diff --git "a/1203.jsonl" "b/1203.jsonl" new file mode 100644--- /dev/null +++ "b/1203.jsonl" @@ -0,0 +1,459 @@ +{"seq_id": "233481371", "text": "from numpy import linalg, array, asarray, matrix, random, dot, cross, pi, arccos\nfrom mpl_toolkits.mplot3d import Axes3D\nimport matplotlib.pyplot as plt\nfrom sympy import Matrix as spMatrix\n\nA = matrix([[2,-3,1],[0,1,3],[-4,2,1]])\n#som array, men uten komponentvise operasjoner * / **\nrandMat = random.rand(2,6) #2x6-matrise med tilfeldige verdier mellom 0 og 1\nA_inv = linalg.inv(A)\nA_det = linalg.det(A)\nE_val, E_vec = linalg.eig(A)\n\nv = matrix([2,4,6])\nv_transp = v.T\na = asarray(v)\nb = array([3,5,7])\nc = cross(a,b)\nd = arccos(dot(a,b)/(linalg.norm(a)*linalg.norm(b)))\n\nM = matrix([[2,-4,6,-2],[-3,2,-1,8],[1,-6,11,4]])\nprint(spMatrix.rref(spMatrix(M))) #radreduksjon via sympy", "sub_path": "linalg/intro.py", "file_name": "intro.py", "file_ext": "py", "file_size_in_byte": 682, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.matrix", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 8, "usage_type": "name"}, {"api_name": "numpy.linalg.inv", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 9, "usage_type": "name"}, {"api_name": "numpy.linalg.det", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 10, "usage_type": "name"}, {"api_name": "numpy.linalg.eig", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 11, "usage_type": "name"}, {"api_name": "numpy.matrix", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.cross", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.arccos", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 18, "usage_type": "name"}, {"api_name": "numpy.matrix", "line_number": 20, "usage_type": "call"}, {"api_name": "sympy.Matrix.rref", "line_number": 21, "usage_type": "call"}, {"api_name": "sympy.Matrix", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "247661400", "text": "__Author__ = 'Prameet Bisht'\n__Version__ = \"0.0.1\"\n__Email__ = \"myprameet09@gmail.com\"\n__Github__ = \"https://github.com/orgs/POC-AWS-services/dashboard\"\n\n\nimport boto3\n\n\n# Create CloudWatchEvents client\ncloudwatch_events = boto3.client('events')\n\n# Put an event rule\nresponse = cloudwatch_events.put_rule(\n Name='DEMO_EVENT',\n RoleArn='IAM_ROLE_ARN',\n ScheduleExpression='rate(5 minutes)',\n State='ENABLED'\n)\nprint(response['RuleArn'])", "sub_path": "Create_a_scheduled_rule.py", "file_name": "Create_a_scheduled_rule.py", "file_ext": "py", "file_size_in_byte": 447, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "boto3.client", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "403650629", "text": "import services.controlers.documentTypeControler\nimport json\nfrom json import JSONEncoder\nfrom json import JSONDecoder\nimport services.controlers.loggControler\nfrom services.exceptions import *\n\n\nclass GetDocumentTypeByCountryIdView:\n\n\tdef post(self, idCountry):\n\t\tresponse_data = {}\n\t\tresponse_data['status']=OK\n\t\tresponse_data['message']=\"DOCUMENTOS_ENCONTRADOS\"\n\t\tresponse_data['data']=''\n\t\tdocumentTypeControler =services.controlers.documentTypeControler.DocumentTypeControler()\n\t\ttry:\n\t\t\tresponse_data['data']=documentTypeControler.getByCountryId(idCountry)\n\t\texcept ExceptionWithCode as e:\n\t\t\tresponse_data['message']=e.message\n\t\t\tresponse_data['status']=e.code\n\n\t\texcept Exception as e:\n\t\t\tresponse_data['status']=ERROR_NO_DEFINIDO\n\t\t\tresponse_data['message']=e.message\n\t\t\tloggControler = services.controlers.loggControler.LoggControler()\n\t\t\tloggControler.addLogg('Critical', ERROR_NO_DEFINIDO, e.message)\n\t\tjsonStringResponse = JSONEncoder().encode(response_data)\n\t\treturn jsonStringResponse", "sub_path": "services/views/getDocumentTypeByCountryIdView.py", "file_name": "getDocumentTypeByCountryIdView.py", "file_ext": "py", "file_size_in_byte": 999, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "services.controlers.documentTypeControler.controlers.documentTypeControler.DocumentTypeControler", "line_number": 16, "usage_type": "call"}, {"api_name": "services.controlers.documentTypeControler.controlers", "line_number": 16, "usage_type": "attribute"}, {"api_name": "services.controlers.documentTypeControler", "line_number": 16, "usage_type": "name"}, {"api_name": "services.controlers.documentTypeControler.controlers.loggControler.LoggControler", "line_number": 26, "usage_type": "call"}, {"api_name": "services.controlers.documentTypeControler.controlers", "line_number": 26, "usage_type": "attribute"}, {"api_name": "services.controlers.documentTypeControler", "line_number": 26, "usage_type": "name"}, {"api_name": "json.JSONEncoder", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "427947269", "text": "#!/usr/bin/env python\n\n\nimport turbotutils.account\nimport boto3\nimport json\n\n\ndef main(account, region):\n session = boto3.Session(profile_name=account)\n client = session.client('iam', region_name=region)\n try:\n response = client.list_users()\n #responseObj = json.loads(response.text)\n for user in response['Users']:\n keys = client.list_access_keys(UserName=user['UserName'])\n if (keys['AccessKeyMetadata'][0]['Status']) == 'Inactive':\n print(keys['AccessKeyMetadata'][0]['UserName'], keys['AccessKeyMetadata'][0]['AccessKeyId'], account)\n except Exception as e:\n print(e, account)\n\nif __name__ == '__main__':\n\n # Set to False if you do not have a valid certificate for your Turbot Host\n turbot_host_certificate_verification = True\n\n # Set to your Turbot Host URL\n turbot_host = turbotutils.get_turbot_host()\n\n turbot_user_id = turbotutils.get_turbot_user()\n\n # Get the turbot version\n api_version = turbotutils.get_api_version()\n\n # Get the access and secret key pairs\n (turbot_api_access_key, turbot_api_secret_key) = turbotutils.get_turbot_access_keys()\n region_name='us-east-1'\n accounts = turbotutils.cluster.get_turbot_account_ids(turbot_api_access_key, turbot_api_secret_key, turbot_host_certificate_verification, turbot_host, api_version)\n\n for account in accounts:\n turbot_account = account\n main(account, region_name)\n", "sub_path": "examples/find_unused_access_keys.py", "file_name": "find_unused_access_keys.py", "file_ext": "py", "file_size_in_byte": 1454, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "boto3.Session", "line_number": 10, "usage_type": "call"}, {"api_name": "turbotutils.account.get_turbot_host", "line_number": 28, "usage_type": "call"}, {"api_name": "turbotutils.account", "line_number": 28, "usage_type": "name"}, {"api_name": "turbotutils.account.get_turbot_user", "line_number": 30, "usage_type": "call"}, {"api_name": "turbotutils.account", "line_number": 30, "usage_type": "name"}, {"api_name": "turbotutils.account.get_api_version", "line_number": 33, "usage_type": "call"}, {"api_name": "turbotutils.account", "line_number": 33, "usage_type": "name"}, {"api_name": "turbotutils.account.get_turbot_access_keys", "line_number": 36, "usage_type": "call"}, {"api_name": "turbotutils.account", "line_number": 36, "usage_type": "name"}, {"api_name": "turbotutils.account.cluster.get_turbot_account_ids", "line_number": 38, "usage_type": "call"}, {"api_name": "turbotutils.account.cluster", "line_number": 38, "usage_type": "attribute"}, {"api_name": "turbotutils.account", "line_number": 38, "usage_type": "name"}]} +{"seq_id": "574322406", "text": "from django.urls import path\n\nfrom . import views\n\napp_name = 'orders'\n\nurlpatterns = [\n path(\"\", views.index, name=\"index\"),\n path(\"register\", views.register, name='register'),\n path(\"enter\", views.enter, name='enter'),\n path(\"exit\", views.exit, name='exit'),\n path(\"pizza//\", views.process_order, name='process_order'),\n path(\"cart/\", views.process_cart, name='process_cart'),\n path(\"display_orders\", views.display_orders, name='display_orders'),\n path(\"clear_cart\", views.clear_cart, name='clear_cart')\n\n\n]", "sub_path": "orders/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 553, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "501895107", "text": "import cv2\nimport pymysql\nrecognizer = cv2.face.LBPHFaceRecognizer_create()\n\n# Load the trained mode\nrecognizer.read('auth/trainer/trainer.yml')\n\n# Load prebuilt model for Frontal Face\ncascadePath = \"auth/cascades/haarcascade_frontalface_default.xml\"\nface_id=26723\n# Create classifier from prebuilt model\nfaceCascade = cv2.CascadeClassifier(cascadePath);\ncap = cv2.VideoCapture(0)\nhieght=600\nwidht=1200\n# Initialize sample face image\ncount =1\nclass CaptureImages(object):\n\n\n def getCaptureImages(self):\n conn = pymysql.connect(\"localhost\", \"xolani\", \"phpXOLANI6565x.,\", \"register\")\n curs = conn.cursor()\n\n curs.execute(\"SELECT * FROM users WHERE registerNumber =(SELECT min(registerNumber) FROM users)\")\n val = curs.fetchall()\n for row in val:\n global face_id\n #face_id = row[0]\n face_id = row[0]\n conn.commit()\n conn.close()\n\n global count\n\n img, frame_image = cap.read()\n # Convert frame to grayscale\n gray = cv2.cvtColor(frame_image, cv2.COLOR_BGR2GRAY)\n\n # Detect frames of different sizes, list of faces rectangles\n faces = faceCascade.detectMultiScale(gray, 1.2, 5)\n\n # Loops for each faces\n for (x, y, w, h) in faces:\n # Crop the image frame into rectangle\n cv2.rectangle(frame_image, (x, y), (x + w, y + h), (255, 0, 0), 2)\n # Increment sample face image\n count += 1\n\n if (count < 60):\n\n # Save the captured image into the datasets folder\n cv2.imwrite(\"auth/dataset/User.\" + str(face_id) + '.' + str(count) + \".jpg\", gray[y:y + h, x:x + w])\n print(\"busy with ur staff\")\n else:\n print(\"im done with you staff\")\n\n # if img:\n # self.out.write(frame_image)\n\n# while True:\n# CaptureImages().getCaptureImages()\n", "sub_path": "auth/capture_image.py", "file_name": "capture_image.py", "file_ext": "py", "file_size_in_byte": 1904, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "cv2.face.LBPHFaceRecognizer_create", "line_number": 3, "usage_type": "call"}, {"api_name": "cv2.face", "line_number": 3, "usage_type": "attribute"}, {"api_name": "cv2.CascadeClassifier", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 13, "usage_type": "call"}, {"api_name": "pymysql.connect", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 38, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 46, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "49349721", "text": "import numpy as np\nfrom easydict import EasyDict as edict\n\n\ncfgs = edict()\ncfgs.file_list = ['training.txt', 'validation.txt']\ncfgs.seq_num = 0\ncfgs.cur_channel = 3\n\ncfgs.NUM_OF_CLASSESS = 2\n#cfgs.IMAGE_SIZE = [270, 480]\ncfgs.IMAGE_SIZE = [540, 960]\ncfgs.batch_size = 8\n", "sub_path": "experiment/finetune_fcn/cfgs/config_train_m0717.py", "file_name": "config_train_m0717.py", "file_ext": "py", "file_size_in_byte": 270, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "easydict.EasyDict", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "468250146", "text": "import random\n\nfrom tools.unionfind import UnionFind\n\n\ndef maze_generator_search(height, width):\n # Random breast/depth first search in an implicit graph starting from square (0,0)\n\n maze = [[0 if x % 2 == y % 2 == 1 else 1 for y in range(2 * width + 1)] for x in range(2 * height + 1)]\n\n queue = {(0, 0)} # waiting queue\n seen = {(0, 0)} # seen nodes\n\n # get coordinates of a node's neighbors\n neighbors = lambda X: filter(\n lambda Y: 0 <= Y[0] < height and 0 <= Y[1] < width,\n [(X[0] + d[0], X[1] + d[1]) for d in ((1, 0), (-1, 0), (0, 1), (0, -1))]\n )\n\n # get the coordinates of the wall beetween 2 nodes\n wall = lambda X, Y: tuple((2 * X[i] + 1 + 2 * Y[i] + 1) // 2 for i in range(2))\n\n while queue:\n # random waiting node\n square = queue.pop() # lot faster than random.sample\n\n # its neighbors (without the already seen ones)\n neighbors_ = set(neighbors(square)).difference(seen)\n\n if neighbors_:\n # random neighbor\n neighbor = random.sample(neighbors_, 1)[0]\n\n # add to queue and seen\n seen.add(neighbor)\n queue.add(neighbor)\n\n # break wall beetween the nodes\n wall_ = wall(square, neighbor)\n maze[wall_[0]][wall_[1]] = 0\n\n # put back node to in the queue it it has at least one unseen neighbor\n if len(neighbors_) > 1:\n queue.add(square)\n\n return maze\n\n\ndef maze_generator_union(height, width):\n # Random expansion of connected components\n\n # empty maze\n maze = [[0 if x % 2 == y % 2 == 1 else 1 for y in range(2 * width + 1)] for x in range(2 * height + 1)]\n\n # squares that can be randomly selected by the algorithm\n squares = {(x, y) for x in range(height) for y in range(width)}\n\n # union-find structure to keep track of connected components\n unionfind = UnionFind(squares)\n\n # get coordinates of a node's neighbors\n neighbors = lambda square: filter(\n lambda Y: 0 <= Y[0] < height and 0 <= Y[1] < width,\n [(square[0] + d[0], square[1] + d[1]) for d in ((1, 0), (-1, 0), (0, 1), (0, -1))]\n )\n\n # get the coordinates of the wall beetween 2 nodes\n wall = lambda X, Y: tuple((2 * X[i] + 1 + 2 * Y[i] + 1) // 2 for i in range(2))\n\n while squares:\n # random waiting node\n square = squares.pop()\n\n # its neighbors (without the ones which already are in the same connected component)\n neighbors_ = set(filter(\n lambda X: unionfind.find(square) != unionfind.find(X),\n neighbors(square)\n ))\n\n if neighbors_:\n # random neighbor\n neighbor = random.sample(neighbors_, 1)[0]\n\n # union of the connected components\n unionfind.union(square, neighbor)\n\n # break wall beetween the nodes\n wall_ = wall(square, neighbor)\n maze[wall_[0]][wall_[1]] = 0\n\n # put back node to in the queue it it has at least one unconnected neighbor\n if len(neighbors_) > 1:\n squares.add(square)\n\n return maze\n", "sub_path": "maze/maze_generator.py", "file_name": "maze_generator.py", "file_ext": "py", "file_size_in_byte": 3124, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "random.sample", "line_number": 32, "usage_type": "call"}, {"api_name": "tools.unionfind.UnionFind", "line_number": 59, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "338567987", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\n# CODE NAME HERE\n\n# CODE DESCRIPTION HERE\n\nCreated on 2018-12-18 at 10:47\n\n@author: cook\n\"\"\"\n\nimport numpy as np\nimport os\nfrom astropy.io import fits\n\n\n# =============================================================================\n# Define variables\n# =============================================================================\nWORKSPACE = '/spirou/cfht_nights/mtl/telluDB'\nDATABASE = os.path.join(WORKSPACE, 'master_tellu_SPIROU.txt')\n# -----------------------------------------------------------------------------\n\n# =============================================================================\n# Define functions\n# =============================================================================\ndef read_data_base(databasepath):\n # open file\n f = open(databasepath, 'r')\n lines = f.readlines()\n f.close()\n # split by space\n dlines = []\n for line in lines:\n dlines.append(line.split())\n # return database lines\n return dlines\n\n\n\n# =============================================================================\n# Start of code\n# =============================================================================\n# Main code here\nif __name__ == \"__main__\":\n # ----------------------------------------------------------------------\n # load database file\n database = read_data_base(DATABASE)\n\n # get filename sna obj name for all TELL_OBJ\n tell_file, tell_obj = [], []\n for entry in database:\n if len(entry) == 0:\n continue\n if entry[0] == 'TELL_OBJ':\n tell_file.append(entry[1].strip())\n tell_obj.append(entry[4].strip())\n\n # loop around files and check header\n hdr_obj = []\n for it, filename in enumerate(tell_file):\n # print progress\n print('Reading file {0} of {1}'.format(it + 1, len(tell_file)))\n # load header\n fhdr = fits.getheader(os.path.join(WORKSPACE, filename))\n # get objname from header\n objname_hdr = str(fhdr['OBJNAME']).strip()\n hdr_obj.append(objname_hdr)\n # check for consistency\n if objname_hdr.upper() != tell_obj[it].upper():\n wmsg = '\\tFile {0} OBJNAME match fail ({1} != {2})'\n print(wmsg.format(filename, tell_obj[it], objname_hdr))\n\n tell_obj = np.array(tell_obj)\n hdr_obj = np.array(hdr_obj)\n mask = tell_obj != hdr_obj\n print('Number OBJNAMES not equal = {0}'.format(np.nansum(mask)))\n\n\n# =============================================================================\n# End of code\n# =============================================================================\n", "sub_path": "old_code/INTROOT/misc/test_db_values.py", "file_name": "test_db_values.py", "file_ext": "py", "file_size_in_byte": 2639, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "astropy.io.fits.getheader", "line_number": 66, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 66, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.nansum", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "352762794", "text": "#!/usr/bin/python\n# -*- coding:UTF-8 -*-\n\nfrom test.common_package import myunit\nfrom time import sleep\nfrom test.common_package.searchcommon import SearchCommon\nfrom test.common_package.selectcity import SelectCity\nfrom test.common_package.basic import Page\n\nclass Bj_ErShouFang_All_PaiXu(myunit.MyTest,Page):\n #单元测试使用\n #def __init__(self,driver):\n # self.driver = driver\n\n def open_url(self):\n SelectCity(self.driver).dakaiwangzhan() # 打开网站\n sleep(2)\n SelectCity(self.driver).maitian_online_city_select(cityname=2) # 选择城市\n self.shouye_handle = self.driver.current_window_handle\n SelectCity(self.driver).second_hand_house() # 二手房\n sleep(3)\n self.all_handle = self.driver.window_handles\n for self.handle in self.all_handle:\n if self.handle != self.shouye_handle:\n self.driver.switch_to.window(self.handle)\n sleep(1)\n\n def test_1(self):\n self.open_url()\n SearchCommon(self.driver).paixu_list(paixuname='总价')\n SearchCommon(self.driver).paixu_list(paixuname='单价')\n SearchCommon(self.driver).paixu_list(paixuname='面积')\n\nif __name__==\"__main__\":\n from selenium import webdriver\n import data.urldata as URL\n\n driver = webdriver.Chrome()\n url = URL.maitian_online_url\n driver.get(url)\n # driver.get('http://bj-test.imtfc.com/esfway/B1D1/T3L1')\n driver.implicitly_wait(2)\n driver.maximize_window()\n Bj_ErShouFang_All_PaiXu(driver).test_1()\n", "sub_path": "test/xiamen_TestCase/xm_ershoufang_paixu_testcase.py", "file_name": "xm_ershoufang_paixu_testcase.py", "file_ext": "py", "file_size_in_byte": 1543, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "test.common_package.myunit.MyTest", "line_number": 10, "usage_type": "attribute"}, {"api_name": "test.common_package.myunit", "line_number": 10, "usage_type": "name"}, {"api_name": "test.common_package.basic.Page", "line_number": 10, "usage_type": "name"}, {"api_name": "test.common_package.selectcity.SelectCity", "line_number": 16, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 17, "usage_type": "call"}, {"api_name": "test.common_package.selectcity.SelectCity", "line_number": 18, "usage_type": "call"}, {"api_name": "test.common_package.selectcity.SelectCity", "line_number": 20, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 21, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 26, "usage_type": "call"}, {"api_name": "test.common_package.searchcommon.SearchCommon", "line_number": 30, "usage_type": "call"}, {"api_name": "test.common_package.searchcommon.SearchCommon", "line_number": 31, "usage_type": "call"}, {"api_name": "test.common_package.searchcommon.SearchCommon", "line_number": 32, "usage_type": "call"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 38, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 38, "usage_type": "name"}, {"api_name": "data.urldata.maitian_online_url", "line_number": 39, "usage_type": "attribute"}, {"api_name": "data.urldata", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "35300069", "text": "from models import networkgcn, networktcn \nimport torch \nimport numpy as np \nfrom TorchSUL import Model as M \nimport datareader \nfrom tqdm import tqdm\nimport torch.nn.functional as F \n\nbone_pairs = [[8,9],[9,10], [8,14],[14,15],[15,16], [8,11],[12,13],[11,12], [8,7],[7,0], [4,5],[5,6],[0,4], [0,1],[1,2],[2,3]]\nbone_matrix = np.zeros([16,17], dtype=np.float32)\nfor i, pair in enumerate(bone_pairs):\n\tbone_matrix[i, pair[0]] = -1\n\tbone_matrix[i, pair[1]] = 1\nbone_matrix_inv = np.linalg.pinv(bone_matrix)\nbone_matrix_inv = torch.from_numpy(bone_matrix_inv)\nbone_matrix = torch.from_numpy(bone_matrix)\n\nbsize = 32\nseq_len = 243\n\nnetgcn = networkgcn.TransNet(256, 17)\nnettcn = networktcn.Refine2dNet(17, seq_len)\n\n# initialize the network with dumb input \nx_dumb = torch.zeros(2,17,2)\naffb = torch.ones(2,16,16) / 16\naffpts = torch.ones(2,17,17) / 17\nnetgcn(x_dumb, affpts, affb, bone_matrix, bone_matrix_inv)\nx_dumb = torch.zeros(2,243, 17*3)\nnettcn(x_dumb)\n\n# load networks \nM.Saver(netgcn).restore('./ckpts/model_gcn/')\nM.Saver(nettcn).restore('./ckpts/model_tcn/')\n\n# push to gpu \nnetgcn.eval()\nnettcn.eval()\n\n# get loader \ndataset = datareader.PtsData(seq_len)\n\n# start testing \nsample_num = 0\nloss_total = 0\nfor i in tqdm(range(len(dataset))):\n\tp2d,p3d = dataset[i]\n\tbsize = p2d.shape[0]\n\taffb = torch.ones(bsize,16,16) / 16\n\taffpts = torch.ones(bsize,17,17) / 17\n\n\twith torch.no_grad():\n\t\tpred = netgcn(p2d, affpts, affb, bone_matrix, bone_matrix_inv)\n\t\tpred = pred.unsqueeze(0).unsqueeze(0)\n\t\tpred = F.pad(pred, (0,0,0,0,seq_len//2, seq_len//2), mode='replicate')\n\t\tpred = pred.squeeze()\n\t\tpred = nettcn.evaluate(pred)\n\t\tloss = torch.sqrt(torch.pow(pred - p3d, 2).sum(dim=-1)) # [N, 17]\n\t\tloss = loss.mean(dim=1).sum()\n\t\tloss_total = loss_total + loss\n\t\tsample_num = sample_num + bsize\n\nprint('MPJPE: %.4f'%(loss_total / sample_num))\n", "sub_path": "eval_gt_h36m_cpu.py", "file_name": "eval_gt_h36m_cpu.py", "file_ext": "py", "file_size_in_byte": 1840, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.zeros", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 10, "usage_type": "attribute"}, {"api_name": "numpy.linalg.pinv", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 16, "usage_type": "call"}, {"api_name": "models.networkgcn.TransNet", "line_number": 21, "usage_type": "call"}, {"api_name": "models.networkgcn", "line_number": 21, "usage_type": "name"}, {"api_name": "models.networktcn.Refine2dNet", "line_number": 22, "usage_type": "call"}, {"api_name": "models.networktcn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 29, "usage_type": "call"}, {"api_name": "TorchSUL.Model.Saver", "line_number": 33, "usage_type": "call"}, {"api_name": "TorchSUL.Model", "line_number": 33, "usage_type": "name"}, {"api_name": "TorchSUL.Model.Saver", "line_number": 34, "usage_type": "call"}, {"api_name": "TorchSUL.Model", "line_number": 34, "usage_type": "name"}, {"api_name": "datareader.PtsData", "line_number": 41, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn.functional.pad", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 55, "usage_type": "name"}, {"api_name": "torch.sqrt", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 58, "usage_type": "call"}]} +{"seq_id": "378608248", "text": "# -*- coding:ascii -*-\nfrom mako import runtime, filters, cache\nUNDEFINED = runtime.UNDEFINED\n__M_dict_builtin = dict\n__M_locals_builtin = locals\n_magic_number = 10\n_modified_time = 1421880533.820617\n_enable_loop = True\n_template_filename = 'C:\\\\Python34\\\\text_dmp\\\\homepage\\\\templates/terms.html'\n_template_uri = 'terms.html'\n_source_encoding = 'ascii'\nimport os, os.path, re\n_exports = ['content3', 'content1', 'content2']\n\n\ndef _mako_get_namespace(context, name):\n try:\n return context.namespaces[(__name__, name)]\n except KeyError:\n _mako_generate_namespaces(context)\n return context.namespaces[(__name__, name)]\ndef _mako_generate_namespaces(context):\n pass\ndef _mako_inherit(template, context):\n _mako_generate_namespaces(context)\n return runtime._inherit_from(context, 'base.htm', _template_uri)\ndef render_body(context,**pageargs):\n __M_caller = context.caller_stack._push_frame()\n try:\n __M_locals = __M_dict_builtin(pageargs=pageargs)\n def content3():\n return render_content3(context._locals(__M_locals))\n def content1():\n return render_content1(context._locals(__M_locals))\n def content2():\n return render_content2(context._locals(__M_locals))\n __M_writer = context.writer()\n __M_writer('\\n\\n \\n')\n if 'parent' not in context._data or not hasattr(context._data['parent'], 'content1'):\n context['self'].content1(**pageargs)\n \n\n __M_writer('\\n')\n if 'parent' not in context._data or not hasattr(context._data['parent'], 'content2'):\n context['self'].content2(**pageargs)\n \n\n __M_writer('\\n \\n')\n if 'parent' not in context._data or not hasattr(context._data['parent'], 'content3'):\n context['self'].content3(**pageargs)\n \n\n return ''\n finally:\n context.caller_stack._pop_frame()\n\n\ndef render_content3(context,**pageargs):\n __M_caller = context.caller_stack._push_frame()\n try:\n def content3():\n return render_content3(context)\n __M_writer = context.writer()\n __M_writer('\\n
\\n\\n
\\n \\n
\\n
\\n
\\n
\\n

Google Web Fonts and
Font Awesome Icons

\\n

This template features the \\'Lato\\' font, part of the Google Web Font library, as well as icons from Font Awesome.

\\n
\\n
\\n \"\"\\n
\\n
\\n \\n
\\n \\n
\\n')\n return ''\n finally:\n context.caller_stack._pop_frame()\n\n\ndef render_content1(context,**pageargs):\n __M_caller = context.caller_stack._push_frame()\n try:\n def content1():\n return render_content1(context)\n __M_writer = context.writer()\n __M_writer('\\n
\\n\\n
\\n \\n
\\n \\n
\\n \\n
\\n \\n\\n
\\n \\n')\n return ''\n finally:\n context.caller_stack._pop_frame()\n\n\ndef render_content2(context,**pageargs):\n __M_caller = context.caller_stack._push_frame()\n try:\n def content2():\n return render_content2(context)\n __M_writer = context.writer()\n __M_writer('\\n
\\n\\n
\\n \\n
\\n
\\n
\\n
\\n

3D Device Mockups
by PSDCovers

\\n

Turn your 2D designs into high quality, 3D product shots in seconds using free Photoshop actions by PSDCovers! Visit their website to download some of their awesome, free photoshop actions!

\\n
\\n
\\n \"\"\\n
\\n
\\n \\n
\\n \\n\\n
\\n')\n return ''\n finally:\n context.caller_stack._pop_frame()\n\n\n\"\"\"\n__M_BEGIN_METADATA\n{\"uri\": \"terms.html\", \"filename\": \"C:\\\\Python34\\\\text_dmp\\\\homepage\\\\templates/terms.html\", \"line_map\": {\"48\": 40, \"64\": 42, \"82\": 19, \"27\": 0, \"70\": 4, \"38\": 1, \"88\": 19, \"58\": 42, \"43\": 18, \"76\": 4, \"94\": 88}, \"source_encoding\": \"ascii\"}\n__M_END_METADATA\n\"\"\"\n", "sub_path": "homepage/cached_templates/templates/terms.html.py", "file_name": "terms.html.py", "file_ext": "py", "file_size_in_byte": 5344, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "mako.runtime.UNDEFINED", "line_number": 3, "usage_type": "attribute"}, {"api_name": "mako.runtime", "line_number": 3, "usage_type": "name"}, {"api_name": "mako.runtime._inherit_from", "line_number": 26, "usage_type": "call"}, {"api_name": "mako.runtime", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "69393030", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\navg_reward = np.load('no_target_net/average_data.npy')\nreward = np.load('no_target_net/episode_data.npy')\n\nplt.rc('font', family='serif')\nplt.figure(figsize=(12, 14))\n\nax = plt.subplot(111) \nax.spines[\"top\"].set_visible(False) \nax.spines[\"right\"].set_visible(False) \n\nax.get_xaxis().tick_bottom() \nax.get_yaxis().tick_left() \nplt.xticks(fontsize=16)\nplt.yticks(fontsize=16)\n\n# plt.plot(np.arange(2000), reward, color=[0.866, 0.596, 0.850])\n# plt.plot(np.arange(2000), avg_reward, color=[0.6, 0.384, 0.239], linewidth=2.5)\nplt.plot(np.arange(2000), reward, color=[0.709, 0.341, 0.050])\nplt.plot(np.arange(2000), avg_reward, color=[0.105, 0.207, 0.733], linewidth=2.5)\nplt.xlabel('Episode', fontsize=17)\nplt.ylabel('Reward', fontsize=17)\n\nplt.grid()\nplt.show()", "sub_path": "CS6700_AE16B011_PA3/Code/Q2/plotting.py", "file_name": "plotting.py", "file_ext": "py", "file_size_in_byte": 823, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.load", "line_number": 4, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 5, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 7, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "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": "414566947", "text": "from collections import namedtuple\n\nimport os\n\nROOT_PATH = os.path.dirname(os.path.abspath(__file__))\n\nNNConfig = namedtuple('NNConfig', ['embeding_size', 'rnn_specs', 'rnn_doc_specs', 'projection_size', 'use_word2vec'])\nDataSetConfig = namedtuple('DataSetConfig', ['dict_size'])\nOptimizationConfig = namedtuple('OptimizationConfig', ['batch_size', 'num_epochs', 'gradient_norm'])\nCheckpointConfig = namedtuple('CheckpointConfig', ['checkpoint_dir', 'save_checkpoint_steps'])\nSummaryConfig = namedtuple('SummaryConfig', ['summary_dir', 'save_test_steps', 'save_eval_steps', 'eval_batch', 'save_graph'])\nTrainingConfig = namedtuple('TrainingConfig', ['nn_cfg', 'dataset_cfg', 'opt_cfg', 'checkpoint_cfg', 'summary_cfg', 'debug'])\n\nnn_cfg = NNConfig(\n embeding_size = 100,\n rnn_specs = [(\"GRU\",64)],\n rnn_doc_specs = [(\"GRU\",64)],\n projection_size = None,\n use_word2vec = False,\n)\n\nopt_cfg = OptimizationConfig(\n batch_size=500*5,\n num_epochs = 1000,\n gradient_norm = 5.\n)\n\ncheckpoint_cfg = CheckpointConfig(\n checkpoint_dir = 'checkpoint_dist',\n save_checkpoint_steps = 10000,\n)\nsummary_cfg = SummaryConfig(\n summary_dir='summary_dist',\n save_test_steps=10,\n save_eval_steps=30,\n eval_batch=200*5,\n save_graph=False,\n)\n\nif __name__ == '__main__':\n print(ROOT_PATH)\n", "sub_path": "Arsenii/config_dist.py", "file_name": "config_dist.py", "file_ext": "py", "file_size_in_byte": 1312, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.dirname", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 5, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 7, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 8, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 9, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 10, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 11, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "176849951", "text": "#!/usr/bin/python\n\n# Imported Modules:\n\nfrom __future__ import print_function\nfrom config import os, params\nfrom flask import Flask\nfrom copy import deepcopy\nfrom gitbot import main, printdebug\n\n# Setting Up:\n\nglobal working\nworking = False\n\napp = Flask(__name__) # Creates the application\n\n# Running The Main Function:\n\n@app.route(\"/\", methods=['GET', 'POST']) # Registers the script to run on hook or visit\ndef run():\n printdebug(params, \"Initializing...\")\n newparams = deepcopy(params)\n printdebug(params, \"Using parameters: \"+repr(newparams))\n global working\n if working: # Prevents two scripts running at the same time\n printdebug(newparams, \" Failed due to concurrent boot.\")\n elif not newparams[\"orgname\"]:\n printdebug(newparams, \" Failed due to abscence of organization name. Set the BOT_ORGNAME environment variable to the name of the organization.\")\n elif not newparams[\"token\"]:\n printdebug(newparams, \" Failed due to abscence of login token. Set the BOT_TOKEN environment variable to the bot's login token.\")\n else:\n working = True\n try:\n openpulls, merges = main(newparams) # Runs the script and extracts the parameters\n finally:\n working = False\n printdebug(newparams, \"Pull Requests: \"+repr(openpulls)+\"\\nMerges: \"+repr(merges)) # Displays a message with the output parameters\n return \"GitHub pull requests succesfully analyzed. Merged \"+str(len(merges))+\" pull requests.\" # Returns a summary string for website visitors\n return \"Failed to boot up pull request analyzer. Check logs for more information.\"\n", "sub_path": "web.py", "file_name": "web.py", "file_ext": "py", "file_size_in_byte": 1833, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Flask", "line_number": 16, "usage_type": "call"}, {"api_name": "gitbot.printdebug", "line_number": 22, "usage_type": "call"}, {"api_name": "config.params", "line_number": 22, "usage_type": "argument"}, {"api_name": "copy.deepcopy", "line_number": 23, "usage_type": "call"}, {"api_name": "config.params", "line_number": 23, "usage_type": "argument"}, {"api_name": "gitbot.printdebug", "line_number": 24, "usage_type": "call"}, {"api_name": "config.params", "line_number": 24, "usage_type": "argument"}, {"api_name": "gitbot.printdebug", "line_number": 27, "usage_type": "call"}, {"api_name": "gitbot.printdebug", "line_number": 29, "usage_type": "call"}, {"api_name": "gitbot.printdebug", "line_number": 31, "usage_type": "call"}, {"api_name": "gitbot.main", "line_number": 35, "usage_type": "call"}, {"api_name": "gitbot.printdebug", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "245057918", "text": "from json import loads, dump, load\nfrom pandas import DataFrame\nfrom re import compile, DOTALL\nfrom requests import get\nfrom fake_useragent import UserAgent\nfrom codecs import open\nfrom time import sleep\nfrom random import randint\n\n\nclass ArtFactsArtistScraper(object):\n def __init__(self):\n with open(\"artfacts_artists.json\", \"r\", \"utf-8\") as f:\n self.artists = load(f)\n self.data = None\n self.base_url = \"https://artfacts.net/api/v0/\"\n self.ua = UserAgent()\n\n def get_artists(self):\n bu = self.base_url + \"artists/\"\n for i in range(1, 702500):\n print(i)\n bu_i = bu + str(i)\n headers = {'user-agent': self.ua.random}\n r = get(bu_i, headers=headers)\n if r.status_code == 200:\n text = r.text\n d = loads(text)\n self.artists.append(d)\n self.persist()\n sleep(randint(3, 10))\n\n def persist(self):\n with open(\"artfacts_artists.json\", \"w\", \"utf-8\") as f:\n dump(self.artists, f, indent=4)\n\n\nif __name__ == \"__main__\":\n afas = ArtFactsArtistScraper()\n afas.get_artists()\n", "sub_path": "artfacts.py", "file_name": "artfacts.py", "file_ext": "py", "file_size_in_byte": 1174, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "codecs.open", "line_number": 13, "usage_type": "call"}, {"api_name": "json.load", "line_number": 14, "usage_type": "call"}, {"api_name": "fake_useragent.UserAgent", "line_number": 17, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 25, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 28, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 31, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 31, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 34, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "119334481", "text": "from typing import List\nfrom collections import deque\n\n\n# Definition for a binary tree node.\nclass TreeNode:\n def __init__(self, x):\n self.val = x\n self.left = None\n self.right = None\n\n\nclass Solution:\n\n def pathSum(self, root: TreeNode, su: int) -> List[List[int]]:\n\n if not root:\n return list()\n\n ans = list()\n dq = deque()\n dq.append((root, root.val, [root.val]))\n while dq:\n node, d, path = dq.popleft()\n if not node.left and not node.right:\n if d == su:\n ans.append(path)\n if node.left:\n k = path[:]\n k.append(node.left.val)\n dq.append((node.left, d + node.left.val, k))\n if node.right:\n k = path[:]\n k.append(node.right.val)\n dq.append((node.right, d + node.right.val, k))\n return ans\n", "sub_path": "offer/NO.34/solution.py", "file_name": "solution.py", "file_ext": "py", "file_size_in_byte": 939, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "collections.deque", "line_number": 21, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "517842859", "text": "# -*- coding: utf-8 -*-\nimport unittest\nfrom mock import patch, Mock\nfrom prediction_model.pytorch.model import IntentPytorch\nfrom proto import rest_api_pb2\nimport functools\n\nDOC_1 = rest_api_pb2.PredictItem()\nDOC_1.content = 'doc 1'\nDOC_1.id = 'id 1'\n\n\nDOC_2 = rest_api_pb2.PredictItem()\nDOC_2.content = 'doc 2'\nDOC_2.id = 'id 2'\n\n\nDOC_3 = rest_api_pb2.PredictItem()\nDOC_3.content = 'doc 3'\nDOC_3.id = 'id 3'\n\n\nclass TestIntentPytorch(unittest.TestCase):\n @patch('prediction_model.pytorch.model.IntentPytorch.__init__', return_value=None)\n def test_rate_doc1(self, mock_model):\n '''test result have PIRCE intent'''\n model = IntentPytorch()\n with patch.object(model, \n \"_IntentPytorch__rate_mentions_proba\", \n return_value = [[(rest_api_pb2.IntentType.PRICE, 0.75)]]):\n\n result = model.predict(DOC_1)\n \n self.assertEqual(result[0].list_predict[0].intent_type, rest_api_pb2.IntentType.PRICE)\n", "sub_path": "Thanos/intent/main/prediction_model/pytorch/model_test.py", "file_name": "model_test.py", "file_ext": "py", "file_size_in_byte": 1005, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "proto.rest_api_pb2.PredictItem", "line_number": 8, "usage_type": "call"}, {"api_name": "proto.rest_api_pb2", "line_number": 8, "usage_type": "name"}, {"api_name": "proto.rest_api_pb2.PredictItem", "line_number": 13, "usage_type": "call"}, {"api_name": "proto.rest_api_pb2", "line_number": 13, "usage_type": "name"}, {"api_name": "proto.rest_api_pb2.PredictItem", "line_number": 18, "usage_type": "call"}, {"api_name": "proto.rest_api_pb2", "line_number": 18, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 23, "usage_type": "attribute"}, {"api_name": "prediction_model.pytorch.model.IntentPytorch", "line_number": 27, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 28, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 28, "usage_type": "name"}, {"api_name": "proto.rest_api_pb2.IntentType", "line_number": 30, "usage_type": "attribute"}, {"api_name": "proto.rest_api_pb2", "line_number": 30, "usage_type": "name"}, {"api_name": "proto.rest_api_pb2.IntentType", "line_number": 34, "usage_type": "attribute"}, {"api_name": "proto.rest_api_pb2", "line_number": 34, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "110456376", "text": "import heapq\r\nimport time\r\nimport math\r\nfrom pathlib import Path\r\nimport random\r\n\r\nrandomness_complexity = 25\r\n\r\n\"\"\"File with utility functions for agents and environments to use.\"\"\"\r\n\r\n\r\ndef get_reward_from_bitstring(s: str) -> float:\r\n \"\"\"Calculate reward from bit string\"\"\"\r\n reward = 0\r\n sign = 1 if s[0] == \"0\" else -1\r\n for idx in range(len(s) - 1):\r\n reward += int(s[idx + 1]) * pow(0.5, idx)\r\n return sign * reward\r\n\r\n\r\ndef get_decimal_from_bitstring(s: str) -> float:\r\n \"\"\"Calculate decimal number from bitstring\"\"\"\r\n decimal = 0\r\n for idx in range((len(s) - 1)):\r\n decimal += int(s[len(s) - 1 - idx]) * pow(2, idx)\r\n return decimal\r\n\r\n\r\ndef get_bitstring_from_decimal(decimal: int, length: int) -> str:\r\n \"\"\"Calculate bitstring from decimal\"\"\"\r\n return format(decimal, 'b').zfill(length)\r\n\r\n\r\ndef get_random_bit() -> int:\r\n \"\"\"Returns a pseudorandom bit from a timestamp. Used for calculating random bit complexity\"\"\"\r\n seed = time.time()\r\n return pow(2, int(str(seed).replace('.', '')[-5:])) % 3 % 2\r\n\r\n\r\ndef get_data_path() -> Path:\r\n \"\"\"Returns data path.\"\"\"\r\n return Path(__file__).parent.parent.joinpath('resources/data')\r\n\r\n\r\ndef get_plots_path() -> Path:\r\n \"\"\"Returns plots path.\"\"\"\r\n return Path(__file__).parent.parent.joinpath('resources/plots')\r\n\r\n\r\ndef is_saved(training_step: int) -> bool:\r\n \"\"\"Returns boolean indicating if the given training step is to be saved or loaded.\"\"\"\r\n if training_step == 0:\r\n return False\r\n if math.log10(training_step).is_integer():\r\n return True\r\n if math.log10(training_step * 2).is_integer():\r\n return True\r\n return False\r\n\r\n\r\ndef nested_set(dic, keys, value):\r\n \"\"\"Sets a nested value in a dictionary\"\"\"\r\n for key in keys[:-1]:\r\n dic = dic.setdefault(key, {})\r\n dic[keys[-1]] = value\r\n\r\n\r\ndef random_action(length: int) -> str:\r\n \"\"\"Returns a random action\"\"\"\r\n action = ''\r\n for i in range(length):\r\n action += str(random.randint(0, 1))\r\n return action\r\n\r\n\r\ndef sort_dict(dic: dict, keys: list):\r\n \"\"\"Sorts the keys in a dict with a sorted list of the keys. The list must contain only valid keys.\"\"\"\r\n temp_dic = {}\r\n for key in keys:\r\n temp_dic[key] = dic.pop(key)\r\n for key in temp_dic:\r\n dic[key] = temp_dic[key]\r\n\r\n\r\ndef init_heap(length: int):\r\n \"\"\"initialize action heap for pi agents\"\"\"\r\n reward_statistics = []\r\n for action_idx in range(pow(2, length)):\r\n action = get_bitstring_from_decimal(action_idx, length)\r\n heapq.heappush(reward_statistics, (1, action, 0))\r\n return reward_statistics\r\n\r\n\r\ndef heapq_siftdown(heap, startpos, pos):\r\n \"\"\"Taken from heapq internal code since it might be deprecated\r\n https://hg.python.org/cpython/file/3.6/Lib/heapq.py\r\n Implements decrease_key\"\"\"\r\n newitem = heap[pos]\r\n # Follow the path to the root, moving parents down until finding a place\r\n # newitem fits.\r\n while pos > startpos:\r\n parentpos = (pos - 1) >> 1\r\n parent = heap[parentpos]\r\n if newitem < parent:\r\n heap[pos] = parent\r\n pos = parentpos\r\n continue\r\n break\r\n heap[pos] = newitem\r\n\r\n\r\ndef heapq_siftup(heap, pos):\r\n \"\"\"Taken from heapq internal code since it might be deprecated.\r\n https://hg.python.org/cpython/file/3.6/Lib/heapq.py\r\n Implements increase_key\"\"\"\r\n endpos = len(heap)\r\n startpos = pos\r\n newitem = heap[pos]\r\n # Bubble up the smaller child until hitting a leaf.\r\n childpos = 2*pos + 1 # leftmost child position\r\n while childpos < endpos:\r\n # Set childpos to index of smaller child.\r\n rightpos = childpos + 1\r\n if rightpos < endpos and not heap[childpos] < heap[rightpos]:\r\n childpos = rightpos\r\n # Move the smaller child up.\r\n heap[pos] = heap[childpos]\r\n pos = childpos\r\n childpos = 2*pos + 1\r\n # The leaf at pos is empty now. Put newitem there, and bubble it up\r\n # to its final resting place (by sifting its parents down).