\n :param connection_dict: a dictionary in the structure\n {\n \"connection\": {\n \"host\": \"\", \"ip\": \"\", \"port\": \"\",\n \"sid\": \"\", \"user\": \"\", \"password\": \"\"\n }\n }\n :type connection_dict: dictionary\n :returns: Connection object to database (according to input details)\n :rtype: cx_Oracle.Connection\n '''\n connection_dict = connection_dict['connection'] # contains a dictionary\n db_ip = connection_dict['ip']\n db_port = connection_dict['port']\n db_sid = connection_dict['sid']\n db_user = connection_dict['user']\n db_password = connection_dict['password']\n\n # Return a string suitable for use as the dsn parameter for connect().\n # This string is identical to the strings in the tnsnames.ora file.\n db_ora_dsn = cx_Oracle.makedsn(db_ip, db_port, db_sid)\n db_connection = cx_Oracle.connect(db_user, db_password, db_ora_dsn)\n return db_connection\n\n\ndef __create_connection(conn_add_prms):\n try:\n try:\n conn_details = get_security_details(configdetails = get_dict(Entity=conn_add_prms,\n lookuptype=\"Database\"))\n except Exception as ex:\n logging.error(\"Exception while fetching security details : \" + str(ex))\n raise Exception(\"Exception while fetching security details : \" + str(ex))\n db_connection = db_connect({\"connection\": conn_details})\n return db_connection\n except Exception as e:\n raise e\n\n\n\ndef __return_response(error, db_cursor, is_select_query=False):\n if error is None:\n if not is_select_query:\n response = constants.get_success(),db_cursor.rowcount\n else:\n response = constants.get_success(), result_to_json(db_cursor)\n else:\n response = constants.get_failed(),str(error)\n return response\n\n", "sub_path": "python_fission/lib/database/oracle.py", "file_name": "oracle.py", "file_ext": "py", "file_size_in_byte": 5890, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.info", "line_number": 52, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 61, "usage_type": "call"}, {"api_name": "cx_Oracle.DatabaseError", "line_number": 62, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 66, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 85, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 95, "usage_type": "call"}, {"api_name": "cx_Oracle.DatabaseError", "line_number": 96, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 100, "usage_type": "call"}, {"api_name": "cx_Oracle.makedsn", "line_number": 128, "usage_type": "call"}, {"api_name": "cx_Oracle.connect", "line_number": 129, "usage_type": "call"}, {"api_name": "python_fission.lib.atomiq.classes.security.get_security_details", "line_number": 136, "usage_type": "call"}, {"api_name": "python_fission.lib.atomiq.classes.helper.get_dict", "line_number": 136, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 139, "usage_type": "call"}, {"api_name": "python_fission.lib.atomiq.classes.constants.get_success", "line_number": 151, "usage_type": "call"}, {"api_name": "python_fission.lib.atomiq.classes.constants", "line_number": 151, "usage_type": "name"}, {"api_name": "python_fission.lib.atomiq.classes.constants.get_success", "line_number": 153, "usage_type": "call"}, {"api_name": "python_fission.lib.atomiq.classes.constants", "line_number": 153, "usage_type": "name"}, {"api_name": "python_fission.lib.atomiq.classes.constants.get_failed", "line_number": 155, "usage_type": "call"}, {"api_name": "python_fission.lib.atomiq.classes.constants", "line_number": 155, "usage_type": "name"}]} +{"seq_id": "325938672", "text": "# Copyright (c) Microsoft Corporation. Licensed under the MIT license.\n\nimport argparse\nimport logging\nimport os\nimport random\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader, SequentialSampler, RandomSampler, Dataset\nfrom tqdm import tqdm, trange\nfrom transformers import (\n BertForSequenceClassification, BertTokenizer, XLMForSequenceClassification, XLMTokenizer,\n XLMRobertaForSequenceClassification, XLMRobertaTokenizer, AdamW, get_linear_schedule_with_warmup\n)\nfrom sklearn.metrics import f1_score, precision_score, recall_score\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef set_seed(args):\n random.seed(args.seed)\n np.random.seed(args.seed)\n torch.manual_seed(args.seed)\n\n\ndef simple_accuracy(preds, labels):\n return (preds == labels).mean()\n\n\ndef acc_and_f1(preds, labels):\n acc = simple_accuracy(preds, labels)\n f1 = f1_score(y_true=labels, y_pred=preds, average='weighted')\n precision = precision_score(\n y_true=labels, y_pred=preds, average='weighted')\n recall = recall_score(y_true=labels, y_pred=preds, average='weighted')\n return{\n \"acc\": acc,\n \"f1\": f1,\n \"acc_and_f1\": (acc + f1) / 2,\n \"precision\": precision,\n \"recall\": recall\n }\n\n\ndef read_examples_from_file(data_dir, mode):\n file_path = os.path.join(data_dir, \"{}.txt\".format(mode))\n examples = []\n with open(file_path, 'r') as infile:\n lines = infile.read().strip().split('\\n')\n for line in lines:\n x = line.split('\\t')\n text = x[0]\n label = x[1]\n examples.append({'text': text, 'label': label})\n if mode == 'test':\n for i in range(len(examples)):\n if examples[i]['text'] == 'not found':\n examples[i]['present'] = False\n else:\n examples[i]['present'] = True\n return examples\n\n\ndef convert_examples_to_features(examples,\n label_list,\n tokenizer,\n max_seq_length=128):\n\n label_map = {label: i for i, label in enumerate(label_list)}\n\n features = []\n\n for (ex_index, example) in enumerate(examples):\n\n sentence = example['text']\n label = example['label']\n\n sentence_tokens = tokenizer.tokenize(sentence)[:max_seq_length - 2]\n sentence_tokens = [tokenizer.cls_token] + \\\n sentence_tokens + [tokenizer.sep_token]\n input_ids = tokenizer.convert_tokens_to_ids(sentence_tokens)\n\n label = label_map[label]\n features.append({'input_ids': input_ids,\n 'label': label})\n if 'present' in example:\n features[-1]['present'] = example['present']\n\n return features\n\n\ndef get_labels(data_dir):\n all_path = os.path.join(data_dir, \"all.txt\")\n labels = []\n with open(all_path, \"r\") as infile:\n lines = infile.read().strip().split('\\n')\n\n for line in lines:\n splits = line.split('\\t')\n label = splits[-1]\n if label not in labels:\n labels.append(label)\n return labels\n\n\ndef train(args, train_dataset, valid_dataset, model, tokenizer, labels):\n\n # Prepare train data\n train_sampler = RandomSampler(train_dataset)\n train_dataloader = DataLoader(\n train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, collate_fn=collate)\n train_batch_size = args.train_batch_size\n\n # Prepare optimizer\n t_total = len(train_dataloader) * args.num_train_epochs\n no_decay = ['bias', 'LayerNorm.weight']\n optimizer_grouped_parameters = [\n {'params': [p for n, p in model.named_parameters() if not any(\n nd in n for nd in no_decay)], 'weight_decay': args.weight_decay},\n {'params': [p for n, p in model.named_parameters() if any(\n nd in n for nd in no_decay)], 'weight_decay': 0.0}\n ]\n optimizer = AdamW(optimizer_grouped_parameters,\n lr=args.learning_rate, eps=args.adam_epsilon)\n scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps=t_total // 10, num_training_steps=t_total)\n\n # Train!\n logger.info(\"***** Running training *****\")\n logger.info(\" Num examples = %d\", len(train_dataset))\n logger.info(\" Num Epochs = %d\", args.num_train_epochs)\n logger.info(\" Instantaneous batch size per GPU = %d\", train_batch_size)\n\n global_step = 0\n tr_loss, logging_loss = 0.0, 0.0\n model.zero_grad()\n train_iterator = trange(int(args.num_train_epochs), desc=\"Epoch\")\n set_seed(args)\n best_f1_score = 0\n if not os.path.exists(args.output_dir):\n os.makedirs(args.output_dir)\n for _ in train_iterator:\n epoch_iterator = tqdm(train_dataloader, desc=\"Iteration\")\n for step, batch in enumerate(epoch_iterator):\n model.train()\n batch = tuple(t.to(args.device) for t in batch)\n inputs = {'input_ids': batch[0],\n 'attention_mask': batch[1],\n 'labels': batch[2]}\n outputs = model(**inputs, return_dict=False)\n # model outputs are always tuple in transformers (see doc)\n loss = outputs[0]\n\n loss.backward()\n torch.nn.utils.clip_grad_norm_(\n model.parameters(), args.max_grad_norm)\n\n tr_loss += loss.item()\n optimizer.step()\n scheduler.step()\n model.zero_grad()\n global_step += 1\n\n # Checking for validation accuracy and stopping after drop in accuracy for 3 epochs\n results = evaluate(args, model, tokenizer, labels, 'validation')\n if results.get('f1') > best_f1_score and args.save_steps > 0:\n best_f1_score = results.get('f1')\n model_to_save = model.module if hasattr(model, \"module\") else model\n model_to_save.save_pretrained(args.output_dir)\n tokenizer.save_pretrained(args.output_dir)\n torch.save(args, os.path.join(\n args.output_dir, \"training_args.bin\"))\n\n return global_step, tr_loss / global_step\n\n\ndef evaluate(args, model, tokenizer, labels, mode, prefix=\"\"):\n\n eval_dataset = load_and_cache_examples(args, tokenizer, labels, mode=mode)\n eval_sampler = SequentialSampler(eval_dataset)\n eval_dataloader = DataLoader(\n eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=collate)\n results = {}\n\n # Evaluation\n logger.info(\"***** Running evaluation %s *****\", prefix)\n logger.info(\" Num examples = %d\", len(eval_dataset))\n logger.info(\" Batch size = %d\", args.eval_batch_size)\n eval_loss = 0.0\n nb_eval_steps = 0\n preds = None\n out_label_ids = None\n model.eval()\n\n for batch in tqdm(eval_dataloader, desc=\"Evaluating\"):\n model.eval()\n batch = tuple(t.to(args.device) for t in batch)\n\n with torch.no_grad():\n inputs = {\"input_ids\": batch[0],\n \"attention_mask\": batch[1],\n \"labels\": batch[2]}\n '''print(inputs[\"input_ids\"])\n print(inputs[\"attention_mask\"])\n print(inputs[\"token_type_ids\"])'''\n outputs = model(**inputs, return_dict=False)\n tmp_eval_loss, logits = outputs[:2]\n eval_loss += tmp_eval_loss.mean().item()\n\n nb_eval_steps += 1\n\n if preds is None:\n preds = logits.detach().cpu().numpy()\n out_label_ids = inputs[\"labels\"].detach().cpu().numpy()\n else:\n preds = np.append(preds, logits.detach().cpu().numpy(), axis=0)\n out_label_ids = np.append(\n out_label_ids, inputs[\"labels\"].detach().cpu().numpy(), axis=0)\n\n eval_loss = eval_loss / nb_eval_steps\n\n preds = np.argmax(preds, axis=1)\n if mode == \"test\":\n preds_list = []\n label_map = {i: label for i, label in enumerate(labels)}\n\n for i in range(out_label_ids.shape[0]):\n if eval_dataset[i][2] == 0:\n preds_list.append('not found')\n else:\n preds_list.append(label_map[preds[i]])\n\n return preds_list\n\n else:\n result = acc_and_f1(preds, out_label_ids)\n results.update(result)\n\n logger.info(\"***** Eval results %s *****\", prefix)\n for key in sorted(result.keys()):\n logger.info(\" %s = %s\", key, str(result[key]))\n\n return results\n\n\nclass CustomDataset(Dataset):\n def __init__(self, input_ids, labels, present=None):\n self.input_ids = input_ids\n self.labels = labels\n self.present = present\n\n def __len__(self):\n return len(self.labels)\n\n def __getitem__(self, i):\n if self.present:\n return torch.tensor(self.input_ids[i], dtype=torch.long), torch.tensor(self.labels[i], dtype=torch.long), self.present[i]\n else:\n return torch.tensor(self.input_ids[i], dtype=torch.long), torch.tensor(self.labels[i], dtype=torch.long)\n\n\ndef collate(examples):\n padding_value = 0\n\n first_sentence = [t[0] for t in examples]\n first_sentence_padded = torch.nn.utils.rnn.pad_sequence(\n first_sentence, batch_first=True, padding_value=padding_value)\n\n max_length = first_sentence_padded.shape[1]\n first_sentence_attn_masks = torch.stack([torch.cat([torch.ones(len(t[0]), dtype=torch.long), torch.zeros(\n max_length - len(t[0]), dtype=torch.long)]) for t in examples])\n\n labels = torch.stack([t[1] for t in examples])\n\n return first_sentence_padded, first_sentence_attn_masks, labels\n\n\ndef load_and_cache_examples(args, tokenizer, labels, mode):\n\n logger.info(\"Creating features from dataset file at %s\", args.data_dir)\n examples = read_examples_from_file(args.data_dir, mode)\n features = convert_examples_to_features(examples, labels, tokenizer, args.max_seq_length)\n\n # Convert to Tensors and build dataset\n all_input_ids = [f['input_ids'] for f in features]\n all_labels = [f['label'] for f in features]\n args = [all_input_ids, all_labels]\n if 'present' in features[0]:\n present = [1 if f['present'] else 0 for f in features]\n args.append(present)\n\n dataset = CustomDataset(*args)\n return dataset\n\n\ndef main():\n\n parser = argparse.ArgumentParser()\n\n # Required parameters\n parser.add_argument(\"--data_dir\", default=None, type=str, required=True,\n help=\"The input data dir\")\n\n parser.add_argument(\"--output_dir\", default=None, type=str, required=True,\n help=\"The output directory where the model predictions and checkpoints will be written.\")\n # Optional Parameters\n parser.add_argument(\"--learning_rate\", default=5e-5, type=float,\n help=\"The initial learning rate for Adam.\")\n parser.add_argument(\"--weight_decay\", default=0.0, type=float,\n help=\"Weight decay if we apply some.\")\n parser.add_argument(\"--adam_epsilon\", default=1e-8, type=float,\n help=\"Epsilon for Adam optimizer.\")\n parser.add_argument(\"--max_grad_norm\", default=1.0, type=float,\n help=\"Max gradient norm.\")\n parser.add_argument(\"--num_train_epochs\", default=10, type=int,\n help=\"Total number of training epochs to perform.\")\n parser.add_argument(\"--train_batch_size\", default=64, type=int,\n help=\"Batch size per GPU/CPU for training.\")\n parser.add_argument(\"--eval_batch_size\", default=64, type=int,\n help=\"Batch size per GPU/CPU for evaluation.\")\n parser.add_argument(\"--seed\", type=int, default=42,\n help=\"random seed for initialization\")\n parser.add_argument(\"--model_type\", type=str,\n default='bert', help='type of model xlm/xlm-roberta/bert')\n parser.add_argument(\"--model_name\", default='bert-base-multilingual-cased',\n type=str, help='name of pretrained model/path to checkpoint')\n parser.add_argument(\"--save_steps\", type=int, default=1, help='set to -1 to not save model')\n parser.add_argument(\"--max_seq_length\", default=128, type=int, help=\"max seq length after tokenization\")\n\n args = parser.parse_args()\n device = torch.device(\"cuda\" if torch.cuda.is_available() else 'cpu')\n args.device = device\n\n # Set up logging\n logging.basicConfig(format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n datefmt=\"%m/%d/%Y %H:%M:%S\",\n level=logging.INFO)\n\n # Set seed\n set_seed(args)\n\n # Prepare data\n labels = get_labels(args.data_dir)\n num_labels = len(labels)\n\n # Initialize model\n tokenizer_class = {\"xlm\": XLMTokenizer, \"bert\": BertTokenizer, \"xlm-roberta\": XLMRobertaTokenizer}\n if args.model_type not in tokenizer_class.keys():\n print(\"Model type has to be xlm/xlm-roberta/bert\")\n exit(0)\n tokenizer = tokenizer_class[args.model_type].from_pretrained(\n args.model_name, do_lower_case=True)\n model_class = {\"xlm\": XLMForSequenceClassification, \"bert\": BertForSequenceClassification, \"xlm-roberta\": XLMRobertaForSequenceClassification}\n model = model_class[args.model_type].from_pretrained(\n args.model_name, num_labels=num_labels)\n\n model.to(args.device)\n\n # Training\n\n logger.info(\"Training/evaluation parameters %s\", args)\n\n train_dataset = load_and_cache_examples(\n args, tokenizer, labels, mode=\"train\")\n valid_dataset = load_and_cache_examples(\n args, tokenizer, labels, mode=\"validation\")\n global_step, tr_loss = train(\n args, train_dataset, valid_dataset, model, tokenizer, labels)\n logger.info(\" global_step = %s, average loss = %s\", global_step, tr_loss)\n\n # Evaluation\n\n results = {}\n\n result = evaluate(args, model, tokenizer, labels, mode=\"validation\")\n preds = evaluate(args, model, tokenizer, labels, mode=\"test\")\n\n # Saving predictions\n output_test_predictions_file = os.path.join(args.output_dir, \"test_predictions.txt\")\n with open(output_test_predictions_file, \"w\") as writer:\n writer.write('\\n'.join(preds))\n\n return results\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "Code/BertSequence.py", "file_name": "BertSequence.py", "file_ext": "py", "file_size_in_byte": 14189, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 23, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 33, "usage_type": "call"}, {"api_name": "sklearn.metrics.precision_score", "line_number": 34, "usage_type": "call"}, {"api_name": "sklearn.metrics.recall_score", "line_number": 36, "usage_type": "call"}, {"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": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "torch.utils.data.RandomSampler", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 111, "usage_type": "call"}, {"api_name": "transformers.AdamW", "line_number": 124, "usage_type": "call"}, {"api_name": "transformers.get_linear_schedule_with_warmup", "line_number": 126, "usage_type": "call"}, {"api_name": "tqdm.trange", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path", "line_number": 140, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 141, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 143, "usage_type": "call"}, {"api_name": "torch.nn.utils.clip_grad_norm_", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 155, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path", "line_number": 171, "usage_type": "attribute"}, {"api_name": "torch.utils.data.SequentialSampler", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 181, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 195, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 222, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 246, "usage_type": "name"}, {"api_name": "torch.tensor", "line_number": 257, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 257, "usage_type": "attribute"}, {"api_name": "torch.tensor", "line_number": 259, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 259, "usage_type": "attribute"}, {"api_name": "torch.nn.utils.rnn.pad_sequence", "line_number": 266, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 266, "usage_type": "attribute"}, {"api_name": "torch.stack", "line_number": 270, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 270, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 270, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 270, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 270, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 271, "usage_type": "attribute"}, {"api_name": "torch.stack", "line_number": 273, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 298, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 331, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 331, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 331, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 335, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 337, "usage_type": "attribute"}, {"api_name": "transformers.XLMTokenizer", "line_number": 347, "usage_type": "name"}, {"api_name": "transformers.BertTokenizer", "line_number": 347, "usage_type": "name"}, {"api_name": "transformers.XLMRobertaTokenizer", "line_number": 347, "usage_type": "name"}, {"api_name": "transformers.XLMForSequenceClassification", "line_number": 353, "usage_type": "name"}, {"api_name": "transformers.BertForSequenceClassification", "line_number": 353, "usage_type": "name"}, {"api_name": "transformers.XLMRobertaForSequenceClassification", "line_number": 353, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 379, "usage_type": "call"}, {"api_name": "os.path", "line_number": 379, "usage_type": "attribute"}]} +{"seq_id": "452173145", "text": "# coding: utf-8\n\nfrom .utils import _convert_int_to_float\n\ndef _element_matrix_name(i,j):\n return 'mat_{i}{j}'.format(i=i,j=j)\n\ndef _global_matrix_name(i,j):\n return 'M_{i}{j}'.format(i=i,j=j)\n\ndef construct_element_matrix_names(n_rows, n_cols):\n mat_args = []\n for i in range(0, n_rows):\n ls = []\n for j in range(0, n_cols):\n mat = _element_matrix_name(i,j)\n ls.append(mat)\n mat_args.append(ls)\n\n return mat_args\n\ndef print_element_matrix_args(n_rows, n_cols, mat_args):\n ls = []\n for i in range(0, n_rows):\n for j in range(0, n_cols):\n ls.append(mat_args[i][j])\n mat_args_str = ', '.join(mat for mat in ls)\n return mat_args_str\n\ndef print_element_matrix_init(n_rows, n_cols, mat_args, size, tab):\n slices = ','.join(':' for i in range(0, size))\n\n lines = []\n for i in range(0, n_rows):\n for j in range(0, n_cols):\n mat = mat_args[i][j]\n\n line = '{mat}[{slices}] = 0.0'.format(mat=mat,slices=slices)\n line = tab + line\n\n lines.append(line)\n\n mat_init_str = '\\n'.join(line for line in lines)\n return mat_init_str\n\ndef print_accumulation_var_init(n_rows, n_cols, tab):\n lines = []\n for i in range(0, n_rows):\n for j in range(0, n_cols):\n line = 'v_{i}{j} = 0.0'.format(i=i,j=j)\n line = tab + line\n\n lines.append(line)\n\n accum_init_str = '\\n'.join(line for line in lines)\n return accum_init_str\n\ndef print_accumulation_var(n_rows, n_cols, expr, tab):\n lines = []\n for i in range(0, n_rows):\n for j in range(0, n_cols):\n line = 'v_{i}{j} += ({__WEAK_FORM__}) * wvol'\n e = _convert_int_to_float(expr[i,j].evalf())\n # we call evalf to avoid having fortran doing the evaluation of rational\n # division\n line = line.format(i=i, j=j, __WEAK_FORM__=e)\n line = tab + line\n\n lines.append(line)\n\n accum_str = '\\n'.join(line for line in lines)\n return accum_str\n\ndef print_bilinear_accumulation_assign(n_rows, n_cols, dim, tab):\n if dim == 1:\n e_pattern = 'mat_{i}{j}[il_1, test_p1 + jl_1 - il_1] = v_{i}{j}'\n\n elif dim == 2:\n e_pattern = 'mat_{i}{j}[il_1, il_2, test_p1 + jl_1 - il_1, test_p2 + jl_2 - il_2] = v_{i}{j}'\n\n elif dim ==3:\n e_pattern = 'mat_{i}{j}[il_1, il_2, il_3, test_p1 + jl_1 - il_1, test_p2 + jl_2 - il_2, test_p3 + jl_3 - il_3] = v_{i}{j}'\n\n else:\n raise NotImplementedError('only 1d, 2d and 3d are available')\n\n lines = []\n for i in range(0, n_rows):\n for j in range(0, n_cols):\n line = e_pattern.format(i=i,j=j)\n line = tab + line\n\n lines.append(line)\n\n accum_assign_str = '\\n'.join(line for line in lines)\n return accum_assign_str\n\ndef print_linear_accumulation_assign(n_rows, n_cols, dim, tab):\n if dim == 1:\n e_pattern = 'mat_{i}{j}[il_1] = v_{i}{j}'\n\n elif dim == 2:\n e_pattern = 'mat_{i}{j}[il_1, il_2] = v_{i}{j}'\n\n elif dim ==3:\n e_pattern = 'mat_{i}{j}[il_1, il_2, il_3] = v_{i}{j}'\n\n else:\n raise NotImplementedError('only 1d, 2d and 3d are available')\n\n lines = []\n for i in range(0, n_rows):\n for j in range(0, n_cols):\n line = e_pattern.format(i=i,j=j)\n line = tab + line\n\n lines.append(line)\n\n accum_assign_str = '\\n'.join(line for line in lines)\n return accum_assign_str\n\n# TODO use space degrees\ndef print_element_matrix_decs(n_rows, n_cols, dim, mat_args, tab):\n if dim == 1:\n pattern = 'zeros( (test_p1+1, 2*trial_p1+1) )'\n\n elif dim == 2:\n pattern = 'zeros( (test_p1+1, test_p2+1, 2*trial_p1+1, 2*trial_p2+1) )'\n\n elif dim == 3:\n pattern = 'zeros( (test_p1+1, test_p2+1, test_p3+1, 2*trial_p1+1, 2*trial_p2+1, 2*trial_p3+1) )'\n\n else:\n raise NotImplementedError('only 1d, 2d and 3d are available')\n\n lines = []\n for i in range(0, n_rows):\n for j in range(0, n_cols):\n mat = mat_args[i][j]\n line = '{mat} = {pattern}'.format(mat=mat, pattern=pattern)\n line = tab + line\n lines.append(line)\n\n decs = '\\n'.join(i for i in lines)\n return decs\n\ndef construct_global_matrix_names(n_rows, n_cols):\n mat_args = []\n for i in range(0, n_rows):\n ls = []\n for j in range(0, n_cols):\n mat = _global_matrix_name(i,j)\n ls.append(mat)\n mat_args.append(ls)\n\n return mat_args\n\ndef print_global_matrix_args(n_rows, n_cols, mat_args):\n ls = []\n for i in range(0, n_rows):\n for j in range(0, n_cols):\n ls.append(mat_args[i][j])\n mat_args_str = ', '.join(mat for mat in ls)\n return mat_args_str\n\n# TODO use spaces\ndef print_global_matrix_decs(n_rows, n_cols, mat_args):\n pattern = 'StencilMatrix( test_space.vector_space, trial_space.vector_space )'\n\n lines = []\n for i in range(0, n_rows):\n for j in range(0, n_cols):\n mat = mat_args[i][j]\n line = '{mat} = {pattern}'.format(mat=mat, pattern=pattern)\n lines.append(line)\n\n decs = '\\n'.join(i for i in lines)\n return decs\n\n# TODO use spaces\ndef print_global_matrix_update(n_rows, n_cols, dim,\n element_mat_args,\n global_mat_args, tab):\n\n lslices = ','.join(':' for i in range(0, 2*dim))\n suffix = ','.join(':' for i in range(0, dim))\n\n lines = []\n for i in range(0, n_rows):\n for j in range(0, n_cols):\n lmat = element_mat_args[i][j]\n gmat = global_mat_args[i][j]\n\n # ... every matrix should have access to its corresponding space and\n # degrees\n if dim == 1:\n gslices = 'is1-test_p1:is1+1'\n\n elif dim == 2:\n gslices = 'is1-test_p1:is1+1, is2-test_p2:is2+1'\n\n elif dim == 3:\n gslices = 'is1-test_p1:is1+1, is2-test_p2:is2+1, is3-test_p3:is3+1'\n\n gslices = '{gslices}, {suffix}'.format(gslices=gslices,\n suffix=suffix)\n # ...\n\n line = '{gmat}[{gslices}] += {lmat}[{lslices}]'.format(lmat=lmat,\n gmat=gmat,\n lslices=lslices,\n gslices=gslices)\n line = tab + line\n lines.append(line)\n\n decs = '\\n'.join(i for i in lines)\n return decs\n\ndef construct_argument_matrix_name(n_rows, n_cols):\n if (n_rows == 1) and (n_cols == 1):\n return _global_matrix_name(0,0)\n else:\n return 'd_matrix'\n\ndef print_argument_matrix_kwargs(argument_mat):\n return ', {}=None'.format(argument_mat)\n\ndef print_import_stencil_matrix():\n return 'from spl.linalg.stencil import StencilMatrix'\n\ndef print_set_dict_matrix(n_rows, n_cols, argument_mat, mat_args):\n if (n_rows == 1) and (n_cols == 1):\n return ''\n\n lines = [argument_mat + ' = {}']\n for i in range(0, n_rows):\n for j in range(0, n_cols):\n M = _global_matrix_name(i,j)\n line = '{d}[{i},{j}] = {M}'.format(d=argument_mat, i=i, j=j, M=M)\n lines.append(line)\n\n return '\\n'.join(i for i in lines)\n\ndef print_get_dict_matrix(n_rows, n_cols, argument_mat, mat_args):\n if (n_rows == 1) and (n_cols == 1):\n return ''\n\n lines = []\n for i in range(0, n_rows):\n for j in range(0, n_cols):\n M = _global_matrix_name(i,j)\n line = '{M} = {d}[{i},{j}]'.format(d=argument_mat, i=i, j=j, M=M)\n lines.append(line)\n\n return '\\n'.join(i for i in lines)\n\n\n_template_define_global_matrix = \"\"\"\nif {__ARGUMENT_MAT__} is None:\n{__IMPORT_STENCIL__}\n{__DECS__}\n{__SET_DICT__}\n{__ELSE__}\n{__GET_DICT__}\n\"\"\"\n# TODO add comment to the generated code\ndef print_define_global_matrix(n_rows, n_cols, global_mat_args, argument_mat, tab):\n # ...\n def _indent_block(txt):\n indent = ' '*4\n\n lines = []\n for line in txt.split('\\n'):\n line = indent + line\n lines.append(line)\n\n return '\\n'.join(line for line in lines)\n # ...\n\n # ...\n global_mat_decs_str = print_global_matrix_decs(n_rows, n_cols, global_mat_args)\n global_mat_decs_str = _indent_block( global_mat_decs_str )\n # ...\n\n # ...\n import_str = print_import_stencil_matrix()\n import_str = _indent_block( import_str )\n # ...\n\n # ...\n set_dict_str = print_set_dict_matrix(n_rows, n_cols, argument_mat, global_mat_args)\n set_dict_str = _indent_block( set_dict_str )\n # ...\n\n # ...\n get_dict_str = print_get_dict_matrix(n_rows, n_cols, argument_mat, global_mat_args)\n get_dict_str = _indent_block( get_dict_str )\n # ...\n\n # ...\n if (n_rows == 1) and (n_cols == 1):\n else_str = ''\n else:\n else_str = 'else:'\n # ...\n\n pattern = _template_define_global_matrix\n code = pattern.format(__ARGUMENT_MAT__=argument_mat,\n __IMPORT_STENCIL__=import_str,\n __DECS__=global_mat_decs_str,\n __SET_DICT__=set_dict_str,\n __GET_DICT__=get_dict_str,\n __ELSE__=else_str)\n\n lines = []\n for line in code.split('\\n'):\n line = tab + line\n lines.append(line)\n\n code = '\\n'.join(line for line in lines)\n\n return code\n", "sub_path": "symfe/codegen/matrix.py", "file_name": "matrix.py", "file_ext": "py", "file_size_in_byte": 9560, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "utils._convert_int_to_float", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "622097539", "text": "# This file is maintained on https://github.com/jfcherng-sublime/ST-API-stubs\n\nfrom typing import (\n Any,\n Callable,\n Dict,\n List,\n Sequence,\n Tuple,\n TypedDict,\n TypeVar,\n Union,\n)\nimport sublime\n\n# ----- #\n# types #\n# ----- #\n\nT = TypeVar(\"T\")\nExpandableVar = TypeVar(\"ExpandableVar\", None, bool, int, float, str, Dict, List, Tuple)\n\nCallback0 = Callable[[], Any]\nCallback1 = Callable[[T], Any]\n\nPoint = int\nDip = float\nStr = str # alias in case we have a variable named as \"str\"\nValue = Union[dict, list, tuple, str, int, float, bool, None]\n\nCompletion = Union[str, Sequence[str], Tuple[str, str], sublime.CompletionItem]\nCompletionKind = Tuple[int, str, str]\nCompletionNormalized = Tuple[\n str, # trigger\n str, # annotation\n str, # details\n str, # completion\n str, # kind_name\n int, # icon letter (Unicode code point, decimal form)\n int, # completion_format\n int, # flags\n int, # kind\n]\n\nclass Layout(TypedDict):\n cols: Sequence[float]\n rows: Sequence[float]\n cells: Sequence[Sequence[int]]\n\nLocation = Tuple[str, str, Tuple[int, int]]\nVector = Tuple[Dip, Dip]\n", "sub_path": "sublime_typing.pyi", "file_name": "sublime_typing.pyi", "file_ext": "pyi", "file_size_in_byte": 1138, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "typing.TypeVar", "line_number": 20, "usage_type": "call"}, {"api_name": "typing.TypeVar", "line_number": 21, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 21, "usage_type": "argument"}, {"api_name": "typing.List", "line_number": 21, "usage_type": "argument"}, {"api_name": "typing.Tuple", "line_number": 21, "usage_type": "argument"}, {"api_name": "typing.Callable", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 24, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 29, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 31, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 31, "usage_type": "name"}, {"api_name": "sublime.CompletionItem", "line_number": 31, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 32, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 33, "usage_type": "name"}, {"api_name": "typing.TypedDict", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 46, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 47, "usage_type": "name"}, {"api_name": "typing.Sequence", "line_number": 48, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 50, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 51, "usage_type": "name"}]} +{"seq_id": "556758362", "text": "import numpy as np\nimport matplotlib as mpl\nfrom matplotlib import pyplot as plt\n\nplt.rcParams['axes.unicode_minus'] = False\nplt.rcParams['font.sans-serif'] = ['SimHei']\nplt.rcParams['lines.linewidth'] = 3\n\nprint('including Tree, Anomaly Detection, PCA, ...')\nfrom sklearn.datasets import load_breast_cancer\nbc = load_breast_cancer()\ndata_X = np.array(bc['data'])\ndata_Y = np.array(bc['target'])\nprint(data_X.shape,data_Y.shape)\n# print(bc['feature_names'])\nprint('-------------------------------------')\n\n# Decision Tree:\nprint('tree:')\nfrom sklearn.tree import DecisionTreeClassifier, plot_tree\nTR = DecisionTreeClassifier(criterion='entropy')\nTR.fit(data_X,data_Y)\nresult1 = TR.predict(data_X)\nfrom sklearn import metrics\nprint('accuracy:',metrics.accuracy_score(y_true=data_Y,y_pred=result1))\n\n# visualise the tree:\nplt.figure(figsize=(10,5))\nplot_tree(TR,filled=True,feature_names=bc['feature_names'],class_names=['yes','no'])\nplt.show()\n\nfrom sklearn.model_selection import train_test_split\nX_train,X_test,Y_train,Y_test = train_test_split(data_X,data_Y,test_size=0.3,random_state=1)\nTR1 = DecisionTreeClassifier(criterion='entropy')\nTR1.fit(X_train,Y_train)\nresult1_1 = TR1.predict(X_test)\nprint('accuracy of test-data:',metrics.accuracy_score(y_true=Y_test,y_pred=result1_1))\n\n# Some tecs to raise the accuracy:\n# Add the min_samples_leaf coefficient:\nTR2 = DecisionTreeClassifier(criterion='entropy',min_samples_leaf=10)\nTR2.fit(X_train,Y_train)\nresult1_2 = TR2.predict(X_test)\nprint('accuracy after adjustment:',metrics.accuracy_score(y_true=Y_test,y_pred=result1_2))\nprint('--------------------------------------')\n\nprint('Anomaly Detection:')\nfrom sklearn.datasets import load_iris\niris = load_iris()\n# print(iris['feature_names'])\ndata_X1 = np.array(iris['data'])\ndata_Y1 = np.array(iris['target'])\n# print(data_X1.shape,data_Y1.shape)\nwhole_data = np.hstack((data_X1,np.array([iris['target']]).T))\nlabel1 = []\nfor item in whole_data:\n if item[4] == 0:\n label1.append(item)\nlabel1 = np.array(label1)\nlabel1 = label1[:,[0,2]]\nplt.scatter(label1[:,0],label1[:,1],color='blue',marker='o')\nplt.xlim((0,10))\nplt.ylim((0,5))\nplt.xlabel('sepal length')\nplt.ylabel('petal length')\nplt.show()\n# Add a anomaly data:\nlabel1_for_AD = np.vstack((label1,np.array([[7,1.5]])))\nprint(label1_for_AD.shape)\n\n# Visualise the distribution of each variable:\nfrom matplotlib.pyplot import subplot\nsubplot(121)\nplt.hist(label1_for_AD[:,0],bins=100)\nplt.title('sepal length distribution')\nsubplot(122)\nplt.hist(label1_for_AD[:,1],bins=100)\nplt.title('petal length distribution')\nplt.show()\n\n# calculate information of the variables:\nx1_mean = np.mean(label1_for_AD[:,0])\nx1_std = np.std(label1_for_AD[:,0])\nprint(f'mean of sepal length={x1_mean}, std of sepal length={x1_std}')\nx2_mean = np.mean(label1_for_AD[:,1])\nx2_std = np.std(label1_for_AD[:,1])\nprint(f'mean of petal length={x2_mean}, std of petal length={x2_std}')\n\n# calculate the Gaussian distribution\nfrom scipy.stats import norm\nx1_normal = norm.pdf(np.linspace(0,10,100),x1_mean,x1_std)\nx2_normal = norm.pdf(np.linspace(0,5,100),x2_mean,x2_std)\nsubplot(121)\nplt.plot(np.linspace(0,10,100),x1_normal)\nplt.title('normal--sepal length')\nsubplot(122)\nplt.plot(np.linspace(0,5,100),x2_normal)\nplt.title('normal--petal length')\nplt.show()\n\n# Find the anomaly:\nfrom sklearn.covariance import EllipticEnvelope\nAD = EllipticEnvelope()\nAD.fit(label1_for_AD)\nresult2 = AD.predict(label1_for_AD)\n# print(result2)\nresult2_2 = np.array([result2]).T\nlabel1_for_AD = np.hstack((label1_for_AD,result2_2))\n# print(label1_for_AD.shape)\n\n# Visualise the result:\nsubplot(121)\nplt.scatter(label1_for_AD[:,0],label1_for_AD[:,1],color='blue',marker='o')\nplt.xlim((0,10))\nplt.ylim((0,5))\nplt.xlabel('sepal length')\nplt.ylabel('petal length')\nplt.title('original data')\nsubplot(122)\nplt.scatter(label1_for_AD[label1_for_AD[:,2]==1,0],label1_for_AD[label1_for_AD[:,2]==1,1],color='blue',marker='o')\nplt.scatter(label1_for_AD[label1_for_AD[:,2]==-1,0],label1_for_AD[label1_for_AD[:,2]==-1,1],color='red',marker='x',linewidths=2)\nplt.xlim((0,10))\nplt.ylim((0,5))\nplt.xlabel('sepal length')\nplt.ylabel('petal length')\nplt.title('Anomaly Detection')\nplt.show()\nnum = 0\nfor item in label1_for_AD:\n if item[2] == -1:\n num += 1\nprint('the number of anomalies:',num)\nprint('---------------------------------------')\n\n# PCA\nprint('principle component analysis:')\nbc1 = load_breast_cancer()\ndata_X2 = bc1['data']\ndata_Y2 = bc1['target']\nprint(data_X2.shape,data_Y2.shape)\nfrom sklearn.neighbors import KNeighborsClassifier\nclf = KNeighborsClassifier(n_neighbors=2)\nclf.fit(data_X2,data_Y2)\nresult3 = clf.predict(data_X2)\nprint('accuracy:',metrics.accuracy_score(y_true=data_Y2,y_pred=result3))\n# To get the data standard(mean=0,std=1):\nfrom sklearn.preprocessing import StandardScaler\nX2_norm = StandardScaler().fit_transform(data_X2)\n# To apply the PCA analysis:\nfrom sklearn.decomposition import PCA\npca = PCA(n_components=30)\nX2_pca = pca.fit_transform(X2_norm)\nprint(pca.explained_variance_ratio_)\n# We can see that the principle components can be the first 10 terms\npca1 = PCA(n_components=10)\nX2_pca1 = pca1.fit_transform(X2_norm)\nclf1 = KNeighborsClassifier(n_neighbors=2)\nclf1.fit(X2_pca1,data_Y2)\nresult3_2 = clf1.predict(X2_pca1)\nprint('accuracy after PCA:',metrics.accuracy_score(y_true=data_Y2,y_pred=result3_2))\n\n# The most important utilization for PCA is visualising:\ndata_X3 = np.array(iris['data'])\ndata_Y3 = np.array(iris['target'])\nprint(data_X3.shape,data_Y3.shape)\npca_vis = PCA(n_components=4)\ndata_X3_update = pca_vis.fit_transform(data_X3)\nprint(pca_vis.explained_variance_ratio_)\npca_vis2 = PCA(n_components=3)\ndata_X3_reupdate = pca_vis2.fit_transform(data_X3)\nfrom sklearn.linear_model import LogisticRegression\nlogic_model = LogisticRegression()\nlogic_model.fit(data_X3_reupdate,data_Y3)\nresult4 = logic_model.predict(data_X3_reupdate)\nprint('accuracy:',metrics.accuracy_score(y_true=data_Y3,y_pred=result4))\nfrom mpl_toolkits.mplot3d import Axes3D\nax = plt.gca(projection='3d')\nax.scatter(data_X3_reupdate[result4==0,0],data_X3_reupdate[result4==0,1],data_X3_reupdate[result4==0,2],color='red')\nax.scatter(data_X3_reupdate[result4==1,0],data_X3_reupdate[result4==1,1],data_X3_reupdate[result4==1,2],color='blue')\nax.scatter(data_X3_reupdate[result4==2,0],data_X3_reupdate[result4==2,1],data_X3_reupdate[result4==2,2],color='lightgreen')\nplt.show()\n", "sub_path": "other_tecs_in_ML.py", "file_name": "other_tecs_in_ML.py", "file_ext": "py", "file_size_in_byte": 6412, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "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.rcParams", "line_number": 6, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 6, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 7, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "sklearn.datasets.load_breast_cancer", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 21, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 25, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "sklearn.tree.plot_tree", "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": "sklearn.model_selection.train_test_split", "line_number": 33, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 34, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 37, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 37, "usage_type": "name"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 41, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 44, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 44, "usage_type": "name"}, {"api_name": "sklearn.datasets.load_iris", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "numpy.vstack", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 86, "usage_type": "call"}, {"api_name": "scipy.stats.norm.pdf", "line_number": 91, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 91, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 91, "usage_type": "call"}, {"api_name": "scipy.stats.norm.pdf", "line_number": 92, "usage_type": "call"}, {"api_name": "scipy.stats.norm", "line_number": 92, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "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": "numpy.linspace", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 98, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "sklearn.covariance.EllipticEnvelope", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 116, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "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.subplot", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "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.title", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "sklearn.datasets.load_breast_cancer", "line_number": 137, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 142, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 145, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 145, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 148, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 151, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 155, "usage_type": "call"}, {"api_name": "sklearn.neighbors.KNeighborsClassifier", "line_number": 157, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 160, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 160, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 164, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 166, "usage_type": "call"}, {"api_name": "sklearn.decomposition.PCA", "line_number": 169, "usage_type": "call"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 172, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 175, "usage_type": "call"}, {"api_name": "sklearn.metrics", "line_number": 175, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 181, "usage_type": "name"}]} +{"seq_id": "248386811", "text": "import cv2\n\nprint(\"hello world\")\n\nface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\neye_cascade = cv2.CascadeClassifier('haarcascade_eye.xml')\n\ndef detect(gray, frame):\n\tfaces = face_cascade.detectMultiScale(gray, 1.3, 5)\n\tprint(faces)\n\n\nvideo_capture = cv2.VideoCapture(0)\nwhile True:\n # Read each frame\n\tif video_capture.read():\n\t\t_, frame = video_capture.read()\n\t\tgray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n\t\tcanvas = detect(gray, frame)\n\t\t# Show the image in the screen\n\t\tcv2.imshow(\"Video\", canvas)\n\t\t# Put the condition which triggers the end of program\n\t\tif cv2.waitKey(1) & 0xFF == ord('q'):\n\t\t\tbreak\n\t\tvideo_capture.release()\n\t\tcv2.destroyAllWindows()\n\n", "sub_path": "DeepFakeDetector.py", "file_name": "DeepFakeDetector.py", "file_ext": "py", "file_size_in_byte": 693, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cv2.CascadeClassifier", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.CascadeClassifier", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "582893082", "text": "import dataloaders_v2\nimport numpy as np\nfrom tqdm import tqdm\nimport os, sys\nimport time\n\nimport torch\nfrom torchvision import transforms\nimport torch.nn.functional as F\nimport torch.nn as nn\n\nfrom ops.utils_blocks import block_module\nfrom model.gray_group import ListaParams\nfrom model.gray_group import groupLista as Lista\n\n\n# parameters\n# the same setting used in the original code\nkernel_size = 9\nnum_filters = 256\nstride = 1\nunfoldings = 24\nfreq_corr_update_test = 6\nfreq_corr_update_train = 100\ncorr_update_test = 3\ncorr_update_train = 2\nlmbda_prox = 0.02\nrescaling_init_val = 1.0\nspams_init = 1\nmulti_theta = 1\ncenter_windows = 1\ndiag_rescale_gamma = 1\ndiag_rescale_patch = 1\npatch_size = 56\nnu_init = 1\nmask_windows = 1\nmulti_std = 0\ntrain_batch = 25\naug_scale = 0\n\npad_block = 1\npad_patch = 0\nno_pad = False\ncustom_pad = None\nstride_test = 12\ntest_batch = 10\n\nmodel_name = 'trained_model/gray/corr_update%3_freq%6_kernel_size%9_lr_step%80_noise_level%25_train_batch%25_/ckpt'\nsigma = 25\nnoise_std = sigma / 255\ndataset_name='BSD68'\n\nlr = 1e-5\nepochs = 2 # epoch is going through all the patches of the image\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\ncriterion = nn.MSELoss(reduction='sum')\n\nout_dir = os.path.join(model_name)\nckpt_path = os.path.join(out_dir)\ncheckpoint = torch.load(ckpt_path, map_location=device)\n\ndata_path = 'datasets'\ntest_path = [f'{data_path}/{dataset_name}/']\ntrain_path = [f'{data_path}/BSD400/']\nval_path = train_path\nloaders = dataloaders_v2.get_dataloaders(train_path, test_path,train_path, crop_size=patch_size,\n\t\t\t\t\t\t\t\t batch_size=train_batch, downscale=aug_scale,concat=1)\n\nloader = loaders['test']\n\nparams = ListaParams(kernel_size=kernel_size, num_filters=num_filters, stride=stride,\n\t\t\t\t\t unfoldings=unfoldings, freq=freq_corr_update_test, corr_update=corr_update_test,\n\t\t\t\t\t lmbda_init=lmbda_prox, h=rescaling_init_val, spams=spams_init,\n\t\t\t\t\t multi_lmbda=multi_theta,\n\t\t\t\t\t center_windows=center_windows, std_gamma=diag_rescale_gamma,\n\t\t\t\t\t std_y=diag_rescale_patch, block_size=patch_size, nu_init=nu_init,\n\t\t\t\t\t mask=mask_windows, multi_std=multi_std)\n\nblock_params = {\n\t\t\t'crop_out_blocks': 0,\n\t\t\t'ponderate_out_blocks': 1,\n\t\t\t'sum_blocks': 0,\n\t\t\t'pad_even': 1, \n\t\t\t'centered_pad': 0,\n\t\t\t'pad_block': pad_block,\n\t\t\t'pad_patch': pad_patch,\n\t\t\t'no_pad': no_pad,\n\t\t\t'custom_pad': custom_pad,\n\t\t\t'avg': 1}\n\nl = kernel_size // 2\nmask = F.conv_transpose2d(torch.ones(1, 1, patch_size - 2 * l, patch_size - 2 * l),\n\t\t\t\t\t torch.ones(1, 1, kernel_size, kernel_size))\nmask /= mask.max()\nmask = mask.to(device=device)\n\npsnr_before = []\npsnr_after = []\n\nfor idx,batch in enumerate(tqdm(loader)):\n\n\tstart_time = time.time()\n\n\t# load model\n\tmodel = Lista(params).to(device)\n\tmodel.load_state_dict(checkpoint['state_dict'],strict=True)\n\tmodel.eval()\n\n\tbatch = batch.to(device=device)\n\ttorch.manual_seed(0) # for reproducibility\n\tnoise = torch.randn_like(batch) * noise_std\n\tnoisy_batch = batch + noise\n\n\t# denoise the image by denoising each patch and reconstructe the image \n\twith torch.no_grad():\n\t block = block_module(patch_size, stride_test, kernel_size, block_params)\n\t batch_noisy_blocks = block._make_blocks(noisy_batch)\n\t patch_loader = torch.utils.data.DataLoader(batch_noisy_blocks, batch_size=test_batch, drop_last=False)\n\t batch_out_blocks = torch.zeros_like(batch_noisy_blocks)\n\n\t for i, inp in enumerate(patch_loader):\n\t\t id_from, id_to = i * patch_loader.batch_size, (i + 1) * patch_loader.batch_size\n\t\t batch_out_blocks[id_from:id_to] = model(inp)\n\n\t output = block._agregate_blocks(batch_out_blocks)\n\t psnr_batch = -10 * torch.log10((output.clamp(0., 1.) - batch).pow(2).flatten(2, 3).mean(2)).mean()\n\t psnr_before.append(psnr_batch.item())\n\n\t# internal adaptation\n\tmodel.train()\n\toptimizer = torch.optim.Adam(model.parameters(), lr=lr)\n\n\tfor epoch in range(epochs):\n\t\n\t block = block_module(patch_size, stride_test, kernel_size, block_params)\n\t batch_noisy_blocks = block._make_blocks(output)\n\t patch_loader = torch.utils.data.DataLoader(batch_noisy_blocks, batch_size=test_batch, drop_last=False)\n\t batch_out_blocks = torch.zeros_like(batch_noisy_blocks)\n\n\t # add noise to each patch and optimize with it and the noisy patch\n\t for inp in tqdm(patch_loader):\n\n\t\t noise = torch.randn_like(inp) * noise_std\n\t\t noisy_inp = inp + noise\n\t\t optimizer.zero_grad()\n\t\t retrain_output = model(noisy_inp)\n\t\t loss = (mask * (retrain_output - inp)).pow(2).sum() / retrain_output.shape[0]\n\t\t loss.backward()\n\t\t optimizer.step() \n\n\t# denoise the image again after adaptation\n\tmodel.eval()\n\twith torch.no_grad():\n\t\n\t block = block_module(patch_size, stride_test, kernel_size, block_params)\n\t batch_noisy_blocks = block._make_blocks(noisy_batch)\n\t patch_loader = torch.utils.data.DataLoader(batch_noisy_blocks, batch_size=test_batch, drop_last=False)\n\t batch_out_blocks = torch.zeros_like(batch_noisy_blocks)\n\n\t for i, inp in enumerate(patch_loader): # if it doesnt fit in memory\n\t\t id_from, id_to = i * patch_loader.batch_size, (i + 1) * patch_loader.batch_size\n\t\t batch_out_blocks[id_from:id_to] = model(inp)\n\n\t output = block._agregate_blocks(batch_out_blocks)\n\n\tpsnr_batch = -10 * torch.log10((output.clamp(0., 1.) - batch).pow(2).flatten(2, 3).mean(2)).mean()\n\tpsnr_after.append(psnr_batch.item())\n\n\telapsed_time = time.time() - start_time\n\tprint('{} - {:.2f} second - Before adaptation: {:.2f} dB; After adaptation {:.2f} dB'.format(idx, elapsed_time, psnr_before[-1], psnr_after[-1]))\n\navg_psnr_before = np.mean(psnr_before)\navg_psnr_after = np.mean(psnr_after)\nprint('Average PSNR - {} - Before adaptation: {:.2f} dB; After adaptation {:.2f} dB'.format(idx, avg_psnr_before, avg_psnr_after))", "sub_path": "GroupSC_internal_adaptation.py", "file_name": "GroupSC_internal_adaptation.py", "file_ext": "py", "file_size_in_byte": 5706, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.device", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 55, "usage_type": "attribute"}, {"api_name": "torch.nn.MSELoss", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 60, "usage_type": "call"}, {"api_name": "dataloaders_v2.get_dataloaders", "line_number": 66, "usage_type": "call"}, {"api_name": "model.gray_group.ListaParams", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn.functional.conv_transpose2d", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 93, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 100, "usage_type": "call"}, {"api_name": "time.time", "line_number": 102, "usage_type": "call"}, {"api_name": "model.gray_group", "line_number": 105, "usage_type": "name"}, {"api_name": "model.gray_group.groupLista", "line_number": 105, "usage_type": "call"}, {"api_name": "model.gray_group.load_state_dict", "line_number": 106, "usage_type": "call"}, {"api_name": "model.gray_group", "line_number": 106, "usage_type": "name"}, {"api_name": "model.gray_group.eval", "line_number": 107, "usage_type": "call"}, {"api_name": "model.gray_group", "line_number": 107, "usage_type": "name"}, {"api_name": "torch.manual_seed", "line_number": 110, "usage_type": "call"}, {"api_name": "torch.randn_like", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 115, "usage_type": "call"}, {"api_name": "ops.utils_blocks.block_module", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 118, "usage_type": "attribute"}, {"api_name": "torch.zeros_like", "line_number": 119, "usage_type": "call"}, {"api_name": "model.gray_group", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.log10", "line_number": 126, "usage_type": "call"}, {"api_name": "model.gray_group.train", "line_number": 130, "usage_type": "call"}, {"api_name": "model.gray_group", "line_number": 130, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 131, "usage_type": "attribute"}, {"api_name": "model.gray_group.parameters", "line_number": 131, "usage_type": "call"}, {"api_name": "model.gray_group", "line_number": 131, "usage_type": "name"}, {"api_name": "ops.utils_blocks.block_module", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 137, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 137, "usage_type": "attribute"}, {"api_name": "torch.zeros_like", "line_number": 138, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.randn_like", "line_number": 143, "usage_type": "call"}, {"api_name": "model.gray_group", "line_number": 146, "usage_type": "call"}, {"api_name": "model.gray_group.eval", "line_number": 152, "usage_type": "call"}, {"api_name": "model.gray_group", "line_number": 152, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 153, "usage_type": "call"}, {"api_name": "ops.utils_blocks.block_module", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 157, "usage_type": "attribute"}, {"api_name": "torch.zeros_like", "line_number": 158, "usage_type": "call"}, {"api_name": "model.gray_group", "line_number": 162, "usage_type": "call"}, {"api_name": "torch.log10", "line_number": 166, "usage_type": "call"}, {"api_name": "time.time", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 173, "usage_type": "call"}]} +{"seq_id": "436686155", "text": "import os\nimport sys\n\nfrom django import *\nfrom PIL import Image\nfrom django.core.mail import BadHeaderError, send_mail\nfrom django.http import HttpResponse, HttpRequest\nfrom django.shortcuts import redirect\nfrom django.contrib.auth.forms import UserCreationForm\nimport urllib3\n\ntry:\n from web.models.social import *\nexcept:\n from models.social import *\ntry:\n from web.models.user_auth import *\nexcept:\n from models.user_auth import *\nfrom django.db import models\ntry:\n from web.mysite.models import User, Article, RoomImage, Fab, ArticleRoom, ArticleFloor, ArticleLive, ArticleCreate\nexcept:\n from mysite.models import User, Article, RoomImage, Fab, ArticleRoom, ArticleFloor, ArticleLive, ArticleCreate\nfrom django.contrib.auth import get_user_model\nfrom django.contrib.auth.models import User\n\nfrom django.core.mail import EmailMessage\nfrom django.template.loader import get_template\nfrom django.views.decorators.http import require_POST\n\nimport cloudinary\nimport cloudinary.uploader\nimport cloudinary.api\n\nfrom cloudinary.forms import cl_init_js_callbacks\n\nimport requests\n\nUser = get_user_model()\n\n\"\"\"\nGoogle, ログイン認証\n\"\"\"\nfrom httplib2 import Http\nfrom oauth2client.client import flow_from_clientsecrets\nfrom oauth2client.file import Storage\nfrom apiclient.discovery import build\nimport hmac\nimport hashlib\n\nSCOPE = 'https://www.googleapis.com/auth/plus.profile.emails.read'\n\n# flow = flow_from_clientsecrets(\n# './client_id.json',\n# scope=SCOPE,\n# redirect_uri= \"http://localhost:3031/auth/complete/google-oauth2/\")\n\n\"\"\"\nfacebook\n# \"\"\"\n# FACEBOOK_ID = '292183621408680'\n# FACEBOOK_SECRET = '1077fcc7e686d3c4ff08fbb05fcc94ab'\n# FACEBOOK_CALLBACK_URL = 'http://localhost:8000/callback/facebook'\n\n\"\"\"\nSettingファイル\n\"\"\"\nsys.path.append(os.getcwd())\ntry:\n from web.config.settings import *\nexcept ImportError:\n from config.settings import *\n\n\n\"\"\"\nimgファイル保存\n\"\"\"\nUPLOAD_FOLDER = '/static/img/'\n\ndef upload(img, **options):\n try:\n cloudinary.uploader.upload(img, options={\"folder\": \"./Model_image\", \"tags\": \"roomii_image\"})\n except:\n pass\n\n\"\"\"\nSNSログイン\n\"\"\"\n\ndef create_socils_user(data):\n\n try:\n user_name = data[\"displayName\"].replace(\" \", \"\").replace(\"%20\", \"\")\n\n secret = 'google'\n password = hmac.new(\n secret.encode('UTF-8'),\n SECRET_KEY.encode('UTF-8'),\n hashlib.sha256\n ).hexdigest()\n\n user = User.objects.create(\n username = user_name,\n email = data[\"emails\"][0]['value'],\n password = password,\n is_staff = 0,\n is_active = 1,\n image='/static/img/profile.png',\n )\n\n if user:\n user.save()\n\n return user\n else:\n return redirect(\"apps:login\")\n except:\n user_name = data[\"name\"].replace(\" \", \"\").replace(\"%20\", \"\")\n\n secret = 'facebook'\n password = hmac.new(\n secret.encode('UTF-8'),\n SECRET_KEY.encode('UTF-8'),\n hashlib.sha256\n ).hexdigest()\n\n user = User.objects.create(\n username = user_name,\n email = data['email'],\n password = password,\n is_staff = 0,\n is_active = 1,\n image='/static/img/profile.png',\n )\n\n if user:\n user.save()\n\n return user\n else:\n return redirect(\"apps:login\")\n\n\ndef create_socials(user_id , data, provider):\n if provider == 'google':\n social = Social(\n user_id = user_id,\n provider = provider,\n provider_id = data['id']\n )\n elif provider == 'facebook':\n social = Social(\n user_id = user_id.id,\n provider = provider,\n provider_id = data['id']\n )\n\n session.add(social)\n session.commit()\n\ndef check_socials(data, provider):\n\n if provider == 'google':\n social = session.query(Social).filter(\n Social.provider == 'google',\n Social.provider_id == data\n ).first()\n elif provider == 'facebook':\n social = session.query(Social).filter(\n Social.provider == 'facebook',\n Social.provider_id == data['id']\n ).first()\n\n if social is None:\n return False\n else:\n return social\n\n\"\"\"\nFacebook, ログイン認証\n\"\"\"\n\n# facebookアクセストークンの取得\ndef get_facebook_access_token(code):\n\n url = 'https://graph.facebook.com/v3.2/oauth/access_token'\n params = {\n 'redirect_uri': FACEBOOK_CALLBACK_URL,\n 'client_id': SOCIAL_AUTH_FACEBOOK_KEY,\n 'client_secret': SOCIAL_AUTH_FACEBOOK_SECRET,\n 'code': code,\n }\n r = requests.get(url, params=params)\n return r.json()['access_token']\n\ndef check_facebook_access_token(access_token):\n\n url = 'https://graph.facebook.com/debug_token'\n params = {\n 'input_token': access_token,\n 'access_token': '%s|%s' % (SOCIAL_AUTH_FACEBOOK_KEY, SOCIAL_AUTH_FACEBOOK_SECRET)\n }\n r = requests.get(url, params=params)\n return r.json()['data']\n\ndef get_facebook_user_info(access_token, user_id):\n\n url = 'https://graph.facebook.com/%s' % (user_id)\n params = {\n 'fields': 'name, email',\n 'access_token': access_token,\n }\n return requests.get(url, params=params).json()\n\n\"\"\"\nGoogle, ログイン認証\n\"\"\"\n\ndef google_login_flow(code):\n\n # 取得したアクセストークンから認証情報を作成\n credentials = flow.step2_exchange(code)\n\n # 認証情報を開発フォルダに保存\n CREDENTIALS_FILE = \"./credentials\"\n Storage(CREDENTIALS_FILE).put(credentials)\n\n # 保存したファイルに記載されている認証情報から必要情報取得\n credentials = Storage(CREDENTIALS_FILE).get()\n http_auth = credentials.authorize(Http())\n service = build('plus', 'v1', http=http_auth)\n\n # json形式でユーザー情報を渡す\n result = service.people().get(userId='me').execute()\n\n return result\n", "sub_path": "web/mysite/library.py", "file_name": "library.py", "file_ext": "py", "file_size_in_byte": 6176, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.contrib.auth.models.User", "line_number": 40, "usage_type": "name"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 40, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 69, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 69, "usage_type": "call"}, {"api_name": "cloudinary.uploader.upload", "line_number": 83, "usage_type": "call"}, {"api_name": "cloudinary.uploader", "line_number": 83, "usage_type": "attribute"}, {"api_name": "hmac.new", "line_number": 97, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 100, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User.objects.create", "line_number": 103, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 103, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 103, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 117, "usage_type": "call"}, {"api_name": "hmac.new", "line_number": 122, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 125, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User.objects.create", "line_number": 128, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 128, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 128, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 142, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 194, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 204, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 214, "usage_type": "call"}, {"api_name": "oauth2client.file.Storage", "line_number": 227, "usage_type": "call"}, {"api_name": "oauth2client.file.Storage", "line_number": 230, "usage_type": "call"}, {"api_name": "httplib2.Http", "line_number": 231, "usage_type": "call"}, {"api_name": "apiclient.discovery.build", "line_number": 232, "usage_type": "call"}]} +{"seq_id": "460280885", "text": "import re\r\nimport dns\r\nfrom dns import resolver\r\nimport socket\r\nimport smtplib\r\n\r\nfrom openpyxl import load_workbook\r\n\r\nwb = load_workbook('Book1.xlsx', data_only=True)\r\nsh = wb[\"Sheet1\"]\r\n\r\nfor row in sh['A{}:A{}'.format(sh.min_row + 1, sh.max_row)]:\r\n for cell in row:\r\n try:\r\n wb.save('Book1.xlsx')\r\n addressToVerify = cell.value\r\n match = re.match('^[_a-z0-9-]+(\\.[_a-z0-9-]+)*@[a-z0-9-]+(\\.[a-z0-9-]+)*(\\.[a-z]{2,4})$', addressToVerify)\r\n if match == None:\r\n print('Bad Syntax for ' + addressToVerify)\r\n\r\n resolver = dns.resolver.Resolver()\r\n records = dns.resolver.query('dnspython.org', 'MX')\r\n\r\n mxRecord = records[0].exchange\r\n mxRecord = str(mxRecord)\r\n\r\n # Get local server hostname\r\n host = socket.gethostname()\r\n\r\n # SMTP lib setup (use debug level for full output)\r\n server = smtplib.SMTP()\r\n server.set_debuglevel(0)\r\n\r\n # SMTP Conversation\r\n server.connect(mxRecord)\r\n server.helo(host)\r\n server.mail('surajk@lambdatest.com')\r\n print(addressToVerify)\r\n code, message = server.rcpt(str(addressToVerify))\r\n server.quit()\r\n print(code)\r\n # Assume 250 as Success\r\n # Assume 550 as Failure\r\n if code == 550:\r\n sh.cell(row=cell.row, column=2).value = \"Soft\"\r\n elif code == 250:\r\n sh.cell(row=cell.row, column=2).value = \"Success\"\r\n elif code == 520:\r\n sh.cell(row=cell.row, column=2).value=\"Soft1\"\r\n elif code == 521:\r\n sh.cell(row=cell.row, column=2).value=\"Soft1\"\r\n elif code == 522:\r\n sh.cell(row=cell.row, column=2).value=\"Soft1\"\r\n elif code == 531:\r\n sh.cell(row=cell.row, column=2).value=\"Soft1\"\r\n elif code == 545:\r\n sh.cell(row=cell.row, column=2).value=\"Soft1\"\r\n elif code == 553:\r\n sh.cell(row=cell.row, column=2).value=\"Soft1\"\r\n elif code == 421:\r\n sh.cell(row=cell.row, column=2).value=\"Soft1\"\r\n elif code == 450:\r\n sh.cell(row=cell.row, column=2).value=\"Soft1\"\r\n elif code == 451:\r\n sh.cell(row=cell.row, column=2).value=\"Soft1\"\r\n elif code == 452:\r\n sh.cell(row=cell.row, column=2).value=\"Soft1\"\r\n\r\n else:\r\n sh.cell(row=cell.row, column=2).value = \"Fail\"\r\n\r\n except Exception as e:\r\n print(\"error\",e,\" for address \",addressToVerify)\r\n\r\nwb.save('Book1.xlsx')\r\nprint(\"Done\")\r\n", "sub_path": "EV3.py", "file_name": "EV3.py", "file_ext": "py", "file_size_in_byte": 2732, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "openpyxl.load_workbook", "line_number": 9, "usage_type": "call"}, {"api_name": "re.match", "line_number": 17, "usage_type": "call"}, {"api_name": "dns.resolver", "line_number": 21, "usage_type": "name"}, {"api_name": "dns.resolver.Resolver", "line_number": 21, "usage_type": "call"}, {"api_name": "dns.resolver.query", "line_number": 22, "usage_type": "call"}, {"api_name": "dns.resolver", "line_number": 22, "usage_type": "attribute"}, {"api_name": "socket.gethostname", "line_number": 28, "usage_type": "call"}, {"api_name": "smtplib.SMTP", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "363354033", "text": "#!/usr/bin/env python\nimport signal\nimport sys\nimport time\nimport struct\nfrom textile_udpclient import UdpInstance\nfrom pythonosc import udp_client\nimport socket\nimport numpy as np\n\nfrom PyQt5 import QtBluetooth as QtBt\nfrom PyQt5 import QtCore\n\nfrom qenum import qenum_key\n\n\nclass ServiceHandler(object):\n def __init__(self, device, uuid):\n self.device = device\n self.uuid = uuid\n self.characteristics = {}\n s = device.connection.createServiceObject(uuid)\n s.stateChanged.connect(self.stateChanged)\n s.characteristicChanged.connect(self.characteristicChanged)\n s.descriptorWritten.connect(self.descriptorWritten)\n s.descriptorRead.connect(self.descriptorRead)\n s.error.connect(self.error)\n\n self.service = s\n device.services[uuid.toString()] = self\n\n self.last = time.time()\n\n s.discoverDetails()\n\n def stateChanged(self, state):\n print(\"stateChanged()\", self.device.address, self.uuid.toString(), qenum_key(QtBt.QLowEnergyService, state))\n if state == QtBt.QLowEnergyService.ServiceState.ServiceDiscovered:\n self.characteristics = self.service.characteristics()\n\n for c in self.characteristics:\n print(c.name(), qenum_key(QtBt.QLowEnergyCharacteristic, c.properties()))\n if ServiceHandler.supportsNotify(c):\n self.enableNotify(c)\n\n def supportsNotify(char):\n return char.properties() & 0x10\n\n def enableNotify(self, char):\n notification = char.descriptors()[0]\n if notification.isValid():\n print(\"enabling notifications on \", notification)\n self.service.writeDescriptor(notification, QtCore.QByteArray.fromHex(b\"0100\"))\n\n def disconnected(self):\n print(\"Sevice.disconnected()\", self.device.address, self.uuid.toString())\n\n def characteristicChanged(self, char, data):\n print(\"Sevice.characteristicChanged()\", self.device.address, self.uuid.toString(), data, now - self.last)\n\n def descriptorWritten(self, desc, data):\n print(\"Sevice.descriptorWritten()\", self.device.address, self.uuid.toString(), desc, data)\n\n def descriptorRead(self, *args, **kwargs):\n print(\"Sevice.descriptorRead()\", self.device.address, self.uuid.toString(), args, kwargs)\n\n def error(self, error):\n print(\"Sevice.error()\", self.device.address, self.uuid.toString(), qenum_key(QtBt.QLowEnergyService, error))\n\n\nclass EtextileServiceHandler(ServiceHandler):\n uuid = \"{00004e20-0000-1000-8000-00805f9b34fb}\"\n\n def __init__(self, device, uuid):\n super().__init__(device, uuid)\n\n def characteristicChanged(self, char, data):\n array = struct.unpack(\"H\" * int((len(data) / 2)), data)\n EtextileServiceHandler.etextile_handle_data(self.device_number(self.device.address), array)\n\n def etextile_handle_data(device_address, data):\n print(\"etextile data:\", device_address, data)\n\n # convert address to Riot1 Riot2 etc:\n def device_number(self, i):\n if device_name_dict.get(i) is None:\n new_device_num = 'Riot' + str(len(device_name_dict) + 1)\n device_name_dict[i] = new_device_num\n print(\"Unknown Riot device added\" + i + \" - gets Number \" + new_device_num)\n return device_name_dict.get(i, \"Invalid device. Add to List!\")\n\n\nclass DeviceConnection(object):\n def __init__(self, app, device, service_handlers):\n self.app = app\n if sys.platform != \"darwin\":\n self.address = device.address().toString()\n else:\n self.address = device.deviceUuid().toString()\n\n self.service_handlers = {}\n for s in service_handlers:\n self.service_handlers[s.uuid] = s\n\n c = QtBt.QLowEnergyController.createCentral(device)\n c.connected.connect(self.connected)\n c.disconnected.connect(self.disconnected)\n c.error.connect(self.error)\n\n c.serviceDiscovered.connect(self.serviceDiscovered)\n c.discoveryFinished.connect(self.discoveryFinished)\n\n self.connection = c\n\n self.services = {}\n\n self.app.connections[self.address] = self\n\n def connect(self):\n print(\"device.connect()\", self.address)\n self.connection.connectToDevice()\n\n def connected(self):\n print(\"device.connected()\", self.address)\n self.connection.discoverServices()\n\n def disconnected(self):\n print(\"device.disconnected()\", self.address)\n QtCore.QTimer.singleShot(2000, self.cleanup)\n\n def cleanup(self):\n print(\"device.cleanup()\")\n if self.app.connections.get(self.address) == self:\n self.app.connections.pop(self.address, None)\n\n def serviceDiscovered(self, uuid):\n print(\"device.serviceDiscovered()\", self.address, uuid.toString())\n\n def discoveryFinished(self):\n print(\"device.discoveryFinished()\", self.address)\n\n for uuid in self.connection.services():\n service_handler = self.service_handlers.get(uuid.toString())\n if service_handler is not None:\n service_handler(self, uuid)\n\n def error(self, error):\n print(\"device.error()\", self.address, qenum_key(QtBt.QLowEnergyController, error))\n if qenum_key(QtBt.QLowEnergyController, error) == \"UnknownError\":\n self.connection.disconnectFromDevice()\n QtCore.QTimer.singleShot(5000, self.cleanup)\n\n\nclass Application(QtCore.QCoreApplication):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.service_handlers = [EtextileServiceHandler]\n self.connections = {}\n self.riotUuid = []\n self.scan_for_devices()\n self.exec()\n\n def display_status(self):\n pass\n\n def device_discovered(self, device):\n Application.device_print(device)\n\n if device.name().startswith(\"RIOT\"):\n if device.deviceUuid().toString() not in self.riotUuid:\n self.riotUuid.append(device.deviceUuid().toString())\n pass\n\n def device_print(device):\n print(device.address().toString(), device.name(), '\\t', device.deviceUuid().toString())\n\n def error(self, error):\n print(\"error():\", qenum_key(QtBt.QBluetoothDeviceDiscoveryAgent, error))\n\n def finished(self, *args, **kwargs):\n for device in self.agent.discoveredDevices():\n if sys.platform != \"darwin\":\n if device.name().startswith(\"RIOT\"):\n if device.address().toString() not in self.connections:\n connection = DeviceConnection(self, device, self.service_handlers)\n connection.connect()\n else:\n # on OSX the name can't be read, so use the Uudi\n if device.deviceUuid().toString() in self.riotUuid:\n if device.deviceUuid().toString() not in self.connections:\n if device.deviceUuid().toString() not in device_name_dict_exclude:\n connection = DeviceConnection(self, device, self.service_handlers)\n connection.connect()\n\n self.agent.start()\n\n def scan_for_devices(self):\n self.agent = QtBt.QBluetoothDeviceDiscoveryAgent(self)\n self.agent.deviceDiscovered.connect(self.device_discovered)\n self.agent.finished.connect(self.finished)\n self.agent.error.connect(self.error)\n self.agent.setLowEnergyDiscoveryTimeout(1000)\n\n timer = QtCore.QTimer(self.agent)\n timer.start(2000)\n timer.timeout.connect(self.display_status)\n\n self.agent.start()\n\n\n# provide\ndef handle_data(device_num, array):\n print(device_num, array)\n udp.sendUdp(device_num, array)\n\n\nEtextileServiceHandler.etextile_handle_data = handle_data\n\nif __name__ == \"__main__\":\n if sys.platform == \"darwin\":\n import os\n\n os.environ[\"QT_EVENT_DISPATCHER_CORE_FOUNDATION\"] = \"1\"\n\n # setup OSC client\n localIP = \"127.0.0.1\"\n localPortSender = 7001\n device_name_dict = {\n # List of device names, add here:\n \"{94e00f41-7d5a-4851-bd0a-1f7e02c1350f}\": 'Riot4',\n \"{0b0b4431-b150-4279-9f6a-ce112144b99e}\": 'Riot5',\n \"{620eb416-51d3-47d5-a4d4-7d7233cc08ec}\": 'Riot6',\n \"{167bfbf0-b0c3-430e-8eb6-5c6890a3233f}\": 'Riot1',\n \"{16c02af7-bb9e-4dc1-ae42-32e476ce7734}\": 'Riot2',\n \"{f68e4c23-7ee4-40e1-a923-060853a4d138}\": 'Riot3',\n }\n\n device_name_dict_exclude = {\n # List of devices that shouldn't connect:\n \"{f904c92a-55dc-499f-87da-4e9e0d3e7894}\": 'Melissa'\n }\n\n if len(sys.argv) > 1:\n localPortSender = int(sys.argv[1])\n\n print(\"set port to \", localPortSender)\n\n udp = UdpInstance(localIP, localPortSender, \"firstSender\")\n\n app = Application(sys.argv)\n\n udp.close()\n", "sub_path": "host/ble.py", "file_name": "ble.py", "file_ext": "py", "file_size_in_byte": 8844, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "time.time", "line_number": 32, "usage_type": "call"}, {"api_name": "qenum.qenum_key", "line_number": 37, "usage_type": "call"}, {"api_name": "PyQt5.QtBluetooth.QLowEnergyService", "line_number": 37, "usage_type": "attribute"}, {"api_name": "PyQt5.QtBluetooth", "line_number": 37, "usage_type": "name"}, {"api_name": "PyQt5.QtBluetooth.QLowEnergyService", "line_number": 38, "usage_type": "attribute"}, {"api_name": "PyQt5.QtBluetooth", "line_number": 38, "usage_type": "name"}, {"api_name": "qenum.qenum_key", "line_number": 42, "usage_type": "call"}, {"api_name": "PyQt5.QtBluetooth.QLowEnergyCharacteristic", "line_number": 42, "usage_type": "attribute"}, {"api_name": "PyQt5.QtBluetooth", "line_number": 42, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QByteArray.fromHex", "line_number": 53, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QByteArray", "line_number": 53, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 53, "usage_type": "name"}, {"api_name": "qenum.qenum_key", "line_number": 68, "usage_type": "call"}, {"api_name": "PyQt5.QtBluetooth.QLowEnergyService", "line_number": 68, "usage_type": "attribute"}, {"api_name": "PyQt5.QtBluetooth", "line_number": 68, "usage_type": "name"}, {"api_name": "struct.unpack", "line_number": 78, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 96, "usage_type": "attribute"}, {"api_name": "PyQt5.QtBluetooth.QLowEnergyController.createCentral", "line_number": 105, "usage_type": "call"}, {"api_name": "PyQt5.QtBluetooth.QLowEnergyController", "line_number": 105, "usage_type": "attribute"}, {"api_name": "PyQt5.QtBluetooth", "line_number": 105, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QTimer.singleShot", "line_number": 129, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QTimer", "line_number": 129, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 129, "usage_type": "name"}, {"api_name": "qenum.qenum_key", "line_number": 148, "usage_type": "call"}, {"api_name": "PyQt5.QtBluetooth.QLowEnergyController", "line_number": 148, "usage_type": "attribute"}, {"api_name": "PyQt5.QtBluetooth", "line_number": 148, "usage_type": "name"}, {"api_name": "qenum.qenum_key", "line_number": 149, "usage_type": "call"}, {"api_name": "PyQt5.QtBluetooth.QLowEnergyController", "line_number": 149, "usage_type": "attribute"}, {"api_name": "PyQt5.QtBluetooth", "line_number": 149, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QTimer.singleShot", "line_number": 151, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QTimer", "line_number": 151, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 151, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QCoreApplication", "line_number": 154, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 154, "usage_type": "name"}, {"api_name": "qenum.qenum_key", "line_number": 178, "usage_type": "call"}, {"api_name": "PyQt5.QtBluetooth.QBluetoothDeviceDiscoveryAgent", "line_number": 178, "usage_type": "attribute"}, {"api_name": "PyQt5.QtBluetooth", "line_number": 178, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 182, "usage_type": "attribute"}, {"api_name": "PyQt5.QtBluetooth.QBluetoothDeviceDiscoveryAgent", "line_number": 198, "usage_type": "call"}, {"api_name": "PyQt5.QtBluetooth", "line_number": 198, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QTimer", "line_number": 204, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 204, "usage_type": "name"}, {"api_name": "sys.platform", "line_number": 220, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 223, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 243, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 244, "usage_type": "attribute"}, {"api_name": "textile_udpclient.UdpInstance", "line_number": 248, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 250, "usage_type": "attribute"}]} +{"seq_id": "243952605", "text": "from losses import completion_network_loss, noise_loss\nfrom utils import *\nfrom classify import *\nfrom generator import *\nfrom discri import *\nfrom torch.utils.data import DataLoader\nfrom torch.optim import Adadelta, Adam\nfrom torch.nn import BCELoss, DataParallel\nfrom torchvision.utils import save_image\nfrom torch.autograd import grad\nimport torchvision.transforms as transforms\nimport torch\nimport time\nimport random\nimport os, logging\nimport numpy as np\nfrom attack import inversion, inversion_grad_constraint\nfrom generator import Generator\nfrom sklearn.model_selection import GridSearchCV\n\n\n#logger\ndef get_logger():\n logger_name = \"main-logger\"\n logger = logging.getLogger(logger_name)\n logger.setLevel(logging.INFO)\n handler = logging.StreamHandler()\n fmt = \"[%(asctime)s %(levelname)s %(filename)s line %(lineno)d %(process)d] %(message)s\"\n handler.setFormatter(logging.Formatter(fmt))\n logger.addHandler(handler)\n return logger\n\ndef get_acc(G, D, T, E, lamda, lamda2):\n total_acc, total_acc5 = 0, 0\n # no auxilary\n for i in range(3):\n iden = torch.from_numpy(np.arange(60))\n\n for idx in range(5):\n print(\"--------------------- Attack batch [%s]------------------------------\" % idx)\n acc, acc5 = inversion_grad_constraint(G, D, T, E, iden, lr=2e-2, momentum=0.9, lamda=lamda, lamda2=lamda2, iter_times=1500, clip_range=1, improved=False)\n iden = iden + 60\n total_acc += acc\n total_acc5 += acc5\n\n aver_acc = total_acc / 15\n aver_acc5 = total_acc5 / 15\n print(\"Average Acc:{:.2f}\\tAverage Acc5:{:.2f}\".format(aver_acc, aver_acc5))\n \n return aver_acc, aver_acc5\n\n\n\nif __name__ == \"__main__\":\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0, 1, 2, 3'\n # os.environ[\"CUDA_VISIBLE_DEVICES\"] = '4, 5, 6, 7'\n\n global args, logger\n logger = get_logger()\n model_name_T = \"VGG16\"\n model_name_E = \"FaceNet\"\n dataset_name = \"celeba\"\n improved_flag = False\n\n file = \"./config/attack\" + \".json\"\n args = load_json(json_file=file)\n logger.info(args)\n print(\"Using improved GAN:\", improved_flag)\n \n z_dim = 100\n\n path_G = '/home/sichen/models/GAN/celeba_G.tar'\n path_D = '/home/sichen/models/GAN/celeba_D.tar'\n path_T = '/home/sichen/models/target_model/target_ckp/VGG16_88.26.tar'\n path_E = '/home/sichen/models/target_model/target_ckp/FaceNet_95.88.tar'\n\n ###########################################\n ########### load model ##########\n ###########################################\n # no mask\n G = Generator(z_dim)\n G = torch.nn.DataParallel(G).cuda()\n if improved_flag == True:\n # D = Discriminator(3, 64, 1000)\n D = MinibatchDiscriminator()\n else:\n D = DGWGAN(3)\n \n D = torch.nn.DataParallel(D).cuda()\n ckp_G = torch.load(path_G)\n G.load_state_dict(ckp_G['state_dict'], strict=False)\n ckp_D = torch.load(path_D)\n D.load_state_dict(ckp_D['state_dict'], strict=False)\n\n if model_name_T.startswith(\"VGG16\"):\n T = VGG16(1000)\n elif model_name_T.startswith('IR152'):\n T = IR152(1000)\n elif model_name_T == \"FaceNet64\":\n T = FaceNet64(1000)\n\n \n T = torch.nn.DataParallel(T).cuda()\n ckp_T = torch.load(path_T)\n T.load_state_dict(ckp_T['state_dict'], strict=False)\n\n E = FaceNet(1000)\n E = torch.nn.DataParallel(E).cuda()\n ckp_E = torch.load(path_E)\n E.load_state_dict(ckp_E['state_dict'], strict=False)\n\n \n ################ param search #############\n\n dict_acc = {}\n dict_acc5 = {}\n best_acc, best_acc5 = 0, 0\n \n\n # lamda_list = [10, 50, 100, 150, 200, 500] # iden loss\n # lamda1_list = [0.1, 1] # prior loss\n # lamda2_list = [1, 10, 50, 100, 500] # grad loss\n\n lamda_list = [100] # iden loss\n lamda2_list = [8, 125, 200] # grad loss\n\n \n for lamda in lamda_list:\n for lamda2 in lamda2_list:\n\n aver_acc, aver_acc5 = get_acc(G, D, T, E, lamda, lamda2)\n \n params = 'lamda1=' + str(1) + ' lamda=' + str(lamda) + ' lamda2=' + str(lamda2)\n print(params)\n \n dict_acc[params] = aver_acc\n dict_acc5[params] = aver_acc5\n\n if aver_acc > best_acc:\n best_acc = aver_acc\n best_params = params\n if aver_acc5 > best_acc5:\n best_acc5 = aver_acc5\n best_params_5 = params\n\n # print(dict_acc)\n\n filename = open('./gc_search_acc.txt','w')#dict转txt\n for k,v in dict_acc.items():\n filename.write(k+':\\t'+str(v))\n filename.write('\\n')\n filename.close()\n\n filename = open('./gc_search_acc5.txt','w')#dict转txt\n for k,v in dict_acc5.items():\n filename.write(k+':\\t'+str(v))\n filename.write('\\n')\n filename.close()\n\n print(\"Best acc: \" + str(best_acc) + \"\\tparams are: \" + best_params)\n print(\"Best acc5: \" + str(best_acc5) + \"\\tparams are: \" + best_params_5)\n\n \n\n\n \n\n ", "sub_path": "GMI-code/Celeba/gc_search.py", "file_name": "gc_search.py", "file_ext": "py", "file_size_in_byte": 4993, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 25, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 26, "usage_type": "attribute"}, {"api_name": "logging.StreamHandler", "line_number": 27, "usage_type": "call"}, {"api_name": "logging.Formatter", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 37, "usage_type": "call"}, {"api_name": "attack.inversion_grad_constraint", "line_number": 41, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 55, "usage_type": "attribute"}, {"api_name": "generator.Generator", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn.DataParallel", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "attribute"}, {"api_name": "torch.nn.DataParallel", "line_number": 89, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 89, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn.DataParallel", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.nn.DataParallel", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 108, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 109, "usage_type": "call"}]} +{"seq_id": "142734000", "text": "import glob\nimport json\nimport os\nimport random\nimport sys\n\npatterns = {\n 'Hypernymy_S*_1_A.txt': 'hp_s_a_train.jsonl',\n 'Hypernymy_S*_2_A.txt': 'hp_s_a_test.jsonl',\n 'Hypernymy_S*_2_B.txt': 'hp_s_b_test.jsonl',\n 'Hypernymy_S*_2_IB.txt': 'hp_s_ib_test.jsonl',\n}\n\n\ndef main(indir, val_ratio=0.15):\n os.makedirs('hypernymy_dataset', exist_ok=True)\n\n for pattern, outfname in patterns.items():\n infiles = list(glob.glob(os.path.join(indir, pattern)))\n samples = [s for f in infiles for s in read_file(f)]\n\n if outfname == 'hp_s_a_train.jsonl':\n random.seed(1337)\n random.shuffle(samples)\n split_pos = int(len(samples) * (1 - val_ratio))\n train_samples = samples[:split_pos]\n valid_samples = samples[split_pos:]\n write_file(train_samples, outfname, infiles)\n write_file(valid_samples, outfname.replace('train', 'valid'), infiles)\n else:\n write_file(samples, outfname, infiles)\n\n\ndef read_file(fname):\n with open(fname) as fp:\n lines = [l.strip() for l in fp if l.strip() and not l.startswith('#')]\n samples = [\n {'sentence1': s1, 'sentence2': s2, 'gold_label': l}\n for s1, s2, l in chunkwise(lines, size=3)\n ]\n\n # validate dataset\n for s in samples:\n if not (\n s['gold_label'] in ('contradiction', 'entailment', 'neutral')\n and len(s['sentence1']) > 3\n and len(s['sentence2']) > 3\n ):\n raise Exception(f'invalid sample - {s}')\n return samples\n\n\ndef chunkwise(t, size):\n \"\"\"variable length chunks from iterable\"\"\"\n it = iter(t)\n return zip(*[it]*size)\n\n\ndef write_file(lines, fname, infiles=(), outdir='hypernymy_dataset'):\n print(f'saving {fname} from {infiles} with {len(lines)} samples')\n\n filename = os.path.join(outdir, fname)\n with open(filename, 'w') as fp:\n for l in lines:\n fp.write(json.dumps(l))\n fp.write('\\n')\n\n\nif __name__ == '__main__':\n main(indir=sys.argv[1])\n", "sub_path": "build_hypernymy_dataset.py", "file_name": "build_hypernymy_dataset.py", "file_ext": "py", "file_size_in_byte": 2053, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.makedirs", "line_number": 16, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 19, "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": "random.seed", "line_number": 23, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 24, "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": "json.dumps", "line_number": 65, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 70, "usage_type": "attribute"}]} +{"seq_id": "380634519", "text": "\n\n#==============================================================================\n# import soundfile as sf\n# import os\n# import numpy as np\n# #audio = np.array([-0.23, 0.025,0.025, 0.025, 0.02365])\n# #print(audio.shape)\n# fs = 44100\n# path = \"C:\\\\Users\\\\User\\\\Google Drive\\\\testaudio.npy\"\n# path1 = \"C:\\\\Users\\\\User\\\\Google Drive\\\\testaudio.wav\"\n# # Only needed here to simulate closing & reopening file\n# audio = np.load(path)\n# sf.write(file=path1, data=audio, samplerate=fs, subtype='PCM_24')\n#==============================================================================\n\n\n#==============================================================================\n# from scipy.io.wavfile import read\n# filename = 'C:\\\\Users\\\\User\\\\Google Drive\\\\Summer 2017\\\\Internship\\\\' +\\\n# 'TUT-rare-sound-events-2017-development\\\\data\\\\' +\\\n# 'mixture_data\\\\devtrain\\\\20b255387a2d0cddc0a3dff5014875e7'+\\\n# '\\\\audio\\\\mixture_devtrain_babycry_000_07a75692b15446e9fbf6cc3afaf96097.wav'\n# \n# \n# sr, data = scipy.io.wavfile.read(filename, mmap=False)\n# print(data.shape)\n#==============================================================================\n\n\nimport os\nimport wave\nimport numpy\nimport librosa\n\n\ndef load_audio(filename, mono=True, fs=44100):\n \"\"\"Load audio file into numpy array\n\n Supports 24-bit wav-format, and flac audio through librosa.\n\n Parameters\n ----------\n filename: str\n Path to audio file\n\n mono : bool\n In case of multi-channel audio, channels are averaged into single channel.\n (Default value=True)\n\n fs : int > 0 [scalar]\n Target sample rate, if input audio does not fulfil this, audio is resampled.\n (Default value=44100)\n\n Returns\n -------\n audio_data : numpy.ndarray [shape=(signal_length, channel)]\n Audio\n\n sample_rate : integer\n Sample rate\n\n \"\"\"\n\n file_base, file_extension = os.path.splitext(filename)\n if file_extension == '.wav':\n audio_file = wave.open(filename)\n\n # Audio info\n sample_rate = audio_file.getframerate()\n sample_width = audio_file.getsampwidth()\n number_of_channels = audio_file.getnchannels()\n number_of_frames = audio_file.getnframes()\n #print ('sample_rate: {}'.format(sample_rate))\n #print ('sample_width: {}'.format(sample_width))\n #print ('number_of_channels: {}'.format(number_of_channels))\n #print ('number_of_frames: {}'.format(number_of_frames))\n\n # Read raw bytes\n data = audio_file.readframes(number_of_frames)\n audio_file.close()\n\n # Convert bytes based on sample_width\n num_samples, remainder = divmod(len(data),\n sample_width * number_of_channels)\n if remainder > 0:\n raise ValueError('The length of data is not a multiple of '\n 'sample size * number of channels.')\n if sample_width > 4:\n raise ValueError('Sample size cannot be bigger than 4 bytes.')\n\n if sample_width == 3:\n # 24 bit audio\n a = numpy.empty((num_samples, number_of_channels, 4), dtype=numpy.uint8)\n raw_bytes = numpy.fromstring(data, dtype=numpy.uint8)\n a[:, :, :sample_width] = raw_bytes.reshape(-1, number_of_channels, sample_width)\n a[:, :, sample_width:] = (a[:, :, sample_width - 1:sample_width] >> 7) * 255\n audio_data = a.view('', '', '', '', 'unique']:\n continue # we don't need modules for the placeholders\n \n # figure out which module we want we use\n if module_name == 'scene':\n # scene is just a flag that indicates the start of a new line of reasoning\n # we set `module` to `None` because we still need the 'scene' flag in forward()\n module = None\n elif module_name == 'intersect':\n module =AndModule(ConceptNode(\"And_Module\"))\n elif module_name == 'union':\n module = OrModule(ConceptNode(\"Or_Module\"))\n elif 'equal' in module_name or module_name in {'less_than', 'greater_than'}:\n module = ComparisonModule(ConceptNode(\"Comparison_Module\"),module_dim)\n elif 'query' in module_name or module_name in {'exist', 'count'}:\n module = QueryModule(ConceptNode(\"Query_Module\"),module_dim)\n elif 'relate' in module_name:\n module = RelateModule(ConceptNode(\"Relate_Module\"),module_dim)\n elif 'same' in module_name:\n module = SameModule(ConceptNode(\"Same_Module\"),module_dim)\n else:\n module = AttentionModule(ConceptNode(\"Attention_Module\"),module_dim)\n\n # add the module to our dictionary \n # and register its parameters so it can learn\n function_modules[module_name] = module\n # add_module(module_name, module)\n \n torch.set_grad_enabled(False)\n\n \n\n def form_bindlink(atomspace, features, program, inheritance_set=None):\n\n print (\"in form_bindlink we have\")\n print (\"atomspace = {}, program = {}\".format(atomspace, program))\n \n # create a ConceptNode to hold our features\n bbox_instance = atomspace.add_node(types.ConceptNode, 'BoundingBox_instance')\n \n \n # now fill it with actual features\n set_value(bbox_instance, features)\n \n # create another ConceptNode, just for the BoundingBox concept\n box_concept = atomspace.add_node(types.ConceptNode, 'BoundingBox')\n \n # link BoundingBox and and instance of it. \n # do we need this?\n atomspace.add_link(types.InheritanceLink, [bbox_instance, box_concept])\n\n\n\n current, rest = program[0], program[1:]\n \n if inheritance_set is None:\n inheritance_set = set()\n \n scene = atomspace.add_node(types.VariableNode, \"$Scene\")\n \n\n if current.startswith('query'):\n query_type = current.split('_')[-1]\n features_atom, attention_atom, out, left, inh = form_bindlink(atomspace, features, rest)\n print (\"features_atom.execute() = {}\".format(features_atom.execute()))\n print (\"attention_atom = {}\".format(attention_atom))\n # sys.exit(0)\n\n inheritance_set |= inh\n var = atomspace.add_node(types.VariableNode, \"$X\")\n concept = atomspace.add_node(types.ConceptNode, query_type)\n inh_link = atomspace.add_link(types.InheritanceLink, [var, concept])\n print (\"inh_link = {}\".format(inh_link))\n \n assert(inh_link not in inheritance_set)\n inheritance_set.add(inh_link)\n\n # link = build_filter(atomspace, concept, var, exec_out_sub=sub_prog)\n \n \n varlist = []\n for inh in inheritance_set:\n for atom in inh.get_out():\n if atom.type == types.VariableNode:\n varlist.append(atom)\n\n print (\"varlist = {}\".format(varlist))\n \n \n variable_list = atomspace.add_link(types.VariableList, varlist)\n print (\"variable_list = {}\".format(variable_list))\n \n conj = atomspace.add_link(types.AndLink, [*inheritance_set])\n print (\"conj = {}\".format(conj))\n \n list_link = atomspace.add_link(types.ListLink, [var])\n print (\"list_link = {}\".format(list_link))\n \n \n bind_link = BindLink(variable_list, conj, list_link)\n print (\"bind_link = {}\".format(bind_link))\n result = execute_atom(atomspace, bind_link)\n print (\"result = {}\".format(result))\n \n for atom in result.get_out():\n # print (\"atom.get_out()[0] is {}\".format(atom.get_out()[0]))\n var2 = atom.get_out()[0]\n\n module_type = 'filter_' + query_type + '[' + var2.name + ']'\n module = function_modules[module_type]\n\n link = module.execute(features_atom.execute(), out)\n \n varlist = []\n for inh in inheritance_set:\n for atom in inh.get_out():\n if atom.type == types.VariableNode:\n varlist.append(atom)\n\n # print (\"varlist = {}\".format(varlist))\n # print (\"link = {}\".format(link))\n \n \n variable_list = atomspace.add_link(types.VariableList, varlist)\n # print (\"variable_list = {}\".format(variable_list))\n \n conj = atomspace.add_link(types.AndLink, [*inheritance_set])\n # print (\"conj = {}\".format(conj))\n \n list_link = atomspace.add_link(types.ListLink, varlist + [link])\n # print (\"list_link = {}\".format(list_link))\n \n \n bind_link2 = BindLink(variable_list, conj, list_link)\n print (\"bind_link2 = {}\".format(bind_link2))\n result2 = execute_atom(atomspace, bind_link2)\n print (\"result2 = {}\".format(result2))\n \n\n # return link, left, inheritance_set\n # return bind_link\n return result2\n \n\n elif current.startswith('scene'):\n \n \n # let's use two distinct atoms\n features_atom = InputModule(ConceptNode(\"Data-{}\".format(str(uuid.uuid4()))), features)\n attention_atom = InputModule(ConceptNode(\"Attention-{}\".format(str(uuid.uuid4()))), ones_var)\n\n out = None # we need this, we'll later hold temp results in there\n\n return features_atom, attention_atom, out, rest, inheritance_set\n \n\n elif current.startswith('filter'):\n print (\"in filter branch, we have current {}\".format(current))\n \n \n \n filter_type, filter_arg = filter_reg.match(current).groups()\n features_atom, attention_atom, out, left, inh = form_bindlink(atomspace, features, rest)\n \n filter_type_atom = atomspace.add_node(types.ConceptNode, filter_type)\n filter_arg_atom = atomspace.add_node(types.ConceptNode, filter_arg)\n \n \n inheritance_set |= inh\n \n \n print (\"we have filter_type {} and filter_arg {}\".format(filter_type_atom.name, filter_arg_atom.name))\n inh_filter = InheritanceLink(filter_arg_atom, filter_type_atom)\n print (\"InheritanceLink was created:{}\".format(inh_filter))\n # TODO: now that we have an inheritance link, \n # how do we use it?\n \n \n module_type = 'filter_' + filter_type_atom.name + '[' + filter_arg_atom.name + ']'\n module = function_modules[module_type]\n if isinstance(attention_atom, CogModule):\n print (\"attention_atom was a CogModule\")\n out = module.execute(features_atom.execute(), attention_atom.execute())\n else:\n # out = module.execute(features_atom.execute(), attention_atom)\n out = module.execute(features_atom.execute(), out)\n \n print (\"we now have attention_atom = {}\".format(attention_atom))\n \n return features_atom, attention_atom, out, left, inheritance_set \n\n elif current.startswith('relate'):\n relate_arg = relate_reg.match(current).groups()[0]\n sub_prog, left, inh = form_bindlink(atomspace, rest)\n inheritance_set |= inh\n return build_relate(atomspace, relate_argument=relate_arg,\n exec_out_sub=sub_prog), left, inheritance_set\n \n elif current.startswith('same'):\n same_arg = current.split('_')[-1]\n sub_prog, left, inh = form_bindlink(atomspace, rest)\n inheritance_set |= inh\n return build_same(atomspace, same_argument=same_arg,\n exec_out_sub=sub_prog), left, inheritance_set\n \n elif current.startswith('intersect'):\n sub_prog0, left, inh = form_bindlink(atomspace, features, rest)\n inheritance_set |= inh\n sub_prog1, right, inh = form_bindlink(atomspace, features, left)\n inheritance_set |= inh\n return build_intersect(atomspace, arg0=sub_prog0, arg1=sub_prog1), right, inheritance_set\n \n elif current == '':\n return form_bindlink(atomspace, features, rest)\n \n elif current == 'unique':\n return form_bindlink(atomspace, features, rest)\n \n else:\n raise NotImplementedError(current)\n\n \n\n BATCH_SIZE = 64\n h5_path = Path('/media/enoch/0645F864324D53D4/neural_stuff/CLEVR_v1/data/')\n h5_files = [x for x in h5_path.iterdir() if x.is_file()]\n \n val_loader_kwargs = {\n 'question_h5':h5_files[1], \n 'feature_h5': h5_files[0], \n 'batch_size': BATCH_SIZE,\n 'num_workers': 1,\n 'shuffle': False\n }\n\n\n loader = ClevrDataLoaderH5(**val_loader_kwargs)\n \n for i, batch in enumerate(tqdm(loader)):\n print (\"working with batch #{}\".format(i))\n\n _, _, feats, expected_answers, programs = batch\n feats = feats.to(device)\n\n feats_module = InputModule(ConceptNode(\"batch_features\"), feats)\n\n programs = programs.to(device)\n\n features = feats_module\n\n batch_size = features().size(0)\n\n \n for n in range(batch_size):\n \n output = stem(features())[n:n + 1]\n\n program_list = [vocab['program_idx_to_token'][i] \\\n for i in reversed(programs.data[n].cpu().numpy()) \\\n if vocab['program_idx_to_token'][i] != ''] \n \n rev_prog = tuple(reversed(program_list))\n\n #eval_link, left, inheritance_set = form_bindlink(atomspace, output, rev_prog)\n # bind_link = form_bindlink(atomspace, output, rev_prog)\n results = form_bindlink(atomspace, output, rev_prog)\n print (\"results = {}\".format(results))\n sys.exit(0)\n \n\nif __name__ == '__main__':\n main()\n", "sub_path": "experiments/opencog/pattern_matcher_vqa/bindlink_former.py", "file_name": "bindlink_former.py", "file_ext": "py", "file_size_in_byte": 27118, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "module.CogModule", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 60, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "module.CogModule", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.min", "line_number": 97, "usage_type": "call"}, {"api_name": "module.CogModule", "line_number": 101, "usage_type": "name"}, {"api_name": "torch.max", "line_number": 118, "usage_type": "call"}, {"api_name": "module.CogModule", "line_number": 123, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 150, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 151, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 152, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 153, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 153, "usage_type": "attribute"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 154, "usage_type": "attribute"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 155, "usage_type": "attribute"}, {"api_name": "torch.mul", "line_number": 175, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 178, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 187, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 187, "usage_type": "name"}, {"api_name": "torch.sigmoid", "line_number": 188, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 192, "usage_type": "call"}, {"api_name": "module.CogModule", "line_number": 198, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 220, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 220, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 221, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 221, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 222, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 222, "usage_type": "attribute"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 223, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 223, "usage_type": "attribute"}, {"api_name": "torch.mul", "line_number": 228, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 229, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 229, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 230, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 230, "usage_type": "name"}, {"api_name": "module.CogModule", "line_number": 234, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 253, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 253, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 254, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 254, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 255, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 255, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 256, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 256, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 257, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 257, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 258, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 258, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 259, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 259, "usage_type": "attribute"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 260, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 260, "usage_type": "attribute"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 261, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 261, "usage_type": "attribute"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 262, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 262, "usage_type": "attribute"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 263, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 263, "usage_type": "attribute"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 264, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 264, "usage_type": "attribute"}, {"api_name": "torch.mul", "line_number": 271, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 272, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 272, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 273, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 273, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 274, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 274, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 275, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 275, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 276, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 276, "usage_type": "name"}, {"api_name": "torch.sigmoid", "line_number": 277, "usage_type": "call"}, {"api_name": "module.CogModule", "line_number": 281, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 305, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 305, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 306, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 306, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.max_pool2d", "line_number": 314, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 314, "usage_type": "name"}, {"api_name": "torch.mul", "line_number": 317, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 318, "usage_type": "call"}, {"api_name": "torch.sigmoid", "line_number": 319, "usage_type": "call"}, {"api_name": "module.CogModule", "line_number": 323, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 345, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 345, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 346, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 346, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 347, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 347, "usage_type": "name"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 348, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 348, "usage_type": "attribute"}, {"api_name": "torch.nn.init.kaiming_normal_", "line_number": 349, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 349, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 353, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 354, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 354, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 355, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 355, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 356, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 356, "usage_type": "name"}, {"api_name": "opencog.scheme_wrapper.scheme_eval_as", "line_number": 362, "usage_type": "call"}, {"api_name": "opencog.scheme_wrapper.scheme_eval", "line_number": 363, "usage_type": "call"}, {"api_name": "opencog.scheme_wrapper.scheme_eval", "line_number": 364, "usage_type": "call"}, {"api_name": "opencog.scheme_wrapper.scheme_eval", "line_number": 365, "usage_type": "call"}, {"api_name": "opencog.scheme_wrapper.scheme_eval", "line_number": 366, "usage_type": "call"}, {"api_name": "opencog.scheme_wrapper.scheme_eval", "line_number": 367, "usage_type": "call"}, {"api_name": "opencog.scheme_wrapper.scheme_eval", "line_number": 369, "usage_type": "call"}, {"api_name": "opencog.scheme_wrapper.scheme_eval", "line_number": 371, "usage_type": "call"}, {"api_name": "opencog.scheme_wrapper.scheme_eval", "line_number": 373, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 383, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 383, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 383, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 388, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 389, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 397, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 401, "usage_type": "call"}, {"api_name": "tbd.utils.clevr.load_vocab", "line_number": 405, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 405, "usage_type": "call"}, {"api_name": "opencog.utilities.initialize_opencog", "line_number": 410, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 415, "usage_type": "call"}, {"api_name": "torch.set_grad_enabled", "line_number": 462, "usage_type": "call"}, {"api_name": "module.set_value", "line_number": 476, "usage_type": "call"}, {"api_name": "opencog.bindlink.execute_atom", "line_number": 535, "usage_type": "call"}, {"api_name": "module.execute", "line_number": 545, "usage_type": "call"}, {"api_name": "opencog.bindlink.execute_atom", "line_number": 569, "usage_type": "call"}, {"api_name": "module.InputModule", "line_number": 582, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 582, "usage_type": "call"}, {"api_name": "module.InputModule", "line_number": 583, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 583, "usage_type": "call"}, {"api_name": "module.CogModule", "line_number": 614, "usage_type": "argument"}, {"api_name": "module.execute", "line_number": 616, "usage_type": "call"}, {"api_name": "module.execute", "line_number": 619, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 658, "usage_type": "call"}, {"api_name": "tbd.utils.clevr.ClevrDataLoaderH5", "line_number": 670, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 672, "usage_type": "call"}, {"api_name": "module.InputModule", "line_number": 678, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 701, "usage_type": "call"}]} +{"seq_id": "433517038", "text": "from django.urls import path\n\nfrom api.users.views import *\n\napp_name = 'users'\nurlpatterns = [\n # 사용자 회원가입/로그인 URL\n path('signup', SignUpView.as_view(), name='signup'),\n path('login', LoginView.as_view(), name='login'),\n\n # 사용자 프로필 조회 URL\n path('profile/main', UserProfileView.as_view(), name='mainprofile'),\n path('profile/trainee/sub', TraineeSubProfileView.as_view(), name='trainee_subprofile'),\n path('profile/coach/sub', CoachSubProfileView.as_view(), name='coach_subprofile'),\n\n # 사용자 프로필 업데이트 URL\n path('profile/edit', ProfileUpdateView.as_view(), name='edit_profile'),\n\n # 서비스 코치 list URL\n path('coach/all', CoachListView.as_view(), name='coach_all'),\n]\n", "sub_path": "api/users/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 762, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "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": 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": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "338136271", "text": "try:\n import random\n import math\n import pyttsx3\n #changing voice to girl\n engine = pyttsx3.init()\n voices = engine.getProperty('voices') #getting details of current voice\n #engine.setProperty('voice', voices[0].id) #changing index, changes voices. o for male\n engine.setProperty('voice', voices[1].id)\n # Computer options\n options = [\"rock\", \"paper\", \"scissors\"]\n # User input\n guess = input(\"rock, paper or scissors?\\n\")\n while not guess in options:\n print(\"Please choose rock, paper or scissors\")\n guess = input()\n\n computer = options[math.floor(random.random()*len(options))]\n\n # Rock, paper, scissors! (countdown)\n print(\"Rock, \")\n print(\"Paper, \")\n print(\"Scissors\")\n pyttsx3.speak(\"Rock, Paper, Scissor\")\n print(\"Shoot!\\n\")\n pyttsx3.speak(\"Shoot\")\n # Rock\n if guess == \"rock\":\n if computer == \"rock\":\n print(\"Draw!\")\n pyttsx3.speak(\"Draw\")\n\n elif computer == \"paper\":\n print(\"Rock looses to paper, you loose!\")\n pyttsx3.speak(\"Rock looses to paper, you loose!\")\n\n else:\n print(\"Rock beats scissors, you win!\")\n pyttsx3.speak(\"Rock beats scissors, you win!\")\n # Paper\n elif guess == \"paper\":\n if computer == \"rock\":\n print(\"Paper beats rock, you win!\")\n pyttsx3.speak(\"Paper beats rock, you win!\")\n \n elif computer == \"paper\":\n print(\"Draw!\")\n pyttsx3.speak(\"Draw!\")\n\n else:\n print(\"Paper loses to scissors, you loose!\")\n pyttsx3.speak(\"Paper loses to scissors, you loose!\")\n\n # Scissors\n else:\n if computer == \"rock\":\n print(\"Scissors looses to rock, you loose!\")\n pyttsx3.speak(\"Scissors looses to rock, you loose!\")\n\n elif computer == \"paper\":\n print(\"Scissors beats paper, you win!\")\n pyttsx3.speak(\"Scissors beats paper, you win!\")\n\n else:\n print(\"Draw!\")\n pyttsx3.speak(\"Draw\")\n\nexcept:\n print(\"Enter the correct spelling\")\n pyttsx3.speak(\"Enter the correct spelling\")\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2157, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pyttsx3.init", "line_number": 6, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 18, "usage_type": "call"}, {"api_name": "random.random", "line_number": 18, "usage_type": "call"}, {"api_name": "pyttsx3.speak", "line_number": 24, "usage_type": "call"}, {"api_name": "pyttsx3.speak", "line_number": 26, "usage_type": "call"}, {"api_name": "pyttsx3.speak", "line_number": 31, "usage_type": "call"}, {"api_name": "pyttsx3.speak", "line_number": 35, "usage_type": "call"}, {"api_name": "pyttsx3.speak", "line_number": 39, "usage_type": "call"}, {"api_name": "pyttsx3.speak", "line_number": 44, "usage_type": "call"}, {"api_name": "pyttsx3.speak", "line_number": 48, "usage_type": "call"}, {"api_name": "pyttsx3.speak", "line_number": 52, "usage_type": "call"}, {"api_name": "pyttsx3.speak", "line_number": 58, "usage_type": "call"}, {"api_name": "pyttsx3.speak", "line_number": 62, "usage_type": "call"}, {"api_name": "pyttsx3.speak", "line_number": 66, "usage_type": "call"}, {"api_name": "pyttsx3.speak", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "571065970", "text": "from typing import Callable, Type, Tuple, Union\n\nfrom main.model.character.class_ import Class\nfrom main.model.character.race import Race\nfrom main.model.int_types.natural import Natural\n\n_races = (\n\n)\n\n_classes = (\n\n)\n\n\nclass CharacterBuilder:\n \"\"\"\n Manages the character creation process\n \"\"\"\n\n def __init__(self):\n \"\"\"\n Initializes the class\n \"\"\"\n\n # TODO: refactor prompt generators into an inner method\n\n race_setter_prompt = \"\"\n\n for i in range(0, len(_races)):\n race_setter_prompt += str(i + 1) + \" - \" + _races[i].get_name() + \"\\n\"\n\n race_setter_prompt += \"Enter the character's race index:\"\n\n class_setter_prompt = \"\"\n\n for i in range(0, len(_classes)):\n class_setter_prompt += str(i + 1) + \" - \" + _classes[i].get_name() + \"\\n\"\n\n class_setter_prompt += \"Enter the character's class index:\"\n\n self._name: str\n self._age: Natural\n self._race: Race\n self._class: Class\n\n # TODO: make sure documentation is correct\n\n def parameter_setter(\n exception_types: Tuple[Type[Exception], ...],\n error_feedback: str,\n next_prompt: str,\n next_parameter_setter: Callable[[], None]\n ):\n \"\"\"\n Convert a function to be a parameter_setter\n\n :param exception_types: The exceptions that, if raised by the function, cause the next feedback to be set to\n the error_feedback and prevents both the next prompt and next operation from being set to the given values\n :param error_feedback: The message to set next feedback to should one of the exception_types be caught\n :param next_prompt: The message to set the next prompt to should no exception be caught\n :param next_parameter_setter: The function to call should no exception be caught\n :return: A function that will return a function that will call the converted function\n \"\"\"\n\n def get_caller(fn: Callable[[str], None]):\n \"\"\"\n Gets a function that will call the given fn\n\n :param fn: The function to be called by the returned function\n :return: The quit_on_exception inner function\n \"\"\"\n\n def quit_on_exception(fn_value: str):\n \"\"\"\n Tries to call the given fn with the given fn_value as its only parameter; if it succeeds, sets the\n next feedback to a confirmation response, the next prompt to the given next_prompt, and the next\n operation to given next_operation; if one of the given exception_types are caught, sets the next\n feedback to error_feedback\n\n :param fn_value: The value to pass to the given fn\n \"\"\"\n\n try:\n fn(fn_value)\n\n self._next_feedback = \"The response: \" + fn_value + \" was accepted.\"\n self._next_prompt = next_prompt\n self._next_parameter_setter = next_parameter_setter\n except exception_types:\n self._next_feedback = error_feedback\n\n return quit_on_exception\n\n return get_caller\n\n @parameter_setter(\n (ValueError, IndexError),\n \"The entered class index was not within acceptable bounds.\",\n None,\n None\n )\n def set_class(value: str):\n \"\"\"\n Sets the character's class to a class in the class list that is at the given value - 1; raises ValueError if\n value is not an integer; raises IndexError if value is less than 0 or greater than or equal to the number of\n races\n\n :param value: The index + 1 of the class in the class list that the character's class will be set to\n \"\"\"\n self._class = _classes[int(value) - 1]\n\n @parameter_setter(\n (ValueError, IndexError),\n \"The entered race index was not within acceptable bounds.\",\n class_setter_prompt,\n set_class\n )\n def set_race(value: str):\n \"\"\"\n Sets the character's race to a race in the race list that is at the given value - 1; raises ValueError if\n value is not an integer; raises IndexError if value is less than 0 or greater than or equal to the number of\n races\n\n :param value: The index + 1 of the race in the race list that the character's race will be set to\n \"\"\"\n\n self._race = _races[int(value) - 1]\n\n @parameter_setter(\n (ValueError,),\n \"The entered age was not within acceptable bounds.\",\n race_setter_prompt,\n set_race\n )\n def set_age(value: str):\n \"\"\"\n Set the age to the given value; raises ValueError if value is not an integer or is less than 0\n\n :param value: The value to set the age to\n \"\"\"\n\n self._age = Natural(int(value))\n\n @parameter_setter((), \"\", \"Enter the character's age (can be a positive number):\", set_age)\n def set_name(value: str):\n \"\"\"\n Set the name to the given value\n\n :param value: The value to set the name to\n \"\"\"\n\n self._name = value\n\n self._next_prompt: str = \"Enter the character's name:\"\n self._next_parameter_setter: Callable[[str], None] = set_name\n self._next_feedback: str = \"\"\n\n @property\n def next_prompt(self) -> str:\n return self._next_prompt\n\n @property\n def next_parameter_setter(self) -> Callable[[str], None]:\n return self._next_parameter_setter\n\n @property\n def next_feedback(self) -> str:\n return self._next_feedback\n\n @property\n def races(self) -> Tuple[Type[Race], ...]:\n return _races\n\n @property\n def classes(self) -> Tuple[Type[Class], ...]:\n return _classes\n", "sub_path": "main/model/character_builder.py", "file_name": "character_builder.py", "file_ext": "py", "file_size_in_byte": 6116, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "main.model.int_types.natural.Natural", "line_number": 43, "usage_type": "name"}, {"api_name": "main.model.character.race.Race", "line_number": 44, "usage_type": "name"}, {"api_name": "main.model.character.class_.Class", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 50, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 50, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 53, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 66, "usage_type": "name"}, {"api_name": "main.model.int_types.natural.Natural", "line_number": 143, "usage_type": "call"}, {"api_name": "typing.Callable", "line_number": 156, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 164, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 172, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 172, "usage_type": "name"}, {"api_name": "main.model.character.race.Race", "line_number": 172, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 176, "usage_type": "name"}, {"api_name": "typing.Type", "line_number": 176, "usage_type": "name"}, {"api_name": "main.model.character.class_.Class", "line_number": 176, "usage_type": "name"}]} +{"seq_id": "374134059", "text": "# sysrsync.py\n\nimport os\nimport sys\nimport pipes\nimport subprocess\nimport collections\n\nfrom functools import reduce\nfrom operator import iconcat\n\nfrom typing import Any, Iterable, List, Tuple, Optional\n\nimport pprint as pprint_lib \npprint = pprint_lib.PrettyPrinter(indent=1).pprint \n\n__version__ = '0.2.0-alpha'\n\n\n\ndef run(cwd=None, strict=True, verbose=False, timeout=60*3, **kwargs):\n rsync_command = get_rsync_command(**kwargs)\n rsync_string = ' '.join(rsync_command)\n\n if cwd is None:\n cwd = os.getcwd()\n\n if verbose is True:\n print('[sysrsync runner] running command (timeout={} secs):'.format())\n print(rsync_string)\n\n subprocess_result = run_command.run(rsync_command, cwd=cwd, timeout=timeout)\n\n if strict is True:\n code = subprocess_result[\"returncode\"]\n _check_return_code(code, rsync_string, subprocess_result)\n\n return subprocess_result\n\n\ndef _check_return_code(return_code: int, action: str, subprocess_result: dict):\n if return_code != 0:\n pprint(subprocess_result)\n msg = \"[sysrsync runner] {action} exited with code {return_code}\".format(action=action, return_code=return_code)\n raise RsyncError(msg)\n\n\n\n#####################################################################\n## command_maker\n\ndef get_rsync_command(source: str,\n destination: str,\n source_ssh: Optional[str] = None,\n destination_ssh: Optional[str] = None,\n exclusions: Iterable[str] = None,\n sync_source_contents: bool = True,\n options: Iterable[str] = None) -> List[str]:\n if (source_ssh is not None and destination_ssh is not None):\n raise RemotesError()\n\n if exclusions is None:\n exclusions = []\n if options is None:\n options = []\n\n source = get_directory_with_ssh(source, source_ssh)\n destination = get_directory_with_ssh(destination, destination_ssh)\n\n if is_path_to_file(source, (source_ssh is not None)):\n sync_source_contents = False\n\n source, destination = sanitize_trailing_slash(\n source, destination, sync_source_contents)\n\n exclusions = get_exclusions(exclusions)\n\n return ['rsync',\n *options,\n source,\n destination,\n *exclusions]\n\n\ndef get_exclusions(exclusions: Iterable[str]) -> Iterable[str]:\n return flatten((('--exclude', exclusion) for exclusion in exclusions if exclusion != '--exclude'))\n\n\n#####################################################################\n## exceptions\n\nclass RemotesError(Exception):\n def __init__(self):\n message = 'source and destination cannot both be remote'\n super().__init__(message)\n\n\nclass RsyncError(Exception):\n pass\n\n\n#####################################################################\n## helpers: directory\n\n\ndef get_directory_with_ssh(directory: str, ssh: Optional[str]) -> str:\n if ssh is None:\n return directory\n\n return '{ssh}:{directory}'.format(ssh=ssh, directory=directory)\n\n\ndef sanitize_trailing_slash(source_dir: str, target_dir: str, sync_sourcedir_contents: bool = True) -> Tuple[str, str]:\n target_dir = strip_trailing_slash(target_dir)\n\n if sync_sourcedir_contents is True:\n source_dir = add_trailing_slash(source_dir)\n else:\n source_dir = strip_trailing_slash(source_dir)\n\n return source_dir, target_dir\n\n\ndef strip_trailing_slash(directory: str) -> str:\n return (directory[:-1]\n if directory.endswith('/')\n else directory)\n\n\ndef add_trailing_slash(directory: str) -> str:\n return (directory\n if directory.endswith('/')\n else '{directory}/'.format(directory=directory))\n\n\n#####################################################################\n## helpers: files\n\n\ndef is_path_to_file(path, is_remote) -> bool:\n if is_remote is True:\n return exists_remote(path)\n\n return os.path.isfile(path)\n\n\ndef exists_remote(host_with_path):\n \"Test if a file exists at path on a host accessible with SSH.\"\n host, path = host_with_path.split(':', 1)\n return subprocess.call(['ssh', host, 'test -f {}'.format(pipes.quote(path))]) == 0\n\n\n#####################################################################\n## helpers: iterators\n\n\ndef flatten(input_iter: Iterable[Any]) -> List[Any]:\n list_of_lists = (element if isinstance(element, collections.Iterable)\n else [element]\n for element in input_iter)\n\n return reduce(iconcat, list_of_lists, [])\n\n\n#####################################################################\n## subprocess.run() wrapper with nice return and timeout option\n\ndef run_command(command_list, timeout=3, cwd=None):\n '''\n wrapper on subprocess.run()\n returns results in dict along with many diagnostics for failure cases\n '''\n assert isinstance(command_list, list)\n\n # explicit conversion of num to str\n command_list = [str(elem) for elem in command_list]\n\n try:\n completed_process = subprocess.run(\n command_list, \n check=True, # exception on non-zero error code\n timeout=timeout, # exception on timeout secs\n # capture both stderr and stdout\n # decode() byte strings to normal utf strings, not necessary\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n universal_newlines=True,\n cwd=cwd\n )\n except subprocess.CalledProcessError as error:\n result = {\n \"result\" : \"CalledProcessError\",\n \"cmd\" : error.cmd,\n \"returncode\" : error.returncode,\n \"stdout\" : error.stdout,\n \"stderr\" : error.stderr\n }\n except subprocess.TimeoutExpired as error:\n result = {\n \"result\" : \"TimeoutExpired\",\n \"cmd\" : error.cmd,\n \"timeout\" : error.timeout,\n \"stdout\" : error.stdout,\n \"stderr\" : error.stderr,\n \"returncode\" : 1,\n }\n except:\n err_type, err_value, traceback = sys.exc_info()\n result = {\n \"result\" : \"UnexpectedError\",\n \"type\" : err_type,\n \"value\" : err_value,\n \"traceback\" : traceback,\n \"returncode\" : 1,\n }\n # HACK: for testing, uncomment this to get crash here\n # raise \n else:\n # all okay\n result = {\n \"result\" : \"OK\",\n \"cmd\" : completed_process.args,\n \"stdout\" : completed_process.stdout,\n \"stderr\" : completed_process.stderr,\n \"returncode\" : completed_process.returncode\n }\n \n # these function inputs\n # note, \n # cmd is a return item from subprocess.run()\n # cmd_list is what we pass to subprocess.run()\n # cmd_line should be cut and pasteable but \n # not always, watch quoting args with spaces\n command_line = ' '.join([str(elem) for elem in command_list])\n result[\"inputs\"] = {\n \"cmd_list\" : command_list,\n \"cmd_line\" : command_line,\n \"cwd\" : cwd,\n \"timeout\" : timeout,\n }\n\n return result", "sub_path": "sysrsync.py", "file_name": "sysrsync.py", "file_ext": "py", "file_size_in_byte": 7323, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pprint.PrettyPrinter", "line_number": 15, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 26, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 54, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 55, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 58, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 58, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 85, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 106, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 113, "usage_type": "name"}, {"api_name": "os.path.isfile", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "attribute"}, {"api_name": "subprocess.call", "line_number": 150, "usage_type": "call"}, {"api_name": "pipes.quote", "line_number": 150, "usage_type": "call"}, {"api_name": "typing.Iterable", "line_number": 157, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 157, "usage_type": "name"}, {"api_name": "collections.Iterable", "line_number": 158, "usage_type": "attribute"}, {"api_name": "functools.reduce", "line_number": 162, "usage_type": "call"}, {"api_name": "operator.iconcat", "line_number": 162, "usage_type": "argument"}, {"api_name": "typing.List", "line_number": 157, "usage_type": "name"}, {"api_name": "subprocess.run", "line_number": 179, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 185, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 186, "usage_type": "attribute"}, {"api_name": "subprocess.CalledProcessError", "line_number": 190, "usage_type": "attribute"}, {"api_name": "subprocess.TimeoutExpired", "line_number": 198, "usage_type": "attribute"}, {"api_name": "sys.exc_info", "line_number": 208, "usage_type": "call"}]} +{"seq_id": "8462282", "text": "from collections import defaultdict\r\nimport string\r\nclass Solution:\r\n def findLadders(self, beginWord: str, endWord: str, wordList: List[str]) -> List[List[str]]:\r\n # (0) edge case\r\n if endWord not in wordList: \r\n return []\r\n \r\n # (1) initialize data structure\r\n dic = set(wordList)\r\n level = {beginWord}\r\n parents = defaultdict(set)\r\n \r\n # (2) bfs\r\n while level and endWord not in parents:\r\n next_level = defaultdict(set)\r\n for node in level:\r\n for char in string.ascii_lowercase: # for char in 'abcdefghijklmnopqrstuvwxyz':\r\n for i in range(len(beginWord)):\r\n temp = node[:i] + char + node[i+1:]\r\n if temp in dic and temp not in parents:\r\n next_level[temp].add(node)\r\n level = next_level\r\n parents.update(next_level)\r\n \r\n # (3) save path from parent \r\n res = [[endWord]]\r\n while res and res[0][0] != beginWord:\r\n res = [[p]+r for r in res for p in parents[r[0]]]\r\n return res\r\n\r\n \r\n# node: hit\r\n# temp: hot\r\n# next_level: defaultdict(, {'hot': {'hit'}})\r\n# parent: defaultdict(, {'hot': {'hit'}})\r\n# -------------\r\n# node: hot\r\n# temp: dot\r\n# next_level: defaultdict(, {'dot': {'hot'}})\r\n# temp: lot\r\n# next_level: defaultdict(, {'dot': {'hot'}, 'lot': {'hot'}})\r\n# parent: defaultdict(, {'hot': {'hit'}, 'dot': {'hot'}, 'lot': {'hot'}})\r\n# -------------\r\n# node: dot\r\n# temp: dog\r\n# next_level: defaultdict(, {'dog': {'dot'}})\r\n# node: lot\r\n# temp: log\r\n# next_level: defaultdict(, {'dog': {'dot'}, 'log': {'lot'}})\r\n# parent: defaultdict(, {'hot': {'hit'}, 'dot': {'hot'}, 'lot': {'hot'}, 'dog': {'dot'}, 'log': {'lot'}})\r\n# -------------\r\n# node: dog\r\n# temp: cog\r\n# next_level: defaultdict(, {'cog': {'dog'}})\r\n# node: log\r\n# temp: cog\r\n# next_level: defaultdict(, {'cog': {'dog', 'log'}})\r\n# parent: defaultdict(, {'hot': {'hit'}, 'dot': {'hot'}, 'lot': {'hot'}, 'dog': {'dot'}, 'log': {'lot'}, 'cog': {'dog', 'log'}})\r\n# -------------\r\n# [['cog']]\r\n# [['dog', 'cog'], ['log', 'cog']]\r\n# [['dot', 'dog', 'cog'], ['lot', 'log', 'cog']]\r\n# [['hot', 'dot', 'dog', 'cog'], ['hot', 'lot', 'log', 'cog']]\r\n# [['hit', 'hot', 'dot', 'dog', 'cog'], ['hit', 'hot', 'lot', 'log', 'cog']]", "sub_path": "07 Graph/126. Word Ladder II.py", "file_name": "126. Word Ladder II.py", "file_ext": "py", "file_size_in_byte": 2529, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "collections.defaultdict", "line_number": 12, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 16, "usage_type": "call"}, {"api_name": "string.ascii_lowercase", "line_number": 18, "usage_type": "attribute"}]} +{"seq_id": "57253953", "text": "\nimport math\nimport random\nimport pprint\nimport pickle\nimport sys\nimport os\nfrom datetime import datetime\nimport util\n\nclass Chromosome:\n def __init__(self, bounds, NPC_size, time_size):\n self.y = 0\n self.scenario = [[[] for i in range(time_size)] for j in range(NPC_size)] # This scenario\n self.bounds = bounds\n self.code_x1_length = NPC_size \n self.code_x2_length = time_size\n self.timeoutTime = 300 # in seconds, timeout timer for simulator execution per each scenario simulation\n\n def fix_init(self):\n for i in range(self.code_x1_length): # For every NPC\n for j in range(self.code_x2_length): # For every time slice\n v = (self.bounds[0][0] + self.bounds[0][1]) / float(2) #random.uniform(self.bounds[0][0], self.bounds[0][1]) # Init velocity\n a = 3 # Keep straight #random.randrange(self.bounds[1][0], self.bounds[1][1]) # Init action\n self.scenario[i][j].append(v)\n self.scenario[i][j].append(a)\n\n def rand_init(self):\n for i in range(self.code_x1_length): # For every NPC\n for j in range(self.code_x2_length): # For every time slice\n v = random.uniform(self.bounds[0][0], self.bounds[0][1]) # Init velocity\n a = random.randrange(self.bounds[1][0], self.bounds[1][1]) # Init action\n self.scenario[i][j].append(v)\n self.scenario[i][j].append(a)\n\n def foo_obj_func(self):\n speedSum = 0\n for npc in self.scenario:\n for nt in npc:\n speedSum += nt[0]\n speedSum += nt[0] * 34\n return speedSum\n\n def decoding(self):\n fitness_score = 0\n\n # Send scenario object to simulation script\n s_f = open('scenario.obj', 'wb')\n pickle.dump(self.scenario, s_f)\n s_f.truncate() \n s_f.close() \n \n for x in range(0, 100):\t\n\n if os.path.isfile('result.obj') == True:\n os.remove(\"result.obj\")\n\n os.system(\"python3 simulation.py scenario.obj result.obj\")\n resultObj = None\n\n # Read fitness score\n if os.path.isfile('result.obj') == True:\n f_f = open('result.obj', 'rb')\n resultObj = pickle.load(f_f)\n f_f.close()\n\n if resultObj != None and resultObj['fitness'] != '':\n return resultObj\n break\n else:\n util.print_debug(\" ***** \" + str(x) + \"th/10 trial: Fail to get fitness, try again ***** \")\n\n return None\n\n # Get fitness score of the scenario\n def func(self, gen=None, lisFlag=False):\n\n resultObj = self.decoding()\n self.y = float(resultObj['fitness'])\n if resultObj['fault'] == 'ego':\n # Found a bug of ego ADS \n util.print_debug(\" ***** Found an accident where ego is at fault ***** \")\n # Dump the scenario where causes the accident\n if os.path.exists('AccidentScenario') == False:\n os.mkdir('AccidentScenario')\n now = datetime.now()\n date_time = now.strftime(\"%m-%d-%Y-%H-%M-%S\")\n ckName = 'AccidentScenario/accident-gen' + str(gen) + '-' + date_time\n if lisFlag == True:\n ckName = ckName + \"-LIS\"\n a_f = open(ckName, 'wb')\n pickle.dump(self, a_f)\n a_f.truncate() \n a_f.close()\n \nif __name__ == '__main__':\n a = [[10, 30], [0, 2]]\n chromosome = Chromosome(a, 5, 10, 10)\n pprint.pprint(chromosome.scene)\n\n", "sub_path": "urban/Chromosome.py", "file_name": "Chromosome.py", "file_ext": "py", "file_size_in_byte": 3731, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "random.uniform", "line_number": 31, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 32, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 56, "usage_type": "call"}, {"api_name": "os.system", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 64, "usage_type": "call"}, {"api_name": "util.print_debug", "line_number": 71, "usage_type": "call"}, {"api_name": "util.print_debug", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 85, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 86, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 86, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 92, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 99, "usage_type": "call"}]} +{"seq_id": "452567037", "text": "#############################################################################\n## jhRibbonSetup ##\n#############################################################################\n\n\"\"\"\nSCRIPT: jhRibbonSetup.mel\nDESCRIPTION:Creates a nurbs surface with controls across the hulls for use with\n\t\t\tfollicles and as a wrap to create tube/motion path style animation.\n \nINSTRUCTIONS:Select a nurbs curve, run jhRibbonSetup. If you have trouble,\n\t\t\ttry running in a clean scene. \n \nUPDATE: Converted to python by Zed Bennett; February 21 2011\n\"\"\"\nimport maya.cmds as cmds\nimport math as m\nimport random as r\n##================== ribbon UI window ======================##\ndef ribbonControlUI(*args, **kwargs):\n if (cmds.window(\"ribbonUI\", ex=1)!=True):\n cmds.window(\"ribbonUI\", title=\"Ribbon Path and Controls\", s=0, tlb=1)\n cmds.columnLayout(\"mainLayout\", w=300)\n ##====================================== instructions text field ==================================##\n cmds.setParent(\"mainLayout\")\n cmds.frameLayout(\"descriptionFrame\", l=\"Instructions:\", cll=1, w=300)\n cmds.columnLayout(co=(\"both\",1))\n cmds.text(\"Poop pooop poop poooop\", ww=1, w=300, align='left')\n cmds.text(\"Test test test test test poooooop\", ww=1, w=300, align='left')\n ##====================================== controls frame ==================================##\n cmds.setParent(\"mainLayout\")\n cmds.frameLayout(\"controlsFrame\", l=\"Controls:\", cll=1, cl=1, w=300)\n cmds.columnLayout(\"controls\", adj=1, rs=1)\n ##==================================== Shape Scale Slider =====================================##\n cmds.setParent(\"controls\")\n cmds.columnLayout(\"scaleSliderLayout\", adj=1)\n cmds.floatSliderGrp('shapeScale', l=\"Shape Scale:\", min=0.01, max=10, fmn=.0001, fmx=1000, v=1, pre=2, s=0.01, f=1, cw3=[65,65,100])\n ##==================================== Pri Color Slider =====================================##\n cmds.setParent(\"controls\")\n cmds.rowLayout(\"priColorSliderFrame\", nc=2, cw2=[90, 205], ct2=[\"both\",\"right\"])\n cmds.text(\"Primary Color: \")\n cmds.colorIndexSliderGrp('primaryColor', min=1, max=32, v=18, cw2=[30,190])\n ##==================================== Pri Color Slider =====================================##\n cmds.setParent(\"controls\")\n cmds.rowLayout(\"secColorSliderFrame\", nc=2, cw2=[90, 205], ct2=[\"both\",\"right\"])\n cmds.text(\"Secondary Color:\")\n cmds.colorIndexSliderGrp('secondaryColor', min=1, max=32, v=18, cw2=[30,190])\n ##====================================== build button ==================================##\n cmds.setParent(\"controls\")\n cmds.rowLayout(\"buttonLayout\", nc=2, cw2=[100,100], ct2=[\"both\", \"both\"], co2=[30,0])\n cmds.button(l='Build Ribbon', c=__buildRibbon, w=100)\n cmds.button(l='Attach Objects', c=__attachObjs, w=100)\n cmds.showWindow(\"ribbonUI\")\n else:\n cmds.showWindow(\"ribbonUI\")\n##=================== build ribbon from curve system ===================##\ndef __buildRibbon(*args, **kwargs):\n ##=========run fixNm function to make sure names are correct===========##\n sel=cmds.ls(sl=1, fl=1)\n ##========================ERROR CHECK==============================##\n if(len(sel)!=1):\n cmds.error(\"Too many curves selected - please select only 1\")\n if cmds.objectType(cmds.listRelatives(sel[0],c=1,s=1))!=\"nurbsCurve\":\n cmds.error(\"You must select a nurbs curve in order for this to work\")\n __fixNm(sel)\n cmds.cycleCheck(e=0)\n ##==================what is the name of this system==================##\n __sysPrompt()\n sysNm=__d.sysNm\n ##==================make base heirarchy nodes and parent them accordingly=====================##\n __d.rootGrp=\"%s_ROOT\"%(sysNm)\n __d.ctrlGrp=\"%s_CTRL\"%(sysNm)\n __d.rigGrp=\"%s_RIG\"%(sysNm)\n __d.ctrlsGrp=\"%s_ctrls_grp\"%(sysNm)\n cmds.createNode('transform',n=__d.rootGrp)\n cmds.createNode('transform',n=__d.ctrlGrp)\n cmds.createNode('transform',n=__d.rigGrp)\n cmds.createNode('transform',n=__d.ctrlsGrp)\n cmds.parent(__d.ctrlGrp, __d.rootGrp)\n cmds.parent(__d.ctrlsGrp, __d.ctrlGrp)\n cmds.parent(__d.rigGrp, __d.rootGrp)\n ##===========================sort selection=============================##\n crv=\"%s_pathCrv\"%(sysNm)\n ##if object has a shape check for curve\n cmds.rename(sel[0],crv)\n cmds.parent(crv,__d.rigGrp)\n ##now to get the follicles to move later on we need to add a position attribute to our path curve\n crvPos=\"%s.position\"%(crv)\n zeroPos=\"%s.zeroPos\"%(crv)\n cmds.addAttr(crv, ln=\"position\", at='double', k=1)\n cmds.addAttr(crv, ln=\"zeroPos\", at='double', min=0, max=1, k=1)\n ##how long is our curve and normalize the parametrization\n spans=cmds.getAttr(\"%s.spans\"%(crv))\n degree=cmds.getAttr(\"%s.degree\"%(crv))\n arclen=cmds.arclen(crv)\n ySupp=((arclen/spans)/8)\n crvTransY=\"%s.translateY\"%(crv)\n cmds.rebuildCurve(crv, ch=0, rpo=1, rt=0, end=1, kr=0, kcp=1, kep=1, kt=1, s=7, d=3, tol=0.01)\n cmds.delete(crv, ch=1)\n aimCrv=\"%s_aimCurve\"%(sysNm)\n aimCrvY=\"%s.translateY\"%(aimCrv)\n cmds.duplicate(crv, rr=1, n=aimCrv)\n cmds.setAttr(crvTransY,((cmds.getAttr(crvTransY))-ySupp))\n cmds.setAttr(aimCrvY,((cmds.getAttr(aimCrvY))+ySupp))\n ##=============create a surface and name it after the curve===============##\n surf=\"%s_surf\"%sysNm\n cmds.loft(crv, aimCrv, n=surf)\n ##==================make surface template option=========================##\n cmds.parent(surf,__d.ctrlGrp)\n shdr=\"%s_xRay\"%sysNm\n cmds.shadingNode(\"lambert\", asShader=1, n=shdr)\n shdSet=\"%sSG\"%shdr\n cmds.sets(r=1, nss=1, em=1, n=shdSet)\n cmds.connectAttr(\"%s.outColor\"%shdr,\"%s.surfaceShader\"%shdSet, f=1)\n cmds.setAttr((\"%s.transparency\"%shdr),0.75,0.75,0.75,type='double3')\n cmds.setAttr(\"%s.doubleSided\"%surf,0)\n cmds.setAttr(\"%s.doubleSided\"%surf,1)\n cmds.setAttr(\"%s.castsShadows\"%surf,0)\n cmds.setAttr(\"%s.receiveShadows\"%surf,0)\n cmds.setAttr(\"%s.motionBlur\"%surf,0)\n cmds.setAttr(\"%s.primaryVisibility\"%surf,0)\n cmds.setAttr(\"%s.smoothShading\"%surf,0)\n cmds.setAttr(\"%s.visibleInReflections\"%surf,0)\n cmds.setAttr(\"%s.visibleInRefractions\"%surf,0)\n cmds.setAttr(\"%sShape.curvePrecisionShaded\"%surf,15)\n ##================== make controls for ribbon ===================##\t\n clsGrp=\"%s_clsGrp\"%sysNm\n cmds.createNode('transform',n=clsGrp)\n cmds.parent(clsGrp, __d.rigGrp)\n ctrls=[]\n for n in range(spans+degree):\n ##=========== set some variables ============##\n numStr=str(n)\n numNm=numStr.zfill(2)\n pathCv=\"%s.cv[%s]\"%(crv, numStr)\n aimCv=\"%s.cv[%s]\"%(aimCrv, numStr)\n pClsNm=\"%s_%s_pathCls\"%(sysNm,numNm)\n aClsNm=\"%s_%s_aimCls\"%(sysNm,numNm)\n clsGrpNm=\"%s_%s_clsGrp\"%(sysNm,numNm)\n ctrlNm=\"%s_ori_%s_ctrl\"%(sysNm,numNm)\n __d.ctrlGrpNm=\"%s_grp\"%ctrlNm\n ##====== calculate distance between the path and aim CVs for scale purposes ==============##\n p1=cmds.pointPosition(pathCv)\n p2=cmds.pointPosition(aimCv)\n ctrlScl=(m.sqrt(((m.exp(p1[0]-p2[0]))+(m.exp(p1[1]-p2[1]))+(m.exp(p1[2]-p2[2])))))\n ##====== create clusters to control surface =======##\n cmds.cluster(pathCv,n=pClsNm)\n cmds.cluster(aimCv,n=aClsNm)\n pClsNm=\"%sHandle\"%pClsNm\n aClsNm=\"%sHandle\"%aClsNm\n cmds.group(pClsNm, aClsNm, n=clsGrpNm)\n cmds.xform(clsGrpNm, cp=1)\n ##====== create a controller for orienting and positioning the clusters ========##\n cmds.curve(d=1, p=((0,1,4),(0,4,0),(0,1,-4),(0,1,-3),(0,3,0),(0,1,3),(0,1,4)),\n k=(0,1,2,3,4,5,6), n=ctrlNm)\n shpNm=cmds.listRelatives(ctrlNm, s=1)\n cmds.setAttr(\"%s.overrideEnabled\"%shpNm[0], 1)\n cmds.setAttr(\"%s.overrideColor\"%shpNm[0], 17)\n cmds.group(ctrlNm, n=__d.ctrlGrpNm)\n cmds.scale(ctrlScl, ctrlScl, ctrlScl, ctrlNm)\n cmds.makeIdentity(ctrlNm,apply=1,t=1,r=1,s=1,n=0)\n cmds.pointConstraint(pClsNm, aClsNm, __d.ctrlGrpNm, n=\"deleteMe\")\n cmds.delete(\"deleteMe\")\n cmds.aimConstraint(aClsNm, __d.ctrlGrpNm, aim=(0,1,0), n=\"deleteMe\")\n cmds.delete(\"deleteMe\")\n cmds.parentConstraint(ctrlNm, clsGrpNm, n=\"%s_parentConstraint\"%clsGrpNm)\n cmds.scaleConstraint(ctrlNm, clsGrpNm, n=\"%s_scaleConstraint\"%clsGrpNm)\n cmds.parent(__d.ctrlGrpNm, __d.ctrlsGrp)\n cmds.parent(clsGrpNm, clsGrp)\n cmds.xform(__d.ctrlsGrp, cp=1)\n pathCtrlNm=\"%s_path_ctrl\"%sysNm\n cmds.circle(ch=0, r=ySupp, nr=(0,1,0), n=pathCtrlNm)\n cmds.setAttr(\"%s.overrideEnabled\"%surf,1)\n cmds.addAttr(pathCtrlNm, ln=\"Extra\", at=\"enum\", en=\"Controls\", h=0, k=0)\n cmds.setAttr(\"%s.Extra\"%pathCtrlNm, cb=1)\n cmds.addAttr(pathCtrlNm, ln=\"templateSurface\", k=1, min=0, max=1, dv=0, at='short')\n cmds.connectAttr(\"%s.templateSurface\"%pathCtrlNm,\"%s.overrideDisplayType\"%surf, f=1)\n cShpNm=cmds.listRelatives(pathCtrlNm, s=1)\n cmds.setAttr(\"%s.overrideEnabled\"%cShpNm[0], 1)\n cmds.setAttr(\"%s.overrideColor\"%cShpNm[0], 13)\n cmds.parent(pathCtrlNm, __d.ctrlGrp)\n cmds.parentConstraint(__d.ctrlsGrp, pathCtrlNm, n=\"deleteMe\")\n cmds.delete(\"deleteMe\")\n cmds.parentConstraint(pathCtrlNm, __d.ctrlsGrp, n=\"%s_parentConstraint\"%pathCtrlNm)\n cmds.hide(__d.rigGrp)\n ##==== reset cycle check =====##\n cmds.cycleCheck(e=1)\n##=================== shape name check ===================##\ndef __fixNm(sel, *args, **kwargs):\n for i in sel:\n shapeNm=\"%sShape\"%i\n shapes=cmds.listRelatives(i, s=1)\n if (len(shapes) > 0):\n for s in shapes:\n if (s.endswith(\"Shape\")==0):\n cmds.rename(s, shapeNm)\n##=================== system name propmt window ===================## \ndef __sysPrompt(*args, **kwargs):\n result=cmds.promptDialog(\n\tt=\"Name System\",\n\tm=\"Enter Name:\",\n\tb=[\"OK\",\"Cancel\"],\n\tdb=\"OK\",\n\tcb=\"Cancel\",\n\tds=\"Cancel\")\n if result == \"OK\":\n __d.sysNm=cmds.promptDialog(q=1, text=1)\n else:\n cmds.error(\"Operation Cancelled\")\n##=================== flow objects ========================##\ndef __attachObjs(*args, **kwargs):\n ##============== select objects then the surface they animate across ===============##\n ##============== sort selection ====================##\n sel=cmds.ls(sl=1,fl=1)\n selSize=len(sel)\n flowSurf=(sel[-1])\n objs=sel[:-1]\n fols=[]\n __d.folGrp=\"follicle_grp\"\n cmds.group(em=1, n=__d.folGrp)\n cmds.parent(__d.folGrp, __d.rigGrp)\n ##=============== ask user for information =========================##\n dup=cmds.confirmDialog(\n t=\"Use Current Objs?