\r\n heap[pos] = newitem\r\n heapq_siftdown(heap, startpos, pos)\r\n", "sub_path": "python/src/Utility.py", "file_name": "Utility.py", "file_ext": "py", "file_size_in_byte": 4150, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "time.time", "line_number": 36, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 42, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 40, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 47, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 45, "usage_type": "name"}, {"api_name": "math.log10", "line_number": 54, "usage_type": "call"}, {"api_name": "math.log10", "line_number": 56, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 72, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 90, "usage_type": "call"}]} +{"seq_id": "384947808", "text": "import json\n\nimport torch\nfrom torch.autograd import Variable\nfrom parlai.core.params import ParlaiParser\n\nfrom bots import Questioner, Answerer\nfrom dataloader import ShapesQADataset\nfrom world import QAWorld\n\n\ndef parse_options():\n parser = ParlaiParser()\n pth_file = \"world_best.pth\"\n # pth_file = 'checkpoints/world-07-Nov-2019-15:56:51/world_epoch_02900.pth'\n parser.add_argument(\n \"--load-path\",\n type=str,\n default=pth_file,\n help=\"path to pth file of the world checkpoint\",\n )\n parser.add_argument(\n \"--print-conv\",\n default=False,\n action=\"store_true\",\n help=\"whether to print the conversation between bots or not\",\n )\n parser.add_argument(\n \"--conv-save-path\",\n type=str,\n default=None,\n help=\"whether to print the conversation between bots or not\",\n )\n return parser.parse_args()\n\n\ndef load_world_dataset():\n world_dict = torch.load(OPT[\"load_path\"], map_location=torch.device(\"cpu\"))\n world_dict[\"opt\"][\"use_gpu\"] = torch.cuda.is_available()\n dataset = ShapesQADataset(world_dict[\"opt\"])\n questioner = Questioner(world_dict[\"opt\"])\n answerer = Answerer(world_dict[\"opt\"])\n if world_dict[\"opt\"].get(\"use_gpu\"):\n questioner, answerer = questioner.cuda(), answerer.cuda()\n questioner.load_state_dict(world_dict[\"qbot\"])\n answerer.load_state_dict(world_dict[\"abot\"])\n world = QAWorld(world_dict[\"opt\"], questioner, answerer)\n print(\"Loaded world from checkpoint: %s\" % OPT[\"load_path\"])\n print(\"Questioner and Answerer Bots: \")\n print(world.qbot)\n print(world.abot)\n return world, dataset\n\n\ndef run_evaluation(world, dataset):\n world.qbot.eval()\n world.abot.eval()\n first_accuracy = {\"train\": 0, \"val\": 0}\n second_accuracy = {\"train\": 0, \"val\": 0}\n atleast_accuracy = {\"train\": 0, \"val\": 0}\n both_accuracy = {\"train\": 0, \"val\": 0}\n\n for dtype in [\"train\", \"val\"]:\n batch = dataset.complete_data(dtype)\n # make variables volatile because graph construction is not required for eval\n batch[\"image\"] = Variable(batch[\"image\"], volatile=True)\n batch[\"task\"] = Variable(batch[\"task\"], volatile=True)\n world.qbot.observe({\"batch\": batch, \"episode_done\": True})\n\n for _ in range(world.opt[\"num_rounds\"]):\n world.parley()\n guess_token, guess_distr = world.qbot.predict(batch[\"task\"], 2)\n\n # check how much do first attribute, second attribute, both and at least one match\n first_match = guess_token[0].data == batch[\"labels\"][:, 0].long()\n second_match = guess_token[1].data == batch[\"labels\"][:, 1].long()\n both_matches = first_match & second_match\n atleast_match = first_match | second_match\n\n # compute accuracy according to matches\n first_accuracy[dtype] = 100 * torch.mean(first_match.float())\n second_accuracy[dtype] = 100 * torch.mean(second_match.float())\n atleast_accuracy[dtype] = 100 * torch.mean(atleast_match.float())\n both_accuracy[dtype] = 100 * torch.mean(both_matches.float())\n\n for dtype in [\"train\", \"val\"]:\n print(\n \"Overall accuracy [%s]: %.2f (first: %.2f, second: %.2f, atleast_one: %.2f)\"\n % (\n dtype,\n both_accuracy[dtype],\n first_accuracy[dtype],\n second_accuracy[dtype],\n atleast_accuracy[dtype],\n )\n )\n\n\n\nif __name__ == \"__main__\":\n\n OPT = parse_options()\n\n world, dataset = load_world_dataset()\n\n run_evaluation(\n world, dataset\n )\n\n \"\"\"\n world_best.pth\n Overall accuracy [train]: 97.12 (first: 98.08, second: 99.04, atleast_one: 100.00)\n Overall accuracy [val]: 95.83 (first: 97.22, second: 98.61, atleast_one: 100.00)\n \"\"\"\n", "sub_path": "evaluate.py", "file_name": "evaluate.py", "file_ext": "py", "file_size_in_byte": 3827, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "parlai.core.params.ParlaiParser", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 39, "usage_type": "attribute"}, {"api_name": "dataloader.ShapesQADataset", "line_number": 40, "usage_type": "call"}, {"api_name": "bots.Questioner", "line_number": 41, "usage_type": "call"}, {"api_name": "bots.Answerer", "line_number": 42, "usage_type": "call"}, {"api_name": "world.QAWorld", "line_number": 47, "usage_type": "call"}, {"api_name": "world.qbot", "line_number": 50, "usage_type": "attribute"}, {"api_name": "world.abot", "line_number": 51, "usage_type": "attribute"}, {"api_name": "world.qbot.eval", "line_number": 56, "usage_type": "call"}, {"api_name": "world.qbot", "line_number": 56, "usage_type": "attribute"}, {"api_name": "world.abot.eval", "line_number": 57, "usage_type": "call"}, {"api_name": "world.abot", "line_number": 57, "usage_type": "attribute"}, {"api_name": "torch.autograd.Variable", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 67, "usage_type": "call"}, {"api_name": "world.qbot.observe", "line_number": 68, "usage_type": "call"}, {"api_name": "world.qbot", "line_number": 68, "usage_type": "attribute"}, {"api_name": "world.opt", "line_number": 70, "usage_type": "attribute"}, {"api_name": "world.parley", "line_number": 71, "usage_type": "call"}, {"api_name": "world.qbot.predict", "line_number": 72, "usage_type": "call"}, {"api_name": "world.qbot", "line_number": 72, "usage_type": "attribute"}, {"api_name": "torch.mean", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 84, "usage_type": "call"}]} +{"seq_id": "376193552", "text": "import discord, sqlite3, asyncio\nfrom discord.ext import commands\nfrom discord_slash import cog_ext, SlashContext\nfrom discord_slash.utils.manage_commands import create_option\n\nclass Slash(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n\n @cog_ext.cog_slash(name=\"give\", description=\"Donner des crédits à quelqu'un qui est inscrit à l'aventure ISO land !\", options=[\n create_option(\n name=\"membre\",\n description=\"Membre de discord à qui donner des crédits\",\n option_type=6,\n required=True\n ),\n create_option(\n name=\"argent\",\n description=\"Montant de crédits à donner (avec une taxe de 2%)\",\n option_type=4,\n required=True\n )])\n async def _give(self, ctx, membre: discord.Member, argent: int):\n connection = sqlite3.connect(\"iso_card.db\")\n cursor = connection.cursor()\n if membre.bot == True:\n await ctx.send(f\"{ctx.author.mention} Tu ne peux pas donner d'argent aux bots... :wink:\")\n if membre.bot == False:\n if membre == ctx.author:\n await ctx.send(\"Tu ne peux pas te donner de l'argent à toi-même ! :stuck_out_tongue:\")\n else:\n member_id = (f\"{membre.id}\",)\n cursor.execute('SELECT * FROM tt_iso_card WHERE user_id = ?', member_id)\n member_values = cursor.fetchone()\n author_id = (f\"{ctx.author.id}\",)\n cursor.execute('SELECT * FROM tt_iso_card WHERE user_id = ?', author_id)\n author_values = cursor.fetchone()\n if member_values == None:\n await ctx.send(f\"{ctx.author.mention} Tu ne peux pas donner d'argent à cette personne car elle ne s'est pas inscrite à l'aventure ISO land ! (Pour qu'elle inscrive : **/start**)\")\n elif author_values == None:\n await ctx.send(f\"{ctx.author.mention} Tu ne peux pas donner d'argent car tu ne t'es pas inscrit à l'aventure ISO land ! (Pour t'inscrire : **/start**)\")\n else:\n argent_de_author = author_values[5]\n if argent > argent_de_author:\n await ctx.send(f\"{ctx.author.mention} Tu ne peux pas donner autant d'argent car tu n'en as pas assez sur ta carte !\")\n else:\n if argent < 1:\n await ctx.send(f\"{ctx.author.mention} Tu ne peux pas effectuer cette transaction car le montant est trop bas (minimum 1<:aCoin:822427301488623620> ) !\")\n else:\n argent_a_donner = argent\n ancient_argent_author = argent_de_author\n taxe = argent*0.02 # le complément est la taxe de 2%\n new_argent_author = argent_de_author - argent_a_donner - taxe\n new_argent_author = round(new_argent_author, 2)\n\n ancient_argent_member = member_values[5]\n new_argent_member = ancient_argent_member + argent_a_donner\n\n updated_author = (f\"{new_argent_author}\", f\"{ctx.author.id}\",)\n cursor.execute('UPDATE tt_iso_card SET dailies = ? WHERE user_id = ?', updated_author)\n updated_member = (f\"{new_argent_member}\", f\"{membre.id}\",)\n cursor.execute('UPDATE tt_iso_card SET dailies = ? WHERE user_id = ?', updated_member)\n connection.commit()\n await ctx.send(embed=None, content=f\"**Transaction** effectuée par : {ctx.author.mention}\\ncréditeur : {membre.mention}\\nMontant : {argent_a_donner}<:aCoin:822427301488623620> (Montant total : {argent_a_donner + taxe}<:aCoin:822427301488623620> )\\nTaxe : {taxe}<:aCoin:822427301488623620> (2%)\")\n\n connection.close()\n\ndef setup(bot):\n bot.add_cog(Slash(bot))\n\ndef teardown(bot):\n bot.remove_cog(\"give\")", "sub_path": "cogs/give.py", "file_name": "give.py", "file_ext": "py", "file_size_in_byte": 4133, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "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": "discord.Member", "line_number": 23, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 24, "usage_type": "call"}, {"api_name": "discord_slash.cog_ext.cog_slash", "line_number": 10, "usage_type": "call"}, {"api_name": "discord_slash.cog_ext", "line_number": 10, "usage_type": "name"}, {"api_name": "discord_slash.utils.manage_commands.create_option", "line_number": 11, "usage_type": "call"}, {"api_name": "discord_slash.utils.manage_commands.create_option", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "303469921", "text": "import tensorflow as tf\nimport tensorflow_probability as tfp\nimport numpy as np\n\ntf.compat.v1.logging.set_verbosity(tf.compat.v1.logging.ERROR)\n\ntfk = tf.keras\ntfkl = tf.keras.layers\ntfd = tfp.distributions\ntfpl = tfp.layers\n\n# Load data.\nn = int(1e3)\nscale_tril = np.array([[1.6180, 0.],\n [-2.7183, 3.1416]]).astype(np.float32)\nx = tfd.Normal(loc=0, scale=1).sample([n, 2])\neps = tfd.Normal(loc=0, scale=0.01).sample([n, 2])\ny = tf.matmul(x, scale_tril) + eps\n\n# Create model.\nd = tf.dimension_value(y.shape[-1])\nmodel = tfk.Sequential([\n tfkl.Dense(tfpl.MultivariateNormalTriL.params_size(d), input_shape=(2,)),\n tfpl.MultivariateNormalTriL(d),\n])\n\n# Fit.\nmodel.compile(optimizer=tf.train.AdamOptimizer(learning_rate=0.02),\n loss=lambda y, model: -model.log_prob(y),\n metrics=[])\nbatch_size = 100\nmodel.fit(x, y,\n batch_size=batch_size,\n epochs=1,\n # epochs=500,\n steps_per_epoch=n // batch_size,\n verbose=False,\n shuffle=True)\n# print(x.shape, y.shape)\n# print([w.shape for w in model.get_weights()])\n# print(model(y[:1]))\nprint(model.predict(y[:1], steps=1))\nprint()\nfor layer in model.layers:\n\tprint(layer.output)\n# ==> [[ 1.61842895e+00 1.34138885e-04]\n# [ -2.71818233e+00 3.14186454e+00]]\n# model.summary()\n\n\n\n\n\n", "sub_path": "test_stuff.py", "file_name": "test_stuff.py", "file_ext": "py", "file_size_in_byte": 1337, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "tensorflow.compat.v1.logging.set_verbosity", "line_number": 5, "usage_type": "call"}, {"api_name": "tensorflow.compat", "line_number": 5, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 7, "usage_type": "attribute"}, {"api_name": "tensorflow.keras", "line_number": 8, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.distributions", "line_number": 9, "usage_type": "attribute"}, {"api_name": "tensorflow_probability.layers", "line_number": 10, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 15, "usage_type": "attribute"}, {"api_name": "tensorflow.matmul", "line_number": 18, "usage_type": "call"}, {"api_name": "tensorflow.dimension_value", "line_number": 21, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 28, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 28, "usage_type": "attribute"}]} +{"seq_id": "203202361", "text": "#Functions needed to handle the Audi A2D2 dataset, according to https://www.a2d2.audi/a2d2/en/tutorial.html\n\nimport json\nimport pprint\nimport numpy as np\nimport numpy.linalg as la\nimport open3d as o3\n\ndef skew_sym_matrix(u):\n return np.array([[ 0, -u[2], u[1]], \n [ u[2], 0, -u[0]], \n [-u[1], u[0], 0]])\n\n\ndef axis_angle_to_rotation_mat(axis, angle):\n return np.cos(angle) * np.eye(3) + \\\n np.sin(angle) * skew_sym_matrix(axis) + \\\n (1 - np.cos(angle)) * np.outer(axis, axis)\n\n\ndef read_bounding_boxes(file_name_bboxes):\n # open the file\n with open (file_name_bboxes, 'r') as f:\n bboxes = json.load(f)\n \n boxes = [] # a list for containing bounding boxes \n print(bboxes.keys())\n \n for bbox in bboxes.keys():\n bbox_read = {} # a dictionary for a given bounding box\n bbox_read['class'] = bboxes[bbox]['class']\n bbox_read['truncation']= bboxes[bbox]['truncation']\n bbox_read['occlusion']= bboxes[bbox]['occlusion']\n bbox_read['alpha']= bboxes[bbox]['alpha']\n bbox_read['top'] = bboxes[bbox]['2d_bbox'][0]\n bbox_read['left'] = bboxes[bbox]['2d_bbox'][1]\n bbox_read['bottom'] = bboxes[bbox]['2d_bbox'][2]\n bbox_read['right']= bboxes[bbox]['2d_bbox'][3]\n bbox_read['center'] = np.array(bboxes[bbox]['center'])\n bbox_read['size'] = np.array(bboxes[bbox]['size'])\n angle = bboxes[bbox]['rot_angle']\n axis = np.array(bboxes[bbox]['axis'])\n bbox_read['rotation'] = axis_angle_to_rotation_mat(axis, angle) \n boxes.append(bbox_read)\n\n return boxes \n\n\ndef extract_bboxes_file_name_from_image_file_name(file_name_image):\n file_name_bboxes = file_name_image.split('/')\n file_name_bboxes = file_name_bboxes[-1].split('.')[0]\n file_name_bboxes = file_name_bboxes.split('_')\n file_name_bboxes = file_name_bboxes[0] + '_' + \\\n 'label3D_' + \\\n file_name_bboxes[2] + '_' + \\\n file_name_bboxes[3] + '.json'\n \n return file_name_bboxes\n\n\ndef get_points(bbox):\n half_size = bbox['size'] / 2.\n \n if half_size[0] > 0:\n # calculate unrotated corner point offsets relative to center\n brl = np.asarray([-half_size[0], +half_size[1], -half_size[2]])\n bfl = np.asarray([+half_size[0], +half_size[1], -half_size[2]])\n bfr = np.asarray([+half_size[0], -half_size[1], -half_size[2]])\n brr = np.asarray([-half_size[0], -half_size[1], -half_size[2]])\n trl = np.asarray([-half_size[0], +half_size[1], +half_size[2]])\n tfl = np.asarray([+half_size[0], +half_size[1], +half_size[2]])\n tfr = np.asarray([+half_size[0], -half_size[1], +half_size[2]])\n trr = np.asarray([-half_size[0], -half_size[1], +half_size[2]])\n \n # rotate points\n points = np.asarray([brl, bfl, bfr, brr, trl, tfl, tfr, trr])\n points = np.dot(points, bbox['rotation'].T)\n \n # add center position\n points = points + bbox['center']\n \n return points\n\n\n# Create or update open3d wire frame geometry for the given bounding boxes\ndef _get_bboxes_wire_frames(bboxes, linesets=None, color=None):\n\n num_boxes = len(bboxes)\n \n # initialize linesets, if not given\n if linesets is None:\n linesets = [o3.geometry.LineSet() for _ in range(num_boxes)]\n\n # set default color\n if color is None:\n #color = [1, 0, 0]\n color = [0, 0, 1]\n\n assert len(linesets) == num_boxes, \"Number of linesets must equal number of bounding boxes\"\n\n # point indices defining bounding box edges\n lines = [[0, 1], [1, 2], [2, 3], [3, 0],\n [0, 4], [1, 5], [2, 6], [3, 7],\n [4, 5], [5, 6], [6, 7], [7, 4], \n [5, 2], [1, 6]]\n\n # loop over all bounding boxes\n for i in range(num_boxes):\n # get bounding box corner points\n points = get_points(bboxes[i])\n # update corresponding Open3d line set\n colors = [color for _ in range(len(lines))]\n line_set = linesets[i]\n line_set.points = o3.utility.Vector3dVector(points)\n line_set.lines = o3.utility.Vector2iVector(lines)\n line_set.colors = o3.utility.Vector3dVector(colors)\n\n return linesets\n\nprint(\"Function import done.\")", "sub_path": "functions.py", "file_name": "functions.py", "file_ext": "py", "file_size_in_byte": 4314, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.array", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.outer", "line_number": 18, "usage_type": "call"}, {"api_name": "json.load", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 77, "usage_type": "call"}, {"api_name": "open3d.geometry.LineSet", "line_number": 92, "usage_type": "call"}, {"api_name": "open3d.geometry", "line_number": 92, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 114, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 114, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector2iVector", "line_number": 115, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 115, "usage_type": "attribute"}, {"api_name": "open3d.utility.Vector3dVector", "line_number": 116, "usage_type": "call"}, {"api_name": "open3d.utility", "line_number": 116, "usage_type": "attribute"}]} +{"seq_id": "226741108", "text": "from unittest import TestCase\ntry:\n from unittest import mock\nexcept ImportError:\n import mock\nfrom tox_ansible.tox_test_case import ToxTestCase\nfrom tox_ansible.ansible.role import Role\nfrom tox_ansible.ansible.scenario import Scenario\nfrom tox_ansible.options import Options\nfrom tox_ansible.tox_helper import Tox\n\n\nDOCKER_DRIVER = {\"driver\": {\"name\": \"docker\"}}\nOPENSTACK_DRIVER = {\"driver\": {\"name\": \"openstack\"}}\nBASE_DEPS = [\"molecule\", \"ansible-lint\", \"yamllint\", \"flake8\", \"pytest\",\n \"testinfra\"]\n\n\n@mock.patch.object(Scenario, \"config\", new_callable=mock.PropertyMock,\n return_value={})\nclass TestToxTestCase(TestCase):\n @mock.patch.object(Options, \"get_global_opts\", return_value=[])\n @mock.patch.object(Tox, \"posargs\", new_callable=mock.PropertyMock,\n return_value=[])\n def test_case_is_simple(self, pos_mock, opts_mock, config_mock):\n t = ToxTestCase(self.role, self.scenario)\n self.assertEqual(t.get_name(), \"derp-my_test\")\n self.assertEqual(t.get_working_dir(), \"roles/derp\")\n self.assertEqual(t.get_dependencies(), BASE_DEPS + [\"ansible\"])\n cmds = [[\"molecule\", \"test\", \"-s\", self.scenario.name]]\n self.assertEqual(t.get_commands(self.opts), cmds)\n self.assertIsNone(t.get_basepython())\n\n @mock.patch.object(Options, \"get_global_opts\", return_value=[\"-c\", \"derp\"])\n @mock.patch.object(Tox, \"posargs\", new_callable=mock.PropertyMock,\n return_value=[])\n def test_case_has_global_opts(self, pos_mock, opts_mock, config_mock):\n t = ToxTestCase(self.role, self.scenario)\n cmds = [[\"molecule\", \"-c\", \"derp\", \"test\", \"-s\", self.scenario.name]]\n self.assertEqual(t.get_commands(self.opts), cmds)\n\n def test_case_expand_ansible(self, config_mock):\n t = ToxTestCase(self.role, self.scenario)\n ts = t.expand_ansible(\"2.7\")\n self.assertEqual(ts.ansible, \"2.7\")\n self.assertEqual(ts.get_name(), \"ansible27-derp-my_test\")\n self.assertEqual(ts.get_dependencies(), BASE_DEPS + [\"ansible==2.7.*\"])\n self.assertIsNone(ts.get_basepython())\n\n def test_case_expand_python(self, config_mock):\n t = ToxTestCase(self.role, self.scenario)\n ts = t.expand_python(\"4.1\")\n self.assertEqual(ts.python, \"4.1\")\n self.assertEqual(ts.get_name(), \"py41-derp-my_test\")\n self.assertEqual(ts.get_basepython(), \"python4.1\")\n\n def test_case_expand_twice(self, config_mock):\n t = ToxTestCase(self.role, self.scenario)\n t1 = t.expand_python(\"4.1\")\n t2 = t1.expand_ansible(\"1.0\")\n self.assertEqual(t2.get_name(), \"ansible10-py41-derp-my_test\")\n\n @mock.patch.object(Scenario, \"driver\", new_callable=mock.PropertyMock,\n return_value=\"docker\")\n def test_case_includes_docker_deps(self, driver_mock, config_mock):\n s = Scenario(\"moelcule/my_test\")\n t = ToxTestCase(self.role, s)\n self.assertIn(\"docker\", t.get_dependencies())\n\n @mock.patch.object(Scenario, \"driver\", new_callable=mock.PropertyMock,\n return_value=\"openstack\")\n def test_case_includes_openstack_deps(self, driver_mock, config_mock):\n s = Scenario(\"molecule/osp_test\")\n t = ToxTestCase(self.role, s)\n self.assertIn(\"openstacksdk\", t.get_dependencies())\n\n @classmethod\n def setUp(cls):\n cls.role = Role(\"roles/derp\")\n cls.scenario = Scenario(\"molecule/my_test\")\n cls.opts = Options(mock.Mock())\n", "sub_path": "tests/test_tox_test_case.py", "file_name": "test_tox_test_case.py", "file_ext": "py", "file_size_in_byte": 3533, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "unittest.TestCase", "line_number": 21, "usage_type": "name"}, {"api_name": "tox_ansible.tox_test_case.ToxTestCase", "line_number": 26, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 22, "usage_type": "call"}, {"api_name": "tox_ansible.options.Options", "line_number": 22, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 22, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 23, "usage_type": "call"}, {"api_name": "tox_ansible.tox_helper.Tox", "line_number": 23, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 23, "usage_type": "attribute"}, {"api_name": "mock.PropertyMock", "line_number": 23, "usage_type": "attribute"}, {"api_name": "tox_ansible.tox_test_case.ToxTestCase", "line_number": 38, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 34, "usage_type": "call"}, {"api_name": "tox_ansible.options.Options", "line_number": 34, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 34, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 35, "usage_type": "call"}, {"api_name": "tox_ansible.tox_helper.Tox", "line_number": 35, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 35, "usage_type": "attribute"}, {"api_name": "mock.PropertyMock", "line_number": 35, "usage_type": "attribute"}, {"api_name": "tox_ansible.tox_test_case.ToxTestCase", "line_number": 43, "usage_type": "call"}, {"api_name": "tox_ansible.tox_test_case.ToxTestCase", "line_number": 51, "usage_type": "call"}, {"api_name": "tox_ansible.tox_test_case.ToxTestCase", "line_number": 58, "usage_type": "call"}, {"api_name": "tox_ansible.ansible.scenario.Scenario", "line_number": 66, "usage_type": "call"}, {"api_name": "tox_ansible.tox_test_case.ToxTestCase", "line_number": 67, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 63, "usage_type": "call"}, {"api_name": "tox_ansible.ansible.scenario.Scenario", "line_number": 63, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 63, "usage_type": "attribute"}, {"api_name": "mock.PropertyMock", "line_number": 63, "usage_type": "attribute"}, {"api_name": "tox_ansible.ansible.scenario.Scenario", "line_number": 73, "usage_type": "call"}, {"api_name": "tox_ansible.tox_test_case.ToxTestCase", "line_number": 74, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 70, "usage_type": "call"}, {"api_name": "tox_ansible.ansible.scenario.Scenario", "line_number": 70, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 70, "usage_type": "attribute"}, {"api_name": "mock.PropertyMock", "line_number": 70, "usage_type": "attribute"}, {"api_name": "tox_ansible.ansible.role.Role", "line_number": 79, "usage_type": "call"}, {"api_name": "tox_ansible.ansible.scenario.Scenario", "line_number": 80, "usage_type": "call"}, {"api_name": "tox_ansible.options.Options", "line_number": 81, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 81, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 19, "usage_type": "call"}, {"api_name": "tox_ansible.ansible.scenario.Scenario", "line_number": 19, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 19, "usage_type": "attribute"}, {"api_name": "mock.PropertyMock", "line_number": 19, "usage_type": "attribute"}]} +{"seq_id": "241590963", "text": "from .. import ma\nfrom ..models import User\nfrom marshmallow import fields, pre_dump\n\n\nclass _UserSchema(ma.Schema):\n email = fields.Email()\n token = fields.String()\n username = fields.String()\n bio = fields.String()\n image = fields.URL()\n following = fields.Boolean(default=None)\n\n @pre_dump(pass_many=False)\n def fill_following(self, data):\n logged_user = User.get_logged_user(raise_exceptipn=False)\n if logged_user:\n data.following = data.is_following_by(logged_user)\n return data\n\n\nclass UserSchema(ma.Schema):\n user = fields.Nested(\n _UserSchema, only=[\"email\", \"token\", \"username\", \"bio\", \"image\"])\n\n\nclass ProfileSchema(ma.Schema):\n profile = fields.Nested(\n _UserSchema, only=[\"username\", \"bio\", \"image\", \"following\"])\n\n\nclass _ArticleSchema(ma.Schema):\n slug = fields.String()\n title = fields.String()\n description = fields.String()\n body = fields.String()\n createdAt = fields.DateTime(attribute='created_at')\n updatedAt = fields.DateTime(attribute='updated_at')\n favorited = fields.Boolean(default=None)\n favoritesCount = fields.Integer(default=0)\n tagList = fields.List(fields.String())\n author = fields.Nested(\n _UserSchema, only=[\"username\", \"bio\", \"image\", \"following\"])\n\n @pre_dump(pass_many=False)\n def fill_favorited(self, data):\n logged_user = User.get_logged_user(raise_exceptipn=False)\n if logged_user:\n data.favorited = data.is_favorited_by(logged_user)\n return data\n\n\nclass ArticleSchema(ma.Schema):\n article = fields.Nested(_ArticleSchema)\n\n\nclass ArticlesSchema(ma.Schema):\n articles = fields.Nested(_ArticleSchema, many=True)\n articlesCount = fields.Integer(default=0)\n\n @pre_dump(pass_many=False)\n def calucate_count(self, data):\n if 'articlesCount' not in data:\n data['articlesCount'] = len(data['articles'])\n return data\n\n\nclass TagsSchema(ma.Schema):\n tags = fields.List(fields.String())\n\n\nclass _CommentSchema(ma.Schema):\n id = fields.Integer()\n body = fields.String()\n createdAt = fields.DateTime(attribute='created_at')\n updatedAt = fields.DateTime(attribute='updated_at')\n author = fields.Nested(\n _UserSchema, only=[\"username\", \"bio\", \"image\", \"following\"])\n\n\nclass CommentSchema(ma.Schema):\n comment = fields.Nested(_CommentSchema)\n\n\nclass CommentsSchema(ma.Schema):\n comments = fields.Nested(_CommentSchema, many=True)\n\n\nuser_schema = UserSchema()\nprofile_schema = ProfileSchema()\narticle_schema = ArticleSchema()\narticles_schema = ArticlesSchema()\ntags_schema = TagsSchema()\ncomment_schema = CommentSchema()\ncomments_schema = CommentsSchema()\n", "sub_path": "conduit/views/schemas.py", "file_name": "schemas.py", "file_ext": "py", "file_size_in_byte": 2702, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "marshmallow.fields.Email", "line_number": 7, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 7, "usage_type": "name"}, {"api_name": "marshmallow.fields.String", "line_number": 8, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 8, "usage_type": "name"}, {"api_name": "marshmallow.fields.String", "line_number": 9, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 9, "usage_type": "name"}, {"api_name": "marshmallow.fields.String", "line_number": 10, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 10, "usage_type": "name"}, {"api_name": "marshmallow.fields.URL", "line_number": 11, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 11, "usage_type": "name"}, {"api_name": "marshmallow.fields.Boolean", "line_number": 12, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 12, "usage_type": "name"}, {"api_name": "models.User.get_logged_user", "line_number": 16, "usage_type": "call"}, {"api_name": "models.User", "line_number": 16, "usage_type": "name"}, {"api_name": "marshmallow.pre_dump", "line_number": 14, "usage_type": "call"}, {"api_name": "marshmallow.fields.Nested", "line_number": 23, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 23, "usage_type": "name"}, {"api_name": "marshmallow.fields.Nested", "line_number": 28, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 28, "usage_type": "name"}, {"api_name": "marshmallow.fields.String", "line_number": 33, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 33, "usage_type": "name"}, {"api_name": "marshmallow.fields.String", "line_number": 34, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 34, "usage_type": "name"}, {"api_name": "marshmallow.fields.String", "line_number": 35, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 35, "usage_type": "name"}, {"api_name": "marshmallow.fields.String", "line_number": 36, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 36, "usage_type": "name"}, {"api_name": "marshmallow.fields.DateTime", "line_number": 37, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 37, "usage_type": "name"}, {"api_name": "marshmallow.fields.DateTime", "line_number": 38, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 38, "usage_type": "name"}, {"api_name": "marshmallow.fields.Boolean", "line_number": 39, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 39, "usage_type": "name"}, {"api_name": "marshmallow.fields.Integer", "line_number": 40, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 40, "usage_type": "name"}, {"api_name": "marshmallow.fields.List", "line_number": 41, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 41, "usage_type": "name"}, {"api_name": "marshmallow.fields.String", "line_number": 41, "usage_type": "call"}, {"api_name": "marshmallow.fields.Nested", "line_number": 42, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 42, "usage_type": "name"}, {"api_name": "models.User.get_logged_user", "line_number": 47, "usage_type": "call"}, {"api_name": "models.User", "line_number": 47, "usage_type": "name"}, {"api_name": "marshmallow.pre_dump", "line_number": 45, "usage_type": "call"}, {"api_name": "marshmallow.fields.Nested", "line_number": 54, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 54, "usage_type": "name"}, {"api_name": "marshmallow.fields.Nested", "line_number": 58, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 58, "usage_type": "name"}, {"api_name": "marshmallow.fields.Integer", "line_number": 59, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 59, "usage_type": "name"}, {"api_name": "marshmallow.pre_dump", "line_number": 61, "usage_type": "call"}, {"api_name": "marshmallow.fields.List", "line_number": 69, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 69, "usage_type": "name"}, {"api_name": "marshmallow.fields.String", "line_number": 69, "usage_type": "call"}, {"api_name": "marshmallow.fields.Integer", "line_number": 73, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 73, "usage_type": "name"}, {"api_name": "marshmallow.fields.String", "line_number": 74, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 74, "usage_type": "name"}, {"api_name": "marshmallow.fields.DateTime", "line_number": 75, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 75, "usage_type": "name"}, {"api_name": "marshmallow.fields.DateTime", "line_number": 76, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 76, "usage_type": "name"}, {"api_name": "marshmallow.fields.Nested", "line_number": 77, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 77, "usage_type": "name"}, {"api_name": "marshmallow.fields.Nested", "line_number": 82, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 82, "usage_type": "name"}, {"api_name": "marshmallow.fields.Nested", "line_number": 86, "usage_type": "call"}, {"api_name": "marshmallow.fields", "line_number": 86, "usage_type": "name"}]} +{"seq_id": "648538672", "text": "from collections import defaultdict, deque\nimport sys\n\nn, m = map(int, input().split())\nc = 0\nnameToInt = {}\ngraph = defaultdict(list)\nindeg = defaultdict(int)\noutdeg = defaultdict(int)\ngood = [True for _ in range(n)]\nfor _ in range(n):\n stuff = input().split()\n name = stuff[0]\n if name not in nameToInt:\n nameToInt[name] = c\n c += 1\n a = nameToInt[name]\n edges = stuff[2:]\n for nxt in edges:\n if nxt not in nameToInt:\n nameToInt[nxt] = c\n c += 1\n b = nameToInt[nxt]\n if a != b:\n graph[a].append(b)\n graph[b].append(a)\n outdeg[a] += 1\n indeg[b] += 1\n good[a] = False\n good[b] = False\n start = a\nassert c == n\n\ndef solve():\n if good.count(True) == n:\n return 'FALSE ALARM'\n p1 = 0\n m1 = 0\n for i in range(c):\n deg = outdeg[i] - indeg[i]\n if 1 < deg or -1 > deg:\n return False\n if deg == -1:\n m1 += 1\n elif deg == 1:\n p1 += 1\n if p1 == m1 == 0 or p1 == m1 == 1:\n visited = set()\n q = deque([start])\n while len(q) > 0:\n cur = q.popleft()\n if cur in visited:\n continue\n visited.add(cur)\n for nxt in graph[cur]:\n q.append(nxt)\n return len(visited) == good.count(False)\n return False\n\nx = solve()\nif x == False:\n print('IMPOSSIBLE')\nelif x == True:\n print('POSSIBLE')\nelse:\n print(x)\n", "sub_path": "kattis/grandopening.py", "file_name": "grandopening.py", "file_ext": "py", "file_size_in_byte": 1528, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "collections.defaultdict", "line_number": 7, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 8, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 9, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "268446088", "text": "\"\"\"Define the MyQ API.\"\"\"\nimport logging\n\nfrom aiohttp import ClientSession\nfrom aiohttp.client_exceptions import ClientError\n\nfrom .device import MyQDevice, MyQDoorDevice, MyQLightDevice\nfrom .errors import MyQError, RequestError, UnsupportedBrandError\n\n_LOGGER = logging.getLogger(__name__)\n\nAPI_BASE = 'https://myqexternal.myqdevice.com'\nLOGIN_ENDPOINT = \"api/v4/User/Validate\"\nDEVICE_LIST_ENDPOINT = \"api/v4/UserDeviceDetails/Get\"\n\nDEFAULT_TIMEOUT = 10\nDEFAULT_USER_AGENT = \"Chamberlain/3773 (iPhone; iOS 11.0.3; Scale/2.00)\"\n\nBRAND_MAPPINGS = {\n 'liftmaster': {\n 'app_id':\n 'Vj8pQggXLhLy0WHahglCD4N1nAkkXQtGYpq2HrHD7H1nvmbT55KqtN6RSF4ILB/i'\n },\n 'chamberlain': {\n 'app_id':\n 'OA9I/hgmPHFp9RYKJqCKfwnhh28uqLJzZ9KOJf1DXoo8N2XAaVX6A1wcLYyWsnnv'\n },\n 'craftsman': {\n 'app_id':\n 'YmiMRRS1juXdSd0KWsuKtHmQvh5RftEp5iewHdCvsNB77FnQbY+vjCVn2nMdIeN8'\n },\n 'merlin': {\n 'app_id':\n '3004cac4e920426c823fa6c2ecf0cc28ef7d4a7b74b6470f8f0d94d6c39eb718'\n }\n}\n\n\nclass API:\n \"\"\"Define a class for interacting with the MyQ iOS App API.\"\"\"\n\n def __init__(self, brand: str, websession: ClientSession) -> None:\n \"\"\"Initialize the API object.\"\"\"\n if brand not in BRAND_MAPPINGS:\n raise UnsupportedBrandError('Unknown brand: {0}'.format(brand))\n\n self._brand = brand\n self._security_token = None\n self._websession = websession\n\n async def _request(\n self,\n method: str,\n endpoint: str,\n *,\n headers: dict = None,\n params: dict = None,\n data: dict = None,\n json: dict = None,\n **kwargs) -> dict:\n \"\"\"Make a request.\"\"\"\n url = '{0}/{1}'.format(API_BASE, endpoint)\n\n if not headers:\n headers = {}\n if self._security_token:\n headers['SecurityToken'] = self._security_token\n headers.update({\n 'MyQApplicationId': BRAND_MAPPINGS[self._brand]['app_id'],\n 'User-Agent': DEFAULT_USER_AGENT,\n })\n\n try:\n async with self._websession.request(\n method, url, headers=headers, params=params, data=data,\n json=json, timeout=DEFAULT_TIMEOUT, **kwargs) as resp:\n resp.raise_for_status()\n return await resp.json(content_type=None)\n except ClientError as err:\n raise RequestError(\n 'Error requesting data from {0}: {1}'.format(endpoint, err))\n\n async def authenticate(self, username: str, password: str) -> None:\n \"\"\"Authenticate against the API.\"\"\"\n login_resp = await self._request(\n 'post',\n LOGIN_ENDPOINT,\n json={\n 'username': username,\n 'password': password\n })\n\n if int(login_resp['ReturnCode']) != 0:\n raise MyQError(login_resp['ErrorMessage'])\n\n self._security_token = login_resp['SecurityToken']\n\n async def _get_devices(self, device_class: 'MyQDevice' = None) -> list:\n \"\"\"Get a list of all devices associated with the account.\n Optionally filtered by class.\n \"\"\"\n devices_resp = await self._request('get', DEVICE_LIST_ENDPOINT)\n return [\n MyQDevice.get_device(device, self._brand, self._request)\n for device in devices_resp['Devices'] if not device_class\n or MyQDevice.get_device_class(device) == device_class\n ]\n\n async def get_devices(self, covers_only: bool = True) -> list:\n \"\"\"Get a list of all devices associated with the account.\"\"\"\n return await self._get_devices(MyQDoorDevice if covers_only else None)\n\n async def get_covers(self) -> list:\n \"\"\"Get a list of all covers associated with the account.\"\"\"\n return await self._get_devices(MyQDoorDevice)\n\n async def get_lights(self) -> list:\n \"\"\"Get a list of all lights associated with the account.\"\"\"\n return await self._get_devices(MyQLightDevice)\n\n\nasync def login(\n username: str, password: str, brand: str,\n websession: ClientSession) -> API:\n \"\"\"Log in to the API.\"\"\"\n api = API(brand, websession)\n await api.authenticate(username, password)\n return api\n", "sub_path": "pymyq/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 4313, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "aiohttp.ClientSession", "line_number": 42, "usage_type": "name"}, {"api_name": "errors.UnsupportedBrandError", "line_number": 45, "usage_type": "call"}, {"api_name": "aiohttp.client_exceptions.ClientError", "line_number": 79, "usage_type": "name"}, {"api_name": "errors.RequestError", "line_number": 80, "usage_type": "call"}, {"api_name": "errors.MyQError", "line_number": 94, "usage_type": "call"}, {"api_name": "device.MyQDevice.get_device", "line_number": 104, "usage_type": "call"}, {"api_name": "device.MyQDevice", "line_number": 104, "usage_type": "name"}, {"api_name": "device.MyQDevice.get_device_class", "line_number": 106, "usage_type": "call"}, {"api_name": "device.MyQDevice", "line_number": 106, "usage_type": "name"}, {"api_name": "device.MyQDoorDevice", "line_number": 111, "usage_type": "name"}, {"api_name": "device.MyQDoorDevice", "line_number": 115, "usage_type": "argument"}, {"api_name": "device.MyQLightDevice", "line_number": 119, "usage_type": "argument"}, {"api_name": "aiohttp.ClientSession", "line_number": 124, "usage_type": "name"}]} +{"seq_id": "404123668", "text": "import read_imf_nan\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as mdates\nimport datetime as dt\n\n# read the data\ndata = read_imf_nan.read_imf_nan( )\n\n# create plotting canvas\n# save a reference so we have it for later\nfig = plt.figure(1)\n\n# create first subplot\nax1 = plt.subplot(2,1,1)\nplt.plot(data['date'], data['AE'], color='black')\n# add labels\nax1.set_ylabel('AE')\n# set the x axis limits to 00:00\nbt = dt.datetime(2012, 3, 5, 0, 0, 0)\nft = dt.datetime(2012, 3, 15, 0, 0, 0)\nax1.set_xlim(bt, ft)\n# make the y axis symmetric\nax1.set_ylim(0, 3000)\n# overplot a line at zero\nxli = ax1.get_xlim()\nplt.plot( xli, [0,0], '--', color='gray' )\n\n#create subplot\nax2 = plt.subplot(2,1,2)\nplt.plot(data['date'], data['SYM'], color='black')\n# add labels\nax2.set_ylabel('SYM/H')\n# set the x axis limits to 00:00\nbt = dt.datetime(2012, 3, 5, 0, 0, 0)\nft = dt.datetime(2012, 3, 15, 0, 0, 0)\nax2.set_xlim(bt, ft)\n# make the y axis symmetric\nax2.set_ylim(-150, 60)\n# overplot a line at zero\nxli = ax2.get_xlim()\nplt.plot( xli, [0,0], '--', color='gray' )\n\n\n\n#show plot\nplt.show()\n", "sub_path": "plot_imf_nice_sub.py", "file_name": "plot_imf_nice_sub.py", "file_ext": "py", "file_size_in_byte": 1084, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "read_imf_nan.read_imf_nan", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 14, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "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": "datetime.datetime", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "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": "124876065", "text": "import logging\n\nimport requests\nfrom bs4 import BeautifulSoup\n\nfrom http_request_randomizer.requests.parsers.UrlParser import UrlParser\nfrom http_request_randomizer.requests.proxy.ProxyObject import ProxyObject, AnonymityLevel, Protocol\n\nlogger = logging.getLogger(__name__)\n__author__ = 'pgaref'\n\n\nclass FreeProxyParser(UrlParser):\n def __init__(self, id, web_url, timeout=None):\n UrlParser.__init__(self, id=id, web_url=web_url, timeout=timeout)\n\n def parse_proxyList(self):\n curr_proxy_list = []\n try:\n response = requests.get(self.get_url(), timeout=self.timeout)\n if not response.ok:\n logger.warning(\"Proxy Provider url failed: {}\".format(self.get_url()))\n return []\n\n content = response.content\n soup = BeautifulSoup(content, \"html.parser\")\n #table = soup.find(\"table\", attrs={\"id\": \"proxylisttable\"})\n table = soup.find(\"table\")\n\n\n # The first tr contains the field names.\n headings = [th.get_text() for th in table.find(\"tr\").find_all(\"th\")]\n\n datasets = []\n for row in table.find_all(\"tr\")[1:-1]:\n dataset = zip(headings, (td.get_text() for td in row.find_all(\"td\")))\n if dataset:\n datasets.append(dataset)\n\n for dataset in datasets:\n proxy_obj = self.create_proxy_object(dataset)\n # Make sure it is a Valid Proxy Address\n if proxy_obj is not None and UrlParser.valid_ip_port(proxy_obj.get_address()):\n curr_proxy_list.append(proxy_obj)\n else:\n logger.debug(\"Proxy Invalid: {}\".format(dataset))\n except AttributeError as e:\n logger.error(\"Provider {0} failed with Attribute error: {1}\".format(self.id, e))\n except KeyError as e:\n logger.error(\"Provider {0} failed with Key error: {1}\".format(self.id, e))\n except Exception as e:\n logger.error(\"Provider {0} failed with Unknown error: {1}\".format(self.id, e))\n finally:\n return curr_proxy_list\n\n def create_proxy_object(self, dataset):\n # Check Field[0] for tags and field[1] for values!\n ip = \"\"\n port = None\n anonymity = AnonymityLevel.UNKNOWN\n country = None\n protocols = []\n for field in dataset:\n if field[0] == 'IP Address':\n # Make sure it is a Valid IP\n ip = field[1].strip() # String strip()\n # Make sure it is a Valid IP\n if not UrlParser.valid_ip(ip):\n logger.debug(\"IP with Invalid format: {}\".format(ip))\n return None\n elif field[0] == 'Port':\n port = field[1].strip() # String strip()\n elif field[0] == 'Anonymity':\n anonymity = AnonymityLevel.get(field[1].strip()) # String strip()\n elif field[0] == 'Country':\n country = field[1].strip() # String strip()\n elif field[0] == 'Https':\n if field[1].strip().lower() == 'yes': protocols.extend([Protocol.HTTP, Protocol.HTTPS])\n elif field[1].strip().lower() == 'no': protocols.append(Protocol.HTTP)\n return ProxyObject(source=self.id, ip=ip, port=port, anonymity_level=anonymity, country=country, protocols=protocols)\n\n def __str__(self):\n return \"{0} parser of '{1}' with required bandwidth: '{2}' KBs\" \\\n .format(self.id, self.url, self.minimum_bandwidth_in_KBs)\n", "sub_path": "http_request_randomizer/requests/parsers/FreeProxyParser.py", "file_name": "FreeProxyParser.py", "file_ext": "py", "file_size_in_byte": 3592, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "http_request_randomizer.requests.parsers.UrlParser.UrlParser", "line_number": 13, "usage_type": "name"}, {"api_name": "http_request_randomizer.requests.parsers.UrlParser.UrlParser.