\", \n m=\"Select Yes to flow current object. No to duplicate N times.\",\n b=[\"Yes\",\"No\"],\n db=\"Yes\",\n cb=\"No\", \n ds=\"No\")\n ##=============== create system =======================##\n if dup==\"No\":\n result=cmds.promptDialog(\n t=\"How Many objs\",\n m=\"Integer:\",\n b=[\"OK\",\"Cancel\"],\n db=\"OK\",\n cb=\"Cancel\",\n ds=\"Cancel\")\n ##================= create system ==================##\n if result == \"OK\":\n numObjs=cmds.promptDialog(q=1, t=1)\n for n in range(int(numObjs)):\n pad=len(numObjs)\n nStr=str(n).zfill(pad)\n rand=r.seed(len(objs))\n obj=objs[rand]\n cmds.duplicate(obj, n=\"%s_%s\"%(obj, nStr))\n ##=============== create follicles ==================##\n fol=\"%s_%s_follicle\"%(obj, nStr)\n folShape=\"%sShape\"%fol\n cmds.createNode('follicle', n=folShape)\n folDAG=cmds.listRelatives(folShape, p=1)\n cmds.rename(folDAG[0], fol)\n cmds.connectAttr(\"%s.local\"%flowSurf,\"%s.inputSurface\"%folShape)\n cmds.connectAttr (\"%s.worldMatrix[0]\"%flowSurf,\"%s.inputWorldMatrix\"%folShape)\n cmds.connectAttr (\"%s.outRotate\"%folShape,\"%s.rotate\"%fol)\n cmds.connectAttr (\"%s.outTranslate\"%folShape,\"%s.translate\"%fol)\n cmds.parentConstraint(fol, obj, n=\"%s_parentConstraint\"%obj)\n cmds.setAttr(\"%s.parameterU\"%folShape,(r.seed(1)))\n children=cmds.listRelatives(obj, c=1)\n ctrl=obj\n for child in children:\n if cmds.objectType(child)!='transform':\n parents=cmds.listRelatives(child, p=1)\n for i in parents:\n ctrl=i\n break\n cmds.addAttr(ctrl, ln=\"speed\",at='double')\n cmds.setDrivenKeyframe(\"%s.parameterV\"%folShape, cd=\"%s.speed\"%ctrl)\n cmds.setAttr(\"%s.speed\"%ctrl,100)\n cmds.setAttr(\"&s.parameterV\"%folShape,1)\n cmds.setDrivenKeyframe(\"%s.parameterV\"%folShape, cd=\"%s.speed\"%ctrl)\n cmds.setAttr(\"%s.parameterV.preInfinity\"%folShape,3)\n cmds.setAttr(\"%s.parameterV.postInfinity\"%folShape,3)\n cmds.setAttr(\"%s.speed\"%ctrl,(r.seed(100)))\n else: \n numObjs=(len(objs))\n cPOS=cmds.createNode('closestPointOnSurface')\n cmds.connectAttr(\"%s.worldSpace\"%flowSurf,\"%s.inputSurface\"%cPOS)\n for n in range(numObjs):\n obj=objs[n]\n pad=len(str(numObjs))\n nStr=str(n).zfill(pad)\n fol=\"%s_%s_follicle\"%(obj, nStr)\n folShape=\"%sShape\"%fol\n cmds.createNode('follicle', n=folShape)\n folDAG=cmds.listRelatives(folShape, p=1)\n cmds.rename(folDAG[0], fol) \n cmds.connectAttr(\"%s.local\"%flowSurf,\"%s.inputSurface\"%folShape)\n cmds.connectAttr (\"%s.worldMatrix[0]\"%flowSurf,\"%s.inputWorldMatrix\"%folShape)\n cmds.connectAttr (\"%s.outRotate\"%folShape,\"%s.rotate\"%fol)\n cmds.connectAttr (\"%s.outTranslate\"%folShape,\"%s.translate\"%fol) \n pos=cmds.xform(obj, q=1, ws=1, piv=1)\n cmds.setAttr (\"%s.inPositionX\"%cPOS, pos[0])\n cmds.setAttr (\"%s.inPositionY\"%cPOS, pos[1])\n cmds.setAttr (\"%s.inPositionZ\"%cPOS, pos[2])\n curU=cmds.getAttr(\"%s.parameterU\"%cPOS)\n curV=cmds.getAttr(\"%s.parameterV\"%cPOS)\n cmds.setAttr(\"%s.parameterU\"%folShape, curU)\n cmds.setAttr(\"%s.parameterV\"%folShape, curV)\n cmds.addAttr(obj, ln=\"pos\", at=\"double\")\n cmds.setAttr(\"%s.pos\"%obj, e=1, k=1)\n cmds.setAttr(\"%s.pos\"%obj, curV*100)\n parentX=cmds.listRelatives(obj, p=1)\n cmds.parentConstraint(fol, obj, mo=1)\n cmds.parent(fol, __d.folGrp)\n cmds.delete(cPOS)\t\n##=================== dictionary function ===================##\ndef __d(*args, **kwargs):\n d='poop'", "sub_path": "scripts/python/maya/rigging/ribbonPathTool.py", "file_name": "ribbonPathTool.py", "file_ext": "py", "file_size_in_byte": 15045, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "maya.cmds.window", "line_number": 20, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 20, "usage_type": "name"}, {"api_name": "maya.cmds.window", "line_number": 21, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 21, "usage_type": "name"}, {"api_name": "maya.cmds.columnLayout", "line_number": 22, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 22, "usage_type": "name"}, {"api_name": "maya.cmds.setParent", "line_number": 24, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 24, "usage_type": "name"}, {"api_name": "maya.cmds.frameLayout", "line_number": 25, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 25, "usage_type": "name"}, {"api_name": "maya.cmds.columnLayout", "line_number": 26, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 26, "usage_type": "name"}, {"api_name": "maya.cmds.text", "line_number": 27, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 27, "usage_type": "name"}, {"api_name": "maya.cmds.text", "line_number": 28, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 28, "usage_type": "name"}, {"api_name": "maya.cmds.setParent", "line_number": 30, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 30, "usage_type": "name"}, {"api_name": "maya.cmds.frameLayout", "line_number": 31, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 31, "usage_type": "name"}, {"api_name": "maya.cmds.columnLayout", "line_number": 32, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 32, "usage_type": "name"}, {"api_name": "maya.cmds.setParent", "line_number": 34, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 34, "usage_type": "name"}, {"api_name": "maya.cmds.columnLayout", "line_number": 35, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 35, "usage_type": "name"}, {"api_name": "maya.cmds.floatSliderGrp", "line_number": 36, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 36, "usage_type": "name"}, {"api_name": "maya.cmds.setParent", "line_number": 38, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 38, "usage_type": "name"}, {"api_name": "maya.cmds.rowLayout", "line_number": 39, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 39, "usage_type": "name"}, {"api_name": "maya.cmds.text", "line_number": 40, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 40, "usage_type": "name"}, {"api_name": "maya.cmds.colorIndexSliderGrp", "line_number": 41, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 41, "usage_type": "name"}, {"api_name": "maya.cmds.setParent", "line_number": 43, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 43, "usage_type": "name"}, {"api_name": "maya.cmds.rowLayout", "line_number": 44, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 44, "usage_type": "name"}, {"api_name": "maya.cmds.text", "line_number": 45, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 45, "usage_type": "name"}, {"api_name": "maya.cmds.colorIndexSliderGrp", "line_number": 46, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 46, "usage_type": "name"}, {"api_name": "maya.cmds.setParent", "line_number": 48, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 48, "usage_type": "name"}, {"api_name": "maya.cmds.rowLayout", "line_number": 49, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 49, "usage_type": "name"}, {"api_name": "maya.cmds.button", "line_number": 50, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 50, "usage_type": "name"}, {"api_name": "maya.cmds.button", "line_number": 51, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 51, "usage_type": "name"}, {"api_name": "maya.cmds.showWindow", "line_number": 52, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 52, "usage_type": "name"}, {"api_name": "maya.cmds.showWindow", "line_number": 54, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 54, "usage_type": "name"}, {"api_name": "maya.cmds.ls", "line_number": 58, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 58, "usage_type": "name"}, {"api_name": "maya.cmds.error", "line_number": 61, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 61, "usage_type": "name"}, {"api_name": "maya.cmds.objectType", "line_number": 62, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 62, "usage_type": "name"}, {"api_name": "maya.cmds.listRelatives", "line_number": 62, "usage_type": "call"}, {"api_name": "maya.cmds.error", "line_number": 63, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 63, "usage_type": "name"}, {"api_name": "maya.cmds.cycleCheck", "line_number": 65, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 65, "usage_type": "name"}, {"api_name": "maya.cmds.createNode", "line_number": 74, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 74, "usage_type": "name"}, {"api_name": "maya.cmds.createNode", "line_number": 75, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 75, "usage_type": "name"}, {"api_name": "maya.cmds.createNode", "line_number": 76, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 76, "usage_type": "name"}, {"api_name": "maya.cmds.createNode", "line_number": 77, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 77, "usage_type": "name"}, {"api_name": "maya.cmds.parent", "line_number": 78, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 78, "usage_type": "name"}, {"api_name": "maya.cmds.parent", "line_number": 79, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 79, "usage_type": "name"}, {"api_name": "maya.cmds.parent", "line_number": 80, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 80, "usage_type": "name"}, {"api_name": "maya.cmds.rename", "line_number": 84, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 84, "usage_type": "name"}, {"api_name": "maya.cmds.parent", "line_number": 85, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 85, "usage_type": "name"}, {"api_name": "maya.cmds.addAttr", "line_number": 89, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 89, "usage_type": "name"}, {"api_name": "maya.cmds.addAttr", "line_number": 90, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 90, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 92, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 92, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 93, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 93, "usage_type": "name"}, {"api_name": "maya.cmds.arclen", "line_number": 94, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 94, "usage_type": "name"}, {"api_name": "maya.cmds.rebuildCurve", "line_number": 97, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 97, "usage_type": "name"}, {"api_name": "maya.cmds.delete", "line_number": 98, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 98, "usage_type": "name"}, {"api_name": "maya.cmds.duplicate", "line_number": 101, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 101, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 102, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 102, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 102, "usage_type": "call"}, {"api_name": "maya.cmds.setAttr", "line_number": 103, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 103, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 103, "usage_type": "call"}, {"api_name": "maya.cmds.loft", "line_number": 106, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 106, "usage_type": "name"}, {"api_name": "maya.cmds.parent", "line_number": 108, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 108, "usage_type": "name"}, {"api_name": "maya.cmds.shadingNode", "line_number": 110, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 110, "usage_type": "name"}, {"api_name": "maya.cmds.sets", "line_number": 112, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 112, "usage_type": "name"}, {"api_name": "maya.cmds.connectAttr", "line_number": 113, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 113, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 114, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 114, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 115, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 115, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 116, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 116, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 117, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 117, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 118, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 118, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 119, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 119, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 120, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 120, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 121, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 121, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 122, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 122, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 123, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 123, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 124, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 124, "usage_type": "name"}, {"api_name": "maya.cmds.createNode", "line_number": 127, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 127, "usage_type": "name"}, {"api_name": "maya.cmds.parent", "line_number": 128, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 128, "usage_type": "name"}, {"api_name": "maya.cmds.pointPosition", "line_number": 142, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 142, "usage_type": "name"}, {"api_name": "maya.cmds.pointPosition", "line_number": 143, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 143, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 144, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 144, "usage_type": "call"}, {"api_name": "maya.cmds.cluster", "line_number": 146, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 146, "usage_type": "name"}, {"api_name": "maya.cmds.cluster", "line_number": 147, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 147, "usage_type": "name"}, {"api_name": "maya.cmds.group", "line_number": 150, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 150, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 151, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 151, "usage_type": "name"}, {"api_name": "maya.cmds.curve", "line_number": 153, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 153, "usage_type": "name"}, {"api_name": "maya.cmds.listRelatives", "line_number": 155, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 155, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 156, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 156, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 157, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 157, "usage_type": "name"}, {"api_name": "maya.cmds.group", "line_number": 158, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 158, "usage_type": "name"}, {"api_name": "maya.cmds.scale", "line_number": 159, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 159, "usage_type": "name"}, {"api_name": "maya.cmds.makeIdentity", "line_number": 160, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 160, "usage_type": "name"}, {"api_name": "maya.cmds.pointConstraint", "line_number": 161, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 161, "usage_type": "name"}, {"api_name": "maya.cmds.delete", "line_number": 162, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 162, "usage_type": "name"}, {"api_name": "maya.cmds.aimConstraint", "line_number": 163, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 163, "usage_type": "name"}, {"api_name": "maya.cmds.delete", "line_number": 164, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 164, "usage_type": "name"}, {"api_name": "maya.cmds.parentConstraint", "line_number": 165, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 165, "usage_type": "name"}, {"api_name": "maya.cmds.scaleConstraint", "line_number": 166, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 166, "usage_type": "name"}, {"api_name": "maya.cmds.parent", "line_number": 167, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 167, "usage_type": "name"}, {"api_name": "maya.cmds.parent", "line_number": 168, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 168, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 169, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 169, "usage_type": "name"}, {"api_name": "maya.cmds.circle", "line_number": 171, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 171, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 172, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 172, "usage_type": "name"}, {"api_name": "maya.cmds.addAttr", "line_number": 173, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 173, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 174, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 174, "usage_type": "name"}, {"api_name": "maya.cmds.addAttr", "line_number": 175, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 175, "usage_type": "name"}, {"api_name": "maya.cmds.connectAttr", "line_number": 176, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 176, "usage_type": "name"}, {"api_name": "maya.cmds.listRelatives", "line_number": 177, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 177, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 178, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 178, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 179, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 179, "usage_type": "name"}, {"api_name": "maya.cmds.parent", "line_number": 180, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 180, "usage_type": "name"}, {"api_name": "maya.cmds.parentConstraint", "line_number": 181, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 181, "usage_type": "name"}, {"api_name": "maya.cmds.delete", "line_number": 182, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 182, "usage_type": "name"}, {"api_name": "maya.cmds.parentConstraint", "line_number": 183, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 183, "usage_type": "name"}, {"api_name": "maya.cmds.hide", "line_number": 184, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 184, "usage_type": "name"}, {"api_name": "maya.cmds.cycleCheck", "line_number": 186, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 186, "usage_type": "name"}, {"api_name": "maya.cmds.listRelatives", "line_number": 191, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 191, "usage_type": "name"}, {"api_name": "maya.cmds.rename", "line_number": 195, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 195, "usage_type": "name"}, {"api_name": "maya.cmds.promptDialog", "line_number": 198, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 198, "usage_type": "name"}, {"api_name": "maya.cmds.promptDialog", "line_number": 206, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 206, "usage_type": "name"}, {"api_name": "maya.cmds.error", "line_number": 208, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 208, "usage_type": "name"}, {"api_name": "maya.cmds.ls", "line_number": 213, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 213, "usage_type": "name"}, {"api_name": "maya.cmds.group", "line_number": 219, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 219, "usage_type": "name"}, {"api_name": "maya.cmds.parent", "line_number": 220, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 220, "usage_type": "name"}, {"api_name": "maya.cmds.confirmDialog", "line_number": 222, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 222, "usage_type": "name"}, {"api_name": "maya.cmds.promptDialog", "line_number": 231, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 231, "usage_type": "name"}, {"api_name": "maya.cmds.promptDialog", "line_number": 240, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 240, "usage_type": "name"}, {"api_name": "random.seed", "line_number": 244, "usage_type": "call"}, {"api_name": "maya.cmds.duplicate", "line_number": 246, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 246, "usage_type": "name"}, {"api_name": "maya.cmds.createNode", "line_number": 250, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 250, "usage_type": "name"}, {"api_name": "maya.cmds.listRelatives", "line_number": 251, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 251, "usage_type": "name"}, {"api_name": "maya.cmds.rename", "line_number": 252, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 252, "usage_type": "name"}, {"api_name": "maya.cmds.connectAttr", "line_number": 253, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 253, "usage_type": "name"}, {"api_name": "maya.cmds.connectAttr", "line_number": 254, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 254, "usage_type": "name"}, {"api_name": "maya.cmds.connectAttr", "line_number": 255, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 255, "usage_type": "name"}, {"api_name": "maya.cmds.connectAttr", "line_number": 256, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 256, "usage_type": "name"}, {"api_name": "maya.cmds.parentConstraint", "line_number": 257, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 257, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 258, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 258, "usage_type": "name"}, {"api_name": "random.seed", "line_number": 258, "usage_type": "call"}, {"api_name": "maya.cmds.listRelatives", "line_number": 259, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 259, "usage_type": "name"}, {"api_name": "maya.cmds.objectType", "line_number": 262, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 262, "usage_type": "name"}, {"api_name": "maya.cmds.listRelatives", "line_number": 263, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 263, "usage_type": "name"}, {"api_name": "maya.cmds.addAttr", "line_number": 267, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 267, "usage_type": "name"}, {"api_name": "maya.cmds.setDrivenKeyframe", "line_number": 268, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 268, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 269, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 269, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 270, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 270, "usage_type": "name"}, {"api_name": "maya.cmds.setDrivenKeyframe", "line_number": 271, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 271, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 272, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 272, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 273, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 273, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 274, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 274, "usage_type": "name"}, {"api_name": "random.seed", "line_number": 274, "usage_type": "call"}, {"api_name": "maya.cmds.createNode", "line_number": 277, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 277, "usage_type": "name"}, {"api_name": "maya.cmds.connectAttr", "line_number": 278, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 278, "usage_type": "name"}, {"api_name": "maya.cmds.createNode", "line_number": 285, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 285, "usage_type": "name"}, {"api_name": "maya.cmds.listRelatives", "line_number": 286, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 286, "usage_type": "name"}, {"api_name": "maya.cmds.rename", "line_number": 287, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 287, "usage_type": "name"}, {"api_name": "maya.cmds.connectAttr", "line_number": 288, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 288, "usage_type": "name"}, {"api_name": "maya.cmds.connectAttr", "line_number": 289, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 289, "usage_type": "name"}, {"api_name": "maya.cmds.connectAttr", "line_number": 290, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 290, "usage_type": "name"}, {"api_name": "maya.cmds.connectAttr", "line_number": 291, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 291, "usage_type": "name"}, {"api_name": "maya.cmds.xform", "line_number": 292, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 292, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 293, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 293, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 294, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 294, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 295, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 295, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 296, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 296, "usage_type": "name"}, {"api_name": "maya.cmds.getAttr", "line_number": 297, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 297, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 298, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 298, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 299, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 299, "usage_type": "name"}, {"api_name": "maya.cmds.addAttr", "line_number": 300, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 300, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 301, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 301, "usage_type": "name"}, {"api_name": "maya.cmds.setAttr", "line_number": 302, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 302, "usage_type": "name"}, {"api_name": "maya.cmds.listRelatives", "line_number": 303, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 303, "usage_type": "name"}, {"api_name": "maya.cmds.parentConstraint", "line_number": 304, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 304, "usage_type": "name"}, {"api_name": "maya.cmds.parent", "line_number": 305, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 305, "usage_type": "name"}, {"api_name": "maya.cmds.delete", "line_number": 306, "usage_type": "call"}, {"api_name": "maya.cmds", "line_number": 306, "usage_type": "name"}]} +{"seq_id": "202572473", "text": "from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.template import loader\nfrom django.urls import reverse\n\n\nfrom .models import Kilpailu, Kisaaja\n\n\ndef index(request):\n kisalista = Kilpailu.objects.order_by('vuosi')\n objects = ', '.join([k.nimi for k in kisalista])\n template = loader.get_template('jinja2/index.html.j2')\n context = {\n 'kisalista': kisalista,\n 'navigation_bar': [\n ['/kisa', 'index', 'Kisat'],\n ['/kisa/kisaajat', 'kisaajat', 'Kisaajat']\n ],\n 'active_page': { 'id': 'index', 'name': 'Etusivu'}\n }\n return HttpResponse(template.render(context, request))\n\n\ndef detail(request, kilpailu_id):\n template = loader.get_template('jinja2/kisa.html.j2')\n context = {\n 'kisa': kilpailu_id,\n 'navigation_bar': [\n ['/kisa', 'kisa', 'Kisat'],\n ['/kisa/kisaajat', 'kisaajat', 'Kisaajat']\n ],\n 'active_page': { 'id': 'kisa', 'name': 'Kisa'}\n }\n return HttpResponse(template.render(context, request))\n\n\ndef lisaa_kisaaja(request):\n\n template = loader.get_template('jinja2/lisaa_kisaaja.html.j2')\n if request.method == 'POST':\n uusi_kisaaja = Kisaaja(nimi_etu=request.POST['nimi_etu'], nimi_suku=request.POST['nimi_suku'], ruoka_allergiat=request.POST['allergiat'])\n # uusi_kisaaja['nimi_etu'] = request.POST['nimi_etu']\n # uusi_kisaaja['nimi_suku'] = request.POST['nimi_suku']\n # uusi_kisaaja['ruoka_allergiat'] = request.POST['allergiat']\n uusi_kisaaja.save()\n context = {\n 'navigation_bar': [\n ['/kisa', 'kisa', 'Kisat'],\n ['/kisa/kisaajat', 'kisaajat', 'Kisaajat']\n ],\n 'active_page': { 'id': 'lisaa_kisaaja', 'name': 'Kisaajan nimi'}\n }\n return HttpResponse(template.render(context, request))\n else:\n context = {\n 'navigation_bar': [\n ['/kisa', 'kisa', 'Kisat'],\n ['/kisa/kisaajat', 'kisaajat', 'Kisaajat']\n ],\n 'active_page': { 'id': 'lisaa_kisaaja', 'name': 'Lisää kisaaja'}\n }\n return HttpResponse(template.render(context, request))\n\n\ndef add_lajipisteet(request, lajipisteet_id):\n return HttpResponse('Aseta lajipisteet: {}'.format(lajipisteet_id))\n\n\ndef kisaajat(request, kilpailu_id):\n template = loader.get_template('jinja2/kisaajat.html.j2')\n context = {\n 'navigation_bar': [\n ['/kisa', 'kisa', 'Kisat'],\n ['/kisa/kisaajat', 'kisaajat', 'Kisaajat']\n ],\n 'active_page': { 'id': 'kisaajat', 'name': 'Kisa ' + kilpailu_id + ' > Kisaajat'}\n }\n return HttpResponse(template.render(context, request))\n\n\ndef kaikki_kisaajat(request):\n template = loader.get_template('jinja2/kisaajat.html.j2')\n context = {\n 'navigation_bar': [\n ['/kisa', 'kisa', 'Kisat'],\n ['/kisa/kisaajat', 'kisaajat', 'Kisaajat']\n ],\n 'active_page': { 'id': 'kisaajat', 'name': 'Kaikki kisaajat'}\n }\n return HttpResponse(template.render(context, request))\n\n\ndef kisaaja(request, kilpailu_id, kisaaja_id):\n template = loader.get_template('jinja2/kisaaja.html.j2')\n context = {\n 'navigation_bar': [\n ['/kisa', 'kisa', 'Kisat'],\n ['/kisa/kisaajat', 'kisaajat', 'Kisaajat']\n ],\n 'kisaaja_id': kisaaja_id,\n 'active_page': { 'id': 'kisaaja', 'name': 'Kisa ' + kilpailu_id + ' > Kisaaja ' + kisaaja_id}\n }\n return HttpResponse(template.render(context, request))\n", "sub_path": "kisa/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3541, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "models.Kilpailu.objects.order_by", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Kilpailu.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.Kilpailu", "line_number": 11, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 13, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 13, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 22, "usage_type": "call"}, {"api_name": "django.template.loader.get_template", "line_number": 26, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 26, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 35, "usage_type": "call"}, {"api_name": "django.template.loader.get_template", "line_number": 40, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 40, "usage_type": "name"}, {"api_name": "models.Kisaaja", "line_number": 42, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 54, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 63, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 67, "usage_type": "call"}, {"api_name": "django.template.loader.get_template", "line_number": 71, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 71, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 79, "usage_type": "call"}, {"api_name": "django.template.loader.get_template", "line_number": 83, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 83, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 91, "usage_type": "call"}, {"api_name": "django.template.loader.get_template", "line_number": 95, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 95, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 104, "usage_type": "call"}]} +{"seq_id": "106709438", "text": "\"\"\"\n Copyright 2021 Ian Housman, RedCastle Resources Inc.\n\n Licensed under the Apache License, Version 2.0 (the \"License\");\n you may not use this file except in compliance with the License.\n You may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\n Unless required by applicable law or agreed to in writing, software\n distributed under the License is distributed on an \"AS IS\" BASIS,\n WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n See the License for the specific language governing permissions and\n limitations under the License.\n\"\"\"\n#Script to view RTFD outputs\n####################################################################################################\n####################################################################################################\nimport geeViz.geeView as geeView\nee = geeView.ee\nMap = geeView.Map\nfrom google.cloud import storage\nimport os,sys\nos.environ['GOOGLE_APPLICATION_CREDENTIALS'] = r\"Q:\\RTFD_gee_method\\credentials\\gtac-rtfd-b50238099cd8.json\"\n#Clear any layers added to Map object\n#If map is not cleared, layers are simply appended to the existing list of layers if layers have been added previously\nMap.clearMap()\n####################################################################################################\nbucket = 'rtfd-delivery'\n\nstudy_areas = ['CONUS','AK']\noutput_types = ['Z','TDD']\n\n\ncontinuous_palette_chastain = ['a83800','ff5500','e0e0e0','a4ff73','38a800']\neight_bit_viz = {'min':0,'max':254,'palette':continuous_palette_chastain,'dateFormat':'YYYYMMdd','advanceInterval':'day'}\npersistence_viz = {'min':0,'max':3,'palette':'e1e1e1,ffaa00,e10000,e100c5','dateFormat':'YYYYMMdd','advanceInterval':'day','classLegendDict':{'0 Detections':'e1e1e1','1 Detection':'ffaa00','2 Detections':'e10000','3 Detections':'e100c5'}}\n\ndef list_blobs(bucket_name):\n \"\"\"Lists all the blobs in the bucket.\"\"\"\n # bucket_name = \"your-bucket-name\"\n\n storage_client = storage.Client()\n\n # Note: Client.list_blobs requires at least package version 1.17.0.\n blobs = storage_client.list_blobs(bucket_name)\n return [i.name for i in blobs]\n # for blob in blobs:\n # print(blob.name)\n\nfiles = list_blobs(bucket)\ntifs = [i for i in files if os.path.splitext(i)[1] == '.tif']\n\ndef getDate(name,jd_split_string = '_jd'):\n yr = int(name.split(jd_split_string)[0][-4:])\n day = int(name.split(jd_split_string)[1].split('-')[0])\n d = ee.Date.fromYMD(yr,1,1).advance(day-1,'day')\n return d.millis()\n # print(name,yr,day,d.format('YYYY-MM-dd').getInfo())\nfor study_area in study_areas:\n for output_type in output_types:\n tifsT = [i for i in tifs if i.find(study_area)>-1]\n tifsT = [i for i in tifsT if i.find(output_type)>-1]\n eight_bits= [i for i in tifsT if i.find('_8bit')>-1]\n persistence = [i for i in tifsT if i.find('_persistence')>-1]\n raws = [i for i in tifsT if i.find('_persistence')==-1 and i.find('_8bit')==-1]\n \n eight_bit_c = []\n for t in eight_bits:\n img = ee.Image.loadGeoTIFF('gs://{}/{}'.format(bucket,t))\n \n d = getDate(t)\n img = img.set('system:time_start',d)\n eight_bit_c.append(img)\n eight_bit_c = ee.ImageCollection(eight_bit_c)\n Map.addTimeLapse(eight_bit_c,eight_bit_viz,'{} {} 8 bit timelapse'.format(study_area,output_type),False)\n\n persistence_c = []\n print(persistence)\n for t in persistence:\n img = ee.Image.loadGeoTIFF('gs://{}/{}'.format(bucket,t))\n \n d = getDate(t,'_jds')\n img = img.set('system:time_start',d)\n persistence_c.append(img)\n persistence_c = ee.ImageCollection(persistence_c)\n Map.addTimeLapse(persistence_c,persistence_viz,'{} {} persistence timelapse'.format(study_area,output_type),False)\n\nMap.view()", "sub_path": "GEE_RTFD_Migration/RTFD_GEEViz_Viewer.py", "file_name": "RTFD_GEEViz_Viewer.py", "file_ext": "py", "file_size_in_byte": 3788, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "geeViz.geeView.ee", "line_number": 20, "usage_type": "attribute"}, {"api_name": "geeViz.geeView", "line_number": 20, "usage_type": "name"}, {"api_name": "geeViz.geeView.Map", "line_number": 21, "usage_type": "attribute"}, {"api_name": "geeViz.geeView", "line_number": 21, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 24, "usage_type": "attribute"}, {"api_name": "google.cloud.storage.Client", "line_number": 43, "usage_type": "call"}, {"api_name": "google.cloud.storage", "line_number": 43, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "attribute"}]} +{"seq_id": "76040955", "text": "# Licensed to Modin Development Team under one or more contributor license agreements.\n# See the NOTICE file distributed with this work for additional information regarding\n# copyright ownership. The Modin Development Team licenses this file to you under the\n# Apache License, Version 2.0 (the \"License\"); you may not use this file except in\n# compliance with the License. You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software distributed under\n# the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF\n# ANY KIND, either express or implied. See the License for the specific language\n# governing permissions and limitations under the License.\n\n\"\"\"\nModule contains class ``BaseQueryCompiler``.\n\n``BaseQueryCompiler`` is a parent abstract class for any other query compiler class.\n\"\"\"\n\nimport abc\n\nfrom modin.data_management.functions.default_methods import (\n DataFrameDefault,\n SeriesDefault,\n DateTimeDefault,\n StrDefault,\n BinaryDefault,\n ResampleDefault,\n RollingDefault,\n CatDefault,\n GroupByDefault,\n)\nfrom modin.error_message import ErrorMessage\nimport modin.backends.base.doc_utils as doc_utils\n\nfrom pandas.core.dtypes.common import is_scalar\nimport pandas.core.resample\nimport pandas\nimport numpy as np\nfrom typing import List, Hashable\n\n\ndef _get_axis(axis):\n \"\"\"\n Build index labels getter of the specified axis.\n\n Parameters\n ----------\n axis : {0, 1}\n Axis to get labels from.\n\n Returns\n -------\n callable(BaseQueryCompiler) -> pandas.Index\n \"\"\"\n\n def axis_getter(self):\n ErrorMessage.default_to_pandas(f\"DataFrame.get_axis({axis})\")\n return self.to_pandas().axes[axis]\n\n return axis_getter\n\n\ndef _set_axis(axis):\n \"\"\"\n Build index labels setter of the specified axis.\n\n Parameters\n ----------\n axis : {0, 1}\n Axis to set labels on.\n\n Returns\n -------\n callable(BaseQueryCompiler)\n \"\"\"\n\n def axis_setter(self, labels):\n new_qc = DataFrameDefault.register(pandas.DataFrame.set_axis)(\n self, axis=axis, labels=labels\n )\n self.__dict__.update(new_qc.__dict__)\n\n return axis_setter\n\n\n# FIXME: many of the BaseQueryCompiler methods are hiding actual arguments\n# by using *args and **kwargs. They should be spread into actual parameters.\n# Currently actual arguments are placed in the methods docstrings, but since they're\n# not presented in the function's signature it makes linter to raise `PR02: unknown parameters`\n# warning. For now, they're silenced by using `noqa` (Modin issue #3108).\nclass BaseQueryCompiler(abc.ABC):\n \"\"\"\n Abstract class that handles the queries to Modin dataframes.\n\n This class defines common query compilers API, most of the methods\n are already implemented and defaulting to pandas.\n\n Attributes\n ----------\n lazy_execution : bool\n Whether underlying execution engine is designed to be executed in a lazy mode only.\n If True, such QueryCompiler will be handled differently at the front-end in order\n to reduce execution triggering as much as possible.\n\n Notes\n -----\n See the Abstract Methods and Fields section immediately below this\n for a list of requirements for subclassing this object.\n \"\"\"\n\n @abc.abstractmethod\n def default_to_pandas(self, pandas_op, *args, **kwargs):\n \"\"\"\n Do fallback to pandas for the passed function.\n\n Parameters\n ----------\n pandas_op : callable(pandas.DataFrame) -> object\n Function to apply to the casted to pandas frame.\n *args : iterable\n Positional arguments to pass to `pandas_op`.\n **kwargs : dict\n Key-value arguments to pass to `pandas_op`.\n\n Returns\n -------\n BaseQueryCompiler\n The result of the `pandas_op`, converted back to ``BaseQueryCompiler``.\n \"\"\"\n pass\n\n # Abstract Methods and Fields: Must implement in children classes\n # In some cases, there you may be able to use the same implementation for\n # some of these abstract methods, but for the sake of generality they are\n # treated differently.\n\n lazy_execution = False\n\n # Metadata modification abstract methods\n def add_prefix(self, prefix, axis=1):\n \"\"\"\n Add string prefix to the index labels along specified axis.\n\n Parameters\n ----------\n prefix : str\n The string to add before each label.\n axis : {0, 1}, default: 1\n Axis to add prefix along. 0 is for index and 1 is for columns.\n\n Returns\n -------\n BaseQueryCompiler\n New query compiler with updated labels.\n \"\"\"\n if axis:\n return DataFrameDefault.register(pandas.DataFrame.add_prefix)(\n self, prefix=prefix\n )\n else:\n return SeriesDefault.register(pandas.Series.add_prefix)(self, prefix=prefix)\n\n def add_suffix(self, suffix, axis=1):\n \"\"\"\n Add string suffix to the index labels along specified axis.\n\n Parameters\n ----------\n suffix : str\n The string to add after each label.\n axis : {0, 1}, default: 1\n Axis to add suffix along. 0 is for index and 1 is for columns.\n\n Returns\n -------\n BaseQueryCompiler\n New query compiler with updated labels.\n \"\"\"\n if axis:\n return DataFrameDefault.register(pandas.DataFrame.add_suffix)(\n self, suffix=suffix\n )\n else:\n return SeriesDefault.register(pandas.Series.add_suffix)(self, suffix=suffix)\n\n # END Metadata modification abstract methods\n\n # Abstract copy\n\n def copy(self):\n \"\"\"\n Make a copy of this object.\n\n Returns\n -------\n BaseQueryCompiler\n Copy of self.\n\n Notes\n -----\n For copy, we don't want a situation where we modify the metadata of the\n copies if we end up modifying something here. We copy all of the metadata\n to prevent that.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.copy)(self)\n\n # END Abstract copy\n\n # Abstract join and append helper functions\n\n def concat(self, axis, other, **kwargs): # noqa: PR02\n \"\"\"\n Concatenate `self` with passed query compilers along specified axis.\n\n Parameters\n ----------\n axis : {0, 1}\n Axis to concatenate along. 0 is for index and 1 is for columns.\n other : BaseQueryCompiler or list of such\n Objects to concatenate with `self`.\n join : {'outer', 'inner', 'right', 'left'}, default: 'outer'\n Type of join that will be used if indices on the other axis are different.\n (note: if specified, has to be passed as ``join=value``).\n ignore_index : bool, default: False\n If True, do not use the index values along the concatenation axis.\n The resulting axis will be labeled 0, …, n - 1.\n (note: if specified, has to be passed as ``ignore_index=value``).\n sort : bool, default: False\n Whether or not to sort non-concatenation axis.\n (note: if specified, has to be passed as ``sort=value``).\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n Concatenated objects.\n \"\"\"\n concat_join = [\"inner\", \"outer\"]\n\n def concat(df, axis, other, **kwargs):\n kwargs.pop(\"join_axes\", None)\n ignore_index = kwargs.get(\"ignore_index\", False)\n if kwargs.get(\"join\", \"outer\") in concat_join:\n if not isinstance(other, list):\n other = [other]\n other = [df] + other\n result = pandas.concat(other, axis=axis, **kwargs)\n else:\n if isinstance(other, (list, np.ndarray)) and len(other) == 1:\n other = other[0]\n ignore_index = kwargs.pop(\"ignore_index\", None)\n kwargs[\"how\"] = kwargs.pop(\"join\", None)\n result = df.join(other, rsuffix=\"r_\", **kwargs)\n if ignore_index:\n if axis == 0:\n result = result.reset_index(drop=True)\n else:\n result.columns = pandas.RangeIndex(len(result.columns))\n return result\n\n return DataFrameDefault.register(concat)(self, axis=axis, other=other, **kwargs)\n\n # END Abstract join and append helper functions\n\n # Data Management Methods\n @abc.abstractmethod\n def free(self):\n \"\"\"Trigger a cleanup of this object.\"\"\"\n pass\n\n @abc.abstractmethod\n def finalize(self):\n \"\"\"Finalize constructing the dataframe calling all deferred functions which were used to build it.\"\"\"\n pass\n\n # END Data Management Methods\n\n # To/From Pandas\n @abc.abstractmethod\n def to_pandas(self):\n \"\"\"\n Convert underlying query compilers data to ``pandas.DataFrame``.\n\n Returns\n -------\n pandas.DataFrame\n The QueryCompiler converted to pandas.\n \"\"\"\n pass\n\n @classmethod\n @abc.abstractmethod\n def from_pandas(cls, df, data_cls):\n \"\"\"\n Build QueryCompiler from pandas DataFrame.\n\n Parameters\n ----------\n df : pandas.DataFrame\n The pandas DataFrame to convert from.\n data_cls : type\n :py:class:`~modin.engines.base.frame.data.BasePandasFrame` class\n (or its descendant) to convert to.\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler containing data from the pandas DataFrame.\n \"\"\"\n pass\n\n # END To/From Pandas\n\n # From Arrow\n @classmethod\n @abc.abstractmethod\n def from_arrow(cls, at, data_cls):\n \"\"\"\n Build QueryCompiler from Arrow Table.\n\n Parameters\n ----------\n at : Arrow Table\n The Arrow Table to convert from.\n data_cls : type\n :py:class:`~modin.engines.base.frame.data.BasePandasFrame` class\n (or its descendant) to convert to.\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler containing data from the pandas DataFrame.\n \"\"\"\n pass\n\n # END From Arrow\n\n # To NumPy\n\n def to_numpy(self, **kwargs): # noqa: PR02\n \"\"\"\n Convert underlying query compilers data to NumPy array.\n\n Parameters\n ----------\n dtype : dtype\n The dtype of the resulted array.\n copy : bool\n Whether to ensure that the returned value is not a view on another array.\n na_value : object\n The value to replace missing values with.\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n np.ndarray\n The QueryCompiler converted to NumPy array.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.to_numpy)(self, **kwargs)\n\n # END To NumPy\n\n # Abstract inter-data operations (e.g. add, sub)\n # These operations require two DataFrames and will change the shape of the\n # data if the index objects don't match. An outer join + op is performed,\n # such that columns/rows that don't have an index on the other DataFrame\n # result in NaN values.\n\n @doc_utils.doc_binary_method(operation=\"addition\", sign=\"+\")\n def add(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.add)(self, other=other, **kwargs)\n\n @doc_utils.add_refer_to(\"DataFrame.combine\")\n def combine(self, other, **kwargs): # noqa: PR02\n \"\"\"\n Perform column-wise combine with another QueryCompiler with passed `func`.\n\n If axes are not equal, perform frames alignment first.\n\n Parameters\n ----------\n other : BaseQueryCompiler\n Left operand of the binary operation.\n func : callable(pandas.Series, pandas.Series) -> pandas.Series\n Function that takes two ``pandas.Series`` with aligned axes\n and returns one ``pandas.Series`` as resulting combination.\n fill_value : float or None\n Value to fill missing values with after frame alignment occurred.\n overwrite : bool\n If True, columns in `self` that do not exist in `other`\n will be overwritten with NaNs.\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n Result of combine.\n \"\"\"\n return BinaryDefault.register(pandas.DataFrame.combine)(\n self, other=other, **kwargs\n )\n\n @doc_utils.add_refer_to(\"DataFrame.combine_first\")\n def combine_first(self, other, **kwargs): # noqa: PR02\n \"\"\"\n Fill null elements of `self` with value in the same location in `other`.\n\n If axes are not equal, perform frames alignment first.\n\n Parameters\n ----------\n other : BaseQueryCompiler\n Provided frame to use to fill null values from.\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n \"\"\"\n return BinaryDefault.register(pandas.DataFrame.combine_first)(\n self, other=other, **kwargs\n )\n\n @doc_utils.doc_binary_method(operation=\"equality comparison\", sign=\"==\")\n def eq(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.eq)(self, other=other, **kwargs)\n\n @doc_utils.doc_binary_method(operation=\"integer division\", sign=\"//\")\n def floordiv(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.floordiv)(\n self, other=other, **kwargs\n )\n\n @doc_utils.doc_binary_method(\n operation=\"greater than or equal comparison\", sign=\">=\", op_type=\"comparison\"\n )\n def ge(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.ge)(self, other=other, **kwargs)\n\n @doc_utils.doc_binary_method(\n operation=\"greater than comparison\", sign=\">\", op_type=\"comparison\"\n )\n def gt(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.gt)(self, other=other, **kwargs)\n\n @doc_utils.doc_binary_method(\n operation=\"less than or equal comparison\", sign=\"<=\", op_type=\"comparison\"\n )\n def le(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.le)(self, other=other, **kwargs)\n\n @doc_utils.doc_binary_method(\n operation=\"less than comparison\", sign=\"<\", op_type=\"comparison\"\n )\n def lt(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.lt)(self, other=other, **kwargs)\n\n @doc_utils.doc_binary_method(operation=\"modulo\", sign=\"%\")\n def mod(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.mod)(self, other=other, **kwargs)\n\n @doc_utils.doc_binary_method(operation=\"multiplication\", sign=\"*\")\n def mul(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.mul)(self, other=other, **kwargs)\n\n @doc_utils.add_refer_to(\"DataFrame.corr\")\n def corr(self, **kwargs): # noqa: PR02\n \"\"\"\n Compute pairwise correlation of columns, excluding NA/null values.\n\n Parameters\n ----------\n method : {'pearson', 'kendall', 'spearman'} or callable(pandas.Series, pandas.Series) -> pandas.Series\n Correlation method.\n min_periods : int\n Minimum number of observations required per pair of columns\n to have a valid result. If fewer than `min_periods` non-NA values\n are present the result will be NA.\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n Correlation matrix.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.corr)(self, **kwargs)\n\n @doc_utils.add_refer_to(\"DataFrame.cov\")\n def cov(self, **kwargs): # noqa: PR02\n \"\"\"\n Compute pairwise covariance of columns, excluding NA/null values.\n\n Parameters\n ----------\n min_periods : int\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n Covariance matrix.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.cov)(self, **kwargs)\n\n def dot(self, other, **kwargs): # noqa: PR02\n \"\"\"\n Compute the matrix multiplication of `self` and `other`.\n\n Parameters\n ----------\n other : BaseQueryCompiler or NumPy array\n The other query compiler or NumPy array to matrix multiply with `self`.\n squeeze_self : boolean\n If `self` is a one-column query compiler, indicates whether it represents Series object.\n squeeze_other : boolean\n If `other` is a one-column query compiler, indicates whether it represents Series object.\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n A new query compiler that contains result of the matrix multiply.\n \"\"\"\n if kwargs.get(\"squeeze_self\", False):\n applyier = pandas.Series.dot\n else:\n applyier = pandas.DataFrame.dot\n return BinaryDefault.register(applyier)(self, other=other, **kwargs)\n\n @doc_utils.doc_binary_method(\n operation=\"not equal comparison\", sign=\"!=\", op_type=\"comparison\"\n )\n def ne(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.ne)(self, other=other, **kwargs)\n\n @doc_utils.doc_binary_method(operation=\"exponential power\", sign=\"**\")\n def pow(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.pow)(self, other=other, **kwargs)\n\n @doc_utils.doc_binary_method(\n operation=\"integer division\", sign=\"//\", self_on_right=True\n )\n def rfloordiv(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.rfloordiv)(\n self, other=other, **kwargs\n )\n\n @doc_utils.doc_binary_method(operation=\"modulo\", sign=\"%\", self_on_right=True)\n def rmod(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.rmod)(\n self, other=other, **kwargs\n )\n\n @doc_utils.doc_binary_method(\n operation=\"exponential power\", sign=\"**\", self_on_right=True\n )\n def rpow(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.rpow)(\n self, other=other, **kwargs\n )\n\n @doc_utils.doc_binary_method(operation=\"substraction\", sign=\"-\", self_on_right=True)\n def rsub(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.rsub)(\n self, other=other, **kwargs\n )\n\n @doc_utils.doc_binary_method(operation=\"division\", sign=\"/\", self_on_right=True)\n def rtruediv(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.rtruediv)(\n self, other=other, **kwargs\n )\n\n @doc_utils.doc_binary_method(operation=\"substraction\", sign=\"-\")\n def sub(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.sub)(self, other=other, **kwargs)\n\n @doc_utils.doc_binary_method(operation=\"division\", sign=\"/\")\n def truediv(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.truediv)(\n self, other=other, **kwargs\n )\n\n @doc_utils.doc_binary_method(operation=\"conjunction\", sign=\"&\", op_type=\"logical\")\n def __and__(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.__and__)(\n self, other=other, **kwargs\n )\n\n @doc_utils.doc_binary_method(operation=\"disjunction\", sign=\"|\", op_type=\"logical\")\n def __or__(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.__or__)(\n self, other=other, **kwargs\n )\n\n @doc_utils.doc_binary_method(\n operation=\"conjunction\", sign=\"&\", op_type=\"logical\", self_on_right=True\n )\n def __rand__(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.__rand__)(\n self, other=other, **kwargs\n )\n\n @doc_utils.doc_binary_method(\n operation=\"disjunction\", sign=\"|\", op_type=\"logical\", self_on_right=True\n )\n def __ror__(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.__ror__)(\n self, other=other, **kwargs\n )\n\n @doc_utils.doc_binary_method(\n operation=\"exclusive or\", sign=\"^\", op_type=\"logical\", self_on_right=True\n )\n def __rxor__(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.__rxor__)(\n self, other=other, **kwargs\n )\n\n @doc_utils.doc_binary_method(operation=\"exclusive or\", sign=\"^\", op_type=\"logical\")\n def __xor__(self, other, **kwargs): # noqa: PR02\n return BinaryDefault.register(pandas.DataFrame.__xor__)(\n self, other=other, **kwargs\n )\n\n # FIXME: query compiler shoudln't care about differences between Frame and Series.\n # We should combine `df_update` and `series_update` into one method (Modin issue #3101).\n @doc_utils.add_refer_to(\"DataFrame.update\")\n def df_update(self, other, **kwargs): # noqa: PR02\n \"\"\"\n Update values of `self` using non-NA values of `other` at the corresponding positions.\n\n If axes are not equal, perform frames alignment first.\n\n Parameters\n ----------\n other : BaseQueryCompiler\n Frame to grab replacement values from.\n join : {\"left\"}\n Specify type of join to align frames if axes are not equal\n (note: currently only one type of join is implemented).\n overwrite : bool\n Whether to overwrite every corresponding value of self, or only if it's NAN.\n filter_func : callable(pandas.Series, pandas.Series) -> numpy.ndarray\n Function that takes column of the self and return bool mask for values, that\n should be overwriten in the self frame.\n errors : {\"raise\", \"ignore\"}\n If \"raise\", will raise a ``ValueError`` if `self` and `other` both contain\n non-NA data in the same place.\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler with updated values.\n \"\"\"\n return BinaryDefault.register(pandas.DataFrame.update, inplace=True)(\n self, other=other, **kwargs\n )\n\n @doc_utils.add_refer_to(\"Series.update\")\n def series_update(self, other, **kwargs): # noqa: PR02\n \"\"\"\n Update values of `self` using values of `other` at the corresponding indices.\n\n Parameters\n ----------\n other : BaseQueryCompiler\n One-column query compiler with updated values.\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler with updated values.\n \"\"\"\n return BinaryDefault.register(pandas.Series.update, inplace=True)(\n self,\n other=other,\n squeeze_self=True,\n squeeze_other=True,\n **kwargs,\n )\n\n @doc_utils.add_refer_to(\"DataFrame.clip\")\n def clip(self, lower, upper, **kwargs): # noqa: PR02\n \"\"\"\n Trim values at input threshold.\n\n Parameters\n ----------\n lower : float or list-like\n upper : float or list-like\n axis : {0, 1}\n inplace : {False}\n This parameter serves the compatibility purpose. Always has to be False.\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler with values limited by the specified thresholds.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.clip)(\n self, lower=lower, upper=upper, **kwargs\n )\n\n @doc_utils.add_refer_to(\"DataFrame.where\")\n def where(self, cond, other, **kwargs): # noqa: PR02\n \"\"\"\n Update values of `self` using values from `other` at positions where `cond` is False.\n\n Parameters\n ----------\n cond : BaseQueryCompiler\n Boolean mask. True - keep the self value, False - replace by `other` value.\n other : BaseQueryCompiler or pandas.Series\n Object to grab replacement values from.\n axis : {0, 1}\n Axis to align frames along if axes of self, `cond` and `other` are not equal.\n 0 is for index, when 1 is for columns.\n level : int or label, optional\n Level of MultiIndex to align frames along if axes of self, `cond`\n and `other` are not equal. Currently `level` parameter is not implemented,\n so only None value is acceptable.\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler with updated data.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.where)(\n self, cond=cond, other=other, **kwargs\n )\n\n @doc_utils.add_refer_to(\"DataFrame.merge\")\n def merge(self, right, **kwargs): # noqa: PR02\n \"\"\"\n Merge QueryCompiler objects using a database-style join.\n\n Parameters\n ----------\n right : BaseQueryCompiler\n QueryCompiler of the right frame to merge with.\n how : {\"left\", \"right\", \"outer\", \"inner\", \"cross\"}\n on : label or list of such\n left_on : label or list of such\n right_on : label or list of such\n left_index : bool\n right_index : bool\n sort : bool\n suffixes : list-like\n copy : bool\n indicator : bool or str\n validate : str\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler that contains result of the merge.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.merge)(\n self, right=right, **kwargs\n )\n\n @doc_utils.add_refer_to(\"DataFrame.join\")\n def join(self, right, **kwargs): # noqa: PR02\n \"\"\"\n Join columns of another QueryCompiler.\n\n Parameters\n ----------\n right : BaseQueryCompiler\n QueryCompiler of the right frame to join with.\n on : label or list of such\n how : {\"left\", \"right\", \"outer\", \"inner\"}\n lsuffix : str\n rsuffix : str\n sort : bool\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler that contains result of the join.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.join)(self, right, **kwargs)\n\n # END Abstract inter-data operations\n\n # Abstract Transpose\n def transpose(self, *args, **kwargs): # noqa: PR02\n \"\"\"\n Transpose this QueryCompiler.\n\n Parameters\n ----------\n copy : bool\n Whether to copy the data after transposing.\n *args : iterable\n Serves the compatibility purpose. Does not affect the result.\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n Transposed new QueryCompiler.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.transpose)(\n self, *args, **kwargs\n )\n\n def columnarize(self):\n \"\"\"\n Transpose this QueryCompiler if it has a single row but multiple columns.\n\n This method should be called for QueryCompilers representing a Series object,\n i.e. ``self.is_series_like()`` should be True.\n\n Returns\n -------\n BaseQueryCompiler\n Transposed new QueryCompiler or self.\n \"\"\"\n if len(self.columns) != 1 or (\n len(self.index) == 1 and self.index[0] == \"__reduced__\"\n ):\n return self.transpose()\n return self\n\n def is_series_like(self):\n \"\"\"\n Check whether this QueryCompiler can represent ``modin.pandas.Series`` object.\n\n Returns\n -------\n bool\n Return True if QueryCompiler has a single column or row, False otherwise.\n \"\"\"\n return len(self.columns) == 1 or len(self.index) == 1\n\n # END Abstract Transpose\n\n # Abstract reindex/reset_index (may shuffle data)\n @doc_utils.add_refer_to(\"DataFrame.reindex\")\n def reindex(self, axis, labels, **kwargs): # noqa: PR02\n \"\"\"\n Align QueryCompiler data with a new index along specified axis.\n\n Parameters\n ----------\n axis : {0, 1}\n Axis to align labels along. 0 is for index, 1 is for columns.\n labels : list-like\n Index-labels to align with.\n method : {None, \"backfill\"/\"bfill\", \"pad\"/\"ffill\", \"nearest\"}\n Method to use for filling holes in reindexed frame.\n fill_value : scalar\n Value to use for missing values in the resulted frame.\n limit : int\n tolerance : int\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler with aligned axis.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.reindex)(\n self, axis=axis, labels=labels, **kwargs\n )\n\n @doc_utils.add_refer_to(\"DataFrame.reset_index\")\n def reset_index(self, **kwargs): # noqa: PR02\n \"\"\"\n Reset the index, or a level of it.\n\n Parameters\n ----------\n drop : bool\n Whether to drop the reset index or insert it at the beginning of the frame.\n level : int or label, optional\n Level to remove from index. Removes all levels by default.\n col_level : int or label\n If the columns have multiple levels, determines which level the labels\n are inserted into.\n col_fill : label\n If the columns have multiple levels, determines how the other levels\n are named.\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler with reset index.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.reset_index)(self, **kwargs)\n\n def set_index_from_columns(\n self, keys: List[Hashable], drop: bool = True, append: bool = False\n ):\n \"\"\"\n Create new row labels from a list of columns.\n\n Parameters\n ----------\n keys : list of hashable\n The list of column names that will become the new index.\n drop : bool, default: True\n Whether or not to drop the columns provided in the `keys` argument.\n append : bool, default: True\n Whether or not to add the columns in `keys` as new levels appended to the\n existing index.\n\n Returns\n -------\n BaseQueryCompiler\n A new QueryCompiler with updated index.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.set_index)(\n self, keys=keys, drop=drop, append=append\n )\n\n # END Abstract reindex/reset_index\n\n # Full Reduce operations\n #\n # These operations result in a reduced dimensionality of data.\n # Currently, this means a Pandas Series will be returned, but in the future\n # we will implement a Distributed Series, and this will be returned\n # instead.\n\n def is_monotonic_increasing(self):\n \"\"\"\n Return boolean if values in the object are monotonicly increasing.\n\n Returns\n -------\n bool\n \"\"\"\n return SeriesDefault.register(pandas.Series.is_monotonic_increasing)(self)\n\n def is_monotonic_decreasing(self):\n \"\"\"\n Return boolean if values in the object are monotonicly decreasing.\n\n Returns\n -------\n bool\n \"\"\"\n return SeriesDefault.register(pandas.Series.is_monotonic_decreasing)(self)\n\n @doc_utils.doc_reduce_agg(\n method=\"number of non-NaN values\", refer_to=\"count\", extra_params=[\"**kwargs\"]\n )\n def count(self, **kwargs): # noqa: PR02\n return DataFrameDefault.register(pandas.DataFrame.count)(self, **kwargs)\n\n @doc_utils.doc_reduce_agg(\n method=\"maximum value\", refer_to=\"max\", extra_params=[\"skipna\", \"**kwargs\"]\n )\n def max(self, **kwargs): # noqa: PR02\n return DataFrameDefault.register(pandas.DataFrame.max)(self, **kwargs)\n\n @doc_utils.doc_reduce_agg(\n method=\"mean value\", refer_to=\"mean\", extra_params=[\"skipna\", \"**kwargs\"]\n )\n def mean(self, **kwargs): # noqa: PR02\n return DataFrameDefault.register(pandas.DataFrame.mean)(self, **kwargs)\n\n @doc_utils.doc_reduce_agg(\n method=\"minimum value\", refer_to=\"min\", extra_params=[\"skipna\", \"**kwargs\"]\n )\n def min(self, **kwargs): # noqa: PR02\n return DataFrameDefault.register(pandas.DataFrame.min)(self, **kwargs)\n\n @doc_utils.doc_reduce_agg(\n method=\"production\",\n refer_to=\"prod\",\n extra_params=[\"**kwargs\"],\n params=\"axis : {0, 1}\",\n )\n def prod(self, **kwargs): # noqa: PR02\n return DataFrameDefault.register(pandas.DataFrame.prod)(self, **kwargs)\n\n @doc_utils.doc_reduce_agg(\n method=\"sum\",\n refer_to=\"sum\",\n extra_params=[\"**kwargs\"],\n params=\"axis : {0, 1}\",\n )\n def sum(self, **kwargs): # noqa: PR02\n return DataFrameDefault.register(pandas.DataFrame.sum)(self, **kwargs)\n\n @doc_utils.add_refer_to(\"to_datetime\")\n def to_datetime(self, *args, **kwargs):\n \"\"\"\n Convert columns of the QueryCompiler to the datetime dtype.\n\n Parameters\n ----------\n *args : iterable\n **kwargs : dict\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler with all columns converted to datetime dtype.\n \"\"\"\n return SeriesDefault.register(pandas.to_datetime)(self, *args, **kwargs)\n\n # END Abstract full Reduce operations\n\n # Abstract map partitions operations\n # These operations are operations that apply a function to every partition.\n def abs(self):\n \"\"\"\n Get absolute numeric value of each element.\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler with absolute numeric value of each element.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.abs)(self)\n\n def applymap(self, func):\n \"\"\"\n Apply passed function elementwise.\n\n Parameters\n ----------\n func : callable(scalar) -> scalar\n Function to apply to each element of the QueryCompiler.\n\n Returns\n -------\n BaseQueryCompiler\n Transformed QueryCompiler.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.applymap)(self, func=func)\n\n # FIXME: `**kwargs` which follows `numpy.conj` signature was inherited\n # from ``PandasQueryCompiler``, we should get rid of this dependency.\n # (Modin issue #3108)\n def conj(self, **kwargs):\n \"\"\"\n Get the complex conjugate for every element of self.\n\n Parameters\n ----------\n **kwargs : dict\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler with conjugate applied element-wise.\n\n Notes\n -----\n Please refer to ``numpy.conj`` for parameters description.\n \"\"\"\n\n def conj(df, *args, **kwargs):\n return pandas.DataFrame(np.conj(df))\n\n return DataFrameDefault.register(conj)(self, **kwargs)\n\n # FIXME:\n # 1. This function takes Modin Series and DataFrames via `values` parameter,\n # we should avoid leaking of the high-level objects to the query compiler level.\n # (Modin issue #3106)\n # 2. Spread **kwargs into actual arguments (Modin issue #3108).\n def isin(self, **kwargs): # noqa: PR02\n \"\"\"\n Check for each element of `self` whether it's contained in passed `values`.\n\n Parameters\n ----------\n values : list-like, modin.pandas.Series, modin.pandas.DataFrame or dict\n Values to check elements of self in.\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n Boolean mask for self of whether an element at the corresponding\n position is contained in `values`.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.isin)(self, **kwargs)\n\n def isna(self):\n \"\"\"\n Check for each element of self whether it's NaN.\n\n Returns\n -------\n BaseQueryCompiler\n Boolean mask for self of whether an element at the corresponding\n position is NaN.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.isna)(self)\n\n # FIXME: this method is not supposed to take any parameters (Modin issue #3108).\n def negative(self, **kwargs):\n \"\"\"\n Change the sign for every value of self.\n\n Parameters\n ----------\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n\n Notes\n -----\n Be aware, that all QueryCompiler values have to be numeric.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.__neg__)(self, **kwargs)\n\n def notna(self):\n \"\"\"\n Check for each element of `self` whether it's existing (non-missing) value.\n\n Returns\n -------\n BaseQueryCompiler\n Boolean mask for `self` of whether an element at the corresponding\n position is not NaN.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.notna)(self)\n\n @doc_utils.add_refer_to(\"DataFrame.round\")\n def round(self, **kwargs): # noqa: PR02\n \"\"\"\n Round every numeric value up to specified number of decimals.\n\n Parameters\n ----------\n decimals : int or list-like\n Number of decimals to round each column to.\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler with rounded values.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.round)(self, **kwargs)\n\n # FIXME:\n # 1. high-level objects leaks to the query compiler (Modin issue #3106).\n # 2. remove `inplace` parameter.\n @doc_utils.add_refer_to(\"DataFrame.replace\")\n def replace(self, **kwargs): # noqa: PR02\n \"\"\"\n Replace values given in `to_replace` by `value`.\n\n Parameters\n ----------\n to_replace : scalar, list-like, regex, modin.pandas.Series, or None\n value : scalar, list-like, regex or dict\n inplace : {False}\n This parameter serves the compatibility purpose. Always has to be False.\n limit : int or None\n regex : bool or same types as `to_replace`\n method : {\"pad\", \"ffill\", \"bfill\", None}\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler with all `to_replace` values replaced by `value`.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.replace)(self, **kwargs)\n\n @doc_utils.add_one_column_warning\n # FIXME: adding refer-to note will create two instances of the \"Notes\" section,\n # this breaks numpydoc style rules and also crashes the doc-style checker script.\n # For now manually added the refer-to message.\n # @doc_utils.add_refer_to(\"Series.view\")\n def series_view(self, **kwargs): # noqa: PR02\n \"\"\"\n Reinterpret underlying data with new dtype.\n\n Parameters\n ----------\n dtype : dtype\n Data type to reinterpret underlying data with.\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler of the same data in memory, with reinterpreted values.\n\n Notes\n -----\n - Be aware, that if this method do fallback to pandas, then newly created\n QueryCompiler will be the copy of the original data.\n - Please refer to ``modin.pandas.Series.view`` for more information\n about parameters and output format.\n \"\"\"\n return SeriesDefault.register(pandas.Series.view)(self, **kwargs)\n\n @doc_utils.add_one_column_warning\n @doc_utils.add_refer_to(\"to_numeric\")\n def to_numeric(self, *args, **kwargs): # noqa: PR02\n \"\"\"\n Convert underlying data to numeric dtype.\n\n Parameters\n ----------\n errors : {\"ignore\", \"raise\", \"coerce\"}\n downcast : {\"integer\", \"signed\", \"unsigned\", \"float\", None}\n *args : iterable\n Serves the compatibility purpose. Does not affect the result.\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler with converted to numeric values.\n \"\"\"\n return SeriesDefault.register(pandas.to_numeric)(self, *args, **kwargs)\n\n # FIXME: get rid of `**kwargs` parameter (Modin issue #3108).\n @doc_utils.add_one_column_warning\n @doc_utils.add_refer_to(\"Series.unique\")\n def unique(self, **kwargs):\n \"\"\"\n Get unique values of `self`.\n\n Parameters\n ----------\n **kwargs : dict\n Serves compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler with unique values.\n \"\"\"\n return SeriesDefault.register(pandas.Series.unique)(self, **kwargs)\n\n @doc_utils.add_one_column_warning\n @doc_utils.add_refer_to(\"Series.searchsorted\")\n def searchsorted(self, **kwargs): # noqa: PR02\n \"\"\"\n Find positions in a sorted `self` where `value` should be inserted to maintain order.\n\n Parameters\n ----------\n value : list-like\n side : {\"left\", \"right\"}\n sorter : list-like, optional\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n One-column QueryCompiler which contains indices to insert.\n \"\"\"\n return SeriesDefault.register(pandas.Series.searchsorted)(self, **kwargs)\n\n # END Abstract map partitions operations\n\n @doc_utils.add_one_column_warning\n @doc_utils.add_refer_to(\"Series.value_counts\")\n def value_counts(self, **kwargs): # noqa: PR02\n \"\"\"\n Count unique values of one-column `self`.\n\n Parameters\n ----------\n normalize : bool\n sort : bool\n ascending : bool\n bins : int, optional\n dropna : bool\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n One-column QueryCompiler which index labels is a unique elements of `self`\n and each row contains the number of times corresponding value was met in the `self`.\n \"\"\"\n return SeriesDefault.register(pandas.Series.value_counts)(self, **kwargs)\n\n @doc_utils.add_refer_to(\"DataFrame.stack\")\n def stack(self, level, dropna):\n \"\"\"\n Stack the prescribed level(s) from columns to index.\n\n Parameters\n ----------\n level : int or label\n dropna : bool\n\n Returns\n -------\n BaseQueryCompiler\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.stack)(\n self, level=level, dropna=dropna\n )\n\n # Abstract map partitions across select indices\n def astype(self, col_dtypes, **kwargs): # noqa: PR02\n \"\"\"\n Convert columns dtypes to given dtypes.\n\n Parameters\n ----------\n col_dtypes : dict\n Map for column names and new dtypes.\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler with updated dtypes.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.astype)(\n self, dtype=col_dtypes, **kwargs\n )\n\n @property\n def dtypes(self):\n \"\"\"\n Get columns dtypes.\n\n Returns\n -------\n pandas.Series\n Series with dtypes of each column.\n \"\"\"\n return self.to_pandas().dtypes\n\n # END Abstract map partitions across select indices\n\n # Abstract column/row partitions reduce operations\n #\n # These operations result in a reduced dimensionality of data.\n # Currently, this means a Pandas Series will be returned, but in the future\n # we will implement a Distributed Series, and this will be returned\n # instead.\n\n # FIXME: we're handling level parameter at front-end, it shouldn't\n # propagate to the query compiler (Modin issue #3102)\n @doc_utils.add_refer_to(\"DataFrame.all\")\n def all(self, **kwargs): # noqa: PR02\n \"\"\"\n Return whether all the elements are true, potentially over an axis.\n\n Parameters\n ----------\n axis : {0, 1}, optional\n bool_only : bool, optional\n skipna : bool\n level : int or label\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n If axis was specified return one-column QueryCompiler with index labels\n of the specified axis, where each row contains boolean of whether all elements\n at the corresponding row or column are True. Otherwise return QueryCompiler\n with a single bool of whether all elements are True.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.all)(self, **kwargs)\n\n @doc_utils.add_refer_to(\"DataFrame.any\")\n def any(self, **kwargs): # noqa: PR02\n \"\"\"\n Return whether any element is true, potentially over an axis.\n\n Parameters\n ----------\n axis : {0, 1}, optional\n bool_only : bool, optional\n skipna : bool\n level : int or label\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n If axis was specified return one-column QueryCompiler with index labels\n of the specified axis, where each row contains boolean of whether any element\n at the corresponding row or column is True. Otherwise return QueryCompiler\n with a single bool of whether any element is True.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.any)(self, **kwargs)\n\n def first_valid_index(self):\n \"\"\"\n Return index label of first non-NaN/NULL value.\n\n Returns\n -------\n scalar\n \"\"\"\n return (\n DataFrameDefault.register(pandas.DataFrame.first_valid_index)(self)\n .to_pandas()\n .squeeze()\n )\n\n @doc_utils.add_refer_to(\"DataFrame.idxmax\")\n def idxmax(self, **kwargs): # noqa: PR02\n \"\"\"\n Get position of the first occurence of the maximum for each row or column.\n\n Parameters\n ----------\n axis : {0, 1}\n skipna : bool\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n One-column QueryCompiler with index labels of the specified axis,\n where each row contains position of the maximum element for the\n corresponding row or column.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.idxmax)(self, **kwargs)\n\n @doc_utils.add_refer_to(\"DataFrame.idxmin\")\n def idxmin(self, **kwargs): # noqa: PR02\n \"\"\"\n Get position of the first occurence of the minimum for each row or column.\n\n Parameters\n ----------\n axis : {0, 1}\n skipna : bool\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n One-column QueryCompiler with index labels of the specified axis,\n where each row contains position of the minimum element for the\n corresponding row or column.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.idxmin)(self, **kwargs)\n\n def last_valid_index(self):\n \"\"\"\n Return index label of last non-NaN/NULL value.\n\n Returns\n -------\n scalar\n \"\"\"\n return (\n DataFrameDefault.register(pandas.DataFrame.last_valid_index)(self)\n .to_pandas()\n .squeeze()\n )\n\n @doc_utils.doc_reduce_agg(\n method=\"median value\", refer_to=\"median\", extra_params=[\"skipna\", \"**kwargs\"]\n )\n def median(self, **kwargs): # noqa: PR02\n return DataFrameDefault.register(pandas.DataFrame.median)(self, **kwargs)\n\n @doc_utils.add_refer_to(\"DataFrame.memory_usage\")\n def memory_usage(self, **kwargs): # noqa: PR02\n \"\"\"\n Return the memory usage of each column in bytes.\n\n Parameters\n ----------\n index : bool\n deep : bool\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n One-column QueryCompiler with index labels of `self`, where each row\n contains the memory usage for the corresponding column.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.memory_usage)(self, **kwargs)\n\n @doc_utils.doc_reduce_agg(\n method=\"number of unique values\",\n refer_to=\"nunique\",\n params=\"\"\"\n axis : {0, 1}\n dropna : bool\"\"\",\n extra_params=[\"**kwargs\"],\n )\n def nunique(self, **kwargs): # noqa: PR02\n return DataFrameDefault.register(pandas.DataFrame.nunique)(self, **kwargs)\n\n @doc_utils.doc_reduce_agg(\n method=\"value at the given quantile\",\n refer_to=\"quantile\",\n params=\"\"\"\n q : float\n axis : {0, 1}\n numeric_only : bool\n interpolation : {\"linear\", \"lower\", \"higher\", \"midpoint\", \"nearest\"}\"\"\",\n extra_params=[\"**kwargs\"],\n )\n def quantile_for_single_value(self, **kwargs): # noqa: PR02\n return DataFrameDefault.register(pandas.DataFrame.quantile)(self, **kwargs)\n\n @doc_utils.doc_reduce_agg(\n method=\"unbiased skew\", refer_to=\"skew\", extra_params=[\"skipna\", \"**kwargs\"]\n )\n def skew(self, **kwargs): # noqa: PR02\n return DataFrameDefault.register(pandas.DataFrame.skew)(self, **kwargs)\n\n @doc_utils.doc_reduce_agg(\n method=\"standard deviation of the mean\",\n refer_to=\"sem\",\n extra_params=[\"skipna\", \"ddof\", \"**kwargs\"],\n )\n def sem(self, **kwargs): # noqa: PR02\n return DataFrameDefault.register(pandas.DataFrame.sem)(self, **kwargs)\n\n @doc_utils.doc_reduce_agg(\n method=\"standard deviation\",\n refer_to=\"std\",\n extra_params=[\"skipna\", \"ddof\", \"**kwargs\"],\n )\n def std(self, **kwargs): # noqa: PR02\n return DataFrameDefault.register(pandas.DataFrame.std)(self, **kwargs)\n\n @doc_utils.doc_reduce_agg(\n method=\"variance\", refer_to=\"var\", extra_params=[\"skipna\", \"ddof\", \"**kwargs\"]\n )\n def var(self, **kwargs): # noqa: PR02\n return DataFrameDefault.register(pandas.DataFrame.var)(self, **kwargs)\n\n # END Abstract column/row partitions reduce operations\n\n # Abstract column/row partitions reduce operations over select indices\n #\n # These operations result in a reduced dimensionality of data.\n # Currently, this means a Pandas Series will be returned, but in the future\n # we will implement a Distributed Series, and this will be returned\n # instead.\n @doc_utils.add_refer_to(\"DataFrame.describe\")\n def describe(self, **kwargs): # noqa: PR02\n \"\"\"\n Generate descriptive statistics.\n\n Parameters\n ----------\n percentiles : list-like\n include : \"all\" or list of dtypes, optional\n exclude : list of dtypes, optional\n datetime_is_numeric : bool\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler object containing the descriptive statistics\n of the underlying data.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.describe)(self, **kwargs)\n\n # END Abstract column/row partitions reduce operations over select indices\n\n # Map across rows/columns\n # These operations require some global knowledge of the full column/row\n # that is being operated on. This means that we have to put all of that\n # data in the same place.\n\n @doc_utils.doc_cum_agg(method=\"sum\", refer_to=\"cumsum\")\n def cumsum(self, **kwargs): # noqa: PR02\n return DataFrameDefault.register(pandas.DataFrame.cumsum)(self, **kwargs)\n\n @doc_utils.doc_cum_agg(method=\"maximum\", refer_to=\"cummax\")\n def cummax(self, **kwargs): # noqa: PR02\n return DataFrameDefault.register(pandas.DataFrame.cummax)(self, **kwargs)\n\n @doc_utils.doc_cum_agg(method=\"minimum\", refer_to=\"cummin\")\n def cummin(self, **kwargs): # noqa: PR02\n return DataFrameDefault.register(pandas.DataFrame.cummin)(self, **kwargs)\n\n @doc_utils.doc_cum_agg(method=\"product\", refer_to=\"cumprod\")\n def cumprod(self, **kwargs): # noqa: PR02\n return DataFrameDefault.register(pandas.DataFrame.cumprod)(self, **kwargs)\n\n @doc_utils.add_refer_to(\"DataFrame.diff\")\n def diff(self, **kwargs): # noqa: PR02\n \"\"\"\n First discrete difference of element.\n\n Parameters\n ----------\n periods : int\n axis : {0, 1}\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler of the same shape as `self`, where each element is the difference\n between the corresponding value and the previous value in this row or column.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.diff)(self, **kwargs)\n\n @doc_utils.add_refer_to(\"DataFrame.dropna\")\n def dropna(self, **kwargs): # noqa: PR02\n \"\"\"\n Remove missing values.\n\n Parameters\n ----------\n axis : {0, 1}\n how : {\"any\", \"all\"}\n thresh : int, optional\n subset : list of labels\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler with null values dropped along given axis.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.dropna)(self, **kwargs)\n\n @doc_utils.add_refer_to(\"DataFrame.nlargest\")\n def nlargest(self, n=5, columns=None, keep=\"first\"):\n \"\"\"\n Return the first `n` rows ordered by `columns` in descending order.\n\n Parameters\n ----------\n n : int, default: 5\n columns : list of labels, optional\n Column labels to order by.\n (note: this parameter can be omitted only for a single-column query compilers\n representing Series object, otherwise `columns` has to be specified).\n keep : {\"first\", \"last\", \"all\"}, default: \"first\"\n\n Returns\n -------\n BaseQueryCompiler\n \"\"\"\n if columns is None:\n return SeriesDefault.register(pandas.Series.nlargest)(self, n=n, keep=keep)\n else:\n return DataFrameDefault.register(pandas.DataFrame.nlargest)(\n self, n=n, columns=columns, keep=keep\n )\n\n @doc_utils.add_refer_to(\"DataFrame.nsmallest\")\n def nsmallest(self, n=5, columns=None, keep=\"first\"):\n \"\"\"\n Return the first `n` rows ordered by `columns` in ascending order.\n\n Parameters\n ----------\n n : int, default: 5\n columns : list of labels, optional\n Column labels to order by.\n (note: this parameter can be omitted only for a single-column query compilers\n representing Series object, otherwise `columns` has to be specified).\n keep : {\"first\", \"last\", \"all\"}, default: \"first\"\n\n Returns\n -------\n BaseQueryCompiler\n \"\"\"\n if columns is None:\n return SeriesDefault.register(pandas.Series.nsmallest)(self, n=n, keep=keep)\n else:\n return DataFrameDefault.register(pandas.DataFrame.nsmallest)(\n self, n=n, columns=columns, keep=keep\n )\n\n @doc_utils.add_refer_to(\"DataFrame.eval\")\n def eval(self, expr, **kwargs):\n \"\"\"\n Evaluate string expression on QueryCompiler columns.\n\n Parameters\n ----------\n expr : str\n **kwargs : dict\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler containing the result of evaluation.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.eval)(\n self, expr=expr, **kwargs\n )\n\n @doc_utils.add_refer_to(\"DataFrame.mode\")\n def mode(self, **kwargs): # noqa: PR02\n \"\"\"\n Get the modes for every column or row.\n\n Parameters\n ----------\n axis : {0, 1}\n numeric_only : bool\n dropna : bool\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler with modes calculated alogn given axis.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.mode)(self, **kwargs)\n\n @doc_utils.add_refer_to(\"DataFrame.fillna\")\n def fillna(self, **kwargs): # noqa: PR02\n \"\"\"\n Replace NaN values using provided method.\n\n Parameters\n ----------\n value : scalar or dict\n method : {\"backfill\", \"bfill\", \"pad\", \"ffill\", None}\n axis : {0, 1}\n inplace : {False}\n This parameter serves the compatibility purpose. Always has to be False.\n limit : int, optional\n downcast : dict, optional\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler with all null values filled.\n \"\"\"\n squeeze_self = kwargs.pop(\"squeeze_self\", False)\n squeeze_value = kwargs.pop(\"squeeze_value\", False)\n\n def fillna(df, value, **kwargs):\n if squeeze_self:\n df = df.squeeze(axis=1)\n if squeeze_value:\n value = value.squeeze(axis=1)\n return df.fillna(value, **kwargs)\n\n return DataFrameDefault.register(fillna)(self, **kwargs)\n\n @doc_utils.add_refer_to(\"DataFrame.query\")\n def query(self, expr, **kwargs):\n \"\"\"\n Query columns of the QueryCompiler with a boolean expression.\n\n Parameters\n ----------\n expr : str\n **kwargs : dict\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler containing the rows where the boolean expression is satisfied.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.query)(\n self, expr=expr, **kwargs\n )\n\n @doc_utils.add_refer_to(\"DataFrame.rank\")\n def rank(self, **kwargs): # noqa: PR02\n \"\"\"\n Compute numerical rank along the specified axis.\n\n By default, equal values are assigned a rank that is the average of the ranks\n of those values, this behaviour can be changed via `method` parameter.\n\n Parameters\n ----------\n axis : {0, 1}\n method : {\"average\", \"min\", \"max\", \"first\", \"dense\"}\n numeric_only : bool\n na_option : {\"keep\", \"top\", \"bottom\"}\n ascending : bool\n pct : bool\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler of the same shape as `self`, where each element is the\n numerical rank of the corresponding value along row or column.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.rank)(self, **kwargs)\n\n @doc_utils.add_refer_to(\"DataFrame.sort_index\")\n def sort_index(self, **kwargs): # noqa: PR02\n \"\"\"\n Sort data by index or column labels.\n\n Parameters\n ----------\n axis : {0, 1}\n level : int, label or list of such\n ascending : bool\n inplace : bool\n kind : {\"quicksort\", \"mergesort\", \"heapsort\"}\n na_position : {\"first\", \"last\"}\n sort_remaining : bool\n ignore_index : bool\n key : callable(pandas.Index) -> pandas.Index, optional\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler containing the data sorted by columns or indices.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.sort_index)(self, **kwargs)\n\n @doc_utils.add_refer_to(\"DataFrame.melt\")\n def melt(self, *args, **kwargs): # noqa: PR02\n \"\"\"\n Unpivot QueryCompiler data from wide to long format.\n\n Parameters\n ----------\n id_vars : list of labels, optional\n value_vars : list of labels, optional\n var_name : label\n value_name : label\n col_level : int or label\n ignore_index : bool\n *args : iterable\n Serves the compatibility purpose. Does not affect the result.\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler with unpivoted data.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.melt)(self, *args, **kwargs)\n\n @doc_utils.add_refer_to(\"DataFrame.sort_values\")\n def sort_columns_by_row_values(self, rows, ascending=True, **kwargs): # noqa: PR02\n \"\"\"\n Reorder the columns based on the lexicographic order of the given rows.\n\n Parameters\n ----------\n rows : label or list of labels\n The row or rows to sort by.\n ascending : bool, default: True\n Sort in ascending order (True) or descending order (False).\n kind : {\"quicksort\", \"mergesort\", \"heapsort\"}\n na_position : {\"first\", \"last\"}\n ignore_index : bool\n key : callable(pandas.Index) -> pandas.Index, optional\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler that contains result of the sort.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.sort_values)(\n self, by=rows, axis=1, ascending=ascending, **kwargs\n )\n\n @doc_utils.add_refer_to(\"DataFrame.sort_values\")\n def sort_rows_by_column_values(\n self, columns, ascending=True, **kwargs\n ): # noqa: PR02\n \"\"\"\n Reorder the rows based on the lexicographic order of the given columns.\n\n Parameters\n ----------\n columns : label or list of labels\n The column or columns to sort by.\n ascending : bool, default: True\n Sort in ascending order (True) or descending order (False).\n kind : {\"quicksort\", \"mergesort\", \"heapsort\"}\n na_position : {\"first\", \"last\"}\n ignore_index : bool\n key : callable(pandas.Index) -> pandas.Index, optional\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler that contains result of the sort.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.sort_values)(\n self, by=columns, axis=0, ascending=ascending, **kwargs\n )\n\n # END Abstract map across rows/columns\n\n # Map across rows/columns\n # These operations require some global knowledge of the full column/row\n # that is being operated on. This means that we have to put all of that\n # data in the same place.\n @doc_utils.doc_reduce_agg(\n method=\"value at the given quantile\",\n refer_to=\"quantile\",\n params=\"\"\"\n q : list-like\n axis : {0, 1}\n numeric_only : bool\n interpolation : {\"linear\", \"lower\", \"higher\", \"midpoint\", \"nearest\"}\"\"\",\n extra_params=[\"**kwargs\"],\n )\n def quantile_for_list_of_values(self, **kwargs): # noqa: PR02\n return DataFrameDefault.register(pandas.DataFrame.quantile)(self, **kwargs)\n\n # END Abstract map across rows/columns\n\n # Abstract __getitem__ methods\n def getitem_array(self, key):\n \"\"\"\n Mask QueryCompiler with `key`.\n\n Parameters\n ----------\n key : BaseQueryCompiler, np.ndarray or list of column labels\n Boolean mask represented by QueryCompiler or ``np.ndarray`` of the same\n shape as `self`, or enumerable of columns to pick.\n\n Returns\n -------\n BaseQueryCompiler\n New masked QueryCompiler.\n \"\"\"\n\n def getitem_array(df, key):\n return df[key]\n\n return DataFrameDefault.register(getitem_array)(self, key)\n\n def getitem_column_array(self, key, numeric=False):\n \"\"\"\n Get column data for target labels.\n\n Parameters\n ----------\n key : list-like\n Target labels by which to retrieve data.\n numeric : bool, default: False\n Whether or not the key passed in represents the numeric index\n or the named index.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler that contains specified columns.\n \"\"\"\n\n def get_column(df, key):\n if numeric:\n return df.iloc[:, key]\n else:\n return df[key]\n\n return DataFrameDefault.register(get_column)(self, key=key)\n\n def getitem_row_array(self, key):\n \"\"\"\n Get row data for target indices.\n\n Parameters\n ----------\n key : list-like\n Numeric indices of the rows to pick.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler that contains specified rows.\n \"\"\"\n\n def get_row(df, key):\n return df.iloc[key]\n\n return DataFrameDefault.register(get_row)(self, key=key)\n\n # END Abstract __getitem__ methods\n\n # Abstract insert\n # This method changes the shape of the resulting data. In Pandas, this\n # operation is always inplace, but this object is immutable, so we just\n # return a new one from here and let the front end handle the inplace\n # update.\n def insert(self, loc, column, value):\n \"\"\"\n Insert new column.\n\n Parameters\n ----------\n loc : int\n Insertion position.\n column : label\n Label of the new column.\n value : One-column BaseQueryCompiler, 1D array or scalar\n Data to fill new column with.\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler with new column inserted.\n \"\"\"\n\n def inserter(df, loc, column, value):\n if isinstance(value, pandas.DataFrame):\n value = value.squeeze(axis=1)\n df.insert(loc, column, value)\n return df\n\n return DataFrameDefault.register(inserter)(\n self, loc=loc, column=column, value=value\n )\n\n # END Abstract insert\n\n # Abstract drop\n def drop(self, index=None, columns=None):\n \"\"\"\n Drop specified rows or columns.\n\n Parameters\n ----------\n index : list of labels, optional\n Labels of rows to drop.\n columns : list of labels, optional\n Labels of columns to drop.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler with removed data.\n \"\"\"\n if index is None and columns is None:\n return self\n else:\n return DataFrameDefault.register(pandas.DataFrame.drop)(\n self, index=index, columns=columns\n )\n\n # END drop\n\n # UDF (apply and agg) methods\n # There is a wide range of behaviors that are supported, so a lot of the\n # logic can get a bit convoluted.\n def apply(self, func, axis, *args, **kwargs):\n \"\"\"\n Apply passed function across given axis.\n\n Parameters\n ----------\n func : callable(pandas.Series) -> scalar, str, list or dict of such\n The function to apply to each column or row.\n axis : {0, 1}\n Target axis to apply the function along.\n 0 is for index, 1 is for columns.\n *args : iterable\n Positional arguments to pass to `func`.\n **kwargs : dict\n Keyword arguments to pass to `func`.\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler that contains the results of execution and is built by\n the following rules:\n\n - Labels of specified axis are the passed functions names.\n - Labels of the opposite axis are preserved.\n - Each element is the result of execution of `func` against\n corresponding row/column.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.apply)(\n self, func=func, axis=axis, *args, **kwargs\n )\n\n # END UDF\n\n # Manual Partitioning methods (e.g. merge, groupby)\n # These methods require some sort of manual partitioning due to their\n # nature. They require certain data to exist on the same partition, and\n # after the shuffle, there should be only a local map required.\n\n # FIXME: `map_args` and `reduce_args` leaked there from `PandasQueryCompiler.groupby_*`,\n # pandas backend implements groupby via MapReduce approach, but for other backends these\n # parameters make no sense, they shouldn't be present in a base class.\n\n @doc_utils.doc_groupby_method(\n action=\"count non-null values\",\n result=\"number of non-null values\",\n refer_to=\"count\",\n )\n def groupby_count(\n self,\n by,\n axis,\n groupby_args,\n map_args,\n reduce_args=None,\n numeric_only=True,\n drop=False,\n ):\n return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.count)(\n self,\n by=by,\n axis=axis,\n groupby_args=groupby_args,\n map_args=map_args,\n reduce_args=reduce_args,\n numeric_only=numeric_only,\n drop=drop,\n )\n\n @doc_utils.doc_groupby_method(\n action=\"check whether any element is True\",\n result=\"boolean of whether there is any element which is True\",\n refer_to=\"any\",\n )\n def groupby_any(\n self,\n by,\n axis,\n groupby_args,\n map_args,\n reduce_args=None,\n numeric_only=True,\n drop=False,\n ):\n return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.any)(\n self,\n by=by,\n axis=axis,\n groupby_args=groupby_args,\n map_args=map_args,\n reduce_args=reduce_args,\n numeric_only=numeric_only,\n drop=drop,\n )\n\n @doc_utils.doc_groupby_method(\n action=\"get the minimum value\", result=\"minimum value\", refer_to=\"min\"\n )\n def groupby_min(\n self,\n by,\n axis,\n groupby_args,\n map_args,\n reduce_args=None,\n numeric_only=True,\n drop=False,\n ):\n return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.min)(\n self,\n by=by,\n axis=axis,\n groupby_args=groupby_args,\n map_args=map_args,\n reduce_args=reduce_args,\n numeric_only=numeric_only,\n drop=drop,\n )\n\n @doc_utils.doc_groupby_method(result=\"product\", refer_to=\"prod\")\n def groupby_prod(\n self,\n by,\n axis,\n groupby_args,\n map_args,\n reduce_args=None,\n numeric_only=True,\n drop=False,\n ):\n return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.prod)(\n self,\n by=by,\n axis=axis,\n groupby_args=groupby_args,\n map_args=map_args,\n reduce_args=reduce_args,\n numeric_only=numeric_only,\n drop=drop,\n )\n\n @doc_utils.doc_groupby_method(\n action=\"get the maximum value\", result=\"maximum value\", refer_to=\"max\"\n )\n def groupby_max(\n self,\n by,\n axis,\n groupby_args,\n map_args,\n reduce_args=None,\n numeric_only=True,\n drop=False,\n ):\n return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.max)(\n self,\n by=by,\n axis=axis,\n groupby_args=groupby_args,\n map_args=map_args,\n reduce_args=reduce_args,\n numeric_only=numeric_only,\n drop=drop,\n )\n\n @doc_utils.doc_groupby_method(\n action=\"check whether all elements are True\",\n result=\"boolean of whether all elements are True\",\n refer_to=\"all\",\n )\n def groupby_all(\n self,\n by,\n axis,\n groupby_args,\n map_args,\n reduce_args=None,\n numeric_only=True,\n drop=False,\n ):\n return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.all)(\n self,\n by=by,\n axis=axis,\n groupby_args=groupby_args,\n map_args=map_args,\n reduce_args=reduce_args,\n numeric_only=numeric_only,\n drop=drop,\n )\n\n @doc_utils.doc_groupby_method(result=\"sum\", refer_to=\"sum\")\n def groupby_sum(\n self,\n by,\n axis,\n groupby_args,\n map_args,\n reduce_args=None,\n numeric_only=True,\n drop=False,\n ):\n return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.sum)(\n self,\n by=by,\n axis=axis,\n groupby_args=groupby_args,\n map_args=map_args,\n reduce_args=reduce_args,\n numeric_only=numeric_only,\n drop=drop,\n )\n\n @doc_utils.doc_groupby_method(\n action=\"get the number of elements\",\n result=\"number of elements\",\n refer_to=\"size\",\n )\n def groupby_size(\n self,\n by,\n axis,\n groupby_args,\n map_args,\n reduce_args=None,\n numeric_only=True,\n drop=False,\n ):\n return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.size)(\n self,\n by=by,\n axis=axis,\n groupby_args=groupby_args,\n map_args=map_args,\n reduce_args=reduce_args,\n numeric_only=numeric_only,\n drop=drop,\n method=\"size\",\n )\n\n @doc_utils.add_refer_to(\"GroupBy.aggregate\")\n def groupby_agg(\n self,\n by,\n is_multi_by,\n axis,\n agg_func,\n agg_args,\n agg_kwargs,\n groupby_kwargs,\n drop=False,\n ):\n \"\"\"\n Group QueryCompiler data and apply passed aggregation function.\n\n Parameters\n ----------\n by : BaseQueryCompiler, column or index label, Grouper or list of such\n Object that determine groups.\n is_multi_by : bool\n If `by` is a QueryCompiler or list of such indicates whether it's\n grouping on multiple columns/rows.\n axis : {0, 1}\n Axis to group and apply aggregation function along.\n 0 is for index, when 1 is for columns.\n agg_func : dict or callable(DataFrameGroupBy) -> DataFrame\n Function to apply to the GroupBy object.\n agg_args : dict\n Positional arguments to pass to the `agg_func`.\n agg_kwargs : dict\n Key arguments to pass to the `agg_func`.\n groupby_kwargs : dict\n GroupBy parameters as expected by ``modin.pandas.DataFrame.groupby`` signature.\n drop : bool, default: False\n If `by` is a QueryCompiler indicates whether or not by-data came\n from the `self`.\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler containing the result of groupby aggregation.\n \"\"\"\n if isinstance(by, type(self)) and len(by.columns) == 1:\n by = by.columns[0] if drop else by.to_pandas().squeeze()\n elif isinstance(by, type(self)):\n by = list(by.columns)\n\n return GroupByDefault.register(pandas.core.groupby.DataFrameGroupBy.aggregate)(\n self,\n by=by,\n is_multi_by=is_multi_by,\n axis=axis,\n agg_func=agg_func,\n groupby_args=groupby_kwargs,\n agg_args=agg_kwargs,\n drop=drop,\n )\n\n # END Manual Partitioning methods\n\n @doc_utils.add_refer_to(\"DataFrame.unstack\")\n def unstack(self, level, fill_value):\n \"\"\"\n Pivot a level of the (necessarily hierarchical) index labels.\n\n Parameters\n ----------\n level : int or label\n fill_value : scalar or dict\n\n Returns\n -------\n BaseQueryCompiler\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.unstack)(\n self, level=level, fill_value=fill_value\n )\n\n @doc_utils.add_refer_to(\"DataFrame.pivot\")\n def pivot(self, index, columns, values):\n \"\"\"\n Produce pivot table based on column values.\n\n Parameters\n ----------\n index : label or list of such, pandas.Index, optional\n columns : label or list of such\n values : label or list of such, optional\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler containing pivot table.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.pivot)(\n self, index=index, columns=columns, values=values\n )\n\n @doc_utils.add_refer_to(\"DataFrame.pivot_table\")\n def pivot_table(\n self,\n index,\n values,\n columns,\n aggfunc,\n fill_value,\n margins,\n dropna,\n margins_name,\n observed,\n sort,\n ):\n \"\"\"\n Create a spreadsheet-style pivot table from underlying data.\n\n Parameters\n ----------\n index : label, pandas.Grouper, array or list of such\n values : label, optional\n columns : column, pandas.Grouper, array or list of such\n aggfunc : callable(pandas.Series) -> scalar, dict of list of such\n fill_value : scalar, optional\n margins : bool\n dropna : bool\n margins_name : str\n observed : bool\n sort : bool\n\n Returns\n -------\n BaseQueryCompiler\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.pivot_table)(\n self,\n index=index,\n values=values,\n columns=columns,\n aggfunc=aggfunc,\n fill_value=fill_value,\n margins=margins,\n dropna=dropna,\n margins_name=margins_name,\n observed=observed,\n sort=sort,\n )\n\n @doc_utils.add_refer_to(\"get_dummies\")\n def get_dummies(self, columns, **kwargs): # noqa: PR02\n \"\"\"\n Convert categorical variables to dummy variables for certain columns.\n\n Parameters\n ----------\n columns : label or list of such\n Columns to convert.\n prefix : str or list of such\n prefix_sep : str\n dummy_na : bool\n drop_first : bool\n dtype : dtype\n **kwargs : dict\n Serves the compatibility purpose. Does not affect the result.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler with categorical variables converted to dummy.\n \"\"\"\n\n def get_dummies(df, columns, **kwargs):\n return pandas.get_dummies(df, columns=columns, **kwargs)\n\n return DataFrameDefault.register(get_dummies)(self, columns=columns, **kwargs)\n\n @doc_utils.add_one_column_warning\n @doc_utils.add_refer_to(\"Series.repeat\")\n def repeat(self, repeats):\n \"\"\"\n Repeat each element of one-column QueryCompiler given number of times.\n\n Parameters\n ----------\n repeats : int or array of ints\n The number of repetitions for each element. This should be a\n non-negative integer. Repeating 0 times will return an empty\n QueryCompiler.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler with repeated elements.\n \"\"\"\n return SeriesDefault.register(pandas.Series.repeat)(self, repeats=repeats)\n\n # Indexing\n\n index = property(_get_axis(0), _set_axis(0))\n columns = property(_get_axis(1), _set_axis(1))\n\n def get_axis(self, axis):\n \"\"\"\n Return index labels of the specified axis.\n\n Parameters\n ----------\n axis : {0, 1}\n Axis to return labels on.\n 0 is for index, when 1 is for columns.\n\n Returns\n -------\n pandas.Index\n \"\"\"\n return self.index if axis == 0 else self.columns\n\n def view(self, index=None, columns=None):\n \"\"\"\n Mask QueryCompiler with passed keys.\n\n Parameters\n ----------\n index : list of ints, optional\n Positional indices of rows to grab.\n columns : list of ints, optional\n Positional indices of columns to grab.\n\n Returns\n -------\n BaseQueryCompiler\n New masked QueryCompiler.\n \"\"\"\n index = [] if index is None else index\n columns = [] if columns is None else columns\n\n def applyier(df):\n return df.iloc[index, columns]\n\n return DataFrameDefault.register(applyier)(self)\n\n def insert_item(self, axis, loc, value, how=\"inner\", replace=False):\n \"\"\"\n Insert rows/columns defined by `value` at the specified position.\n\n If frames are not aligned along specified axis, perform frames alignment first.\n\n Parameters\n ----------\n axis : {0, 1}\n Axis to insert along. 0 means insert rows, when 1 means insert columns.\n loc : int\n Position to insert `value`.\n value : BaseQueryCompiler\n Rows/columns to insert.\n how : {\"inner\", \"outer\", \"left\", \"right\"}, default: \"inner\"\n Type of join that will be used if frames are not aligned.\n replace : bool, default: False\n Whether to insert item after column/row at `loc-th` position or to replace\n it by `value`.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler with inserted values.\n \"\"\"\n assert isinstance(value, type(self))\n\n def mask(idx):\n if len(idx) == len(self.get_axis(axis)):\n return self\n return (\n self.getitem_column_array(idx, numeric=True)\n if axis\n else self.getitem_row_array(idx)\n )\n\n if 0 <= loc < len(self.get_axis(axis)):\n first_mask = mask(list(range(loc)))\n second_mask_loc = loc + 1 if replace else loc\n second_mask = mask(list(range(second_mask_loc, len(self.get_axis(axis)))))\n return first_mask.concat(axis, [value, second_mask], join=how, sort=False)\n else:\n return self.concat(axis, [value], join=how, sort=False)\n\n def setitem(self, axis, key, value):\n \"\"\"\n Set the row/column defined by `key` to the `value` provided.\n\n Parameters\n ----------\n axis : {0, 1}\n Axis to set `value` along. 0 means set row, 1 means set column.\n key : label\n Row/column label to set `value` in.\n value : BaseQueryCompiler, list-like or scalar\n Define new row/column value.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler with updated `key` value.\n \"\"\"\n\n def setitem(df, axis, key, value):\n if is_scalar(key) and isinstance(value, pandas.DataFrame):\n value = value.squeeze()\n if not axis:\n df[key] = value\n else:\n df.loc[key] = value\n return df\n\n return DataFrameDefault.register(setitem)(self, axis=axis, key=key, value=value)\n\n def write_items(self, row_numeric_index, col_numeric_index, broadcasted_items):\n \"\"\"\n Update QueryCompiler elements at the specified positions by passed values.\n\n In contrast to ``setitem`` this method allows to do 2D assignments.\n\n Parameters\n ----------\n row_numeric_index : list of ints\n Row positions to write value.\n col_numeric_index : list of ints\n Column positions to write value.\n broadcasted_items : 2D-array\n Values to write. Have to be same size as defined by `row_numeric_index`\n and `col_numeric_index`.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler with updated values.\n \"\"\"\n\n def write_items(df, broadcasted_items):\n if isinstance(df.iloc[row_numeric_index, col_numeric_index], pandas.Series):\n broadcasted_items = broadcasted_items.squeeze()\n df.iloc[\n list(row_numeric_index), list(col_numeric_index)\n ] = broadcasted_items\n return df\n\n return DataFrameDefault.register(write_items)(\n self, broadcasted_items=broadcasted_items\n )\n\n # END Abstract methods for QueryCompiler\n\n @property\n def __constructor__(self):\n \"\"\"\n Get query compiler constructor.\n\n By default, constructor method will invoke an init.\n\n Returns\n -------\n callable\n \"\"\"\n return type(self)\n\n # __delitem__\n # This will change the shape of the resulting data.\n def delitem(self, key):\n \"\"\"\n Drop `key` column.\n\n Parameters\n ----------\n key : label\n Column name to drop.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler without `key` column.\n \"\"\"\n return self.drop(columns=[key])\n\n # END __delitem__\n\n def has_multiindex(self, axis=0):\n \"\"\"\n Check if specified axis is indexed by MultiIndex.\n\n Parameters\n ----------\n axis : {0, 1}, default: 0\n The axis to check (0 - index, 1 - columns).\n\n Returns\n -------\n bool\n True if index at specified axis is MultiIndex and False otherwise.\n \"\"\"\n if axis == 0:\n return isinstance(self.index, pandas.MultiIndex)\n assert axis == 1\n return isinstance(self.columns, pandas.MultiIndex)\n\n def get_index_name(self, axis=0):\n \"\"\"\n Get index name of specified axis.\n\n Parameters\n ----------\n axis : {0, 1}, default: 0\n Axis to get index name on.\n\n Returns\n -------\n hashable\n Index name, None for MultiIndex.\n \"\"\"\n return self.get_axis(axis).name\n\n def set_index_name(self, name, axis=0):\n \"\"\"\n Set index name for the specified axis.\n\n Parameters\n ----------\n name : hashable\n New index name.\n axis : {0, 1}, default: 0\n Axis to set name along.\n \"\"\"\n self.get_axis(axis).name = name\n\n def get_index_names(self, axis=0):\n \"\"\"\n Get index names of specified axis.\n\n Parameters\n ----------\n axis : {0, 1}, default: 0\n Axis to get index names on.\n\n Returns\n -------\n list\n Index names.\n \"\"\"\n return self.get_axis(axis).names\n\n def set_index_names(self, names, axis=0):\n \"\"\"\n Set index names for the specified axis.\n\n Parameters\n ----------\n names : list\n New index names.\n axis : {0, 1}, default: 0\n Axis to set names along.\n \"\"\"\n self.get_axis(axis).names = names\n\n # DateTime methods\n\n @doc_utils.doc_dt_round(refer_to=\"ceil\")\n def dt_ceil(self, freq, ambiguous=\"raise\", nonexistent=\"raise\"):\n return DateTimeDefault.register(pandas.Series.dt.ceil)(\n self, freq, ambiguous, nonexistent\n )\n\n @doc_utils.add_one_column_warning\n @doc_utils.add_refer_to(\"Series.dt.components\")\n def dt_components(self):\n \"\"\"\n Spread each date-time value into its components (days, hours, minutes...).\n\n Returns\n -------\n BaseQueryCompiler\n \"\"\"\n return DateTimeDefault.register(pandas.Series.dt.components)(self)\n\n @doc_utils.doc_dt_timestamp(\n prop=\"the date without timezone information\", refer_to=\"date\"\n )\n def dt_date(self):\n return DateTimeDefault.register(pandas.Series.dt.date)(self)\n\n @doc_utils.doc_dt_timestamp(prop=\"day component\", refer_to=\"day\")\n def dt_day(self):\n return DateTimeDefault.register(pandas.Series.dt.day)(self)\n\n @doc_utils.doc_dt_timestamp(\n prop=\"day name\", refer_to=\"day_name\", params=\"locale : str, optional\"\n )\n def dt_day_name(self, locale=None):\n return DateTimeDefault.register(pandas.Series.dt.day_name)(self, locale)\n\n @doc_utils.doc_dt_timestamp(prop=\"integer day of week\", refer_to=\"dayofweek\")\n # FIXME: `dt_dayofweek` is an alias for `dt_weekday`, one of them should\n # be removed (Modin issue #3107).\n def dt_dayofweek(self):\n return DateTimeDefault.register(pandas.Series.dt.dayofweek)(self)\n\n @doc_utils.doc_dt_timestamp(prop=\"day of year\", refer_to=\"dayofyear\")\n def dt_dayofyear(self):\n return DateTimeDefault.register(pandas.Series.dt.dayofyear)(self)\n\n @doc_utils.doc_dt_interval(prop=\"days\", refer_to=\"days\")\n def dt_days(self):\n return DateTimeDefault.register(pandas.Series.dt.days)(self)\n\n @doc_utils.doc_dt_timestamp(\n prop=\"number of days in month\", refer_to=\"days_in_month\"\n )\n # FIXME: `dt_days_in_month` is an alias for `dt_daysinmonth`, one of them should\n # be removed (Modin issue #3107).\n def dt_days_in_month(self):\n return DateTimeDefault.register(pandas.Series.dt.days_in_month)(self)\n\n @doc_utils.doc_dt_timestamp(prop=\"number of days in month\", refer_to=\"daysinmonth\")\n def dt_daysinmonth(self):\n return DateTimeDefault.register(pandas.Series.dt.daysinmonth)(self)\n\n @doc_utils.doc_dt_period(prop=\"the timestamp of end time\", refer_to=\"end_time\")\n def dt_end_time(self):\n return DateTimeDefault.register(pandas.Series.dt.end_time)(self)\n\n @doc_utils.doc_dt_round(refer_to=\"floor\")\n def dt_floor(self, freq, ambiguous=\"raise\", nonexistent=\"raise\"):\n return DateTimeDefault.register(pandas.Series.dt.floor)(\n self, freq, ambiguous, nonexistent\n )\n\n @doc_utils.add_one_column_warning\n @doc_utils.add_refer_to(\"Series.dt.freq\")\n def dt_freq(self):\n \"\"\"\n Get the time frequency of the underlying time-series data.\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler containing a single value, the frequency of the data.\n \"\"\"\n return DateTimeDefault.register(pandas.Series.dt.freq)(self)\n\n @doc_utils.doc_dt_timestamp(prop=\"hour\", refer_to=\"hour\")\n def dt_hour(self):\n return DateTimeDefault.register(pandas.Series.dt.hour)(self)\n\n @doc_utils.doc_dt_timestamp(\n prop=\"the boolean of whether corresponding year is leap\",\n refer_to=\"is_leap_year\",\n )\n def dt_is_leap_year(self):\n return DateTimeDefault.register(pandas.Series.dt.is_leap_year)(self)\n\n @doc_utils.doc_dt_timestamp(\n prop=\"the boolean of whether the date is the last day of the month\",\n refer_to=\"is_month_end\",\n )\n def dt_is_month_end(self):\n return DateTimeDefault.register(pandas.Series.dt.is_month_end)(self)\n\n @doc_utils.doc_dt_timestamp(\n prop=\"the boolean of whether the date is the first day of the month\",\n refer_to=\"is_month_start\",\n )\n def dt_is_month_start(self):\n return DateTimeDefault.register(pandas.Series.dt.is_month_start)(self)\n\n @doc_utils.doc_dt_timestamp(\n prop=\"the boolean of whether the date is the last day of the quarter\",\n refer_to=\"is_quarter_end\",\n )\n def dt_is_quarter_end(self):\n return DateTimeDefault.register(pandas.Series.dt.is_quarter_end)(self)\n\n @doc_utils.doc_dt_timestamp(\n prop=\"the boolean of whether the date is the first day of the quarter\",\n refer_to=\"is_quarter_start\",\n )\n def dt_is_quarter_start(self):\n return DateTimeDefault.register(pandas.Series.dt.is_quarter_start)(self)\n\n @doc_utils.doc_dt_timestamp(\n prop=\"the boolean of whether the date is the last day of the year\",\n refer_to=\"is_year_end\",\n )\n def dt_is_year_end(self):\n return DateTimeDefault.register(pandas.Series.dt.is_year_end)(self)\n\n @doc_utils.doc_dt_timestamp(\n prop=\"the boolean of whether the date is the first day of the year\",\n refer_to=\"is_year_start\",\n )\n def dt_is_year_start(self):\n return DateTimeDefault.register(pandas.Series.dt.is_year_start)(self)\n\n @doc_utils.doc_dt_timestamp(prop=\"microseconds component\", refer_to=\"microsecond\")\n def dt_microsecond(self):\n return DateTimeDefault.register(pandas.Series.dt.microsecond)(self)\n\n @doc_utils.doc_dt_interval(prop=\"microseconds component\", refer_to=\"microseconds\")\n def dt_microseconds(self):\n return DateTimeDefault.register(pandas.Series.dt.microseconds)(self)\n\n @doc_utils.doc_dt_timestamp(prop=\"minute component\", refer_to=\"minute\")\n def dt_minute(self):\n return DateTimeDefault.register(pandas.Series.dt.minute)(self)\n\n @doc_utils.doc_dt_timestamp(prop=\"month component\", refer_to=\"month\")\n def dt_month(self):\n return DateTimeDefault.register(pandas.Series.dt.month)(self)\n\n @doc_utils.doc_dt_timestamp(\n prop=\"the month name\", refer_to=\"month name\", params=\"locale : str, optional\"\n )\n def dt_month_name(self, locale=None):\n return DateTimeDefault.register(pandas.Series.dt.month_name)(self, locale)\n\n @doc_utils.doc_dt_timestamp(prop=\"nanoseconds component\", refer_to=\"nanosecond\")\n def dt_nanosecond(self):\n return DateTimeDefault.register(pandas.Series.dt.nanosecond)(self)\n\n @doc_utils.doc_dt_interval(prop=\"nanoseconds component\", refer_to=\"nanoseconds\")\n def dt_nanoseconds(self):\n return DateTimeDefault.register(pandas.Series.dt.nanoseconds)(self)\n\n @doc_utils.add_one_column_warning\n @doc_utils.add_refer_to(\"Series.dt.normalize\")\n def dt_normalize(self):\n \"\"\"\n Set the time component of each date-time value to midnight.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler containing date-time values with midnight time.\n \"\"\"\n return DateTimeDefault.register(pandas.Series.dt.normalize)(self)\n\n @doc_utils.doc_dt_timestamp(prop=\"quarter component\", refer_to=\"quarter\")\n def dt_quarter(self):\n return DateTimeDefault.register(pandas.Series.dt.quarter)(self)\n\n @doc_utils.doc_dt_period(prop=\"the fiscal year\", refer_to=\"qyear\")\n def dt_qyear(self):\n return DateTimeDefault.register(pandas.Series.dt.qyear)(self)\n\n @doc_utils.doc_dt_round(refer_to=\"round\")\n def dt_round(self, freq, ambiguous=\"raise\", nonexistent=\"raise\"):\n return DateTimeDefault.register(pandas.Series.dt.round)(\n self, freq, ambiguous, nonexistent\n )\n\n @doc_utils.doc_dt_timestamp(prop=\"seconds component\", refer_to=\"second\")\n def dt_second(self):\n return DateTimeDefault.register(pandas.Series.dt.second)(self)\n\n @doc_utils.doc_dt_interval(prop=\"seconds component\", refer_to=\"seconds\")\n def dt_seconds(self):\n return DateTimeDefault.register(pandas.Series.dt.seconds)(self)\n\n @doc_utils.doc_dt_period(prop=\"the timestamp of start time\", refer_to=\"start_time\")\n def dt_start_time(self):\n return DateTimeDefault.register(pandas.Series.dt.start_time)(self)\n\n @doc_utils.add_refer_to(\"Series.dt.strftime\")\n def dt_strftime(self, date_format):\n \"\"\"\n Format underlying date-time data using specified format.\n\n Parameters\n ----------\n date_format : str\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler containing formated date-time values.\n \"\"\"\n return DateTimeDefault.register(pandas.Series.dt.strftime)(self, date_format)\n\n @doc_utils.doc_dt_timestamp(prop=\"time component\", refer_to=\"time\")\n def dt_time(self):\n return DateTimeDefault.register(pandas.Series.dt.time)(self)\n\n @doc_utils.doc_dt_timestamp(\n prop=\"time component with timezone information\", refer_to=\"timetz\"\n )\n def dt_timetz(self):\n return DateTimeDefault.register(pandas.Series.dt.timetz)(self)\n\n @doc_utils.add_one_column_warning\n @doc_utils.add_refer_to(\"Series.dt.to_period\")\n def dt_to_period(self, freq=None):\n \"\"\"\n Convert underlying data to the period at a particular frequency.\n\n Parameters\n ----------\n freq : str, optional\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler containing period data.\n \"\"\"\n return DateTimeDefault.register(pandas.Series.dt.to_period)(self, freq)\n\n @doc_utils.add_one_column_warning\n @doc_utils.add_refer_to(\"Series.dt.to_pydatetime\")\n def dt_to_pydatetime(self):\n \"\"\"\n Convert underlying data to array of python native ``datetime``.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler containing 1D array of ``datetime`` objects.\n \"\"\"\n return DateTimeDefault.register(pandas.Series.dt.to_pydatetime)(self)\n\n # FIXME: there are no references to this method, we should either remove it\n # or add a call reference at the DataFrame level (Modin issue #3103).\n @doc_utils.add_one_column_warning\n @doc_utils.add_refer_to(\"Series.dt.to_pytimedelta\")\n def dt_to_pytimedelta(self):\n \"\"\"\n Convert underlying data to array of python native ``datetime.timedelta``.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler containing 1D array of ``datetime.timedelta``.\n \"\"\"\n return DateTimeDefault.register(pandas.Series.dt.to_pytimedelta)(self)\n\n @doc_utils.doc_dt_period(\n prop=\"the timestamp representation\", refer_to=\"to_timestamp\"\n )\n def dt_to_timestamp(self):\n return DateTimeDefault.register(pandas.Series.dt.to_timestamp)(self)\n\n @doc_utils.doc_dt_interval(prop=\"duration in seconds\", refer_to=\"total_seconds\")\n def dt_total_seconds(self):\n return DateTimeDefault.register(pandas.Series.dt.total_seconds)(self)\n\n @doc_utils.add_one_column_warning\n @doc_utils.add_refer_to(\"Series.dt.tz\")\n def dt_tz(self):\n \"\"\"\n Get the time-zone of the underlying time-series data.\n\n Returns\n -------\n BaseQueryCompiler\n QueryCompiler containing a single value, time-zone of the data.\n \"\"\"\n return DateTimeDefault.register(pandas.Series.dt.tz)(self)\n\n @doc_utils.add_one_column_warning\n @doc_utils.add_refer_to(\"Series.dt.tz_convert\")\n def dt_tz_convert(self, tz):\n \"\"\"\n Convert time-series data to the specified time zone.\n\n Parameters\n ----------\n tz : str, pytz.timezone\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler containing values with converted time zone.\n \"\"\"\n return DateTimeDefault.register(pandas.Series.dt.tz_convert)(self, tz)\n\n @doc_utils.add_one_column_warning\n @doc_utils.add_refer_to(\"Series.dt.tz_localize\")\n def dt_tz_localize(self, tz, ambiguous=\"raise\", nonexistent=\"raise\"):\n \"\"\"\n Localize tz-naive to tz-aware.\n\n Parameters\n ----------\n tz : str, pytz.timezone, optional\n ambiguous : {\"raise\", \"inner\", \"NaT\"} or bool mask, default: \"raise\"\n nonexistent : {\"raise\", \"shift_forward\", \"shift_backward, \"NaT\"} or pandas.timedelta, default: \"raise\"\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler containing values with localized time zone.\n \"\"\"\n return DateTimeDefault.register(pandas.Series.dt.tz_localize)(\n self, tz, ambiguous, nonexistent\n )\n\n @doc_utils.doc_dt_timestamp(prop=\"week component\", refer_to=\"week\")\n def dt_week(self):\n return DateTimeDefault.register(pandas.Series.dt.week)(self)\n\n @doc_utils.doc_dt_timestamp(prop=\"integer day of week\", refer_to=\"weekday\")\n def dt_weekday(self):\n return DateTimeDefault.register(pandas.Series.dt.weekday)(self)\n\n @doc_utils.doc_dt_timestamp(prop=\"week of year\", refer_to=\"weekofyear\")\n def dt_weekofyear(self):\n return DateTimeDefault.register(pandas.Series.dt.weekofyear)(self)\n\n @doc_utils.doc_dt_timestamp(prop=\"year component\", refer_to=\"year\")\n def dt_year(self):\n return DateTimeDefault.register(pandas.Series.dt.year)(self)\n\n # End of DateTime methods\n\n # Resample methods\n\n # FIXME:\n # 1. Backend shouldn't care about differences between Series and DataFrame\n # so `resample_agg_df` and `resample_agg_ser` should be combined (Modin issue #3104).\n # 2. In DataFrame API `Resampler.aggregate` is an alias for `Resampler.apply`\n # we should remove one of these methods: `resample_agg_*` or `resample_app_*` (Modin issue #3107).\n @doc_utils.doc_resample_agg(\n action=\"apply passed aggregation function\",\n params=\"func : str, dict, callable(pandas.Series) -> scalar, or list of such\",\n output=\"function names\",\n refer_to=\"agg\",\n )\n def resample_agg_df(self, resample_args, func, *args, **kwargs):\n return ResampleDefault.register(pandas.core.resample.Resampler.aggregate)(\n self, resample_args, func, *args, **kwargs\n )\n\n @doc_utils.add_deprecation_warning(replacement_method=\"resample_agg_df\")\n @doc_utils.doc_resample_agg(\n action=\"apply passed aggregation function in a one-column query compiler\",\n params=\"func : str, dict, callable(pandas.Series) -> scalar, or list of such\",\n output=\"function names\",\n refer_to=\"agg\",\n )\n def resample_agg_ser(self, resample_args, func, *args, **kwargs):\n return ResampleDefault.register(\n pandas.core.resample.Resampler.aggregate, squeeze_self=True\n )(self, resample_args, func, *args, **kwargs)\n\n @doc_utils.add_deprecation_warning(replacement_method=\"resample_agg_df\")\n @doc_utils.doc_resample_agg(\n action=\"apply passed aggregation function\",\n params=\"func : str, dict, callable(pandas.Series) -> scalar, or list of such\",\n output=\"function names\",\n refer_to=\"apply\",\n )\n def resample_app_df(self, resample_args, func, *args, **kwargs):\n return ResampleDefault.register(pandas.core.resample.Resampler.apply)(\n self, resample_args, func, *args, **kwargs\n )\n\n @doc_utils.add_deprecation_warning(replacement_method=\"resample_agg_df\")\n @doc_utils.doc_resample_agg(\n action=\"apply passed aggregation function in a one-column query compiler\",\n params=\"func : str, dict, callable(pandas.Series) -> scalar, or list of such\",\n output=\"function names\",\n refer_to=\"apply\",\n )\n def resample_app_ser(self, resample_args, func, *args, **kwargs):\n return ResampleDefault.register(\n pandas.core.resample.Resampler.apply, squeeze_self=True\n )(self, resample_args, func, *args, **kwargs)\n\n def resample_asfreq(self, resample_args, fill_value):\n \"\"\"\n Resample time-series data and get the values at the new frequency.\n\n Group data into intervals by time-series row/column with\n a specified frequency and get values at the new frequency.\n\n Parameters\n ----------\n resample_args : list\n Resample parameters as expected by ``modin.pandas.DataFrame.resample`` signature.\n fill_value : scalar\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler containing values at the specified frequency.\n \"\"\"\n return ResampleDefault.register(pandas.core.resample.Resampler.asfreq)(\n self, resample_args, fill_value\n )\n\n # FIXME: `resample_backfill` is an alias for `resample_bfill`, one of these method\n # should be removed (Modin issue #3107).\n @doc_utils.doc_resample_fillna(method=\"back-fill\", refer_to=\"backfill\")\n def resample_backfill(self, resample_args, limit):\n return ResampleDefault.register(pandas.core.resample.Resampler.backfill)(\n self, resample_args, limit\n )\n\n @doc_utils.doc_resample_fillna(method=\"back-fill\", refer_to=\"bfill\")\n def resample_bfill(self, resample_args, limit):\n return ResampleDefault.register(pandas.core.resample.Resampler.bfill)(\n self, resample_args, limit\n )\n\n @doc_utils.doc_resample_reduction(\n result=\"number of non-NA values\", refer_to=\"count\", compatibility_params=False\n )\n def resample_count(self, resample_args):\n return ResampleDefault.register(pandas.core.resample.Resampler.count)(\n self, resample_args\n )\n\n # FIXME: `resample_ffill` is an alias for `resample_pad`, one of these method\n # should be removed (Modin issue #3107).\n @doc_utils.doc_resample_fillna(method=\"forward-fill\", refer_to=\"ffill\")\n def resample_ffill(self, resample_args, limit):\n return ResampleDefault.register(pandas.core.resample.Resampler.ffill)(\n self, resample_args, limit\n )\n\n # FIXME: we should combine all resample fillna methods into `resample_fillna`\n # (Modin issue #3107)\n @doc_utils.doc_resample_fillna(\n method=\"specified\", refer_to=\"fillna\", params=\"method : str\"\n )\n def resample_fillna(self, resample_args, method, limit):\n return ResampleDefault.register(pandas.core.resample.Resampler.fillna)(\n self, resample_args, method, limit\n )\n\n @doc_utils.doc_resample_reduction(\n result=\"first element\", refer_to=\"first\", params=\"_method : str\"\n )\n def resample_first(self, resample_args, _method, *args, **kwargs):\n return ResampleDefault.register(pandas.core.resample.Resampler.first)(\n self, resample_args, _method, *args, **kwargs\n )\n\n # FIXME: This function takes Modin DataFrame via `obj` parameter,\n # we should avoid leaking of the high-level objects to the query compiler level.\n # (Modin issue #3106)\n def resample_get_group(self, resample_args, name, obj):\n \"\"\"\n Resample time-series data and get the specified group.\n\n Group data into intervals by time-series row/column with\n a specified frequency and get the values of the specified group.\n\n Parameters\n ----------\n resample_args : list\n Resample parameters as expected by ``modin.pandas.DataFrame.resample`` signature.\n name : object\n obj : modin.pandas.DataFrame, optional\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler containing the values from the specified group.\n \"\"\"\n return ResampleDefault.register(pandas.core.resample.Resampler.get_group)(\n self, resample_args, name, obj\n )\n\n @doc_utils.doc_resample_fillna(\n method=\"specified interpolation\",\n refer_to=\"interpolate\",\n params=\"\"\"\n method : str\n axis : {0, 1}\n limit : int\n inplace : {False}\n This parameter serves the compatibility purpose. Always has to be False.\n limit_direction : {\"forward\", \"backward\", \"both\"}\n limit_area : {None, \"inside\", \"outside\"}\n downcast : str, optional\n **kwargs : dict\n \"\"\",\n overwrite_template_params=True,\n )\n def resample_interpolate(\n self,\n resample_args,\n method,\n axis,\n limit,\n inplace,\n limit_direction,\n limit_area,\n downcast,\n **kwargs,\n ):\n return ResampleDefault.register(pandas.core.resample.Resampler.interpolate)(\n self,\n resample_args,\n method,\n axis,\n limit,\n inplace,\n limit_direction,\n limit_area,\n downcast,\n **kwargs,\n )\n\n @doc_utils.doc_resample_reduction(\n result=\"last element\", params=\"_method : str\", refer_to=\"last\"\n )\n def resample_last(self, resample_args, _method, *args, **kwargs):\n return ResampleDefault.register(pandas.core.resample.Resampler.last)(\n self, resample_args, _method, *args, **kwargs\n )\n\n @doc_utils.doc_resample_reduction(\n result=\"maximum value\", params=\"_method : str\", refer_to=\"max\"\n )\n def resample_max(self, resample_args, _method, *args, **kwargs):\n return ResampleDefault.register(pandas.core.resample.Resampler.max)(\n self, resample_args, _method, *args, **kwargs\n )\n\n @doc_utils.doc_resample_reduction(\n result=\"mean value\", params=\"_method : str\", refer_to=\"mean\"\n )\n def resample_mean(self, resample_args, _method, *args, **kwargs):\n return ResampleDefault.register(pandas.core.resample.Resampler.mean)(\n self, resample_args, _method, *args, **kwargs\n )\n\n @doc_utils.doc_resample_reduction(\n result=\"median value\", params=\"_method : str\", refer_to=\"median\"\n )\n def resample_median(self, resample_args, _method, *args, **kwargs):\n return ResampleDefault.register(pandas.core.resample.Resampler.median)(\n self, resample_args, _method, *args, **kwargs\n )\n\n @doc_utils.doc_resample_reduction(\n result=\"minimum value\", params=\"_method : str\", refer_to=\"min\"\n )\n def resample_min(self, resample_args, _method, *args, **kwargs):\n return ResampleDefault.register(pandas.core.resample.Resampler.min)(\n self, resample_args, _method, *args, **kwargs\n )\n\n @doc_utils.doc_resample_fillna(method=\"'nearest'\", refer_to=\"nearest\")\n def resample_nearest(self, resample_args, limit):\n return ResampleDefault.register(pandas.core.resample.Resampler.nearest)(\n self, resample_args, limit\n )\n\n @doc_utils.doc_resample_reduction(\n result=\"number of unique values\", params=\"_method : str\", refer_to=\"nunique\"\n )\n def resample_nunique(self, resample_args, _method, *args, **kwargs):\n return ResampleDefault.register(pandas.core.resample.Resampler.nunique)(\n self, resample_args, _method, *args, **kwargs\n )\n\n # FIXME: Backend shouldn't care about differences between Series and DataFrame\n # so `resample_ohlc_df` and `resample_ohlc_ser` should be combined (Modin issue #3104).\n @doc_utils.doc_resample_agg(\n action=\"compute open, high, low and close values\",\n params=\"_method : str\",\n output=\"labels of columns containing computed values\",\n refer_to=\"ohlc\",\n )\n def resample_ohlc_df(self, resample_args, _method, *args, **kwargs):\n return ResampleDefault.register(pandas.core.resample.Resampler.ohlc)(\n self, resample_args, _method, *args, **kwargs\n )\n\n @doc_utils.doc_resample_agg(\n action=\"compute open, high, low and close values\",\n params=\"_method : str\",\n output=\"labels of columns containing computed values\",\n refer_to=\"ohlc\",\n )\n def resample_ohlc_ser(self, resample_args, _method, *args, **kwargs):\n return ResampleDefault.register(\n pandas.core.resample.Resampler.ohlc, squeeze_self=True\n )(self, resample_args, _method, *args, **kwargs)\n\n @doc_utils.doc_resample_fillna(method=\"'pad'\", refer_to=\"pad\")\n def resample_pad(self, resample_args, limit):\n return ResampleDefault.register(pandas.core.resample.Resampler.pad)(\n self, resample_args, limit\n )\n\n # FIXME: This method require us to build high-level resampler object\n # which we shouldn't do at the backend. We need to move this at the front.\n # (Modin issue #3105)\n @doc_utils.add_refer_to(\"Resampler.pipe\")\n def resample_pipe(self, resample_args, func, *args, **kwargs):\n \"\"\"\n Resample time-series data and apply aggregation on it.\n\n Group data into intervals by time-series row/column with\n a specified frequency, build equivalent ``pandas.Resampler`` object\n and apply passed function to it.\n\n Parameters\n ----------\n resample_args : list\n Resample parameters as expected by ``modin.pandas.DataFrame.resample`` signature.\n func : callable(pandas.Resampler) -> object or tuple(callable, str)\n *args : iterable\n Positional arguments to pass to function.\n **kwargs : dict\n Keyword arguments to pass to function.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler containing the result of passed function.