__init__", "line_number": 15, "usage_type": "call"}, {"api_name": "http_request_randomizer.requests.parsers.UrlParser.UrlParser", "line_number": 15, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 20, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 26, "usage_type": "call"}, {"api_name": "http_request_randomizer.requests.parsers.UrlParser.UrlParser.valid_ip_port", "line_number": 43, "usage_type": "call"}, {"api_name": "http_request_randomizer.requests.parsers.UrlParser.UrlParser", "line_number": 43, "usage_type": "name"}, {"api_name": "http_request_randomizer.requests.proxy.ProxyObject.AnonymityLevel.UNKNOWN", "line_number": 60, "usage_type": "attribute"}, {"api_name": "http_request_randomizer.requests.proxy.ProxyObject.AnonymityLevel", "line_number": 60, "usage_type": "name"}, {"api_name": "http_request_randomizer.requests.parsers.UrlParser.UrlParser.valid_ip", "line_number": 68, "usage_type": "call"}, {"api_name": "http_request_randomizer.requests.parsers.UrlParser.UrlParser", "line_number": 68, "usage_type": "name"}, {"api_name": "http_request_randomizer.requests.proxy.ProxyObject.AnonymityLevel.get", "line_number": 74, "usage_type": "call"}, {"api_name": "http_request_randomizer.requests.proxy.ProxyObject.AnonymityLevel", "line_number": 74, "usage_type": "name"}, {"api_name": "http_request_randomizer.requests.proxy.ProxyObject.Protocol.HTTP", "line_number": 78, "usage_type": "attribute"}, {"api_name": "http_request_randomizer.requests.proxy.ProxyObject.Protocol", "line_number": 78, "usage_type": "name"}, {"api_name": "http_request_randomizer.requests.proxy.ProxyObject.Protocol.HTTPS", "line_number": 78, "usage_type": "attribute"}, {"api_name": "http_request_randomizer.requests.proxy.ProxyObject.Protocol.HTTP", "line_number": 79, "usage_type": "attribute"}, {"api_name": "http_request_randomizer.requests.proxy.ProxyObject.Protocol", "line_number": 79, "usage_type": "name"}, {"api_name": "http_request_randomizer.requests.proxy.ProxyObject.ProxyObject", "line_number": 80, "usage_type": "call"}]} +{"seq_id": "315233768", "text": "import json\nfrom math import sqrt\nfrom os.path import exists\n\n\ndef load_data(filepath):\n if not exists(filepath):\n return None\n with open(filepath, encoding='windows-1251') as file_handler:\n json_object = json.load(file_handler, encoding='windows-1251')\n return json_object\n\n\ndef get_biggest_bar(data):\n return max(data, key=lambda x: x['SeatsCount'])\n\n\ndef get_smallest_bar(data):\n return min(data, key=lambda x: x['SeatsCount'])\n\n\ndef get_closest_bar(data, longitude, latitude):\n def distance_calculation(bar, longitude=longitude, latitude=latitude):\n\n bar_latitude = bar['geoData']['coordinates'][1]\n bar_longitude = bar['geoData']['coordinates'][0]\n x2 = latitude\n x1 = bar_latitude\n y2 = longitude\n y1 = bar_longitude\n\n return sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)\n\n return min(data, key=distance_calculation)\n\n\ndef print_result(filepath, longitude, latitude):\n json_data = load_data(filepath)\n\n print('Самый большой бар: {}\\n'.format(json.dumps(\n get_biggest_bar(json_data), indent=4, ensure_ascii=False)))\n print('Самый маленький бар: {}\\n'.format(json.dumps(\n get_smallest_bar(json_data), indent=4, ensure_ascii=False)))\n print('Ближайший бар: {}\\n'.format(json.dumps(\n get_closest_bar(json_data, longitude, latitude), indent=4, ensure_ascii=False)))\n\n\ndef get_data():\n filepath = input('Введите путь к файлу: ')\n longitude = int(input('Введите свои кординаты (долгота): '))\n latitude = int(input('Введите свои кординаты (широта): '))\n\n return filepath, longitude, latitude\n\n\nif __name__ == '__main__':\n users_data = get_data()\n print_result(*users_data)\n", "sub_path": "bars.py", "file_name": "bars.py", "file_ext": "py", "file_size_in_byte": 1816, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.exists", "line_number": 7, "usage_type": "call"}, {"api_name": "json.load", "line_number": 10, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 32, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 40, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 42, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "269956366", "text": "import traceback\nimport urllib\nfrom pathlib import Path\nimport wget as wget\nfrom selenium import webdriver\nfrom bs4 import BeautifulSoup, element\nimport time\nimport re\nimport requests\nimport urllib3\nfrom urllib.parse import urljoin\n\nfrom datetime import datetime\nfrom selenium.webdriver.common.action_chains import ActionChains\n# from selenium.webdriver import ActionChains\nfrom selenium.webdriver.common import by\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nimport sys\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\n\nfrom selenium.webdriver.remote.webelement import WebElement\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as cond\nimport mysql.connector\nfrom multiprocessing.pool import ThreadPool\n\n\n\ndef runChromeOverServer():\n while True:\n try:\n # proo = AmazonProxies.objects.order_by('count')[0]\n # proxies = proo.proxy\n # proo.count += 1\n # proo.save()\n proxy= '172.254.124.231:3128'\n # proxy = '162.243.108.161:8080'\n from pyvirtualdisplay import Display\n # display = Display(visible=0, size=(1024, 768))\n # display.start()\n display = ''\n options = webdriver.ChromeOptions()\n options.add_argument(\"--start-maximized\")\n # options.add_argument('--proxy-server=%s' % proxy)\n options.add_argument('--disable-notifications')\n options.add_argument('--disable-dev-shm-usage')\n options.add_argument('--shm-size=2g')\n options.add_argument('--no-sandbox')\n while True:\n try:\n driver = webdriver.Chrome(executable_path='E:\\Emsgroup\\web_driver\\chromedriver.exe')\n except:\n driver = webdriver.Chrome(executable_path='E:\\Emsgroup\\web_driver\\chromedriver.exe',\n chrome_options=options)\n break\n return driver,display\n\n except Exception as e:\n print('driver while exception')\n print(e)\n pass\n\n\n\ndef fetching_categories():\n driver, display = runChromeOverServer()\n try:\n driver.execute_script(\"window.open('about:blank','tab1')\")\n driver.switch_to.window(\"tab1\")\n driver.get(\"https://www.sheetplastics.co.uk/\")\n WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.CSS_SELECTOR, \".sub-nav__column\")))\n main_nav = driver.find_elements_by_css_selector(\".sub-nav__column\")[0:3]\n p_id = 0\n for sublinks in main_nav:\n class_li = sublinks.find_elements_by_tag_name(\"li\")\n for li in class_li:\n a_ = li.find_element_by_tag_name(\"a\")\n main_cat_link = a_.get_attribute(\"href\")\n main_cat_name = a_.get_property(\"innerHTML\")\n print(main_cat_name , main_cat_link)\n\n # try:\n # mydb = mysql.connector.connect(\n # host=\"localhost\",\n # user=\"root\",\n # passwd=\"\",\n # database=\"plasticsheet\"\n # )\n # mycursor = mydb.cursor()\n # sql = \"INSERT INTO category (category_name,meta_title,meta_description,status,IsHome,IsMenu,parent,category_url,gfeed_status)\" \\\n # \" VALUES (%s, %s,%s, %s,%s, %s, %s,%s, %s)\"\n # val = (\n # main_cat_name, main_cat_name, main_cat_name, \"Yes\", \"Yes\", \"Yes\", p_id,\n # main_cat_name.replace('-', '').replace('/', '').replace(' ','').strip(), \"Yes\")\n # mycursor.execute(sql, val)\n # mydb.commit()\n # print(mycursor.rowcount, \"record inserted.\")\n # mydb.close()\n #\n # except Exception as e:\n # print('Database query errror')\n # print(e)\n try:\n mydb = mysql.connector.connect(\n host=\"localhost\",\n user=\"root\",\n passwd=\"\",\n database=\"plasticsheet\"\n )\n mycursor = mydb.cursor()\n\n sql = \"SELECT category_id FROM category WHERE category_name ='%s'\" % main_cat_name\n mycursor.execute(sql)\n myresult = mycursor.fetchall()\n for x in myresult:\n category_id = x[0]\n except Exception as e:\n print(e)\n pass\n\n try:\n catelist = []\n mydb = mysql.connector.connect(\n host=\"localhost\",\n user=\"root\",\n passwd=\"\",\n database=\"plasticsheet\"\n )\n mycursor = mydb.cursor()\n sql = \"SELECT category_id FROM products\"\n mycursor.execute(sql)\n myresult = mycursor.fetchall()\n for x in myresult:\n cat_id = x[0]\n catelist.append(cat_id)\n if category_id in catelist:\n pass\n print(\"Already Exist in Database\")\n else:\n extractproduct_urls(main_cat_link , category_id)\n except:\n pass\n\n except Exception as e:\n print(e)\n\n driver.quit()\n\ndef extractproduct_urls(main_cat_link , category_id):\n driver , display = runChromeOverServer()\n driver.execute_script(\"window.open('about:blank','tab2')\")\n driver.switch_to.window(\"tab2\")\n driver.get(main_cat_link)\n WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.CSS_SELECTOR, \".product-image\")))\n product_url_div = driver.find_elements_by_css_selector(\".product-image\")\n product_urls_list =[]\n for anchor_tags in product_url_div:\n product_urls = anchor_tags.get_attribute(\"href\")\n product_urls_list.append((product_urls , category_id))\n multipooling(product_urls_list)\n driver.quit()\n\n\ndef multipooling(product_url_list):\n print(len(product_url_list))\n chunksList = []\n for i in range(0, len(product_url_list), 1):\n chunk = product_url_list[i: i+1]\n chunksList.append(chunk)\n\n # print(chunksList)\n print(chunksList)\n # print(len(chunksList))\n # print(len(chunksList), \" : \", chunksList)\n pool_size = 5\n pool = ThreadPool(pool_size)\n pool.map(products_data, chunksList)\n pool.close()\n pool.join()\n print('Done All Pool')\n\ndef products_data(chunksList):\n parent_url = 'https://www.sheetplastics.co.uk/'\n driver, display = runChromeOverServer()\n data_ = 10\n for url in chunksList:\n pro_url = url[0]\n cat_id = url[1]\n driver.execute_script(\"window.open('about:blank','tab\" + str(data_) + \"')\")\n driver.switch_to.window(\"tab\" + str(data_))\n driver.get(pro_url)\n try:\n product_title = driver.find_element_by_css_selector(\".product-name\").text\n print(product_title)\n except Exception as e:\n print(e)\n data_ += 1\n driver.quit()\n\ndef products():\n driver , display = runChromeOverServer()\n data_ = 1\n driver.execute_script(\"window.open('about:blank','tab\" + str(data_) + \"')\")\n driver.switch_to.window(\"tab\" + str(data_))\n driver.get(\"https://www.sheetplastics.co.uk/2mm-clear-acrylic-sheet-cut-to-size.html\")\n try:\n product_title = driver.find_element_by_css_selector(\".product-name\").text\n print(product_title)\n except Exception as e:\n print(e)\n try:\n price_div = driver.find_element_by_css_selector(\".regular-price\")\n product_price = price_div.text\n print(product_price)\n except Exception as e:\n print(e)\n\n try:\n product_image_div = driver.find_element_by_css_selector(\".product-image\").find_element_by_tag_name(\"a\")\n product_image = product_image_div.get_attribute(\"href\")\n print(product_image)\n except Exception as e:\n print(e)\n try:\n desc_ = driver.find_element_by_css_selector(\".tabbed-content__content.cf.tabbed-content__content--active\").text\n print(desc_)\n except Exception as e:\n print(e)\n\n data_ += 1\n driver.quit()\n\n\nif __name__ == '__main__':\n # fetching_categories()\n products()", "sub_path": "plasticsheet.py", "file_name": "plasticsheet.py", "file_ext": "py", "file_size_in_byte": 8806, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "selenium.webdriver.ChromeOptions", "line_number": 44, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 44, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 53, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 53, "usage_type": "name"}, {"api_name": "selenium.webdriver.Chrome", "line_number": 55, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 55, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 73, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 73, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 73, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 73, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 73, "usage_type": "name"}, {"api_name": "mysql.connector.connector.connect", "line_number": 106, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 106, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 106, "usage_type": "name"}, {"api_name": "mysql.connector.connector.connect", "line_number": 125, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 125, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 125, "usage_type": "name"}, {"api_name": "selenium.webdriver.support.wait.WebDriverWait", "line_number": 156, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions.presence_of_element_located", "line_number": 156, "usage_type": "call"}, {"api_name": "selenium.webdriver.support.expected_conditions", "line_number": 156, "usage_type": "name"}, {"api_name": "selenium.webdriver.common.by.By.CSS_SELECTOR", "line_number": 156, "usage_type": "attribute"}, {"api_name": "selenium.webdriver.common.by.By", "line_number": 156, "usage_type": "name"}, {"api_name": "multiprocessing.pool.ThreadPool", "line_number": 178, "usage_type": "call"}]} +{"seq_id": "270617133", "text": "import json\nimport locale\nimport datetime\nimport time\nimport random\nimport logging\nimport web\n\nimport random\nimport string\n\nfrom utils.decorator import authentication\nfrom base import Base, NBase\nfrom dbapi.ComputingClient import ComputingClient\nfrom dbapi.CMDB import CMDB\n\ndef randomStr(size = 6, chars=string.ascii_letters + string.digits):\n return ''.join(random.choice(chars) for x in range(size))\n\nclass PageAnalyze(Base):\n def getAnalyze(self, mode):\n logging.debug('PageAnalyze.getAnalyze')\n params = self.validateParams()\n analyze = self.dcli.PageAnalyze(params, params['collector'], params['service'])\n if not analyze:\n analyze = []\n logging.debug(analyze)\n if mode == 'hit':\n ret = {'time': [], 'data': [[], []]}\n for row in analyze:\n m = int(row['start_time'])\n ret['time'].append(datetime.datetime.fromtimestamp(m).strftime(self.fmt))\n ret['data'][0].append(row['available_hits'])\n ret['data'][1].append(row['unavailable_hits'])\n elif mode == 'time':\n ret = {'time': [], 'data': [[], [], [], [], []]}\n for row in analyze:\n m = int(row['start_time'])\n ret['time'].append(datetime.datetime.fromtimestamp(m).strftime(self.fmt))\n ret['data'][0].append(float(row['tcp_retry_time']))\n ret['data'][1].append(float(row['tcp_connection_time']))\n ret['data'][2].append(float(row.get('http_network_time', 0)))\n ret['data'][3].append(float(row['http_server_time']))\n ret['data'][4].append(float(row['http_download_time']))\n elif mode == 'all':\n ret = {'time': [], 'data': [[], [], [], [], [], [], [], []], 'line': []}\n cmdb = CMDB()\n service_info = cmdb.GetServiceInfo(params['collector'], params['service'])\n if not service_info:\n service_info = {}\n ret['line'].append(service_info.get('performance_critical', 0))\n ret['line'].append(service_info.get('performance_warning', 0))\n for row in analyze:\n m = int(row['start_time'])\n ret['time'].append(datetime.datetime.fromtimestamp(m).strftime(self.fmt))\n ret['data'][0].append(float(row['tcp_retry_time']))\n ret['data'][1].append(float(row['tcp_connection_time']))\n ret['data'][2].append(float(row.get('http_network_time', 0)))\n ret['data'][3].append(float(row['http_server_time']))\n ret['data'][4].append(float(row['http_download_time']))\n ret['data'][5].append(row['available_hits'])\n ret['data'][6].append(row['unavailable_hits'])\n ret['data'][7].append(float(row['availability']))\n return ret\n\nclass PageHit(PageAnalyze):\n @authentication\n def POST(self, **kwagrs):\n logging.debug('PageHit.POST')\n web.header('Content-Type', 'application/json')\n #param = self.validateParams()\n ret = self.getAnalyze('hit')\n #now = datetime.datetime.now()\n #ret = {'time': [], 'data': []}\n #ret['time'] = [t.strftime(locale.nl_langinfo(locale.T_FMT)) for t in [now - datetime.timedelta(minutes = i) for i in range(10)]]\n #ret['data'] = [[random.randrange(100) for i in range(10)] for i in range(2)]\n return json.dumps(ret)\n\nclass PageTime(PageAnalyze):\n @authentication\n def POST(self, **kwagrs):\n logging.debug('PageTime.POST')\n web.header('Content-Type', 'application/json')\n #param = self.validateParams()\n ret = self.getAnalyze('time')\n #now = datetime.datetime.now()\n #ret = {'time': [], 'data': []}\n #ret['time'] = [t.strftime(locale.nl_langinfo(locale.T_FMT)) for t in [now - datetime.timedelta(minutes = i) for i in range(10)]]\n #ret['data'] = [[random.randrange(100) for i in range(10)] for i in range(5)]\n return json.dumps(ret)\n\nclass PageAnalytics(PageAnalyze):\n @authentication\n def POST(self, **kwagrs):\n logging.debug('PageTime.POST')\n web.header('Content-Type', 'application/json')\n ret = self.getAnalyze('all')\n return json.dumps(ret)\n\nclass PageSummary(NBase):\n def __init__(self):\n logging.debug('PageSummary.__init__')\n self.dcli = ComputingClient()\n\n @authentication\n def POST(self, **kwargs):\n logging.debug('PageSummary.POST')\n web.header('Content-Type', 'application/json')\n param = self.validateParams()\n perf_dict = self.dcli.PageSummary(param['start_time'], param['end_time'], param['collector'])\n logging.debug(perf_dict)\n ret = []\n for service in perf_dict:\n logging.info(service)\n tmp = {}\n tmp['service_name'] = service['service_name']\n tmp['availability'] = float(service['availability'])\n tmp['download_time'] = float(service['http_download_time'])\n tmp['server_time'] = float(service['http_server_time'])\n tmp['total_hits'] = service['total_hits']\n tmp['download_size'] = float(service['http_download_size'])\n tmp['event_count'] = service['event_count']\n ret.append(tmp)\n return json.dumps(ret)\n", "sub_path": "backend/handlers/page.py", "file_name": "page.py", "file_ext": "py", "file_size_in_byte": 5358, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "string.ascii_letters", "line_number": 17, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 17, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 18, "usage_type": "call"}, {"api_name": "base.Base", "line_number": 20, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 22, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 32, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "attribute"}, {"api_name": "dbapi.CMDB.CMDB", "line_number": 47, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 55, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 55, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 69, "usage_type": "call"}, {"api_name": "web.header", "line_number": 70, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 77, "usage_type": "call"}, {"api_name": "utils.decorator.authentication", "line_number": 67, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 82, "usage_type": "call"}, {"api_name": "web.header", "line_number": 83, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 90, "usage_type": "call"}, {"api_name": "utils.decorator.authentication", "line_number": 80, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 95, "usage_type": "call"}, {"api_name": "web.header", "line_number": 96, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 98, "usage_type": "call"}, {"api_name": "utils.decorator.authentication", "line_number": 93, "usage_type": "name"}, {"api_name": "base.NBase", "line_number": 100, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 102, "usage_type": "call"}, {"api_name": "dbapi.ComputingClient.ComputingClient", "line_number": 103, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 107, "usage_type": "call"}, {"api_name": "web.header", "line_number": 108, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 111, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 114, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 124, "usage_type": "call"}, {"api_name": "utils.decorator.authentication", "line_number": 105, "usage_type": "name"}]} +{"seq_id": "363118791", "text": "import os\nfrom django.conf import settings\nfrom django.contrib.staticfiles import finders\nfrom datetime import datetime\nfrom app.constants import MESES\n\ndef link_callback(uri, rel):\n sUrl = settings.STATIC_URL # Typically /static/\n sRoot = settings.STATIC_ROOT # Typically /home/userX/project_static/\n mUrl = settings.MEDIA_URL # Typically /media/\n mRoot = settings.MEDIA_ROOT # Typically /home/userX/project_static/media/\n\n if uri.startswith(mUrl):\n path = os.path.join(mRoot, uri.replace(mUrl, \"\"))\n elif uri.startswith(sUrl):\n path = os.path.join(sRoot, uri.replace(sUrl, \"\"))\n else:\n return uri\n\n # make sure that file exists\n if not os.path.isfile(path):\n raise Exception('media URI must start with %s or %s' % (sUrl, mUrl))\n return path\n\ndef convertir_fecha(fecha: str) -> datetime:\n formato_fecha = '%B %d, %Y' # January 28, 2021\n for m in MESES:\n if m == fecha.split(' ')[0]:\n return datetime.strptime(fecha.replace(m, MESES[m]), formato_fecha) ", "sub_path": "gentelella/app/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 1062, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.conf.settings.STATIC_URL", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.settings.STATIC_ROOT", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 9, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_URL", "line_number": 10, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 10, "usage_type": "name"}, {"api_name": "django.conf.settings.MEDIA_ROOT", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 11, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "app.constants.MESES", "line_number": 27, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 29, "usage_type": "name"}, {"api_name": "app.constants.MESES", "line_number": 29, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "258971737", "text": "\"\"\"\nAuthorization views\n\"\"\"\nfrom itsdangerous import BadSignature, BadPayload\nfrom requests import post\n\nfrom flask import render_template, request, redirect, url_for, Blueprint\nfrom flask_login import LoginManager, logout_user, UserMixin, login_user\nfrom flask_wtf import FlaskForm\nfrom wtforms import StringField, PasswordField\nfrom wtforms.validators import DataRequired\n\nfrom . import login_serializer\nfrom .utils import get_cida_auth_token, generate_auth_header, get_url_endpoint, is_safe_url\nfrom .. import app\n\n\nauth = Blueprint('auth', __name__,\n template_folder='templates',\n static_folder='static',\n static_url_path='/auth/static')\n\nAUTH_ENDPOINT_URL = app.config.get('AUTH_ENDPOINT_URL')\n# should requests verify the certificates for ssl connections\nVERIFY_CERT = app.config['VERIFY_CERT']\n\n\nclass LoginForm(FlaskForm):\n \"\"\"\n Authorization login form\n \"\"\"\n username = StringField('AD Username:', validators=[DataRequired()])\n password = PasswordField('AD Password:', validators=[DataRequired()])\n\n# Flask-Login Login Manager\nlogin_manager = LoginManager()\nlogin_manager.init_app(app)\nlogin_manager.login_view = 'auth.login_page'\n\n\nclass User(UserMixin):\n \"\"\"\n User Class for flask-Login\n \"\"\"\n def __init__(self, username=None, cida_auth_token=None):\n self.id = username\n self.cida_auth_token = cida_auth_token\n\n def is_authenticated(self):\n return True\n\n def is_active(self):\n return True\n\n def is_anonymous(self):\n return False\n\n def get_auth_token(self):\n \"\"\"\n Encode a secure token for cookie.\n\n The Token is encrypted using itsdangerous.URLSafeTimedSerializer which\n allows us to have a max_age on the token itself. When the cookie is stored\n on the users computer it also has a exipry date, but could be changed by\n the user, so this feature allows us to enforce the exipry date of the token\n server side and not rely on the users cookie to exipre.\n \"\"\"\n data = [str(self.id), self.cida_auth_token]\n return login_serializer.dumps(data)\n\n @staticmethod\n def get(username, cida_auth_token):\n '''\n :param username: AD username\n :param cida_auth_token: token returned by CIDA auth service\n :return User object if userid is valid, otherwise return None:\n '''\n if username:\n user = User(username, cida_auth_token)\n else:\n user = None\n return user\n\n\n@login_manager.user_loader\ndef load_user(username):\n \"\"\"\n Flask-Login user_loader callback.\n The user_loader function reloads the user object from the user ID stored in the session.\n \"\"\"\n cida_auth_token = get_cida_auth_token(request.cookies)\n if cida_auth_token:\n user = User.get(username, cida_auth_token)\n else:\n user = None\n\n return user\n\n\n@login_manager.token_loader\ndef load_token(token):\n \"\"\"\n Flask-Login token_loader callback.\n The token_loader function asks this function to take the token that was\n stored on the users computer process it to check if its valid and then\n return a User Object if its valid or None if its not valid.\n \"\"\"\n\n # The Token itself was generated by User.get_auth_token. So it is up to\n # us to known the format of the token data itself.\n # Decrypt the Security Token, data = [ad_user_username, user_ad_token]\n try:\n data = login_serializer.loads(token, max_age=app.config['REMEMBER_COOKIE_DURATION'].total_seconds())\n except (BadSignature, BadPayload):\n user = None\n else:\n # generate the user object based on the contents of the cookie, if the cookie isn't expired\n if data:\n user = User(data[0], data[1])\n else:\n user = None\n\n return user\n\n\n@auth.route(\"/logout/\")\ndef logout_page(forward):\n \"\"\"\n Web Page to Logout User, then Redirect them to Index Page.\n \"\"\"\n auth_header = generate_auth_header(request)\n logout_url = AUTH_ENDPOINT_URL + 'logout'\n response = post(logout_url, headers=auth_header, verify=VERIFY_CERT)\n\n logout_user()\n\n return redirect(url_for(forward))\n\n\n@auth.route(\"/login/\", methods=[\"GET\", \"POST\"])\ndef login_page():\n \"\"\"\n Web Page to Display Login Form and process form.\n \"\"\"\n form = LoginForm()\n error = None\n if request.method == \"POST\":\n # take the form data and put it into the payload to send to the pubs auth endpoint\n payload = {'username': request.form['username'], 'password': request.form['password']}\n # POST the payload to the pubs auth endpoint\n pubs_login_url = AUTH_ENDPOINT_URL + 'token'\n mp_response = post(pubs_login_url, data=payload, verify=VERIFY_CERT)\n # if the pubs endpoint login is successful, then proceed with logging in\n if mp_response.status_code == 200:\n user = User(request.form['username'], mp_response.json().get('token'))\n login_user(user, remember=True)\n\n next_page = request.args.get(\"next\")\n app.logger.info('Next page: %s', next_page)\n\n if next_page is not None and is_safe_url(next_page, request.host_url):\n endpoint = get_url_endpoint(next_page, request.environ['SERVER_NAME'], ('pubswh.index', {}))\n url = url_for(endpoint[0], **endpoint[1])\n return redirect(url)\n\n return redirect(url_for('pubswh.index'))\n else:\n error = 'Username or Password is invalid '+str(mp_response.status_code)\n\n return render_template('auth/login.html', form=form, error=error)\n\n\n@auth.route('/loginservice/', methods=[\"POST\"])\ndef login_service():\n \"\"\"\n Login service view\n \"\"\"\n resp = post(AUTH_ENDPOINT_URL + 'token', data=request.form, verify=VERIFY_CERT)\n if resp.status_code == 200:\n user = User(request.form['username'], resp.json().get('token'))\n login_user(user, remember=True)\n\n # This fixed an an ERR_INVALID_CHUNKED_ENCODING when the app was run on the deployment server.\n if 'transfer-encoding' in resp.headers:\n del resp.headers['transfer-encoding']\n return (resp.text, resp.status_code, list(resp.headers.items()))\n", "sub_path": "server/pubs_ui/auth/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 6257, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Blueprint", "line_number": 18, "usage_type": "call"}, {"api_name": "flask_wtf.FlaskForm", "line_number": 28, "usage_type": "name"}, {"api_name": "wtforms.StringField", "line_number": 32, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 32, "usage_type": "call"}, {"api_name": "wtforms.PasswordField", "line_number": 33, "usage_type": "call"}, {"api_name": "wtforms.validators.DataRequired", "line_number": 33, "usage_type": "call"}, {"api_name": "flask_login.LoginManager", "line_number": 36, "usage_type": "call"}, {"api_name": "flask_login.UserMixin", "line_number": 41, "usage_type": "name"}, {"api_name": "utils.get_cida_auth_token", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.request.cookies", "line_number": 91, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 91, "usage_type": "name"}, {"api_name": "itsdangerous.BadSignature", "line_number": 114, "usage_type": "name"}, {"api_name": "itsdangerous.BadPayload", "line_number": 114, "usage_type": "name"}, {"api_name": "utils.generate_auth_header", "line_number": 131, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 131, "usage_type": "argument"}, {"api_name": "requests.post", "line_number": 133, "usage_type": "call"}, {"api_name": "flask_login.logout_user", "line_number": 135, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 137, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 137, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 147, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 147, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 149, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 149, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 152, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 155, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 155, "usage_type": "name"}, {"api_name": "flask_login.login_user", "line_number": 156, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 158, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 158, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 158, "usage_type": "name"}, {"api_name": "utils.is_safe_url", "line_number": 161, "usage_type": "call"}, {"api_name": "flask.request.host_url", "line_number": 161, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 161, "usage_type": "name"}, {"api_name": "utils.get_url_endpoint", "line_number": 162, "usage_type": "call"}, {"api_name": "flask.request.environ", "line_number": 162, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 162, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 163, "usage_type": "call"}, {"api_name": "flask.redirect", "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": "flask.render_template", "line_number": 170, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 178, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 178, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 178, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 180, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 180, "usage_type": "name"}, {"api_name": "flask_login.login_user", "line_number": 181, "usage_type": "call"}]} +{"seq_id": "632142613", "text": "import PyCore\nimport PyDataProcess\nimport QtConversion\nimport ResNetActionRecognition_process as processMod\nimport cv2\nimport os\nimport glob\n\n#PyQt GUI framework\nfrom PyQt5.QtWidgets import *\n\nbackend_names = {\n cv2.dnn.DNN_BACKEND_DEFAULT: \"Default\",\n cv2.dnn.DNN_BACKEND_HALIDE: \"Halide\",\n cv2.dnn.DNN_BACKEND_INFERENCE_ENGINE: \"Inference engine\",\n cv2.dnn.DNN_BACKEND_OPENCV: \"OpenCV\",\n cv2.dnn.DNN_BACKEND_VKCOM: \"VKCOM\",\n cv2.dnn.DNN_BACKEND_CUDA: \"CUDA\",\n}\n\ntarget_names = {\n cv2.dnn.DNN_TARGET_CPU: \"CPU\",\n cv2.dnn.DNN_TARGET_OPENCL: \"OpenCL FP32\",\n cv2.dnn.DNN_TARGET_OPENCL_FP16: \"OpenCL FP16\",\n cv2.dnn.DNN_TARGET_MYRIAD: \"MYRIAD\",\n cv2.dnn.DNN_TARGET_VULKAN: \"VULKAN\",\n cv2.dnn.DNN_TARGET_FPGA: \"FPGA\",\n cv2.dnn.DNN_TARGET_CUDA: \"CUDA FP32\",\n cv2.dnn.DNN_TARGET_CUDA_FP16: \"CUDA FP16\",\n}\n\nbackend_targets = {\n cv2.dnn.DNN_BACKEND_DEFAULT: [cv2.dnn.DNN_TARGET_CPU, cv2.dnn.DNN_TARGET_OPENCL, cv2.dnn.DNN_TARGET_OPENCL_FP16],\n cv2.dnn.DNN_BACKEND_HALIDE: [cv2.dnn.DNN_TARGET_CPU],\n cv2.dnn.DNN_BACKEND_INFERENCE_ENGINE: [cv2.dnn.DNN_TARGET_CPU],\n cv2.dnn.DNN_BACKEND_OPENCV: [cv2.dnn.DNN_TARGET_CPU, cv2.dnn.DNN_TARGET_OPENCL, cv2.dnn.DNN_TARGET_OPENCL_FP16],\n cv2.dnn.DNN_BACKEND_VKCOM: [cv2.dnn.DNN_TARGET_CPU],\n cv2.dnn.DNN_BACKEND_CUDA: [cv2.dnn.DNN_TARGET_CUDA, cv2.dnn.DNN_TARGET_CUDA_FP16],\n}\n\n# --------------------\n# - Class which implements widget associated with the process\n# - Inherits PyCore.CProtocolTaskWidget from Ikomia API\n# --------------------\nclass ResNetActionRecognitionWidget(PyCore.CProtocolTaskWidget):\n\n def __init__(self, param, parent):\n PyCore.CProtocolTaskWidget.__init__(self, parent)\n\n if param is None:\n self.param = processMod.ResNetActionRecognitionParam()\n else:\n self.param = param\n\n self.param_changed = False\n\n # Create layout : QGridLayout by default\n self.grid_layout = QGridLayout()\n\n\n # Sample duration\n label_duration = QLabel(\"Sample duration (in frame)\")\n self.spin_duration = QSpinBox()\n self.spin_duration.setRange(1, 100)\n self.spin_duration.setSingleStep(1)\n self.spin_duration.setValue(self.param.sample_duration)\n\n # Rolling prediction on/off\n self.check_rolling = QCheckBox(\"Rolling prediction\")\n self.check_rolling.setChecked(self.param.rolling)\n\n # Combobox for models\n label_model = QLabel(\"Model\")\n self.combo_models = QComboBox()\n self.fill_combo_models()\n self.combo_models.currentIndexChanged.connect(self.on_param_changed)\n self.combo_models.setCurrentIndex(self.combo_models.findData(self.param.model_path))\n\n # Combobox for inference backend\n label_backend = QLabel(\"DNN backend\")\n self.combo_backend = QComboBox()\n self.fill_combo_backend() \n self.combo_backend.setCurrentIndex(self.combo_backend.findData(self.param.backend))\n self.combo_backend.currentIndexChanged.connect(self.on_backend_changed)\n\n # Combobox for inference target\n label_target = QLabel(\"DNN target\")\n self.combo_target = QComboBox()\n self.fill_combo_target(self.param.backend)\n self.combo_target.setCurrentIndex(self.combo_target.findData(self.param.target))\n self.combo_target.currentIndexChanged.connect(self.on_param_changed)\n\n # Fill layout \n self.grid_layout.addWidget(label_backend, 0, 0, 1, 1)\n self.grid_layout.addWidget(self.combo_backend, 0, 1, 1, 1)\n self.grid_layout.addWidget(label_target, 1, 0, 1, 1)\n self.grid_layout.addWidget(self.combo_target, 1, 1, 1, 1)\n self.grid_layout.addWidget(label_model, 2, 0, 1, 1)\n self.grid_layout.addWidget(self.combo_models, 2, 1, 1, 1)\n self.grid_layout.addWidget(label_duration, 3, 0, 1, 1)\n self.grid_layout.addWidget(self.spin_duration, 3, 1, 1, 1)\n self.grid_layout.addWidget(self.check_rolling, 4, 0, 1, 2)\n \n\n # PyQt -> Qt wrapping\n layoutPtr = QtConversion.PyQtToQt(self.grid_layout)\n\n # Set widget layout\n self.setLayout(layoutPtr)\n\n \n def fill_combo_models(self):\n self.combo_models.clear()\n models_folder = os.path.dirname(os.path.realpath(__file__)) + \"/models\"\n model_files = glob.glob(models_folder + \"/*.onnx\")\n\n for f in model_files:\n self.combo_models.addItem(os.path.basename(f), f)\n\n \n def fill_combo_backend(self):\n self.combo_backend.clear()\n for backend in backend_names: \n self.combo_backend.addItem(backend_names[backend], backend)\n\n\n def fill_combo_target(self, backend):\n targets = backend_targets[backend]\n self.combo_target.clear()\n\n for target in targets:\n self.combo_target.addItem(target_names[target], target)\n\n\n def on_backend_changed(self, index):\n backend = self.combo_backend.currentData()\n self.fill_combo_target(backend)\n self.param_changed = True\n\n\n def on_param_changed(self, index):\n self.param_changed = True\n\n\n def onApply(self):\n # Apply button clicked slot\n # Get parameters from widget\n self.param.sample_duration = self.spin_duration.value()\n self.param.rolling = self.check_rolling.isChecked()\n self.param.model_path = self.combo_models.currentData()\n self.param.update = self.param_changed\n self.param.backend = self.combo_backend.currentData()\n self.param.target = self.combo_target.currentData()\n\n # Send signal to launch the process\n self.emitApply(self.param)\n\n\n#--------------------\n#- Factory class to build process widget object\n#- Inherits PyDataProcess.CWidgetFactory from Ikomia API\n#--------------------\nclass ResNetActionRecognitionWidgetFactory(PyDataProcess.CWidgetFactory):\n\n def __init__(self):\n PyDataProcess.CWidgetFactory.__init__(self)\n # Set the name of the process -> it must be the same as the one declared in the process factory class\n self.name = \"ResNet Action Recognition\"\n\n\n def create(self, param):\n # Create widget object\n return ResNetActionRecognitionWidget(param, None)\n", "sub_path": "ResNetActionRecognition/ResNetActionRecognition_widget.py", "file_name": "ResNetActionRecognition_widget.py", "file_ext": "py", "file_size_in_byte": 6244, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "cv2.dnn", "line_number": 13, "usage_type": "attribute"}, {"api_name": "cv2.dnn", "line_number": 14, "usage_type": "attribute"}, {"api_name": "cv2.dnn", "line_number": 15, "usage_type": "attribute"}, {"api_name": "cv2.dnn", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.dnn", "line_number": 17, "usage_type": "attribute"}, {"api_name": "cv2.dnn", "line_number": 18, "usage_type": "attribute"}, {"api_name": "cv2.dnn", "line_number": 22, "usage_type": "attribute"}, {"api_name": "cv2.dnn", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.dnn", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cv2.dnn", "line_number": 25, "usage_type": "attribute"}, {"api_name": "cv2.dnn", "line_number": 26, "usage_type": "attribute"}, {"api_name": "cv2.dnn", "line_number": 27, "usage_type": "attribute"}, {"api_name": "cv2.dnn", "line_number": 28, "usage_type": "attribute"}, {"api_name": "cv2.dnn", "line_number": 29, "usage_type": "attribute"}, {"api_name": "cv2.dnn", "line_number": 33, "usage_type": "attribute"}, {"api_name": "cv2.dnn", "line_number": 34, "usage_type": "attribute"}, {"api_name": "cv2.dnn", "line_number": 35, "usage_type": "attribute"}, {"api_name": "cv2.dnn", "line_number": 36, "usage_type": "attribute"}, {"api_name": "cv2.dnn", "line_number": 37, "usage_type": "attribute"}, {"api_name": "cv2.dnn", "line_number": 38, "usage_type": "attribute"}, {"api_name": "PyCore.CProtocolTaskWidget", "line_number": 45, "usage_type": "attribute"}, {"api_name": "PyCore.CProtocolTaskWidget.__init__", "line_number": 48, "usage_type": "call"}, {"api_name": "PyCore.CProtocolTaskWidget", "line_number": 48, "usage_type": "attribute"}, {"api_name": "ResNetActionRecognition_process.ResNetActionRecognitionParam", "line_number": 51, "usage_type": "call"}, {"api_name": "QtConversion.PyQtToQt", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 114, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "PyDataProcess.CWidgetFactory", "line_number": 163, "usage_type": "attribute"}, {"api_name": "PyDataProcess.CWidgetFactory.__init__", "line_number": 166, "usage_type": "call"}, {"api_name": "PyDataProcess.CWidgetFactory", "line_number": 166, "usage_type": "attribute"}]} +{"seq_id": "426790747", "text": "#!/usr/bin/env python3\n\ndef main():\n from ken2015nov.xlsxcsv import rows_to_csv, rows_to_xlsx,\\\n CsvToRows, XlsxToRows\n _prepare_a_csv()\n with CsvToRows(r'a.csv') as rows:\n rows_to_xlsx(r'a.xlsx', rows)\n\n with CsvToRows(r'a.csv') as rows:\n rows_to_csv(r'0.csv', rows)\n with CsvToRows(r'a.csv') as rows:\n rows_to_xlsx(r'1.xlsx', rows)\n with XlsxToRows(r'a.xlsx') as rows:\n rows_to_csv(r'2.csv', rows)\n with XlsxToRows(r'a.xlsx') as rows:\n rows_to_xlsx(r'3.xlsx', rows)\n\n_a_csv = r'''\na,b\n1,2\n3,4\n'''[1:]\n\ndef _prepare_a_csv():\n from pathlib import Path\n with Path(__file__).with_name(r'a.csv').open(r'wt') as ostream:\n ostream.write(_a_csv)\n\nif __name__ == r'__main__':\n main()\n", "sub_path": "xlsxcsvdemo.py", "file_name": "xlsxcsvdemo.py", "file_ext": "py", "file_size_in_byte": 756, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "ken2015nov.xlsxcsv.CsvToRows", "line_number": 7, "usage_type": "call"}, {"api_name": "ken2015nov.xlsxcsv.rows_to_xlsx", "line_number": 8, "usage_type": "call"}, {"api_name": "ken2015nov.xlsxcsv.CsvToRows", "line_number": 10, "usage_type": "call"}, {"api_name": "ken2015nov.xlsxcsv.rows_to_csv", "line_number": 11, "usage_type": "call"}, {"api_name": "ken2015nov.xlsxcsv.CsvToRows", "line_number": 12, "usage_type": "call"}, {"api_name": "ken2015nov.xlsxcsv.rows_to_xlsx", "line_number": 13, "usage_type": "call"}, {"api_name": "ken2015nov.xlsxcsv.XlsxToRows", "line_number": 14, "usage_type": "call"}, {"api_name": "ken2015nov.xlsxcsv.rows_to_csv", "line_number": 15, "usage_type": "call"}, {"api_name": "ken2015nov.xlsxcsv.XlsxToRows", "line_number": 16, "usage_type": "call"}, {"api_name": "ken2015nov.xlsxcsv.rows_to_xlsx", "line_number": 17, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "647594131", "text": "import collections\nfrom datetime import datetime\nfrom contextlib import contextmanager\n\nimport cx_Oracle\nfrom sqlalchemy import create_engine, func\nfrom sqlalchemy.orm import Session\n\nfrom .model import Base, Body, Ticker\nfrom .util import is_test_mode, read_config\n\n\nclass Database:\n def __init__(self, connection_string, **kwargs):\n self.__engine = create_engine(\n connection_string,\n **kwargs)\n\n @classmethod\n def initialize(cls, **kwargs):\n database_config = read_config()[\"database\"]\n\n connection_string = 'oracle://{user}:{password}@{sid}'.format(\n user=database_config[\"username\"],\n password=database_config[\"password\"],\n sid=database_config[\"dsn\"])\n\n if is_test_mode():\n connection_string = 'sqlite:///:memory:'\n\n del kwargs['test']\n\n database = Database(connection_string, **kwargs)\n # Create all tables\n Base.metadata.create_all(database.__engine)\n return database\n\n @contextmanager\n def session_scope(self):\n \"\"\"Provide a transactional scope around a series of operations.\"\"\"\n session = Session(self.__engine)\n try:\n yield session\n session.commit()\n except:\n session.rollback()\n raise\n finally:\n session.close()\n\n def add(self, obj):\n with self.session_scope() as session:\n if isinstance(obj, collections.Iterable):\n for item in obj:\n session.merge(item)\n else:\n session.merge(obj)\n session.commit()\n\n def get_all_tickers(self) -> list[Ticker]:\n with self.session_scope() as session:\n return session.query(Ticker).all()\n\n def get_ticker_mentions(self, \n start: datetime = None, \n end: datetime = None\n ):\n with self.session_scope() as session:\n return session.query(Body.ticker, func.count(Body.content)).group_by(Body.ticker).order_by(func.count(Body.content).desc())\n", "sub_path": "wsbdiscordbot/database.py", "file_name": "database.py", "file_ext": "py", "file_size_in_byte": 2065, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sqlalchemy.create_engine", "line_number": 15, "usage_type": "call"}, {"api_name": "util.read_config", "line_number": 21, "usage_type": "call"}, {"api_name": "util.is_test_mode", "line_number": 28, "usage_type": "call"}, {"api_name": "model.Base.metadata.create_all", "line_number": 35, "usage_type": "call"}, {"api_name": "model.Base.metadata", "line_number": 35, "usage_type": "attribute"}, {"api_name": "model.Base", "line_number": 35, "usage_type": "name"}, {"api_name": "sqlalchemy.orm.Session", "line_number": 41, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 38, "usage_type": "name"}, {"api_name": "collections.Iterable", "line_number": 53, "usage_type": "attribute"}, {"api_name": "model.Ticker", "line_number": 62, "usage_type": "argument"}, {"api_name": "model.Ticker", "line_number": 60, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 65, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 66, "usage_type": "name"}, {"api_name": "model.Body.ticker", "line_number": 69, "usage_type": "attribute"}, {"api_name": "model.Body", "line_number": 69, "usage_type": "name"}, {"api_name": "sqlalchemy.func.count", "line_number": 69, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 69, "usage_type": "name"}, {"api_name": "model.Body.content", "line_number": 69, "usage_type": "attribute"}]} +{"seq_id": "499586236", "text": "import numpy as np\nfrom scipy.spatial import distance\nimport math\nimport random\n\nDEBUG = False\nUNIFORM = True\n\nclass gonzalez:\n def get_csv(self,fileName):\n return np.genfromtxt(fileName, delimiter=',')\n\n def __init__(self,data, it, r):\n self.it = it\n self.data = data\n self.r2 = r * 2\n \n def gonzalez(self):\n size = len(self.data)\n\n #Random initialization\n winners = [[random.randint(0, size-1)]]\n centers = np.array([self.data[winners[0][0]]])\n\n #Populating dist array\n dist = distance.cdist(self.data, np.array([centers[len(centers)-1]]))\n dist = np.array([item for sublist in dist for item in sublist])\n if(DEBUG):\n \tprint(dist[0:100])\n \n \n for i in range(self.it-1):\n if(DEBUG):\n print(\"-----------------------------\\n\", centers.shape)\n #Get distance to new center\n if(DEBUG):\n print(dist[0:100])\n tempdist = distance.cdist(self.data, np.array([centers[len(centers)-1]]))\n tempdist = np.array([item for sublist in tempdist for item in sublist])\n if(DEBUG):\n print(tempdist[0:100])\n #For each entry, if leq, replace\n dist = np.array([dist[i] if dist[i] <= tempdist[i] else tempdist[i] for i in range(size)])\n if(DEBUG):\n print(dist[0:100])\n #Picking center\n winnerInd = [np.argmax(dist)]\n winners.append(winnerInd)\n if(DEBUG):\n print(winnerInd, \"\\n\", self.data[winnerInd])\n #Adding center\n centers = np.append(centers, self.data[winnerInd], axis = 0)\n\n if(DEBUG):\n print(winners)\n \n return np.array(centers)\n\n\n\n#kcent = kcentersOutliers(\"syntheticData/data.txt\",10,10.0)\n#kcent.kcentersOutliers()\n\n", "sub_path": "lib/gonzalez.py", "file_name": "gonzalez.py", "file_ext": "py", "file_size_in_byte": 1906, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.genfromtxt", "line_number": 11, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.cdist", "line_number": 26, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.cdist", "line_number": 38, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 38, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "121821869", "text": "# cnMaestro sample API test code. From page 86-87 of the cnMaestro 2.4.1 RESTful API documentation\n# Copyright (C) 2017 Cambium Networks, LTD.