\n \"\"\"\n return ResampleDefault.register(pandas.core.resample.Resampler.pipe)(\n self, resample_args, func, *args, **kwargs\n )\n\n @doc_utils.doc_resample_reduction(\n result=\"product\",\n params=\"\"\"\n _method : str\n min_count : int\"\"\",\n refer_to=\"prod\",\n )\n def resample_prod(self, resample_args, _method, min_count, *args, **kwargs):\n return ResampleDefault.register(pandas.core.resample.Resampler.prod)(\n self, resample_args, _method, min_count, *args, **kwargs\n )\n\n @doc_utils.doc_resample_reduction(\n result=\"quantile\", params=\"q : float\", refer_to=\"quantile\"\n )\n def resample_quantile(self, resample_args, q, *args, **kwargs):\n return ResampleDefault.register(pandas.core.resample.Resampler.quantile)(\n self, resample_args, q, *args, **kwargs\n )\n\n @doc_utils.doc_resample_reduction(\n result=\"standart error of the mean\",\n params=\"ddof : int, default: 1\",\n refer_to=\"sem\",\n )\n def resample_sem(self, resample_args, ddof=1, *args, **kwargs):\n return ResampleDefault.register(pandas.core.resample.Resampler.sem)(\n self, resample_args, ddof, *args, **kwargs\n )\n\n @doc_utils.doc_resample_reduction(\n result=\"number of elements in a group\", refer_to=\"size\"\n )\n def resample_size(self, resample_args, *args, **kwargs):\n return ResampleDefault.register(pandas.core.resample.Resampler.size)(\n self, resample_args, *args, **kwargs\n )\n\n @doc_utils.doc_resample_reduction(\n result=\"standart deviation\", params=\"ddof : int\", refer_to=\"std\"\n )\n def resample_std(self, resample_args, ddof, *args, **kwargs):\n return ResampleDefault.register(pandas.core.resample.Resampler.std)(\n self, resample_args, ddof, *args, **kwargs\n )\n\n @doc_utils.doc_resample_reduction(\n result=\"sum\",\n params=\"\"\"\n _method : str\n min_count : int\"\"\",\n refer_to=\"sum\",\n )\n def resample_sum(self, resample_args, _method, min_count, *args, **kwargs):\n return ResampleDefault.register(pandas.core.resample.Resampler.sum)(\n self, resample_args, _method, min_count, *args, **kwargs\n )\n\n def resample_transform(self, resample_args, arg, *args, **kwargs):\n \"\"\"\n Resample time-series data and apply aggregation on it.\n\n Group data into intervals by time-series row/column with\n a specified frequency and call passed function on each group.\n In contrast to ``resample_app_df`` apply function to the whole group,\n instead of a single axis.\n\n Parameters\n ----------\n resample_args : list\n Resample parameters as expected by ``modin.pandas.DataFrame.resample`` signature.\n arg : callable(pandas.DataFrame) -> pandas.Series\n *args : iterable\n Positional arguments to pass to function.\n **kwargs : dict\n Keyword arguments to pass to function.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler containing the result of passed function.\n \"\"\"\n return ResampleDefault.register(pandas.core.resample.Resampler.transform)(\n self, resample_args, arg, *args, **kwargs\n )\n\n @doc_utils.doc_resample_reduction(\n result=\"variance\", params=\"ddof : int\", refer_to=\"var\"\n )\n def resample_var(self, resample_args, ddof, *args, **kwargs):\n return ResampleDefault.register(pandas.core.resample.Resampler.var)(\n self, resample_args, ddof, *args, **kwargs\n )\n\n # End of Resample methods\n\n # Str methods\n\n @doc_utils.doc_str_method(refer_to=\"capitalize\", params=\"\")\n def str_capitalize(self):\n return StrDefault.register(pandas.Series.str.capitalize)(self)\n\n @doc_utils.doc_str_method(\n refer_to=\"center\",\n params=\"\"\"\n width : int\n fillchar : str, default: ' '\"\"\",\n )\n def str_center(self, width, fillchar=\" \"):\n return StrDefault.register(pandas.Series.str.center)(self, width, fillchar)\n\n @doc_utils.doc_str_method(\n refer_to=\"contains\",\n params=\"\"\"\n pat : str\n case : bool, default: True\n flags : int, default: 0\n na : object, default: np.NaN\n regex : bool, default: True\"\"\",\n )\n def str_contains(self, pat, case=True, flags=0, na=np.NaN, regex=True):\n return StrDefault.register(pandas.Series.str.contains)(\n self, pat, case, flags, na, regex\n )\n\n @doc_utils.doc_str_method(\n refer_to=\"count\",\n params=\"\"\"\n pat : str\n flags : int, default: 0\n **kwargs : dict\"\"\",\n )\n def str_count(self, pat, flags=0, **kwargs):\n return StrDefault.register(pandas.Series.str.count)(self, pat, flags, **kwargs)\n\n @doc_utils.doc_str_method(\n refer_to=\"endswith\",\n params=\"\"\"\n pat : str\n na : object, default: np.NaN\"\"\",\n )\n def str_endswith(self, pat, na=np.NaN):\n return StrDefault.register(pandas.Series.str.endswith)(self, pat, na)\n\n @doc_utils.doc_str_method(\n refer_to=\"find\",\n params=\"\"\"\n sub : str\n start : int, default: 0\n end : int, optional\"\"\",\n )\n def str_find(self, sub, start=0, end=None):\n return StrDefault.register(pandas.Series.str.find)(self, sub, start, end)\n\n @doc_utils.doc_str_method(\n refer_to=\"findall\",\n params=\"\"\"\n pat : str\n flags : int, default: 0\n **kwargs : dict\"\"\",\n )\n def str_findall(self, pat, flags=0, **kwargs):\n return StrDefault.register(pandas.Series.str.findall)(\n self, pat, flags, **kwargs\n )\n\n @doc_utils.doc_str_method(refer_to=\"get\", params=\"i : int\")\n def str_get(self, i):\n return StrDefault.register(pandas.Series.str.get)(self, i)\n\n @doc_utils.doc_str_method(\n refer_to=\"index\",\n params=\"\"\"\n sub : str\n start : int, default: 0\n end : int, optional\"\"\",\n )\n def str_index(self, sub, start=0, end=None):\n return StrDefault.register(pandas.Series.str.index)(self, sub, start, end)\n\n @doc_utils.doc_str_method(refer_to=\"isalnum\", params=\"\")\n def str_isalnum(self):\n return StrDefault.register(pandas.Series.str.isalnum)(self)\n\n @doc_utils.doc_str_method(refer_to=\"isalpha\", params=\"\")\n def str_isalpha(self):\n return StrDefault.register(pandas.Series.str.isalpha)(self)\n\n @doc_utils.doc_str_method(refer_to=\"isdecimal\", params=\"\")\n def str_isdecimal(self):\n return StrDefault.register(pandas.Series.str.isdecimal)(self)\n\n @doc_utils.doc_str_method(refer_to=\"isdigit\", params=\"\")\n def str_isdigit(self):\n return StrDefault.register(pandas.Series.str.isdigit)(self)\n\n @doc_utils.doc_str_method(refer_to=\"islower\", params=\"\")\n def str_islower(self):\n return StrDefault.register(pandas.Series.str.islower)(self)\n\n @doc_utils.doc_str_method(refer_to=\"isnumeric\", params=\"\")\n def str_isnumeric(self):\n return StrDefault.register(pandas.Series.str.isnumeric)(self)\n\n @doc_utils.doc_str_method(refer_to=\"isspace\", params=\"\")\n def str_isspace(self):\n return StrDefault.register(pandas.Series.str.isspace)(self)\n\n @doc_utils.doc_str_method(refer_to=\"istitle\", params=\"\")\n def str_istitle(self):\n return StrDefault.register(pandas.Series.str.istitle)(self)\n\n @doc_utils.doc_str_method(refer_to=\"isupper\", params=\"\")\n def str_isupper(self):\n return StrDefault.register(pandas.Series.str.isupper)(self)\n\n @doc_utils.doc_str_method(refer_to=\"join\", params=\"sep : str\")\n def str_join(self, sep):\n return StrDefault.register(pandas.Series.str.join)(self, sep)\n\n @doc_utils.doc_str_method(refer_to=\"len\", params=\"\")\n def str_len(self):\n return StrDefault.register(pandas.Series.str.len)(self)\n\n @doc_utils.doc_str_method(\n refer_to=\"ljust\",\n params=\"\"\"\n width : int\n fillchar : str, default: ' '\"\"\",\n )\n def str_ljust(self, width, fillchar=\" \"):\n return StrDefault.register(pandas.Series.str.ljust)(self, width, fillchar)\n\n @doc_utils.doc_str_method(refer_to=\"lower\", params=\"\")\n def str_lower(self):\n return StrDefault.register(pandas.Series.str.lower)(self)\n\n @doc_utils.doc_str_method(refer_to=\"lstrip\", params=\"to_strip : str, optional\")\n def str_lstrip(self, to_strip=None):\n return StrDefault.register(pandas.Series.str.lstrip)(self, to_strip)\n\n @doc_utils.doc_str_method(\n refer_to=\"match\",\n params=\"\"\"\n pat : str\n case : bool, default: True\n flags : int, default: 0\n na : object, default: np.NaN\"\"\",\n )\n def str_match(self, pat, case=True, flags=0, na=np.NaN):\n return StrDefault.register(pandas.Series.str.match)(self, pat, case, flags, na)\n\n @doc_utils.doc_str_method(\n refer_to=\"normalize\", params=\"form : {'NFC', 'NFKC', 'NFD', 'NFKD'}\"\n )\n def str_normalize(self, form):\n return StrDefault.register(pandas.Series.str.normalize)(self, form)\n\n @doc_utils.doc_str_method(\n refer_to=\"pad\",\n params=\"\"\"\n width : int\n side : {'left', 'right', 'both'}, default: 'left'\n fillchar : str, default: ' '\"\"\",\n )\n def str_pad(self, width, side=\"left\", fillchar=\" \"):\n return StrDefault.register(pandas.Series.str.pad)(self, width, side, fillchar)\n\n @doc_utils.doc_str_method(\n refer_to=\"partition\",\n params=\"\"\"\n sep : str, default: ' '\n expand : bool, default: True\"\"\",\n )\n def str_partition(self, sep=\" \", expand=True):\n return StrDefault.register(pandas.Series.str.partition)(self, sep, expand)\n\n @doc_utils.doc_str_method(refer_to=\"repeat\", params=\"repeats : int\")\n def str_repeat(self, repeats):\n return StrDefault.register(pandas.Series.str.repeat)(self, repeats)\n\n @doc_utils.doc_str_method(\n refer_to=\"replace\",\n params=\"\"\"\n pat : str\n repl : str or callable\n n : int, default: -1\n case : bool, optional\n flags : int, default: 0\n regex : bool, default: True\"\"\",\n )\n def str_replace(self, pat, repl, n=-1, case=None, flags=0, regex=True):\n return StrDefault.register(pandas.Series.str.replace)(\n self, pat, repl, n, case, flags, regex\n )\n\n @doc_utils.doc_str_method(\n refer_to=\"rfind\",\n params=\"\"\"\n sub : str\n start : int, default: 0\n end : int, optional\"\"\",\n )\n def str_rfind(self, sub, start=0, end=None):\n return StrDefault.register(pandas.Series.str.rfind)(self, sub, start, end)\n\n @doc_utils.doc_str_method(\n refer_to=\"rindex\",\n params=\"\"\"\n sub : str\n start : int, default: 0\n end : int, optional\"\"\",\n )\n def str_rindex(self, sub, start=0, end=None):\n return StrDefault.register(pandas.Series.str.rindex)(self, sub, start, end)\n\n @doc_utils.doc_str_method(\n refer_to=\"rjust\",\n params=\"\"\"\n width : int\n fillchar : str, default: ' '\"\"\",\n )\n def str_rjust(self, width, fillchar=\" \"):\n return StrDefault.register(pandas.Series.str.rjust)(self, width, fillchar)\n\n @doc_utils.doc_str_method(\n refer_to=\"rpartition\",\n params=\"\"\"\n sep : str, default: ' '\n expand : bool, default: True\"\"\",\n )\n def str_rpartition(self, sep=\" \", expand=True):\n return StrDefault.register(pandas.Series.str.rpartition)(self, sep, expand)\n\n @doc_utils.doc_str_method(\n refer_to=\"rsplit\",\n params=\"\"\"\n pat : str, optional\n n : int, default: -1\n expand : bool, default: False\"\"\",\n )\n def str_rsplit(self, pat=None, n=-1, expand=False):\n return StrDefault.register(pandas.Series.str.rsplit)(self, pat, n, expand)\n\n @doc_utils.doc_str_method(refer_to=\"rstrip\", params=\"to_strip : str, optional\")\n def str_rstrip(self, to_strip=None):\n return StrDefault.register(pandas.Series.str.rstrip)(self, to_strip)\n\n @doc_utils.doc_str_method(\n refer_to=\"slice\",\n params=\"\"\"\n start : int, optional\n stop : int, optional\n step : int, optional\"\"\",\n )\n def str_slice(self, start=None, stop=None, step=None):\n return StrDefault.register(pandas.Series.str.slice)(self, start, stop, step)\n\n @doc_utils.doc_str_method(\n refer_to=\"slice_replace\",\n params=\"\"\"\n start : int, optional\n stop : int, optional\n repl : str or callable, optional\"\"\",\n )\n def str_slice_replace(self, start=None, stop=None, repl=None):\n return StrDefault.register(pandas.Series.str.slice_replace)(\n self, start, stop, repl\n )\n\n @doc_utils.doc_str_method(\n refer_to=\"split\",\n params=\"\"\"\n pat : str, optional\n n : int, default: -1\n expand : bool, default: False\"\"\",\n )\n def str_split(self, pat=None, n=-1, expand=False):\n return StrDefault.register(pandas.Series.str.split)(self, pat, n, expand)\n\n @doc_utils.doc_str_method(\n refer_to=\"startswith\",\n params=\"\"\"\n pat : str\n na : object, default: np.NaN\"\"\",\n )\n def str_startswith(self, pat, na=np.NaN):\n return StrDefault.register(pandas.Series.str.startswith)(self, pat, na)\n\n @doc_utils.doc_str_method(refer_to=\"strip\", params=\"to_strip : str, optional\")\n def str_strip(self, to_strip=None):\n return StrDefault.register(pandas.Series.str.strip)(self, to_strip)\n\n @doc_utils.doc_str_method(refer_to=\"swapcase\", params=\"\")\n def str_swapcase(self):\n return StrDefault.register(pandas.Series.str.swapcase)(self)\n\n @doc_utils.doc_str_method(refer_to=\"title\", params=\"\")\n def str_title(self):\n return StrDefault.register(pandas.Series.str.title)(self)\n\n @doc_utils.doc_str_method(refer_to=\"translate\", params=\"table : dict\")\n def str_translate(self, table):\n return StrDefault.register(pandas.Series.str.translate)(self, table)\n\n @doc_utils.doc_str_method(refer_to=\"upper\", params=\"\")\n def str_upper(self):\n return StrDefault.register(pandas.Series.str.upper)(self)\n\n @doc_utils.doc_str_method(\n refer_to=\"wrap\",\n params=\"\"\"\n width : int\n **kwargs : dict\"\"\",\n )\n def str_wrap(self, width, **kwargs):\n return StrDefault.register(pandas.Series.str.wrap)(self, width, **kwargs)\n\n @doc_utils.doc_str_method(refer_to=\"zfill\", params=\"width : int\")\n def str_zfill(self, width):\n return StrDefault.register(pandas.Series.str.zfill)(self, width)\n\n # End of Str methods\n\n # Rolling methods\n\n # FIXME: most of the rolling/window methods take *args and **kwargs parameters\n # which are only needed for the compatibility with numpy, this behaviour is inherited\n # from the API level, we should get rid of it (Modin issue #3108).\n\n @doc_utils.doc_window_method(\n result=\"the result of passed functions\",\n action=\"apply specified functions\",\n refer_to=\"aggregate\",\n params=\"\"\"\n func : str, dict, callable(pandas.Series) -> scalar, or list of such\n *args : iterable\n **kwargs : dict\"\"\",\n build_rules=\"udf_aggregation\",\n )\n def rolling_aggregate(self, rolling_args, func, *args, **kwargs):\n return RollingDefault.register(pandas.core.window.rolling.Rolling.aggregate)(\n self, rolling_args, func, *args, **kwargs\n )\n\n # FIXME: at the query compiler method `rolling_apply` is an alias for `rolling_aggregate`,\n # one of these should be removed (Modin issue #3107).\n @doc_utils.add_deprecation_warning(replacement_method=\"rolling_aggregate\")\n @doc_utils.doc_window_method(\n result=\"the result of passed function\",\n action=\"apply specified function\",\n refer_to=\"apply\",\n params=\"\"\"\n func : callable(pandas.Series) -> scalar\n raw : bool, default: False\n engine : None, default: None\n This parameters serves the compatibility purpose. Always has to be None.\n engine_kwargs : None, default: None\n This parameters serves the compatibility purpose. Always has to be None.\n args : tuple, optional\n kwargs : dict, optional\"\"\",\n build_rules=\"udf_aggregation\",\n )\n def rolling_apply(\n self,\n rolling_args,\n func,\n raw=False,\n engine=None,\n engine_kwargs=None,\n args=None,\n kwargs=None,\n ):\n return RollingDefault.register(pandas.core.window.rolling.Rolling.apply)(\n self, rolling_args, func, raw, engine, engine_kwargs, args, kwargs\n )\n\n @doc_utils.doc_window_method(\n result=\"correlation\",\n refer_to=\"corr\",\n params=\"\"\"\n other : modin.pandas.Series, modin.pandas.DataFrame, list-like, optional\n pairwise : bool, optional\n *args : iterable\n **kwargs : dict\"\"\",\n )\n def rolling_corr(self, rolling_args, other=None, pairwise=None, *args, **kwargs):\n return RollingDefault.register(pandas.core.window.rolling.Rolling.corr)(\n self, rolling_args, other, pairwise, *args, **kwargs\n )\n\n @doc_utils.doc_window_method(result=\"number of non-NA values\", refer_to=\"count\")\n def rolling_count(self, rolling_args):\n return RollingDefault.register(pandas.core.window.rolling.Rolling.count)(\n self, rolling_args\n )\n\n @doc_utils.doc_window_method(\n result=\"covariance\",\n refer_to=\"cov\",\n params=\"\"\"\n other : modin.pandas.Series, modin.pandas.DataFrame, list-like, optional\n pairwise : bool, optional\n ddof : int, default: 1\n **kwargs : dict\"\"\",\n )\n def rolling_cov(self, rolling_args, other=None, pairwise=None, ddof=1, **kwargs):\n return RollingDefault.register(pandas.core.window.rolling.Rolling.cov)(\n self, rolling_args, other, pairwise, ddof, **kwargs\n )\n\n @doc_utils.doc_window_method(\n result=\"unbiased kurtosis\", refer_to=\"kurt\", params=\"**kwargs : dict\"\n )\n def rolling_kurt(self, rolling_args, **kwargs):\n return RollingDefault.register(pandas.core.window.rolling.Rolling.kurt)(\n self, rolling_args, **kwargs\n )\n\n @doc_utils.doc_window_method(\n result=\"maximum value\",\n refer_to=\"max\",\n params=\"\"\"\n *args : iterable\n **kwargs : dict\"\"\",\n )\n def rolling_max(self, rolling_args, *args, **kwargs):\n return RollingDefault.register(pandas.core.window.rolling.Rolling.max)(\n self, rolling_args, *args, **kwargs\n )\n\n @doc_utils.doc_window_method(\n result=\"mean value\",\n refer_to=\"mean\",\n params=\"\"\"\n *args : iterable\n **kwargs : dict\"\"\",\n )\n def rolling_mean(self, rolling_args, *args, **kwargs):\n return RollingDefault.register(pandas.core.window.rolling.Rolling.mean)(\n self, rolling_args, *args, **kwargs\n )\n\n @doc_utils.doc_window_method(\n result=\"median value\", refer_to=\"median\", params=\"**kwargs : dict\"\n )\n def rolling_median(self, rolling_args, **kwargs):\n return RollingDefault.register(pandas.core.window.rolling.Rolling.median)(\n self, rolling_args, **kwargs\n )\n\n @doc_utils.doc_window_method(\n result=\"minimum value\",\n refer_to=\"min\",\n params=\"\"\"\n *args : iterable\n **kwargs : dict\"\"\",\n )\n def rolling_min(self, rolling_args, *args, **kwargs):\n return RollingDefault.register(pandas.core.window.rolling.Rolling.min)(\n self, rolling_args, *args, **kwargs\n )\n\n @doc_utils.doc_window_method(\n result=\"quantile\",\n refer_to=\"quantile\",\n params=\"\"\"\n quantile : float\n interpolation : {'linear', 'lower', 'higher', 'midpoint', 'nearest'}, default: 'linear'\n **kwargs : dict\"\"\",\n )\n def rolling_quantile(\n self, rolling_args, quantile, interpolation=\"linear\", **kwargs\n ):\n return RollingDefault.register(pandas.core.window.rolling.Rolling.quantile)(\n self, rolling_args, quantile, interpolation, **kwargs\n )\n\n @doc_utils.doc_window_method(\n result=\"unbiased skewness\", refer_to=\"skew\", params=\"**kwargs : dict\"\n )\n def rolling_skew(self, rolling_args, **kwargs):\n return RollingDefault.register(pandas.core.window.rolling.Rolling.skew)(\n self, rolling_args, **kwargs\n )\n\n @doc_utils.doc_window_method(\n result=\"standart deviation\",\n refer_to=\"std\",\n params=\"\"\"\n ddof : int, default: 1\n *args : iterable\n **kwargs : dict\"\"\",\n )\n def rolling_std(self, rolling_args, ddof=1, *args, **kwargs):\n return RollingDefault.register(pandas.core.window.rolling.Rolling.std)(\n self, rolling_args, ddof, *args, **kwargs\n )\n\n @doc_utils.doc_window_method(\n result=\"sum\",\n refer_to=\"sum\",\n params=\"\"\"\n *args : iterable\n **kwargs : dict\"\"\",\n )\n def rolling_sum(self, rolling_args, *args, **kwargs):\n return RollingDefault.register(pandas.core.window.rolling.Rolling.sum)(\n self, rolling_args, *args, **kwargs\n )\n\n @doc_utils.doc_window_method(\n result=\"variance\",\n refer_to=\"var\",\n params=\"\"\"\n ddof : int, default: 1\n *args : iterable\n **kwargs : dict\"\"\",\n )\n def rolling_var(self, rolling_args, ddof=1, *args, **kwargs):\n return RollingDefault.register(pandas.core.window.rolling.Rolling.var)(\n self, rolling_args, ddof, *args, **kwargs\n )\n\n # End of Rolling methods\n\n # Window methods\n\n @doc_utils.doc_window_method(\n win_type=\"window of the specified type\",\n result=\"mean\",\n refer_to=\"mean\",\n params=\"\"\"\n *args : iterable\n **kwargs : dict\"\"\",\n )\n def window_mean(self, window_args, *args, **kwargs):\n return RollingDefault.register(pandas.core.window.Window.mean)(\n self, window_args, *args, **kwargs\n )\n\n @doc_utils.doc_window_method(\n win_type=\"window of the specified type\",\n result=\"standart deviation\",\n refer_to=\"std\",\n params=\"\"\"\n ddof : int, default: 1\n *args : iterable\n **kwargs : dict\"\"\",\n )\n def window_std(self, window_args, ddof=1, *args, **kwargs):\n return RollingDefault.register(pandas.core.window.Window.std)(\n self, window_args, ddof, *args, **kwargs\n )\n\n @doc_utils.doc_window_method(\n win_type=\"window of the specified type\",\n result=\"sum\",\n refer_to=\"sum\",\n params=\"\"\"\n *args : iterable\n **kwargs : dict\"\"\",\n )\n def window_sum(self, window_args, *args, **kwargs):\n return RollingDefault.register(pandas.core.window.Window.sum)(\n self, window_args, *args, **kwargs\n )\n\n @doc_utils.doc_window_method(\n win_type=\"window of the specified type\",\n result=\"variance\",\n refer_to=\"var\",\n params=\"\"\"\n ddof : int, default: 1\n *args : iterable\n **kwargs : dict\"\"\",\n )\n def window_var(self, window_args, ddof=1, *args, **kwargs):\n return RollingDefault.register(pandas.core.window.Window.var)(\n self, window_args, ddof, *args, **kwargs\n )\n\n # End of Window methods\n\n # Categories methods\n\n @doc_utils.add_one_column_warning\n @doc_utils.add_refer_to(\"Series.cat.codes\")\n def cat_codes(self):\n \"\"\"\n Convert underlying categories data into its codes.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler containing the integer codes of the underlying\n categories.\n \"\"\"\n return CatDefault.register(pandas.Series.cat.codes)(self)\n\n # End of Categories methods\n\n # DataFrame methods\n\n def invert(self):\n \"\"\"\n Apply bitwise invertion for each element of the QueryCompiler.\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler containing bitwise invertion for each value.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.__invert__)(self)\n\n @doc_utils.doc_reduce_agg(\n method=\"mean absolute deviation\",\n params=\"\"\"\n axis : {0, 1}\n skipna : bool\n level : None, default: None\n Serves the compatibility purpose. Always has to be None.\"\"\",\n refer_to=\"mad\",\n )\n def mad(self, axis, skipna, level=None):\n return DataFrameDefault.register(pandas.DataFrame.mad)(\n self, axis=axis, skipna=skipna, level=level\n )\n\n @doc_utils.doc_reduce_agg(\n method=\"unbiased kurtosis\", refer_to=\"kurt\", extra_params=[\"skipna\", \"**kwargs\"]\n )\n def kurt(self, axis, level=None, numeric_only=None, skipna=True, **kwargs):\n return DataFrameDefault.register(pandas.DataFrame.kurt)(\n self, axis=axis, skipna=skipna, numeric_only=numeric_only, **kwargs\n )\n\n sum_min_count = sum\n prod_min_count = prod\n\n @doc_utils.add_refer_to(\"DataFrame.compare\")\n def compare(self, other, align_axis, keep_shape, keep_equal):\n \"\"\"\n Compare data of two QueryCompilers and highlight the difference.\n\n Parameters\n ----------\n other : BaseQueryCompiler\n Query compiler to compare with. Have to be the same shape and the same\n labeling as `self`.\n align_axis : {0, 1}\n keep_shape : bool\n keep_equal : bool\n\n Returns\n -------\n BaseQueryCompiler\n New QueryCompiler containing the differences between `self` and passed\n query compiler.\n \"\"\"\n return DataFrameDefault.register(pandas.DataFrame.compare)(\n self,\n other=other,\n align_axis=align_axis,\n keep_shape=keep_shape,\n keep_equal=keep_equal,\n )\n\n # End of DataFrame methods\n", "sub_path": "modin/backends/base/query_compiler.py", "file_name": "query_compiler.py", "file_ext": "py", "file_size_in_byte": 142354, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "modin.error_message.ErrorMessage.default_to_pandas", "line_number": 58, "usage_type": "call"}, {"api_name": "modin.error_message.ErrorMessage", "line_number": 58, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault.register", "line_number": 79, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault", "line_number": 79, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 79, "usage_type": "attribute"}, {"api_name": "abc.ABC", "line_number": 92, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 112, "usage_type": "attribute"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault.register", "line_number": 158, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault", "line_number": 158, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 158, "usage_type": "attribute"}, {"api_name": "modin.data_management.functions.default_methods.SeriesDefault.register", "line_number": 162, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.SeriesDefault", "line_number": 162, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 162, "usage_type": "attribute"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault.register", "line_number": 181, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault", "line_number": 181, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 181, "usage_type": "attribute"}, {"api_name": "modin.data_management.functions.default_methods.SeriesDefault.register", "line_number": 185, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.SeriesDefault", "line_number": 185, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 185, "usage_type": "attribute"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault.register", "line_number": 206, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault", "line_number": 206, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 206, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 251, "usage_type": "attribute"}, {"api_name": "pandas.RangeIndex", "line_number": 260, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault.register", "line_number": 263, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault", "line_number": 263, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 268, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 273, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 281, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 294, "usage_type": "attribute"}, {"api_name": "abc.abstractmethod", "line_number": 318, "usage_type": "attribute"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault.register", "line_number": 362, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault", "line_number": 362, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 362, "usage_type": "attribute"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault.register", "line_number": 374, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault", "line_number": 374, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 374, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_binary_method", "line_number": 372, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 372, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault.register", "line_number": 403, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault", "line_number": 403, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 403, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.add_refer_to", "line_number": 376, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 376, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault.register", "line_number": 425, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault", "line_number": 425, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 425, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.add_refer_to", "line_number": 407, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 407, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault.register", "line_number": 431, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault", "line_number": 431, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 431, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_binary_method", "line_number": 429, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 429, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault.register", "line_number": 435, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault", "line_number": 435, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 435, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_binary_method", "line_number": 433, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 433, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault.register", "line_number": 443, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault", "line_number": 443, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 443, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_binary_method", "line_number": 439, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 439, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault.register", "line_number": 449, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault", "line_number": 449, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 449, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_binary_method", "line_number": 445, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 445, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault.register", "line_number": 455, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault", "line_number": 455, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 455, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_binary_method", "line_number": 451, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 451, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault.register", "line_number": 461, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault", "line_number": 461, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 461, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_binary_method", "line_number": 457, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 457, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault.register", "line_number": 465, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault", "line_number": 465, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 465, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_binary_method", "line_number": 463, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 463, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault.register", "line_number": 469, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.BinaryDefault", "line_number": 469, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 469, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_binary_method", "line_number": 467, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 467, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault.register", "line_number": 492, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault", "line_number": 492, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 492, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.add_refer_to", "line_number": 471, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 471, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault.register", "line_number": 510, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault", "line_number": 510, "usage_type": "name"}, {"api_name": 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"modin.data_management.functions.default_methods.RollingDefault", "line_number": 3972, "usage_type": "name"}, {"api_name": "pandas.core", "line_number": 3972, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_window_method", "line_number": 3962, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 3962, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault.register", "line_number": 3980, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault", "line_number": 3980, "usage_type": "name"}, {"api_name": "pandas.core", "line_number": 3980, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_window_method", "line_number": 3976, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 3976, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault.register", "line_number": 3992, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault", "line_number": 3992, "usage_type": "name"}, {"api_name": "pandas.core", "line_number": 3992, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_window_method", "line_number": 3984, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 3984, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault.register", "line_number": 4004, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault", "line_number": 4004, "usage_type": "name"}, {"api_name": "pandas.core", "line_number": 4004, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_window_method", "line_number": 3996, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 3996, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault.register", "line_number": 4012, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault", "line_number": 4012, "usage_type": "name"}, {"api_name": "pandas.core", "line_number": 4012, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_window_method", "line_number": 4008, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 4008, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault.register", "line_number": 4024, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault", "line_number": 4024, "usage_type": "name"}, {"api_name": "pandas.core", "line_number": 4024, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_window_method", "line_number": 4016, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 4016, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault.register", "line_number": 4039, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault", "line_number": 4039, "usage_type": "name"}, {"api_name": "pandas.core", "line_number": 4039, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_window_method", "line_number": 4028, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 4028, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault.register", "line_number": 4047, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault", "line_number": 4047, "usage_type": "name"}, {"api_name": "pandas.core", "line_number": 4047, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_window_method", "line_number": 4043, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 4043, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault.register", "line_number": 4060, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault", "line_number": 4060, "usage_type": "name"}, {"api_name": "pandas.core", "line_number": 4060, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_window_method", "line_number": 4051, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 4051, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault.register", "line_number": 4072, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault", "line_number": 4072, "usage_type": "name"}, {"api_name": "pandas.core", "line_number": 4072, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_window_method", "line_number": 4064, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 4064, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault.register", "line_number": 4085, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault", "line_number": 4085, "usage_type": "name"}, {"api_name": "pandas.core", "line_number": 4085, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_window_method", "line_number": 4076, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 4076, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault.register", "line_number": 4102, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault", "line_number": 4102, "usage_type": "name"}, {"api_name": "pandas.core", "line_number": 4102, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_window_method", "line_number": 4093, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 4093, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault.register", "line_number": 4116, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault", "line_number": 4116, "usage_type": "name"}, {"api_name": "pandas.core", "line_number": 4116, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_window_method", "line_number": 4106, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 4106, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault.register", "line_number": 4129, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault", "line_number": 4129, "usage_type": "name"}, {"api_name": "pandas.core", "line_number": 4129, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_window_method", "line_number": 4120, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 4120, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault.register", "line_number": 4143, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.RollingDefault", "line_number": 4143, "usage_type": "name"}, {"api_name": "pandas.core", "line_number": 4143, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_window_method", "line_number": 4133, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 4133, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.CatDefault.register", "line_number": 4163, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.CatDefault", "line_number": 4163, "usage_type": "name"}, {"api_name": "pandas.Series", "line_number": 4163, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.add_one_column_warning", "line_number": 4151, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 4151, "usage_type": "name"}, {"api_name": "modin.backends.base.doc_utils.add_refer_to", "line_number": 4152, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 4152, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault.register", "line_number": 4178, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault", "line_number": 4178, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 4178, "usage_type": "attribute"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault.register", "line_number": 4190, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault", "line_number": 4190, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 4190, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_reduce_agg", "line_number": 4180, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 4180, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault.register", "line_number": 4198, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault", "line_number": 4198, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 4198, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.doc_reduce_agg", "line_number": 4194, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 4194, "usage_type": "name"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault.register", "line_number": 4225, "usage_type": "call"}, {"api_name": "modin.data_management.functions.default_methods.DataFrameDefault", "line_number": 4225, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 4225, "usage_type": "attribute"}, {"api_name": "modin.backends.base.doc_utils.add_refer_to", "line_number": 4205, "usage_type": "call"}, {"api_name": "modin.backends.base.doc_utils", "line_number": 4205, "usage_type": "name"}]} +{"seq_id": "92747773", "text": "from trac.mimeview.api import Mimeview, IContentConverter, Context\nfrom trac.core import *\nfrom trac.perm import IPermissionRequestor\nfrom trac.resource import Resource, IResourceManager, get_resource_url, ResourceNotFound\nfrom trac.config import BoolOption, IntOption, ListOption\nfrom trac.web.chrome import INavigationContributor, ITemplateProvider, \\\n add_stylesheet, add_javascript, add_link, \\\n add_ctxtnav, prevnext_nav, add_notice\nfrom trac.web.main import IRequestHandler\nfrom trac.timeline.api import ITimelineEventProvider\nfrom trac.util.translation import _\nfrom trac.attachment import Attachment, AttachmentModule\nfrom trac.util.compat import any, partial\nfrom trac.wiki.api import IWikiSyntaxProvider\nfrom trac.util.datefmt import format_datetime, utc, to_timestamp\nfrom trac.search import ISearchSource, search_to_sql, shorten_result\nfrom trac.util.presentation import Paginator\n\nfrom datetime import datetime\nimport re\nfrom genshi.builder import tag\n\nfrom mailinglistplugin.api import MailinglistSystem\nfrom mailinglistplugin.model import Mailinglist, MailinglistConversation, MailinglistMessage\n\nimport pkg_resources\n\nclass MailinglistModule(Component):\n implements(IRequestHandler, ITemplateProvider, INavigationContributor,\n IWikiSyntaxProvider, ISearchSource, ITimelineEventProvider)\n\n limit = IntOption(\"mailinglist\", \"page_size\", 20,\n \"Number of conversations to show per page\")\n \n # ITemplateProvider methods\n\n def get_htdocs_dirs(self):\n return [('mailinglist', pkg_resources.resource_filename(__name__, 'htdocs'))]\n\n def get_templates_dirs(self):\n return [pkg_resources.resource_filename(__name__, 'templates')]\n\n # INavigationContributor methods\n def get_active_navigation_item(self, req):\n return 'mailinglist'\n\n def get_navigation_items(self, req):\n yield ('mainnav', 'mailinglist',\n tag.a(_('Mailinglist'), href=req.href.mailinglist()))\n\n # IWikiSyntaxProvider\n def get_wiki_syntax(self):\n yield (r'\\bmailinglist:(?P\\d+)\\b', \n lambda f, m, fm: self._format_link(f, 'mailinglist', \n fm.group('list_id'), \n m, fm))\n\n def get_link_resolvers(self):\n yield ('mailinglist', self._format_link)\n \n def _format_link(self, formatter, ns, target, label, match=None):\n resource = Resource('mailinglist', target)\n try:\n instance = MailinglistSystem(self.env).get_instance_for_resource(resource)\n except ResourceNotFound:\n return tag.a(label, class_='missing mailinglist')\n if isinstance(instance,Mailinglist):\n return tag.a(\"Mailinglist: %s\" % instance.name, href=formatter.href.mailinglist(target))\n elif isinstance(instance,MailinglistConversation):\n return tag.a(\"%s: %s\" % (instance.mailinglist.name, instance.subject),\n href=formatter.href.mailinglist(target),\n title=\"Dated %s\" % format_datetime(instance.date, tzinfo=formatter.req.tz))\n elif isinstance(instance,MailinglistMessage):\n return tag.a(\"%s: %s\" % (instance.conversation.mailinglist.name, instance.subject),\n href=formatter.href.mailinglist(target),\n title=\"Dated %s\" % format_datetime(instance.date, tzinfo=formatter.req.tz))\n else:\n return tag.a(label, href=formatter.href.mailinglist(target)) \n\n # ISearchSource methods\n\n def get_search_filters(self, req):\n if 'MAILINGLIST_VIEW' in req.perm:\n yield ('mailinglist', _(\"Mailinglist\"))\n\n def get_search_results(self, req, terms, filters):\n if not 'mailinglist' in filters:\n return\n mailinglist_realm = Resource('mailinglist')\n\n lists = {}\n for mailinglist in Mailinglist.select(self.env):\n if \"MAILINGLIST_VIEW\" in req.perm(mailinglist.resource): \n lists[mailinglist.id] = mailinglist\n \n if not lists:\n self.log.debug(\"This user can't view any lists, so not searching.\")\n return\n \n db = self.env.get_read_db()\n sql, args = search_to_sql(db, ['subject','body','from_email','from_name'], terms)\n\n cursor = db.cursor()\n query = \"\"\"\n SELECT id, subject, body, from_name, from_email, date, list, conversation\n FROM mailinglistmessages\n WHERE list IN (%s) AND %s\n \"\"\" % (\",\".join(map(str,lists.keys())), sql,)\n self.log.debug(\"Search query: %s\", query)\n cursor.execute(query, args)\n for mid, subject, body, from_name, from_email, date, mlist, conversation in cursor:\n # build resource ourself to speed things up\n m = mailinglist_realm(id=\"%s/%d/%d\" % (lists[mlist].emailaddress,\n conversation,\n mid))\n if 'MAILINGLIST_VIEW' in req.perm(m):\n yield (req.href.mailinglist(m.id),\n tag(\"%s: %s\" % (lists[mlist].name, subject)),\n datetime.fromtimestamp(date, utc),\n \"%s <%s>\" % (from_name, from_email),\n shorten_result(body, terms))\n \n # Attachments\n for result in AttachmentModule(self.env).get_search_results(\n req, mailinglist_realm, terms):\n yield result \n \n\n # IRequestHandler methods\n def match_request(self, req):\n if req.path_info.startswith(\"/mailinglist\"):\n match = re.match(r'/mailinglist/([^/]+)$', req.path_info)\n if match:\n req.args['listname'] = match.group(1)\n match = re.match(r'/mailinglist/[^/]+/([0-9]+)$', req.path_info)\n if match:\n req.args['conversationid'] = match.group(1)\n match = re.match(r'/mailinglist/[^/]+/[0-9]+/([0-9]+)$', req.path_info)\n if match:\n req.args['messageid'] = match.group(1)\n return True\n\n def process_request(self, req):\n offset = req.args.get(\"offset\",0)\n page = req.args.get('page', 1)\n try:\n offset = int(offset)\n except:\n raise TracError(_('Invalid offset used: %(offset)s', offset=offset)) \n \n try:\n page = int(page)\n except:\n raise TracError(_('Invalid page used: %(page)s', page=page))\n \n offset = (page - 1) * self.limit\n \n add_stylesheet(req, 'mailinglist/css/mailinglist.css')\n add_javascript(req, 'mailinglist/mailinglist.js')\n \n mailinglists = [m for m in Mailinglist.select(self.env)\n if \"MAILINGLIST_VIEW\" in req.perm(m.resource)]\n\n data = {\"mailinglists\": mailinglists,\n \"offset\": offset,\n \"limit\": self.limit}\n\n if req.method == 'POST':\n\n if 'subscribe' in req.args:\n subscribe = True\n unsubscribe = False\n mailinglist_email = req.args.get('subscribe')\n elif 'unsubscribe' in req.args:\n subscribe = False\n unsubscribe = True\n mailinglist_email = req.args.get('unsubscribe')\n else:\n # at the moment we only post subscription info to\n # mailing list page - so if there is none in req.args we \n # can just redirect to mailing list page\n req.redirect(req.href.mailinglist())\n\n # get mailing list object and check permissions\n mailinglist = Mailinglist.select_by_address(self.env,\n mailinglist_email, localpart=True)\n req.perm(mailinglist.resource).require(\"MAILINGLIST_VIEW\")\n\n if subscribe:\n mailinglist.subscribe(user=req.authname)\n # subscribe does not return a value to indicate if it \n # was successful, so we have to explicitly check\n if mailinglist.is_subscribed(req.authname):\n add_notice(req, _('You have been subscribed to %s.' % mailinglist.name))\n else:\n add_notice(req, _('Unable to subscribe to %s.' % mailinglist.name))\n elif unsubscribe:\n mailinglist.unsubscribe(user=req.authname)\n # unsubscribe does not return a value to indicate if it \n # was successful, so we have to explicitly check\n if not mailinglist.is_subscribed(req.authname):\n add_notice(req, _('You have been unsubscribed from %s.' % mailinglist.name))\n else:\n add_notice(req, _('Unable to unsubscribe from %s.' % mailinglist.name))\n\n if req.path_info.endswith('/mailinglist'):\n # overview mailing list page\n req.redirect(req.href.mailinglist())\n elif 'conversationid' in req.args:\n # individual mailing list conversation log\n req.redirect(req.href.mailinglist(mailinglist_email, req.args['conversationid']))\n else:\n # individual mailing list homepage\n req.redirect(req.href.mailinglist(mailinglist_email))\n\n #for mailinglist in mailinglists:\n # add_ctxtnav(req,\n # _(\"List: %s\") % mailinglist.name,\n # req.href.mailinglist(mailinglist.emailaddress))\n\t\t\n if 'messageid' in req.args:\n message = MailinglistMessage(self.env, req.args['messageid'])\n # leaks the subject of the email in the error, wonder if\n # that's a problem...\n req.perm(message.resource).require(\"MAILINGLIST_VIEW\")\n if req.args.get('format') == \"raw\":\n req.send_header('Content-Disposition', 'attachment')\n req.send_response(200)\n content = message.raw.bytes\n req.send_header('Content-Type', 'application/mbox')\n req.send_header('Content-Length', len(content))\n req.end_headers()\n if req.method != 'HEAD':\n req.write(content)\n return\n\n context = Context.from_request(req, message.resource)\n \n data['message'] = message\n data['attachments'] = AttachmentModule(self.env).attachment_data(context)\n\n add_link(req, 'up', get_resource_url(self.env, message.conversation.resource, req.href,\n offset=data['offset']),\n _(\"Back to conversation\"))\n\n prevnext_nav(req, _(\"Newer message\"), _(\"Older message\"), \n _(\"Back to conversation\"))\n\n raw_href = get_resource_url(self.env, message.resource,\n req.href, format='raw')\n add_link(req, 'alternate', raw_href, _('mbox'), \"application/mbox\")\n\n if 'MAILINGLIST_ADMIN' in req.perm:\n add_ctxtnav(req, tag.a(tag.i(class_=\"fa fa-cog\"), ' Manage List',\n href=req.href.admin('mailinglist', 'lists', message.conversation.mailinglist.emailaddress),\n title='Manage and subscribe users to the %s mailing list' % message.conversation.mailinglist.name))\n\n return 'mailinglist_message.html', data, None\n \n if 'conversationid' in req.args:\n conversation = MailinglistConversation(self.env, req.args['conversationid'])\n # also leaks the subject of the first email in the error message\n req.perm(conversation.resource).require(\"MAILINGLIST_VIEW\")\n data['conversation'] = conversation\n data['attachmentselect'] = partial(Attachment.select, self.env)\n \n results = Paginator(conversation.messages(), page - 1, self.limit)\n if results.has_next_page:\n next_href = get_resource_url(self.env, conversation.resource, req.href, page=page + 1) \n add_link(req, 'next', next_href, _('Next Page'))\n\n if results.has_previous_page:\n prev_href = get_resource_url(self.env, conversation.resource, req.href, page=page - 1) \n add_link(req, 'prev', prev_href, _('Previous Page'))\n \n shown_pages = results.get_shown_pages()\n pagedata = [{'href': get_resource_url(self.env,\n conversation.resource,\n req.href, page=page),\n 'class': None, 'string': str(page),\n 'title': _('Page %(num)d', num=page)}\n for page in shown_pages]\n results.shown_pages = pagedata\n results.current_page = {'href': None, 'class': 'current',\n 'string': str(results.page + 1),\n 'title': None}\n data['paginator'] = results\n add_link(req, 'up', get_resource_url(self.env, conversation.mailinglist.resource, req.href,\n offset=data['offset']),\n _(\"List of conversations\"))\n\n prevnext_nav(req, _(\"Newer conversation\"), _(\"Older conversation\"), \n _(\"Back to list of conversations\"))\n\n if 'MAILINGLIST_ADMIN' in req.perm:\n add_ctxtnav(req, tag.a(tag.i(class_=\"fa fa-cog\"), ' Manage List',\n href=req.href.admin('mailinglist', 'lists', conversation.mailinglist.emailaddress),\n title='Manage and subscribe users to the %s mailing list' % conversation.mailinglist.name))\n\n\n # Check if user is already subscribed to mailing list \n # and add the appropriate subscribe / unsubscribe ribbon option\n if conversation.mailinglist.is_subscribed(req.authname):\n add_ctxtnav(req, tag.form(tag.input(tag.a(tag.i(class_='fa fa-eye-slash'),\n ' Unsubscribe', title='Unsubscribe from the %s mailing list' % conversation.mailinglist.name, id='subscribe-link'),\n name='unsubscribe', value=conversation.mailinglist.emailaddress, class_='hidden'),\n method_='post', action='', id='subscribe-form', class_='hidden'))\n else:\n add_ctxtnav(req, tag.form(tag.input(tag.a(tag.i(class_='fa fa-eye'),\n ' Subscribe', title='Subscribe to the %s mailing list' % conversation.mailinglist.name, id='subscribe-link'),\n name='subscribe', value=conversation.mailinglist.emailaddress, class_='hidden'),\n method_='post', action='', id='subscribe-form', class_='hidden'))\n\n return 'mailinglist_conversation.html', data, None\n\n elif 'listname' in req.args:\n mailinglist = Mailinglist.select_by_address(self.env,\n req.args['listname'], localpart=True)\n # leaks the name of the mailinglist\n req.perm(mailinglist.resource).require(\"MAILINGLIST_VIEW\")\n\n data['mailinglist'] = mailinglist\n\n results = Paginator(mailinglist.conversations(),\n page - 1,\n self.limit)\n\n if results.has_next_page:\n next_href = get_resource_url(self.env, mailinglist.resource, req.href, page=page + 1) \n add_link(req, 'next', next_href, _('Next Page'))\n\n if results.has_previous_page:\n prev_href = get_resource_url(self.env, mailinglist.resource, req.href, page=page - 1) \n add_link(req, 'prev', prev_href, _('Previous Page'))\n\n shown_pages = results.get_shown_pages()\n pagedata = [{'href': get_resource_url(self.env,\n mailinglist.resource,\n req.href, page=page),\n 'class': None, 'string': str(page),\n 'title': _('Page %(num)d', num=page)}\n for page in shown_pages]\n results.shown_pages = pagedata\n results.current_page = {'href': None, 'class': 'current',\n 'string': str(results.page + 1),\n 'title': None}\n data['paginator'] = results\n\n if data['offset'] + data['limit'] < mailinglist.count_conversations():\n add_link(req, 'next',\n get_resource_url(self.env, mailinglist.resource, req.href,\n offset=data['offset']+data['limit']),\n _(\"Older conversations\"))\n\n if offset > 0:\n add_link(req, 'prev',\n get_resource_url(self.env, mailinglist.resource, req.href,\n offset=data['offset']-data['limit']),\n _(\"Newer conversations\"))\n\n add_link(req, 'up', req.href.mailinglist(), _(\"List of mailinglists\"))\n\n prevnext_nav(req, _(\"Newer conversations\"), _(\"Older conversations\"), (\"Back to Mailinglists\"))\n\n if 'MAILINGLIST_ADMIN' in req.perm:\n add_ctxtnav(req, tag.a(tag.i(class_=\"fa fa-cog\"), ' Manage List',\n href=req.href.admin('mailinglist', 'lists', mailinglist.emailaddress),\n title='Manage and subscribe users to the %s mailing list' % mailinglist.name))\n\n # Check if user is already subscribed to mailing list \n # and add the appropriate subscribe / unsubscribe ribbon option\n if mailinglist.is_subscribed(req.authname):\n add_ctxtnav(req, tag.form(tag.input(tag.a(tag.i(class_='fa fa-eye-slash'),\n ' Unsubscribe', title='Unsubscribe from the %s mailing list' % mailinglist.name, id='subscribe-link'),\n name='unsubscribe', value=mailinglist.emailaddress, class_='hidden'),\n method_='post', action='', id='subscribe-form', class_='hidden'))\n else:\n add_ctxtnav(req, tag.form(tag.input(tag.a(tag.i(class_='fa fa-eye'),\n ' Subscribe', title='Subscribe to the %s mailing list' % mailinglist.name, id='subscribe-link'),\n name='subscribe', value=mailinglist.emailaddress, class_='hidden'),\n method_='post', action='', id='subscribe-form', class_='hidden'))\n\n return 'mailinglist_conversations.html', data, None\n\n else:\n return 'mailinglist_list.html', data, None\n\n # ITimelineEventProvider methods\n\n def get_timeline_filters(self, req):\n if 'MAILINGLIST_VIEW' in req.perm:\n yield ('mailinglist', _(\"Mailinglist messages\"))\n\n def get_timeline_events(self, req, start, stop, filters):\n if 'mailinglist' in filters:\n mailinglist_realm = Resource('mailinglist')\n\n lists = {}\n for mailinglist in Mailinglist.select(self.env):\n if \"MAILINGLIST_VIEW\" in req.perm(mailinglist.resource): \n lists[mailinglist.id] = mailinglist\n\n if not lists:\n self.log.debug(\"This user can't view any lists, so not listing timeline events.\")\n return\n\n self.log.debug(\"Searching for timeline events in %s\", lists)\n\n db = self.env.get_read_db()\n\n cursor = db.cursor()\n cursor.execute(\"SELECT id, subject, body, from_name, from_email, date, list, conversation \"\n \"FROM mailinglistmessages \"\n \"WHERE date>=%%s AND date<=%%s AND list IN (%s)\" % \",\".join(map(str,lists.keys())),\n (to_timestamp(start), to_timestamp(stop)))\n # \n for mid, subject, body, from_name, from_email, date, mlist, conversation in cursor:\n # build resource ourself to speed things up\n m = mailinglist_realm(id=\"%s/%d/%d\" % (lists[mlist].emailaddress,\n conversation,\n mid))\n if 'MAILINGLIST_VIEW' in req.perm(m):\n yield ('mailinglist', \n datetime.fromtimestamp(date, utc),\n \"%s\" % (from_name,),\n (mid,\n subject, \n body.lstrip()[:200],\n lists[mlist].name, \n lists[mlist].emailaddress, \n conversation))\n\n # Attachments\n for event in AttachmentModule(self.env).get_timeline_events(\n req, mailinglist_realm, start, stop):\n yield event\n\n def render_timeline_event(self, context, field, event):\n mid, subject, snippet, listname, listemailaddress, conversation = event[3]\n if field == 'url':\n return context.href.mailinglist(listemailaddress, conversation, mid)\n elif field == 'title':\n return \"%s: %s\" % (listname, subject)\n elif field == 'description':\n return snippet\n\n", "sub_path": "mailinglistplugin/web_ui.py", "file_name": "web_ui.py", "file_ext": "py", "file_size_in_byte": 21612, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "trac.web.main.IRequestHandler", "line_number": 29, "usage_type": "argument"}, {"api_name": "trac.web.chrome.ITemplateProvider", "line_number": 29, "usage_type": "argument"}, {"api_name": "trac.web.chrome.INavigationContributor", "line_number": 29, "usage_type": "argument"}, {"api_name": "trac.wiki.api.IWikiSyntaxProvider", "line_number": 30, "usage_type": "argument"}, {"api_name": "trac.search.ISearchSource", "line_number": 30, "usage_type": "argument"}, {"api_name": "trac.timeline.api.ITimelineEventProvider", "line_number": 30, "usage_type": "argument"}, {"api_name": "trac.config.IntOption", "line_number": 32, "usage_type": "call"}, {"api_name": "pkg_resources.resource_filename", "line_number": 38, "usage_type": "call"}, {"api_name": "pkg_resources.resource_filename", "line_number": 41, "usage_type": "call"}, {"api_name": "genshi.builder.tag.a", "line_number": 49, "usage_type": "call"}, {"api_name": "genshi.builder.tag", "line_number": 49, "usage_type": "name"}, {"api_name": "trac.util.translation._", "line_number": 49, "usage_type": "call"}, {"api_name": "trac.resource.Resource", "line_number": 62, "usage_type": "call"}, {"api_name": "mailinglistplugin.api.MailinglistSystem", "line_number": 64, "usage_type": "call"}, {"api_name": "trac.resource.ResourceNotFound", "line_number": 65, "usage_type": "name"}, {"api_name": "genshi.builder.tag.a", "line_number": 66, "usage_type": "call"}, {"api_name": "genshi.builder.tag", "line_number": 66, "usage_type": "name"}, {"api_name": "mailinglistplugin.model.Mailinglist", "line_number": 67, "usage_type": "argument"}, {"api_name": "genshi.builder.tag.a", "line_number": 68, "usage_type": "call"}, {"api_name": "genshi.builder.tag", "line_number": 68, "usage_type": "name"}, {"api_name": "mailinglistplugin.model.MailinglistConversation", "line_number": 69, "usage_type": "argument"}, {"api_name": "genshi.builder.tag.a", "line_number": 70, "usage_type": "call"}, {"api_name": "genshi.builder.tag", "line_number": 70, "usage_type": "name"}, {"api_name": "trac.util.datefmt.format_datetime", "line_number": 72, "usage_type": "call"}, {"api_name": "mailinglistplugin.model.MailinglistMessage", "line_number": 73, "usage_type": "argument"}, {"api_name": "genshi.builder.tag.a", "line_number": 74, "usage_type": "call"}, {"api_name": "genshi.builder.tag", "line_number": 74, "usage_type": "name"}, {"api_name": "trac.util.datefmt.format_datetime", "line_number": 76, "usage_type": "call"}, {"api_name": "genshi.builder.tag.a", "line_number": 78, "usage_type": "call"}, {"api_name": "genshi.builder.tag", "line_number": 78, "usage_type": "name"}, {"api_name": "trac.util.translation._", "line_number": 84, "usage_type": "call"}, {"api_name": "trac.resource.Resource", "line_number": 89, "usage_type": "call"}, {"api_name": "mailinglistplugin.model.Mailinglist.select", "line_number": 92, "usage_type": "call"}, {"api_name": "mailinglistplugin.model.Mailinglist", "line_number": 92, "usage_type": "name"}, {"api_name": "trac.search.search_to_sql", "line_number": 101, "usage_type": "call"}, {"api_name": "genshi.builder.tag", "line_number": 118, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 119, "usage_type": "call"}, {"api_name": "trac.util.datefmt.utc", "line_number": 119, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 119, "usage_type": "name"}, {"api_name": "trac.search.shorten_result", "line_number": 121, "usage_type": "call"}, {"api_name": "trac.attachment.AttachmentModule", "line_number": 124, "usage_type": "call"}, {"api_name": "re.match", "line_number": 132, "usage_type": "call"}, {"api_name": "re.match", "line_number": 135, "usage_type": "call"}, {"api_name": "re.match", "line_number": 138, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 149, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 154, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_stylesheet", "line_number": 158, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_javascript", "line_number": 159, "usage_type": "call"}, {"api_name": "mailinglistplugin.model.Mailinglist.select", "line_number": 161, "usage_type": "call"}, {"api_name": "mailinglistplugin.model.Mailinglist", "line_number": 161, "usage_type": "name"}, {"api_name": "mailinglistplugin.model.Mailinglist.select_by_address", "line_number": 185, "usage_type": "call"}, {"api_name": "mailinglistplugin.model.Mailinglist", "line_number": 185, "usage_type": "name"}, {"api_name": "trac.web.chrome.add_notice", "line_number": 194, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 194, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_notice", "line_number": 196, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 196, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_notice", "line_number": 202, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 202, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_notice", "line_number": 204, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 204, "usage_type": "call"}, {"api_name": "mailinglistplugin.model.MailinglistMessage", "line_number": 222, "usage_type": "call"}, {"api_name": "trac.mimeview.api.Context.from_request", "line_number": 237, "usage_type": "call"}, {"api_name": "trac.mimeview.api.Context", "line_number": 237, "usage_type": "name"}, {"api_name": "trac.attachment.AttachmentModule", "line_number": 240, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_link", "line_number": 242, "usage_type": "call"}, {"api_name": "trac.resource.get_resource_url", "line_number": 242, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 244, "usage_type": "call"}, {"api_name": "trac.web.chrome.prevnext_nav", "line_number": 246, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 246, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 247, "usage_type": "call"}, {"api_name": "trac.resource.get_resource_url", "line_number": 249, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_link", "line_number": 251, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 251, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_ctxtnav", "line_number": 254, "usage_type": "call"}, {"api_name": "genshi.builder.tag.a", "line_number": 254, "usage_type": "call"}, {"api_name": "genshi.builder.tag", "line_number": 254, "usage_type": "name"}, {"api_name": "genshi.builder.tag.i", "line_number": 254, "usage_type": "call"}, {"api_name": "mailinglistplugin.model.MailinglistConversation", "line_number": 261, "usage_type": "call"}, {"api_name": "trac.util.compat.partial", "line_number": 265, "usage_type": "call"}, {"api_name": "trac.attachment.Attachment.select", "line_number": 265, "usage_type": "attribute"}, {"api_name": "trac.attachment.Attachment", "line_number": 265, "usage_type": "name"}, {"api_name": "trac.util.presentation.Paginator", "line_number": 267, "usage_type": "call"}, {"api_name": "trac.resource.get_resource_url", "line_number": 269, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_link", "line_number": 270, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 270, "usage_type": "call"}, {"api_name": "trac.resource.get_resource_url", "line_number": 273, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_link", "line_number": 274, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 274, "usage_type": "call"}, {"api_name": "trac.resource.get_resource_url", "line_number": 277, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 281, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_link", "line_number": 288, "usage_type": "call"}, {"api_name": "trac.resource.get_resource_url", "line_number": 288, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 290, "usage_type": "call"}, {"api_name": "trac.web.chrome.prevnext_nav", "line_number": 292, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 292, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 293, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_ctxtnav", "line_number": 296, "usage_type": "call"}, {"api_name": "genshi.builder.tag.a", "line_number": 296, "usage_type": "call"}, {"api_name": "genshi.builder.tag", "line_number": 296, "usage_type": "name"}, {"api_name": "genshi.builder.tag.i", "line_number": 296, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_ctxtnav", "line_number": 304, "usage_type": "call"}, {"api_name": "genshi.builder.tag.form", "line_number": 304, "usage_type": "call"}, {"api_name": "genshi.builder.tag", "line_number": 304, "usage_type": "name"}, {"api_name": "genshi.builder.tag.input", "line_number": 304, "usage_type": "call"}, {"api_name": "genshi.builder.tag.a", "line_number": 304, "usage_type": "call"}, {"api_name": "genshi.builder.tag.i", "line_number": 304, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_ctxtnav", "line_number": 309, "usage_type": "call"}, {"api_name": "genshi.builder.tag.form", "line_number": 309, "usage_type": "call"}, {"api_name": "genshi.builder.tag", "line_number": 309, "usage_type": "name"}, {"api_name": "genshi.builder.tag.input", "line_number": 309, "usage_type": "call"}, {"api_name": "genshi.builder.tag.a", "line_number": 309, "usage_type": "call"}, {"api_name": "genshi.builder.tag.i", "line_number": 309, "usage_type": "call"}, {"api_name": "mailinglistplugin.model.Mailinglist.select_by_address", "line_number": 317, "usage_type": "call"}, {"api_name": "mailinglistplugin.model.Mailinglist", "line_number": 317, "usage_type": "name"}, {"api_name": "trac.util.presentation.Paginator", "line_number": 324, "usage_type": "call"}, {"api_name": "trac.resource.get_resource_url", "line_number": 329, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_link", "line_number": 330, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 330, "usage_type": "call"}, {"api_name": "trac.resource.get_resource_url", "line_number": 333, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_link", "line_number": 334, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 334, "usage_type": "call"}, {"api_name": "trac.resource.get_resource_url", "line_number": 337, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 341, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_link", "line_number": 350, "usage_type": "call"}, {"api_name": "trac.resource.get_resource_url", "line_number": 351, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 353, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_link", "line_number": 356, "usage_type": "call"}, {"api_name": "trac.resource.get_resource_url", "line_number": 357, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 359, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_link", "line_number": 361, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 361, "usage_type": "call"}, {"api_name": "trac.web.chrome.prevnext_nav", "line_number": 363, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 363, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_ctxtnav", "line_number": 366, "usage_type": "call"}, {"api_name": "genshi.builder.tag.a", "line_number": 366, "usage_type": "call"}, {"api_name": "genshi.builder.tag", "line_number": 366, "usage_type": "name"}, {"api_name": "genshi.builder.tag.i", "line_number": 366, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_ctxtnav", "line_number": 373, "usage_type": "call"}, {"api_name": "genshi.builder.tag.form", "line_number": 373, "usage_type": "call"}, {"api_name": "genshi.builder.tag", "line_number": 373, "usage_type": "name"}, {"api_name": "genshi.builder.tag.input", "line_number": 373, "usage_type": "call"}, {"api_name": "genshi.builder.tag.a", "line_number": 373, "usage_type": "call"}, {"api_name": "genshi.builder.tag.i", "line_number": 373, "usage_type": "call"}, {"api_name": "trac.web.chrome.add_ctxtnav", "line_number": 378, "usage_type": "call"}, {"api_name": "genshi.builder.tag.form", "line_number": 378, "usage_type": "call"}, {"api_name": "genshi.builder.tag", "line_number": 378, "usage_type": "name"}, {"api_name": "genshi.builder.tag.input", "line_number": 378, "usage_type": "call"}, {"api_name": "genshi.builder.tag.a", "line_number": 378, "usage_type": "call"}, {"api_name": "genshi.builder.tag.i", "line_number": 378, "usage_type": "call"}, {"api_name": "trac.util.translation._", "line_number": 392, "usage_type": "call"}, {"api_name": "trac.resource.Resource", "line_number": 396, "usage_type": "call"}, {"api_name": "mailinglistplugin.model.Mailinglist.select", "line_number": 399, "usage_type": "call"}, {"api_name": "mailinglistplugin.model.Mailinglist", "line_number": 399, "usage_type": "name"}, {"api_name": "trac.util.datefmt.to_timestamp", "line_number": 415, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 424, "usage_type": "call"}, {"api_name": "trac.util.datefmt.utc", "line_number": 424, "usage_type": "argument"}, {"api_name": "datetime.datetime", "line_number": 424, "usage_type": "name"}, {"api_name": "trac.attachment.AttachmentModule", "line_number": 434, "usage_type": "call"}]} +{"seq_id": "70000840", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os\nimport time\nimport logging\nimport traceback\nimport six\nimport utils.timestamp as TimeStampProvider\nimport cloudio.mqtt_helpers as mqtt\nfrom cloudio.cloudio_node import CloudioNode\nfrom cloudio.properties_endpoint_configuration import PropertiesEndpointConfiguration\nfrom cloudio.interface.node_container import CloudioNodeContainer\nfrom cloudio.interface.message_format import CloudioMessageFormat\nfrom cloudio.message_format.factory import MessageFormatFactory\nfrom cloudio.exception.cloudio_modification_exception import CloudioModificationException\nfrom cloudio.exception.invalid_property_exception import InvalidPropertyException\nfrom utils.resource_loader import ResourceLoader\nfrom cloudio.message_format.json_format import JsonMessageFormat\nfrom utils import path_helpers\nfrom cloudio.pending_update import PendingUpdate\nfrom cloudio.topicuuid import TopicUuid\n\nversion = ''\n# Get endpoint python version info from init file\nwith open(os.path.dirname(os.path.realpath(__file__)) + '/__init__.py') as vf:\n content = vf.readlines()\n for line in content:\n if '__version__' in line:\n values = line.split('=')\n version = values[1]\n version = version.strip('\\n')\n version = version.strip('\\r')\n version = version.replace('\\'', '')\n version = version.strip(' ')\n break\n\n# Enable logging\nlogging.basicConfig(format='%(asctime)s.%(msecs)03d - %(name)s - %(levelname)s - %(message)s',\n datefmt='%Y-%m-%d %H:%M:%S',\n level=logging.DEBUG)\nlogging.getLogger(__name__).setLevel(logging.INFO) # DEBUG, INFO, WARNING, ERROR, CRITICAL\n\nlogging.getLogger(__name__).info('cloudio-endpoint-python version: %s' % version)\n\nclass CloudioEndpoint(CloudioNodeContainer):\n \"\"\"The cloud.iO endpoint.\n\n Contains among other things the mqtt client to talk to the cloudio broker.\n \"\"\"\n\n # Constants ######################################################################################\n MQTT_HOST_URI_PROPERTY = u'ch.hevs.cloudio.endpoint.hostUri'\n MQTT_PERSISTENCE_MEMORY = u'memory'\n MQTT_PERSISTENCE_FILE = u'file'\n MQTT_PERSISTENCE_NONE = u'none'\n MQTT_PERSISTENCE_PROPERTY = u'ch.hevs.cloudio.endpoint.persistence'\n MQTT_PERSISTENCE_DEFAULT = MQTT_PERSISTENCE_FILE\n MQTT_PERSISTENCE_LOCATION = u'ch.hevs.cloudio.endpoint.persistenceLocation'\n\n CERT_AUTHORITY_FILE_PROPERTY = u'ch.hevs.cloudio.endpoint.ssl.authorityCert'\n ENDPOINT_IDENTITY_TLS_VERSION_PROPERTY = u'ch.hevs.cloudio.endpoint.ssl.version' # tlsv1.0 or tlsv1.2\n ENDPOINT_IDENTITY_FILE_PROPERTY = u'ch.hevs.cloudio.endpoint.ssl.clientCert' # PKCS12 based file (*.p12)\n ENDPOINT_IDENTITY_CERT_FILE_PROPERTY = u'ch.hevs.cloudio.endpoint.ssl.clientCert' # (*.pem)\n ENDPOINT_IDENTITY_KEY_FILE_PROPERTY = u'ch.hevs.cloudio.endpoint.ssl.clientKey' # (*.pem)\n\n log = logging.getLogger(__name__)\n\n def __init__(self, uuid, configuration=None):\n self._endPointIsReady = False # Set to true after connection and subscription\n\n self.uuid = uuid # type: str\n self.nodes = {} # type: dict as CloudioNode\n self.cleanSession = True\n self.messageFormat = None # type: CloudioMessageFormat\n self.persistence = None # type MqttClientPersistence\n\n self.log.debug('Creating Endpoint %s' % uuid)\n\n # Check if a configuration with properties is given\n if configuration is None:\n # Try to load properties using a config file\n properties = ResourceLoader.loadFromLocations(self.uuid + '.properties',\n ['home:' + '/.config/cloud.io/', 'file:/etc/cloud.io/'])\n configuration = PropertiesEndpointConfiguration(properties)\n\n self._retryInterval = 10 # Connect retry interval in seconds\n self.messageFormat = JsonMessageFormat()\n\n # Check if 'host' property is present in config file\n host = configuration.getProperty(self.MQTT_HOST_URI_PROPERTY)\n if host == '':\n exit('Missing mandatory property \"' + self.MQTT_HOST_URI_PROPERTY + '\"')\n\n # Create persistence object.\n persistenceType = configuration.getProperty(self.MQTT_PERSISTENCE_PROPERTY, self.MQTT_PERSISTENCE_DEFAULT)\n if persistenceType == self.MQTT_PERSISTENCE_MEMORY:\n self.persistence = mqtt.MemoryPersistence()\n elif persistenceType == self.MQTT_PERSISTENCE_FILE:\n persistenceLocation = configuration.getProperty(self.MQTT_PERSISTENCE_LOCATION)\n self.persistence = mqtt.MqttDefaultFilePersistence(directory=persistenceLocation)\n elif persistenceType == self.MQTT_PERSISTENCE_NONE:\n self.persistence = None\n else:\n raise InvalidPropertyException('Unknown persistence implementation ' +\n '(ch.hevs.cloudio.endpoint.persistence): ' +\n '\\'' + persistenceType + '\\'')\n # Open peristence storage\n if self.persistence:\n self.persistence.open(clientId=self.uuid, serverUri=host)\n\n self.options = mqtt.MqttConnectOptions()\n\n # Last will is a message with the UUID of the endpoint and no payload.\n willMessage = 'DEAD'\n self.options.setWill(u'@offline/' + uuid, willMessage, 1, False)\n\n self.options._caFile = configuration.getProperty(self.CERT_AUTHORITY_FILE_PROPERTY, None)\n self.options._clientCertFile = configuration.getProperty(self.ENDPOINT_IDENTITY_CERT_FILE_PROPERTY, None)\n self.options._clientKeyFile = configuration.getProperty(self.ENDPOINT_IDENTITY_KEY_FILE_PROPERTY, None)\n self.options._username = configuration.getProperty('username')\n self.options._password = configuration.getProperty('password')\n self.options._tlsVersion = configuration.getProperty(self.ENDPOINT_IDENTITY_TLS_VERSION_PROPERTY, 'tlsv1.2')\n\n # Make path usable\n self.options._caFile = path_helpers.prettify(self.options._caFile)\n self.options._clientCertFile = path_helpers.prettify(self.options._clientCertFile)\n self.options._clientKeyFile = path_helpers.prettify(self.options._clientKeyFile)\n\n self._client = mqtt.MqttReconnectClient(host,\n clientId=self.uuid + '-endpoint-',\n clean_session=self.cleanSession,\n options=self.options)\n #  Register callback method for connection established\n self._client.setOnConnectedCallback(self._onConnected)\n # Register callback method to be called when receiving a message over MQTT\n self._client.setOnMessageCallback(self._onMessageArrived)\n # Start the client\n self._client.start()\n\n def close(self):\n # Stop Mqtt client\n self._client.stop()\n\n def _onMessageArrived(self, client, userdata, msg):\n #print(msg.topic + ': ' + str(msg.payload))\n try:\n if six.PY3:\n # Need to convert from bytes to string\n payload = msg.payload.decode('utf-8')\n else:\n payload = msg.payload\n\n # First determine the message format (first byte identifies the message format).\n messageFormat = MessageFormatFactory.messageFormat(payload[0])\n if messageFormat == None:\n self.log.error('Message-format ' + payload[0] + \" not supported!\")\n return\n\n topicLevels = msg.topic.split('/')\n # Create attribute location path stack.\n location = []\n for topicLevel in topicLevels:\n location.insert(0, topicLevel)\n\n # Read the action tag from the topic\n action = topicLevels[0]\n if action == '@set':\n location.pop()\n self._set(msg.topic, location, messageFormat, payload)\n else:\n self.log.error('Method \\\"' + action + '\\\" not supported!')\n except Exception as exception:\n self.log.error(u'Exception :' + exception.message)\n traceback.print_exc()\n\n def subscribeToSetCommands(self):\n (result, mid) = self._client.subscribe(u'@set/' + self.getUuid().toString() + '/#', 1)\n return True if result == self._client.MQTT_ERR_SUCCESS else False\n\n def addNode(self, nodeName, clsOrObject):\n if nodeName != '' and clsOrObject != None:\n node = None\n\n self.log.debug('Adding node %s' % nodeName)\n\n # Add node to endpoint\n if isinstance(clsOrObject, CloudioNode):\n node = clsOrObject\n pass # All right. We have the needed object\n else:\n raise RuntimeError(u'Wrong cloud.iO object type')\n\n if node:\n # We got an object\n node.setName(nodeName)\n node.setParentNodeContainer(self)\n\n assert not nodeName in self.nodes, u'Node with given name already present!'\n self.nodes[nodeName] = node\n\n # If the endpoint is online, send node add message\n if self.isOnline():\n data = self.messageFormat.serializeNode(node)\n self._client.publish(u'@nodeAdded/' + node.getUuid().toString(), data, 1, False)\n else:\n self.log.info(u'Not sending \\'@nodeAdded\\' message. No connection to broker!')\n\n def getNode(self, nodeName):\n \"\"\"Returns the node identified by the given name\n :param nodeName The Name of the node\n :type nodeName str\n \"\"\"\n return self.nodes.get(nodeName, None)\n\n def _set(self, topic, location, messageFormat, data):\n \"\"\"Assigns a new value to a cloud.iO attribute.\n\n :param topic: Topic representing the attribute\n :param location: Location stack\n :type location list\n :param messageFormat: Message format according to the data parameter\n :param data: Contains among other things the value to be assigned\n :return:\n \"\"\"\n # The path to the location must be start with the actual UUID of the endpoint.\n if location and self.uuid == location.pop() and \\\n location and 'nodes' == location.pop() and \\\n location:\n # Get the node with the name according to the topic\n node = self.nodes.get(location[-1])\n if node:\n location.pop()\n # Get the attribute reference\n attribute = node.findAttribute(location)\n if attribute:\n # Deserialize the message into the attribute\n messageFormat.deserializeAttribute(data, attribute)\n else:\n self.log.error('Attribute \\\"' + location[0] + '\\\" in node \\\"' + node.getName() + '\\\" not found!')\n else:\n self.log.error('Node \\\"' + location.pop() + '\\\" not found!')\n else:\n self.log.error('Invalid topic: ' + topic)\n\n ######################################################################\n # Interface implementations\n #\n def getUuid(self):\n return TopicUuid(self)\n\n def getName(self):\n return self.uuid\n\n def setName(self, name):\n raise CloudioModificationException(u'CloudioEndpoint name can not be changed!')\n\n def attributeHasChangedByEndpoint(self, attribute):\n \"\"\"\n :param attribute:\n :type attribute: CloudioAttribute\n :return:\n \"\"\"\n # Create the MQTT message using the given message format.\n data = self.messageFormat.serializeAttribute(attribute)\n\n messageQueued = False\n if self.isOnline():\n try:\n messageQueued = self._client.publish(u'@update/' + attribute.getUuid().toString(), data, 1, False)\n except Exception as exception:\n self.log.error(u'Exception :' + exception.message)\n\n # If the message could not be send for any reason, add the message to the pending\n # updates persistence if available.\n if not messageQueued and self.persistence:\n try:\n self.persistence.put('PendingUpdate-' + attribute.getUuid().toString().replace('/', ';')\n + '-' + str(TimeStampProvider.getTimeInMilliseconds()),\n PendingUpdate(data))\n except Exception as exception:\n self.log.error(u'Exception :' + exception.message)\n traceback.print_exc()\n\n # Check if there are messages in the persistence store to send\n if messageQueued and self.persistence and len(self.persistence.keys()) > 0:\n # Try to send stored messages to cloud.iO\n self._purgePersistentDataStore()\n\n def attributeHasChangedByCloud(self, attribute):\n \"\"\"Informs the endpoint that an underlying attribute has changed (initiated from the cloud).\n\n Attribute changes initiated from the cloud (@set) are directly received\n by the concerning cloud.iO attribute. The cloud.iO attribute forwards the information\n up to the parents till the endpoint.\n \"\"\"\n pass\n\n def _onConnected(self):\n \"\"\"This callback is called after the MQTT client has successfully connected to cloud.iO.\n \"\"\"\n # Announce our presence to the broker\n # self.announce()\n # It is too early here because the endpoint model\n # is not loaded at this moment\n\n success = self.subscribeToSetCommands()\n if not success:\n self.log.critical('Could not subscribe to @set topic!')\n\n # Try to send stored messages to cloud.iO\n # self._purgePersistentDataStore()\n # It may not be a good idea to send this data to cloud.iO using\n # the connection thread!\n\n self._endPointIsReady = True\n\n time.sleep(4) # Give the clients time to connect to cloud.iO and to setup the mqtt queue\n\n def _onConnectionThreadFinished(self):\n self.log.info('Connection thread finished')\n self.thread = None\n\n def isOnline(self):\n return self._client.isConnected() and self._endPointIsReady\n\n def announce(self):\n # Send birth message\n self.log.info(u'Sending birth message...')\n strMessage = self.messageFormat.serializeEndpoint(self)\n self._client.publish(u'@online/' + self.uuid, strMessage, 1, True)\n\n def _purgePersistentDataStore(self):\n \"\"\"Tries to send stored messages to cloud.iO.\n \"\"\"\n if self.persistence:\n print(str(len(self.persistence.keys())) + ' in persistence')\n for key in self.persistence.keys():\n if self.isOnline():\n # Is it a pending update?\n if key.startswith('PendingUpdate-'):\n # Get the pending update persistent object from store\n pendingUpdate = self.persistence.get(key)\n\n if pendingUpdate is not None:\n if six.PY3:\n # getHeaderBytes() already returns a string. No decode() call necessary\n print('Copy pers: ' + key + ': ' + pendingUpdate.getHeaderBytes())\n else:\n print('Copy pers: ' + key + ': ' + pendingUpdate.getHeaderBytes().decode('utf-8'))\n\n # Get the uuid of the endpoint\n uuid = pendingUpdate.getUuidFromPersistenceKey(key)\n\n # Try to send the update to the broker and remove it from the storage\n if self._client.publish(u'@update/' + uuid, pendingUpdate.getHeaderBytes(), 1, False):\n # Remove key from store\n self.persistence.remove(key)\n time.sleep(0) # Give other threads time to do its job\n else:\n break\n\n\nif __name__ == '__main__':\n pass\n", "sub_path": "src/cloudio/endpoint/endpoint.py", "file_name": "endpoint.py", "file_ext": "py", "file_size_in_byte": 16349, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.dirname", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 39, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 41, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 42, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 44, "usage_type": "call"}, {"api_name": "cloudio.interface.node_container.CloudioNodeContainer", "line_number": 46, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 67, "usage_type": "call"}, {"api_name": "utils.resource_loader.ResourceLoader.loadFromLocations", "line_number": 83, "usage_type": "call"}, {"api_name": "utils.resource_loader.ResourceLoader", "line_number": 83, "usage_type": "name"}, {"api_name": "cloudio.properties_endpoint_configuration.PropertiesEndpointConfiguration", "line_number": 85, "usage_type": "call"}, {"api_name": "cloudio.message_format.json_format.JsonMessageFormat", "line_number": 88, "usage_type": "call"}, {"api_name": "cloudio.mqtt_helpers.MemoryPersistence", "line_number": 98, "usage_type": "call"}, {"api_name": "cloudio.mqtt_helpers", "line_number": 98, "usage_type": "name"}, {"api_name": "cloudio.mqtt_helpers.MqttDefaultFilePersistence", "line_number": 101, "usage_type": "call"}, {"api_name": "cloudio.mqtt_helpers", "line_number": 101, "usage_type": "name"}, {"api_name": "cloudio.exception.invalid_property_exception.InvalidPropertyException", "line_number": 105, "usage_type": "call"}, {"api_name": "cloudio.mqtt_helpers.MqttConnectOptions", "line_number": 112, "usage_type": "call"}, {"api_name": "cloudio.mqtt_helpers", "line_number": 112, "usage_type": "name"}, {"api_name": "utils.path_helpers.prettify", "line_number": 126, "usage_type": "call"}, {"api_name": "utils.path_helpers", "line_number": 126, "usage_type": "name"}, {"api_name": "utils.path_helpers.prettify", "line_number": 127, "usage_type": "call"}, {"api_name": "utils.path_helpers", "line_number": 127, "usage_type": "name"}, {"api_name": "utils.path_helpers.prettify", "line_number": 128, "usage_type": "call"}, {"api_name": "utils.path_helpers", "line_number": 128, "usage_type": "name"}, {"api_name": "cloudio.mqtt_helpers.MqttReconnectClient", "line_number": 130, "usage_type": "call"}, {"api_name": "cloudio.mqtt_helpers", "line_number": 130, "usage_type": "name"}, {"api_name": "six.PY3", "line_number": 148, "usage_type": "attribute"}, {"api_name": "cloudio.message_format.factory.MessageFormatFactory.messageFormat", "line_number": 155, "usage_type": "call"}, {"api_name": "cloudio.message_format.factory.MessageFormatFactory", "line_number": 155, "usage_type": "name"}, {"api_name": "traceback.print_exc", "line_number": 175, "usage_type": "call"}, {"api_name": "cloudio.cloudio_node.CloudioNode", "line_number": 188, "usage_type": "argument"}, {"api_name": "cloudio.topicuuid.TopicUuid", "line_number": 250, "usage_type": "call"}, {"api_name": "cloudio.exception.cloudio_modification_exception.CloudioModificationException", "line_number": 256, "usage_type": "call"}, {"api_name": "utils.timestamp.getTimeInMilliseconds", "line_number": 279, "usage_type": "call"}, {"api_name": "utils.timestamp", "line_number": 279, "usage_type": "name"}, {"api_name": "cloudio.pending_update.PendingUpdate", "line_number": 280, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 283, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 318, "usage_type": "call"}, {"api_name": "six.PY3", "line_number": 346, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 359, "usage_type": "call"}]} +{"seq_id": "120643026", "text": "\nimport pygame, sys, time\nfrom pygame.locals import *\n\n\n\n# Create the window\n\n\ndef main():\n\n # Initialize pygame\n pygame.init()\n\n # Set window size and title, and frame delay\n surfaceSize = (500, 400) # window size\n windowTitle = 'Pong' # title\n frameDelay = 0.02 # smaller is faster game\n surface = pygame.display.set_mode(surfaceSize, 0, 0)\n pygame.display.set_caption(windowTitle)\n gameOver = False\n \n center1 = [50, 75] \n color1=pygame.Color('white')\n radius1=5\n speed1=[1,2]\n pygame.draw.circle(surface,color1, center1,radius1,0) \n color2=pygame.Color('white')\n rect1=pygame.Rect(50,200,10,50)\n color3=pygame.Color('white') \n rect2=pygame.Rect(450,200,10,50)\n pygame.draw.rect(surface, color2, rect1)\n pygame.draw.rect(surface, color3, rect2)\n pygame.display.update() \n while True:\n # Handle events\n for event in pygame.event.get():\n if event.type == QUIT:\n pygame.quit()\n sys.exit()\n pygame.display.update()\n # Set the frame speed by pausing between frames\n time.sleep(frameDelay)\n update(surface,color1,center1,speed1,radius1)\n moveDot(surface,color1,center1,speed1,radius1)\ndef update(surface,color1,center1,speed1,radius1): \n if False:\n return True\n else:\n color2=pygame.Color('white')\n rect1=pygame.Rect(50,200,10,50)\n color3=pygame.Color('white') \n rect2=pygame.Rect(450,200,10,50) \n eraseColor=pygame.Color('black')\n surface.fill(eraseColor)\n pygame.draw.rect(surface, color2, rect1)\n pygame.draw.rect(surface, color3, rect2) \n for index in range(0, 2):\n center1[index] = center1[index] + 1\n pygame.draw.circle(surface, pygame.Color('white'), center1, 5 , 0)\n return False\n \ndef moveDot(surface,color1,center1,speed1,radius1):\n center1=center1+speed1\n if center1surfaceSize:\n speed1=-speed1\n \n return center1, speed1\n\n\nmain()\n", "sub_path": "lab3 version one.py", "file_name": "lab3 version one.py", "file_ext": "py", "file_size_in_byte": 2011, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pygame.init", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pygame.display.set_caption", "line_number": 20, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.Color", "line_number": 24, "usage_type": "call"}, {"api_name": "pygame.draw.circle", "line_number": 27, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 27, "usage_type": "attribute"}, {"api_name": "pygame.Color", "line_number": 28, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 29, "usage_type": "call"}, {"api_name": "pygame.Color", "line_number": 30, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 31, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 32, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 33, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 33, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 34, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 34, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 37, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 39, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 40, "usage_type": "call"}, {"api_name": "pygame.display.update", "line_number": 41, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 41, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.Color", "line_number": 50, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 51, "usage_type": "call"}, {"api_name": "pygame.Color", "line_number": 52, "usage_type": "call"}, {"api_name": "pygame.Rect", "line_number": 53, "usage_type": "call"}, {"api_name": "pygame.Color", "line_number": 54, "usage_type": "call"}, {"api_name": "pygame.draw.rect", "line_number": 56, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pygame.draw.rect", "line_number": 57, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 60, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pygame.Color", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "198886851", "text": "import click\n\n# modificamos los comandos dentro del grupo clientes\n# definimos nuestros comandos básicos\n\n\nfrom clientes.servicios import ServiciosClientes\nfrom clientes.modelo import Cliente\n\n\n@click.group() # con esto los convertimos en comandos de click\ndef clientes():\n \"\"\"Administrador de ciclo de vida de clientes\"\"\"\n pass\n\n\n@clientes.command()\n@click.option(\n '-n', # abreviación\n '--nombre', # nombre completo del comando\n type=str, #  tipo de dato de entrada\n prompt=True, # si no viene el nombre incluido, la consola se lo pide\n help='El nombre del cliente') # mensaje de ayuda\n@click.option(\n '-e',\n '--empresa',\n type=str,\n prompt=True,\n help='La empresa del cliente')\n@click.option(\n '-em',\n '--email',\n type=str,\n prompt=True,\n help='El email del cliente')\n@click.option(\n '-r',\n '--rol',\n type=str,\n prompt=True,\n help='El rol del cliente')\n@click.pass_context\ndef crear(contexto, nombre, empresa, email, rol):\n \"\"\" Crea un nuevo cliente \"\"\"\n cliente = Cliente(nombre, empresa, email, rol)\n servicios_cliente = ServiciosClientes(contexto.obj['tabla_clientes'])\n servicios_cliente.crear_cliente(cliente)\n\n\n@clientes.command()\n@click.pass_context\ndef listar(contexto):\n \"\"\"Lista todo los clientes\"\"\"\n servicios_cliente = ServiciosClientes(contexto.obj['tabla_clientes'])\n lista_clientes = servicios_cliente.listar_clientes()\n # para imprimir algo en la consola hacemos uso de click.echo y no de print porque la forma en que funciona...\n # la libreria click en los distintos SO varia, y asi aseguramos mostrar todo bajo un mismo formato\n click.echo(' ID | NOMBRE | EMPRESA | EMAIL | ROL')\n click.echo('*' * 100)\n for cliente in lista_clientes:\n click.echo('{uid} | {nombre} | {empresa} | {email} | {rol}'.format(\n uid=cliente['uid'],\n nombre=cliente['nombre'],\n empresa=cliente['empresa'],\n email=cliente['email'],\n rol=cliente['rol']))\n\n\n@clientes.command()\n@click.argument(\n 'cliente_id',\n type=str)\n@click.pass_context\ndef actualizar(contexto, cliente_id):\n \"\"\"Actualiza el cliente\"\"\"\n servicio_cliente = ServiciosClientes(contexto.obj['tabla_clientes'])\n cliente = _buscar_cliente_por_id(servicio_cliente, cliente_id)\n if cliente != None: # si la lista no es vacía entonces...\n # creamos un flujo de actualización\n # desempaqueto al primer elemento de la lista que es el cliente que quiero actualizar\n # debo instanciar al cliente en su clase Clientes por lo que le pasamos la referencia como: **cliente[0]\n cliente_actualizado = _flujo_de_cliente_actualizado(\n _diccionario_a_objeto(cliente))\n servicio_cliente.actualizar_cliente(cliente_actualizado)\n click.echo('El cliente fue actualizado')\n else:\n click.echo('El cliente no fue encontrado')\n\n\n@clientes.command()\n@click.argument(\n 'cliente_id',\n type=str)\n@click.pass_context\ndef eliminar(contexto, cliente_id):\n \"\"\"Elimina el cliente\"\"\"\n servicio_cliente = ServiciosClientes(contexto.obj['tabla_clientes'])\n cliente = _buscar_cliente_por_id(servicio_cliente, cliente_id)\n print(cliente)\n if cliente != None: # si la lista no es vacía entonces...\n servicio_cliente.borrar_cliente(_diccionario_a_objeto(cliente))\n click.echo('El cliente fue eliminado')\n else:\n click.echo('El cliente no fue encontrado')\n\n\ndef _buscar_cliente_por_id(servicio_cliente, cliente_id):\n lista_clientes = servicio_cliente.listar_clientes()\n # queremos al cliente de todos los clientes que se encuentren en la lista de clientes...\n # que cumpla con la condición de que su id es la que nos pasaron por parametro\n cliente = [\n cliente for cliente in lista_clientes if cliente['uid'] == cliente_id]\n if len(cliente) > 0:\n return cliente[0]\n return None\n\n\ndef _diccionario_a_objeto(cliente_dic):\n return Cliente(**cliente_dic)\n\n\ndef _flujo_de_cliente_actualizado(cliente):\n click.echo('Deja vacío si no quiere modificar el valor')\n cliente.nombre = click.prompt(\n 'Nuevo nombre', type=str, default=cliente.nombre)\n cliente.empresa = click.prompt(\n 'Nuevo empresa', type=str, default=cliente.empresa)\n cliente.email = click.prompt(\n 'Nuevo email', type=str, default=cliente.email)\n cliente.rol = click.prompt(\n 'Nuevo rol', type=str, default=cliente.rol)\n return cliente\n\n\ncomandos_declarados = clientes\n", "sub_path": "clientes/comandos.py", "file_name": "comandos.py", "file_ext": "py", "file_size_in_byte": 4536, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "click.group", "line_number": 11, "usage_type": "call"}, {"api_name": "clientes.modelo.Cliente", "line_number": 45, "usage_type": "call"}, {"api_name": "clientes.servicios.ServiciosClientes", "line_number": 46, "usage_type": "call"}, {"api_name": "clientes.servicios.command", "line_number": 17, "usage_type": "call"}, {"api_name": "clientes.servicios", "line_number": 17, "usage_type": "name"}, {"api_name": "click.option", "line_number": 18, "usage_type": "call"}, {"api_name": "click.option", "line_number": 24, "usage_type": "call"}, {"api_name": "click.option", "line_number": 30, "usage_type": "call"}, {"api_name": "click.option", "line_number": 36, "usage_type": "call"}, {"api_name": "click.pass_context", "line_number": 42, "usage_type": "attribute"}, {"api_name": "clientes.servicios.ServiciosClientes", "line_number": 54, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 58, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 59, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 61, "usage_type": "call"}, {"api_name": "clientes.servicios.command", "line_number": 50, "usage_type": "call"}, {"api_name": "clientes.servicios", "line_number": 50, "usage_type": "name"}, {"api_name": "click.pass_context", "line_number": 51, "usage_type": "attribute"}, {"api_name": "clientes.servicios.ServiciosClientes", "line_number": 76, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 85, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 87, "usage_type": "call"}, {"api_name": "clientes.servicios.command", "line_number": 69, "usage_type": "call"}, {"api_name": "clientes.servicios", "line_number": 69, "usage_type": "name"}, {"api_name": "click.argument", "line_number": 70, "usage_type": "call"}, {"api_name": "click.pass_context", "line_number": 73, "usage_type": "attribute"}, {"api_name": "clientes.servicios.ServiciosClientes", "line_number": 97, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 102, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 104, "usage_type": "call"}, {"api_name": "clientes.servicios.command", "line_number": 90, "usage_type": "call"}, {"api_name": "clientes.servicios", "line_number": 90, "usage_type": "name"}, {"api_name": "click.argument", "line_number": 91, "usage_type": "call"}, {"api_name": "click.pass_context", "line_number": 94, "usage_type": "attribute"}, {"api_name": "clientes.modelo.Cliente", "line_number": 119, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 123, "usage_type": "call"}, {"api_name": "click.prompt", "line_number": 124, "usage_type": "call"}, {"api_name": "click.prompt", "line_number": 126, "usage_type": "call"}, {"api_name": "click.prompt", "line_number": 128, "usage_type": "call"}, {"api_name": "click.prompt", "line_number": 130, "usage_type": "call"}, {"api_name": "clientes.servicios", "line_number": 135, "usage_type": "name"}]} +{"seq_id": "406060839", "text": "from flask import Flask, request\nimport sys\n'''\n3.1\n- apply argv[1] as port number\n- handle multiple connection \n- waiting time for therm thread to 0.5 second \n\n3.2\n- handle command line arguments for server's port number\n- handle no available sensors [NA]\n\n'''\nfrom w1thermsensor import W1ThermSensor\nimport json\n\n# wrapper class for w1thermsensor as a thread\nfrom therm import ThermThreading\nimport time\n\n# return a string with commar seperated temperatures\ndef build_temp_str(w1sensor):\n L = []\n for k, v in w1sensor.items():\n print (\"({0}, {1})\".format(k,v))\n L.append(v)\n temp_str = ','.join(str(x) for x in L)\n return temp_str\n \nWATCHDOG_DELAY = 0.2 \nMAX_TRY = 2\n\napp = Flask(__name__)\n@app.route(\"/w1-dev\")\ndef w1term_dev():\n global t\n cnt = 0\n available = False\n while True:\n if len(t.w1sensor) == t.num_w1sensor:\n available = True\n break\n else:\n if cnt > MAX_TRY: \n break\n cnt += 1\n time.sleep(WATCHDOG_DELAY)\n\n if available is False: \n return json.dump(\"[NA]\") \n else:\n L = [] \n for k in t.w1sensor.keys():\n L.append(k)\n return (json.dumps(L)) \n\n\n@app.route(\"/w1-id\")\ndef w1term_id():\n global t\n sid = request.args.get('id')\n return (json.dumps(t.w1sensor[sid]))\n\n\n@app.route(\"/w1\")\ndef w1term():\n global t\n cnt = 0\n available = False\n while True:\n if len(t.w1sensor) == t.num_w1sensor:\n available = True\n break\n else:\n if cnt > 2: \n break\n cnt += 1\n time.sleep(WATCHDOG_DELAY)\n\n if available is False: \n return json.dump(\"[NA]\") \n else: \n return (json.dumps(t.w1sensor)) \n\n \n@app.route(\"/\")\ndef hello():\n w1sensor = dict()\n for sensor in W1ThermSensor.get_available_sensors():\n w1sensor[sensor.id] = sensor.get_temperature()\n\n if len(w1sensor) == 0:\n return \"[NA]\"\n else:\n return build_temp_str(w1sensor)\n #return (json.dumps(L)) # list of termperatures\n #return (json.dumps(w1sensor)) # dictionary of a pair of sensor id and its temperature\n\n \nif __name__ == \"__main__\":\n\n if len(sys.argv) != 2:\n sys.stderr.write(\"wrong argument: check port number\\n\")\n exit(4)\n t = ThermThreading()\n time.sleep(3)\n\n #to use threading and to spawn three processes to handle incoming requests.\n app.run(host='0.0.0.0', port=int(sys.argv[1]), threaded=True)\n", "sub_path": "ds1820_flask3_2.py", "file_name": "ds1820_flask3_2.py", "file_ext": "py", "file_size_in_byte": 2460, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Flask", "line_number": 33, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 47, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 50, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 61, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 61, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 62, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 78, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 81, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 83, "usage_type": "call"}, {"api_name": "w1thermsensor.W1ThermSensor.get_available_sensors", "line_number": 89, "usage_type": "call"}, {"api_name": "w1thermsensor.W1ThermSensor", "line_number": 89, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 102, "usage_type": "attribute"}, {"api_name": "sys.stderr.write", "line_number": 103, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 103, "usage_type": "attribute"}, {"api_name": "therm.ThermThreading", "line_number": 105, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 106, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 109, "usage_type": "attribute"}]} +{"seq_id": "35572988", "text": "# coding: utf-8\n# Team : Quality Management Center\n# Author：Yi\n# Date ：2020/5/9 20:14\n# Tool ：PyCharm\nfrom sqlalchemy import create_engine, Column, String, Integer\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy.orm import sessionmaker\nfrom datetime import datetime\nimport time\nimport sys\n\ntry:\n engine = create_engine('mysql+mysqlconnector://root:wangyi200801@localhost:3306/test')\n DBSession = sessionmaker(bind=engine)\n print('数据库连接成功...')\n session = DBSession()\n Base = declarative_base()\nexcept Exception as e:\n print(e)\n\nclass Book(Base):\n __tablename__ = 'mes_book'\n id = Column(String(255), primary_key=True)\n content = Column(String(255))\n name = Column(String(20))\n time_now = Column(String(50))\n is_delete = Column(Integer)\n\n# 添加操作\ndef insert_mes():\n b_id, b_name = input(\"请输入ID和姓名：（空格隔开）\").split()\n b_content = input('请输入留言信息：')\n b_time_now = datetime.strftime(datetime.now().replace(microsecond=0),'%Y-%m-%d %H:%M:%S')\n b = Book(id=b_id, name=b_name, content=b_content, time_now=b_time_now, is_delete=0)\n try:\n session.add(b)\n session.commit()\n print('insert successfully!\\n')\n except Exception as e:\n print('insert failed!')\n print(e)\n\n# 删除操作\ndef delete_mes():\n try:\n b_id = input('请输入要删除的ID：')\n query1 = session.query(Book).filter(Book.id == b_id).first()\n session.delete(query1)\n session.commit()\n print('delete successfully!\\n')\n except Exception as e:\n print('delete failed!')\n print(e)\n\n# 修改操作\ndef update_mes():\n try:\n b_id = input('请输入要修改的ID：')\n b_content=input('请输入修改后的留言信息：')\n query1=session.query(Book).filter(Book.id == b_id).one()\n query1.content = b_content\n session.commit()\n print('update successfully!\\n')\n except Exception as e:\n print('update failed!')\n print(e)\n\n# 查询操作\ndef select_mes():\n try:\n query1 = session.query(Book).all()\n print('留言信息如下：')\n for data in query1:\n print(data.id, data.name, data.content, data.time_now, data.is_delete)\n print()\n except Exception as e:\n print(e)\n\ndef choice():\n print('1.添加留言信息\\n2.删除留言信息\\n3.修改留言信息（根据ID修改）\\n4.显示留言信息\\n5.退出\\n')\n print('请输入选项：')\n while True:\n try:\n ch = int(input())\n if isinstance(ch,int) and ch>=1 and ch<=5:\n return ch\n else:\n raise Exception\n except:\n print('输入1-5的整数，请重新输入...')\n\nif __name__ == '__main__':\n while True:\n time.sleep(1)\n ch = choice()\n if ch == 1:\n insert_mes()\n elif ch == 2:\n delete_mes()\n elif ch == 3:\n update_mes()\n elif ch == 4:\n select_mes()\n elif ch == 5:\n session.close()\n sys.exit(0)", "sub_path": "homework10/q3.py", "file_name": "q3.py", "file_ext": "py", "file_size_in_byte": 3155, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 14, "usage_type": "call"}, {"api_name": "sqlalchemy.orm.sessionmaker", "line_number": 15, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.declarative.declarative_base", "line_number": 18, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 27, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 28, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 28, "usage_type": "argument"}, {"api_name": "datetime.datetime.strftime", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 34, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 34, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 95, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 107, "usage_type": "call"}]} +{"seq_id": "560298843", "text": "import cfn_sweeper.resources.AWS_EC2_Instance as ec2\nimport pytest\nimport boto3\nfrom moto import mock_ec2\n\n@mock_ec2\ndef test_ec2_resource():\n ec2_scanner_resource = ec2.resource()\n \n scan_result = ec2_scanner_resource.gather('us-east-1')\n assert len(scan_result) == 0\n \n client = boto3.resource('ec2', region_name='us-east-1')\n client.create_instances(\n ImageId= 'aki-00806369',\n MinCount=1,\n MaxCount=1,\n InstanceType='t2.micro',\n)\n\n \n assert ec2_scanner_resource.resource_name == 'AWS::EC2::Instance'\n \n scan_result = ec2_scanner_resource.gather(region='us-east-1')\n\n assert len(scan_result) == 1\n\n \n \n", "sub_path": "tests/resources/test_AWS_EC2_Instance.py", "file_name": "test_AWS_EC2_Instance.py", "file_ext": "py", "file_size_in_byte": 659, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cfn_sweeper.resources.AWS_EC2_Instance.resource", "line_number": 8, "usage_type": "call"}, {"api_name": "cfn_sweeper.resources.AWS_EC2_Instance", "line_number": 8, "usage_type": "name"}, {"api_name": "boto3.resource", "line_number": 13, "usage_type": "call"}, {"api_name": "moto.mock_ec2", "line_number": 6, "usage_type": "name"}]} +{"seq_id": "212925579", "text": "from django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.contrib.auth.models import User\nfrom .models import Client,Book\nfrom .forms import ClientForm,BookForm\n\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.shortcuts import redirect, render\nfrom django.urls import reverse, reverse_lazy\nfrom django.http import HttpResponse\nfrom django.views import generic\n\nfrom django.utils import timezone\nfrom dateutil.relativedelta import relativedelta\nfrom datetime import date\n\nfrom django.db.models import Q\nfrom django.conf import settings\n\n#-----------------------------------------\n# 顧客リスト項目\n#-----------------------------------------\n\nclass ClientIndexView(generic.ListView):\n\ttemplate_name = 'clientapp/index.html'\n\tmodel = Client\n\tqueryset = Client.objects.order_by('-updated_at')\n\tpaginate_by = 5\n\n\tdef get_queryset(self, *args, **kwargs):\n\t\tq_word = self.request.GET.get('query')\n\t\tif q_word:\n\t\t\tobject_list = Client.objects.filter(\n\t\t\t\tQ(name__icontains=q_word)|\n\t\t\t\tQ(org__icontains=q_word)|\n\t\t\t\tQ(scale__icontains=q_word)|\n\t\t\t\tQ(mat_trend__icontains=q_word)|\n\t\t\t\tQ(mat_total__icontains=q_word)|\n\t\t\t\t# Q(author__icontains=q_word)|\n\t\t\t\tQ(priority__icontains=q_word)\n\t\t\t\t)\n\t\telse:\n\t\t\tobject_list = Client.objects.all()\n\t\treturn object_list\n\nclass ClientDetailView(generic.DetailView):\n\ttemplate_name = 'clientapp/detail.html'\n\tmodel = Client\n\n\tdef get_context_data(self, *wargs, **kwargs):\n\t\tcontext = super().get_context_data(**kwargs)\n\t\tcontext.update({\n\t\t\t'client': Client.objects.filter(name=self.object)\n\t\t\t})\n\t\treturn context\n\nclass ClientCreateView(LoginRequiredMixin, generic.edit.CreateView):\n\ttemplate_name = 'clientapp/client_form.html'\n\tmodel = Client\n\tfields = ['name','org','scale','proposals', 'mat_trend','mat_total','priority','memo','author']\n\nclass ClientUpdateView(LoginRequiredMixin, generic.edit.UpdateView):\n\ttemplate_name = 'clientapp/client_form.html'\n\tmodel = Client\n\tfields = ['name','org','scale','proposals', 'mat_trend','mat_total','priority','memo','author']\n\nclass ClientDeleteView(LoginRequiredMixin, generic.edit.DeleteView):\n\ttemplate_name = 'clientapp/delete.html'\n\tmodel = Client\n\tsuccess_url = reverse_lazy('clientapp:index')\n\n#-----------------------------------------\n# 写真アップロード項目\n#-----------------------------------------\n\nclass FiileListView(generic.ListView):\n\t\"\"\"ファイル一覧ビュー\"\"\"\n\ttemplate_name = 'clientapp/file_list.html'\n\tmodel = Book\n\t# form_class = BookForm\n\nclass FiileCreateView(generic.CreateView):\n \"\"\"ファイルモデルのクリエイトビュー\"\"\"\n model = Book\n fields = [\"image\"]\n template_name = 'clientapp/file_form.html'\n success_url = reverse_lazy('clientapp:file_list')\n\nclass FiileUpdateView(generic.UpdateView):\n\t\"\"\"ファイルモデルのアップデートビュー\"\"\"\n\ttemplate_name = 'clientapp/file_form.html'\n\tmodel = Book\n\tfields = [\"image\"]\n\tdef my_view(request):\n\t\tif foo:\n\t\t\treturn HttpResponseNotFound('

Page not found

')\n\t\telse:\n\t\t\treturn HttpResponse('

Page was found

')\n\nclass FiileDeleteView(generic.DeleteView):\n\t\"\"\"ファイルモデルのデリートビュー\"\"\"\n\ttemplate_name = 'clientapp/file_delete.html'\n\tmodel = Book\n\tfields = [\"__all__\"]\n\tsuccess_url = reverse_lazy('clientapp:file_list')", "sub_path": "clientapp/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3310, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.views.generic.ListView", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 23, "usage_type": "name"}, {"api_name": "models.Client", "line_number": 25, "usage_type": "name"}, {"api_name": "models.Client.objects.order_by", "line_number": 26, "usage_type": "call"}, {"api_name": "models.Client.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.Client", "line_number": 26, "usage_type": "name"}, {"api_name": "models.Client.objects.filter", "line_number": 32, "usage_type": "call"}, {"api_name": "models.Client.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "models.Client", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 39, "usage_type": "call"}, {"api_name": "models.Client.objects.all", "line_number": 42, "usage_type": "call"}, {"api_name": "models.Client.objects", "line_number": 42, "usage_type": "attribute"}, {"api_name": "models.Client", "line_number": 42, "usage_type": "name"}, {"api_name": "django.views.generic.DetailView", "line_number": 45, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 45, "usage_type": "name"}, {"api_name": "models.Client", "line_number": 47, "usage_type": "name"}, {"api_name": "models.Client.objects.filter", "line_number": 52, "usage_type": "call"}, {"api_name": "models.Client.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "models.Client", "line_number": 52, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 56, "usage_type": "name"}, {"api_name": "django.views.generic.edit", "line_number": 56, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 56, "usage_type": "name"}, {"api_name": "models.Client", "line_number": 58, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 61, "usage_type": "name"}, {"api_name": "django.views.generic.edit", "line_number": 61, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 61, "usage_type": "name"}, {"api_name": "models.Client", "line_number": 63, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 66, "usage_type": "name"}, {"api_name": "django.views.generic.edit", "line_number": 66, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 66, "usage_type": "name"}, {"api_name": "models.Client", "line_number": 68, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 69, "usage_type": "call"}, {"api_name": "django.views.generic.ListView", "line_number": 75, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 75, "usage_type": "name"}, {"api_name": "models.Book", "line_number": 78, "usage_type": "name"}, {"api_name": "django.views.generic.CreateView", "line_number": 81, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 81, "usage_type": "name"}, {"api_name": "models.Book", "line_number": 83, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 86, "usage_type": "call"}, {"api_name": "django.views.generic.UpdateView", "line_number": 88, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 88, "usage_type": "name"}, {"api_name": "models.Book", "line_number": 91, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 97, "usage_type": "call"}, {"api_name": "django.views.generic.DeleteView", "line_number": 99, "usage_type": "attribute"}, {"api_name": "django.views.generic", "line_number": 99, "usage_type": "name"}, {"api_name": "models.Book", "line_number": 102, "usage_type": "name"}, {"api_name": "django.urls.reverse_lazy", "line_number": 104, "usage_type": "call"}]} +{"seq_id": "43661432", "text": "# Читаем txt и записываем в dbf файл\nimport dbf\nimport os\nimport sys\n\nos.chdir(r\"D:\\python_lab\\test_file_db\")\n\n\n'''\n# Пример из Интернета - рабочий!\ntable = dbf.Table('test.dbf', 'cod N(1,0); name C(30)')\ntable.open(mode=dbf.READ_WRITE)\nfor x in range(0, 10):\n row_tuple = (str(int(x)), \"name\" + str(int(x)))\n table.append(row_tuple)\ntable.close\n'''\n# s = {'ФИО': [номер счета, сумма на счете]}\ns = {}\n\nf = open('text_01.txt', 'r', encoding='utf-8')\nline = f.readline()\n\nwhile line:\n # print(line)\n # print(\"\\nНомер счета: \" + line[0:11])\n # print(\"\\tФИО владельца счета: \" + line[11:41])\n # print(\"\\tСумма на счете: \" + line[41:47])\n a = line[0:11]\n b = line[11:41]\n c = line[41:47]\n s[b.strip()] = [a, c] # тут убираем все лишние пробелы вокруг ФИО\n # s[b] = [a, c]\n line = f.readline()\n f.close\n# print(s)\n\n\ntable = dbf.Table(\n 'test.dbf', 'fio C(47); numberbank N(11,0); money N(6,0)', codepage='utf8')\ntable.open(mode=dbf.READ_WRITE)\nfor x, y in s.items():\n row_tuple = (str(x), str(int(y[1])), str(int(y[0])))\n table.append(row_tuple)\ntable.close\n", "sub_path": "test_file_db/r_txt_w_dbf.py", "file_name": "r_txt_w_dbf.py", "file_ext": "py", "file_size_in_byte": 1247, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.chdir", "line_number": 6, "usage_type": "call"}, {"api_name": "dbf.Table", "line_number": 39, "usage_type": "call"}, {"api_name": "dbf.READ_WRITE", "line_number": 41, "usage_type": "attribute"}]} +{"seq_id": "382677690", "text": "\nimport json\nimport requests\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nplt.style.use('ggplot')\nplt.rcParams['axes.unicode_minus'] = False \n\nurl = 'https://opendata.epa.gov.tw/ws/Data/CFPCarbon/?$format=json'\nresponse = requests.get(url, verify=False)\ndata_df = pd.DataFrame(response.json())\nprint(data_df.head())\n\ndata = []\ndata_lis = []\n\ndef get_name (CFPL_Code):\n\n CFPL_Code = str(CFPL_Code)\n\n for i in range (0, len(response.json())):\n if response.json()[i]['CFPL_Code'] == CFPL_Code:\n data = [response.json()[i]['Product_Name'], response.json()[i]['Product_Carbon_Footprint_Data']]\n data_lis.append(data)\n return data_lis\n\nget_name(1800714004)\nget_name(1802202001)\ndata_lis[0][1] = '650'\ndata_lis[1][1] = '380'\nprint('早上八點吃早餐', data_lis[0], data_lis[1])\n\nget_name(1604931002)\nget_name(1716412002)\ndata_lis[2][1] = '80'\ndata_lis[3][1] = '2000'\nprint('早上九點搭公車去銀行辦事', data_lis[2], data_lis[3])\n\nget_name(1800203002)\nget_name(1800203010)\nget_name(1800203011)\ndata_lis[4][1] = '4500'\ndata_lis[5][1] = '7500'\ndata_lis[6][1] = '7000'\nprint('早上十點市場買肉', data_lis[4], data_lis[5], data_lis[6])\n\nget_name('R1701905001')\nprint('中午簡單吃兩個鳳梨酥', data_lis[7])\ndata_lis[7][1] = '340'\nprint('中午簡單吃兩個鳳梨酥', data_lis[7])\n\nget_name(1716312002)\nprint('下午待在家看影片', data_lis[8])\ndata_lis[8][1] = '3680'\nprint('下午待在家看影片', data_lis[8])\n\nget_name(1803305002)\ndata_lis[9][1] = '80'\nprint('晚上洗頭', data_lis[9])\n\nget_name(1716312002)\ndata_lis[10][1] = '1200'\nprint('繼續看影片', data_lis[10])\n\ntotal = 0\nfor i in range (0, 11):\n total = total + int(data_lis[i][1])\nprint(total)\n\ndf = pd.DataFrame(data_lis, columns = ['事件', '碳足跡(g)'])\nlabels = list(df['事件'])\nsizes = [int(list(df['碳足跡(g)'])[int(i)]) for i in range(11)]\n\nexplode = (0,0,0.2,0,0.1,0.1,0.1,0,0,0.2,0)\nfig1, ax1 = plt.subplots()\n\nax1.pie(sizes, labels=labels, explode=explode, autopct='%1.0f%%', shadow=False, startangle=180, textprops = {'fontsize':8})\nax1.axis('equal')\nplt.title('Carbon_Foot per day = 27.41(kg)', loc='right')\nplt.show()", "sub_path": "CFPL.py", "file_name": "CFPL.py", "file_ext": "py", "file_size_in_byte": 2184, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.pyplot.style.use", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 7, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 8, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}]} +{"seq_id": "276514871", "text": "import zipfile\n\n\nname = '90052'\nlist_com = []\nlist_fi = []\nwith zipfile.ZipFile('channel.zip') as zip:\n for num in range(len(zip.infolist()) - 1):\n list_com.append(zip.getinfo(str(name) + \".txt\").comment.decode('utf-8'))\n with zip.open(str(name) + \".txt\") as act:\n name = str(str(act.read()).split()[-1][:-1])\nprint(''.join(list_com))\n", "sub_path": "level6.py", "file_name": "level6.py", "file_ext": "py", "file_size_in_byte": 363, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "zipfile.ZipFile", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "381755550", "text": "from typing import List\n\nfrom asgard.conf import settings\nfrom asgard.workers.autoscaler.decision_component_interface import (\n DecisionComponentInterface,\n)\nfrom asgard.workers.models.decision import Decision\nfrom asgard.workers.models.scalable_app import ScalableApp\n\n\nclass DecisionComponent(DecisionComponentInterface):\n def decide_scaling_actions(self, apps: List[ScalableApp]) -> List[Decision]:\n decisions = []\n for app in apps:\n if app.app_stats:\n decision = Decision(app.id)\n deploy_decision = False\n\n cpu_usage = app.app_stats.cpu_usage / 100\n mem_usage = app.app_stats.mem_usage / 100\n\n if app.is_set_to_scale_cpu():\n\n if (\n cpu_usage\n > app.cpu_threshold\n + settings.AUTOSCALER_MARGIN_THRESHOLD\n or cpu_usage\n < app.cpu_threshold\n - settings.AUTOSCALER_MARGIN_THRESHOLD\n ):\n new_cpu = (\n cpu_usage * app.cpu_allocated\n ) / app.cpu_threshold\n\n decision.cpu = (\n app.min_cpu_scale_limit\n if new_cpu < app.min_cpu_scale_limit\n else app.max_cpu_scale_limit\n if new_cpu > app.max_cpu_scale_limit\n else new_cpu\n )\n\n deploy_decision = True\n if app.is_set_to_scale_mem():\n if (\n mem_usage\n > app.mem_threshold\n + settings.AUTOSCALER_MARGIN_THRESHOLD\n or mem_usage\n < app.mem_threshold\n - settings.AUTOSCALER_MARGIN_THRESHOLD\n ):\n new_mem = (\n mem_usage * app.mem_allocated\n ) / app.mem_threshold\n\n decision.mem = (\n app.min_mem_scale_limit\n if new_mem < app.min_mem_scale_limit\n else app.max_mem_scale_limit\n if new_mem > app.max_mem_scale_limit\n else new_mem\n )\n\n deploy_decision = True\n\n if deploy_decision:\n decisions.append(decision)\n\n return decisions\n", "sub_path": "asgard/workers/autoscaler/simple_decision_component.py", "file_name": "simple_decision_component.py", "file_ext": "py", "file_size_in_byte": 2629, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "asgard.workers.autoscaler.decision_component_interface.DecisionComponentInterface", "line_number": 11, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 12, "usage_type": "name"}, {"api_name": "asgard.workers.models.scalable_app.ScalableApp", "line_number": 12, "usage_type": "name"}, {"api_name": "asgard.workers.models.decision.Decision", "line_number": 16, "usage_type": "call"}, {"api_name": "asgard.conf.settings.AUTOSCALER_MARGIN_THRESHOLD", "line_number": 27, "usage_type": "attribute"}, {"api_name": "asgard.conf.settings", "line_number": 27, "usage_type": "name"}, {"api_name": "asgard.conf.settings.AUTOSCALER_MARGIN_THRESHOLD", "line_number": 30, "usage_type": "attribute"}, {"api_name": "asgard.conf.settings", "line_number": 30, "usage_type": "name"}, {"api_name": "asgard.conf.settings.AUTOSCALER_MARGIN_THRESHOLD", "line_number": 49, "usage_type": "attribute"}, {"api_name": "asgard.conf.settings", "line_number": 49, "usage_type": "name"}, {"api_name": "asgard.conf.settings.AUTOSCALER_MARGIN_THRESHOLD", "line_number": 52, "usage_type": "attribute"}, {"api_name": "asgard.conf.settings", "line_number": 52, "usage_type": "name"}, {"api_name": "asgard.workers.models.decision.Decision", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "613318564", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom urllib.request import urlopen\nfrom urllib.parse import urlparse\nfrom bs4 import BeautifulSoup\nimport re\nimport datetime\nimport random\n\npages = set()\nrandom.seed(datetime.datetime.now())\n\n# 获取页面所有内链的列表\ndef get_Internal_Links(soup, include_Url):\n include_Url = urlparse(include_Url).scheme+\"://\"+urlparse(include_Url).netloc\n internal_Links = []\n # 找出所有以\"/\"开头的链接\n for link in soup.findAll(\"a\", href=re.compile(\"^(/|.*\"+include_Url+\")\")):\n if link.attrs['href'] is not None:\n if link.attrs['href'] not in internal_Links:\n if(link.attrs['href'].startswith(\"/\")):\n internal_Links.append(include_Url+link.attrs['href'])\n else:\n internal_Links.append(link.attrs['href'])\n return internal_Links\n \n# 获取页面所有外链的列表\ndef get_External_Links(soup, exclude_Url):\n external_Links = []\n # 找出所有以\"http\"或\"www\"开头且不包含当前URL的链接\n for link in soup.findAll(\"a\",\n href=re.compile(\"^(http|www)((?!\"+exclude_Url+\").)*$\")):\n if link.attrs['href'] is not None:\n if link.attrs['href'] not in external_Links:\n external_Links.append(link.attrs['href'])\n return external_Links\n \ndef split_Address(address):\n address_Parts = address.replace(\"http://\", \"\").split(\"/\")\n return address_Parts\n \ndef get_Random_External_Link(starting_Page):\n html = urlopen(starting_Page)\n soup = BeautifulSoup(html,'lxml')\n external_Links = get_External_Links(soup, urlparse(starting_Page).netloc)\n if len(external_Links) == 0:\n print(\"No external links, looking around the site for one\")\n domain = urlparse(starting_Page).scheme+\"://\"+urlparse(starting_Page).netloc\n internal_Links = get_Internal_Links(soup, domain)\n return get_Random_External_Link(internal_Links[random.randint(0,len(internal_Links)-1)])\n else:\n return external_Links[random.randint(0, len(external_Links)-1)]\n \ndef follow_External_Only(starting_Site):\n external_Link = get_Random_External_Link(starting_Site)\n print(\"Random external link is: \"+external_Link)\n follow_External_Only(external_Link)\n \nfollow_External_Only(\"https://www.wstx.com/\")", "sub_path": "script/example.py", "file_name": "example.py", "file_ext": "py", "file_size_in_byte": 2342, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "random.seed", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 12, "usage_type": "attribute"}, {"api_name": "urllib.parse.urlparse", "line_number": 16, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 19, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 33, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 44, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 45, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 46, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 49, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 51, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "74498845", "text": "\ntry:\n from unittest.mock import Mock\nexcept ImportError:\n from mock import Mock\nimport pytest\n\nfrom chapter10.datatypes import analog\n\n\n@pytest.mark.parametrize(\n ('data_type',),\n ((0x20,), (0x22,), (0x23,), (0x24,), (0x25,), (0x26,), (0x27,)))\ndef test_reserved(data_type):\n with pytest.raises(NotImplementedError):\n a = analog.Analog(Mock(\n file=Mock(tell=Mock(return_value=0),\n read=Mock(return_value=b'1234')),\n pos=0,\n data_type=data_type,\n data_length=2))\n a.parse()\n", "sub_path": "tests/unit/datatypes/test_analog.py", "file_name": "test_analog.py", "file_ext": "py", "file_size_in_byte": 568, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pytest.raises", "line_number": 15, "usage_type": "call"}, {"api_name": "chapter10.datatypes.analog.Analog", "line_number": 16, "usage_type": "call"}, {"api_name": "chapter10.datatypes.analog", "line_number": 16, "usage_type": "name"}, {"api_name": "mock.Mock", "line_number": 16, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 17, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 18, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 11, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 11, "usage_type": "attribute"}]} +{"seq_id": "323836143", "text": "from flask import Blueprint, render_template\nfrom flask_login import login_required\nfrom flask_menu import register_menu\n\nblueprint = Blueprint(\n 'scoap3_tools',\n __name__,\n url_prefix='/tools',\n template_folder='templates',\n)\n\n\n@blueprint.route('/test')\n@register_menu(blueprint, 'general.tools', text=' Tools')\n@login_required\ndef test():\n return render_template('scoap3_tools/test.html')\n", "sub_path": "scoap3/modules/tools/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 437, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Blueprint", "line_number": 5, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 17, "usage_type": "call"}, {"api_name": "flask_menu.register_menu", "line_number": 14, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "171515158", "text": "import json\nimport easygui\nimport logging\nimport findLabels\nlogging.basicConfig(level=logging.INFO, filename='logs/parserLog.txt')\n\nlabels = []\npath = easygui.fileopenbox()\n\n\ndef parseDoccanoToSpacy():\n try:\n\n fopen = open(path, 'rt')\n lines = fopen.readlines()\n data = []\n labels = findLabels.getLabels(path)\n\n for line in lines:\n line = json.loads(line)\n\n if 'labels' in line:\n line['entities'] = line.pop('labels')\n else:\n line['entities'] = []\n\n entities = []\n\n for e in line['entities']:\n if e[2] in labels:\n entities.append(\n {\"start\": e[0], \"end\": e[1], \"label\": e[2]})\n line['entities'] = entities\n\n entJson = {\"entities\": []}\n if (len(line[\"text\"]) > 5):\n text = (line[\"text\"])\n\n for e in line[\"entities\"]:\n entJson[\"entities\"].append(\n (e[\"start\"], e[\"end\"], e[\"label\"]))\n\n data.append((text, entJson))\n\n return data\n\n except Exception as ex:\n logging.info(ex)\n \n\n\ndef getLabels():\n return labels\n", "sub_path": "parse.py", "file_name": "parse.py", "file_ext": "py", "file_size_in_byte": 1242, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.basicConfig", "line_number": 5, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 5, "usage_type": "attribute"}, {"api_name": "easygui.fileopenbox", "line_number": 8, "usage_type": "call"}, {"api_name": "findLabels.getLabels", "line_number": 17, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "10920576", "text": "# Project SiteSeer\nimport argparse\nimport base64\nfrom googleapiclient import discovery\nfrom oauth2client.client import GoogleCredentials\n\nfrom google.cloud import storage\nfrom google.cloud import automl_v1beta1 as automl\nimport os\nimport picamera\nimport datetime\nimport time\nimport RPi.GPIO as GPIO\nfrom firebase import firebase\nimport urllib.request, json\nimport math\nfirebase = firebase.FirebaseApplication('https://siteseer.firebaseio.com', None)\n\n# Camera class used to setup Raspberry Pi camera and functions such as takephoto and analyze for object detection\nclass Camera:\n # Setup camera and object detection model\n def __init__(self):\n self.response_display_name = \"\"\n self.project_id = 'siteseer'\n self.compute_region = 'us-central1'\n self.model_id = 'ICN3897396801327187329' # model 3 'ICN626954994675902736'\n self.file_path = '/home/pi/image.jpg'\n self.score_threshold = '0.5'\n os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"]=\"/home/pi/siteseer-030a672b14ba.json\"\n self.project = 'siteseer'\n self.storage_client = storage.Client(project=self.project)\n self.bucket = self.storage_client.get_bucket('siteseer')\n self.automl_client = automl.AutoMlClient()\n self.model_full_id = self.automl_client.model_path(self.project_id, self.compute_region, self.model_id)\n self.prediction_client = automl.PredictionServiceClient()\n # Function to take photo using camera of Raspberry Pi\n def takephoto(self):\n self.camera = picamera.PiCamera()\n self.camera.resolution = (640, 480)\n self.camera.vflip = True\n self.camera.hflip = True\n self.camera.capture('image.jpg', quality=100, resize=(640, 480))\n self.camera.close()\n # Use Google API for object detection\n def analyze(self):\n '''\n credentials = GoogleCredentials.get_application_default()\n service = discovery.build('vision', 'v1', credentials=credentials)\n with open('image.jpg', 'rb') as image:\n image_content = base64.b64encode(image.read())\n service_request = service.images().annotate(body={\n 'requests': [{\n 'image': {\n 'content': image_content.decode('UTF-8')\n },\n 'features': [{\n 'type': 'LABEL_DETECTION',\n 'maxResults': 1\n }]\n }]\n })\n response = service_request.execute()['responses'][0]['labelAnnotations'][0]['description']\n firebase.put('sight', 'speech/1', response)\n print(\"Label Detection:\", response)\n '''\n with open(self.file_path, \"rb\") as image_file:\n content = image_file.read()\n payload = {\"image\": {\"image_bytes\": content}}\n\n params = { }\n\n if self.score_threshold:\n params = {\"score_threshold\": self.score_threshold}\n\n response = self.prediction_client.predict(self.model_full_id, payload, params)\n for result in response.payload:\n # print(\"Date: {} Prediction: {} {}\".format(str(datetime.datetime.now()), result.display_name, result.classification.score))\n if not result.display_name == self.response_display_name:\n self.response_display_name = result.display_name\n firebase.put('sight', 'speech/1', result.display_name)\n print(\"Object Detection:\", result.display_name, result.classification.score)\n\n image = self.bucket.blob('Sidewalk')\n image.upload_from_filename('image.jpg')\n\n# Directions class used to provide user with directions using Google Maps API\nclass Directions:\n # Setup up Directions API\n def __init__(self):\n # Directions Objects\n self.output = \"\"\n self.legs = [ ]\n self.current_leg = 0\n self.current_location = (0,0)\n self.current_end_location = (0,0)\n self.set_radius = 0\n\n # Firebase https://siteseer.firebaseio.com/\n self.touch = 11\n self.touch_original = firebase.get('restart', 'triggeredPressed')\n firebase.put('restart', 'triggeredPressed', False) #Yellow Light = False\n\n # Directions API\n self.endpoint = 'https://maps.googleapis.com/maps/api/directions/json?'\n self.api_key = '*key*'\n self.triggerDir_original = firebase.get('maps', 'trigger/1')\n firebase.put('maps', 'trigger/1', False)\n self.mode = \"walking\"\n self.alternatives = \"false\" # one\n self.threshold = 0.00003\n\n # GPIO Setup\n GPIO.setmode(GPIO.BCM)\n GPIO.setup(self.touch, GPIO.IN)\n # Check to see if trigger (touch sensor) is pressed by user\n def touch_sensor(self):\n touch_pressed = GPIO.input(self.touch)\n if touch_pressed == self.touch_original:\n # reset\n self.output = \"\"\n self.legs = [ ]\n self.current_leg = 0\n self.current_location = (0,0)\n self.current_end_location = (0,0)\n self.set_radius = 0\n\n self.touch_original = (not self.touch_original)\n firebase.put('restart', 'triggeredPressed', (not self.touch_original))\n # Using Google Directions API to get each step of a route\n def calculate_route(self):\n firebase.put('maps', 'trigger/1', False)\n\n origin = str(firebase.get('maps', 'latitude'))+\",\"+str(firebase.get('maps', 'longitude'))\n destination = firebase.get('maps', 'destination')\n\n #Building the URL for the request\n nav_request = 'origin={}&destination={}&mode={}&alternatives={}&key={}'.format(origin.replace(' ','+'),destination.replace(' ','+'),self.mode,self.alternatives,self.api_key)\n request = self.endpoint + nav_request\n #Sends the request and reads the response.\n response = urllib.request.urlopen(request).read()\n #Loads response as JSON\n directions = json.loads(response.decode('utf-8'))\n print(\"*********************************************\")\n try:\n print(\"Origin:\", origin, \"Destination:\", destination)\n response = urllib.request.urlopen(request).read()\n directions = json.loads(response.decode('utf-8'))\n # steps = directions['routes'][0]['legs'][0]['steps']\n self.output = self.parse_gang_sign(directions['routes'][0]['legs'][0]['steps'][0]['html_instructions'])\n self.legs = directions['routes'][0]['legs'][0]['steps']\n self.current_end_location = (directions['routes'][0]['legs'][0]['steps'][0][\"end_location\"][\"lat\"], directions['routes'][0]['legs'][0]['steps'][0][\"end_location\"][\"lng\"])\n print(\"Current Route End Location:\", self.current_end_location)\n self.current_location = (firebase.get('maps', 'latitude'), firebase.get('maps', 'longitude'))\n lat_diff = (abs(self.current_location[0] - self.current_end_location[0])) ** 2\n long_diff = (abs(self.current_location[1] - self.current_end_location[1])) ** 2\n if self.set_radius == 0:\n # self.output = self.orient_initial(self.output)\n firebase.put('maps', 'order/1', self.output)\n # else: # gets rid of directions for inital orienting (north, etc.), after initial, no more orienting (north, etc.)\n # self.output = self.parse_orientation(self.output)\n self.set_radius = math.sqrt(lat_diff + long_diff) # this is the current distance/length of route\n print(\"Length of Legs\", len(self.legs))\n except:\n self.output = \"NO POSSIBLE ROUTE\"\n # self.output = self.parse_orientation(self.output)\n firebase.put('maps', 'order/1', self.output)\n print(\"Route:\", self.output)\n print(\"*********************************************\")\n # parse north, south, east, west\n def parse_orientation(self, sentense):\n lst = [\"north\", \"south\", \"east\", \"west\", \"northeast\", \"southeast\", \"southwest\", \"northwest\"]\n if any(s in sentense for s in lst):\n for w in lst:\n loc = sentense.find(w)\n if loc != -1:\n sentense = sentense[loc:]\n loc = sentense.find(\" \")\n sentense = sentense[loc:]\n return \"Head Straight \" + sentense\n # Initial Orientation\n def orient_initial(self, sentense):\n output = \"orientation not found\"\n current_orientation = firebase.get('maps', 'heading')\n if \"north\" in sentense:\n destination_orientation = 0\n elif \"south\" in sentense:\n destination_orientation = 180\n elif \"east\" in sentense:\n destination_orientation = 90\n elif \"west\" in sentense:\n destination_orientation = 270\n elif \"northeast\" in sentense:\n destination_orientation = 45\n elif \"southeast\" in sentense:\n destination_orientation = 135\n elif \"southwest\" in sentense:\n destination_orientation = 225\n elif \"northwest\" in sentense:\n destination_orientation = 320\n turn_degree = destination_orientation - current_orientation\n if turn_degree < 0:\n turn_degree = 360 + turn_degree\n if turn_degree > 0 and turn_degree <= 180:\n output = \"Please Turn .Right. \" + str(int(turn_degree)) + \" degrees to orient correctly. \"\n elif turn_degree > 180 and turn_degree <= 360:\n output = \"Please Turn .Left. \" + str(int(360 - turn_degree)) + \" degrees to orient correctly. \"\n return output\n # Get rid of HTML Markups\n def parse_gang_sign(self, str):\n while str.find(\">\") != -1:\n less_than = str.find(\"<\")\n greater_than = str.find(\">\")\n str = str[0:less_than] + \" \" + str[greater_than + 1:]\n return str\n # Check to see if user is near next checkpoint for next direction\n def checkpoint_calibration(self):\n # New Directions Requested\n triggerDir_now = firebase.get('maps', 'trigger/1')\n if triggerDir_now == True:\n self.calculate_route()\n\n # Checking for Next Direction\n self.current_location = (firebase.get('maps', 'latitude'), firebase.get('maps', 'longitude'))\n\n seconds = time.time()\n lat_diff = (abs(self.current_location[0] - self.current_end_location[0])) ** 2\n long_diff = (abs(self.current_location[1] - self.current_end_location[1])) ** 2\n radius = math.sqrt(lat_diff + long_diff)\n\n print(\"------------------------------------------------\")\n print(\"Time:\", int(seconds) % 25)\n print(\"Radius from Curent Location to Next Checkpoint:\", float(radius))\n print(\"A to B Current Route Entire Radius:\", self.set_radius)\n print(\"Current Location:\", self.current_location)\n print(\"Current End Location:\", self.current_end_location)\n print(\"------------------------------------------------\")\n\n if ( radius <= self.threshold ): # if at checkpoint\n if (len(self.legs) == 1):\n firebase.put('maps', 'order/1', \"You have arrived at your destination\")\n else:\n firebase.put('maps', 'trigger/1', True)\n elif ( radius <= self.threshold + 0.00003): # if near checkpoint\n firebase.put('maps', 'order/1', \"Slow Down. Next direction is coming up\")\n elif ( self.set_radius != 0 and radius > self.set_radius + self.threshold ): # if wrong direction\n firebase.put('sight', 'speech/1', \"Wrong Direction\")\n if ( (not len(self.legs) == 1) and self.set_radius != 0 and 0 <= int(seconds) % 25 <= 6): # if not reached destination and another path is found (rerouting)\n self.calculate_route()\n '''\n new_route = get_route()\n if new_route != \"\":\n pass\n '''\n # Gets the directions/route for the user\n def get_route(self):\n origin = str(firebase.get('maps', 'latitude'))+\",\"+str(firebase.get('maps', 'longitude'))\n destination = firebase.get('maps', 'destination')\n nav_request = 'origin={}&destination={}&mode={}&alternatives={}&key={}'.format(origin.replace(' ','+'),destination.replace(' ','+'),self.mode,self.alternatives,self.api_key)\n request = self.endpoint + nav_request\n response = urllib.request.urlopen(request).read()\n directions = json.loads(response.decode('utf-8'))\n try:\n response = urllib.request.urlopen(request).read()\n directions = json.loads(response.decode('utf-8'))\n route = self.parse_gang_sign(directions['routes'][0]['legs'][0]['steps'][0]['html_instructions'])\n except:\n route = \"NO POSSIBLE ROUTE\"\n return route\n\ndef main():\n cam = Camera()\n dir = Directions()\n while True:\n # Takes picture and analyzes and continuously checks for checkpoint\n cam.takephoto()\n cam.analyze()\n dir.touch_sensor()\n dir.checkpoint_calibration()\n\nmain()\n", "sub_path": "street.py", "file_name": "street.py", "file_ext": "py", "file_size_in_byte": 13085, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "firebase.firebase", "line_number": 17, "usage_type": "name"}, {"api_name": "firebase.firebase.FirebaseApplication", "line_number": 17, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 29, "usage_type": "attribute"}, {"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": "google.cloud.automl_v1beta1.AutoMlClient", "line_number": 33, "usage_type": "call"}, {"api_name": "google.cloud.automl_v1beta1", "line_number": 33, "usage_type": "name"}, {"api_name": "google.cloud.automl_v1beta1.PredictionServiceClient", "line_number": 35, "usage_type": "call"}, {"api_name": "google.cloud.automl_v1beta1", "line_number": 35, "usage_type": "name"}, {"api_name": "picamera.PiCamera", "line_number": 38, "usage_type": "call"}, {"api_name": "firebase.firebase.put", "line_number": 80, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 80, "usage_type": "name"}, {"api_name": "firebase.firebase.get", "line_number": 100, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 100, "usage_type": "name"}, {"api_name": "firebase.firebase.put", "line_number": 101, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 101, "usage_type": "name"}, {"api_name": "firebase.firebase.get", "line_number": 106, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 106, "usage_type": "name"}, {"api_name": "firebase.firebase.put", "line_number": 107, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 107, "usage_type": "name"}, {"api_name": "RPi.GPIO.setmode", "line_number": 113, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 113, "usage_type": "name"}, {"api_name": "RPi.GPIO.BCM", "line_number": 113, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.setup", "line_number": 114, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 114, "usage_type": "name"}, {"api_name": "RPi.GPIO.IN", "line_number": 114, "usage_type": "attribute"}, {"api_name": "RPi.GPIO.input", "line_number": 117, "usage_type": "call"}, {"api_name": "RPi.GPIO", "line_number": 117, "usage_type": "name"}, {"api_name": "firebase.firebase.put", "line_number": 128, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 128, "usage_type": "name"}, {"api_name": "firebase.firebase.put", "line_number": 131, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 131, "usage_type": "name"}, {"api_name": "firebase.firebase.get", "line_number": 133, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 133, "usage_type": "name"}, {"api_name": "firebase.firebase.get", "line_number": 134, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 134, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 140, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 140, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 140, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 142, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 146, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 146, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 146, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 147, "usage_type": "call"}, {"api_name": "firebase.firebase.get", "line_number": 153, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 153, "usage_type": "name"}, {"api_name": "firebase.firebase.put", "line_number": 158, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 158, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 161, "usage_type": "call"}, {"api_name": "firebase.firebase.put", "line_number": 166, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 166, "usage_type": "name"}, {"api_name": "firebase.firebase.get", "line_number": 183, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 183, "usage_type": "name"}, {"api_name": "firebase.firebase.get", "line_number": 218, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 218, "usage_type": "name"}, {"api_name": "firebase.firebase.get", "line_number": 223, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 223, "usage_type": "name"}, {"api_name": "time.time", "line_number": 225, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 228, "usage_type": "call"}, {"api_name": "firebase.firebase.put", "line_number": 240, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 240, "usage_type": "name"}, {"api_name": "firebase.firebase.put", "line_number": 242, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 242, "usage_type": "name"}, {"api_name": "firebase.firebase.put", "line_number": 244, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 244, "usage_type": "name"}, {"api_name": "firebase.firebase.put", "line_number": 246, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 246, "usage_type": "name"}, {"api_name": "firebase.firebase.get", "line_number": 256, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 256, "usage_type": "name"}, {"api_name": "firebase.firebase.get", "line_number": 257, "usage_type": "call"}, {"api_name": "firebase.firebase", "line_number": 257, "usage_type": "name"}, {"api_name": "urllib.request.request.urlopen", "line_number": 260, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 260, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 260, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 261, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 263, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 263, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 263, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 264, "usage_type": "call"}]} +{"seq_id": "119513218", "text": "from django.conf import settings\nfrom django.core.mail.backends.base import BaseEmailBackend\nfrom django.utils.translation import ugettext as _\n\nfrom mailer.models import Message\n\n\nclass DbBackend(BaseEmailBackend):\n\n def send_messages(self, email_messages):\n num_sent = 0\n subject_prefix = getattr(settings, 'MAILER_EMAIL_SUBJECT_PREFIX', None)\n for email in email_messages:\n if subject_prefix:\n email.subject = u'%s %s' % (_(subject_prefix), email.subject)\n msg = Message()\n msg.email = email\n msg.save()\n num_sent += 1\n return num_sent\n", "sub_path": "mailer/backend.py", "file_name": "backend.py", "file_ext": "py", "file_size_in_byte": 640, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.core.mail.backends.base.BaseEmailBackend", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.settings", "line_number": 12, "usage_type": "argument"}, {"api_name": "django.utils.translation.ugettext", "line_number": 15, "usage_type": "call"}, {"api_name": "mailer.models.Message", "line_number": 16, "usage_type": "call"}]} +{"seq_id": "613966959", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Feb 22 07:52:20 2019\r\n\r\n@author: RayomandVatcha\r\n\"\"\"\r\nfrom __future__ import print_function, division, absolute_import, unicode_literals\r\n#from Crypto.Cipher import AES\r\nfrom SecuritySuite import AESCipher\r\nimport numpy\r\nimport sys\r\n\r\nclass PacketDetails:\r\n \r\n TotalSent = 0\r\n TotalRecieved = 0\r\n SendPacketSize = 0\r\n RecievePacketSize = 0\r\n @staticmethod\r\n def Details():\r\n print(\"Sent : Size = \" + str(PacketDetails.SendPacketSize) + \" Total = \" + str(PacketDetails.TotalSent) +\r\n \" Recieved : Size = \" + str(PacketDetails.RecievePacketSize) + \" Total = \" + str(PacketDetails.TotalRecieved), end='\\r')\r\n sys.stdout.flush()\r\n \r\n\r\nimport simplejson as json\r\nclass PersonProfile:\r\n \r\n def __init__(self, KeyDetails):\r\n self.ProfileInformationObject = json.loads(KeyDetails)\r\n print(\"[Info :] Successfully parsed the keys\")\r\n \r\n def getKey(self, ID):\r\n return self.ProfileInformationObject[ID]['Pwd']\r\n\r\nclass ProcessBytes:\r\n UserProfile = None\r\n\r\n def __init__(self):\r\n self.MaxOrderNumber = 16\r\n self.reInitialise()\r\n\r\n def reInitialise(self):\r\n self.OrderedBuffer = [None] * self.MaxOrderNumber\r\n self.lengthSeqNo = len(str(self.MaxOrderNumber))\r\n self.ctr = 0\r\n self.security_suite = None\r\n self.startPointer = -1\r\n self.rem = 0\r\n self.multiCastIncrementer = 0\r\n self.IsItP2P = True\r\n self.notGetPacket = 0\r\n self.ID = None\r\n\r\n\r\n\r\n def enableKeyForGroupID(self, GroupID):\r\n self.ID = GroupID\r\n if (ProcessBytes.UserProfile is None):\r\n key = \"1234567890abcdef\"\r\n print(\"[Alert :] Your call may not be secure as default key is being used. Please attach the physical key\")\r\n else:\r\n key = ProcessBytes.UserProfile.getKey(GroupID)\r\n if(GroupID != \"P2P\"):\r\n self.IsItP2P = False\r\n else:\r\n self.IsItP2P = True\r\n self.security_suite = AESCipher(key)\r\n\r\n def NumberTheRawBytes(self, rawBytes):\r\n # print(len(rawBytes))\r\n self.ctr = (self.ctr + 1) % self.MaxOrderNumber\r\n dt = str(self.ctr).zfill(self.lengthSeqNo) + rawBytes\r\n # print(len(dt))\r\n # return dt\r\n #i = len(dt)\r\n #self.rem = min(16 - i % 16, i % 16)\r\n # print(self.rem)\r\n #for k in range(0, self.rem):\r\n # dt = '0' + dt\r\n # print(len(dt))\r\n return dt\r\n\r\n def EncryptBytes(self, bytesData):\r\n if (self.security_suite is not None):\r\n dt = self.security_suite.encrypt(bytesData)\r\n return dt\r\n else:\r\n return bytesData\r\n\r\n def DecryptBytes(self, bytesData):\r\n return self.security_suite.decrypt(bytesData)\r\n\r\n def GetSeqNoData(self, bytesData):\r\n return bytesData[:self.lengthSeqNo + self.rem], bytesData[self.lengthSeqNo + self.rem:]\r\n\r\n def AddUnorderedBytes(self, packetData):\r\n # print(seqNo)\r\n\r\n if (packetData is None or len(packetData) == 0): return\r\n \r\n dataMultiple = []\r\n seqMultiple = []\r\n for bt in packetData:\r\n if (self.security_suite is not None):\r\n bytesData = self.DecryptBytes(bt)\r\n else:\r\n bytesData = bt\r\n \r\n seqNo, data = self.GetSeqNoData(bytesData)\r\n #print(seqNo)\r\n #print(len(data))\r\n \r\n # print(bytesData)\r\n try:\r\n er = seqNo.lstrip('0')\r\n if (len(er) == 0):\r\n seqNo = 0\r\n else:\r\n seqNo = int(er)\r\n except Exception as e:\r\n print(\"[Warning :] Error while decrypting. Scanning keys again...\")\r\n self.enableKeyForGroupID(self.ID)\r\n seqNo = 0\r\n\r\n # print(seqNo)\r\n dataMultiple.append(numpy.fromstring(data, dtype=numpy.uint8))\r\n seqMultiple.append(seqNo)\r\n\r\n if(self.IsItP2P):#len(dataMultiple) == 1):\r\n\r\n seqNo = seqMultiple[0]\r\n dataBlend = dataMultiple[0]\r\n self.OrderedBuffer[seqNo] = dataBlend.tostring()\r\n # print(self.OrderedBuffer)\r\n if (self.startPointer < 0 or self.startPointer >= self.MaxOrderNumber):\r\n self.startPointer = seqNo\r\n else:\r\n #print(\"**\")\r\n ratio = 1.0 / len(dataMultiple)\r\n dataBlend = dataMultiple[0] #* ratio\r\n for dt in range(1, len(dataMultiple)):\r\n dataBlend = dataBlend + dataMultiple[dt] #* ratio\r\n #dataBlend = dataBlend.astype(numpy.uint8)\r\n\r\n #print(self.multiCastIncrementer)\r\n\r\n self.OrderedBuffer[self.multiCastIncrementer] = dataBlend.tostring()\r\n if (self.startPointer < 0 or self.startPointer >= self.MaxOrderNumber):\r\n self.startPointer = self.multiCastIncrementer\r\n self.multiCastIncrementer = self.multiCastIncrementer + 1\r\n if (self.multiCastIncrementer < 0 or self.multiCastIncrementer >= self.MaxOrderNumber):\r\n self.multiCastIncrementer = 0\r\n #self.startPointer = self.startPointer + 1\r\n \r\n return data\r\n\r\n def makeOrderedByte(self, rawData):\r\n bytesData = self.NumberTheRawBytes(rawData)\r\n bytesData = self.EncryptBytes(bytesData)\r\n #print(len(bytesData))\r\n return bytesData\r\n\r\n def getLatestOrderedByte(self):\r\n if (self.startPointer >= 0 and self.OrderedBuffer[self.startPointer] is not None):\r\n self.notGetPacket = 0\r\n data = self.OrderedBuffer[self.startPointer]\r\n self.OrderedBuffer[self.startPointer] = None\r\n self.startPointer = self.startPointer + 1\r\n #print(self.startPointer)\r\n #print(self.OrderedBuffer)\r\n return data\r\n else:\r\n self.notGetPacket = self.notGetPacket + 1\r\n if(self.notGetPacket > 3):\r\n self.startPointer = self.startPointer + 1\r\n return None\r\n\r\n\r\nif __name__ == \"__main__\":\r\n bytesData1 = 'A' * 10\r\n bytesData2 = 'B' * 10\r\n bytesData3 = 'C' * 10\r\n pr = ProcessBytes()\r\n pr.enableKeyForGroupID(\"P2P\")\r\n b1 = pr.makeOrderedByte(bytesData1)\r\n print(b1)\r\n b2 = pr.makeOrderedByte(bytesData2)\r\n print(b2)\r\n b3 = pr.makeOrderedByte(bytesData3)\r\n print(b3)\r\n print(\"####################################\")\r\n pr.AddUnorderedBytes(b2)\r\n pr.AddUnorderedBytes(b3)\r\n pr.AddUnorderedBytes(b1)\r\n while (True):\r\n tp = pr.getLatestOrderedByte()\r\n if (tp is not None):\r\n print(tp)\r\n print(pr.OrderedBuffer)\r\n\r\n\r\n\r\n", "sub_path": "Encrytpsy1/EncrytedVOIP_latest/ProcessBytes.py", "file_name": "ProcessBytes.py", "file_ext": "py", "file_size_in_byte": 6786, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.stdout.flush", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 23, "usage_type": "attribute"}, {"api_name": "simplejson.loads", "line_number": 30, "usage_type": "call"}, {"api_name": "SecuritySuite.AESCipher", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.fromstring", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 127, "usage_type": "attribute"}]} +{"seq_id": "34266619", "text": "import os\n\nimport firebase_admin\nimport flask\nimport json\nfrom jinja2 import Environment, ChoiceLoader, ModuleLoader, FileSystemLoader\nfrom firebase_admin import credentials, firestore\n# from lxml import html\n\n\n# === Globals ===\ncred = credentials.ApplicationDefault()\nfirebase_admin.initialize_app(cred, {\n 'projectId': os.environ.get('GCP_PROJECT')\n })\ndb = firestore.client()\njinja = Environment(\n loader=ChoiceLoader([\n ModuleLoader(os.path.abspath(os.curdir) + '/jinja.cache'),\n FileSystemLoader(os.path.abspath(os.curdir) + '/templates')\n ]))\n\n# === Helper Functions ===\n\n\ndef request_wants_json(request):\n best = request.accept_mimetypes \\\n .best_match(['application/json', 'text/html'])\n return best == 'application/json' and \\\n request.accept_mimetypes[best] > \\\n request.accept_mimetypes['text/html']\n\n\n# === Entrypoints ===\n\n\ndef list_comics(request: flask.Request):\n \"\"\"List all registered comics\n Args:\n request: The request object.\n \n Returns:\n The response text, or any of the values that can be turned into a\n Response object using `make_response`\n .\n \"\"\"\n comics_ref = db.collection(u'comics')\n comics = {doc.id: doc.to_dict() for doc in comics_ref.get()}\n response = flask.Response()\n\n if request_wants_json(request):\n response.content_type = 'application/json'\n response.set_data(json.dumps(comics))\n else:\n template = jinja.get_template('list.jinja2')\n response.set_data(template.render(comics=comics))\n\n return response\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1718, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "firebase_admin.credentials.ApplicationDefault", "line_number": 12, "usage_type": "call"}, {"api_name": "firebase_admin.credentials", "line_number": 12, "usage_type": "name"}, {"api_name": "firebase_admin.initialize_app", "line_number": 13, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 14, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 14, "usage_type": "attribute"}, {"api_name": "firebase_admin.firestore.client", "line_number": 16, "usage_type": "call"}, {"api_name": "firebase_admin.firestore", "line_number": 16, "usage_type": "name"}, {"api_name": "jinja2.Environment", "line_number": 17, "usage_type": "call"}, {"api_name": "jinja2.ChoiceLoader", "line_number": 18, "usage_type": "call"}, {"api_name": "jinja2.ModuleLoader", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.curdir", "line_number": 19, "usage_type": "attribute"}, {"api_name": "jinja2.FileSystemLoader", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.curdir", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.Request", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.Response", "line_number": 49, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "407715676", "text": "#!/usr/bin/env python\n# encoding: utf-8\n\nimport objc\nfrom Foundation import *\nfrom AppKit import *\nimport sys, os, re\n\nGlyphsPaletteProtocol = objc.protocolNamed( \"GlyphsPalette\" )\n\nclass SmileyPalette ( NSObject, GlyphsPaletteProtocol ):\n\t_theView = objc.IBOutlet()\n\t_theImageView = objc.IBOutlet()\n\n\tdef init( self ):\n\t\t\"\"\"\n\t\tDo all initializing here.\n\t\t\"\"\"\n\t\ttry:\n\t\t\tself.controller = None\n\t\t\tif not NSBundle.loadNibNamed_owner_( \"SmileyPaletteView\", self ):\n\t\t\t\tself.logToConsole( \"Palette Layers: Error loading Nib!\" )\n\t\t\n\t\t\ts = objc.selector( self.update, signature='v@:' )\n\t\t\tNSNotificationCenter.defaultCenter().addObserver_selector_name_object_( self, s, \"GSUpdateInterface\", None )\n\t\t\tNSNotificationCenter.defaultCenter().addObserver_selector_name_object_( self, s, \"GSDocumentCloseNotification\", None )\n\t\t\tNSNotificationCenter.defaultCenter().addObserver_selector_name_object_( self, s, \"GSDocumentActivateNotification\", None )\n\t\t\n\t\t\tFrame = self._theView.frame()\n\t\t\n\t\t\tif NSUserDefaults.standardUserDefaults().objectForKey_( \"com.GeorgSeifert.SmileyPalette.ViewHeight\" ):\n\t\t\t\tFrame.size.height = NSUserDefaults.standardUserDefaults().integerForKey_( \"com.GeorgSeifert.SmileyPalette.ViewHeight\" )\n\t\t\t\tself._theView.setFrame_(Frame)\n\t\t\n\t\t\treturn self\n\t\texcept Exception as e:\n\t\t\tself.logToConsole( \"init: %s\" % str(e) )\n\t\t\t\n\tdef __del__(self):\n\t\tNSNotificationCenter.defaultCenter().removeObserver_( self )\n\t\n\tdef title( self ):\n\t\t\"\"\"\n\t\tThis is the name as it appears in the Palette section header.\n\t\t\"\"\"\n\t\ttry:\n\t\t\treturn \"Smiley\"\n\t\texcept Exception as e:\n\t\t\tself.logToConsole( \"title: %s\" % str(e) )\n\t\n\tdef interfaceVersion( self ):\n\t\t\"\"\"\n\t\tDistinguishes the API version the plugin was built for. \n\t\tReturn 1.\n\t\t\"\"\"\n\t\ttry:\n\t\t\treturn 1\n\t\texcept Exception as e:\n\t\t\tself.logToConsole( \"interfaceVersion: %s\" % str(e) )\n\t\n\tdef theView( self ):\n\t\t\"\"\"\n\t\tReturns an NSView to be displayed in the palette.\n\t\tThis is the grey background in the palette, on which you can place UI items.\n\t\t\"\"\"\n\t\ttry:\n\t\t\treturn self._theView\n\t\texcept Exception as e:\n\t\t\tself.logToConsole( \"theView: %s\" % str(e) )\n\t\n\tdef minHeight( self ):\n\t\t\"\"\"\n\t\tThe minimum height of the view in pixels.\n\t\t\"\"\"\n\t\ttry:\n\t\t\treturn 78\n\t\texcept Exception as e:\n\t\t\tself.logToConsole( \"minHeight: %s\" % str(e) )\n\t\n\tdef maxHeight( self ):\n\t\t\"\"\"\n\t\tThe maximum height of the view in pixels.\n\t\tMust be equal to or bigger than minHeight.\n\t\t\"\"\"\n\t\ttry:\n\t\t\treturn 78\n\t\texcept Exception as e:\n\t\t\tself.logToConsole( \"maxHeight: %s\" % str(e) )\n\t\n\tdef currentHeight( self ):\n\t\t\"\"\"\n\t\tThe current height of the Palette section.\n\t\tUsed for storing the current resized state.\n\t\tIf you have a fixed height, you can return the height in pixels\n\t\t\"\"\"\n\t\ttry:\n\t\t\treturn 78\n\t\t\t# NSUserDefaults.standardUserDefaults().integerForKey_( \"com.GeorgSeifert.SmileyPalette.ViewHeight\" )\n\t\texcept Exception as e:\n\t\t\tself.logToConsole( \"currentHeight: %s\" % str(e) )\n\t\n\tdef setCurrentHeight_( self, newHeight ):\n\t\t\"\"\"\n\t\tSets a new height for the Palette section.\n\t\t\"\"\"\n\t\ttry:\n\t\t\tif newHeight >= self.minHeight() and newHeight <= self.maxHeight():\n\t\t\t\tNSUserDefaults.standardUserDefaults().setInteger_forKey_( newHeight, \"com.GeorgSeifert.SmileyPalette.ViewHeight\" )\n\t\texcept Exception as e:\n\t\t\tself.logToConsole( \"setCurrentHeight_: %s\" % str(e) )\n\n\tdef setWindowController_(self, Controller):\n\t\tself.controller = Controller\n\t\n\tdef windowController(self):\n\t\treturn self.controller\n\t\n\tdef currentWindowController( self, sender ):\n\t\t\"\"\"\n\t\tReturns a window controller object.\n\t\tUse self.currentWindowController() to access it.\n\t\t\"\"\"\n\t\ttry:\n\t\t\twindowController = None\n\t\t\ttry:\n\t\t\t\twindowController = self.controller\n\t\t\t\tif not windowController:\n\t\t\t\t\twindowController = NSDocumentController.sharedDocumentController().currentDocument().windowController()\n\t\t\t\tif not windowController and sender.respondsToSelector_( \"object\" ):\n\t\t\t\t\tif sender.object().__class__ == NSClassFromString( \"GSFont\" ):\n\t\t\t\t\t\tFont = sender.object()\n\t\t\t\t\t\twindowController = Font.parent().windowControllers()[0]\n\t\t\t\t\t\tself.logToConsole( \"__windowController1\", windowController )\n\t\t\t\t\telse:\n\t\t\t\t\t\twindowController = sender.object()\n\t\t\t\t\t\tself.logToConsole( \"__windowController2\", windowController )\n\t\t\texcept:\n\t\t\t\tpass\n\t\t\treturn windowController\n\t\texcept Exception as e:\n\t\t\tself.logToConsole( \"currentWindowController: %s\" % str(e) )\n\t\n\tdef update( self, sender ):\n\t\t\"\"\"\n\t\tCalled from the notificationCenter if the info in the current Glyph window has changed.\n\t\tThis can be called quite a lot, so keep this method fast.\n\t\t\"\"\"\n\t\ttry:\n\t\t\tLayer = None\n\t\t\t\n\t\t\ttry:\n\t\t\t\twindowController = self.currentWindowController( sender )\n\t\t\t\tLayer = windowController.activeLayer()\n\t\t\texcept:\n\t\t\t\tpass\n\t\t\t\t\n\t\t\tif Layer:\n\t\t\t\tself._theImageView.setHidden_( False )\n\t\t\telse:\n\t\t\t\tself._theImageView.setHidden_( True )\n\t\texcept Exception as e:\n\t\t\tself.logToConsole( \"update: %s\" % str(e) )\n\t\t\t\n\t\t\n\t\n\tdef logToConsole( self, message ):\n\t\t\"\"\"\n\t\tThe variable 'message' will be passed to Console.app.\n\t\tUse self.logToConsole( \"bla bla\" ) for debugging.\n\t\t\"\"\"\n\t\tmyLog = \"Show %s plugin:\\n%s\" % ( self.title(), message )\n\t\tNSLog( myLog )\n\t\n", "sub_path": "Python Samples/Smiley Panel Plugin/Smiley Palette.glyphsPalette/Contents/Resources/SmileyPalette.py", "file_name": "SmileyPalette.py", "file_ext": "py", "file_size_in_byte": 5110, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "objc.protocolNamed", "line_number": 9, "usage_type": "call"}, {"api_name": "objc.IBOutlet", "line_number": 12, "usage_type": "call"}, {"api_name": "objc.IBOutlet", "line_number": 13, "usage_type": "call"}, {"api_name": "objc.selector", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "389078642", "text": "# %% Setup\nimport os\nimport time\nfrom glob import glob\nfrom os.path import join\n\nimport GPUtil\nimport numpy as np\nimport tensorflow as tf\nfrom keras.backend.tensorflow_backend import set_session\nfrom keras.callbacks import ModelCheckpoint, ReduceLROnPlateau\nfrom keras.optimizers import Adam\nfrom matplotlib import pyplot as plt\nfrom natsort import natsorted\nfrom prettytable import PrettyTable\nfrom sklearn.metrics import auc, confusion_matrix, roc_curve\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.utils import class_weight\n\nfrom Datagen import PngClassDataGenerator, PngDataGenerator\nfrom HelperFunctions import (RenameWeights, get_class_datagen, get_seg_datagen,\n get_train_params, get_val_params)\nfrom Losses import dice_coef_loss\nfrom Models import Inception_model\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\n\nconfig = tf.ConfigProto()\n# dynamically grow the memory used on the GPU\nconfig.gpu_options.allow_growth = True\nsess = tf.Session(config=config)\n# set this TensorFlow session as the default session for Keras\nset_session(sess)\n\n\nrng = np.random.RandomState(seed=1)\n\ntry:\n if not 'DEVICE_ID' in locals():\n DEVICE_ID = GPUtil.getFirstAvailable()[0]\n print('Using GPU', DEVICE_ID)\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = str(DEVICE_ID)\nexcept Exception as e:\n raise('No GPU available')\n\n\n# ~~~~~~~~~~~~~~~~~~~~~~~\n# ~~~~~~~~ SETUP~~~~~~~\n# ~~~~~~~~~~~~~~~~~~~~~~\n\n# Setup data\n# pre_train_datapath = '/data/Kaggle/nih-chest-dataset/images_resampled_sorted_into_categories/Pneumothorax_norm/'\n# pre_train_negative_datapath = '/data/Kaggle/nih-chest-dataset/images_resampled_sorted_into_categories/No_Finding_norm/'\npre_train_datapath = '/data/Kaggle/nih-chest-dataset/images_resampled_sorted_into_categories/Pneumothorax/'\npre_train_negative_datapath = '/data/Kaggle/nih-chest-dataset/images_resampled_sorted_into_categories/No_Finding/'\n\n# train_pos_datapath = '/data/Kaggle/pos-norm-png'\n# train_neg_datapath = '/data/Kaggle/neg-norm-png'\ntrain_pos_datapath = '/data/Kaggle/pos-filt-png'\ntrain_neg_datapath = '/data/Kaggle/neg-filt-png'\n\npretrain_weights_filepath = 'Best_pretrain_class_weights.h5'\ntrain_weights_filepath = 'Best_Kaggle_Classification_Weights_{}_v4.h5'\n\n# pre-train parameters\npre_im_dims = (512, 512)\npre_n_channels = 1\npre_batch_size = 16\npre_val_split = .15\npre_epochs = 10\npre_multi_process = False\nskip_pretrain = True\n\n# train parameters\nim_dims = (512, 512)\nn_channels = 1\nbatch_size = 4\nlearnRate = 1e-4\nfilt_nums = 16\nnum_blocks = 5\nval_split = .15\nepochs = 10\nfull_epochs = 30 # epochs trained on 1024 data\nmulti_process = False\n\n# datagen params\npre_train_params = get_train_params(\n pre_batch_size, pre_im_dims, pre_n_channels)\npre_val_params = get_val_params(pre_batch_size, pre_im_dims, pre_n_channels)\ntrain_params = get_train_params(batch_size, im_dims, n_channels)\nval_params = get_val_params(batch_size, im_dims, n_channels)\nfull_train_params = get_train_params(2, (1024, 1024), 1)\nfull_val_params = get_val_params(2, (1024, 1024), 1)\n\n# %% ~~~~~~~~~~~~~~~~~~~~~~~\n# ~~~~Pre-training~~~~~~\n# ~~~~~~~~~~~~~~~~~~~~~~~\n\nif not skip_pretrain:\n\n print('---------------------------------')\n print('---- Setting up pre-training ----')\n print('---------------------------------')\n\n # Get datagens for pre-training\n pre_train_gen, pre_val_gen, class_weights = get_class_datagen(\n pre_train_datapath, pre_train_negative_datapath, pre_train_params, pre_val_params, pre_val_split)\n\n # Create model\n model = Inception_model(input_shape=pre_im_dims+(pre_n_channels,))\n\n # Compile model\n model.compile(Adam(lr=learnRate), loss='binary_crossentropy',\n metrics=['accuracy'])\n\n # Create callbacks\n cb_check = ModelCheckpoint(pretrain_weights_filepath, monitor='val_loss',\n verbose=1, save_best_only=True, save_weights_only=True, mode='auto', period=1)\n\n print('---------------------------------')\n print('----- Starting pre-training -----')\n print('---------------------------------')\n\n # Train model\n pre_history = model.fit_generator(generator=pre_train_gen,\n epochs=pre_epochs, use_multiprocessing=pre_multi_process,\n workers=8, verbose=1, callbacks=[cb_check],\n class_weight=class_weights,\n validation_data=pre_val_gen)\n\n # Load best weights\n model.load_weights(pretrain_weights_filepath)\n\n # Calculate confusion matrix\n print('Calculating classification confusion matrix...')\n pre_val_gen.shuffle = False\n preds = model.predict_generator(pre_val_gen, verbose=1)\n labels = [pre_val_gen.labels[f] for f in pre_val_gen.list_IDs]\n y_pred = np.rint(preds)\n totalNum = len(y_pred)\n y_true = np.rint(labels)[:totalNum]\n tn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\n\n print('--------------------------------------')\n print('Classification Results on pre-training')\n print('--------------------------------------')\n print('True positives: {}'.format(tp))\n print('True negatives: {}'.format(tn))\n print('False positives: {}'.format(fp))\n print('False negatives: {}'.format(fn))\n print('% Positive: {:.02f}'.format(100*(tp+fp)/totalNum))\n print('% Negative: {:.02f}'.format(100*(tn+fn)/totalNum))\n print('% Accuracy: {:.02f}'.format(100*(tp+tn)/totalNum))\n print('% Sensitivity: {:.02f}'.format(100*(tp)/(tp+fn)))\n print('% Specificity: {:.02f}'.format(100*(tn)/(tn+fp)))\n print('-----------------------')\n\nelse:\n # skip pretraining, load weights and go to regular training\n print('Skipping pre-training, setting up model')\n # Create model\n model = Inception_model(input_shape=pre_im_dims+(pre_n_channels,))\n # Compile model\n model.compile(Adam(lr=learnRate), loss='binary_crossentropy',\n metrics=['accuracy'])\n model.load_weights(pretrain_weights_filepath)\n\n# %% ~~~~~~~~~~~~~~~~~~~~~~~~~~\n# ~~~~~~~ 512 Training~~~~~~~~~\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nprint('---------------------------------')\nprint('---- Setting up 512 training ----')\nprint('---------------------------------')\n\n# Get datagens for training\ntrain_gen, val_gen, class_weights = get_class_datagen(\n train_pos_datapath, train_neg_datapath, train_params, val_params, val_split)\n\n# Create callbacks\ncur_weights_path = train_weights_filepath.format('512train')\ncb_check = ModelCheckpoint(cur_weights_path, monitor='val_loss', verbose=1,\n save_best_only=True, save_weights_only=True, mode='auto', period=1)\n\nprint('---------------------------------')\nprint('----- Starting 512 training -----')\nprint('---------------------------------')\n\n# Train model\nhistory = model.fit_generator(generator=train_gen,\n epochs=epochs, use_multiprocessing=multi_process,\n workers=8, verbose=1, callbacks=[cb_check],\n class_weight=class_weights,\n validation_data=val_gen)\n\n# Load best weights\nmodel.load_weights(cur_weights_path)\n\n# Calculate confusion matrix\nprint('Calculating classification confusion matrix...')\nval_gen.shuffle = False\npreds = model.predict_generator(val_gen, verbose=1)\nlabels = [val_gen.labels[f] for f in val_gen.list_IDs]\ny_pred = np.rint(preds)\ntotalNum = len(y_pred)\ny_true = np.rint(labels)[:totalNum]\ntn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\n\nprint('--------------------------------------')\nprint('Classification Results on 512 training')\nprint('--------------------------------------')\nprint('True positives: {}'.format(tp))\nprint('True negatives: {}'.format(tn))\nprint('False positives: {}'.format(fp))\nprint('False negatives: {}'.format(fn))\nprint('% Positive: {:.02f}'.format(100*(tp+fp)/totalNum))\nprint('% Negative: {:.02f}'.format(100*(tn+fn)/totalNum))\nprint('% Accuracy: {:.02f}'.format(100*(tp+tn)/totalNum))\nprint('% Sensitivity: {:.02f}'.format(100*(tp)/(tp+fn)))\nprint('% Specificity: {:.02f}'.format(100*(tn)/(tn+fp)))\nprint('-----------------------')\n\n# %% ~~~~~~~~~~~~~~~~~~~~~~~~~~~\n# ~~~~~~~ 1024 Training~~~~~~~~~\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nprint('----------------------------------')\nprint('---- Setting up 1024 training ----')\nprint('----------------------------------')\n\n# rebuild model\nfull_model = Inception_model(input_shape=(1024, 1024)+(n_channels,))\nfull_model.load_weights(cur_weights_path)\n\n# Compile model\nfull_model.compile(Adam(lr=learnRate), loss='binary_crossentropy',\n metrics=['accuracy'])\n\n# Get datagens for training\nfull_train_gen, full_val_gen, class_weights = get_class_datagen(\n train_pos_datapath, train_neg_datapath, full_train_params, full_val_params, val_split)\n\n# Create callbacks\ncur_weights_path = train_weights_filepath.format('1024train')\ncb_check = ModelCheckpoint(cur_weights_path, monitor='val_loss', verbose=1,\n save_best_only=True, save_weights_only=True, mode='auto', period=1)\n\nprint('----------------------------------')\nprint('----- Starting 1024 training -----')\nprint('----------------------------------')\n\n# Train model\nhistory = full_model.fit_generator(generator=full_train_gen,\n epochs=full_epochs, use_multiprocessing=multi_process,\n workers=8, verbose=1, callbacks=[cb_check],\n class_weight=class_weights,\n validation_data=full_val_gen)\n\n# Load best weights\nfull_model.load_weights(cur_weights_path)\n\n# Calculate confusion matrix\nprint('Calculating classification confusion matrix...')\nfull_val_gen.shuffle = False\npreds = full_model.predict_generator(full_val_gen, verbose=1)\nlabels = [full_val_gen.labels[f] for f in full_val_gen.list_IDs]\ny_pred = np.rint(preds)\ntotalNum = len(y_pred)\ny_true = np.rint(labels)[:totalNum]\ntn, fp, fn, tp = confusion_matrix(y_true, y_pred).ravel()\n\nprint('---------------------------------------')\nprint('Classification Results on 1024 training')\nprint('---------------------------------------')\nprint('True positives: {}'.format(tp))\nprint('True negatives: {}'.format(tn))\nprint('False positives: {}'.format(fp))\nprint('False negatives: {}'.format(fn))\nprint('% Positive: {:.02f}'.format(100*(tp+fp)/totalNum))\nprint('% Negative: {:.02f}'.format(100*(tn+fn)/totalNum))\nprint('% Accuracy: {:.02f}'.format(100*(tp+tn)/totalNum))\nprint('% Sensitivity: {:.02f}'.format(100*(tp)/(tp+fn)))\nprint('% Specificity: {:.02f}'.format(100*(tn)/(tn+fp)))\nprint('-----------------------')\n\n\n# Make ROC curve\nfpr, tpr, thresholds = roc_curve(y_true, preds, pos_label=1)\nroc_auc = auc(fpr, tpr)\nplt.figure()\nlw = 2\nplt.plot(fpr, tpr, color='darkorange',\n lw=lw, label='ROC curve (area = {:0.2f})'.format(roc_auc))\nplt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('Receiver operating characteristic for pneumothorax')\nplt.legend(loc=\"lower right\")\nplt.show()\n\n# print threshold table\ntable = PrettyTable(['Threshold', 'True Positive Rate', 'False Positive Rate'])\nfor t, tp, fp in zip(thresholds, tpr, fpr):\n table.add(['{:.034f}'.format(t), '{:.034f}'.format(\n tp), '{:.034f}'.format(fp)])\nprint(table)\n\n\n# Get and display a few predictions\n# for ind in range(5):\n# b_ind = np.random.randint(0, len(full_val_gen))\n# valX, valY = full_val_gen.__getitem__(b_ind)\n# preds = full_model.predict_on_batch(valX)\n# cur_im = valX[0]\n# disp_im = np.concatenate([cur_im[..., c]\n# for c in range(cur_im.shape[-1])], axis=1)\n# plt.imshow(disp_im, cmap='gray')\n# plt.title('Predicted: {} Actual: {}'.format(preds[0], valY[0]))\n# plt.show()\n", "sub_path": "TrainClassificationModel.py", "file_name": "TrainClassificationModel.py", "file_ext": "py", "file_size_in_byte": 11926, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.environ", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tensorflow.ConfigProto", "line_number": 29, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 32, "usage_type": "call"}, {"api_name": "keras.backend.tensorflow_backend.set_session", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.random.RandomState", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 37, "usage_type": "attribute"}, {"api_name": "GPUtil.getFirstAvailable", "line_number": 41, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 43, "usage_type": "attribute"}, {"api_name": "HelperFunctions.get_train_params", "line_number": 88, "usage_type": "call"}, {"api_name": "HelperFunctions.get_val_params", "line_number": 90, "usage_type": "call"}, {"api_name": "HelperFunctions.get_train_params", "line_number": 91, "usage_type": "call"}, {"api_name": "HelperFunctions.get_val_params", "line_number": 92, "usage_type": "call"}, {"api_name": "HelperFunctions.get_train_params", "line_number": 93, "usage_type": "call"}, {"api_name": "HelperFunctions.get_val_params", "line_number": 94, "usage_type": "call"}, {"api_name": "HelperFunctions.get_class_datagen", "line_number": 107, "usage_type": "call"}, {"api_name": "Models.Inception_model", "line_number": 111, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 114, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.rint", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.rint", "line_number": 142, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 143, "usage_type": "call"}, {"api_name": "Models.Inception_model", "line_number": 163, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 165, "usage_type": "call"}, {"api_name": "HelperFunctions.get_class_datagen", "line_number": 178, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.rint", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.rint", "line_number": 207, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 208, "usage_type": "call"}, {"api_name": "Models.Inception_model", "line_number": 233, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 237, "usage_type": "call"}, {"api_name": "HelperFunctions.get_class_datagen", "line_number": 241, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.rint", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.rint", "line_number": 270, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 271, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 289, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 290, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 291, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 291, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 293, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 293, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 295, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 295, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 296, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 296, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 297, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 297, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 298, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 298, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 299, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 299, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 300, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 300, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 301, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 301, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 302, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 302, "usage_type": "name"}, {"api_name": "prettytable.PrettyTable", "line_number": 305, "usage_type": "call"}]} +{"seq_id": "212941547", "text": "from __future__ import absolute_import\nfrom __future__ import unicode_literals\n\nfrom django import forms\nfrom django.utils.translation import ugettext_lazy as _\nimport json\nfrom pdt import json_schema_check_fhir\nimport requests\nfrom .models import Practitioner, Address\nfrom collections import OrderedDict\n\n\nclass PractitionerModelForm(forms.ModelForm):\n def __init__(self, *args, **kwargs):\n super(PractitionerModelForm, self).__init__(*args, **kwargs)\n instance = getattr(self, 'instance', None)\n if instance and instance.pk:\n self.fields['npi'].widget.attrs['readonly'] = True\n self.fields['fhir_id'].widget.attrs['readonly'] = True\n self.fields['first_name'].widget.attrs['required'] = True\n self.fields['last_name'].widget.attrs['required'] = True\n\n class Meta:\n model = Practitioner\n fields = ('npi','fhir_id','first_name', 'last_name',)\n\n\n\nclass FetchPractitionerForm(forms.Form):\n npi = forms.CharField(label='NPI', max_length=10,\n help_text =_(\"Enter a valid NPI\"))\n\n required_css_class = 'required'\n def clean_npi(self):\n npi = self.cleaned_data.get('npi')\n url = \"https://registry.npi.io/search/fhir/Practitioner.\" \\\n \"json?identifier.value=%s\" % (npi)\n response = requests.get(url)\n try:\n jr = json.loads(response.text)\n\n if 'results' not in jr:\n msg=_(\"The lookup failed. Invalid response from server\")\n raise forms.ValidationError(msg)\n\n if not jr['results']:\n msg=_(\"Invalid NPI\")\n raise forms.ValidationError(msg)\n except ValueError:\n msg=_(\"The lookup failed. JSON was not returned by the server.\")\n raise forms.ValidationError(msg)\n return npi\n\n\n\nclass AddressForm(forms.Form):\n line_1= forms.CharField(max_length=255)\n line_2 = forms.CharField(max_length=255, required=False)\n city= forms.CharField(max_length=255)\n state= forms.CharField(max_length=2)\n postal_code= forms.CharField(max_length=15)\n country= forms.CharField(max_length=2)\n use = forms.ChoiceField(choices = (('home','home'),\n ('work','work'),('mailing', 'mailing')))\n required_css_class = 'required'\n\n def create_fhir_json(self):\n json_fhir_dict = OrderedDict()\n json_fhir_dict['line']=[]\n json_fhir_dict['line'].append(self.cleaned_data.get('line_1'))\n json_fhir_dict['line'].append(self.cleaned_data.get('line_2'))\n json_fhir_dict['city'] = self.cleaned_data.get('city')\n json_fhir_dict['state'] = self.cleaned_data.get('state')\n json_fhir_dict['postalCode'] = self.cleaned_data.get('postal_code')\n json_fhir_dict['country'] = self.cleaned_data.get('country')\n json_fhir_dict['use'] = self.cleaned_data.get('use')\n return json.dumps(json_fhir_dict, indent=4)\n\nclass AffiliationForm(forms.Form):\n purpose = forms.ChoiceField(choices =\n (('PROVIDER-NETWORK', 'Provider-Network'),\n ('MEDICARE-NETWORK', 'Medicare-Network')))\n npi = forms.CharField(max_length=10, label=\"Organization's NPI (Type 2)\")\n endpoint_data_type = forms.ChoiceField(\n choices = (('DIRECT-EMAIL-ADDRESS', 'Direct-Email-Address')),\n required=False)\n endpoint = forms.CharField(max_length=512, required=False)\n required_css_class = 'required'\n\n def create_fhir_json(self):\n json_fhir_dict = OrderedDict()\n json_fhir_dict['purpose'] = self.cleaned_data.get('purpose')\n json_fhir_dict['npi'] = self.cleaned_data.get('npi')\n json_fhir_dict['endpoint_data_type'] = self.cleaned_data.get('endpoint_data_type)')\n json_fhir_dict['endpoint'] = self.cleaned_data.get('endpoint')\n\n return json.dumps(json_fhir_dict, indent=4)\n\n\nclass PractitionerHumanForm(forms.Form):\n first_name = forms.CharField(max_length=255)\n last_name = forms.CharField(max_length=255)\n\n\n required_css_class = 'required'\n\n\nclass OrganizationHumanForm(forms.Form):\n organization_name = forms.CharField(max_length=255)\n\n required_css_class = 'required'\n\n\n\nclass JsonForm(forms.Form):\n json = forms.CharField(label='JSON body', max_length=10000,\n widget=forms.Textarea,\n help_text =_(\"This field must contain a JSON object e.g. {}\"))\n\n required_css_class = 'required'\n\n\n def clean_json(self):\n jsonf = self.cleaned_data.get('json')\n\n try:\n j = json.loads(jsonf)\n if type (j) != type({}):\n msg=_(\"The field does not contain a valid JSON object.\")\n raise forms.ValidationError(msg)\n\n except ValueError:\n msg=_(\"The field does not contain valid JSON.\")\n raise forms.ValidationError(msg)\n\n return jsonf\n\nclass PractitionerForm(forms.Form):\n json = forms.CharField(label='JSON body',\n max_length=10000, widget=forms.Textarea,\n help_text =_(\"This field must contain a Practitioner\" \\\n \"FHIR JSON object e.g. {}\"))\n\n required_css_class = 'required'\n\n\n def clean_json(self):\n jsonf = self.cleaned_data.get('json')\n try:\n j = json.loads(jsonf)\n json_pract_result = json_schema_check_fhir.json_schema_check_fhir(\n 'Practitioner', jsonf)\n if json_pract_result['errors'] != []:\n msg=_(\"The field does not contain a valid FHIR Practitioner\" \\\n \"JSON object: \", json_pract_result['errors'])\n raise forms.ValidationError(msg)\n\n except ValueError:\n msg=_(\"The field does not contain valid JSON.\")\n raise forms.ValidationError(msg)\n\n return jsonf\n\nclass OrganizationForm(forms.Form):\n json = forms.CharField(label='JSON body', max_length=10000,\n widget=forms.Textarea,\n help_text =_(\"This field must contain an Organization FHIR JSON \\\n object e.g. {}\"))\n\n required_css_class = 'required'\n\n\n def clean_json(self):\n jsonf = self.cleaned_data.get('json')\n try:\n j = json.loads(jsonf)\n json_org_result = json_schema_check_fhir.json_schema_check_fhir(\n 'Organization',\n jsonf)\n if json_org_result['errors'] != []:\n msg=_(\"The field does not contain a valid FHIR Organization \\\n JSON object: \", json_org_result['errors'])\n raise forms.ValidationError(msg)\n\n except ValueError:\n msg=_(\"The field does not contain valid JSON.\")\n raise forms.ValidationError(msg)\n\n return jsonf\n", "sub_path": "apps/provider/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 6888, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.forms.ModelForm", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 13, "usage_type": "name"}, {"api_name": "models.Practitioner", "line_number": 24, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 29, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 29, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 30, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 38, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 40, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 43, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 44, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 44, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 47, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 48, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 48, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 50, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 51, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 51, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 56, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 56, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 57, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 57, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 58, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 58, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 59, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 59, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 60, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 60, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 61, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 61, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 62, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 62, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 63, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 63, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 68, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 77, "usage_type": "call"}, {"api_name": "django.forms.Form", "line_number": 79, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 79, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 80, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 80, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 83, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 83, "usage_type": "name"}, {"api_name": "django.forms.ChoiceField", "line_number": 84, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 84, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 87, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 87, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 91, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 97, "usage_type": "call"}, {"api_name": "django.forms.Form", "line_number": 100, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 100, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 101, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 101, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 102, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 102, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 108, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 108, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 109, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 109, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 115, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 115, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 116, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 116, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 117, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 117, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 118, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 127, "usage_type": "call"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 129, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 130, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 130, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 133, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 134, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 134, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 138, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 138, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 139, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 139, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 140, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 140, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 141, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 150, "usage_type": "call"}, {"api_name": "pdt.json_schema_check_fhir.json_schema_check_fhir", "line_number": 151, "usage_type": "call"}, {"api_name": "pdt.json_schema_check_fhir", "line_number": 151, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 154, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 156, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 156, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 159, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 160, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 160, "usage_type": "name"}, {"api_name": "django.forms.Form", "line_number": 164, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 164, "usage_type": "name"}, {"api_name": "django.forms.CharField", "line_number": 165, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 165, "usage_type": "name"}, {"api_name": "django.forms.Textarea", "line_number": 166, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 166, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 167, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 176, "usage_type": "call"}, {"api_name": "pdt.json_schema_check_fhir.json_schema_check_fhir", "line_number": 177, "usage_type": "call"}, {"api_name": "pdt.json_schema_check_fhir", "line_number": 177, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 181, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 183, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 183, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 186, "usage_type": "call"}, {"api_name": "django.forms.ValidationError", "line_number": 187, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 187, "usage_type": "name"}]} +{"seq_id": "403555101", "text": "'''\nCreated on 26.06.2014\n\n@author: twilker\n\nThis module holds all model objects\n'''\nfrom model.event import EventHook\nfrom model.control import GameControl\nfrom timeit import itertools\nfrom random import shuffle\nfrom enum import Enum\nimport random\n\nclass BaseObject(object):\n ''' This class provides basic event and builder functionality\n '''\n \n def __init__(self,builder,configObj,eventBus):\n self._builder = builder\n self._configuration = configObj\n self.on_changed = EventHook(\"model_object_bus\",eventBus)\n \n def clone(self,new,eventBus,builder):\n ''' Clone this instance but changes the event bus\n '''\n new._builder = builder\n new.on_changed = EventHook(\"model_object_bus\",eventBus)\n new._configuration = self._configuration\n \n def setup_new_game(self):\n '''Clears all caches and sets up a new game\n '''\n pass\n \nclass Game(BaseObject):\n ''' This class is a container for the game components\n '''\n \n def __init__(self,builder,configObj,eventBus,gameBoard,cardStock,goalDice,drawDie):\n super(Game,self).__init__(builder,configObj,eventBus)\n self.board = gameBoard\n self.card_stock = cardStock\n self.goal_dice = goalDice\n self.draw_die = drawDie\n self.shortcut_used = False\n self.last_move = None, None\n self.__init_players()\n \n def clone(self,eventBus,builder):\n clone = Game.__new__(Game)\n super(Game,self).clone(clone,eventBus,builder)\n clone.board = self.board.clone(eventBus,builder)\n clone.card_stock = self.card_stock.clone(eventBus,builder)\n clone.goal_dice = self.goal_dice.clone(eventBus,builder)\n clone.draw_die = self.draw_die.clone(eventBus,builder)\n clone.shortcut_used = self.shortcut_used\n clone.players = []\n for player in self.players:\n clone.players.append(player.clone(eventBus,builder,clone.board,clone.card_stock))\n clone.active_player = clone.players[self.players.index(self.active_player)]\n clone._next_player = clone.players[self.players.index(self._next_player)] if self._next_player != None else None\n clone._player_cycle = itertools.cycle(clone.players)\n while next(clone._player_cycle) != clone.active_player:\n pass #move player cycle to expected position\n if self.last_move[0] == None:\n clone.last_move = None, None\n else:\n clone.last_move = clone.players[self.players.index(self.last_move[0])], self.last_move[1]\n return clone\n \n def switch_player(self):\n old_player = self.active_player\n if self._next_player != None:\n actual_player = self.active_player\n self.active_player = self._next_player\n while actual_player != self.active_player:\n actual_player = next(self._player_cycle)\n self._next_player = None\n else:\n self.active_player = next(self._player_cycle)\n while self.active_player.lose_turn:\n self.active_player.lose_turn = False\n self.active_player = next(self._player_cycle)\n self.on_changed(self,'player_changed',old_player,self.active_player)\n \n def setup_new_game(self,user_control):\n self.board.setup_new_game()\n self.card_stock.setup_new_game()\n for player in self.players:\n player.setup_new_game()\n self._player_cycle = itertools.cycle(self.players)\n self.active_player = next(self._player_cycle)\n self._next_player = None\n self._builder.add_object(user_control,\"USERCONTROL\")\n return self._builder.build(GameControl)\n \n def __init_players(self):\n self.players = []\n for _ in range(0,self._configuration.as_int('PLAYER_COUNT')):\n self.players.append(self._builder.build(Player))\n \nclass DrawDie(BaseObject):\n ''' The die to determine the dra\n '''\n \n def __init__(self,builder,configObj,eventBus):\n super(DrawDie,self).__init__(builder,configObj,eventBus)\n self.__setup_die_value()\n eventBus.subscribe('draw_die_rolling',self.__on_draw_die_rolling)\n \n def clone(self,eventBus,builder):\n clone = DrawDie.__new__(DrawDie)\n super(DrawDie,self).clone(clone,eventBus,builder)\n clone._value = self._value\n eventBus.subscribe('draw_die_rolling',clone.__on_draw_die_rolling)\n return clone\n \n def __on_draw_die_rolling(self,args):\n args.draw_die_used = self\n \n def __setup_die_value(self):\n self._value = 6\n \n def __call__(self):\n return {1 : DrawDieTypes.event,\n 2 : DrawDieTypes.event,\n 3 : DrawDieTypes.event,\n 4 : DrawDieTypes.rule,\n 5 : DrawDieTypes.rule,\n 6 : DrawDieTypes.goal}[random.randint(1, self._value)]\n \nclass GoalDice(BaseObject):\n ''' The Goal die\n '''\n \n def __init__(self,builder,configObj,eventBus):\n super(GoalDice,self).__init__(builder,configObj,eventBus)\n self.__value = self._configuration.as_int('FIELD_COUNT') - 1\n \n def clone(self,eventBus,builder):\n clone = GoalDice.__new__(GoalDice)\n super(GoalDice,self).clone(clone,eventBus,builder)\n clone.__value = self.__value\n return clone\n \n @property\n def value(self):\n return self.__value\n \n def reroll(self):\n old_value = self.__value\n self.__value = self()\n self.on_changed(self,'goal_dice_value_changed',old_value,self.__value)\n \n def __call__(self):\n return random.randint(0, self._configuration.as_int('FIELD_COUNT') - 1)\n \nclass Player(BaseObject):\n ''' One player\n '''\n \n def __init__(self,builder,configObj,eventBus,gameBoard,cardStock):\n super(Player,self).__init__(builder,configObj,eventBus)\n self.hand = []\n self.meeples = []\n self._card_stock = cardStock\n self.lose_turn = False\n self._board = gameBoard\n self.moved_meeples = []\n \n def clone(self,eventBus,builder,gameBoard,cardStock):\n clone = Player.__new__(Player)\n super(Player,self).clone(clone,eventBus,builder)\n clone._card_stock = cardStock\n clone._board = gameBoard\n clone.lose_turn = self.lose_turn\n clone.meeples = []\n for meeple in self.meeples:\n clone_meeple = meeple.clone(eventBus,builder,gameBoard)\n clone_meeple.player = clone\n clone.meeples.append(clone_meeple)\n clone.hand = []\n for card in self.hand:\n clone.hand.append(cardStock[self._card_stock.index(card)])\n clone.crossings = self.crossings\n clone._default_rules = []\n for rule in self._default_rules:\n clone._default_rules.append(rule.clone(eventBus,builder))\n clone.active_rules = []\n for rule in self.active_rules:\n if rule in self._card_stock:\n clone.active_rules.append(cardStock[self._card_stock.index(rule)])\n else:\n clone.active_rules.append(clone._default_rules[self._default_rules.index(rule)])\n clone._default_goal = self._default_goal.clone(eventBus,builder)\n if self.goal in self._card_stock:\n clone.goal = cardStock[self._card_stock.index(self.goal)]\n else:\n clone.goal = clone._default_goal\n clone.moved_meeples = []\n for meeple in self.moved_meeples:\n if meeple in self.meeples:\n clone.moved_meeples.append(clone.meeples[self.meeples.index(meeple)])\n return clone\n \n def setup_new_game(self):\n self.__init_rules()\n self.__init_meeples()\n self.__init_goal()\n self.crossings = 0 \n\n def __retrieve_infos(self,meeples):\n infos = {}\n for meeple in meeples:\n infos[meeple.field]=meeple.waited\n return infos\n \n def get_custom_rules(self):\n rules = []\n for i in range(0,len(self.active_rules)):\n if self.active_rules[i] != self._default_rules[i]:\n rules.append(i)\n return rules\n \n @property\n def has_custom_goal(self):\n return self.goal != self._default_goal\n\n def switch_meeples_with(self,target_player):\n self.on_changed(self,'switching_meeples',self,target_player)\n source_meeples = self.__retrieve_infos(self.meeples)\n target_meeples = self.__retrieve_infos(target_player.meeples)\n self.clear_meeple()\n target_player.clear_meeple()\n for meeple in target_meeples:\n new = self.add_meeple()\n new.move_to(self._board.index(meeple))\n new.waited = target_meeples[meeple]\n for meeple in source_meeples:\n new = target_player.add_meeple()\n new.move_to(self._board.index(meeple))\n new.waited = source_meeples[meeple]\n self.on_changed(self,'switched_meeples',self,target_player)\n \n def switch_rules_with(self,target_player):\n self.on_changed(self,'switching_rules',self,target_player)\n source_rules = self.__get_switch_rule_set(target_player)\n target_rules = target_player.__get_switch_rule_set(self)\n for i in range(0,len(target_rules)):\n self.active_rules[i] = target_rules[i]\n for i in range(0,len(source_rules)):\n target_player.active_rules[i] = source_rules[i]\n self.on_changed(self,'rule_changed',self,target_player)\n self.on_changed(self,'switched_rules',self,target_player)\n \n def __get_switch_rule_set(self,target_player):\n rules = []\n for i in range(0,len(self.active_rules)):\n if self.active_rules[i] != self._default_rules[i]:\n rules.append(self.active_rules[i])\n else:\n rules.append(target_player._default_rules[i])\n return rules\n\n def add_meeple(self):\n if len(self.meeples) >= 8:\n return None\n \n meeple = self._builder.build(Meeple)\n meeple.player = self\n self.meeples.append(meeple)\n return meeple\n\n def remove_meeple(self,meeple):\n self.meeples.remove(meeple)\n meeple.dispose()\n\n def clear_meeple(self):\n for meeple in self.meeples.copy():\n self.remove_meeple(meeple)\n \n def change_rule(self,index,card,fire_event=True):\n old_rule = self.active_rules[index]\n self.active_rules[index] = card\n if fire_event:\n self.on_changed(self,'rule_changed',old_rule,card)\n if old_rule not in self._default_rules:\n self._card_stock.discard(old_rule)\n \n def change_goal(self,card):\n old_goal = self.goal\n self.goal = card\n self.on_changed(self,'goal_changed',old_goal,card)\n if old_goal != self._default_goal:\n self._card_stock.discard(old_goal)\n\n def reset_rule(self, index):\n changed = False\n if self.active_rules[index] != self._default_rules[index]:\n self.change_rule(index, self._default_rules[index], False)\n changed = True\n if changed:\n self.on_changed(self,'rule_changed',None,None)\n\n def reset_rules(self):\n for i in range(0,len(self._default_rules)):\n self.reset_rule(i)\n \n def reset_goal(self):\n if self._default_goal != self.goal:\n self.change_goal(self._default_goal)\n \n def __init_rules(self):\n self._default_rules = []\n for rule in self._configuration['DEFAULT_RULE_SET']:\n rule_config = CardConfig(rule, 'RULE')\n self._builder.add_object(rule_config)\n self._default_rules.append(self._builder.build(Card))\n self.active_rules = self._default_rules.copy()\n \n def __init_goal(self):\n goal_config = CardConfig('DEFAULT_GOAL', 'GOAL')\n self._builder.add_object(goal_config)\n self._default_goal = self._builder.build(Card) \n self.goal = self._default_goal\n\n def __init_meeples(self):\n self.meeples = []\n for _ in range(0,self._configuration.as_int('START_MEEPLE_COUNT')):\n self.add_meeple()\n \nclass Meeple(BaseObject):\n '''An meeple of a player\n '''\n \n def __init__(self,builder,configObj,eventBus,gameBoard):\n super(Meeple,self).__init__(builder,configObj,eventBus)\n self._board = gameBoard\n start_field = int(self._configuration['FIELDS']['START_FIELD']['POSITIONS'][0])\n self.field = gameBoard[start_field] \n self.field.add_meeple(self)\n self.waited = 0\n self.deleted = False\n \n def clone(self,eventBus,builder,gameBoard):\n clone = Meeple.__new__(Meeple)\n super(Meeple,self).clone(clone,eventBus,builder)\n clone._board = gameBoard\n clone.waited = self.waited\n clone.deleted = self.deleted\n clone.field = gameBoard[self._board.index(self.field)]\n clone.field.add_meeple(clone)\n return clone\n \n def move_to(self,new_field):\n old_field = -1\n if self.field != None:\n old_field = self._board.index(self.field)\n self.field.remove_meeple(self)\n self.field = self._board[new_field] \n self.field.add_meeple(self)\n self.on_changed(self,'moved',old_field,new_field)\n \n def wait(self):\n field = self._board.index(self.field)\n self.on_changed(self,'waited',field,field)\n \n def dispose(self):\n if self.field != None:\n self.field.remove_meeple(self)\n self.field = None\n self.deleted = True\n self.player = None\n \nclass GameBoard(BaseObject,list):\n ''' The game board\n '''\n \n def __init__(self,builder,configObj,eventBus):\n super(GameBoard,self).__init__(builder,configObj,eventBus)\n self.__init_fields()\n self.__init_shortcuts()\n \n def clone(self,eventBus,builder):\n clone = GameBoard.__new__(GameBoard)\n super(GameBoard,self).clone(clone,eventBus,builder)\n for field in self:\n clone.append(field.clone(eventBus,builder))\n clone.shortcuts = {}\n for color in self.shortcuts:\n clone.shortcuts[color] = self.shortcuts[color].clone()\n return clone\n \n def get_shortcut_goals(self,current_field):\n shortcuts = {}\n for color in self.shortcuts:\n shortcut = self.shortcuts[color]\n if current_field in shortcut.positions:\n shortcuts[color] = list(filter(lambda p: p != current_field,shortcut.positions)) \n return shortcuts\n \n def __init_shortcuts(self):\n self.shortcuts = {}\n for configured in self._configuration['SHORTCUTS']:\n color = configured.lower()\n positions = []\n for position in self._configuration['SHORTCUTS'][configured]['POSITIONS']:\n positions.append(int(position))\n self.shortcuts[color] = self.Shortcut(positions)\n \n def __init_fields(self):\n special_fields = self.__find_special_fields()\n for counter in range(0,self._configuration.as_int('FIELD_COUNT')):\n if counter in special_fields:\n field_config = special_fields[counter], self._configuration['FIELDS'][special_fields[counter]]\n else:\n field_config = 'DEFAULT_FIELD', self._configuration['FIELDS']['DEFAULT_FIELD']\n self._builder.add_object(field_config,'FIELDCONFIG')\n self.append(self._builder.build(Field))\n \n def __find_special_fields(self):\n special_fields = {}\n for config in self._configuration['FIELDS']:\n if config == 'DEFAULT_FIELD':\n continue\n for position in self._configuration['FIELDS'][config]['POSITIONS']:\n position = int(position)\n special_fields[position] = config\n return special_fields\n \n class Shortcut(object):\n \n def __init__(self,positions):\n self.positions = positions \n \n def clone(self):\n clone_positions = self.positions.copy()\n return GameBoard.Shortcut(clone_positions) \n \nclass Field(BaseObject):\n ''' A game field\n '''\n \n def __init__(self,builder,configObj,eventBus,fieldConfig):\n super(Field,self).__init__(builder,configObj,eventBus)\n self.name = fieldConfig[0]\n self.meeples = []\n \n def clone(self,eventBus,builder):\n clone = Field.__new__(Field)\n super(Field,self).clone(clone,eventBus,builder)\n clone.meeples = []\n #meeples are filled with the meeples themself\n clone.name = self.name\n return clone\n \n def add_meeple(self,meeple):\n self.meeples.append(meeple)\n \n def remove_meeple(self,meeple):\n self.meeples.remove(meeple)\n \nclass CardStock(BaseObject, list):\n ''' The game board\n '''\n \n def __init__(self,builder,configObj,eventBus):\n super(CardStock,self).__init__(builder,configObj,eventBus)\n self.rule_draw_pile = []\n self.event_draw_pile = []\n self.goal_draw_pile = []\n self.rule_discard_pile = []\n self.event_discard_pile = []\n self.goal_discard_pile = []\n self.__build_cards()\n \n def clone(self,eventBus,builder):\n clone = CardStock.__new__(CardStock)\n super(CardStock,self).clone(clone,eventBus,builder)\n for card in self:\n clone.append(card.clone(eventBus,builder))\n clone.rule_draw_pile = []\n for card in self.rule_draw_pile:\n clone.rule_draw_pile.append(clone[self.index(card)])\n clone.event_draw_pile = []\n for card in self.event_draw_pile:\n clone.event_draw_pile.append(clone[self.index(card)])\n clone.goal_draw_pile = []\n for card in self.goal_draw_pile:\n clone.goal_draw_pile.append(clone[self.index(card)])\n clone.rule_discard_pile = []\n for card in self.rule_discard_pile:\n clone.rule_discard_pile.append(clone[self.index(card)])\n clone.event_discard_pile = []\n for card in self.event_discard_pile:\n clone.event_discard_pile.append(clone[self.index(card)])\n clone.goal_discard_pile = []\n for card in self.goal_discard_pile:\n clone.goal_discard_pile.append(clone[self.index(card)])\n return clone\n \n def setup_new_game(self):\n cards_copy = self.copy()\n self.rule_draw_pile = list(filter(lambda c:c.type == 'RULE',cards_copy))\n self.event_draw_pile = list(filter(lambda c:c.type == 'EVENT',cards_copy))\n self.goal_draw_pile = list(filter(lambda c:c.type == 'GOAL',cards_copy))\n shuffle(self.rule_draw_pile)\n shuffle(self.event_draw_pile)\n shuffle(self.goal_draw_pile)\n self.rule_discard_pile.clear()\n self.event_discard_pile.clear()\n self.goal_discard_pile.clear()\n\n def draw_card(self,draw_type):\n draw_pile, discard_pile = self.__get_piles(draw_type)\n card = draw_pile.pop(0)\n self.on_changed(self,'card_drawn',card,card)\n if len(draw_pile) == 0:\n self.__reset_draw_pile(discard_pile,draw_type)\n return card\n\n def __reset_draw_pile(self, discard_pile, draw_type):\n if draw_type == DrawDieTypes.event:\n self.event_draw_pile = discard_pile.copy()\n shuffle(self.event_draw_pile)\n elif draw_type == DrawDieTypes.rule:\n self.rule_draw_pile = discard_pile.copy()\n shuffle(self.rule_draw_pile)\n elif draw_type == DrawDieTypes.goal:\n self.goal_draw_pile = discard_pile.copy()\n shuffle(self.goal_draw_pile)\n else:\n raise NotImplementedError()\n discard_pile.clear()\n \n def __get_piles(self,draw_type):\n return {DrawDieTypes.event : (self.event_draw_pile,self.event_discard_pile),\n DrawDieTypes.rule : (self.rule_draw_pile,self.rule_discard_pile),\n DrawDieTypes.goal : (self.goal_draw_pile,self.goal_discard_pile)}[draw_type]\n \n def discard(self,card):\n if card.type == 'RULE':\n self.rule_discard_pile.append(card)\n elif card.type == 'EVENT':\n self.event_discard_pile.append(card)\n elif card.type == 'GOAL':\n self.goal_discard_pile.append(card)\n else:\n raise NotImplementedError()\n self.on_changed(self,'card_discarded',card,card)\n \n def __build_cards(self):\n for card in self._configuration['CARDS']:\n card_config = CardConfig(card, self._configuration['CARDS'][card]['TYPE'])\n self._builder.add_object(card_config)\n for _ in range(0,self._configuration['CARDS'][card].as_int('QUANTITY')):\n self.append(self._builder.build(Card))\n \nclass Card(BaseObject):\n ''' A playing card\n '''\n \n def __init__(self,builder,configObj,eventBus,cardConfig):\n super(Card,self).__init__(builder,configObj,eventBus)\n self.name = cardConfig.name\n self.type = cardConfig.type\n \n def clone(self,eventBus,builder):\n clone = Card.__new__(Card)\n super(Card,self).clone(clone,eventBus,builder)\n clone.name = self.name\n clone.type = self.type\n return clone\n \nclass DrawDieTypes(Enum):\n event = 1\n rule = 2\n goal = 3\n \nclass CardConfig(object):\n '''Configuration for a card\n '''\n \n def __init__(self,name,_type):\n self.name = name\n self.type = _type", "sub_path": "model/objects.py", "file_name": "objects.py", "file_ext": "py", "file_size_in_byte": 21872, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "model.event.EventHook", "line_number": 22, "usage_type": "call"}, {"api_name": "model.event.EventHook", "line_number": 28, "usage_type": "call"}, {"api_name": "timeit.itertools.cycle", "line_number": 63, "usage_type": "call"}, {"api_name": "timeit.itertools", "line_number": 63, "usage_type": "name"}, {"api_name": "timeit.itertools.cycle", "line_number": 92, "usage_type": "call"}, {"api_name": "timeit.itertools", "line_number": 92, "usage_type": "name"}, {"api_name": "model.control.GameControl", "line_number": 96, "usage_type": "argument"}, {"api_name": "random.randint", "line_number": 131, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 157, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 508, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 509, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 510, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 526, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 529, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 532, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 576, "usage_type": "name"}]} +{"seq_id": "383732907", "text": "from flask import Blueprint, current_app, request, jsonify\nfrom maintain_frontend.exceptions import ApplicationError\nfrom maintain_frontend.dependencies.search_api.address_service import AddressesService\nimport re\nfrom maintain_frontend.dependencies.search_api.search_type import SearchType\n\n# Blueprint Definition\naddress_finder_bp = Blueprint('address_finder', __name__,\n static_url_path='/static/address_finder',\n static_folder='static',\n template_folder='templates')\n\n\n@address_finder_bp.route('/address-finder/_search')\ndef get_addresses():\n current_app.logger.info(\"Search by address requested\")\n if not request.is_xhr:\n current_app.logger.error(\"Search request not xhr\")\n raise ApplicationError(500)\n\n postcode = request.args.get('search_term')\n addresses_service = AddressesService(current_app.config)\n\n if not postcode:\n current_app.logger.info(\"No search query provided\")\n validation_errors = {\n \"search_postcode_message\": \"Enter postcode or choose 'Enter address manually'\",\n \"search_message_inline_message\": \"Enter postcode or choose 'Enter address manually'\",\n \"status\": \"error\"\n }\n\n return jsonify(validation_errors)\n\n search_query = postcode.strip().replace(\"'\", \"\").upper()\n postcode_regex_check = '^([Gg][Ii][Rr] 0[Aa]{2})|((([A-Za-z][0-9]{1,2})|(([A-Za-z]' \\\n '[A-Ha-hJ-Yj-y][0-9]{1,2})|(([A-Za-z][0-9][A-Za-z])|' \\\n '([A-Za-z][A-Ha-hJ-Yj-y][0-9]?[A-Za-z])))) [0-9][A-Za-z]{2})$'\n\n valid_postcode = re.match(postcode_regex_check, search_query)\n\n if valid_postcode is not None:\n current_app.logger.info(\"Valid postcode provided: %s\", search_query)\n response = addresses_service.get_by(SearchType.POSTCODE.value, search_query)\n else:\n current_app.logger.info(\"Invalid postcode provided: %s\", search_query)\n validation_errors = {\n \"search_postcode_message\": \"Invalid postcode, please try again\",\n \"status\": \"error\"\n }\n return jsonify(validation_errors)\n\n if response.status_code == 200:\n current_app.logger.info(\"Search results found\")\n addresses = response.json()\n response_data = {\n \"addresses\": addresses,\n \"status\": \"success\"\n }\n return jsonify(response_data)\n elif response.status_code == 400:\n current_app.logger.info(\"Invalid postcode provided: %s\", search_query)\n validation_errors = {\n \"search_postcode_message\": \"Invalid postcode, please try again\",\n \"search_message_inline_message\": \"Invalid postcode, please try again\",\n \"status\": \"error\"\n }\n return jsonify(validation_errors)\n elif response.status_code == 404:\n current_app.logger.info(\"Valid search format but no results found\")\n validation_errors = {\n \"search_postcode_message\": \"Results not found. Try another search\",\n \"search_message_inline_message\": \"Results not found. Try another search\",\n \"status\": \"error\"\n }\n return jsonify(validation_errors)\n else:\n current_app.logger.error(\"Error returned from a get_by function\")\n raise ApplicationError(500)\n", "sub_path": "maintain_frontend/address_finder/address_finder.py", "file_name": "address_finder.py", "file_ext": "py", "file_size_in_byte": 3348, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Blueprint", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.current_app.logger.info", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.request.is_xhr", "line_number": 17, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.current_app.logger.error", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 18, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 18, "usage_type": "name"}, {"api_name": "maintain_frontend.exceptions.ApplicationError", "line_number": 19, "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": "maintain_frontend.dependencies.search_api.address_service.AddressesService", "line_number": 22, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.current_app.logger.info", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 32, "usage_type": "call"}, {"api_name": "re.match", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.current_app.logger.info", "line_number": 42, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 42, "usage_type": "name"}, {"api_name": "maintain_frontend.dependencies.search_api.search_type.SearchType.POSTCODE", "line_number": 43, "usage_type": "attribute"}, {"api_name": "maintain_frontend.dependencies.search_api.search_type.SearchType", "line_number": 43, "usage_type": "name"}, {"api_name": "flask.current_app.logger.info", "line_number": 45, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 45, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 50, "usage_type": "call"}, {"api_name": "flask.current_app.logger.info", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 53, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.current_app.logger.info", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 61, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 61, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 67, "usage_type": "call"}, {"api_name": "flask.current_app.logger.info", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 69, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 69, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 75, "usage_type": "call"}, {"api_name": "flask.current_app.logger.error", "line_number": 77, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 77, "usage_type": "name"}, {"api_name": "maintain_frontend.exceptions.ApplicationError", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "549291387", "text": "from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n path('', views.listing, name='listing'),\n path('', views.detail, name='detail'),\n path('search/', views.search, name='search'),\n path('booked/', views.booked, name='booked')\n]", "sub_path": "store/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 281, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "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"}]} +{"seq_id": "205481928", "text": "'''\r\nCreated on 2019年4月16日\r\n@author: rocky\r\n'''\r\nimport pika\r\n\r\n\r\ndef on_message(channel, method_frame, header_frame, body):\r\n\tprint(method_frame.delivery_tag)\r\n\tprint(body)\r\n\tprint()\r\n\tchannel.basic_ack(delivery_tag=method_frame.delivery_tag)\r\n\r\n\r\ndef confirm_handler(frame):\r\n\tprint(\"confirm_handler\")\r\n\tprint(frame)\r\n\r\n\r\ndef main():\r\n\r\n\thost = '192.168.177.143'\r\n\tcredentials = pika.PlainCredentials('guest', 'guest')\r\n\t\r\n\tparam = pika.ConnectionParameters(host=host, port=5672, credentials=credentials, heartbeat=300)\r\n\tconnection = pika.BlockingConnection(param)\r\n\tchannel = connection.channel()\r\n\tchannel.confirm_delivery(callback=confirm_handler)\r\n\r\n\tmsg = \"Hello\"\r\n\tchannel.basic_publish(exchange=\"TEST\", routing_key=\"test.a\", body=msg)\r\n\t\t\t\r\n\tconnection.close()\r\n\r\nif __name__ == '__main__':\r\n\tmain()", "sub_path": "rabbitmq/pika003.py", "file_name": "pika003.py", "file_ext": "py", "file_size_in_byte": 815, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pika.PlainCredentials", "line_number": 23, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 25, "usage_type": "call"}, {"api_name": "pika.BlockingConnection", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "15636177", "text": "#!/usr/bin/env python3\n\n# Plater\n# -----\n# Easily create a starter file template for different project\n# -----\n# https://github.com/aquadzn/plater\n# William Jacques\n# -----\n# Licensed under MIT License\n# -----\n# plater.py\n# -----\n\nimport argparse\nimport inspect\n\nfrom . import templates\n\n\nDICT = {\n \"dockerfile\": templates.dockerfile,\n \"dockerignore\": templates.dockerignore,\n \"readme\": templates.readme,\n \"actions_python\": templates.actions_python,\n \"mit_license\": templates.mit_license,\n \"setup\": templates.setup,\n \"conda_env\": templates.conda_env,\n \"flask\": templates.flask,\n \"pytorch_mnist\": templates.pytorch_mnist,\n \"tensorflow_mnist\": templates.tensorflow_mnist,\n \"bash\": templates.bash,\n \"html\": templates.html,\n}\n\n\ndef generate_template(args):\n \"\"\"\n Creates file of chosen template.\n \"\"\"\n if len(args.template) == 1:\n print(f\"Creating {''.join(args.template)} template ...\")\n if args.filename is not None:\n DICT.get(\n ''.join(args.template),\n lambda: 'Invalid function!'\n )(args.filename)\n else:\n DICT.get(''.join(args.template), lambda: 'Invalid function!')()\n\n else:\n print(f\"Creating {' - '.join(args.template)} template ...\")\n for i in args.template:\n if args.filename is not None:\n for j in args.filename:\n DICT.get(i, lambda: 'Invalid function!')(j)\n else:\n DICT.get(i, lambda: 'Invalid function!')()\n\n\ndef get_args():\n \"\"\"\n Returns the CLI arguments.\n \"\"\"\n functions_list = [x.__name__ for x in templates.__dict__.values()\n if inspect.isfunction(x)]\n\n parser = argparse.ArgumentParser(\n description=\"If you use both flags,\\\n number of args for each flag must be the same.\"\n )\n parser.add_argument(\n '-t',\n '--template',\n nargs='+',\n metavar='Name of template',\n help=f\"Select one or more template in {functions_list}.\",\n required=True,\n choices=functions_list\n )\n parser.add_argument(\n '-f',\n '--filename',\n nargs='+',\n metavar='Output filename',\n help=\"Select custom filename. Must be as long as template argument.\"\n )\n\n return parser.parse_args()\n", "sub_path": "plater/plater.py", "file_name": "plater.py", "file_ext": "py", "file_size_in_byte": 2349, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "inspect.isfunction", "line_number": 66, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 68, "usage_type": "call"}]} +{"seq_id": "154121949", "text": "import os\nimport datetime\nimport re\nimport uuid\nimport asyncio\nimport shutil\nfrom threading import Thread\nfrom pathlib import Path\nfrom typing import List\n\nimport docker\nfrom flask import Blueprint, request, json\nfrom flask_restful import Api, Resource\nfrom flask_socketio import emit\n\nfrom .socket import socketio\n\nbp = Blueprint('executor', __name__, url_prefix='/executor')\napi = Api(bp)\n\nclass Executor:\n # List of supported languages and versions\n SUPPORTED_LANGS = ['c++17']\n # Max time allowed for a sbumission to run a test case in seconds\n TEST_CASE_TIME_LIMIT = 5\n # Docker container memory limit in mb\n CONTAINER_MEMORY_LIMIT = 50\n # Docker container max CPU allotment (% of 1 CPU)\n CONTAINER_CPU_LIMIT = 0.05\n # Number of containers to run in parallel for one submission\n CONTAINERS_PER_SUBMISSION = 3\n\n \"\"\"Socket status message return types\"\"\"\n BUILDING_DOCKER_IMAGE = 0\n DOCKER_IMAGE_BUILT = 1\n STARTING_DOCKER_CONTAINER = 2\n DOCKER_CONTAINER_STARTED = 3\n RUNNING_TEST_CASE = 4\n FINISHED_TEST_CASE = 5\n CLEANING_UP = 6\n FINISHED = 7\n DOCKER_IMAGE_FAILED = 8\n\n \"\"\"Test case statuses\"\"\"\n TEST_CASE_PASSED = 0\n TEST_CASE_FAILED = 1\n TEST_CASE_TIMED_OUT = 2\n \n async def build_and_run_submission(self,\n source_codes: List[str], source_code_filenames: List[str], test_case_inputs: List[str], test_case_outputs: List[str],\n temp_dir: str, lang: str, entry_point: str) -> None:\n \"\"\"\n Builds the program from source and runs the code against all the test case pairs of inputs and outputs.\n\n Args:\n source_codes: List of source codes, each string being its own file.\n source_code_filenames: List of the names of the file that each source code will be put in\n test_case_inputs: List of test case inputs, each string being its own file.\n test_case_outputs: List of test case outputs, each string being its own file.\n temp_dir: Directory where a temporary folder will be created for building the program.\n lang: The programming language used. Must be one of Executor.SUPPORTED_LANGS.\n entry_point: If the programming language does not require compilation, then the entry point\n specifies the name of the file that should be run. If the programming language requires compilation,\n then the entry point specifies the name of the output executable after compilation.\n \"\"\"\n submission_time = datetime.datetime.now()\n\n # Generate a unique id for this submission\n # This will be used to name the Docker image, the Docker containers, and the temporary build directories\n unique_id = str(uuid.uuid1())\n\n # Create a temporary build directory where the Docker image will be created from\n build_dir = os.path.join(temp_dir, unique_id)\n\n submission_path = os.path.join(build_dir, 'main.c')\n test_case_path = os.path.join(build_dir, 'testcase0.in')\n try:\n # Make the build directory\n os.makedirs(build_dir)\n\n # Write each source code to its own file\n for source_code, filename in zip(source_codes, source_code_filenames):\n file_path = os.path.join(build_dir, filename)\n with open(file_path, 'w') as f:\n f.write(source_code)\n \n # Write each test case input to its own file\n for idx, input_text in enumerate(test_case_inputs):\n file_path = os.path.join(build_dir, f'test_case_{idx}.in')\n with open(file_path, 'w') as f:\n f.write(input_text)\n\n dockerfile_path = os.path.join(build_dir, 'Dockerfile')\n compilation_args = ' '.join(source_code_filenames)\n s = \"\"\"\n FROM gcc:4.9\n COPY . /usr/src/myapp\n WORKDIR /usr/src/myapp\n RUN g++ -o \"\"\" + f'{entry_point} {compilation_args}'\n\n print('Loading Docker client')\n docker_client = docker.from_env()\n docker_api_client = docker.APIClient()\n dockerfile = open(dockerfile_path, 'w')\n dockerfile.write(s)\n dockerfile.close()\n\n socketio.emit('status', json.dumps({ 'type': self.BUILDING_DOCKER_IMAGE, 'message': 'Building Docker image', 'data': {} }))\n print('Building Docker image')\n\n # Form a unique image name\n image_name = f'codematic-{unique_id}'\n\n # Build the Docker image using the low-level APIClient as\n # it can return raw build output messages\n try:\n err_msg = ''\n for line in (docker_api_client.build(\n rm=True, path=build_dir, tag=image_name,\n encoding='utf-8', decode=True)):\n\n # Build generator output is a dict\n # Look for the 'stream' key as these contain the build messages\n if 'stream' in line:\n line = line['stream']\n print(line, end='')\n # The build messages include the Docker messages which should be ignored\n if not line.startswith('Step ') and not line.startswith(' ---> ') and not line.strip() == '':\n err_msg += line\n print(err_msg)\n except docker.errors.APIError as e:\n print('Failed to build Docker image (server error).')\n raise e\n \n # If the Docker image build was successful, then the image will\n # be successfully retrieved\n try:\n docker_image = docker_client.images.get(image_name)\n except docker.errors.ImageNotFound as e:\n # If not found, then there was an error in the build\n socketio.emit('status', json.dumps({ 'type': self.DOCKER_IMAGE_FAILED, 'message': err_msg }))\n print('Failed to build Docker image.')\n raise e\n\n print('Docker image built successfully')\n socketio.emit('status', json.dumps({ 'type': self.DOCKER_IMAGE_BUILT, 'message': 'Docker image built successfully', 'data': {} }))\n\n print('Starting Docker container')\n socketio.emit('status', json.dumps({ 'type': self.STARTING_DOCKER_CONTAINER, 'message': 'Starting Docker container', 'data': {} }))\n container = docker_client.containers.run(docker_image.id,\n remove=True, # --rm, removes the container after it finishes running\n tty=True, # -t, TTY\n stdin_open=True, # -i, interactive\n name=f'codematic-{unique_id}', # --name, specifies the name of the container\n detach=True, # -d, place the container into the background after it is created\n mem_limit=f'{self.CONTAINER_MEMORY_LIMIT}M' # --mem-limit, maximum amount of memory that the container can use\n )\n\n api_client = docker.APIClient(timeout=10)\n\n print('Docker container started')\n socketio.emit('status', json.dumps({ 'type': self.DOCKER_CONTAINER_STARTED, 'message': 'Docker container started', 'data': {} }))\n\n def run_test_case(idx, entry_point, actual_outputs):\n exit_code, container_output = container.exec_run(f'sh -c \"./{entry_point} < test_case_{idx}.in\"')\n container_output = container_output.decode('utf-8')\n actual_outputs[idx] = container_output\n \n actual_outputs = [''] * len(test_case_outputs)\n\n for idx, expected_output in enumerate(test_case_outputs):\n print(f'Running test case {idx}')\n socketio.emit('status', json.dumps({ 'type': self.RUNNING_TEST_CASE, 'message': f'Running test case {idx}', 'data': { 'testCase': idx } }))\n\n # Start the test case on a new thread\n thread = Thread(target=run_test_case, args=(idx, entry_point, actual_outputs))\n thread.start()\n thread.join(timeout=5.0)\n \n # Check if the test case times out\n timed_out = False\n if thread.is_alive():\n timed_out = True\n \n # Get the output of the container and then determine the status\n container_output = actual_outputs[idx]\n if timed_out:\n status = self.TEST_CASE_TIMED_OUT\n elif container_output != expected_output:\n status = self.TEST_CASE_FAILED\n else:\n status = self.TEST_CASE_PASSED\n print(f'Finished test case {idx}, Status: {status}')\n message = f'Test case {idx} ' + ('passed' if status == self.TEST_CASE_PASSED else 'failed')\n socketio.emit('status', json.dumps({ 'type': self.FINISHED_TEST_CASE, 'message': message, 'data': { 'testCase': idx, 'status': status } }))\n\n print('Cleaning up')\n socketio.emit('status', json.dumps({ 'type': self.CLEANING_UP, 'message': 'Cleaning up', 'data': {} }))\n\n container.kill()\n docker_client.images.remove(docker_image.id, force=True)\n\n print('Finished')\n socketio.emit('status', json.dumps({ 'type': self.FINISHED, 'message': 'Finished', 'data': {} }))\n\n except Exception as e:\n try:\n shutil.rmtree(build_dir, ignore_errors=True)\n container.kill()\n docker_client.images.remove(docker_image.id, force=True)\n except:\n pass\n print(f'Failed to build and run submission.')\n print(e)\n raise e\n finally:\n try:\n shutil.rmtree(build_dir, ignore_errors=True)\n container.kill()\n docker_client.images.remove(docker_image.id, force=True)\n except:\n pass\n\n\n\n\n\n\n\n\nclass ExecutorEndpoint(Resource):\n def get(self):\n return { 'message': 'Hello' }\n def post(self):\n form_data = request.get_json()\n if 'sourceCodes' not in form_data:\n return { 'message': 'No source codes provided.' }, 400\n if 'sourceCodeFilenames' not in form_data:\n return { 'message': 'No source code filenames provided.' }, 400\n if 'testCaseInputs' not in form_data:\n return { 'message': 'No test case inputs provided.' }, 400\n if 'testCaseOutputs' not in form_data:\n return { 'message': 'No test case outputs provided.' }, 400\n\n source_codes = form_data['sourceCodes'] \n source_code_filenames = form_data['sourceCodeFilenames']\n test_case_inputs = form_data['testCaseInputs']\n test_case_outputs = form_data['testCaseOutputs']\n\n print(source_codes)\n print(source_code_filenames)\n print(test_case_inputs)\n print(test_case_outputs)\n\n if len(source_codes) != len(source_code_filenames):\n return { 'message': 'Number of source codes differs from number of source code filenames' }, 400\n if len(test_case_inputs) != len(test_case_outputs):\n return { 'message': 'Number of test case inputs differs from number of test case outputs' }, 400\n\n # Unescape escaped characters such as \\n in the source code and test case inputs and outputs\n for i in range(len(source_codes)):\n source_codes[i] = source_codes[i].encode('utf-8').decode('unicode_escape')\n for i in range(len(test_case_inputs)):\n test_case_inputs[i] = test_case_inputs[i].encode('utf-8').decode('unicode_escape')\n test_case_outputs[i] = test_case_outputs[i].encode('utf-8').decode('unicode_escape')\n \n # Ensure that all source code filenames contain only alphanumeric characters and periods\n for filename in source_code_filenames:\n for c in filename:\n if not re.match('[a-zA-Z0.9\\.]', c):\n return { 'message': f'Invalid filename: {filename}'}\n \n # Set the temporary directory where all the code and build files will go\n temp_dir = os.path.join(Path.home(), 'codematic', 'temp')\n\n try:\n executor = Executor()\n run_result = asyncio.run(executor.build_and_run_submission(\n source_codes, source_code_filenames, test_case_inputs, test_case_outputs, temp_dir, 'c++17', 'main'))\n print(run_result)\n except:\n print('Submission did not successfully complete.')\n return { 'message': 'Submission failed.' }, 400\n return { 'message': 'Success!' }\n\n@socketio.on('message')\ndef handle_message(data):\n print('Received message:', data)\n\n@socketio.on('connect')\ndef handle_connection(data):\n print('A user connected.')\n\n@socketio.on('disconnect')\ndef test_disconnect():\n print('Client disconnected')\n\napi.add_resource(ExecutorEndpoint, '/run')", "sub_path": "codematic/executor.py", "file_name": "executor.py", "file_ext": "py", "file_size_in_byte": 13104, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Blueprint", "line_number": 18, "usage_type": "call"}, {"api_name": "flask_restful.Api", "line_number": 19, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 50, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 66, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 66, "usage_type": "attribute"}, {"api_name": "uuid.uuid1", "line_number": 70, "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": "os.path.join", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.makedirs", "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": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "docker.from_env", "line_number": 102, "usage_type": "call"}, {"api_name": "docker.APIClient", "line_number": 103, "usage_type": "call"}, {"api_name": "socket.socketio.emit", "line_number": 108, "usage_type": "call"}, {"api_name": "socket.socketio", "line_number": 108, "usage_type": "name"}, {"api_name": "flask.json.dumps", "line_number": 108, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 108, "usage_type": "name"}, {"api_name": "docker.errors", "line_number": 131, "usage_type": "attribute"}, {"api_name": "docker.errors", "line_number": 139, "usage_type": "attribute"}, {"api_name": "socket.socketio.emit", "line_number": 141, "usage_type": "call"}, {"api_name": "socket.socketio", "line_number": 141, "usage_type": "name"}, {"api_name": "flask.json.dumps", "line_number": 141, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 141, "usage_type": "name"}, {"api_name": "socket.socketio.emit", "line_number": 146, "usage_type": "call"}, {"api_name": "socket.socketio", "line_number": 146, "usage_type": "name"}, {"api_name": "flask.json.dumps", "line_number": 146, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 146, "usage_type": "name"}, {"api_name": "socket.socketio.emit", "line_number": 149, "usage_type": "call"}, {"api_name": "socket.socketio", "line_number": 149, "usage_type": "name"}, {"api_name": "flask.json.dumps", "line_number": 149, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 149, "usage_type": "name"}, {"api_name": "docker.APIClient", "line_number": 159, "usage_type": "call"}, {"api_name": "socket.socketio.emit", "line_number": 162, "usage_type": "call"}, {"api_name": "socket.socketio", "line_number": 162, "usage_type": "name"}, {"api_name": "flask.json.dumps", "line_number": 162, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 162, "usage_type": "name"}, {"api_name": "socket.socketio.emit", "line_number": 173, "usage_type": "call"}, {"api_name": "socket.socketio", "line_number": 173, "usage_type": "name"}, {"api_name": "flask.json.dumps", "line_number": 173, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 173, "usage_type": "name"}, {"api_name": "threading.Thread", "line_number": 176, "usage_type": "call"}, {"api_name": "socket.socketio.emit", "line_number": 195, "usage_type": "call"}, {"api_name": "socket.socketio", "line_number": 195, "usage_type": "name"}, {"api_name": "flask.json.dumps", "line_number": 195, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 195, "usage_type": "name"}, {"api_name": "socket.socketio.emit", "line_number": 198, "usage_type": "call"}, {"api_name": "socket.socketio", "line_number": 198, "usage_type": "name"}, {"api_name": "flask.json.dumps", "line_number": 198, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 198, "usage_type": "name"}, {"api_name": "socket.socketio.emit", "line_number": 204, "usage_type": "call"}, {"api_name": "socket.socketio", "line_number": 204, "usage_type": "name"}, {"api_name": "flask.json.dumps", "line_number": 204, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 204, "usage_type": "name"}, {"api_name": "shutil.rmtree", "line_number": 208, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 218, "usage_type": "call"}, {"api_name": "flask_restful.Resource", "line_number": 231, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 235, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 235, "usage_type": "name"}, {"api_name": "re.match", "line_number": 270, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 274, "usage_type": "call"}, {"api_name": "os.path", "line_number": 274, "usage_type": "attribute"}, {"api_name": "pathlib.Path.home", "line_number": 274, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 274, "usage_type": "name"}, {"api_name": "asyncio.run", "line_number": 278, "usage_type": "call"}, {"api_name": "socket.socketio.on", "line_number": 286, "usage_type": "call"}, {"api_name": "socket.socketio", "line_number": 286, "usage_type": "name"}, {"api_name": "socket.socketio.on", "line_number": 290, "usage_type": "call"}, {"api_name": "socket.socketio", "line_number": 290, "usage_type": "name"}, {"api_name": "socket.socketio.on", "line_number": 294, "usage_type": "call"}, {"api_name": "socket.socketio", "line_number": 294, "usage_type": "name"}]} +{"seq_id": "243200144", "text": "import logging\nfrom itertools import product\n\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\n\nfrom greenguard.demo import load_demo\nfrom greenguard.metrics import METRICS\nfrom greenguard.pipeline import GreenGuardPipeline, generate_init_params, generate_preprocessing\n\nLOGGER = logging.getLogger(__name__)\n\n\ndef _build_init_params(template, window_size, rule, template_params):\n if 'dfs' in template:\n window_size_rule_params = {\n 'pandas.DataFrame.resample#1': {\n 'rule': rule,\n },\n 'featuretools.dfs.json#1': {\n 'training_window': window_size,\n }\n }\n elif 'lstm' in template:\n window_size_rule_params = {\n 'pandas.DataFrame.resample#1': {\n 'rule': rule,\n },\n 'mlprimitives.custom.timeseries_preprocessing.cutoff_window_sequences#1': {\n 'window_size': window_size,\n }\n }\n\n for primitive, params in window_size_rule_params.items():\n primitive_params = template_params.get(primitive, {})\n primitive_params.update(params)\n\n return template_params\n\n\ndef evaluate_template(template, target_times, readings, metric='f1', tuning_iterations=50,\n preprocessing=0, init_params=None, cost=False, test_size=0.25,\n cv_splits=3, random_state=0, cache_path=None):\n \"\"\"Returns the scores for a given template.\n\n Args:\n template (str):\n Given template to evaluate.\n target_times (DataFrame):\n Contains the specefication problem that we are solving, which has three columns:\n\n * turbine_id: Unique identifier of the turbine which this label corresponds to.\n * cutoff_time: Time associated with this target.\n * target: The value that we want to predict. This can either be a numerical value\n or a categorical label. This column can also be skipped when preparing\n data that will be used only to make predictions and not to fit any\n pipeline.\n\n readings (DataFrame):\n Contains the signal data from different sensors, with the following columns:\n\n * turbine_id: Unique identifier of the turbine which this reading comes from.\n * signal_id: Unique identifier of the signal which this reading comes from.\n * timestamp (datetime): Time where the reading took place, as a datetime.\n * value (float): Numeric value of this reading.\n\n metric (function or str):\n Metric to use. If an ``str`` is give it must be one of the metrics\n defined in the ``greenguard.metrics.METRICS`` dictionary.\n tuning_iterations (int):\n Number of iterations to be used.\n preprocessing (int, list or dict):\n Number of preprocessing steps to be used.\n init_params (list):\n Initialization parameters for the pipeline.\n cost (bool):\n Wheter the metric is a cost function (the lower the better) or not.\n test_size (float):\n Percentage of the data set to be used for the test.\n cv_splits (int):\n Amount of splits to create.\n random_state (int):\n Random number of train_test split.\n cache_path (str):\n If given, cache the generated cross validation splits in this folder.\n Defatuls to ``None``.\n\n Returns:\n scores (dict):\n Stores the four types of scores that are being evaluate.\n \"\"\"\n scores = dict()\n\n train, test = train_test_split(target_times, test_size=test_size, random_state=random_state)\n\n if isinstance(metric, str):\n metric, cost = METRICS[metric]\n\n pipeline = GreenGuardPipeline(\n template,\n metric,\n cost=cost,\n cv_splits=cv_splits,\n init_params=init_params,\n preprocessing=preprocessing,\n cache_path=cache_path\n )\n\n # Computing the default test score\n pipeline.fit(train, readings)\n predictions = pipeline.predict(test, readings)\n\n scores['default_test'] = metric(test['target'], predictions)\n\n # Computing the default cross validation score\n session = pipeline.tune(train, readings)\n session.run(1)\n\n scores['default_cv'] = pipeline.cv_score\n\n # Computing the cross validation score with tuned hyperparameters\n session.run(tuning_iterations)\n\n scores['tuned_cv'] = pipeline.cv_score\n\n # Computing the test score with tuned hyperparameters\n pipeline.fit(train, readings)\n predictions = pipeline.predict(test, readings)\n\n scores['tuned_test'] = metric(test['target'], predictions)\n\n return scores\n\n\ndef evaluate_templates(templates, window_size_rule, metric='f1',\n tuning_iterations=50, init_params=None, target_times=None,\n readings=None, preprocessing=0, cost=False, test_size=0.25,\n cv_splits=3, random_state=0, cache_path=None, output_path=None):\n \"\"\"Execute the benchmark process and optionally store the result as a ``CSV``.\n\n Args:\n templates (list):\n List of templates to try.\n window_size_rule (list):\n List of tupples (int, str or Timedelta object).\n metric (function or str):\n Metric to use. If an ``str`` is give it must be one of the metrics\n defined in the ``greenguard.metrics.METRICS`` dictionary.\n tuning_iterations (int):\n Number of iterations to be used.\n init_params (dict):\n Initialization parameters for the pipelines.\n target_times (DataFrame):\n Contains the specefication problem that we are solving, which has three columns:\n\n * turbine_id: Unique identifier of the turbine which this label corresponds to.\n * cutoff_time: Time associated with this target.\n * target: The value that we want to predict. This can either be a numerical value\n or a categorical label. This column can also be skipped when preparing\n data that will be used only to make predictions and not to fit any\n pipeline.\n\n readings (DataFrame):\n Contains the signal data from different sensors, with the following columns:\n\n * turbine_id: Unique identifier of the turbine which this reading comes from.\n * signal_id: Unique identifier of the signal which this reading comes from.\n * timestamp (datetime): Time where the reading took place, as a datetime.\n * value (float): Numeric value of this reading.\n\n preprocessing (int, list or dict):\n Number of preprocessing steps to be used.\n cost (bool):\n Wheter the metric is a cost function (the lower the better) or not.\n test_size (float):\n Percentage of the data set to be used for the test.\n cv_splits (int):\n Amount of splits to create.\n random_state (int):\n Random number of train_test split.\n output_path (str):\n Path where to save the benchmark report.\n cache_path (str):\n If given, cache the generated cross validation splits in this folder.\n Defatuls to ``None``.\n\n Returns:\n pandas.DataFrame or None:\n If ``output_path`` is ``None`` it will return a ``pandas.DataFrame`` object,\n else it will dump the results in the specified ``output_path``.\n\n Example:\n >>> from sklearn.metrics import f1_score\n >>> templates = [\n ... 'normalize_dfs_xgb_classifier',\n ... 'unstack_lstm_timeseries_classifier'\n ... ]\n >>> window_size_rule = [\n ... ('30d','12h'),\n ... ('7d','4h')\n ... ]\n >>> preprocessing = [0, 1]\n >>> scores_df = evaluate_templates(\n ... templates=templates,\n ... window_size_rule=window_size_rule,\n ... metric=f1_score,\n ... tuning_iterations=5,\n ... preprocessing=preprocessing,\n ... cost=False,\n ... test_size=0.25,\n ... cv_splits=3,\n ... random_state=0\n ... )\n >>> scores_df\n template window_size resample_rule default_test default_cv tuned_cv tuned_test status\n 0 unstack_lstm_timeseries_classifier 30d 12h 0.720000 0.593634 0.627883 0.775510 OK\n 1 unstack_lstm_timeseries_classifier 7d 4h 0.723404 0.597440 0.610766 0.745098 OK\n 2 normalize_dfs_xgb_classifier 30d 12h 0.581818 0.619698 0.637123 0.596491 OK\n 3 normalize_dfs_xgb_classifier 7d 4h 0.581818 0.619698 0.650367 0.603774 OK\n\n \"\"\" # noqa\n\n if readings is None and target_times is None:\n target_times, readings = load_demo()\n\n init_params = generate_init_params(templates, init_params)\n preprocessing = generate_preprocessing(templates, preprocessing)\n\n scores_list = []\n for template, window_rule in product(templates, window_size_rule):\n window_size, rule = window_rule\n\n scores = dict()\n scores['template'] = template\n scores['window_size'] = window_size\n scores['resample_rule'] = rule\n\n try:\n template_params = init_params[template]\n template_params = _build_init_params(template, window_size, rule, template_params)\n template_preprocessing = preprocessing[template]\n\n result = evaluate_template(\n template=template,\n target_times=target_times,\n readings=readings,\n metric=metric,\n tuning_iterations=tuning_iterations,\n preprocessing=template_preprocessing,\n init_params=template_params,\n cost=cost,\n test_size=test_size,\n cv_splits=cv_splits,\n random_state=random_state,\n cache_path=cache_path\n )\n\n scores.update(result)\n scores['status'] = 'OK'\n\n except Exception:\n scores['status'] = 'ERRORED'\n LOGGER.exception('Could not score template %s ', template)\n\n scores_list.append(scores)\n\n results = pd.DataFrame.from_records(scores_list)\n results = results.reindex(['template', 'window_size', 'resample_rule', 'default_test',\n 'default_cv', 'tuned_cv', 'tuned_test', 'status'], axis=1)\n\n if output_path:\n LOGGER.info('Saving benchmark report to %s', output_path)\n results.to_csv(output_path)\n else:\n return results\n", "sub_path": "greenguard/benchmark.py", "file_name": "benchmark.py", "file_ext": "py", "file_size_in_byte": 11046, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 94, "usage_type": "call"}, {"api_name": "greenguard.metrics.METRICS", "line_number": 97, "usage_type": "name"}, {"api_name": "greenguard.pipeline.GreenGuardPipeline", "line_number": 99, "usage_type": "call"}, {"api_name": "greenguard.demo.load_demo", "line_number": 224, "usage_type": "call"}, {"api_name": "greenguard.pipeline.generate_init_params", "line_number": 226, "usage_type": "call"}, {"api_name": "greenguard.pipeline.generate_preprocessing", "line_number": 227, "usage_type": "call"}, {"api_name": "itertools.product", "line_number": 230, "usage_type": "call"}, {"api_name": "pandas.DataFrame.from_records", "line_number": 267, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 267, "usage_type": "attribute"}]} +{"seq_id": "122860589", "text": "#coding:utf-8\nimport time,os\nimport datetime \nimport unittest\nfrom appium.webdriver.common.touch_action import TouchAction\n#from robot.utils.asserts import *\nfrom appium import webdriver\nfrom public import login\nfrom public import logout\nfrom public.extend import Appium_Extend \nfrom public.clear_massage import clear_massage\nfrom public.clear_massage import clear_allmassage\nfrom public.set_driver import set_driver\nfrom public import set\nfrom public import get\n \nclass Imtest(unittest.TestCase):\n #def __init__(self,driver):\n #self.driver = driver \n def setUp(self):\n wq=set_driver()\n self.driver=wq.get_driver('3a11d697','4725')#联想\n self.verificationErrors = []\n self.driver.implicitly_wait(10) \n \n def test_send_txt(self):\n '''退出讨论组，@成员，发消息'''\n try:\n self.driver.find_element_by_id(\"com.yuntongxun.rongxin.lite:id/ytx_negative_btn\").click()#点击取消下载\n except:\n print (u\"未弹出更新页\")\n self.driver.find_element_by_id(\"com.yuntongxun.rongxin.lite:id/icon\").click()#点击加号\n self.driver.find_element_by_name(u\"发起群聊\").click()#发起群��\n self.driver.find_element_by_name(u\"魏阳阳\").click()#点击魏阳阳\n self.driver.find_element_by_id(\"com.yuntongxun.rongxin.lite:id/action_option_style_button\").click()#点击确认\n time.sleep(1)\n #验证创建群组的名称及人数显示\n el = self.driver.find_element_by_id(\"com.yuntongxun.rongxin.lite:id/ytx_action_title\").get_attribute(\"text\")\n self.assertEqual(el,u'温强、魏阳阳')\n el1 = self.driver.find_element_by_id(\"com.yuntongxun.rongxin.lite:id/ytx_action_title_count\").get_attribute(\"text\") \n self.assertEqual(el1,'(2)')\n #修改群名称、群公告、昵称 \n self.driver.tap([(940,80),(1060,200)], 100)#点击群组设置图标\n self.driver.find_element_by_name(u\"群组名称\").click()#点击确认\n self.driver.find_element_by_id(\"com.yuntongxun.rongxin.lite:id/content\").click()#点击\n self.driver.find_element_by_id(\"com.yuntongxun.rongxin.lite:id/content\").clear()\n self.driver.find_element_by_id(\"com.yuntongxun.rongxin.lite:id/content\").send_keys(u'自动化测试创建群组')\n self.driver.find_element_by_id(\"com.yuntongxun.rongxin.lite:id/action_option_style_button\").click()#点击\n \n \n \n def tearDown(self):\n self.driver.quit()\n self.assertEqual([], self.verificationErrors)\n \n \nif __name__ == \"__main__\":\n # 构造测试集\n suite = unittest.TestSuite()\n suite.addTest(Imtest(\"test_send_txt\"))\n # 执行测试\n runner = unittest.TextTestRunner()\n runner.run(suite) ", "sub_path": "rongxin/test_c_group_1creatB.py", "file_name": "test_c_group_1creatB.py", "file_ext": "py", "file_size_in_byte": 2823, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "unittest.TestCase", "line_number": 17, "usage_type": "attribute"}, {"api_name": "public.set_driver.set_driver", "line_number": 21, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 36, "usage_type": "call"}, {"api_name": "unittest.TestSuite", "line_number": 59, "usage_type": "call"}, {"api_name": "unittest.TextTestRunner", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "319951047", "text": "import requests\nimport time\nimport json\nimport urllib3\nimport re\nfrom lxml import html\n\nurllib3.disable_warnings()\n\nrequest_session = requests.session()\n\nurl = \"https://api.bevol.cn/search/goods/index3\"\n\ntimestamp = int(round(time.time() * 1000))\n\nresult = request_session.get(\n url,\n params={\"p\": \"1\", \"category\": \"6\", \"_\": timestamp},\n verify=False\n)\njsonp_str = json.loads(result.text)\nfor i in range (0,20):\n mid = jsonp_str[\"data\"][\"items\"][i][\"mid\"]\n name = jsonp_str[\"data\"][\"items\"][i][\"title\"]\n # print (\"mid:%s;name:%s\" %(mid,name))\n detail_url = 'https://www.bevol.cn/product/' + mid + '.html'\n result = request_session.get(\n detail_url,\n verify=False\n )\n content = str(result.content, 'utf-8')\n # print(content)\n tree = html.fromstring(result.content)\n for i in range(0, 2):\n star_elems0 = tree.findall('.//img[@src=\"https://img' + str(i) + '.bevol.cn/xiaostar.png\"]')\n star_nums0 = [stars.text for stars in star_elems0]\n star_level0 = len(star_nums0)\n\n star_elems1 = tree.findall('.//img[@src=\"https://img' + str(i) + '.bevol.cn/xiaobanstar.png\"]')\n star_nums1 = [stars.text for stars in star_elems1]\n star_level1 = len(star_nums1)/2\n\n star_level = star_level0 + star_level1\n if star_level > 4:\n print(\"产品名称:%s ---星级为: %s\" % (name, star_level))\n", "sub_path": "Python/mlxxly.py", "file_name": "mlxxly.py", "file_ext": "py", "file_size_in_byte": 1392, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "urllib3.disable_warnings", "line_number": 8, "usage_type": "call"}, {"api_name": "requests.session", "line_number": 10, "usage_type": "call"}, {"api_name": "time.time", "line_number": 14, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 21, "usage_type": "call"}, {"api_name": "lxml.html.fromstring", "line_number": 33, "usage_type": "call"}, {"api_name": "lxml.html", "line_number": 33, "usage_type": "name"}]} +{"seq_id": "592875380", "text": "from django.conf.urls import url\nfrom django.contrib.auth import views\nfrom web.views import *\n# We are adding a URL called /home\nurlpatterns = [\n url(r'^portfolio/(?P\\d+)/$', portfolio),\n url(r'^tasks', MyTasksListView.as_view()),\n url(r'^applications/(?P\\d+)/$', applicaions),\n url(r'^$', AllProjectsListView.as_view()),\n url(r'^myprojects', MyProjectsListView.as_view()),\n url(r'^project/(?P\\d+)/$', ProjectDetailView.as_view()),\n url(r'^signup$', signup),\n url(r'^(accounts/)?logout/$', views.logout, {'next_page': '/login'}),\n url(r'^(accounts/)?login/$', views.login, {'template_name': 'registration/login.html'}, name='login'),\n\n]", "sub_path": "web/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 676, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"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"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.logout", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.views", "line_number": 13, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.contrib.auth.views.login", "line_number": 14, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.views", "line_number": 14, "usage_type": "name"}]} +{"seq_id": "394915279", "text": "from repo.transaction import Transaction\nfrom model.account import Account\nfrom model.source import Source, CanineInfo, EnvironmentInfo\nfrom repo.kit_repo import KitRepo\nfrom repo.account_repo import AccountRepo\nfrom repo.source_repo import SourceRepo\nfrom repo.survey_template_repo import SurveyTemplateRepo\nfrom repo.survey_answers_repo import SurveyAnswersRepo\nimport datetime\nimport json\nimport util.vue_adapter\n\n# TODO: Refactor me into proper unit tests!\n\n\ndef json_converter(o):\n if isinstance(o, datetime.datetime):\n return str(o)\n return o.__dict__\n\n\nACCT_ID = \"aaaaaaaa-bbbb-cccc-dddd-eeeeffffffff\"\nDOGGY_ID = \"dddddddd-dddd-dddd-dddd-dddddddddddd\"\nPLANTY_ID = \"eeeeeeee-eeee-eeee-eeee-eeeeeeeeeeee\"\n\nwith Transaction() as t:\n kit_repo = KitRepo(t)\n kit = kit_repo.get_kit(\"eba20873-b88d-33cc-e040-8a80115d392c\", \"#6á$E\")\n print(\"Kit: \")\n print(json.dumps(kit, default=json_converter, indent=2))\n\n acct_repo = AccountRepo(t)\n acct_repo.delete_account(ACCT_ID)\n acc = Account(ACCT_ID,\n \"foo@bar.com\",\n \"globus\",\n \"Dan\",\n \"H\",\n '{\"a\":5, \"b\":7}',\n \"USER\")\n print(acct_repo.create_account(acc))\n t.commit()\n\nwith Transaction() as t:\n acct_repo = AccountRepo(t)\n acc = acct_repo.get_account(ACCT_ID)\n print(\"Account: \")\n print(json.dumps(acc, default=json_converter, indent=2))\n\nwith Transaction() as t:\n acct_repo = AccountRepo(t)\n acc = acct_repo.get_account(ACCT_ID)\n acc.last_name = \"The Greatest\"\n acct_repo.update_account(acc)\n acc = acct_repo.get_account(ACCT_ID)\n print(\"Account: \")\n print(json.dumps(acc, default=json_converter, indent=2))\n t.commit()\n\nwith Transaction() as t:\n source_repo = SourceRepo(t)\n source_repo.delete_source(ACCT_ID, DOGGY_ID)\n source_repo.delete_source(ACCT_ID, PLANTY_ID)\n source_repo.create_source(Source.create_canine(\n DOGGY_ID,\n ACCT_ID,\n CanineInfo(\"Doggy\")))\n source_repo.create_source(Source.create_environment(\n PLANTY_ID,\n ACCT_ID,\n EnvironmentInfo(\"Planty\", \"The green one\")))\n\n doggy = source_repo.get_source(ACCT_ID, DOGGY_ID)\n planty = source_repo.get_source(ACCT_ID, PLANTY_ID)\n all_sources = source_repo.get_sources_in_account(ACCT_ID)\n just_plants = source_repo.get_sources_in_account(ACCT_ID, \"environment\")\n\n print(\"Doggy:\")\n print(json.dumps(doggy, default=json_converter, indent=2))\n\n print(\"Planty:\")\n print(json.dumps(planty, default=json_converter, indent=2))\n\n print(\"All:\")\n print(json.dumps(all_sources, default=json_converter, indent=2))\n\n print(\"Just Plants:\")\n print(json.dumps(just_plants, default=json_converter, indent=2))\n t.commit()\n\nwith Transaction() as t:\n survey_template_repo = SurveyTemplateRepo(t)\n ids = survey_template_repo.list_survey_ids()\n print(ids)\n\n the_stuff = survey_template_repo.get_survey_template(ids[0])\n # print(json.dumps(the_stuff.groups[0].questions[10],\n # default=json_converter,\n # indent=2))\n\n in_vue = util.vue_adapter.to_vue_schema(the_stuff)\n # print(json.dumps(in_vue, default=json_converter, indent=2))\n\n with open(\"surveySchema.json\", \"w\") as outFile:\n outFile.write(json.dumps(in_vue, default=json_converter, indent=2))\n\nwith Transaction() as t:\n survey_answers_repo = SurveyAnswersRepo(t)\n survey_ids = survey_answers_repo.list_answered_surveys(\n 'd8592c74-7fc4-2135-e040-8a80115d6401',\n 'Name - 7O],Gß[1Y1')\n\n print(survey_ids)\n\n survey_model = survey_answers_repo.get_answered_survey(\n 'd8592c74-7fc4-2135-e040-8a80115d6401',\n survey_ids[0])\n\n print(survey_model)\n\n answer_id = survey_answers_repo.submit_answered_survey(\n 'd8592c74-7fc4-2135-e040-8a80115d6401',\n \"DOGGY!\",\n \"en_us\",\n 1,\n survey_model\n )\n\n survey_model2 = survey_answers_repo.get_answered_survey(\n 'd8592c74-7fc4-2135-e040-8a80115d6401',\n answer_id)\n\n print(survey_model2)\n print(survey_model == survey_model2)\n\n survey_answers_repo.delete_answered_survey(ACCT_ID, answer_id)\n\n", "sub_path": "repo_test_scratch.py", "file_name": "repo_test_scratch.py", "file_ext": "py", "file_size_in_byte": 4217, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datetime.datetime", "line_number": 17, "usage_type": "attribute"}, {"api_name": "repo.transaction.Transaction", "line_number": 26, "usage_type": "call"}, {"api_name": "repo.kit_repo.KitRepo", "line_number": 27, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 30, "usage_type": "call"}, {"api_name": "repo.account_repo.AccountRepo", "line_number": 32, "usage_type": "call"}, {"api_name": "model.account.Account", "line_number": 34, "usage_type": "call"}, {"api_name": "repo.transaction.Transaction", "line_number": 44, "usage_type": "call"}, {"api_name": "repo.account_repo.AccountRepo", "line_number": 45, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 48, "usage_type": "call"}, {"api_name": "repo.transaction.Transaction", "line_number": 50, "usage_type": "call"}, {"api_name": "repo.account_repo.AccountRepo", "line_number": 51, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 57, "usage_type": "call"}, {"api_name": "repo.transaction.Transaction", "line_number": 60, "usage_type": "call"}, {"api_name": "repo.source_repo.SourceRepo", "line_number": 61, "usage_type": "call"}, {"api_name": "model.source.Source.create_canine", "line_number": 64, "usage_type": "call"}, {"api_name": "model.source.Source", "line_number": 64, "usage_type": "name"}, {"api_name": "model.source.CanineInfo", "line_number": 67, "usage_type": "call"}, {"api_name": "model.source.Source.create_environment", "line_number": 68, "usage_type": "call"}, {"api_name": "model.source.Source", "line_number": 68, "usage_type": "name"}, {"api_name": "model.source.EnvironmentInfo", "line_number": 71, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 79, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 82, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 85, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 88, "usage_type": "call"}, {"api_name": "repo.transaction.Transaction", "line_number": 91, "usage_type": "call"}, {"api_name": "repo.survey_template_repo.SurveyTemplateRepo", "line_number": 92, "usage_type": "call"}, {"api_name": "util.vue_adapter.vue_adapter.to_vue_schema", "line_number": 101, "usage_type": "call"}, {"api_name": "util.vue_adapter.vue_adapter", "line_number": 101, "usage_type": "attribute"}, {"api_name": "util.vue_adapter", "line_number": 101, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 105, "usage_type": "call"}, {"api_name": "repo.transaction.Transaction", "line_number": 107, "usage_type": "call"}, {"api_name": "repo.survey_answers_repo.SurveyAnswersRepo", "line_number": 108, "usage_type": "call"}]} +{"seq_id": "321872307", "text": "import arcpy\nimport os\nimport uuid\n\n# Input WebMap json\nWeb_Map_as_JSON = arcpy.GetParameterAsText(0)\n\n# The template location in the server data store\ntemplateMxd = '//MyComputer/MyDataStore/BasicTutorial/LG_062912_10.1/LG_062912_10.1/LGIM_World_Topo_Map_v1.5.mxd'\n \n# Convert the WebMap to a map document\nresult = arcpy.mapping.ConvertWebMapToMapDocument(Web_Map_as_JSON, templateMxd)\nmxd = result.mapDocument\n\n# Reference the data frame that contains the webmap\n# Note: ConvertWebMapToMapDocument renames the active dataframe in the template_mxd to \"Webmap\"\ndf = arcpy.mapping.ListDataFrames(mxd, 'Webmap')[0]\n\n# Remove the service layer\n# This will just leave the vector layers from the template\nfor lyr in arcpy.mapping.ListLayers(mxd, data_frame=df):\n if lyr.isServiceLayer:\n arcpy.mapping.RemoveLayer(df, lyr)\n \n# Use the uuid module to generate a GUID as part of the output name\n# This will ensure a unique output name\noutput = 'WebMap_{}.pdf'.format(str(uuid.uuid1()))\nOutput_File = os.path.join(arcpy.env.scratchFolder, output)\n\n# Export the WebMap\narcpy.mapping.ExportToPDF(mxd, Output_File) \n\n# Set the output parameter to be the output file of the server job\narcpy.SetParameterAsText(1, Output_File)\n\n# Clean up - delete the map document reference\nfilePath = mxd.filePath\ndel mxd, result\nos.remove(filePath)", "sub_path": "CountyMaps/BasicTutorial.py", "file_name": "BasicTutorial.py", "file_ext": "py", "file_size_in_byte": 1339, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "arcpy.GetParameterAsText", "line_number": 6, "usage_type": "call"}, {"api_name": "arcpy.mapping.ConvertWebMapToMapDocument", "line_number": 12, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 12, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.ListDataFrames", "line_number": 17, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 17, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.ListLayers", "line_number": 21, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 21, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.RemoveLayer", "line_number": 23, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 23, "usage_type": "attribute"}, {"api_name": "uuid.uuid1", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "arcpy.env", "line_number": 28, "usage_type": "attribute"}, {"api_name": "arcpy.mapping.ExportToPDF", "line_number": 31, "usage_type": "call"}, {"api_name": "arcpy.mapping", "line_number": 31, "usage_type": "attribute"}, {"api_name": "arcpy.SetParameterAsText", "line_number": 34, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "205903321", "text": "import csv\nimport os\nfrom datetime import datetime\n\nimport serial\n\nfrom .keypress import KBHit\n\n\ndef logear():\n carpeta = \"data\"\n fecha_inicio = datetime.now().strftime(\"%Y-%m-%d %H-%M-%S\")\n print(\"Logeando informacion, para detener el proceso pulse 'q'...\")\n # Se establece la conexion serie\n if os.name == \"nt\":\n ser = serial.Serial(\"COM3\")\n else:\n ser = serial.Serial(\"/dev/ttyUSB0\")\n # Vacia el buffer por las dudas\n ser.reset_input_buffer()\n kb = KBHit()\n with open(f\"{carpeta}/log.csv\", \"w+\", newline=\"\") as f:\n escritor = csv.writer(f)\n while True:\n # Si ingresaron \"q\" entonces terminar de logear\n if kb.kbhit() and kb.getch() == \"q\":\n kb.set_normal_term()\n break\n try:\n # Leer una linea del output del arduino\n ser_bytes = ser.readline().decode()\n _, humedad, _, luz, estado = ser_bytes.rstrip().split()\n estado = int(estado)\n # Para logear la hora, minutos y segundos junto con cada\n # medicion\n now = datetime.now()\n current_time = now.strftime(\"%Y-%m-%d;%H:%M:%S\")\n # El estado es 0 si esta abierto, y 1 si esta cerrado\n abierto = \"Abierto\" if estado else \"Cerrado\"\n escritor.writerow([current_time, humedad, luz, abierto])\n except BaseException:\n print(\"Keyboard Interrupt\")\n break\n fecha_final = datetime.now().strftime(\"%Y-%m-%d %H-%M-%S\")\n\n old_file = os.path.join(carpeta, \"log.csv\")\n new_file = os.path.join(carpeta, f\"{fecha_inicio}__{fecha_final}.csv\")\n os.rename(old_file, new_file)\n", "sub_path": "funcionalidades/logear_info.py", "file_name": "logear_info.py", "file_ext": "py", "file_size_in_byte": 1743, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datetime.datetime.now", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 12, "usage_type": "name"}, {"api_name": "os.name", "line_number": 15, "usage_type": "attribute"}, {"api_name": "serial.Serial", "line_number": 16, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 18, "usage_type": "call"}, {"api_name": "keypress.KBHit", "line_number": 21, "usage_type": "call"}, {"api_name": "csv.writer", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 36, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 44, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 44, "usage_type": "name"}, {"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": "os.rename", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "66014636", "text": "from PyQt5.QtWidgets import QApplication, QTabWidget\nfrom testcase_table import TestCase\nfrom test_log_browser import ProgressBrowser\nimport sys\n\n__Author__ = \"Richard_Wen@wistron.com\"\n__Copyright__ = \"Copyright (c) 2019\"\n__Version__ = \"Version 1.0\"\n\n\nclass TestProgressTab(QTabWidget):\n\n def __init__(self):\n super(TestProgressTab, self).__init__()\n\n self.testCaseTab = TestCase()\n self.progressTab = ProgressBrowser()\n\n self.addTab(self.testCaseTab, \"Test Case\")\n self.addTab(self.progressTab, \"Progress Log\")\n\n self.setWindowTitle(\"Test Progress\")\n\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n demo = TestProgressTab()\n demo.show()\n sys.exit(app.exec_())\n\n\n\n\n ", "sub_path": "view/test_progress_tab.py", "file_name": "test_progress_tab.py", "file_ext": "py", "file_size_in_byte": 737, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "PyQt5.QtWidgets.QTabWidget", "line_number": 11, "usage_type": "name"}, {"api_name": "testcase_table.TestCase", "line_number": 16, "usage_type": "call"}, {"api_name": "test_log_browser.ProgressBrowser", "line_number": 17, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 26, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "187845314", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Sep 14 16:53:08 2019\n\n@author: cl\n\"\"\"\n\nimport cv2\nimport numpy as np\nimport h5py\n\nfrom sklearn.model_selection import train_test_split\n\ndef read_img_h5py(h5_file='data.h5'):\n data_set = None\n lbl_set = None\n test_index = 0\n \n with h5py.File(h5_file, 'r') as hf:\n \n start_index = int(hf['start_index'].value)\n end_index = int(hf['end_index'].value)\n print('start_index:', start_index, 'end_index:', end_index)\n \n for i in range(start_index, end_index):\n test_index += 1\n \n img_raw = hf['X'+str(i)]\n img_data = np.array(img_raw[:,:,:])\n img_data = cv2.cvtColor(img_data, cv2.COLOR_BGR2RGB)\n \n img_data = img_data.reshape(-1, 128, 128, 3)\n \n img_lbl = np.array(hf['y'+str(i)].value)\n \n if data_set is None:\n data_set = img_data\n lbl_set = img_lbl\n else:\n data_set = np.concatenate((data_set, img_data))\n lbl_set = np.concatenate((lbl_set, img_lbl))\n \n if i % 100 == 0:\n print('img data shape:', img_data.shape)\n print('data_set shape', data_set.shape)\n print('img_lbl:', img_lbl)\n print('lbl_set shape:', lbl_set.shape)\n \n return (data_set, lbl_set) \n\ndef write_data_set(X_train, X_test, y_train, y_test, h5_file='out.h5'):\n dt = h5py.special_dtype(vlen=str)\n \n with h5py.File(h5_file, 'w') as hf:\n \n hf.create_dataset(name=\"X_train\", shape=X_train.shape, dtype=np.int8,\n compression=\"gzip\", compression_opts=9)\n hf['X_train'][...] = X_train\n print('Save X_train: ', X_train.shape)\n \n hf.create_dataset(name=\"y_train\", shape=y_train.shape, \n dtype=dt,\n compression=\"gzip\", compression_opts=9)\n hf['y_train'][...] = y_train\n print('Save y_train: ', y_train.shape)\n \n hf.create_dataset(name=\"X_test\", shape=X_test.shape, dtype=np.int8,\n compression=\"gzip\", compression_opts=9)\n hf['X_test'][...] = X_test\n print('Save X_test: ', X_test.shape)\n \n hf.create_dataset(name=\"y_test\", shape=y_test.shape, \n dtype=dt,\n compression=\"gzip\", compression_opts=9)\n hf['y_test'][...] = y_test\n print('Save y_test: ', y_test.shape)\n \ndef read_data_set(h5_file='out.h5'):\n with h5py.File(h5_file, 'r') as hf:\n X_train = hf['X_train'].value\n print('Read X_train: ', X_train.shape) \n \n y_train = hf['y_train'].value\n print('Read y_train: ', y_train.shape) \n \n X_test = hf['X_test'].value\n print('Read X_test: ', X_test.shape) \n \n y_test = hf['y_test'].value\n print('Read y_test: ', y_test.shape) \n \n return (X_train, y_train, X_test, y_test)\n \n(X, y) = read_img_h5py(h5_file='..\\\\data\\\\data128.h5')\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, \n test_size=1 / 3, \n random_state=1)\nprint('X_train: ', X_train.shape, 'X_test: ', X_test.shape)\nprint('y_train: ', y_train.shape, 'y_test: ', y_test.shape)\n\nwrite_data_set(X_train, X_test, y_train, y_test, h5_file='..\\\\data\\\\ca2data.h5')\n\nX_train_data, y_train_data, X_test_data, y_test_data = read_data_set(h5_file='..\\\\data\\\\ca2data.h5' )\n", "sub_path": "PRMLS-CA2-Submission/sources/sampling/HDF5_Dataset.py", "file_name": "HDF5_Dataset.py", "file_ext": "py", "file_size_in_byte": 3672, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "h5py.File", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 41, "usage_type": "call"}, {"api_name": "h5py.special_dtype", "line_number": 52, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.int8", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.int8", "line_number": 67, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 79, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 96, "usage_type": "call"}]} +{"seq_id": "592865570", "text": "from django.utils import timezone\n\nfrom django.contrib.auth.models import User\nfrom .models import League, Theme, Tune, Membership\nfrom django.core.exceptions import ObjectDoesNotExist\n\n# This function generates songs for a Theme\ndef generate_songs(number, theme_obj):\n\tuser_obj = User.objects.get(username='admin')\n\tfor i in range(number):\n\t\ttune = Tune(\n\t\t\t\ttheme=theme_obj,\n\t\t\t\tsubmitter=user_obj,\n\t\t\t\turl='http://www.soundcloud.com',\n\t\t\t\ttitle='Song' + str(i),\n\t\t\t\tartist='Artist' + str(i))\n\t\ttune.save()\n\ndef generate_songs_for_status(number, league_obj, status):\n\ttry: theme_obj = Theme.objects.filter(league=league_obj).get(status=status)\n\texcept ObjectDoesNotExist:\n\t\treturn 'That theme does not exist'\n\telse:\n\t\tgenerate_songs(number, theme_obj)\n\t\treturn '%d tunes were created for status %d theme for the league %s' %(number, status, league_obj.name)", "sub_path": "league/dev.py", "file_name": "dev.py", "file_ext": "py", "file_size_in_byte": 859, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 9, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 9, "usage_type": "name"}, {"api_name": "models.Tune", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Theme.objects.filter", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Theme.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.Theme", "line_number": 20, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "117179487", "text": "import binascii\nimport os\n\nimport pytest\nimport requests\nfrom requests.models import Response\n\nfrom aqt import helper\nfrom aqt.metadata import Version\n\n\ndef test_helper_altlink(monkeypatch):\n class Message:\n headers = {\"content-type\": \"text/plain\", \"length\": 300}\n text = \"\"\"\n\n MirrorBrain/2.17.0\n http://download.example.io/boo.7z.meta4\n 2020-03-04T01:11:48Z\n \n Example Project\n https://download.example.io\n \n\n \n 651\n d49eba3937fb063caa48769e8f28377c\n 25d3a33d00c1e5880679a17fd4b8b831134cfa6f\n 37e50248cf061109e2cb92105cd2c36a6e271701d6d4a72c4e73c6d82aad790a\n \n bec628a149ed24a3a9b83747776ecca5a1fad11c\n 98b1dee3f741de51167a9428b0560cd2d1f4d945\n 8717a0cb3d14c1958de5981635c9b90b146da165\n 78cd2ae3ae37ca7c080a56a2b34eb33ec44a9ef1\n \n http://mirrors.geekpie.club/boo.7z\n http://ftp.jaist.ac.jp/pub/boo.7z\n http://ftp.yz.yamagata-u.ac.jp/pub/boo.7z\n \n\n\"\"\"\n\n def mock_return(url):\n return Message()\n\n monkeypatch.setattr(helper, \"_get_meta\", mock_return)\n\n url = \"http://foo.baz/qtproject/boo.7z\"\n alt = \"http://mirrors.geekpie.club/boo.7z\"\n newurl = helper.altlink(url, alt)\n assert newurl.startswith(\"http://ftp.jaist.ac.jp/\")\n\n\ndef test_settings(tmp_path):\n helper.Settings.load_settings(\n os.path.join(os.path.dirname(__file__), \"data\", \"settings.ini\")\n )\n assert helper.Settings.concurrency == 3\n assert \"http://mirror.example.com\" in helper.Settings.blacklist\n\n\ndef mocked_iter_content(chunk_size):\n with open(\n os.path.join(os.path.dirname(__file__), \"data\", \"windows-5150-update.xml\"), \"rb\"\n ) as f:\n data = f.read(chunk_size)\n while len(data) > 0:\n yield data\n data = f.read(chunk_size)\n return b\"\"\n\n\ndef mocked_requests_get(*args, **kwargs):\n response = Response()\n response.status_code = 200\n response.iter_content = mocked_iter_content\n return response\n\n\ndef test_helper_downloadBinary_md5(tmp_path, monkeypatch):\n\n monkeypatch.setattr(requests.Session, \"get\", mocked_requests_get)\n\n expected = binascii.unhexlify(\"1d41a93e4a585bb01e4518d4af431933\")\n out = tmp_path.joinpath(\"text.xml\")\n helper.downloadBinaryFile(\"http://example.com/test.xml\", out, \"md5\", expected, 60)\n\n\ndef test_helper_downloadBinary_sha256(tmp_path, monkeypatch):\n\n monkeypatch.setattr(requests.Session, \"get\", mocked_requests_get)\n\n expected = binascii.unhexlify(\n \"07b3ef4606b712923a14816b1cfe9649687e617d030fc50f948920d784c0b1cd\"\n )\n out = tmp_path.joinpath(\"text.xml\")\n helper.downloadBinaryFile(\n \"http://example.com/test.xml\", out, \"sha256\", expected, 60\n )\n\n\n@pytest.mark.parametrize(\n \"version, expect\",\n [\n (\"1.33.1\", Version(major=1, minor=33, patch=1)),\n (\n \"1.33.1-202102101246\",\n Version(major=1, minor=33, patch=1, build=(\"202102101246\",)),\n ),\n (\n \"1.33-202102101246\",\n Version(major=1, minor=33, patch=0, build=(\"202102101246\",)),\n ),\n (\"2020-05-19-1\", Version(major=2020, minor=0, patch=0, build=(\"05-19-1\",))),\n ],\n)\ndef test_helper_to_version_permissive(version, expect):\n assert Version.permissive(version) == expect\n", "sub_path": "tests/test_helper.py", "file_name": "test_helper.py", "file_ext": "py", "file_size_in_byte": 3801, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "aqt.helper", "line_number": 46, "usage_type": "argument"}, {"api_name": "aqt.helper.altlink", "line_number": 50, "usage_type": "call"}, {"api_name": "aqt.helper", "line_number": 50, "usage_type": "name"}, {"api_name": "aqt.helper.Settings.load_settings", "line_number": 55, "usage_type": "call"}, {"api_name": "aqt.helper.Settings", "line_number": 55, "usage_type": "attribute"}, {"api_name": "aqt.helper", "line_number": 55, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 56, "usage_type": "call"}, {"api_name": "aqt.helper.Settings", "line_number": 58, "usage_type": "attribute"}, {"api_name": "aqt.helper", "line_number": 58, "usage_type": "name"}, {"api_name": "aqt.helper.Settings", "line_number": 59, "usage_type": "attribute"}, {"api_name": "aqt.helper", "line_number": 59, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 64, "usage_type": "call"}, {"api_name": "requests.models.Response", "line_number": 74, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 82, "usage_type": "attribute"}, {"api_name": "binascii.unhexlify", "line_number": 84, "usage_type": "call"}, {"api_name": "aqt.helper.downloadBinaryFile", "line_number": 86, "usage_type": "call"}, {"api_name": "aqt.helper", "line_number": 86, "usage_type": "name"}, {"api_name": "requests.Session", "line_number": 91, "usage_type": "attribute"}, {"api_name": "binascii.unhexlify", "line_number": 93, "usage_type": "call"}, {"api_name": "aqt.helper.downloadBinaryFile", "line_number": 97, "usage_type": "call"}, {"api_name": "aqt.helper", "line_number": 97, "usage_type": "name"}, {"api_name": "aqt.metadata.Version.permissive", "line_number": 118, "usage_type": "call"}, {"api_name": "aqt.metadata.Version", "line_number": 118, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 102, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 102, "usage_type": "attribute"}, {"api_name": "aqt.metadata.Version", "line_number": 105, "usage_type": "call"}, {"api_name": "aqt.metadata.Version", "line_number": 108, "usage_type": "call"}, {"api_name": "aqt.metadata.Version", "line_number": 112, "usage_type": "call"}, {"api_name": "aqt.metadata.Version", "line_number": 114, "usage_type": "call"}]} +{"seq_id": "416675277", "text": "#!