\n# Implemented/reworked by Adrien K\n\n# \"API test code for cnMaestro that demonstrates session establishment and API\n# api. The client connects to cnMaestro using the Client Id and Client\n# Secret downloaded from the Client API page in the cnMaestro UI. The Client\n# receives a URL, Access Token, and Expiration Interval (in seconds)\n# defining how long the token is valid. The URL and Access Token are used\n# for subsequent API requests.\" -- cnMaestro 2.4.1 RESTful API documentation\n\nimport sys\nimport requests\nimport json\nimport base64\n\ndef check_http_return(section, url, code, request):\n if int(code) != 200:\n print('{0} failed with HTTP status {1}'.format(section, code))\n print('URL: {}'.format(url))\n try:\n print(json.dumps(request.json(), indent=2))\n except:\n pass\n sys.exit(1)\n\n\n# Retrieve access parameters (url, access_token, and expires_in).\ndef get_access_parameters(host, client_id, client_secret):\n token_url = 'https://{}/api/v1/access/token'.format(host)\n encoded_credentials = base64.b64encode('{}:{}'.format(client_id, client_secret).encode()).decode()\n headers = {\n 'Authorization': 'Basic {}'.format(encoded_credentials),\n 'Content-Type': 'application/x-www-form-urlencoded'\n }\n print(encoded_credentials)\n body = 'grant_type=client_credentials'\n r = requests.post(token_url, body, headers=headers, verify=False)\n check_http_return('Access Parameters', token_url, r.status_code, r)\n return r.json()['access_token'], r.json()['expires_in']\n\n\n# Validate the expiration of the access token.\ndef validate_access_token(host, access_token):\n validate_url = 'https://{}/api/v1/access/validate_token'.format(host)\n headers = {\n 'Authorization': 'Bearer {}'.format(access_token),\n }\n r = requests.get(validate_url, headers=headers, verify=False)\n check_http_return('Validate Access Token', validate_url, r.status_code, r)\n return r.json()['expires_in']\n\n\ndef generate_api_session(host, client_id, client_secret):\n # Retrieve access parameters and generate API session\n print('\\nRetrieve Access Parameters')\n access_token, expires_in = get_access_parameters(host, client_id, client_secret)\n print('Success: access_token ({}) expires_in ({})\\n'.format(access_token, expires_in))\n\n # Validate time remaining for the access token\n print('Validating expiration time')\n expires_in_check = validate_access_token(host, access_token)\n print('Success: expiresIn ({})\\n'.format(expires_in_check))\n return access_token\n\n\n# Execute API using URL returned in access parameters. Currently unused\ndef call_api(host, path, access_token):\n api_url = 'https://{}{}'.format(host, path)\n headers = {\n 'Authorization': 'Bearer {}'.format(access_token),\n }\n r = requests.get(api_url, headers=headers, verify=False)\n check_http_return(\"API\", api_url, r.status_code, r)\n return r.json()\n", "sub_path": "cnm_usage/api/auth.py", "file_name": "auth.py", "file_ext": "py", "file_size_in_byte": 3094, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "json.dumps", "line_number": 22, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 25, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 38, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 49, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "234742174", "text": "\"\"\"\nGeneral drawing methods for graphs using Bokeh.\n\"\"\"\n\nfrom bokeh.io import show, output_file\nfrom bokeh.plotting import figure\nfrom bokeh.models import (GraphRenderer, StaticLayoutProvider, Circle, LabelSet,\n ColumnDataSource)\nfrom graph import Graph\nimport random\nimport math\n\n\nclass BokehGraph:\n \"\"\"Class that takes a graph and exposes drawing methods.\"\"\"\n def __init__(self, graph):\n if not graph.vertices:\n raise Exception(\"Graph should contain vertices\")\n self.graph = graph\n self.pos = {}\n\n def _setup_labels(self):\n label_data = {'x': [], 'y': [], 'names': []}\n for vertex, position in self.pos.items():\n label_data['x'].append(position[0])\n label_data['y'].append(position[1])\n label_data['names'].append(vertex)\n label_source = ColumnDataSource(label_data)\n labels = LabelSet(x='x', y='y', text='names', level='glyph',\n text_align='center', text_baseline='middle',\n source=label_source, render_mode='canvas')\n return labels\n\n def _get_edges(self):\n start = []\n end = []\n checked = set()\n for startpoint, endpoints in self.graph.vertices.items():\n for endpoint in endpoints:\n if (startpoint.label, endpoint.label) not in checked:\n checked.add((startpoint.label, endpoint.label))\n start.append(startpoint.label)\n end.append(endpoint.label)\n return dict(start=start, end=end)\n\n def _get_colors(self):\n colors = []\n for vertex in self.graph.vertices.keys():\n color = vertex.color\n colors.append(color)\n return colors\n\n def _map_coords(self, width, height):\n cells = math.ceil(len(self.graph.vertices.keys())**(1/2))\n cube = (width-1)/cells\n x_grid = 0.5\n y_grid = 0.5\n for vertex in self.graph.vertices.keys():\n self.pos[vertex.label] = (random.uniform(x_grid,x_grid+cube), \n random.uniform(y_grid,y_grid+cube))\n if x_grid + cube >= (width - cube):\n x_grid = 0.5\n y_grid += cube\n else:\n x_grid += cube\n \n def _get_indices(self):\n indices = []\n for vertex in self.graph.vertices.keys():\n indices.append(vertex.label)\n return indices\n \n def draw(self, title='Graph', width=10, height=10,\n show_axis=False, show_grid=False, circle_size=25):\n plot = figure(title=title, x_range=(0,width), y_range=(0,height))\n \n plot.axis.visible = show_axis\n plot.grid.visible = show_grid\n\n graph = GraphRenderer()\n graph.node_renderer.data_source.add(\n self._get_indices(), 'index')\n graph.node_renderer.data_source.add(\n self._get_colors(), 'color')\n graph.node_renderer.glyph = Circle(size=circle_size,\n fill_color='color')\n graph.edge_renderer.data_source.data = self._get_edges()\n\n self._map_coords(width, height)\n graph.layout_provider = StaticLayoutProvider(graph_layout=self.pos)\n plot.renderers.append(graph)\n\n labels = self._setup_labels()\n plot.add_layout(labels)\n\n output_file('./graph.html')\n show(plot)\n\ndef randomize_graph(vertices=10, connections=5):\n graph = Graph()\n for i in range(vertices):\n graph.add_vertex(str(i))\n for i in range(connections):\n start, end = random.sample(list(graph.vertices), 2)\n graph.add_edge(start, end)\n colors = ['#FF395B', '#FC928F', '#F9C6A3', '#C0BF9F',\n '#79A792', '#1A8CC1', '#FECE6B', '#F69D61']\n color_index = 0\n searched = set()\n for vertex in graph.vertices:\n if vertex not in searched:\n if color_index > len(colors):\n color_index = 0\n color = colors[-color_index]\n searched.update(graph.search(vertex, color))\n color_index += 1\n bokeh = BokehGraph(graph)\n bokeh.draw()\n \nif __name__ == '__main__':\n randomize_graph(16, 6)", "sub_path": "projects/graph/src/draw.py", "file_name": "draw.py", "file_ext": "py", "file_size_in_byte": 4214, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "graph.vertices", "line_number": 17, "usage_type": "attribute"}, {"api_name": "bokeh.models.ColumnDataSource", "line_number": 28, "usage_type": "call"}, {"api_name": "bokeh.models.LabelSet", "line_number": 29, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 54, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 59, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 60, "usage_type": "call"}, {"api_name": "bokeh.plotting.figure", "line_number": 75, "usage_type": "call"}, {"api_name": "bokeh.models.GraphRenderer", "line_number": 80, "usage_type": "call"}, {"api_name": "graph.node_renderer.data_source.add", "line_number": 81, "usage_type": "call"}, {"api_name": "graph.node_renderer", "line_number": 81, "usage_type": "attribute"}, {"api_name": "graph.node_renderer.data_source.add", "line_number": 83, "usage_type": "call"}, {"api_name": "graph.node_renderer", "line_number": 83, "usage_type": "attribute"}, {"api_name": "graph.node_renderer", "line_number": 85, "usage_type": "attribute"}, {"api_name": "bokeh.models.Circle", "line_number": 85, "usage_type": "call"}, {"api_name": "graph.edge_renderer", "line_number": 87, "usage_type": "attribute"}, {"api_name": "graph.layout_provider", "line_number": 90, "usage_type": "attribute"}, {"api_name": "bokeh.models.StaticLayoutProvider", "line_number": 90, "usage_type": "call"}, {"api_name": "bokeh.io.output_file", "line_number": 96, "usage_type": "call"}, {"api_name": "bokeh.io.show", "line_number": 97, "usage_type": "call"}, {"api_name": "graph.Graph", "line_number": 100, "usage_type": "call"}, {"api_name": "graph.add_vertex", "line_number": 102, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 104, "usage_type": "call"}, {"api_name": "graph.vertices", "line_number": 104, "usage_type": "attribute"}, {"api_name": "graph.add_edge", "line_number": 105, "usage_type": "call"}, {"api_name": "graph.vertices", "line_number": 110, "usage_type": "attribute"}, {"api_name": "graph.search", "line_number": 115, "usage_type": "call"}, {"api_name": "bokeh.io", "line_number": 117, "usage_type": "name"}, {"api_name": "bokeh.io.draw", "line_number": 118, "usage_type": "call"}, {"api_name": "bokeh.io", "line_number": 118, "usage_type": "name"}]} +{"seq_id": "547428187", "text": "import requests\nimport json\n\n\nclass RunMethod(object):\n\n def post_main(self, url, data=None, header=None, cookies=None):\n res = None\n if header != None:\n res = requests.post(url=url, data=data, headers=header, cookies=cookies)\n # 如果是https请求无法发送加上verify=False���忽略https\n else:\n res = requests.post(url=url, data=data, cookies=cookies)\n return res\n\n def get_main(self, url, params=None, header=None, cookies=None):\n res = None\n if header != None:\n res = requests.get(url=url, params=params, headers=header, cookies=cookies)\n else:\n res = requests.get(url=url, params=params, cookies=cookies)\n return res\n\n def run_main(self, method, url, data=None, header=None, params=None, cookies=None):\n res = None\n if method == \"post\":\n res = self.post_main(url, data, header, cookies)\n else:\n res = self.post_main(url, params, header, cookies)\n return res\n #return json.dumps(res, ensure_ascii=False)\n\nif __name__ == \"__main__\":\n run = RunMethod()\n url = \"http://hubskins.zzbtest.com/front/member/login\"\n data = {\"user_email\": \"1102055693@qq.com\",\"user_pass\": \"wu123456\"}\n res = run.run_main(\"post\", url, data)\n print(res)\n print(type(res))\n\n\n\n\n", "sub_path": "base/runmain.py", "file_name": "runmain.py", "file_ext": "py", "file_size_in_byte": 1353, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "requests.post", "line_number": 10, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 13, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 19, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "75309091", "text": "from django.urls import path\n\nfrom .views import *\n\napp_name = 'blog'\n\nurlpatterns = [\n path('', PostList.as_view(), name='home'),\n path('', PostDetail.as_view(), name='post_detail'),\n path('accounts/signup/', signup_view, name='signup'),\n path('accounts/profile/', profile_view, name='profile'),\n path('accounts/login/', login_view, name='login'),\n path('accounts/logout/', logout_view, name='logout')\n]\n", "sub_path": "blog/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 434, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"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": "649779314", "text": "import sys\nsys.path.append(\"/home/andrewliao11/gail-tf\")\nfrom baselines import logger\nimport pickle as pkl\nimport numpy as np\nfrom tqdm import tqdm\nimport ipdb\n\nclass Mujoco_Dset(object):\n def __init__(self, expert_path, ret_threshold=None, traj_limitation=np.inf, random=True):\n with open(expert_path, \"rb\") as f:\n traj_data = pkl.load(f)\n obs = []\n acs = []\n rets = []\n lens = []\n for traj in tqdm(traj_data):\n if ret_threshold is not None and traj[\"ep_ret\"] < ret_threshold:\n pass\n if len(rets) >= traj_limitation:\n break\n rets.append(traj[\"ep_ret\"])\n lens.append(len(traj[\"ob\"]))\n obs.append(traj[\"ob\"])\n acs.append(traj[\"ac\"])\n self.num_traj = len(rets)\n self.avg_ret = sum(rets)/len(rets)\n self.avg_len = sum(lens)/len(lens)\n self.rets = np.array(rets)\n self.lens = np.array(lens)\n self.obs = np.array([v for ob in obs for v in ob])\n self.acs = np.array([v for ac in acs for v in ac])\n if len(self.acs) > 2:\n self.acs = np.squeeze(self.acs)\n assert len(self.obs) == len(self.acs)\n self.num_transition = len(self.obs)\n self.randomize = random\n self.init_pointer()\n self.log_info()\n\n def log_info(self):\n logger.log(\"Total trajectories: %d\"%self.num_traj)\n logger.log(\"Total transitions: %d\"%self.num_transition)\n logger.log(\"Average episode length: %f\"%self.avg_len)\n logger.log(\"Average returns: %f\"%self.avg_ret)\n\n def init_pointer(self):\n self.pointer = 0\n if self.randomize:\n idx = np.arange(self.num_transition)\n np.random.shuffle(idx)\n self.obs = self.obs[idx, :]\n self.acs = self.acs[idx, :]\n\n def get_next_batch(self, batch_size):\n if self.pointer + batch_size >= self.num_transition:\n self.init_pointer()\n end = self.pointer + batch_size\n obs = self.obs[self.pointer:end, :]\n acs = self.acs[self.pointer:end, :]\n self.pointer = end\n return obs, acs\n\n def plot(self):\n import matplotlib.pyplot as plt\n plt.hist(self.rets)\n plt.savefig(\"histogram_rets.png\")\n plt.close()\n\n\ndef test(expert_path):\n dset = Mujoco_Dset(expert_path)\n dset.plot()\n\nif __name__ == '__main__':\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--expert_path\", type=str, default=\"../baselines/ppo1/ppo.Hopper.0.00.pkl\")\n args = parser.parse_args()\n test(args.expert_path)\n\n", "sub_path": "dataset/mujoco.py", "file_name": "mujoco.py", "file_ext": "py", "file_size_in_byte": 2639, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.path.append", "line_number": 2, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 2, "usage_type": "attribute"}, {"api_name": "numpy.inf", "line_number": 10, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 12, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 17, "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": 31, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 34, "usage_type": "call"}, {"api_name": "baselines.logger.log", "line_number": 42, "usage_type": "call"}, {"api_name": "baselines.logger", "line_number": 42, "usage_type": "name"}, {"api_name": "baselines.logger.log", "line_number": 43, "usage_type": "call"}, {"api_name": "baselines.logger", "line_number": 43, "usage_type": "name"}, {"api_name": "baselines.logger.log", "line_number": 44, "usage_type": "call"}, {"api_name": "baselines.logger", "line_number": 44, "usage_type": "name"}, {"api_name": "baselines.logger.log", "line_number": 45, "usage_type": "call"}, {"api_name": "baselines.logger", "line_number": 45, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 51, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "{'plt': 'matplotlib.pyplot'}", "line_number": 72, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "234215852", "text": "# Copyright (c) 2018 Anki, 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 in the file LICENSE.txt or 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\"\"\"Support for Vector's camera.\n\nVector has a built-in camera which he uses to observe the world around him.\n\nThe :class:`CameraComponent` class defined in this module is made available as\n:attr:`anki_vector.robot.Robot.camera` and can be used to enable/disable image\nsending and observe images being sent by the robot.\n\nThe camera resolution is 1280 x 720 with a field of view of 90 deg (H) x 50 deg (V).\n\"\"\"\n\n# __all__ should order by constants, event classes, other classes, functions.\n__all__ = ['CameraComponent']\n\nimport asyncio\nfrom concurrent.futures import CancelledError\nimport sys\n\ntry:\n import cv2\nexcept ImportError as exc:\n sys.exit(\"Cannot import opencv-python: Do `pip3 install opencv-python` to install\")\n\nfrom . import util\nfrom .messaging import protocol\n\ntry:\n import numpy as np\nexcept ImportError as exc:\n sys.exit(\"Cannot import numpy: Do `pip3 install numpy` to install\")\n\ntry:\n from PIL import Image\nexcept ImportError:\n sys.exit(\"Cannot import from PIL: Do `pip3 install --user Pillow` to install\")\n\n\nclass CameraComponent(util.Component):\n \"\"\"Represents Vector's camera.\n\n The CameraComponent object receives images from Vector's camera, unpacks the data,\n composes it and makes it available as latest_image.\n\n The :class:`anki_vector.robot.Robot` or :class:`anki_vector.robot.AsyncRobot` instance observes the camera.\n\n .. testcode::\n\n import anki_vector\n import time\n\n with anki_vector.Robot(enable_camera_feed=True) as robot:\n time.sleep(1)\n image = robot.camera.latest_image\n image.show()\n\n :param robot: A reference to the owner Robot object.\n \"\"\"\n\n def __init__(self, robot):\n super().__init__(robot)\n\n self._latest_image: Image.Image = None\n self._latest_image_id: int = None\n self._camera_feed_task: asyncio.Task = None\n\n @property\n def latest_image(self) -> Image.Image:\n \"\"\":class:`Image.Image`: The most recently processed image received from the robot.\n\n :getter: Returns the Pillow Image representing the latest image\n\n .. testcode::\n\n import anki_vector\n import time\n\n with anki_vector.Robot(enable_camera_feed=True) as robot:\n time.sleep(1)\n image = robot.camera.latest_image\n image.show()\n \"\"\"\n\n return self._latest_image\n\n @property\n def latest_image_id(self) -> int:\n \"\"\"The most recently processed image's id received from the robot.\n\n Used only to track chunks of the same image.\n\n :getter: Returns the id for the latest image\n\n .. testcode::\n\n import anki_vector\n import time\n\n with anki_vector.Robot(enable_camera_feed=True) as robot:\n time.sleep(1)\n image = robot.camera.latest_image\n image.show()\n print(f\"latest_image_id: {robot.camera.latest_image_id}\")\n \"\"\"\n return self._latest_image_id\n\n def init_camera_feed(self) -> None:\n \"\"\"Begin camera feed task.\"\"\"\n if not self._camera_feed_task or self._camera_feed_task.done():\n self._camera_feed_task = self.conn.loop.create_task(self._request_and_handle_images())\n\n def close_camera_feed(self) -> None:\n \"\"\"Cancel camera feed task.\"\"\"\n if self._camera_feed_task:\n self._camera_feed_task.cancel()\n future = self.conn.run_coroutine(self._camera_feed_task)\n future.result()\n\n def _unpack_image(self, msg: protocol.CameraFeedResponse) -> None:\n \"\"\"Processes raw data from the robot into a more more useful image structure.\"\"\"\n size = len(msg.data)\n\n # Constuct numpy array out of source data\n array = np.empty(size, dtype=np.uint8)\n array[0:size] = list(msg.data)\n\n # Decode compressed source data into uncompressed image data\n imageArray = cv2.imdecode(array, -1)\n imageArray = cv2.cvtColor(imageArray, cv2.COLOR_BGR2RGB)\n\n # Convert to Pillow Image\n self._latest_image = Image.fromarray(imageArray)\n self._latest_image_id = msg.image_id\n self.robot.viewer.enqueue_frame(self._latest_image)\n\n async def _request_and_handle_images(self) -> None:\n \"\"\"Queries and listens for camera feed events from the robot.\n Received events are parsed by a helper function.\"\"\"\n try:\n req = protocol.CameraFeedRequest()\n async for evt in self.grpc_interface.CameraFeed(req):\n # If the camera feed is disabled after stream is setup, exit the stream\n # (the camera feed on the robot is disabled internally on stream exit)\n if not self.robot.enable_camera_feed:\n self.logger.warning('Camera feed has been disabled. Enable the feed to start/continue receiving camera feed data')\n return\n self._unpack_image(evt)\n except CancelledError:\n self.logger.debug('Camera feed task was cancelled. This is expected during disconnection.')\n", "sub_path": "anki_vector/camera.py", "file_name": "camera.py", "file_ext": "py", "file_size_in_byte": 5722, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.exit", "line_number": 36, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 49, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 76, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 76, "usage_type": "name"}, {"api_name": "asyncio.Task", "line_number": 78, "usage_type": "attribute"}, {"api_name": "PIL.Image.Image", "line_number": 81, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 81, "usage_type": "name"}, {"api_name": "messaging.protocol.CameraFeedResponse", "line_number": 132, "usage_type": "attribute"}, {"api_name": "messaging.protocol", "line_number": 132, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 137, "usage_type": "attribute"}, {"api_name": "cv2.imdecode", "line_number": 141, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 142, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 142, "usage_type": "attribute"}, {"api_name": "PIL.Image.fromarray", "line_number": 145, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 145, "usage_type": "name"}, {"api_name": "messaging.protocol.CameraFeedRequest", "line_number": 153, "usage_type": "call"}, {"api_name": "messaging.protocol", "line_number": 153, "usage_type": "name"}, {"api_name": "concurrent.futures.CancelledError", "line_number": 161, "usage_type": "name"}]} +{"seq_id": "233430559", "text": "#!/usr/bin/env python\n\nimport yaml\nimport IPython\nimport ROOT\nimport DMesonJetUtils\nimport DMesonJetCompare\n\nglobalList = []\n\ndef ResolutionComparison(config):\n fname = \"{0}/{1}/{2}.root\".format(config[\"input_path\"], config[\"train\"], config[\"name\"])\n file = ROOT.TFile(fname)\n if not file or file.IsZombie():\n print(\"Could not open file {0}\".format(fname))\n exit(1)\n\n spectrumName = \"JetPtSpectrum_DPt_30\"\n jetName = \"Jet_AKTChargedR040_pt_scheme\"\n dmesonName = \"D0_D0toKpiCuts\"\n prefix = \"Prompt_{}_{}_{}\".format(dmesonName, jetName, spectrumName)\n\n pt_lim = [(5, 6), (8, 10), (20, 30)]\n histos = []\n for (minJetPt, maxJetPt) in pt_lim:\n resolutionName = \"{0}/DetectorResponse/{0}_DetectorResponse_{1}_{2}\".format(prefix, minJetPt * 10, maxJetPt * 10)\n h = DMesonJetUtils.GetObject(file, resolutionName)\n h.SetTitle(\"{} < #it{{p}}_{{T,gen jet}}^{{ch}} < {} GeV/#it{{c}}\".format(minJetPt, maxJetPt))\n globalList.append(h)\n histos.append(h)\n\n cname = \"ResolutionVsJetPt_Paper\"\n comp = DMesonJetCompare.DMesonJetCompare(cname)\n comp.fDoSpectraPlot = \"lineary\"\n comp.fDoRatioPlot = None\n comp.fMarkerSize = 1.5\n comp.fX1LegSpectrum = 0.14\n comp.fX2LegSpectrum = 0.41\n comp.fY1LegSpectrum = 0.53\n comp.fLinUpperSpace = 0.50\n comp.fLegLineHeight = 0.075\n comp.fColors = [ROOT.kRed + 2, ROOT.kBlue + 2, ROOT.kGreen + 2]\n comp.fMarkers = [ROOT.kOpenCircle, ROOT.kOpenSquare, ROOT.kOpenDiamond]\n r = comp.CompareSpectra(histos[0], histos[1:])\n histos[2].SetMarkerSize(2.2)\n for obj in r:\n globalList.append(obj)\n\n canvas = comp.fCanvasSpectra\n canvas.SetTicks(1, 1)\n canvas.SetLeftMargin(0.13)\n canvas.SetRightMargin(0.05)\n canvas.SetTopMargin(0.05)\n canvas.SetBottomMargin(0.15)\n canvas.cd()\n\n h = comp.fMainHistogram\n\n h.GetYaxis().SetTitle(\"Probability Density\")\n #h.GetXaxis().SetTitle(\"(#it{p}_{T,det jet}^{ch} #font[122]{-} #it{p}_{T,gen jet}^{ch}) / #it{p}_{T,gen jet}^{ch}\")\n h.GetXaxis().SetTitle(\"#Delta_{#it{p}_{T}}\")\n h.GetXaxis().SetTitleFont(43)\n h.GetXaxis().SetTitleSize(30)\n h.GetXaxis().SetTitleOffset(1.0)\n h.GetXaxis().SetLabelFont(43)\n h.GetXaxis().SetLabelSize(22)\n h.GetXaxis().SetLabelOffset(0.02)\n h.GetXaxis().SetRangeUser(-0.6, 0.6)\n h.GetYaxis().SetTitleFont(43)\n h.GetYaxis().SetTitleSize(26)\n h.GetYaxis().SetLabelFont(43)\n h.GetYaxis().SetLabelSize(22)\n h.GetYaxis().SetTitleOffset(0.9)\n h.GetYaxis().SetRangeUser(0, 14)\n\n paveALICE = ROOT.TPaveText(0.14, 0.62, 0.53, 0.95, \"NB NDC\")\n globalList.append(paveALICE)\n paveALICE.SetBorderSize(0)\n paveALICE.SetFillStyle(0)\n paveALICE.SetTextFont(43)\n paveALICE.SetTextSize(21)\n paveALICE.SetTextAlign(13)\n paveALICE.AddText(\"ALICE PYTHIA 6\")\n paveALICE.AddText(\"pp, #sqrt{#it{s}} = 7 TeV\")\n paveALICE.AddText(\"Prompt D^{0} #rightarrow K^{#font[122]{-}}#pi^{+}\")\n paveALICE.AddText(\"and charge conj.\")\n paveALICE.AddText(\"#it{p}_{T,D} > 3 GeV/#it{c}\")\n paveALICE.Draw()\n \n paveALICE = ROOT.TPaveText(0.65, 0.75, 0.90, 0.95, \"NB NDC\")\n globalList.append(paveALICE)\n paveALICE.SetBorderSize(0)\n paveALICE.SetFillStyle(0)\n paveALICE.SetTextFont(43)\n paveALICE.SetTextSize(21)\n paveALICE.SetTextAlign(13)\n paveALICE.AddText(\"Charged Jets\")\n paveALICE.AddText(\"Anti-#it{k}_{T}, #it{R} = 0.4\")\n paveALICE.AddText(\"|#it{#eta}_{jet}| < 0.5\")\n paveALICE.Draw()\n\n return canvas\n\ndef main():\n ROOT.TH1.AddDirectory(False)\n ROOT.gStyle.SetOptTitle(0)\n ROOT.gStyle.SetOptStat(0)\n\n f = open(\"LHC15i2response_Train1399_efficiency.yaml\", 'r')\n config = yaml.load(f)\n f.close()\n\n canvas = ResolutionComparison(config)\n canvas.SaveAs(\"{0}/ResolutionVsJetPt_Paper.pdf\".format(config[\"input_path\"]))\n canvas.SaveAs(\"{0}/ResolutionVsJetPt_Paper.C\".format(config[\"input_path\"]))\n\n\nif __name__ == '__main__':\n\n main()\n\n IPython.embed()\n", "sub_path": "DMesonJetAnalysis/ResolutionVsJetPt_Paper.py", "file_name": "ResolutionVsJetPt_Paper.py", "file_ext": "py", "file_size_in_byte": 3995, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "ROOT.TFile", "line_number": 13, "usage_type": "call"}, {"api_name": "DMesonJetUtils.GetObject", "line_number": 27, "usage_type": "call"}, {"api_name": "DMesonJetCompare.DMesonJetCompare", "line_number": 33, "usage_type": "call"}, {"api_name": "ROOT.kRed", "line_number": 42, "usage_type": "attribute"}, {"api_name": "ROOT.kBlue", "line_number": 42, "usage_type": "attribute"}, {"api_name": "ROOT.kGreen", "line_number": 42, "usage_type": "attribute"}, {"api_name": "ROOT.kOpenCircle", "line_number": 43, "usage_type": "attribute"}, {"api_name": "ROOT.kOpenSquare", "line_number": 43, "usage_type": "attribute"}, {"api_name": "ROOT.kOpenDiamond", "line_number": 43, "usage_type": "attribute"}, {"api_name": "ROOT.TPaveText", "line_number": 76, "usage_type": "call"}, {"api_name": "ROOT.TPaveText", "line_number": 90, "usage_type": "call"}, {"api_name": "ROOT.TH1.AddDirectory", "line_number": 105, "usage_type": "call"}, {"api_name": "ROOT.TH1", "line_number": 105, "usage_type": "attribute"}, {"api_name": "ROOT.gStyle.SetOptTitle", "line_number": 106, "usage_type": "call"}, {"api_name": "ROOT.gStyle", "line_number": 106, "usage_type": "attribute"}, {"api_name": "ROOT.gStyle.SetOptStat", "line_number": 107, "usage_type": "call"}, {"api_name": "ROOT.gStyle", "line_number": 107, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 110, "usage_type": "call"}, {"api_name": "IPython.embed", "line_number": 122, "usage_type": "call"}]} +{"seq_id": "80852717", "text": "from logging import getLogger\n\nfrom django_eth.constants import NULL_ADDRESS\nfrom ethereum.utils import (check_checksum, checksum_encode, ecrecover_to_pub,\n privtoaddr, sha3)\nfrom functools import wraps\nfrom hexbytes import HexBytes\nfrom typing import Dict, Union\nfrom web3 import HTTPProvider, Web3\nfrom web3.middleware import geth_poa_middleware\nfrom web3.utils.threads import Timeout\n\nfrom .contracts import get_erc20_contract\n\nlogger = getLogger(__name__)\n\n\nclass TransactionAlreadyImported(ValueError):\n pass\n\n\nclass ReplacementTransactionUnderpriced(ValueError):\n pass\n\n\nclass FromAddressNotFound(ValueError):\n pass\n\n\nclass InvalidNonce(ValueError):\n pass\n\n\nclass InsufficientFunds(ValueError):\n pass\n\n\ndef tx_with_exception_handling(func):\n error_with_exception: Dict[str, Exception] = {\n 'Transaction with the same hash was already imported': TransactionAlreadyImported,\n 'replacement transaction underpriced': ReplacementTransactionUnderpriced,\n 'from not found': FromAddressNotFound,\n 'correct nonce': InvalidNonce,\n 'insufficient funds': InsufficientFunds,\n \"doesn't have enough funds\": InsufficientFunds,\n }\n\n @wraps(func)\n def with_exception_handling(*args, **kwargs):\n try:\n return func(*args, **kwargs)\n except ValueError as exc:\n str_exc = str(exc).lower()\n for reason, custom_exception in error_with_exception.items():\n if reason.lower() in str_exc:\n raise custom_exception(str(exc)) from exc\n raise exc\n return with_exception_handling\n\n\nclass EthereumServiceProvider:\n def __new__(cls):\n if not hasattr(cls, 'instance'):\n from django.conf import settings\n cls.instance = EthereumService(settings.ETHEREUM_NODE_URL,\n settings.SAFE_FUNDER_MAX_ETH,\n settings.SAFE_FUNDER_PRIVATE_KEY)\n return cls.instance\n\n\nclass EthereumService:\n NULL_ADDRESS = NULL_ADDRESS\n\n def __init__(self, ethereum_node_url, max_eth_to_send=0.1, funder_private_key=None):\n self.ethereum_node_url = ethereum_node_url\n self.max_eth_to_send = max_eth_to_send\n self.funder_private_key = funder_private_key\n self.w3 = Web3(HTTPProvider(self.ethereum_node_url))\n try:\n if self.w3.net.chainId != 1:\n self.w3.middleware_stack.inject(geth_poa_middleware, layer=0)\n # For tests using dummy connections (like IPC)\n except (ConnectionError, FileNotFoundError):\n self.w3.middleware_stack.inject(geth_poa_middleware, layer=0)\n\n def get_nonce_for_account(self, address, block_identifier=None):\n return self.w3.eth.getTransactionCount(address, block_identifier=block_identifier)\n\n @property\n def current_block_number(self):\n return self.w3.eth.blockNumber\n\n @staticmethod\n def estimate_data_gas(data: bytes):\n if isinstance(data, str):\n data = HexBytes(data)\n\n gas = 0\n for byte in data:\n if not byte:\n gas += 4 # Byte 0 -> 4 Gas\n else:\n gas += 68 # Any other byte -> 68 Gas\n return gas\n\n def get_balance(self, address: str, block_identifier=None):\n return self.w3.eth.getBalance(address, block_identifier)\n\n def get_erc20_balance(self, address: str, erc20_address: str):\n return get_erc20_contract(self.w3, erc20_address).functions.balanceOf(address).call()\n\n def get_transaction(self, tx_hash):\n return self.w3.eth.getTransaction(tx_hash)\n\n def get_transaction_receipt(self, tx_hash, timeout=None):\n if not timeout:\n return self.w3.eth.getTransactionReceipt(tx_hash)\n else:\n try:\n tx_receipt = self.w3.eth.waitForTransactionReceipt(tx_hash, timeout=timeout)\n # Parity returns tx_receipt even is tx is still pending, so we check `blockNumber` is not None\n return None if tx_receipt['blockNumber'] is None else tx_receipt\n except Timeout:\n return None\n\n def get_block(self, block_number, full_transactions=False):\n return self.w3.eth.getBlock(block_number, full_transactions=full_transactions)\n\n @tx_with_exception_handling\n def send_transaction(self, transaction_dict: Dict[str, any]) -> bytes:\n return self.w3.eth.sendTransaction(transaction_dict)\n\n @tx_with_exception_handling\n def send_raw_transaction(self, raw_transaction) -> bytes:\n return self.w3.eth.sendRawTransaction(bytes(raw_transaction))\n\n def send_unsigned_transaction(self, tx: Dict[str, any], private_key: Union[None, str]=None,\n public_key: Union[None, str]=None, retry: bool=False,\n block_identifier: Union[None, str]=None) -> bytes:\n \"\"\"\n Send a tx using an unlocked public key in the node or a private key. Both `public_key` and\n `private_key` cannot be `None`\n :param tx:\n :param private_key:\n :param public_key:\n :param retry: Retry if a problem with nonce is found\n :param block_identifier:\n :return:\n \"\"\"\n if private_key:\n address = self.private_key_to_address(private_key)\n elif public_key:\n address = public_key\n else:\n logger.error('No ethereum account provided. Need a public_key or private_key')\n raise ValueError(\"Ethereum account was not configured or unlocked in the node\")\n\n nonce = tx.get('nonce')\n if nonce is None:\n nonce = self.get_nonce_for_account(address, block_identifier=block_identifier)\n tx['nonce'] = nonce\n\n number_errors = 0\n while number_errors != 5: # Retry if a problem with a nonce arises\n try:\n if private_key:\n signed_tx = self.w3.eth.account.signTransaction(tx, private_key=private_key)\n logger.debug('Sending %d wei from %s to %s', tx['value'], address, tx['to'])\n return self.w3.eth.sendRawTransaction(signed_tx.rawTransaction)\n elif public_key:\n tx['from'] = public_key\n if 'nonce' not in tx:\n tx['nonce'] = self.get_nonce_for_account(public_key, block_identifier=block_identifier)\n return self.send_transaction(tx)\n except ValueError as e:\n str_e = str(e).lower()\n if retry and 'replacement transaction underpriced' in str_e:\n logger.error('Tx with same nonce was already sent, retrying with nonce + 1')\n tx['nonce'] += 1\n elif retry and \"the tx doesn't have the correct nonce\" in str_e:\n logger.error('Tx does not have the correct nonce, retrying recovering nonce again')\n tx['nonce'] = self.get_nonce_for_account(address, block_identifier='latest')\n number_errors += 1\n else:\n raise e\n\n def send_eth_to(self, to: str, gas_price: int, value: int, gas: int=22000, retry: bool=False,\n block_identifier=None) -> bytes:\n \"\"\"\n Send ether using configured account\n :param to: to\n :param gas_price: gas_price\n :param value: value(wei)\n :param gas: gas, defaults to 22000\n :param retry: Retry if a problem is found\n :param block_identifier: None default, 'pending' not confirmed txs\n :return: tx_hash\n \"\"\"\n\n assert check_checksum(to)\n assert value < self.w3.toWei(self.max_eth_to_send, 'ether')\n\n private_key = None\n public_key = None\n\n if self.funder_private_key:\n private_key = self.funder_private_key\n elif self.w3.eth.accounts:\n public_key = self.w3.eth.accounts[0]\n else:\n logger.error('No ethereum account configured')\n raise ValueError(\"Ethereum account was not configured or unlocked in the node\")\n\n tx = {\n 'to': to,\n 'value': value,\n 'gas': gas,\n 'gasPrice': gas_price,\n }\n\n return self.send_unsigned_transaction(tx, private_key=private_key, public_key=public_key,\n retry=retry, block_identifier=block_identifier)\n\n def check_tx_with_confirmations(self, tx_hash: str, confirmations: int) -> bool:\n \"\"\"\n Check tx hash and make sure it has the confirmations required\n :param w3: Web3 instance\n :param tx_hash: Hash of the tx\n :param confirmations: Minimum number of confirmations required\n :return: True if tx was mined with the number of confirmations required, False otherwise\n \"\"\"\n tx_receipt = self.w3.eth.getTransactionReceipt(tx_hash)\n if not tx_receipt or tx_receipt['blockNumber'] is None:\n # If tx_receipt exists but blockNumber is None, tx is still pending (just Parity)\n return False\n else:\n return (self.w3.eth.blockNumber - tx_receipt['blockNumber']) >= confirmations\n\n @staticmethod\n def private_key_to_address(private_key):\n return checksum_encode(privtoaddr(private_key))\n\n @staticmethod\n def get_signing_address(hash, v, r, s) -> str:\n \"\"\"\n :return: checksum encoded address starting by 0x, for example `0x568c93675A8dEb121700A6FAdDdfE7DFAb66Ae4A`\n :rtype: str\n \"\"\"\n encoded_64_address = ecrecover_to_pub(hash, v, r, s)\n address_bytes = sha3(encoded_64_address)[-20:]\n return checksum_encode(address_bytes)\n", "sub_path": "gnosis/safe/ethereum_service.py", "file_name": "ethereum_service.py", "file_ext": "py", "file_size_in_byte": 9802, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 39, "usage_type": "name"}, {"api_name": "functools.wraps", "line_number": 48, "usage_type": "call"}, {"api_name": "django.conf.settings.ETHEREUM_NODE_URL", "line_number": 65, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 65, "usage_type": "name"}, {"api_name": "django.conf.settings.SAFE_FUNDER_MAX_ETH", "line_number": 66, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 66, "usage_type": "name"}, {"api_name": "django.conf.settings.SAFE_FUNDER_PRIVATE_KEY", "line_number": 67, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 67, "usage_type": "name"}, {"api_name": "django_eth.constants.NULL_ADDRESS", "line_number": 72, "usage_type": "name"}, {"api_name": "web3.Web3", "line_number": 78, "usage_type": "call"}, {"api_name": "web3.HTTPProvider", "line_number": 78, "usage_type": "call"}, {"api_name": "web3.middleware.geth_poa_middleware", "line_number": 81, "usage_type": "argument"}, {"api_name": "web3.middleware.geth_poa_middleware", "line_number": 84, "usage_type": "argument"}, {"api_name": "hexbytes.HexBytes", "line_number": 96, "usage_type": "call"}, {"api_name": "contracts.get_erc20_contract", "line_number": 110, "usage_type": "call"}, {"api_name": "web3.utils.threads.Timeout", "line_number": 123, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 130, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 137, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 137, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 138, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 139, "usage_type": "name"}, {"api_name": "ethereum.utils.check_checksum", "line_number": 200, "usage_type": "call"}, {"api_name": "ethereum.utils.checksum_encode", "line_number": 241, "usage_type": "call"}, {"api_name": "ethereum.utils.privtoaddr", "line_number": 241, "usage_type": "call"}, {"api_name": "ethereum.utils.ecrecover_to_pub", "line_number": 249, "usage_type": "call"}, {"api_name": "ethereum.utils.sha3", "line_number": 250, "usage_type": "call"}, {"api_name": "ethereum.utils.checksum_encode", "line_number": 251, "usage_type": "call"}]} +{"seq_id": "108975117", "text": "\nfrom pybind11 import get_cmake_dir\nfrom pybind11.setup_helpers import Pybind11Extension, build_ext\nfrom setuptools import Extension\nfrom .utils import get_incs, get_srcs\n\n\n\n# libi2c_module = Extension('pylibi2c', include_dirs=[\n# 'ext_modules/libi2c/src'], sources=get_srcs('ext_modules/libi2c/src'))\n\next_so = \"./ext_modules/libmaix/components/libmaix/lib/arch/r329\"\n\n_maix_module = Extension('_maix', include_dirs=['ext_modules/_maix/include', 'ext_modules/libmaix/components/libmaix/include'],\n sources=get_srcs('ext_modules/_maix'),\n libraries=[\n \"jpeg\"\n ],\n)\n\n# python3.8 -m pip install pybind11\n# _maix_vivo_module = Pybind11Extension(\"_maix_vivo\",\n# include_dirs=[\n# get_incs(\n# 'ext_modules/libmaix/components/libmaix/include')\n# ],\n# sources=get_srcs(\n# 'ext_modules/_maix_vivo'),\n# libraries=[\n# # \"dl\", \n# # \"rt\", \n# # \"log\", \n# # \"ion\", \n# \"pthread\", \n# # \"cdc_base\",\n# # \"MemAdapter\", \n# # \"media_utils\",\n# # \"mpp_vi\", \n# # \"mpp_isp\", \n# # \"ISP\",\n# # \"venc_base\", \n# # \"mpp_component\", \n# # \"adecoder\", \n# # \"asound\", \n# # \"venc_base\", \n# # \"hwdisplay\",\n# # \"maix_utils\", \n# # \"maix_cam\", \n# # \"maix_image\",\n# ],\n# library_dirs=[ ext_so, ],\n# extra_link_args=[-Wl,-rpath=/usr/lib/python3.8/site-packages/maix -DR329],\n# # define_macros=[('V831Camera', None)],\n# )\n\n# python3.8 -m pip install pybind11\n_maix_opencv_module = Pybind11Extension(\n name = \"_maix_opencv\",\n include_dirs=[get_incs('ext_modules/libmaix/components/libmaix/lib/arch/r329/include/opencv4/')],\n sources=get_srcs('ext_modules/_maix_opencv'),\n libraries=[\n \"opencv_aruco\", \n \"opencv_dnn\", \n \"opencv_hfs\", \n \"opencv_optflow\", \n \"opencv_shape\", \n \"opencv_videoio\",\n \"opencv_bgsegm\", \n \"opencv_dpm\", \n \"opencv_highgui\", \n \"opencv_phase_unwrapping\", \n \"opencv_stereo\",\n \"opencv_video\", \n \"opencv_bioinspired\", \n \"opencv_face\", \n \"opencv_imgcodecs\", \n \"opencv_photo\",\n \"opencv_stitching\", \n \"opencv_videostab\", \n \"opencv_calib3d\", \n \"opencv_features2d\", \n \"opencv_img_hash\",\n \"opencv_plot\", \n \"opencv_structured_light\", \n \"opencv_ccalib\", \n \"opencv_flann\",\n \"opencv_imgproc\", \n \"opencv_quality\", \n \"opencv_superres\", \n \"opencv_ximgproc\", \n \"opencv_core\", \n \"opencv_freetype\",\n \"opencv_line_descriptor\", \n \"opencv_reg\", \n \"opencv_surface_matching\", \n \"opencv_xobjdetect\", \n \"opencv_datasets\",\n \"opencv_fuzzy\", \n \"opencv_ml\", \n \"opencv_rgbd\", \n \"opencv_text\", \n \"opencv_xphoto\", \n \"opencv_dnn_objdetect\",\n \"opencv_objdetect\", \n \"opencv_saliency\", \n \"opencv_tracking\"\n ],\n library_dirs=[\"./ext_modules/libmaix/components/libmaix/lib/arch/r329/opencv4\", ],\n extra_link_args=[\"-Wl,-rpath=/usr/local/lib/python3.9/dist-packages/maix/_maix_opencv\"],\n extra_compile_args=['-std=c++11', '-std=gnu++11' ],\n )\n\n_maix_camera_module = Pybind11Extension(\n name = '_maix_camera', \n include_dirs=['ext_modules/_maix_camera/include', 'ext_modules/libmaix/components/libmaix/include'],\n sources=get_srcs('ext_modules/_maix_camera'),\n libraries=[\n # \"dl\", \n # \"rt\", \n # \"log\", \n # \"ion\", \n \"pthread\", \n # \"cdc_base\",\n # \"MemAdapter\", \n # \"media_utils\", \n # \"mpp_vi\", \n # \"mpp_isp\", \n # \"ISP\",\n # \"venc_base\", \n # \"mpp_component\", \n # \"adecoder\", \n # \"asound\", \n # \"venc_base\", \n # \"hwdisplay\",\n # \"maix_utils\", \n \"maix_cam\", \n # \"maix_image\",\n],\n library_dirs=[\"/lib\", \"/usr/lib\", ext_so, ],\n # extra_link_args = [ \"-Wl,-z,origin\", \"-Wl,-rpath='$ORIGIN/maix'\" ]\n extra_compile_args=['-DR329Camera', '-std=c++11', '-std=gnu++11' ],\n extra_link_args=[\"-Wl,-rpath=/usr/local/lib/python3.9/dist-packages/maix\"]\n)\n\n_maix_display_module = Pybind11Extension(\n name = \"_maix_display\",\n include_dirs=['ext_modules/_maix_display/include', 'ext_modules/libmaix/components/libmaix/include'],\n sources=get_srcs('ext_modules/_maix_display'),\n libraries=[\n # \"dl\", \n # \"rt\", \n # \"log\", \n # \"ion\", \n \"pthread\", \n # \"cdc_base\",\n # \"maix_utils\", \n \"maix_disp\", \n # \"maix_image\",\n ],\n library_dirs=[\"/lib\", \"/usr/lib\", ext_so, ],\n extra_compile_args=['-DR329Display', '-std=c++11', '-std=gnu++11' ],\n extra_link_args=[\"-Wl,-rpath=/usr/local/lib/python3.9/dist-packages/maix\"]\n )\n# max_nn_srcs = get_srcs('ext_modules/_maix_nn/src')\n# max_nn_srcs.extend(get_srcs('ext_modules/libmaix/components/libmaix/src'))\n# max_nn_srcs.remove(\"ext_modules/libmaix/components/libmaix/src/libmaix.c\")\n# _maix_nn_module = Extension('_maix_nn', include_dirs=['ext_modules/_maix_nn/include', 'ext_modules/libmaix/components/libmaix/include'],\n# sources=max_nn_srcs,\n# libraries=[\n# \"maix_utils\", \"maix_nn\",\n# ],\n# library_dirs=[\"/lib\", \"/usr/lib\", ext_so, ],\n# # extra_link_args = [ \"-Wl,-z,origin\", \"-Wl,-rpath='$ORIGIN/maix'\" ]\n# extra_link_args=[-Wl,-rpath=/usr/lib/python3.8/site-packages/maix -DR329]\n# )\n\n_maix_modules = [\n # libi2c_module,\n _maix_module,\n # _maix_vivo_module,\n _maix_opencv_module,\n _maix_camera_module,\n _maix_display_module,\n # _maix_nn_module\n]\n\n_maix_data_files = [\n ('/maix', get_srcs(ext_so, ['so'])),\n ('/maix/_maix_opencv/', get_srcs(\"ext_modules/libmaix/components/libmaix/lib/arch/r329/opencv4\", ['so'])), # depend system provide\n]\n\n_maix_py_modules = [\n \"Pillow\",\n \"rpyc\",\n \"gpiod\",\n \"evdev\",\n \"spidev\",\n \"pyserial\"\n \"zbarlight\",\n]\n", "sub_path": "envs/maix_r329.py", "file_name": "maix_r329.py", "file_ext": "py", "file_size_in_byte": 7214, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "setuptools.Extension", "line_number": 14, "usage_type": "call"}, {"api_name": "utils.get_srcs", "line_number": 15, "usage_type": "call"}, {"api_name": "pybind11.setup_helpers.Pybind11Extension", "line_number": 57, "usage_type": "call"}, {"api_name": "utils.get_incs", "line_number": 59, "usage_type": "call"}, {"api_name": "utils.get_srcs", "line_number": 60, "usage_type": "call"}, {"api_name": "pybind11.setup_helpers.Pybind11Extension", "line_number": 113, "usage_type": "call"}, {"api_name": "utils.get_srcs", "line_number": 116, "usage_type": "call"}, {"api_name": "pybind11.setup_helpers.Pybind11Extension", "line_number": 145, "usage_type": "call"}, {"api_name": "utils.get_srcs", "line_number": 148, "usage_type": "call"}, {"api_name": "utils.get_srcs", "line_number": 188, "usage_type": "call"}, {"api_name": "utils.get_srcs", "line_number": 189, "usage_type": "call"}]} +{"seq_id": "403060909", "text": "from typing import List\r\n\r\nfrom discord.ext.commands import Bot, Cog, command, context\r\n\r\nfrom cogs.utils.const import GameStatusConst\r\nfrom cogs.utils.werewolf_bot import WerewolfBot\r\nfrom setup_logger import setup_logger\r\n\r\nlogger = setup_logger(__name__)\r\n\r\n\r\nclass GameStatusCog(Cog):\r\n def __init__(self, bot: WerewolfBot):\r\n logger.debug(\"GameStatusCogのinit\")\r\n self.bot: WerewolfBot = bot\r\n\r\n @command(aliases=[\"cre\"])\r\n async def create(self, ctx: context) -> None:\r\n \"\"\"人狼ゲーム作成(エイリアス[cre])\"\"\"\r\n if self.bot.game.status == GameStatusConst.PLAYING.value:\r\n await ctx.send(\"現在ゲーム中です。createコマンドは使えません\")\r\n return\r\n if self.bot.game.status == GameStatusConst.WAITING.value:\r\n await ctx.send(\"現在参加者募集中です\")\r\n return\r\n\r\n self.bot.game.status = GameStatusConst.WAITING.value\r\n await ctx.send(\"参加者の募集を開始しました。\")\r\n\r\n @command(aliases=[\"sgs\"])\r\n async def show_game_status(self, ctx: context) -> None:\r\n \"\"\"コマンド:現在のゲームステータスを表示\r\n\r\n :param ctx:\r\n :return:\r\n \"\"\"\r\n await ctx.send(\"show_game_statusコマンドが実行されました\")\r\n status: str = self.bot.game.status\r\n await ctx.send(f\"現在のゲームのステータスは{status}です\")\r\n\r\n @command(aliases=[\"setgs\"])\r\n async def set_game_status(self, ctx: context, status: str = \"\") -> None:\r\n \"\"\"コマンド：ゲームステータスを引数statusに設定\r\n\r\n :param ctx:\r\n :param status:ゲームのステータス。GameStatusConst参照\r\n :return:\r\n \"\"\"\r\n status_list: List[str] = [x.value for x in GameStatusConst]\r\n\r\n if status == \"\":\r\n await ctx.send(f\"引数がありません。引数は以下からえらんでください。 {status_list}\")\r\n return\r\n\r\n if status not in status_list:\r\n await ctx.send(f\"引数が間違っています。引数は以下からえらんでください。{status_list}\")\r\n return\r\n\r\n self.bot.game.status = status\r\n await ctx.send(f\"ゲームのステータスを{status}にセットしました\")\r\n\r\n\r\ndef setup(bot: Bot) -> None:\r\n bot.add_cog(GameStatusCog(bot))\r\n", "sub_path": "cogs/game_status_cog.py", "file_name": "game_status_cog.py", "file_ext": "py", "file_size_in_byte": 2394, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "setup_logger.setup_logger", "line_number": 9, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 12, "usage_type": "name"}, {"api_name": "cogs.utils.werewolf_bot.WerewolfBot", "line_number": 13, "usage_type": "name"}, {"api_name": "cogs.utils.werewolf_bot.WerewolfBot", "line_number": 15, "usage_type": "name"}, {"api_name": "discord.ext.commands.context", "line_number": 18, "usage_type": "name"}, {"api_name": "cogs.utils.const.GameStatusConst.PLAYING", "line_number": 20, "usage_type": "attribute"}, {"api_name": "cogs.utils.const.GameStatusConst", "line_number": 20, "usage_type": "name"}, {"api_name": "cogs.utils.const.GameStatusConst.WAITING", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cogs.utils.const.GameStatusConst", "line_number": 23, "usage_type": "name"}, {"api_name": "cogs.utils.const.GameStatusConst.WAITING", "line_number": 27, "usage_type": "attribute"}, {"api_name": "cogs.utils.const.GameStatusConst", "line_number": 27, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 17, "usage_type": "call"}, {"api_name": "discord.ext.commands.context", "line_number": 31, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 30, "usage_type": "call"}, {"api_name": "discord.ext.commands.context", "line_number": 42, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 49, "usage_type": "name"}, {"api_name": "cogs.utils.const.GameStatusConst", "line_number": 49, "usage_type": "name"}, {"api_name": "discord.ext.commands.command", "line_number": 41, "usage_type": "call"}, {"api_name": "discord.ext.commands.Bot", "line_number": 63, "usage_type": "name"}]} +{"seq_id": "503731084", "text": "# import sys\n# reload(sys)\n# sys.setdefaultencoding('utf8')\nimport numpy as np\nimport matplotlib.pyplot as plt \n\ndef f1 (x):\n\treturn (-0.2*x**4+x**2 -x)\ndef f1p(x):\n\treturn(-0.8*x**3+2*x-1) \ndef f1pp(x):\n\treturn(-0.24*x**2+2)\n\ndef f2(x):\n\treturn(1-np.exp(-x**2) )\ndef f2p(x):\n\treturn(-2*x*np.exp(-x**2) )\ndef f2pp(x):\n\treturn(np.exp(-x**2)*(-2+4*x**2) )\n\ndef f3(x):\n\treturn( (x**2+0.3)**(1./2) )\ndef f3p(x):\n\treturn( x/(x**2+0.3)**(1./2) )\ndef f3pp(x):\n\treturn(3/(x**2+0.3)**(3./2) ) \n\ndef newtonMethod(x0, f, fp, fpp): \n\ttolerance = 10**(-7)\n\teps = 10**(-14) \n\tp = []\n\tres = []\n\n\tmaxIt = 10\n\thaveSol = False \n\n\tfor i in range(maxIt): \n\t\ty = f(x0)\n\t\typ = fp(x0) \n\t\typp = fpp(x0) \n\t\tprint (i, x0, y, \"###\", yp, ypp)\n\t\tif (abs(ypp) < eps):\n\t\t\tprint (\"denominator too small\" )\n\t\t\tbreak\n\t\tx1 = x0 - yp/ypp \n\t\tp.append(x0) \n\t\tres.append(y) \n\t\tif (abs(x1-x0) <= tolerance * abs(x1)):\n\t\t\thaveSol = True\n\t\t\tprint (\"Found Solution\")\n\t\t\tbreak\n\t\tx0 = x1\n\t\t# print i, x0, y\n\t# print x1-x0, f(x1), f(x0) \n\treturn res, p \n\n# newtonMethod(-1, f1,f1p, f1pp) \n# print \"-----\"\n# newtonMethod(-0.1, f2, f2p, f2pp) \n# newtonMethod(-2.5, f2, f2p, f2pp) \n# print \"-----\"\n# newtonMethod(-2,f3,f3p,f3pp) \n\nprint (\"---------\")\nres, p = newtonMethod(-1,f1,f1p, f1pp) \ny = [f1(x) for x in np.linspace(-1,1,len(res))]\nres = [x for x in res] \nx =np.linspace(-1,1,len(res))\n# print len(x), len(y)\n# print len(p), len(res) \nplt.plot(x,y)\nplt.hold(True) \nplt.scatter(p, res)\nplt.axis([-1,1,-2,2])\nplt.title('Newton search, f(x) = -0.2x^4+x^2-x')\n\np = ['%.2f' %elem for elem in p]\nres = ['%.2f' %elem for elem in res] \ntabl = plt.table(cellText = [p,res], \n\tloc = 'top',\n\tcolWidths = [0.1]*len(res), colLoc = 'bottom', \n\trowLabels = ['p', 'f(p)'], rowLoc = 'left',\n\tbbox = [0,-0.2,1,0.1] )\ntabl.auto_set_font_size(False) \ntabl.set_fontsize(12)\ntabl.scale(1, 1)\nplt.hold(False) \n# plt.savefig('f1newt')\nprint(\"----------\")\n\nplt.figure() \nprint (\"---------\")\nres, p = newtonMethod(-2.5,f2,f2p,f2pp) \ny = [f2(x) for x in np.linspace(-3,3,len(res))]\nres = [x for x in res] \nx =np.linspace(-3,3,len(res))\n# print y\n# print len(x), len(y)\n# print len(p), len(res) \nplt.plot(x,y)\nplt.hold(True) \nplt.scatter(p, res)\nplt.axis([-3,3,-2,2])\nplt.title('Newton search, 1-exp(-x^2)') \np = ['%.2f' %elem for elem in p]\nres = ['%.2f' %elem for elem in res] \ntabl = plt.table(cellText = [p,res], \n\tloc = 'top',\n\tcolWidths = [0.1]*len(res), colLoc = 'center', \n\trowLabels = ['p', 'f(p)'], rowLoc = 'left',\n\tbbox = [0,-0.2,1,0.1] )\ntabl.auto_set_font_size(False) \ntabl.set_fontsize(12)\ntabl.scale(1, 1)\nplt.hold(False) \nprint(\"----------\")\n\nplt.figure() \nprint (\"---------\")\nres, p = newtonMethod(-2,f3,f3p,f3pp) \ny = [f3(x) for x in np.linspace(-1,1,len(res))]\nres = [x for x in res] \nx =np.linspace(-1,1,len(res))\n# print y\n# print len(x), len(y)\n# print len(p), len(res) \nplt.plot(x,y)\nplt.hold(True) \nplt.scatter(p, res)\nplt.title('Newton search, (x^2+3)^(1/2)')\nplt.axis([-1,1,0,3])\np = ['%.2f' %elem for elem in p]\nres = ['%.2f' %elem for elem in res] \ntabl = plt.table(cellText = [p,res], \n\tloc = 'top',\n\tcolWidths = [0.1]*len(res), colLoc = 'center', \n\trowLabels = ['p', 'f(p)'], rowLoc = 'left',\n\tbbox = [0,-0.2,1,0.1] )\ntabl.auto_set_font_size(False) \ntabl.set_fontsize(12)\ntabl.scale(1, 1)\nplt.hold(False) \nplt.show(block = False) \nplt.show() \nprint(\"----------\")", "sub_path": "4th_sem_grad/OPTIMIZATION/hwk1/newt.py", "file_name": "newt.py", "file_ext": "py", "file_size_in_byte": 3327, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.exp", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hold", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "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.table", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hold", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hold", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.table", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hold", "line_number": 115, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 115, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 118, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hold", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.table", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.hold", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}]} +{"seq_id": "476281184", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n# This python script require vpython to be installed (see vpython.org)\n# vpython is used for the structural visualization\nfrom vpython import *\nimport sys\nsys.path.append(\"../Spectra\")\nsys.path.append(\"../Structure\")\nimport Spectra\nimport CD\nimport Structure\n\n# Global parameters\nd2eA=0.20819434 # Debye to eÅ\nbohr3=0.529177210 # bohr in Å\nEh2icm=219500 # Hartree to cm-1\nA=Eh2icm*bohr3*d2eA**2\nprint(A)\n\n# Define Parameters\nd=16.0 # Distance between molecules along spiral in Ångstrøm\nr=3.0 # Distance between layers in Ångstrøm\nN=20 # Number of molecules\nn0=2 # Number of first point\nd2r=np.pi/180.0 # Degree to radians\nalpha=4.0*d2r # Alpha angle for transition dipole\nbeta=55.0*d2r # Beta angle for transition dipole\nmum=5.5 # Dipole in Debye\n\n# Create positions\nx=np.zeros((N,3))\nmu=np.zeros((N,3))\nfor n in range(N):\n p=np.sqrt(2*d*(n+n0)/r) # Leave our first n0 points\n x[n,0]=r*p*np.cos(p)\n x[n,1]=r*p*np.sin(p)\n mu[n,2]=mum*np.cos(beta)\n mu[n,0]=mum*np.sin(beta)*(-np.sin(p)*np.cos(alpha)+np.cos(p)*np.sin(alpha))\n mu[n,1]=mum*np.sin(beta)*(np.cos(p)*np.cos(alpha)+np.sin(p)*np.sin(alpha))\n lmu=np.linalg.norm(mu[n,:])\n# print(lmu) \n\n# Plot the structure in 2D\nplt.plot(x[:,0],x[:,1])\nfor n in range(N):\n plt.arrow(x[n,0],x[n,1],mu[n,0],mu[n,1])\n# Make x and y direction equivalent on screen\nplt.axis('equal')\nplt.show()\n\n# Create Hamiltonian\nH=np.zeros((N,N))\nfor n in range(N):\n for m in range(n+1,N):\n dx=x[n,:]-x[m,:]\n dd=np.linalg.norm(dx)\n d3=dd*dd*dd\n d5=d3*dd*dd\n J=np.inner(mu[n,:],mu[m,:])/d3-3*np.inner(mu[n,:],dx)*np.inner(dx,mu[m,:])/d5\n H[n,m]=J*A\n H[m,n]=J*A\n\n# Plot structure\nStructure.visual(x,mu,N,1)\n# Make spectrum\nSpectra.absorption(H,mu,N,10)\nCD.CD(H,mu,x,N,10)\n# Visualize state\n#visual_exciton(x,mu,c,index,N,scale)\n\n", "sub_path": "Spiral/Spiral.py", "file_name": "Spiral.py", "file_ext": "py", "file_size_in_byte": 1839, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.path.append", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "numpy.pi", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.sin", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 40, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.arrow", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "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": "numpy.zeros", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.inner", "line_number": 59, "usage_type": "call"}, {"api_name": "Structure.visual", "line_number": 64, "usage_type": "call"}, {"api_name": "Spectra.absorption", "line_number": 66, "usage_type": "call"}, {"api_name": "CD.CD", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "297042210", "text": "from flask import Flask, render_template, request\nfrom solver import solve\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef index():\n return render_template('index.html')\n\n@app.route('/translate', methods = ['POST'])\ndef translate():\n inputText = request.form['inputText']\n translationOption = request.form['translationOption']\n algoOption = request.form['algoOption']\n\n res = solve(inputText, algoOption, translationOption)\n\n return render_template('index.html', result=res)\n\nif __name__ == \"__main__\":\n app.run()", "sub_path": "src/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 529, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 11, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 11, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 13, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 13, "usage_type": "name"}, {"api_name": "solver.solve", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "129264654", "text": "from collections import OrderedDict\n\nfrom flask import Blueprint, current_app as app, url_for, redirect\n\nfrom ..domain import Image\n\n\nblueprint = Blueprint('link', __name__, url_prefix=\"/\")\n\n\n@blueprint.route(\"//\")\ndef get(**kwargs):\n data = OrderedDict()\n data['visible'] = OrderedDict()\n data['hidden'] = OrderedDict()\n for kind in Image.KINDS:\n url = url_for('image.get', kind=kind, _external=True, **kwargs)\n data['visible'][kind] = url\n code = app.link_service.encode(**kwargs)\n url = url_for('image.get_encoded', kind=kind, _external=True, code=code)\n data['hidden'][kind] = url\n return data\n\n\n@blueprint.route(\"\")\ndef get_encoded(code):\n key, top, bottom = app.link_service.decode(code)\n url = url_for('.get', key=key, top=top, bottom=bottom)\n return redirect(url)\n", "sub_path": "memegen/routes/link.py", "file_name": "link.py", "file_ext": "py", "file_size_in_byte": 855, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Blueprint", "line_number": 8, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 13, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 14, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 15, "usage_type": "call"}, {"api_name": "domain.Image.KINDS", "line_number": 16, "usage_type": "attribute"}, {"api_name": "domain.Image", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.current_app.link_service.encode", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.current_app.link_service", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.current_app.link_service.decode", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.current_app.link_service", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "623454831", "text": "#!/usr/bin/python\nimport numpy as np\nfrom keras.models import Sequential\nfrom keras.layers.convolutional import Conv2D, MaxPooling2D\nfrom keras.layers.core import Flatten, Dense\n\nimport input_data\n\nmnist = input_data.read_data_sets(\"Mnist_data/\", one_hot=True)\n\nX_train = np.expand_dims(mnist.train.images.reshape(-1, 28, 28), axis=3)\nY_train = mnist.train.labels\n\nX_test = np.expand_dims(mnist.test.images.reshape(-1, 28, 28), axis=3)\nY_test = mnist.test.labels\n\nMODEL_FILENAME = \"number_model2.hdf5\"\n\n# Build the neural network!\nmodel = Sequential()\n\nmodel.add(Conv2D(32, (5, 5), padding='same', input_shape=(28,28,1), activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n\nmodel.add(Conv2D(64, (5, 5), padding='same', activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2,2), strides=(2,2)))\n\nmodel.add(Flatten())\nmodel.add(Dense(1024, activation='relu'))\n\n# Output layer with 32 nodes (one for each possible letter/number we predict)\nmodel.add(Dense(10, activation=\"softmax\"))\n\n# Ask Keras to build the TensorFlow model behind the scenes\nmodel.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n\n# Train the neural network\nmodel.fit(X_train, Y_train, validation_data=(X_test, Y_test), batch_size=50, epochs=3, verbose=1)\n\n# Save the trained model to disk\nmodel.save(MODEL_FILENAME)\n", "sub_path": "keras_train_deep.py", "file_name": "keras_train_deep.py", "file_ext": "py", "file_size_in_byte": 1340, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "input_data.read_data_sets", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 14, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 20, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 22, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.MaxPooling2D", "line_number": 23, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.Conv2D", "line_number": 25, "usage_type": "call"}, {"api_name": "keras.layers.convolutional.MaxPooling2D", "line_number": 26, "usage_type": "call"}, {"api_name": "keras.layers.core.Flatten", "line_number": 28, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 29, "usage_type": "call"}, {"api_name": "keras.layers.core.Dense", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "331137373", "text": "from config.auth_config import JWT_SECRET, JWT_CLIENT_ID, ADMIN_CLIENT_ID, ADMIN_CLIENT_SECRET, \\\n AUTH_CONNECTION, AUTH_URL, USE_AUTH, NO_AUTH_EMAIL\nfrom config.portal_config import PORTAL_URL\nfrom datetime import datetime, timedelta\nfrom functools import wraps\nfrom flask import request, Response, jsonify\nimport jwt\nimport logging\nfrom membership.database.base import Session\nfrom membership.database.models import Member\nimport pkg_resources\nimport random\nimport requests\nimport string\n\nPASSWORD_CHARS = string.ascii_letters + string.digits\n\n\ndef deny(reason: str= '') -> Response:\n \"\"\"Sends a 401 response that enables basic auth\"\"\"\n response = jsonify({\n 'status': 'error',\n 'err': 'Could not verify your access level for that URL.\\n'\n 'You have to login with proper credentials and' + reason\n })\n response.status_code = 401\n return response\n\n\ndef requires_auth(admin=False):\n \"\"\" This defines a decorator which when added to a route function in flask requires authorization to\n view the route.\n \"\"\"\n def decorator(f):\n @wraps(f)\n def decorated(*args, **kwargs):\n if USE_AUTH:\n auth = request.headers.get('authorization')\n if not auth:\n return deny('Authorization not found.')\n token = auth.split()[1]\n try:\n token = jwt.decode(token, JWT_SECRET, audience=JWT_CLIENT_ID)\n except Exception as e:\n return deny(str(e))\n email = token.get('email')\n else:\n email = NO_AUTH_EMAIL\n session = Session()\n try:\n member = session.query(Member).filter_by(email_address=email).one()\n authenticated = False\n if admin:\n for role in member.roles:\n if role.committee_id is None and role.role == 'admin':\n authenticated = True\n else:\n authenticated = True\n if authenticated:\n kwargs['requester'] = member\n kwargs['session'] = session\n return f(*args, **kwargs)\n return deny('not enough access')\n finally:\n session.close()\n\n return decorated\n return decorator\n\n\ncurrent_token = {}\n\n\ndef get_auth0_token():\n if not current_token or datetime.now() > current_token['expiry']:\n current_token.update(generate_auth0_token())\n return current_token['token']\n\n\ndef generate_auth0_token():\n payload = {'grant_type': \"client_credentials\",\n 'client_id': ADMIN_CLIENT_ID,\n 'client_secret': ADMIN_CLIENT_SECRET,\n 'audience': AUTH_URL + 'api/v2/'}\n response = requests.post(AUTH_URL + 'oauth/token', json=payload).json()\n return {'token': response['access_token'],\n 'expiry': datetime.now() + timedelta(seconds=response['expires_in'])}\n\n\ndef create_auth0_user(email):\n if not USE_AUTH:\n return PORTAL_URL\n # create the user\n payload = {\n 'connection': AUTH_CONNECTION,\n 'email': email,\n 'password': ''.join(random.SystemRandom().choice(PASSWORD_CHARS) for _ in range(12)),\n 'user_metadata': {},\n 'email_verified': False,\n 'verify_email': False\n }\n headers = {'Authorization': 'Bearer ' + get_auth0_token()}\n r = requests.post(AUTH_URL + 'api/v2/users', json=payload, headers=headers)\n if r.status_code > 299:\n logging.error(r.json())\n raise Exception('Failed to create user')\n user_id = r.json()['user_id']\n\n # get a password change URL\n payload = {\n 'result_url': PORTAL_URL,\n 'user_id': user_id\n }\n r = requests.post(AUTH_URL + 'api/v2/tickets/password-change', json=payload, headers=headers)\n if r.status_code > 299:\n logging.error(r.json())\n raise Exception('Failed to get password url')\n reset_url = r.json()['ticket']\n\n # get email verification link\n payload = {\n 'result_url': reset_url,\n 'user_id': user_id\n }\n r = requests.post(AUTH_URL + 'api/v2/tickets/email-verification', json=payload, headers=headers)\n if r.status_code > 299:\n logging.error(r.json())\n raise Exception('Failed to get verify url')\n validate_url = r.json()['ticket']\n return validate_url\n\n", "sub_path": "membership/web/auth.py", "file_name": "auth.py", "file_ext": "py", "file_size_in_byte": 4446, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "string.ascii_letters", "line_number": 16, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 16, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 19, "usage_type": "name"}, {"api_name": "config.auth_config.USE_AUTH", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.request.headers.get", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "jwt.decode", "line_number": 43, "usage_type": "call"}, {"api_name": "config.auth_config.JWT_SECRET", "line_number": 43, "usage_type": "argument"}, {"api_name": "config.auth_config.JWT_CLIENT_ID", "line_number": 43, "usage_type": "name"}, {"api_name": "config.auth_config.NO_AUTH_EMAIL", "line_number": 48, "usage_type": "name"}, {"api_name": "membership.database.base.Session", "line_number": 49, "usage_type": "call"}, {"api_name": "membership.database.models.Member", "line_number": 51, "usage_type": "argument"}, {"api_name": "functools.wraps", "line_number": 35, "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": "config.auth_config.ADMIN_CLIENT_ID", "line_number": 82, "usage_type": "name"}, {"api_name": "config.auth_config.ADMIN_CLIENT_SECRET", "line_number": 83, "usage_type": "name"}, {"api_name": "config.auth_config.AUTH_URL", "line_number": 84, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 85, "usage_type": "call"}, {"api_name": "config.auth_config.AUTH_URL", "line_number": 85, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 87, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 87, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 87, "usage_type": "call"}, {"api_name": "config.auth_config.USE_AUTH", "line_number": 91, "usage_type": "name"}, {"api_name": "config.portal_config.PORTAL_URL", "line_number": 92, "usage_type": "name"}, {"api_name": "config.auth_config.AUTH_CONNECTION", "line_number": 95, "usage_type": "name"}, {"api_name": "random.SystemRandom", "line_number": 97, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 103, "usage_type": "call"}, {"api_name": "config.auth_config.AUTH_URL", "line_number": 103, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 105, "usage_type": "call"}, {"api_name": "config.portal_config.PORTAL_URL", "line_number": 111, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 114, "usage_type": "call"}, {"api_name": "config.auth_config.AUTH_URL", "line_number": 114, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 116, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 125, "usage_type": "call"}, {"api_name": "config.auth_config.AUTH_URL", "line_number": 125, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 127, "usage_type": "call"}]} +{"seq_id": "445205190", "text": "#!/usr/bin/python3\n\"\"\"city flask triggers\"\"\"\nfrom api.v1.views import app_views\nfrom flask import Flask, jsonify, abort, make_response, request\nfrom models import storage\nfrom models.state import State\nfrom models.city import City\n\n\n@app_views.route('/states//cities',\n methods=['GET'], strict_slashes=False)\ndef citiesretr(state_id):\n \"\"\"Retrieves the list of all City objects of a State\"\"\"\n idstate = storage.get(State, state_id)\n if idstate is None:\n abort(404)\n cities = []\n for city in idstate.cities:\n cities.append(city.to_dict())\n return jsonify(cities)\n\n\n@app_views.route('/cities/',\n methods=['GET'], strict_slashes=False)\ndef cityid(city_id):\n \"\"\"Retrieves a City object\"\"\"\n cityobj = storage.get(City, city_id)\n if cityobj is None:\n abort(404)\n return jsonify(cityobj.to_dict())\n\n\n@app_views.route('/cities/',\n methods=['DELETE'], strict_slashes=False)\ndef delete(city_id):\n \"\"\"deletes a city object\"\"\"\n cityobj = storage.get(City, city_id)\n if cityobj is None:\n abort(404)\n storage.delete(cityobj)\n storage.save()\n return (jsonify({}), 200)\n\n\n@app_views.route('/states//cities',\n methods=['POST'], strict_slashes=False)\ndef createcity(state_id):\n \"\"\"Creates a City\"\"\"\n citystate = storage.get(State, state_id)\n if citystate is None:\n abort(404)\n if not request.get_json():\n return make_response(jsonify({\"error\": \"not a JSON\"}), 400)\n if 'name' not in request.get_json():\n return make_response(jsonify({\"error\": \"missing name\"}), 400)\n post_json = request.get_json()\n post_json['state_id'] = state_id\n jsoncity = City(**post_json)\n jsoncity.save()\n return make_response(jsonify(jsoncity.to_dict()), 201)\n\n\n@app_views.route('/cities/',\n methods=['PUT'], strict_slashes=False)\ndef updatecity(city_id):\n \"\"\"update a city as json\"\"\"\n update = storage.get(City, city_id)\n if update is None:\n abort(404)\n if not request.get_json():\n return make_response(jsonify({\"error\": \"Not a JSON\"}), 400)\n for key, value in request.get_json().items():\n if key not in ['id', 'state_id', 'created_at', 'updated_at']:\n setattr(update, key, value)\n update.save()\n return jsonify(update.to_dict())\n", "sub_path": "api/v1/views/cities.py", "file_name": "cities.py", "file_ext": "py", "file_size_in_byte": 2430, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "models.storage.get", "line_number": 14, "usage_type": "call"}, {"api_name": "models.state.State", "line_number": 14, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 14, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 20, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 10, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 10, "usage_type": "name"}, {"api_name": "models.storage.get", "line_number": 27, "usage_type": "call"}, {"api_name": "models.city.City", "line_number": 27, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 30, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 23, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 23, "usage_type": "name"}, {"api_name": "models.storage.get", "line_number": 37, "usage_type": "call"}, {"api_name": "models.city.City", "line_number": 37, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 37, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 39, "usage_type": "call"}, {"api_name": "models.storage.delete", "line_number": 40, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 40, "usage_type": "name"}, {"api_name": "models.storage.save", "line_number": 41, "usage_type": "call"}, {"api_name": "models.storage", "line_number": 41, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 42, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 33, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 33, "usage_type": "name"}, {"api_name": "models.storage.get", "line_number": 49, "usage_type": "call"}, {"api_name": "models.state.State", "line_number": 49, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 49, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 52, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 52, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 54, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 56, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 56, "usage_type": "name"}, {"api_name": "models.city.City", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 60, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 45, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 45, "usage_type": "name"}, {"api_name": "models.storage.get", "line_number": 67, "usage_type": "call"}, {"api_name": "models.city.City", "line_number": 67, "usage_type": "argument"}, {"api_name": "models.storage", "line_number": 67, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 70, "usage_type": "name"}, {"api_name": "flask.make_response", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 72, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 76, "usage_type": "call"}, {"api_name": "api.v1.views.app_views.route", "line_number": 63, "usage_type": "call"}, {"api_name": "api.v1.views.app_views", "line_number": 63, "usage_type": "name"}]} +{"seq_id": "82211507", "text": "# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build\\bdist.win-amd64\\egg\\opentnsim\\cli.py\n# Compiled at: 2019-07-18 03:25:05\n# Size of source mod 2**32: 775 bytes\n\"\"\"Console script for opentnsim.\"\"\"\nimport sys, click, opentnsim.server\n\n@click.group()\ndef cli(args=None):\n \"\"\"OpenTNSim simulation.\"\"\"\n click.echo('Replace this message by putting your code into opentnsim.cli.main')\n click.echo('See click documentation at http://click.pocoo.org/')\n return 0\n\n\n@cli.command()\n@click.option('--host', default='0.0.0.0')\n@click.option('--port', default=5000, type=int)\n@click.option('--debug/--no-debug', default=False)\ndef serve(host, port, debug, args=None):\n \"\"\"Run a flask server with the backend code\"\"\"\n app = opentnsim.server.app\n app.run(host=host, debug=debug, port=port)\n\n\nif __name__ == '__main__':\n sys.exit(cli())", "sub_path": "pycfiles/opentnsim-0.0.1-py3.7/cli.cpython-37.py", "file_name": "cli.cpython-37.py", "file_ext": "py", "file_size_in_byte": 957, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "click.echo", "line_number": 14, "usage_type": "call"}, {"api_name": "click.echo", "line_number": 15, "usage_type": "call"}, {"api_name": "click.group", "line_number": 11, "usage_type": "call"}, {"api_name": "opentnsim.server.server", "line_number": 25, "usage_type": "attribute"}, {"api_name": "opentnsim.server", "line_number": 25, "usage_type": "name"}, {"api_name": "click.option", "line_number": 20, "usage_type": "call"}, {"api_name": "click.option", "line_number": 21, "usage_type": "call"}, {"api_name": "click.option", "line_number": 22, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "16977627", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals, print_function\nfrom abc import ABCMeta, abstractmethod\n\nfrom six import with_metaclass\n\nfrom .utils import parse_query_parameters\n\n\nclass CollectionMixin(with_metaclass(ABCMeta, object)):\n @abstractmethod\n def list(self, size=100, offset=None, **filter_fields):\n \"\"\"\n :param size: A limit on the number of objects to be returned.\n :type size: int\n :param offset: A cursor used for pagination. offset is an object identifier that defines a place in the list.\n :type offset: uuid.UUID\n :param filter_fields: Dictionary containing values to filter for\n :type filter_fields: dict\n :rtype: dict\n :return: Dictionary containing dictionaries\n \"\"\"\n\n def iterate(self, window_size=10, **filter_fields):\n current_offset = None\n while True:\n response = self.list(size=window_size, offset=current_offset, **filter_fields)\n for item in response['data']:\n yield item\n next_url = response.get('next', None)\n if next_url is None:\n return\n current_offset = parse_query_parameters(next_url).get('offset')[0]\n", "sub_path": "src/kong/mixins.py", "file_name": "mixins.py", "file_ext": "py", "file_size_in_byte": 1233, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "six.with_metaclass", "line_number": 10, "usage_type": "call"}, {"api_name": "abc.ABCMeta", "line_number": 10, "usage_type": "argument"}, {"api_name": "abc.abstractmethod", "line_number": 11, "usage_type": "name"}, {"api_name": "utils.parse_query_parameters", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "612246260", "text": "import datetime\nimport json\nimport os\nimport platform\nimport re\nfrom enum import Enum, auto\nfrom pathlib import Path\n\nimport numpy as np\nfrom PyQt5.QtWidgets import QLabel, QPushButton, QRadioButton, QStatusBar, \\\n QMessageBox\nfrom netCDF4 import Dataset\n\n\nclass PlotType(Enum):\n TIME_SERIES = auto()\n HEAT_MAP = auto()\n\n\nclass DataAction(Enum):\n EXPORT = auto()\n PLOT = auto()\n\n\nclass ExportDataType(Enum):\n CSV = \".csv\"\n ZIP = \".zip\"\n EXCEL = \".xlsx\"\n HTML = \".html\"\n\n\nclass PlotDataType(Enum):\n PDF = \"pdf\"\n PNG = \"png\"\n EPS = \"eps\"\n SVG = \"svg\"\n JPEG = \"jpeg\"\n\n\nclass FileExtension(Enum):\n NETCDF = \"*.nc\"\n NETCDF4 = \"*.nc4\"\n DATA_FILE = \".npz\"\n TMP_DATA_FILE = \"_tmp.npz\"\n META_FILE = \"metadata.json\"\n TMP_META_FILE = \"metadata_tmp.json\"\n PLOT_PNG = \".png\"\n PLOT_PDF = \".pdf\"\n\n\nclass DirectorySeparator(Enum):\n UNIX = \"/\"\n WINDOWS = \"/\"\n\n\nclass HelperFunction:\n @staticmethod\n def format_directory_path(path: str) -> str:\n separator = HelperFunction.get_dir_separator()\n reg = r\"{0}$\".format(separator)\n if not re.findall(reg, path):\n path += separator\n return path\n\n @staticmethod\n def can_read_directory(src_path: str) -> bool:\n return os.access(src_path, os.R_OK)\n\n @staticmethod\n def can_write_directory(src_path: str) -> bool:\n return os.access(src_path, os.W_OK)\n\n @staticmethod\n def get_qt_text_width(element, text: str) -> int:\n return 1.1 * element.fontMetrics().boundingRect(text).width()\n\n @staticmethod\n def replace_array_fill_value(data: np.ndarray, fill_value) -> np.ndarray:\n if len(data.shape) == 3:\n new_d = np.where(data[:, :, :] != fill_value, data[:, :, :],\n np.NaN)\n else:\n new_d = np.where(data[:, :, :, :] != fill_value, data[:, :, :, :],\n np.NaN)\n return new_d\n\n @staticmethod\n def get_long_variable_name(src_path: str, variable_name: str) -> str:\n sorted_file_list = sorted(\n Path(src_path).glob(FileExtension.NETCDF4.value))\n if len(sorted_file_list) == 0:\n sorted_file_list = sorted(\n Path(src_path).glob(FileExtension.NETCDF.value))\n if len(sorted_file_list) == 0:\n return \"\"\n with Dataset(sorted_file_list[0], 'r') as d:\n return HelperFunction.format_variable_name(\n d.variables[variable_name].long_name)\n\n @staticmethod\n def get_available_variables(src_path: str) -> [str]:\n sorted_file_list = sorted(\n Path(src_path).glob(FileExtension.NETCDF4.value))\n if len(sorted_file_list) == 0:\n sorted_file_list = sorted(\n Path(src_path).glob(FileExtension.NETCDF.value))\n var_info_list = []\n if HelperFunction.is_valid_nc_source_directory(src_path):\n with Dataset(sorted_file_list[0], 'r') as d:\n for var in d.variables.keys():\n if var != 'time' and var != 'lat' and var != 'lon' and var != 'lev':\n var_info_list.append(var)\n return var_info_list\n\n @staticmethod\n def is_valid_nc_source_directory(src_path: str) -> bool:\n sorted_file_list = sorted(\n Path(src_path).glob(FileExtension.NETCDF4.value))\n if len(sorted_file_list) == 0:\n sorted_file_list = sorted(\n Path(src_path).glob(FileExtension.NETCDF.value))\n if len(sorted_file_list) == 0:\n return False\n return True\n\n @staticmethod\n def is_valid_npz_source_directory(src_path: str) -> bool:\n dir_separator = HelperFunction.get_dir_separator()\n reg = r\"{0}$\".format(dir_separator)\n if not re.findall(reg, src_path):\n src_path += dir_separator\n\n metadata_path = src_path + FileExtension.META_FILE.value\n\n metadata_dictionary = None\n try:\n with open(metadata_path, 'r') as f:\n metadata_dictionary = json.load(f)\n except:\n return False\n\n if metadata_dictionary is None:\n return False\n\n var_name = metadata_dictionary['name']\n data_path = src_path + var_name + FileExtension.DATA_FILE.value\n try:\n np.load(data_path)\n return True\n except:\n return False\n\n @staticmethod\n def get_dir_separator() -> str:\n system = platform.system()\n if system == 'Darwin' or system == 'Linux':\n return DirectorySeparator.UNIX.value\n elif system == 'Windows':\n return DirectorySeparator.WINDOWS.value\n\n @staticmethod\n def format_variable_name(name: str) -> str:\n n = re.sub(\"_\", \" \", name)\n return \" \".join(w.capitalize() for w in n.split())\n\n @staticmethod\n def get_data_info(src_folder: str):\n with open(src_folder + FileExtension.META_FILE.value, 'r') as f:\n return json.load(f)\n\n @staticmethod\n def round_number(number, places):\n return round(10 ** places * number) / 10 ** places\n\n @staticmethod\n def create_label(parent, text: str, x_position: int, y_position: int,\n height: int) -> QLabel:\n label = HelperFunction.create_label_with_width(parent, text,\n x_position, y_position,\n 1, height)\n label.setFixedWidth(HelperFunction.get_qt_text_width(label, text))\n return label\n\n @staticmethod\n def create_label_with_width(parent, text: str, x: int, y: int, width: int,\n height: int) -> QLabel:\n label = QLabel(parent)\n label.setText(text)\n label.setGeometry(x, y, width, height)\n return label\n\n @staticmethod\n def create_button(parent, text: str, x: int, y: int, width: int,\n height: int) -> QPushButton:\n button = QPushButton(parent)\n button.setText(text)\n button.setGeometry(x, y, width, height)\n return button\n\n @staticmethod\n def create_radio_button(parent, text: str, x: int, y: int, width: int,\n height: int) -> QRadioButton:\n radio_button = QRadioButton(parent)\n radio_button.setText(text)\n radio_button.setGeometry(x, y, width, height)\n return radio_button\n\n @staticmethod\n def create_status_bar(parent, text: str, x: int, y: int, width: int,\n height: int) -> QStatusBar:\n status_bar = QStatusBar(parent)\n status_bar.showMessage(text)\n status_bar.setGeometry(x, y, width, height)\n return status_bar\n\n @staticmethod\n def show_error_message(parent, text: str):\n error = QMessageBox(parent)\n error.setWindowTitle(\"An Error Occurred!\")\n error.setText(text)\n error.exec_()\n\n @staticmethod\n def get_datetime_from_str(string: str):\n return datetime.datetime.strptime(string, \"%Y-%m-%d %H:%M\")\n\n @staticmethod\n def get_str_from_datetime(dt: datetime.datetime):\n return datetime.datetime.strftime(dt, \"%Y-%m-%d %H:%M\")\n", "sub_path": "programs/gui_program/HelperFunctions.py", "file_name": "HelperFunctions.py", "file_ext": "py", "file_size_in_byte": 7214, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "enum.Enum", "line_number": 15, "usage_type": "name"}, {"api_name": "enum.auto", "line_number": 16, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 17, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 20, "usage_type": "name"}, {"api_name": "enum.auto", "line_number": 21, "usage_type": "call"}, {"api_name": "enum.auto", "line_number": 22, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 25, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 32, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 40, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 51, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 61, "usage_type": "call"}, {"api_name": "os.access", "line_number": 67, "usage_type": "call"}, {"api_name": "os.R_OK", "line_number": 67, "usage_type": "attribute"}, {"api_name": "os.access", "line_number": 71, "usage_type": "call"}, {"api_name": "os.W_OK", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 78, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.NaN", "line_number": 81, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.NaN", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 90, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 93, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 96, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 103, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 106, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 109, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 118, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 121, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 130, "usage_type": "call"}, {"api_name": "json.load", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 148, "usage_type": "call"}, {"api_name": "platform.system", "line_number": 155, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 163, "usage_type": "call"}, {"api_name": "json.load", "line_number": 169, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 177, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 187, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 186, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 195, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 194, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 203, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QRadioButton", "line_number": 202, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QStatusBar", "line_number": 211, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QStatusBar", "line_number": 210, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 218, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 225, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 225, "usage_type": "attribute"}, {"api_name": "datetime.datetime", "line_number": 228, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strftime", "line_number": 229, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 229, "usage_type": "attribute"}]} +{"seq_id": "171186073", "text": "import os\nimport re\nimport json\nimport threading\nimport subprocess\n\nimport sublime\nimport sublime_plugin\n\n\ndef _get_workspace_json_path(wind):\n proj_file_path = wind.project_file_name()\n if proj_file_path is not None:\n proj_file = os.path.basename(proj_file_path)\n proj_file_dir = os.path.dirname(proj_file_path)\n\n json_file = re.sub(\n r\"\\.sublime-project\",\n r\".p4basic.json\",\n proj_file)\n json_file_path = os.path.join(proj_file_dir, json_file)\n\n return json_file_path\n else:\n return None\n\n\ndef _get_setting(wind, key, default=None):\n # First check dedicated .json file in project file dir\n json_file_path = _get_workspace_json_path(wind)\n if json_file_path is not None and os.path.isfile(json_file_path):\n with open(json_file_path) as f:\n json_sett = json.load(f)\n\n if key in json_sett:\n return json_sett[key]\n\n # Next check embedded p4basic section in project file\n proj_sett = wind.project_data().get(\"p4basic\")\n if proj_sett is not None and key in proj_sett:\n return proj_sett[key]\n\n # Check user preferences\n sett = sublime.load_settings(\"p4basic.sublime-settings\")\n return sett.get(key, default=default)\n\n\ndef _get_p4_base_cmd(wind):\n cmd = _get_setting(wind, \"p4_path\", \"p4\")\n port = _get_setting(wind, \"port\")\n client = _get_setting(wind, \"client\")\n host = _get_setting(wind, \"host\")\n user = _get_setting(wind, \"user\")\n\n if port is not None:\n cmd += \" -p {}\".format(port)\n if client is not None:\n cmd += \" -c {}\".format(client)\n if host is not None:\n cmd += \" -H {}\".format(host)\n if user is not None:\n cmd += \" -u {}\".format(user)\n\n return cmd\n\n\ndef _out_msg(wind, msg):\n view = wind.create_output_panel(\"p4basic\")\n\n wind.run_command(\"show_panel\", {\"panel\": \"output.p4basic\"})\n view.run_command(\"p4basic_panel_append\", {'text': msg})\n\n\nclass P4basicPanelAppend(sublime_plugin.TextCommand):\n def run(self, edit, text):\n self.view.insert(edit, self.view.size(), text)\n\n\ndef _where_path(wind, path):\n cmd_base = _get_p4_base_cmd(wind)\n\n path_dir = os.path.dirname(path)\n\n cmd = cmd_base + ' -Ztag where \"{}\"'.format(path)\n p = subprocess.Popen(\n cmd,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n shell=True,\n cwd=path_dir)\n _out_msg(wind, \"[COMMAND]{}\\n\".format(cmd))\n result, err = p.communicate()\n result = result.decode(\"utf-8\")\n err = err.decode(\"utf-8\")\n\n if err != '':\n output = \"[COMMAND]{}\\n[ERROR]{}\\n\".format(cmd, err)\n _out_msg(wind, output)\n sublime.error_message(output)\n else:\n output = \"[COMMAND]{}\\n[OUTPUT]{}\\n\".format(cmd, result)\n _out_msg(wind, output)\n\n\nclass P4basicWhereSidebar(sublime_plugin.WindowCommand):\n def run(self, paths=[]):\n if len(paths) != 1:\n _out_msg(self.window, \"Only where of single path supported.