/usr/bin/python\n\nANSIBLE_METADATA = {\n 'metadata_version': '1.0',\n 'status': ['preview'],\n 'supported_by': '@globalclouddev'\n}\n\nDOCUMENTATION = '''\n---\nmodule: amavar_client\n\nshort_description: Manages agents for Avamar\n\ndescription:\n - \"Manages clients for Avamar Backup Solution by Dell Technologies\"\n\noptions:\n endpoint:\n description:\n - Avamar endpoint. Accepts IP address or FQDN\n required: True\n oath_token:\n description:\n - OAuth2 token\n required: True\n client:\n description:\n - Client FQDN or IP Address\n required: False\n domain:\n description:\n - Avamar Domain. Default is /\n required: False\n policy:\n description:\n - Group Policy\n required: True\n retention:\n description:\n - Avamar retention. Default is 'Default Retention'\n required: False\n debug:\n description:\n - Shows additional log output. True or False\n required: False\n state:\n description:\n - present or absent\n required: True\n\nauthor:\n - DellEMC Cloud Development (@globalclouddev)\n'''\n\nEXAMPLES = '''\n\n- name: Add client to avamar\n avamar_client:\n endpoint: \"avamar.local\"\n oauth_token: \"access_token\"\n client: \"avamar-test.local\"\n domain: \"/\"\n policy: \"Test Group\"\n retention: \"Default Retention\"\n state: present\n\n- name: Remove client to avamar\n avamar_client:\n endpoint: \"avamar.local\"\n oauth_token: \"access_token\"\n client: \"avamar-test.local\"\n domain: \"/\"\n policy: \"Test Group\"\n retention: \"Default Retention\"\n state: absent\n'''\n\nRETURN = '''\nmsg:\n description: The status of the resource\n'''\n\nfrom ansible.module_utils.basic import AnsibleModule\nfrom ansible.module_utils.urls import fetch_url, url_argument_spec\nimport json\n\nSOCKET_TIMEOUT = 30\n\ndef run_module():\n module = AnsibleModule(\n argument_spec = dict(\n endpoint = dict(required=True),\n oauth_token = dict(required=True, no_log=True),\n client = dict(required=True),\n domain = dict(required=False, default='/'),\n policy = dict(required=False, default='Default Group'),\n retention = dict(required=False, default='Default Retention'),\n backup = dict(required=False, type='bool', default=False),\n validate_certs = dict(required=False, type='bool', default=False),\n debug = dict(required=False, type='bool', default=False),\n state = dict(required=False, default='present', choices=['present', 'absent'])\n ),\n supports_check_mode = True\n )\n\n result = dict(\n changed=False,\n )\n\n #===========================================================================\n # Variables\n #===========================================================================\n endpoint = module.params['endpoint']\n debug = module.params['debug']\n SOCKET_TIMEOUT = 30\n CLIENT_FOUND = False\n RETENTION_ID = False\n CLIENT_ID = False\n client_json = False\n MEMBERS_LIST = []\n GROUP_ID = False\n CLIENT_INVITED = False\n CLIENT_FOUND_IN_GROUP = False\n CLIENT_ADDED = False\n BACKUP_STARTED = False\n\n #===========================================================================\n # Make Authentication\n #===========================================================================\n authorization_token = \"Bearer %s\" % module.params['oauth_token']\n\n #===========================================================================\n # Get Clients\n #===========================================================================\n method = 'GET'\n url = 'https://' + endpoint + '/api/v1/clients?domain=' + module.params['domain']\n headers = {'Accept':'application/json',\n 'Content-Type':'application/json',\n 'Authorization': authorization_token,\n }\n # Make the REST call \n response, info = fetch_url(module,\n url,\n data={},\n headers=headers,\n method=method,\n timeout=SOCKET_TIMEOUT)\n\n if debug:\n result['info'] = info\n\n try:\n response_content = response.read()\n response_json = json.loads(response_content)\n clients = response_json[\"content\"]\n\n except Exception as e:\n module.fail_json(msg=\"Failed to get clients\", info=info, error=e)\n\n for client in clients:\n client_name = client.get('name')\n if client_name == module.params['client']:\n CLIENT_FOUND = True\n client_json = client\n CLIENT_ID = client.get('id')\n\n #module.fail_json(msg=clients)\n\n #===========================================================================\n # Get Retention\n #===========================================================================\n del response, info\n method = 'GET'\n url = 'https://' + endpoint + '/api/v1/retentions?domain=' + module.params['domain']\n # Make the REST call \n response, info = fetch_url(module,\n url,\n data={},\n headers=headers,\n method=method,\n timeout=SOCKET_TIMEOUT)\n\n try:\n response_content = response.read()\n response_json = json.loads(response_content)\n retentions = response_json['content']\n except Exception as e:\n module.fail_json(msg=\"Cannot find retention\", info=info, error=e)\n\n if retentions and len(retentions) > 0:\n for retention in retentions:\n retention_name = retention.get('name')\n if retention_name == module.params['retention']:\n RETENTION_ID = retention.get('id')\n break\n else:\n RETENTION_ID = 'Default:POLICYID'\n\n #===========================================================================\n # Get Groups\n #===========================================================================\n del response, info\n method = 'GET'\n url = 'https://' + endpoint + '/api/v1/groups'\n # Make the REST call \n response, info = fetch_url(module,\n url,\n data={},\n headers=headers,\n method=method,\n timeout=SOCKET_TIMEOUT)\n\n try:\n response_content = response.read()\n response_json = json.loads(response_content)\n groups = response_json['content']\n except Exception as e:\n module.fail_json(msg=\"Cannot find groups\", info=info, error=e)\n\n if len(groups) > 0:\n for group in groups:\n group_name = group.get('name')\n if group_name == module.params['policy']:\n group_policy = group\n GROUP_ID = group.get('id')\n break\n else:\n module.fail_json(msg=\"Policy %s does not exist\" % module.params['policy'])\n \n if GROUP_ID:\n #===========================================================================\n # Get Group Members\n #===========================================================================\n del response, info\n method = 'POST'\n body = dict(\n domainFqdn = module.params['domain'],\n enableRule = False,\n groupId = GROUP_ID,\n recursive = True\n )\n url = 'https://' + endpoint + '/api/v1/groups/backup-groups/candidate-members'\n # Make the REST call \n response, info = fetch_url(module,\n url,\n data=module.jsonify(body),\n headers=headers,\n method=method,\n timeout=SOCKET_TIMEOUT)\n\n try:\n response_content = response.read()\n response_json = json.loads(response_content)\n members = response_json\n except Exception as e:\n module.fail_json(msg=\"Cannot find group members\", info=info, error=e)\n\n if len(members) > 0:\n for member in members:\n\n if member.get('name') == module.params['client']:\n CLIENT_FOUND_IN_GROUP = True\n break\n\n if not CLIENT_FOUND_IN_GROUP:\n for member in members:\n if member.get('joinType') == \"INCLUDED_BY_USER\":\n payload = dict(\n cid = member.get('cid'),\n joinType = \"INCLUDED_BY_USER\",\n datasetId = None,\n )\n\n MEMBERS_LIST.append( payload )\n\n #===========================================================================\n # Check Mode\n #===========================================================================\n if module.check_mode:\n result['check_mode'] = True\n if CLIENT_FOUND:\n result['client'] = client_json\n if CLIENT_ID and module.params['state'] == \"present\":\n result['msg'] = \"Resource is present\"\n if not CLIENT_ID and module.params['state'] == \"present\":\n result['msg'] = \"Resource gets created\"\n if CLIENT_ID and module.params['state'] == \"absent\":\n result['msg'] = \"Resource gets removed\"\n if not CLIENT_ID and module.params['state'] == \"absent\":\n result['msg'] = \"Resource is deleted\"\n else:\n #===========================================================================\n # State Present\n #===========================================================================\n if module.params['state'] == \"present\":\n\n #===========================================================================\n # Client does not exists\n #===========================================================================\n if not CLIENT_ID:\n #===========================================================================\n # Create Client\n #===========================================================================\n del response, info\n method = 'POST'\n url = 'https://' + endpoint + '/api/v1/clients'\n body = dict(\n contact = dict(\n email = \"\",\n location = \"\",\n name = \"\",\n notes = \"\",\n phone = \"\",\n ),\n domain = module.params['domain'],\n enabled = True,\n encrytion = \"HIGH\",\n name = module.params['client'],\n overrideEncryption = True,\n overrideRetention = True,\n overtimeOption = \"NEVER\",\n #retention = \"Default:POLICYID\",\n retention = RETENTION_ID,\n )\n # Make the REST call \n response, info = fetch_url(module,\n url,\n data=module.jsonify(body),\n headers=headers,\n method=method,\n timeout=SOCKET_TIMEOUT)\n\n if debug:\n result['info'] = info\n result['payload'] = body\n\n try:\n response_content = response.read()\n response_json = json.loads(response_content)\n CLIENT_ID = response_json['id']\n CLIENT_ADDED = True\n except Exception as e:\n module.fail_json(msg=\"Cannot create client\", info=info, payload=body, error=e)\n\n #===========================================================================\n # Client exists\n #===========================================================================\n if CLIENT_ID:\n #===========================================================================\n # Invite Client\n #===========================================================================\n del response, info\n method = 'POST'\n url = 'https://' + endpoint + '/api/v1/clients/' + CLIENT_ID + '/invite'\n body = dict(\n cid = CLIENT_ID,\n )\n # Make the REST call \n response, info = fetch_url(module,\n url,\n data=module.jsonify(body),\n headers=headers,\n method=method,\n timeout=SOCKET_TIMEOUT)\n\n if debug:\n result['info'] = info\n result['payload'] = body\n\n if info.get('status') == 200:\n CLIENT_INVITED = True\n \n if not CLIENT_INVITED:\n module.fail_json(msg='Client invite failed', info=info)\n\n #===========================================================================\n # Client not in policy\n #===========================================================================\n if not CLIENT_FOUND_IN_GROUP:\n #===========================================================================\n # Add Client to Group Policy\n #===========================================================================\n\n payload = dict(\n cid = CLIENT_ID,\n joinType = \"INCLUDED_BY_USER\",\n datasetId = None,\n )\n\n MEMBERS_LIST.append( payload )\n group_policy['memberList'] = MEMBERS_LIST\n group_policy['retentionPolicyId'] = RETENTION_ID\n\n del response, info\n method = 'PUT'\n url = 'https://' + endpoint + '/api/v1/groups/backup-groups/' + GROUP_ID\n body = group_policy\n # Make the REST call \n response, info = fetch_url(module,\n url,\n data=module.jsonify(body),\n headers=headers,\n method=method,\n timeout=SOCKET_TIMEOUT)\n\n if debug:\n result['info'] = info\n result['payload'] = body\n\n if info.get('status') == 201 or info.get('status') == 200:\n result['msg'] = \"Resource Created\"\n result['changed'] = True\n CLIENT_FOUND_IN_GROUP = True\n else:\n module.fail_json(msg=\"Cannot add client to group policy\")\n\n #===========================================================================\n # Backup Requested\n #===========================================================================\n if module.params['backup']:\n #===========================================================================\n # Get Plugins\n #===========================================================================\n del response, info\n method = 'GET'\n body = {}\n url = 'https://' + endpoint + '/api/v1/clients/' + CLIENT_ID + '/plugin-builds'\n # Make the REST call \n response, info = fetch_url(module,\n url,\n data=module.jsonify(body),\n headers=headers,\n method=method,\n timeout=SOCKET_TIMEOUT)\n\n try:\n response_content = response.read()\n response_json = json.loads(response_content)\n plugins = response_json\n except Exception as e:\n module.fail_json(msg=\"Cannot find plugins\", info=info, error=e)\n\n if plugins and len(plugins) > 0:\n for plugin in plugins:\n if 'File System' in plugin.get('description'):\n PLUGIN_ID = plugin.get('pluginNumber')\n break\n\n if PLUGIN_ID:\n #===========================================================================\n # Run Backup\n #===========================================================================\n del response, info\n method = 'POST'\n body = {\n \"encryption\": \"HIGH\",\n \"expireTime\": 1568505600000,\n \"flags\": [\n {\n \"key\": \"ddr\",\n \"pid\": \"1001\",\n \"value\": True,\n \"alternatePluginNumber\": 0,\n \"defaultValue\": \"\",\n \"flagId\": \"ddr\",\n \"flagType\": \"checkbox\"\n },\n {\n \"key\": \"ddr-index\",\n \"pid\": \"1001\",\n \"value\": \"1\",\n \"alternatePluginNumber\": 0,\n \"defaultValue\": \"\",\n \"flagId\": \"ddr-index\",\n \"flagType\": \"pulldown\"\n },\n {\n \"key\": \"ddr-encrypt-strength\",\n \"pid\": \"1001\",\n \"value\": \"default\",\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"default\",\n \"flagId\": \"ddr-encrypt-strengthB\",\n \"flagType\": \"pulldown\"\n },\n {\n \"key\": \"label\",\n \"pid\": \"1001\",\n \"value\": \"\",\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"\",\n \"flagId\": \"label\",\n \"flagType\": \"string\"\n },\n {\n \"key\": \"verbose\",\n \"pid\": \"1001\",\n \"value\": \"0\",\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"0\",\n \"flagId\": \"verbose\",\n \"flagType\": \"pulldown\"\n },\n {\n \"key\": \"informationals\",\n \"pid\": \"1001\",\n \"value\": \"2\",\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"2\",\n \"flagId\": \"informationals\",\n \"flagType\": \"pulldown\"\n },\n {\n \"key\": \"statistics\",\n \"pid\": \"1001\",\n \"value\": False,\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"\",\n \"flagId\": \"statistics1\",\n \"flagType\": \"checkbox\"\n },\n {\n \"key\": \"debug\",\n \"pid\": \"1001\",\n \"value\": False,\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"\",\n \"flagId\": \"debug1\",\n \"flagType\": \"checkbox\"\n },\n {\n \"key\": \"one-file-system\",\n \"pid\": \"1001\",\n \"value\": False,\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"\",\n \"flagId\": \"one-file-system\",\n \"flagType\": \"checkbox\"\n },\n {\n \"key\": \"[]default-traversal\",\n \"pid\": \"1001\",\n \"value\": True,\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"true\",\n \"flagId\": \"none\",\n \"flagType\": \"checkbox\"\n },\n {\n \"key\": \"force\",\n \"pid\": \"1001\",\n \"value\": False,\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"\",\n \"flagId\": \"force\",\n \"flagType\": \"checkbox\"\n },\n {\n \"key\": \"forcefs\",\n \"pid\": \"1001\",\n \"value\": \"\",\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"\",\n \"flagId\": \"forcefs\",\n \"flagType\": \"string\"\n },\n {\n \"key\": \"ignorefs\",\n \"pid\": \"1001\",\n \"value\": \"\",\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"\",\n \"flagId\": \"ignorefs\",\n \"flagType\": \"string\"\n },\n {\n \"key\": \"run-at-start\",\n \"pid\": \"1001\",\n \"value\": \"\",\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"\",\n \"flagId\": \"run-at-start1\",\n \"flagType\": \"textbox\"\n },\n {\n \"key\": \"run-at-start-exit\",\n \"pid\": \"1001\",\n \"value\": True,\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"true\",\n \"flagId\": \"run-at-start-exit1\",\n \"flagType\": \"checkbox\"\n },\n {\n \"key\": \"run-at-end\",\n \"pid\": \"1001\",\n \"value\": \"\",\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"\",\n \"flagId\": \"run-at-end1\",\n \"flagType\": \"textbox\"\n },\n {\n \"key\": \"run-at-end-exit\",\n \"pid\": \"1001\",\n \"value\": True,\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"true\",\n \"flagId\": \"run-at-end-exit1\",\n \"flagType\": \"checkbox\"\n },\n {\n \"key\": \"checkcache\",\n \"pid\": \"1001\",\n \"value\": False,\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"\",\n \"flagId\": \"checkcache\",\n \"flagType\": \"checkbox\"\n },\n {\n \"key\": \"repaircache\",\n \"pid\": \"1001\",\n \"value\": False,\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"\",\n \"flagId\": \"repaircache\",\n \"flagType\": \"checkbox\"\n },\n {\n \"key\": \"filecachemax\",\n \"pid\": \"1001\",\n \"value\": \"-8\",\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"-8\",\n \"flagId\": \"filecachemax\",\n \"flagType\": \"integer\"\n },\n {\n \"key\": \"hashcachemax\",\n \"pid\": \"1001\",\n \"value\": \"-16\",\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"-16\",\n \"flagId\": \"hashcachemax\",\n \"flagType\": \"integer\"\n },\n {\n \"key\": \"flagfile\",\n \"pid\": \"1001\",\n \"value\": \"\",\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"\",\n \"flagId\": \"flagfile\",\n \"flagType\": \"string\"\n },\n {\n \"key\": \"throttle\",\n \"pid\": \"1001\",\n \"value\": \"\",\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"\",\n \"flagId\": \"throttle\",\n \"flagType\": \"string\"\n },\n {\n \"key\": \"soft-quota-limit\",\n \"pid\": \"1001\",\n \"value\": \"\",\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"\",\n \"flagId\": \"soft-quota-limit\",\n \"flagType\": \"integer\"\n },\n {\n \"key\": \"hard-quota-limit\",\n \"pid\": \"1001\",\n \"value\": \"\",\n \"alternatePluginNumber\": 1001,\n \"defaultValue\": \"\",\n \"flagId\": \"hard-quota-limit\",\n \"flagType\": \"integer\"\n }\n ],\n \"ignoreIndependentAndRawDisks\": True,\n \"proxy\": \"\",\n \"targets\": [\n {\n \"pid\": PLUGIN_ID,\n \"value\": \"ALL\"\n }\n ]\n }\n url = 'https://' + endpoint + '/api/v1/clients/' + CLIENT_ID + '/backup'\n # Make the REST call \n response, info = fetch_url(module,\n url,\n data=module.jsonify(body),\n headers=headers,\n method=method,\n timeout=SOCKET_TIMEOUT)\n \n if info.get(\"status\") == 200:\n BACKUP_STARTED = True\n result['msg'] = \"Backup started\"\n result['changed'] = True\n else:\n module.fail_json(msg=\"Backup failed to start\", info=info)\n else:\n module.fail_json(msg=\"No suitable plugin found\", info=info)\n\n\n #===========================================================================\n # Module Output\n #===========================================================================\n if CLIENT_ADDED:\n result['msg'] = \"Resource added\"\n result['changed'] = True\n\n if CLIENT_INVITED:\n result['msg'] = \"Resource invited\"\n result['changed'] = True\n\n if CLIENT_FOUND_IN_GROUP:\n result['msg'] = \"Resource present\"\n result['changed'] = True\n\n if BACKUP_STARTED:\n result['msg'] = \"Backup started\"\n result['changed'] = True\n\n #===========================================================================\n # State Absent\n #===========================================================================\n elif module.params['state'] == \"absent\":\n \n if CLIENT_ID:\n #===========================================================================\n # Delete Share\n #===========================================================================\n del response, info\n method = 'DELETE'\n url = 'https://' + endpoint + '/api/v1/clients/' + CLIENT_ID\n # Make the REST call \n response, info = fetch_url(module,\n url,\n data={},\n headers=headers,\n method=method,\n timeout=SOCKET_TIMEOUT)\n\n if debug:\n result['info'] = info\n result['payload'] = body\n\n if info.get(\"status\") == 204:\n result['msg'] = \"Resource removed\"\n result['changed'] = True\n else:\n module.fail_json(msg=\"Resource removal failed\", info=info)\n else:\n result['changed'] = False\n result['msg'] = \"Resource already removed\"\n\n module.exit_json(**result)\n\n\ndef main():\n run_module()\n\nif __name__ == '__main__':\n main()", "sub_path": "playbook/library/avamar_client.py", "file_name": "avamar_client.py", "file_ext": "py", "file_size_in_byte": 32132, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "ansible.module_utils.basic.AnsibleModule", "line_number": 91, "usage_type": "call"}, {"api_name": "ansible.module_utils.urls.fetch_url", "line_number": 143, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 155, "usage_type": "call"}, {"api_name": "ansible.module_utils.urls.fetch_url", "line_number": 177, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 186, "usage_type": "call"}, {"api_name": "ansible.module_utils.urls.fetch_url", "line_number": 207, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 216, "usage_type": "call"}, {"api_name": "ansible.module_utils.urls.fetch_url", "line_number": 245, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 254, "usage_type": "call"}, {"api_name": "ansible.module_utils.urls.fetch_url", "line_number": 327, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 340, "usage_type": "call"}, {"api_name": "ansible.module_utils.urls.fetch_url", "line_number": 360, "usage_type": "call"}, {"api_name": "ansible.module_utils.urls.fetch_url", "line_number": 400, "usage_type": "call"}, {"api_name": "ansible.module_utils.urls.fetch_url", "line_number": 430, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 439, "usage_type": "call"}, {"api_name": "ansible.module_utils.urls.fetch_url", "line_number": 697, "usage_type": "call"}, {"api_name": "ansible.module_utils.urls.fetch_url", "line_number": 746, "usage_type": "call"}]} +{"seq_id": "469335773", "text": "import socket\nimport os\nimport sys\nimport json\nimport pickle\nimport time\n\n#host\nHOST = \"127.0.0.1\"\n\n# input port\nif len(sys.argv) != 2:\n print(usage)\n sys.exit(0)\n\nif len(sys.argv) > 1:\n PORT = int(sys.argv[1])\n\n\n# function to receive client data and processing json format\ndef getRecv(sconn, tcpsize):\n # listen to socket\n recv = sconn.recv(tcpsize)\n # load data with json format\n recv = json.loads(pickle.loads(recv))\n # if response got ok, than get data\n if recv['response'] == 'ok':\n data = recv['data']\n # server error, response not ok \n else:\n print('server error, bye')\n # exit program\n exit()\n # return data\n return data\n\n# function to create json formate before sending to client\ndef createJSON(response, data):\n data = {\n 'response' : response,\n 'data' : data,\n }\n return pickle.dumps(json.dumps(data))\n\n# prepare socket\ns = socket.socket()\n# connect socket\ns.connect((HOST, PORT))\n# get current directory of server\ncur_dir = getRecv(s, 1024)\n\nwhile True:\n # show input on terminal\n cmd = input(\"\"+str(\"ftp@ \") + str(cur_dir) + \" > \")\n # send inputed string\n s.send(createJSON('ok', cmd))\n # split string by space\n s_cmd = cmd.split(\" \")\n # get command\n cm = s_cmd[0]\n\n # if any argument on command, use for get and put command\n try:\n fname = s_cmd[1]\n except:\n fname = \"\"\n\n if cm == \"bye\":\n exit()\n elif cmd != 'ls':\n print (cm+\" NO such command, use ls command\")\n else:\n # get server response\n data= getRecv(s, 102400)\n\n # looping data from client\n for i in data['files']:\n # convert size to Kb\n size= i['size'] / 1000\n size= str(size)\n # print\n print(str(i['name'])+\"\\t\\t\"+size+\" Kb\")\n", "sub_path": "tugas4/client/client_ls.py", "file_name": "client_ls.py", "file_ext": "py", "file_size_in_byte": 1841, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 14, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 17, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 25, "usage_type": "call"}, {"api_name": "pickle.loads", "line_number": 25, "usage_type": "call"}, {"api_name": "pickle.dumps", "line_number": 43, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 43, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "63461768", "text": "from flask import Flask, session, send_from_directory, render_template\nfrom flask_session import Session\nimport os\napp = Flask(__name__)\nSESSION_TYPE = 'filesystem'\napp.secret_key = os.urandom(24)\napp.config.from_object(__name__)\nSession(app)\n\nquestions = [\n {\n 'file': '1.jpg',\n 'answers': ['cat', 'crete cat', 'dog'],\n 'query': 'The question is can you find this with >99% accuracy?',\n 'hint': 'Is it a cat or a dog?'\n },\n {\n 'file': '2.png',\n 'answers': ['tux'],\n 'query': 'Not just any penguin!',\n 'hint': 'The Linux Mascot!'\n },\n {\n 'file': '3.png',\n 'answers': ['fedora hat', 'fedora'],\n 'query': 'And this is not just any hat',\n 'hint': 'Has the same name as a popular linux distro'\n },\n {\n 'file': '4.png',\n 'answers': ['fedora project', 'fedora'],\n 'query': 'Who said that this is not a hat?',\n 'hint': 'The GCI organisation with the most tasks solved'\n },\n {\n 'file': '5.jpg',\n 'answers': ['flask', 'python flask'],\n 'query': 'Hint: This is a hard one. FFFFFFFFound it yet?',\n 'hint': 'A python framework that was used to make this app'\n },\n {\n 'file': '6.jpeg',\n 'answers': ['linus torvalds'],\n 'query': 'Doesn\\'t he look lovely?',\n 'hint': 'The founder of Linux'\n },\n {\n 'file': '7.png',\n 'answers': ['fedora pagure', 'pagure'],\n 'query': 'Don\\'t. Be. Like. A. Snail. Answer. Quickly.',\n 'hint': 'Github but good'\n },\n {\n 'file': '8.png',\n 'answers': ['gnome project', 'gnome desktop', 'gnome'],\n 'query': 'What is this insane multitasking?',\n 'hint': 'The default desktop environment on many distros'\n },\n {\n 'file': '9.png',\n 'answers': ['shebang', 'hashbang'],\n 'query': 'Wait a minute, I\\'ve seen this before',\n 'hint': 'The starting line for scripts on linux'\n },\n {\n 'file': '10.jpg',\n 'answers': ['ibm pc 5150', '5150'],\n 'query': 'Wow, who\\'s old now?',\n 'hint': 'The model number of the first IBM PC'\n }\n]\n\n@app.route('/images/')\ndef send_img(path):\n return send_from_directory('images', path)\n\n\n@app.route('/')\ndef index():\n if 'qid' not in session:\n session['qid']=0\n\n current_qid = session['qid']\n print(current_qid)\n if current_qid>=len(questions):\n session['qid'] = 0\n return render_template(\"game_completed.html\")\n return render_template(\"show_question.html\", qid=current_qid+1, image_path=questions[current_qid]['file'], query_text=questions[current_qid]['query'], correct_answers=questions[current_qid]['answers'], hint=questions[current_qid]['hint'])\n\n@app.route('/next_question')\ndef next_question():\n session['qid'] +=1\n return 'OK' \n\n@app.route('/game_over')\ndef game_over():\n session['qid'] = 0\n return 'You need to start over :('\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 3021, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Flask", "line_number": 4, "usage_type": "call"}, {"api_name": "os.urandom", "line_number": 6, "usage_type": "call"}, {"api_name": "flask_session.Session", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.send_from_directory", "line_number": 75, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 80, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 83, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 86, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 87, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 92, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 97, "usage_type": "name"}]} +{"seq_id": "299701214", "text": "# %load q04_encoding/build.py\n# Default imports\nimport pandas as pd\nfrom sklearn.preprocessing import LabelEncoder\n\nny_housing = pd.read_csv('data/train.csv')\nhousing_data = ny_housing[['MasVnrArea', 'GrLivArea', 'LotShape', 'GarageType', 'SalePrice']]\n\n\n# Write your code here:\n\ndef encoding(dataset):\n le = LabelEncoder()\n dataset['LotShape'] = dataset['LotShape'].fillna(dataset['LotShape'].mode()[0])\n dataset['GarageType'] = dataset['GarageType'].fillna(dataset['GarageType'].mode()[0])\n dataset['LotShape_Label'] = le.fit_transform(dataset['LotShape'])\n df_dummy = pd.get_dummies(dataset['GarageType'])\n dataset = dataset.drop(['GarageType'],1)\n dataset = pd.concat([dataset,df_dummy],1)\n return dataset\n\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "q04_encoding/build.py", "file_name": "build.py", "file_ext": "py", "file_size_in_byte": 747, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_csv", "line_number": 6, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.get_dummies", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 19, "usage_type": "call"}]} +{"seq_id": "275340070", "text": "\"\"\"\nSorters\n=======\n\nEach of the functions contained in this module take something that is unsorted\nand sort it. In some cases the sort happens entirely in-memory, but in most\ncases the sort happens by by chunking the input stream into small pieces,\nsorting each piece into its own file, and then doing a final merge sort.\n\nWith a reasonable `chunksize` the limitations to the amount of data that can be\nsorted is available disk space for incidental I/O, and the amount of time the\ncaller is willing to wait.\n\n\n**kwargs\n--------\n\nSome functions take a generic, undocumented `**kwargs`. When not documented\nthis is `chunksize`, `jobs`, `key`, and `reverse`.\n\"\"\"\n\n\nimport functools\nimport itertools as it\nimport logging\nimport tempfile\n\nfrom tinysort import tools\nimport tinysort.io\n\nfrom tinysort._backport_heapq import merge as heapq_merge\n\n\n__all__ = [\n 'file2file', 'file2stream', 'files2stream', 'stream2file', 'stream2stream']\n\n\nlogger = logging.getLogger('tinysort')\n\n\nCHUNKSIZE = 100000\n_PICKLE_IO = tinysort.io.Pickle()\n\n\ndef _mp_sort_into_tempfile(kwargs):\n\n \"\"\"\n Used by `_stream2tempfiles()`.\n\n Parameters\n ----------\n kwargs : dict\n data : tuple\n writer : tinysort.io.BaseSerializer\n sort_args : dict\n \"\"\"\n\n data = kwargs['data']\n writer = kwargs['writer']\n sort_args = kwargs['sort_args']\n _, tmp = tempfile.mkstemp()\n\n with writer.open(tmp, 'w') as f:\n for item in sorted(data, **sort_args):\n f.write(item)\n\n return tmp\n\n\ndef _stream2tempfiles(\n stream, jobs=1, chunksize=CHUNKSIZE, writer=_PICKLE_IO, **kwargs):\n\n \"\"\"\n Sort a stream of data into temporary files. Caller is responsible for\n deleting files. Tempfile paths are generated with `tempfile.mkstemp()`.\n\n Parameters\n ----------\n stream : iter\n Input stream to sort.\n jobs : int, optional\n Sort data with a pool of N workers.\n chunksize : int, optional\n Process this many objects from the input stream in each job. Also\n the maximum amount of objects per tempfile.\n writer : None or tinysort.io.BaseSerializer, optional\n Instance of the serializer for writing the stream to disk.\n kwargs : **kwargs, optional\n Keyword arguments for `sorted()`.\n\n Returns\n -------\n list\n Temporary file paths.\n \"\"\"\n\n tasks = ({\n 'data': data,\n 'writer': writer,\n 'sort_args': kwargs\n } for data in tools.slicer(stream, chunksize))\n\n with tools.runner(_mp_sort_into_tempfile, tasks, jobs) as run:\n return list(run)\n\n\ndef _file2tempfiles(infile, reader, **kwargs):\n\n \"\"\"\n Sort the contents of a single file into a bunch of temporary files. Mostly\n a convenience wrapper for `_stream2tempfiles()` to allow reading from a file\n on disk.\n\n Parameters\n ----------\n infile : str\n Input file to read.\n reader : tinysort.io.BaseSerializer\n Instance of the serializer for reading `infile`.\n kwargs : **kwargs\n Keyword arguments for `_stream2tempfiles()`.\n\n Returns\n -------\n See `_stream2tempfiles()`.\n \"\"\"\n\n with reader.open(infile) as f:\n return _stream2tempfiles(f, **kwargs)\n\n\ndef _mergefiles2stream(*infiles, **kwargs):\n\n \"\"\"\n Merge sort a bunch of tempfiles into a sorted stream of objects. Tempfiles\n are not deleted.\n\n Parameters\n ----------\n infiles : str\n Input paths to merge sort.\n reader : tinysort.io.BaseSerializer\n Instance of the serializer for reading the `paths`.\n kwargs : **kwargs, optional\n Keyword arguments for `heapq.merge()`.\n\n Yields\n ------\n object\n \"\"\"\n\n if 'reader' not in kwargs:\n raise TypeError(\"reader parameter is required.\")\n else:\n reader = kwargs.pop('reader')\n\n with tools.batch_open(*infiles, opener=reader.open) as handles:\n for item in heapq_merge(*handles, **kwargs):\n yield item\n\n\ndef stream2stream(stream, serializer=_PICKLE_IO, jobs=1, **kwargs):\n\n \"\"\"\n Take an unsorted stream of data and turn it into a sorted stream of data.\n Data is chunked into tempfiles with `_stream2tempfiles()`, and then merged\n with `_mergefiles2stream()`. Intermediary tempfiles are written and read\n with `serializer` and are deleted automatically.\n\n Parameters\n ----------\n stream : iter\n Sort this stream of data.\n serializer : tinysort.io.BaseSerializer, optional\n Instance of the class to use for writing and reading intermediary\n tempfiles.\n jobs : int, optional\n Process data in parallel with a pool of N workers. Passed to\n `_mergefiles2stream()`.\n kwargs : **kwargs, optional\n Keyword arguments for `_stream2tempfiles()`. The `key` and\n `reverse` value are extracted for `_mergefiles2stream()` as well.\n \n Yields\n ------\n object\n Sorted objects.\n \"\"\"\n\n # We know we already have the data in-memory, so just doing a straight up\n # sort is almost certainly faster\n if isinstance(stream, (list, tuple, dict)):\n for item in sorted(\n stream,\n key=kwargs.get('key'),\n reverse=kwargs.get('reverse', False)):\n yield item\n\n else:\n\n # Reader, writer, and serializer have different meanings from an API and\n # documentation perspective, so we don't want this to create an error.\n kwargs.update(writer=serializer)\n\n chunk_paths = _stream2tempfiles(\n stream,\n jobs=jobs,\n **kwargs)\n\n with tools.delete_files(*chunk_paths) as paths:\n for item in _mergefiles2stream(\n *paths,\n reader=serializer,\n key=kwargs.get('key'),\n reverse=kwargs.get('reverse', False)):\n yield item\n\n\ndef file2stream(infile, reader, **kwargs):\n \n \"\"\"\n Convenience wrapper for `stream2stream()` for working with an input file\n on disk.\n \n Parameters\n ----------\n infile : str\n Sort this file.\n reader : tinysort.io.BaseSerializer\n Instance of the serializer for reading `infile`.\n kwargs : **kwargs, optional\n Keyword arguments for `stream2stream()`.\n\n Yields\n ------\n See `stream2stream()`.\n \"\"\"\n\n with reader.open(infile) as src:\n for item in stream2stream(src, **kwargs):\n yield item\n\n\ndef files2stream(*infiles, **kwargs):\n\n \"\"\"\n Sort a batch of files into a single stream.\n\n Parameters\n ----------\n paths : *str\n Input files to sort.\n reader : tinysort.io.BaseSerializer\n Instance of the serializer for reading `infile`.\n kwargs : **kwargs, optional\n Keyword arguments for `file2stream()`.\n\n Yields\n ------\n object\n \"\"\"\n\n if 'reader' not in kwargs:\n raise TypeError(\"reader parameter is required\")\n else:\n reader = kwargs.pop('reader')\n\n tfiles = []\n try:\n srt = functools.partial(_file2tempfiles, reader=reader, **kwargs)\n tfiles += list(it.chain(*map(srt, infiles)))\n finally:\n with tools.delete_files(*tfiles) as merge:\n for item in _mergefiles2stream(\n *merge,\n reader=reader,\n key=kwargs.get('key'),\n reverse=kwargs.get('reverse', False)):\n yield item\n\n\ndef stream2file(stream, outfile, writer, **kwargs):\n\n \"\"\"\n Convenience wrapper for `stream2stream()` for writing to a file on disk.\n\n Parameters\n ----------\n stream : iter\n Sort this stream of data.\n outfile : str\n Write sorted data to a file at this path. Will be overwritten if it\n already exists.\n writer : tinysort.io.BaseSerializer\n Instance of the serializer for writing `outfile`.\n kwargs : **kwargs\n Keyword arguments for `stream2stream`.\n\n Returns\n -------\n str\n `outfile`\n \"\"\"\n\n with writer.open(outfile, 'w') as dst:\n\n # We already have everything in-memory so this is faster\n if isinstance(stream, (list, tuple, dict)):\n sorted_data = sorted(\n stream,\n key=kwargs.get('key'),\n reverse=kwargs.get('reverse', False))\n\n else:\n sorted_data = stream2stream(stream, **kwargs)\n\n for item in sorted_data:\n dst.write(item)\n\n return outfile\n\n\ndef file2file(infile, outfile, reader, writer, **kwargs):\n\n \"\"\"\n A _super_ convenient wrapper for reading a file on disk and writing to a\n file on disk.\n\n Parameters\n ----------\n infile : str\n Path to input file.\n outfile : str\n Path to output file. Will be overwritten if it already exists.\n reader : tinysort.io.BaseSerializer\n Instance of the serializer for reading `infile`.\n writer : tinysort.io.BaseSerializer\n Instance of the serializer for writing `outfile`.\n kwargs : **kwargs, optional\n Keyword arguments for `file2stream()`.\n\n Returns\n -------\n str\n `outfile`\n \"\"\"\n\n with writer.open(outfile, 'w') as dst:\n for item in file2stream(infile, reader=reader, **kwargs):\n dst.write(item)\n\n return outfile\n", "sub_path": "tinysort/_sort.py", "file_name": "_sort.py", "file_ext": "py", "file_size_in_byte": 9283, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 38, "usage_type": "call"}, {"api_name": "tinysort.io.Pickle", "line_number": 42, "usage_type": "call"}, {"api_name": "tinysort.io", "line_number": 42, "usage_type": "attribute"}, {"api_name": "tempfile.mkstemp", "line_number": 61, "usage_type": "call"}, {"api_name": "tinysort.tools.slicer", "line_number": 101, "usage_type": "call"}, {"api_name": "tinysort.tools", "line_number": 101, "usage_type": "name"}, {"api_name": "tinysort.tools.runner", "line_number": 103, "usage_type": "call"}, {"api_name": "tinysort.tools", "line_number": 103, "usage_type": "name"}, {"api_name": "tinysort.tools.batch_open", "line_number": 157, "usage_type": "call"}, {"api_name": "tinysort.tools", "line_number": 157, "usage_type": "name"}, {"api_name": "tinysort._backport_heapq.merge", "line_number": 158, "usage_type": "call"}, {"api_name": "tinysort.tools.delete_files", "line_number": 210, "usage_type": "call"}, {"api_name": "tinysort.tools", "line_number": 210, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 270, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 271, "usage_type": "call"}, {"api_name": "tinysort.tools.delete_files", "line_number": 273, "usage_type": "call"}, {"api_name": "tinysort.tools", "line_number": 273, "usage_type": "name"}]} +{"seq_id": "311152435", "text": "from aiogram import Bot, types\nfrom aiogram.dispatcher import Dispatcher\nfrom aiogram.utils import executor\nfrom aiogram.dispatcher.filters import Text\n\nfrom config import TOKEN\n\nfrom aiogram.types import ReplyKeyboardRemove, \\\n ReplyKeyboardMarkup, KeyboardButton, \\\n InlineKeyboardMarkup, InlineKeyboardButton\n\nbot = Bot(token=TOKEN)\ndp = Dispatcher(bot)\n\n@dp.message_handler(commands=['start'])\nasync def process_start_command(message: types.Message):\n keyboard = types.ReplyKeyboardMarkup(resize_keyboard=True)\n buttons = [\"Первая кнопка\", \"Вторая кнопка\"]\n keyboard.add(*buttons)\n await message.reply(\"Привет!\\nНапиши мне что-нибудь!\", reply_markup=keyboard)\n\n@dp.message_handler(Text(equals=\"Первая кнопка\"))\nasync def with_puree(message: types.Message):\n await message.reply(\"Выбрана Первая кнопка!\")\n\n\n\n@dp.message_handler(commands=['help'])\nasync def process_help_command(message: types.Message):\n await message.reply(\"Напиши мне что-нибудь, и я отпрпавлю этот текст тебе в ответ!\")\n\n\n@dp.message_handler()\nasync def echo_message(msg: types.Message):\n await bot.send_message(msg.from_user.id, msg.text)\n\n\nif __name__ == '__main__':\n executor.start_polling(dp)", "sub_path": "bot.py", "file_name": "bot.py", "file_ext": "py", "file_size_in_byte": 1325, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "aiogram.Bot", "line_number": 12, "usage_type": "call"}, {"api_name": "config.TOKEN", "line_number": 12, "usage_type": "name"}, {"api_name": "aiogram.dispatcher.Dispatcher", "line_number": 13, "usage_type": "call"}, {"api_name": "aiogram.types.Message", "line_number": 16, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 16, "usage_type": "name"}, {"api_name": "aiogram.types.ReplyKeyboardMarkup", "line_number": 17, "usage_type": "call"}, {"api_name": "aiogram.types", "line_number": 17, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 23, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 23, "usage_type": "name"}, {"api_name": "aiogram.dispatcher.filters.Text", "line_number": 22, "usage_type": "call"}, {"api_name": "aiogram.types.Message", "line_number": 29, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 29, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 34, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 34, "usage_type": "name"}, {"api_name": "aiogram.utils.executor.start_polling", "line_number": 39, "usage_type": "call"}, {"api_name": "aiogram.utils.executor", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "176983146", "text": "from enum import Enum, unique\nfrom entorno import Entorno\nimport storageManager.jsonMode as DBMS\nimport typeChecker.typeReference as TRef\nimport typeChecker.typeEnum as TEnum\nimport sqlErrors\nfrom reporteErrores.errorReport import ErrorReport\nfrom useDB.instanciaDB import DB_ACTUAL\n\nclass IS(Enum):\n TRUE = 1\n FALSE = 2\n NULL = 3\n DISTINCT = 4\n UNKNOWN = 5\n \nclass ALTER_TABLE_DROP(Enum):\n COLUMN = 1\n CONSTRAINT = 2\n\nclass ALTER_TABLE_ADD(Enum):\n COLUMN = 1\n UNIQUE = 2\n FOREIGN_KEY = 3\n MULTI_FOREIGN_KEY = 3\n CHECKS = 4\n\nclass CONSTRAINT_FIELD(Enum):\n UNIQUE = 1\n PRIMARY_KEY = 2\n NULL = 3\n\nclass TYPE_COLUMN(Enum):\n SMALLINT = 'SMALLINT'\n BIGINT = 'BIGINT'\n INTEGER = 'INTEGER'\n DECIMAL = 'DECIMAL'\n NUMERIC = 'NUMERIC'\n REAL = 'REAL'\n DOUBLE_PRECISION = 'DOUBLE_PRECISION'\n MONEY = 'MONEY'\n CHAR = 'CHAR'\n VARCHAR = 'VARCHAR'\n TEXT = 'TEXT'\n BOOLEAN = 'BOOLEAN'\n # No implementadas\n TIME = 'TIME'\n TIMESTAMP = 'TIMESTAMP'\n DATE = 'DATE'\n INTERVAL = 'INTERVAL'\n\n# ------------------------ DDL ----------------------------\n# Instruccion (Abstracta)\nclass Instruccion:\n def ejecutar(self,ts):\n pass\n\n def dibujar(self):\n pass\n\nclass CreateType(Instruccion):\n def __init__(self, nombre, lista):\n self.nombre = nombre\n self.lista = lista\n\n def ejecutar(self, ts):\n lista = list()\n for item in self.lista:\n if not item.val in lista:\n lista.append(item.val)\n\n if not TEnum.insertEnum(self.nombre, lista):\n return ErrorReport('Semantico', 'Invalid Enum Declaration', self.lista[0].linea)\n return 'Enum \\'' + self.nombre + '\\' succesful created'\n\n\n# Create Database\nclass CreateDatabase(Instruccion):\n def __init__(self, nombre, reemplazo = False, existencia = False, duenio = None, modo = 0):\n self.nombre = nombre\n self.reemplazo = reemplazo\n self.existencia = existencia\n self.duenio = duenio\n self.modo = modo\n\n def dibujar(self):\n identificador = str(hash(self))\n\n nodo = \"\\n\" + identificador + \"[ label = \\\"CREATE DATABASE\\\" ];\"\n\n if self.reemplazo:\n nodo += \"\\nREPLACE\" + identificador + \"[ label = \\\"OR REPLACE\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> REPLACE\" + identificador + \";\"\n\n nodo += \"\\nNAME\" + identificador + \"[ label = \\\"\" + self.nombre + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> NAME\" + identificador + \";\"\n\n if self.existencia:\n nodo += \"\\nEXISTS\" + identificador + \"[ label = \\\"IF EXISTS\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> EXISTS\" + identificador + \";\"\n\n if self.duenio:\n nodo += \"\\nOWNER\" + identificador + \"[ label = \\\"OWNER\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> OWNER\" + identificador + \";\"\n nodo += \"\\nOWNERNAME\" + identificador + \"[ label = \\\"\"+ self.duenio + \"\\\" ];\"\n nodo += \"\\nOWNER\" + identificador + \" -> OWNERNAME\" + identificador + \";\"\n\n if self.modo > 0:\n nodo += \"\\nMODE\" + identificador + \"[ label = \\\"\" + self.modo + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> MODE\" + identificador + \";\"\n\n return nodo\n\n def ejecutar(self,ts):\n if TRef.databaseExist(self.nombre):\n if not self.existencia:\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.duplicate_database), 0)\n\n exito = 0\n databases = DBMS.showDatabases()\n\n if self.reemplazo:\n if self.nombre in databases: #Eliminamos si existe \n DBMS.dropDatabase(self.nombre)\n TRef.dropDatabase(self.nombre)\n exito = DBMS.createDatabase(self.nombre)\n elif self.existencia:\n if not self.nombre in databases:\n exito = DBMS.createDatabase(self.nombre)\n else:\n exito = DBMS.createDatabase(self.nombre)\n\n #Si tenemos exito se crea en el type reference\n if exito == 1:\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.invalid_schema_definition), 0)\n elif exito == 2:\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.duplicate_database), 0)\n \n TRef.createDatabase(self.nombre, self.modo)\n return \"Database '\" + self.nombre + \"' succesful created\"\n\n# Create Table\nclass CreateTable(Instruccion):\n def __init__(self, nombre, columnas, herencia = None):\n self.nombre = nombre\n self.columnas = columnas\n self.herencia = herencia\n\n def dibujar(self):\n identificador = str(hash(self))\n\n nodo = \"\\n\" + identificador + \"[ label = \\\"CREATE TABLE\\\" ];\"\n nodo += \"\\nNAME\" + identificador + \"[ label=\\\"\" + self.nombre + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> NAME\" + identificador + \";\\n//COLUMNAS DE LA TABLA\" + identificador + \"\\n\"\n\n for col in self.columnas:\n nodo += \"\\n\" + identificador + \" -> \" + str(hash(col)) + \";\"\n nodo += col.dibujar()\n\n if self.herencia:\n nodo += \"\\nINHERITS\" + identificador + \"[ label=\\\"INHERITS\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> INHERITS\" + identificador + \";\"\n nodo += \"SUPER\" + identificador + \"[ label=\\\"\" + self.herencia + \"\\\" ];\"\n nodo += \"\\nINHERITS\" + identificador + \" -> SUPER\" + identificador + \";\"\n\n return nodo\n\n def ejecutar(self, ts):\n if DB_ACTUAL.getName() == None:\n return ErrorReport('Semantico', 'Not defined database to used', 0)\n elif not TRef.databaseExist(DB_ACTUAL.getName()):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_invalid_schema_name.invalid_schema_name), 0)\n elif TRef.tableExist(DB_ACTUAL.getName(), self.nombre):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.duplicate_table), 0)\n\n # Aux de comprobacion y almacenamiento\n columns = dict()\n auxFK = list()\n auxPK = list()\n auxUnique = list()\n auxCheck = list()\n\n # Proceso de las distintas columnas recibidas en la consulta\n for col in self.columnas:\n if isinstance(col, CreateField): #Columna nueva\n #Obtenemos cada columna y corroboramos que tengan nombres distintos\n if col.nombre in columns:\n return 1\n else: \n colSint = col.ejecutar(ts)\n if isinstance(colSint, ErrorReport):\n return colSint\n columns[col.nombre] = colSint\n elif isinstance(col, ConstraintMultipleFields): #Multiples Constraints\n if col.tipo == CONSTRAINT_FIELD.UNIQUE:\n auxUnique.extend(col.ejecutar(ts))\n else:\n auxPK.extend(col.ejecutar(ts))\n elif isinstance(col, ForeignKeyMultipleFields): #Multiples Llaves Foraneas\n colSint = col.ejecutar(ts)\n if isinstance(colSint, ErrorReport):\n return colSint\n auxFK.extend(colSint)\n elif isinstance(col, CheckMultipleFields): #Multiple chequeos\n auxCheck.extend(col.ejecutar(ts))\n else:\n return col\n \n #Modificamos los valores dependiendo de las columnas multiples\n # Primary Key\n for pk in auxPK:\n # Se verifica que cada constraint haga referencia a un campo, de lo contrario será invalido\n if not pk in columns:\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.invalid_column_reference), 0)\n columns[pk]['PK'] = True\n\n # Foreign Key\n for fk in auxFK:\n # Se verifica que cada constraint haga referencia a un campo, de lo contrario será invalido\n if not fk[0] in columns:\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.invalid_column_reference), 0)\n columns[fk[0]]['FK'] = True\n columns[fk[0]]['References'] = {'Table':fk[1],'Field':fk[0]}\n\n for chequeo in auxCheck:\n # Se verifica que cada constraint haga referencia a un campo, de lo contrario será invalido\n if not chequeo[0] in columns:\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.invalid_column_reference), 0)\n #TODO Mejorar implementacion de checks\n columns[chequeo[0]]['Check'] = chequeo[1]\n\n # Unique\n for unico in auxUnique:\n # Se verifica que cada constraint haga referencia a un campo, de lo contrario será invalido\n if not unico in columns:\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.invalid_column_reference), 0)\n columns[unico]['Unique'] = True\n \n #--------- Herencia\n if self.herencia:\n if not TRef.tableExist(DB_ACTUAL.getName(), self.herencia):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_fdw_error.fdw_table_not_found), 0)\n else:\n colsPadre = TRef.getColumns(DB_ACTUAL.getName(), self.herencia)\n for col in colsPadre:\n # Verificamos que no existan columnas repetidas con el padre, ya que no existe el polimorfismo de campos\n if col in columns:\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.duplicate_column), 0)\n # De no existir columnas duplicadas, se agregan las columnas a la tabla\n columns.update(colsPadre)\n\n # Ahora procedemos a crear\n result = DBMS.createTable(DB_ACTUAL.getName(), self.nombre, len(columns))\n\n if result == 0:\n TRef.createTable(DB_ACTUAL.getName(), self.nombre, columns, self.herencia)\n return result\n\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.duplicate_table), 0)\n \n# Create Field\nclass CreateField(Instruccion):\n def __init__(self, nombre, tipo, atributos = None):\n self.nombre = nombre\n self.tipo = tipo\n self.atributos = atributos\n\n def dibujar(self):\n identificador = str(hash(self))\n\n nodo = \"\\n\" + identificador + \"[ label = \\\"NEW FIELD\\\" ];\"\n nodo += \"\\nNAME\" + identificador + \"[ label = \\\"\" + self.nombre + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> NAME\" + identificador + \";\\n//ATRIBUTOS DE CREAR UN CAMPO \" + identificador + \"\\n\"\n\n if self.atributos:\n for atributo in self.atributos:\n nodo += \"\\n\" + identificador + \" -> \" + str(hash(atributo))\n nodo += atributo.dibujar()\n\n nodo += \"\\n//FIN DE ATRIBUTOS DE CREAR CAMPO \" + identificador + \"\\n\"\n\n return nodo\n\n def ejecutar(self, ts):\n #Guardamos el tipo y largo si es necesario\n tipo = None\n largo = None\n\n if isinstance(self.tipo, tuple):\n tipo = self.tipo[0].value\n largo = self.tipo[1]\n else:\n tipo = self.tipo.value\n\n #Bajo la logica de que puede venir parametros repetidos, tomaremos el ultimo en venir como valido\n atributos = dict(\n {\n \"Type\": tipo,\n \"Lenght\": largo,\n \"Default\": None,\n \"Null\": True,\n \"PK\": False,\n \"PKConst\": None,\n \"FK\": False,\n \"References\": None,\n \"FKConst\": None,\n \"Unique\": False,\n \"UniqueConst\": None,\n \"Check\": None,\n \"CheckConst\": None\n }\n )\n\n if self.atributos:\n for atr in self.atributos:\n if isinstance(atr, ConstraintField):\n if atr.tipo == CONSTRAINT_FIELD.PRIMARY_KEY:\n atributos['PK'] = True\n\n elif atr.tipo == CONSTRAINT_FIELD.UNIQUE:\n atributos['Unique'] = True\n atributos['UniqueConst'] = atr.ejecutar(ts)\n\n elif atr.tipo == CONSTRAINT_FIELD.NULL:\n atributos['Null'] = atr.ejecutar(ts)\n elif isinstance(atr, ForeignKeyField):\n fk = atr.ejecutar(ts)\n if isinstance(fk, ErrorReport):\n return fk\n else:\n colFK = TRef.getColumns(DB_ACTUAL.getName(), fk['Table'])[fk['Field']]\n if colFK['Type'] != tipo:\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_fdw_error.fdw_invalid_data_type), 0)\n atributos['References'] = fk\n atributos['FK'] = True\n elif isinstance(atr, DefaultField):\n atributos['Default'] = atr.ejecutar(ts)\n elif isinstance(atr,CheckField):\n #TODO Mejorar implementacion de checks\n cheq = atr.ejecutar(ts)\n atributos['Check'] = cheq[1]\n atributos['CheckConst'] = cheq[0]\n else:\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.invalid_table_definition), 0)\n\n return atributos\n\n# Default Field\nclass DefaultField(Instruccion):\n def __init__(self, valor):\n self.valor = valor\n\n def dibujar(self):\n identificador = str(hash(self))\n\n nodo = \"\\n\" + identificador + \"[ label = \\\"DEFAULT\\\" ];\"\n nodo += \"\\nDEFAULT\" + identificador + \"[ label = \\\"\" + self.valor + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> DEFAULT\" + identificador + \";\\n\"\n\n return nodo\n\n def ejecutar(self, ts):\n return self.valor\n\n# Check Field\nclass CheckField(Instruccion):\n def __init__(self, condiciones, nombre = None):\n self.condiciones = condiciones\n self.nombre = nombre\n \n def dibujar(self):\n identificador = str(hash(self))\n\n nodo = \"\\n\" + identificador + \"[ label = \\\"CHECK\\\" ];\"\n\n if self.nombre:\n nodo += \"\\nNAME\" + identificador + \"[ label = \\\"\" + self.nombre + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> NAME\" + identificador + \";\"\n\n for condicion in self.condiciones:\n nodo += \"\\n\" + identificador + \" -> \" + str(hash(condicion)) + \";\"\n nodo += condicion.dibujar()\n\n return nodo\n\n def ejecutar(self, ts):\n return (self.nombre,self.condiciones)\n\n# Constraint Field\nclass ConstraintField(Instruccion):\n def __init__(self, tipo, valor = None):\n self.tipo = tipo\n self.valor = valor\n\n def dibujar(self):\n identificador = str(hash(self))\n\n nodo = \"\\n\" + identificador\n\n if self.tipo == CONSTRAINT_FIELD.UNIQUE:\n nodo += \"[ label = \\\"UNIQUE\\\" ];\"\n elif self.tipo == CONSTRAINT_FIELD.NULL:\n nodo += \"[ label = \\\"NULLS\\\" ];\"\n else:\n nodo += \"[ label = \\\"PRIMARY KEY\\\" ];\"\n\n if self.valor:\n nodo += \"\\nNAME\" + identificador + \"[ label = \\\"\" + self.valor + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> NAME\" + identificador + \";\"\n\n return nodo\n \n def ejecutar(self, ts):\n return self.valor\n\n#ForeignKey Field\nclass ForeignKeyField(Instruccion):\n def __init__(self, tabla, campo):\n self.tabla = tabla\n self.campo = campo\n\n def ejecutar(self, ts):\n if TRef.columnExist(DB_ACTUAL.getName(), self.tabla, self.campo):\n return {'Table': self.tabla, 'Field': self.campo}\n else:\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_fdw_error.fdw_column_name_not_found), 0)\n\n# Constraint Multiple Fields: Comprende tanto Unique como Primary Key\nclass ConstraintMultipleFields(Instruccion):\n def __init__(self, tipo, lista):\n self.tipo = tipo\n self.lista = lista\n\n def dibujar(self):\n identificador = str(hash(self))\n\n nodo = \"\\n\" + identificador\n\n if self.tipo == CONSTRAINT_FIELD.UNIQUE:\n nodo += \"[ label = \\\"UNIQUE MULTIPLE\\\" ];\"\n else:\n nodo += \"[ label = \\\"PRIMARY KEY MULTIPLE\\\" ];\"\n\n for item in self.lista:\n nodo += \"\\n\" + identificador + \" -> \" + str(hash(item)) + \";\"\n nodo += item.dibujar()\n\n return nodo\n\n def ejecutar(self, ts):\n return self.lista\n \n# Foreign Key Multiple Fields\nclass ForeignKeyMultipleFields(Instruccion):\n def __init__(self, listaPropia, otraTabla, listaOtraTabla):\n self.lista = listaPropia\n self.otraTabla = otraTabla\n self.listaOtraTabla = listaOtraTabla\n\n def dibujar(self):\n identificador = str(hash(self))\n\n nodo = \"\\n\" + identificador + \"[ label = \\\"FOREIGN KEY MULTIPLE\\\" ];\"\n\n nodo += \"\\nLOCAL\" + identificador + \"[ label = \\\"LOCAL FIELDS\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> LOCAL\" + identificador + \";\"\n\n for item in self.lista:\n nodo += \"\\nLOCAL\" + identificador + \" -> \" + str(hash(item)) + \";\"\n nodo += item.dibujar()\n\n nodo += \"\\nFOREIGN\" + identificador + \"[ label = \\\"\" + self.otraTabla + \" FIELDS\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> FOREIGN\" + identificador + \";\"\n\n for item in self.listaOtraTabla:\n nodo += \"\\nFOREIGN\" + identificador + \" -> \" + str(hash(item)) + \";\"\n nodo += item.dibujar()\n\n return nodo\n\n def ejecutar(self, ts):\n if not TRef.databaseExist(DB_ACTUAL.getName()):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_fdw_error.fdw_schema_not_found), 0)\n elif not TRef.tableExist(DB_ACTUAL.getName(),self.otraTabla):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_fdw_error.fdw_table_not_found), 0) \n\n #Comparamos que la misma cantidad de ids propios sea igual a la foranea\n if len(self.lista) != len(self.listaOtraTabla):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_data_exception.data_exception), 0)\n\n for col in self.listaOtraTabla:\n if not TRef.columnExist(DB_ACTUAL.getName(), self.otraTabla, col):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_fdw_error.fdw_invalid_column_number), 0)\n\n listaSin = list()\n for i in range(len(self.lista)):\n listaSin.append( (self.lista[i], self.otraTabla, self.listaOtraTabla[i]) )\n\n return listaSin\n\n# Check Multiple Fields\nclass CheckMultipleFields(Instruccion):\n def __init__(self, campo, condiciones):\n self.campo = campo\n self.condiciones = condiciones\n\n def dibujar(self):\n identificador = str(hash(self))\n\n nodo = \"\\n\" + identificador + \"[ label = \\\"CHECK MULTIPLE\\\" ];\"\n\n for condicion in self.condiciones:\n nodo += \"\\n\" + identificador + \" -> \" + str(hash(condicion)) + \";\"\n nodo += condicion.dibujar()\n\n return nodo\n\n def ejecutar(self, ts):\n return (self.campo, self.condiciones.simular())\n\n# Alter Database\nclass AlterDatabase(Instruccion):\n def __init__(self, nombre, accion):\n self.nombre = nombre\n self.accion = accion\n\n def dibujar(self):\n identificador = str(hash(self))\n \n nodo = \"\\n\" + identificador + \"[ label = \\\"ALTER DATABASE\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> \" + str(hash(self.accion)) + \";\"\n \n if self.accion[0] == 'OWNER':\n subid = str(hash(self.accion))\n nodo += \"\\n\" + subid + \"[ label = \\\"OWNER\\\" ];\"\n nodo += \"\\n\" + subid + \" -> OWNER\" + subid + \";\"\n nodo += \"\\nOWNER\" + subid + \"[ label = \\\"\" + self.accion[1] + \"\\\" ];\"\n nodo += \"\\n\" + subid + \" -> OWNER\" + subid + \";\\n\"\n else:\n subid = str(hash(self.accion))\n nodo += \"\\n\" + subid + \"[ label = \\\"NAME\\\" ];\"\n nodo += \"\\n\" + subid + \" -> NAME\" + subid + \";\"\n nodo += \"\\nNAME\" + subid + \"[ label = \\\"\" + self.accion[1] + \"\\\" ];\"\n nodo += \"\\n\" + subid + \" -> NAME\" + subid + \";\\n\"\n\n return nodo\n \n def ejecutar(self, ts):\n if not TRef.databaseExist(self.nombre):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_invalid_schema_name.invalid_schema_name), 0)\n\n if self.accion[0] == 'OWNER':\n pass\n else:\n #Comprobamos que no exista una base de datos con ese nombre\n if TRef.databaseExist(self.accion[1]):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.duplicate_database), 0)\n DBMS.alterDatabase(self.nombre, self.accion[1])\n TRef.alterDatabase(self.nombre, self.accion[1])\n return 0\n\n# Alter Table\nclass AlterTable(Instruccion):\n def __init__(self, tabla, accion):\n self.tabla = tabla\n self.accion = accion\n\n def dibujar(self):\n identificador = str(hash(self))\n \n nodo = \"\\n\" + identificador + \"[ label = \\\"ALTER TABLE\\\" ];\"\n\n nodo += \"\\nNAME\" + identificador + \"[ label = \\\"\" + self.tabla + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> NAME\" + identificador + \";\"\n\n nodo += \"\\n\" + identificador + \" -> \" + str(hash(self.accion)) + \";\"\n nodo += self.accion.dibujar()\n\n return nodo\n \n def ejecutar(self, ts):\n if DB_ACTUAL.getName() == None:\n return ErrorReport('Semantico', 'Not defined database to used', 0)\n elif not TRef.databaseExist(DB_ACTUAL.getName()):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_invalid_schema_name.invalid_schema_name), 0)\n elif not TRef.tableExist(DB_ACTUAL.getName(), self.tabla):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.undefined_table), 0)\n\n if isinstance(self.accion, list):\n for subaccion in self.accion:\n sint = subaccion.ejecutar(ts)\n #Si es un error, solo se retorna\n if isinstance(sint, ErrorReport):\n return sint\n elif isinstance(self.accion, AlterField):\n #Comprobamos la existencia del campo\n if not TRef.columnExist(DB_ACTUAL.getName(),self.tabla,self.accion.campo):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.undefined_column), 0)\n \n if self.accion.cantidad:\n sint = self.accion.ejecutar(ts)\n if isinstance(sint, int):\n print(TRef.alterField(DB_ACTUAL.getName(), self.tabla, self.accion.campo, 'Type', TYPE_COLUMN.VARCHAR.value))\n print(TRef.alterField(DB_ACTUAL.getName(), self.tabla, self.accion.campo, 'Lenght', sint))\n elif isinstance(sint, tuple):\n print(TRef.alterField(DB_ACTUAL.getName(), self.tabla, self.accion.campo, 'Type', sint[0].value))\n print(TRef.alterField(DB_ACTUAL.getName(), self.tabla, self.accion.campo, 'Lenght', sint[1]))\n else:\n print(TRef.alterField(DB_ACTUAL.getName(), self.tabla, self.accion.campo, 'Type', sint))\n print(TRef.alterField(DB_ACTUAL.getName(), self.tabla, self.accion.campo, 'Lenght', None))\n else:\n print(TRef.alterField(DB_ACTUAL.getName(), self.tabla, self.accion.campo, 'Null', False))\n elif isinstance(self.accion, AlterTableDrop):\n if self.accion.tipo == ALTER_TABLE_DROP.COLUMN:\n sint = self.accion.ejecutar(ts)\n #Comprobamos la existencia del campo\n if not TRef.columnExist(DB_ACTUAL.getName(),self.tabla,self.accion.campo):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.undefined_column), 0)\n dropField = TRef.alterDropColumn(DB_ACTUAL.getName(), self.tabla, sint)\n if dropField == 1:\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_data_exception.data_exception), 0)\n elif dropField == 4:\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_integrity_constraint_violation.integrity_constraint_violation), 0)\n elif dropField == 6:\n return ErrorReport('Semantico', 'Error: A table cannot be empty', 0)\n else:\n if not TRef.constraintExist(self.accion.nombre):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_integrity_constraint_violation.integrity_constraint_violation), 0)\n colPres = TRef.getConstraint(DB_ACTUAL.getName(),self.tabla, self.accion.nombre)\n if not isinstance(colPres, tuple):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_data_exception.data_exception), 0)\n TRef.alterField(DB_ACTUAL.getName(), self.tabla, colPres[0], colPres[1], None)\n\n\n return 'Alter table complete'\n \n \n \n \n\n\n# Alter Field: Cambia al tipo varchar o cambia ser nulo\nclass AlterField(Instruccion):\n def __init__(self, campo, cantidad = None):\n self.campo = campo\n self.cantidad = cantidad\n\n def dibujar(self):\n identificador = str(hash(self))\n\n nodo = \"\\n\" + identificador \n\n if self.cantidad:\n nodo += \"[ label = \\\"ALTER COLUMN \" + self.campo + \" TYPE\\\" ];\"\n\n nodo += \"\\nTYPE\" + identificador + \"[ label = \\\"VARCHAR(\" + str(self.cantidad) + \")\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> TYPE\" + identificador + \";\\n\"\n else:\n nodo += \"[ label = \\\"ALTER COLUMN \" + self.campo + \" SET\\\" ];\"\n\n nodo += \"\\nVALUE\" + identificador + \"[ label = \\\"NOT NULL\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> VALUE\" + identificador + \";\\n\"\n\n return nodo\n \n def ejecutar(self, ts):\n # Verificar si existe la columna\n if self.cantidad:\n if isinstance(self.cantidad, int):\n if self.cantidad < 0:\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_data_exception.numeric_value_out_of_range),0)\n return self.cantidad\n elif isinstance(self.cantidad, tuple):\n return self.cantidad\n else:\n return self.cantidad.value\n else:\n return False\n\n# Alter Table Drop: Encapsula tanto constraints como columna\nclass AlterTableDrop(Instruccion):\n def __init__(self, nombre, tipo):\n self.nombre = nombre\n self.tipo = tipo\n\n def dibujar(self):\n identificador = str(hash(self))\n\n nodo = \"\\n\" + identificador\n\n if self.tipo == ALTER_TABLE_DROP.CONSTRAINT:\n nodo += \"[ label = \\\"DROP CONSTRAINT\\\" ];\"\n else:\n nodo += \"[ label = \\\"DROP COLUMN\\\" ];\"\n\n nodo += \"\\nNAME\" + identificador + \"[ label = \\\"\" + self.nombre + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> NAME\" + identificador + \";\\n\"\n\n return nodo \n\n def ejecutar(self, ts):\n return self.nombre\n\n# Alter add \nclass AlterTableAdd(Instruccion):\n def __init__(self, nombre, tipo, accion):\n self.nombre = nombre\n self.tipo = tipo\n self.accion = accion\n\n def dibujar(self):\n identificador = str(hash(self))\n\n nodo = \"\\n\" + identificador \n\n if self.tipo == ALTER_TABLE_ADD.UNIQUE:\n nodo += \"[ label = \\\"ADD UNIQUE\\\" ];\"\n nodo += \"\\nNAME\" + identificador + \"[ label = \\\"\" + self.nombre + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> NAME\" + identificador + \";\"\n nodo += \"\\nID\" + identificador + \"[ label = \\\"\" + self.accion + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> ID\" + identificador + \";\\n\"\n elif self.tipo == ALTER_TABLE_ADD.FOREIGN_KEY:\n nodo += \"[ label = \\\"ADD FOREIGN KEY\\\" ];\"\n for local in self.nombre:\n nodo += \"\\n\" + str(hash(local)) + \"[ label =\\\"\" + local + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> \" + str(hash(local)) + \";\"\n nodo += \"\\nTABLA\" + identificador + \"[ label = \\\"\" + self.accion[0] + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> TABLA\" + identificador + \";\"\n for foraneo in self.accion[1]:\n nodo += \"\\n\" + str(hash(foraneo)) + \"[ label =\\\"\" + foraneo + \"\\\" ];\"\n nodo += \"\\nTABLA\" + identificador + \" -> \" + str(hash(foraneo)) + \";\"\n elif self.tipo == ALTER_TABLE_ADD.CHECKS:\n nodo += \"[ label = \\\"ADD CHECKS\\\" ]\"\n nodo += \"\\nNAME\" + identificador + \"[ label = \\\"\" + self.nombre + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> NAME\" + identificador + \";\"\n nodo += \"\\nACTION\" + identificador + \"[ label = \\\"\" + self.accion + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> ACTION\" + identificador + \";\\n\"\n else:\n nodo += \"[ label = \\\"ADD COLUMN\\\" ];\"\n nodo += \"\\nNAME\" + identificador + \"[ label = \\\"\" + self.nombre + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> NAME\" + identificador + \";\"\n nodo += \"\\nTYPE\" + identificador + \"[ label = \\\"\" + self.accion + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> TYPE\" + identificador + \";\\n\"\n return nodo\n\n def ejecutar(self, ts):\n if self.tipo == ALTER_TABLE_ADD.FOREIGN_KEY:\n if not TRef.tableExist(DB_ACTUAL.getName(),self.accion[0]):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_fdw_error.fdw_table_not_found), 0) \n if not TRef.columnExist(DB_ACTUAL.getName(), self.accion[0], self.accion[1]):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_fdw_error.fdw_invalid_column_number), 0)\n return (self.nombre,self.accion)\n elif self.tipo == ALTER_TABLE_ADD.MULTI_FOREIGN_KEY:\n if not TRef.tableExist(DB_ACTUAL.getName(),self.accion[0]):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_fdw_error.fdw_table_not_found), 0) \n\n #Comparamos que la misma cantidad de ids propios sea igual a la foranea\n if len(self.nombre) != len(self.accion[1]):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_data_exception.data_exception), 0)\n\n for col in self.accion[1]:\n if not TRef.columnExist(DB_ACTUAL.getName(), self.accion[0], col):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_fdw_error.fdw_invalid_column_number), 0)\n\n listaSin = list()\n for i in range(len(self.nombre)):\n listaSin.append( (self.nombre[i], self.accion[0], self.accion[1][i]) )\n\n return listaSin\n\n return (self.nombre, self.accion)\n# Show Database\nclass ShowDatabase(Instruccion):\n def __init__(self, like = None):\n self.like = like\n\n def dibujar(self):\n identificador = str(hash(self))\n\n nodo = \"\\n\" + identificador + \"[ label = \\\"SHOW DATABASE\\\" ];\"\n nodo += \"\\nNAME\" + identificador + \"[ label = \\\"\" + self.db + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> NAME\" + identificador + \";\"\n if self.like:\n nodo += \"\\nLIKE\" + identificador + \"[ label = \\\"\" + self.like + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> LIKE\" + identificador + \";\"\n return nodo\n\n def ejecutar(self, ts):\n display = 'Databases\\n---------------------\\n'\n databases = TRef.showDatabases()\n\n for db in databases:\n display += db + '\\n'\n\n return display\n\n# Drop Database\nclass DropDatabase(Instruccion):\n def __init__(self, db, existencia = False):\n self.db = db\n self.existencia = existencia\n\n def dibujar(self):\n identificador = str(hash(self))\n\n nodo = \"\\n\" + identificador + \"[ label = \\\"DROP DATABASE\\\" ];\"\n nodo += \"\\nNAME\" + identificador + \"[ label = \\\"\" + self.db + \"\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> NAME\" + identificador + \";\"\n if self.existencia:\n nodo += \"\\nLIKE\" + identificador + \"[ label = \\\"IF EXISTS\\\" ];\"\n nodo += \"\\n\" + identificador + \" -> LIKE\" + identificador + \";\"\n return nodo\n\n def ejecutar(self, ts):\n if not TRef.databaseExist(self.db):\n if self.existencia:\n return \"Drop Database: Database doesn't exist\"\n else:\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_invalid_schema_name.invalid_schema_name), 0)\n\n DBMS.dropDatabase(self.db)\n TRef.dropDatabase(self.db)\n\n return 'Successful database dropped'\n\nclass DropTable(Instruccion):\n def __init__(self, tabla):\n self.tabla = tabla\n\n def ejecutar(self, ts):\n if DB_ACTUAL.getName() == None:\n return ErrorReport('Semantico', 'Not defined database to used', 0)\n elif not TRef.databaseExist(DB_ACTUAL.getName()):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_invalid_schema_name.invalid_schema_name), 0)\n elif TRef.tableExist(DB_ACTUAL.getName(), self.tabla):\n return ErrorReport('Semantico', sqlErrors.sqlErrorToString(sqlErrors.sql_error_syntax_error_or_access_rule_violation.undefined_table), 0)\n\n DBMS.dropTable(DB_ACTUAL.getName(), self.tabla)\n TRef.dropTable(DB_ACTUAL.getName(), self.tabla)\n return 'Successful table dropped' \n\ndef __comprobarTipo(ts,exp):\n pass", "sub_path": "parser/team25/code/astDDL.py", "file_name": "astDDL.py", "file_ext": "py", "file_size_in_byte": 34982, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "enum.Enum", "line_number": 10, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 17, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 21, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 28, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 33, "usage_type": "name"}, {"api_name": "typeChecker.typeEnum.insertEnum", "line_number": 72, "usage_type": "call"}, {"api_name": "typeChecker.typeEnum", "line_number": 72, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 73, "usage_type": "call"}, {"api_name": "typeChecker.typeReference.databaseExist", "line_number": 115, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 115, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 117, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 117, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_syntax_error_or_access_rule_violation", "line_number": 117, "usage_type": "attribute"}, {"api_name": "storageManager.jsonMode.showDatabases", "line_number": 120, "usage_type": "call"}, {"api_name": "storageManager.jsonMode", "line_number": 120, "usage_type": "name"}, {"api_name": "storageManager.jsonMode.dropDatabase", "line_number": 124, "usage_type": "call"}, {"api_name": "storageManager.jsonMode", "line_number": 124, "usage_type": "name"}, {"api_name": "typeChecker.typeReference.dropDatabase", "line_number": 125, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 125, "usage_type": "name"}, {"api_name": "storageManager.jsonMode.createDatabase", "line_number": 126, "usage_type": "call"}, {"api_name": "storageManager.jsonMode", "line_number": 126, "usage_type": "name"}, {"api_name": "storageManager.jsonMode.createDatabase", "line_number": 129, "usage_type": "call"}, {"api_name": "storageManager.jsonMode", "line_number": 129, "usage_type": "name"}, {"api_name": "storageManager.jsonMode.createDatabase", "line_number": 131, "usage_type": "call"}, {"api_name": "storageManager.jsonMode", "line_number": 131, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 135, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 135, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_syntax_error_or_access_rule_violation", "line_number": 135, "usage_type": "attribute"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 137, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 137, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_syntax_error_or_access_rule_violation", "line_number": 137, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.createDatabase", "line_number": 139, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 139, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 169, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 169, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 170, "usage_type": "call"}, {"api_name": "typeChecker.typeReference.databaseExist", "line_number": 171, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 171, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 171, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 171, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 172, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 172, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_invalid_schema_name", "line_number": 172, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.tableExist", "line_number": 173, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 173, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 173, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 173, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 174, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 174, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_syntax_error_or_access_rule_violation", "line_number": 174, "usage_type": "attribute"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 191, "usage_type": "argument"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 201, "usage_type": "argument"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 214, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 214, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_syntax_error_or_access_rule_violation", "line_number": 214, "usage_type": "attribute"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 221, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 221, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_syntax_error_or_access_rule_violation", "line_number": 221, "usage_type": "attribute"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 228, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 228, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_syntax_error_or_access_rule_violation", "line_number": 228, "usage_type": "attribute"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 236, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 236, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_syntax_error_or_access_rule_violation", "line_number": 236, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.tableExist", "line_number": 241, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 241, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 241, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 241, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 242, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 242, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_fdw_error", "line_number": 242, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.getColumns", "line_number": 244, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 244, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 244, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 244, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 248, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 248, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_syntax_error_or_access_rule_violation", "line_number": 248, "usage_type": "attribute"}, {"api_name": "storageManager.jsonMode.createTable", "line_number": 253, "usage_type": "call"}, {"api_name": "storageManager.jsonMode", "line_number": 253, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 253, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 253, "usage_type": "name"}, {"api_name": "typeChecker.typeReference.createTable", "line_number": 256, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 256, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 256, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 256, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 259, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 259, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_syntax_error_or_access_rule_violation", "line_number": 259, "usage_type": "attribute"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 328, "usage_type": "argument"}, {"api_name": "typeChecker.typeReference.getColumns", "line_number": 331, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 331, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 331, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 331, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 333, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 333, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_fdw_error", "line_number": 333, "usage_type": "attribute"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 344, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 344, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_syntax_error_or_access_rule_violation", "line_number": 344, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.columnExist", "line_number": 423, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 423, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 423, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 423, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 426, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 426, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_fdw_error", "line_number": 426, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.databaseExist", "line_number": 482, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 482, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 482, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 482, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 483, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 483, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_fdw_error", "line_number": 483, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.tableExist", "line_number": 484, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 484, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 484, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 484, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 485, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 485, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_fdw_error", "line_number": 485, "usage_type": "attribute"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 489, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 489, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_data_exception", "line_number": 489, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.columnExist", "line_number": 492, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 492, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 492, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 492, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 493, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 493, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_fdw_error", "line_number": 493, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.databaseExist", "line_number": 549, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 549, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 550, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 550, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_invalid_schema_name", "line_number": 550, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.databaseExist", "line_number": 556, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 556, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 557, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 557, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_syntax_error_or_access_rule_violation", "line_number": 557, "usage_type": "attribute"}, {"api_name": "storageManager.jsonMode.alterDatabase", "line_number": 558, "usage_type": "call"}, {"api_name": "storageManager.jsonMode", "line_number": 558, "usage_type": "name"}, {"api_name": "typeChecker.typeReference.alterDatabase", "line_number": 559, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 559, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 582, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 582, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 583, "usage_type": "call"}, {"api_name": "typeChecker.typeReference.databaseExist", "line_number": 584, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 584, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 584, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 584, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 585, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 585, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_invalid_schema_name", "line_number": 585, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.tableExist", "line_number": 586, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 586, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 586, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 586, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 587, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 587, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_syntax_error_or_access_rule_violation", "line_number": 587, "usage_type": "attribute"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 593, "usage_type": "argument"}, {"api_name": "typeChecker.typeReference.columnExist", "line_number": 597, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 597, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 597, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 597, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 598, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 598, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_syntax_error_or_access_rule_violation", "line_number": 598, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.alterField", "line_number": 603, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 603, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 603, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 603, "usage_type": "name"}, {"api_name": "typeChecker.typeReference.alterField", "line_number": 604, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 604, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 604, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 604, "usage_type": "name"}, {"api_name": "typeChecker.typeReference.alterField", "line_number": 606, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 606, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 606, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 606, "usage_type": "name"}, {"api_name": "typeChecker.typeReference.alterField", "line_number": 607, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 607, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 607, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 607, "usage_type": "name"}, {"api_name": "typeChecker.typeReference.alterField", "line_number": 609, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 609, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 609, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 609, "usage_type": "name"}, {"api_name": "typeChecker.typeReference.alterField", "line_number": 610, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 610, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 610, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 610, "usage_type": "name"}, {"api_name": "typeChecker.typeReference.alterField", "line_number": 612, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 612, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 612, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 612, "usage_type": "name"}, {"api_name": "typeChecker.typeReference.columnExist", "line_number": 617, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 617, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 617, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 617, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 618, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 618, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_syntax_error_or_access_rule_violation", "line_number": 618, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.alterDropColumn", "line_number": 619, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 619, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 619, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 619, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 621, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 621, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_data_exception", "line_number": 621, "usage_type": "attribute"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 623, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 623, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_integrity_constraint_violation", "line_number": 623, "usage_type": "attribute"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 625, "usage_type": "call"}, {"api_name": "typeChecker.typeReference.constraintExist", "line_number": 627, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 627, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 628, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 628, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_integrity_constraint_violation", "line_number": 628, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.getConstraint", "line_number": 629, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 629, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 629, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 629, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 631, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 631, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_data_exception", "line_number": 631, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.alterField", "line_number": 632, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 632, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 632, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 632, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 671, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 671, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_data_exception", "line_number": 671, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.tableExist", "line_number": 748, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 748, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 748, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 748, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 749, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 749, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_fdw_error", "line_number": 749, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.columnExist", "line_number": 750, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 750, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 750, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 750, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 751, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 751, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_fdw_error", "line_number": 751, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.tableExist", "line_number": 754, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 754, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 754, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 754, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 755, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 755, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_fdw_error", "line_number": 755, "usage_type": "attribute"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 759, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 759, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_data_exception", "line_number": 759, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.columnExist", "line_number": 762, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 762, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 762, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 762, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 763, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 763, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_fdw_error", "line_number": 763, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.showDatabases", "line_number": 790, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 790, "usage_type": "name"}, {"api_name": "typeChecker.typeReference.databaseExist", "line_number": 815, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 815, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 819, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 819, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_invalid_schema_name", "line_number": 819, "usage_type": "attribute"}, {"api_name": "storageManager.jsonMode.dropDatabase", "line_number": 821, "usage_type": "call"}, {"api_name": "storageManager.jsonMode", "line_number": 821, "usage_type": "name"}, {"api_name": "typeChecker.typeReference.dropDatabase", "line_number": 822, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 822, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 831, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 831, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 832, "usage_type": "call"}, {"api_name": "typeChecker.typeReference.databaseExist", "line_number": 833, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 833, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 833, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 833, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 834, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 834, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_invalid_schema_name", "line_number": 834, "usage_type": "attribute"}, {"api_name": "typeChecker.typeReference.tableExist", "line_number": 835, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 835, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 835, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 835, "usage_type": "name"}, {"api_name": "reporteErrores.errorReport.ErrorReport", "line_number": 836, "usage_type": "call"}, {"api_name": "sqlErrors.sqlErrorToString", "line_number": 836, "usage_type": "call"}, {"api_name": "sqlErrors.sql_error_syntax_error_or_access_rule_violation", "line_number": 836, "usage_type": "attribute"}, {"api_name": "storageManager.jsonMode.dropTable", "line_number": 838, "usage_type": "call"}, {"api_name": "storageManager.jsonMode", "line_number": 838, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 838, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 838, "usage_type": "name"}, {"api_name": "typeChecker.typeReference.dropTable", "line_number": 839, "usage_type": "call"}, {"api_name": "typeChecker.typeReference", "line_number": 839, "usage_type": "name"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL.getName", "line_number": 839, "usage_type": "call"}, {"api_name": "useDB.instanciaDB.DB_ACTUAL", "line_number": 839, "usage_type": "name"}]} +{"seq_id": "156194418", "text": "from smtplib import SMTP\nimport smtplib\nimport email\nimport time\nfrom email.mime.text import MIMEText\nfrom email.mime.multipart import MIMEMultipart\n\nclass SendMessage():\n\n def __init__(self): #sending gmail\n # google server\n self.server = smtplib.SMTP('smtp.gmail.com', 587) # host name and port\n\n # connect to the server\n self.server.ehlo()\n self.server.starttls()\n self.server.ehlo()\n\n\n\n def sendEmail(self, error):\n '''\n https://docs.python.org/3/library/email-examples.html\n https://en.wikibooks.org/wiki/Python_Programming/Email\n\n This function is the core of the email that will be sent out.\n :param error:\n :return: This will return the error given and added to the email.\n '''\n fromaddr = \"vegeta.jerkface@gmail.com\"\n toaddr = \"it@sixpackshortcuts.com\"\n #toaddr = \"lmayfield@spscoach.com\"\n msg = MIMEMultipart()\n msg['From'] = fromaddr\n msg['To'] = toaddr\n msg['Subject'] = 'ATTENTION!!! An Error has occurred with one of our sites.'\n\n body = \"At \" + time.strftime(\" %H:%M:%S \") + \" One of our websites has experienced the following problem. \\n \" \\\n \"The error is as follows: \\n\" + str(error)\n\n msg.attach(MIMEText(body, 'plain'))\n\n # login\n self.server.login(\"vegeta.jerkface@gmail.com\", \"Vegetasps1\")\n text = msg.as_string()\n #self.server.sendmail(fromaddr, toaddr, text)\n self.server.sendmail(msg['From'], msg['To'], text)\n\n\n\n#https://stackoverflow.com/questions/8856117/how-to-send-email-to-multiple-recipients-using-python-smtplib\n#https://stackoverflow.com/questions/8856117/how-to-send-email-to-multiple-recipients-using-python-smtplib\n#https://docs.python.org/3/library/email-examples.html\n\n# txt messaging? https://www.twilio.com/sms/pricing", "sub_path": "python_tools/utilities/smtp.py", "file_name": "smtp.py", "file_ext": "py", "file_size_in_byte": 1870, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "smtplib.SMTP", "line_number": 12, "usage_type": "call"}, {"api_name": "email.mime.multipart.MIMEMultipart", "line_number": 33, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 38, "usage_type": "call"}, {"api_name": "email.mime.text.MIMEText", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "528426096", "text": "from django.db import models\nfrom phone_field import PhoneField\nfrom phonenumber_field.modelfields import PhoneNumberField\n\n# Create your models here.\n\nclass Detail(models.Model):\n name \t\t = models.CharField(max_length=100,blank=False,null=True)\n url \t\t = models.URLField(max_length = 200, blank=False)\n phone_number = PhoneNumberField(unique=True)\n # phone_number = PhoneField(blank=False, help_text='Contact phone number')\n\nclass GetImageSearch(models.Model):\n\tname \t\t = models.CharField(max_length = 200, blank=False,null=True)\n\tupload \t\t = models.ImageField(upload_to ='uploads/')\n\tdate_added = models.DateTimeField(auto_now_add=True)\n\n\nclass ShowImage(models.Model):\n\tshow_image = models.BooleanField(default=False)\n\n\n", "sub_path": "Assignment/userapp/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 741, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.db.models.Model", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.URLField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "phonenumber_field.modelfields.PhoneNumberField", "line_number": 10, "usage_type": "call"}, {"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.CharField", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 19, "usage_type": "attribute"}, {"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"}]} +{"seq_id": "246104261", "text": "from crawler.spiders import BaseSpider\nimport scrapy\nfrom utils.util_old import *\nfrom crawler.items import *\nfrom bs4 import BeautifulSoup\nfrom scrapy.http import Request, Response\nimport re\nimport time\n\nclass TempoSpider(BaseSpider):\n name = 'tempo'\n allowed_domains = ['tempo.com.ph']\n start_urls = ['http://tempo.com.ph/']\n website_id = 197 # 网站的id(必填)\n language_id = 1866 # 所用语言的id\n sql = { # sql配置\n 'host': '192.168.235.162',\n 'user': 'dg_admin',\n 'password': 'dg_admin',\n 'db': 'dg_crawler'\n }\n\n \n \n \n\n def parse(self, response):\n soup = BeautifulSoup(response.text, 'html.parser')\n for i in soup.select('li.current-cat ~ li a'):\n url = i.get('href')\n yield scrapy.Request(url, callback=self.parse_essay)\n\n # def parse_menu(self, response):\n # soup = BeautifulSoup(response.text, 'html.parser')\n # allPages = soup.select_one('div.numbered-pagination > span').text.split()[-1] if soup.select_one('div.numbered-pagination > span').text else '0' # 翻页\n # for i in range(int(allPages)):\n # url = response.url + 'page/' + str(i + 1) + '/'\n # yield scrapy.Request(url, callback=self.parse_essay)\n\n def parse_essay(self, response):\n soup = BeautifulSoup(response.text, 'html.parser')\n flag = True\n for i in soup.select('#container > div')[1:-2]: # 每页的文章\n url = i.select_one('a').get('href')\n try:\n pub_time = i.select_one('.entryDate').text if i.select_one('.entryDate').text else i.select_one('.meta_date').text\n except Exception:\n continue\n if self.time == None or Util.format_time3(Util.format_time2(pub_time)) >= int(self.time):\n yield scrapy.Request(url, callback=self.parse_item)\n else:\n flag = False\n self.logger.info('时间截止')\n break\n if flag:\n if soup.select('.pagi-next') != []:\n yield Request(soup.select('.pagi-next')[0].attrs['href'], callback=self.parse_essay)\n else:\n for i in soup.select('.numbered-pagination a'):\n yield Request(i.attrs['href'], callback=self.parse_essay)\n\n def parse_item(self, response):\n soup = BeautifulSoup(response.text, 'html.parser')\n item = NewsItem()\n category = soup.select('#bcrum > a')\n item['category1'] = category[1].text\n item['category2'] = category[2].text if category[2].text else None\n item['title'] = soup.select_one('h1.entry_title').text\n item['pub_time'] = Util.format_time2(soup.select_one('span.postDate').text)\n item['images'] = [i.get('src') for i in soup.select('#bcrum ~div >p >a>img')]\n item['abstract'] = soup.select_one('h1.entry_title').text\n ss = ''\n for i in soup.select('#bcrum ~div > p'):\n ss += i.text + r'\\n'\n for i in soup.select('#bcrum ~ div >ol'):\n ss += i.text + r'\\n'\n item['body'] = ss\n return item\n", "sub_path": "crawler/v1/tempo.py", "file_name": "tempo.py", "file_ext": "py", "file_size_in_byte": 3152, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "crawler.spiders.BaseSpider", "line_number": 10, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 28, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 31, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 41, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 50, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 57, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 60, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "52808209", "text": "#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Dec 9 20:15:39 2019\n\n@author: wenyi\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nfrom skimage import img_as_float\n\nfrom nltk.tokenize import word_tokenize \n\nfrom sklearn import preprocessing\nfrom sklearn.linear_model import Ridge\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom sklearn.feature_extraction.text import CountVectorizer\n\n\n##########################################################\n# Image features (label)\n##########################################################\ntraining_labels = pd.read_csv(\n './data/features_train/training-image-feature-2.csv', \n sep=\",\", header=None)\n\nprint(training_labels.shape)\n\ntesting_labels = pd.read_csv(\n './data/features_test/testing-image-feature-2.csv', \n sep=\",\", header=None)\n\nprint(testing_labels.shape)\nprint(\"****** Done loading images features labels ******\")\n\n\n\n##########################################################\n# tag (label)\n##########################################################\ndef load_tag(isTrain):\n all_tag = []\n tag_for_one_image = []\n if isTrain:\n folder_path = \"data/tags_train/\"\n num = 10000\n else:\n folder_path = \"data/tags_test/\"\n num = 2000\n \n for n in range(num):\n path = folder_path + str(n) + \".txt\"\n txtfile = open(path, \"r\")\n lines = txtfile.read().split('\\n')\n # format: vehicle:airplane \n temp = ''\n cat = ''\n for line in lines:\n words = line.split(':')\n if words[0] == '':\n break\n tags = words[1]\n cat = cat + ' '\n tags = tags.replace(\" \", \"\")\n temp = temp + ' ' + tags\n all_tag.append(tags)\n tag_for_one_image.append(temp)\n \n all_tag = list(set(all_tag))\n \n return tag_for_one_image, all_tag\n\n#training word to vector\ntag2image_train, tag_train = load_tag(True) #tag_train length:80\ncv_train = CountVectorizer(vocabulary = tag_train)\ntraining_tag = cv_train.fit_transform(tag2image_train).toarray() #10000*80\n\n#testing word to vector\ntag2image_test, tag_test = load_tag(False) \ncv_test = CountVectorizer(vocabulary = tag_train) \ntesting_tag = cv_test.fit_transform(tag2image_test).toarray() #2000*80\nprint(training_tag.shape)\nprint(testing_tag.shape)\nprint(\"****** Done processing training/testing tag labels ******\")\n\n\n\n\n##########################################################\n# Descrptions features (data)\n##########################################################\ntraining_data_df = pd.read_csv('./train_des.csv', header=None)\ntesting_data_df = pd.read_csv('./test_des.csv', header=None)\n\ntraining_data = []\ntesting_data = []\n\nfor row in training_data_df.iterrows():\n training_data.append(\" \".join(row[1]))\nfor row in testing_data_df.iterrows():\n testing_data.append(\" \".join(row[1]))\n\ntraining_data = np.array(training_data)\ntesting_data = np.array(testing_data)\n\nprint(training_data.shape)\nprint(testing_data.shape)\nprint(\"****** Done loading training/testing description data ******\")\n\n\n# Bag of Words Model \nword_dict = {}\nword_voc = []\n\n# iterate thru all reviews in the training set\nfor i in range(len(training_data)):\n review = training_data[i]\n \n for w in word_tokenize(review):\n if w not in word_dict:\n word_voc.append(w)\n word_dict[w] = 0\n\nprint(len(word_dict)) #6719\nprint(len(word_voc))\nprint(\"****** Done building word dictionary ******\")\n\n#description training word to vector\ntraining_vectors = CountVectorizer(vocabulary = word_voc).fit_transform(\n training_data).toarray() #10000*7322\ntesting_vectors = CountVectorizer(vocabulary = word_voc).fit_transform(\n testing_data).toarray() #10000*7322\nprint(training_vectors.shape) #10000*6719\nprint(testing_vectors.shape) #10000*6719\n\nprint(\"****** Done processing training/testing description data ******\")\n\n\n\n\n\n##########################################################\n# Model \n##########################################################\nridge_tag = Ridge(alpha=1.0)\nridge_tag.fit(training_vectors, training_tag)\nridge_m1_pred = ridge_tag.predict(testing_vectors)\n\nprint(ridge_m1_pred.shape)\nprint(\"\\n**** Done Ridge Model 1 ****\") \n\nnp.savetxt(\"./output/ridge_m1_pred.csv\", ridge_m1_pred, delimiter=\",\")\nprint(\"\\n**** Done saving output ****\") \n\n\n\n\nridge_img = Ridge(alpha=1.0)\nridge_img.fit(training_vectors, training_labels)\nridge_m2_pred = ridge_img.predict(testing_vectors)\n\nprint(ridge_m2_pred.shape)\nprint(\"\\n**** Done Ridge Model 2 ****\") \n\nnp.savetxt(\"./output/ridge_m2_pred.csv\", ridge_m2_pred, delimiter=\",\")\nprint(\"\\n**** Done saving output ****\") \n\n\n\n\n##########################################################\n# cos similarity\n##########################################################\nsamples = preprocessing.normalize(testing_tag, norm='l2') \npred = preprocessing.normalize(ridge_m1_pred, norm='l2') \n\nprint(samples.shape)\nprint(pred.shape)\n\ncos_1 = cosine_similarity(pred, samples)\nprint(cos_1.shape)\nprint(cos_1)\n\n##########################################################\nsamples = preprocessing.normalize(testing_labels, norm='l2') \npred = preprocessing.normalize(ridge_m2_pred, norm='l2') \n\nprint(samples.shape)\nprint(pred.shape)\n\ncos_2 = cosine_similarity(pred, samples)\nprint(cos_2.shape)\nprint(cos_2)\n\n\n##########################################################\n# cos similarity combined\n##########################################################\ncos = cos_1 + cos_2\nprint(cos.shape)\nprint(cos)\n\ntext_df = pd.read_csv('./test_des.csv', header=None)\nsubmission = []\n\n##########################################################\n# display description and images\n##########################################################\n\nfor i in range(cos.shape[0]):\n#for i in range(10):\n txt = str(i)+\".txt\"\n img_ids = \"\"\n\n img_list = cos[i].argsort()[-20:][::-1]\n # print(img_list)\n \n # print description\n # print(\"\\n\" + str(i))\n # print(\" \".join(text_df.iloc[i,:1])) \n \n # plot preview images\n # fig, axes = plt.subplots(5,4,figsize=(10,10)) \n \n for j in range(20):\n img_ids = img_ids + str(img_list[j]) +\".jpg \"\n # path = \"./data/images_test/\"+ str(img_list[j]) + \".jpg\"\n\n # img = img_as_float(mpimg.imread(path))\n # ax = axes[j//4, j%4]\n # ax.set_xticks([])\n # ax.set_yticks([])\n # ax.imshow(img)\n \n # plt.show()\n # print(txt)\n # print(img_ids)\n submission.append([txt, img_ids.strip()])\n submission_df = pd.DataFrame(submission, \n columns = ['Descritpion_ID', 'Top_20_Image_IDs']) \n \n submission_df.to_csv (\"./output/final_submission.csv\", index = None, header=True)\n\nprint(\"\\n**** Done saving submission ****\") \n\n", "sub_path": "checkpoint/v9-cv-cv-ridge.py", "file_name": "v9-cv-cv-ridge.py", "file_ext": "py", "file_size_in_byte": 6924, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_csv", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 33, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 79, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 84, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 96, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 97, "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": "nltk.tokenize.word_tokenize", "line_number": 123, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 133, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.CountVectorizer", "line_number": 135, "usage_type": "call"}, {"api_name": "sklearn.linear_model.Ridge", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 156, "usage_type": "call"}, {"api_name": "sklearn.linear_model.Ridge", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 169, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.normalize", "line_number": 178, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 178, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.normalize", "line_number": 179, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 179, "usage_type": "name"}, {"api_name": "sklearn.metrics.pairwise.cosine_similarity", "line_number": 184, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.normalize", "line_number": 189, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 189, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.normalize", "line_number": 190, "usage_type": "call"}, {"api_name": "sklearn.preprocessing", "line_number": 190, "usage_type": "name"}, {"api_name": "sklearn.metrics.pairwise.cosine_similarity", "line_number": 195, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 207, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 243, "usage_type": "call"}]} +{"seq_id": "316112021", "text": "from django.shortcuts import render\nfrom django.http import HttpResponse\n\nfrom neo4j import GraphDatabase, basic_auth\nneo_client = GraphDatabase.driver(\"bolt://localhost:7687\", auth=basic_auth(\"neo4j\", \"life\"), encrypted=False)\nsession = neo_client.session()\n\ndef form(request):\n context = dict(title='Form')\n return render(request, 'form.html', context)\n\ndef entry(request):\n form = request.POST\n print(form, flush=True)\n name = form['name']\n organization = form['organization']\n application = form['application']\n tools = form['tools']\n session.run('MERGE (a:Application {name: \\'' + application + '\\'})')\n for tool in tools.split(','):\n session.run('MERGE (t:Tool {name: \\'' + tool + '\\'})')\n session.run('MATCH (a:Application) WHERE a.name = \\'' + application + '\\' MATCH (t:Tool) WHERE t.name = \\'' + tool + '\\'' + 'MERGE (a)-[:uses]->(t)')\n session.run('MERGE (u:User {name: \\'' + name + '\\', organization: \\'' + organization + '\\'})')\n session.run('MATCH (u:User) WHERE u.name = \\'' + name + '\\' MATCH (a:Application) WHERE a.name = \\'' + application + '\\' MERGE (u)-[:works_on]->(a)')\n return render(request, 'done.html', dict())\n", "sub_path": "site/directory/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1207, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "neo4j.GraphDatabase.driver", "line_number": 5, "usage_type": "call"}, {"api_name": "neo4j.GraphDatabase", "line_number": 5, "usage_type": "name"}, {"api_name": "neo4j.basic_auth", "line_number": 5, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 10, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "72752094", "text": "import pandas as pd, numpy as np, seaborn as sns\nfrom matplotlib import pyplot as plt\nimport os\nimport datetime\nimport math\nfrom IPython.core.display import display, HTML\n\nfrom urllib.request import urlopen\nimport urllib.error\nfrom zipfile import ZipFile\n\nmarketHolidays = [\n datetime.datetime(2020, 2, 21),\n datetime.datetime(2020, 3, 10),\n datetime.datetime(2020, 4, 2),\n datetime.datetime(2020, 4, 6),\n datetime.datetime(2020, 4, 10),\n datetime.datetime(2020, 4, 14),\n datetime.datetime(2020, 5, 1),\n datetime.datetime(2020, 5, 25),\n datetime.datetime(2020, 10, 2),\n datetime.datetime(2020, 11, 16),\n datetime.datetime(2020, 11, 30),\n datetime.datetime(2020, 12, 25),\n datetime.datetime(2021, 1, 26),\n datetime.datetime(2021, 3, 11)\n]\n\nmarketWeekendOpen = [\n datetime.datetime(2020, 2, 24) #open for Diwali Muhurat\n]\n\nclass DataSet:\n bhavcopyOld: None\n bhavcopyNew: None\n opOld: None\n opNew: None\n\n def __init__(self, bcOld, bcNew, oOld, oNew):\n self.bhavcopyOld = bcOld\n self.bhavcopyNew = bcNew\n self.opOld = oOld\n self.opNew = oNew\n\n def printSampleData(self):\n print(self.bhavcopyOld.head(1))\n print(self.bhavcopyNew.head(1))\n print(self.opOld.head(1))\n print(self.opNew.head(1))\n\nclass DataReader:\n def readFundamentals(self, path):\n return pd.read_csv(path)\n \n def downloadParticipantWiseOiData(self, date):\n #https://www1.nseindia.com/content/nsccl/fao_participant_oi_29092020.csv\n basePath = \"https://www1.nseindia.com/content/nsccl/\"\n fileName = \"fao_participant_oi_\" + date.strftime(\"%d%m%Y\").upper() + \".csv\"\n url = basePath + fileName\n print(url)\n urldata = urlopen(url)\n with open('data/' + fileName,'wb') as output:\n output.write(urldata.read())\n\n def readParticipantWiseOiData(self, date):\n fileName = \"fao_participant_oi_\" + date.strftime(\"%d%m%Y\").upper() + \".csv\"\n filepath = \"Data/\" + fileName\n \n if not os.path.isfile(filepath):\n self.downloadParticipantWiseOiData(date)\n \n df = pd.read_csv(filepath, skiprows=1)\n df['ForDate'] = date\n df.columns = df.columns.str.strip()\n return df\n \n def readParticipantWiseOiDataForDateRange(self, fromDate, toDate = datetime.datetime.today()):\n datelist = pd.date_range(fromDate, toDate, freq='B').tolist()\n combined_df = None\n for date in datelist:\n if date not in marketHolidays:\n if combined_df is None:\n combined_df = self.readParticipantWiseOiData(date)\n else:\n try:\n df = self.readParticipantWiseOiData(date)\n combined_df = combined_df.append(df, ignore_index=True)\n except urllib.error.HTTPError as e:\n print(e, date)\n return combined_df\n\n def downloadExtractZip(self, zipurl, fileName, extractPath):\n zipresp = urlopen(zipurl)\n zipPath = \"Data/\" + fileName\n tempzip = open(zipPath, \"wb\")\n tempzip.write(zipresp.read())\n tempzip.close()\n zf = ZipFile(zipPath)\n zf.extractall(path = \"Data/\" + extractPath)\n zf.close()\n\n def trimDatapointValues(self, data, datapoints):\n for dp in datapoints:\n data[dp] = data[dp].apply(lambda x: str(x).strip())\n \n def readFile(self, filepath):\n df = pd.read_csv(filepath)\n df.columns = df.columns.str.strip()\n return df\n\n def downloadBhavCopy(self, date):\n #https://www1.nseindia.com/content/historical/EQUITIES/2020/JUN/cm12JUN2020bhav.csv.zip\n basePath = \"https://www1.nseindia.com/content/historical/EQUITIES/\" + date.strftime(\"%Y\") + \"/\" + date.strftime(\"%b\").upper() + \"/\"\n fileName = \"cm\" + date.strftime(\"%d%b%Y\").upper() + \"bhav.csv\" + \".zip\"\n url = basePath + fileName\n print(url)\n self.downloadExtractZip(url, fileName, \"\")\n\n def downloadBhavCopy2(self, date):\n #https://www1.nseindia.com/archives/equities/bhavcopy/pr/PR080121.zip\n basePath = \"https://www1.nseindia.com/archives/equities/bhavcopy/pr/\"\n folderName = \"PR\" + date.strftime(\"%d%m%y\").upper()\n fileName = folderName + \".zip\"\n url = basePath + fileName\n print(url)\n self.downloadExtractZip(url, fileName, folderName)\n \n def readBhavcopy2(self, date):\n fileName = \"Pd\" + date.strftime(\"%d%m%y\").upper() + \".csv\"\n filepath = \"Data/PR\" + date.strftime(\"%d%m%y\").upper() + \"/\" + fileName\n \n if not os.path.isfile(filepath):\n self.downloadBhavCopy2(date)\n \n df = pd.read_csv(filepath)\n df.columns = df.columns.str.strip()\n df.rename(columns = {\n 'OPEN_PRICE': 'OPEN',\n 'HIGH_PRICE': 'HIGH',\n 'LOW_PRICE': 'LOW',\n 'CLOSE_PRICE': 'CMP'\n }, inplace=True)\n self.trimDatapointValues(df, ['SYMBOL', 'SECURITY'])\n df['ForDate'] = date\n df = df[(df['SERIES'] == 'EQ') | (df['SECURITY'].isin(['Nifty 50', 'Nifty Bank']))]\n df = df[df['MKT'].isin(['Y', 'N', 'G'])]\n df = df.astype({'PREV_CL_PR': 'float', \n 'OPEN': 'float', \n 'HIGH': 'float', \n 'LOW': 'float', \n 'CMP': 'float', \n 'NET_TRDVAL': 'float', \n 'NET_TRDQTY': 'float',\n 'TRADES': 'int64',\n 'HI_52_WK': 'float', \n 'LO_52_WK': 'float'})\n df['SYMBOL'] = df.apply(lambda x: x['SYMBOL'] if x['MKT'] == 'N' else x['SECURITY'], axis=1)\n df.loc[df['SYMBOL'] == 'Nifty 50', 'SYMBOL'] = 'NIFTY'\n df.loc[df['SYMBOL'] == 'Nifty Bank', 'SYMBOL'] = 'BANKNIFTY'\n #df['OPEN'] = pd.to_numeric(df['OPEN'], downcast='float')\n return df\n\n def readBhavcopy(self, date, niftyBhav=None):\n fileName = \"cm\" + date.strftime(\"%d%b%Y\").upper() + \"bhav.csv\"\n filepath = \"Data/\" + fileName\n \n if not os.path.isfile(filepath):\n self.downloadBhavCopy(date)\n \n df = pd.read_csv(filepath)\n df.columns = df.columns.str.strip()\n self.trimDatapointValues(df, ['SYMBOL'])\n df['ForDate'] = date\n df = df[df['SERIES'] == 'EQ']\n if (niftyBhav != None):\n df = df.append({'SYMBOL':'NIFTY', 'CLOSE': niftyBhav}, ignore_index=True)\n return df\n\n def readBhavcopyForDateRange(self, fromDate, toDate = datetime.datetime.today()):\n datelist = pd.date_range(fromDate, toDate, freq='B').tolist()\n combined_df = None\n for date in datelist:\n if date not in marketHolidays:\n if combined_df is None:\n combined_df = self.readBhavcopy2(date)\n else:\n try:\n df = self.readBhavcopy2(date)\n combined_df = combined_df.append(df, ignore_index=True)\n except urllib.error.HTTPError as e:\n print(e, date)\n return combined_df\n\n def downloadFOData(self, date):\n #https://www1.nseindia.com/archives/fo/mkt/fo12062020.zip\n basePath = \"https://www1.nseindia.com/archives/fo/mkt/\"\n folderName = \"fo\" + date.strftime(\"%d%m%Y\")\n fileName = folderName + \".zip\"\n url = basePath + fileName\n print(url)\n self.downloadExtractZip(url, fileName, folderName)\n\n def readFuturesData(self, date):\n folderName = \"fo\" + date.strftime(\"%d%m%Y\")\n path = \"Data/\" + folderName\n \n if not os.path.isdir(path):\n self.downloadFOData(date)\n \n df = pd.read_csv(path + \"/\" + \"fo\" + date.strftime(\"%d%m%Y\") + \".csv\")\n df.columns = df.columns.str.strip()\n self.trimDatapointValues(df, ['SYMBOL'])\n df['ForDate'] = date.strftime(\"%Y-%m-%d\")\n return df\n\n def readOptionsDataForDateRange(self, fromDate, toDate = datetime.datetime.today()):\n datelist = pd.date_range(fromDate, toDate, freq='B').tolist()\n combined_df = None\n for date in datelist:\n if date not in marketHolidays:\n if combined_df is None:\n combined_df = self.readOptionsData(date)\n else:\n try:\n df = self.readOptionsData(date)\n combined_df = combined_df.append(df, ignore_index=True)\n except urllib.error.HTTPError as e:\n print(e, date)\n return combined_df\n\n def readOptionsData(self, date):\n folderName = \"fo\" + date.strftime(\"%d%m%Y\")\n path = \"Data/\" + folderName\n \n if not os.path.isdir(path):\n self.downloadFOData(date)\n\n filepath = path + \"/\" + \"op\" + date.strftime(\"%d%m%Y\") + \".csv\"\n df = self.readFile(filepath)\n df['ForDate'] = date.strftime(\"%Y-%m-%d\")\n self.trimDatapointValues(df, ['SYMBOL', 'OPT_TYPE'])\n return df\n\n def downloadEquityDeliveryData(self, date):\n #https://www1.nseindia.com/archives/equities/mto/MTO_23112020.DAT\n basePath = \"https://www1.nseindia.com/archives/equities/mto/\"\n fileName = \"MTO_\" + date.strftime(\"%d%m%Y\") + \".DAT\"\n url = basePath + fileName\n print(url)\n urldata = urlopen(url)\n with open('data/' + fileName,'wb') as output:\n output.write(urldata.read())\n \n def readEquityDeliveryData(self, date):\n fileName = \"MTO_\" + date.strftime(\"%d%m%Y\") + \".DAT\"\n filepath = \"Data/\" + fileName\n \n if not os.path.isfile(filepath):\n self.downloadEquityDeliveryData(date)\n \n df = pd.read_csv(filepath, skiprows=4, names=['RecordTypeCode', 'SrNo', 'SYMBOL', 'RecordType', 'QuantityTraded', 'DeliverableQuantity', 'DeliverableQuantityPercent'])\n df['ForDate'] = date\n df.columns = df.columns.str.strip()\n return df\n\n def readEquityDeliveryDataForDateRange(self, fromDate, toDate = datetime.datetime.today()):\n datelist = pd.date_range(fromDate, toDate, freq='B').tolist()\n combined_df = None\n for date in datelist:\n if date not in marketHolidays:\n if combined_df is None:\n combined_df = self.readEquityDeliveryData(date)\n else:\n try:\n df = self.readEquityDeliveryData(date)\n combined_df = combined_df.append(df, ignore_index=True)\n except urllib.error.HTTPError as e:\n print(e, date)\n return combined_df\n\nclass CompiledData:\n old: None\n new: None\n keyColumns: None\n\n def __init__(self, old, new):\n self.old = old\n self.new = new\n self.keyColumns = ['SYMBOL', 'OPT_TYPE','STR_PRICE', 'EXP_DATE', 'CMP', 'Moneyness', \\\n 'StrikePriceDiff', 'StrikePriceDiffPer', 'StrikePriceDiffPerAbs', 'LotSize', 'Premium', 'SpotAdjPremium', 'SpotPriceGrowthPer', 'PriceGrowthPer', 'PremiumGrowth', 'PremiumGrowthPer']\n\nclass DataProcessor:\n originalData: None\n compiledData: None\n indexCols: None\n optionsDisplayColumns: None\n futDisplayCols: None\n reader: None\n niftySymbols: None\n bankniftySymbols: None\n\n def __init__(self):\n self.reader = DataReader()\n self.optionsDisplayColumns = ['SYMBOL', 'STR_PRICE', 'OPT_TYPE', 'CMP', 'CLOSE_PRICE', 'Premium', 'OPEN_INT*', 'LotSize', 'StrikePriceDiffPer', 'OpIntradayChange', 'CmpIntradayChange']\n self.futDisplayCols = ['SYMBOL', 'EXP_DATE', 'CLOSE_PRICE', 'CMP', 'Fwd', 'OPEN_INT*', 'PriceDiffPer', 'PriceDiffPerAbs', 'FutureIntradayChange', 'CmpIntradayChange']\n self.niftySymbols = ['ADANIPORTS', 'ASIANPAINT', 'AXISBANK', 'BAJAJ-AUTO', 'BAJAJFINSV', 'BAJFINANCE', 'BHARTIARTL', 'BPCL', 'BRITANNIA', 'CIPLA', 'COALINDIA', 'DIVISLAB', 'DRREDDY', 'EICHERMOT', 'GAIL', 'GRASIM', 'HCLTECH', 'HDFC', 'HDFCBANK', 'HDFCLIFE', 'HEROMOTOCO', 'HINDALCO', 'HINDUNILVR', 'ICICIBANK', 'INDUSINDBK', 'INFY', 'IOC', 'ITC', 'JSWSTEEL', 'KOTAKBANK', 'LT', 'M&M', 'MARUTI', 'NESTLEIND', 'NTPC', 'ONGC', 'POWERGRID', 'RELIANCE', 'SBILIFE', 'SBIN', 'SHREECEM', 'SUNPHARMA', 'TATAMOTORS', 'TATASTEEL', 'TCS', 'TECHM', 'TITAN', 'ULTRACEMCO', 'UPL', 'WIPRO']\n self.bankniftySymbols = ['RBLBANK', 'AXISBANK', 'HDFCBANK', 'SBIN', 'ICICIBANK', 'BANDHANBNK', 'KOTAKBANK', 'PNB', 'FEDERALBNK', 'INDUSINDBK', 'BANKBARODA', 'IDFCFIRSTB']\n \n # ## Read and standardise data\n def readData(self, dateOld, dateNew):\n bhavcopyOld = self.reader.readBhavcopy(dateOld)\n bhavcopyNew = self.reader.readBhavcopy(dateNew)\n optionsOld = self.reader.readOptionsData(dateOld)\n optionsNew = self.reader.readOptionsData(dateNew)\n\n self.originalData = DataSet(bhavcopyOld, bhavcopyNew, optionsOld, optionsNew)\n\n def mergeBhavAndOp(self, op, bhav):\n mergedData = pd.merge(op, bhav, how='inner', left_on=['SYMBOL'], right_on=['SYMBOL'])\n mergedData.rename(columns={'CLOSE': 'CMP'}, inplace=True)\n return mergedData\n\n def mergeFundamentalsAndOp(self, op, fdm):\n fdmDisplayCols = ['Ticker', 'Last Close', 'Price 52 Wk Low', 'Price 52 Wk High', '% above 52w low']\n mergedData = pd.merge(op, fdm[fdmDisplayCols], how='inner', left_on=['SYMBOL'], right_on=['Ticker'])\n return mergedData\n\n def mergeBhavAndFutures(self, futures, bhav):\n mergedData = pd.merge(futures, bhav, how='inner', left_on=['SYMBOL'], right_on=['SYMBOL'])\n mergedData.rename(columns={'CLOSE': 'CMP'}, inplace=True)\n return mergedData\n\n def compile(self, niftyOldBhav, niftyNewBhav):\n newData = self.compileData(self.originalData.opNew, self.originalData.bhavcopyNew, None, niftyNewBhav)\n oldData = self.compileData(self.originalData.opOld, self.originalData.bhavcopyOld, newData, niftyOldBhav)\n self.compiledData = CompiledData(oldData, newData)\n return self.compiledData\n\n def trimDatapointValues(self, data, datapoints):\n for dp in datapoints:\n data[dp] = data[dp].apply(lambda x: str(x).strip())\n\n def compileData(self, op, bhavcopy, newCompiledData, niftyBhav):\n self.trimDatapointValues(op, ['SYMBOL', 'OPT_TYPE'])\n compiledData = self.mergeBhavAndOp(op, bhavcopy)\n self.computeContractData(compiledData)\n \n if newCompiledData is not None:\n self.computeOldAndNewVariances(newCompiledData, compiledData)\n \n return compiledData\n\n def computeContractData(self, mergedData):\n mergedData['Moneyness'], mergedData['StrikePriceDiff'], mergedData['StrikePriceDiffPer'], mergedData['StrikePriceDiffPerAbs'], \\\n mergedData['LotSize'], mergedData['Premium'], mergedData['SpotAdjPremium'], \\\n mergedData['OpIntradayChange'], mergedData['CmpIntradayChange'] = \\\n zip(*mergedData.apply(self.getComputedData, axis=1))\n\n def computeFuturesData(self, mergedData):\n mergedData['Fwd'], mergedData['PriceDiff'], mergedData['PriceDiffPer'], mergedData['PriceDiffPerAbs'], \\\n mergedData['FutureIntradayChange'], mergedData['CmpIntradayChange'] = \\\n zip(*mergedData.apply(self.getFuturesComputedData, axis=1))\n\n def getFuturesComputedData(self, contract):\n cmp = contract['CMP']\n futPrice = contract['CLOSE_PRICE']\n\n fwd = ''\n if cmp == futPrice:\n fwd = 'SPOT'\n else:\n if futPrice > cmp:\n fwd = 'Premium'\n else:\n fwd = 'Discount'\n\n priceDiff = futPrice - cmp\n priceDiffPer = round(priceDiff / cmp * 100, 2)\n priceDiffPerAbs = abs(priceDiffPer)\n futIntradayChange = contract['CLOSE_PRICE'] - contract['OPEN_PRICE']\n cmpIntradayChange = contract['CMP'] - contract['OPEN']\n \n return fwd, priceDiff, priceDiffPer, priceDiffPerAbs, futIntradayChange, cmpIntradayChange\n\n #def determineCategory(self, contract):\n def getComputedData(self, contract):\n cmp = contract['CMP']\n strikePrice = contract['STR_PRICE']\n optionType = contract['OPT_TYPE']\n closePrice = contract['CLOSE_PRICE']\n\n cat = ''\n if cmp == strikePrice:\n cat = 'ATM'\n else:\n if optionType.strip() == 'CE':\n if strikePrice > cmp:\n cat = 'OTM'\n else:\n cat = 'ITM'\n else:\n if strikePrice > cmp:\n cat = 'ITM'\n else:\n cat = 'OTM'\n \n strikePriceDiff = (strikePrice - cmp)\n strikePriceDiffPer = round((strikePrice - cmp) / cmp * 100, 2)\n strikePriceDiffPerAbs = abs(strikePriceDiffPer)\n lotSize = round(contract['TRD_QTY'] / contract['NO_OF_CONT'])\n premium = round(lotSize * contract['CLOSE_PRICE'], 0)\n opIntradayChange = contract['CLOSE_PRICE'] - contract['OPEN_PRICE']\n cmpIntradayChange = contract['CMP'] - contract['OPEN']\n\n if cat == 'OTM':\n spotAdjPremium = closePrice + abs(strikePriceDiff)\n else:\n spotAdjPremium = closePrice - abs(strikePriceDiff)\n\n return cat, strikePriceDiff, strikePriceDiffPer, strikePriceDiffPerAbs, lotSize, premium, spotAdjPremium, opIntradayChange, cmpIntradayChange\n\n def filterOptions(self, filter, opt):\n options = opt\n if 'openIntThreshold' in filter:\n options = options[options['OPEN_INT*_x'] >= filter['openIntThreshold']]\n if 'optTypes' in filter:\n options = options[options.OPT_TYPE.isin(filter['optTypes'])]\n if 'strikePriceThreshold' in filter:\n options = options[options.StrikePriceDiffPerAbs >= filter['strikePriceThreshold']]\n if 'moneyness' in filter:\n options = options[options.Moneyness.isin(filter['moneyness'])] \n if 'moneyness_x' in filter:\n options = options[options.Moneyness_x.isin(filter['moneyness_x'])]\n if 'strikePrice' in filter:\n options = options[options.STR_PRICE==filter['strikePrice']]\n if 'strikePrices' in filter:\n options = options[options.STR_PRICE.isin(filter['strikePrices'])]\n if 'prefExpiry' in filter:\n options = options[options.EXP_DATE==filter['prefExpiry']]\n if 'prefExpiries' in filter:\n options = options[options.EXP_DATE.isin(filter['prefExpiries'])]\n if 'symbol' in filter:\n options = options[options.SYMBOL==filter['symbol']]\n if '52weekLowThreshold' in filter:\n options = options[options['% above 52w low']<=filter['52weekLowThreshold']]\n if 'ForDate' in filter:\n options = options[options['ForDate']==filter['ForDate']]\n if 'sortBy' in filter:\n if 'sortAscending' in filter:\n options = options.sort_values(filter['sortBy'], ascending=filter['sortAscending'])\n else:\n options = options.sort_values(filter['sortBy'], ascending=False)\n \n if 'returnFullset' in filter:\n return options\n else:\n return options[self.optionsDisplayColumns]\n\n def filterFutures(self, filter, fut):\n futures = fut\n if 'openIntThreshold' in filter:\n futures = futures[futures['OPEN_INT*'] >= filter['openIntThreshold']]\n if 'Fwd' in filter:\n futures = futures[futures.fwd.isin(filter['Fwd'])]\n if 'prefExpiry' in filter:\n futures = futures[futures.EXP_DATE >= filter['prefExpiry']] \n if 'symbol' in filter:\n futures = futures[futures.SYMBOL==filter['symbol']]\n if 'sortBy' in filter:\n futures = futures.sort_values(filter['sortBy'], ascending=False)\n return futures[self.futDisplayCols]\n\n def computeOldAndNewVariances(self, newData, oldData):\n oldData['NewSpotPrice'], oldData['SpotPriceGrowth'], oldData['SpotPriceGrowthPer'], \\\n oldData['NewPrice'], oldData['PriceGrowth'], oldData['PriceGrowthPer'], \\\n oldData['NewPremium'], oldData['PremiumGrowth'], oldData['PremiumGrowthPer'] = \\\n zip(*oldData.apply(self.getVarianceComputation, axis=1, newData=newData))\n\n def getDataVariances(self, newContract, oldContract, datapoint):\n newValue = newContract[datapoint].iloc[0]\n oldValue = oldContract[datapoint]\n growth = newValue - oldValue\n growthPer = round(growth / oldValue * 100, 1)\n return (newValue, growth, growthPer)\n\n def getVarianceComputation(self, contract, newData):\n #print(type(oldData))\n #return\n\n symbol = contract['SYMBOL']\n strikePrice = contract['STR_PRICE']\n optionType = contract['OPT_TYPE']\n expiry = contract['EXP_DATE']\n newContract = newData.loc[(newData['SYMBOL'] == symbol) & \\\n (newData['STR_PRICE'] == strikePrice) & \\\n (newData['OPT_TYPE'] == optionType) & \\\n (newData['EXP_DATE'] == expiry) ]\n \n #print(oldContract['CMP']))\n\n if newContract.shape[0] > 0:\n spotPriceVariances = self.getDataVariances(newContract, contract, 'CMP')\n priceVariances = self.getDataVariances(newContract, contract, 'CLOSE_PRICE')\n premiumVariances = self.getDataVariances(newContract, contract, 'Premium')\n \n return spotPriceVariances[0], spotPriceVariances[1], spotPriceVariances[2], \\\n priceVariances[0], priceVariances[1], priceVariances[2], \\\n premiumVariances[0], premiumVariances[1], premiumVariances[2]\n else:\n return 0, 0, 0, 0, 0, 0, 0, 0, 0\n\n def getOptionsForDate(self, date, bhavCopy=None):\n options = self.reader.readOptionsData(date)\n if bhavCopy is not None:\n options = self.mergeBhavAndOp(options, bhavCopy)\n self.computeContractData(options)\n return options\n\n def getFuturesForDate(self, date, bhavCopy=None):\n futures = self.reader.readFuturesData(date)\n if bhavCopy is not None:\n futures = self.mergeBhavAndFutures(futures, bhavCopy)\n self.computeFuturesData(futures)\n return futures\n\n def compareFutures(self, fut1, fut2):\n comparison = pd.merge(fut1, fut2, how='inner', left_on=['SYMBOL', 'EXP_DATE'], right_on=['SYMBOL', 'EXP_DATE'])\n comparison['OiChange'] = comparison['OPEN_INT*_x'] - comparison['OPEN_INT*_y']\n comparison['OiChangePer'] = comparison['OiChange'] / comparison['OPEN_INT*_y'] * 100\n comparison['OiChangePerAbs'] = abs(comparison['OiChangePer'])\n return comparison[['SYMBOL', 'EXP_DATE', 'CLOSE_PRICE_x', 'CLOSE_PRICE_y', 'Fwd_x', 'Fwd_y', \\\n 'OiChangePer', 'OiChangePerAbs', 'CmpIntradayChange_x', 'FutureIntradayChange_x', 'OPEN_INT*_x', 'OPEN_INT*_y']]\n\n def compareOptions(self, op1, op2):\n comparison = pd.merge(op1, op2, how='inner', left_on=['SYMBOL', 'STR_PRICE', 'EXP_DATE', 'OPT_TYPE'], right_on=['SYMBOL', 'STR_PRICE', 'EXP_DATE', 'OPT_TYPE'])\n comparison['OiChange'] = comparison['OPEN_INT*_x'] - comparison['OPEN_INT*_y']\n comparison['OiChangePer'] = comparison['OiChange'] / comparison['OPEN_INT*_y'] * 100\n comparison['OiChangePerAbs'] = abs(comparison['OiChangePer'])\n return comparison[['SYMBOL', 'STR_PRICE', 'EXP_DATE', 'OPT_TYPE', \\\n 'CLOSE_PRICE_x', 'CLOSE_PRICE_y', 'OPEN_INT*_x', 'OPEN_INT*_y', \\\n 'Premium_x', 'Moneyness_x', \\\n 'OiChange', 'OiChangePer', 'OiChangePerAbs']]\n\n def mergeEquityBhavAndDelivery(self, bhav, delivery):\n mergedData = pd.merge(delivery, bhav, how='inner', left_on=['SYMBOL', 'ForDate'], right_on=['SYMBOL', 'ForDate'])\n mergedData.rename(columns={'CLOSE': 'CMP'}, inplace=True)\n return mergedData\n\nclass SpreadStrategyCheck:\n NeutralSpread = None\n BullBiasSpread = None\n BearBiasSpread = None\n\n HighGrowthSelection = None\n HighCreditSelection = None\n\n KeyColumns = None\n\n def __init__(self):\n self.KeyColumns = ['SYMBOL', 'OPT_TYPE', 'EXP_DATE', 'LotSize', 'STR_PRICE', 'CMP', \\\n 'Moneyness', 'CLOSE_PRICE', 'NewPrice', 'Premium', 'PremiumGrowth', 'PremiumGrowthPer', \\\n 'SpotPriceGrowthPer', 'PriceGrowthPer', 'StrikePriceDiff', 'StrikePriceDiffPer', 'SpotAdjPremium']\n\n self.NeutralSpread = SpreadParams(['ITM'], ['ITM', 'OTM'], \\\n unhedgedCEPer = 0.05, unhedgedPEPer=0.05, itmThresholdCEPer=0.1, itmThresholdPEPer=0.1)\n self.BullBiasSpread = SpreadParams(['ITM'], ['ITM', 'OTM'], \\\n unhedgedCEPer = 0.05, unhedgedPEPer=0.05, itmThresholdCEPer=0.1, itmThresholdPEPer=0.15)\n self.BearBiasSpread = SpreadParams(['ITM'], ['ITM', 'OTM'], \\\n unhedgedCEPer = 0.05, unhedgedPEPer=0.05, itmThresholdCEPer=0.15, itmThresholdPEPer=0.1)\n\n\n self.HighCreditSelection = SelectionParams('Premium', highestForSell=True, highestForBuy=False)\n self.HighGrowthSelection = SelectionParams('PremiumGrowth', highestForSell=False, highestForBuy=True)\n \n\n def filterContracts(self, compiled, expLimits):\n contracts = compiled.old[(compiled.old['SYMBOL'] == 'NIFTY') \\\n & (compiled.old['EXP_DATE'].isin(expLimits)) \\\n & (compiled.old['CLOSE_PRICE'] > 0) & (compiled.old['NewPrice'] > 0) \\\n & (compiled.old['OPEN_INT*'] >= 100)][self.KeyColumns]\n return contracts\n\n\n def getContract(self, contracts, optType, categoriesLimit, strikeLow, strikeHigh, sortBy, sortByAsc):\n print(strikeLow, strikeHigh)\n\n contract = contracts[(contracts['OPT_TYPE'] == optType) & \\\n (contracts['STR_PRICE'] <= strikeHigh) & (contracts['STR_PRICE'] >= strikeLow) \\\n & (contracts['Moneyness'].isin(categoriesLimit))\\\n ].sort_values(sortBy, ascending=sortByAsc).iloc[0]\n return contract\n\n\n def bestSpread(self, compiled, cmp, spreadParams, selectionParams, expLimits):\n unhedgedPE = cmp - (cmp * spreadParams.UnhedgedPEPer)\n unhedgedCE = cmp + (cmp * spreadParams.UnhedgedCEPer)\n itmThresholdCE = cmp - (cmp * spreadParams.ItmThresholdCEPer)\n itmThresholdPE = cmp + (cmp * spreadParams.ItmThresholdPEPer)\n \n contracts = self.filterContracts(compiled, expLimits)\n \n sellPE = self.getContract(contracts, 'PE', spreadParams.SellCategories, cmp, itmThresholdPE, selectionParams.SortBy, not selectionParams.HighestForSell)\n buyPE = self.getContract(contracts, 'PE', spreadParams.BuyCategories, unhedgedPE, cmp, selectionParams.SortBy, not selectionParams.HighestForBuy)\n sellCE = self.getContract(contracts, 'CE', spreadParams.SellCategories, itmThresholdCE, cmp, selectionParams.SortBy, not selectionParams.HighestForSell)\n buyCE = self.getContract(contracts, 'CE', spreadParams.BuyCategories, cmp, unhedgedCE, selectionParams.SortBy, not selectionParams.HighestForBuy)\n \n \n profit = (sellPE.PremiumGrowth * -1) + buyPE.PremiumGrowth + (sellCE.PremiumGrowth * -1) + buyCE.PremiumGrowth\n strategy = pd.DataFrame([sellPE, buyPE, sellCE, buyCE])\n \n display(strategy)\n print('Profit: ' + str(round(profit, 2)))\n\nclass SelectionParams:\n SortBy: None\n HighestForSell: None\n HighestForBuy: None\n\n def __init__(self, sortBy, highestForSell, highestForBuy):\n self.SortBy = sortBy\n self.HighestForSell = highestForSell\n self.HighestForBuy = highestForBuy\n\nclass SpreadParams:\n SellCategories = None\n BuyCategories = None\n UnhedgedCEPer = None\n UnhedgedPEPer = None\n ItmThresholdCEPer = None\n ItmThresholdPEPer = None\n\n def __init__(self, sellCategories, buyCategories, unhedgedCEPer, unhedgedPEPer, itmThresholdCEPer, itmThresholdPEPer):\n self.SellCategories = sellCategories\n self.BuyCategories = buyCategories\n self.UnhedgedCEPer = unhedgedCEPer\n self.UnhedgedPEPer = unhedgedPEPer\n self.ItmThresholdCEPer = itmThresholdCEPer\n self.ItmThresholdPEPer = itmThresholdPEPer\n\nclass DataVisualizer:\n compiledData: None\n\n def __init__(self, compiledData):\n self.compiledData = compiledData\n\n def boxplot(self, x, y, title, xlabel, ylabel, ylim_min, ylim_max):\n plt.figure(figsize=(12,6))\n plot = sns.boxplot(x=x, y=y, data=self.compiledData.new, fliersize=2, notch=True).set_ylim(ylim_min, ylim_max)\n plt.title(title)\n plt.xlabel(xlabel)\n plt.ylabel(ylabel)\n plt.show()\n return plot\n\nclass OiParticipantsVisualizer:\n oi_datapoints: None\n\n def init(self):\n self.oi_datapoints = {\n 'pairs':[]\n }\n\n self.add_oi_datapoint('Future Index')\n self.add_oi_datapoint('Option Index Call')\n self.add_oi_datapoint('Option Index Put', bullishPosition = False)\n self.add_oi_datapoint('Future Stock')\n self.add_oi_datapoint('Option Stock Call')\n self.add_oi_datapoint('Option Stock Put', bullishPosition = False)\n \n def add_delta_datapoints(self, data):\n for pair in self.oi_datapoints['pairs']:\n data[pair['name'] +' Delta'] = data[pair['name'] + ' Long'] - data[pair['name'] + ' Short']\n return data\n\n #def add_oi_datapoint(name, dp1, dp2):\n def add_oi_datapoint(self, name, bullishPosition = True):\n longSignal, shortSignal, deltaSignal = 'bullish', 'bearish', 'delta'\n \n if bullishPosition == False:\n longSignal, shortSignal = 'bearish', 'bullish'\n \n self.oi_datapoints['pairs'].append({\n 'name': name, \n 'dp': [\n {'name': name + ' Long', 'signal': longSignal}, \n {'name': name + ' Short', 'signal': shortSignal},\n {'name': name + ' Delta', 'signal': deltaSignal}\n ]\n })\n \n def renderChart(self, oi, col, color, title):\n sns.light_palette(\"seagreen\", as_cmap=True)\n chart = sns.lineplot(data=oi, x='ForDate', y=col, legend=\"full\", palette=color, hue=\"Client Type\", markers=True)\n chart.set_title(title)\n \n def oiCharts(self, oi, clientTypes=['FII'], months = None):\n if clientTypes is not None:\n filtered = oi[oi['Client Type'].isin(clientTypes)]\n else:\n filtered = oi[oi['Client Type'] != 'TOTAL']\n \n if months is not None:\n filtered = filtered[filtered['ForDate'].dt.month.isin(months)]\n \n signalPalette = {\n 'bullish': 'Greens',\n 'bearish': 'Reds'\n }\n \n for pair in self.oi_datapoints['pairs']:\n #for pair in [oi_datapoints['pairs'][0]]:\n fig, ax = plt.subplots()\n fig.set_size_inches(18, 4)\n self.renderChart(filtered, pair['dp'][0]['name'], signalPalette[pair['dp'][0]['signal']], pair['name'])\n self.renderChart(filtered, pair['dp'][1]['name'], signalPalette[pair['dp'][1]['signal']], pair['name'])\n sns.despine()\n\n return filtered\n\nclass EquityDeliveryVisualizer:\n def renderDeliveryChart(self, data, datapoint):\n fig, ax = plt.subplots()\n fig.set_size_inches(18, 4)\n sns.lineplot(data=data, x='ForDate', y=datapoint, legend=\"full\", markers=True)\n sns.despine()\n\n def renderPriceChart(self, data):\n fig, ax = plt.subplots()\n fig.set_size_inches(18, 4)\n sns.lineplot(data=data, x='ForDate', y='CMP', legend=\"full\", markers=True)\n sns.despine()\n\n def showDeliveryChartsForSymbols(self, symbols, data):\n grp_data = data[(data['SYMBOL'].isin(symbols)) & (data['RecordType'] == 'EQ')]\n grp_data = grp_data.groupby('ForDate').sum().reset_index()\n self.renderDeliveryChart(grp_data, datapoint='DeliverableQuantity')\n \n def showDeliveryPercChartsForSymbols(self, symbols, data):\n grp_data = data[(data['SYMBOL'].isin(symbols)) & (data['RecordType'] == 'EQ')]\n grp_data = grp_data.groupby('ForDate').mean().reset_index()\n self.renderDeliveryChart(grp_data, datapoint='DeliverableQuantityPercent')", "sub_path": "dataparser.py", "file_name": "dataparser.py", "file_ext": "py", "file_size_in_byte": 32597, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datetime.datetime", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 18, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 21, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 30, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 53, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 77, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 77, "usage_type": "attribute"}, {"api_name": "pandas.date_range", "line_number": 78, "usage_type": "call"}, {"api_name": "urllib.request.error", "line_number": 88, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 88, "usage_type": "name"}, {"api_name": "urllib.request.urlopen", "line_number": 93, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 98, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path", "line_number": 132, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 167, "usage_type": "call"}, {"api_name": "os.path", "line_number": 167, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 170, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 179, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 179, "usage_type": "attribute"}, {"api_name": "pandas.date_range", "line_number": 180, "usage_type": "call"}, {"api_name": "urllib.request.error", "line_number": 190, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 190, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 207, "usage_type": "call"}, {"api_name": "os.path", "line_number": 207, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 210, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 216, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 216, "usage_type": "attribute"}, {"api_name": "pandas.date_range", "line_number": 217, "usage_type": "call"}, {"api_name": "urllib.request.error", "line_number": 227, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 227, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 235, "usage_type": "call"}, {"api_name": "os.path", "line_number": 235, "usage_type": "attribute"}, {"api_name": "urllib.request.urlopen", "line_number": 250, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 258, "usage_type": "call"}, {"api_name": "os.path", "line_number": 258, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 261, "usage_type": "call"}, {"api_name": "datetime.datetime.today", "line_number": 266, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 266, "usage_type": "attribute"}, {"api_name": "pandas.date_range", "line_number": 267, "usage_type": "call"}, {"api_name": "urllib.request.error", "line_number": 277, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 277, "usage_type": "name"}, {"api_name": "pandas.merge", "line_number": 319, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 325, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 329, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 527, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 535, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 545, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 609, "usage_type": "call"}, {"api_name": "IPython.core.display.display", "line_number": 611, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 647, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 647, "usage_type": "name"}, {"api_name": "seaborn.boxplot", "line_number": 648, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 649, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 649, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 650, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 650, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 651, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 651, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 652, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 652, "usage_type": "name"}, {"api_name": "seaborn.light_palette", "line_number": 692, "usage_type": "call"}, {"api_name": "seaborn.lineplot", "line_number": 693, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 712, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 712, "usage_type": "name"}, {"api_name": "seaborn.despine", "line_number": 716, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 722, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 722, "usage_type": "name"}, {"api_name": "seaborn.lineplot", "line_number": 724, "usage_type": "call"}, {"api_name": "seaborn.despine", "line_number": 725, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 728, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 728, "usage_type": "name"}, {"api_name": "seaborn.lineplot", "line_number": 730, "usage_type": "call"}, {"api_name": "seaborn.despine", "line_number": 731, "usage_type": "call"}]} +{"seq_id": "393858578", "text": "import csv\nimport numpy as np\nimport pandas as pd\nfrom dataprocess.load_descriptions import load_description\nfrom collections import defaultdict\nimport os\n\n\n'''\nload_description: return {code: code_des}\n'''\ndef gen_batch(data_dir,dicts,max_len=2500,batch_size=16,des_embed=False):\n print('Load data ... ')\n c2ind = dicts['c2ind']#{code:ind_vec}\n ind2c = dicts['ind2c']\n w2ind = dicts['w2ind']\n des_inds_dict = dicts['des_inds_dict'] #{code:dex_ind}\n with open(data_dir,'r') as data:\n rows = csv.reader(data)\n next(rows)\n x_batch = []\n y_batch = []\n des_batch = []\n length = 0\n for row in rows:\n des = []\n if len(x_batch) == batch_size:\n x_batch = np.array(pad_text(x_batch,length))\n yield x_batch,np.array(y_batch),np.array(des_batch)\n x_batch = []\n y_batch = []\n des_batch = []\n l_inds,code_set,labelled = process_label(row[3],c2ind,labelled=False)\n if not labelled:\n continue\n y_batch.append(l_inds)\n xi = [int(w2ind[w]) if w in w2ind.keys() else len(w2ind)+1 for w in row[2].split()]\n if len(xi) > max_len:\n xi = xi[:max_len]\n x_batch.append(xi)\n length = min(int(row[4]),max_len)\n if des_embed:\n for ind in code_set:\n c = ind2c[ind]\n if c in des_inds_dict.keys():\n des.append(des_inds_dict[c][:])\n else:\n des.append([len(w2ind)+1])\n des_batch.append(pad_des(des))\n\n x_batch = np.array(pad_text(x_batch,length))\n yield x_batch,np.array(y_batch),np.array(des_batch)\n\n\n\ndef process_label(y,c2ind,labelled=False):\n num_labels = len(c2ind)\n y_matrix = np.zeros(num_labels)\n code_set = set()\n labels = y.split(';')\n\n for l in labels:\n if l in c2ind.keys():\n y_matrix[int(c2ind[l])] = 1\n code_set.add(int(c2ind[l])) #inds of codes\n labelled = True\n\n return y_matrix,code_set,labelled\n\n\n\ndef batch_iter(df_set,dicts,batch_size=16):\n '''\n\n :param train_dir: os.getcwd()/dev_data/train_dataset.csv\n training set\n vocab_dir: vocab in training set:VOCAB_TRAIN.csv\n codes_dir: code_filtered_dir\n :param batch_size:16\n :return:\n '''\n #dftrain_set = pd.read_csv(train_dir,dtype={\"LABELS\": str})\n c2ind = dicts['c2ind']#{code:ind_vec}\n ind2c = dicts['ind2c']\n w2ind = dicts['w2ind']\n des_inds_dict = dicts['des_inds_dict'] #{code:dex_ind}\n\n #df_set = clean_unlabel_data(dftrain_set,c2ind)\n\n data_len = df_set.shape[0]\n num_batch = int((data_len - 1)/batch_size) + 1\n\n for i in range(num_batch):\n start_id = i * batch_size\n end_id = min((i + 1) * batch_size, data_len)\n rows = df_set.iloc[start_id:end_id] #dataframe\n x = list(rows['TEXT'])\n y = list(rows['LABELS']) #code_set: list of tuple\n # the unique code inds in each instance\n x_ = process_note(x,w2ind)\n y_,code_set = multi_hot_label(y,c2ind) #batch_size,num_labels np.array\n des = process_des(des_inds_dict,w2ind,ind2c,code_set)\n yield x_, y_, des\n\ndef clean_unlabel_data(train_dir,dicts):\n df = pd.read_csv(train_dir,dtype={\"LABELS\": str})\n c2ind = dicts['c2ind']\n inds_del = []\n for i in range(df.shape[0]):\n y = df.iloc[i]['LABELS']\n labels = str(y).split(';')\n inds = [c2ind[c] for c in labels if c in c2ind.keys()]\n if len(inds) == 0:\n inds_del.append(i)\n clean_df = df.drop(df.index[inds_del])\n\n return clean_df\n\n\n\ndef process_note(x,w2ind,max_length=2500):\n '''\n icd9_codes = all icd9_codes in dataset -- list\n :return:\n '''\n\n x_ = []\n length = 0\n for xi in x:\n text = [int(w2ind[w]) if w in w2ind.keys() else len(w2ind)+1 for w in xi.strip().split()]\n length = len(text)\n if length >= max_length:\n text = text[0:max_length]\n x_.append(text)\n\n if length < max_length:\n max_length = length\n notes = pad_text(x_,max_length)\n\n return notes\n\n\ndef pad_text(notes,max_length):\n\n for note in notes:\n length = len(note)\n note.extend([0 for i in range(max_length-length)])\n return notes\n\n\ndef multi_hot_label(y,c2ind):\n batch_size = len(y)\n num_labels = len(c2ind)\n\n y_matrix = np.zeros((batch_size,num_labels))\n code_set = []\n\n for i in range(batch_size):\n codes = set()\n labels = str(y[i]).split(';')\n #label = str(y[i]).split(';')\n for l in labels:\n if l in c2ind.keys():\n c = int(c2ind[l])\n y_matrix[i,c] = 1\n #codes.add(l)\n codes.add(c) #inds of codes\n code_set.append(codes)\n return y_matrix,code_set\n\n\ndef process_des(des_inds_dict,w2ind,ind2c,code_set):\n '''\n\n :param y:\n :param icd_di_dic:\n :param w2ind:\n :param code_set: list[(code inds),(),...]