\")\n return\n\n _where_path(self.window, paths[0])\n\n def is_enabled(self, paths=[]):\n return len(paths) == 1\n\n\nclass P4basicWhereText(sublime_plugin.TextCommand):\n def run(self, edit):\n _where_path(self.view.window(), self.view.file_name())\n\n\nclass P4basicEditSidebar(sublime_plugin.WindowCommand):\n def run(self, paths=[]):\n _edit_paths(self.window, paths)\n\n\nclass P4basicEditText(sublime_plugin.TextCommand):\n def run(self, edit):\n _edit_paths(self.view.window(), [self.view.file_name()])\n\n\ndef _edit_paths(wind, paths):\n cmd_base = _get_p4_base_cmd(wind)\n\n for path in paths:\n path_dir = os.path.dirname(path)\n\n if os.path.isdir(path):\n path = os.path.join(path, \"...\")\n\n cmd = cmd_base + ' edit \"{}\"'.format(path)\n p = subprocess.Popen(\n cmd,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n shell=True,\n cwd=path_dir)\n\n result, err = p.communicate()\n result = result.decode(\"utf-8\")\n err = err.decode(\"utf-8\")\n\n if err != '':\n output = \"[COMMAND]{}\\n[ERROR]{}\\n\".format(cmd, err)\n _out_msg(wind, output)\n sublime.error_message(err)\n else:\n output = \"[COMMAND]{}\\n[OUTPUT]{}\\n\".format(cmd, result)\n _out_msg(wind, output)\n\n\nclass P4basicDiffSidebar(sublime_plugin.WindowCommand):\n def run(self, files=[]):\n if len(files) != 1:\n _out_msg(self.window, \"Only diff of single file supported.\")\n return\n\n _diff_file(self.window, files[0])\n\n def is_enabled(self, files=[]):\n return len(files) == 1\n\n\nclass P4basicDiffText(sublime_plugin.TextCommand):\n def run(self, edit):\n _diff_file(self.view.window(), self.view.file_name())\n\n\ndef _diff_file(wind, file):\n cmd_base = _get_p4_base_cmd(wind)\n\n file_dir = os.path.dirname(file)\n\n env = dict(os.environ)\n\n diff_path = _get_setting(wind, \"diff_path\")\n if diff_path is not None:\n env['P4DIFF'] = diff_path\n\n cmd = cmd_base + ' diff \"{}\"'.format(file)\n def _func():\n p = subprocess.Popen(\n cmd,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n shell=True,\n cwd=file_dir,\n env=env)\n _out_msg(wind, \"[COMMAND]{}\\n\".format(cmd))\n result, err = p.communicate()\n result = result.decode(\"utf-8\")\n err = err.decode(\"utf-8\")\n\n if err != '':\n output = \"[COMMAND]{}\\n[ERROR]{}\\n\".format(cmd, err)\n _out_msg(wind, output)\n sublime.error_message(output)\n else:\n output = \"[COMMAND]{}\\n[OUTPUT]{}\\n\".format(cmd, result)\n _out_msg(wind, output)\n\n t = threading.Thread(target=_func)\n t.start() \n\n\nclass P4basicP4vSidebar(sublime_plugin.WindowCommand):\n def run(self, paths=[]):\n if len(paths) != 1:\n _out_msg(\"Only p4v on single path supported.\")\n return\n\n _open_p4v(self.window, paths[0])\n\n def is_enabled(self, paths=[]):\n return len(paths) == 1\n\n\nclass P4basicP4vText(sublime_plugin.TextCommand):\n def run(self, edit):\n _open_p4v(self.view.window(), self.view.file_name())\n\n\ndef _open_p4v(wind, path):\n path_dir = os.path.dirname(path)\n\n cmd = _get_setting(wind, \"p4v_path\", \"p4v\")\n\n port = _get_setting(wind, \"port\")\n client = _get_setting(wind, \"client\")\n user = _get_setting(wind, \"user\")\n\n if port is not None:\n cmd += \" -p {}\".format(port)\n if user is not None:\n cmd += \" -u {}\".format(user)\n if client is not None:\n cmd += \" -c {}\".format(client)\n\n cmd += ' -s \"{}\"'.format(path)\n\n def _func():\n p = subprocess.Popen(\n cmd,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n shell=True,\n cwd=path_dir)\n _out_msg(wind, \"[COMMAND]{}\\n\".format(cmd))\n result, err = p.communicate()\n result = result.decode(\"utf-8\")\n err = err.decode(\"utf-8\")\n\n if err != '':\n output = \"[COMMAND]{}\\n[ERROR]{}\\n\".format(cmd, err)\n _out_msg(wind, output)\n sublime.error_message(output)\n\n t = threading.Thread(target=_func)\n t.start()\n\n\nclass P4basicOpenWorkspaceSettings(sublime_plugin.WindowCommand):\n def run(self):\n path = _get_workspace_json_path(self.window)\n\n if path is None:\n return\n\n self.window.open_file(path)\n\n def is_enabled(self):\n return self.window.project_file_name() is not None\n", "sub_path": "p4basic.py", "file_name": "p4basic.py", "file_ext": "py", "file_size_in_byte": 7621, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.basename", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 33, "usage_type": "call"}, {"api_name": "sublime.load_settings", "line_number": 44, "usage_type": "call"}, {"api_name": "sublime_plugin.TextCommand", "line_number": 74, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 85, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 87, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 88, "usage_type": "attribute"}, {"api_name": "sublime.error_message", "line_number": 99, "usage_type": "call"}, {"api_name": "sublime_plugin.WindowCommand", "line_number": 105, "usage_type": "attribute"}, {"api_name": "sublime_plugin.TextCommand", "line_number": 117, "usage_type": "attribute"}, {"api_name": "sublime_plugin.WindowCommand", "line_number": 122, "usage_type": "attribute"}, {"api_name": "sublime_plugin.TextCommand", "line_number": 127, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path", "line_number": 138, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 142, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 144, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 145, "usage_type": "attribute"}, {"api_name": "sublime.error_message", "line_number": 156, "usage_type": "call"}, {"api_name": "sublime_plugin.WindowCommand", "line_number": 162, "usage_type": "attribute"}, {"api_name": "sublime_plugin.TextCommand", "line_number": 174, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 182, "usage_type": "call"}, {"api_name": "os.path", "line_number": 182, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 184, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 192, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 194, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 195, "usage_type": "attribute"}, {"api_name": "sublime.error_message", "line_number": 207, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 212, "usage_type": "call"}, {"api_name": "sublime_plugin.WindowCommand", "line_number": 216, "usage_type": "attribute"}, {"api_name": "sublime_plugin.TextCommand", "line_number": 228, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 234, "usage_type": "call"}, {"api_name": "os.path", "line_number": 234, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 252, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 254, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 255, "usage_type": "attribute"}, {"api_name": "sublime.error_message", "line_number": 266, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 268, "usage_type": "call"}, {"api_name": "sublime_plugin.WindowCommand", "line_number": 272, "usage_type": "attribute"}]} +{"seq_id": "445463415", "text": "\nfrom g1.arithmeticgenome import ArithmeticGenome\nfrom g1.population import Population, PopulationAndSelectionConfig\nfrom g1.multithreading import PrintThread, ConstantDiscoveryTask, TaskRunner, createAndStartPrintAndTaskQueues, goGentleIntoThatGoodNight\nfrom g1.individual import Individual\n\nimport random\nimport logging\n# import cProfile, pstats\nimport datetime, time\n\n### Setup\nrandom.seed()\n\nlogFormat = '%(asctime)-15s %(message)s'\nlogging.basicConfig(format=logFormat)\nsystemLog = logging.getLogger(__name__)\nsystemLog.setLevel(logging.WARNING)\n\nst = datetime.datetime.fromtimestamp(time.time()).strftime('%Y%m%d%H%M%S')\ndataLogFileName = 'data/Log0.333.' + st + \".tsv\"\n\n# Creates queues which facilitate multi-threaded execution of populations simultaneously, and safe multi-threaded logging\ntaskQueue, printQueue = createAndStartPrintAndTaskQueues(dataLogFileName, systemLog, threads=1)\n\n### Example to discover a constant value, looping through different dna lengths, with multi-threading\n\ndef problem(dummmy):\n return 3.141\n\npopulationSize=60\niterations = 500\n\nfor dnaLength in range(10,51,5):\n for k in range(0,100):\n populationConfig = PopulationAndSelectionConfig(populationSize,0.0001, 0.33, 2, 0.16, 0.32, 0, 0.33, 1, 1, 1, 1, 0, 1, 0.25, 0.25, 0.4, 0.5, 1, 0)\n genomeConfig = {\"length\" : dnaLength}\n t = ConstantDiscoveryTask(problem, ArithmeticGenome, genomeConfig, populationConfig, iterations)\n taskQueue.put(t)\n\n\n# Wait for everything to finish, and close peacefully. Without this end of execution of the main threads will kill off all other threads that are probably still running\ngoGentleIntoThatGoodNight(taskQueue, printQueue)\n", "sub_path": "multiThreadedConstantDiscovery.py", "file_name": "multiThreadedConstantDiscovery.py", "file_ext": "py", "file_size_in_byte": 1685, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "random.seed", "line_number": 13, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 16, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 17, "usage_type": "call"}, {"api_name": "logging.WARNING", "line_number": 18, "usage_type": "attribute"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 20, "usage_type": "call"}, {"api_name": "g1.multithreading.createAndStartPrintAndTaskQueues", "line_number": 24, "usage_type": "call"}, {"api_name": "g1.population.PopulationAndSelectionConfig", "line_number": 36, "usage_type": "call"}, {"api_name": "g1.multithreading.ConstantDiscoveryTask", "line_number": 38, "usage_type": "call"}, {"api_name": "g1.arithmeticgenome.ArithmeticGenome", "line_number": 38, "usage_type": "argument"}, {"api_name": "g1.multithreading.goGentleIntoThatGoodNight", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "389038622", "text": "\"\"\"\nCode modified from: https://github.com/alshedivat/lola/tree/master/lola\n\"\"\"\nimport numpy as np\nfrom gym.envs.multi_agent.matrix_social_dilemma import MatrixSocialDilemma\n\ndef test_MatrixSocialDilemma():\n n_test_games=20\n n_test_step=5\n # Play n games\n for i in range(n_test_games):\n payout_matrix = np.random.randint(-10, 10, (2,2))\n social_dilemma = MatrixSocialDilemma(payout_matrix=payout_matrix)\n o = social_dilemma.reset()\n\n for agent_num in range(len(o)):\n assert o[agent_num] == (social_dilemma.NUM_STATES -1)\n\n # Play n steps\n for n in range(n_test_step):\n action = np.random.randint(0, 2, (2,)).tolist()\n o, r, done, info = social_dilemma.step(action=action)\n\n # Assume 2 agents\n for agent_num in range(len(r)):\n current_agent_a = action[agent_num]\n other_agent_a = action[(agent_num +1 ) % len(r)]\n assert (r[agent_num] ==\n social_dilemma.payout_mat[current_agent_a][other_agent_a])\n", "sub_path": "gym/envs/tests/multi_agent/test_maxtric_social_dilemma.py", "file_name": "test_maxtric_social_dilemma.py", "file_ext": "py", "file_size_in_byte": 1074, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.random.randint", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 12, "usage_type": "attribute"}, {"api_name": "gym.envs.multi_agent.matrix_social_dilemma.MatrixSocialDilemma", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 21, "usage_type": "attribute"}]} +{"seq_id": "178525330", "text": "#!/usr/bin/python3\n\nimport array\nfrom PIL import Image\n\n\nimage = Image.open('dog.ppm')\nvar = image.readline()\nprint(\"esto es: \",var)\n\n\nnew_image = image.resize((400, 400))\nnew_image.save('image_400.ppm')\n\nprint(image.size) # Output: (1200, 776)\nprint(new_image.size) # Output: (400, 400)\nprint(image.mode)", "sub_path": "computacion2/img_3.py", "file_name": "img_3.py", "file_ext": "py", "file_size_in_byte": 305, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "PIL.Image.open", "line_number": 7, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 7, "usage_type": "name"}]} +{"seq_id": "541773517", "text": "# -*- coding: utf-8 -*-\n\n'''\nCreated on 2017-11-05 16:40:35\n一等函数.\n@author: zhoujiagen\n'''\nfrom operator import add # 操作符模块\nimport random\nimport unittest\nimport functools\nfrom inspect import signature\n\ndef factorial(number):\n \"\"\"计算阶乘.\n\n Args:\n number: 数.\n\n Returns:\n number!.\n\n Raises:\n None\n \"\"\"\n if number < 2:\n return 1\n return number * factorial(number - 1)\n\n\ndef reverse(word):\n \"\"\"单词的反向拼写.\n\n Args:\n word: 单词.\n\n Returns:\n 单词的反向拼写.\n\n Raises:\n None\n \"\"\"\n return word[::-1]\n\n\ndef tag(name, *content, cls=None, **attrs):\n \"\"\"生成一个或多个HTML标签.\n\n 作为函数形参和实参的示例.\n\n Args:\n name: 标签的名称.\n *content: 标签的内容.\n cls: 标签的属性类.\n **attrs: 标签的属性键值.\n\n Returns:\n HTML便签片段.\n\n Raises:\n None\n \"\"\"\n if cls is not None:\n attrs['class'] = cls\n if attrs:\n attr_str = ''.join(' %s=\"%s\"' % (attr, value)\n for attr, value in sorted(attrs.items()))\n else:\n attr_str = ''\n\n if content:\n return '\\n'.join('<%s%s>%s' % (name, attr_str, c, name)\n for c in content)\n return '<%s%s />' % (name, attr_str)\n\n\ndef clip_with_annotation(text:str, max_len:'int > 0'=80) -> str:\n \"\"\"带注解版本的clip(text, max_len)\"\"\"\n return clip(text, max_len)\n\n\ndef clip(text, max_len=80):\n \"\"\"在max_len前面或者后面的第一个空格处截断文本.\n\n 用于演示函数内省.\n\n Args:\n text: 文本.\n max_len: 文本截断的参考最大长度.\n\n Returns:\n 截断后的文本.\n\n Raises:\n None\n \"\"\"\n end = None\n if(len(text) > max_len):\n space_before = text.rfind(' ', 0, max_len)\n if space_before >= 0:\n end = space_before\n else:\n space_after = text.rfind(' ', max_len)\n if space_after >= 0:\n end = space_after\n\n if end is None:\n end = len(text)\n\n return text[:end].rstrip()\n\n\nclass BingoCage(object):\n \"\"\"自定义可调用类型实例.\n\n 使用可迭代对象创建, 内部存储随机排列的列表.\n 调用实例时取出一个元素.\n\n Attributes:\n likes_spam: A boolean indicating if we like SPAM or not.\n eggs: An integer count of the eggs we have laid.\n \"\"\"\n\n def __init__(self, items):\n \"\"\"使用可迭代对象创建, 内部存储随机排列的列表.\"\"\"\n self._items = list(items)\n random.shuffle(self._items) # 随机排列\n\n def pick(self):\n \"\"\"取出一个元素\"\"\"\n try:\n return self._items.pop()\n except IndexError:\n raise LookupError('pick form empty BingoCage')\n\n def __call__(self):\n return self.pick()\n\n\nclass TestFunctionAsFirstLevelObject(unittest.TestCase):\n \"\"\"函数作为一等对象的Spike单元测试.\n\n Attributes:\n None\n \"\"\"\n def test_function_property(self):\n \"\"\"函数对象的属性\"\"\"\n print(type(factorial))\n print(factorial.__doc__)\n\n self.assertEqual(1, 1)\n\n class DummyClass(object):\n \"\"\"演示用类\"\"\"\n pass\n obj = DummyClass()\n def func():\n \"\"\"演示用函数\"\"\"\n pass\n print(sorted(set(dir(func)) - set(dir(obj))))\n\n def test_function_alias(self):\n \"\"\"函数对象别名\"\"\"\n fact = factorial\n print(fact)\n print(fact(5))\n self.assertEqual(120, fact(5))\n\n def test_function_as_parameter(self):\n \"\"\"函数对象作为参数\"\"\"\n self.assertEqual([1, 1, 2, 6, 24, 120, 720, 5040, 40320, 362880],\n map(factorial, range(10)))\n\n def test_functional_operator(self):\n \"\"\"map, filter, reduce的替代品\"\"\"\n # 使用列表推导\n self.assertEqual(list(map(factorial, range(6))),\n [factorial(n) for n in range(6)])\n # self.assertEqual(list(map(factorial,\n # filter(lambda n: n % 2, range(6)))),\n # [factorial(n) for n in range(6) if n % 2])\n # 使用内建函数\n self.assertEqual(functools.reduce(add, range(100)), sum(range(100)))\n\n def test_reverse_word(self):\n \"\"\"测试单词的反向拼写\"\"\"\n word = 'testing'\n self.assertEqual('gnitset', reverse(word))\n\n def test_highorder_function(self):\n \"\"\"高阶函数\"\"\"\n fruits = ['strawberry', 'fig', 'apple', 'cherry', 'raspberry',\n 'banana']\n # 使用BIF len()\n self.assertEqual(['fig', 'apple', 'cherry', 'banana', 'raspberry',\n 'strawberry'], sorted(fruits, key=len))\n self.assertEqual(['banana', 'apple', 'fig', 'raspberry', 'strawberry',\n 'cherry'], sorted(fruits, key=reverse))\n\n def test_lambda(self):\n \"\"\"匿名函数\"\"\"\n fruits = ['strawberry', 'fig', 'apple', 'cherry', 'raspberry',\n 'banana']\n self.assertEqual(['banana', 'apple', 'fig', 'raspberry', 'strawberry',\n 'cherry'],\n sorted(fruits, key=lambda word: word[::-1]))\n\n def test_callable(self):\n \"\"\"可调用对象\"\"\"\n bingo = BingoCage(range(3))\n self.assertTrue(bingo.pick() in range(3))\n self.assertTrue(bingo.pick() in range(3))\n self.assertTrue(callable(bingo)) # 判断是否是可调用对象\n\n def test_function_parameters(self):\n \"\"\"函数的形参和实参\"\"\"\n print(tag('br')) # 单个定位参数\n print(tag('p', 'hello')) # 第一个参数后的参数被*content捕获, 存入元组\n print(tag('p', 'hello', 'world'))\n print(tag('p', 'hello', id=33)) # 未在签名中指定的关键字参数被**attrs捕获, 存入字典\n print(tag('p', 'hello', 'world', cls='siderbar')) # cls作为关键字参数传入\n print(tag(content='testing', name='img')) # 位置参数也可作为关键字参数传入\n # 实参字典中所有元素作为单个参数传入\n # 同名键绑定到具名参数上, 余下的被**attrs捕获\n tags = {'name': 'img', 'title': 'Sunset Boulevard',\n 'src': 'sunset.jpg', 'cls': 'framed'}\n print(tag(**tags))\n\n def f(a, *, b):\n \"\"\"仅限关键字参数\"\"\"\n return a, b\n print(f(1, b=2))\n # print(f(1, 2, b=2)) # ERROR\n # print(f(b=2, 1)) # ERROR\n\n self.assertTrue(1 == 1)\n\n def test_function_introspection(self):\n \"\"\"函数内省\"\"\"\n # 函数的位置参数和关键字参数的默认值\n print(clip.__defaults__)\n # 仅限关键字参数的默认值\n print(clip.__kwdefaults__)\n # 函数的代码对象属性\n print(clip.__code__)\n # 函数的局部变量名称和数量\n print(clip.__code__.co_varnames)\n print(clip.__code__.co_argcount)\n\n # 提取函数的签名\n tag_sig = signature(tag)\n print(tag_sig)\n for name, param in tag_sig.parameters.items():\n print(param.kind, ':', name, '=', param.default)\n # 绑定实参\n tags = {'name': 'img', 'title': 'Sunset Boulevard',\n 'src': 'sunset.jpg', 'cls': 'framed'}\n bound_args = tag_sig.bind(**tags)\n print(bound_args)\n for name, value in bound_args.arguments.items():\n print(name, '=', value)\n\n self.assertTrue(1 == 1)\n\n def test_function_annotation(self):\n \"\"\"函数注解\"\"\"\n print(clip_with_annotation.__annotations__)\n\n clip_sig = signature(clip_with_annotation)\n for param in clip_sig.parameters.values():\n note = repr(param.annotation).ljust(13)\n print(note, ':', param.name, '=', param.default)\n\n self.assertTrue(1 == 1)\n", "sub_path": "python/src3/com/spike/functional/function_object_v3.py", "file_name": "function_object_v3.py", "file_ext": "py", "file_size_in_byte": 8044, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "random.shuffle", "line_number": 127, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 140, "usage_type": "attribute"}, {"api_name": "functools.reduce", "line_number": 183, "usage_type": "call"}, {"api_name": "operator.add", "line_number": 183, "usage_type": "argument"}, {"api_name": "inspect.signature", "line_number": 251, "usage_type": "call"}, {"api_name": "inspect.signature", "line_number": 269, "usage_type": "call"}]} +{"seq_id": "67920672", "text": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error\n\nfrom sklearn.linear_model import LinearRegression\n\ndef select_feature(train_set, vbias=0.5, wbias=0.002, loop_times=200, rate=0.075):\n '''\n train_set: 训练集，用于数据划分\n vbias: 方差阈值, 各特征数据方差低于该值删除\n wbias: 线性函数各特征系数阈值， 特征系数低于该值删除\n loop_times: 循环轮数， 减小偶然事件导致特征删除误差\n rate: 特征系数阈值与循环轮数比率， 如果高于该值，那么该特征在循环loop_times次后，低于wbias值次数较多\n\n 参数调整:\n vbias小 -> 该特征数据较平稳，无明显波动\n wbias小 -> 该特征对于目标影响能力小，\n loop_times大 -> 减小偶然误差\n rate小 -> loop_times * rate = 特征系数低于wbias最大次数\n '''\n \n feature_names = []\n\n # 根据各特征方差选取特征\n features = train_set.columns[:-1] \n for feature in features.values:\n vals = train_set[feature].values\n var = vals.var()\n if var > vbias:\n feature_names.append(feature)\n\n X = train_set[feature_names].values\n Y_= train_set[train_set.columns[-1:]].values\n\n # 根据线性模型特征系数选取特征\n drop = {}\n drop_keys = []\n for i in range(loop_times): #迭代轮数，\n clf = LinearRegression()\n X_train, X_test, y_train, y_test = train_test_split(X, Y_, test_size=0.2, random_state=None)\n clf = clf.fit(X_train, y_train)\n coef_ = clf.coef_[0]\n\n for w in range(len(coef_)):\n if np.abs(coef_[w]) < wbias: # 系数小于wbias的特征删除\n if feature_names[w] in drop.keys():\n drop[feature_names[w]] += 1\n else:\n drop[feature_names[w]] = 0\n # v32 v21 v34 v20 v11 v18 v15 v25 v30 v8\n for k, v in drop.items():\n if rate < (v/loop_times): # 特征系数低于wbias出现次数 比 循环次数 高于rate删除\n feature_names.remove(k)\n drop_keys.append(k)\n print(sorted(drop.items(), key=lambda d: d[1], reverse=True))\n print('drop keys: \\n', drop_keys)\n print('num of features: %d'%len(feature_names))\n print('features:\\n', feature_names)\n return feature_names \n\nif __name__=='__main__':\n train_set = pd.read_csv('data/zhengqi_train.csv')\n select_feature(train_set, 0.5)\n", "sub_path": "zhengqi/feature_selection.py", "file_name": "feature_selection.py", "file_ext": "py", "file_size_in_byte": 2568, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sklearn.linear_model.LinearRegression", "line_number": 42, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 48, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 65, "usage_type": "call"}]} +{"seq_id": "371670388", "text": "from Bio import SeqIO\nimport os\nfile = \"../data/megaphage_contigs.fasta\"\n#loci = [\"SRS015941_NODE_2_length_315556_cov_47.6479\", \"SRS014470_NODE_1_length_389119_cov_8.88358\"]\nloci = [\"SRS078431_NODE_11_length_258293_cov_26.0675\", \"SRS017304_NODE_5_length_258288_cov_116.449\"]\ncluster = \"VC_1419_6\"\n\nfor record in SeqIO.parse(file, \"fasta\"):\n for locus in loci:\n if record.id == locus:\n SeqIO.write(record, \"../data/\" + cluster + \"/\" + locus + \".fasta\", \"fasta\")\n", "sub_path": "helper_scripts/extract_fasta_files.py", "file_name": "extract_fasta_files.py", "file_ext": "py", "file_size_in_byte": 482, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "Bio.SeqIO.parse", "line_number": 8, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 8, "usage_type": "name"}, {"api_name": "Bio.SeqIO.write", "line_number": 11, "usage_type": "call"}, {"api_name": "Bio.SeqIO", "line_number": 11, "usage_type": "name"}]} +{"seq_id": "560382632", "text": "import os\nimport argparse\n\nimport numpy as np\nimport torch\n\nfrom src.general.envs.gym_env import GymEnv\nfrom src.sac.sac_agent import SACAgent\n\ndef main(args,\n train_steps=1000000,\n random_steps=1000,\n train_freq=1,\n target_update_freq=1,\n actor_lr=0.0001,\n q_lr=0.0001,\n entropy_lr=0.001,\n gamma=0.99,\n alpha=1,\n tau=0.005,\n buffer_size=500000,\n batch_size=256,\n gradient_steps=1,\n actor_fc=(256, 256),\n critic_fc=(256, 256),\n conv_size=None,\n logging_period=25,\n checkpoint_period=5000,\n gpu=False):\n\n if args.wandb:\n import wandb\n if args.wandb_name != None:\n wandb.init(name=args.wandb_name,\n project=\"hexapod-sac\",\n entity=\"olin-robolab\")\n else:\n wandb.init(project=\"hexapod-sac\",\n entity=\"olin-robolab\")\n wandb.config.update({\"train_steps\": train_steps,\n \"random_steps\": random_steps,\n \"train_freq\": train_freq,\n \"target_update_freq\": target_update_freq,\n \"actor_lr\": actor_lr,\n \"q_lr\": q_lr,\n \"entropy_lr\": entropy_lr,\n \"gamma\": gamma,\n \"alpha\": alpha,\n \"tau\": tau,\n \"buffer_size\": buffer_size,\n \"batch_size\": batch_size,\n \"gradient_steps\": gradient_steps,\n \"actor_fc\": actor_fc,\n \"critic_fc\": critic_fc,\n \"conv_size\": conv_size})\n else: wandb = None\n\n env = GymEnv(\"BipedalWalker-v3\")\n if args.render:\n env.render()\n\n if torch.cuda.is_available() and args.gpu: \n device = \"cuda:0\"\n else:\n if args.gpu:\n print(\"GPU flag set, but no GPU found! Using CPU.\")\n device = \"cpu\"\n\n print(\"Building agent...\")\n agent = SACAgent(train_steps=train_steps,\n random_steps=random_steps,\n train_freq=train_freq,\n target_update_freq=target_update_freq,\n actor_lr=actor_lr,\n q_lr=q_lr,\n entropy_lr=entropy_lr,\n gamma=gamma,\n alpha=alpha,\n tau=tau,\n buffer_size=buffer_size,\n batch_size=batch_size,\n gradient_steps=gradient_steps,\n env=env,\n actor_fc=actor_fc,\n critic_fc=critic_fc,\n conv_size=conv_size,\n device=device,\n logging_period=logging_period,\n checkpoint_period=checkpoint_period,\n output_dir=args.output_dir,\n restore_dir=args.restore,\n wandb=wandb)\n print(\"Agent built!\")\n\n print(\"Starting train...\")\n try:\n agent.train()\n finally:\n # Make sure out environment is closed\n # PLEASE DONT HIT CTRL C TWICE\n env.close()\n print(\"Train done!\")\n\n env.close()\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser('Train PPO')\n # Directory path arguments\n parser.add_argument(\n '--output-dir',\n type=str,\n default='/tmp/sac')\n\n # File path arguments\n parser.add_argument(\n '--restore',\n type=str,\n default=None)\n\n # Run mode arguments\n parser.add_argument(\n '--render',\n default=False,\n action='store_true')\n parser.add_argument(\n '--gpu',\n default=False,\n action='store_true')\n\n # WandB flags\n parser.add_argument(\n '--wandb',\n default=False,\n action='store_true')\n parser.add_argument(\n '--wandb-name',\n type=str,\n default=None)\n args = parser.parse_args()\n\n #logging.getLogger().setLevel(logging.INFO)\n\n main(args)\n", "sub_path": "src/sac/train_sac.py", "file_name": "train_sac.py", "file_ext": "py", "file_size_in_byte": 4225, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "wandb.init", "line_number": 34, "usage_type": "call"}, {"api_name": "wandb.init", "line_number": 38, "usage_type": "call"}, {"api_name": "wandb.config.update", "line_number": 40, "usage_type": "call"}, {"api_name": "wandb.config", "line_number": 40, "usage_type": "attribute"}, {"api_name": "src.general.envs.gym_env.GymEnv", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 62, "usage_type": "attribute"}, {"api_name": "src.sac.sac_agent.SACAgent", "line_number": 70, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 108, "usage_type": "call"}]} +{"seq_id": "621458370", "text": "import sys\nfrom dependency_injector.wiring import inject, Provide\nfrom menu.menu import Menu\nfrom app.b2c2_client import B2C2Client\nfrom dependency_injection.container import Container\n\n\n@inject\ndef main_menu(\n b2c2_client: B2C2Client = Provide[Container.b2c2_client],\n):\n my_menu = Menu(b2c2_client)\n my_menu.main_menu()\n\n\nif __name__ == \"__main__\":\n container = Container()\n container.init_resources()\n container.config.from_ini(\"config.ini\")\n container.wire(modules=[sys.modules[__name__]])\n\n main_menu()", "sub_path": "cli_client.py", "file_name": "cli_client.py", "file_ext": "py", "file_size_in_byte": 531, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "app.b2c2_client.B2C2Client", "line_number": 10, "usage_type": "name"}, {"api_name": "dependency_injector.wiring.Provide", "line_number": 10, "usage_type": "name"}, {"api_name": "dependency_injection.container.Container.b2c2_client", "line_number": 10, "usage_type": "attribute"}, {"api_name": "dependency_injection.container.Container", "line_number": 10, "usage_type": "name"}, {"api_name": "menu.menu.Menu", "line_number": 12, "usage_type": "call"}, {"api_name": "dependency_injector.wiring.inject", "line_number": 8, "usage_type": "name"}, {"api_name": "dependency_injection.container.Container", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.modules", "line_number": 20, "usage_type": "attribute"}]} +{"seq_id": "584545774", "text": "import pandas as pd\nimport pymysql\nimport time\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.pairwise import linear_kernel\n\nconn = pymysql.connect(host='34.64.88.80', port=3306, user='root',\n passwd='root', db='django_db', charset='utf8')\n\ndef contents_based_filtering(item_id, num,tag_data, ingredi):\n curs=conn.cursor()\n\n splited_data=ingredi.split(',')\n\n getAll=\"select * from recipes where ingredients like \"\n \n for idx, data in enumerate(splited_data):\n if idx==len(splited_data)-1:\n break\n getAll = getAll + \"\\\"%\" + data + \"%\\\"\" + \" or ingredients like \"\n\n getAll = getAll + \"\\\"%\" + splited_data[len(splited_data)-1] + \"%\\\"\"\n\n print(getAll)\n\n curs.execute(getAll)\n temp = curs.fetchall()\n\n\n allcontent=list(temp)\n\n allcontent.append((999999,'name','timereq','cookmethod','img',tag_data,'category','ingredients'))\n\n df=pd.DataFrame(allcontent, columns=['id','name','time_req','cook_method','img','tags','category','ingredients'])\n\n tf = TfidfVectorizer(analyzer='word', stop_words='english')\n tfidf_matrix = tf.fit_transform(df['tags'].values.astype('U'))\n cosine_similarities = linear_kernel(tfidf_matrix, tfidf_matrix)\n\n results = {}\n for idx, row in df.iterrows():\n similar_indices = cosine_similarities[idx].argsort()[:-100:-1]\n similar_items = [(cosine_similarities[idx][i], df['id'][i]) for i in similar_indices]\n results[row['id']] = similar_items[1:]\n\n print(\"Recommending \" + str(num) + \" products similar to \" + df.loc[df['id'] == item_id]['name'].tolist()[0] + \"...\")\n print(\"-------\")\n recs = results[item_id][:num]\n dict = {}\n\n for rec in recs:\n print(\"Recommended: \" + df.loc[df['id'] == rec[1]]['name'].tolist()[0] + \" (score:\" + str(rec[0]) + \")\")\n dict[df.loc[df['id'] == rec[1]]['name'].tolist()[0]] = str(rec[0])\n\n return dict\n\n\ncontents_based_filtering(item_id=999999, num=5, tag_data='#피자#���탈리안#술안주', ingredi='치즈,옥수수,고추장')\n", "sub_path": "backEnd/200424_subtask_hybridSystem/algorithm/tfidf.py", "file_name": "tfidf.py", "file_ext": "py", "file_size_in_byte": 2064, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pymysql.connect", "line_number": 7, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 34, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 36, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise.linear_kernel", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "191039656", "text": "from __future__ import division\nfrom __future__ import print_function\n\nimport auc_heuristic_computation\nfrom collections import defaultdict\nfrom enum import Enum\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport re\nimport sys\n\nNUMERIC_CONST_PATTERN_TEXT = r\"\"\"\n [-+]? # optional sign\n (?: \n (?: \\d* \\. \\d+ ) # .1 .12 .123 etc 9.1 etc 98.1 etc \n | \n (?: \\d+ \\.? ) # 1. 12. 123. etc 1 12 123 etc \n ) \n # followed by optional exponent part if desired \n (?: [Ee] [+-]? \\d+ ) ? \n \"\"\"\nNUMERIC_CONST_PATTERN = re.compile(NUMERIC_CONST_PATTERN_TEXT, re.VERBOSE)\nRESULTS_DIR = sys.argv[1]\nHEURISTIC_VERSION = 1\nDPI_FOR_SAVING_PNG = 200\nDATASET_TYPES = auc_heuristic_computation.print_dataset_types()\nUSER_TYPE_TO_NAME = {\n 0: \"non-recurring\",\n# 1: \"sporadic\",\n 2: \"frequent\"#,\n# 3: \"permanent\"\n}\nDATASET_TYPE_TO_NAME = {\n 'B': 'Sustainable',\n 'C': 'Transitioning',\n 'A': 'Emerging'\n}\nACTIVITY_TYPE_TO_NAME = {\n \"posts\": \"questions\",\n \"replies\": \"answers\"\n}\n\nclass activityType(Enum):\n Q = \"posts\"\n A = \"replies\"\n\ndef get_user_type_aucs(dataset_list, activityType):\n user_type_aucs = defaultdict(list)\n for dataset in dataset_list:\n dataset_aucs = []\n with open(RESULTS_DIR + dataset + \"/auc_activity_per_cluster_{}.txt\".format(activityType.value), \"r\") as f:\n for line in f:\n if line.strip() == \"\":\n break\n dataset_aucs.append(float(NUMERIC_CONST_PATTERN.findall(line)[-1]))\n dataset_auc_sum = sum(dataset_aucs)\n normalized_aucs = [auc / dataset_auc_sum for auc in dataset_aucs]\n for auc_index, auc in enumerate(normalized_aucs):\n user_type_aucs[auc_index].append(auc)\n return user_type_aucs\n\ndataset_types_aucs = {}\nfor dataset_type, dataset_list in DATASET_TYPES.items():\n dataset_types_aucs[dataset_type + \"_\" + activityType.Q.value] = get_user_type_aucs(dataset_list, activityType.Q)\n dataset_types_aucs[dataset_type + \"_\" + activityType.A.value] = get_user_type_aucs(dataset_list, activityType.A)\n\nfor activity_type in [activityType.Q.value, activityType.A.value]:\n fig, ax_list = plt.subplots(1, 2, figsize=(9, 6))\n #fig, ax_list = plt.subplots(1, 4)\n first_plot = True\n for user_type in [0, 2]:\n array_to_plot = []\n labels_to_plot = []\n user_type_name = USER_TYPE_TO_NAME[user_type]\n ax_list_index = 0 if user_type == 0 else 1\n for dataset_type in sorted(DATASET_TYPES.keys(), reverse=True):\n dataset_type_name = DATASET_TYPE_TO_NAME[dataset_type]\n label = dataset_type_name\n labels_to_plot.append(label)\n array_to_plot.append(np.array(dataset_types_aucs[dataset_type + \"_\" + activity_type][user_type]).reshape((-1, 1)))\n ax_list[ax_list_index].boxplot(array_to_plot, labels=labels_to_plot)\n if first_plot:\n first_plot = False\n ax_list[ax_list_index].set_ylabel(\"Area-Under-the-Curve (% of total)\")\n else:\n ax_list[ax_list_index].tick_params(axis='y', which='both', bottom='off', top='off', labelbottom='off')\n ax_list[ax_list_index].set_title(user_type_name)\n fig.suptitle(\"Distribution of Area-Under-the-Curve Ratios per User Type of \" + ACTIVITY_TYPE_TO_NAME[activity_type].capitalize() + \"-based Activity Time Series\")\n #fig.tight_layout(h_pad=5)\n fig.subplots_adjust(wspace=0.3)\n plt.savefig(\"auc_ratios_per_user_per_dataset_types_{}.png\".format(activity_type))\n", "sub_path": "src/auc_boxplot.py", "file_name": "auc_boxplot.py", "file_ext": "py", "file_size_in_byte": 3536, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "re.compile", "line_number": 23, "usage_type": "call"}, {"api_name": "re.VERBOSE", "line_number": 23, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 24, "usage_type": "attribute"}, {"api_name": "auc_heuristic_computation.print_dataset_types", "line_number": 27, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 44, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}]} +{"seq_id": "496859301", "text": "# -*- coding:utf-8 -*-\nimport sys\nreload(sys)\nsys.setdefaultencoding('utf-8')\nimport os\nmain_dir = os.path.dirname(os.path.abspath(__file__))\nsys.path.append(os.path.dirname(main_dir))\n\nimport argparse\nfrom org.org import Org\nfrom user.user import User\nfrom function.function import IndustryAndFunction\nfrom db.mongoExe import MongoExe\nfrom main import get_manual_match_dict, cleanYXT\n\nENV = 'line'\nUSER_TABLE = 'user'\nmongoConnections = MongoExe()\n\n\ndef update_user_info(orgid):\n # create obj\n org_obj = Org(ENV)\n user_obj = User(ENV, 'elearning.CORE_USERPROFILE', USER_TABLE)\n mongo_conn_alias, mongoConnection = mongoConnections.getConnection()\n\n function_obj = IndustryAndFunction(\n org_obj.get_single_org_info_from_slave(orgid),\n user_obj.get_user_list_from_master(orgid),\n get_manual_match_dict(mongoConnection, orgid))\n\n # set detail\n user_list = function_obj.set_region().set_industry().set_function().user_list\n user_obj.delete_records(orgid)\n user_obj.insert_user_list_into_slave(user_list)\n\n # close all connections\n user_obj.close()\n org_obj.close()\n mongoConnections.close()\n\n cleanYXT()\n\n\ndef config_argparser():\n parser = argparse.ArgumentParser(description='set the orgId')\n parser.add_argument(\n '--orgid',\n required=True,\n dest='orgid',\n action='store',\n help='set the orgid you want to update users')\n return parser.parse_args()\n\nif __name__ == '__main__':\n args = config_argparser()\n update_user_info(args.orgid)\n # update_user_info('78558c63-22fe-4e84-88f2-f5bb4df64855')\n", "sub_path": "skyeye/skyeyeops_backend/jobs/UserprofileCleaning/main_change_info.py", "file_name": "main_change_info.py", "file_ext": "py", "file_size_in_byte": 1609, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.setdefaultencoding", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "db.mongoExe.MongoExe", "line_number": 18, "usage_type": "call"}, {"api_name": "org.org.Org", "line_number": 23, "usage_type": "call"}, {"api_name": "user.user.User", "line_number": 24, "usage_type": "call"}, {"api_name": "function.function.IndustryAndFunction", "line_number": 27, "usage_type": "call"}, {"api_name": "main.get_manual_match_dict", "line_number": 30, "usage_type": "call"}, {"api_name": "main.cleanYXT", "line_number": 42, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "504499537", "text": "#!/usr/bin/env python3\n#'description': ''This module automatically creates API keys for every available region. There is an included cleanup feature to remove old \"AWSc2\" keys that are referenced by name.',\nimport datetime\nimport argparse\nfrom copy import deepcopy\nfrom botocore.exceptions import ClientError\n\ndef cleanup(awsattack_main, regions):\n print = awsattack_main.print\n for region in regions:\n client = awsattack_main.get_boto3_client('apigateway', region)\n try:\n keys = client.get_api_keys()['items']\n if len(keys) < 1:\n print(' No keys were found in {}'.format(region))\n for key in keys:\n if key['name'] == 'AWSc2':\n try:\n client.delete_api_key(apiKey=key['id'])\n print(' Key deletion successful for: {}'.format(region))\n except ClientError as error:\n if error.response['Error']['Code'] == 'AccessDeniedException':\n print(' FAILURE: ')\n print(' MISSING NEEDED PERMISSIONS')\n return False\n except ClientError as error:\n if error.response['Error']['Code'] == 'AccessDeniedException':\n print(' FAILURE: ')\n print(' MISSING NEEDED PERMISSIONS')\n return False\n return True\n\n\ndef main(args, awsattack_main):\n session = awsattack_main.get_active_session()\n\n print = awsattack_main.print\n get_regions = awsattack_main.get_regions\n regions = args.regions.split(',') if args.regions else get_regions('apigateway')\n\n summary_data = {}\n api_keys = {}\n \n if cleanup(awsattack_main, regions):\n print(' Old Keys Cleaned')\n summary_data['cleanup'] = True\n else:\n print(' Failed to Cleanup Keys')\n summary_data['cleanup'] = False\n \n\n return summary_data\n\n", "sub_path": "ttp/src/api_gateway_cleanup_api_keys_src.py", "file_name": "api_gateway_cleanup_api_keys_src.py", "file_ext": "py", "file_size_in_byte": 1971, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "botocore.exceptions.ClientError", "line_number": 21, "usage_type": "name"}, {"api_name": "botocore.exceptions.ClientError", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "539895812", "text": "#%%\nfrom time import time\nfrom numpy import load\nfrom scipy.spatial.distance import cosine\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.neural_network import MLPClassifier as MLP\nfrom sklearn.externals.joblib import dump\n\n#%%\n# The data is load\nM = load(\"use_data_normalized.npy\")\n\nXtrain = M.item().get('X_train')\nXtest = M.item().get('X_test')\nytrain = M.item().get('y_train')\nytest = M.item().get('y_test')\n\n#%%\n\"\"\"\nfrom sklearn.metrics import confusion_matrix\n\nypred = clf.predict(Xtest)\n\ncm = confusion_matrix(ytest, ypred)\n\nprint(f\"FMR: {100*cm[0][1]/(cm[0][1] + cm[0][0])}%\")\nprint(f\"FNMR: {100*cm[1][0]/(cm[1][0] + cm[1][1])}%\")\n\"\"\"\n\n#%%\n\nimport numpy as np\nfrom math import sqrt\nfrom numpy import linalg as LA\n\n\ndef get_d_prime(clf, Xtest, ytest):\n \n \n values_genuines = []\n values_impostors = []\n \n for pr, real in zip(Xtest, ytest):\n v1, v2 = pr[:512], pr[512:]\n v1 = v1/LA.norm(v1)\n v2 = v2/LA.norm(v2)\n distance = cosine(v1, v2)\n distance = LA.norm(distance)\n \n if real == 0:\n values_impostors.append(distance)\n else:\n values_genuines.append(distance)\n \n impostors = np.array(values_impostors)\n genuines = np.array(values_genuines)\n \n std_impostors = np.std(impostors)\n std_genuines = np.std(genuines)\n \n print(f\"std genuinos: {std_genuines}\")\n print(f\"std impostores: {std_impostors}\")\n \n mean_impostors = np.mean(impostors)\n mean_genuines = np.mean(genuines)\n \n print(f\"mean genuinos: {mean_genuines}\")\n print(f\"mean impostores: {mean_impostors}\")\n \n d_prime = abs(mean_genuines-mean_impostors)/(sqrt(0.5*(std_impostors+std_genuines)))\n \n print(d_prime)\n return d_prime\n\nget_d_prime(None, Xtest, ytest)\n \nprint(\"\\n\\n\\n\\n\\n\")", "sub_path": "distance.py", "file_name": "distance.py", "file_ext": "py", "file_size_in_byte": 1823, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.load", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 45, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 46, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.cosine", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 65, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "90072507", "text": "import logging\nimport socket\nimport uuid\n\nimport docker\nimport pytest\n\nfrom viz import Client\nfrom viz.instance import set_shared_chain_instance\n\nlog = logging.getLogger(\"vizapi\")\nlog.setLevel(logging.DEBUG)\n\n\n@pytest.fixture(scope=\"session\")\ndef private_keys():\n return [\n \"5JabcrvaLnBTCkCVFX5r4rmeGGfuJuVp4NAKRNLTey6pxhRQmf4\",\n \"5Hw9YPABaFxa2LooiANLrhUK5TPryy8f7v9Y1rk923PuYqbYdfC\",\n \"5J9DBCRX5D2ZUUuy9qV2ef9p5sfA3ydHsDs2G531bob7wbEigDJ\",\n ]\n\n\n@pytest.fixture(scope=\"session\")\ndef default_account():\n return \"viz\"\n\n\n@pytest.fixture(scope=\"session\")\ndef session_id():\n \"\"\"\n Generate unique session id.\n\n This is needed in case testsuite may run in parallel on the same server, for example if CI/CD is being used. CI/CD\n infrastructure may run tests for each commit, so these tests should not influence each other.\n \"\"\"\n return str(uuid.uuid4())\n\n\n@pytest.fixture(scope=\"session\")\ndef unused_port():\n \"\"\"Obtain unused port to bind some service.\"\"\"\n\n def _unused_port():\n with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n s.bind((\"127.0.0.1\", 0))\n return s.getsockname()[1]\n\n return _unused_port\n\n\n@pytest.fixture(scope=\"session\")\ndef docker_manager():\n \"\"\"Initialize docker management client.\"\"\"\n return docker.from_env(version=\"auto\")\n\n\n@pytest.fixture(scope=\"session\")\ndef viz_testnet(session_id, unused_port, docker_manager):\n \"\"\"Run vizd inside local docker container.\"\"\"\n port_http = unused_port()\n port_ws = unused_port()\n container = docker_manager.containers.run(\n image=\"vizblockchain/vizd:testnet\",\n name=\"viz-testnet-{}\".format(session_id),\n ports={\"8090\": port_http, \"8091\": port_ws},\n detach=True,\n )\n container.http_port = port_http\n container.ws_port = port_ws\n yield container\n container.remove(v=True, force=True)\n\n\n@pytest.fixture(scope=\"session\")\ndef viz_instance_ws(viz_testnet, private_keys):\n \"\"\"Initialize BitShares instance connected to a local testnet.\"\"\"\n viz = Client(node=\"ws://127.0.0.1:{}\".format(viz_testnet.ws_port), keys=private_keys, num_retries=-1)\n set_shared_chain_instance(viz)\n\n return viz\n\n\n@pytest.fixture(scope=\"session\")\ndef viz(viz_instance_ws):\n \"\"\"Shortcut to ws instance.\"\"\"\n\n return viz_instance_ws\n", "sub_path": "tests/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 2324, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 15, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 24, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 37, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 29, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 45, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 45, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 40, "usage_type": "call"}, {"api_name": "docker.from_env", "line_number": 55, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 52, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 58, "usage_type": "call"}, {"api_name": "viz.Client", "line_number": 78, "usage_type": "call"}, {"api_name": "viz.instance.set_shared_chain_instance", "line_number": 79, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 75, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 84, "usage_type": "call"}]} +{"seq_id": "527501907", "text": "# -*- coding:utf-8 -*-\n\n__auth__ = 'peic'\n\n''' \nPython 协程和异步IO \nyield 、yield from的使用\nasyncio 模块的使用\n'''\n\n# -*- 协程 -*-\n# 首先需要理解yield\n# 函数中有yield语句而变成generator的函数，在每次调用next()的时候执行，遇到yield语句返回，再次执行时从上次返回的yield语句处继续执行，类似CPU的中断处理\n\n\ndef consumer():\n c_r = ''\n while True:\n \t# 此处yield接受调用者发出的参数，通过send进行调用\n \t# c.send(p_n)中n的值通过yield返回，赋值给r_n\n \t# send(value)：The value argument becomes the result of the current yield expression. \n # consumer通过yield拿到消息，处理，又通过yield把结果传回。拿到的值是send传递的值，赋给c_n，返回的值是c_r\n c_n = yield c_r\n if not c_n:\n return\n print('[CONSUMER] Consuming %s...' % c_n)\n c_r = '200 OK'\n\ndef produce(c):\n c.send(None)\n p_n = 0\n while p_n < 5:\n p_n = p_n + 1\n print('[PRODUCER] Producing %s...' % p_n)\n\n\n # 调用send的generator(此处就是c)，send语句会将参数(也就是p_n)的值传给这个生成器目前yield表达式的值(c_n)\n # 而send表达式的值(也就是传给p_r的值)会是generator的下一个值(next(c_r))\n # 通过调用返回的形式，c.send()就完成了函数的调用返回，即执行了一步generator(consumer)然后返回到原函数(produce)，在这个过程中，yield起到中断返回作用\n p_r = c.send(p_n)\n print('[PRODUCER] Consumer return: %s' % p_r)\n c.close()\n\nc = consumer()\nproduce(c)\n\n\n\n\n# -*- 异步IO -*-\nimport asyncio\nimport threading\n\n# @asyncio.coroutine把一个generator标记为coroutine类型\n@asyncio.coroutine\ndef sub():\n print('sub start: ...')\n n = 10\n while True:\n print('yield start')\n # asyncio.sleep()也是一个coroutine类型的generator，所以线程不会中断，而是直接执行下一个循环，等待yield from的返回\n # 可以简单的理解为出现yield之后则开启一个协程(类似开启一个新线程),不管这个协程是否执行完毕，继续下一个循环\n # 开启新协程后，print('yield start')会因为继续执行循环被立即执行，可以通过打印结果观察\n r = yield from asyncio.sleep(1)\n n = n - 1\n print('---sub: %s, thread:%s' %(n, threading.currentThread()))\n if n == 0:\n break\n\n@asyncio.coroutine\ndef add():\n print('add start: ...')\n n = 10\n while True:\n print('yield start')\n r = yield from asyncio.sleep(2)\n n = n + 1\n print('+++add: %s, thread:%s' %(n, threading.currentThread()))\n if n > 20:\n break\n\n\n# 获取EventLoop:\nloop = asyncio.get_event_loop()\n# 执行coroutine\ntasks = [add(),sub()]\nloop.run_until_complete(asyncio.wait(tasks))\nloop.close()\n", "sub_path": "python-toys/learn-python/Asyncio_Coroutine.py", "file_name": "Asyncio_Coroutine.py", "file_ext": "py", "file_size_in_byte": 2935, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "asyncio.sleep", "line_number": 64, "usage_type": "call"}, {"api_name": "threading.currentThread", "line_number": 66, "usage_type": "call"}, {"api_name": "asyncio.coroutine", "line_number": 55, "usage_type": "attribute"}, {"api_name": "asyncio.sleep", "line_number": 76, "usage_type": "call"}, {"api_name": "threading.currentThread", "line_number": 78, "usage_type": "call"}, {"api_name": "asyncio.coroutine", "line_number": 70, "usage_type": "attribute"}, {"api_name": "asyncio.get_event_loop", "line_number": 84, "usage_type": "call"}, {"api_name": "asyncio.wait", "line_number": 87, "usage_type": "call"}]} +{"seq_id": "570212221", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Mar 2 01:45:54 2020\n\n@author: Larry Juang\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport glob\n\n\ndef assets_list(ext=\"\"):\n \"Returns files with an extension\"\n return [f[5:-4] for f in glob.glob(\"Data/\" + f\"*{ext}\")]\n\ndef backtest(daily_fluc, allocation, commission):\n \"\"\"the decision to for investment allocation was already made on 0th, (i-1)th day\"\"\"\n \"\"\"the gain/loss occurs on 1st, ith day\"\"\"\n assert len(daily_fluc) == len(allocation) # daily_fluc is \n weights = np.array([-1, -0.75, 0.5, 0.25, 0, 0.25, 0.5, 0.75, 1]) # only 100%, 50%, and 0 positions available\n total_return = 1\n max_return = 1\n drawdown = []\n return_series = []\n for i in range(len(daily_fluc)):\n pos = allocation[i]\n assert(np.sum(pos) == 1)\n transaction = 0\n if i > 0:\n if np.array_equal(pos, allocation[i-1]) == False:\n transaction = 1\n \n total_return = total_return*(1+np.sum(weights*pos)*daily_fluc[i]) - transaction*commission\n max_return = max(max_return, total_return)\n drawdown.append(total_return/max_return)\n return_series.append(total_return)\n total_return = max(0, total_return)\n\n\n return total_return, np.array(drawdown), np.array(return_series)\n\ndef data_processing(ticker = None):\n try:\n assert ticker != None\n assert ticker in assets_list(\"csv\")\n data = pd.read_csv(\"Data/\" + ticker + \".csv\")\n data[\"Date\"] = pd.to_datetime(data[\"Date\"])\n data[\"Adj Close Yesterday\"] = data[\"Adj Close\"].shift(1)\n data[\"High 5\"] = data[\"Adj Close\"].rolling(5).max()\n data[\"High 10\"] = data[\"Adj Close\"].rolling(10).max()\n data[\"High 20\"] = data[\"Adj Close\"].rolling(20).max()\n data[\"High 50\"] = data[\"Adj Close\"].rolling(50).max()\n data[\"High 75\"] = data[\"Adj Close\"].rolling(75).max()\n data[\"High 100\"] = data[\"Adj Close\"].rolling(100).max()\n data[\"High 125\"] = data[\"Adj Close\"].rolling(125).max()\n data[\"Low 20\"] = data[\"Adj Close\"].rolling(20).min()\n data[\"Low 30\"] = data[\"Adj Close\"].rolling(30).min()\n data[\"Low 40\"] = data[\"Adj Close\"].rolling(40).min()\n data[\"Low 50\"] = data[\"Adj Close\"].rolling(50).min()\n data[\"Low 75\"] = data[\"Adj Close\"].rolling(75).min()\n data[\"Low 100\"] = data[\"Adj Close\"].rolling(100).min()\n data[\"Low 125\"] = data[\"Adj Close\"].rolling(125).min()\n data[\"Daily Fluctuation\"] = (data[\"Adj Close\"].values-data[\"Adj Close Yesterday\"].values)/data[\"Adj Close Yesterday\"].values\n data = data.dropna()\n data = data.reset_index(drop = True)\n\n return data\n except: \n print(\"The ticker is incorrect, or does not exist in Data folder.\")\n\ndef action_generation(data):\n # this function takes in the expanded data set and generate the action \n # sequence based on the rules. Action sequence is [0,0,1,1,2,2,1,1,0,-1], etc\n action = [0]\n for n in range(1, len(data)):\n if (data[\"Adj Close\"][n] >= data[\"High 50\"][n]) and (action[n-1] == 0):\n position = 1\n action.append(position)\n continue\n if (data[\"Adj Close\"][n] >= data[\"High 75\"][n]) and (action[n-1] == 1):\n position = 2\n action.append(position)\n continue\n if (data[\"Adj Close\"][n] >= data[\"High 100\"][n]) and (action[n-1] == 2):\n position = 3\n action.append(position)\n continue\n if (data[\"Adj Close\"][n] >= data[\"High 125\"][n]) and (action[n-1] == 3):\n position = 4\n action.append(position)\n continue \n if (data[\"Adj Close\"][n] <= data[\"Low 20\"][n]) and (action[n-1] > 0):\n position = action[n-1] - 1\n action.append(position)\n continue\n if (data[\"Adj Close\"][n] <= data[\"Low 30\"][n]) and (action[n-1] == 0):\n position = -1\n action.append(position)\n continue\n if (data[\"Adj Close\"][n] <= data[\"Low 40\"][n]) and (action[n-1] == -1):\n position = -2\n action.append(position)\n continue\n if (data[\"Adj Close\"][n] <= data[\"Low 50\"][n]) and (action[n-1] == -2):\n position = -3\n action.append(position)\n continue\n if (data[\"Adj Close\"][n] <= data[\"Low 75\"][n]) and (action[n-1] == -3):\n position = -4\n action.append(position)\n continue \n if (data[\"Adj Close\"][n] >= data[\"High 5\"][n]) and (action[n-1] < 0):\n position = action[n-1] + 1\n action.append(position)\n continue \n position = action[n-1]\n action.append(position)\n return action\n\ndef action_allocation_conversion(action):\n # each action is a integer, e.g. -2 or 3.\n # This function converts the action into the 9 bits allocation array.\n allocation = []\n for n in range(len(action)):\n if action[n] == 0:\n allocation.append(np.array([0, 0, 0, 0, 1, 0, 0, 0, 0]))\n continue\n if action[n] == 1:\n allocation.append(np.array([0, 0, 0, 0, 0, 1, 0, 0, 0]))\n continue\n if action[n] == 2:\n allocation.append(np.array([0, 0, 0, 0, 0, 0, 1, 0, 0]))\n continue\n if action[n] == 3:\n allocation.append(np.array([0, 0, 0, 0, 0, 0, 0, 1, 0]))\n continue\n if action[n] == 4:\n allocation.append(np.array([0, 0, 0, 0, 0, 0, 0, 0, 1]))\n continue\n if action[n] == -1:\n allocation.append(np.array([0, 0, 0, 1, 0, 0, 0, 0, 0]))\n continue\n if action[n] == -2:\n allocation.append(np.array([0, 0, 1, 0, 0, 0, 0, 0, 0]))\n continue\n if action[n] == -3:\n allocation.append(np.array([0, 1, 0, 0, 0, 0, 0, 0, 0]))\n continue\n if action[n] == -4:\n allocation.append(np.array([1, 0, 0, 0, 0, 0, 0, 0, 0]))\n continue\n return allocation\n\ndef main(ticker = \"SPY\"):\n data = data_processing(ticker)\n action = action_generation(data)\n allocation = action_allocation_conversion(action)\n total_return, drawdown, return_series = backtest(data[\"Daily Fluctuation\"], allocation, 0.0003)\n # Plot the Asset's historic return\n plt.figure(1)\n plt.plot(data[\"Date\"], data[\"Adj Close\"])\n plt.title(\"Historic Adj. Close\")\n plt.xlabel(\"Date\")\n plt.ylabel(\"Price\")\n \n # Plot the strategy's backtested return\n plt.figure(2)\n plt.plot(data[\"Date\"], return_series)\n plt.title(\"Backtested Strategy Return\")\n plt.xlabel(\"Date\")\n plt.ylabel(\"Return\")\n \n # Plot the Strategy's backtested drawdown\n plt.figure(3)\n plt.plot(data[\"Date\"], drawdown)\n plt.title(\"Backtested Strategy Drawdown\")\n plt.xlabel(\"Date\")\n plt.ylabel(\"Drawdown\") \n \n \n # Plot the Strategy's Action over the backtested period\n plt.figure(4)\n plt.plot(data[\"Date\"], np.array(action)/4)\n plt.title(\"Backtested Strategy Action\")\n plt.xlabel(\"Date\")\n plt.ylabel(\"Allocated Portion\") \n\nif __name__ == \"__main__\":\n #main()\n main() \n \n \n \n \n\n \n\n \n \n \n \n\n\n \n\n \n\n", "sub_path": "Donchian with Future Knowledge Unrealistic Script.py", "file_name": "Donchian with Future Knowledge Unrealistic Script.py", "file_ext": "py", "file_size_in_byte": 7325, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "glob.glob", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array_equal", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 165, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 166, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 166, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 172, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 174, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 174, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 177, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 177, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 180, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 180, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 181, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "numpy.array", "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.xlabel", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}]} +{"seq_id": "202944470", "text": "from django.shortcuts import render\n\n# Create your views here.\nfrom django.http import HttpResponse\nimport requests\n\n\ndef index(request):\n\n soon_url = \"https://imdb-api.com/en/API/ComingSoon/k_pftzqnp0\"\n soon = requests.get(soon_url).json()\n release_url = (\"https://imdb-api.com/en/API/InTheaters/k_pftzqnp0\")\n release = requests.get(release_url).json()\n top_url = \"https://imdb-api.com/en/API/BoxOffice/k_pftzqnp0\"\n top = requests.get(top_url).json()\n lastest_url = \"https://imdb-api.com/en/API/InTheaters/k_pftzqnp0\"\n lastest = requests.get(lastest_url).json()\n context = {\"soon\": soon, \"release\": release,\n \"top\": top, \"lastest\": lastest}\n return render(request, \"home.html\", context)\n\n\ndef movies(request):\n return render(request, \"movies.html\")\n\n\ndef login(request):\n return render(request, \"login.html\")\n\n\ndef celebrities(request):\n return render(request, \"celebrities.html\")\n\n\ndef moviedetails(request, pk):\n print(pk)\n url = 'https://imdb-api.com/en/API/Title/k_lwm5x736/'+pk+'/Trailer,Ratings,Wikipedia,'\n details = requests.get(url).json()\n context = {\"details\": details}\n\n return render(request, \"movie-details.html\", context)\n\n\ndef top_movies(request):\n return render(request, \"top-movies.html\")\n\n\ndef blog(request):\n return render(request, \"blog.html\")\n\n\ndef blog_details(request):\n return render(request, \"blog-details.html\")\n\n\ndef register_user(request):\n\n if request.method == 'POST':\n user_form = userForm(request.POST)\n user_info_form = userInfoForm(request.POST, request.FILES)\n\n if user_form.is_valid() and user_info_form.is_valid():\n user = user_form.save()\n user.set_password(user.password)\n user.save()\n\n user_info = user_info_form.save(commit=False)\n user_info.user = user\n user_info.save()\n\n username = request.POST.get('username')\n password = request.POST.get('password')\n\n user = authenticate(username=username, password=password)\n\n if user:\n login(request, user)\n\n return redirect('home')\n\n else:\n context = {'user_form.errors': user_form.errors,\n 'user_info_form.errors': user_info_form.errors}\n return render(request, 'user/register.html', context)\n else:\n\n user_form = userForm()\n user_info_form = userInfoForm()\n\n context = {'user_form': user_form,\n 'user_info_form': user_info_form}\n\n return render(request, 'user/register.html', context)\n\n\ndef searchresult(request):\n\n if request.method == \"POST\":\n Query = request.POST.get(\"Query\")\n query_url = \"https://imdb-api.com/en/API/SearchMovie/k_08ug9l32/\"+Query\n query = requests.get(query_url).json()\n print(query)\n context = {\"query\": query}\n return render(request, 'result.html', context)\n", "sub_path": "movieapp/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2948, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "requests.get", "line_number": 11, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 17, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 20, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 24, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 28, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 32, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 38, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 41, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 45, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 49, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 53, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 84, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 93, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 101, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 104, "usage_type": "call"}]} +{"seq_id": "395327049", "text": "from __future__ import annotations\nfrom dataclasses import dataclass, field\nfrom travelport.models.type_profile_type_7 import TypeProfileType7\n\n__NAMESPACE__ = \"http://www.travelport.com/schema/uprofile_v37_0\"\n\n\n@dataclass\nclass TypeProfileParentHistory2:\n \"\"\"\n Parameters\n ----------\n profile_id\n Agency in which the field group is created.\n profile_type\n The type of profile this profile is for (e.g., branch, account,\n traveler). The profile type identifies which default\n attributes/elements (minimum data set) the system will insert.\n profile_name\n The name of the profile. Either the concatenated first name or last\n name of a Agent or Traveler or the name of the other profile.\n provisioning_code\n The Provisioning Code for this profile.\n \"\"\"\n class Meta:\n name = \"typeProfileParentHistory\"\n\n profile_id: None | int = field(\n default=None,\n metadata={\n \"name\": \"ProfileID\",\n \"type\": \"Attribute\",\n }\n )\n profile_type: None | TypeProfileType7 = field(\n default=None,\n metadata={\n \"name\": \"ProfileType\",\n \"type\": \"Attribute\",\n }\n )\n profile_name: None | str = field(\n default=None,\n metadata={\n \"name\": \"ProfileName\",\n \"type\": \"Attribute\",\n }\n )\n provisioning_code: None | str = field(\n default=None,\n metadata={\n \"name\": \"ProvisioningCode\",\n \"type\": \"Attribute\",\n \"min_length\": 1,\n \"max_length\": 25,\n }\n )\n", "sub_path": "travelport/models/type_profile_parent_history_2.py", "file_name": "type_profile_parent_history_2.py", "file_ext": "py", "file_size_in_byte": 1612, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "dataclasses.field", "line_number": 28, "usage_type": "call"}, {"api_name": "travelport.models.type_profile_type_7.TypeProfileType7", "line_number": 35, "usage_type": "name"}, {"api_name": "dataclasses.field", "line_number": 35, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 42, "usage_type": "call"}, {"api_name": "dataclasses.field", "line_number": 49, "usage_type": "call"}, {"api_name": "dataclasses.dataclass", "line_number": 8, "usage_type": "name"}]} +{"seq_id": "609478117", "text": "# -*- coding:utf-8 -*-\nimport string\n\nfrom bs4 import BeautifulSoup # 解析html结构的模块\nfrom src.tool.ExcelManager import writeExcel\nimport urllib.request\n\ndef makeBookListInfo(url_content):\n \"\"\"\n\t抓取图书列表信息\n\t\"\"\"\n books = [] # 初始化为一个空列表\n soup = BeautifulSoup(url_content, 'html.parser') # 开始解析\n list = soup.select(\"div.article div#subject_list ul.subject-list\")\n listsoup = BeautifulSoup(str(list), 'html.parser') # 开始解析\n booktable1 = listsoup.findAll(\"li\") # 找到所有图书信息所在标记\n for book in booktable1: # 循环遍历图书列表\n simplebook = book # book为booktable1下面的一个dl标签\n subsoup = BeautifulSoup(str(simplebook), 'html.parser') # 单本书进行解析\n book_large_img = subsoup.img['src'] # 直接使用标签，然后获得属性的值 # 图书封面：\n # print(str(book_large_img)) # 打印图片链接\n img_temp = book_large_img.split('/') # 将该链接以‘/’进行切分\n img_temp[len(img_temp) - 2] = 'spic'\n message = subsoup.find('div', attrs={\"class\": \"info\"}) # 图书信息\n book_link = message.a['href'] # 图书链接\n book_name = message.a['title'] # 图书名称\n book_info = subsoup.find('div', attrs={\"class\": \"pub\"}).string.replace('\\n ', '').replace(' \\n', '').replace(' ', '') # 图书出版信息\n try: book_star = subsoup.find('span', attrs={\"class\": \"rating_nums\"}).string # 图书星级\n except:\n book_star = '-'\n pass\n book_info = book_info.strip(' \\n') # strip用来去掉换行符和空格\n books.append([book_name, book_link, book_large_img, book_info, book_star]) # 构建自己的信息结构\n return books # 返回图书列表\n\n\"\"\"\ndef makeBookInfo(url_content):\n\n#抓取单本书\n\n soup = BeautifulSoup(url_content, 'html.parser') # 开始解析\n # 查找对应的meta标签，返回的是整个标签的内容\n book_no = soup.find('meta', attrs={'http-equiv': 'mobile-agent'})\n # 这里是有问题的，meta标签没闭合\n # 去除那本书的编号\n book_no = book_no['content'].split('subject/')[1].replace('/', '')\n # 书名，不知道为什么是有空格和换行符的，看html源文件是没有的，但是取出来就有了。\n book_name = soup.find('h1').text.replace('\\n', '')\n book_info = soup.find('div', attrs={\"id\": \"info\"}) # 出版信息\n peoples = soup.find('a', attrs={\"class\", \"rating_people\"}) # 评分的人数\n books = soup.findAll('span', attrs={\"class\", \"rating_per\"}) # 人数的比例\n\n book_intro = soup.findAll('div', attrs={\"class\": \"intro\"}) # 书籍及作者介绍\n book_alot = soup.findAll('div', attrs={\"class\": \"subject_show block5\"}) # 丛书信息 可能不存在\n\n # 使用css的选择器来获得标签。\n # 认真看了一下这部分获取的东西，html代码里面，这部分只是一些文字，用css装饰了一下。好像通过标签确实找不到\n # 而且有些分了好多层，所以才用css的选择器拉获取吧。\n\n mu_lu = soup.select('div[id*=\"dir\"]') # 表示获得所有id属性中包含dir的div\n #bookhotcomment1 = soup.select('div#wt_1 div.ctsh div.tlst div.ilst a') # 评论头像\n #bookhotcomment2 = soup.select('div#wt_1 div.ctsh div.tlst div.nlst h3 > a') # 评论详情\n #bookhotcomment3 = soup.select('div#wt_1 div.ctsh div.tlst div.clst span.starb') # 用户简介\n comments = []\n # 获取评论信息\n bookhotcomment = soup.findAll('li', attrs=['class', 'comment-item'])\n for comment in bookhotcomment:\n # print(comment)\n simple_comment = BeautifulSoup(str(comment), 'html.parser')\n book_hot_comment1 = simple_comment.find('span', attrs=['class', 'comment-info']) # 评论ID\n book_hot_comment2 = simple_comment.find('p', attrs=['class', 'comment-content']) # 评论详情\n book_hot_comment3 = simple_comment.find('span', attrs=['class', 'user-stars allstar50 rating']) # 评价等级\n try:\n book_hot_comment33 = book_hot_comment3.title()\n except:\n book_hot_comment33 = 'none'\n comments.append(\n '
'.join(['评论ID：'+book_hot_comment1.find('a').get_text(), '\\n评论内容：'+book_hot_comment2.get_text(), '\\n星级：'+book_hot_comment33.replace('\\xa0', '\\n')])) # \\xa0是不间断空白符\n mu_lu = soup.select('div[id*=\"dir\"]') # 表示获得所有id属性中包含dir的div\n\n try:\n # 获取出来的东西很多换行符和空格，不知道什么原理。这个串联有点像C++的输入输出哇。\n book_info = book_info.text.replace(' \\n', '').replace('\\n ', '').replace(' ', '')\n except:\n book_info = ''\n # 内容里面分了好多的段落，这些写得有点硬了，稍微变更一下就不能用了。\n\n try:\n bookintro1 = book_intro[0].findAll('p')\n except:\n bookintro1 = []\n try:\n bookintro2 = book_intro[1].findAll('p')\n except:\n bookintro2 = []\n book_intro = ''\n author_intro = ''\n # 相当于去除

和

\n for i in bookintro1:\n book_intro = book_intro + i.text + '\\n'\n for i in bookintro2:\n author_intro = author_intro + i.text + '\\n'\n try:\n bookalot = book_alot[0].text.replace('\\n', '').replace(' ', '')\n except:\n bookalot = '.>_<.无丛书信息'\n peoples = peoples.text\n try:\n mu_lu = mu_lu[0].text.replace(' ', '')\n mu_lu = mu_lu[1].text.replace(' ', '')\n except:\n mu_lu = '.>_<.未检索到目录信息'\n\n\n stars = []\n for i in books:\n stars.append(i.text)\n # 写在函数最后，这个函数就是将书本页面的内容提取出来，按自己的格式构造一下。\n # 所以，核心内容是那几个提取的函数find，findAll，select。理解这三个函数。其他部分都不重要了。\n # 构造成一个列表\n\n return [book_no, book_name, book_info, book_intro, author_intro, int(peoples.replace('人评价', '')), ' ,'.join(stars), bookalot, mu_lu,\n '
'.join(comments)]\n\"\"\" \"\"\"\n 抓取单本书\n \"\"\"\ndef makeBookInfo(url_content):\n soup = BeautifulSoup(url_content, 'html.parser') # 开始解析\n # 查找对应的meta标签，返回的是整个标签的内容\n book_no = soup.find('meta', attrs={'http-equiv': 'mobile-agent'})\n book_no = book_no['content'].split('subject/')[1].replace('/', '') # 书编号\n # print('编号：' + book_no)\n book_name = soup.find('h1').text.replace('\\n', '')\n # print('书名：' + book_name)\n book_info = soup.find('div', attrs={\"id\": \"info\"}) # 出版信息\n try: # 作者\n book_au = soup.select(\"div.article div#info a:nth-of-type(1)\")\n # print(book_au)\n soup_au = BeautifulSoup(str(book_au), 'html.parser')\n book_author = soup_au.get_text().replace(' \\n', '').replace('\\n ', '').replace(' ','') # .replace('[', '').replace(']', '')\n except:\n book_author = '未检索到作者信息'\n #print(\"作者：\" + book_author)\n book_tra = soup.select(\"div.article div#info a:nth-of-type(2)\") # 译者\n book_trans = u'-'\n try:\n # print(book_tra)\n soup_tra = BeautifulSoup(str(book_tra), 'html.parser')\n if not soup_tra.text.replace(' \\n', '').replace('\\n ', '').replace(' ', ''):\n book_trans = str(soup_tra.text.replace(' \\n', '').replace('\\n ', '').replace(' ', ''))\n except:\n book_trans = '-'\n #print('译者：' + book_trans)\n # 出版年，页数，定价，出版社\n book_message = book_info.findAll('span', attrs={'class': 'pl'})\n try:\n book_year = u'-'\n book_price = u'-'\n book_page = 0\n book_public = u'-'\n for item in book_message:\n if item.string == u\"出版年:\":\n book_year = item.nextSibling.strip().split(\"/\")[0].strip() # nextSibling查找下一个兄弟节点 ，strip()去掉空格，split用来分割\n if item.string == u\"页数:\":\n book_page = item.nextSibling.strip().split(\"/\")[0].strip()\n if item.string == u\"定价:\":\n book_price = item.nextSibling.strip().split(\"/\")[0].strip()\n if item.string == u\"出版社:\":\n book_public = item.nextSibling.strip().split(\"/\")[0].strip()\n except:\n print(\"抓取出版年和页数，定价时出错\")\n book_year = '未检索到年份信息'\n book_price = '未检索到价格'\n book_page = '未检索到页数'\n book_public = '未检索到出版社信息'\n #print('出版社：' + book_public)\n #print('出版年：' + book_year)\n #print('页数:' + book_page)\n #print('定价:' + book_price)\n\n # 评论人数：\n try:\n infoVoteNum = soup.find('span', {'property': 'v:votes'})\n votenum = infoVoteNum.get_text().strip()\n except:\n # print(\"评论人数不足\")\n votenum = '0'\n\n # print(\"评论人数：\" + votenum)\n\n # 星级评价：\n infostar = soup.find('strong', {'property': 'v:average'})\n # print(infostar)\n try:\n stars = \"0\"\n stars = infostar.get_text().strip()\n if stars == '':\n stars = \"0\"\n except:\n print(\"抓取评价出错\")\n stars = \"0\" # 使用u或者U处理unicode文本\n # print(\"星级评价：\" + stars)\n\n # 评价人数的比例\n try:\n ratio = soup.findAll('span', attrs={\"class\", \"rating_per\"})\n voteratio = []\n for i in ratio:\n voteratio.append(i.text)\n except:\n voteratio = \"0\"\n # print(\"人数比例：\")\n # print(voteratio)\n try:\n book_intro = ''\n author_intro = ''\n total = soup.findAll('h2')\n for previous in total:\n #print(previous)\n #print(previous.text.replace('·', '').replace('\\n', '').replace(' \\n', '').replace(' ', ''))\n word = previous.text.replace('·', '').replace('\\n', '').replace(' \\n', '').replace(' ', '')\n if word == '内容简介      ':\n book = previous.find_next_sibling() # 找下一个节点\n #print(book)\n soup_book = BeautifulSoup(str(book), 'html.parser')\n book_intro1 = soup_book.findAll('div', attrs={\"class\": \"intro\"})\n #print(book_intro1)\n #print(len(book_intro1))\n if len(book_intro1) == 1:\n intro1 = book_intro1[0].findAll('p')\n if len(book_intro1) == 2:\n intro1 = book_intro1[1].findAll('p')\n if word == '作者简介      ':\n author = previous.find_next_sibling() # 找下一个节点\n #print(author)\n soup_author = BeautifulSoup(str(author), 'html.parser')\n author_intro1 = soup_author.findAll('div', attrs={\"class\": \"intro\"})\n if len(author_intro1) == 1:\n intro2 = author_intro1[0].findAll('p')\n if len(author_intro1) == 2:\n intro2 = author_intro1[1].findAll('p')\n for i in intro1:\n book_intro = book_intro + i.text + '\\n'\n for i in intro2:\n author_intro = author_intro + i.text + '\\n'\n #print(book_intro)\n #print(author_intro)\n except:\n book_intro = '-'\n author_intro = '-'\n\n # 丛书信息 可能不存在\n book_oth = soup.findAll('div', attrs={\"class\": \"subject_show block5\"})\n try:\n book_others = book_oth[0].text.replace('\\n', '').replace(' ', '')\n except:\n book_others = '-'\n #print('丛书：' + book_others)\n # 目录\n mu_lu = soup.select('div[id*=\"dir\"]') # 表示获得所有id属性中包含dir的div\n # print(mu_lu)\n if len(mu_lu) == 1:\n try:\n mu_lu = mu_lu[0].text.replace(' ', '')\n except:\n mu_lu = '-'\n if len(mu_lu) == 2:\n try:\n mu_lu = mu_lu[1].text.replace(' · · · · · · (收起)', '').replace(' ', '').replace('\\n', '|')\n except:\n mu_lu = '-'\n else:\n mu_lu = '-'\n\n #print('目录：' + mu_lu)\n\n # 推荐书籍\n try:\n movie_like = soup.find('div', attrs={\"class\": \"content clearfix\"}) # 相关电影推荐 可能不存在\n recomm = ''\n movie = movie_like.findAll('dl')\n for i in movie:\n recomm += i.dd.a.string + ','\n recommendations = recomm.strip(string.punctuation).strip()\n except:\n recommendations = '暂无推荐'\n #print('推荐书籍:' + recommendations)\n\n # 获取评论信息\n comments = []\n bookcomment = soup.findAll('li', attrs=['class', 'comment-item'])\n for comment in bookcomment:\n simple_comment = BeautifulSoup(str(comment), 'html.parser')\n book_comment1 = simple_comment.find('span', attrs=['class', 'comment-info']) # 评论ID\n comment_id = book_comment1.find('a').string.replace(\"'\", \"\\\\'\").replace('\"', '\\\\\"')\n book_comment2 = simple_comment.find('p', attrs=['class', 'comment-content']).get_text().replace(' \\n', '').replace('\\n ', '').replace(' ', '').replace(\"'\", \"\\\\'\").replace('\"', '\\\\\"') # 评论详情\n book_comment11 = BeautifulSoup(str(book_comment1), 'html.parser')\n book_comment3 = book_comment11.select(\"span:nth-of-type(2)\") # 评价等级\n # print(book_comment3)\n try:\n book_comment33 = str(book_comment3).split('title=\"')[1].split('\">')[0]\n if book_comment33 == '力荐':\n book_comment33 = '★★★★★'\n elif book_comment33 == '推荐':\n book_comment33 = '★★★★'\n elif book_comment33 == '还行':\n book_comment33 = '★★★'\n elif book_comment33 == '较差':\n book_comment33 = '★★'\n elif book_comment33 == '很差':\n book_comment33 = '★'\n else:\n book_comment33 = '该用户未评分'\n except:\n book_comment33 = '该用户未评分'\n comments.append(\n '
'.join(['评论ID：' + comment_id, '评论内容：' + book_comment2,\n '评价等级：' + book_comment33.replace('\\xa0', '\\n')])\n )# \\xa0是不间断空白符\n #-----------如果是写入excel，由于是把每个list元素拆分开写，所以应该是string类型-------\n return [book_no, book_name, book_author, book_public, book_trans, book_year, book_page, book_price, votenum,\n stars, ','.join(voteratio), book_intro, author_intro, book_others, mu_lu, recommendations, '
'.join(comments), book_info.text.replace(' \\n', '').replace('\\n ', '').replace(' ', '').replace(\"'\", '\"')]\n\n\ndef makeBookTag(url_content, path='D:\\workplace\\pythonwork\\douban_book_catch_zzq\\darabase/bookTag.xlsx'):\n \"\"\"\n\t抓取标签提取写入Excel\n\t\"\"\"\n soup = BeautifulSoup(url_content, 'html.parser') # 开始解析\n booktag1 = soup.select('div#content div.article div div')\n print(str(booktag1))\n taglist = [['标签类别', '标签名', '标签链接', '点击量']] # Excel里面的标题\n for booktag2 in booktag1:\n # print(str(booktag2))\n soup1 = BeautifulSoup(str(booktag2), 'html.parser') # 开始解析\n booktag2 = soup1.find('a', attrs={'class': 'tag-title-wrapper'})\n tagType = booktag2['name'] # 标签类别\n booktag3 = soup1.findAll(\"a\")\n booktag4 = soup1.findAll(\"b\") # 该标签下的图书数量\n for i in range(0, len(booktag4)): # len(booktag4)就可以表示一行有多少种不同类型的分类\n tag = booktag3[i + 1].string # 标签名\n taglink = 'https://book.douban.com'+booktag3[i + 1]['href'] # 链接\n tagnum = booktag4[i].string\n taglist.append([tagType, tag, taglink, tagnum]) # 把内容加进去，按照标题的顺序\n writeExcel(path, taglist) # 将内容写入excel中\n print(\"写入EXCEL成功\")\n\n\ndef testBookTag():\n file = open('D:\\workplace\\pythonwork\\douban_book_catch_zzq\\darabase/booktag.html', 'rb')\n content = file.read()\n makeBookTag(content, r'D:\\workplace\\pythonwork\\douban_book_catch_zzq\\darabase/booktag.xlsx')\n\n\ndef testManyBook():\n file = open('D:/workplace/pythonwork/douban_book_catch_zzq/web抓取/0.html', 'rb')\n content = file.read()\n books = makeBookListInfo(content)\n for i in books:\n print(i)\n\n\ndef testBookInfo():\n print('D:/workplace/pythonwork/HelloWorld/book/流行/1000134三毛流浪记全集.html')\n file = open('D:/workplace/pythonwork/HelloWorld/book/流行/1000134三毛流浪记全集.html', 'rb') # 读取文件\n content = file.read()\n book = makeBookInfo(content)\n for i in book:\n print('*' * 50) # 分割一下，方便查看\n print(i) # 打印一下内容\n\n\nif __name__ == '__main__':\n # testManyBook()\n testBookInfo() # 提取书详细页面的信息\n # testBookTag() # 测试图书标签抓取是否成功\n", "sub_path": "src/tool/TagManager.py", "file_name": "TagManager.py", "file_ext": "py", "file_size_in_byte": 16944, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 13, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 15, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 19, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 128, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 139, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 148, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 225, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 236, "usage_type": "call"}, {"api_name": "string.punctuation", "line_number": 284, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 293, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 297, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 329, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 335, "usage_type": "call"}, {"api_name": "src.tool.ExcelManager.writeExcel", "line_number": 345, "usage_type": "call"}]} +{"seq_id": "250432081", "text": "#\n# voice-skill-sdk\n#\n# (C) 2021, Deutsche Telekom AG\n#\n# This file is distributed under the terms of the MIT license.\n# For details see the file LICENSE in the top directory.\n#\n\n#\n# Internationalization\n#\n\nimport re\nimport random\nimport logging\nimport subprocess\nfrom pathlib import Path\nfrom functools import reduce\nfrom types import MappingProxyType\nfrom typing import Dict, Iterable, List, Optional, Mapping, Text, Tuple, Union\n\nimport yaml\nfrom yaml.reader import ReaderError\nfrom yaml.scanner import ScannerError\nfrom babel import dates, lists, support\n\n# Place your `[lang].po` files to `locale` directory\nLOCALE_DIR = \"locale\"\n\nPROGRAM = \"pybabel\"\nPROGRAM_NOT_FOUND = f'Failed to launch \"{PROGRAM} %s\": not found. Make sure \"{PROGRAM}\" is in your PATH.'\n\nRE_TRANSLATIONS = re.compile(r\"^[a-z]{2}(-[A-Z]{2})?$\")\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_locale_dir(locale_dir: Text = None) -> Path:\n \"\"\"Returns locales folder location\"\"\"\n return Path(locale_dir or LOCALE_DIR)\n\n\ndef make_lazy(func, alt=None):\n \"\"\"\n Make lazy translation function\n\n :param func: function to call\n :param alt: alternative function if translation is not set\n :return:\n \"\"\"\n\n def lazy_func(*args, **kwargs):\n \"\"\"Lazy translations wrapper\"\"\"\n\n from skill_sdk.intents import r\n\n try:\n return getattr(r.get_translation(), func)(*args, **kwargs)\n except TypeError:\n logger.error(\"Calling translation functions outside of request context.\")\n except AttributeError as e:\n logger.exception(\"%s\", repr(e))\n return alt(*args, **kwargs) if callable(alt) else None\n\n return lazy_func\n\n\n_ = make_lazy(\"gettext\", lambda m, *a, **kw: m)\n_n = make_lazy(\n \"ngettext\", lambda singular, plural, n, *a, **kw: singular if n == 1 else plural\n)\n_a = make_lazy(\"getalltexts\", lambda m, *a, **kw: [m])\n\n\nclass TranslationError(Exception):\n \"\"\"\n Exception raised when a translation could not be performed due to a missing ``.mo`` file, a missing translation\n key or if there are no suitable translations available in the text service.\n \"\"\"\n\n\nclass Message(str):\n \"\"\"String object that encapsulates formatting parameters\"\"\"\n\n # Message id\n key: Text\n\n # Message string (un-formatted)\n value: Text\n\n # Positional arguments\n args: Tuple\n\n # Keyword arguments\n kwargs: Dict\n\n def __new__(cls, value, key=None, *args, **kwargs):\n \"\"\"\n Create a message with msgstr/msgid and format parameters\n\n :return:\n \"\"\"\n message = (\n value.format(*args, **kwargs)\n if isinstance(value, str) and (args or kwargs)\n else value\n )\n string = super().__new__(cls, message)\n string.key = key or value\n string.args = args\n string.kwargs = kwargs\n string.value = value\n return string\n\n def format(self, *args, **kwargs) -> \"Message\":\n \"\"\"\n Create and return new Message object with given format parameters\n\n :return:\n \"\"\"\n message = Message(self.value, self.key, *args, **kwargs)\n return message\n\n def __add__(self, other: Union[\"Message\", Text]) -> \"Message\":\n \"\"\"\n Concatenate messages (or Message and str)\n\n @param other:\n @return:\n \"\"\"\n if isinstance(other, Message):\n value = self.value + other.value\n args = self.args + other.args\n kwargs = {**self.kwargs, **other.kwargs}\n else:\n value = self.value + other\n args, kwargs = self.args, self.kwargs\n\n return Message(value, self.key, *args, **kwargs)\n\n def join(self, iterable: Iterable[Union[\"Message\", Text]]):\n \"\"\"\n Join messages in iterable and return a concatenated Message.\n\n @param iterable:\n @return:\n \"\"\"\n return reduce(lambda x, y: x + self + y, iterable)\n\n def strip(self, __chars: Optional[Text] = None) -> \"Message\":\n \"\"\"\n Return new Message object with stripped value\n\n :return:\n \"\"\"\n message = Message(\n self.value.strip(__chars), self.key, *self.args, **self.kwargs\n )\n return message\n\n\nclass Translations(support.Translations):\n \"\"\"Lazy translations, return Message object instead of formatted string\"\"\"\n\n def __init__(self, lang: Text = None, fp=None):\n self.lang = lang\n super().__init__(fp)\n\n def gettext(self, message, *args, **kwargs) -> Message:\n return Message(super().gettext(message), message, *args, **kwargs)\n\n def ngettext(self, singular, plural, n, *args, **kwargs) -> Message:\n return Message(super().ngettext(singular, plural, n), singular, *args, **kwargs)\n\n def format_list(self, elements: List[Text], style=\"standard\"):\n \"\"\"\n Join list elements\n [items, item2, item3] -> 'item1, item2 and item3'\n\n :param elements:\n :param style:\n :return:\n \"\"\"\n return lists.format_list(elements, style=style, locale=self.lang)\n\n # Backward compatibility\n nl_join = format_list\n\n def nl_build(self, header: Text, elements: List[Text]) -> Text:\n \"\"\"\n Build list in natural language:\n (header, [items, item2, item3]) -> 'Header: item1, item2 and item3.'\n\n :param header: list header\n :param elements: list elements\n :return:\n \"\"\"\n return Message(\": \").join((header, self.format_list(elements)))\n\n def format_datetime(self, datetime=None, format=\"medium\", tzinfo=None) -> Text:\n \"\"\"Format datetime according to the locale\"\"\"\n return dates.format_datetime(datetime, format, tzinfo, self.lang)\n\n def format_date(self, date=None, format=\"medium\") -> Text:\n \"\"\"Format date according to the locale\"\"\"\n return dates.format_date(date, format, self.lang)\n\n def format_time(self, time=None, format=\"medium\", tzinfo=None) -> Text:\n \"\"\"Format time according to the locale\"\"\"\n return dates.format_time(time, format, tzinfo, self.lang)\n\n def format_timedelta(\n self,\n delta,\n granularity=\"second\",\n threshold=0.85,\n add_direction=False,\n format=\"long\",\n ) -> Text:\n \"\"\"Format a time delta according to the rules of the given locale\"\"\"\n return dates.format_timedelta(\n delta, granularity, threshold, add_direction, format, self.lang\n )\n\n\nclass MultiStringTranslation(Translations):\n \"\"\"Translations that allows single key to have multiple values\"\"\"\n\n def _parse(self, fp):\n \"\"\"\n Load catalogue from YAML file\n\n @param fp:\n @return:\n \"\"\"\n\n try:\n catalog = yaml.safe_load(fp)\n self._catalog = {\n k: v if isinstance(v, list) else [v] for k, v in catalog.items()\n }\n except (ReaderError, ScannerError) as ex:\n logger.exception(\n \"Could not load translations from %s: %s\", repr(fp), repr(ex)\n )\n raise RuntimeError from ex\n\n def __repr__(self):\n return f\"<{type(self).__name__}: {repr(self.files)}>\"\n\n def gettext(self, message, *args, **kwargs):\n logger.debug(\"Translating message %s to %s\", repr(message), repr(self.lang))\n try:\n candidates = self._catalog[message]\n logger.debug(\"%s candidates: %s\", len(candidates), repr(candidates))\n return Message(random.choice(candidates), message, *args, **kwargs)\n except LookupError:\n logger.warning(\"No translation for key: %s\", repr(message))\n return super().gettext(message, *args, **kwargs)\n\n def ngettext(self, singular, plural, n, *args, **kwargs):\n logger.debug(\"Translating %s/%s/%s to %s\", singular, plural, n, self.lang)\n return self.gettext(singular if n == 1 else plural, *args, **kwargs)\n\n def getalltexts(self, key, *args, **kwargs):\n logger.debug(\"Retrieving all translation messages for %s in %s\", key, self.lang)\n try:\n candidates = self._catalog[key]\n logger.debug(\"%s candidates: %s\", len(candidates), repr(candidates))\n return [Message(value, key, *args, **kwargs) for value in candidates]\n except LookupError:\n logger.warning(\"No translation for key: %s\", key)\n return [super().gettext(key, *args, **kwargs)]\n\n\ndef compile_locales(locale_dir: Text = None, force: bool = False):\n \"\"\"\n Compile all languages available in locale_dir:\n launches `pybabel compile` to compile .po to .mo files\n\n :param locale_dir:\n :param force: force compilation even if *.mo files exist\n :return:\n \"\"\"\n command = \"compile\"\n\n for po_file in get_locale_dir(locale_dir).glob(\"*.po\"):\n\n mo_file = po_file.with_suffix(\".mo\")\n if mo_file.exists() and not force:\n logger.info(\"Skipping %s: %s exists\", po_file.name, mo_file)\n continue\n\n logger.info(\"Compiling %s ...\", po_file.name)\n try:\n\n result = subprocess.check_output(\n [\n PROGRAM,\n command,\n \"-i\",\n str(po_file),\n \"-o\",\n str(mo_file),\n ],\n text=True,\n stderr=subprocess.STDOUT,\n )\n logger.info(result)\n\n except FileNotFoundError:\n logger.error(PROGRAM_NOT_FOUND, command)\n\n except subprocess.CalledProcessError as ex:\n logger.error(\"Failed to compile %s: %s\", po_file.name, ex.stdout)\n raise\n\n\ndef _load_yaml(locale_dir: Text = None) -> Dict[Text, MultiStringTranslation]:\n \"\"\"\n Load multi-string translations from YAML files\n\n @param locale_dir:\n @return:\n \"\"\"\n\n logger.info(\"Loading YAML translations...\")\n\n return {\n yaml_file.stem: MultiStringTranslation(yaml_file.stem, yaml_file.open(mode=\"r\"))\n for yaml_file in get_locale_dir(locale_dir).glob(\"*.yaml\")\n if RE_TRANSLATIONS.match(yaml_file.stem)\n }\n\n\ndef _load_gettext(locale_dir: Text = None) -> Dict[Text, Translations]:\n \"\"\"\n Load `gettext` translations from *.po/*.mo files\n\n @param locale_dir:\n @return:\n \"\"\"\n\n logger.info(\"Loading gettext translations...\")\n\n compile_locales(locale_dir)\n return {\n mo_file.stem: Translations(mo_file.stem, mo_file.open(mode=\"rb\"))\n for mo_file in get_locale_dir(locale_dir).glob(\"*.mo\")\n if RE_TRANSLATIONS.match(mo_file.stem)\n }\n\n\ndef load_translations(locale_dir: Text = None) -> Mapping[Text, Translations]:\n \"\"\"\n Load local languages available in locale_dir\n\n :param locale_dir:\n :return:\n \"\"\"\n\n translations = _load_yaml(locale_dir) or _load_gettext(locale_dir)\n\n return MappingProxyType(translations)\n", "sub_path": "skill_sdk/i18n.py", "file_name": "i18n.py", "file_ext": "py", "file_size_in_byte": 10928, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "re.compile", "line_number": 34, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 36, "usage_type": "call"}, {"api_name": "typing.Text", "line_number": 39, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 41, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 39, "usage_type": "name"}, {"api_name": "skill_sdk.intents.r.get_translation", "line_number": 59, "usage_type": "call"}, {"api_name": "skill_sdk.intents.r", "line_number": 59, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 87, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 90, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 93, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 96, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 125, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 125, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 142, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 142, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 142, "usage_type": "name"}, {"api_name": "functools.reduce", "line_number": 149, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 151, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 151, "usage_type": "name"}, {"api_name": "babel.support.Translations", "line_number": 163, "usage_type": "attribute"}, {"api_name": "babel.support", "line_number": 163, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 166, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 176, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 176, "usage_type": "name"}, {"api_name": "babel.lists.format_list", "line_number": 185, "usage_type": "call"}, {"api_name": "babel.lists", "line_number": 185, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 190, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 190, "usage_type": "name"}, {"api_name": "babel.dates.format_datetime", "line_number": 203, "usage_type": "call"}, {"api_name": "babel.dates", "line_number": 203, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 201, "usage_type": "name"}, {"api_name": "babel.dates.format_date", "line_number": 207, "usage_type": "call"}, {"api_name": "babel.dates", "line_number": 207, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 205, "usage_type": "name"}, {"api_name": "babel.dates.format_time", "line_number": 211, "usage_type": "call"}, {"api_name": "babel.dates", "line_number": 211, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 209, "usage_type": "name"}, {"api_name": "babel.dates.format_timedelta", "line_number": 222, "usage_type": "call"}, {"api_name": "babel.dates", "line_number": 222, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 220, "usage_type": "name"}, {"api_name": "yaml.safe_load", "line_number": 239, "usage_type": "call"}, {"api_name": "yaml.reader.ReaderError", "line_number": 243, "usage_type": "name"}, {"api_name": "yaml.scanner.ScannerError", "line_number": 243, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 257, "usage_type": "call"}, {"api_name": "typing.Text", "line_number": 277, "usage_type": "name"}, {"api_name": "subprocess.check_output", "line_number": 298, "usage_type": "call"}, {"api_name": "subprocess.STDOUT", "line_number": 308, "usage_type": "attribute"}, {"api_name": "subprocess.CalledProcessError", "line_number": 315, "usage_type": "attribute"}, {"api_name": "typing.Text", "line_number": 320, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 320, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 337, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 337, "usage_type": "name"}, {"api_name": "typing.Text", "line_number": 355, "usage_type": "name"}, {"api_name": "types.MappingProxyType", "line_number": 365, "usage_type": "call"}, {"api_name": "typing.Mapping", "line_number": 355, "usage_type": "name"}]} +{"seq_id": "307441123", "text": "from flask import Flask, render_template, request\nfrom script import assistant_gui\n\n\napp = Flask(__name__)\n\n\n@app.route(\"/\")\ndef load_home():\n name = \"Ratnesh\"\n return render_template('home.html', name=name)\n\n\n@app.route(\"/func\", methods={'GET', 'POST'})\ndef process():\n name = request.form.get('input')\n '''name=assistant_gui.record_audio()\n assistant_gui.respond(name)'''\n assistant_gui.respond(name)\n return render_template('home.html', name=name)\n\nif __name__ == \"__main__\":\n app.run(debug=False)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 525, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 16, "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": "script.assistant_gui.respond", "line_number": 19, "usage_type": "call"}, {"api_name": "script.assistant_gui", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "372000362", "text": "import json\nimport re\n\n\nsource_file=open('./soruce_tifu.txt', 'a')\ntarget_file=open('./target_tifu.txt', 'a')\n\n# Read entire file\nposts = []\nwith open('tifu_all_tokenized_and_filtered.json', 'r') as fp:\n for line in fp:\n posts.append(json.loads(line))\n\n# Json entries\n#print(posts[50000].keys())\n\n#print(posts[168].get('selftext_without_tldr').replace('\\n', ' '))\n# print(posts[50000].get('tldr'))\n# print(posts[50000].get('title'))\n\n# exit()\n\n\ni=0\nfor element in posts:\n\n if not element.get('tldr') is None:\n\n \n target_text=element.get('tldr')\n\n else:\n target_text='shamane'\n \n\n\n source_text=element.get('selftext_without_tldr')\n\n\n \n\n source_text=source_text.replace(\"\\n\", \" \").lstrip(' ')\n target_text=target_text.replace(\"\\n\", \" \").lstrip(' ')\n\n\n source_text=\" \".join(source_text.split())\n target_text=\" \".join(target_text.split())\n\n\n\n soruce_wordList = re.sub(\"[^\\w]\", \" \", source_text).split()\n target_wordList = re.sub(\"[^\\w]\", \" \", target_text).split()\n\n\n\n if len(soruce_wordList) < 100:\n continue\n\n if len(target_wordList)<25:\n continue\n\n \n if len(target_wordList) >= len(source_text):\n continue\n\n \n i=i+1\n\n\n\n source_file.write(source_text+'\\n')\n target_file.write(target_text+'\\n')\n\n\n # if i==170:\n \n # print(source_text)\n # exit()\n\n\n\n\n\n\n\n\n\nexit()\n# [u'title_tokenized',\n# u'permalink',\n# u'title',\n# u'url',\n# u'num_comments',\n# u'tldr', # (optional)\n# u'created_utc',\n# u'trimmed_title_tokenized',\n# u'ups',\n# u'selftext_html',\n# u'score',\n# u'upvote_ratio',\n# u'tldr_tokenized', # (optional)\n# u'selftext',\n# u'trimmed_title',\n# u'selftext_without_tldr_tokenized',\n# u'id',\n# u'selftext_without_tldr']", "sub_path": "Trainning data/Reddit-TIFU-data-pre/clean.py", "file_name": "clean.py", "file_ext": "py", "file_size_in_byte": 1771, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "json.loads", "line_number": 12, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 51, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "601811749", "text": "# 실습\n# cifar10 과 cifar 100 으로 모델 만들것\n# trainable=True, False\n# FC 로 만든것과 Avarage Pooling 으로 만든거 비교\n\nfrom tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.applications import VGG16, VGG19\nimport tensorflow as tf\nfrom tensorflow.keras.datasets import cifar100, cifar10\n\n\n# 1. 