\n :return:\n '''\n\n des = []\n for inds in code_set:\n codes = [ind2c[i] for i in inds]\n ind_vec = [des_inds_dict[c][:] if c in des_inds_dict.keys() else [len(w2ind)+1] for c in codes]#！！！\n des_padded = pad_des(ind_vec)\n des.append(des_padded)\n #des_padded [batch_size,max_label,max_seqlen]\n\n des_arr = np.array(des)\n\n return des_arr\n\ndef pad_des(des):\n\n max_len = max([len(v) for v in des])\n pad_vecs = []\n for v in des:\n if len(v) < max_len:\n v.extend([0 for i in range(max_len-len(v))])\n pad_vecs.append(v)\n return pad_vecs\n\n\ndef load_all_dict(label_set='full'):\n dicts = {}\n c2ind,ind2c = all_codes_list(label_set)\n w2ind,ind2w = all_vocab_list()\n des_inds_dict = load_des_vector()\n dicts['c2ind'] = c2ind\n dicts['ind2c'] = ind2c\n dicts['w2ind'] = w2ind\n dicts['ind2w'] = ind2w\n dicts['des_inds_dict'] = des_inds_dict\n return dicts\n\n\ndef all_vocab_list():\n vocabs_dir = os.path.join(os.pardir,'mimicdata/mimic3/vocab.csv')\n vocabs = set()\n '''\n with open(vocabs_dir,'r') as f:\n reader = csv.reader(f)\n for row in reader:\n vocabs.add(row[0])\n '''\n with open(vocabs_dir,'r') as f:\n for i, row in enumerate(f):\n row = row.rstrip()\n if row != '':\n vocabs.add(row.strip())\n\n ind2w = {i+1:w for i,w in enumerate(sorted(vocabs))}\n w2ind = {w:i for i,w in ind2w.items()}\n #ind2w = {i+1:w for i,w in enumerate(sorted(vocabs))}\n #w2ind = {w:i+1 for i,w in enumerate(sorted(vocabs))}\n\n return w2ind,ind2w\n\n\ndef all_codes_list(label_set='full'):\n codes = set()\n if label_set == 'full':\n for s in ['train','dev','test']:\n code_dir = os.path.join(os.pardir,'mimicdata/mimic3/%s_full.csv' %s)\n with open(code_dir) as f:\n reader = csv.reader(f)\n next(reader)\n for row in reader:\n cs = row[3].split(';')\n for c in cs:\n if c != '':\n codes.add(c)\n '''\n code_dir = os.path.join(os.pardir,'mimicdata/mimic3/ALL_CODES_filtered.csv')\n with open(code_dir) as f:\n reader = csv.reader(f)\n next(reader)\n for row in reader:\n if row[2] != '':\n codes.add(row[2])\n '''\n else:\n code_dir = os.path.join(os.pardir,'mimicdata/mimic3/TOP_50_CODES.csv')\n with open(code_dir) as f:\n reader = csv.reader(f)\n for row in reader:\n if row[0] != '':\n codes.add(row[2])\n ind2c = defaultdict(str, {i:c for i,c in enumerate(sorted(codes))})\n c2ind = {c:i for i,c in ind2c.items()}\n #c2ind = {c:i for i,c in enumerate(sorted(codes))}\n #ind2c = {i:c for i,c in enumerate(sorted(codes))}\n\n return c2ind,ind2c\n\n\ndef load_embedding_vector():\n\n vocabs_matrix_dir = os.path.join(os.pardir,'mimicdata/mimic3/processed_full.embed')#vocab_matrix.w2v')##\n embed_matrix = []\n with open(vocabs_matrix_dir) as f:\n for l in f:\n wv = l.rstrip().split()[1:]\n vec = np.array(wv).astype(np.float)\n vec = vec / (np.linalg.norm(vec) + 1e-6)\n embed_matrix.append(vec)\n vec = np.random.randn(len(embed_matrix[-1]))\n vec = vec / (np.linalg.norm(vec) + 1e-6)\n embed_matrix.append(vec)\n embed_matrix = np.array(embed_matrix)\n return embed_matrix\n\n\ndef load_des_vector():\n ''':return {code: des_inds}'''\n\n w2ind,ind2w = all_vocab_list()\n\n icd_des_dic=load_description() #get {code, [des_words_list]}\n des_inds_dict = {}\n\n for code, des in icd_des_dic.items():\n des_inds = [w2ind[i] if i in w2ind.keys() else len(w2ind)+1 for i in des]\n des_inds_dict[code] = des_inds\n return des_inds_dict\n\n\n", "sub_path": "train/load_data_multiattn.py", "file_name": "load_data_multiattn.py", "file_ext": "py", "file_size_in_byte": 9013, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "csv.reader", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 58, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 219, "usage_type": "call"}, {"api_name": "os.path", "line_number": 219, "usage_type": "attribute"}, {"api_name": "os.pardir", "line_number": 219, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 245, "usage_type": "call"}, {"api_name": "os.path", "line_number": 245, "usage_type": "attribute"}, {"api_name": "os.pardir", "line_number": 245, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 247, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 264, "usage_type": "call"}, {"api_name": "os.path", "line_number": 264, "usage_type": "attribute"}, {"api_name": "os.pardir", "line_number": 264, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 266, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 270, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 280, "usage_type": "call"}, {"api_name": "os.path", "line_number": 280, "usage_type": "attribute"}, {"api_name": "os.pardir", "line_number": 280, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 285, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 286, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 288, "usage_type": "attribute"}, {"api_name": "numpy.linalg.norm", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 289, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 291, "usage_type": "call"}, {"api_name": "dataprocess.load_descriptions.load_description", "line_number": 300, "usage_type": "call"}]} +{"seq_id": "622658492", "text": "import os\nimport sys\nimport csv\nimport rtree\nimport math\nimport pandas as pd\nimport numpy as np\nfrom data_types import get_dtypes_dict\n\nBASE_DIR = os.path.dirname(os.path.dirname(__file__))\nDATA_DIR = os.path.join(BASE_DIR, 'build')\n\nOUTPUT_DIR = os.path.join(BASE_DIR, 'build', 'scatterplot', 'schools', 'reduced')\n\ndef dist(p, q):\n \"Return the Euclidean distance between points p and q.\"\n return math.hypot(p[0] - q[0], p[1] - q[1])\n\ndef get_subset(points, r):\n \"\"\"Return a maximal list of elements of points such that no pairs of\n points in the result have distance less than r.\n \"\"\"\n result = []\n index = rtree.index.Index()\n for i, p in enumerate(points):\n px = p[1]\n py = p[2]\n pz = p[3]\n if np.isnan(px) or np.isnan(py) or np.isnan(pz) or px == -999 or py == -999 or pz == -999:\n continue\n nearby = index.intersection((px - r, py - r, px + r, py + r))\n if all(dist([px, py], [points[j][1], points[j][2]]) >= r for j in nearby):\n result.append(p)\n index.insert(i, (px, py, px, py))\n return result\n\ndef extract_tuples(df, cols):\n \"\"\"Return tuples containing the id, and values for\n the provided xVar, yVar, zVar\n \"\"\"\n subset = df[cols]\n tuples = [tuple(x) for x in subset.values]\n return tuples\n\ndef create_pair_csv(region, df, xVar, yVar, zVar, radius):\n \"\"\"Write a csv file from the dataframe with id, xVar, yVar columns\n sampling only one point from within the provided radius.\n Points with a higher zVar value have priority. \n \"\"\"\n\n # make sure the columns exist in the data set\n if xVar == yVar or xVar not in df.columns or yVar not in df.columns or zVar not in df.columns:\n print(\"skipping x / y pair \" + xVar + \", \" + yVar ,file=sys.stderr)\n return\n\n # extract data into tuples\n output_cols = [ 'id', xVar, yVar, zVar ]\n tuples = extract_tuples(df, output_cols)\n\n # get subset of points\n subset = get_subset(tuples, radius)\n\n # convert tuples to new csv\n output_file = os.path.join(OUTPUT_DIR, xVar + '-' + yVar + '.csv')\n output_df = pd.DataFrame(subset)\n # output_df['id'] = output_df.index\n output_df = output_df[[0]]\n # output_df = output_df.round(3)\n try:\n output_df.columns = ['id']\n output_df.to_csv(output_file, index=False)\n print(\"reduced\", xVar, \"/\", yVar, \"pair to\",str(output_df.shape[0]),\"points. (\", 100*output_df.shape[0]/df.shape[0], \"%)\")\n except ValueError:\n print(\"error witing file\", xVar, yVar)\n\nif __name__ == '__main__':\n\n region = sys.argv[1]\n radius = float(sys.argv[2])\n dtypes = get_dtypes_dict(region)\n zVar = 'all_sz'\n\n # create pairs with these columns\n y_vars = ['all_avg', 'all_grd', 'all_coh']\n\n # Read the data dictionary from stdin\n data_df = pd.read_csv(\n os.path.join(DATA_DIR, region + '.csv'),\n dtype=dtypes\n )\n\n # sort by zVar so the largest are selected\n data_df = data_df.sort_values(by=[zVar], ascending=False)\n\n # sort the columns in alphabetic order for consistent var names\n data_df = data_df.reindex(sorted(data_df.columns), axis=1)\n\n # loop through all columns and make pairs\n for col in y_vars:\n create_pair_csv(region, data_df, col, 'all_frl', zVar, radius)\n\n\n", "sub_path": "scripts/create_pairs.py", "file_name": "create_pairs.py", "file_ext": "py", "file_size_in_byte": 3132, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "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": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "attribute"}, {"api_name": "math.hypot", "line_number": 17, "usage_type": "call"}, {"api_name": "rtree.index.Index", "line_number": 24, "usage_type": "call"}, {"api_name": "rtree.index", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.isnan", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 65, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 78, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 79, "usage_type": "attribute"}, {"api_name": "data_types.get_dtypes_dict", "line_number": 80, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}]} +{"seq_id": "62290878", "text": "import enum\nimport torch\nfrom torch.utils.data import Dataset\nimport random\nimport numpy as np\nfrom ffindex import *\nimport codecs\nfrom parsers import parse_a3m, read_templates\nimport os\nimport get_true_pdb_name\ndef read_data_mock(data_path):\n data = []\n for i in range(10):\n L = random.randint(80, 120)\n N = 10\n msa = torch.randint(0, 21, (N, L))\n xyz_t = torch.randn(N, L, 3, 3)\n t1d = torch.randn(N, L, 3)\n t0d = torch.randn(N,3)\n xyz_label = torch.randn(L, 3, 3)\n prob_s_label = torch.randint(0, 64, (L * L, ))\n\n feat = msa, xyz_t, t1d, t0d\n label = xyz_label, prob_s_label\n data.append((feat, label))\n return data\ndef read_xyz(path):\n \"\"\"\n x y z x y z x y z\n x y z x y z x y z\n \"\"\"\n return np.load(path).astype(np.float)\n\ndef get_dis_class(v):\n segs = np.arange(2.5, 20.5, 0.5)\n for i,x in enumerate(segs):\n if i == 0 and v < x:\n return i\n if v < x and v > segs[i-1]:\n return i\n return len(segs)\ndef read_dis(path):\n \"\"\"\n L * L\n \"\"\"\n data = np.load(path)\n for i in range(data.shape[0]):\n for j in range(data.shape[1]):\n data[i][j] = get_dis_class(data[i][j])\n return data.astype(np.int)\ndef read_dis_angle(path):\n \"\"\"\n L * L * 4\n dist\n omega\n theta\n phi\n \"\"\"\n data = np.load(path)\n dis_distribute = data[..., 0]\n omega_distribute = data[..., 1]\n theta_distribute = data[..., 2]\n phi_distribute = data[..., 3]\n return [dis_distribute.astype(np.int), omega_distribute.astype(np.int), theta_distribute.astype(np.int), phi_distribute.astype(np.int)]\n\ndef read_mask(path):\n data = np.load(path)\n return data.astype(np.int)\n\n\ndef read_data_true_test(data_path):\n data = []\n for line in codecs.open(data_path):\n if len(data) >= 1:\n break\n line = line.strip().split(\",\")\n seq_name,seq_feat_path = line\n msa = parse_a3m(os.path.join(seq_feat_path, \"t000_.msa0.a3m\"))\n N, L = msa.shape\n if L > 50:\n continue\n xyz_t = torch.randn(N, L, 3, 3)\n t1d = torch.randn(N, L, 3)\n t0d = torch.randn(N,3)\n xyz_label = read_xyz(os.path.join(seq_feat_path, seq_name + \".xyz.npy\"))\n prob_s_label = read_dis(os.path.join(seq_feat_path, seq_name + \".dis.npy\"))\n label = torch.from_numpy(xyz_label).float(), torch.from_numpy(prob_s_label).long()\n feat = torch.from_numpy(msa).long(), xyz_t, t1d, t0d\n data.append((feat, label))\n # break\n\n print(\"data reader over\")\n return data\ndef read_data_true(data_path):\n f = open(data_path, \"rb\")\n data = pickle.load(f)\n data = data[2:]\n \n train_data = []\n for feat, label in data:\n feat_new = [torch.tensor(f).float() for f in feat]\n feat_new[0] = feat_new[0].long()\n print(f\"data shape {feat_new[0].shape}\", )\n label_new = [torch.tensor(label[0]).float(), torch.tensor(label[1]).long()]\n train_data.append((tuple(feat_new), tuple(label_new)))\n if len(train_data) == 1:\n break\n f.close()\n print(\"data reader over\")\n return train_data\ndef read_data_true_mask(data_path):\n f = open(data_path, \"rb\")\n data = pickle.load(f)\n # data = data[2:]\n \n train_data = []\n for feat, label, masks in data:\n feat_new = [torch.tensor(f).float() for f in feat]\n feat_new[0] = feat_new[0].long()\n print(f\"data shape {feat_new[0].shape}\", )\n label_new = []\n label_new.append(torch.tensor(label[0]).float()) # xyz\n label_new.extend([torch.tensor(i).long() for i in label[1:]])\n\n masks_new = [torch.tensor(i).long() for i in masks]\n train_data.append((tuple(feat_new), tuple(label_new), tuple(masks_new)))\n f.close()\n print(\"data reader over\")\n return train_data\ndef read_data_forsave(data_path):\n FFDB=\"pdb100_2021Mar03/pdb100_2021Mar03/pdb100_2021Mar03\"\n FFindexDB = namedtuple(\"FFindexDB\", \"index, data\")\n ffdb = None\n ffdb = FFindexDB(read_index(FFDB+'_pdb.ffindex'),\n read_data(FFDB+'_pdb.ffdata'))\n data = []\n def check_file_ok(seq_feat_path, seq_name):\n files = [\"t000_.msa0.a3m\", \"t000_.hhr\", \"t000_.atab\", seq_name + \".xyz.npy\", seq_name + \".dis.npy\", seq_name + \".dis_angle.npy\", seq_name + \".mask.npy\"]\n return all([os.path.exists(os.path.join(seq_feat_path, i)) for i in files])\n for line in codecs.open(data_path):\n # if len(data) >= 10:\n # break\n line = line.strip().split(\",\")\n seq_name,seq_feat_path = line\n if not check_file_ok(seq_feat_path, seq_name):\n continue\n msa = parse_a3m(os.path.join(seq_feat_path, \"t000_.msa0.a3m\"))\n N, L = msa.shape\n # if L > 100:\n # continue\n xyz_t, t1d, t0d = read_templates(L, ffdb, os.path.join(seq_feat_path, \"t000_.hhr\"), \\\n os.path.join(seq_feat_path, \"t000_.atab\"), n_templ=10)\n if xyz_t is None:\n continue\n # print(seq_name,seq_feat_path)\n xyz_label = read_xyz(os.path.join(seq_feat_path, seq_name + \".xyz.npy\"))\n # prob_s_label = read_dis(os.path.join(seq_feat_path, seq_name + \".dis.npy\"))\n prob_s_label_2 = read_dis_angle(os.path.join(seq_feat_path, seq_name + \".dis_angle.npy\"))\n dis_masks = read_mask(os.path.join(seq_feat_path, seq_name + \".mask.npy\"))\n xyz_mask = np.zeros(L)\n sel_xyz = np.unique(np.where(~np.isnan(xyz_label.reshape(-1, 3*3)))[0])\n xyz_mask[sel_xyz] = 1\n # label = torch.from_numpy(xyz_label).float(), torch.from_numpy(prob_s_label).long()\n label = []\n label.append(torch.from_numpy(xyz_label).float())\n label.extend([torch.from_numpy(i) for i in prob_s_label_2])\n\n masks = []\n masks.append(torch.from_numpy(dis_masks).long())\n masks.append(torch.from_numpy(xyz_mask).long())\n\n feat = torch.from_numpy(msa).long(), xyz_t, t1d, t0d\n\n print(f\"debug {seq_name} msa {torch.from_numpy(msa).shape} xyz_t {xyz_t.shape} \\\n xyz_label {torch.from_numpy(xyz_label).shape}\")\n data.append((feat, label, masks))\n\n del(ffdb)\n get_true_pdb_name.clear()\n print(\"data reader over\")\n return data\nclass DataRead(Dataset):\n def __init__(self, data_path) -> None:\n super().__init__()\n # self.data = read_data_true(data_path)\n self.data = read_data_true_mask(data_path)\n pass\n def __len__(self):\n return len(self.data)\n\n def __getitem__(self, index):\n return self.data[index]\n\nimport pickle\nif __name__ == '__main__':\n data = read_data_forsave(\"./generate_feat/train-feat.list\")\n data_new = []\n for feat, label, masks in data:\n feat_new = []\n label_new = []\n masks_new = []\n for j, f in enumerate(feat):\n feat_new.append(f.tolist())\n \n for j, l in enumerate(label):\n label_new.append(l.tolist())\n\n for j, l in enumerate(masks):\n masks_new.append(l.tolist())\n data_new.append((feat_new, label_new, masks_new))\n with open(\"./generate_feat/train_data.pickle\", \"wb\") as f:\n pickle.dump(data_new, f)", "sub_path": "network/data_reader.py", "file_name": "data_reader.py", "file_ext": "py", "file_size_in_byte": 7258, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "random.randint", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.randint", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.randint", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.float", "line_number": 32, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 50, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 64, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.int", "line_number": 68, "usage_type": "attribute"}, {"api_name": "codecs.open", "line_number": 73, "usage_type": "call"}, {"api_name": "parsers.parse_a3m", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "torch.randn", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 140, "usage_type": "call"}, {"api_name": "parsers.parse_a3m", "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": "parsers.read_templates", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path", "line_number": 151, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path", "line_number": 152, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path", "line_number": 156, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path", "line_number": 158, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 159, "usage_type": "call"}, {"api_name": "os.path", "line_number": 159, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 161, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 172, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 174, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 175, "usage_type": "call"}, {"api_name": "get_true_pdb_name.clear", "line_number": 179, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 182, "usage_type": "name"}, {"api_name": "pickle.dump", "line_number": 212, "usage_type": "call"}]} +{"seq_id": "339617802", "text": "from django.shortcuts import render\nfrom rest_framework import views\nfrom rest_framework import status\nfrom rest_framework.response import Response\nfrom image.models import Images\nfrom image.serializers import UploadImageSerializer\nfrom django.core.files.uploadedfile import InMemoryUploadedFile\n\n\nfrom PIL import Image\nfrom io import BytesIO\nimport base64\nimport uuid\nimport sys\n\nclass UploadView(views.APIView):\n def post(self, request):\n try:\n serializer = UploadImageSerializer(data=request.data)\n serializer.is_valid(raise_exception=True)\n data = serializer.data\n option = data.get('option')\n origin_image = data.get('original_image').split(\",\")[-1]\n original_file = None\n square_file = None\n small_file = None\n\n # Image Validation\n try:\n img = Image.open(BytesIO(base64.b64decode(origin_image)))\n except Exception as e:\n return Response(data={\n \"message\": \"Please select image file\",\n \"code\": status.HTTP_400_BAD_REQUEST\n })\n file_format = img.format\n if option == 'SQ' or option == 'AL':\n img = img.resize(\n (img.width, img.width), Image.ANTIALIAS)\n square_file = BytesIO()\n img.save(square_file, format=f\"{file_format}\")\n square_file=InMemoryUploadedFile(square_file, None, 'foo.jpg', 'image/jpeg', len(square_file.getvalue()), None)\n if option == 'SM' or option == 'AL':\n img = img.resize((256, 256), Image.ANTIALIAS)\n small_file = BytesIO()\n img.save(small_file, format=f\"{file_format}\")\n small_file=InMemoryUploadedFile(small_file, None, 'foo.jpg', 'image/jpeg', len(small_file.getvalue()), None)\n if option == 'OG' or option == 'AL':\n original_file = BytesIO()\n img.save(original_file, format=f\"{file_format}\")\n original_file=InMemoryUploadedFile(original_file, None, 'foo.jpg', 'image/jpeg', len(original_file.getvalue()), None)\n try:\n Images.objects.create(\n uuid = uuid.uuid4(),\n option=option,\n original_file=original_file,\n small_file=small_file,\n square_file=square_file,\n )\n except Exception as e:\n return Response(data={\n \"message\": f\"Can not save image: {e}\",\n \"code\": status.HTTP_400_BAD_REQUEST\n })\n\n return Response(data={\n \"message\": \"Save image successfully\",\n \"code\": status.HTTP_200_OK\n })\n except Exception as e:\n print(e)\n return Response(data={\n \"message\": \"Failed saving image\",\n \"code\": status.HTTP_400_BAD_REQUEST\n })\n", "sub_path": "image_backend/image/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3031, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "rest_framework.views.APIView", "line_number": 16, "usage_type": "attribute"}, {"api_name": "rest_framework.views", "line_number": 16, "usage_type": "name"}, {"api_name": "image.serializers.UploadImageSerializer", "line_number": 19, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 30, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 30, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 30, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 30, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 32, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 34, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 34, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 39, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 39, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 40, "usage_type": "call"}, {"api_name": "django.core.files.uploadedfile.InMemoryUploadedFile", "line_number": 42, "usage_type": "call"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 44, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 44, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 45, "usage_type": "call"}, {"api_name": "django.core.files.uploadedfile.InMemoryUploadedFile", "line_number": 47, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 49, "usage_type": "call"}, {"api_name": "django.core.files.uploadedfile.InMemoryUploadedFile", "line_number": 51, "usage_type": "call"}, {"api_name": "image.models.Images.objects.create", "line_number": 53, "usage_type": "call"}, {"api_name": "image.models.Images.objects", "line_number": 53, "usage_type": "attribute"}, {"api_name": "image.models.Images", "line_number": 53, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 54, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 61, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 63, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 63, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 66, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 68, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 68, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 72, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 74, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 74, "usage_type": "name"}]} +{"seq_id": "558669121", "text": "from config import Config\nfrom core.database.repository.get_group_language import GroupLanguageRepository\nfrom languages import (EN,IT)\n\ndef get(update, context):\n chat = update.effective_message.chat_id\n row = GroupLanguageRepository().getById([chat])\n if row is None:\n return None\n else: \n return row['languages']\n\ndef languages(update,context):\n LANGUAGE = get(update,context)\n \n if LANGUAGE == \"\" or LANGUAGE is None:\n LANGUAGE = Config.DEFAULT_LANGUAGE\n\n if LANGUAGE == \"IT\":\n setLang = IT.Italian\n elif LANGUAGE == \"EN\":\n setLang = EN.English\n\n languages.start = setLang[\"START_COMMAND\"]\n languages.helps = setLang[\"HELP_COMMAND\"]\n languages.group_info = setLang[\"GROUP_INFO\"]\n return LANGUAGE", "sub_path": "languages/getLang.py", "file_name": "getLang.py", "file_ext": "py", "file_size_in_byte": 773, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "core.database.repository.get_group_language.GroupLanguageRepository", "line_number": 7, "usage_type": "call"}, {"api_name": "config.Config.DEFAULT_LANGUAGE", "line_number": 17, "usage_type": "attribute"}, {"api_name": "config.Config", "line_number": 17, "usage_type": "name"}, {"api_name": "languages.IT.Italian", "line_number": 20, "usage_type": "attribute"}, {"api_name": "languages.IT", "line_number": 20, "usage_type": "name"}, {"api_name": "languages.EN.English", "line_number": 22, "usage_type": "attribute"}, {"api_name": "languages.EN", "line_number": 22, "usage_type": "name"}, {"api_name": "languages.start", "line_number": 24, "usage_type": "attribute"}, {"api_name": "languages.helps", "line_number": 25, "usage_type": "attribute"}, {"api_name": "languages.group_info", "line_number": 26, "usage_type": "attribute"}]} +{"seq_id": "498984946", "text": "from urllib.request import urlopen\r\nfrom math import pi\r\nimport re\r\nimport datetime\r\n\r\ndef count(s, char):\r\n return len(s.split(char))\r\n\r\ndef collect_data(balise, jour=datetime.date.today().isoformat().replace(\"-\", \"\"), heure=None):\r\n \"\"\"\r\n Use to retrieve data from infoclimat.fr giving a certain location and a certain day.\r\n \r\n Arguments:\r\n balise: int\r\n jour: int (YMD)\r\n heure: str (hour\"h\"00)\r\n \r\n Return:\r\n Dictionary:\r\n - heure: h\r\n - temp: °C\r\n - temp_eau: °C\r\n - amp: m\r\n - periode: s\r\n - freq: Hz\r\n - puls: rad.s-1\r\n - hum: %\r\n - vent: km.h-1\r\n - pression: hPa \r\n \"\"\"\r\n page = urlopen(\"https://www.infoclimat.fr/mer/bouees.php?id={}&jour={}\".format(balise, jour))\r\n content = str(page.read()).split(\"\")[1].split(r\"<\\tbody>\")[0]\r\n datas = [content.split('id=\"cdata{}\"'.format(i))[1].split(\"\")[0] for i in range(count(content, \"cdata\") - 1)]\r\n\r\n ele = []\r\n to_load = None\r\n for i, data in enumerate(datas):\r\n e = {}\r\n h = data.split('UTC\">')[1].split(\"\")[0]\r\n num_float = [float(x) for x in re.findall(\"(\\d+\\.\\d+)\", data)]\r\n\r\n e['heure'] = h\r\n\r\n e[\"temp\"] = num_float[1]\r\n e[\"temp_eau\"] = num_float[2]\r\n\r\n e[\"amp\"] = num_float[4]\r\n e[\"periode\"] = num_float[5]\r\n e[\"freq\"] = 1/e[\"periode\"]\r\n e[\"puls\"] = 2 * pi / e[\"periode\"]\r\n\r\n e[\"hum\"] = float(re.findall(\">(\\d\\d).*?%\", data)[0])\r\n e[\"vent\"] = float(re.findall('bold\">(\\d+).*?km/h', data)[0])\r\n e[\"pression\"] = num_float[9]\r\n\r\n if h == heure:\r\n to_load = i\r\n break\r\n\r\n ele.append(e)\r\n\r\n if to_load == None:\r\n return ele\r\n\r\n return ele[to_load]\r\n\r\n", "sub_path": "API.py", "file_name": "API.py", "file_ext": "py", "file_size_in_byte": 1807, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datetime.date.today", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 9, "usage_type": "attribute"}, {"api_name": "urllib.request.urlopen", "line_number": 31, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 40, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 50, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 52, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "155043572", "text": "\"\"\"Add a full-text search index to S3Blob\n\nRevision ID: 9576b2ed4073\nRevises: 858454adaad2\nCreate Date: 2018-02-16 18:13:37.534812\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\n# revision identifiers, used by Alembic.\nrevision = '9576b2ed4073'\ndown_revision = '858454adaad2'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n op.add_column('s3_blob', sa.Column('preview_tsv', postgresql.TSVECTOR(), nullable=True))\n op.create_index('idx_tsv', 's3_blob', ['preview_tsv'], unique=False, postgresql_using='gin')\n\n\ndef downgrade():\n op.drop_index('idx_tsv', table_name='s3_blob')\n op.drop_column('s3_blob', 'preview_tsv')\n", "sub_path": "registry/migrations/versions/9576b2ed4073_add_a_full_text_search_index_to_s3blob.py", "file_name": "9576b2ed4073_add_a_full_text_search_index_to_s3blob.py", "file_ext": "py", "file_size_in_byte": 686, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "alembic.op.add_column", "line_number": 20, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 20, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql.TSVECTOR", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.dialects.postgresql", "line_number": 20, "usage_type": "name"}, {"api_name": "alembic.op.create_index", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 21, "usage_type": "name"}, {"api_name": "alembic.op.drop_index", "line_number": 25, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 25, "usage_type": "name"}, {"api_name": "alembic.op.drop_column", "line_number": 26, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "277502613", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n__author__ = 'wu_yong'\n\nimport sys\nfrom ics.scheduler import app\nfrom ics.task.gsxt.cdrcb.task_logic import *\nfrom ics.scheduler.new_task import StableTask\nfrom ics.task.gsxt.cdrcb.constant import *\nfrom ics.utils.decorator import stable2\nfrom ics.utils.exception_util import LogicException, DownloaderException\n\nreload(sys)\nsys.setdefaultencoding('utf-8')\n\n\n@app.task(bind=True, base=StableTask, rate_limit='120/m', ignore_result=True)\n@stable2((LogicException, DownloaderException), logger=logger)\ndef start(self, seed_dict):\n logger.info(u'开始了:{}'.format(seed_dict))\n if seed_dict.get('target_name'):\n seed_dict['company_key'] = seed_dict['target_name']\n elif seed_dict.get('target_id'):\n seed_dict['company_key'] = seed_dict['target_id']\n else:\n logger.error(u'输入种子不合法，不包含搜索关键字')\n return\n value_dict['seed_dict'] = seed_dict\n page_dict['seed_dict'] = seed_dict\n logger.info(u'开始了value_dict:{}'.format(value_dict))\n logger.info(u'开始抓取种子: {}'.format(seed_dict))\n init_home()\n get_validate()\n init_search_list()\n iter_search_list()\n\n pass\n\n\nif __name__ == '__main__':\n seed_dict = {\n \"task_name\": \"gsxt\",\n \"task_id\": str(uuid.uuid4()),\n \"target_name\": u\"安徽洽洽食品有限公司\", # 四川众和源餐饮管理有限公司\n \"target_id\": \"\",\n \"target_type\": 1,\n \"company_key\": u\"安徽洽洽食品有限公司\",\n }\n\n start(seed_dict)", "sub_path": "project/spiders/ics/task/gsxt/cdrcb/task.py", "file_name": "task.py", "file_ext": "py", "file_size_in_byte": 1561, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.setdefaultencoding", "line_number": 15, "usage_type": "call"}, {"api_name": "ics.scheduler.app.task", "line_number": 18, "usage_type": "call"}, {"api_name": "ics.scheduler.app", "line_number": 18, "usage_type": "name"}, {"api_name": "ics.scheduler.new_task.StableTask", "line_number": 18, "usage_type": "name"}, {"api_name": "ics.utils.decorator.stable2", "line_number": 19, "usage_type": "call"}, {"api_name": "ics.utils.exception_util.LogicException", "line_number": 19, "usage_type": "name"}, {"api_name": "ics.utils.exception_util.DownloaderException", "line_number": 19, "usage_type": "name"}]} +{"seq_id": "306601068", "text": "\"\"\"\nSet up and build the support pages for various types of problems.\n\"\"\"\n\nfrom __future__ import (absolute_import, division, print_function)\n\nimport inspect\nimport os\n\nfrom jinja2 import Environment, FileSystemLoader\n\nimport fitbenchmarking\n\n\ndef create(results_per_test, group_name, support_pages_dir,\n options):\n \"\"\"\n Iterate through problem results and create a support html page for\n each.\n\n :param results_per_test: results object\n :type results_per_test: list[list[list]]\n :param group_name: name of the problem group\n :type group_name: str\n :param support_pages_dir: directory in which the results are saved\n :type support_pages_dir: str\n :param options: The options used in the fitting problem and plotting\n :type options: fitbenchmarking.utils.options.Options\n \"\"\"\n\n name_count = {}\n for prob_result in results_per_test:\n name = prob_result[0].problem.sanitised_name\n name_count[name] = 1 + name_count.get(name, 0)\n count = name_count[name]\n\n create_prob_group(prob_result,\n group_name,\n support_pages_dir,\n count,\n options)\n\n\ndef create_prob_group(prob_results, group_name, support_pages_dir,\n count, options):\n \"\"\"\n Creates a support page containing figures and other\n details about the fit for a problem.\n A link to the support page is stored in the results object.\n\n :param prob_results: problem results objects containing results for\n each minimizer and a certain fitting function\n :type prob_results: list[fitbenchmarking.utils.fitbm_result.FittingResult]\n :param group_name: name of the problem group\n :type group_name: str\n :param support_pages_dir: directory to store the support pages in\n :type support_pages_dir: str\n :param count: number of times a problem with the same name was\n passed through this function\n :type count: int\n :param options: The options used in the fitting problem and plotting\n :type options: fitbenchmarking.utils.options.Options\n \"\"\"\n\n for result in prob_results:\n prob_name = result.problem.sanitised_name\n\n file_name = '{}_{}_{}_{}.html'.format(\n group_name, prob_name, count, result.minimizer).lower()\n file_path = os.path.join(support_pages_dir, file_name)\n\n # Bool for print message/insert image\n fit_success = init_success = options.make_plots\n\n if options.make_plots:\n fig_fit, fig_start = get_figure_paths(result, count)\n if fig_fit == '':\n fig_fit = result.figure_error\n fit_success = False\n if fig_start == '':\n fig_start = result.figure_error\n init_success = False\n else:\n fig_fit = fig_start = 'Re-run with make_plots set to yes in the ' \\\n 'ini file to generate plots.'\n\n root = os.path.dirname(inspect.getfile(fitbenchmarking))\n template_dir = os.path.join(root, \"templates\")\n env = Environment(loader=FileSystemLoader(template_dir))\n style_css = os.path.join(template_dir, 'main_style.css')\n table_css = os.path.join(template_dir, 'table_style.css')\n custom_style = os.path.join(template_dir, 'custom_style.css')\n template = env.get_template(\"support_page_template.html\")\n\n with open(file_path, 'w') as fh:\n fh.write(template.render(\n css_style_sheet=style_css,\n table_style=table_css,\n custom_style=custom_style,\n title=result.problem.name,\n equation=result.problem.equation,\n initial_guess=result.ini_function_params,\n minimiser=result.minimizer,\n is_best_fit=result.is_best_fit,\n initial_plot_available=init_success,\n initial_plot=fig_start,\n min_params=result.fin_function_params,\n fitted_plot_available=fit_success,\n fitted_plot=fig_fit))\n\n result.support_page_link = file_path\n\n\ndef get_figure_paths(result, count):\n \"\"\"\n Get the paths to the figures used in the support page.\n\n :param result: The result to get the figures for\n :type result: fitbenchmarking.utils.fitbm_result.FittingProblem\n :param count: number of times a problem with the same name was\n passed through this function, consecutively\n :type count: int\n\n :return: the paths to the required figures\n :rtype: tuple(str, str)\n \"\"\"\n\n figures_dir = \"figures\"\n\n output = []\n for link in [result.figure_link, result.start_figure_link]:\n if link == '':\n output.append('')\n else:\n path = os.path.join(figures_dir, link)\n output.append(path)\n\n return output[0], output[1]\n", "sub_path": "fitbenchmarking/results_processing/support_page.py", "file_name": "support_page.py", "file_ext": "py", "file_size_in_byte": 4954, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.join", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path", "line_number": 70, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "inspect.getfile", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "jinja2.Environment", "line_number": 89, "usage_type": "call"}, {"api_name": "jinja2.FileSystemLoader", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 92, "usage_type": "call"}, {"api_name": "os.path", "line_number": 92, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "attribute"}]} +{"seq_id": "455333645", "text": "import json\n\nimport pytest\nfrom py42.response import Py42Response\nfrom requests import Response\n\nfrom code42cli.main import cli\n\n\nTEST_ROLE_RETURN_DATA = {\n \"data\": [{\"roleName\": \"Customer Cloud Admin\", \"roleId\": \"1234543\"}]\n}\nTEST_USERS_RESPONSE = {\n \"users\": [\n {\n \"firstName\": \"test\",\n \"lastName\": \"username\",\n \"orgId\": 4321,\n \"orgUid\": \"44444444\",\n \"orgName\": \"ORG_NAME\",\n \"status\": \"Active\",\n \"notes\": \"This is a note.\",\n \"active\": True,\n \"blocked\": False,\n \"creationDate\": \"2021-03-12T20:07:40.898Z\",\n \"modificationDate\": \"2021-03-12T20:07:40.938Z\",\n \"userId\": 1234,\n \"username\": \"test.username@example.com\",\n \"userUid\": \"911162111513111325\",\n \"invited\": False,\n \"quotaInBytes\": 55555,\n }\n ]\n}\nTEST_EMPTY_USERS_RESPONSE = {\"users\": []}\nTEST_USERNAME = TEST_USERS_RESPONSE[\"users\"][0][\"username\"]\nTEST_USER_ID = TEST_USERS_RESPONSE[\"users\"][0][\"userId\"]\nTEST_ROLE_NAME = TEST_ROLE_RETURN_DATA[\"data\"][0][\"roleName\"]\n\n\ndef _create_py42_response(mocker, text):\n response = mocker.MagicMock(spec=Response)\n response.text = text\n response._content_consumed = mocker.MagicMock()\n response.status_code = 200\n return Py42Response(response)\n\n\ndef get_all_users_generator():\n yield TEST_USERS_RESPONSE\n\n\n@pytest.fixture\ndef get_available_roles_response(mocker):\n return _create_py42_response(mocker, json.dumps(TEST_ROLE_RETURN_DATA))\n\n\n@pytest.fixture\ndef get_all_users_success(cli_state):\n cli_state.sdk.users.get_all.return_value = get_all_users_generator()\n\n\n@pytest.fixture\ndef get_user_id_success(cli_state):\n cli_state.sdk.users.get_by_username.return_value = TEST_USERS_RESPONSE\n\n\n@pytest.fixture\ndef get_user_id_failure(cli_state):\n cli_state.sdk.users.get_by_username.return_value = TEST_EMPTY_USERS_RESPONSE\n\n\n@pytest.fixture\ndef get_available_roles_success(cli_state, get_available_roles_response):\n cli_state.sdk.users.get_available_roles.return_value = get_available_roles_response\n\n\ndef test_list_when_non_table_format_outputs_expected_columns(\n runner, cli_state, get_all_users_success\n):\n result = runner.invoke(cli, [\"users\", \"list\", \"-f\", \"CSV\"], obj=cli_state)\n assert \"firstName\" in result.output\n assert \"lastName\" in result.output\n assert \"orgId\" in result.output\n assert \"orgUid\" in result.output\n assert \"orgName\" in result.output\n assert \"status\" in result.output\n assert \"notes\" in result.output\n assert \"active\" in result.output\n assert \"blocked\" in result.output\n assert \"creationDate\" in result.output\n assert \"modificationDate\" in result.output\n assert \"userId\" in result.output\n assert \"username\" in result.output\n assert \"userUid\" in result.output\n assert \"invited\" in result.output\n assert \"quotaInBytes\" in result.output\n\n\ndef test_list_when_table_format_outputs_expected_columns(\n runner, cli_state, get_all_users_success\n):\n result = runner.invoke(cli, [\"users\", \"list\", \"-f\", \"TABLE\"], obj=cli_state)\n assert \"orgUid\" in result.output\n assert \"status\" in result.output\n assert \"username\" in result.output\n assert \"userUid\" in result.output\n\n assert \"firstName\" not in result.output\n assert \"lastName\" not in result.output\n assert \"orgId\" not in result.output\n assert \"orgName\" not in result.output\n assert \"notes\" not in result.output\n assert \"active\" not in result.output\n assert \"blocked\" not in result.output\n assert \"creationDate\" not in result.output\n assert \"modificationDate\" not in result.output\n assert \"userId\" not in result.output\n assert \"invited\" not in result.output\n assert \"quotaInBytes\" not in result.output\n\n\ndef test_list_users_calls_users_get_all_with_expected_role_id(\n runner, cli_state, get_available_roles_success, get_all_users_success\n):\n ROLE_NAME = \"Customer Cloud Admin\"\n runner.invoke(cli, [\"users\", \"list\", \"--role-name\", ROLE_NAME], obj=cli_state)\n cli_state.sdk.users.get_all.assert_called_once_with(\n active=None, org_uid=None, role_id=\"1234543\"\n )\n\n\ndef test_list_users_calls_get_all_users_with_correct_parameters(\n runner, cli_state, get_all_users_success\n):\n org_uid = \"TEST_ORG_UID\"\n runner.invoke(\n cli, [\"users\", \"list\", \"--org-uid\", org_uid, \"--active\"], obj=cli_state\n )\n cli_state.sdk.users.get_all.assert_called_once_with(\n active=True, org_uid=org_uid, role_id=None\n )\n\n\ndef test_list_users_when_given_inactive_uses_active_equals_false(\n runner, cli_state, get_available_roles_success, get_all_users_success\n):\n runner.invoke(cli, [\"users\", \"list\", \"--inactive\"], obj=cli_state)\n cli_state.sdk.users.get_all.assert_called_once_with(\n active=False, org_uid=None, role_id=None\n )\n\n\ndef test_list_users_when_given_active_and_inactive_raises_error(\n runner, cli_state, get_available_roles_success, get_all_users_success\n):\n result = runner.invoke(\n cli, [\"users\", \"list\", \"--active\", \"--inactive\"], obj=cli_state\n )\n assert \"Error: --inactive can't be used with: --active\" in result.output\n\n\ndef test_list_users_when_given_excluding_active_and_inactive_uses_active_equals_none(\n runner, cli_state, get_available_roles_success, get_all_users_success\n):\n runner.invoke(cli, [\"users\", \"list\"], obj=cli_state)\n cli_state.sdk.users.get_all.assert_called_once_with(\n active=None, org_uid=None, role_id=None\n )\n\n\ndef test_add_user_role_adds(\n runner, cli_state, get_user_id_success, get_available_roles_success\n):\n command = [\n \"users\",\n \"add-role\",\n \"--username\",\n \"test.username@example.com\",\n \"--role-name\",\n \"Customer Cloud Admin\",\n ]\n runner.invoke(cli, command, obj=cli_state)\n cli_state.sdk.users.add_role.assert_called_once_with(TEST_USER_ID, TEST_ROLE_NAME)\n\n\ndef test_add_user_role_raises_error_when_role_does_not_exist(\n runner, cli_state, get_user_id_success, get_available_roles_success\n):\n command = [\n \"users\",\n \"add-role\",\n \"--username\",\n \"test.username@example.com\",\n \"--role-name\",\n \"test\",\n ]\n result = runner.invoke(cli, command, obj=cli_state)\n assert result.exit_code == 1\n assert \"Role with name 'test' not found.\" in result.output\n\n\ndef test_add_user_role_raises_error_when_username_does_not_exist(\n runner, cli_state, get_user_id_failure, get_available_roles_success\n):\n command = [\n \"users\",\n \"add-role\",\n \"--username\",\n \"not_a_username@example.com\",\n \"--role-name\",\n \"Desktop User\",\n ]\n result = runner.invoke(cli, command, obj=cli_state)\n assert result.exit_code == 1\n assert \"User 'not_a_username@example.com' does not exist.\" in result.output\n\n\ndef test_remove_user_role_removes(\n runner, cli_state, get_user_id_success, get_available_roles_success\n):\n command = [\n \"users\",\n \"remove-role\",\n \"--username\",\n \"test.username@example.com\",\n \"--role-name\",\n \"Customer Cloud Admin\",\n ]\n runner.invoke(cli, command, obj=cli_state)\n cli_state.sdk.users.remove_role.assert_called_once_with(\n TEST_USER_ID, TEST_ROLE_NAME\n )\n\n\ndef test_remove_user_role_raises_error_when_role_does_not_exist(\n runner, cli_state, get_user_id_success, get_available_roles_success\n):\n command = [\n \"users\",\n \"remove-role\",\n \"--username\",\n \"test.username@example.com\",\n \"--role-name\",\n \"test\",\n ]\n result = runner.invoke(cli, command, obj=cli_state)\n assert result.exit_code == 1\n assert \"Role with name 'test' not found.\" in result.output\n\n\ndef test_remove_user_role_raises_error_when_username_does_not_exist(\n runner, cli_state, get_user_id_failure, get_available_roles_success\n):\n command = [\n \"users\",\n \"remove-role\",\n \"--username\",\n \"not_a_username@example.com\",\n \"--role-name\",\n \"Desktop User\",\n ]\n result = runner.invoke(cli, command, obj=cli_state)\n assert result.exit_code == 1\n assert \"User 'not_a_username@example.com' does not exist.\" in result.output\n", "sub_path": "tests/cmds/test_users.py", "file_name": "test_users.py", "file_ext": "py", "file_size_in_byte": 8246, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.Response", "line_number": 42, "usage_type": "name"}, {"api_name": "py42.response.Py42Response", "line_number": 46, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 55, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 63, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 68, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 73, "usage_type": "attribute"}, {"api_name": "code42cli.main.cli", "line_number": 81, "usage_type": "argument"}, {"api_name": "code42cli.main.cli", "line_number": 103, "usage_type": "argument"}, {"api_name": "code42cli.main.cli", "line_number": 127, "usage_type": "argument"}, {"api_name": "code42cli.main.cli", "line_number": 138, "usage_type": "argument"}, {"api_name": "code42cli.main.cli", "line_number": 148, "usage_type": "argument"}, {"api_name": "code42cli.main.cli", "line_number": 158, "usage_type": "argument"}, {"api_name": "code42cli.main.cli", "line_number": 166, "usage_type": "argument"}, {"api_name": "code42cli.main.cli", "line_number": 183, "usage_type": "argument"}, {"api_name": "code42cli.main.cli", "line_number": 198, "usage_type": "argument"}, {"api_name": "code42cli.main.cli", "line_number": 214, "usage_type": "argument"}, {"api_name": "code42cli.main.cli", "line_number": 230, "usage_type": "argument"}, {"api_name": "code42cli.main.cli", "line_number": 247, "usage_type": "argument"}, {"api_name": "code42cli.main.cli", "line_number": 263, "usage_type": "argument"}]} +{"seq_id": "367687082", "text": "from twisted.internet import reactor\nfrom twisted.internet.defer import Deferred\nfrom twisted.internet.interfaces import ISSLTransport\nfrom twisted.internet.task import LoopingCall\nfrom twisted.names import client as dnsClient\nfrom twisted.words.protocols import irc\nfrom txircd import version\nfrom txircd.ircbase import IRCBase\nfrom txircd.utils import CaseInsensitiveDictionary, expandIPv6Address, ipIsV4, isValidHost, isValidMetadataKey, ModeType, now, splitMessage\n\nirc.ERR_ALREADYREGISTERED = \"462\"\n\nclass IRCUser(IRCBase):\n\tdef __init__(self, ircd, ip, uuid = None, host = None):\n\t\tself.ircd = ircd\n\t\tself.uuid = ircd.createUUID() if uuid is None else uuid\n\t\t\n\t\tregistrationTimeout = self.ircd.config.get(\"user_registration_timeout\", 10)\n\t\t\n\t\tself.nick = None\n\t\tself.ident = None\n\t\tif ip[0] == \":\": # Normalize IPv6 address for IRC\n\t\t\tip = \"0{}\".format(ip)\n\t\tif host is None:\n\t\t\tself.realHost = ip\n\t\telse:\n\t\t\tself.realHost = host\n\t\tself.ip = ip\n\t\tself._hostStack = []\n\t\tself._hostsByType = {}\n\t\tself.gecos = None\n\t\tself._metadata = CaseInsensitiveDictionary()\n\t\tself.cache = {}\n\t\tself.channels = []\n\t\tself.modes = {}\n\t\tself.connectedSince = now()\n\t\tself.nickSince = now()\n\t\tself.idleSince = now()\n\t\tself._registerHolds = set((\"connection\", \"dns\", \"NICK\", \"USER\"))\n\t\tself.disconnectedDeferred = Deferred()\n\t\tself._messageBatches = {}\n\t\tself._errorBatchName = None\n\t\tself._errorBatch = []\n\t\tself.ircd.users[self.uuid] = self\n\t\tself.localOnly = False\n\t\tself.secureConnection = False\n\t\tself._pinger = LoopingCall(self._ping)\n\t\tself._registrationTimeoutTimer = reactor.callLater(registrationTimeout, self._timeoutRegistration)\n\t\tself._connectHandlerTimer = None\n\t\tself._startDNSResolving(registrationTimeout)\n\t\n\tdef _startDNSResolving(self, timeout):\n\t\tip = self.ip\n\t\tif ipIsV4(ip):\n\t\t\taddr = \"{}.in-addr.arpa\".format(\".\".join(reversed(ip.split(\".\"))))\n\t\telse:\n\t\t\taddr = reversed(expandIPv6Address(ip).replace(\":\", \"\"))\n\t\t\taddr = \"{}.ip6.arpa\".format(\".\".join(addr))\n\t\tresolveDeferred = dnsClient.lookupPointer(addr, ((timeout/2),))\n\t\tresolveDeferred.addCallbacks(callback=self._verifyDNSResolution, callbackArgs=(timeout,), errback=self._cancelDNSResolution)\n\t\n\tdef _verifyDNSResolution(self, result, timeout):\n\t\tname = result[0][0].payload.name.name\n\t\tif len(name) > self.ircd.config.get(\"hostname_length\", 64):\n\t\t\tself._cancelDNSResolution()\n\t\t\treturn\n\t\tif not isValidHost(name):\n\t\t\tself._cancelDNSResolution()\n\t\t\treturn\n\t\tresolveDeferred = dnsClient.getHostByName(name, ((timeout/2),))\n\t\tresolveDeferred.addCallbacks(callback=self._completeDNSResolution, errback=self._cancelDNSResolution, callbackArgs=(name,))\n\t\n\tdef _completeDNSResolution(self, result, name):\n\t\tif result == self.ip:\n\t\t\tself.realHost = name\n\t\tself.register(\"dns\")\n\t\n\tdef _cancelDNSResolution(self, error = None):\n\t\tself.register(\"dns\")\n\t\n\tdef connectionMade(self):\n\t\t# We need to callLater the connect action call because the connection isn't fully set up yet,\n\t\t# nor is it fully set up even with a delay of zero, which causes the message buffer not to be sent\n\t\t# when the connection is closed.\n\t\t# The \"connection\" register hold is used basically solely for the purposes of this to prevent potential\n\t\t# race conditions with registration.\n\t\tself._connectHandlerTimer = reactor.callLater(0.1, self._callConnectAction)\n\t\tif ISSLTransport.providedBy(self.transport):\n\t\t\tself.secureConnection = True\n\t\n\tdef _callConnectAction(self):\n\t\tself._connectHandlerTimer = None\n\t\tif self.ircd.runActionUntilFalse(\"userconnect\", self, users=[self]):\n\t\t\tself.transport.loseConnection()\n\t\telse:\n\t\t\tself.register(\"connection\")\n\t\n\tdef dataReceived(self, data):\n\t\tself.ircd.runActionStandard(\"userrecvdata\", self, data, users=[self])\n\t\ttry:\n\t\t\tIRCBase.dataReceived(self, data)\n\t\texcept Exception:\n\t\t\tself.ircd.log.failure(\"An error occurred while processing incoming data.\")\n\t\t\tif self.uuid in self.ircd.users:\n\t\t\t\tself.disconnect(\"Error occurred\")\n\t\n\tdef sendLine(self, line):\n\t\tself.ircd.runActionStandard(\"usersenddata\", self, line, users=[self])\n\t\tIRCBase.sendLine(self, line)\n\t\n\tdef sendMessage(self, command, *args, **kw):\n\t\t\"\"\"\n\t\tSends the given message to this user.\n\t\tAccepts the following keyword arguments:\n\t\t- prefix: The message prefix or None to suppress the default prefix\n\t\t If not given, defaults to the server name.\n\t\t- to: The destination of the message or None if the message has no\n\t\t destination. The implicit destination is this user if this\n\t\t argument isn't specified.\n\t\t- tags: Dict of message tags to send.\n\t\t- alwaysPrefixLastParam: For compatibility with some broken clients,\n\t\t you might want some messages to always have the last parameter\n\t\t prefixed with a colon. To do that, pass this as True.\n\t\t\"\"\"\n\t\tif \"prefix\" not in kw:\n\t\t\tkw[\"prefix\"] = self.ircd.name\n\t\tif kw[\"prefix\"] is None:\n\t\t\tdel kw[\"prefix\"]\n\t\tto = self.nick if self.nick else \"*\"\n\t\tif \"to\" in kw:\n\t\t\tto = kw[\"to\"]\n\t\t\tdel kw[\"to\"]\n\t\tif to:\n\t\t\targs = [to] + list(args)\n\t\tself.ircd.runActionStandard(\"modifyoutgoingmessage\", self, command, args, kw)\n\t\tIRCBase.sendMessage(self, command, *args, **kw)\n\t\n\tdef handleCommand(self, command, params, prefix, tags):\n\t\tif self.uuid not in self.ircd.users:\n\t\t\treturn # we have been disconnected - ignore all further commands\n\t\tif command in self.ircd.userCommands:\n\t\t\thandlers = self.ircd.userCommands[command]\n\t\t\tif not handlers:\n\t\t\t\treturn\n\t\t\tdata = None\n\t\t\tspewRegWarning = True\n\t\t\taffectedUsers = []\n\t\t\taffectedChannels = []\n\t\t\tfor handler in handlers:\n\t\t\t\tif handler[0].forRegistered is not None:\n\t\t\t\t\tif (handler[0].forRegistered is True and not self.isRegistered()) or (handler[0].forRegistered is False and self.isRegistered()):\n\t\t\t\t\t\tcontinue\n\t\t\t\tspewRegWarning = False\n\t\t\t\tdata = handler[0].parseParams(self, params, prefix, tags)\n\t\t\t\tif data is not None:\n\t\t\t\t\taffectedUsers = handler[0].affectedUsers(self, data)\n\t\t\t\t\taffectedChannels = handler[0].affectedChannels(self, data)\n\t\t\t\t\tif self not in affectedUsers:\n\t\t\t\t\t\taffectedUsers.append(self)\n\t\t\t\t\tbreak\n\t\t\tif data is None:\n\t\t\t\tif spewRegWarning:\n\t\t\t\t\tif self.isRegistered():\n\t\t\t\t\t\tself.sendMessage(irc.ERR_ALREADYREGISTERED, \"You may not reregister\")\n\t\t\t\t\telse:\n\t\t\t\t\t\tself.sendMessage(irc.ERR_NOTREGISTERED, command, \"You have not registered\")\n\t\t\t\telif self._hasBatchedErrors():\n\t\t\t\t\tself._dispatchErrorBatch()\n\t\t\t\treturn\n\t\t\tself._clearErrorBatch()\n\t\t\tif self.ircd.runComboActionUntilValue(((\"commandpermission-{}\".format(command), self, data), (\"commandpermission\", self, command, data)), users=affectedUsers, channels=affectedChannels) is False:\n\t\t\t\treturn\n\t\t\tself.ircd.runComboActionStandard(((\"commandmodify-{}\".format(command), self, data), (\"commandmodify\", self, command, data)), users=affectedUsers, channels=affectedChannels) # This allows us to do processing without the \"stop on empty\" feature of runActionProcessing\n\t\t\tfor handler in handlers:\n\t\t\t\tif handler[0].execute(self, data):\n\t\t\t\t\tif handler[0].resetsIdleTime:\n\t\t\t\t\t\tself.idleSince = now()\n\t\t\t\t\tbreak # If the command executor returns True, it was handled\n\t\t\telse:\n\t\t\t\treturn # Don't process commandextra if it wasn't handled\n\t\t\tself.ircd.runComboActionStandard(((\"commandextra-{}\".format(command), self, data), (\"commandextra\", self, command, data)), users=affectedUsers, channels=affectedChannels)\n\t\telse:\n\t\t\tif not self.ircd.runActionFlagTrue(\"commandunknown\", self, command, params, {}):\n\t\t\t\tself.sendMessage(irc.ERR_UNKNOWNCOMMAND, command, \"Unknown command\")\n\t\n\tdef createMessageBatch(self, batchName, batchType, batchParameters = None):\n\t\t\"\"\"\n\t\tStart a new message batch with the given batch name, type, and list of parameters.\n\t\tIf a batch with the given name already exists, that batch will be overwritten.\n\t\t\"\"\"\n\t\tself._messageBatches[batchName] = { \"type\": batchType, \"parameters\": batchParameters, \"messages\": [] }\n\t\n\tdef sendMessageInBatch(self, batchName, command, *args, **kw):\n\t\t\"\"\"\n\t\tAdds a message to the batch with the given name.\n\t\t\"\"\"\n\t\tif batchName not in self._messageBatches:\n\t\t\treturn\n\t\tself._messageBatches[batchName][\"messages\"].append((command, args, kw))\n\t\n\tdef sendBatch(self, batchName):\n\t\t\"\"\"\n\t\tSends the messages in the given batch to the user.\n\t\t\"\"\"\n\t\tif batchName not in self._messageBatches:\n\t\t\treturn\n\t\tbatchType = self._messageBatches[batchName][\"type\"]\n\t\tbatchParameters = self._messageBatches[batchName][\"parameters\"]\n\t\tself.ircd.runActionStandard(\"startbatchsend\", self, batchName, batchType, batchParameters)\n\t\tfor messageData in self._messageBatches[batchName][\"messages\"]:\n\t\t\tself.sendMessage(messageData[0], *messageData[1], **messageData[2])\n\t\tself.ircd.runActionStandard(\"endbatchsend\", self, batchName, batchType, batchParameters)\n\t\n\tdef startErrorBatch(self, batchName):\n\t\t\"\"\"\n\t\tUsed to start an error batch when sending multiple error messages to a\n\t\tuser from a command's parseParams or from the commandpermission action.\n\t\t\"\"\"\n\t\tif not self._errorBatchName or not self._errorBatch: # Only the first batch should apply\n\t\t\tself._errorBatchName = batchName\n\t\t\n\tdef sendBatchedError(self, batchName, command, *args, **kw):\n\t\t\"\"\"\n\t\tAdds an error to the current error batch if the specified error batch\n\t\tis the current error batch.\n\t\t\"\"\"\n\t\tif batchName and self._errorBatchName == batchName:\n\t\t\tself._errorBatch.append((command, args, kw))\n\t\n\tdef sendSingleError(self, batchName, command, *args, **kw):\n\t\t\"\"\"\n\t\tCreates a batch containing a single error and adds the specified error\n\t\tto it.\n\t\t\"\"\"\n\t\tif not self._errorBatchName:\n\t\t\tself._errorBatchName = batchName\n\t\t\tself._errorBatch.append((command, args, kw))\n\t\n\tdef _hasBatchedErrors(self):\n\t\tif self._errorBatch:\n\t\t\treturn True\n\t\treturn False\n\t\n\tdef _clearErrorBatch(self):\n\t\tself._errorBatchName = None\n\t\tself._errorBatch = []\n\t\n\tdef _dispatchErrorBatch(self):\n\t\tfor error in self._errorBatch:\n\t\t\tself.sendMessage(error[0], *error[1], **error[2])\n\t\tself._clearErrorBatch()\n\t\n\tdef filterConditionalTags(self, conditionalTags):\n\t\tapplyTags = {}\n\t\tfor tag, data in conditionalTags.iteritems():\n\t\t\tvalue, check = data\n\t\t\tif check(self):\n\t\t\t\tapplyTags[tag] = value\n\t\treturn applyTags\n\t\n\tdef connectionLost(self, reason):\n\t\tif self.uuid in self.ircd.users:\n\t\t\tself.disconnect(\"Connection reset\")\n\t\tself.disconnectedDeferred.callback(None)\n\t\n\tdef disconnect(self, reason):\n\t\t\"\"\"\n\t\tDisconnects the user from the server.\n\t\t\"\"\"\n\t\tself.ircd.log.debug(\"Disconnecting user {user.uuid} ({user.hostmask()}): {reason}\", user=self, reason=reason)\n\t\t# Sometimes, actions deferred from initial connection may cause registration to occur after disconnection if\n\t\t# disconnection happens before registration completes. If the user is unregistered on disconnection, this prevents\n\t\t# the user from completing registration.\n\t\tself.addRegisterHold(\"QUIT\")\n\t\tif self._pinger:\n\t\t\tif self._pinger.running:\n\t\t\t\tself._pinger.stop()\n\t\t\tself._pinger = None\n\t\tif self._registrationTimeoutTimer:\n\t\t\tif self._registrationTimeoutTimer.active():\n\t\t\t\tself._registrationTimeoutTimer.cancel()\n\t\t\tself._registrationTimeoutTimer = None\n\t\tif self._connectHandlerTimer and self._connectHandlerTimer.active():\n\t\t\tself._connectHandlerTimer.cancel()\n\t\t\tself._connectHandlerTimer = None\n\t\tself.ircd.recentlyQuitUsers[self.uuid] = now()\n\t\tdel self.ircd.users[self.uuid]\n\t\tif self.isRegistered():\n\t\t\tdel self.ircd.userNicks[self.nick]\n\t\tuserSendList = [self]\n\t\twhile self.channels:\n\t\t\tchannel = self.channels[0]\n\t\t\tuserSendList.extend(channel.users.keys())\n\t\t\tself._leaveChannel(channel)\n\t\tuserSendList = [u for u in set(userSendList) if u.uuid[:3] == self.ircd.serverID]\n\t\tuserSendList.remove(self)\n\t\tself.ircd.runActionProcessing(\"quitmessage\", userSendList, self, reason, users=[self] + userSendList)\n\t\tself.ircd.runActionStandard(\"quit\", self, reason, users=self)\n\t\tself.transport.loseConnection()\n\t\n\tdef _timeoutRegistration(self):\n\t\tif self.isRegistered():\n\t\t\tself._pinger.start(self.ircd.config.get(\"user_ping_frequency\", 60), False)\n\t\t\treturn\n\t\tself.disconnect(\"Registration timeout\")\n\t\n\tdef _ping(self):\n\t\tself.ircd.runActionStandard(\"pinguser\", self)\n\t\n\tdef isRegistered(self):\n\t\t\"\"\"\n\t\tReturns True if this user session is fully registered.\n\t\t\"\"\"\n\t\treturn not self._registerHolds\n\t\n\tdef register(self, holdName):\n\t\t\"\"\"\n\t\tRemoves the specified hold on a user's registration. If this is the\n\t\tlast hold on a user, completes registration on the user.\n\t\t\"\"\"\n\t\tif holdName not in self._registerHolds:\n\t\t\treturn\n\t\tself._registerHolds.remove(holdName)\n\t\tif not self._registerHolds:\n\t\t\tif not self.nick or self.nick in self.ircd.userNicks:\n\t\t\t\tself._registerHolds.add(\"NICK\")\n\t\t\tif not self.ident or not self.gecos:\n\t\t\t\tself._registerHolds.add(\"USER\")\n\t\t\tif self._registerHolds:\n\t\t\t\treturn\n\t\t\tself._registerHolds.add(\"registercheck\") # The user shouldn't be considered registered until we complete these final checks\n\t\t\tif self.ircd.runActionUntilFalse(\"register\", self, users=[self]):\n\t\t\t\tself.transport.loseConnection()\n\t\t\t\treturn\n\t\t\tself._registerHolds.remove(\"registercheck\")\n\t\t\tself.ircd.userNicks[self.nick] = self.uuid\n\t\t\tself.ircd.log.debug(\"Registering user {user.uuid} ({user.hostmask()})\", user=self)\n\t\t\tversionWithName = \"txircd-{}\".format(version)\n\t\t\tself.sendMessage(irc.RPL_WELCOME, \"Welcome to the {} Internet Relay Chat Network {}\".format(self.ircd.config[\"network_name\"], self.hostmask()))\n\t\t\tself.sendMessage(irc.RPL_YOURHOST, \"Your host is {}, running version {}\".format(self.ircd.name, versionWithName))\n\t\t\tself.sendMessage(irc.RPL_CREATED, \"This server was created {}\".format(self.ircd.startupTime.replace(microsecond=0)))\n\t\t\tchanModes = \"\".join([\"\".join(modes.keys()) for modes in self.ircd.channelModes])\n\t\t\tchanModes += \"\".join(self.ircd.channelStatuses.keys())\n\t\t\tself.sendMessage(irc.RPL_MYINFO, self.ircd.name, versionWithName, \"\".join([\"\".join(modes.keys()) for modes in self.ircd.userModes]), chanModes)\n\t\t\tself.sendISupport()\n\t\t\tself.ircd.runActionStandard(\"welcome\", self, users=[self])\n\t\n\tdef addRegisterHold(self, holdName):\n\t\t\"\"\"\n\t\tAdds a register hold to this user if the user is not yet registered.\n\t\t\"\"\"\n\t\tif not self._registerHolds:\n\t\t\treturn\n\t\tself._registerHolds.add(holdName)\n\t\n\tdef sendISupport(self):\n\t\t\"\"\"\n\t\tSends ISUPPORT to this user.\"\"\"\n\t\tisupportList = self.ircd.generateISupportList()\n\t\tisupportMsgList = splitMessage(\" \".join(isupportList), 350)\n\t\tfor line in isupportMsgList:\n\t\t\tlineArgs = line.split(\" \")\n\t\t\tlineArgs.append(\"are supported by this server\")\n\t\t\tself.sendMessage(irc.RPL_ISUPPORT, *lineArgs)\n\t\n\tdef hostmask(self):\n\t\t\"\"\"\n\t\tReturns the user's hostmask.\n\t\t\"\"\"\n\t\treturn \"{}!{}@{}\".format(self.nick, self.ident, self.host())\n\t\n\tdef hostmaskWithRealHost(self):\n\t\t\"\"\"\n\t\tReturns the user's hostmask using the user's real host rather than any\n\t\tvhost that may have been applied.\n\t\t\"\"\"\n\t\treturn \"{}!{}@{}\".format(self.nick, self.ident, self.realHost)\n\t\n\tdef hostmaskWithIP(self):\n\t\t\"\"\"\n\t\tReturns the user's hostmask using the user's IP address instead of the\n\t\thost.\n\t\t\"\"\"\n\t\treturn \"{}!{}@{}\".format(self.nick, self.ident, self.ip)\n\t\n\tdef changeNick(self, newNick, fromServer = None):\n\t\t\"\"\"\n\t\tChanges this user's nickname. If initiated by a remote server, that\n\t\tserver should be specified in the fromServer parameter.\n\t\t\"\"\"\n\t\tif newNick == self.nick:\n\t\t\treturn\n\t\tif newNick in self.ircd.userNicks and self.ircd.userNicks[newNick] != self.uuid:\n\t\t\treturn\n\t\toldNick = self.nick\n\t\tif oldNick and oldNick in self.ircd.userNicks:\n\t\t\tdel self.ircd.userNicks[self.nick]\n\t\tself.nick = newNick\n\t\tself.nickSince = now()\n\t\tif self.isRegistered():\n\t\t\tself.ircd.userNicks[self.nick] = self.uuid\n\t\t\tuserSendList = [self]\n\t\t\tfor channel in self.channels:\n\t\t\t\tuserSendList.extend(channel.users.keys())\n\t\t\tuserSendList = [u for u in set(userSendList) if u.uuid[:3] == self.ircd.serverID]\n\t\t\tself.ircd.runActionProcessing(\"changenickmessage\", userSendList, self, oldNick, users=userSendList)\n\t\t\tself.ircd.runActionStandard(\"changenick\", self, oldNick, fromServer, users=[self])\n\t\n\tdef changeIdent(self, newIdent, fromServer = None):\n\t\t\"\"\"\n\t\tChanges this user's ident. If initiated by a remote server, that server\n\t\tshould be specified in the fromServer parameter.\n\t\t\"\"\"\n\t\tif newIdent == self.ident:\n\t\t\treturn\n\t\tif len(newIdent) > self.ircd.config.get(\"ident_length\", 12):\n\t\t\treturn\n\t\toldIdent = self.ident\n\t\tself.ident = newIdent\n\t\tif self.isRegistered():\n\t\t\tself.ircd.runActionStandard(\"changeident\", self, oldIdent, fromServer, users=[self])\n\t\n\tdef host(self):\n\t\tif not self._hostStack:\n\t\t\treturn self.realHost\n\t\treturn self._hostsByType[self._hostStack[-1]]\n\t\n\tdef changeHost(self, hostType, newHost, fromServer = None):\n\t\t\"\"\"\n\t\tChanges a user's host. If initiated by a remote server, that server\n\t\tshould be specified in the fromServer parameter.\n\t\t\"\"\"\n\t\tif hostType == \"*\":\n\t\t\treturn\n\t\tif len(newHost) > self.ircd.config.get(\"hostname_length\", 64):\n\t\t\treturn\n\t\tif hostType in self._hostsByType and self._hostsByType[hostType] == newHost:\n\t\t\treturn\n\t\toldHost = self.host()\n\t\tself._hostsByType[hostType] = newHost\n\t\tif hostType in self._hostStack:\n\t\t\tself._hostStack.remove(hostType)\n\t\tself._hostStack.append(hostType)\n\t\tif self.isRegistered():\n\t\t\tself.ircd.runComboActionStandard(((\"changehost\", self, hostType, oldHost, fromServer), (\"updatehost\", self, hostType, oldHost, newHost, fromServer)), users=[self])\n\t\n\tdef updateHost(self, hostType, newHost, fromServer = None):\n\t\t\"\"\"\n\t\tUpdates the host of a given host type for the user. If initiated by\n\t\ta remote server, that server should be specified in the fromServer\n\t\tparameter.\n\t\t\"\"\"\n\t\tif hostType not in self._hostStack:\n\t\t\tself.changeHost(hostType, newHost, fromServer)\n\t\t\treturn\n\t\tif hostType == \"*\":\n\t\t\treturn\n\t\tif len(newHost) > self.ircd.config.get(\"hostname_length\", 64):\n\t\t\treturn\n\t\tif hostType in self._hostsByType and self._hostsByType[hostType] == newHost:\n\t\t\treturn\n\t\toldHost = self.host()\n\t\toldHostOfType = None\n\t\tif hostType in self._hostsByType:\n\t\t\toldHostOfType = self._hostsByType[hostType]\n\t\tself._hostsByType[hostType] = newHost\n\t\tchangedUserHost = (oldHost != self.host())\n\t\tchangedHostOfType = (oldHostOfType != newHost)\n\t\tif self.isRegistered():\n\t\t\tif changedUserHost and changedHostOfType:\n\t\t\t\tself.ircd.runComboActionStandard(((\"changehost\", self, hostType, oldHost, fromServer), (\"updatehost\", self, hostType, oldHost, newHost, fromServer)), users=[self])\n\t\t\telif changedHostOfType:\n\t\t\t\tself.ircd.runActionStandard(\"updatehost\", self, hostType, oldHost, newHost, fromServer, users=[self])\n\t\n\tdef resetHost(self, hostType, fromServer = None):\n\t\t\"\"\"\n\t\tResets the user's host to the real host.\n\t\t\"\"\"\n\t\tif hostType not in self._hostsByType:\n\t\t\treturn\n\t\toldHost = self.host()\n\t\tif hostType in self._hostStack:\n\t\t\tself._hostStack.remove(hostType)\n\t\tdel self._hostsByType[hostType]\n\t\tcurrentHost = self.host()\n\t\tif currentHost != oldHost:\n\t\t\tself.ircd.runComboActionStandard(((\"changehost\", self, hostType, oldHost, fromServer), (\"updatehost\", self, hostType, oldHost, None, fromServer)), users=[self])\n\t\telse:\n\t\t\tself.ircd.runActionStandard(\"updatehost\", self, hostType, oldHost, None, fromServer, users=[self])\n\t\n\tdef currentHostType(self):\n\t\tif self._hostStack:\n\t\t\treturn self._hostStack[-1]\n\t\treturn \"*\"\n\t\n\tdef changeGecos(self, newGecos, fromServer = None):\n\t\t\"\"\"\n\t\tChanges a user's real name. If initiated by a remote server, that\n\t\tserver should be specified in the fromServer parameter.\n\t\t\"\"\"\n\t\tif len(newGecos) > self.ircd.config.get(\"gecos_length\", 128):\n\t\t\treturn\n\t\tif newGecos == self.gecos:\n\t\t\treturn\n\t\toldGecos = self.gecos\n\t\tself.gecos = newGecos\n\t\tif self.isRegistered():\n\t\t\tself.ircd.runActionStandard(\"changegecos\", self, oldGecos, fromServer, users=[self])\n\t\n\tdef metadataKeyExists(self, key):\n\t\t\"\"\"\n\t\tChecks whether the specified key exists in the user's metadata.\n\t\t\"\"\"\n\t\treturn key in self._metadata\n\t\n\tdef metadataKeyCase(self, key):\n\t\t\"\"\"\n\t\tReturns the specified key in the user's metadata in its original case.\n\t\tReturns None if the given key is not in the user's metadata.\n\t\t\"\"\"\n\t\tif key not in self._metadata:\n\t\t\treturn None\n\t\treturn self._metadata[key][0]\n\t\n\tdef metadataValue(self, key):\n\t\t\"\"\"\n\t\tReturns the value of the given key in the user's metadata or None if\n\t\tthe given key is not in the user's metadata.\n\t\t\"\"\"\n\t\tif key not in self._metadata:\n\t\t\treturn None\n\t\treturn self._metadata[key][1]\n\t\n\tdef metadataVisibility(self, key):\n\t\t\"\"\"\n\t\tReturns the visibility value of the given key in the user's metadata or\n\t\tNone if the given key is not in the user's metadata.\n\t\t\"\"\"\n\t\tif key not in self._metadata:\n\t\t\treturn None\n\t\treturn self._metadata[key][2]\n\t\n\tdef metadataSetByUser(self, key):\n\t\t\"\"\"\n\t\tReturns whether the given key in the user's metadata was set by a user\n\t\tor None if the given key is not in the user's metadata.\n\t\t\"\"\"\n\t\tif key not in self._metadata:\n\t\t\treturn None\n\t\treturn self._metadata[key][3]\n\t\n\tdef metadataList(self):\n\t\t\"\"\"\n\t\tReturns the list of metadata keys/values for the user as a list of\n\t\ttuples in the format\n\t\t[ (key, value, visibility, setByUser) ]\n\t\t\"\"\"\n\t\treturn self._metadata.values()\n\t\n\tdef setMetadata(self, key, value, visibility, setByUser, fromServer = None):\n\t\t\"\"\"\n\t\tSets metadata for the user. If initiated by a remote server, that\n\t\tserver should be specified in the fromServer parameter.\n\t\tIf the value is None, deletes the metadata at the provided key.\n\t\t\"\"\"\n\t\tif not isValidMetadataKey(key):\n\t\t\treturn False\n\t\toldData = None\n\t\tif key in self._metadata:\n\t\t\toldData = self._metadata[key]\n\t\tif setByUser and oldData and not oldData[3]:\n\t\t\treturn False\n\t\tif setByUser and self.ircd.runActionUntilValue(\"usercansetmetadata\", key, users=[self]) is False:\n\t\t\treturn False\n\t\tif value is None:\n\t\t\tif key in self._metadata:\n\t\t\t\tdel self._metadata[key]\n\t\telif not visibility:\n\t\t\treturn False\n\t\telse:\n\t\t\tself._metadata[key] = (key, value, visibility, setByUser)\n\t\toldValue = oldData[1] if oldData else None\n\t\tself.ircd.runActionStandard(\"usermetadataupdate\", self, key, oldValue, value, visibility, setByUser, fromServer, users=[self])\n\t\treturn True\n\t\n\tdef canSeeMetadataVisibility(self, visibility):\n\t\tif visibility == \"*\":\n\t\t\treturn True\n\t\treturn self.ircd.runActionUntilValue(\"usercanseemetadata\", self, visibility) is not False\n\t\n\tdef joinChannel(self, channel, override = False):\n\t\t\"\"\"\n\t\tJoins the user to a channel. Specify the override parameter only if all\n\t\tpermission checks should be bypassed.\n\t\t\"\"\"\n\t\tif channel in self.channels:\n\t\t\treturn\n\t\tif not override:\n\t\t\tif self.ircd.runActionUntilValue(\"joinpermission\", channel, self, users=[self], channels=[channel]) is False:\n\t\t\t\treturn\n\t\tchannel.users[self] = { \"status\": \"\" }\n\t\tself.channels.append(channel)\n\t\tnewChannel = False\n\t\tif channel.name not in self.ircd.channels:\n\t\t\tnewChannel = True\n\t\t\tself.ircd.channels[channel.name] = channel\n\t\t\tself.ircd.recentlyDestroyedChannels[channel.name] = False\n\t\t# We need to send the JOIN message before doing other processing, as chancreate will do things like\n\t\t# mode defaulting, which will send messages about the channel before the JOIN message, which is bad.\n\t\tmessageUsers = [u for u in channel.users.iterkeys() if u.uuid[:3] == self.ircd.serverID]\n\t\tself.ircd.runActionProcessing(\"joinmessage\", messageUsers, channel, self, users=messageUsers, channels=[channel])\n\t\tif newChannel:\n\t\t\tself.ircd.runActionStandard(\"channelcreate\", channel, self, channels=[channel])\n\t\tself.ircd.runActionStandard(\"join\", channel, self, users=[self], channels=[channel])\n\t\n\tdef leaveChannel(self, channel, partType = \"PART\", typeData = {}, fromServer = None):\n\t\t\"\"\"\n\t\tRemoves the user from a channel. The partType and typeData are used for\n\t\tthe leavemessage action to send the parting message. If the channel\n\t\tleaving is initiated by a remote server, that server should be\n\t\tspecified in the fromServer parameter.\n\t\t\"\"\"\n\t\tif channel not in self.channels:\n\t\t\treturn\n\t\tmessageUsers = [u for u in channel.users.iterkeys() if u.uuid[:3] == self.ircd.serverID]\n\t\tself.ircd.runActionProcessing(\"leavemessage\", messageUsers, channel, self, partType, typeData, fromServer, users=[self], channels=[channel])\n\t\tself._leaveChannel(channel)\n\t\n\tdef _leaveChannel(self, channel):\n\t\tself.ircd.runActionStandard(\"leave\", channel, self, users=[self], channels=[channel])\n\t\tself.channels.remove(channel)\n\t\tdel channel.users[self]\n\t\n\tdef setModes(self, modes, defaultSource):\n\t\t\"\"\"\n\t\tSets modes on the user. Accepts modes as a list of tuples in the\n\t\tformat:\n\t\t[ (adding, mode, param, setBy, setTime) ]\n\t\t- adding: True if we're setting the mode; False if unsetting\n\t\t- mode: The mode letter\n\t\t- param: The mode's parameter; None if no parameter is needed for that\n\t\t mode\n\t\t- setBy: Optional, only used for list modes; a human-readable string\n\t\t (typically server name or nick!user@host) for who/what set this\n\t\t mode)\n\t\t- setTime: Optional, only used for list modes; a datetime object\n\t\t containing when the mode was set\n\t\t\n\t\tThe defaultSource is a valid user ID or server ID of someone who set\n\t\tthe modes. It is used as the source for announcements about the mode\n\t\tchange and as the default setter for any list modes who do not have the\n\t\tsetBy parameter specified.\n\t\tThe default time for list modes with no setTime specified is now().\n\t\t\"\"\"\n\t\tmodeChanges = []\n\t\tdefaultSourceName = self._sourceName(defaultSource)\n\t\tif defaultSourceName is None:\n\t\t\traise ValueError (\"Source must be a valid user or server ID.\")\n\t\tnowTime = now()\n\t\tfor modeData in modes:\n\t\t\tmode = modeData[1]\n\t\t\tif mode not in self.ircd.userModeTypes:\n\t\t\t\tcontinue\n\t\t\tsetBy = defaultSourceName\n\t\t\tsetTime = nowTime\n\t\t\tmodeType = self.ircd.userModeTypes[mode]\n\t\t\tadding = modeData[0]\n\t\t\tif modeType in (ModeType.List, ModeType.ParamOnUnset, ModeType.Param):\n\t\t\t\tparam = modeData[2]\n\t\t\telse:\n\t\t\t\tparam = None\n\t\t\tif modeType == ModeType.List:\n\t\t\t\tdataCount = len(modeData)\n\t\t\t\tif dataCount >= 4:\n\t\t\t\t\tsetBy = modeData[3]\n\t\t\t\tif dataCount >= 5:\n\t\t\t\t\tsetTime = modeData[4]\n\t\t\tif adding:\n\t\t\t\tparamList = self.ircd.userModes[modeType][mode].checkSet(self, param)\n\t\t\telse:\n\t\t\t\tparamList = self.ircd.userModes[modeType][mode].checkUnset(self, param)\n\t\t\tif paramList is None:\n\t\t\t\tcontinue\n\t\t\t\n\t\t\tfor parameter in paramList:\n\t\t\t\tif self._applyMode(adding, modeType, mode, parameter, setBy, setTime):\n\t\t\t\t\tmodeChanges.append((adding, mode, parameter, setBy, setTime))\n\t\t\n\t\tself._notifyModeChanges(modeChanges, defaultSource, defaultSourceName)\n\t\treturn modeChanges\n\t\n\tdef setModesByUser(self, user, modes, params, override = False):\n\t\t\"\"\"\n\t\tParses a mode string specified by a user and sets those modes on the\n\t\tuser.\n\t\tThe user parameter should be the user who set the modes (usually, but\n\t\tnot always, this user).\n\t\tThe modes parameter is the actual modes string; parameters specified by\n\t\tthe user should be as a list of strings in params.\n\t\tThe override parameter should be used only when all permission checks\n\t\tshould be overridden.\n\t\t\"\"\"\n\t\tadding = True\n\t\tchanges = []\n\t\tsetBy = self._sourceName(user.uuid)\n\t\tsetTime = now()\n\t\tfor mode in modes:\n\t\t\tif len(changes) >= self.ircd.config.get(\"modes_per_line\", 20):\n\t\t\t\tbreak\n\t\t\tif mode == \"+\":\n\t\t\t\tadding = True\n\t\t\t\tcontinue\n\t\t\tif mode == \"-\":\n\t\t\t\tadding = False\n\t\t\t\tcontinue\n\t\t\tif mode not in self.ircd.userModeTypes:\n\t\t\t\tuser.sendMessage(irc.ERR_UNKNOWNMODE, mode, \"is unknown mode char to me\")\n\t\t\t\tcontinue\n\t\t\tmodeType = self.ircd.userModeTypes[mode]\n\t\t\tparam = None\n\t\t\tif modeType in (ModeType.List, ModeType.ParamOnUnset) or (adding and modeType == ModeType.Param):\n\t\t\t\ttry:\n\t\t\t\t\tparam = params.pop(0)\n\t\t\t\texcept IndexError:\n\t\t\t\t\tif modeType == ModeType.List:\n\t\t\t\t\t\tself.ircd.userModes[modeType][mode].showListParams(user, self)\n\t\t\t\t\tcontinue\n\t\t\tif adding:\n\t\t\t\tparamList = self.ircd.userModes[modeType][mode].checkSet(self, param)\n\t\t\telse:\n\t\t\t\tparamList = self.ircd.userModes[modeType][mode].checkUnset(self, param)\n\t\t\tif paramList is None:\n\t\t\t\tcontinue\n\t\t\t\n\t\t\tfor parameter in paramList:\n\t\t\t\tif len(changes) >= self.ircd.config.get(\"modes_per_line\", 20):\n\t\t\t\t\tbreak\n\t\t\t\tif not override and self.ircd.runActionUntilValue(\"modepermission-user-{}\".format(mode), self, user, adding, parameter, users=[self, user]) is False:\n\t\t\t\t\tcontinue\n\t\t\t\tif adding:\n\t\t\t\t\tif modeType == ModeType.List:\n\t\t\t\t\t\tif mode in self.modes and len(self.modes[mode]) > self.ircd.config.get(\"user_listmode_limit\", 128):\n\t\t\t\t\t\t\tuser.sendMessage(irc.ERR_BANLISTFULL, self.name, parameter, \"Channel +{} list is full\".format(mode))\n\t\t\t\t\t\t\tcontinue\n\t\t\t\tif self._applyMode(adding, modeType, mode, parameter, setBy, setTime):\n\t\t\t\t\tchanges.append((adding, mode, parameter, setBy, setTime))\n\t\tself._notifyModeChanges(changes, user.uuid, setBy)\n\t\treturn changes\n\t\n\tdef _applyMode(self, adding, modeType, mode, parameter, setBy, setTime):\n\t\tif parameter:\n\t\t\tif len(parameter) > 255:\n\t\t\t\treturn False\n\t\t\tif \" \" in parameter:\n\t\t\t\treturn False\n\t\t\n\t\tif adding:\n\t\t\tif modeType == ModeType.List:\n\t\t\t\tif mode in self.modes:\n\t\t\t\t\tif len(self.modes[mode]) > self.ircd.config.get(\"user_listmode_limit\", 128):\n\t\t\t\t\t\treturn False\n\t\t\t\t\tfor paramData in self.modes[mode]:\n\t\t\t\t\t\tif parameter == paramData[0]:\n\t\t\t\t\t\t\treturn False\n\t\t\t\telse:\n\t\t\t\t\tself.modes[mode] = []\n\t\t\t\tself.modes[mode].append((parameter, setBy, setTime))\n\t\t\t\treturn True\n\t\t\tif mode in self.modes and self.modes[mode] == parameter:\n\t\t\t\treturn False\n\t\t\tself.modes[mode] = parameter\n\t\t\treturn True\n\t\t\n\t\tif modeType == ModeType.List:\n\t\t\tif mode not in self.modes:\n\t\t\t\treturn False\n\t\t\tfor index, paramData in enumerate(self.modes[mode]):\n\t\t\t\tif paramData[0] == parameter:\n\t\t\t\t\tdel self.modes[mode][index]\n\t\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\treturn False\n\t\t\tif not self.modes[mode]:\n\t\t\t\tdel self.modes[mode]\n\t\t\treturn True\n\t\tif mode not in self.modes:\n\t\t\treturn False\n\t\tif modeType == ModeType.ParamOnUnset and parameter != self.modes[mode]:\n\t\t\treturn False\n\t\tdel self.modes[mode]\n\t\treturn True\n\t\n\tdef _notifyModeChanges(self, modeChanges, source, sourceName):\n\t\tif not modeChanges:\n\t\t\treturn \n\t\tfor change in modeChanges:\n\t\t\tself.ircd.runActionStandard(\"modechange-user-{}\".format(change[1]), self, change[3], change[0], change[2], users=[self])\n\t\t\n\t\tusers = []\n\t\tif source in self.ircd.users and source[:3] == self.ircd.serverID:\n\t\t\tusers.append(self.ircd.users[source])\n\t\tif self.uuid[:3] == self.ircd.serverID:\n\t\t\tusers.append(self)\n\t\tif users:\n\t\t\tself.ircd.runActionProcessing(\"modemessage-user\", users, self, source, sourceName, modeChanges, users=users)\n\t\tself.ircd.runActionStandard(\"modechanges-user\", self, source, sourceName, modeChanges, users=[self])\n\t\n\tdef _sourceName(self, source):\n\t\tif source in self.ircd.users:\n\t\t\treturn self.ircd.users[source].hostmask()\n\t\tif source == self.ircd.serverID:\n\t\t\treturn self.ircd.name\n\t\tif source in self.ircd.servers:\n\t\t\treturn self.ircd.servers[source].name\n\t\treturn None\n\t\n\tdef modeString(self, toUser):\n\t\t\"\"\"\n\t\tGet a user-reportable mode string for the modes set on the user.\n\t\t\"\"\"\n\t\tmodeStr = [\"+\"]\n\t\tparams = []\n\t\tfor mode in self.modes:\n\t\t\tmodeType = self.ircd.userModeTypes[mode]\n\t\t\tif modeType not in (ModeType.ParamOnUnset, ModeType.Param, ModeType.NoParam):\n\t\t\t\tcontinue\n\t\t\tif modeType != ModeType.NoParam:\n\t\t\t\tparam = None\n\t\t\t\tif toUser:\n\t\t\t\t\tparam = self.ircd.userModes[modeType][mode].showParam(toUser, self)\n\t\t\t\tif not param:\n\t\t\t\t\tparam = self.modes[mode]\n\t\t\telse:\n\t\t\t\tparam = None\n\t\t\tmodeStr.append(mode)\n\t\t\tif param:\n\t\t\t\tparams.append(param)\n\t\tif params:\n\t\t\treturn \"{} {}\".format(\"\".join(modeStr), \" \".join(params))\n\t\treturn \"\".join(modeStr)\n\nclass RemoteUser(IRCUser):\n\tdef __init__(self, ircd, ip, uuid = None, host = None):\n\t\tIRCUser.__init__(self, ircd, ip, uuid, host)\n\t\tself._registrationTimeoutTimer.cancel()\n\t\n\tdef _startDNSResolving(self, timeout):\n\t\tself.register(\"dns\", True)\n\t\n\tdef sendMessage(self, command, *params, **kw):\n\t\tpass # Messages can't be sent directly to remote users.\n\t\n\tdef register(self, holdName, fromRemote = False):\n\t\t\"\"\"\n\t\tHandles registration of a remote user.\n\t\t\"\"\"\n\t\tif not fromRemote:\n\t\t\treturn\n\t\tif holdName not in self._registerHolds:\n\t\t\treturn\n\t\tself.ircd.log.debug(\"Registered remote user {user.uuid} ({user.hostmask()})\", user=self)\n\t\tself._registerHolds.remove(holdName)\n\t\tif not self._registerHolds:\n\t\t\tself.ircd.runActionStandard(\"remoteregister\", self, users=[self])\n\t\t\tself.ircd.userNicks[self.nick] = self.uuid\n\t\n\tdef addRegisterHold(self, holdName):\n\t\tpass # We're just not going to allow this here.\n\t\n\tdef disconnect(self, reason, fromRemote = False):\n\t\t\"\"\"\n\t\tDisconnects the remote user from the remote server.\n\t\t\"\"\"\n\t\tif fromRemote:\n\t\t\tif self.isRegistered():\n\t\t\t\tdel self.ircd.userNicks[self.nick]\n\t\t\tself.ircd.recentlyQuitUsers[self.uuid] = now()\n\t\t\tdel self.ircd.users[self.uuid]\n\t\t\tuserSendList = []\n\t\t\twhile self.channels:\n\t\t\t\tchannel = self.channels[0]\n\t\t\t\tuserSendList.extend(channel.users.keys())\n\t\t\t\tself._leaveChannel(channel)\n\t\t\tuserSendList = [u for u in set(userSendList) if u.uuid[:3] == self.ircd.serverID]\n\t\t\tself.ircd.runActionProcessing(\"quitmessage\", userSendList, self, reason, users=userSendList)\n\t\t\tself.ircd.runActionStandard(\"remotequit\", self, reason, users=[self])\n\t\telse:\n\t\t\tself.ircd.runActionUntilTrue(\"remotequitrequest\", self, reason, users=[self])\n\t\n\tdef changeNick(self, newNick, fromServer = None):\n\t\t\"\"\"\n\t\tChanges the nickname of the user. If the change was initiated by a\n\t\tremote server, that server should be specified as the fromServer\n\t\tparameter.\n\t\t\"\"\"\n\t\toldNick = self.nick\n\t\tif self.nick and self.nick in self.ircd.userNicks and self.ircd.userNicks[self.nick] == self.uuid:\n\t\t\tdel self.ircd.userNicks[self.nick]\n\t\tself.nick = newNick\n\t\tself.ircd.userNicks[self.nick] = self.uuid\n\t\tif self.isRegistered():\n\t\t\tuserSendList = [self]\n\t\t\tfor channel in self.channels:\n\t\t\t\tuserSendList.extend(channel.users.keys())\n\t\t\tuserSendList = [u for u in set(userSendList) if u.uuid[:3] == self.ircd.serverID]\n\t\t\tself.ircd.runActionProcessing(\"changenickmessage\", userSendList, self, oldNick, users=userSendList)\n\t\t\tself.ircd.runActionStandard(\"remotechangenick\", self, oldNick, fromServer, users=[self])\n\t\n\tdef changeIdent(self, newIdent, fromServer = None):\n\t\t\"\"\"\n\t\tChanges the ident of the user. If the change was initiated by a remote\n\t\tserver, that server should be specified as the fromServer parameter.\n\t\t\"\"\"\n\t\tif len(newIdent) > self.ircd.config.get(\"ident_length\", 12):\n\t\t\treturn\n\t\toldIdent = self.ident\n\t\tself.ident = newIdent\n\t\tif self.isRegistered():\n\t\t\tself.ircd.runActionStandard(\"remotechangeident\", self, oldIdent, fromServer, users=[self])\n\t\n\tdef changeGecos(self, newGecos, fromServer = None):\n\t\t\"\"\"\n\t\tChanges the real name of the user. If the change was initiated by a\n\t\tremote server, that server should be specified as the fromServer\n\t\tparameter.\n\t\t\"\"\"\n\t\toldGecos = self.gecos\n\t\tself.gecos = newGecos\n\t\tif self.isRegistered():\n\t\t\tself.ircd.runActionStandard(\"remotechangegecos\", self, oldGecos, fromServer, users=[self])\n\t\n\tdef joinChannel(self, channel, override = False, fromRemote = False):\n\t\t\"\"\"\n\t\tJoins the user to a channel.\n\t\t\"\"\"\n\t\tif fromRemote:\n\t\t\tif channel in self.channels:\n\t\t\t\treturn\n\t\t\tnewChannel = False\n\t\t\tif channel.name not in self.ircd.channels:\n\t\t\t\tnewChannel = True\n\t\t\t\tself.ircd.channels[channel.name] = channel\n\t\t\tchannel.users[self] = { \"status\": \"\" }\n\t\t\tself.channels.append(channel)\n\t\t\tmessageUsers = [u for u in channel.users.iterkeys() if u.uuid[:3] == self.ircd.serverID]\n\t\t\tself.ircd.runActionProcessing(\"joinmessage\", messageUsers, channel, self, users=[self], channels=[channel])\n\t\t\tif newChannel:\n\t\t\t\tself.ircd.runActionStandard(\"channelcreate\", channel, self, channels=[channel])\n\t\t\tself.ircd.runActionStandard(\"remotejoin\", channel, self, users=[self], channels=[channel])\n\t\telse:\n\t\t\tself.ircd.runActionUntilTrue(\"remotejoinrequest\", self, channel, users=[self], channels=[channel])\n\t\n\tdef _leaveChannel(self, channel):\n\t\tself.ircd.runActionStandard(\"remoteleave\", channel, self, users=[self], channels=[channel])\n\t\tself.channels.remove(channel)\n\t\tdel channel.users[self]\n\nclass LocalUser(IRCUser):\n\t\"\"\"\n\tLocalUser is a fake user created by a module, which is not\n\tpropagated to other servers.\n\t\"\"\"\n\tdef __init__(self, ircd, nick, ident, host, ip, gecos):\n\t\tIRCUser.__init__(self, ircd, ip, None, host)\n\t\tself.localOnly = True\n\t\tself._sendMsgFunc = lambda self, command, *args, **kw: None\n\t\tself._registrationTimeoutTimer.cancel()\n\t\tdel self._registerHolds\n\t\tself._pinger = None\n\t\tself.nick = nick\n\t\tself.ident = ident\n\t\tself.gecos = gecos\n\t\tself.ircd.log.debug(\"Created new local user {user.uuid} ({user.hostmask()})\", user=self)\n\t\tself.ircd.runActionStandard(\"localregister\", self, users=[self])\n\t\tself.ircd.userNicks[self.nick] = self.uuid\n\t\n\tdef register(self, holdName):\n\t\tpass\n\t\n\tdef setSendMsgFunc(self, func):\n\t\t\"\"\"\n\t\tSets the function to call when a message is sent to this user.\n\t\t\"\"\"\n\t\tself._sendMsgFunc = func\n\t\n\tdef sendMessage(self, command, *args, **kw):\n\t\t\"\"\"\n\t\tSends a message to this user.\n\t\t\"\"\"\n\t\tself._sendMsgFunc(self, command, *args, **kw)\n\t\n\tdef disconnect(self, reason):\n\t\t\"\"\"\n\t\tCleans up and removes the user.\n\t\t\"\"\"\n\t\tdel self.ircd.users[self.uuid]\n\t\tdel self.ircd.userNicks[self.nick]\n\t\tuserSendList = [self]\n\t\tfor channel in self.channels:\n\t\t\tuserSendList.extend(channel.users.keys())\n\t\tuserSendList = [u for u in set(userSendList) if u.uuid[:3] == self.ircd.serverID]\n\t\tuserSendList.remove(self)\n\t\tself.ircd.log.debug(\"Removing local user {user.uuid} ({user.hostmask()}): {reason}\", user=self, reason=reason)\n\t\tself.ircd.runActionProcessing(\"quitmessage\", userSendList, self, reason, users=userSendList)\n\t\tself.ircd.runActionStandard(\"localquit\", self, reason, users=[self])\n\t\n\tdef joinChannel(self, channel, override = False):\n\t\t\"\"\"\n\t\tJoins the user to a channel.\n\t\t\"\"\"\n\t\tIRCUser.joinChannel(self, channel, True)\n", "sub_path": "txircd/user.py", "file_name": "user.py", "file_ext": "py", "file_size_in_byte": 37421, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "twisted.words.protocols.irc.ERR_ALREADYREGISTERED", "line_number": 11, "usage_type": "attribute"}, {"api_name": "twisted.words.protocols.irc", "line_number": 11, "usage_type": "name"}, {"api_name": "txircd.ircbase.IRCBase", "line_number": 13, "usage_type": "name"}, {"api_name": "txircd.utils.CaseInsensitiveDictionary", "line_number": 32, "usage_type": "call"}, {"api_name": "txircd.utils.now", "line_number": 36, "usage_type": "call"}, {"api_name": "txircd.utils.now", "line_number": 37, "usage_type": "call"}, {"api_name": "txircd.utils.now", "line_number": 38, "usage_type": "call"}, {"api_name": "twisted.internet.defer.Deferred", "line_number": 40, "usage_type": "call"}, {"api_name": "twisted.internet.task.LoopingCall", "line_number": 47, "usage_type": "call"}, {"api_name": "twisted.internet.reactor.callLater", "line_number": 48, "usage_type": "call"}, {"api_name": "twisted.internet.reactor", "line_number": 48, "usage_type": "name"}, {"api_name": "txircd.utils.ipIsV4", "line_number": 54, "usage_type": "call"}, {"api_name": "txircd.utils.expandIPv6Address", "line_number": 57, "usage_type": "call"}, {"api_name": "twisted.names.client.lookupPointer", "line_number": 59, "usage_type": "call"}, {"api_name": "twisted.names.client", "line_number": 59, "usage_type": "name"}, {"api_name": "txircd.utils.isValidHost", "line_number": 67, "usage_type": "call"}, {"api_name": "twisted.names.client.getHostByName", "line_number": 70, "usage_type": "call"}, {"api_name": "twisted.names.client", "line_number": 70, "usage_type": "name"}, {"api_name": "twisted.internet.reactor.callLater", "line_number": 87, "usage_type": "call"}, {"api_name": "twisted.internet.reactor", "line_number": 87, "usage_type": "name"}, {"api_name": "twisted.internet.interfaces.ISSLTransport.providedBy", "line_number": 88, "usage_type": "call"}, {"api_name": "twisted.internet.interfaces.ISSLTransport", "line_number": 88, "usage_type": "name"}, {"api_name": "txircd.ircbase.IRCBase.dataReceived", "line_number": 101, "usage_type": "call"}, {"api_name": "txircd.ircbase.IRCBase", "line_number": 101, "usage_type": "name"}, {"api_name": "txircd.ircbase.IRCBase.sendLine", "line_number": 109, "usage_type": "call"}, {"api_name": "txircd.ircbase.IRCBase", "line_number": 109, "usage_type": "name"}, {"api_name": "txircd.ircbase.IRCBase.sendMessage", "line_number": 136, "usage_type": "call"}, {"api_name": "txircd.ircbase.IRCBase", "line_number": 136, "usage_type": "name"}, {"api_name": "twisted.words.protocols.irc.ERR_ALREADYREGISTERED", "line_number": 164, "usage_type": "attribute"}, {"api_name": "twisted.words.protocols.irc", "line_number": 164, "usage_type": "name"}, {"api_name": "twisted.words.protocols.irc.ERR_NOTREGISTERED", "line_number": 166, "usage_type": "attribute"}, {"api_name": "twisted.words.protocols.irc", "line_number": 166, "usage_type": "name"}, {"api_name": "txircd.utils.now", "line_number": 177, "usage_type": "call"}, {"api_name": "twisted.words.protocols.irc.ERR_UNKNOWNCOMMAND", "line_number": 184, "usage_type": "attribute"}, {"api_name": "twisted.words.protocols.irc", "line_number": 184, "usage_type": "name"}, {"api_name": "txircd.utils.now", "line_number": 286, "usage_type": "call"}, {"api_name": "txircd.version", "line_number": 338, "usage_type": "argument"}, {"api_name": "twisted.words.protocols.irc.RPL_WELCOME", "line_number": 339, "usage_type": "attribute"}, {"api_name": "twisted.words.protocols.irc", "line_number": 339, "usage_type": "name"}, {"api_name": "twisted.words.protocols.irc.RPL_YOURHOST", "line_number": 340, "usage_type": "attribute"}, {"api_name": "twisted.words.protocols.irc", "line_number": 340, "usage_type": "name"}, {"api_name": "twisted.words.protocols.irc.RPL_CREATED", "line_number": 341, "usage_type": "attribute"}, {"api_name": "twisted.words.protocols.irc", "line_number": 341, "usage_type": "name"}, {"api_name": "twisted.words.protocols.irc.RPL_MYINFO", "line_number": 344, "usage_type": "attribute"}, {"api_name": "twisted.words.protocols.irc", "line_number": 344, "usage_type": "name"}, {"api_name": "txircd.utils.splitMessage", "line_number": 360, "usage_type": "call"}, {"api_name": "twisted.words.protocols.irc.RPL_ISUPPORT", "line_number": 364, "usage_type": "attribute"}, {"api_name": "twisted.words.protocols.irc", "line_number": 364, "usage_type": "name"}, {"api_name": "txircd.utils.now", "line_number": 399, "usage_type": "call"}, {"api_name": "txircd.utils.isValidMetadataKey", "line_number": 566, "usage_type": "call"}, {"api_name": "txircd.utils.now", "line_number": 659, "usage_type": "call"}, {"api_name": "txircd.utils.ModeType.List", "line_number": 668, "usage_type": "attribute"}, {"api_name": "txircd.utils.ModeType", "line_number": 668, "usage_type": "name"}, {"api_name": "txircd.utils.ModeType.ParamOnUnset", "line_number": 668, "usage_type": "attribute"}, {"api_name": "txircd.utils.ModeType.Param", "line_number": 668, "usage_type": "attribute"}, {"api_name": "txircd.utils.ModeType.List", "line_number": 672, "usage_type": "attribute"}, {"api_name": "txircd.utils.ModeType", "line_number": 672, "usage_type": "name"}, {"api_name": "txircd.utils.now", "line_number": 706, "usage_type": "call"}, {"api_name": "twisted.words.protocols.irc.ERR_UNKNOWNMODE", "line_number": 717, "usage_type": "attribute"}, {"api_name": "twisted.words.protocols.irc", "line_number": 717, "usage_type": "name"}, {"api_name": "txircd.utils.ModeType.List", "line_number": 721, "usage_type": "attribute"}, {"api_name": "txircd.utils.ModeType", "line_number": 721, "usage_type": "name"}, {"api_name": "txircd.utils.ModeType.ParamOnUnset", "line_number": 721, "usage_type": "attribute"}, {"api_name": "txircd.utils.ModeType.Param", "line_number": 721, "usage_type": "attribute"}, {"api_name": "txircd.utils.ModeType.List", "line_number": 725, "usage_type": "attribute"}, {"api_name": "txircd.utils.ModeType", "line_number": 725, "usage_type": "name"}, {"api_name": "txircd.utils.ModeType.List", "line_number": 741, "usage_type": "attribute"}, {"api_name": "txircd.utils.ModeType", "line_number": 741, "usage_type": "name"}, {"api_name": "twisted.words.protocols.irc.ERR_BANLISTFULL", "line_number": 743, "usage_type": "attribute"}, {"api_name": "twisted.words.protocols.irc", "line_number": 743, "usage_type": "name"}, {"api_name": "txircd.utils.ModeType.List", "line_number": 758, "usage_type": "attribute"}, {"api_name": "txircd.utils.ModeType", "line_number": 758, "usage_type": "name"}, {"api_name": "txircd.utils.ModeType.List", "line_number": 774, "usage_type": "attribute"}, {"api_name": "txircd.utils.ModeType", "line_number": 774, "usage_type": "name"}, {"api_name": "txircd.utils.ModeType.ParamOnUnset", "line_number": 788, "usage_type": "attribute"}, {"api_name": "txircd.utils.ModeType", "line_number": 788, "usage_type": "name"}, {"api_name": "txircd.utils.ModeType.ParamOnUnset", "line_number": 825, "usage_type": "attribute"}, {"api_name": "txircd.utils.ModeType", "line_number": 825, "usage_type": "name"}, {"api_name": "txircd.utils.ModeType.Param", "line_number": 825, "usage_type": "attribute"}, {"api_name": "txircd.utils.ModeType.NoParam", "line_number": 825, "usage_type": "attribute"}, {"api_name": "txircd.utils.ModeType.NoParam", "line_number": 827, "usage_type": "attribute"}, {"api_name": "txircd.utils.ModeType", "line_number": 827, "usage_type": "name"}, {"api_name": "txircd.utils.now", "line_number": 877, "usage_type": "call"}]} +{"seq_id": "100123412", "text": "import datetime\nimport pandas as pd\nimport numpy as np\nimport cmq_crv_defn\nfrom cmq_inst import CMQInstrument\nimport cmq_curve\nimport misc\n\nclass CMQCalendarSwap(CMQInstrument):\n class_params = dict(CMQInstrument.class_params, **{ 'strike': 0.0,\n 'fwd_index': 'SGXIRO',\n 'need_disc': True})\n inst_key = ['fwd_index', 'strike', 'start', 'end', 'ccy']\n\n def __init__(self, trade_data, market_data = {}, model_settings = {}):\n super(CMQCalendarSwap, self).__init__(trade_data, market_data, model_settings)\n\n def set_trade_data(self, trade_data):\n super(CMQCalendarSwap, self).set_trade_data(trade_data)\n num_days = (self.end - self.start).days + 1\n day_range = [ self.start + datetime.timedelta(days = d) for d in range(num_days)]\n crv_info = cmq_crv_defn.COM_Curve_Map[self.fwd_index]\n self.spotID = crv_info['spotID']\n self.fixing_dates = [d for d in day_range if misc.is_workday(d, crv_info['calendar'])]\n self.fwd_tenors = cmq_crv_defn.curve_expiry(crv_info['exch'], self.fwd_index, self.start, self.end)\n self.mkt_deps['COMFix'] = { self.spotID: self.fixing_dates }\n self.mkt_deps['COMFwd'] = { self.fwd_index: [ x1 for x1, x2 in self.fwd_tenors] }\n if self.need_disc:\n self.mkt_deps['IRCurve'] = { self.ccy.lower() + '_disc': ['ALL'] }\n\n def set_market_data(self, market_data):\n super(CMQCalendarSwap, self).set_market_data(market_data)\n if len(market_data) == 0:\n self.fwd_curve = None\n self.fix_series = None\n self.past_fix = []\n self.past_avg = 0.0\n self.fwd_avg = 0.0\n self.df = 1.0\n return\n fwd_quotes = market_data['COMFwd'][self.fwd_index]\n fwd_tenors = [ (self.value_date - quote[1]).days for quote in fwd_quotes]\n fwd_prices = [ quote[2] for quote in fwd_quotes]\n #fwd_quotes = map(list, zip(*fwd_quotes))\n mode = cmq_curve.ForwardCurve.InterpMode.PiecewiseConst\n self.fwd_curve = cmq_curve.ForwardCurve.from_array(fwd_tenors, fwd_prices, interp_mode = mode)\n fix_quotes = market_data['COMFix'][self.spotID]\n fix_quotes = map(list, zip(*fix_quotes))\n self.fix_series = pd.Series(fix_quotes[1], index = fix_quotes[0])\n self.past_fix = [d for d in self.fixing_dates if\n (d < self.value_date) or ((d == self.value_date) and self.eod_flag)]\n fut_t = [(self.value_date - d).days for d in self.fixing_dates if d not in self.past_fix]\n if len(self.past_fix) > 0:\n self.past_avg = np.mean(self.fix_series[self.past_fix])\n else:\n self.past_avg = 0.0\n self.fwd_avg = np.mean(self.fwd_curve(fut_t))\n if self.need_disc and (self.end >= self.value_date):\n rate_quotes = market_data['IRCurve'][self.ccy.lower() + '_disc']\n tenors = [(quote[1] - self.value_date).days for quote in rate_quotes]\n irates = [quote[2] for quote in rate_quotes]\n mode = cmq_curve.ForwardCurve.InterpMode.Linear\n rate_curve = cmq_curve.ForwardCurve.from_array(tenors, irates, interp_mode = mode)\n t_exp = (self.end-self.value_date).days\n self.df = np.exp(-rate_curve(t_exp)*t_exp/365.0)\n else:\n self.df = 1.0\n\n def clean_price(self):\n r = float(len(self.past_fix))/float(len(self.fixing_dates))\n avg = self.past_avg * r + self.fwd_avg * (1-r)\n return (avg - self.strike) * self.df\n", "sub_path": "my algorithms/risk_engine/cmq_calendarswap.py", "file_name": "cmq_calendarswap.py", "file_ext": "py", "file_size_in_byte": 3626, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cmq_inst.CMQInstrument", "line_number": 9, "usage_type": "name"}, {"api_name": "cmq_inst.CMQInstrument.class_params", "line_number": 10, "usage_type": "attribute"}, {"api_name": "cmq_inst.CMQInstrument", "line_number": 10, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 21, "usage_type": "call"}, {"api_name": "cmq_crv_defn.COM_Curve_Map", "line_number": 22, "usage_type": "attribute"}, {"api_name": "misc.is_workday", "line_number": 24, "usage_type": "call"}, {"api_name": "cmq_crv_defn.curve_expiry", "line_number": 25, "usage_type": "call"}, {"api_name": "cmq_curve.ForwardCurve", "line_number": 45, "usage_type": "attribute"}, {"api_name": "cmq_curve.ForwardCurve.from_array", "line_number": 46, "usage_type": "call"}, {"api_name": "cmq_curve.ForwardCurve", "line_number": 46, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 57, "usage_type": "call"}, {"api_name": "cmq_curve.ForwardCurve", "line_number": 62, "usage_type": "attribute"}, {"api_name": "cmq_curve.ForwardCurve.from_array", "line_number": 63, "usage_type": "call"}, {"api_name": "cmq_curve.ForwardCurve", "line_number": 63, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 65, "usage_type": "call"}]} +{"seq_id": "66582664", "text": "# ----------------------------------------------------------------------------\n# \"THE BEER-WARE LICENSE\" (Revision 42):\n# dkratzert@gmx.de> wrote this file. As long as you retain\n# this notice you can do whatever you want with this stuff. If we meet some day,\n# and you think this stuff is worth it, you can buy me a beer in return. \n# Dr. Daniel Kratzert\n# ----------------------------------------------------------------------------\n#\nimport hashlib\nimport itertools as it\nimport re\nfrom math import sqrt\nfrom os import path\nfrom pathlib import Path\nfrom typing import Union, Tuple, List\n\n# protected space character:\nprotected_space = u'\\u00A0'\n# Angstrom character:\n# angstrom = u'\\u212B' # Unicode angstrom sign (only for compatibility)\nangstrom = u'\\u00C5' # Latin capital A with ring above. The Unicode consortium recommends to use the regular letter\n# capital theta symbol:\nTheta_symbol = u'\\u03F4'\n# bigger or equal:\nbequal = u'\\u2265'\n# small_sigma:\nsigma_sm = u'\\u03C3'\n# en dash:\nhalbgeviert = u'\\u2013'\n# minus sign:\nminus_sign = u'\\u2212'\n# degree sign:\ndegree_sign = u'\\u00B0'\n# middle ellipsis\nellipsis_mid = u'\\u22EF'\n# ellipsis\nellipsis_char = u'\\u2026'\n# less or equal sign\nless_or_equal = u'\\u2264'\n# times (cross) symbol\ntimessym = u'\\u00d7'\n# lambda\nlambdasym = u'\\u03bb'\n# one bar\none_bar = u'\\u0031\\u0305'\n# Zero-with space ZWSP\nzero_width_space = u'\\u200B'\n\n\ndef isnumeric(value: str) -> bool:\n \"\"\"\n Determines if a string can be converted to a number.\n \"\"\"\n value = value.split('(')[0]\n try:\n float(value)\n except ValueError:\n return False\n return True\n\n\ndef sha512_checksum_of_file(filename: str, block_size=65536):\n \"\"\"\n Calculates a SHA512 checksum from a file.\n \"\"\"\n sha512 = hashlib.sha512()\n with open(filename, 'rb') as f:\n for block in iter(lambda: f.read(block_size), b''):\n sha512.update(block)\n return sha512.hexdigest()\n\n\ndef distance(x1: float, y1: float, z1: float, x2: float, y2: float, z2: float) -> float:\n \"\"\"\n distance between two points in space for orthogonal axes.\n \"\"\"\n return sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2 + (z1 - z2) ** 2)\n\n\ndef grouper(inputs, n, fillvalue=None):\n iters = [iter(inputs)] * n\n return it.zip_longest(*iters, fillvalue=fillvalue)\n\n\ndef get_file_with_new_ending(file: Path, new_ending: str, strip_from_name: str = '') -> Path:\n \"\"\"\n Retruns a file path with a new ending. If strip_strip_from_name is given, this string is also\n removed from the file name before the suffix.\n \"\"\"\n basename = file.stem\n if strip_from_name:\n basename = re.sub('{}$'.format(strip_from_name), '', basename)\n return file.parent.joinpath(Path(basename + new_ending))\n\n\ndef get_error_from_value(value: str) -> Tuple[float, float]:\n \"\"\"\n Returns the error value from a number string.\n \"\"\"\n try:\n value = value.replace(\" \", \"\")\n except AttributeError:\n return float(value), 0.0\n if \"(\" in value:\n vval, err = value.split(\"(\")\n val = vval.split('.')\n err = err.split(\")\")[0]\n if not err:\n return float(vval), 0.0\n if len(val) > 1:\n return float(vval), int(err) * (10 ** (-1 * len(val[1])))\n else:\n return float(vval), float(err)\n else:\n try:\n return float(value), 0.0\n except ValueError:\n return 0.0, 0.0\n\n\ndef next_path(path_pattern: str) -> str:\n \"\"\"\n Finds the next free path in an sequentially named list of files\n\n e.g. path_pattern = 'file-%s.txt':\n\n file-1.txt\n file-2.txt\n file-3.txt\n\n Runs in log(n) time where n is the number of existing files in sequence\n https://stackoverflow.com/questions/17984809/how-do-i-create-a-incrementing-filename-in-python/47087513\n \"\"\"\n i = 1\n # First do an exponential search\n while path.exists(path_pattern % i):\n i = i * 2\n # Result lies somewhere in the interval (i/2..i]\n # We call this interval (a..b] and narrow it down until a + 1 = b\n a, b = (i // 2, i)\n while a + 1 < b:\n c = (a + b) // 2 # interval midpoint\n a, b = (c, b) if path.exists(path_pattern % c) else (a, c)\n return path_pattern % b\n\n\nclass Multilog(object):\n \"\"\"\n This class copies all output from stdout and stderr to a file\n It acts like tee with following usage:\n sys.stdout = multifile([sys.stdout, lstfileobj])\n \"\"\"\n\n def __init__(self, files):\n self._files = files\n\n def __getattr__(self, attr, *args):\n return self._wrap(attr, *args)\n\n def _wrap(self, attr, *args):\n def g(*a, **kw):\n res = ''\n for f in self._files:\n res = getattr(f, attr, *args)(*a, **kw)\n return res\n\n return g\n\n\ndef strip_finalcif_of_name(pth: str) -> str:\n \"\"\"\n Strips '-finalcif' from the stem path\n \"\"\"\n return re.sub('-finalcif$', '', pth)\n\n\ndef flatten(lis: list) -> list:\n \"\"\"\n Given a list, possibly nested to any level, return it flattened.\n From: http://code.activestate.com/recipes/578948-flattening-an-arbitrarily-nested-list-in-python/\n \"\"\"\n new_lis = []\n for item in lis:\n if isinstance(item, list):\n new_lis.extend(flatten(item))\n else:\n new_lis.append(item)\n return new_lis\n\n\ndef find_line(inputlist: list, regex: str) -> int:\n for num, string in enumerate(inputlist):\n if re.match(regex, string, re.IGNORECASE):\n return num # returns the index number if regex found\n return 0\n\n\ndef this_or_quest(value: Union[str, int, float, None]) -> Union[str, int, float]:\n \"\"\"\n Returns the value or a question mark if the value is None.\n \"\"\"\n if value == '' or value is None:\n return '?'\n else:\n return value\n\n\ndef to_float(st) -> Union[float, List[float], None]:\n if isinstance(st, list):\n try:\n return [float(x) for x in st[-2:]]\n except ValueError:\n return None\n else:\n try:\n return float(st.split('(')[0])\n except ValueError:\n return None\n\n\ndef to_int(st: Union[str, List[Union[str, int]]]) -> Union[int, List[int], None]:\n if isinstance(st, list):\n try:\n return [int(x) for x in st[-2:]]\n except ValueError:\n return None\n else:\n try:\n return int(float(st.split('(')[0]))\n except ValueError:\n return None\n\n\n# '_space_group_centring_type', # seems to be used nowere\n# '_exptl_absorpt_special_details', # This is not official?!?\nessential_keys = {\n # '_atom_sites_solution_secondary' : 'Codes which identify how the remaining non-hydrogen sites were located',\n # '_diffrn_measurement_specimen_adhesive' : 'Adhesive used to hold the crystal on the _diffrn_measurement_specimen_support during intensity measurement.',\n # '_diffrn_source_power' : 'The power in kilowatts at which the radiation source was operated',\n # '_diffrn_source_target' : 'The chemical element symbol for the X-ray target (usually the anode) used to generate X-rays.',\n # '_olex2_diffrn_ambient_temperature_device' : 'Device to cool the crystal during measurement',\n '_atom_sites_solution_hydrogens' : 'Codes which identify the methods used to locate the initial hydrogen atom sites',\n '_atom_sites_solution_primary' : 'Codes which identify the methods used to locate the initial atom sites',\n '_audit_contact_author_address' : 'The address of the cif author',\n '_audit_contact_author_email' : 'The email address of the cif author',\n '_audit_contact_author_name' : 'The name of the cif author',\n '_audit_contact_author_phone' : 'The phone number of the cif author',\n '_audit_creation_method' : 'The program that created this cif file after refinement',\n '_cell_angle_alpha' : 'Unit-cell angle in degree',\n '_cell_angle_beta' : 'Unit-cell angle in degree',\n '_cell_angle_gamma' : 'Unit-cell angle in degree',\n '_cell_formula_units_Z' : 'The number of the formula units in the unit cell as specified by _chemical_formula_sum',\n '_cell_length_a' : 'Unit-cell length in angstroms',\n '_cell_length_b' : 'Unit-cell length in angstroms',\n '_cell_length_c' : 'Unit-cell length in angstroms',\n '_cell_measurement_reflns_used' : 'The total number of reflections used to determine the unit cell',\n '_cell_measurement_temperature' : 'The temperature in kelvins at which the unit-cell parameters were measured',\n '_cell_measurement_theta_max' : 'The minimum theta angles of reflections used to measure the unit cell in degrees',\n '_cell_measurement_theta_min' : 'The maximum theta angles of reflections used to measure the unit cell in degrees',\n '_cell_volume' : 'Unit-cell volume in cubic angstroms',\n '_chemical_absolute_configuration' : 'Method how the absolute configuration was established',\n '_chemical_formula_moiety' : 'Formula with each discrete bonded residue or ion separated',\n '_chemical_formula_sum' : 'The sum formula specifies the composition of the compound',\n '_chemical_formula_weight' : 'Formula mass in daltons',\n '_chemical_melting_point' : 'The temperature in kelvins at which the crystalline solid changes to a liquid',\n '_chemical_name_common' : 'Trivial name by which the compound is commonly known',\n '_chemical_name_systematic' : 'IUPAC or Chemical Abstracts full name of the compound',\n '_computing_cell_refinement' : 'Computer program used to index and refine the unit cell parameters',\n '_computing_data_collection' : 'Computer program used to collect the intensity data',\n '_computing_data_reduction' : 'Computer program used to integrate the intensity data',\n '_computing_molecular_graphics' : 'Computer program used to make molecular graphics',\n '_computing_publication_material' : 'Computer program used to generate publication material',\n '_computing_structure_refinement' : 'Computer program used for structure refinement',\n '_computing_structure_solution' : 'Computer program used for structure solution',\n '_diffrn_ambient_environment' : 'The gas or liquid surrounding the sample, if not air.',\n '_diffrn_ambient_temperature' : 'The mean temperature in kelvins at which the intensities were measured',\n '_diffrn_detector' : 'The general class of the radiation detector.',\n '_diffrn_detector_area_resol_mean' : 'The resolution of an area detector, in pixels/mm.',\n '_diffrn_detector_type' : 'The make, model or name of the detector device used.',\n '_diffrn_measured_fraction_theta_full' : 'Fraction of unique (symmetry-independent) reflections measured out to _diffrn_reflns_theta_full',\n '_diffrn_measured_fraction_theta_max' : 'Fraction of unique (symmetry-independent) reflections measured out to _diffrn_reflns_theta_max',\n '_diffrn_measurement_device' : 'The general class of goniometer or device used to support and orient the specimen.',\n '_diffrn_measurement_device_type' : 'The make, model or name of the measurement device used.',\n '_diffrn_measurement_method' : \"Method used to measure the intensities, eg.g 'omega scans'\",\n '_diffrn_measurement_specimen_support' : 'The physical device used to support the crystal during data collection.',\n '_diffrn_radiation_monochromator' : r'The typ monochromator type to get _diffrn_radiation_wavelength',\n '_diffrn_radiation_probe' : 'The nature of the radiation used',\n '_diffrn_radiation_type' : r'The type of the radiation, e.g. Mo K\\a',\n '_diffrn_radiation_wavelength' : 'The radiation wavelength in angstroms',\n '_diffrn_reflns_Laue_measured_fraction_full' : 'Fraction of Laue unique reflections measured out to the resolution given in _diffrn_reflns_theta_full',\n '_diffrn_reflns_Laue_measured_fraction_max' : 'Fraction of Laue unique reflections measured out to the resolution given in _diffrn_reflns_theta_max',\n '_diffrn_reflns_av_R_equivalents' : 'The residual for symmetry-equivalent reflections used to calculate the average intensity',\n '_diffrn_reflns_av_unetI/netI' : 'Measure [sum |u(net I)|/sum|net I|] for all measured reflections',\n '_diffrn_reflns_number' : 'The total number of measured intensities excluding systematic absent',\n '_diffrn_reflns_point_group_measured_fraction_full': 'Fraction of crystal point-group unique reflections measured out to the resolution given in _diffrn_reflns_theta_full',\n '_diffrn_reflns_point_group_measured_fraction_max' : 'Fraction of crystal point-group unique reflections measured out to the resolution given in _diffrn_reflns_theta_max',\n '_diffrn_reflns_theta_full' : 'The theta angle at which the measured reflection count is close to complete',\n '_diffrn_reflns_theta_max' : 'Maximum theta angle in degrees for the measured intensities',\n '_diffrn_reflns_theta_min' : 'Minimum theta angle in degrees for the measured intensities',\n '_diffrn_source' : \"The general class of the source of radiation, e.g.'sealed X-ray tube'\",\n '_diffrn_source_current' : 'The current in milliamperes at which the radiation source was operated',\n '_diffrn_source_type' : 'The make, model or name of the source of radiation.',\n '_diffrn_source_voltage' : 'The voltage in kilovolts at which the radiation source was operated',\n '_exptl_absorpt_coefficient_mu' : 'The absorption coefficient mu in reciprocal millimetres',\n '_exptl_absorpt_correction_T_max' : 'The calculated maximum value of the transmission factor for the specimen',\n '_exptl_absorpt_correction_T_min' : 'The calculated minimum value of the transmission factor for the specimen',\n '_exptl_absorpt_correction_type' : 'The absorption-correction type and method',\n '_exptl_absorpt_process_details' : 'Description of the absorption process applied to the intensities',\n '_exptl_crystal_F_000' : 'The effective number of electrons in the crystal unit cell contributing to F(000)',\n '_exptl_crystal_colour' : 'The colour of the crystal',\n '_exptl_crystal_density_diffrn' : 'Density values calculated from the crystal cell and contents',\n '_exptl_crystal_density_meas' : 'Density value measured using standard chemical and physical methods',\n '_exptl_crystal_density_method' : 'The method used to measure _exptl_crystal_density_meas',\n '_exptl_crystal_description' : 'A description of the quality and habit of the crystal',\n '_exptl_crystal_recrystallization_method' : 'Describes the method used to crystallize the sample',\n '_exptl_crystal_size_max' : 'Maximum dimension of the crystal in mm',\n '_exptl_crystal_size_mid' : 'Medium dimension of the crystal in mm',\n '_exptl_crystal_size_min' : 'Minimum dimension of the crystal in mm',\n '_exptl_special_details' : 'Any details about the experimental work prior to the measurement',\n '_geom_special_details' : 'The description of geometrical extra information such as least-squares planes',\n '_publ_contact_author_id_orcid' : 'The ORCID ID of the author submitting the manuscript and data block',\n '_publ_section_references' : 'References for programs used to process the data',\n '_refine_ls_R_factor_all' : 'Residual factor for all reflections. This is the conventional R factor',\n '_refine_ls_R_factor_gt' : 'Residual R1 factor for the reflections satisfying the _reflns_threshold_expression',\n '_refine_ls_abs_structure_Flack' : 'The measure of absolute structure as defined by Flack (1983)',\n '_refine_ls_abs_structure_details' : 'The nature of the absolute structure and how it was determined',\n '_refine_ls_extinction_coef' : 'The extinction coefficient used to calculate the correction factor applied to the structure-factor data',\n '_refine_ls_extinction_method' : 'A description of the extinction-correction method applied',\n '_refine_ls_goodness_of_fit_ref' : 'The l.s. goodness-of-fit parameter S for all reflections in the refinement',\n '_refine_ls_hydrogen_treatment' : 'Treatment of hydrogen atoms in the least-squares refinement',\n '_refine_ls_matrix_type' : 'Type of matrix used to accumulate the least-squares derivatives',\n '_refine_ls_number_parameters' : 'The number of parameters refined in the least-squares process',\n '_refine_ls_number_reflns' : 'The number of unique reflections contributing to the least-squares refinement calculation',\n '_refine_ls_number_restraints' : 'The number of restrained parameters',\n '_refine_ls_restrained_S_all' : \"The l.s. goodness-of-fit parameter S' for all reflections in the refinement and including the restraints applied\",\n '_refine_ls_shift/su_max' : 'The largest ratio of the final least-squares parameter shift to the final standard uncertainty',\n '_refine_ls_shift/su_mean' : 'The average ratio of the final least-squares parameter shift to the final standard uncertainty',\n '_refine_ls_structure_factor_coef' : 'Structure-factor coefficient |F|, F^2^ or I used in the least-squares refinement process',\n '_refine_ls_wR_factor_gt' : 'Weighted residual factor for reflections satisfying _reflns_threshold_expression',\n '_refine_ls_wR_factor_ref' : 'Weighted residual factors wR2 for all reflections included in the refinement.',\n '_refine_ls_weighting_details' : 'A description of special aspects of the weighting scheme used in the least-squares refinement',\n '_refine_ls_weighting_scheme' : 'The weighting scheme applied in the least-squares process',\n '_refine_special_details' : 'Detailed refinement description, e.g. information about a disorder model',\n '_reflns_Friedel_coverage' : 'The proportion of Friedel-related reflections present in the number of independent reflections',\n '_reflns_Friedel_fraction_full' : 'The number of Friedel pairs measured out to _diffrn_reflns_theta_full',\n '_reflns_Friedel_fraction_max' : 'The number of Friedel pairs measured out to _diffrn_reflns_theta_max',\n '_reflns_number_gt' : 'The number of reflections in the _refln_ list that are significantly intense',\n '_reflns_number_total' : 'The total number of reflections in the _refln_ list (not the _diffrn_refln_ list)',\n '_reflns_special_details' : 'Description of the properties of the reported reflection list',\n '_reflns_threshold_expression' : 'The threshold that serves to identify significantly intense reflections',\n '_space_group_IT_number' : 'The number as assigned in International Tables for Crystallography Vol. A',\n '_space_group_crystal_system' : 'The name of the crystal system to which the space group belongs',\n '_space_group_name_H-M_alt' : 'Hermann-Mauguin symbol to describe the space group',\n '_space_group_name_Hall' : 'Space-group symbol defined by S. R. Hall (1981)',\n # '_space_group_symop_operation_xyz' : 'Symmetry operations of the space group',\n}\n\ntwin_keys = {\n '_twin_individual_id' : 'The unique identifier for this twin individual',\n '_twin_individual_twin_lattice_type' : 'Identification of the symmetry relationships between the twin lattices',\n '_twin_special_details' : 'Information about twinning in the sample not described elsewhere',\n '_twin_individual_mass_fraction_refined': 'The refined mass fraction of this twin individual',\n '_twin_individual_twin_matrix_11' : 'Elements of the matrix',\n '_twin_individual_twin_matrix_12' : 'Elements of the matrix',\n '_twin_individual_twin_matrix_13' : 'Elements of the matrix',\n '_twin_individual_twin_matrix_21' : 'Elements of the matrix',\n '_twin_individual_twin_matrix_22' : 'Elements of the matrix',\n '_twin_individual_twin_matrix_23' : 'Elements of the matrix',\n '_twin_individual_twin_matrix_31' : 'Elements of the matrix',\n '_twin_individual_twin_matrix_32' : 'Elements of the matrix',\n '_twin_individual_twin_matrix_33' : 'Elements of the matrix',\n}\n\nnon_centrosymm_keys = ('_chemical_absolute_configuration', '_refine_ls_abs_structure_Flack',\n '_refine_ls_abs_structure_details')\n\n# Keys that get a text field in the main list. These fields have more hight.\ntext_field_keys = ('_refine_special_details',\n '_refine_ls_weighting_details',\n '_reflns_special_details',\n '_exptl_absorpt_process_details',\n '_publ_section_references',\n '_audit_contact_author_address',\n '_exptl_crystal_recrystallization_method',\n '_exptl_special_details',\n '_geom_special_details',\n '_diffrn_measurement_details',\n '_diffrn_oxdiff_ac3_digest_frames',\n '_diffrn_oxdiff_ac3_digest_hkl',\n '_oxdiff_exptl_absorpt_empirical_details',\n )\n\ndo_not_import_keys = (\n '_cell_length_a',\n '_cell_length_b',\n '_cell_length_c',\n '_cell_angle_alpha',\n '_cell_angle_beta',\n '_cell_angle_gamma',\n '_space_group_IT_number',\n '_space_group_crystal_system',\n '_space_group_name_H-M_alt',\n '_shelx_res_file',\n '_shelx_hkl_file',\n '_shelx_res_checksum',\n '_shelx_hkl_checksum',\n '_shelx_fab_file',\n '_shelx_fab_checksum',\n '_shelx_fcf_file',\n '_shelx_fcf_checksum',\n '_exptl_absorpt_coefficient_mu',\n '_exptl_crystal_F_000',\n '_exptl_crystal_density_diffrn',\n '_reflns_number_total',\n '_reflns_number_gt',\n)\n\ndo_not_import_from_stoe_cfx = (\n '_diffrn_measured_fraction_theta_max',\n '_diffrn_measured_fraction_theta_full',\n '_diffrn_reflns_av_R_equivalents',\n '_diffrn_reflns_av_unetI/netI',\n '_diffrn_reflns_limit_h_min',\n '_diffrn_reflns_limit_h_max',\n '_diffrn_reflns_limit_k_min',\n '_diffrn_reflns_limit_k_max',\n '_diffrn_reflns_limit_l_min',\n '_diffrn_reflns_limit_l_max',\n '_diffrn_reflns_number',\n '_diffrn_reflns_theta_min',\n '_diffrn_reflns_theta_max',\n '_diffrn_reflns_theta_full',\n '_reflns_special_details',\n '_audit_author_name',\n '_audit_contact_author',\n '_audit_contact_author_address',\n '_audit_contact_author_email',\n '_audit_contact_author_fax',\n '_audit_contact_author_phone',\n '_audit_creation_method',\n '',\n '',\n '',\n)\n\nABSORPTION_CORRECTION_TYPES = (\n (0, ''), # , ''),\n (1, 'multi-scan'), # , 'symmetry-related measurements'),\n (2, 'numerical'), # , 'numerical from crystal shape'),\n (3, 'empirical'), # , 'empirical from intensities'),\n (4, 'gaussian'), # , 'Gaussian from crystal shape'),\n (5, 'integration'), # , 'integration from crystal shape'),\n (6, 'analytical'), # , 'analytical from crystal shape'),\n (7, 'none'), # , 'no absorption correction applied'),\n (8, 'cylinder'), # , 'cylindrical'),\n (9, 'psi-scan'), # , 'psi-scan corrections'),\n (10, 'refdelf'), # , 'refined from delta-F'),\n (11, 'sphere'), # , 'spherical'),\n)\n\nCOLOUR_CHOICES = (\n (0, ''),\n (1, 'colourless'),\n (2, 'white'),\n (3, 'black'),\n (4, 'gray'),\n (5, 'brown'),\n (6, 'red'),\n (7, 'pink'),\n (8, 'orange'),\n (9, 'yellow'),\n (10, 'green'),\n (11, 'blue'),\n (12, 'violet')\n)\n\nSPECIMEN_SUPPORT = (\n (0, ''),\n (1, 'MiTeGen micromount'),\n (2, 'glass capillary'),\n (3, 'quartz capillary'),\n (4, 'glass fiber'),\n (5, 'metal loop'),\n (6, 'nylon loop'),\n (7, 'cactus needle'),\n (8, 'cat whisker'),\n (9, 'carbon fiber'),\n (10, 'beryllium pin'),\n)\n\nADHESIVE = (\n (0, ''),\n (1, 'perfluorether oil'),\n (2, 'epoxy glue'),\n (3, 'motor oil'),\n (4, 'grease'),\n (5, 'honey'),\n)\n\nABSOLUTE_CONFIGURATION_CHOICES = (\n (0, ''), #\n (1, 'ad'), # , 'Anomalous dispersion'),\n (2, 'rm'), # , 'Reference Molecule'),\n (3, 'rmad'), # , 'Reference Molecule and ad'),\n (4, 'syn'), # , 'Synthesis'),\n (5, 'unk'), # , 'Unknown'),\n (6, '.'), # , 'Inapplicable'),\n)\n\nREFINE_LS_HYDROGEN_TREATMENT = (\n (0, ''),\n (1, 'undef'),\n (2, 'mixed'),\n (3, 'constr'),\n (4, 'noref'),\n (5, 'refall'),\n (6, 'refxyz'),\n (7, 'refU'),\n (8, 'hetero'),\n (9, 'heteroxyz'),\n (10, 'heteroU'),\n (11, 'heteronoref'),\n (12, 'hetero-mixed'),\n (13, 'heteroxyz-mixed'),\n (14, 'heteroU-mixed'),\n (15, 'heteronoref-mixed'),\n)\n\nRADIATION_TYPE = (\n (0, r''),\n (1, r'Mo K\\a'),\n (2, r'Cu K\\a'),\n (3, r'Ag K\\a')\n)\n\nSOLUTION_PRIMARY = (\n (0, ''),\n (1, 'direct'),\n (2, 'vecmap'),\n (3, 'heavy'),\n (4, 'difmap'),\n (5, 'geom'),\n (6, 'disper'),\n (7, 'isomor'),\n (8, 'notdet'),\n (9, 'dual'),\n (10, 'iterative'),\n (11, 'other'),\n)\n\nSOLUTION_SECONDARY = (\n (0, ''),\n (1, 'direct'),\n (2, 'vecmap'),\n (3, 'heavy'),\n (4, 'difmap'),\n (5, 'geom'),\n (6, 'disper'),\n (7, 'isomor'),\n (8, 'notdet'),\n (9, 'dual'),\n (10, 'iterative'),\n (11, 'other'),\n)\n\ncombobox_fields = {'_exptl_crystal_colour' : COLOUR_CHOICES,\n '_chemical_absolute_configuration' : ABSOLUTE_CONFIGURATION_CHOICES,\n '_exptl_absorpt_correction_type' : ABSORPTION_CORRECTION_TYPES,\n '_refine_ls_hydrogen_treatment' : REFINE_LS_HYDROGEN_TREATMENT,\n '_diffrn_radiation_type' : RADIATION_TYPE,\n '_atom_sites_solution_primary' : SOLUTION_PRIMARY,\n '_atom_sites_solution_secondary' : SOLUTION_PRIMARY,\n '_diffrn_measurement_specimen_support' : SPECIMEN_SUPPORT,\n '_atom_sites_solution_hydrogens' : SOLUTION_PRIMARY,\n '_diffrn_measurement_specimen_adhesive': ADHESIVE,\n }\n\ninclude_equipment_imports = (\n '_diffrn_detector',\n '_diffrn_detector_area_resol_mean',\n '_diffrn_detector_type',\n '_diffrn_measurement_device',\n '_diffrn_radiation_monochromator',\n '_diffrn_radiation_probe',\n '_diffrn_radiation_type',\n '_diffrn_source',\n '_diffrn_source_type',\n '_exptl_absorpt_process_details',\n '_exptl_absorpt_process_details',\n)\n\ncif_to_header_label = {\n # translates CIF keys into regular headers for loops\n '_atom_site_aniso_label' : 'Displacement Parameters',\n '_atom_site_label' : 'Atomic Coordinates',\n '_atom_type_symbol' : 'Scattering Factors',\n '_audit_author_name' : 'CIF Author',\n '_citation_doi' : 'Citations',\n '_citation_id' : 'Citations',\n '_citation_year' : 'Citations',\n '_geom_angle_atom_site_label_1' : 'Angles',\n '_geom_bond_atom_site_label_1' : 'Bonds',\n '_geom_torsion_atom_site_label_1' : 'Torsion Angles',\n '_shelx_res_file' : 'SHELX res File',\n '_space_group_symop_id' : 'Symmetry',\n '_space_group_symop_operation_xyz' : 'Symmetry',\n '_symmetry_equiv_pos_site_id' : 'Symmetry',\n '_symmetry_equiv_pos_as_xyz' : 'Symmetry',\n '_publ_contact_author_name' : 'Publication Contact Author',\n '_publ_author_name' : 'Publication Authors',\n '_geom_hbond_atom_site_label_D' : 'Hydrogen Bonds',\n '_geom_hbond_atom_site_label_H' : 'Hydrogen Bonds',\n '_geom_hbond_atom_site_label_A' : 'Hydrogen Bonds',\n '_exptl_crystal_face_index_h' : 'Crystal Faces',\n '_exptl_oxdiff_crystal_face_indexfrac_h': 'Crystal Faces Fractional',\n '_platon_squeeze_void_nr' : 'Platon SQUEEZE Voids',\n '_smtbx_masks_void_nr' : 'smtbx Solvent Mask',\n}\n\n\"\"\"\n_publ_section_references\n;\nD. Kratzert, I. Krossing, J. Appl. Cryst. 2018, 51.\n\nDolomanov, O.V., Bourhis, L.J., Gildea, R.J, Howard, J.A.K. & Puschmann, H.\n (2009), J. Appl. Cryst. 42, 339-341.\n\nSheldrick, G.M. (2015). Acta Cryst. A71, 3-8.\n\nSheldrick, G.M. (2015). Acta Cryst. C71, 3-8.\n;\n\"\"\"\n# Equipment templates\n\npredef_equipment_templ = [{'name' : 'D8 VENTURE',\n 'items': [\n ['_diffrn_radiation_monochromator', 'mirror optics'],\n ['_diffrn_measurement_device', 'three-circle diffractometer'],\n ['_diffrn_measurement_device_type', 'Bruker D8 VENTURE dual wavelength Mo/Cu'],\n ['_diffrn_measurement_method', r'\\w and \\f scans'],\n ['_diffrn_source', 'microfocus sealed X-ray tube'],\n # ['_diffrn_source_current', '50'],\n # ['_diffrn_source_voltage', '1.1'],\n ['_diffrn_detector_area_resol_mean', '7.41'],\n ['_diffrn_detector', 'CPAD'],\n ['_diffrn_detector_type', 'Bruker PHOTON III'],\n ['_diffrn_source_type', r'Incoatec I\\ms'],\n ['_diffrn_radiation_probe', 'x-ray'],\n ['_diffrn_measurement_specimen_support', 'MiTeGen micromount'],\n ['_olex2_diffrn_ambient_temperature_device', 'Oxford Cryostream 800'],\n ['_diffrn_ambient_environment', 'N~2~'],\n ]\n },\n {'name' : 'APEX2 QUAZAR',\n 'items': [\n ['_diffrn_radiation_monochromator', 'mirror optics'],\n ['_diffrn_measurement_device', 'three-circle diffractometer'],\n ['_diffrn_measurement_device_type', 'Bruker APEX2 QUAZAR'],\n ['_diffrn_measurement_method', r'\\w and \\f scans'],\n ['_diffrn_source', 'microfocus sealed X-ray tube'],\n ['_diffrn_source_type', r'Incoatec I\\ms'],\n ['_diffrn_detector', 'CCD'],\n ['_diffrn_detector_type', 'Bruker APEXII'],\n ['_diffrn_detector_area_resol_mean', '8.3'],\n ['_diffrn_radiation_probe', 'x-ray'],\n ['_diffrn_measurement_specimen_support', 'MiTeGen micromount'],\n ['_olex2_diffrn_ambient_temperature_device', 'Oxford Cryostream 800'],\n ['_diffrn_ambient_environment', 'N~2~'],\n ]\n },\n {'name' : 'Rigaku Spider',\n 'items': [\n ['_diffrn_radiation_monochromator', 'graphite'],\n ['_diffrn_measurement_device', 'four-circle diffractometer'],\n ['_diffrn_measurement_device_type', 'Rigaku R-AXIS SPIDER'],\n ['_diffrn_measurement_method', r'\\w scans'],\n ['_diffrn_source', 'sealed X-ray tube'], # obsolete: _diffrn_radiation_source\n ['_diffrn_detector', 'Image Plate'],\n ['_diffrn_detector_type', 'Rigaku Image Plate'],\n ['_diffrn_detector_area_resol_mean', '?'],\n ['_diffrn_radiation_probe', 'x-ray'],\n ['_diffrn_measurement_specimen_support', 'MiTeGen micromount'],\n ['_olex2_diffrn_ambient_temperature_device', 'Bruker Kryoflex II'],\n ]\n },\n {'name' : 'Crystallographer Details',\n 'items': [\n ['_audit_contact_author_name', '?'],\n ['_audit_contact_author_address', \"?\"],\n ['_audit_contact_author_email', '?'],\n ['_audit_contact_author_phone', '?'],\n ['_publ_contact_author_id_orcid', '?'],\n ]\n },\n {'name' : 'CCDC number',\n 'items': [\n ['_database_code_depnum_ccdc_archive', '?'],\n ]\n },\n ]\n\n\"\"\"\n{'name' : 'Contact author name and address',\n'items': [\n ['_audit_contact_author_name', 'Dr. Daniel Kratzert'],\n ['_audit_contact_author_address',\n \"Albert-Ludwigs-Universität Freiburg\\n\"\n \"Institut für Anorganische und Analytische Chemie\\n\"\n \"Albertstraße 21\\n\"\n \"Freiburg i. Br.\\n\"\n \"79104\\n\"\n \"Germany\"],\n ['_audit_contact_author_email', 'dkratzert@gmx.de'],\n ['_audit_contact_author_phone', '+497612036156'],\n ['_publ_contact_author_id_orcid', 'https://orcid.org/0000-0003-0970-9780'],\n]\n},\"\"\"\n\n### Property contents:\n\npredef_prop_templ = [{'name' : 'Crystal Color',\n 'values': ['_exptl_crystal_colour',\n ['', 'colourless', 'white', 'black', 'yellow', 'red', 'blue',\n 'green', 'gray', 'pink', 'orange', 'violet', 'brown']]\n },\n {'name' : 'Crystal Habit Description',\n 'values': ['_exptl_crystal_description',\n ['', 'block', 'needle', 'plate', 'prism', 'sphere']]\n },\n {'name' : 'Cell Measurement Temperature',\n 'values': ['_cell_measurement_temperature',\n ['', '15', '80(2)', '100(2)', '110(2)',\n '120(2)', '130(2)', '150(2)', '200(2)', '298(2)']]\n },\n {'name' : 'Measurement Temperature',\n 'values': ['_diffrn_ambient_temperature',\n ['', '15(1)', '80(2)', '100(2)', '110(2)',\n '120(2)', '130(2)', '150(2)', '200(2)', '293.15(2)', '298(2)']]\n },\n {'name' : 'Molecular Graphics',\n 'values': ['_computing_molecular_graphics',\n ['', 'Olex2 (Dolomanov et al., 2009)',\n 'ShelXle (Hübschle 2011)',\n 'ORTEP Farrujia 2012',\n 'Bruker SHELXTL, XP (G. Sheldrick)',\n 'Mercury CSD, C. F. Macrae et al. 2008',\n 'PLATON (A.L.Spek, 2019)'\n ]]\n },\n {'name' : 'Crystal Cooling Device',\n 'values': ['_olex2_diffrn_ambient_temperature_device',\n ['',\n 'Oxford Cryostream',\n 'Oxford Cryostream 800',\n 'Oxford Cryostream 700',\n 'Oxford Cryostream 600',\n 'Bruker Kryofelx II',\n 'Bruker Kryofelx I',\n ]\n ]\n\n },\n {'name' : 'Radiation Type',\n 'values': ['_diffrn_radiation_probe',\n ['',\n 'x-ray',\n 'neutron',\n 'electron',\n 'gamma',\n ]\n ]\n\n },\n {'name' : 'Sample environment',\n 'values': ['_diffrn_ambient_environment',\n ['',\n 'N~2~',\n 'He',\n 'vacuum',\n 'mother liquor',\n 'Ar',\n 'H~2~'\n ]\n ]\n\n },\n {'name' : 'Twin relationship',\n 'values': ['_twin_individual_twin_lattice_type',\n ['',\n 'ref', # reference twin\n 'mt_I', # merohedral class I (simple inversion)\n 'mt_II', # merohedral class II (mirror or twofold)\n 'mt_I+II', # class I and II simultaneously present\n 'rmt', # reticular merohedral\n 'pmt', # pseudo-merohedral\n 'rpmt', # reticular pseudo-merohedral\n 'nmt', # non-merohedral\n ]\n ]\n }\n\n ]\n\ncelltxt = \"\"\"\n \n \n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
a = {0:>7.3f} Å,α = {3:>7.3f}°
b = {1:>7.3f} Å,β = {4:>7.3f}°
c = {2:>7.3f} Å,γ = {5:>7.3f}°
\n
\n
\n Volume = {6:8.2f} Å3, {7}\n
\n \n \n \"\"\"\n\n\ndef is_database_number(input_num: Union[str, int]) -> bool:\n if isinstance(input_num, int):\n input_num = str(input_num)\n state: bool = False\n if len(input_num) == 7 and isnumeric(input_num):\n state = True\n return state\n", "sub_path": "tools/misc.py", "file_name": "misc.py", "file_ext": "py", "file_size_in_byte": 40992, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "hashlib.sha512", "line_number": 66, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 77, "usage_type": "call"}, {"api_name": "itertools.zip_longest", "line_number": 82, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 85, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 92, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 93, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 96, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path", "line_number": 143, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 174, "usage_type": "call"}, {"api_name": "re.match", "line_number": 193, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 193, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 198, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 208, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 208, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 221, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 221, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 837, "usage_type": "name"}]} +{"seq_id": "53184136", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom dataclasses import dataclass, field\nimport logging\nimport sys\nfrom typing import List\nimport xml.etree.ElementTree as ET\n\n# Выполнить индивидуальное задание 2 лабораторной работы 14, добавив аннтотации типов.\n# Выполнить проверку программы с помощью утилиты mypy.\n\n\nclass IllegalMarksError(Exception):\n\n def __init__(self, marks, message=\"Illegal year number\"):\n self.marks = marks\n self.message = message\n super(IllegalMarksError, self).__init__(message)\n\n def __str__(self):\n return f\"{self.marks} -> {self.message}\"\n\n\n# Класс пользовательского исключения в случае, если введенная\n# команда является недопустимой.\nclass UnknownCommandError(Exception):\n\n def __init__(self, command, message=\"Unknown command\"):\n self.command = command\n self.message = message\n super(UnknownCommandError, self).__init__(message)\n\n def __str__(self):\n return f\"{self.command} -> {self.message}\"\n\n\n@dataclass(frozen=True)\nclass Person:\n name: str\n group: str\n marks: list[int]\n\n\n@dataclass\nclass Staff:\n students: List[Person] = field(default_factory=lambda: [])\n\n def add(self, name: str, group: str, marks: list) -> None:\n self.students.append(\n Person(\n name=name,\n group=group,\n marks=marks\n )\n )\n self.students.sort(key=lambda person: person.name)\n\n def __str__(self) -> str:\n # Заголовок таблицы.\n table = []\n line = '+-{}-+-{}-+-{}-+-{}-+-{}-+-{}-+-{}-+-{}-+-{}-+'.format(\n '-' * 4,\n '-' * 30,\n '-' * 20,\n '-' * 8,\n '-' * 8,\n '-' * 8,\n '-' * 8,\n '-' * 8,\n '-' * 11\n )\n table.append(line)\n table.append(\n '| {:^3} | {:^30} | {:^20} | {:^8} | {:^8} | {:^8} | {:^8} | {:^8} |'.format(\n \"№\",\n \"Ф.И.О.\",\n \"Группа\",\n \"1-ая оценка\",\n \"2-ая оценка\",\n \"3-ая оценка\",\n \"4-ая оценка\",\n \"5-ая оценка\"\n )\n )\n table.append(line)\n\n # Вывести данные о всех оценках ученика.\n for idx, person in enumerate(self.students, 1):\n table.append(\n '| {:>3} | {:<30} | {:<20} | {:>11} | {:>11} | {:>11} | {:>11} | {:>11} |'.format(\n idx,\n person.name,\n person.group,\n person.marks[0],\n person.marks[1],\n person.marks[2],\n person.marks[3],\n person.marks[4]\n )\n )\n table.append(line)\n\n return '\\n'.join(table)\n\n def __repr__(self):\n return self.__str__()\n\n def select(self, period) -> List[Person]:\n # Получить данные студентов, которые получили оценку 2.\n parts = command.split(' ', maxsplit=2)\n period = int(parts[1])\n result = []\n count = 0\n for person in self.students:\n if 2 in person.marks:\n count += 1\n result.append(person)\n return result\n\n def load(self, filename) -> None:\n with open(filename, 'r', encoding='utf8') as fin:\n xml = fin.read()\n parser = ET.XMLParser(encoding=\"utf8\")\n tree = ET.fromstring(xml, parser=parser)\n self.students = []\n\n for person_element in tree:\n name, group, marks = None, None, None\n\n for element in person_element:\n if element.tag == 'name':\n name = element.text\n elif element.tag == 'group':\n group = element.text\n elif element.tag == 'marks':\n marks = element.text\n\n if name is not None and group is not None \\\n and marks is not None:\n self.students.append(\n Person(\n name=name,\n group=group,\n marks=marks\n )\n )\n\n def save(self, filename) -> None:\n root = ET.Element('students')\n for person in self.students:\n person_element = ET.Element('person')\n\n name_element = ET.SubElement(person_element, 'name')\n name_element.text = person.name\n\n group_element = ET.SubElement(person_element, 'group')\n group_element.text = person.group\n\n marks_element = ET.SubElement(person_element, 'marks')\n\n # Преобразование списка к строке\n mark = ''.join(str(i) for i in marks)\n marks_element.text = str(mark)\n\n root.append(person_element)\n\n tree = ET.ElementTree(root)\n with open(filename, 'wb') as fout:\n tree.write(fout, encoding='utf8', xml_declaration=True)\n\n\nif __name__ == '__main__':\n # Выполнить настройку логгера.\n logging.basicConfig(\n filename='students_1_individ.log',\n level=logging.INFO\n )\n # Список учеников.\n staff = Staff()\n\n # Организовать бесконечный цикл запроса команд.\n while True:\n try:\n # Запросить команду из терминала.\n command = input(\">>> \").lower()\n\n # Выполнить действие в соответствие с командой.\n if command == 'exit':\n break\n\n elif command == 'add':\n # Запросить данные об учениках.\n n = 5\n name = input(\"Введите фамилию и имя: \")\n group = input(\"Введите группу: \")\n marks = list(map(int, input(\"Введите пять оценок студента, в формате - x y z: \").split(None, n)[:n]))\n # Добавить учеников.\n staff.add(name, group, marks)\n logging.info(\n f\"Добавлен студент: {name}, {group}, \"\n f\"получивший оценки {marks} \"\n )\n\n elif command == 'list':\n # Вывести список.\n print(staff)\n logging.info(\"Отображен список студентов.\")\n\n elif command.startswith('select '):\n parts = command.split(maxsplit=1)\n # Запросить учеников.\n selected = staff.select(parts[1])\n # Вывести результаты запроса.\n if selected:\n for count, person in enumerate(selected, 1):\n print(\n '{:>4}: {}'.format(count, person.name)\n )\n logging.info(\n f\"Найдено {len(selected)} студентов с \"\n f\"оценкой {parts[1]}.\"\n )\n else:\n print(\"Нет студентов, которые получили оценку - 2.\")\n logging.warning(\n f\"Студенты получившие оценку {parts[1]} не найдены.\"\n )\n\n elif command.startswith('load '):\n # Разбить команду на части для имени файла.\n parts = command.split(maxsplit=1)\n # Загрузить данные из файла.\n staff.load(parts[1])\n logging.info(f\"Загружены данные из файла {parts[1]}.\")\n\n elif command.startswith('save '):\n # Разбить команду на части для имени файла.\n parts = command.split(maxsplit=1)\n # Сохранить данные в файл.\n staff.save(parts[1])\n logging.info(f\"Сохранены данные в файл {parts[1]}.\")\n\n elif command == 'help':\n # Вывести справку о работе с программой.\n print(\"Список команд:\\n\")\n print(\"add - добавить студента;\")\n print(\"list - вывести список студентов;\")\n print(\"load <имя файла> - загрузить данные из файла;\")\n print(\"save <имя файла> - сохранить данные в файл;\")\n print(\"select <оценка> - найти студентов которые получили такую оценку;\")\n print(\"help - отобразить справку;\")\n print(\"exit - завершить работу с программой.\")\n else:\n raise UnknownCommandError(command)\n except Exception as exc:\n logging.error(f\"Ошибка: {exc}\")\n print(exc, file=sys.stderr)\n", "sub_path": "individ_1.py", "file_name": "individ_1.py", "file_ext": "py", "file_size_in_byte": 9689, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "dataclasses.dataclass", "line_number": 38, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 47, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 47, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 109, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree", "line_number": 123, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.XMLParser", "line_number": 124, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 124, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.fromstring", "line_number": 125, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 125, "usage_type": "argument"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 150, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 150, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 152, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 152, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 154, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 154, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 157, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 157, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 160, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 160, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.ElementTree", "line_number": 168, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 168, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 45, "usage_type": "name"}, {"api_name": "logging.basicConfig", "line_number": 175, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 177, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 200, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 208, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 220, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 226, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 235, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 242, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 257, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 258, "usage_type": "attribute"}]} +{"seq_id": "126926249", "text": "# QM\n\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport geopandas\n\n#reading in pm and population data\npm = pd.read_csv('pm.csv')\ndemo = pd.read_csv('2019demo.csv')\n\n\n\npm = pm.merge(demo, on=['state', 'county'], how = 'inner', validate='1:1', indicator=True)\npm['total_black'] = pm['nonhisp_black'].sum()\npm['total_white'] = pm['nonhisp_white'].sum()\npm['black_ratio'] = pm['nonhisp_black']/pm['total_black']\npm['white_ratio'] = pm['nonhisp_white']/pm['total_white']\n\npm['pm_round']= round(pm['pm']).astype('int')\ngroup = pm.groupby(['pm_round'], as_index=False)\nsum_pm = group.sum()\n\n\nsns.set_style(\"whitegrid\")\n\n\nfig = sns.barplot(data = sum_pm, x= 'pm_round', y = 'black_ratio')\nfig = fig.set(ylim= (0, 0.25))\nfig = plt.title(\"BAU 2030: Black pm Exposure\")\nplt.savefig('black_pm_weighted.png')\n\n \n\n \nfig = sns.barplot(data = sum_pm, x= 'pm_round', y = 'white_ratio')\nfig = fig.set(ylim= (0, 0.25))\nfig = plt.title(\"BAU 2030: White pm Exposure\")\nplt.savefig('white_pm_weighted.png')\n\n\n\n\nsum_pm = sum_pm [['pm_round', 'white_ratio', 'black_ratio']]\nscenarios = sum_pm [['pm_round', 'white_ratio', 'black_ratio']]\nsum_pm.loc[(sum_pm['pm_round'] >=9), 'flag'] = 1\nsum_pm['flag'] = sum_pm['flag'].fillna(0)\nsum_pm['flag']= (sum_pm['flag']).astype('int')\n\ngroup = sum_pm.groupby('flag')\nredist = group.sum()\nredist['b_move'] = redist['black_ratio'] - redist['white_ratio']\n\n\n\nnum = redist.loc[1, \"b_move\"]\nredist['b_scale'] = (num + redist['black_ratio'])/redist['black_ratio']\nscale = redist.loc[0, \"b_scale\"]\n\n\n\n\nsum_pm['scaled_b'] = sum_pm['black_ratio'] * scale\nsum_pm.loc[sum_pm['flag'] ==1, 'scaled_b'] = sum_pm['white_ratio']\n\n\n\n\nfig = sns.barplot(data = sum_pm, x= 'pm_round', y = 'scaled_b')\nfig = fig.set(ylim= (0, 0.25))\nfig = plt.title(\"BAU 2030: Black pm Exposure (Scaled)\")\nplt.savefig('black_pm_weighted_scaled.png', dpi =300)\n\n\n\npop_sum_pm['da'] = pop_sum_pm['black_ratio'] - pop_sum_pm['white_ratio']\npop_sum_pm['dc'] = pop_sum_pm['scaled_b'] - pop_sum_pm['white_ratio']\n\npop_sum_pm['exp_da'] = pop_sum_pm['da']*pop_sum_pm['pm_round']\npop_sum_pm['exp_dc'] = pop_sum_pm['dc']*pop_sum_pm['pm_round']\nstack = pop_sum_pm[['pm_round', 'exp_da','exp_dc']]\nstacky = stack.set_index(['pm_round'])\nstacky = stacky.stack()\nstacky = stacky.reset_index(level=['pm_round'])\nstacky['type'] = stacky.index\nstacky['value'] = stacky[0]\n\nfig = sns.barplot(data = stacky, x= 'pm_round', y = 'value', hue=\"type\")\nplt.savefig('barplot_exp.png', dpi =300)\n\n\nprint(round((pop_sum_pm['da']*pop_sum_pm['pm_round']).sum(), 2))\nprint(round((pop_sum_pm['dc']*pop_sum_pm['pm_round']).sum(), 2))\n\n## By race for 3 scenarios\n## Scenario 1: Real\nscenarios = scenarios[['pm_round', 'white_ratio', 'black_ratio']]\nscenario_stack = scenarios.set_index(['pm_round'])\nscenario_stack = scenario_stack.stack()\nscenario_stack = scenario_stack.reset_index(level=['pm_round'])\nscenario_stack['type'] = scenario_stack.index\nscenario_stack['ratio'] = scenario_stack[0]\nfig, ax = plt.subplots()\nax.set_ylim(0,.3)\nsns.barplot(data = scenario_stack, x= 'pm_round', y = 'ratio', hue=\"type\")\nplt.title(\"BAU 2030: Base Scenario\")\n\nplt.savefig('base_scenario.png', dpi =300)\n\n## Scenrio 2: Counter-factual \ncf1 = scenarios.copy()\n\ncf1['flag'] = [1 if val == 13 or val == 20 else 0 for val in cf1['pm_round']]\n\n\n\ncf1['flag'] = cf1['flag'].fillna(0)\ncf1['flag']= (cf1['flag']).astype('int')\n\ngroup = cf1.groupby('flag')\nredist = group.sum()\nredist['b_move'] = redist['black_ratio'] - redist['white_ratio']\n\n\n\nnum = redist.loc[1, \"b_move\"]\nredist['b_scale'] = (num + redist['black_ratio'])/redist['black_ratio']\nscale = redist.loc[0, \"b_scale\"]\n\n\n\n\ncf1['black_ratio'] = cf1['black_ratio'] * scale\ncf1.loc[cf1['flag'] ==1, 'black_ratio'] = cf1['white_ratio']\ncf1 = cf1[['pm_round', 'white_ratio', 'black_ratio']]\n\n\nscenario_stack = cf1.set_index(['pm_round'])\nscenario_stack = scenario_stack.stack()\nscenario_stack = scenario_stack.reset_index(level=['pm_round'])\nscenario_stack['type'] = scenario_stack.index\nscenario_stack['ratio'] = scenario_stack[0]\n\nfig, ax = plt.subplots()\nax.set_ylim(0,.3)\nsns.barplot(data = scenario_stack, x= 'pm_round', y = 'ratio', hue=\"type\")\nplt.title(\"BAU 2030: Shift in PM2.5 '13' and '20' exp\")\nplt.savefig('base_scenario2.png', dpi =300)\n\n## Scenario 3: Counter-factual 2\ncf2 = scenarios.copy()\n\ncf2['flag'] = [1 if val > 9 else 0 for val in cf2['pm_round']]\n\n\n\ncf2['flag'] = cf2['flag'].fillna(0)\ncf2['flag']= (cf2['flag']).astype('int')\n\ngroup = cf2.groupby('flag')\nredist = group.sum()\nredist['b_move'] = redist['black_ratio'] - redist['white_ratio']\n\n\n\nnum = redist.loc[1, \"b_move\"]\nredist['b_scale'] = (num + redist['black_ratio'])/redist['black_ratio']\nscale = redist.loc[0, \"b_scale\"]\n\n\n\n\ncf2['black_ratio'] = cf2['black_ratio'] * scale\ncf2.loc[cf2['flag'] ==1, 'black_ratio'] = cf2['white_ratio']\ncf2 = cf2[['pm_round', 'white_ratio', 'black_ratio']]\n\n\nscenario_stack = cf2.set_index(['pm_round'])\nscenario_stack = scenario_stack.stack()\nscenario_stack = scenario_stack.reset_index(level=['pm_round'])\nscenario_stack['type'] = scenario_stack.index\nscenario_stack['ratio'] = scenario_stack[0]\n\nfig, ax = plt.subplots()\nax.set_ylim(0,.3)\nsns.barplot(data = scenario_stack, x= 'pm_round', y = 'ratio', hue=\"type\")\nplt.title(\"BAU 2030: Shift in PM2.5 > 9 exp\")\nplt.savefig('base_scenario3.png', dpi =300)", "sub_path": "Analysis/ej.py", "file_name": "ej.py", "file_ext": "py", "file_size_in_byte": 5359, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "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": "seaborn.set_style", "line_number": 25, "usage_type": "call"}, {"api_name": "seaborn.barplot", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 69, "usage_type": "call"}, {"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.savefig", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "seaborn.barplot", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 187, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}]} +{"seq_id": "367821906", "text": "import json\nfrom math import ceil\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom matplotlib.backends.backend_pdf import PdfPages\n\nplt.style.use(\"ggplot\")\n\n\ndef load_json(filepath):\n with open(filepath) as file:\n return json.load(file)\n\n\ndef parse(string):\n return [float(value) for value in string[1:-1].split(\" \") if value != \"\"]\n\n\ndef plot_ci(ax, molecule_name, intensity, upper_bound, lower_bound, mols_lags):\n x = np.arange(mols_lags + 1)\n ax.step(x, np.exp(intensity), where=\"mid\")\n ax.fill_between(x, np.exp(lower_bound), np.exp(upper_bound), alpha=0.5, step=\"mid\")\n ax.hlines(1, 0, max(x))\n ax.set_title(molecule_name)\n ax.set_ylim(0, 3)\n return ax\n\n\ndef plot_intensities(df: pd.DataFrame, mols_lags, n_cols=3):\n fig, axes = plt.subplots(\n ceil(len(df) / n_cols), n_cols, sharey=False, figsize=(16, 82)\n )\n for (i, row) in enumerate(df.iterrows()):\n plot_ci(\n axes[i // n_cols][i % n_cols],\n row[0],\n parse(row[1].refit_coeffs),\n parse(row[1].upper_bound),\n parse(row[1].lower_bound),\n mols_lags,\n )\n plt.tight_layout()\n return fig, axes\n\n\nif __name__ == \"__main__\":\n cv_track = load_json(\"cv_track.json\")\n with open(\"mapping.json\", \"r\") as mapping_file:\n mapping = json.load(mapping_file)\n params = load_json(\"parameters.json\")\n\n coeffs = pd.read_csv(\"ci.csv\", index_col=0)\n coeffs[\"molecule_name\"] = mapping[:-8]\n coeffs = coeffs.set_index(\"molecule_name\")\n fig, axes = plot_intensities(coeffs, params[\"lag\"])\n\n file_name = \"ci-gender={}-bucket-size={}-lag={}.pdf\".format(\n params[\"gender\"], params[\"bucket\"], params[\"lag\"]\n )\n with PdfPages(file_name) as pdf:\n pdf.savefig(fig)\n", "sub_path": "plots/produce_ci.py", "file_name": "produce_ci.py", "file_ext": "py", "file_size_in_byte": 1805, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.pyplot.style.use", "line_number": 9, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 9, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "json.load", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 24, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 31, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "json.load", "line_number": 51, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.backends.backend_pdf.PdfPages", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "591964270", "text": "# -*- encoding: utf-8 -*-\n# Copyright (c) 2016 b<>com\n#\n# Authors: Vincent FRANCOISE \n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or\n# implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport datetime\n\nfrom monascaclient import exc\n\nfrom watcher.common import clients\n\n\nclass MonascaHelper(object):\n\n def __init__(self, osc=None):\n \"\"\":param osc: an OpenStackClients instance\"\"\"\n self.osc = osc if osc else clients.OpenStackClients()\n self.monasca = self.osc.monasca()\n\n def query_retry(self, f, *args, **kwargs):\n try:\n return f(*args, **kwargs)\n except exc.HTTPUnauthorized:\n self.osc.reset_clients()\n self.monasca = self.osc.monasca()\n return f(*args, **kwargs)\n except Exception:\n raise\n\n def _format_time_params(self, start_time, end_time, period):\n \"\"\"Format time-related params to the correct Monasca format\n\n :param start_time: Start datetime from which metrics will be used\n :param end_time: End datetime from which metrics will be used\n :param period: interval in seconds (int)\n :return: start ISO time, end ISO time, period\n \"\"\"\n\n if not period:\n period = int(datetime.timedelta(hours=3).total_seconds())\n if not start_time:\n start_time = (\n datetime.datetime.utcnow() -\n datetime.timedelta(seconds=period))\n\n start_timestamp = None if not start_time else start_time.isoformat()\n end_timestamp = None if not end_time else end_time.isoformat()\n\n return start_timestamp, end_timestamp, period\n\n def statistics_list(self, meter_name, dimensions, start_time=None,\n end_time=None, period=None,):\n \"\"\"List of statistics.\"\"\"\n start_timestamp, end_timestamp, period = self._format_time_params(\n start_time, end_time, period\n )\n raw_kwargs = dict(\n name=meter_name,\n start_time=start_timestamp,\n end_time=end_timestamp,\n dimensions=dimensions,\n )\n\n kwargs = {k: v for k, v in raw_kwargs.items() if k and v}\n\n statistics = self.query_retry(\n f=self.monasca.metrics.list_measurements, **kwargs)\n\n return statistics\n\n def statistic_aggregation(self,\n meter_name,\n dimensions,\n start_time=None,\n end_time=None,\n period=None,\n aggregate='avg',\n group_by='*'):\n \"\"\"Representing a statistic aggregate by operators\n\n :param meter_name: meter names of which we want the statistics\n :param dimensions: dimensions (dict)\n :param start_time: Start datetime from which metrics will be used\n :param end_time: End datetime from which metrics will be used\n :param period: Sampling `period`: In seconds. If no period is given,\n only one aggregate statistic is returned. If given, a\n faceted result will be returned, divided into given\n periods. Periods with no data are ignored.\n :param aggregate: Should be either 'avg', 'count', 'min' or 'max'\n :return: A list of dict with each dict being a distinct result row\n \"\"\"\n start_timestamp, end_timestamp, period = self._format_time_params(\n start_time, end_time, period\n )\n\n raw_kwargs = dict(\n name=meter_name,\n start_time=start_timestamp,\n end_time=end_timestamp,\n dimensions=dimensions,\n period=period,\n statistics=aggregate,\n group_by=group_by,\n )\n\n kwargs = {k: v for k, v in raw_kwargs.items() if k and v}\n\n statistics = self.query_retry(\n f=self.monasca.metrics.list_statistics, **kwargs)\n\n return statistics\n", "sub_path": "python-watcher-1.0.1/watcher/datasource/monasca.py", "file_name": "monasca.py", "file_ext": "py", "file_size_in_byte": 4489, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "watcher.common.clients.OpenStackClients", "line_number": 30, "usage_type": "call"}, {"api_name": "watcher.common.clients", "line_number": 30, "usage_type": "name"}, {"api_name": "monascaclient.exc.HTTPUnauthorized", "line_number": 36, "usage_type": "attribute"}, {"api_name": "monascaclient.exc", "line_number": 36, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 56, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 56, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "223260912", "text": "\nfrom killer.db import filter\nfrom killer.models import Port\n\nimport logging\nlogging.getLogger(\"scapy.runtime\").setLevel(logging.ERROR)\n\nfrom scapy.all import *\n\nfrom pprint import pprint\n\nfrom multiprocessing import Pool\n\nimport time\n\nports = []\nports.append({ 'protocol' : 'tcp', 'number' : 80, 'ranking' : 484133 })\nports.append({ 'protocol' : 'udp', 'number' : 631, 'ranking' : 450281 })\nports.append({ 'protocol' : 'udp', 'number' : 161, 'ranking' : 433467 })\nports.append({ 'protocol' : 'udp', 'number' : 137, 'ranking' : 365163 })\nports.append({ 'protocol' : 'udp', 'number' : 123, 'ranking' : 330879 })\nports.append({ 'protocol' : 'udp', 'number' : 138, 'ranking' : 297830 })\nports.append({ 'protocol' : 'udp', 'number' : 1434, 'ranking' : 293184 })\nports.append({ 'protocol' : 'udp', 'number' : 445, 'ranking' : 253118})\n\n\ndef suggest_ports(host, quantity = 1):\n\n result = []\n\n for port in ports:\n\n p = filter(Port, host.ports, { 'protocol' : port['protocol'], 'number' : port['number'] })\n\n if not p or not p.state:\n #print(\"We will scan %s\" %(port))\n result.append(port)\n\n if len(result) >= quantity:\n break\n\n return result\n\n\ndef port_check_tcp(ip, port, timeout=2, sleep=0):\n\n response = sr1(IP(dst=ip)/TCP(dport=port, flags=\"S\"), verbose=False, timeout=timeout)\n\n if response:\n if response[TCP].flags == 18:\n sr1(IP(dst=ip)/TCP(dport=port, flags=\"AR\"), verbose=False, timeout=0.0001)\n result = { 'state' : 'open', 'reason' : 'syn/ack' }\n else:\n result = { 'state' : 'closed', 'reason' : 'not syn/ack' }\n else:\n result = { 'state' : 'closed', 'reason' : 'no response' }\n\n time.sleep(sleep)\n\n return result\n\n\ndef port_check_udp(ip, port, timeout=2, sleep=0):\n\n #response = sr1(IP(dst=ip)/TCP(dport=port, flags=\"S\"), verbose=False, timeout=timeout)\n #response = sr1(IP(dst=ip)/UDP(dport=port), verbose=False, timeout=timeout)\n\n #if port == 53:\n # response = sr1(IP(dst=ip)/UDP(dport=port)/DNS(rd=1,qd=DNSQR(qname=\"www.google.com\")),verbose=False, timeout=timeout)\n #else:\n response = sr1(IP(dst=ip)/UDP(dport=port), verbose=False, timeout=timeout)\n\n if response:\n if response.haslayer(ICMP):\n result = { 'state' : 'closed', 'reason' : 'icmp response' }\n #pprint(response)\n # sr1(IP(dst=ip)/TCP(dport=port, flags=\"AR\"), verbose=False, timeout=0.0001)\n else:\n result = { 'state' : 'open', 'reason' : 'udp response' }\n #print(response[DNS].summary())\n\n else:\n result = { 'state' : 'open|filtered', 'reason' : 'no response' }\n\n time.sleep(sleep)\n\n return result\n\n", "sub_path": "killer/portscan.py", "file_name": "portscan.py", "file_ext": "py", "file_size_in_byte": 2716, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 6, "usage_type": "call"}, {"api_name": "logging.ERROR", "line_number": 6, "usage_type": "attribute"}, {"api_name": "killer.db.filter", "line_number": 33, "usage_type": "call"}, {"api_name": "killer.models.Port", "line_number": 33, "usage_type": "argument"}, {"api_name": "time.sleep", "line_number": 58, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "537404957", "text": "import warnings\nwarnings.filterwarnings(\"ignore\")\nimport re\nfrom scipy import sparse\nimport gensim\nfrom tqdm import tqdm_notebook as tqdm\nfrom gensim.models.doc2vec import TaggedDocument\nfrom sklearn.metrics import f1_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\nfrom nltk.tokenize import TweetTokenizer\n\nclass doc2vec:\n\n \n def tokenization(self, document):\n return re.findall(self.w, document)\n # tokenized_words = self.tknzr.tokenize(datapoint[X].lower())\n\n def __init__(self, df, X, Y, build=False):\n self.w = re.compile(\"\\w+\", re.I)\n if 'basestring' not in globals():\n basestring = str\n self.iterations = 10\n self.tknzr = TweetTokenizer(strip_handles=True, reduce_len=True)\n # Hyperparameters : https://arxiv.org/pdf/1607.05368.pdf\n self.vector_size = 300\n self.window_size = 15\n self.min_count = 2\n self.sampling_threshold = 1e-4\n self.negative_size = 5\n self.train_epoch = 50\n # self.dm = 0\n self.worker_count = 7\n\n\n labeled_sentences = []\n df_tags = []\n\n if isinstance(Y, basestring):\n df_tags.append(Y)\n elif isinstance(Y, list):\n df_tags = Y\n elif not isinstance(Y, list):\n raise TypeError\n self.df = df\n# print(self.df)\n self.x = X\n self.y = Y\n self.df_tags = df_tags\n self.testseries = df[df_tags[0]].unique()\n self.testseries_name = df_tags[0]\n if build == True:\n for index, datapoint in df.iterrows():\n tokenized_words = self.tokenization(datapoint[X])\n labeled_sentences.append(TaggedDocument(words=tokenized_words, tags=[datapoint[i] for i in df_tags]))\n model = gensim.models.doc2vec.Doc2Vec(vector_size=self.vector_size,\n window_size=self.window_size,\n min_count=self.min_count,\n sampling_threshold=self.sampling_threshold,\n negative_size=self.negative_size,\n train_epoch=self.train_epoch,\n # dm=self.dm,\n worker_count=self.worker_count)\n model.build_vocab(labeled_sentences)\n model.train(labeled_sentences, total_examples=model.corpus_count, epochs=model.epochs)\n self.model = model\n\n\n def score(self, verbose=False, all_cols=False):\n\n df = self.df\n X = self.x\n Y =self.y\n self.verbose = verbose\n if 'basestring' not in globals():\n basestring = str\n\n labeled_sentences = []\n df_tags = []\n\n if isinstance(Y, basestring):\n df_tags.append(Y)\n elif isinstance(Y, list):\n df_tags = Y\n elif not isinstance(Y, list):\n raise TypeError\n \n cols2try = []\n \n if all_cols == True:\n cols2try = self.df_tags\n \n else:\n cols2try.append(self.df_tags[0])\n \n for col in cols2try:\n print(col)\n total_accuracy = 0\n total_label_accuracy = []\n\n\n\n\n for i in df[col].unique():\n total_label_accuracy.append(0)\n iterations = self.iterations\n for i in (range(iterations)):\n\n train, test = train_test_split(self.df, shuffle=True, test_size=0.05)\n\n for index, datapoint in train.iterrows():\n tokenized_words = self.tokenization(datapoint[X])\n \n\n labeled_sentences.append(TaggedDocument(words=tokenized_words, tags=[datapoint[i] for i in df_tags]))\n\n model = gensim.models.doc2vec.Doc2Vec(vector_size=self.vector_size,\n window_size=self.window_size,\n min_count=self.min_count,\n sampling_threshold=self.sampling_threshold,\n negative_size=self.negative_size,\n train_epoch=self.train_epoch,\n # dm=self.dm,\n worker_count=self.worker_count)\n\n model.build_vocab(labeled_sentences)\n model.train(labeled_sentences, total_examples=model.corpus_count, epochs=model.epochs)\n self.model = model\n\n test['results'] = self.predict(test[X])\n labelaccuracy = f1_score(test[self.testseries_name], test['results'], average=None)\n total_label_accuracy= [x + y for x, y in zip(total_label_accuracy, labelaccuracy)]\n accuracy = accuracy_score(test[self.testseries_name], test['results'])\n total_accuracy = total_accuracy + accuracy\n\n print(\"Accuracy Score: \", total_accuracy/iterations)\n\n total_label_accuracy = [i/iterations for i in total_label_accuracy]\n print(\"Label Score: \", total_label_accuracy)\n\n return [total_label_accuracy, accuracy]\n\n\n def predict_taggedtext(self,\n document): # takes in a taged document and infers vector and returns whether it is releveant or not (1 or 0)\n inferred_vector = document\n inferred_vector = self.model.infer_vector(inferred_vector)\n sims = self.model.docvecs.most_similar([inferred_vector], topn=len(self.model.docvecs))\n return sims\n\n def predict_text(self, document): # takes in a string and infers vector and returns vectors and distance\n tokenized_words = self.tokenization(document)\n\n inferred_vector = TaggedDocument(words=tokenized_words, tags=[\"inferred_vector\"])[0]\n inferred_vector = self.model.infer_vector(inferred_vector)\n sims = self.model.docvecs.most_similar([inferred_vector], topn=len(self.model.docvecs))\n tags = []\n for col in self.df_tags:\n tags.append([rec for rec in sims if rec[0] in set(self.df[col].unique())][0][0])\n return tags\n \n def predict_sims(self, document): # takes in a string and infers vector and returns vectors and distance\n tokenized_words = self.tokenization(document)\n\n\n inferred_vector = TaggedDocument(words=tokenized_words, tags=[\"inferred_vector\"])[0]\n inferred_vector = self.model.infer_vector(inferred_vector)\n sims = self.model.docvecs.most_similar([inferred_vector], topn=len(self.model.docvecs))\n return sims\n \n def get_vector(self, document): # takes in a string and infers vector and returns vectors and distance\n tokenized_words = self.tokenization(document)\n\n\n inferred_vector = TaggedDocument(words=tokenized_words, tags=[\"inferred_vector\"])[0]\n inferred_vector = self.model.infer_vector(inferred_vector)\n return sparse.csr_matrix(inferred_vector).toarray()\n\n def predict_text_main(self, document, col=None): # takes in a string and infers vector and returns vectors and distance\n if col == None:\n col = self.df_tags[0]\n tokenized_words = self.tokenization(document)\n\n\n inferred_vector = TaggedDocument(words=tokenized_words, tags=[\"inferred_vector\"])[0]\n inferred_vector = self.model.infer_vector(inferred_vector)\n sims = self.model.docvecs.most_similar([inferred_vector], topn=len(self.model.docvecs))\n# print([rec for rec in sims if rec[0] in set(self.df[self.df_tags[0]].unique())])\n return [rec for rec in sims if rec[0] in set(self.df[col].unique())][0][0]\n\n\n\n def label_sentences(self, df, X, Y):\n # trick for py2/3 compatibility\n if 'basestring' not in globals():\n basestring = str\n\n labeled_sentences = []\n df_tags = []\n\n if isinstance(Y, basestring):\n df_tags.append(Y)\n elif isinstance(Y, list):\n df_tags = Y\n elif not isinstance(Y, list):\n raise TypeError\n self.df = df\n self.x = X\n self.y = Y\n\n for index, datapoint in df.iterrows():\n tokenized_words = self.tokenization(document)\n\n\n labeled_sentences.append(TaggedDocument(words=tokenized_words, tags=[datapoint[i] for i in df_tags]))\n return labeled_sentences\n\n def predict(self, X): # Takes a series of text and returns a series of predictions\n if self.verbose:\n from tqdm._tqdm_notebook import tqdm_notebook\n tqdm_notebook.pandas()\n return X.apply(self.predict_text_main)\n else:\n return X.apply(self.predict_text_main)\n\n\n\n", "sub_path": "GithubScraper/pd_doc2vec.py", "file_name": "pd_doc2vec.py", "file_ext": "py", "file_size_in_byte": 8990, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "warnings.filterwarnings", "line_number": 2, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 17, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 21, "usage_type": "call"}, {"api_name": "re.I", "line_number": 21, "usage_type": "attribute"}, {"api_name": "nltk.tokenize.TweetTokenizer", "line_number": 25, "usage_type": "call"}, {"api_name": "gensim.models.doc2vec.TaggedDocument", "line_number": 56, "usage_type": "call"}, {"api_name": "gensim.models.doc2vec.Doc2Vec", "line_number": 57, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 57, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 110, "usage_type": "call"}, {"api_name": "gensim.models.doc2vec.TaggedDocument", "line_number": 116, "usage_type": "call"}, {"api_name": "gensim.models.doc2vec.Doc2Vec", "line_number": 118, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 118, "usage_type": "attribute"}, {"api_name": "sklearn.metrics.f1_score", "line_number": 132, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 134, "usage_type": "call"}, {"api_name": "gensim.models.doc2vec.TaggedDocument", "line_number": 155, "usage_type": "call"}, {"api_name": "gensim.models.doc2vec.TaggedDocument", "line_number": 167, "usage_type": "call"}, {"api_name": "gensim.models.doc2vec.TaggedDocument", "line_number": 176, "usage_type": "call"}, {"api_name": "scipy.sparse.csr_matrix", "line_number": 178, "usage_type": "call"}, {"api_name": "scipy.sparse", "line_number": 178, "usage_type": "name"}, {"api_name": "gensim.models.doc2vec.TaggedDocument", "line_number": 186, "usage_type": "call"}, {"api_name": "gensim.models.doc2vec.TaggedDocument", "line_number": 216, "usage_type": "call"}, {"api_name": "tqdm._tqdm_notebook.tqdm_notebook.pandas", "line_number": 222, "usage_type": "call"}, {"api_name": "tqdm._tqdm_notebook.tqdm_notebook", "line_number": 222, "usage_type": "name"}]} +{"seq_id": "545180858", "text": "\"\"\"Dynamically create sde tasks.\"\"\"\nfrom airflow.operators.bash_operator import BashOperator\nfrom airflow.operators.python_operator import PythonOperator\nfrom trident.operators.s3_file_transfer_operator import S3FileTransferOperator\n\nfrom airflow.operators.latest_only_operator import LatestOnlyOperator\n\nfrom airflow.models import DAG\nfrom trident.util.notifications import notify\n\nfrom trident.util.seaboard_updates import update_seaboard_date, get_seaboard_update_dag\n\nfrom trident.util.geospatial import *\n\nfrom trident.util.general import config as conf\n\n\nno_pbf = ('addrapn')\n\n\ndef shp_to_geojson(path_to_file):\n \"\"\"Shapefile to GeoJSON.\"\"\"\n cmd = shp2geojson(path_to_file)\n return cmd\n\n\ndef shp_to_topojson(path_to_file):\n \"\"\"Shapefile to TopoJSON.\"\"\"\n cmd = shp2topojson(path_to_file)\n return cmd\n\n\ndef geojson_to_geobuf(path_to_file):\n \"\"\"Geojson to Geobuf.\"\"\"\n geojson2geobuf(layer=path_to_file)\n return 'Successfully converted geojson to geobuf.'\n\n\ndef geobuf_to_gzip(datasd_name):\n \"\"\"Geobuf to gzip.\"\"\"\n geobuf2gzip(layername=datasd_name)\n return 'Successfully compressed geobuf.'\n\n\ndef shp_to_zip(datasd_name):\n \"\"\"Shapefile to zip.\"\"\"\n shp2zip(layername=datasd_name)\n return 'Successfully transfered shapefiles to zip archive.'\n\n\ndef create_sde_tasks(dag,\n folder,\n layer,\n datasd_name,\n md,\n path_to_file,\n sde_to_shp):\n \"\"\"Dynamically create SDE Airflow tasks.\n\n dag: DAG defined in _dags file.\n folder: subfolder in the sde folder on S3.\n layer: layer name.\n datasd_name: layer name + _datasd.\n md: name of md file on Seaboard.\n path_to_file: poseidon path + datasd_name.\n sde_to_shp: _jobs specific sde_to_shp function\n \"\"\"\n #: Latest Only Operator for sde layer\n sde_latest_only = LatestOnlyOperator(task_id='{layer}_latest_only'\n .format(layer=layer),\n dag=dag)\n\n #: Convert sde table to shapefile format\n to_shp = PythonOperator(\n task_id='{layer}_to_shp'.format(layer=layer),\n python_callable=sde_to_shp,\n on_failure_callback=notify,\n on_retry_callback=notify,\n on_success_callback=notify,\n dag=dag)\n\n #: Convert shapefile to GeoJSON format\n to_geojson = BashOperator(\n task_id='{layer}_to_geojson'.format(layer=layer),\n bash_command=shp_to_geojson(path_to_file),\n on_failure_callback=notify,\n on_retry_callback=notify,\n on_success_callback=notify,\n dag=dag)\n\n #: Convert shapefile to TopoJSON format\n to_topojson = BashOperator(\n task_id='{layer}_to_topojson'.format(layer=layer),\n bash_command=shp_to_topojson(path_to_file),\n on_failure_callback=notify,\n on_retry_callback=notify,\n on_success_callback=notify,\n dag=dag)\n\n #: Compress shapefile components\n to_zip = PythonOperator(\n task_id='{layer}_shp_to_zip'.format(layer=layer),\n python_callable=shp_to_zip,\n op_kwargs={'datasd_name': datasd_name},\n on_failure_callback=notify,\n on_retry_callback=notify,\n on_success_callback=notify,\n dag=dag)\n\n #: Upload shapefile to S3\n shp_to_S3 = S3FileTransferOperator(\n task_id='{layer}_shp_to_S3'.format(layer=layer),\n source_base_path=conf['prod_data_dir'],\n source_key='{datasd_name}.zip'.format(datasd_name=datasd_name),\n dest_s3_conn_id=conf['default_s3_conn_id'],\n dest_s3_bucket=conf['dest_s3_bucket'],\n dest_s3_key='sde/{folder}/{datasd_name}.zip'\n .format(folder=folder, datasd_name=datasd_name),\n on_failure_callback=notify,\n on_retry_callback=notify,\n on_success_callback=notify,\n replace=True,\n dag=dag)\n\n #: Upload geojson to S3\n geojson_to_S3 = S3FileTransferOperator(\n task_id='{layer}_geojson_to_S3'.format(layer=layer),\n source_base_path=conf['prod_data_dir'],\n source_key='{datasd_name}.geojson'.format(datasd_name=datasd_name),\n dest_s3_conn_id=conf['default_s3_conn_id'],\n dest_s3_bucket=conf['dest_s3_bucket'],\n dest_s3_key='sde/{folder}/{datasd_name}.geojson'\n .format(folder=folder, datasd_name=datasd_name),\n on_failure_callback=notify,\n on_retry_callback=notify,\n on_success_callback=notify,\n replace=True,\n dag=dag)\n\n #: Upload topojson to S3\n topojson_to_S3 = S3FileTransferOperator(\n task_id='{layer}_topojson_to_S3'.format(layer=layer),\n source_base_path=conf['prod_data_dir'],\n source_key='{datasd_name}.topojson'.format(datasd_name=datasd_name),\n dest_s3_conn_id=conf['default_s3_conn_id'],\n dest_s3_bucket=conf['dest_s3_bucket'],\n dest_s3_key='sde/{folder}/{datasd_name}.topojson'\n .format(folder=folder, datasd_name=datasd_name),\n on_failure_callback=notify,\n on_retry_callback=notify,\n on_success_callback=notify,\n replace=True,\n dag=dag)\n\n #: Update portal modified date\n update_md = get_seaboard_update_dag('{md}.md'.format(md=md), dag)\n\n if layer not in no_pbf:\n #: Convert GeoJSON to Geobuf format\n to_geobuf = PythonOperator(\n task_id='{layer}_to_geobuf'.format(layer=layer),\n python_callable=geojson_to_geobuf,\n op_kwargs={'path_to_file': path_to_file},\n on_failure_callback=notify,\n on_retry_callback=notify,\n on_success_callback=notify,\n dag=dag)\n\n #: Convert geobuf to gzipped geobuf\n to_gzip = PythonOperator(\n task_id='{layer}_geobuf_to_gzip'.format(layer=layer),\n python_callable=geobuf_to_gzip,\n op_kwargs={'datasd_name': datasd_name},\n on_failure_callback=notify,\n on_retry_callback=notify,\n on_success_callback=notify,\n dag=dag)\n\n #: Upload geobuf to S3\n geobuf_to_S3 = S3FileTransferOperator(\n task_id='{layer}_geobuf_to_S3'.format(layer=layer),\n source_base_path=conf['prod_data_dir'],\n source_key='{datasd_name}.pbf'.format(datasd_name=datasd_name),\n dest_s3_conn_id=conf['default_s3_conn_id'],\n dest_s3_bucket=conf['dest_s3_bucket'],\n dest_s3_key='sde/{folder}/{datasd_name}.pbf'\n .format(folder=folder, datasd_name=datasd_name),\n on_failure_callback=notify,\n on_retry_callback=notify,\n on_success_callback=notify,\n replace=True,\n use_gzip=True,\n dag=dag)\n\n #: Conversion to geobuf is triggered after conversion to geojson.\n to_geobuf.set_upstream(to_geojson)\n\n #: Compression to gzip is triggered after conversion to geobuf.\n to_gzip.set_upstream(to_geobuf)\n\n #: geobuf upload to S3 is triggered after compression to gzipped geobuf.\n geobuf_to_S3.set_upstream(to_gzip)\n\n #: Github update depends on shapefile S3 upload success.\n update_md.set_upstream(geobuf_to_S3)\n\n #: Execution rules:\n #: sde_latest_only must run before shp conversion.\n to_shp.set_upstream(sde_latest_only)\n\n #: Conversion to geojson is triggered after conversion to shp.\n to_geojson.set_upstream(to_shp)\n\n #: Conversion to topojson is triggered after conversion to shapefile.\n to_topojson.set_upstream(to_shp)\n\n #: Compression to zip is triggered after conversion to geojson and topojson.\n to_zip.set_upstream(to_geojson)\n to_zip.set_upstream(to_topojson)\n\n #: shapefile upload to S3 is triggered after conversion to zip.\n shp_to_S3.set_upstream(to_zip)\n\n #: geojson upload to S3 is triggered after conversion to geojson.\n geojson_to_S3.set_upstream(to_geojson)\n\n #: topojson upload to S3 is triggered after conversion to topojson.\n topojson_to_S3.set_upstream(to_topojson)\n\n #: Github update depends on shapefile S3 upload success.\n update_md.set_upstream(shp_to_S3)\n update_md.set_upstream(geojson_to_S3)\n update_md.set_upstream(topojson_to_S3)\n", "sub_path": "poseidon/trident/util/sde_extract_tasks.py", "file_name": "sde_extract_tasks.py", "file_ext": "py", "file_size_in_byte": 8250, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "airflow.operators.latest_only_operator.LatestOnlyOperator", "line_number": 69, "usage_type": "call"}, {"api_name": "airflow.operators.python_operator.PythonOperator", "line_number": 74, "usage_type": "call"}, {"api_name": "trident.util.notifications.notify", "line_number": 77, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 78, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 79, "usage_type": "name"}, {"api_name": "airflow.operators.bash_operator.BashOperator", "line_number": 83, "usage_type": "call"}, {"api_name": "trident.util.notifications.notify", "line_number": 86, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 87, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 88, "usage_type": "name"}, {"api_name": "airflow.operators.bash_operator.BashOperator", "line_number": 92, "usage_type": "call"}, {"api_name": "trident.util.notifications.notify", "line_number": 95, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 96, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 97, "usage_type": "name"}, {"api_name": "airflow.operators.python_operator.PythonOperator", "line_number": 101, "usage_type": "call"}, {"api_name": "trident.util.notifications.notify", "line_number": 105, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 106, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 107, "usage_type": "name"}, {"api_name": "trident.operators.s3_file_transfer_operator.S3FileTransferOperator", "line_number": 111, "usage_type": "call"}, {"api_name": "trident.util.general.config", "line_number": 113, "usage_type": "name"}, {"api_name": "trident.util.general.config", "line_number": 115, "usage_type": "name"}, {"api_name": "trident.util.general.config", "line_number": 116, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 119, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 120, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 121, "usage_type": "name"}, {"api_name": "trident.operators.s3_file_transfer_operator.S3FileTransferOperator", "line_number": 126, "usage_type": "call"}, {"api_name": "trident.util.general.config", "line_number": 128, "usage_type": "name"}, {"api_name": "trident.util.general.config", "line_number": 130, "usage_type": "name"}, {"api_name": "trident.util.general.config", "line_number": 131, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 134, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 135, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 136, "usage_type": "name"}, {"api_name": "trident.operators.s3_file_transfer_operator.S3FileTransferOperator", "line_number": 141, "usage_type": "call"}, {"api_name": "trident.util.general.config", "line_number": 143, "usage_type": "name"}, {"api_name": "trident.util.general.config", "line_number": 145, "usage_type": "name"}, {"api_name": "trident.util.general.config", "line_number": 146, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 149, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 150, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 151, "usage_type": "name"}, {"api_name": "trident.util.seaboard_updates.get_seaboard_update_dag", "line_number": 156, "usage_type": "call"}, {"api_name": "airflow.operators.python_operator.PythonOperator", "line_number": 160, "usage_type": "call"}, {"api_name": "trident.util.notifications.notify", "line_number": 164, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 165, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 166, "usage_type": "name"}, {"api_name": "airflow.operators.python_operator.PythonOperator", "line_number": 170, "usage_type": "call"}, {"api_name": "trident.util.notifications.notify", "line_number": 174, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 175, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 176, "usage_type": "name"}, {"api_name": "trident.operators.s3_file_transfer_operator.S3FileTransferOperator", "line_number": 180, "usage_type": "call"}, {"api_name": "trident.util.general.config", "line_number": 182, "usage_type": "name"}, {"api_name": "trident.util.general.config", "line_number": 184, "usage_type": "name"}, {"api_name": "trident.util.general.config", "line_number": 185, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 188, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 189, "usage_type": "name"}, {"api_name": "trident.util.notifications.notify", "line_number": 190, "usage_type": "name"}]} +{"seq_id": "313205451", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\n\r\nC2017-39\r\n\r\n\r\n\"\"\"\r\nimport scrapy\r\nfrom carbuisness.items import AutohomeFamilyConfigItem\r\nimport time\r\nfrom scrapy.conf import settings\r\nfrom scrapy.mail import MailSender\r\nimport logging\r\nimport json\r\nimport re\r\nimport random\r\nimport hashlib\r\nfrom hashlib import md5\r\nfrom carbuisness.getip import getProxy\r\nfrom selenium import webdriver\r\nfrom scrapy.xlib.pydispatch import dispatcher\r\nfrom scrapy import signals\r\nfrom scrapy.conf import settings\r\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\r\nfrom carbuisness.items import WeatherItem\r\nimport pymongo\r\n\r\nwebsite='autohome_family_config'\r\n\r\nclass CarSpider(scrapy.Spider):\r\n\r\n name=website\r\n start_urls = []\r\n\r\n def __init__(self,**kwargs):\r\n super(CarSpider,self).__init__(**kwargs)\r\n self.mailer=MailSender.from_settings(settings)\r\n self.counts=0\r\n self.carnum=800000\r\n\r\n settings.set('CrawlCar_Num',self.carnum,priority='cmdline')\r\n settings.set('MONGODB_DB','carbusiness',priority='cmdline')\r\n settings.set('MONGODB_COLLECTION',website,priority='cmdline')\r\n\r\n\r\n def start_requests(self):\r\n connection = pymongo.MongoClient(\"192.168.1.94\", 27017)\r\n db = connection[\"newcar\"]\r\n collection = db[\"autohome_newcar\"]\r\n res = collection.distinct(\"familyid\")\r\n for fid in res:\r\n # for fid in ['2533']:\r\n url = \"https://car.autohome.com.cn/price/series-%s.html\" % fid\r\n yield scrapy.Request(url=url, meta={\"fid\":fid})\r\n\r\n def parse(self, response):\r\n item = AutohomeFamilyConfigItem()\r\n item['grabtime'] = time.strftime('%Y-%m-%d %X', time.localtime())\r\n item['url'] = response.url\r\n # item['status'] = re.findall(\"\\d+\", response.xpath(\"//*[@class='main-title']/a/@href\").extract_first().split(\"#\")[0])\r\n item['status'] = response.url\r\n item['brand'] = response.xpath(\"//*[@class='fn-left cartab-title-name']/a/text()\").extract_first()\r\n item['brandid'] = re.findall(\"\\d+\", response.xpath(\"//*[@class='fn-left cartab-title-name']/a/@href\").extract_first())[0]\r\n item['family'] = response.xpath(\"//*[@class='main-title']/a/text()\").extract_first()\r\n item['familyid'] = response.meta[\"fid\"]\r\n item['level'] = response.xpath(\"//*[@class='lever-ul']/li[1]/span/text()\").extract_first()\r\n item['body'] = response.xpath(\"//*[@class='lever-ul']/li[2]/a\")\r\n temp = []\r\n for body in item['body']:\r\n temp.append(body.xpath(\"text()\").extract_first())\r\n item['body'] = temp\r\n item['engine'] = response.xpath(\"//*[@class='lever-ul']/li[3]/span/a\")\r\n temp = []\r\n for engine in item['engine']:\r\n temp.append(engine.xpath(\"text()\").extract_first())\r\n item['engine'] = temp\r\n item['gear'] = response.xpath(\"//*[@class='lever-ul']/li[4]/a\")\r\n temp = []\r\n for gear in item['gear']:\r\n temp.append(gear.xpath(\"text()\").extract_first())\r\n item['gear'] = temp\r\n yield item", "sub_path": "cagey/carbuisness/carbuisness/spiders/autohome_family_config.py", "file_name": "autohome_family_config.py", "file_ext": "py", "file_size_in_byte": 3084, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "scrapy.Spider", "line_number": 30, "usage_type": "attribute"}, {"api_name": "scrapy.mail.MailSender.from_settings", "line_number": 37, "usage_type": "call"}, {"api_name": "scrapy.conf.settings", "line_number": 37, "usage_type": "argument"}, {"api_name": "scrapy.mail.MailSender", "line_number": 37, "usage_type": "name"}, {"api_name": "scrapy.conf.settings.set", "line_number": 41, "usage_type": "call"}, {"api_name": "scrapy.conf.settings", "line_number": 41, "usage_type": "name"}, {"api_name": "scrapy.conf.settings.set", "line_number": 42, "usage_type": "call"}, {"api_name": "scrapy.conf.settings", "line_number": 42, "usage_type": "name"}, {"api_name": "scrapy.conf.settings.set", "line_number": 43, "usage_type": "call"}, {"api_name": "scrapy.conf.settings", "line_number": 43, "usage_type": "name"}, {"api_name": "pymongo.MongoClient", "line_number": 47, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 54, "usage_type": "call"}, {"api_name": "carbuisness.items.AutohomeFamilyConfigItem", "line_number": 57, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 58, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 58, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 63, "usage_type": "call"}]} +{"seq_id": "272484485", "text": "import pandas as pd\nimport os\nimport numpy as np\nfrom keras.utils import to_categorical\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.preprocessing.sequence import pad_sequences\nfrom datetime import date\nfrom fastnumbers import isfloat, isint\nimport re\nfrom gensim import models\nfrom keras.layers import Dropout\nfrom keras.callbacks import ModelCheckpoint, TensorBoard, EarlyStopping\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Activation, LSTM, Bidirectional\nfrom keras.layers import Embedding\n\nos.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\" # see issue #152\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\"\n\nMAX_NB_WORDS = 50000\nEMBEDDING_DIM = 300\nMAX_SEQUENCE_LENGTH = 100\nVALIDATION_SPLIT = 0.1\nRANDOM_SEED = 42\n\nDATA_DIR = '../data'\nEMBEDDING_FILE = 'fasttext_vocab_users.vec'\n\n\ndef load_data():\n data = pd.read_csv('../data/train_set.csv', usecols=range(1, 11), parse_dates=['timestamp', 'thread_timestamp'])\n data = data[data.channel.isin(['career', 'big_data', 'deep_learning', 'kaggle_crackers',\n 'lang_python', 'lang_r', 'nlp', 'theory_and_practice', 'welcome', 'bayesian',\n '_meetings', 'datasets'])\n & data.main_msg]\n\n users_100 = list(data.user_id.value_counts()[:100].index)\n data = data[data[\"user_id\"].isin(users_100)]\n\n mappings = {}\n for c, value in enumerate(users_100, 0):\n mappings[value] = c\n\n # split on data and data val\n date_before = date(2017, 4, 1)\n train = data[data['timestamp'] <= date_before]\n val = data[data['timestamp'] > date_before]\n\n train_data = train[['user_id', 'text']].reset_index()[['user_id', 'text']]\n train_data['user_id'] = train_data.user_id.map(mappings)\n train_data = train_data.sort_values('user_id').reset_index()[['user_id', 'text']]\n\n val_data = val[['user_id', 'text']].reset_index()[['user_id', 'text']]\n val_data['user_id'] = val_data.user_id.map(mappings)\n val_data = val_data.sort_values('user_id').reset_index()[['user_id', 'text']]\n\n train_data.text = train_data.text.astype(str) \\\n .apply(lambda x: re.sub('(<\\S+>:?)|(>)|([\\w\\.]*@[\\w\\.]*)', ' ', x)) \\\n .apply(lambda x: re.sub('\\s+', ' ', x))\n train_data = train_data[~train_data.text.apply(lambda x: isfloat(x) or isint(x) or len(x) < 20)]\n\n val_data.text = val_data.text.astype(str) \\\n .apply(lambda x: re.sub('(<\\S+>:?)|(>)|([\\w\\.]*@[\\w\\.]*)', ' ', x)) \\\n .apply(lambda x: re.sub('\\s+', ' ', x))\n val_data = val_data[~val_data.text.apply(lambda x: isfloat(x) or isint(x) or len(x) < 20)]\n\n train_text = train_data['text'].astype(str).apply(lambda x: x.lower())\n train_labels = np.asarray(train_data['user_id'], dtype='int8')\n\n val_text = val_data['text'].astype(str).apply(lambda x: x.lower())\n val_labels = np.asarray(val_data['user_id'], dtype='int8')\n return train_text, train_labels, val_text, val_labels\n\n\ndef prepare_embeddings(word_indexes):\n\n def load_w2v():\n _fname = os.path.join(DATA_DIR, EMBEDDING_FILE)\n w2v_model = models.KeyedVectors.load_word2vec_format(_fname, binary=False)\n return w2v_model\n\n embeddings = load_w2v()\n # prepare embedding matrix\n nb_words = min(MAX_NB_WORDS, len(word_indexes))\n prepared_embedding_matrix = np.zeros((nb_words, EMBEDDING_DIM))\n for word, n in word_indexes.items():\n if n >= MAX_NB_WORDS:\n continue\n try:\n embedding_vector = embeddings.word_vec(word)\n prepared_embedding_matrix[n] = embedding_vector\n except:\n continue\n\n return prepared_embedding_matrix\n\n\ndef transform(tokenizer_object, train, test):\n sequences_train = tokenizer_object.texts_to_sequences(train) # transform words to its indexes\n sequences_test = tokenizer_object.texts_to_sequences(test)\n\n word_indexes = tokenizer_object.word_index # dictionary of word:index\n\n # transform a list to numpy array with shape (nb_samples, MAX_SEQUENCE_LENGTH)\n # be careful because it takes only last MAX_SEQUENCE_LENGTH words\n train = pad_sequences(sequences_train, maxlen=MAX_SEQUENCE_LENGTH)\n test = pad_sequences(sequences_test, maxlen=MAX_SEQUENCE_LENGTH)\n\n return train, test, word_indexes\n\n\ndef main():\n data_train, labels_train, data_test, labels_test = load_data()\n print(len(data_train), len(data_test))\n tokenizer = Tokenizer(num_words=MAX_NB_WORDS, filters='\"#$%&()*+-/:;<=>@[\\\\]^{|}~\\t\\n,.')\n tokenizer.fit_on_texts(data_train)\n\n X_train, X_test, word_index = transform(tokenizer, data_train, data_test)\n y_train, y_test = to_categorical(np.asarray(labels_train), num_classes=100), to_categorical(np.asarray(labels_test), num_classes=100)\n\n embedding_matrix = prepare_embeddings(word_index)\n\n # инициализируем слой эмбеддингов\n NAME = \"lstm_user_classification\"\n\n # callbacks initialization\n # automatic generation of learning curves\n callback_1 = TensorBoard(log_dir='./logs/logs_{}'.format(NAME), histogram_freq=0,\n write_graph=False, write_images=False)\n # stop training model if accuracy does not increase more than five epochs\n callback_2 = EarlyStopping(monitor='val_acc', min_delta=0, patience=5, verbose=0, mode='auto')\n # best model saving\n callback_3 = ModelCheckpoint(\"../models/model_{}.hdf5\".format(NAME), monitor='val_acc',\n save_best_only=True, verbose=0)\n\n embedding_layer = Embedding(embedding_matrix.shape[0],\n embedding_matrix.shape[1],\n weights=[embedding_matrix],\n input_length=MAX_SEQUENCE_LENGTH,\n trainable=False,\n mask_zero=True)\n\n model = Sequential()\n model.add(embedding_layer)\n model.add(Dropout(0.25))\n model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2, return_sequences=True))\n model.add(LSTM(100, dropout=0.2, recurrent_dropout=0.2))\n model.add(Dropout(0.25))\n model.add(Dense(100))\n model.add(Activation('softmax'))\n\n model.compile(loss='categorical_crossentropy',\n optimizer='adam',\n metrics=['accuracy'])\n\n model.summary()\n model.fit(X_train, y_train, validation_data=[X_test, y_test],\n batch_size=1024, epochs=100, callbacks=[callback_1, callback_2, callback_3])\n\nif __name__ == \"__main__\":\n main()", "sub_path": "projects/p02/src/fasttext_user_classification_lstm_2.py", "file_name": "fasttext_user_classification_lstm_2.py", "file_ext": "py", "file_size_in_byte": 6508, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.environ", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 45, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 58, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 59, "usage_type": "call"}, {"api_name": "fastnumbers.isfloat", "line_number": 60, "usage_type": "call"}, {"api_name": "fastnumbers.isint", "line_number": 60, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 63, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 64, "usage_type": "call"}, {"api_name": "fastnumbers.isfloat", "line_number": 65, "usage_type": "call"}, {"api_name": "fastnumbers.isint", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "gensim.models.KeyedVectors.load_word2vec_format", "line_number": 79, "usage_type": "call"}, {"api_name": "gensim.models.KeyedVectors", "line_number": 79, "usage_type": "attribute"}, {"api_name": "gensim.models", "line_number": 79, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 85, "usage_type": "call"}, {"api_name": "keras.preprocessing.sequence.pad_sequences", "line_number": 106, "usage_type": "call"}, {"api_name": "keras.preprocessing.sequence.pad_sequences", "line_number": 107, "usage_type": "call"}, {"api_name": "keras.preprocessing.text.Tokenizer", "line_number": 115, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 119, "usage_type": "call"}, {"api_name": "keras.callbacks.TensorBoard", "line_number": 128, "usage_type": "call"}, {"api_name": "keras.callbacks.EarlyStopping", "line_number": 131, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 133, "usage_type": "call"}, {"api_name": "keras.layers.Embedding", "line_number": 136, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 143, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 145, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 146, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 147, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 148, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 149, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 150, "usage_type": "call"}]} +{"seq_id": "451853852", "text": "import numpy as np\r\nfrom scipy.spatial import distance as dist\r\nimport copy\r\nimport math\r\nimport cv2\r\nimport serial\r\nimport time\r\nfrom fractions import gcd\r\nimport sys\r\nfrom numpy.linalg import multi_dot\r\nfrom PyQt5.QtWidgets import QApplication, QLabel, QVBoxLayout, QHBoxLayout, QWidget, QPushButton, QMainWindow\r\nfrom PyQt5.QtWidgets import QSizePolicy, QSlider, QRadioButton, QGridLayout, QButtonGroup, QCheckBox\r\nfrom PyQt5.QtGui import QIcon\r\nfrom PyQt5.QtCore import QObject, QThread, pyqtSignal, QSize, Qt\r\nimport matplotlib.pyplot as plt\r\nfrom timeit import default_timer as timer # from feedback_linearization\r\nfrom scipy import interpolate # from path_planning\r\nimport random # from kalman_filter\r\n\r\n\r\n\r\n## Value added imports\r\n#from feedback_linearization import mainLoop2\r\n#from path_planning import mainLoop3, sampleCubicSplinesWithDerivative\r\n#from kalman_filter import mainLoop4\r\n\r\nKL25=serial.Serial('COM7',9600,timeout=1)#open serial port\r\n# two Lists used for real time plottting of the left motor and right motor\r\nRmotor= []\r\nLmotor= []\r\n\r\nclass robotClass:\r\n def __init__(self, pos = [], radius = 0, team = '-no team!-', ID = '-no ID!-'):\r\n self.pos = pos # centre coordinates [x,y]\r\n self.radius = radius # calculated radius of robot (based on centre dot)\r\n self.team = team # team 'B' or 'Y'\r\n self.angle = 999 # angle of orientation \r\n self.ID = ID # ID of robot on a team\r\n # ID markings\r\n self.circles = [] # [x,y,color]\r\n\r\n # this is a method to add a new circle for IDing the robot\r\n def newMarking(self, circle = [0,0,[0,0,0]]): \r\n self.circles.append(circle)\r\n\r\nclass ballClass:\r\n def __init__(self, x = 0, y = 0):\r\n self.pos = [x, y]\r\n\r\nroboList = [] # holds all robots currently seen- resets every loop\r\nroboIDmarks = [] # holds all potential robot ID marks seen ('G' or 'P')\r\nball = None # holds ball position in ballClass type object\r\nIDdRobots = [] # potentially used for previous state- probably updated every loop\r\n\r\nparam1val = 150\r\nparam2val = 15\r\nvalueMin = 125\r\n\r\ndef colorID(hue, sat, val):\r\n color = 'X' # Default case, 'X' will print an error for an unrecognized element\r\n if(val > valueMin):\r\n if (hue < 128 and hue >= 85):\r\n #if (hue < 250 and hue >= 150):\r\n color = 'B' # Blue team circle\r\n #print(hue,sat,val)\r\n elif (hue < 35 and hue > 25 and sat > 100):\r\n color = 'Y' # Yellow team circle\r\n #print(hue,sat,val)\r\n elif (hue >= 128 or (hue <= 9 and sat < 120)): # Must address loop in hue\r\n color = 'P' # Purple ID circle\r\n elif (hue < 85 and hue >= 43):\r\n color = 'G' # Green ID circle\r\n elif ((hue <= 12 and hue >= 3 and sat > 40) or \r\n (hue <=35 and hue > 24 and sat < 100) or (hue <= 24 and hue > 12)):\r\n color = 'O' # Ball!\r\n #print(hue,sat,val)\r\n #else:\r\n # print(\"unrecognized\",hue,sat,val) # good for debugging unrecognized circles\r\n # cv2.waitKey(1000)\r\n return color \r\n\r\n\r\n# IDcircle()\r\n# K.C. & E.H., Nov. 24th, 2018\r\n# This function identifies any given circle based on its color and location. Although it\r\n# does not return anything, it assigns the circle to its appropriate global variable\r\n# (a robot object, ID mark list or ball object).\r\n# (input) -> (function) -> (output)\r\n# [x,y,r] -> ID_circle() -> none\r\n# v1:\r\n# Must implement all identifying functions. closestBot() is not fully developed, and must\r\n# be added when completed.\r\ndef IDcircle(img, circle):\r\n global ball # so we can assign the ball its position globally\r\n\r\n x=int(circle[0])\r\n y=int(circle[1])\r\n #r=int(circle[2])\r\n \r\n # Getting spaced pixels within the circle\r\n #diffx = [int(x+r/2), int(x-r/2)]\r\n #for idx,posx in list(enumerate(diffx)):\r\n # if(posx >= len(img[0])):\r\n # diffx[idx] = x\r\n # elif(posx < 0):\r\n # diffx[idx] = 0\r\n #diffy = [int(y+r/2), int(y-r/2)]\r\n #for idx2,posy in list(enumerate(diffy)):\r\n # if(posy >= len(img)):\r\n # diffy[idx2] = y\r\n # elif(posy < 0):\r\n # diffy[idx2] = 0\r\n #print(\"Colors\",img[y,x,0],img[y,x,1],img[y,x,2])\r\n hue = img[y,x,0]\r\n sat = img[y,x,1]\r\n val = img[y,x,2]\r\n # taking the average hue, saturation and value of a circle\r\n #hue = sum([img[y,x,0],img[y,diffx[0],0],img[y,diffx[1],0],img[diffy[0],x,0],img[diffy[1],x,0]])/5\r\n #sat = sum([img[y,x,1],img[y,diffx[0],1],img[y,diffx[1],1],img[diffy[0],x,1],img[diffy[1],x,1]])/5\r\n #val = sum([img[y,x,2],img[y,diffx[0],2],img[y,diffx[1],2],img[diffy[0],x,2],img[diffy[1],x,2]])/5\r\n\r\n # getting the color of the circle with the averaged pixel\r\n color = colorID(hue, sat, val)\r\n #print(hue, sat, val,' so the Circle is ', color)\r\n #cv2.waitKey(1000)\r\n\r\n # if its blue or (if its yellow) --> Robot center/new robot\r\n if (color == 'B') or (color == 'Y'):\r\n # passes in position [x,y], radius and color\r\n # where the radius is calced with: 25 radius centre in 85 radius robot so ~ 3.4 times radius\r\n roboList.append(robotClass([x,y],circle[2]*4,color))\r\n #print('Robot seen!')\r\n\r\n # if green or purple --> Robot ID marking\r\n elif (color == 'G') or (color == 'P'): \r\n roboIDmarks.append([x,y,color])\r\n \r\n # if orange --> Ball location\r\n elif (color == 'O'):\r\n ball = ballClass(x,y)\r\n\r\n# assignIDmarks()\r\n# E.H., Dec, 2018\r\n# This function cycles through the globally stored roboIDmarks list and assigns them to the\r\n# closest available robot, provided they don't already have 4 assigned\r\ndef assignIDmarks(robot):\r\n #if isinstance(roboList, type(None)) == 0:\r\n # Assign each robot its four closest marks\r\n #for robot in roboList:\r\n closestMarks = [] # indices of the closest four marks to the robot center\r\n # [index in roboIDmarks, euclidean distance]\r\n #furthestMark = [0,0] # [index in closestMarks, euclidean distance]\r\n\r\n # Assign this robot its four closest marks\r\n #for i, mark in enumerate(list(roboIDmarks)):\r\n for mark in roboIDmarks:\r\n markDist = dist.euclidean([mark[0],mark[1]],robot.pos)\r\n\r\n if(markDist < robot.radius and len(robot.circles) < 4):\r\n robot.newMarking(mark)\r\n closestMarks.append(markDist)\r\n elif(markDist < robot.radius):\r\n for i, currentcircle in enumerate(list(robot.circles)):\r\n if markDist < closestMarks[i]:\r\n robot.circles[i] = mark\r\n closestMarks[i] = markDist\r\n\r\n\r\n # If there aren't already four marks given to the robot, \r\n # just give it whatever is available in order to initialize robot.circles[]\r\n #if len(robot.circles) < 4:\r\n # if markDist < robot.radius:\r\n # robot.newMarking(mark)\r\n # closestMarks[i] = [i, markDist]\r\n # if markDist > furthestMark[1]:\r\n # furthestMark = [i, markDist]\r\n\r\n ## If there is a closer value than the furthest currently in robot.circles[]\r\n ## replace the current furthest with this new one \r\n #elif markDist < furthestMark[1]:\r\n # if markDist < robot.radius:\r\n # robot.circles[furthestMark[0]] = mark\r\n # closestMarks[furthestMark[0]] = [i, markDist]\r\n\r\n # furthestMark[1] = markDist\r\n\r\n # # redetermine the furthest mark within the current closest marks\r\n # for j, qark in enumerate(closestMarks):\r\n # if qark[1] > furthestMark[1]:\r\n # furthestMark = [j, qark[1]]\r\n\r\n #else:\r\n #print(robot.radius)\r\n \r\n # * The below code was intended to shorten the list of circles in order to \r\n # improve efficiency, however it had an error due to the index provided by\r\n # \"wark[0]\" being incorrect after the element in the previous iteration \r\n # was deleted... *\r\n ## Remove the marks that were assigned to a robot- this will potentially make \r\n ## assigning the rest of the marks much quicker with larger numbers of robots\r\n #for wark in closestMarks:\r\n # del roboIDmarks[wark[0]]\r\n #else:\r\n # print(\"No robots detected, but there are ID marks..?\")\r\n\r\n# RoboID()\r\n# E.H., Jan, 2019\r\n# This function reads the sorted robot.circles list and assigns an ID (robot.ID = x)\r\n# to the robot. If the IDs are not properly sorted, this will not work\r\n# v2:\r\n# Has all IDs implemented\r\ndef RoboID(robot):\r\n #for robot in roboList:\r\n if len(robot.circles) == 4:\r\n if robot.circles[0][2] == 'P': # circle 1\r\n if robot.circles[1][2] == 'P': # circle 2\r\n if robot.circles[2][2] == 'P': # circle 3\r\n if robot.circles[3][2] == 'P': # circle 4\r\n robot.ID = 'ID9'\r\n elif robot.circles[3][2] == 'G': # circle 4\r\n robot.ID = 'ID4'\r\n elif robot.circles[2][2] == 'G': # circle 3\r\n if robot.circles[3][2] == 'P': # circle 4\r\n robot.ID = 'ID0'\r\n elif robot.circles[3][2] == 'G': # circle 4\r\n robot.ID = 'ID10'\r\n elif robot.circles[1][2] == 'G': # circle 2\r\n if robot.circles[2][2] == 'P': # circle 3\r\n if robot.circles[3][2] == 'G': # circle 4\r\n robot.ID = 'ID7'\r\n elif robot.circles[2][2] == 'G': # circle 3\r\n if robot.circles[3][2] == 'P': # circle 4\r\n robot.ID = 'ID3'\r\n elif robot.circles[0][2] == 'G': # circle 1\r\n if robot.circles[1][2] == 'P': # circle 2\r\n if robot.circles[2][2] == 'P': # circle 3\r\n if robot.circles[3][2] == 'G': # circle 4\r\n robot.ID = 'ID5'\r\n elif robot.circles[2][2] == 'G': # circle 3\r\n if robot.circles[3][2] == 'P': # circle 4\r\n robot.ID = 'ID1'\r\n elif robot.circles[1][2] == 'G': # circle 2\r\n if robot.circles[2][2] == 'P': # circle 3\r\n if robot.circles[3][2] == 'P': # circle 4\r\n robot.ID = 'ID11'\r\n elif robot.circles[3][2] == 'G': # circle 4\r\n robot.ID = 'ID6'\r\n elif robot.circles[2][2] == 'G': # circle 3\r\n if robot.circles[3][2] == 'P': # circle 4\r\n robot.ID = 'ID2'\r\n elif robot.circles[3][2] == 'G': # circle 4\r\n robot.ID = 'ID8'\r\n\r\n\r\n# angle()\r\n# E.H., Jan, 2019\r\n# This function determines the angle of a passed robot using the IDs assigned to the robot\r\n# by observing relative positions of said IDs. The angle determined is assigned to the robot\r\n# at the end of the function, and reassignIDs() is called at the end to allow for identification\r\n# of the robot.\r\n# (input) -> (function) -> (output)\r\n# ID marks -> angle() -> robot.angle\r\ndef angle(robot):\t\r\n topIDs = [] # i.e. the two circles on the flat end of the robot\r\n bottomIDs = []\r\n theta1 = 999 # An impossible number for if statements later\r\n theta2 = 999\r\n\r\n # Uses the cosine law to figure out the angle every possible combo of ID circles makes\r\n # with the center of the robot (team ID), assigning to top or bottom IDs based on this angle\r\n for ii in range(len(robot.circles)-1):\r\n for jj in range(ii + 1, len(robot.circles)):\r\n temp1 = robot.circles[ii]\r\n temp2 = robot.circles[jj]\r\n\r\n # Determining distance between the different IDs\r\n a = dist.euclidean([temp1[0],temp1[1]],robot.pos) # Distance from ID 1 to centre\r\n b = dist.euclidean([temp2[0],temp2[1]],robot.pos) # Distance from ID 2 to centre\r\n c = dist.euclidean([temp1[0],temp1[1]],[temp2[0],temp2[1]]) # Distance from ID 1 to ID 2\r\n try:\r\n theta = math.degrees(math.acos((c**2 - b**2 - a**2)/(-2.0 * a * b))) #CRASHES ON RARE OCCASIONS\r\n except:\r\n print('Theta Error')\r\n if theta > 100 and theta < 130: # Ideally 114.84 degrees\r\n topIDs.append(temp1)\r\n topIDs.append(temp2)\r\n \r\n if theta > 45 and theta < 75: # Ideally 65.16 degrees\r\n bottomIDs.append(temp1)\r\n bottomIDs.append(temp2)\r\n\r\n # the other ID pairs will be either ~180 or ~90 degrees\r\n\r\n # Takes the top two IDs and their average position, creating a vector to that point from the\r\n # center of the robot which the robot's angle can be derived from\r\n if len(topIDs) == 2:\r\n xMean = (topIDs[0][0] + topIDs[1][0])/2\r\n yMean = (topIDs[0][1] + topIDs[1][1])/2\r\n\r\n xDiff = xMean - robot.pos[0]\r\n yDiff = yMean - robot.pos[1]\r\n # Angle points in the direction the robot is facing\r\n theta1 = math.degrees(math.atan2(yDiff,xDiff))\r\n #else:\r\n #print(\"top went wrong...\")\r\n \r\n # Takes the bottom two IDs and their average position, creating a vector from that point to\r\n # the center of the robot which the robot's angle can be derived from\r\n # (this is the opposite direction from the other one so the angle will be the same)\r\n if len(bottomIDs) == 2:\r\n xMean2 = (bottomIDs[0][0] + bottomIDs[1][0])/2\r\n yMean2 = (bottomIDs[0][1] + bottomIDs[1][1])/2\r\n\r\n xDiff2 = robot.pos[0] - xMean2\r\n yDiff2 = robot.pos[1] - yMean2\r\n # Negative for both of these to get an angle that is front facing\r\n theta2 = math.degrees(math.atan2(yDiff2,xDiff2))\r\n #else:\r\n # print(\"bottom is wrong\")\r\n\r\n # Averages the vectors to get a better approx of the true angle\r\n if theta2 != 999 and theta1 != 999:\r\n xMean = (math.cos(math.radians(theta1)) + math.cos(math.radians(theta2)))/2\r\n yMean = (math.sin(math.radians(theta1)) + math.sin(math.radians(theta2)))/2\r\n theta = math.degrees(math.atan2(yMean,xMean))\r\n robot.angle = theta\r\n\r\n # If one of the vector calcs failed, just take the one that worked\r\n elif theta2 != 999 and theta1 == 999:\r\n theta = theta2\r\n robot.angle = theta\r\n elif theta2 == 999 and theta1 != 999:\r\n theta = theta1\r\n robot.angle = theta\r\n else:\r\n return \"ERROR\"\r\n\r\n reassignIDs(robot,topIDs,bottomIDs)\r\n\r\n# reassignIDs()\r\n# E.H., Jan, 2019\r\n# This function reassigns the IDs found in a robot depending on the angle the robot\r\n# is facing. It is helpful to draw out a visualization of this to understand why \r\n# certain angles are associated with certain indices in robot.circles\r\n# (input) -> (function) -> (output)\r\n# angle -> reassignIDs() -> robot.circles (sorted)\r\ndef reassignIDs(robot,topIDs,bottomIDs):\r\n # Reassignment of IDs only works if all four have been recognized\r\n if len(robot.circles) == 4 and len(topIDs) == 2 and len(bottomIDs) == 2:\r\n # I suggest drawing this out if you're having a hard time visualizing it-\r\n # see the design document for further detail on which ID is which\r\n if robot.angle <= 45 and robot.angle >= -45:\r\n if topIDs[0][1] > topIDs[1][1]:\r\n robot.circles[0] = topIDs[1]\r\n robot.circles[1] = topIDs[0]\r\n else:\r\n robot.circles[0] = topIDs[0]\r\n robot.circles[1] = topIDs[1]\r\n if bottomIDs[0][1] > bottomIDs[1][1]:\r\n robot.circles[2] = bottomIDs[1]\r\n robot.circles[3] = bottomIDs[0]\r\n else:\r\n robot.circles[2] = bottomIDs[0]\r\n robot.circles[3] = bottomIDs[1]\r\n if robot.angle < 135 and robot.angle > 45:\r\n if topIDs[0][0] > topIDs[1][0]:\r\n robot.circles[0] = topIDs[0]\r\n robot.circles[1] = topIDs[1]\r\n else:\r\n robot.circles[0] = topIDs[1]\r\n robot.circles[1] = topIDs[0]\r\n if bottomIDs[0][0] > bottomIDs[1][0]:\r\n robot.circles[2] = bottomIDs[0]\r\n robot.circles[3] = bottomIDs[1]\r\n else:\r\n robot.circles[2] = bottomIDs[1]\r\n robot.circles[3] = bottomIDs[0]\r\n if robot.angle < -45 and robot.angle > -135:\r\n if topIDs[0][0] > topIDs[1][0]:\r\n robot.circles[0] = topIDs[1]\r\n robot.circles[1] = topIDs[0]\r\n else:\r\n robot.circles[0] = topIDs[0]\r\n robot.circles[1] = topIDs[1]\r\n if bottomIDs[0][0] > bottomIDs[1][0]:\r\n robot.circles[2] = bottomIDs[1]\r\n robot.circles[3] = bottomIDs[0]\r\n else:\r\n robot.circles[2] = bottomIDs[0]\r\n robot.circles[3] = bottomIDs[1]\r\n if robot.angle <= -135 or robot.angle >= 135: # must be \"or\", as sign swaps at 180\r\n if topIDs[0][1] > topIDs[1][1]:\r\n robot.circles[0] = topIDs[0]\r\n robot.circles[1] = topIDs[1]\r\n else:\r\n robot.circles[0] = topIDs[1]\r\n robot.circles[1] = topIDs[0]\r\n if bottomIDs[0][1] > bottomIDs[1][1]:\r\n robot.circles[2] = bottomIDs[0]\r\n robot.circles[3] = bottomIDs[1]\r\n else:\r\n robot.circles[2] = bottomIDs[1]\r\n robot.circles[3] = bottomIDs[0]\r\n return\r\n\r\n# stoprobot()\r\n# E.H., Mar, 2019\r\n# This function will stop any robots passed into it by issuing stop commands to \r\n# the specified robot. Alternatively, if 'all' is passed in, it will stop all robots.\r\ndef stoprobot(ID):\r\n packet = bytearray()\r\n if(ID == 'all'): # Send stop commands to all robots\r\n print(\"stopping robots\")\r\n for robot in range(0,12):\r\n packet.append(0xFF) # start bit\r\n packet.append(robot) #Robot ID\r\n packet.append(1)\r\n packet.append(0)\r\n packet.append(1)\r\n packet.append(0)\r\n packet.append(0)\r\n packet.append(0xFF) # stop bit\r\n KL25.write(packet)\r\n packet = bytearray()\r\n elif(ID >= 0 and ID <= 11): # Only accept valid IDs\r\n packet.append(0xFF) # start bit\r\n packet.append(ID) #Robot ID\r\n packet.append(1)\r\n packet.append(0)\r\n packet.append(1)\r\n packet.append(0)\r\n packet.append(0)\r\n packet.append(0xFF) # stop bit\r\n KL25.write(packet)\r\n\r\ndef sampleCubicSplinesWithDerivative(points, tangents, resolution):\r\n '''\r\n Compute and sample the cubic splines for a set of input points with\r\n optional information about the tangent (direction AND magnitude). The \r\n splines are parametrized along the traverse line (piecewise linear), with\r\n the resolution being the step size of the parametrization parameter.\r\n The resulting samples have NOT an equidistant spacing.\r\n Arguments: points: a list of n-dimensional points\r\n tangents: a list of tangents\r\n resolution: parametrization step size\r\n Returns: samples\r\n Notes: Lists points and tangents must have equal length. In case a tangent\r\n is not specified for a point, just pass None. For example:\r\n points = [[0,0], [1,1], [2,0]]\r\n tangents = [[1,1], None, [1,-1]]\r\n '''\r\n resolution = float(resolution)\r\n points = np.asarray(points)\r\n nPoints, dim = points.shape\r\n\r\n # Parametrization parameter s.\r\n dp = np.diff(points, axis=0) # difference between points\r\n dp = np.linalg.norm(dp, axis=1) # distance between points\r\n d = np.cumsum(dp) # cumsum along the segments\r\n d = np.hstack([[0],d]) # add distance from first point\r\n l = d[-1] # length of point sequence\r\n nSamples = int(l/resolution) # number of samples\r\n s,r = np.linspace(0,l,nSamples,retstep=True) # sample parameter and step\r\n\r\n # Bring points and (optional) tangent information into correct format.\r\n assert(len(points) == len(tangents))\r\n data = np.empty([nPoints, dim], dtype=object)\r\n for i,p in enumerate(points):\r\n t = tangents[i]\r\n # Either tangent is None or has the same\r\n # number of dimensions as the point p.\r\n assert(t is None or len(t)==dim)\r\n fuse = list(zip(p,t) if t is not None else zip(p,))\r\n data[i,:] = fuse\r\n\r\n # Compute splines per dimension separately.\r\n samples = np.zeros([nSamples, dim])\r\n for i in range(dim):\r\n poly = interpolate.BPoly.from_derivatives(d, data[:,i])\r\n samples[:,i] = poly(s)\r\n return samples\r\n\r\n# Feedback Linearization (Koceila's) global variables:\r\ninit_rob_x = 0\r\ninit_rob_y = 0\r\ninit_rob_angle =0\r\n\r\n# Path Planning (Mike's) global variables:\r\npoints = []\r\ntangents = []\r\nsamples_prev = []\r\nnext_point_x = 0\r\nnext_point_y = 0\r\n\r\n# Kalman Filter (Yan's) global variables:\r\n###\r\n#Mathematical model of a moving mass\r\n\t#this kalman filter regulate on a x-y axis\r\n\t#This Kalman Filter filters out measurement noise and gives best estimate of where the robot is. \r\n## The dt variable should be variable based on the number of frame rates obtained by the CV system. \r\n####### This frame rate should be calculated. #########\r\ndt=1/10 \r\nFx = np.matrix([[1,dt], [0,1]])#A Matrix (state transfer function)(x direction) \r\nBx=np.matrix([[dt**2/2], [dt]]) #B Matrix (control function)(x direction)\r\nXx=np.matrix([[0],[0]])#initial state(belief values) (doesn't matter what it is.)(x direction)\r\nprint(\"Xx belief\",Xx)\r\nFy = np.matrix([[1,dt], [0,1]])#A Matrix (state transfer function)(y direction) \r\nBy=np.matrix([[dt**2/2], [dt]]) #B Matrix (control function)(y direction)\r\nXy=np.matrix([[0],[0]])#initial state(belief values) (doesn't matter what it is.)(y direction)\r\nmu, sigma = 0, 5\r\nnoisex=random.gauss(mu, sigma)#noise of pos from measurement (should be 1*1) assuming gaussian white noise with mean = 0 and variance of 5(fairly certain on the measurement data)\r\nnoisey=noisex\r\n#noise = randn(1)\r\nPx=np.matrix([[1,0],[0,1]])#state covariance matrix (doesn't matter what it is at the start as it will be iterated.)\r\nQx=np.matrix([[0.01,0],[0 ,0.01]])#state predicted noise (when state transformation happens q(k) is process noise and covariance matrix is Q)\r\nHx=np.matrix([[1,0]])#observation matrix (only pos is observable, vel is not)\r\nRx=1#observation noise covariance \r\n\t\r\nPy=np.matrix([[1,0],[0,1]])#state covariance matrix (doesn't matter what it is at the start as it will be iterated.)\r\nQy=np.matrix([[0.01,0],[0 ,0.01]])#state predicted noise (when state transformation happens q(k) is process noise and covariance matrix is Q)\r\nHy=np.matrix([[1,0]])#observation matrix (only pos is observable, vel is not)\r\nRy=1#observation noise covariance \r\n\t\r\n#Mathematical model of a rotating mass\r\n#This Kalman Filter filters out measurement noise and gives best estimate of where the robot is oriented. It is done in 1-d.\r\nF_angle = np.matrix([[1,dt], [0,1]])#A Matrix (state transfer function) \r\nB_angle=np.matrix([[dt**2/2], [dt]]) #B Matrix (control function)\r\nX_angle=np.matrix([0,0])#initial state(belief values) (doesn't matter what it is.)\r\nmu, sigma = 0, 5\r\nnoise=random.gauss(mu, sigma)#noise of angle from measurement (should be 1*1) assuming gaussian white noise with mean = 0 and variance of 10 degrees\r\nP_angle=np.matrix([[1,0],[0,1]])#state covariance matrix (doesn't matter what it is at the start as it will be iterated.)\r\nQ_angle=np.matrix([[1,0],[0 ,1]])#state predicted noise (when state transformation happens q(k) is process noise and covariance matrix is Q)\r\nH_angle=np.matrix([[1,0]])#observation matrix (only pos is observable, vel is not)\r\nR_angle=0.01#observation noise covariance \r\n\r\ntemp_u1=0\r\ntemp_u2=0\r\n\r\n###\r\n\r\n## PD controller Parametters\r\nerror1=0\r\nerror_prior1=0\r\nerror2=0\r\nerror_prior2=0\r\n \r\nderivative1=0 \r\nL=18 #Robot Diameter\r\nR=3.5 #Wheel Radius\r\ndirR=0\r\ndirL=0\r\numax=575 #Max input for position control\r\nu2max= 220\r\nkp1= 1.65 #was 0.65\r\nkp2=1.5#was 1.2, 0.01\r\nkp = 1.5 # same as below\r\nflag = 0\r\nkd1=0.5#was 0.5\r\nkd2=0.5 #was 0.005\r\nkd = 0.5 # fix this <<<<< (need to make it used)\r\nVrMax = 1000\r\nVlMax = VrMax \r\ntemp1=0\r\ntemp2=0\r\ntest = 0\r\ncounter = 0\r\n\r\n# flag for switching mains\r\nradioflag = 0\r\n# flag for path planning\r\nattacker_defender_flag = 0\r\n\r\n# record data flag\r\nrecordFlag = 0\r\n\r\ncap = cv2.VideoCapture(cv2.CAP_DSHOW + 1) # 0 if your pc doesn't have a webcam, probably 1 if it does\r\n\r\n# Plot Lists\r\nDesired_X = []\r\nActual_X = []\r\nDesired_Y = []\r\nActual_Y = []\r\nDesired_Angle = []\r\nActual_Angle = []\r\n\r\n#Inputs \r\nInput_V= []\r\nInput_W= []\r\n\r\nangle_meas_list=[]\r\nangle_bel_list=[]\r\nangle_predicted_list=[]\r\n\r\nangleRecording = []\r\nposRecording = [[],[]]\r\n\r\nstartTime = timer()\r\nendTime = 0\r\nmainLoopTime = []\r\n\r\ndef mainLoop():\r\n global startTime, endTime, mainLoopTime\r\n if(recordFlag == 1):\r\n endTime = timer()\r\n mainLoopTime.append(endTime-startTime)\r\n startTime = timer()\r\n print(\"new loop normal\\n\\r\")\r\n # Declaring global variables so they can be cleared every loop\r\n global roboList, roboIDmarks, circles, ball, IDdRobots\r\n\r\n global error1,error_prior1,error2,error_prior2,dt,derivative1,L,R,dirR,dirL,umax,u2max,kp1\r\n global kp2,flag,kd1,kd2,VrMax,VlMax,temp1,temp2,test,counter\r\n global Desired_X, Actual_X, Desired_Y, Actual_Y, Desired_Angle, Actual_Angle\r\n \r\n global angleRecording, posRecording\r\n \r\n #cap = cv2.VideoCapture(cv2.CAP_DSHOW + 0) # 0 if your pc doesn't have a webcam, probably 1 if it does\r\n # https://stackoverflow.com/questions/52043671/opencv-capturing-imagem-with-black-side-bars\r\n # MSMF doesn't like being scaled up apparently, so switch from it (default) to DirectShow\r\n # so we can scale up the resolution read from the camera\r\n\r\n # Scaling up from 640x480 to HD 1280x720\r\n #cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)\r\n #cap.set(cv2.CAP_PROP_FRAME_HEIGHT,720)\r\n #cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)\r\n #cap.set(cv2.CAP_PROP_FRAME_HEIGHT,720)\r\n \r\n\r\n\r\n #while(True):\r\n # while(KL25.inWaiting()==0):\r\n\r\n ret,frame = cap.read() # reading the video capture into a dummy var and frame\r\n\r\n #cv2.waitKey(50)\r\n \r\n # Reinitializing robot data (prevents buildup of data accross frames)\r\n roboList = []\r\n roboIDmarks= []\r\n circles = []\r\n ball = None\r\n\r\n # Histogram equalization for colors (haven't tested with this)\r\n #img_yuv = cv2.cvtColor(ii, cv2.COLOR_BGR2YUV)\r\n\r\n ### equalize the histogram of the Y channel\r\n #img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])\r\n\r\n ### convert the YUV image back to RGB format\r\n #frame_yuv = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)\r\n\r\n # blurring image for less errant circles and better color recognition later\r\n # d = 5 as that is the recommended nearest neighbour for real time\r\n # sigmaColor = 150 to produce large blending effect\r\n # sigmaSpace is limited by d, so I suspect it doesn't matter\r\n blurred_img = cv2.bilateralFilter(frame,8,150,150) \r\n\r\n # HSV color space conversion\r\n hsv= cv2.cvtColor(blurred_img,cv2.COLOR_BGR2HSV)\r\n\r\n # Color masking, not necessary due to blurring, but might be worth looking into further\r\n #lower_rangeG = np.array([0,0,0]) # Hue, Saturation, Value mask lower limit\r\n #upper_rangeG = np.array([180,255,255]) # \" , \" , \" \" upper limit\r\n\r\n #mask = cv2.inRange(hsv, lower_rangeG, upper_rangeG) # mask for original frame with only good color\r\n #result = cv2.bitwise_and(blurred_img,blurred_img,mask=mask)\r\n result = blurred_img\r\n\r\n #cv2.imshow(\"blurred image\",result)\r\n \r\n hsv_out_gray= cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)\r\n\r\n #cv2.imshow(\"houghin\",hsv_out_gray)\r\n\r\n # Some notes on the HoughCircles function:\r\n # Utilizes edge detection to draw tangent lines, recognizing a circle where perpendicular lines to tangents\r\n # meet, depending on the intensity of the intersecting tangent lines.\r\n # param1: higher threshold for Canny edge detection (lower is half of this)\r\n # param2: accumulator threshold for circle center detection- i.e. the lower it is, the less circular an object\r\n # needs to be to be recognized as a circle\r\n # minDist: Specifies minimum distance between circles (the 4th input to the function)\r\n # \r\n # from documentation: cv2.HoughCircles(image, method, dp, minDist[, circles[, param1[, param2[, minRadius[, maxRadius]]]]]) → circles\r\n circles = cv2.HoughCircles(hsv_out_gray,cv2.HOUGH_GRADIENT,1,minDist=5,param1=param1val,param2=param2val,minRadius=1,maxRadius=20)\r\n\r\n cv2.waitKey(1) # cv2.waitKey() is required to display images- waits 1 millisecond here\r\n\r\n img = copy.deepcopy(frame) # Sometimes if you copy stuff in Python, changes made to a copied variable end up in original\r\n # which necessitates a deepcopy\r\n\r\n if isinstance(circles, type(None)) == 0:\r\n for circle in circles[0,:]:\r\n IDcircle(hsv, circle) # ID all the circles recognized by color\r\n # draw the outer circle\r\n cv2.circle(img,(circle[0],circle[1]),circle[2],(0,255,0),2)\r\n # draw the center of the circle\r\n cv2.circle(img,(circle[0],circle[1]),2,(0,0,255),3)\r\n\r\n if isinstance(ball, type(None)) == 0:\r\n # Draw a blue circle on the ball\r\n cv2.circle(img,(ball.pos[0],ball.pos[1]),10,(200,0,0),5) \r\n cv2.putText(img, str(ball.pos), (ball.pos[0]+20,ball.pos[1]+20), cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n\r\n if (isinstance(roboIDmarks, type(None)) == 0) & (isinstance(roboList, type(None)) == 0):\r\n for robot in roboList:\r\n assignIDmarks(robot) # Assign the ID marks observed to their appropriate robot\r\n angle(robot) # Determine angle of robots seen\r\n RoboID(robot) # Give robots seen an ID\r\n\r\n # Draw the robot circles seen robot by robot\r\n # Draw a black circle on the centre of the robot\r\n cv2.circle(img,(robot.pos[0],robot.pos[1]),5,(0,0,0),2)\r\n #if isinstance(robot.angle, type(None)) == 0:\r\n # # Display the robot's angle\r\n # cv2.putText(img, str(round(robot.angle,1)), (robot.pos[0]+ 100, robot.pos[1] + 130), \r\n # cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # # Display the robot's position\r\n # cv2.putText(img, str(robot.pos), (robot.pos[0]+ 100, robot.pos[1] + 100), \r\n # cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # # Display the robot's ID\r\n # cv2.putText(img, robot.ID, (robot.pos[0]+ 100, robot.pos[1] + 70), \r\n # cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # # Display the robot's Team\r\n # cv2.putText(img, robot.team, (robot.pos[0]+ 100, robot.pos[1] + 40), \r\n # cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n for mark in robot.circles:\r\n # Draw a black circle on every ID mark\r\n cv2.circle(img,(mark[0],mark[1]),5,(0,0,0),2) \r\n flag = 0 # go ahead and print \"no circles detected\" again\r\n\r\n elif(flag == 0):\r\n #print(\"no circles detected\")\r\n flag = 1 # don't print this again\r\n\r\n # Display drawn on frame and original frame\r\n #cv2.imshow('circles on stream',img)\r\n #cv2.imshow('original stream',frame)\r\n\r\n #if cv2.waitKey(1) & 0xFF == ord('\\r'): # if enter is pressed, stop running\r\n # break\r\n\r\n # when the ball does not get detected\r\n if (isinstance(ball, type(None)) != 0):\r\n ball = ballClass(temp1,temp2) \r\n\r\n if(recordFlag == 1):\r\n print(\"recording roboList stuff\")\r\n angleRecording.append(roboList[0].angle)\r\n posRecording[0].append(roboList[0].pos[0])\r\n posRecording[1].append(roboList[0].pos[1])\r\n \r\n packet = bytearray()\r\n #packet.append(0xff)\r\n #packet.append(0x01) #id\r\n #packet.append(0x30) #mtr1\r\n #packet.append(0x01) #dir1\r\n #packet.append(0x30) #mtr2\r\n #packet.append(0x01) #dir2\r\n #packet.append(0x01) #kick\r\n #packet.append(0xff)\r\n #KL25.write(packet)\r\n #data = KL25.read(4) #Reading and Printing slows down the system incredibly, do not use for the demonstration\r\n #print(data.decode('ISO-8859-1')) #Reading and Printing slows down the system incredibly, do not use for the demonstration\r\n\r\n for rob in roboList:\r\n if (rob.ID != '-no ID!-') & (isinstance(roboList, type(None)) == 0):\r\n if(abs(abs(rob.angle)-180)>20 and counter>5):\r\n if (abs(rob.angle-test) >=200 and counter > 5):\r\n rob.angle=test#something is wrong with the angle measurement\r\n \r\n else:\r\n \r\n if (abs(abs( rob.angle)-abs(test)) >=50 and counter>5 ):\r\n rob.angle=test#something is wrong with the angle measurement\r\n test=rob.angle\r\n\r\n #Printing for Responses\r\n #Desired_X.append(ball.pos[0])\r\n #Actual_X.append(rob.pos[0])\r\n #Desired_Y.append(ball.pos[1])\r\n #Actual_Y.append(rob.pos[1])\r\n #Desired_Angle.append(math.degrees((math.atan2(ball.pos[1]-rob.pos[1],ball.pos[0]-rob.pos[0]))))\r\n #Actual_Angle.append(rob.angle)\r\n\r\n #Plot the Graphs\r\n #plt.plot(Actual_X)\r\n\r\n\r\n #print(\"rob.angle\",rob.angle)\r\n # Display the robot's angle\r\n cv2.putText(img, str(round(rob.angle,1)), (rob.pos[0]+ 100, rob.pos[1] + 130), \r\n cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # Display the robot's position\r\n cv2.putText(img, str(rob.pos), (rob.pos[0]+ 100, rob.pos[1] + 100), \r\n cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # Display the robot's ID\r\n cv2.putText(img, rob.ID, (rob.pos[0]+ 100, rob.pos[1] + 70), \r\n cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # Display the robot's Team\r\n cv2.putText(img, rob.team, (rob.pos[0]+ 100, rob.pos[1] + 40), \r\n cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n\r\n ####### Angle Control \r\n if rob.angle == 999:\r\n rob.angle=test\r\n error2=math.degrees((math.atan2(ball.pos[1]-rob.pos[1],ball.pos[0]-rob.pos[0])))-rob.angle\r\n \r\n #regulate the angle to reduce ambiguity\r\n if (abs(error2)<180):\r\n error2=error2\r\n elif (np.sign(error2)==-1):\r\n error2=error2+360\r\n elif (np.sign(error2)==1):\r\n error2=error2-360\r\n else:\r\n print(\"done\")\r\n #print(\"angle\",rob.angle)\r\n #print(\"error2\",error2)\r\n\r\n derivative2=(error2-error_prior2) #Shouldn't this be divided by a dt?\r\n error_prior2=error2\r\n u2= (kp2*error2) + (kd2*derivative2) \r\n #print(\"u2\",u2)\r\n \r\n\t\t\t#Error In Position\r\n ####### Position Control #######\r\n\r\n temp1=ball.pos[0]\r\n temp2=ball.pos[1]\r\n #error1 = ((ball.pos[0]-rob.pos[0])**2+(ball.pos[1]-rob.pos[1])**2)**0.5 * (math.cos(math.atan2(ball.pos[1]-rob.pos[1] / ball.pos[0]-rob.pos[0]) - rob.angle))\r\n error1 = ((ball.pos[0]-rob.pos[0])**2+(ball.pos[1]-rob.pos[1])**2)**0.5\r\n \r\n derivative1=(error1-error_prior1)\r\n error_prior1=error1\r\n #print(\"error1:\",error1)\r\n u1= ( kp1*error1 ) + ( kd1*derivative1 )\r\n #print(error1)\r\n #0 is to rotate forward & 1 is to rotate backward\r\n #if (error1>0):\r\n # dirR=1\r\n # dirL=1\r\n #elif (error1<0):\r\n # dirR=0\r\n # dirL=0\r\n \r\n \r\n\r\n # Assigning the direction of motors based on the wheel velocities sign\r\n\r\n if (error2>=-30 and error2<=30):\r\n error2=error2\r\n dirR=1 #May need to change depending on connection\r\n dirL=0 #May need to change depending on connection\r\n #packet.append(0xFF) #Start Bit\r\n #packet.append(0x01) #ID Bit\r\n #packet.append(0x00) #VrHex\r\n #packet.append(0x00) #dirR\r\n #packet.append(0x0) #VlHex\r\n #packet.append(0x00) #dirL\r\n #packet.append(0x00) #Kick Command \r\n #packet.append(0xFF) #Stop Bit\r\n #break\r\n else:\t\r\n if np.sign(error2)==-1:\r\n dirR=0\r\n dirL=0\r\n elif np.sign(error2)==1:\r\n dirR=1\r\n dirL=1\r\n \r\n #dirR=0\r\n #dirL=0\r\n ## Setting limits to the inputs \r\n if(u1 > umax):\r\n u1=umax\r\n if(u1 < -umax):\r\n u1 = -umax\r\n #u2=0\r\n if(u2 > u2max):\r\n u2=u2max\r\n if(u2 < -u2max):\r\n u2 = -u2max\r\n \r\n # Assigning Individual Wheel velocities\r\n \r\n #u1=0\r\n #u2=0\r\n \r\n vr=u1+u2\r\n vl=u1-u2\r\n #print(\"VR = \",vr)\r\n #print(\"VL = \", vl)\r\n\r\n #if(np.sign(vr) == 1):\r\n #dirR= 0x00\r\n #if(np.sign(vr) == -1):\r\n # dirR= 0x01\r\n\r\n #if(np.sign(vl) == 1):\r\n # dirL= 0x00\r\n\r\n #if(np.sign(vl) == -1):\r\n # dirL= 0x01\r\n\r\n #if(np.sign(error2) == 1):\r\n # dirL = 0x00\r\n # dirR = 0x01\r\n #if(np.sign(error2) == -1):\r\n # dirL = 0x01\r\n # dirR = 0x00\r\n \r\n # Remove the sign in motor velocities\r\n Vr = abs(int(vr))\r\n Vl = abs(int(vl))\r\n #print(Vr)\r\n #print(Vl)\r\n # Assign the motor velocities to 0-256 range to send through 8bit UART\r\n VrHex = int(Vr*255/ (VrMax))\r\n VlHex = int(Vl*255/ (VlMax))\r\n\r\n if(VrHex == 0):\r\n VrHex = 1\r\n if(VlHex == 0):\r\n VlHex = 1\r\n\r\n if (abs(error1) < 20 and abs(error2) <10): \r\n kick= 0x01\r\n else:\r\n kick = 0\r\n if (error1<20 ):\r\n VrHex=0x01\r\n VlHex=0x01\r\n \r\n #print(\"VlHex:\",VlHex)\r\n #print(\"VrHex:\",VrHex)\r\n counter = counter + 1\r\n packet.append(0xFF)\r\n packet.append(0x01) #Robot ID\r\n packet.append(VrHex) #VrHex\r\n packet.append(dirR) #dirR\r\n packet.append(VlHex) #VlHex\r\n packet.append(dirL) #dirL\r\n packet.append(kick) #kick\r\n packet.append(0xFF)\r\n KL25.write(packet)\r\n #data = KL25.read(4)\r\n #print(data.decode('ISO-8859-1'))\r\n \r\n\r\n cv2.imshow('circles on stream',img)\r\n\r\n# Feedback Linearization\r\ndef mainLoop1():\r\n print(\"new loop feedback lin\\n\\r\")\r\n # Declaring global variables so they can be cleared every loop\r\n global roboList, roboIDmarks, circles, ball, IDdRobots\r\n\r\n global error1,error_prior1,error2,error_prior2,dt,derivative1,L,R,dirR,dirL,umax,u2max,kp1\r\n global kp2,flag,kd1,kd2,VrMax,VlMax,temp1,temp2,test,counter\r\n global Desired_X, Actual_X, Desired_Y, Actual_Y, Desired_Angle, Actual_Angle\r\n global startTime\r\n global endTime\r\n global init_rob_angle,init_rob_x,init_rob_y\r\n\r\n # Define parameters\r\n M= 1.2\r\n b = 0.2 # damping\r\n J = (1/2)*M*(R**2) # Moment of Intertia of the robot -- formula of moment of inertia for a cylinder\r\n c = 0.1# damping\r\n k= 1.5 \r\n k2= 0.006\r\n\r\n\r\n #cap = cv2.VideoCapture(cv2.CAP_DSHOW + 0) # 0 if your pc doesn't have a webcam, probably 1 if it does\r\n # https://stackoverflow.com/questions/52043671/opencv-capturing-imagem-with-black-side-bars\r\n # MSMF doesn't like being scaled up apparently, so switch from it (default) to DirectShow\r\n # so we can scale up the resolution read from the camera\r\n\r\n # Scaling up from 640x480 to HD 1280x720\r\n #cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)\r\n #cap.set(cv2.CAP_PROP_FRAME_HEIGHT,720)\r\n #cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)\r\n #cap.set(cv2.CAP_PROP_FRAME_HEIGHT,720)\r\n \r\n\r\n\r\n #while(True):\r\n # while(KL25.inWaiting()==0):\r\n\r\n ret,frame = cap.read() # reading the video capture into a dummy var and frame\r\n\r\n #cv2.waitKey(50)\r\n \r\n # Reinitializing robot data (prevents buildup of data accross frames)\r\n roboList = []\r\n roboIDmarks= []\r\n circles = []\r\n ball = None\r\n\r\n # Histogram equalization for colors (haven't tested with this)\r\n #img_yuv = cv2.cvtColor(ii, cv2.COLOR_BGR2YUV)\r\n\r\n ### equalize the histogram of the Y channel\r\n #img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])\r\n\r\n ### convert the YUV image back to RGB format\r\n #frame_yuv = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)\r\n\r\n # blurring image for less errant circles and better color recognition later\r\n # d = 5 as that is the recommended nearest neighbour for real time\r\n # sigmaColor = 150 to produce large blending effect\r\n # sigmaSpace is limited by d, so I suspect it doesn't matter\r\n blurred_img = cv2.bilateralFilter(frame,8,150,150) \r\n\r\n # HSV color space conversion\r\n hsv= cv2.cvtColor(blurred_img,cv2.COLOR_BGR2HSV)\r\n\r\n # Color masking, not necessary due to blurring, but might be worth looking into further\r\n #lower_rangeG = np.array([0,0,0]) # Hue, Saturation, Value mask lower limit\r\n #upper_rangeG = np.array([180,255,255]) # \" , \" , \" \" upper limit\r\n\r\n #mask = cv2.inRange(hsv, lower_rangeG, upper_rangeG) # mask for original frame with only good color\r\n #result = cv2.bitwise_and(blurred_img,blurred_img,mask=mask)\r\n result = blurred_img\r\n\r\n #cv2.imshow(\"blurred image\",result)\r\n \r\n hsv_out_gray= cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)\r\n\r\n #cv2.imshow(\"houghin\",hsv_out_gray)\r\n\r\n # Some notes on the HoughCircles function:\r\n # Utilizes edge detection to draw tangent lines, recognizing a circle where perpendicular lines to tangents\r\n # meet, depending on the intensity of the intersecting tangent lines.\r\n # param1: higher threshold for Canny edge detection (lower is half of this)\r\n # param2: accumulator threshold for circle center detection- i.e. the lower it is, the less circular an object\r\n # needs to be to be recognized as a circle\r\n # minDist: Specifies minimum distance between circles (the 4th input to the function)\r\n # \r\n # from documentation: cv2.HoughCircles(image, method, dp, minDist[, circles[, param1[, param2[, minRadius[, maxRadius]]]]]) → circles\r\n circles = cv2.HoughCircles(hsv_out_gray,cv2.HOUGH_GRADIENT,1,minDist=5,param1=param1val,param2=param2val,minRadius=1,maxRadius=15)\r\n\r\n cv2.waitKey(1) # cv2.waitKey() is required to display images- waits 1 millisecond here\r\n\r\n img = copy.deepcopy(frame) # Sometimes if you copy stuff in Python, changes made to a copied variable end up in original\r\n # which necessitates a deepcopy\r\n\r\n if isinstance(circles, type(None)) == 0:\r\n for circle in circles[0,:]:\r\n IDcircle(hsv, circle) # ID all the circles recognized by color\r\n # draw the outer circle\r\n cv2.circle(img,(circle[0],circle[1]),circle[2],(0,255,0),2)\r\n # draw the center of the circle\r\n cv2.circle(img,(circle[0],circle[1]),2,(0,0,255),3)\r\n\r\n if isinstance(ball, type(None)) == 0:\r\n # Draw a blue circle on the ball\r\n cv2.circle(img,(ball.pos[0],ball.pos[1]),10,(200,0,0),5) \r\n cv2.putText(img, str(ball.pos), (ball.pos[0]+20,ball.pos[1]+20), cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n\r\n if (isinstance(roboIDmarks, type(None)) == 0) & (isinstance(roboList, type(None)) == 0):\r\n for robot in roboList:\r\n assignIDmarks(robot) # Assign the ID marks observed to their appropriate robot\r\n angle(robot) # Determine angle of robots seen\r\n RoboID(robot) # Give robots seen an ID\r\n\r\n # Draw the robot circles seen robot by robot\r\n # Draw a black circle on the centre of the robot\r\n cv2.circle(img,(robot.pos[0],robot.pos[1]),5,(0,0,0),2)\r\n #if isinstance(robot.angle, type(None)) == 0:\r\n # # Display the robot's angle\r\n # cv2.putText(img, str(round(robot.angle,1)), (robot.pos[0]+ 100, robot.pos[1] + 130), \r\n # cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # # Display the robot's position\r\n # cv2.putText(img, str(robot.pos), (robot.pos[0]+ 100, robot.pos[1] + 100), \r\n # cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # # Display the robot's ID\r\n # cv2.putText(img, robot.ID, (robot.pos[0]+ 100, robot.pos[1] + 70), \r\n # cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # # Display the robot's Team\r\n # cv2.putText(img, robot.team, (robot.pos[0]+ 100, robot.pos[1] + 40), \r\n # cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n for mark in robot.circles:\r\n # Draw a black circle on every ID mark\r\n cv2.circle(img,(mark[0],mark[1]),5,(0,0,0),2) \r\n flag = 0 # go ahead and print \"no circles detected\" again\r\n\r\n elif(flag == 0):\r\n #print(\"no circles detected\")\r\n flag = 1 # don't print this again\r\n\r\n # Display drawn on frame and original frame\r\n #cv2.imshow('circles on stream',img)\r\n cv2.imshow('original stream',frame)\r\n\r\n #if cv2.waitKey(1) & 0xFF == ord('\\r'): # if enter is pressed, stop running\r\n # break\r\n\r\n # when the ball does not get detected\r\n if (isinstance(ball, type(None)) != 0):\r\n ball = ballClass(temp1,temp2) \r\n\r\n \r\n packet = bytearray()\r\n #packet.append(0xff)\r\n #packet.append(0x01) #id\r\n #packet.append(0x30) #mtr1\r\n #packet.append(0x01) #dir1\r\n #packet.append(0x30) #mtr2\r\n #packet.append(0x01) #dir2\r\n #packet.append(0x01) #kick\r\n #packet.append(0xff)\r\n #KL25.write(packet)\r\n #data = KL25.read(4) #Reading and Printing slows down the system incredibly, do not use for the demonstration\r\n #print(data.decode('ISO-8859-1')) #Reading and Printing slows down the system incredibly, do not use for the demonstration\r\n\r\n for rob in roboList:\r\n if (rob.ID != '-no ID!-') & (isinstance(roboList, type(None)) == 0):\r\n if(abs(abs(rob.angle)-180)>20 and counter>5):\r\n if (abs(rob.angle-test) >=200 and counter > 5):\r\n rob.angle=test#something is wrong with the angle measurement\r\n \r\n else:\r\n \r\n if (abs(abs( rob.angle)-abs(test)) >=50 and counter>5 ):\r\n rob.angle=test#something is wrong with the angle measurement\r\n test=rob.angle\r\n\r\n #Printing for Responses\r\n #Desired_X.append(ball.pos[0])\r\n #Actual_X.append(rob.pos[0])\r\n #Desired_Y.append(ball.pos[1])\r\n #Actual_Y.append(rob.pos[1])\r\n #Desired_Angle.append(math.degrees((math.atan2(ball.pos[1]-rob.pos[1],ball.pos[0]-rob.pos[0]))))\r\n #Actual_Angle.append(rob.angle)\r\n\r\n #Plot the Graphs\r\n #plt.plot(Actual_X)\r\n\r\n\r\n #print(\"rob.angle\",rob.angle)\r\n # Display the robot's angle\r\n cv2.putText(img, str(round(rob.angle,1)), (rob.pos[0]+ 100, rob.pos[1] + 130), \r\n cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # Display the robot's position\r\n cv2.putText(img, str(rob.pos), (rob.pos[0]+ 100, rob.pos[1] + 100), \r\n cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # Display the robot's ID\r\n cv2.putText(img, rob.ID, (rob.pos[0]+ 100, rob.pos[1] + 70), \r\n cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # Display the robot's Team\r\n cv2.putText(img, rob.team, (rob.pos[0]+ 100, rob.pos[1] + 40), \r\n cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n\r\n endTime = timer()\r\n dt = endTime - startTime \r\n\r\n # Speed Measurements \r\n V = (((rob.pos[0]-init_rob_x)**2)+((rob.pos[1]-init_rob_y))**2)**0.5 / dt\r\n W = (rob.angle-init_rob_angle)/dt # Differentiating angle over time to find angular velocity\r\n init_rob_angle= rob.angle # Updating the initial state of orientation\r\n init_rob_x= rob.pos[0]\r\n init_rob_y= rob.pos[1]\r\n\r\n startTime = timer()\r\n frameRate= 1/dt\r\n print(\"FPS \",frameRate)\r\n print(\"Speed\", V)\r\n print(\"Angular Speed\", W)\r\n\r\n #if (np.sign(init_rob_angle)==-1):\r\n # init_rob_angle=init_rob_angle+360\r\n #if (np.sign(init_rob_angle)==1):\r\n # init_rob_angle=init_rob_angle-360\r\n\r\n\r\n\r\n\r\n ####### Angle Control \r\n if rob.angle == 999:\r\n rob.angle=test\r\n error2=math.degrees((math.atan2(ball.pos[1]-rob.pos[1],ball.pos[0]-rob.pos[0])))-rob.angle\r\n \r\n #regulate the angle to reduce ambiguity\r\n if (abs(error2)<180):\r\n error2=error2\r\n elif (np.sign(error2)==-1):\r\n error2=error2+360\r\n elif (np.sign(error2)==1):\r\n error2=error2-360\r\n else:\r\n print(\"done\")\r\n print(\"error2\",error2)\r\n\r\n u2 = -k*(M/dt)* ( (b*(dt/M)*error2) + k2*W)\r\n\r\n \r\n #Error In Position\r\n ####### Position Control #######\r\n\r\n temp1=ball.pos[0]\r\n temp2=ball.pos[1]\r\n #error1 = ((ball.pos[0]-rob.pos[0])**2+(ball.pos[1]-rob.pos[1])**2)**0.5 * (math.cos(math.atan2(ball.pos[1]-rob.pos[1] / ball.pos[0]-rob.pos[0]) - rob.angle))\r\n error1 = ((ball.pos[0]-rob.pos[0])**2+(ball.pos[1]-rob.pos[1])**2)**0.5\r\n \r\n u1 = 2*k*(M/dt)* ( (b*(dt/M)*error1) + k2*V);\r\n \r\n print(\"u1\",u1)\r\n print(\"u2\",u2)\r\n\r\n\r\n #print(error1)\r\n #0 is to rotate forward & 1 is to rotate backward\r\n #if (error1>0):\r\n # dirR=1\r\n # dirL=1\r\n #elif (error1<0):\r\n # dirR=0\r\n # dirL=0\r\n \r\n \r\n\r\n # Assigning the direction of motors based on the wheel velocities sign\r\n\r\n if (error2>=-10 and error2<=10):\r\n error2=error2\r\n dirR=1 #May need to change depending on connection\r\n dirL=0 #May need to change depending on connection\r\n # #packet.append(0xFF) #Start Bit\r\n #packet.append(0x01) #ID Bit\r\n #packet.append(0x00) #VrHex\r\n #packet.append(0x00) #dirR\r\n #packet.append(0x0) #VlHex\r\n #packet.append(0x00) #dirL\r\n #packet.append(0x00) #Kick Command \r\n #packet.append(0xFF) #Stop Bit\r\n #break\r\n else:\t\r\n if np.sign(error2)==-1:\r\n dirR=0\r\n dirL=0\r\n elif np.sign(error2)==1:\r\n dirR=1\r\n dirL=1\r\n \r\n \r\n ## Setting limits to the inputs \r\n if(u1 > umax):\r\n u1=umax\r\n if(u1 < -umax):\r\n u1 = -umax\r\n #u2=0\r\n if(u2 > u2max):\r\n u2=u2max\r\n if(u2 < -u2max):\r\n u2 = -u2max\r\n \r\n # Assigning Individual Wheel velocities\r\n \r\n ##Normalizing the control inputs to have them same weight on motor velocities\r\n #U1 = u1*126/umax\r\n #U2 = u2*126/umax\r\n\r\n ##define this up there ^^^^\r\n #e1max= 800\r\n #e2max= 180\r\n\r\n ## Noremalizing the error in the position and angle\r\n #r1 = error1/e1max\r\n #r2 = error2/e2max\r\n #ErrorAvoider = r1+r2\r\n #if (ErrorAvoider == 0): \r\n # k1=0\r\n # k2=0\r\n #else:\r\n # k1= 2*r1/(r1 + r2)\r\n # k2= 2-k1\r\n \r\n vr=u1+u2\r\n vl=u1-u2\r\n #print(\"VR = \",vr)\r\n #print(\"VL = \", vl)\r\n\r\n #if(np.sign(vr) == 1):\r\n #dirR= 0x00\r\n #if(np.sign(vr) == -1):\r\n # dirR= 0x01\r\n\r\n #if(np.sign(vl) == 1):\r\n # dirL= 0x00\r\n\r\n #if(np.sign(vl) == -1):\r\n # dirL= 0x01\r\n\r\n #if(np.sign(error2) == 1):\r\n # dirL = 0x00\r\n # dirR = 0x01\r\n #if(np.sign(error2) == -1):\r\n # dirL = 0x01\r\n # dirR = 0x00\r\n \r\n # Remove the sign in motor velocities\r\n Vr = abs(int(vr))\r\n Vl = abs(int(vl))\r\n #print(Vr)\r\n #print(Vl)\r\n # Assign the motor velocities to 0-256 range to send through 8bit UART\r\n VrHex = int(Vr*255/ (VrMax))\r\n VlHex = int(Vl*255/ (VlMax))\r\n\r\n if(VrHex == 0):\r\n VrHex = 1\r\n if(VlHex == 0):\r\n VlHex = 1\r\n\r\n if (abs(error1) < 20 and abs(error2) <10): \r\n kick= 0x01\r\n else:\r\n kick = 0\r\n if (error1<20 ):\r\n VrHex=0x01\r\n VlHex=0x01\r\n \r\n print(\"VlHex:\",VlHex)\r\n print(\"VrHex:\",VrHex)\r\n counter = counter + 1\r\n packet.append(0xFF)\r\n packet.append(0x01) #Robot ID\r\n packet.append(VrHex) #VrHex\r\n packet.append(dirR) #dirR\r\n packet.append(VlHex) #VlHex\r\n packet.append(dirL) #dirL\r\n packet.append(kick) #kick\r\n packet.append(0xFF)\r\n KL25.write(packet)\r\n #data = KL25.read(4)\r\n #print(data.decode('ISO-8859-1'))\r\n if(recordFlag == 1):\r\n #Printing for Responses\r\n Actual_X.append(rob.pos[0])\r\n Actual_Y.append(rob.pos[1])\r\n Actual_Angle.append(rob.angle)\r\n Input_V.append(u1)\r\n Input_W.append(u2)\r\n \r\n #drawnow(makeFig)\r\n cv2.imshow('circles on stream',img)\r\n\r\n\r\n\r\n\r\n# Kalman Filter\r\ndef mainLoop2():\r\n print(\"new loop\\n\\r\")\r\n # Declaring global variables so they can be cleared every loop\r\n global roboList, roboIDmarks, circles, ball, IDdRobots\r\n\r\n global error1,error_prior1,error2,error_prior2,dt,derivative1,L,R,dirR,dirL,umax,u2max,kp1\r\n global kp2,flag,kd1,kd2,VrMax,VlMax,temp1,temp2,test,counter\r\n \r\n global angle_meas_list\r\n global angle_bel_list\r\n global angle_predicted_list\r\n\r\n global diff,diff2\r\n global u1,temp_u1,temp_u2,u2\r\n global Fx,Bx,Xx,Fy,By,Xy,mu,sigma,noisex,Px,Qx,Hx,Rx,Py,Qy,Hy,Ry,noisey,noise\r\n global F_angle,B_angle,X_angle,P_angle,Q_angle,H_angle,R_angle\r\n\r\n #cap = cv2.VideoCapture(cv2.CAP_DSHOW + 0) # 0 if your pc doesn't have a webcam, probably 1 if it does\r\n # https://stackoverflow.com/questions/52043671/opencv-capturing-imagem-with-black-side-bars\r\n # MSMF doesn't like being scaled up apparently, so switch from it (default) to DirectShow\r\n # so we can scale up the resolution read from the camera\r\n\r\n # Scaling up from 640x480 to HD 1280x720\r\n #cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)\r\n #cap.set(cv2.CAP_PROP_FRAME_HEIGHT,720)\r\n #cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)\r\n #cap.set(cv2.CAP_PROP_FRAME_HEIGHT,720)\r\n\r\n\r\n #while(True):\r\n # while(KL25.inWaiting()==0):\r\n\r\n ret,frame = cap.read() # reading the video capture into a dummy var and frame\r\n\r\n #cv2.waitKey(50)\r\n \r\n # Reinitializing robot data (prevents buildup of data accross frames)\r\n roboList = []\r\n roboIDmarks= []\r\n circles = []\r\n ball = None\r\n\r\n # Histogram equalization for colors (haven't tested with this)\r\n #img_yuv = cv2.cvtColor(ii, cv2.COLOR_BGR2YUV)\r\n\r\n ### equalize the histogram of the Y channel\r\n #img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])\r\n\r\n ### convert the YUV image back to RGB format\r\n #frame_yuv = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)\r\n\r\n # blurring image for less errant circles and better color recognition later\r\n # d = 5 as that is the recommended nearest neighbour for real time\r\n # sigmaColor = 150 to produce large blending effect\r\n # sigmaSpace is limited by d, so I suspect it doesn't matter\r\n blurred_img = cv2.bilateralFilter(frame,8,150,150) \r\n\r\n # HSV color space conversion\r\n hsv= cv2.cvtColor(blurred_img,cv2.COLOR_BGR2HSV)\r\n\r\n # Color masking, not necessary due to blurring, but might be worth looking into further\r\n #lower_rangeG = np.array([0,0,0]) # Hue, Saturation, Value mask lower limit\r\n #upper_rangeG = np.array([180,255,255]) # \" , \" , \" \" upper limit\r\n\r\n #mask = cv2.inRange(hsv, lower_rangeG, upper_rangeG) # mask for original frame with only good color\r\n #result = cv2.bitwise_and(blurred_img,blurred_img,mask=mask)\r\n result = blurred_img\r\n\r\n #cv2.imshow(\"blurred image\",result)\r\n \r\n hsv_out_gray= cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)\r\n\r\n #cv2.imshow(\"houghin\",hsv_out_gray)\r\n\r\n # Some notes on the HoughCircles function:\r\n # Utilizes edge detection to draw tangent lines, recognizing a circle where perpendicular lines to tangents\r\n # meet, depending on the intensity of the intersecting tangent lines.\r\n # param1: higher threshold for Canny edge detection (lower is half of this)\r\n # param2: accumulator threshold for circle center detection- i.e. the lower it is, the less circular an object\r\n # needs to be to be recognized as a circle\r\n # minDist: Specifies minimum distance between circles (the 4th input to the function)\r\n # \r\n # from documentation: cv2.HoughCircles(image, method, dp, minDist[, circles[, param1[, param2[, minRadius[, maxRadius]]]]]) → circles\r\n circles = cv2.HoughCircles(hsv_out_gray,cv2.HOUGH_GRADIENT,1,minDist=5,param1=param1val,param2=param2val,minRadius=1,maxRadius=15)\r\n\r\n cv2.waitKey(1) # cv2.waitKey() is required to display images- waits 1 millisecond here\r\n\r\n img = copy.deepcopy(frame) # Sometimes if you copy stuff in Python, changes made to a copied variable end up in original\r\n # which necessitates a deepcopy\r\n\r\n if isinstance(circles, type(None)) == 0:\r\n for circle in circles[0,:]:\r\n IDcircle(hsv, circle) # ID all the circles recognized by color\r\n # draw the outer circle\r\n cv2.circle(img,(circle[0],circle[1]),circle[2],(0,255,0),2)\r\n # draw the center of the circle\r\n cv2.circle(img,(circle[0],circle[1]),2,(0,0,255),3)\r\n\r\n if isinstance(ball, type(None)) == 0:\r\n # Draw a blue circle on the ball\r\n cv2.circle(img,(ball.pos[0],ball.pos[1]),10,(200,0,0),5) \r\n cv2.putText(img, str(ball.pos), (ball.pos[0]+20,ball.pos[1]+20), cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n\r\n if (isinstance(roboIDmarks, type(None)) == 0) & (isinstance(roboList, type(None)) == 0):\r\n for robot in roboList:\r\n assignIDmarks(robot) # Assign the ID marks observed to their appropriate robot\r\n angle(robot) # Determine angle of robots seen\r\n RoboID(robot) # Give robots seen an ID\r\n\r\n # Draw the robot circles seen robot by robot\r\n # Draw a black circle on the centre of the robot\r\n cv2.circle(img,(robot.pos[0],robot.pos[1]),10,(0,0,0),3)\r\n #if isinstance(robot.angle, type(None)) == 0:\r\n # # Display the robot's angle\r\n # cv2.putText(img, str(round(robot.angle,1)), (robot.pos[0]+ 100, robot.pos[1] + 130), \r\n # cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # # Display the robot's position\r\n # cv2.putText(img, str(robot.pos), (robot.pos[0]+ 100, robot.pos[1] + 100), \r\n # cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # # Display the robot's ID\r\n # cv2.putText(img, robot.ID, (robot.pos[0]+ 100, robot.pos[1] + 70), \r\n # cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # # Display the robot's Team\r\n # cv2.putText(img, robot.team, (robot.pos[0]+ 100, robot.pos[1] + 40), \r\n # cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n for mark in robot.circles:\r\n # Draw a black circle on every ID mark\r\n cv2.circle(img,(mark[0],mark[1]),10,(0,0,0),3) \r\n flag = 0 # go ahead and print \"no circles detected\" again\r\n\r\n elif(flag == 0):\r\n #print(\"no circles detected\")\r\n flag = 1 # don't print this again\r\n\r\n # Display drawn on frame and original frame\r\n #cv2.imshow('circles on stream',img)\r\n cv2.imshow('original stream',frame)\r\n\r\n #if cv2.waitKey(1) & 0xFF == ord('\\r'): # if enter is pressed, stop running\r\n # break\r\n\r\n # when the ball does not get detected\r\n if (isinstance(ball, type(None)) != 0):\r\n ball = ballClass(temp1,temp2) \r\n\r\n \r\n packet = bytearray()\r\n #packet.append(0xff)\r\n #packet.append(0x01) #id\r\n #packet.append(0x30) #mtr1\r\n #packet.append(0x01) #dir1\r\n #packet.append(0x30) #mtr2\r\n #packet.append(0x01) #dir2\r\n #packet.append(0x01) #kick\r\n #packet.append(0xff)\r\n\r\n #KL25.write(packet)\r\n #data = KL25.read(4) #Reading and Printing slows down the system incredibly, do not use for the demonstration\r\n #print(data.decode('ISO-8859-1')) #Reading and Printing slows down the system incredibly, do not use for the demonstration\r\n\r\n for rob in roboList:\r\n if (rob.ID != '-no ID!-') & (isinstance(roboList, type(None)) == 0):\r\n if(abs(abs(rob.angle)-180)>20 and counter>5):\r\n if (abs(rob.angle-test) >=200 and counter > 5):\r\n rob.angle=test#something is wrong with the angle measurement\r\n \r\n else:\r\n \r\n if (abs(abs( rob.angle)-abs(test)) >=50 and counter>5 ):\r\n rob.angle=test#something is wrong with the angle measurement\r\n test=rob.angle\r\n print(\"rob.angle\",rob.angle)\r\n # Display the robot's angle\r\n cv2.putText(img, str(round(rob.angle,1)), (rob.pos[0]+ 100, rob.pos[1] + 130), \r\n cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # Display the robot's position\r\n cv2.putText(img, str(rob.pos), (rob.pos[0]+ 100, rob.pos[1] + 100), \r\n cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # Display the robot's ID\r\n cv2.putText(img, rob.ID, (rob.pos[0]+ 100, rob.pos[1] + 70), \r\n cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # Display the robot's Team\r\n cv2.putText(img, rob.team, (rob.pos[0]+ 100, rob.pos[1] + 40), \r\n cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n\r\n ####### Angle Control \r\n if rob.angle == 999:\r\n rob.angle=test\r\n \r\n \r\n \r\n \r\n #error2=math.degrees((math.atan2(ball.pos[1]-rob.pos[1],ball.pos[0]-rob.pos[0])))-rob.angle\r\n error2=math.degrees((math.atan2(ball.pos[1]-int(Xy[0][0]),ball.pos[0]-int(Xx[0][0]))))-X_angle[0]\r\n #error2=math.degrees((math.atan2(ball.pos[1]-int(Xy[0][0]),ball.pos[0]-int(Xx[0][0]))))-rob.angle\r\n #regulate the angle to reduce ambiguity\r\n if (abs(error2)<180):\r\n error2=error2\r\n elif (np.sign(error2)==-1):\r\n error2=error2+360\r\n elif (np.sign(error2)==1):\r\n error2=error2-360\r\n else:\r\n print(\"done\")\r\n print(\"error2\",error2)\r\n\r\n\r\n\r\n\r\n derivative2=(error2-error_prior2) #Shouldn't this be divided by a dt?\r\n error_prior2=error2\r\n u2= (kp2*error2) + (kd2*derivative2) \r\n print(\"u2\",u2)\r\n diff2=(u2-temp_u2)/dt\r\n temp_u2=u2\r\n u_angle=diff2/dt\r\n print(\"X_angle\",X_angle)\r\n # if x_angle[0]>180:\r\n # x_angle[0] =x_angle[0]-360\r\n # else:\r\n # x_angle=x_angle\r\n Z_angle=rob.angle+noise#i'm not sure if this is right. It should be directly from the measurement\r\n X_angle_bel=np.dot(F_angle,X_angle)+np.dot(B_angle,u_angle)#predicted\r\n #update of the covariance matrix for predicted value\r\n P_angle_bel=np.add(multi_dot([F_angle,P_angle,F_angle.transpose()]),Q_angle)\r\n K_angle=np.dot(P_angle_bel,H_angle.transpose())/(multi_dot([H_angle,P_angle_bel,H_angle.transpose()])+R_angle);# calculating Kalman gain\r\n X_angle=np.add(X_angle_bel,K_angle.dot(np.subtract(Z_angle,H_angle.dot(X_angle_bel))))#belief which is a combination of estimation and measurement\r\n P_angle=multi_dot([np.subtract(np.identity(2),K_angle.dot(H_angle)), P_angle_bel])#update the covariance matrix\r\n\t\t\t#Error In Position\r\n ####### Position Control #######\r\n\r\n temp1=ball.pos[0]\r\n temp2=ball.pos[1]\r\n #error1 = ((ball.pos[0]-rob.pos[0])**2+(ball.pos[1]-rob.pos[1])**2)**0.5 * (math.cos(math.atan2(ball.pos[1]-rob.pos[1] / ball.pos[0]-rob.pos[0]) - rob.angle))\r\n #error1 = ((ball.pos[0]-rob.pos[0])**2+(ball.pos[1]-rob.pos[1])**2)**0.5\r\n error1 = ((ball.pos[0]-int(Xx[0][0]))**2+(ball.pos[1]-int(Xy[0][0]))**2)**0.5\r\n derivative1=(error1-error_prior1)\r\n error_prior1=error1\r\n print(\"error1:\",error1)\r\n u1= ( kp1*error1 ) + ( kd1*derivative1 )\r\n diff=(u1-temp_u1)/dt\r\n temp_u1=u1\r\n u_accel=diff/dt#u is acceleration in this case. and it is supposed to be from the control in the controller\r\n ux=u_accel*math.cos((math.atan2(ball.pos[1]-rob.pos[1],ball.pos[0]-rob.pos[0])))\r\n uy=u_accel*math.sin((math.atan2(ball.pos[1]-rob.pos[1],ball.pos[0]-rob.pos[0])))\r\n \r\n print(\"Xx belief\",Xx) \r\n Zx=rob.pos[0]+noisex#i'm not sure if this is right should be directly from the measurement\r\n X_x=np.dot(Fx,Xx)+np.dot(Bx,ux)#predicted\r\n P_x=np.add(multi_dot([Fx,Px,Fx.transpose()]),Qx)#update of the covariance matrix for predicted value\r\n Kx=np.dot(P_x,Hx.transpose())/(multi_dot([Hx,P_x,Hx.transpose()])+Rx);# calculating Kalman gain\r\n Xx=np.add(X_x,Kx.dot(np.subtract(Zx,Hx.dot(X_x))))#belief which is a combination of estimation and measurement\r\n \r\n Px=multi_dot([np.subtract(np.identity(2),Kx.dot(Hx)), P_x])#update the covariance matrix \r\n \r\n \t#y pos kalman filter\r\n\r\n Zy=rob.pos[1]+noisey#i'm not sure if this is right should be directly from the measurement\r\n X_y=np.dot(Fy,Xy)+np.dot(By,uy)#predicted\r\n P_y=np.add(multi_dot([Fy,Py,Fy.transpose()]),Qy)#update of the covariance matrix for predicted value\r\n Ky=np.dot(P_y,Hy.transpose())/(multi_dot([Hy,P_y,Hy.transpose()])+Ry);# calculating Kalman gain\r\n Xy=np.add(X_y,Ky.dot(np.subtract(Zy,Hy.dot(X_y))))#belief which is a combination of estimation and measurement\r\n print(\"Xy belief\",Xy)\r\n Py=multi_dot([np.subtract(np.identity(2),Ky.dot(Hy)), P_y])#update the covariance matrix \r\n\r\n # Assigning the direction of motors based on the wheel velocities sign\r\n\r\n if (error2>=-10 and error2<=10):\r\n error2=error2\r\n dirR=1 #May need to change depending on connection\r\n dirL=0 #May need to change depending on connection\r\n # #packet.append(0xFF) #Start Bit\r\n #packet.append(0x01) #ID Bit\r\n #packet.append(0x00) #VrHex\r\n #packet.append(0x00) #dirR\r\n #packet.append(0x0) #VlHex\r\n #packet.append(0x00) #dirL\r\n #packet.append(0x00) #Kick Command \r\n #packet.append(0xFF) #Stop Bit\r\n #break\r\n else:\t\r\n if np.sign(error2)==-1:\r\n dirR=0\r\n dirL=0\r\n elif np.sign(error2)==1:\r\n dirR=1\r\n dirL=1\r\n \r\n \r\n ## Setting limits to the inputs \r\n if(u1 > umax):\r\n u1=umax\r\n if(u1 < -umax):\r\n u1 = -umax\r\n #u2=0\r\n if(u2 > u2max):\r\n u2=u2max\r\n if(u2 < -u2max):\r\n u2 = -u2max\r\n \r\n # Assigning Individual Wheel velocities\r\n \r\n #u1=0\r\n #u2=0\r\n \r\n vr=u1+u2\r\n vl=u1-u2\r\n\r\n #if(np.sign(vr) == 1):\r\n #dirR= 0x00\r\n #if(np.sign(vr) == -1):\r\n # dirR= 0x01\r\n\r\n #if(np.sign(vl) == 1):\r\n # dirL= 0x00\r\n\r\n #if(np.sign(vl) == -1):\r\n # dirL= 0x01\r\n\r\n #if(np.sign(error2) == 1):\r\n # dirL = 0x00\r\n # dirR = 0x01\r\n #if(np.sign(error2) == -1):\r\n # dirL = 0x01\r\n # dirR = 0x00\r\n \r\n # Remove the sign in motor velocities\r\n Vr = abs(int(vr))\r\n Vl = abs(int(vl))\r\n #print(Vr)\r\n #print(Vl)\r\n # Assign the motor velocities to 0-256 range to send through 8bit UART\r\n VrHex = int(Vr*255/ VrMax)\r\n VlHex = int(Vl*255/ VlMax)\r\n if(VrHex == 0):\r\n VrHex = 1\r\n if(VlHex == 0):\r\n VlHex = 1\r\n if (abs(error1) < 20 and abs(error2) <10): \r\n kick= 0x01\r\n else:\r\n kick = 0\r\n if (error1<20):\r\n VrHex=0x1\r\n VlHex=0x1\r\n angle_meas_list.append(rob.angle)\r\n angle_bel_list.append(X_angle[0])\r\n print(\"X_angle[0]\",X_angle[0])\r\n angle_predicted_list.append(X_angle_bel[0])\r\n print(\"VlHex:\",VlHex)\r\n print(\"VrHex:\",VrHex)\r\n counter = counter + 1\r\n packet.append(0xFF)\r\n packet.append(0x01) #Robot ID\r\n packet.append(VrHex) #VrHex\r\n packet.append(dirR) #dirR\r\n packet.append(VlHex) #VlHex\r\n packet.append(dirL) #dirL\r\n packet.append(kick) #kick\r\n packet.append(0xFF)\r\n KL25.write(packet)\r\n #data = KL25.read(4)\r\n #print(data.decode('ISO-8859-1'))\r\n\r\n #belief = int(X_angle[0])\r\n #pred= int(X_angle_bel[0])\r\n # Ploting values\r\n \r\n #measurement.append(rob.angle)\r\n #bel.append(belief)\r\n #predicted.append(pred)\r\n\r\n \r\n #drawnow(makeFig)\r\n cv2.imshow('circles on stream',img)\r\n\r\n \r\n# Path Planning\r\ndef mainLoop3():\r\n print(\"new loop path planning\\n\\r\")\r\n # Declaring global variables so they can be cleared every loop\r\n global roboList, roboIDmarks, circles, ball, IDdRobots\r\n\r\n global error1,error_prior1,error2,error_prior2,dt,derivative1,L,R,dirR,dirL,umax,u2max,kp1\r\n global kp2,flag,kd1,kd2,VrMax,VlMax,temp1,temp2,test,counter\r\n\r\n #Mikes Global Variables\r\n global points\r\n global tangents\r\n global samples_prev\r\n global next_point_x, next_point_y\r\n \r\n #cap = cv2.VideoCapture(cv2.CAP_DSHOW + 0) # 0 if your pc doesn't have a webcam, probably 1 if it does\r\n # https://stackoverflow.com/questions/52043671/opencv-capturing-imagem-with-black-side-bars\r\n # MSMF doesn't like being scaled up apparently, so switch from it (default) to DirectShow\r\n # so we can scale up the resolution read from the camera\r\n\r\n # Scaling up from 640x480 to HD 1280x720\r\n #cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)\r\n #cap.set(cv2.CAP_PROP_FRAME_HEIGHT,720)\r\n #cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)\r\n #cap.set(cv2.CAP_PROP_FRAME_HEIGHT,720)\r\n \r\n\r\n #while(True):\r\n # while(KL25.inWaiting()==0):\r\n\r\n ret,frame = cap.read() # reading the video capture into a dummy var and frame\r\n\r\n #cv2.waitKey(50)\r\n \r\n # Reinitializing robot data (prevents buildup of data accross frames)\r\n roboList = []\r\n roboIDmarks= []\r\n circles = []\r\n ball = None\r\n\r\n #Mike's Added Value Parameters\r\n points = []\r\n tangents = []\r\n resolution = 0.2 #was 0.2\r\n\r\n # Histogram equalization for colors (haven't tested with this)\r\n #img_yuv = cv2.cvtColor(ii, cv2.COLOR_BGR2YUV)\r\n\r\n ### equalize the histogram of the Y channel\r\n #img_yuv[:,:,0] = cv2.equalizeHist(img_yuv[:,:,0])\r\n\r\n ### convert the YUV image back to RGB format\r\n #frame_yuv = cv2.cvtColor(img_yuv, cv2.COLOR_YUV2BGR)\r\n\r\n # blurring image for less errant circles and better color recognition later\r\n # d = 5 as that is the recommended nearest neighbour for real time\r\n # sigmaColor = 150 to produce large blending effect\r\n # sigmaSpace is limited by d, so I suspect it doesn't matter\r\n blurred_img = cv2.bilateralFilter(frame,8,150,150) \r\n\r\n # HSV color space conversion\r\n hsv= cv2.cvtColor(blurred_img,cv2.COLOR_BGR2HSV)\r\n\r\n # Color masking, not necessary due to blurring, but might be worth looking into further\r\n #lower_rangeG = np.array([0,0,0]) # Hue, Saturation, Value mask lower limit\r\n #upper_rangeG = np.array([180,255,255]) # \" , \" , \" \" upper limit\r\n\r\n #mask = cv2.inRange(hsv, lower_rangeG, upper_rangeG) # mask for original frame with only good color\r\n #result = cv2.bitwise_and(blurred_img,blurred_img,mask=mask)\r\n result = blurred_img\r\n\r\n #cv2.imshow(\"blurred image\",result)\r\n \r\n hsv_out_gray= cv2.cvtColor(result, cv2.COLOR_BGR2GRAY)\r\n\r\n #cv2.imshow(\"houghin\",hsv_out_gray)\r\n\r\n # Some notes on the HoughCircles function:\r\n # Utilizes edge detection to draw tangent lines, recognizing a circle where perpendicular lines to tangents\r\n # meet, depending on the intensity of the intersecting tangent lines.\r\n # param1: higher threshold for Canny edge detection (lower is half of this)\r\n # param2: accumulator threshold for circle center detection- i.e. the lower it is, the less circular an object\r\n # needs to be to be recognized as a circle\r\n # minDist: Specifies minimum distance between circles (the 4th input to the function)\r\n # \r\n # from documentation: cv2.HoughCircles(image, method, dp, minDist[, circles[, param1[, param2[, minRadius[, maxRadius]]]]]) → circles\r\n circles = cv2.HoughCircles(hsv_out_gray,cv2.HOUGH_GRADIENT,1,minDist=5,param1=param1val,param2=param2val,minRadius=1,maxRadius=15)\r\n\r\n cv2.waitKey(1) # cv2.waitKey() is required to display images- waits 1 millisecond here\r\n\r\n img = copy.deepcopy(frame) # Sometimes if you copy stuff in Python, changes made to a copied variable end up in original\r\n # which necessitates a deepcopy\r\n\r\n #DELETE THIS BLOCK ############\r\n #test_circle = cv2.ellipse(img,(600,220),(100,100),180,90,-90,255,5)\r\n #test_line = cv2.line(img, (230,300), (600,220), 255, 5)\r\n #nx, ny = (500,1) #500 colomns by 1 rows vector]\r\n #x = np.linspace(230,600,nx) #x vector\r\n #y = np.linspace(300,220,nx) #y vector, start and end reverse\r\n #xpoint = x[round(len(x)*8/10)] #Going to rounded 8/10ths the way through the x vector\r\n #ypoint = y[round(len(y)*8/10)] #Going to rounded 8/10ths the way through the y vector\r\n #print(xpoint)\r\n #print(ypoint)\r\n ###########################\r\n\r\n if isinstance(circles, type(None)) == 0:\r\n for circle in circles[0,:]:\r\n IDcircle(hsv, circle) # ID all the circles recognized by color\r\n # draw the outer circle\r\n cv2.circle(img,(circle[0],circle[1]),circle[2],(0,255,0),2)\r\n # draw the center of the circle\r\n cv2.circle(img,(circle[0],circle[1]),2,(0,0,255),3)\r\n\r\n if isinstance(ball, type(None)) == 0:\r\n # Draw a blue circle on the ball\r\n cv2.circle(img,(ball.pos[0],ball.pos[1]),10,(200,0,0),5) \r\n cv2.putText(img, str(ball.pos), (ball.pos[0]+20,ball.pos[1]+20), cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n\r\n if (isinstance(roboIDmarks, type(None)) == 0) & (isinstance(roboList, type(None)) == 0):\r\n for robot in roboList:\r\n assignIDmarks(robot) # Assign the ID marks observed to their appropriate robot\r\n angle(robot) # Determine angle of robots seen\r\n RoboID(robot) # Give robots seen an ID\r\n\r\n # Draw the robot circles seen robot by robot\r\n # Draw a black circle on the centre of the robot\r\n cv2.circle(img,(robot.pos[0],robot.pos[1]),10,(0,0,0),3)\r\n \r\n\r\n #if isinstance(robot.angle, type(None)) == 0:\r\n # # Display the robot's angle\r\n # cv2.putText(img, str(round(robot.angle,1)), (robot.pos[0]+ 100, robot.pos[1] + 130), \r\n # cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # # Display the robot's position\r\n # cv2.putText(img, str(robot.pos), (robot.pos[0]+ 100, robot.pos[1] + 100), \r\n # cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # # Display the robot's ID\r\n # cv2.putText(img, robot.ID, (robot.pos[0]+ 100, robot.pos[1] + 70), \r\n # cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # # Display the robot's Team\r\n # cv2.putText(img, robot.team, (robot.pos[0]+ 100, robot.pos[1] + 40), \r\n # cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n for mark in robot.circles:\r\n # Draw a black circle on every ID mark\r\n cv2.circle(img,(mark[0],mark[1]),10,(0,0,0),3) \r\n flag = 0 # go ahead and print \"no circles detected\" again\r\n\r\n elif(flag == 0):\r\n #print(\"no circles detected\")\r\n flag = 1 # don't print this again\r\n\r\n # Display drawn on frame and original frame\r\n #cv2.imshow('circles on stream',img)\r\n cv2.imshow('original stream',frame)\r\n\r\n #if cv2.waitKey(1) & 0xFF == ord('\\r'): # if enter is pressed, stop running\r\n # break\r\n\r\n # when the ball does not get detected\r\n if (isinstance(ball, type(None)) != 0):\r\n ball = ballClass(temp1,temp2) \r\n \r\n #This if statement is simply for initialization, there has to be a better way of doing this\r\n if(counter == 0):\r\n for rob in roboList:\r\n #Mike's added value stuff Initialization\r\n points = []\r\n tangents = []\r\n resolution = 0.2\r\n points.append([rob.pos[0],rob.pos[1]]) #Robot Position\r\n points.append([ball.pos[0],ball.pos[1]]) #Ball Position\r\n points.append([600,220]) #Net Position\r\n\r\n #Finding the angle at which the robot approaches\r\n approach_x = (points[2][0] - points[1][0])\r\n approach_y = (points[2][1] - points[1][1])\r\n common_divisor = abs(gcd(approach_x,approach_y)) #Absolute value of the greatest common divisor\r\n approach_x = approach_x/common_divisor #Divide by common divisor\r\n approach_y = approach_y/common_divisor #Divide by common divisor\r\n\r\n #Tangents for alligning robot with ball and net\r\n tangents.append([math.tan(45*np.pi/180),1]) #Robot position, Slope converted from radians, this value is whatever angle the robot is currently facing\r\n tangents.append([approach_x, approach_y]) #Ball position\r\n tangents.append([approach_x, approach_y]) #Net position\r\n \r\n packet = bytearray() # ** Should this be within the for loop below?\r\n #packet.append(0xff)\r\n #packet.append(0x01) #id\r\n #packet.append(0x30) #mtr1\r\n #packet.append(0x01) #dir1\r\n #packet.append(0x30) #mtr2\r\n #packet.append(0x01) #dir2\r\n #packet.append(0x01) #kick\r\n #packet.append(0xff)\r\n\r\n #KL25.write(packet)\r\n #data = KL25.read(4) #Reading and Printing slows down the system incredibly, do not use for the demonstration\r\n #print(data.decode('ISO-8859-1')) #Reading and Printing slows down the system incredibly, do not use for the demonstration\r\n\r\n for rob in roboList:\r\n if(rob.ID != '-no ID!-') & (isinstance(roboList, type(None)) == 0):\r\n robotsID = int(''.join(filter(str.isdigit,rob.ID))) # extracting integer ID number from rob.ID\r\n # if the robot has a radius larger than the distance between it and the edge of the frame\r\n # skip over this robot\r\n if(rob.radius < (rob.pos[1] - len(img[1])) or rob.radius < (rob.pos[0] - len(img[0]))):\r\n stoprobot(robotsID)\r\n continue\r\n else:\r\n #if(abs(abs(rob.angle)-180)>20 and counter>5):\r\n # if (abs(rob.angle-test) >=200 and counter > 5):\r\n # rob.angle=test#something is wrong with the angle measurement\r\n \r\n #else:\r\n # if (abs(abs( rob.angle)-abs(test)) >=50 and counter>5 ):\r\n # rob.angle=test#something is wrong with the angle measurement\r\n test=rob.angle\r\n #print(\"rob.angle\",rob.angle)\r\n # Display the robot's angle\r\n cv2.putText(img, str(round(rob.angle,1)), (rob.pos[0]+ 100, rob.pos[1] + 130), \r\n cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # Display the robot's position\r\n cv2.putText(img, str(rob.pos), (rob.pos[0]+ 100, rob.pos[1] + 100), \r\n cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # Display the robot's ID\r\n cv2.putText(img, rob.ID, (rob.pos[0]+ 100, rob.pos[1] + 70), \r\n cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n # Display the robot's Team\r\n cv2.putText(img, rob.team, (rob.pos[0]+ 100, rob.pos[1] + 40), \r\n cv2.FONT_HERSHEY_DUPLEX, 1, (255,255,255), 3)\r\n\r\n #Mike's Added Value \r\n points.append([rob.pos[0],rob.pos[1]]) #Robot Position\r\n points.append([ball.pos[0],ball.pos[1]]) #Ball Position\r\n points.append([600,220]) #Net Position\r\n\r\n #Finding the angle at which the robot approaches FOR THE SPLINE\r\n approach_x = (points[2][0] - points[1][0])\r\n approach_y = (points[2][1] - points[1][1])\r\n #common_divisor = abs(gcd(approach_x,approach_y)) #Absolute value of the greatest common divisor\r\n #approach_x = approach_x/common_divisor #Divide by common divisor\r\n #approach_y = approach_y/common_divisor #Divide by common divisor\r\n\r\n #Tangents for alligning robot with ball and net\r\n tangents.append([math.tan(45*np.pi/180),1]) #Robot position, Slope converted from radians, this value is whatever angle the robot is currently facing\r\n tangents.append([approach_x, approach_y]) #Ball position\r\n tangents.append([approach_x, approach_y]) #Net position\r\n\r\n points = np.asarray(points)\r\n tangents = np.asarray(tangents)\r\n\r\n # Interpolate with different tangent lengths, but equal direction.\r\n scale = 0.01 #Tunable Parameter, the closer to 0 the tighter the spline, 0.01 is a good in between\r\n tangents_new = np.dot(tangents, scale*np.eye(2))\r\n samples_new = np.float32(sampleCubicSplinesWithDerivative(points, tangents_new, resolution))\r\n\r\n #Find the slope to the next point for the robot\r\n next_point_x = np.float32(samples_new[round(len(samples_new)/5)][0] - points[0][0]) #Change in x between the robot and next point\r\n next_point_y = np.float32(samples_new[round(len(samples_new)/5)][1] - points[0][1]) #Change in y between the robot and next point\r\n\r\n print(\"next_point_x\", next_point_x)\r\n print(\"next_point_y\", next_point_y)\r\n\r\n tangents[0][0] = math.atan2(next_point_y, next_point_x) - rob.angle #This value is the angle in radians that the robot must face for its path\r\n #print(\"tangents[0][0]\", math.degrees(tangents[0][0]))\r\n tangents[0][1] = 1\r\n #print(\"tangents[0][1]\", math.degrees(tangents[0][1]))\r\n tangents[1][0] = approach_x #x slope for the spline\r\n #print(\"tangents[1][0]\", math.degrees(tangents[1][0]))\r\n tangents[1][1] = approach_y #y slope for the spline\r\n #print(\"tangents[1][1]\", math.degrees(tangents[1][1]))\r\n tangents[2][0] = approach_x #x slope for the spline\r\n #print(\"tangents[2][0]\", math.degrees(tangents[2][0]))\r\n tangents[2][1] = approach_y #y slope for the spline\r\n #print(\"tangents[2][1]\", math.degrees(tangents[2][1]))\r\n #approach_x = (next_point_x)\r\n #approach_y = (next_point_y)\r\n points = np.asarray(points)\r\n tangents = np.asarray(tangents)\r\n trajectory = math.degrees(tangents[0][0]) #Angle in degrees\r\n\r\n #Printing Mike's Stuff\r\n print(\"X\", samples_new[round(len(samples_new)/20)][0]) #X position with resolution of 10, if this is not good enough divide into smaller pieces perhaps\r\n print(\"Y\", samples_new[round(len(samples_new)/20)][1]) #y position with resolution of 10, if this is not good enough divide into smaller pieces perhaps\r\n print(\"trajectory\", trajectory) #Angle output in degrees\r\n\r\n #Display splines on the live feed & plot\r\n #path_plot = plt.scatter(samples3[:,0], samples3[:,1], marker='o', label='samples3')\r\n if(attacker_defender_flag == 0):\r\n k = 0\r\n for k in range(0,len(samples_new)):\r\n cv2.circle(img, (samples_new[k,0], samples_new[k,1]), 1, (0, 255, 255),5)\r\n\r\n #image1 = path_plot.imshow(grab_frame(cap))\r\n #plt.ion()\r\n #plt.show()\r\n\r\n ####### Angle Control \r\n if rob.angle == 999:\r\n rob.angle=test\r\n\r\n #Mike's Added Value Deciding which error to use Based on Robot Position and Angle\r\n pos_error = ((ball.pos[0]-rob.pos[0])**2+(ball.pos[1]-rob.pos[1])**2)**0.5 #The absolute error between robot and ball\r\n angle_error = math.degrees(math.atan2(ball.pos[1]-rob.pos[1],ball.pos[0]-rob.pos[0])) - rob.angle #Angle error between robot and ball\r\n if (abs(angle_error)<180):\r\n angle_error=angle_error\r\n elif (np.sign(angle_error)==-1):\r\n angle_error=angle_error+360\r\n elif (np.sign(angle_error)==1):\r\n angle_error=angle_error-360\r\n else:\r\n print(\"done\")\r\n\r\n if(attacker_defender_flag == 0):\r\n if(abs(pos_error) <= 30 and abs(angle_error) <=5): #if the robot is close to the ball and is lined up\r\n error2 = math.degrees((math.atan2(ball.pos[1]-rob.pos[1],ball.pos[0]-rob.pos[0])))-rob.angle\r\n error1 = ((ball.pos[0]-rob.pos[0])**2+(ball.pos[1]-rob.pos[1])**2)**0.5\r\n else:\r\n error2 = math.degrees((math.atan2(samples_new[round(len(samples_new)/20)][1]-rob.pos[1], samples_new[round(len(samples_new)/20)][0]-rob.pos[0])))-rob.angle #error2 for Mike's Added Value\r\n error1 = ((next_point_x)**2+(next_point_y)**2)**0.5 #error1 for Mike's Added value\r\n\r\n #error2=math.degrees((math.atan2(ball.pos[1]-rob.pos[1],ball.pos[0]-rob.pos[0])))-rob.angle\r\n #error2=math.degrees((math.atan2(samples_new[round(len(samples_new)/10) + 1][1]-rob.pos[1], samples_new[round(len(samples_new)/10) + 1][0]-rob.pos[0])))-rob.angle #error2 for Mike's Added Value\r\n\r\n #Mike's Added Value Part 2 START\r\n #color = 255\r\n #cv2.line(img, (ball.pos[0],ball.pos[1]), (points[3][0],points[3][1]), color, 5)\r\n if(attacker_defender_flag == 1):\r\n nx, ny = (500,1) #500 colomns by 1 rows vector]\r\n x = np.linspace(ball.pos[0],600,nx) #x vector\r\n y = np.linspace(ball.pos[1],220,nx) #y vector, start and end reversed\r\n\r\n xpoint = x[round(len(x)*8/10)] #Going to rounded 8/10ths the way through the x vector\r\n ypoint = y[round(len(y)*8/10)] #Going to rounded 8/10ths the way through the y vector\r\n error1 = ((xpoint-rob.pos[0])**2+(ypoint-rob.pos[1])**2)**0.5 #error1 for Mike's Added value part 2\r\n error2 = math.degrees((math.atan2(ypoint-rob.pos[1],xpoint-rob.pos[0])))-rob.angle\r\n # print(xpoint)\r\n # print(ypoint)\r\n #Mike's Added Value Part 2 END \r\n\r\n #regulate the angle to reduce ambiguity\r\n if (abs(error2)<180):\r\n error2=error2\r\n elif (np.sign(error2)==-1):\r\n error2=error2+360\r\n elif (np.sign(error2)==1):\r\n error2=error2-360\r\n else:\r\n print(\"done\")\r\n print(\"error2\",error2)\r\n\r\n derivative2=(error2-error_prior2) #Shouldn't this be divided by a dt?\r\n error_prior2=error2\r\n u2= (kp2*error2) + (kd2*derivative2) \r\n print(\"u2\",u2)\r\n \r\n\t\t\t #Error In Position\r\n ####### Position Control #######\r\n temp1=ball.pos[0]\r\n temp2=ball.pos[1]\r\n #error1 = ((ball.pos[0]-rob.pos[0])**2+(ball.pos[1]-rob.pos[1])**2)**0.5\r\n #error1 = ((next_point_x-rob.pos[0])**2+(next_point_y-rob.pos[1])**2)**0.5 #error1 for Mike's Added value\r\n \r\n derivative1=(error1-error_prior1)\r\n error_prior1=error1\r\n print(\"error1:\",error1)\r\n u1= ( kp1*error1 ) + ( kd1*derivative1 )\r\n\r\n\r\n # Assigning the direction of motors based on the wheel velocities sign\r\n if (error2>=-30 and error2<=30):\r\n dirR=1\r\n dirL=0\r\n else:\t\r\n if np.sign(error2)==-1:\r\n dirR=0\r\n dirL=0\r\n elif np.sign(error2)==1:\r\n dirR=1\r\n dirL=1\r\n \r\n \r\n ## Setting limits to the inputs \r\n if(u1 > umax):\r\n u1=umax\r\n if(u1 < -umax):\r\n u1 = -umax\r\n #u2=0\r\n if(u2 > u2max):\r\n u2=u2max\r\n if(u2 < -u2max):\r\n u2 = -u2max\r\n \r\n # Assigning Individual Wheel velocities\r\n vr=u1+u2\r\n vl=u1-u2\r\n \r\n # Remove the sign in motor velocities\r\n Vr = abs(int(vr))\r\n Vl = abs(int(vl))\r\n\r\n # Assign the motor velocities to 0-256 range to send through 8bit UART\r\n #VrHex = int((Vr - VrMin)*255/ (VrMax - VrMin))\r\n #VlHex = int((Vl - VlMin)*255/ (VlMax - VlMin))\r\n VrHex = int(Vr*255/ VrMax)\r\n VlHex = int(Vl*255/ VlMax)\r\n\r\n if(VrHex == 0):\r\n VrHex = 1\r\n if(VlHex == 0):\r\n VlHex = 1\r\n\r\n if (abs(error1) < 20 and abs(error2) <10): \r\n kick= 0x01\r\n else:\r\n kick = 0\r\n\r\n #Mike's Added Value Kicking \r\n if ((error1 < 20) and (error1 == ((ball.pos[0]-rob.pos[0])**2+(ball.pos[1]-rob.pos[1])**2)**0.5)):\r\n VrHex = 0x01\r\n VlHex = 0x01\r\n\r\n #Mike's Added Value Goalie\r\n #if(error1 < 50):\r\n # VrHex = 0x01\r\n # VlHex = 0x01\r\n \r\n print(\"VlHex:\",VlHex)\r\n print(\"VrHex:\",VrHex)\r\n counter = counter + 1\r\n packet.append(0xFF)\r\n packet.append(0x01) #Robot ID\r\n packet.append(VrHex) #VrHex\r\n packet.append(dirR) #dirR\r\n packet.append(VlHex) #VlHex\r\n packet.append(dirL) #dirL\r\n packet.append(kick) #kick\r\n packet.append(0xFF)\r\n KL25.write(packet)\r\n #data = KL25.read(4)\r\n #print(data.decode('ISO-8859-1'))\r\n points = [] #Reset points\r\n tangents = []#Reset tangents\r\n\r\n cv2.imshow('circles on stream',img)\r\n\r\ndef plotData():\r\n if(radioflag == 0):\r\n del angleRecording[0]\r\n anglestddev = np.std(angleRecording)\r\n anglemean = np.mean(angleRecording)\r\n text = '\\u03BC = '+str(round(anglemean,2))+'\\n\\u03C3 = '+str(round(anglestddev,2))\r\n\r\n xstddev = np.std(posRecording[0])\r\n ystddev = np.std(posRecording[1])\r\n xmean = np.mean(posRecording[0])\r\n ymean = np.mean(posRecording[1])\r\n text2 = r'$\\mu_x$ = '+str(int(xmean))+r' $\\mu_y$ = '+str(int(ymean))+'\\n'+r'$\\sigma_x$ = '+str(round(xstddev,2))+r' $\\sigma_y$ = '+str(round(ystddev,2))\r\n\r\n del mainLoopTime[0]\r\n timemean = np.mean(mainLoopTime)\r\n timestddev = np.std(mainLoopTime)\r\n text3 = '\\u03BC = '+str(round(timemean,2))+'\\n\\u03C3 = '+str(round(timestddev,2))\r\n\r\n plt.figure(1)\r\n y,x,_ = plt.hist(angleRecording, color = 'blue', edgecolor = 'black', bins = 100)\r\n x = int(x.max()-0.2*(x.max()-x.min()))\r\n y = int(y.max()*0.8)\r\n plt.title('Histogram of Angular Position Readings')\r\n plt.xlabel('Angle')\r\n plt.ylabel('Number of Readings')\r\n plt.text(x,y,text,bbox=dict(facecolor = 'white',alpha=0.5))\r\n\r\n plt.figure(2)\r\n x2 = int(max(posRecording[0])-(max(posRecording[0])-min(posRecording[0]))*0.25)\r\n y2 = int(max(posRecording[1])-(max(posRecording[1])-min(posRecording[1]))*0.2)\r\n plt.hist2d(posRecording[0],posRecording[1])\r\n plt.title('Heatmap of Positional Readings')\r\n plt.xlabel('x Position')\r\n plt.ylabel('y Position')\r\n plt.text(x2,y2,text2,color = 'w',bbox=dict(facecolor = 'white',alpha=0.5))\r\n\r\n plt.figure(3)\r\n y3,x3,_ = plt.hist(mainLoopTime, color = 'blue', edgecolor = 'black', bins = 50)\r\n x3 = x3.max()-0.2*(x3.max()-x3.min())\r\n y3 = y3.max()*0.8\r\n plt.title('Histogram of Overall Loop Time')\r\n plt.ylabel('Number of Readings')\r\n plt.xlabel('Time Elapsed in Seconds')\r\n plt.text(x3,y3,text3,bbox=dict(facecolor = 'white',alpha=0.5))\r\n\r\n if(radioflag == 1):\r\n plt.figure(1)\r\n plt.plot(Actual_X)\r\n plt.title('X Position')\r\n plt.ylabel('Position (Pixels)')\r\n plt.xlabel('Time Elapsed in Seconds')\r\n\r\n plt.figure(2)\r\n plt.plot(Actual_Y)\r\n plt.title('Y Position')\r\n plt.ylabel('Position (Pixels)')\r\n plt.xlabel('Time Elapsed in Seconds')\r\n\r\n plt.figure(3)\r\n plt.plot(Actual_Angle)\r\n plt.title('Angle (deg)')\r\n plt.ylabel('Angle')\r\n plt.xlabel('Time Elapsed in Seconds')\r\n\r\n plt.figure(4)\r\n plt.plot(Input_V)\r\n plt.title('Forward Velocity Input')\r\n plt.xlabel('Time Elapsed in Seconds')\r\n\r\n plt.figure(5)\r\n plt.plot(Input_W)\r\n plt.title('Angular Velocity Input')\r\n plt.xlabel('Time Elapsed in Seconds')\r\n\r\n plt.show()\r\n\r\ndef run():\r\n app = QApplication(sys.argv)\r\n window = roboGUI()\r\n window.show()\r\n sys.exit(app.exec_())\r\n \r\nbreakpointthing = None\r\nclass Worker(QObject):\r\n finished = pyqtSignal()\r\n\r\n def __init__(self):\r\n super(Worker, self).__init__()\r\n self.working = True\r\n\r\n def work(self):\r\n while(self.working):\r\n sys.settrace = breakpointthing\r\n if(radioflag == 0):\r\n mainLoop()\r\n elif(radioflag == 1):\r\n mainLoop1()\r\n elif(radioflag == 2):\r\n mainLoop2()\r\n elif(radioflag == 3):\r\n mainLoop3()\r\n print(\"program has been stopped\")\r\n while(True):\r\n stoprobot('all') # when stop button pressed, stop robots\r\n \r\n sys.exit()\r\n \r\n\r\nclass roboGUI(QMainWindow):\r\n def __init__(self, *args, **kwargs):\r\n super(roboGUI,self).__init__()\r\n self.window = QWidget(self)\r\n self.setCentralWidget(self.window)\r\n self.resize(400,100)\r\n self.setWindowTitle(\"roboGUI\")\r\n self.layout = QVBoxLayout()\r\n\r\n self.buttons = QHBoxLayout()\r\n\r\n self.startButton = QPushButton(\"START\",self)\r\n #self.startButton.setSizePolicy(QSizePolicy.Fixed,QSizePolicy.Expanding)\r\n #self.startButton.resize(self.startButton.minimumSizeHint())\r\n self.startButton.setMinimumSize(self.startButton.minimumSizeHint())\r\n self.startButton.setMinimumHeight(80)\r\n self.buttons.addWidget(self.startButton)\r\n self.stopButton = QPushButton(\"STOP\",self)\r\n #self.stopButton.setSizePolicy(QSizePolicy.Preferred,QSizePolicy.Expanding)\r\n self.stopButton.setMinimumSize(self.stopButton.minimumSizeHint())\r\n self.stopButton.setMinimumHeight(80)\r\n self.buttons.addWidget(self.stopButton)\r\n\r\n self.layout.addLayout(self.buttons)\r\n\r\n self.sliders = QHBoxLayout()\r\n self.slidersleft = QVBoxLayout()\r\n self.slidersright = QGridLayout()\r\n\r\n self.param1text = QHBoxLayout()\r\n self.param1title = QLabel(\"Parameter 1\")\r\n self.param1value = QLabel(str(param1val))\r\n self.param1slider = QSlider(Qt.Horizontal)\r\n self.param1slider.setMinimum(1)\r\n self.param1slider.setMaximum(250)\r\n self.param1slider.setValue(param1val)\r\n self.param1slider.setTickInterval(1)\r\n self.param1text.addWidget(self.param1title)\r\n self.param1text.addWidget(self.param1value)\r\n self.slidersleft.addLayout(self.param1text)\r\n self.slidersleft.addWidget(self.param1slider)\r\n \r\n self.param2text = QHBoxLayout()\r\n self.param2title = QLabel(\"Parameter 2\")\r\n self.param2value = QLabel(str(param2val))\r\n self.param2slider = QSlider(Qt.Horizontal)\r\n self.param2slider.setMinimum(1)\r\n self.param2slider.setMaximum(50)\r\n self.param2slider.setValue(param2val)\r\n self.param2slider.setTickInterval(1)\r\n self.param2text.addWidget(self.param2title)\r\n self.param2text.addWidget(self.param2value)\r\n self.slidersleft.addLayout(self.param2text)\r\n self.slidersleft.addWidget(self.param2slider)\r\n\r\n self.valuetext = QHBoxLayout()\r\n self.valuetitle = QLabel(\"Minimum Value for Color\")\r\n self.valuevalue = QLabel(str(valueMin))\r\n self.valueslider = QSlider(Qt.Horizontal)\r\n self.valueslider.setMinimum(0)\r\n self.valueslider.setMaximum(255)\r\n self.valueslider.setValue(valueMin)\r\n self.valueslider.setTickInterval(1)\r\n self.valueslider.setSizePolicy(QSizePolicy.Preferred,QSizePolicy.Fixed)\r\n self.valuetext.addWidget(self.valuetitle)\r\n self.valuetext.addWidget(self.valuevalue)\r\n self.slidersleft.addLayout(self.valuetext)\r\n self.slidersleft.addWidget(self.valueslider)\r\n\r\n self.kptext = QHBoxLayout()\r\n self.kptitle = QLabel(\"Kp Value:\")\r\n self.kpvalue = QLabel(str(kp))\r\n self.kpvalue.setFixedWidth(30)\r\n self.kptext.addWidget(self.kptitle)\r\n self.kptext.addWidget(self.kpvalue)\r\n self.kpslider = QSlider(Qt.Vertical)\r\n self.kpslider.setMinimum(0)\r\n self.kpslider.setMaximum(500)\r\n self.kpslider.setValue(kp)\r\n self.kpslider.setTickInterval(5)\r\n self.kpslider.setSizePolicy(QSizePolicy.Fixed,QSizePolicy.Expanding)\r\n\r\n self.kdtext = QHBoxLayout()\r\n self.kdtitle = QLabel(\"Kd Value:\")\r\n self.kdvalue = QLabel(str(kd))\r\n self.kdvalue.setFixedWidth(30)\r\n self.kdtext.addWidget(self.kdtitle)\r\n self.kdtext.addWidget(self.kdvalue)\r\n self.kdslider = QSlider(Qt.Vertical)\r\n self.kdslider.setMinimum(0)\r\n self.kdslider.setMaximum(500)\r\n self.kdslider.setValue(kd)\r\n self.kdslider.setTickInterval(5)\r\n self.kdslider.setSizePolicy(QSizePolicy.Fixed,QSizePolicy.Expanding)\r\n\r\n self.slidersright.addLayout(self.kptext,0,0)\r\n self.slidersright.addLayout(self.kdtext,0,1)\r\n self.slidersright.addWidget(self.kpslider,1,0)\r\n self.slidersright.addWidget(self.kdslider,1,1)\r\n\r\n self.sliders.addLayout(self.slidersleft)\r\n self.sliders.addLayout(self.slidersright)\r\n self.layout.addLayout(self.sliders)\r\n\r\n self.mainButtons = QVBoxLayout()\r\n self.pathPlanningButtons = QHBoxLayout()\r\n self.mainButton_group = QButtonGroup()\r\n\r\n self.normalMain = QRadioButton(\"Normal Main\")\r\n self.normalMain.setChecked(True)\r\n self.normalMain.toggled.connect(self.mainSwitcher)\r\n self.mainButton_group.addButton(self.normalMain)\r\n self.mainButtons.addWidget(self.normalMain)\r\n\r\n self.feedbackLinMain = QRadioButton(\"Feedback Linearization\")\r\n self.feedbackLinMain.toggled.connect(self.mainSwitcher)\r\n self.mainButton_group.addButton(self.feedbackLinMain)\r\n self.mainButtons.addWidget(self.feedbackLinMain)\r\n\r\n self.kalmanFilterMain = QRadioButton(\"Kalman Filter\")\r\n self.kalmanFilterMain.toggled.connect(self.mainSwitcher)\r\n self.mainButton_group.addButton(self.kalmanFilterMain)\r\n self.mainButtons.addWidget(self.kalmanFilterMain)\r\n\r\n self.pathPlanningMain = QRadioButton(\"Path Planning\")\r\n self.pathPlanningMain.toggled.connect(self.mainSwitcher)\r\n self.mainButton_group.addButton(self.pathPlanningMain)\r\n #attacker_defender_flag\r\n self.pathPlanning_group = QButtonGroup()\r\n self.attacker = QRadioButton(\"Offensive Mode\")\r\n self.attacker.toggled.connect(self.pathSwitcher)\r\n self.defender = QRadioButton(\"Defensive Mode\")\r\n self.defender.toggled.connect(self.pathSwitcher)\r\n self.pathPlanning_group.addButton(self.attacker)\r\n self.pathPlanning_group.addButton(self.defender)\r\n self.pathPlanningButtons.addWidget(self.pathPlanningMain)\r\n self.pathPlanningButtons.addWidget(self.attacker)\r\n self.pathPlanningButtons.addWidget(self.defender)\r\n self.mainButtons.addLayout(self.pathPlanningButtons)\r\n\r\n self.layout.addLayout(self.mainButtons)\r\n\r\n self.recordData = QCheckBox(\"Record data\")\r\n self.recordData.stateChanged.connect(self.recordFlagSwitcher)\r\n self.layout.addWidget(self.recordData)\r\n\r\n self.window.setLayout(self.layout)\r\n self.window.setSizePolicy(QSizePolicy.Preferred,QSizePolicy.Expanding)\r\n \r\n self.thread = None\r\n self.worker = None\r\n\r\n self.param1slider.valueChanged[int].connect(self.changedValue_param1)\r\n self.param2slider.valueChanged[int].connect(self.changedValue_param2)\r\n self.valueslider.valueChanged[int].connect(self.changedValue_valueMin)\r\n self.kpslider.valueChanged[int].connect(self.changedValue_kp)\r\n self.kdslider.valueChanged[int].connect(self.changedValue_kd)\r\n self.startButton.clicked.connect(self.startLoop)\r\n\r\n def startLoop(self):\r\n breakpointthing = sys.gettrace()\r\n self.thread = QThread()\r\n self.worker = Worker()\r\n self.worker.moveToThread(self.thread)\r\n\r\n self.thread.started.connect(self.worker.work)\r\n self.stopButton.clicked.connect(self.stopLoop)\r\n self.worker.finished.connect(self.thread.quit)\r\n self.worker.finished.connect(self.worker.deleteLater)\r\n self.worker.finished.connect(self.thread.deleteLater)\r\n\r\n self.thread.start()\r\n\r\n def stopLoop(self):\r\n plotData()\r\n self.worker.working = False\r\n\r\n def changedValue_param1(self, value):\r\n global param1val\r\n param1val = value\r\n self.param1value.setText(str(param1val))\r\n def changedValue_param2(self, value):\r\n global param2val\r\n param2val = value\r\n self.param2value.setText(str(param2val))\r\n def changedValue_valueMin(self, value):\r\n global valueMin\r\n valueMin = value\r\n self.valuevalue.setText(str(valueMin))\r\n def changedValue_kp(self, value):\r\n global kp\r\n kp = value/100\r\n self.kpvalue.setText(str(kp))\r\n def changedValue_kd(self, value):\r\n global kd\r\n kd = value/100\r\n self.kdvalue.setText(str(kd))\r\n\r\n def mainSwitcher(self):\r\n global radioflag\r\n if(self.normalMain.isChecked()):\r\n radioflag = 0\r\n elif(self.feedbackLinMain.isChecked()):\r\n radioflag = 1\r\n elif(self.kalmanFilterMain.isChecked()):\r\n radioflag = 2\r\n elif(self.pathPlanningMain.isChecked()):\r\n radioflag = 3\r\n\r\n def pathSwitcher(self):\r\n global attacker_defender_flag\r\n if(self.attacker.isChecked()):\r\n attacker_defender_flag = 0\r\n print(\"attacker mode\")\r\n if(self.defender.isChecked()):\r\n attacker_defender_flag = 1\r\n print(\"defender mode\")\r\n\r\n def recordFlagSwitcher(self):\r\n global recordFlag\r\n if(self.recordData.isChecked()):\r\n recordFlag = 1\r\n print(\"now recording data...\")\r\n else:\r\n recordFlag = 0\r\n\r\n\r\n sys._excepthook = sys.excepthook \r\n def exception_hook(exctype, value, traceback):\r\n # print(exctype, value, traceback)\r\n sys._excepthook(exctype, value, traceback) \r\n sys.exit(1) \r\n sys.excepthook = exception_hook \r\n\r\nif __name__== \"__main__\":\r\n run()\r\n cv2.destroyAllWindows()", "sub_path": "End of Project Final Documents/End of Project Final Code/FinalMasterDevicePythonCode.py", "file_name": "FinalMasterDevicePythonCode.py", "file_ext": "py", "file_size_in_byte": 108857, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "serial.Serial", "line_number": 27, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.euclidean", "line_number": 157, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 157, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.euclidean", "line_number": 277, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 277, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.euclidean", "line_number": 278, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 278, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.euclidean", "line_number": 279, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 279, "usage_type": "name"}, {"api_name": "math.degrees", "line_number": 281, "usage_type": "call"}, {"api_name": "math.acos", "line_number": 281, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 303, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 303, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 317, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 317, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 323, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 323, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 324, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 324, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 325, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 325, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 453, "usage_type": "call"}, {"api_name": "numpy.diff", "line_number": 457, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 458, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 458, "usage_type": "attribute"}, {"api_name": "numpy.cumsum", "line_number": 459, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 460, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 463, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 467, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 477, "usage_type": "call"}, {"api_name": "scipy.interpolate.BPoly.from_derivatives", "line_number": 479, "usage_type": "call"}, {"api_name": "scipy.interpolate.BPoly", "line_number": 479, "usage_type": "attribute"}, {"api_name": "scipy.interpolate", "line_number": 479, "usage_type": "name"}, {"api_name": "numpy.matrix", "line_number": 503, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 504, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 505, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 507, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 508, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 509, "usage_type": "call"}, {"api_name": "random.gauss", "line_number": 511, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 514, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 515, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 516, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 519, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 520, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 521, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 526, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 527, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 528, "usage_type": "call"}, {"api_name": "random.gauss", "line_number": 530, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 531, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 532, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 533, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 576, "usage_type": "call"}, {"api_name": "cv2.CAP_DSHOW", "line_number": 576, "usage_type": "attribute"}, {"api_name": "timeit.default_timer", "line_number": 597, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 604, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 606, "usage_type": "call"}, {"api_name": "cv2.bilateralFilter", "line_number": 656, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 659, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 659, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 671, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 671, "usage_type": "attribute"}, {"api_name": "cv2.HoughCircles", "line_number": 684, "usage_type": "call"}, {"api_name": "cv2.HOUGH_GRADIENT", "line_number": 684, "usage_type": "attribute"}, {"api_name": "cv2.waitKey", "line_number": 686, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 688, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 695, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 697, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 701, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 702, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 702, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 712, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 728, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 791, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 792, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 794, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 795, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 797, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 798, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 800, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 801, "usage_type": "attribute"}, {"api_name": "math.degrees", "line_number": 806, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 806, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 811, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 813, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 864, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 867, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 950, "usage_type": "call"}, {"api_name": "cv2.bilateralFilter", "line_number": 1013, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 1016, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 1016, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 1028, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 1028, "usage_type": "attribute"}, {"api_name": "cv2.HoughCircles", "line_number": 1041, "usage_type": "call"}, {"api_name": "cv2.HOUGH_GRADIENT", "line_number": 1041, "usage_type": "attribute"}, {"api_name": "cv2.waitKey", "line_number": 1043, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 1045, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 1052, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 1054, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 1058, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 1059, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 1059, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 1069, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 1085, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 1094, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 1143, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 1144, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 1146, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 1147, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 1149, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 1150, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 1152, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 1153, "usage_type": "attribute"}, {"api_name": "timeit.default_timer", "line_number": 1155, "usage_type": "call"}, {"api_name": "timeit.default_timer", "line_number": 1165, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 1182, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 1182, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 1187, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 1189, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 1239, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 1242, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 1347, "usage_type": "call"}, {"api_name": "cv2.bilateralFilter", "line_number": 1408, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 1411, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 1411, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 1423, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 1423, "usage_type": "attribute"}, {"api_name": "cv2.HoughCircles", "line_number": 1436, "usage_type": "call"}, {"api_name": "cv2.HOUGH_GRADIENT", "line_number": 1436, "usage_type": "attribute"}, {"api_name": "cv2.waitKey", "line_number": 1438, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 1440, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 1447, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 1449, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 1453, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 1454, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 1454, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 1464, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 1480, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 1489, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 1526, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 1527, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 1529, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 1530, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 1532, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 1533, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 1535, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 1536, "usage_type": "attribute"}, {"api_name": "math.degrees", "line_number": 1546, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 1546, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 1551, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 1553, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 1575, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 1577, "usage_type": "call"}, {"api_name": "numpy.linalg.multi_dot", "line_number": 1577, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 1578, "usage_type": "call"}, {"api_name": "numpy.linalg.multi_dot", "line_number": 1578, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 1579, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 1579, "usage_type": "call"}, {"api_name": "numpy.linalg.multi_dot", "line_number": 1580, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 1580, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 1580, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 1596, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 1596, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 1597, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 1597, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 1601, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 1602, "usage_type": "call"}, {"api_name": "numpy.linalg.multi_dot", "line_number": 1602, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 1603, "usage_type": "call"}, {"api_name": "numpy.linalg.multi_dot", "line_number": 1603, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 1604, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 1604, "usage_type": "call"}, {"api_name": "numpy.linalg.multi_dot", "line_number": 1606, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 1606, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 1606, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 1611, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 1612, "usage_type": "call"}, {"api_name": "numpy.linalg.multi_dot", "line_number": 1612, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 1613, "usage_type": "call"}, {"api_name": "numpy.linalg.multi_dot", "line_number": 1613, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 1614, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 1614, "usage_type": "call"}, {"api_name": "numpy.linalg.multi_dot", "line_number": 1616, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 1616, "usage_type": "call"}, {"api_name": "numpy.identity", "line_number": 1616, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 1634, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 1637, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 1727, "usage_type": "call"}, {"api_name": "cv2.bilateralFilter", "line_number": 1788, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 1791, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 1791, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 1803, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 1803, "usage_type": "attribute"}, {"api_name": "cv2.HoughCircles", "line_number": 1816, "usage_type": "call"}, {"api_name": "cv2.HOUGH_GRADIENT", "line_number": 1816, "usage_type": "attribute"}, {"api_name": "cv2.waitKey", "line_number": 1818, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 1820, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 1839, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 1841, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 1845, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 1846, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 1846, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 1856, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 1874, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 1883, "usage_type": "call"}, {"api_name": "fractions.gcd", "line_number": 1906, "usage_type": "call"}, {"api_name": "math.tan", "line_number": 1911, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 1911, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 1948, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 1949, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 1951, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 1952, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 1954, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 1955, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 1957, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_DUPLEX", "line_number": 1958, "usage_type": "attribute"}, {"api_name": "math.tan", "line_number": 1973, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 1973, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 1977, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 1978, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 1982, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 1982, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 1983, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 1986, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 1987, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 1992, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 2006, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 2007, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 2008, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 2020, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 2032, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 2032, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 2035, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 2037, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 2044, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 2044, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 2047, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 2047, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 2058, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 2059, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 2064, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 2064, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 2072, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 2074, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 2103, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 2106, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 2173, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 2178, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 2179, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 2182, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 2183, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 2184, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 2185, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 2189, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 2190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 2193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2193, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 2194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 2197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 2198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 2199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2199, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 2200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 2202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2202, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist2d", "line_number": 2205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 2206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 2207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2207, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 2208, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2208, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 2209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2209, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 2211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2211, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 2212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2212, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 2215, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2215, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 2216, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2216, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 2217, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2217, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 2218, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2218, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 2221, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2221, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 2222, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2222, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 2223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2223, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 2224, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2224, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 2225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2225, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 2227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2227, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 2228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2228, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 2229, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2229, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 2230, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2230, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 2231, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2231, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 2233, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2233, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 2234, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2234, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 2235, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2235, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 2236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2236, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 2237, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2237, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 2239, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2239, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 2240, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2240, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 2241, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2241, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 2242, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2242, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 2244, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2244, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 2245, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2245, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 2246, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2246, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 2247, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2247, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 2249, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 2249, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 2252, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 2252, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 2255, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QObject", "line_number": 2258, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 2259, "usage_type": "call"}, {"api_name": "sys.settrace", "line_number": 2267, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 2280, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 2283, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 2286, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 2290, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 2292, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 2294, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 2300, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 2308, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 2309, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QGridLayout", "line_number": 2310, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 2312, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 2313, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 2314, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QSlider", "line_number": 2315, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 2315, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 2315, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 2325, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 2326, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 2327, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QSlider", "line_number": 2328, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 2328, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 2328, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 2338, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 2339, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 2340, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QSlider", "line_number": 2341, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 2341, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 2341, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy.Preferred", "line_number": 2346, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy", "line_number": 2346, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy.Fixed", "line_number": 2346, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 2352, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 2353, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 2354, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QSlider", "line_number": 2358, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.Vertical", "line_number": 2358, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 2358, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy.Fixed", "line_number": 2363, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy", "line_number": 2363, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy.Expanding", "line_number": 2363, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 2365, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 2366, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 2367, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QSlider", "line_number": 2371, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.Vertical", "line_number": 2371, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 2371, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy.Fixed", "line_number": 2376, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy", "line_number": 2376, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy.Expanding", "line_number": 2376, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 2387, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 2388, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QButtonGroup", "line_number": 2389, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 2391, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 2397, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 2402, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 2407, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QButtonGroup", "line_number": 2411, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 2412, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 2414, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QCheckBox", "line_number": 2425, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy.Preferred", "line_number": 2430, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy", "line_number": 2430, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSizePolicy.Expanding", "line_number": 2430, "usage_type": "attribute"}, {"api_name": "sys.gettrace", "line_number": 2443, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QThread", "line_number": 2444, "usage_type": "call"}, {"api_name": "sys._excepthook", "line_number": 2510, "usage_type": "attribute"}, {"api_name": "sys.excepthook", "line_number": 2510, "usage_type": "attribute"}, {"api_name": "sys._excepthook", "line_number": 2513, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 2514, "usage_type": "call"}, {"api_name": "sys.excepthook", "line_number": 2515, "usage_type": "attribute"}, {"api_name": "cv2.destroyAllWindows", "line_number": 2519, "usage_type": "call"}]} +{"seq_id": "230690720", "text": "#!/usr/bin/python\n# author: Mrinmoy sarkar\n# date: 13 FEB 2019\n# email: mrinmoy.pol@gmail.com\n# team: AM2X\n\nfrom amase.TCPClient import AmaseTCPClient\nfrom amase.TCPClient import IDataReceived\nfrom afrl.cmasi.searchai.HazardZoneEstimateReport import HazardZoneEstimateReport\nfrom afrl.cmasi.Circle import Circle\nfrom afrl.cmasi.Polygon import Polygon\nfrom afrl.cmasi.Waypoint import Waypoint\nfrom afrl.cmasi.TurnType import TurnType\nfrom afrl.cmasi.VehicleActionCommand import VehicleActionCommand\nfrom afrl.cmasi.LoiterAction import LoiterAction\nfrom afrl.cmasi.LoiterType import LoiterType\nfrom afrl.cmasi.LoiterDirection import LoiterDirection\nfrom afrl.cmasi.CommandStatusType import CommandStatusType\nfrom afrl.cmasi.AltitudeType import AltitudeType\nfrom afrl.cmasi.searchai.HazardZoneDetection import HazardZoneDetection\nfrom afrl.cmasi.searchai.HazardType import HazardType\nfrom afrl.cmasi.Location3D import Location3D\nfrom afrl.cmasi.Rectangle import Rectangle\nfrom afrl.cmasi.FlightDirectorAction import FlightDirectorAction\nfrom afrl.cmasi.SpeedType import SpeedType\nfrom afrl.cmasi.AltitudeType import AltitudeType\nfrom afrl.cmasi.AirVehicleState import AirVehicleState\nfrom afrl.cmasi.MissionCommand import MissionCommand\nfrom afrl.cmasi.SessionStatus import SessionStatus\nfrom afrl.cmasi.GoToWaypointAction import GoToWaypointAction\nfrom afrl.cmasi.GimbalAngleAction import GimbalAngleAction\nfrom lmcp import LMCPFactory\nfrom afrl.cmasi.KeepInZone import KeepInZone\nfrom afrl.cmasi.searchai.HazardZone import HazardZone\nfrom afrl.cmasi.searchai.HazardZoneChangeCommand import HazardZoneChangeCommand\nfrom afrl.cmasi.AirVehicleConfiguration import AirVehicleConfiguration\nfrom afrl.cmasi.WeatherReport import WeatherReport\nfrom math import sin,cos,atan2,pi,radians,sqrt,tan,pi\nfrom random import randint\nimport pandas as pd\nimport numpy as np\nfrom afrl.cmasi.searchai.HazardType import HazardType\nfrom afrl.cmasi.NavigationMode import NavigationMode\nfrom afrl.cmasi.EntityConfiguration import EntityConfiguration\nfrom afrl.cmasi.EntityState import EntityState\nfrom afrl.cmasi.searchai.RecoveryPoint import RecoveryPoint\nfrom afrl.cmasi.perceive.EntityPerception import EntityPerception\nfrom afrl.cmasi.RemoveEntities import RemoveEntities\nfrom afrl.cmasi.GimbalScanAction import GimbalScanAction\nimport time\nfrom sklearn.cluster import DBSCAN\nfrom sklearn.cluster import MeanShift\nfrom sklearn import metrics\nfrom sklearn.preprocessing import StandardScaler\nfrom scipy.spatial.distance import pdist,squareform\n\nimport cv2\n\n# import utm\n\nfilePath = '../../altitude_data/'\n\nclass PrintLMCPObject(IDataReceived):\n def dataReceived(self, lmcpObject):\n print(lmcpObject.toXMLStr(\"\"))\n \nclass SampleHazardDetector(IDataReceived):\n\n def __init__(self, tcpClient):\n self.__client = tcpClient\n \n self.__estimatedHazardZone = Polygon()\n self.__keepInZone = Rectangle()\n self.__currentLocationofUAV = {}\n self.__searchAreaCenterLat = 0\n self.__searchAreaCenterLong = 0\n self.__searchAreaWidth = 0\n self.__searchAreaHeight = 0\n self.__firezonePoints = {}\n self.__firezoneHintLocation = {}\n self.__centerLocation = Location3D()\n self.__gotHint = False\n \n self.anchor = []\n \n self.__noOfUAVs = 0\n self.__sendReport = False\n self.__maxSpeedofUAV = {}\n self.__resulationOfGrid = 1000\n self.__minidel = 500\n \n self.__waypoints = {}\n self.__uavsInMission = {}\n self.__MissionReady = False\n self.__noOfZone = 0\n self.__zoneassigned = {}\n \n self.__zoneCenter = {}\n self.__zoneboundaryPoints = {}\n \n self.altidata1 = pd.read_csv(filePath+'altidata1.csv',header=None)\n self.altidata1 = self.altidata1.T\n self.altidata2 = pd.read_csv(filePath+'altidata2.csv',header=None)\n self.altidata2 = self.altidata2.T\n self.altidata3 = pd.read_csv(filePath+'altidata3.csv',header=None)\n self.altidata3 = self.altidata3.T\n self.altidata4 = pd.read_csv(filePath+'altidata4.csv',header=None)\n self.altidata4 = self.altidata4.T\n\n self.__safeHeight = 100 # this value is substracted from the max range of the sensor\n self.__surveySafeHeight = 300\n self.__normalSearchAltitude = 450\n \n self.__initLocationOfUAVs = {}\n \n self.__maxAzimuthangle = {}\n self.__minAzimuthangle = {}\n self.__uavsInSearch = {}\n self.__uavsInSarvey = {}\n self.__uavisHeadingtoSurveylocation = {}\n self.__uavisInsmokeZone = {}\n self.__UAVSurvayingZoneId = {}\n \n self.__previousreportsendTime = 0\n self.__previousWeatherReportTime = 0\n \n self.__wspeed = 0\n self.__ditectionTheta = 0\n \n self.__totalWaypointsassignedToUAV = {}\n self.__previouswaypointNo = {}\n self.__visitedTotalwaypoints = {}\n self.__updateArea = False\n \n self.__currentVicleState = {}\n self.__simulationTimemilliSeconds = 0\n self.__hazardSensorStatus = {}\n self.__sensorRefreshrate = 1.0\n self.__recoveryPoints = []\n self.__entityConfigList = []\n self.__airvehicleConfigList = []\n self.__removedairVeicleList = []\n self.__currentEntityState = {}\n self.__uavsInZone = {}\n self.__maxSpeedGlobal = 0\n self.__sensorMaxrange = {}\n self.__windspeedupdateTime = 2000 #in milisecond\n self.__maxsurvayUAVForzone = 3\n self.__maxSpeedForsurvey = 25\n self.__surveyCircleRadius = 1000\n self.__searchCircleRadius = 3000\n self.__uavRecharging = {}\n\n self.__secondaryMergeThreshold = 0\n self.__globalMap = None\n self.__dgrid = None\n self.__glopbalmaxforpercentarea = 0\n self.__boundaryparameterFornewMission = [0,0,0,0]\n self.__stopRecursion = False\n self.__mapResulotion = 50 #in meter\n self.__initialSmallGridW = 0\n self.__initialSmallGridH = 0\n self.__uavInSmokemisssion = {}\n self.__uavisHeadingtoSmokeSurveylocation = {}\n self.__energyThreshold = 90\n self.__energyThresholddist = 4000 #3000\n self.__maxSpeedofUAVduringSurvey = {}\n self.__energyconsumptionRate = 0.1\n self.__mapHold = {}\n self.__secondarysearchareaW = 10000 #in meter\n self.__secondarysearchareaH = 10000 #in meter\n self.__altitudetype = AltitudeType.AGL\n self.__radiusForDeleteOldSample = 100\n self.__minimumNumberOfSamplestokeept = 10\n self.__debug = False\n self.__mission = {}\n self.zoneassigned = {}\n \n def dataReceived(self, lmcpObject):\n if self.__debug:\n print(\"dataReceived enter\")\n if isinstance(lmcpObject, KeepInZone):\n self.__keepInZone = lmcpObject.Boundary\n centerpoint = lmcpObject.Boundary.get_CenterPoint()\n self.__centerLocation = centerpoint\n self.__searchAreaCenterLat = centerpoint.get_Latitude()\n self.__searchAreaCenterLong = centerpoint.get_Longitude()\n self.__searchAreaWidth = lmcpObject.Boundary.get_Width()/2.0 - 500\n self.__searchAreaHeight = lmcpObject.Boundary.get_Height()/2.0 - 500\n\n row = int(lmcpObject.Boundary.get_Width()/self.__mapResulotion)\n col = int(lmcpObject.Boundary.get_Height()/self.__mapResulotion)\n self.__globalMap = np.zeros([row,col])\n self.__dgrid = np.zeros([row,col])\n \n print('found keep in zone') \n \n elif isinstance(lmcpObject, RecoveryPoint):\n self.__recoveryPoints.append(lmcpObject.Boundary)\n \n elif isinstance(lmcpObject, AirVehicleState):\n airVehicleState = lmcpObject\n self.__simulationTimemilliSeconds = airVehicleState.Time\n self.__currentVicleState[airVehicleState.ID] = airVehicleState\n \n if self.__simulationTimemilliSeconds == 0:\n self.__initLocationOfUAVs[airVehicleState.ID] = airVehicleState.Location\n self.__currentLocationofUAV[airVehicleState.ID] = airVehicleState.Location\n \n \n if self.__simulationTimemilliSeconds > 0: \n if (self.__simulationTimemilliSeconds - self.__previousWeatherReportTime) > self.__windspeedupdateTime and airVehicleState.WindSpeed > 0:\n self.__previousWeatherReportTime = self.__simulationTimemilliSeconds\n self.__wspeed = airVehicleState.WindSpeed\n self.__ditectionTheta = airVehicleState.WindDirection\n self.__updateArea = True\n elif airVehicleState.WindSpeed == 0:\n self.__wspeed = 0\n self.__ditectionTheta = 0\n self.__updateArea = False\n\n elif isinstance(lmcpObject, EntityPerception):\n pass\n\n elif isinstance(lmcpObject, RemoveEntities):\n self.__removedairVeicleList.append(lmcpObject.EntityList[0])\n for airVehicleConfig in self.__airvehicleConfigList:\n if airVehicleConfig.ID == lmcpObject.EntityList[0]:\n self.__airvehicleConfigList.remove(airVehicleConfig)\n break\n\n elif isinstance(lmcpObject, AirVehicleConfiguration):\n airvehicleConfiguration = lmcpObject\n self.__maxSpeedofUAV[airvehicleConfiguration.ID] = airvehicleConfiguration.get_MaximumSpeed()\n payloadconfigList = airvehicleConfiguration.PayloadConfigurationList\n self.__maxAzimuthangle[airvehicleConfiguration.ID] = payloadconfigList[0].MaxAzimuth\n self.__minAzimuthangle[airvehicleConfiguration.ID] = payloadconfigList[0].MinAzimuth\n self.__sensorMaxrange[airvehicleConfiguration.ID] = airvehicleConfiguration.PayloadConfigurationList[2].MaxRange\n self.__airvehicleConfigList.append(airvehicleConfiguration)\n self.sendGimbleScanCommand(airvehicleConfiguration.ID,airvehicleConfiguration.PayloadConfigurationList[0].MaxAzimuthSlewRate)\n \n elif isinstance(lmcpObject, EntityConfiguration):\n self.__entityConfigList.append(lmcpObject)\n \n elif isinstance(lmcpObject, HazardZoneDetection):\n hazardDetected = lmcpObject\n detectedLocation = hazardDetected.get_DetectedLocation()\n detectingEntity = hazardDetected.get_DetectingEnitiyID()\n \n fireZoneType = hazardDetected.get_DetectedHazardZoneType()\n \n self.__hazardSensorStatus[detectingEntity] = time.time()\n\n if fireZoneType == HazardType.Fire:\n self.__maxSpeedofUAVduringSurvey[detectingEntity] = self.__maxSpeedofUAV[detectingEntity] if self.__maxSpeedofUAV[detectingEntity] <= self.__maxSpeedForsurvey else self.__maxSpeedForsurvey ## play here\n\n self.__uavsInSarvey[detectingEntity] = True\n \n self.__gotHint = True\n \n [x,y] = self.convertLatLonToxy(detectedLocation.get_Latitude(),detectedLocation.get_Longitude())\n zid = self.getZoneId([x,y])\n if not zid in self.__uavsInZone:\n self.__uavsInZone[zid] = [detectingEntity]\n else:\n if not detectingEntity in self.__uavsInZone[zid]:\n self.__uavsInZone[zid].append(detectingEntity)\n\n self.__firezoneHintLocation[detectingEntity] = detectedLocation\n self.__UAVSurvayingZoneId[detectingEntity] = zid\n if not self.__firezonePoints or not zid in self.__firezonePoints:\n self.__firezonePoints[zid] = [[x,y,time.time()]]\n else:\n self.__firezonePoints[zid].append([x,y,time.time()])\n elif fireZoneType == HazardType.Smoke:\n # print('smoke detected')\n self.__uavisInsmokeZone[detectingEntity] = True\n # pass\n if self.__debug:\n print(\"dataReceived exit\")\n \n def sendMissionCommand(self,veicleid,veicleLocation):\n if self.__debug:\n print(\"sendMissionCommand enter\")\n missionCommand = MissionCommand()\n missionCommand.set_VehicleID(veicleid)\n missionCommand.set_Status(CommandStatusType.Pending)\n missionCommand.set_CommandID(1)\n\n zid,locid = self.getNearestZone(veicleLocation,veicleid)\n vstate = self.getAirVeicleState(veicleid)\n safeHeight = abs(self.__sensorMaxrange[veicleid] * sin(radians(vstate.PayloadStateList[0].Elevation))) - self.__safeHeight\n \n missionCommand.set_FirstWaypoint(locid)\n \n i = 0\n waypoints = self.__waypoints[zid]\n waypointaltimap,waypointconnectingmap = self.mapaltiwithwaypointnumber(waypoints)\n for waypoint in waypoints:\n i = i+1\n alti = max(waypointaltimap[waypoint.get_Number()], waypointaltimap[waypoint.get_NextWaypoint()], waypointaltimap[waypointconnectingmap[waypoint.get_NextWaypoint()]])\n waypoint.set_Altitude(alti + safeHeight)\n waypoint.set_Speed(self.__maxSpeedofUAV[veicleid])\n missionCommand.get_WaypointList().append(waypoint)\n\n self.__totalWaypointsassignedToUAV[veicleid] = i\n self.__visitedTotalwaypoints[veicleid] = 0\n self.__previouswaypointNo[veicleid] = locid\n self.__uavsInMission[veicleid] = True\n\n self.__client.sendLMCPObject(missionCommand) \n if self.__debug:\n print(\"sendMissionCommand exit\") \n \n def sendWaypoint(self,veicleid,initLocation,endLocation,radius=0,torecharge=False):\n if self.__debug:\n print(\"sendWaypoint enter\")\n if radius == 0:\n radius = self.__searchCircleRadius\n missionCommand = MissionCommand()\n missionCommand.set_FirstWaypoint(1)\n missionCommand.set_VehicleID(veicleid)\n missionCommand.set_Status(CommandStatusType.Pending)\n missionCommand.set_CommandID(1)\n\n vstate = self.getAirVeicleState(veicleid)\n safeHeight = abs(self.__sensorMaxrange[veicleid] * sin(radians(vstate.PayloadStateList[0].Elevation))) - self.__safeHeight\n \n\n waypoints = self.getwaypointsBetweenLocations(initLocation,endLocation,veicleid,radius)\n waypointaltimap,waypointconnectingmap = self.mapaltiwithwaypointnumber(waypoints)\n\n i = 0\n for waypoint in waypoints:\n i += 1\n if torecharge:\n alti = 3000#max(list(waypointaltimap.values()))\n else:\n alti = max(waypointaltimap[waypoint.get_Number()], waypointaltimap[waypoint.get_NextWaypoint()], waypointaltimap[waypointconnectingmap[waypoint.get_NextWaypoint()]])\n waypoint.set_Altitude(alti + safeHeight)\n waypoint.set_Speed(self.__maxSpeedofUAV[veicleid])\n missionCommand.get_WaypointList().append(waypoint)\n\n self.__totalWaypointsassignedToUAV[veicleid] = i\n self.__visitedTotalwaypoints[veicleid] = 0\n self.__previouswaypointNo[veicleid] = 1\n self.__uavsInMission[veicleid] = True \n self.__client.sendLMCPObject(missionCommand)\n if self.__debug:\n print(\"sendWaypoint exit\")\n \n def sendOnlywaypoints(self,vstate,waypoints,firstWaypoint=1):\n if self.__debug:\n print(\"sendOnlywaypoints enter\")\n veicleid = vstate.ID\n missionCommand = MissionCommand()\n missionCommand.set_FirstWaypoint(firstWaypoint)\n missionCommand.set_VehicleID(veicleid)\n missionCommand.set_Status(CommandStatusType.Pending)\n missionCommand.set_CommandID(1)\n\n safeHeight = abs(self.__sensorMaxrange[vstate.ID] * sin(radians(vstate.PayloadStateList[0].Elevation))) - self.__safeHeight\n \n waypointaltimap,waypointconnectingmap = self.mapaltiwithwaypointnumber(waypoints)\n\n i = 0\n for waypoint in waypoints:\n i += 1\n alti = max(waypointaltimap[waypoint.get_Number()], waypointaltimap[waypoint.get_NextWaypoint()], waypointaltimap[waypointconnectingmap[waypoint.get_NextWaypoint()]])\n waypoint.set_Altitude(alti + safeHeight)\n waypoint.set_Speed(self.__maxSpeedofUAV[veicleid])\n missionCommand.get_WaypointList().append(waypoint)\n\n self.__totalWaypointsassignedToUAV[veicleid] = i\n self.__visitedTotalwaypoints[veicleid] = 0\n self.__previouswaypointNo[veicleid] = firstWaypoint\n self.__uavsInMission[veicleid] = True\n self.__client.sendLMCPObject(missionCommand)\n if self.__debug:\n print(\"sendOnlywaypoints exit\")\n\n def sendGimbleScanCommand(self,veicleid,slewRate):\n if self.__debug:\n print(\"sendGimbleScanCommand enter\")\n #Setting up the mission to send to the UAV\n vehicleActionCommand = VehicleActionCommand()\n vehicleActionCommand.set_VehicleID(veicleid)\n vehicleActionCommand.set_Status(CommandStatusType.Pending)\n vehicleActionCommand.set_CommandID(1)\n \n \n gimbalScanAction = GimbalScanAction()\n gimbalScanAction.set_PayloadID(1)\n gimbalScanAction.set_AzimuthSlewRate(60)\n gimbalScanAction.set_StartAzimuth(90)\n gimbalScanAction.set_EndAzimuth(-90)\n gimbalScanAction.set_ElevationSlewRate(10)\n gimbalScanAction.set_StartElevation(-45)\n gimbalScanAction.set_EndElevation(-45)\n \n vehicleActionCommand.get_VehicleActionList().append(gimbalScanAction)\n \n self.__client.sendLMCPObject(vehicleActionCommand) \n if self.__debug:\n print(\"sendGimbleScanCommand exit\") \n\n def sendEstimateReport(self,zid):\n if self.__debug:\n print(\"sendEstimateReport enter\")\n #Setting up the mission to send to the UAV\n hazardZoneEstimateReport = HazardZoneEstimateReport()\n hazardZoneEstimateReport.set_EstimatedZoneShape(self.__estimatedHazardZone)\n hazardZoneEstimateReport.set_UniqueTrackingID(zid)\n hazardZoneEstimateReport.set_EstimatedGrowthRate(0)\n hazardZoneEstimateReport.set_PerceivedZoneType(HazardType.Fire)\n hazardZoneEstimateReport.set_EstimatedZoneDirection(0)\n hazardZoneEstimateReport.set_EstimatedZoneSpeed(0)\n\n #Sending the Vehicle Action Command message to AMASE to be interpreted\n self.__client.sendLMCPObject(hazardZoneEstimateReport)\n if self.__debug:\n print(\"sendEstimateReport exit\")\n \n def sendinitialMission(self):\n if self.__debug:\n print(\"sendinitialMission enter\")\n maxSpeed = max(self.__maxSpeedofUAV.values())\n self.__maxSpeedGlobal = maxSpeed\n zi = 0\n for id in self.__maxSpeedofUAV.keys():\n if self.__maxSpeedofUAV[id] == maxSpeed:\n self.sendMissionCommand(id,self.__initLocationOfUAVs[id])\n self.__uavsInMission[id] = True\n self.__uavsInSearch[id] = True\n zi += 1\n if zi == self.__noOfZone:\n break\n \n self.zoneassigned = {}\n for zid in range(1,self.__noOfZone+1):\n self.zoneassigned[zid] = False\n\n for id in self.__maxSpeedofUAV.keys():\n if not id in self.__uavsInMission:\n mind = 10e10\n minid = 0\n minLoc = Location3D()\n minendloc = Location3D()\n minzid = 1\n for zid in range(1,self.__noOfZone+1):\n # print('zloc', self.__zoneCenter[zid])\n if not self.zoneassigned[zid]: \n endLoc = self.__zoneCenter[zid]\n startLoc = self.__initLocationOfUAVs[id]\n d = self.getdistance(startLoc,endLoc)\n if d < mind:\n mind = d\n minid = id\n minLoc = startLoc\n minzid = zid\n minendloc = endLoc\n if minid != 0: \n self.__uavsInMission[minid] = True\n self.sendWaypoint(minid,minLoc,minendloc) \n self.zoneassigned[minzid] = True \n # print('minzid', minzid,'minvid',minid,'endloc',minendloc)\n if self.__debug:\n print(\"sendinitialMission exit\") \n \n def sendSmokeZonemission(self,vstate):\n if self.__debug:\n print(\"sendSmokeZonemission enter\")\n # if self.__maxSpeedofUAV[vstate.ID] < self.__maxSpeedGlobal:\n # print('sendSmokeZonemission',vstate.ID)\n if vstate.EnergyAvailable > 95:\n if (not vstate.ID in self.__uavsInSarvey or (vstate.ID in self.__uavsInSarvey and not self.__uavsInSarvey[vstate.ID])) and (not vstate.ID in self.__uavisHeadingtoSurveylocation):\n waypoints,firstWaypoint = self.smokeZoneMission(vstate)\n self.sendOnlywaypoints(vstate,waypoints,firstWaypoint=firstWaypoint)\n self.__uavInSmokemisssion[vstate.ID] = True\n else:\n # call a available low speed uav\n self.callUAVSForSmokeZoneSurvey(vstate)\n # assign a mission\n waypoints,firstWaypoint = self.smokeZoneMission(vstate)\n self.sendOnlywaypoints(vstate,waypoints,firstWaypoint=firstWaypoint)\n self.__uavInSmokemisssion[vstate.ID] = True\n if self.__debug:\n print(\"sendSmokeZonemission exit\")\n\n def smokeZoneMission(self,vstate): # needs to be debugged\n if self.__debug:\n print(\"smokeZoneMission enter\")\n [xc,yc] = self.convertLatLonToxy(vstate.Location.get_Latitude(),vstate.Location.get_Longitude()) \n r = self.__searchCircleRadius + 1000\n direction = 0\n points = self.GenerateSamplePointsOnACircleforSurvey(xc,yc,r,vstate.Heading,direction)\n vid = vstate.ID\n safeHeight = abs(self.__sensorMaxrange[vid] * sin(radians(vstate.PayloadStateList[0].Elevation))) - self.__safeHeight\n waypoints = []\n for i in range(1,len(points)+1):\n p = points[i-1]\n x = p[0]\n y = p[1]\n [lat,lon] = self.convertxyToLatLon(x,y)\n waypoint = Waypoint()\n waypoint.set_Latitude(lat)\n waypoint.set_Longitude(lon)\n alti = self.getAltitudeLatLon(lat,lon) \n # if alti < self.__normalSearchAltitude:\n # waypoint.set_Altitude(self.__normalSearchAltitude)\n # else:\n waypoint.set_Altitude(alti + safeHeight) #self.__safeHeight)\n waypoint.set_AltitudeType(self.__altitudetype)\n waypoint.set_Number(i)\n if i == len(points):\n waypoint.set_NextWaypoint(1)\n else:\n waypoint.set_NextWaypoint(i+1)\n waypoint.set_Speed(self.__maxSpeedofUAV[vid])\n waypoint.set_SpeedType(SpeedType.Airspeed)\n waypoint.set_ClimbRate(0)\n waypoint.set_TurnType(TurnType.TurnShort)\n waypoint.set_ContingencyWaypointA(0)\n waypoint.set_ContingencyWaypointB(0)\n waypoints.append(waypoint)\n minima = 1e10\n minLocid = 1\n minLoc = Location3D()\n \n for i in range(len(waypoints)):\n loc = waypoints[i]\n d = self.getdistance(loc,vstate.Location)\n if d < minima:\n minima = d\n minLoc = loc\n minLocid = i+1\n \n if sqrt(minima) < 1000:\n if self.__debug:\n print(\"smokeZoneMission exit\")\n return waypoints,minLocid\n \n waypoints1,minLocid = self.getBetweenLatLon(vstate.Location,minLoc,waypointNumber,minima,minLocid,vstate.ID)\n waypoints = waypoints1 + waypoints\n if self.__debug:\n print(\"smokeZoneMission exit\")\n return waypoints,minLocid\n\n def callUAVSForSmokeZoneSurvey(self,vstate): \n if self.__debug:\n print(\"callUAVSForSmokeZoneSurvey enter\")\n for uav in self.__airvehicleConfigList:\n if uav.ID in self.__uavisHeadingtoSmokeSurveylocation or uav.ID in self.__uavInSmokemisssion:\n vstate1 = self.getAirVeicleState(uav.ID)\n d = self.getdistance(vstate.Location,vstate1.Location)\n if d**0.5 < 10000:\n if self.__debug:\n print(\"callUAVSForSmokeZoneSurvey exit\")\n return\n # print('call uav for survey smoke')\n mind = 10e20\n vid = -1\n for uav in self.__airvehicleConfigList:\n if (not uav.ID in self.__uavsInSearch) and (not uav.ID in self.__uavsInSarvey) and (not uav.ID in self.__uavisHeadingtoSurveylocation) and (not uav.ID in self.__uavisHeadingtoSmokeSurveylocation) and (self.__maxSpeedGlobal > uav.MaximumSpeed):\n vstate1 = self.getAirVeicleState(uav.ID)\n d = self.getdistance(vstate.Location,vstate1.Location)\n if d < mind:\n mind = d\n vid = vstate1.ID\n minLoc = vstate1.Location\n if vid != -1:\n self.__uavisHeadingtoSmokeSurveylocation[vid] = True\n self.sendWaypoint(vid,minLoc,vstate.Location) \n else:\n if self.__debug:\n print(\"callUAVSForSmokeZoneSurvey exit\")\n return\n if self.__debug:\n print(\"callUAVSForSmokeZoneSurvey exit\")\n\n def sendServeyCommand(self,vstate,direction,r=0,speed=0):\n if self.__debug:\n print(\"sendServeyCommand enter\")\n veicleid = vstate.ID\n [xc,yc] = self.convertLatLonToxy(vstate.Location.Latitude,vstate.Location.Longitude)\n if r==0:\n r = self.__surveyCircleRadius\n if speed == 0:\n speed = self.__maxSpeedofUAV[veicleid]\n points = self.GenerateSamplePointsOnACircleforSurvey(xc,yc,r,vstate.Heading,direction)\n vid = vstate.ID\n safeHeight = abs(self.__sensorMaxrange[vid] * sin(radians(vstate.PayloadStateList[0].Elevation))) - self.__safeHeight\n missionCommand = MissionCommand()\n missionCommand.set_FirstWaypoint(1)\n missionCommand.set_VehicleID(vid)\n missionCommand.set_Status(CommandStatusType.Pending)\n missionCommand.set_CommandID(1)\n for i in range(1,len(points)+1):\n p = points[i-1]\n x = p[0]\n y = p[1]\n [lat,lon] = self.convertxyToLatLon(x,y)\n waypoint = Waypoint()\n waypoint.set_Latitude(lat)\n waypoint.set_Longitude(lon)\n alti = self.getAltitudeLatLon(lat,lon) \n # if alti < self.__normalSearchAltitude:\n # waypoint.set_Altitude(self.__normalSearchAltitude)\n # else:\n waypoint.set_Altitude(alti + safeHeight) #self.__safeHeight)\n waypoint.set_AltitudeType(self.__altitudetype)\n waypoint.set_Number(i)\n if i == len(points):\n waypoint.set_NextWaypoint(1)\n else:\n waypoint.set_NextWaypoint(i+1)\n waypoint.set_Speed(speed)\n waypoint.set_SpeedType(SpeedType.Airspeed)\n waypoint.set_ClimbRate(0)\n waypoint.set_TurnType(TurnType.TurnShort)\n waypoint.set_ContingencyWaypointA(0)\n waypoint.set_ContingencyWaypointB(0)\n missionCommand.get_WaypointList().append(waypoint)\n \n self.__visitedTotalwaypoints[vstate.ID] = 0\n self.__previouswaypointNo[vstate.ID] = 1 \n self.__totalWaypointsassignedToUAV[vstate.ID] = i\n self.__uavsInMission[veicleid] = True\n\n self.__client.sendLMCPObject(missionCommand)\n if self.__debug:\n print(\"sendServeyCommand exit\")\n\n def callUAVSForSurvey(self,vstate):\n if self.__debug:\n print(\"callUAVSForSurvey enter\")\n while True:\n mind = 10e20\n vid = -1\n zid = self.__UAVSurvayingZoneId[vstate.ID]\n noofsurvayUAVinZone = len(self.__uavsInZone[zid])\n if noofsurvayUAVinZone >= self.__maxsurvayUAVForzone:\n if self.__debug:\n print(\"callUAVSForSurvey exit\")\n return \n\n for uav in self.__airvehicleConfigList:\n if (not uav.ID in self.__uavsInSearch) and (not uav.ID in self.__uavsInSarvey) and (not uav.ID in self.__uavisHeadingtoSurveylocation) and (self.__maxSpeedGlobal > uav.MaximumSpeed):\n vstate1 = self.getAirVeicleState(uav.ID)\n d = self.getdistance(vstate.Location,vstate1.Location)\n if d < mind:\n mind = d\n vid = vstate1.ID\n minLoc = vstate1.Location\n if vid != -1:\n self.__uavisHeadingtoSurveylocation[vid] = True\n self.__UAVSurvayingZoneId[vid] = zid\n self.__uavsInZone[zid].append(vid)\n self.sendWaypoint(vid,minLoc,vstate.Location) \n else:\n if self.__debug:\n print(\"callUAVSForSurvey exit\")\n return\n if self.__debug:\n print(\"callUAVSForSurvey exit\")\n\n def convertLatLonToxy(self,lat,long):\n if self.__debug:\n print(\"convertLatLonToxy enter\")\n R = 111000\n a = lat-self.__searchAreaCenterLat\n b = long-self.__searchAreaCenterLong\n x = R*a\n y = R*cos(radians(lat))*b\n if self.__debug:\n print(\"convertLatLonToxy exit\")\n return [x,y]\n \n def convertxyToLatLon(self,x,y):\n if self.__debug:\n print(\"convertxyToLatLon enter\")\n R = 111000\n lat = x/R + self.__searchAreaCenterLat\n long = y/(R*cos(radians(lat))) + self.__searchAreaCenterLong\n if self.__debug:\n print(\"convertxyToLatLon exit\")\n return [lat,long]\n \n def getCenterofRecoveryZone(self):\n ##############\n self.__mission = {}\n allxypoints = []\n recovpointkey = 1\n assignedMissionNo = {}\n for recovepoint in self.__recoveryPoints:\n [x,y] = self.convertLatLonToxy(recovepoint.CenterPoint.get_Latitude(),recovepoint.CenterPoint.get_Longitude())\n allxypoints.append([x,y])\n self.GenerateVisitPoints([x,y],recovpointkey)\n assignedMissionNo[recovpointkey] = 0\n recovpointkey += 1\n\n # print('error here')\n for uav in self.__airvehicleConfigList:\n vstate = self.__currentVicleState[uav.ID]\n loc = vstate.Location\n recovpointkey = 1\n for recovepoint in self.__recoveryPoints:\n [x,y] = self.convertLatLonToxy(recovepoint.CenterPoint.get_Latitude(),recovepoint.CenterPoint.get_Longitude())\n [x1,y1] = self.convertLatLonToxy(loc.get_Latitude(),loc.get_Longitude())\n if ((x-x1)**2+(y-y1)**2)**0.5 < 5000:\n if assignedMissionNo[recovpointkey] in self.__mission[recovpointkey].keys():\n self.sendOnlywaypoints(vstate,self.__mission[recovpointkey][assignedMissionNo[recovpointkey]])\n assignedMissionNo[recovpointkey] += 1\n break\n recovpointkey += 1\n\n centerForSearch = np.mean(np.array(allxypoints),axis=0)\n zlocation = Location3D()\n [lat,lon] = self.convertxyToLatLon(centerForSearch[0],centerForSearch[1])\n zlocation.set_Latitude(lat)\n zlocation.set_Longitude(lon)\n # self.sendOnlywaypoints(self.__currentVicleState[1],self.__mission[0])\n # self.sendWaypoint(1,self.__initLocationOfUAVs[1],zlocation)\n\n\n # print(centerForSearch)\n\n def GenerateVisitPoints(self,center,recovpointkey,r_min=5000,r_max=10001,r=5000):\n # print('GenerateVisitPoints')\n radiusvec = np.arange(r_min,r_max,r)\n missionkey = 0\n mission = {}\n for rad in radiusvec:\n premiter = 2*rad*pi + 2*rad\n if premiter <= 30000.0:\n mission[missionkey] = self.getSegmentofCircle(center[0],center[1],rad,0,360)\n missionkey += 1 \n else:\n Nm = int(premiter / 30000.0)\n StepSize = int(360/Nm)\n for i in range(Nm):\n mission[missionkey] = self.getSegmentofCircle(center[0],center[1],rad,i*StepSize,(i+1)*StepSize)\n missionkey += 1\n self.__mission[recovpointkey] = mission\n\n def getSegmentofCircle(self,xc,yc,r,startTheta,endTheta):\n # print('getSegmentofCircle')\n Points = []\n StepSize = 5\n Np = round((endTheta-startTheta)/StepSize)\n for i in range(Np):\n angle = startTheta + i * StepSize\n xi = r * cos(radians(angle))\n yi = r * sin(radians(angle))\n Cx = xc + xi\n Cy = yc + yi\n if abs(Cx) > self.__searchAreaWidth:\n if Cx < 0:\n Cx = -self.__searchAreaWidth\n else:\n Cx = self.__searchAreaWidth\n if abs(Cy) > self.__searchAreaWidth:\n if Cy < 0:\n Cy = -self.__searchAreaHeight\n else:\n Cy = self.__searchAreaHeight\n\n Points.append([Cx,Cy])\n waypoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(xc,yc,Points[0][0],Points[0][1],1,0)\n \n for i in range(1,len(Points)):\n [lat,lon] = self.convertxyToLatLon(Points[i][0],Points[i][1])\n waypoint = Waypoint()\n waypoint.set_Latitude(lat)\n waypoint.set_Longitude(lon)\n alti = self.getAltitudeLatLon(lat,lon) \n if alti < self.__normalSearchAltitude:\n waypoint.set_Altitude(self.__normalSearchAltitude)\n else:\n waypoint.set_Altitude(alti + self.__safeHeight)\n waypoint.set_AltitudeType(self.__altitudetype)\n waypoint.set_Number(waypointNumber)\n waypoint.set_NextWaypoint(waypointNumber+1)\n waypoint.set_Speed(30)\n waypoint.set_SpeedType(SpeedType.Airspeed)\n waypoint.set_ClimbRate(15)\n waypoint.set_TurnType(TurnType.TurnShort)\n waypoint.set_ContingencyWaypointA(0)\n waypoint.set_ContingencyWaypointB(0)\n waypointNumber = waypointNumber+1\n waypoints.append(waypoint)\n waypoints1,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(Points[-1][0],Points[-1][1],xc,yc,waypointNumber,1)\n waypoints += waypoints1\n return waypoints \n\n def GenerateSamplePointsOnACircle(self,x,y,r):\n if self.__debug:\n print(\"GenerateSamplePointsOnACircle enter\")\n Points = []\n StepSize = 30\n Np = round(360/StepSize)\n for i in range(Np):\n angle = 360 - i * StepSize\n xi = r * cos(radians(angle))\n yi = r * sin(radians(angle))\n Cx = x + xi\n Cy = y + yi\n Points.append([Cx,Cy])\n if self.__debug:\n print(\"GenerateSamplePointsOnACircle exit\")\n return Points\n \n def GenerateSamplePointsOnACircleforSurvey(self,xc,yc,r,headingangle,direction):\n if self.__debug:\n print(\"GenerateSamplePointsOnACircleforSurvey enter\")\n # xc = xc + r*cos(radians(90-headingangle))\n # yc = yc - r*sin(radians(90-headingangle))\n # headingangle += 90\n Points = []\n Points.append([xc,yc])\n StepSize = 45\n Np = round(360/StepSize)\n for i in range(Np):\n if direction == 0:\n angle = (headingangle+180)%360 - i * StepSize\n angle = angle if angle >= 0 else angle + 360\n elif direction == 1:\n angle = ((headingangle+180)%360 + i * StepSize)%360\n xi = r * cos(radians(angle))\n yi = r * sin(radians(angle))\n Cx = xc + xi\n Cy = yc + yi\n Points.append([Cx,Cy])\n if self.__debug:\n print(\"GenerateSamplePointsOnACircleforSurvey exit\")\n return Points\n\n def calculateGridCoordinate(self):\n if self.__debug:\n print(\"calculateGridCoordinate enter\")\n self.__MissionReady = True\n self.__zoneCenter = {}\n \n self.__waypoints = {}\n w = self.__searchAreaWidth*2\n h = self.__searchAreaHeight*2\n self.__noOfUAVs = len(self.__airvehicleConfigList)\n maxSpeed = []\n for airvehicle in self.__airvehicleConfigList:\n maxSpeed.append(airvehicle.MaximumSpeed)\n\n self.__noOfZone = maxSpeed.count(max(maxSpeed))\n self.__noOfZone = self.__noOfZone if self.__noOfZone%2 == 0 else self.__noOfZone - 1 \n wSeg,hSeg = self.getBig2Factor(self.__noOfZone)\n\n dw = w/wSeg\n dh = h/hSeg\n currCenterx = -w/2\n currCentery = -h/2\n self.__secondaryMergeThreshold = max(dw,dh)\n for ws in range(wSeg):\n for hs in range(hSeg):\n zoneid = ws*hSeg + hs + 1\n # print('zoneid', zoneid)\n\n waypointNumber = 1\n\n x = currCenterx\n y = currCentery\n\n [lat,lon] = self.convertxyToLatLon(x,y)\n waypoint = Waypoint()\n waypoint.set_Latitude(lat)\n waypoint.set_Longitude(lon)\n alti = self.getAltitudeLatLon(lat,lon) \n if alti < self.__normalSearchAltitude:\n waypoint.set_Altitude(self.__normalSearchAltitude)\n else:\n waypoint.set_Altitude(alti + self.__safeHeight)\n waypoint.set_AltitudeType(self.__altitudetype)\n waypoint.set_Number(waypointNumber)\n waypoint.set_NextWaypoint(waypointNumber+1)\n waypoint.set_Speed(30)\n waypoint.set_SpeedType(SpeedType.Airspeed)\n waypoint.set_ClimbRate(15)\n waypoint.set_TurnType(TurnType.TurnShort)\n waypoint.set_ContingencyWaypointA(0)\n waypoint.set_ContingencyWaypointB(0)\n\n waypoints = []\n waypoints.append(waypoint)\n if ws%2==0:\n if hs%2 == 0:\n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(currCenterx,currCentery,currCenterx+dw,currCentery+dh,2,0)\n waypoints = waypoints + wpoints\n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(currCenterx+dw,currCentery+dh,currCenterx,currCentery+dh,waypointNumber,0)\n waypoints = waypoints + wpoints\n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(currCenterx,currCentery+dh,currCenterx+dw,currCentery,waypointNumber,0)\n waypoints = waypoints + wpoints\n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(currCenterx+dw,currCentery,currCenterx,currCentery,waypointNumber,1)\n waypoints = waypoints + wpoints\n else:\n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(currCenterx,currCentery,currCenterx+dw,currCentery+dh,2,0)\n waypoints = waypoints + wpoints\n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(currCenterx+dw,currCentery+dh,currCenterx+dw,currCentery,waypointNumber,0)\n waypoints = waypoints + wpoints\n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(currCenterx+dw,currCentery,currCenterx,currCentery+dh,waypointNumber,0)\n waypoints = waypoints + wpoints\n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(currCenterx,currCentery+dh,currCenterx,currCentery,waypointNumber,1)\n waypoints = waypoints + wpoints\n else:\n if hs%2 != 0:\n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(currCenterx,currCentery,currCenterx+dw,currCentery+dh,2,0)\n waypoints = waypoints + wpoints\n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(currCenterx+dw,currCentery+dh,currCenterx,currCentery+dh,waypointNumber,0)\n waypoints = waypoints + wpoints\n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(currCenterx,currCentery+dh,currCenterx+dw,currCentery,waypointNumber,0)\n waypoints = waypoints + wpoints\n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(currCenterx+dw,currCentery,currCenterx,currCentery,waypointNumber,1)\n waypoints = waypoints + wpoints\n else:\n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(currCenterx,currCentery,currCenterx+dw,currCentery+dh,2,0)\n waypoints = waypoints + wpoints\n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(currCenterx+dw,currCentery+dh,currCenterx+dw,currCentery,waypointNumber,0)\n waypoints = waypoints + wpoints\n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(currCenterx+dw,currCentery,currCenterx,currCentery+dh,waypointNumber,0)\n waypoints = waypoints + wpoints\n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(currCenterx,currCentery+dh,currCenterx,currCentery,waypointNumber,1)\n waypoints = waypoints + wpoints\n\n self.__waypoints[zoneid] = waypoints\n\n zlocation = Location3D()\n [lat,lon] = self.convertxyToLatLon(currCenterx+dw/2,currCentery+dh/2)\n zlocation.set_Latitude(lat)\n zlocation.set_Longitude(lon)\n zlocation.set_Altitude(450)\n # print(zlocation)\n self.__zoneCenter[zoneid] = zlocation\n self.__zoneboundaryPoints[zoneid] = [[currCenterx,currCentery],[currCenterx+dw,currCentery],[currCenterx,currCentery+dh],[currCenterx+dw,currCentery+dh]]\n\n currCentery += dh\n currCenterx += dw\n currCentery = -h/2\n if self.__debug:\n print(\"calculateGridCoordinate exit\")\n \n def getClosestPoint(self,points,refPoint):\n if self.__debug:\n print(\"getClosestPoint enter\")\n mind = 10e10\n closestPoint = []\n for i in len(points):\n d = self.distance(points[i],refPoint)\n if d < mind:\n mind = d\n closestPoint = points[i]\n if self.__debug:\n print(\"getClosestPoint exit\")\n return closestPoint\n \n def getNearestZone(self,location,vid):\n if self.__debug:\n print(\"getNearestZone enter\")\n minima = 1e10\n zoneid = 0\n minLocid = 1\n minLoc = Location3D()\n for z in range(1,self.__noOfZone+1):\n if not z in self.__zoneassigned:\n waypoints = self.__waypoints[z]\n for i in range(len(waypoints)):\n loc = waypoints[i]\n d = self.getdistance(loc,location)\n if d < minima:\n minima = d\n zoneid = z\n minLocid = i+1\n minLoc = loc\n self.__zoneassigned[zoneid]=True\n if sqrt(minima) < 1000:\n if self.__debug:\n print(\"getNearestZone exit\")\n return zoneid,minLocid\n \n waypoints,minLocid = self.getBetweenLatLon(location,minLoc,len(self.__waypoints[zoneid]),minima,minLocid,vid)\n self.__waypoints[zoneid] = waypoints + self.__waypoints[zoneid]\n if self.__debug:\n print(\"getNearestZone exit\")\n return zoneid,minLocid\n \n def getwaypointsBetweenLocations(self,startLoc,endLoc,vid,radius):\n if self.__debug:\n print(\"getwaypointsBetweenLocations enter\") \n vstate = self.getAirVeicleState(vid)\n safeHeight = abs(self.__sensorMaxrange[vid] * sin(radians(vstate.PayloadStateList[0].Elevation))) - self.__safeHeight\n d = self.getdistance(startLoc,endLoc)\n [xs,ys] = self.convertLatLonToxy(startLoc.get_Latitude(),startLoc.get_Longitude())\n [xe,ye] = self.convertLatLonToxy(endLoc.get_Latitude(),endLoc.get_Longitude())\n # radius = self.__searchCircleRadius\n xc = xe\n yc = ye\n points = self.GenerateSamplePointsOnACircle(xc,yc,radius)\n mind = 10e20\n startIndx = 0\n i = 0\n for p in points:\n d = (xs-p[0])**2 + (ys-p[1])**2\n if d < mind:\n mind = d\n xe = p[0]\n ye = p[1]\n startIndx = i\n i += 1\n\n delx = xe-xs\n dely = ye-ys\n m = dely/delx\n ii = int(round(sqrt(d)/self.__minidel))\n delx /= ii\n ii = ii\n x = xs\n waypointNumber = 1\n waypoints = []\n x += delx\n for i in range(ii):\n y = ys + (x-xs)*m\n [lat,lon] = self.convertxyToLatLon(x,y)\n x += delx\n waypoint = Waypoint()\n waypoint.set_Latitude(lat)\n waypoint.set_Longitude(lon)\n alti = self.getAltitudeLatLon(lat,lon) \n # if alti < self.__normalSearchAltitude:\n # waypoint.set_Altitude(self.__normalSearchAltitude)\n # else:\n waypoint.set_Altitude(alti + safeHeight) #self.__safeHeight)\n waypoint.set_AltitudeType(self.__altitudetype)\n waypoint.set_Number(waypointNumber)\n waypoint.set_NextWaypoint(waypointNumber+1)\n waypoint.set_Speed(self.__maxSpeedofUAV[vid])\n waypoint.set_SpeedType(SpeedType.Airspeed)\n waypoint.set_ClimbRate(15)\n waypoint.set_TurnType(TurnType.TurnShort)\n waypoint.set_ContingencyWaypointA(0)\n waypoint.set_ContingencyWaypointB(0)\n waypoints.append(waypoint)\n waypointNumber += 1\n \n wpointnumber = waypointNumber\n\n i = 0\n while i != (len(points)-1):\n startIndx = startIndx % (len(points)-1)\n p = points[startIndx+1]\n i += 1\n startIndx = (startIndx + 1)\n x = p[0]\n y = p[1]\n [lat,lon] = self.convertxyToLatLon(x,y)\n waypoint = Waypoint()\n waypoint.set_Latitude(lat)\n waypoint.set_Longitude(lon)\n alti = self.getAltitudeLatLon(lat,lon) \n # if alti < self.__normalSearchAltitude:\n # waypoint.set_Altitude(self.__normalSearchAltitude)\n # else:\n waypoint.set_Altitude(alti + safeHeight) #self.__safeHeight)\n waypoint.set_AltitudeType(self.__altitudetype)\n waypoint.set_Number(waypointNumber)\n if i == len(points)-1:\n waypoint.set_NextWaypoint(wpointnumber)\n else:\n waypoint.set_NextWaypoint(waypointNumber+1)\n waypoint.set_Speed(self.__maxSpeedofUAV[vid])\n waypoint.set_SpeedType(SpeedType.Airspeed)\n waypoint.set_ClimbRate(15)\n waypoint.set_TurnType(TurnType.TurnShort)\n waypoint.set_ContingencyWaypointA(0)\n waypoint.set_ContingencyWaypointB(0)\n waypoints.append(waypoint)\n waypointNumber += 1\n if self.__debug:\n print(\"getwaypointsBetweenLocations exit\")\n return waypoints\n \n def getBetweenLatLon(self,startLoc,endLoc,startwaypointId,d,connectingwaypointId,vid):\n if self.__debug:\n print(\"getBetweenLatLon enter\")\n vstate = self.getAirVeicleState(vid)\n safeHeight = abs(self.__sensorMaxrange[vid] * sin(radians(vstate.PayloadStateList[0].Elevation))) - self.__safeHeight\n [xs,ys] = self.convertLatLonToxy(startLoc.get_Latitude(),startLoc.get_Longitude())\n [xe,ye] = self.convertLatLonToxy(endLoc.get_Latitude(),endLoc.get_Longitude())\n delx = xe-xs\n dely = ye-ys\n m = dely/delx\n ii = int(round(sqrt(d)/self.__resulationOfGrid))\n delx /= ii\n ii = ii \n x = xs\n waypointNumber = startwaypointId+1\n waypoints = []\n x += delx\n for i in range(ii):\n y = ys + (x-xs)*m\n [lat,lon] = self.convertxyToLatLon(x,y)\n x += delx\n waypoint = Waypoint()\n waypoint.set_Latitude(lat)\n waypoint.set_Longitude(lon)\n alti = self.getAltitudeLatLon(lat,lon) \n # if alti < self.__normalSearchAltitude:\n # waypoint.set_Altitude(self.__normalSearchAltitude)\n # else:\n waypoint.set_Altitude(alti + safeHeight) #self.__safeHeight)\n waypoint.set_AltitudeType(self.__altitudetype)\n waypoint.set_Number(waypointNumber)\n if i == ii-1:\n waypoint.set_NextWaypoint(connectingwaypointId)\n else:\n waypoint.set_NextWaypoint(waypointNumber+1)\n waypoint.set_Speed(self.__maxSpeedofUAV[vid])\n waypoint.set_SpeedType(SpeedType.Airspeed)\n waypoint.set_ClimbRate(15)\n waypoint.set_TurnType(TurnType.TurnShort)\n waypoint.set_ContingencyWaypointA(0)\n waypoint.set_ContingencyWaypointB(0)\n waypoints.append(waypoint)\n waypointNumber += 1\n waypointNumber -= 1\n if self.__debug:\n print(\"getBetweenLatLon exit\")\n return waypoints, startwaypointId+1\n \n def getBetweenLatLonwithoutVIDAlt(self,xs,ys,xe,ye,waypointNumber,option):\n if self.__debug:\n print(\"getBetweenLatLonwithoutVIDAlt enter\")\n delx = xe-xs\n dely = ye-ys\n d = delx**2 + dely**2\n if delx != 0:\n m = dely/delx\n ii = int(round(sqrt(d)/self.__resulationOfGrid))\n delx /= ii\n dely /= ii\n ii = ii - 1\n x = xs\n y = ys\n waypoints = []\n x += delx\n for i in range(ii):\n if delx == 0:\n y += dely\n else:\n y = ys + (x-xs)*m \n [lat,lon] = self.convertxyToLatLon(x,y)\n x += delx\n waypoint = Waypoint()\n waypoint.set_Latitude(lat)\n waypoint.set_Longitude(lon)\n alti = self.getAltitudeLatLon(lat,lon) \n if alti < self.__normalSearchAltitude:\n waypoint.set_Altitude(self.__normalSearchAltitude)\n else:\n waypoint.set_Altitude(alti + self.__safeHeight)\n waypoint.set_AltitudeType(self.__altitudetype)\n waypoint.set_Number(waypointNumber)\n if option==1 and i == ii-1:\n waypoint.set_NextWaypoint(1)\n else:\n waypoint.set_NextWaypoint(waypointNumber+1)\n waypoint.set_Speed(30)\n waypoint.set_SpeedType(SpeedType.Airspeed)\n waypoint.set_ClimbRate(15)\n waypoint.set_TurnType(TurnType.TurnShort)\n waypoint.set_ContingencyWaypointA(0)\n waypoint.set_ContingencyWaypointB(0)\n waypoints.append(waypoint)\n waypointNumber += 1\n if self.__debug:\n print(\"getBetweenLatLonwithoutVIDAlt exit\")\n return waypoints,waypointNumber\n \n def mapaltiwithwaypointnumber(self,waypoints):\n if self.__debug:\n print(\"mapaltiwithwaypointnumber enter\")\n map = {}\n wpointconnectmap = {}\n for waypoint in waypoints:\n alti = self.getAltitudeLatLon(waypoint.get_Latitude(),waypoint.get_Longitude())\n map[waypoint.get_Number()] = alti\n wpointconnectmap[waypoint.get_Number()] = waypoint.get_NextWaypoint()\n if self.__debug:\n print(\"mapaltiwithwaypointnumber exit\")\n return map,wpointconnectmap\n\n def getAltitudeLatLon(self,lat,lon):\n if self.__debug:\n print(\"getAltitudeLatLon enter\")\n if (lat - 39.0) <= 1:\n if abs(lon + 122.0) <= 1:\n i = round((lat - 39) * 3600) \n j = round((lon + 122) * 3600)\n sz = self.altidata1.shape\n if i >= sz[0]:\n i = sz[0] - 1\n elif i < 0:\n i = 0\n if j >= sz[1]:\n j = sz[1] - 1\n elif j<0:\n j=0\n Altitude = self.altidata1[i][j]\n else:\n i = round((lat - 39) * 3600)\n j = round((lon + 121) * 3600)\n sz = self.altidata2.shape\n if i >= sz[0]:\n i = sz[0] - 1\n elif i < 0:\n i = 0\n if j >= sz[1]:\n j = sz[1] - 1\n elif j < 0:\n j = 0\n Altitude = self.altidata2[i][j]\n else:\n if (lon + 122) <= 1:\n i = round((lat - 40) * 3600)\n j = round((lon + 122) * 3600) \n sz = self.altidata3.shape\n if i >= sz[0]:\n i = sz[0] - 1\n elif i < 0:\n i = 0\n if j >= sz[1]:\n j = sz[1] - 1\n elif j < 0:\n j = 0\n Altitude = self.altidata3[i][j] \n else:\n i = round((lat - 40) * 3600)\n j = round((lon + 121) * 3600)\n sz = self.altidata4.shape\n if i >= sz[0]:\n i = sz[0] - 1\n elif i < 0:\n i = 0\n if j >= sz[1]:\n j = sz[1] - 1\n elif j < 0:\n j = 0\n Altitude = self.altidata4[i][j]\n if self.__debug:\n print(\"getAltitudeLatLon exit\")\n return Altitude\n \n def getAltitude(self,location):\n if self.__debug:\n print(\"getAltitude enter\")\n lat = location.get_Latitude()\n lon = location.get_Longitude()\n if (lat - 39.0) <= 1:\n if abs(lon + 122.0) <= 1:\n i = round((lat - 39) * 3600)\n j = round((lon + 122) * 3600)\n sz = self.altidata1.shape\n if i >= sz[0]:\n i = sz[0] - 1\n elif i < 0:\n i = 0\n if j >= sz[1]:\n j = sz[1] - 1\n elif j<0:\n j=0\n Altitude = self.altidata1[i][j]\n else:\n i = round((lat - 39) * 3600)\n j = round((lon + 121) * 3600)\n sz = self.altidata2.shape\n if i >= sz[0]:\n i = sz[0] - 1\n elif i < 0:\n i = 0\n if j >= sz[1]:\n j = sz[1] - 1\n elif j < 0:\n j = 0\n Altitude = self.altidata2[i][j]\n else:\n if (lon + 122) <= 1:\n i = round((lat - 40) * 3600)\n j = round((lon + 122) * 3600)\n sz = self.altidata3.shape\n if i >= sz[0]:\n i = sz[0] - 1\n elif i < 0:\n i = 0\n if j >= sz[1]:\n j = sz[1] - 1\n elif j < 0:\n j = 0\n Altitude = self.altidata3[i][j] \n else:\n i = round((lat - 40) * 3600)\n j = round((lon + 121) * 3600)\n sz = self.altidata4.shape\n if i >= sz[0]:\n i = sz[0] - 1\n elif i < 0:\n i = 0\n if j >= sz[1]:\n j = sz[1] - 1\n elif j < 0:\n j = 0\n Altitude = self.altidata4[i][j]\n if self.__debug:\n print(\"getAltitude exit\")\n return Altitude\n \n def isinKeepInZone(self,location):\n if self.__debug:\n print(\"isinKeepInZone enter\")\n xyposition = self.convertLatLonToxy(location.get_Latitude(),location.get_Longitude())\n if abs(xyposition[0]) <= self.__searchAreaWidth and abs(xyposition[1]) <= self.__searchAreaHeight:\n if self.__debug:\n print(\"isinKeepInZone exit\")\n return True\n if self.__debug:\n print(\"isinKeepInZone exit\")\n return False\n\n def getZoneIdLocation(self,loc):\n if self.__debug:\n print(\"getZoneIdLocation enter\")\n zlocation = loc\n mind =10e20\n zid = 0\n for id in self.__zoneCenter.keys():\n zc = self.__zoneCenter[id]\n d = self.getdistance(zlocation,zc)\n if d < mind:\n mind = d\n zid = id\n if self.__debug:\n print(\"getZoneIdLocation exit\")\n return zid\n \n def getZoneId(self,point):\n if self.__debug:\n print(\"getZoneId enter\")\n zlocation = Location3D()\n [lat,lon] = self.convertxyToLatLon(point[0],point[1])\n zlocation.set_Latitude(lat)\n zlocation.set_Longitude(lon)\n mind = 10e20\n zid = 0\n for id in self.__zoneCenter.keys():\n zc = self.__zoneCenter[id]\n d = self.getdistance(zlocation,zc)\n if d < mind:\n mind = d\n zid = id\n if self.__debug:\n print(\"getZoneId exit\")\n return zid\n \n def getdistance(self,loc1,loc2):\n if self.__debug:\n print(\"getdistance enter\")\n loc1XY = self.convertLatLonToxy(loc1.get_Latitude(),loc1.get_Longitude())\n loc2XY = self.convertLatLonToxy(loc2.get_Latitude(),loc2.get_Longitude())\n d = (loc1XY[0] - loc2XY[0])**2 + (loc1XY[1] - loc2XY[1])**2\n if self.__debug:\n print(\"getdistance exit\")\n return d\n \n def graham_scan(self,points): \n #print('points', points)\n if self.__debug:\n print(\"graham_scan enter\")\n min_idx=None\n for i,(x,y) in enumerate(points):\n if min_idx==None or y= 3:\n\n pointsInZones = self.mergeFireZones(allxypoints[:])\n data = {}\n flag = False\n r = self.__radiusForDeleteOldSample \n Np = self.__minimumNumberOfSamplestokeept \n for key in pointsInZones.keys():\n #print(pointsInZones[key])\n allxypoints = np.array(self.updateSamples(list(pointsInZones[key]),r,Np)) #np.array(pointsInZones[key]) ##\n allxypoints = allxypoints[:,0:2]\n \n if len(allxypoints) >= 3:\n boundarypoints = self.graham_scan(list(allxypoints))\n data[key] = boundarypoints\n flag = True\n pointsInZones[key] = list(allxypoints)\n if flag:\n data = self.secondaryMerge(dict(data))\n else:\n data = self.secondaryMerge(dict(pointsInZones))\n \n for key in data.keys():\n allxypoints = data[key]\n \n if len(allxypoints) >= 3:\n \n boundarypoints = self.graham_scan(list(allxypoints))\n \n for xypoint in boundarypoints:\n [lat,lon]=self.convertxyToLatLon(xypoint[0],xypoint[1])\n locationpoint = Location3D()\n locationpoint.set_Latitude(lat)\n locationpoint.set_Longitude(lon)\n self.__estimatedHazardZone.get_BoundaryPoints().append(locationpoint) \n self.sendEstimateReport(key)\n self.__estimatedHazardZone = Polygon()\n \n if self.__debug:\n print(\"findBoundaryandSendReport exit\") \n\n def getSendReportStatus(self):\n # return self.__sendReport\n return self.__gotHint\n \n def setSendReportStatus(self,status):\n self.__sendReport = status\n\n def getupdateAreaStatus(self):\n return self.__updateArea\n \n def setupdateAreaStatus(self,status):\n self.__updateArea = status\n \n def updateEstimatedArea(self):\n if self.__debug:\n print(\"updateEstimatedArea enter\") \n Rate = self.__wspeed\n Angle = self.__ditectionTheta\n X_Rate = Rate * cos(radians(Angle))\n Y_Rate = Rate * sin(radians(Angle))\n if self.__firezonePoints:\n for key in self.__firezonePoints.keys():\n for i in range(len(self.__firezonePoints[key])):\n self.__firezonePoints[key][i][0] = self.__firezonePoints[key][i][0] - X_Rate\n self.__firezonePoints[key][i][1] = self.__firezonePoints[key][i][1] - Y_Rate\n if self.__debug:\n print(\"updateEstimatedArea exit\") \n \n def translateEstimatedShape(self, WinSpeed, Angle, firepoints):\n if self.__debug:\n print(\"translateEstimatedShape enter\") \n Rate = WinSpeed\n X_Rate = Rate * cos(radians(Angle))\n Y_Rate = Rate * sin(radians(Angle))\n # if self.__firezonePoints:\n # for key in self.__firezonePoints.keys():\n # points = self.__firezonePoints[key]\n # SamplePoints = points\n NewSamplePoint = []\n if firepoints:\n for [x,y] in firepoints:\n x = x - X_Rate\n y = y - Y_Rate\n NewSamplePoint.append([x,y])\n else:\n NewSamplePoint = firepoints\n # self.__firezonePoints[key] = NewSamplePoint\n if self.__debug:\n print(\"translateEstimatedShape exit\")\n return list(NewSamplePoint)\n \n def getBig2Factor(self,num):\n if self.__debug:\n print(\"getBig2Factor enter\")\n ii = int(num/2)+1\n for i in range(ii):\n if i>0 and num%i == 0 and num/i <= i:\n if self.__debug:\n print(\"getBig2Factor exit\")\n return i,int(num/i)\n \n def checksubset(self,y,x):\n if self.__debug:\n print(\"checksubset enter\")\n #test y is a subset of x or not\n counter = 0\n print('check subset')\n for i in range(len(y)):\n for j in range(len(x)):\n # print(x[j] == y[i])\n # print('*************')\n if sum(abs(x[j] - y[i])<[0.001,0.001])==len(y[i]):\n counter += 1\n if self.__debug:\n print(\"checksubset exit\")\n return counter == len(y)\n\n def getSimTime(self):\n return self.__simulationTimemilliSeconds\n\n def getMissionReadyStatus(self):\n return self.__MissionReady\n\n def getAirVeicleState(self,vid):\n # if not vid in self.__hazardSensorStatus:\n # self.__hazardSensorStatus[vid] = 0\n # dt = (time.time()-self.__hazardSensorStatus[vid])\n if not vid in self.__currentVicleState:\n return None\n return self.__currentVicleState[vid]#, 1 if dt < self.__sensorRefreshrate else 0\n\n def getNoOfUAVs(self):\n return self.__noOfUAVs\n\n def getSurveyStatus(self,vid):\n if not vid in self.__uavsInSarvey:\n return False\n return self.__uavsInSarvey[vid]\n \n def setSurveyStatus(self,vid,status):\n self.__uavsInSarvey[vid] = status\n\n def getSmokeZoneStatus(self,vstate):\n if not vstate.ID in self.__uavisInsmokeZone:\n return False\n return self.__uavisInsmokeZone[vstate.ID]\n\n def setSmokeZoneStatus(self,vstate,status):\n self.__uavisInsmokeZone[vstate.ID] = status\n\n def getSmokeMissionStatus(self,vstate):\n if not vstate.ID in self.__uavInSmokemisssion:\n return False\n return self.__uavInSmokemisssion[vstate.ID]\n\n def getAirveicleConfigList(self):\n return self.__airvehicleConfigList\n\n def getSurveyDirection(self,vid):\n if self.__debug:\n print(\"getSurveyDirection enter\")\n zid = self.__UAVSurvayingZoneId[vid]\n uavlist = self.__uavsInZone[zid]\n indx = uavlist.index(vid)\n if indx%2 == 0:\n if self.__debug:\n print(\"getSurveyDirection exit\")\n return 0\n if self.__debug:\n print(\"getSurveyDirection exit\")\n return 1\n\n def isMissionComplete(self,vstate):\n if self.__debug:\n print(\"isMissionComplete enter\")\n veicleid = vstate.ID\n if vstate.Mode == NavigationMode.Waypoint:\n currentwaypointNo = vstate.CurrentWaypoint\n if not vstate.ID in self.__previouswaypointNo:\n self.__previouswaypointNo[vstate.ID] = currentwaypointNo\n if self.__debug:\n print(\"isMissionComplete exit\")\n return False\n\n if currentwaypointNo != self.__previouswaypointNo[vstate.ID]:\n self.__previouswaypointNo[vstate.ID] = currentwaypointNo\n if not vstate.ID in self.__visitedTotalwaypoints:\n self.__visitedTotalwaypoints[vstate.ID] = 0\n self.__visitedTotalwaypoints[vstate.ID] += 1\n if self.__visitedTotalwaypoints[vstate.ID] >= self.__totalWaypointsassignedToUAV[vstate.ID]:\n self.__visitedTotalwaypoints[vstate.ID] = 0\n # print('uav ',vstate.ID, ' finished its mission')\n self.__uavsInMission[vstate.ID] = False\n if self.__debug:\n print(\"isMissionComplete exit\")\n return True\n else:\n if self.__debug:\n print(\"isMissionComplete exit\")\n return True\n\n if veicleid in self.__uavRecharging and not self.__uavRecharging[veicleid] and veicleid in self.__uavsInSearch and not self.__uavsInSearch[veicleid]:\n if (not veicleid in self.__uavisHeadingtoSurveylocation) or (veicleid in self.__uavisHeadingtoSurveylocation and not self.__uavisHeadingtoSurveylocation[veicleid]):\n self.__uavsInSarvey[veicleid] = False\n if self.__debug:\n print(\"isMissionComplete exit\")\n return True\n if self.__debug:\n print(\"isMissionComplete exit\") \n return False\n\n def assignNewMission(self,vstate):\n if self.__debug:\n print(\"assignNewMission enter\")\n vid = vstate.ID\n if vid in self.__uavsInSarvey and self.__uavsInSarvey[vid]:\n #increase the radius of survey by 1km\n direction = self.getSurveyDirection(vstate.ID)\n self.sendServeyCommand(vstate,direction,r=(self.__surveyCircleRadius+1000),speed=self.__maxSpeedofUAVduringSurvey[vid])\n elif (self.__maxSpeedofUAV[vid] < self.__maxSpeedGlobal) and vid in self.__uavisHeadingtoSurveylocation:\n self.sendServeyCommand(vstate,0,r=(self.__searchCircleRadius+1000))\n elif (self.__maxSpeedofUAV[vid] < self.__maxSpeedGlobal) and vid in self.__uavInSmokemisssion:\n self.sendServeyCommand(vstate,0,r=(self.__searchCircleRadius+1000))\n elif (not (self.__maxSpeedofUAV[vid] < self.__maxSpeedGlobal)) and ((vid in self.__uavsInSearch and not self.__uavsInSearch[vid]) or (not vid in self.__uavsInSearch) or (not vid in self.__uavsInMission) or (vid in self.__uavsInMission and not self.__uavsInMission[vid])):\n if vstate.ID in self.__firezoneHintLocation:\n # password\n if not vid in self.__uavisHeadingtoSurveylocation:\n self.sendWaypoint(vstate.ID,vstate.Location,self.__firezoneHintLocation[vstate.ID])\n self.__uavisHeadingtoSurveylocation[vid] = True\n elif not self.__uavisHeadingtoSurveylocation[vid]:\n self.sendWaypoint(vstate.ID,vstate.Location,self.__firezoneHintLocation[vstate.ID])\n self.__uavisHeadingtoSurveylocation[vid] = True\n else:\n self.getNewAreaforSearch(vstate)\n self.__uavsInSearch[vid] = True\n elif not self.__uavsInMission[vstate.ID] and vstate.EnergyAvailable > 99:\n # self.getNewAreaforSearch(vstate)\n mind = 10e10\n minid = 0\n minLoc = Location3D()\n minendloc = Location3D()\n minzid = 1\n for zid in range(1,self.__noOfZone+1):\n # print('zloc', self.__zoneCenter[zid])\n if not self.zoneassigned[zid]: \n endLoc = self.__zoneCenter[zid]\n startLoc = vstate.Location\n d = self.getdistance(startLoc,endLoc)\n if d < mind:\n mind = d\n minid = id\n minLoc = startLoc\n minzid = zid\n minendloc = endLoc\n if minid != 0: \n self.__uavsInMission[minid] = True\n self.sendWaypoint(minid,minLoc,minendloc) \n self.zoneassigned[minzid] = True \n\n if self.__debug:\n print(\"assignNewMission exit\")\n\n def checkpowerStatus(self,AirVehicleState):\n if self.__debug:\n print(\"checkpowerStatus enter\")\n veicleid = AirVehicleState.ID\n Goback = False\n AvailableEnergy = AirVehicleState.EnergyAvailable\n if AvailableEnergy > self.__energyThreshold:\n self.__uavRecharging[veicleid] = False\n if self.__debug:\n print(\"checkpowerStatus exit\")\n return False\n if veicleid in self.__uavRecharging and self.__uavRecharging[veicleid]:\n self.__mapHold[veicleid] = []\n if self.__debug:\n print(\"checkpowerStatus exit\")\n return True\n\n EnergyRate = AirVehicleState.ActualEnergyRate\n Location = AirVehicleState.Location\n Speed = self.__maxSpeedofUAV[veicleid]\n Lat = Location.get_Latitude()\n Lon = Location.get_Longitude()\n [x,y] = self.convertLatLonToxy(Lat,Lon)\n Dist = []\n RecoveryPos = [x,y]\n if EnergyRate > 0.0:\n Dist = []\n for rzone in self.__recoveryPoints:\n [x2,y2] = self.convertLatLonToxy(rzone.CenterPoint.get_Latitude(),rzone.CenterPoint.get_Longitude())\n d = ((x - x2)**2 + (y - y2)**2)**0.5\n Dist.append(d)\n MinIndice = np.argmin(Dist)\n RemainTime = float(AvailableEnergy) / EnergyRate #self.__energyconsumptionRate#\n MaximumDistLeft = Speed * RemainTime - self.__energyThresholddist\n # if veicleid == 4:\n # print('uav',veicleid, AvailableEnergy,'max disthat can be traveled ', MaximumDistLeft, 'recovery zone area', Dist[MinIndice])\n if Dist[MinIndice] >= MaximumDistLeft:\n Goback = True\n RecoveryPos = self.__recoveryPoints[MinIndice].CenterPoint \n radius = self.__recoveryPoints[MinIndice].Radius\n radius = radius*0.95\n self.__uavRecharging[veicleid] = True\n self.__uavsInSearch[veicleid] = False\n self.__uavsInSarvey[veicleid] = False\n self.__uavisHeadingtoSurveylocation[veicleid] = False\n self.sendWaypoint(veicleid, Location, RecoveryPos, radius=radius,torecharge=True)\n #print('uav',veicleid,'recharging')\n # if veicleid == 10:\n # print('uav survey status is reseted 2')\n self.__mapHold[veicleid] = []\n if self.__debug:\n print(\"checkpowerStatus exit\")\n return True\n if self.__debug:\n print(\"checkpowerStatus exit\")\n return False #Goback,RecoveryPos\n\n def updateGlobalMap(self,vstate):\n if self.__debug:\n print(\"updateGlobalMap enter\")\n [x,y] = self.convertLatLonToxy(vstate.Location.get_Latitude(),vstate.Location.get_Longitude())\n\n x = abs(-x + self.__searchAreaWidth)\n y = abs(y + self.__searchAreaHeight)\n i = int(x/self.__mapResulotion)\n j = int(y/self.__mapResulotion)\n\n i = i if i < self.__globalMap.shape[0] else self.__globalMap.shape[0] - 1\n j = j if j < self.__globalMap.shape[1] else self.__globalMap.shape[1] - 1\n\n self.__globalMap[i][j] = 1\n # print(self.__globalMap.shape)\n if self.__debug:\n print(\"updateGlobalMap exit\")\n\n def getNewAreaforSearch(self,vstate): # needs work \n if self.__debug:\n print(\"getNewAreaforSearch enter\")\n [x,y] = self.convertLatLonToxy(vstate.Location.get_Latitude(),vstate.Location.get_Longitude())\n\n x = abs(-x + self.__searchAreaWidth)\n y = abs(y + self.__searchAreaHeight)\n ic = int(x/self.__mapResulotion)\n jc = int(y/self.__mapResulotion)\n\n ic = ic if ic < self.__globalMap.shape[0] else self.__globalMap.shape[0] - 1\n jc = jc if jc < self.__globalMap.shape[1] else self.__globalMap.shape[1] - 1\n\n w = int(self.__secondarysearchareaW/self.__mapResulotion)\n h = int(self.__secondarysearchareaH/self.__mapResulotion)\n\n # print('center of search',ic,jc)\n\n self.__dgrid = np.zeros(self.__dgrid.shape)\n self.__glopbalmaxforpercentarea = 0\n self.__boundaryparameterFornewMission = [0,0,0,0]\n self.__stopRecursion = False\n \n \n flag,self.__boundaryparameterFornewMission = self.recursiveSearch(ic,jc,w,h)\n\n self.__mapHold[vstate.ID] = self.__boundaryparameterFornewMission\n\n # print(self.__boundaryparameterFornewMission)\n if flag:\n x1 = -self.__boundaryparameterFornewMission[0]*self.__mapResulotion + self.__searchAreaWidth\n y1 = self.__boundaryparameterFornewMission[2]*self.__mapResulotion - self.__searchAreaHeight\n\n x2 = -self.__boundaryparameterFornewMission[1]*self.__mapResulotion + self.__searchAreaWidth\n y2 = self.__boundaryparameterFornewMission[2]*self.__mapResulotion - self.__searchAreaHeight\n\n x3 = -self.__boundaryparameterFornewMission[1]*self.__mapResulotion + self.__searchAreaWidth\n y3 = self.__boundaryparameterFornewMission[3]*self.__mapResulotion - self.__searchAreaHeight\n\n x4 = -self.__boundaryparameterFornewMission[0]*self.__mapResulotion + self.__searchAreaWidth\n y4 = self.__boundaryparameterFornewMission[3]*self.__mapResulotion - self.__searchAreaHeight\n\n waypointNumber = 1\n\n x = x1\n y = y1\n\n [lat,lon] = self.convertxyToLatLon(x,y)\n waypoint = Waypoint()\n waypoint.set_Latitude(lat)\n waypoint.set_Longitude(lon)\n alti = self.getAltitudeLatLon(lat,lon) \n if alti < self.__normalSearchAltitude:\n waypoint.set_Altitude(self.__normalSearchAltitude)\n else:\n waypoint.set_Altitude(alti + self.__safeHeight)\n waypoint.set_AltitudeType(self.__altitudetype)\n waypoint.set_Number(waypointNumber)\n waypoint.set_NextWaypoint(waypointNumber+1)\n waypoint.set_Speed(30)\n waypoint.set_SpeedType(SpeedType.Airspeed)\n waypoint.set_ClimbRate(15)\n waypoint.set_TurnType(TurnType.TurnShort)\n waypoint.set_ContingencyWaypointA(0)\n waypoint.set_ContingencyWaypointB(0)\n\n waypoints = []\n waypoints.append(waypoint)\n \n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(x1,y1,x3,y3,2,0)\n waypoints = waypoints + wpoints\n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(x3,y3,x2,y2,waypointNumber,0)\n waypoints = waypoints + wpoints\n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(x2,y2,x4,y4,waypointNumber,0)\n waypoints = waypoints + wpoints\n wpoints,waypointNumber = self.getBetweenLatLonwithoutVIDAlt(x4,y4,x1,y1,waypointNumber,1)\n waypoints = waypoints + wpoints\n\n minima = 1e10\n minLocid = 1\n minLoc = Location3D()\n \n for i in range(len(waypoints)):\n loc = waypoints[i]\n d = self.getdistance(loc,vstate.Location)\n if d < minima:\n minima = d\n minLoc = loc\n minLocid = i+1\n \n if sqrt(minima) < 1000:\n self.sendOnlywaypoints(vstate,waypoints,firstWaypoint=minLocid)\n if self.__debug:\n print(\"getNewAreaforSearch exit\")\n return waypoints,minLocid\n \n waypoints1,minLocid = self.getBetweenLatLon(vstate.Location,minLoc,waypointNumber,minima,minLocid,vstate.ID)\n waypoints = waypoints1 + waypoints\n self.sendOnlywaypoints(vstate,waypoints,firstWaypoint=minLocid)\n if self.__debug:\n print(\"getNewAreaforSearch exit\")\n return waypoints,minLocid\n\n def recursiveSearch(self,i,j,w,h):\n if self.__debug:\n print(\"recursiveSearch enter\")\n # print('in the loop')\n gridw = self.__globalMap.shape[0]\n gridh = self.__globalMap.shape[1]\n ic = i\n jc = j \n l = int(gridw/w)\n m = int(gridh/h)\n uavlist = self.getAirveicleConfigList()\n mindlist = []\n allboundary = []\n\n for i in range(l-1):\n for j in range(m-1):\n area1 = self.__globalMap[i*w:(i+1)*w,j*h:(j+1)*h]\n p = sum(sum(area1))/float(area1.shape[0]*area1.shape[1])\n if (1-p)>0.4:\n minp = min(((i-ic)**2+(j-jc)**2),((i-ic)**2+(j+1-jc)**2),((i+1-ic)**2+(j-jc)**2),((i+1-ic)**2+(j+1-jc)**2))\n mindlist.append(minp)\n allboundary.append([i*w,(i+1)*w,j*h,(j+1)*h])\n \n while True:\n if mindlist:\n indx = np.argmin(mindlist)\n boundary = allboundary[indx]\n flag = True\n for uav in uavlist:\n if uav.ID in self.__mapHold and self.__mapHold[uav.ID] and boundary[0] == self.__mapHold[uav.ID][0] and boundary[1] == self.__mapHold[uav.ID][1] and boundary[2] == self.__mapHold[uav.ID][2] and boundary[3] == self.__mapHold[uav.ID][3]:\n flag = False\n break\n if flag:\n if self.__debug:\n print(\"recursiveSearch exit\")\n return True, boundary\n del mindlist[indx]\n del allboundary[indx]\n else:\n if self.__debug:\n print(\"recursiveSearch exit\")\n return False,[]\n if self.__debug:\n print(\"recursiveSearch exit\")\n return False,[]\n\n def saveMAP(self):\n cv2.imwrite(\"globalMap.png\", self.__globalMap*255)\n\n def getsurveyspeed(self,vstate):\n return self.__maxSpeedofUAVduringSurvey[vstate.ID]\n\n def getrechargeStatus(self,vstate):\n return self.__uavRecharging[vstate.ID]\n\n\n#################\n## Main\n#################\n\nif __name__ == '__main__':\n myHost = 'localhost' # '13.77.73.31'\n myPort = 5555\n amaseClient = AmaseTCPClient(myHost, myPort)\n #amaseClient.addReceiveCallback(PrintLMCPObject())\n smpleHazardDetector = SampleHazardDetector(amaseClient)\n amaseClient.addReceiveCallback(smpleHazardDetector)\n\n try:\n # make a threaded client, listen until a keyboard interrupt (ctrl-c)\n #start client thread\n amaseClient.start()\n\n dt = 0.2\n sensorState = {}\n savetime = time.time()\n previousReportingtime = 0\n reportingTime = 10\n while True:\n #wait for keyboard interrupt\n if smpleHazardDetector.getSimTime() > 0:\n if smpleHazardDetector.getSimTime()>17*60*1000 and smpleHazardDetector.getSendReportStatus() and (time.time() - previousReportingtime) > reportingTime:\n smpleHazardDetector.findBoundaryandSendReport()\n previousReportingtime = time.time()\n \n if not smpleHazardDetector.getMissionReadyStatus():\n smpleHazardDetector.calculateGridCoordinate()\n smpleHazardDetector.sendinitialMission()\n # smpleHazardDetector.getCenterofRecoveryZone()\n\n\n if smpleHazardDetector.getupdateAreaStatus():\n smpleHazardDetector.updateEstimatedArea()\n smpleHazardDetector.setupdateAreaStatus(False)\n\n uavlist = smpleHazardDetector.getAirveicleConfigList()\n for uav in uavlist:\n vstate = smpleHazardDetector.getAirVeicleState(uav.ID)\n if vstate is not None:\n #update global map\n smpleHazardDetector.updateGlobalMap(vstate)\n #check power\n if smpleHazardDetector.checkpowerStatus(vstate):\n #check if mission complete or not\n if smpleHazardDetector.isMissionComplete(vstate):\n print('assign new mission')\n smpleHazardDetector.assignNewMission(vstate)\n \n if smpleHazardDetector.getSurveyStatus(uav.ID):\n if not uav.ID in sensorState:\n direction = smpleHazardDetector.getSurveyDirection(uav.ID)\n smpleHazardDetector.sendServeyCommand(vstate,direction,speed=smpleHazardDetector.getsurveyspeed(vstate))\n smpleHazardDetector.callUAVSForSurvey(vstate)\n sensorState[uav.ID] = 1\n elif vstate.Mode == NavigationMode.Waypoint and vstate.CurrentWaypoint > 2:\n direction = smpleHazardDetector.getSurveyDirection(uav.ID)\n smpleHazardDetector.sendServeyCommand(vstate,direction,speed=smpleHazardDetector.getsurveyspeed(vstate))\n smpleHazardDetector.callUAVSForSurvey(vstate)\n smpleHazardDetector.setSurveyStatus(uav.ID,False)\n elif smpleHazardDetector.getSmokeZoneStatus(vstate) and not smpleHazardDetector.getSmokeMissionStatus(vstate):\n smpleHazardDetector.sendSmokeZonemission(vstate)\n smpleHazardDetector.setSmokeZoneStatus(vstate,False) \n\n time.sleep(dt)\n\n # if (time.time() - savetime) > 850:\n # savetime = time.time()\n # smpleHazardDetector.saveMAP()\n \n except KeyboardInterrupt as ki:\n print(\"Stopping amase tcp client\")\n except Exception as ex:\n print('exception')\n print(ex)\n # print(ex.args)\n smpleHazardDetector.saveMAP()\n amaseClient.stop()\n", "sub_path": "python/hazardzone_detector.py", "file_name": "hazardzone_detector.py", "file_ext": "py", "file_size_in_byte": 89722, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "amase.TCPClient.IDataReceived", "line_number": 63, "usage_type": "name"}, {"api_name": "amase.TCPClient.IDataReceived", "line_number": 67, "usage_type": "name"}, {"api_name": "afrl.cmasi.Polygon.Polygon", "line_number": 72, "usage_type": "call"}, {"api_name": "afrl.cmasi.Rectangle.Rectangle", "line_number": 73, "usage_type": "call"}, {"api_name": "afrl.cmasi.Location3D.Location3D", "line_number": 81, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 101, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 103, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 105, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 107, "usage_type": "call"}, {"api_name": "afrl.cmasi.AltitudeType.AltitudeType.AGL", "line_number": 172, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.AltitudeType.AltitudeType", "line_number": 172, "usage_type": "name"}, {"api_name": "afrl.cmasi.KeepInZone.KeepInZone", "line_number": 182, "usage_type": "argument"}, {"api_name": "numpy.zeros", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 194, "usage_type": "call"}, {"api_name": "afrl.cmasi.searchai.RecoveryPoint.RecoveryPoint", "line_number": 198, "usage_type": "argument"}, {"api_name": "afrl.cmasi.AirVehicleState.AirVehicleState", "line_number": 201, "usage_type": "argument"}, {"api_name": "afrl.cmasi.perceive.EntityPerception.EntityPerception", "line_number": 222, "usage_type": "argument"}, {"api_name": "afrl.cmasi.RemoveEntities.RemoveEntities", "line_number": 225, "usage_type": "argument"}, {"api_name": "afrl.cmasi.AirVehicleConfiguration.AirVehicleConfiguration", "line_number": 232, "usage_type": "argument"}, {"api_name": "afrl.cmasi.EntityConfiguration.EntityConfiguration", "line_number": 242, "usage_type": "argument"}, {"api_name": "afrl.cmasi.searchai.HazardZoneDetection.HazardZoneDetection", "line_number": 245, "usage_type": "argument"}, {"api_name": "time.time", "line_number": 252, "usage_type": "call"}, {"api_name": "afrl.cmasi.searchai.HazardType.HazardType.Fire", "line_number": 254, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.searchai.HazardType.HazardType", "line_number": 254, "usage_type": "name"}, {"api_name": "time.time", "line_number": 272, "usage_type": "call"}, {"api_name": "time.time", "line_number": 274, "usage_type": "call"}, {"api_name": "afrl.cmasi.searchai.HazardType.HazardType.Smoke", "line_number": 275, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.searchai.HazardType.HazardType", "line_number": 275, "usage_type": "name"}, {"api_name": "afrl.cmasi.MissionCommand.MissionCommand", "line_number": 285, "usage_type": "call"}, {"api_name": "afrl.cmasi.CommandStatusType.CommandStatusType.Pending", "line_number": 287, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.CommandStatusType.CommandStatusType", "line_number": 287, "usage_type": "name"}, {"api_name": "math.sin", "line_number": 292, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 292, "usage_type": "call"}, {"api_name": "afrl.cmasi.MissionCommand.MissionCommand", "line_number": 320, "usage_type": "call"}, {"api_name": "afrl.cmasi.CommandStatusType.CommandStatusType.Pending", "line_number": 323, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.CommandStatusType.CommandStatusType", "line_number": 323, "usage_type": "name"}, {"api_name": "math.sin", "line_number": 327, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 327, "usage_type": "call"}, {"api_name": "afrl.cmasi.MissionCommand.MissionCommand", "line_number": 356, "usage_type": "call"}, {"api_name": "afrl.cmasi.CommandStatusType.CommandStatusType.Pending", "line_number": 359, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.CommandStatusType.CommandStatusType", "line_number": 359, "usage_type": "name"}, {"api_name": "math.sin", "line_number": 362, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 362, "usage_type": "call"}, {"api_name": "afrl.cmasi.VehicleActionCommand.VehicleActionCommand", "line_number": 386, "usage_type": "call"}, {"api_name": "afrl.cmasi.CommandStatusType.CommandStatusType.Pending", "line_number": 388, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.CommandStatusType.CommandStatusType", "line_number": 388, "usage_type": "name"}, {"api_name": "afrl.cmasi.GimbalScanAction.GimbalScanAction", "line_number": 392, "usage_type": "call"}, {"api_name": "afrl.cmasi.searchai.HazardZoneEstimateReport.HazardZoneEstimateReport", "line_number": 411, "usage_type": "call"}, {"api_name": "afrl.cmasi.searchai.HazardType.HazardType.Fire", "line_number": 415, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.searchai.HazardType.HazardType", "line_number": 415, "usage_type": "name"}, {"api_name": "afrl.cmasi.Location3D.Location3D", "line_number": 447, "usage_type": "call"}, {"api_name": "afrl.cmasi.Location3D.Location3D", "line_number": 448, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 498, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 498, "usage_type": "call"}, {"api_name": "afrl.cmasi.Waypoint.Waypoint", "line_number": 505, "usage_type": "call"}, {"api_name": "afrl.cmasi.SpeedType.SpeedType.Airspeed", "line_number": 520, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.SpeedType.SpeedType", "line_number": 520, "usage_type": "name"}, {"api_name": "afrl.cmasi.TurnType.TurnType.TurnShort", "line_number": 522, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.TurnType.TurnType", "line_number": 522, "usage_type": "name"}, {"api_name": "afrl.cmasi.Location3D.Location3D", "line_number": 528, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 538, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 592, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 592, "usage_type": "call"}, {"api_name": "afrl.cmasi.MissionCommand.MissionCommand", "line_number": 593, "usage_type": "call"}, {"api_name": "afrl.cmasi.CommandStatusType.CommandStatusType.Pending", "line_number": 596, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.CommandStatusType.CommandStatusType", "line_number": 596, "usage_type": "name"}, {"api_name": "afrl.cmasi.Waypoint.Waypoint", "line_number": 603, "usage_type": "call"}, {"api_name": "afrl.cmasi.SpeedType.SpeedType.Airspeed", "line_number": 618, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.SpeedType.SpeedType", "line_number": 618, "usage_type": "name"}, {"api_name": "afrl.cmasi.TurnType.TurnType.TurnShort", "line_number": 620, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.TurnType.TurnType", "line_number": 620, "usage_type": "name"}, {"api_name": "math.cos", "line_number": 674, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 674, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 684, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 684, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 717, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 717, "usage_type": "call"}, {"api_name": "afrl.cmasi.Location3D.Location3D", "line_number": 718, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 730, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 734, "usage_type": "name"}, {"api_name": "math.cos", "line_number": 753, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 753, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 754, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 754, "usage_type": "call"}, {"api_name": "afrl.cmasi.Waypoint.Waypoint", "line_number": 773, "usage_type": "call"}, {"api_name": "afrl.cmasi.SpeedType.SpeedType.Airspeed", "line_number": 785, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.SpeedType.SpeedType", "line_number": 785, "usage_type": "name"}, {"api_name": "afrl.cmasi.TurnType.TurnType.TurnShort", "line_number": 787, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.TurnType.TurnType", "line_number": 787, "usage_type": "name"}, {"api_name": "math.cos", "line_number": 804, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 804, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 805, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 805, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 829, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 829, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 830, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 830, "usage_type": "call"}, {"api_name": "afrl.cmasi.Waypoint.Waypoint", "line_number": 872, "usage_type": "call"}, {"api_name": "afrl.cmasi.SpeedType.SpeedType.Airspeed", "line_number": 884, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.SpeedType.SpeedType", "line_number": 884, "usage_type": "name"}, {"api_name": "afrl.cmasi.TurnType.TurnType.TurnShort", "line_number": 886, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.TurnType.TurnType", "line_number": 886, "usage_type": "name"}, {"api_name": "afrl.cmasi.Location3D.Location3D", "line_number": 933, "usage_type": "call"}, {"api_name": "afrl.cmasi.Location3D.Location3D", "line_number": 968, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 981, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 996, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 996, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 1019, "usage_type": "call"}, {"api_name": "afrl.cmasi.Waypoint.Waypoint", "line_number": 1030, "usage_type": "call"}, {"api_name": "afrl.cmasi.SpeedType.SpeedType.Airspeed", "line_number": 1042, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.SpeedType.SpeedType", "line_number": 1042, "usage_type": "name"}, {"api_name": "afrl.cmasi.TurnType.TurnType.TurnShort", "line_number": 1044, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.TurnType.TurnType", "line_number": 1044, "usage_type": "name"}, {"api_name": "afrl.cmasi.Waypoint.Waypoint", "line_number": 1061, "usage_type": "call"}, {"api_name": "afrl.cmasi.SpeedType.SpeedType.Airspeed", "line_number": 1076, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.SpeedType.SpeedType", "line_number": 1076, "usage_type": "name"}, {"api_name": "afrl.cmasi.TurnType.TurnType.TurnShort", "line_number": 1078, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.TurnType.TurnType", "line_number": 1078, "usage_type": "name"}, {"api_name": "math.sin", "line_number": 1091, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 1091, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 1097, "usage_type": "call"}, {"api_name": "afrl.cmasi.Waypoint.Waypoint", "line_number": 1108, "usage_type": "call"}, {"api_name": "afrl.cmasi.SpeedType.SpeedType.Airspeed", "line_number": 1123, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.SpeedType.SpeedType", "line_number": 1123, "usage_type": "name"}, {"api_name": "afrl.cmasi.TurnType.TurnType.TurnShort", "line_number": 1125, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.TurnType.TurnType", "line_number": 1125, "usage_type": "name"}, {"api_name": "math.sqrt", "line_number": 1143, "usage_type": "call"}, {"api_name": "afrl.cmasi.Waypoint.Waypoint", "line_number": 1158, "usage_type": "call"}, {"api_name": "afrl.cmasi.SpeedType.SpeedType.Airspeed", "line_number": 1173, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.SpeedType.SpeedType", "line_number": 1173, "usage_type": "name"}, {"api_name": "afrl.cmasi.TurnType.TurnType.TurnShort", "line_number": 1175, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.TurnType.TurnType", "line_number": 1175, "usage_type": "name"}, {"api_name": "afrl.cmasi.Location3D.Location3D", "line_number": 1352, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 1413, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 1448, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 1459, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1459, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 1460, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1460, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 1461, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 1461, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 1478, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1479, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.StandardScaler", "line_number": 1481, "usage_type": "call"}, {"api_name": "sklearn.cluster.MeanShift", "line_number": 1482, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1483, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 1484, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1501, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.squareform", "line_number": 1503, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.pdist", "line_number": 1503, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 1504, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 1506, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1514, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 1515, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 1545, "usage_type": "call"}, {"api_name": "afrl.cmasi.Location3D.Location3D", "line_number": 1567, "usage_type": "call"}, {"api_name": "afrl.cmasi.Polygon.Polygon", "line_number": 1572, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 1595, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 1595, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 1596, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 1596, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 1609, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 1609, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 1610, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 1610, "usage_type": "call"}, {"api_name": "afrl.cmasi.NavigationMode.NavigationMode.Waypoint", "line_number": 1713, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.NavigationMode.NavigationMode", "line_number": 1713, "usage_type": "name"}, {"api_name": "afrl.cmasi.Location3D.Location3D", "line_number": 1776, "usage_type": "call"}, {"api_name": "afrl.cmasi.Location3D.Location3D", "line_number": 1777, "usage_type": "call"}, {"api_name": "afrl.cmasi.AirVehicleState.AirVehicleState.ID", "line_number": 1802, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.AirVehicleState.AirVehicleState", "line_number": 1802, "usage_type": "name"}, {"api_name": "afrl.cmasi.AirVehicleState.AirVehicleState.EnergyAvailable", "line_number": 1804, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.AirVehicleState.AirVehicleState", "line_number": 1804, "usage_type": "name"}, {"api_name": "afrl.cmasi.AirVehicleState.AirVehicleState.ActualEnergyRate", "line_number": 1816, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.AirVehicleState.AirVehicleState", "line_number": 1816, "usage_type": "name"}, {"api_name": "afrl.cmasi.AirVehicleState.AirVehicleState.Location", "line_number": 1817, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.AirVehicleState.AirVehicleState", "line_number": 1817, "usage_type": "name"}, {"api_name": "numpy.argmin", "line_number": 1830, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 1892, "usage_type": "call"}, {"api_name": "afrl.cmasi.Waypoint.Waypoint", "line_number": 1922, "usage_type": "call"}, {"api_name": "afrl.cmasi.SpeedType.SpeedType.Airspeed", "line_number": 1934, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.SpeedType.SpeedType", "line_number": 1934, "usage_type": "name"}, {"api_name": "afrl.cmasi.TurnType.TurnType.TurnShort", "line_number": 1936, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.TurnType.TurnType", "line_number": 1936, "usage_type": "name"}, {"api_name": "afrl.cmasi.Location3D.Location3D", "line_number": 1954, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 1964, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 2002, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 2024, "usage_type": "call"}, {"api_name": "amase.TCPClient.AmaseTCPClient", "line_number": 2040, "usage_type": "call"}, {"api_name": "time.time", "line_number": 2052, "usage_type": "call"}, {"api_name": "time.time", "line_number": 2058, "usage_type": "call"}, {"api_name": "time.time", "line_number": 2060, "usage_type": "call"}, {"api_name": "afrl.cmasi.NavigationMode.NavigationMode.Waypoint", "line_number": 2091, "usage_type": "attribute"}, {"api_name": "afrl.cmasi.NavigationMode.NavigationMode", "line_number": 2091, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 2100, "usage_type": "call"}]}