데이터 구성\n(x_train, y_train), (x_test, y_test) = cifar100.load_data()\n\nx_train = x_train.reshape(50000, 32 * 32 * 3)\nx_test = x_test.reshape(10000, 32 * 32 * 3)\n# 2차원으로 reshpae 하고 다시 4차원으로 원위치\n# print(x_train.shape, x_test.shape) # (50000, 3072) (10000, 3072)\n\nfrom sklearn.preprocessing import MinMaxScaler, StandardScaler, MaxAbsScaler, RobustScaler, QuantileTransformer, PowerTransformer\nscaler = MinMaxScaler()\n# scaler = StandardScaler()\nx_train = scaler.fit_transform(x_train) # 한번에 써줄 수 있음, train 에서만 쓴다\nx_test = scaler.transform(x_test)\n\nx_train = x_train.reshape(x_train.shape[0], 32,32, 3)\nx_test = x_test.reshape(x_test.shape[0], 32,32, 3)\n\nfrom tensorflow.keras.utils import to_categorical\ny_train = to_categorical(y_train)\ny_test = to_categorical(y_test)\n\n\n# 2. 모델링\nvgg19 = VGG19(weights='imagenet', include_top=False, input_shape=(32, 32, 3))\n\n# model = VGG16()\n# model = VGG19()\n\nvgg19.trainable=False\n\nmodel = Sequential()\nmodel.add(vgg19)\nmodel.add(Flatten())\nmodel.add(Dense(100, activation='relu'))\n# model.add(GlobalAveragePooling2D())\nmodel.add(Dense(100, activation='softmax'))\n\n# model.summary()\n# model.trainable=False # 전체 모델 훈련을 동결한다\n\n# 3. 평가, 훈련\nmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) # 다중분류에서 loss 는 categorical_crossentropy\n\nfrom tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\nes = EarlyStopping(monitor='val_loss', patience=5, mode='min', verbose=1)\n# cp = ModelCheckpoint(monitor='val_loss', save_best_only=True, mode='auto',\n# filepath='./_save/ModelCheckPoint/keras48_MCP_cifar10.hdf5')\n\nmodel.fit(x_train, y_train, epochs=100, batch_size=1500, callbacks=[es,], validation_split=0.08, verbose=2)\n\n\nloss = model.evaluate(x_test, y_test)\nprint('loss : ', loss[0])\nprint('accuracy : ', loss[1])\n\n\n\n'''\n결과 출력\n1. cifar 10\ntrainable = True, FC : loss=?, acc=?\nloss : 0.7416892647743225\naccuracy : 0.8048999905586243\ntrainable = True, GAP : loss=?, acc=?\nloss : 0.74931800365448\naccuracy : 0.7973999977111816\ntrainable = False, FC : loss=?, acc=?\nloss : 1.158708930015564\naccuracy : 0.6047000288963318\ntrainable = False, GAP : loss=?, acc=?\nloss : 1.2098288536071777\naccuracy : 0.579800009727478\n\n2. cifar 100\ntrainable = True, FC : loss=?, acc=?\nloss : 3.0773749351501465\naccuracy : 0.29660001397132874\ntrainable = True, GAP : loss=?, acc=?\nloss : 4.605196475982666\naccuracy : 0.009999999776482582\ntrainable = False, FC : loss=?, acc=?\nloss : 2.6485595703125\naccuracy : 0.33880001306533813\ntrainable = False, GAP : loss=?, acc=?\nloss : 2.650249719619751\naccuracy : 0.34630000591278076\n'''\n\n", "sub_path": "keras2/keras72_01_cifar_VGG19.py", "file_name": "keras72_01_cifar_VGG19.py", "file_ext": "py", "file_size_in_byte": 3152, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "tensorflow.keras.datasets.cifar100.load_data", "line_number": 14, "usage_type": "call"}, {"api_name": "tensorflow.keras.datasets.cifar100", "line_number": 14, "usage_type": "name"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 22, "usage_type": "call"}, {"api_name": "tensorflow.keras.utils.to_categorical", "line_number": 31, "usage_type": "call"}, {"api_name": "tensorflow.keras.utils.to_categorical", "line_number": 32, "usage_type": "call"}, {"api_name": "tensorflow.keras.applications.VGG19", "line_number": 36, "usage_type": "call"}, {"api_name": "tensorflow.keras.models.Sequential", "line_number": 43, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Flatten", "line_number": 45, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 46, "usage_type": "call"}, {"api_name": "tensorflow.keras.layers.Dense", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.keras.callbacks.EarlyStopping", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "280569015", "text": "import inspect\nimport platform\nfrom functools import (partial,\n reduce)\nfrom itertools import chain\nfrom types import ModuleType\nfrom typing import (Any,\n List,\n Union)\n\nfrom hypothesis import strategies\n\nfrom paradigm.definitions import (is_supported,\n unsupported)\nfrom paradigm.definitions.utils import (_add,\n _to_contents,\n _update)\nfrom paradigm.hints import (MethodDescriptorType,\n WrapperDescriptorType)\nfrom tests.strategies import modules_list\n\n\ndef to_inner_callables(objects: List[Union[ModuleType, type]]) -> List[Any]:\n return list(filter(callable,\n chain.from_iterable(map(_to_contents, objects))))\n\n\nmodules_callables_list = to_inner_callables(modules_list)\nmodules_callables = strategies.sampled_from(modules_callables_list)\nclasses_list = list(filter(is_supported,\n filter(inspect.isclass, modules_callables_list)))\nclasses_callables_list = to_inner_callables(classes_list)\nclasses = strategies.sampled_from(classes_list)\nclasses_callables = strategies.sampled_from(classes_callables_list)\nmethods = classes_callables.filter(inspect.isfunction)\n\n\ndef is_method_descriptor(object_: Any) -> bool:\n return isinstance(object_, MethodDescriptorType)\n\n\nmethods_descriptors = (classes_callables.filter(is_method_descriptor)\n .filter(is_supported))\n\n\ndef is_wrapper_descriptor(object_: Any) -> bool:\n return isinstance(object_, WrapperDescriptorType)\n\n\nwrappers_descriptors = (classes_callables.filter(is_wrapper_descriptor)\n .filter(is_supported))\nfunctions = (modules_callables.filter(inspect.isfunction)\n .filter(is_supported))\nbuilt_in_functions = (modules_callables.filter(inspect.isbuiltin)\n .filter(is_supported))\ntop_coverage_callables = set()\n_add(top_coverage_callables, '_compression', 'BaseStream')\n_update(top_coverage_callables, 'builtins', ['dict',\n 'set.__init__', 'set.__lt__'])\n_add(top_coverage_callables, 'configparser', 'DuplicateSectionError')\n_add(top_coverage_callables, 'ctypes', 'c_byte')\n_add(top_coverage_callables, 'formatter', 'NullFormatter.pop_alignment')\n_update(top_coverage_callables, 'inspect', ['Signature.__init__',\n 'getinnerframes'])\n_add(top_coverage_callables, 'logging', 'Handler.get_name')\n_add(top_coverage_callables, 'os', 'times_result')\n_add(top_coverage_callables, 'sqlite3', 'Connection.rollback')\n_add(top_coverage_callables, 'symtable', 'Symbol.is_global')\n_add(top_coverage_callables, 'tarfile', 'EOFHeaderError')\n_add(top_coverage_callables, 'telnetlib', 'Telnet.fileno')\n_add(top_coverage_callables, 'time', 'struct_time')\n_update(top_coverage_callables, 'tkinter', ['Misc.focus_force',\n 'Wm.iconmask'])\n_add(top_coverage_callables, 'turtle', 'RawTurtle.turtlesize')\n_add(top_coverage_callables, 'weakref', 'ref')\n_add(top_coverage_callables, 'zipfile', 'error')\ntop_coverage_callables = strategies.sampled_from(list(top_coverage_callables))\ncallables = (built_in_functions\n | classes\n | functions\n | methods\n | methods_descriptors\n | wrappers_descriptors)\npartial_callables = callables.map(partial)\nif platform.python_implementation() == 'PyPy':\n overloaded_callables = strategies.nothing()\nelse:\n overloaded_callables = strategies.sampled_from([int, reduce, super, type])\nunsupported_callables = strategies.sampled_from(\n list(unsupported.built_in_functions\n | unsupported.classes\n | unsupported.methods_descriptors\n | unsupported.wrappers_descriptors))\n", "sub_path": "tests/signatures_tests/strategies.py", "file_name": "strategies.py", "file_ext": "py", "file_size_in_byte": 3893, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "typing.List", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 23, "usage_type": "name"}, {"api_name": "types.ModuleType", "line_number": 23, "usage_type": "name"}, {"api_name": "itertools.chain.from_iterable", "line_number": 25, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 25, "usage_type": "name"}, {"api_name": "paradigm.definitions.utils._to_contents", "line_number": 25, "usage_type": "argument"}, {"api_name": "typing.Any", "line_number": 23, "usage_type": "name"}, {"api_name": "tests.strategies.modules_list", "line_number": 28, "usage_type": "argument"}, {"api_name": "hypothesis.strategies.sampled_from", "line_number": 29, "usage_type": "call"}, {"api_name": "hypothesis.strategies", "line_number": 29, "usage_type": "name"}, {"api_name": "paradigm.definitions.is_supported", "line_number": 30, "usage_type": "argument"}, {"api_name": "inspect.isclass", "line_number": 31, "usage_type": "attribute"}, {"api_name": "hypothesis.strategies.sampled_from", "line_number": 33, "usage_type": "call"}, {"api_name": "hypothesis.strategies", "line_number": 33, "usage_type": "name"}, {"api_name": "hypothesis.strategies.sampled_from", "line_number": 34, "usage_type": "call"}, {"api_name": "hypothesis.strategies", "line_number": 34, "usage_type": "name"}, {"api_name": "inspect.isfunction", "line_number": 35, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 38, "usage_type": "name"}, {"api_name": "paradigm.hints.MethodDescriptorType", "line_number": 39, "usage_type": "argument"}, {"api_name": "paradigm.definitions.is_supported", "line_number": 43, "usage_type": "argument"}, {"api_name": "typing.Any", "line_number": 46, "usage_type": "name"}, {"api_name": "paradigm.hints.WrapperDescriptorType", "line_number": 47, "usage_type": "argument"}, {"api_name": "paradigm.definitions.is_supported", "line_number": 51, "usage_type": "argument"}, {"api_name": "paradigm.definitions.is_supported", "line_number": 53, "usage_type": "argument"}, {"api_name": "inspect.isfunction", "line_number": 52, "usage_type": "attribute"}, {"api_name": "paradigm.definitions.is_supported", "line_number": 55, "usage_type": "argument"}, {"api_name": "inspect.isbuiltin", "line_number": 54, "usage_type": "attribute"}, {"api_name": "paradigm.definitions.utils._add", "line_number": 57, "usage_type": "call"}, {"api_name": "paradigm.definitions.utils._update", "line_number": 58, "usage_type": "call"}, {"api_name": "paradigm.definitions.utils._add", "line_number": 60, "usage_type": "call"}, {"api_name": "paradigm.definitions.utils._add", "line_number": 61, "usage_type": "call"}, {"api_name": "paradigm.definitions.utils._add", "line_number": 62, "usage_type": "call"}, {"api_name": "paradigm.definitions.utils._update", "line_number": 63, "usage_type": "call"}, {"api_name": "paradigm.definitions.utils._add", "line_number": 65, "usage_type": "call"}, {"api_name": "paradigm.definitions.utils._add", "line_number": 66, "usage_type": "call"}, {"api_name": "paradigm.definitions.utils._add", "line_number": 67, "usage_type": "call"}, {"api_name": "paradigm.definitions.utils._add", "line_number": 68, "usage_type": "call"}, {"api_name": "paradigm.definitions.utils._add", "line_number": 69, "usage_type": "call"}, {"api_name": "paradigm.definitions.utils._add", "line_number": 70, "usage_type": "call"}, {"api_name": "paradigm.definitions.utils._add", "line_number": 71, "usage_type": "call"}, {"api_name": "paradigm.definitions.utils._update", "line_number": 72, "usage_type": "call"}, {"api_name": "paradigm.definitions.utils._add", "line_number": 74, "usage_type": "call"}, {"api_name": "paradigm.definitions.utils._add", "line_number": 75, "usage_type": "call"}, {"api_name": "paradigm.definitions.utils._add", "line_number": 76, "usage_type": "call"}, {"api_name": "hypothesis.strategies.sampled_from", "line_number": 77, "usage_type": "call"}, {"api_name": "hypothesis.strategies", "line_number": 77, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 84, "usage_type": "argument"}, {"api_name": "platform.python_implementation", "line_number": 85, "usage_type": "call"}, {"api_name": "hypothesis.strategies.nothing", "line_number": 86, "usage_type": "call"}, {"api_name": "hypothesis.strategies", "line_number": 86, "usage_type": "name"}, {"api_name": "hypothesis.strategies.sampled_from", "line_number": 88, "usage_type": "call"}, {"api_name": "hypothesis.strategies", "line_number": 88, "usage_type": "name"}, {"api_name": "functools.reduce", "line_number": 88, "usage_type": "name"}, {"api_name": "hypothesis.strategies.sampled_from", "line_number": 89, "usage_type": "call"}, {"api_name": "hypothesis.strategies", "line_number": 89, "usage_type": "name"}, {"api_name": "paradigm.definitions.unsupported.built_in_functions", "line_number": 90, "usage_type": "attribute"}, {"api_name": "paradigm.definitions.unsupported", "line_number": 90, "usage_type": "name"}, {"api_name": "paradigm.definitions.unsupported.classes", "line_number": 91, "usage_type": "attribute"}, {"api_name": "paradigm.definitions.unsupported", "line_number": 91, "usage_type": "name"}, {"api_name": "paradigm.definitions.unsupported.methods_descriptors", "line_number": 92, "usage_type": "attribute"}, {"api_name": "paradigm.definitions.unsupported", "line_number": 92, "usage_type": "name"}, {"api_name": "paradigm.definitions.unsupported.wrappers_descriptors", "line_number": 93, "usage_type": "attribute"}, {"api_name": "paradigm.definitions.unsupported", "line_number": 93, "usage_type": "name"}]} +{"seq_id": "220621344", "text": "import requests\nfrom bs4 import BeautifulSoup\n\nclass BTCSpider(object):\n def __init__(self):\n self.url = \"http://8btc.com/forum-61-{}.html\"\n self.headers = {\n \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/68.0.3440.106 Safari/537.36\"\n }\n self.data_list = []\n self.data_detai_list = []\n\n def get_data(self, url):\n response = requests.get(url, headers=self.headers)\n # 当前页面的字符集 是gbk ;\n # 一般都是 转成对应的字符串处理 但是 可能转码的时候有小问题\n # 出现问题之后, 使用原生的 bytes\n data = response.text\n return data\n\n # 批量的url\n def get_url_list(self):\n return [self.url.format(i) for i in range(1, 5)]\n\n def bs4_demo_parse_data(self, data):\n # 解析 bs4\n parse_data = BeautifulSoup(data, 'lxml')\n # 解析数据 title 和url list\n # 拿到了目标标签的 list\n a_list = parse_data.select('.xst')\n for a in a_list:\n dict = {}\n dict['text'] = a.get_text()\n dict['url'] = a.get('href')\n self.data_list.append(dict)\n\n def run(self):\n # 循环遍历 列表页面\n url_list = self.get_url_list()\n for url in url_list[:1]:\n print(url)\n data = self.get_data(url)\n self.bs4_demo_parse_data(data)\n\n # 等列表页 抓取完毕; 在抓取详情页\n for detail in self.data_list:\n detail_url = detail['url']\n print(detail_url)\n\n detail_data = self.get_data(detail_url)\n\n #解析详情页的数据\n detail_parse = BeautifulSoup(detail_data,'lxml')\n\n detail['result'] = detail_parse.select('.t_f')[0].get_text().replace('\\n','')\n print(self.data_list)\n\nspider = BTCSpider()\nspider.run()\n", "sub_path": "requests_bs4_test.py", "file_name": "requests_bs4_test.py", "file_ext": "py", "file_size_in_byte": 1939, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "requests.get", "line_number": 14, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 27, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "384641067", "text": "from django.conf.urls import patterns, url\nfrom usuarios import views\n\nurlpatterns = patterns('',\n\t#urls de compras\n\turl(r'^$', views.index, name=\"index\"),\n\turl(r'^agregar-usuario/$', views.agregarUsuario, name=\"agregarUsuario\"),\n\turl(r'^editar-usuario/$', views.editarUsuario, name=\"editarUsuario\"),\n\t#vistas ajax\n\turl(r'^desactivar-usuario/$', views.desactivarUsuario, name=\"desactivarUsuario\"),\n\turl(r'^cambiar-contrasenia/$', views.cambiarContrasenia, name=\"cambiarContrasenia\"),\n)", "sub_path": "usuarios/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 485, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.conf.urls.patterns", "line_number": 4, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "usuarios.views.index", "line_number": 6, "usage_type": "attribute"}, {"api_name": "usuarios.views", "line_number": 6, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "usuarios.views.agregarUsuario", "line_number": 7, "usage_type": "attribute"}, {"api_name": "usuarios.views", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "usuarios.views.editarUsuario", "line_number": 8, "usage_type": "attribute"}, {"api_name": "usuarios.views", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "usuarios.views.desactivarUsuario", "line_number": 10, "usage_type": "attribute"}, {"api_name": "usuarios.views", "line_number": 10, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "usuarios.views.cambiarContrasenia", "line_number": 11, "usage_type": "attribute"}, {"api_name": "usuarios.views", "line_number": 11, "usage_type": "name"}]} +{"seq_id": "525274191", "text": "#!/usr/bin/env python\nfrom docopt import docopt\n\nfrom poeml.graph import Graph\nfrom poeml.ginst_builder import GraphInstanceBuilder\nfrom poeml.ginst_builder import make_props\nfrom poeml.template import apply_template\n\nusage = \"\"\"ASP XML generator\nUsage:\n\taspo.py \n\"\"\"\n\ndef d_print(s):\n\tprint(s)\n\ndef aspo(graph, roots):\n\td_print('Started')\n\tnodes = len(graph.nodes)\n\ttiles = nodes // roots\n\tassert tiles*roots==nodes, 'Node count is not divisible by root count'\n\n\tdef node_id(tile, id):\n\t\treturn 'node_%d_%s' % (tile, graph.nodes[id] if type(id) is int else id)\n\t\n\tinst = GraphInstanceBuilder()\n\t\n\td_print('Generating devices...')\n\tinst.start_devices()\n\t\n\tfor tile in range(tiles):\n\t\tbase_id = tile*roots\n\t\tfor id in range(nodes):\n\t\t\troot_idx = (id - base_id) if (id>=base_id and id']),\n\t\troots=int(args[''])\n\t)\n\td_print('Done')\n\tprint(xml)\n\n\nif __name__ == \"__main__\":\n\tmain()\n", "sub_path": "aspo.py", "file_name": "aspo.py", "file_ext": "py", "file_size_in_byte": 1887, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "poeml.ginst_builder.GraphInstanceBuilder", "line_number": 26, "usage_type": "call"}, {"api_name": "poeml.ginst_builder.make_props", "line_number": 35, "usage_type": "call"}, {"api_name": "poeml.template.apply_template", "line_number": 67, "usage_type": "call"}, {"api_name": "docopt.docopt", "line_number": 70, "usage_type": "call"}, {"api_name": "poeml.graph.Graph.load", "line_number": 72, "usage_type": "call"}, {"api_name": "poeml.graph.Graph", "line_number": 72, "usage_type": "name"}]} +{"seq_id": "434576582", "text": "from __future__ import absolute_import, division, print_function\n\nimport stripe\nfrom tests.helper import StripeResourceTest\n\n\nclass PayoutTest(StripeResourceTest):\n\n def test_list_payouts(self):\n stripe.Payout.list()\n self.requestor_mock.request.assert_called_with(\n 'get',\n '/v1/payouts',\n {}\n )\n\n def test_cancel_payout(self):\n self.mock_response({\n 'id': 'po_cancel',\n 'status': 'canceled',\n })\n\n payout = stripe.Payout(id='po_cancel')\n\n self.assertTrue(payout is payout.cancel(idempotency_key='idem-foo'))\n self.assertEquals('canceled', payout.status)\n self.assertEquals('po_cancel', payout.id)\n\n self.requestor_mock.request.assert_called_with(\n 'post',\n '/v1/payouts/po_cancel/cancel',\n {},\n {'Idempotency-Key': 'idem-foo'}\n )\n", "sub_path": "tests/api_resources/test_payout.py", "file_name": "test_payout.py", "file_ext": "py", "file_size_in_byte": 914, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "tests.helper.StripeResourceTest", "line_number": 7, "usage_type": "name"}, {"api_name": "stripe.Payout.list", "line_number": 10, "usage_type": "call"}, {"api_name": "stripe.Payout", "line_number": 10, "usage_type": "attribute"}, {"api_name": "stripe.Payout", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "99717270", "text": "#!/usr/bin/python3\n\nimport os\nimport time\nimport logging\nimport logging.config\nimport yaml\nimport click\nfrom certs_net import CertsNet\nfrom datapower_net import DataPowerNet\nfrom manager_net import ManagerNet\nfrom analytics_net import AnalyticsNet\nfrom prometheus_client import start_http_server\nimport metrics_graphite\nfrom prometheus_client import Gauge\n\n\nlogger = logging.getLogger('trawler')\n\nlogging.basicConfig(\n level=logging.getLevelName(logging.INFO),\n format=\"%(levelname)s: %(asctime)s (%(module)s:%(lineno)d): %(message)s\"\n)\n\n\nclass Trawler(object):\n \"\"\" The main trawling \"\"\"\n config = {\n 'prometheus': {'enabled': False},\n 'graphite': {'enabled': False}\n }\n # Default looping frequency\n frequency = 10\n # Default to True, but detected unless overridden in config\n use_kubeconfig = True\n # Default path for secrets in container build - override with envvar SECRETS\n secrets_path = '/app/secrets'\n graphite = None\n gauges = {}\n\n def __init__(self, config_file=None):\n self.secrets_path = os.getenv('SECRETS', self.secrets_path)\n if config_file:\n self.load_config(config_file)\n if 'logging' in self.config:\n logging.config.dictConfig(self.config['logging'])\n self.logger = logging.getLogger(__name__)\n if self.config['prometheus']['enabled']:\n port = self.config['prometheus'].get('port')\n logger.info('Starting prometheus http port at http://0.0.0.0:{}'.format(port))\n start_http_server(port)\n if self.config['graphite']['enabled']:\n self.graphite = metrics_graphite.instance(self.config['graphite'])\n\n use_kubeconfig = False\n if 'trawler' in self.config:\n use_kubeconfig = self.config['trawler'].get('use_kubeconfig')\n self.frequency = self.config['trawler'].get('frequency', self.frequency)\n\n if use_kubeconfig:\n # Explicit override that we want to use kubeconfig rather than in cluster k8s comms\n self.use_kubeconfig = True\n else:\n # Check for KUBERNETES_SERVICE_HOST to determine if running within kubernetes\n if os.getenv('KUBERNETES_SERVICE_HOST'):\n self.use_kubeconfig = False\n\n def read_secret(self, key):\n # Helper function read secrets from mounted k8s secrets\n try:\n with open(\"{}/{}\".format(self.secrets_path, key), 'r') as secret:\n value = secret.read().rstrip()\n return value\n except FileNotFoundError as e:\n logger.exception(e)\n return None\n\n def load_config(self, config_file):\n try:\n with open(config_file, 'r') as config_yaml:\n self.config = yaml.safe_load(config_yaml)\n except FileNotFoundError as e:\n logger.exception(e)\n exit(2)\n\n def set_gauge(self, component, target_name, value, pod_name=None):\n logger.debug(\"Entering set_gauge - params: ({}, {}, {}, {})\".format(component, target_name, value, pod_name))\n logger.debug(type(value))\n if type(value) is float or type(value) is int:\n target_name = target_name.replace('-', '_')\n if self.config['prometheus']['enabled']:\n prometheus_target = \"{}_{}\".format(component, target_name.replace('.', '_'))\n if prometheus_target not in self.gauges:\n logger.info(\"Creating gauge {}\".format(prometheus_target))\n if pod_name:\n self.gauges[prometheus_target] = Gauge(\n prometheus_target,\n prometheus_target, ['pod'])\n else:\n self.gauges[prometheus_target] = Gauge(\n prometheus_target,\n prometheus_target)\n\n logger.debug(\"Setting gauge {} to {}\".format(\n self.gauges[prometheus_target]._name, value))\n if pod_name:\n self.gauges[prometheus_target].labels(pod_name).set(value)\n else:\n self.gauges[prometheus_target].set(value)\n if self.config['graphite']['enabled']:\n if pod_name:\n metric_name = \"{}.{}.{}\".format(component, pod_name, target_name)\n else: \n metric_name = \"{}.{}\".format(component, target_name)\n self.graphite.stage(metric_name, value)\n\n def trawl_metrics(self):\n # Initialise\n logger.info(\"Laying nets...\")\n nets = []\n if 'certs' in self.config['nets'] and self.config['nets']['certs'].get('enabled', True):\n nets.append(CertsNet(self.config['nets']['certs'], self))\n if 'datapower' in self.config['nets'] and self.config['nets']['datapower'].get('enabled', True):\n nets.append(DataPowerNet(self.config['nets']['datapower'], self))\n if 'manager' in self.config['nets'] and self.config['nets']['manager'].get('enabled', True):\n nets.append(ManagerNet(self.config['nets']['manager'], self))\n if 'analytics' in self.config['nets'] and self.config['nets']['analytics'].get('enabled', True):\n nets.append(AnalyticsNet(self.config['nets']['analytics'], self))\n\n while True:\n logger.info(\"Trawling for metrics...\")\n for net in nets:\n net.fish()\n if self.graphite:\n self.graphite.store()\n time.sleep(self.frequency)\n\n\n@click.command()\n@click.version_option()\n@click.option('-c', '--config', required=False, envvar='CONFIG',\n help=\"Specifies an alternative config file\",\n default=None,\n type=click.Path())\ndef cli(config=None):\n trawler = Trawler(config)\n trawler.trawl_metrics()\n\n\nif __name__ == '__main__':\n cli()\n", "sub_path": "trawler.py", "file_name": "trawler.py", "file_ext": "py", "file_size_in_byte": 5921, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.getLevelName", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 42, "usage_type": "call"}, {"api_name": "logging.config.dictConfig", "line_number": 46, "usage_type": "call"}, {"api_name": "logging.config", "line_number": 46, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 47, "usage_type": "call"}, {"api_name": "prometheus_client.start_http_server", "line_number": 51, "usage_type": "call"}, {"api_name": "metrics_graphite.instance", "line_number": 53, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 65, "usage_type": "call"}, {"api_name": "yaml.safe_load", "line_number": 81, "usage_type": "call"}, {"api_name": "prometheus_client.Gauge", "line_number": 96, "usage_type": "call"}, {"api_name": "prometheus_client.Gauge", "line_number": 100, "usage_type": "call"}, {"api_name": "certs_net.CertsNet", "line_number": 122, "usage_type": "call"}, {"api_name": "datapower_net.DataPowerNet", "line_number": 124, "usage_type": "call"}, {"api_name": "manager_net.ManagerNet", "line_number": 126, "usage_type": "call"}, {"api_name": "analytics_net.AnalyticsNet", "line_number": 128, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 136, "usage_type": "call"}, {"api_name": "click.command", "line_number": 139, "usage_type": "call"}, {"api_name": "click.version_option", "line_number": 140, "usage_type": "call"}, {"api_name": "click.option", "line_number": 141, "usage_type": "call"}, {"api_name": "click.Path", "line_number": 144, "usage_type": "call"}]} +{"seq_id": "161275987", "text": "'''Train CIFAR10 with PyTorch.'''\nfrom __future__ import print_function\n\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport torch.backends.cudnn as cudnn\n\nimport torchvision\nimport torchvision.transforms as transforms\n\nimport os\nimport argparse\n\n#from train_test import train, test\nfrom data_utils import trainloader, testloader\n\n#from utils import progress_bar\nfrom torch.autograd import Variable\nfrom cnn2layer import CNN\n\nimport numpy as np\nfrom skopt.callbacks import DeadlineStopper\nfrom skopt import gp_minimize\nfrom skopt import dump\nfrom space_division import HyperSpace\nfrom mpi4py import MPI\n\n\nparser = argparse.ArgumentParser(description='PyTorch CIFAR10 Training')\nparser.add_argument('--lr', default=0.1, type=float, help='learning rate')\nparser.add_argument('--num_epochs', default=20, type=int, help='number of epochs')\nparser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')\nargs = parser.parse_args()\n\nbest_acc = 0 # best test accuracy\nstart_epoch = 0 # start from epoch 0 or last checkpoint epoch\n\ncomm = MPI.COMM_WORLD\nrank = comm.Get_rank()\nsize = comm.Get_size()\n\n\ndef objective(space):\n kernel_size1, stride1, dropout1, kernel_size2, stride2, dropout2, learning_rate = space\n\n # Hyper Parameters\n num_epochs = 10\n kernel_size1 = int(kernel_size1)\n stride1 = int(kernel_size1)\n dropout1 = float(dropout1)\n kernel_size2 = int(kernel_size2)\n stride2 = int(stride2)\n dropout2 = float(dropout2)\n learning_rate = float(learning_rate)\n\n cnn = CNN()\n cnn.cuda()\n\n criterion = nn.CrossEntropyLoss()\n optimizer = torch.optim.Adam(cnn.parameters(), lr=learning_rate)\n\n # Train the Model\n for epoch in range(num_epochs):\n for i, (images, labels) in enumerate(trainloader):\n images = Variable(images).cuda()\n labels = Variable(labels).cuda()\n\n # Forward + Backward + Optimize\n optimizer.zero_grad()\n outputs = cnn(images)\n loss = criterion(outputs, labels)\n loss.backward()\n optimizer.step()\n\n if (i+1) % 100 == 0:\n print ('Epoch [%d/%d], Step [%d/%d], Loss: %.4f'\n %(epoch+1, num_epochs, i+1, 60000//128, loss.data[0]))\n\n # Test the Model\n correct = 0\n total = 0\n for images, labels in testloader:\n images = Variable(images).cuda()\n outputs = cnn(images)\n _, predicted = torch.max(outputs.data, 1)\n total += labels.size(0)\n correct += (predicted.cpu() == labels).sum()\n\n test_accuracy = 100 * correct / total\n return loss.data[0]\n\n\ndef main():\n if rank == 0:\n hyperparameters = {'kernelSize1': np.arange(2,10),\n 'stride1': np.arange(1, 5),\n 'dropout1': np.linspace(0.0, 0.8),\n 'kernelSize2': np.arange(2,10),\n 'stride2': np.arange(1, 5),\n 'dropout2': np.linspace(0.0, 0.8),\n 'learningRate': np.linspace(0.001, 0.1)}\n\n hyperspace = HyperSpace(hyperparameters)\n all_intervals = hyperspace.fold_space()\n hyperspaces = hyperspace.hyper_permute(all_intervals)\n subspace_keys, subspace_boundaries = hyperspace.format_hyperspace(hyperspaces)\n else:\n subspace_keys, subspace_boundaries = None, None\n\n space = comm.scatter(subspace_boundaries, root=0)\n\n deadline = DeadlineStopper(18000)\n # Gaussian process minimization (see scikit-optimize skopt module for other optimizers)\n res_gp = gp_minimize(objective, space, n_calls=50, callback=deadline, random_state=0, verbose=True)\n # Each worker will write their results to disk\n dump(res_gp, '/lustre/atlas/proj-shared/csc237/ygx/safari_zone/vision/pytorch/cifar2/mobilenet/hyper_results/gp_subspace_' + str(rank))\n\n\nif __name__=='__main__':\n main()\n", "sub_path": "hyperdrive_cifar2.py", "file_name": "hyperdrive_cifar2.py", "file_ext": "py", "file_size_in_byte": 3954, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 31, "usage_type": "call"}, {"api_name": "mpi4py.MPI.COMM_WORLD", "line_number": 40, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 40, "usage_type": "name"}, {"api_name": "cnn2layer.CNN", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 62, "usage_type": "attribute"}, {"api_name": "data_utils.trainloader", "line_number": 66, "usage_type": "argument"}, {"api_name": "torch.autograd.Variable", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.autograd.Variable", "line_number": 68, "usage_type": "call"}, {"api_name": "data_utils.testloader", "line_number": 84, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 103, "usage_type": "call"}, {"api_name": "space_division.HyperSpace", "line_number": 105, "usage_type": "call"}, {"api_name": "skopt.callbacks.DeadlineStopper", "line_number": 114, "usage_type": "call"}, {"api_name": "skopt.gp_minimize", "line_number": 116, "usage_type": "call"}, {"api_name": "skopt.dump", "line_number": 118, "usage_type": "call"}]} +{"seq_id": "432648740", "text": "import json\nfrom enum import Enum\n\n'''\n ‘Thoughts are the shadows of our feelings-always darker, emptier, and simpler’. Nietzsche, The gay science p. 203. \n'''\n\nclass Msg(Enum):\n\tsimple_msg = 1\n\tsadness = 2\n\thappiness = 3\n\nclass Connect:\n\tdef __init__(self):\n\t\tself.file_path = 'log.json'\n\t\tself.initData()\n\t\tself.total = 0\n\t\tself.sadness = 0.0\n\t\tself.happiness = 0.0\n\n\tdef initData(self):\n\t\tdata = {}\n\t\tdata['last_msg'] = ''\n\t\tdata['last_sentiment'] = ''\n\t\tdata['sadness_degree'] = 0.5\n\t\tdata['happiness_degree'] = 0.5\n\t\tdata['msg_log'] = []\n\t\tdata['state'] = 0 # Nao alterado\n\t\twith open(self.file_path, 'w+') as file:\n\t\t\tf_line = file.read(1)\n\t\t\tif not f_line:\n\t\t\t\tjson.dump(data, file)\n\t\n\tdef balance(self, msg_type):\n\t\tself.total += 1\n\t\tif msg_type is Msg.happiness:\n\t\t\tself.happiness += 1 \n\t\t\t\n\t\telif msg_type is Msg.sadness:\n\t\t\tself.sadness += 1\n\n\tdef toggleState(self, data):\n\t\tif data['state'] == 0:\n\t\t\tdata['state'] = 1 # Alterado\n\t\telse:\n\t\t\tdata['state'] = 1 # Alterado\n\n\tdef write(self, msg_type, msg):\n\t\twith open(self.file_path, 'r+') as file:\n\t\t\tdata = json.load(file)\n\t\t\tif msg_type is Msg.simple_msg:\n\t\t\t\tdata['last_msg'] = msg\n\t\t\t\tdata['msg_log'].append(msg)\n\t\t\t\tprint(\"Mensagem inserida no json\")\n\t\t\t\t\n\t\t\telif msg_type is Msg.sadness:\n\t\t\t\t\n\t\t\t\tdata['last_msg'] = msg\n\t\t\t\tdata['msg_log'].append(msg)\n\n\t\t\t\tself.balance(msg_type)\n\t\t\t\tdata['last_sentiment'] = 'neg'\n\t\t\t\tdata['sadness_degree'] = self.sadness/self.total\n\t\t\t\tdata['happiness_degree'] = self.happiness/self.total\n\t\t\t\tprint(\"Mensagem inserida no json\")\n\t\t\t\n\t\t\telif msg_type is Msg.happiness:\n\t\t\t\t\n\t\t\t\tdata['last_msg'] = msg\n\t\t\t\tdata['msg_log'].append(msg)\n\n\t\t\t\tself.balance(msg_type)\n\t\t\t\tdata['last_sentiment'] = 'pos'\n\t\t\t\tdata['happiness_degree'] = self.happiness/self.total\n\t\t\t\tdata['sadness_degree'] = self.sadness/self.total\n\t\t\t\tprint(\"Mensagem inserida no json\")\n\n\t\t\tself.toggleState(data)\n\t\t\tfile.seek(0)\n\t\t\tjson.dump(data, file)\n\t\t\tfile.truncate()\n\t\t\t\n\n", "sub_path": "Project2/Modules/Connection/connector.py", "file_name": "connector.py", "file_ext": "py", "file_size_in_byte": 1944, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "enum.Enum", "line_number": 8, "usage_type": "name"}, {"api_name": "json.dump", "line_number": 32, "usage_type": "call"}, {"api_name": "json.load", "line_number": 50, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 80, "usage_type": "call"}]} +{"seq_id": "643305049", "text": "from flask import Flask, render_template\n\napp = Flask(__name__)\n\n@app.route(\"/hello\")\ndef root_page():\n response = render_template('hello.html', greetings=\"Saludos, Amigos\")\n return response\n \n if __name__ == \"__main__\":\n app.run\n", "sub_path": "project/hello02.py", "file_name": "hello02.py", "file_ext": "py", "file_size_in_byte": 249, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "591890311", "text": "# ------------------------------------------------------------------------------\n# The MIT License (MIT)\n#\n# Copyright (c) 2014-2021 Digital Sapphire\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n# ------------------------------------------------------------------------------\nimport logging\nimport typing as t\n\ntry:\n from flask import Flask, g, request\nexcept ImportError:\n Flask = None\n g = None\n request = None\n\nlog = logging.getLogger(__name__)\n\n\nclass DSFlaskResponse:\n bad_request = 400\n conflict = 409\n created = 201\n forbidden = 403\n not_found = 404\n ok = 200\n unauthorized = 401\n\n @staticmethod\n def log_request(**kwargs):\n if Flask is None:\n raise RuntimeError('dsdev-utils[flask] is not installed')\n\n log.info(\"Path: %s\", request.path)\n log.info(\"Method: %s\", request.method)\n log.info(\"Remote Addr: %s\", request.remote_addr)\n if hasattr(g, \"user\"):\n log.info(\"User ID: %s\", g.user.get_id())\n\n parsed_data = dict()\n\n for k, v in request.headers.items():\n if \"Authorization\" in k:\n v = \"*****\"\n parsed_data[k] = v\n\n data = None\n if 'data' in kwargs.keys():\n data = kwargs['data']\n del kwargs['data']\n\n parsed_data.update(kwargs)\n\n msg = f\"Headers: {parsed_data}\"\n log.info(msg)\n if data is not None:\n DSFlaskResponse.log_request_data(data)\n\n @staticmethod\n def log_request_data(data):\n if \"password\" in data.keys():\n temp_password = data[\"password\"]\n data[\"password\"] = \"*****\"\n msg = f\"Request Data: {data}\"\n data[\"password\"] = temp_password\n else:\n msg = f\"Request Data: {data}\"\n\n log.info(msg)\n\n @staticmethod\n def resp_data(\n data: t.Union[t.Dict, t.List], status_code: int\n ) -> t.Tuple[t.Dict[str, t.Any], int]:\n return (\n {\n \"data\": data,\n },\n status_code,\n )\n\n @staticmethod\n def resp_data_created(data: t.Union[t.Dict, t.List]):\n return DSFlaskResponse.resp_data(data, DSFlaskResponse.created)\n\n @staticmethod\n def resp_data_ok(data: t.Union[t.Dict, t.List]):\n return DSFlaskResponse.resp_data(data, DSFlaskResponse.ok)\n\n @staticmethod\n def resp_message(msg, status_code) -> t.Tuple[t.Dict[str, t.Any], int]:\n return (\n {\n \"message\": msg,\n },\n status_code,\n )\n\n @staticmethod\n def resp_message_bad_request(msg=\"Bad Request\") -> t.Tuple[t.Dict[str, t.Any], int]:\n return DSFlaskResponse.resp_message(msg, DSFlaskResponse.bad_request)\n\n @staticmethod\n def resp_message_conflict(msg=\"Conflict\") -> t.Tuple[t.Dict[str, t.Any], int]:\n return DSFlaskResponse.resp_message(msg, DSFlaskResponse.conflict)\n\n @staticmethod\n def resp_message_forbidden(msg=\"Forbidden\") -> t.Tuple[t.Dict[str, t.Any], int]:\n return DSFlaskResponse.resp_message(msg, DSFlaskResponse.forbidden)\n\n @staticmethod\n def resp_message_not_found(msg=\"Not Found\") -> t.Tuple[t.Dict[str, t.Any], int]:\n return DSFlaskResponse.resp_message(msg, DSFlaskResponse.not_found)\n\n @staticmethod\n def resp_message_unauthorized(\n msg=\"Unauthorized\",\n ) -> t.Tuple[t.Dict[str, t.Any], int]:\n return DSFlaskResponse.resp_message(msg, DSFlaskResponse.not_found)\n", "sub_path": "dsdev_utils/flask.py", "file_name": "flask.py", "file_ext": "py", "file_size_in_byte": 4490, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Flask", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.g", "line_number": 31, "usage_type": "name"}, {"api_name": "flask.request", "line_number": 32, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.request.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 51, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 52, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 52, "usage_type": "name"}, {"api_name": "flask.request.remote_addr", "line_number": 53, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 53, "usage_type": "name"}, {"api_name": "flask.g", "line_number": 54, "usage_type": "argument"}, {"api_name": "flask.g.user.get_id", "line_number": 55, "usage_type": "call"}, {"api_name": "flask.g.user", "line_number": 55, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 55, "usage_type": "name"}, {"api_name": "flask.request.headers.items", "line_number": 59, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 59, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 90, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 90, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 90, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 91, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 91, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 91, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 100, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 100, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 100, "usage_type": "attribute"}, {"api_name": "typing.Union", "line_number": 104, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 104, "usage_type": "attribute"}, {"api_name": "typing.List", "line_number": 104, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 108, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 108, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 108, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 117, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 117, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 117, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 121, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 121, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 121, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 125, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 125, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 125, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 129, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 129, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 129, "usage_type": "attribute"}, {"api_name": "typing.Tuple", "line_number": 135, "usage_type": "attribute"}, {"api_name": "typing.Dict", "line_number": 135, "usage_type": "attribute"}, {"api_name": "typing.Any", "line_number": 135, "usage_type": "attribute"}]} +{"seq_id": "533437472", "text": "# -*- coding:utf-8 -*-\n\nfrom __future__ import (absolute_import, division, print_function, unicode_literals)\n\n\nimport tornado.httpserver\nimport tornado.ioloop\nimport tornado.options\nimport tornado.web\nimport tornado.httpclient\nimport tornado.gen\nfrom tornado.httpclient import HTTPRequest\n\ntry:\n from urllib import urlencode, quote\nexcept ImportError:\n from urllib.parse import urlencode, quote\nimport json\n\n\ntry:\n from lxml import etree\nexcept ImportError:\n import sys\n sys.exit(1)\n\n\nfrom tornado.options import define, options\ndefine(\"port\", default=8080, help=\"run on given port\", type=int)\n\n\ndef make_jw_encode(string_to_encode):\n return quote(string_to_encode.encode('gbk'))\n\n\ndef make_jw_weekdays(chinese):\n if chinese == \"一\":\n return \"Mon\"\n elif chinese == \"二\":\n return \"Tue\"\n elif chinese == \"三\":\n return \"Wed\"\n elif chinese == \"四\":\n return \"Thu\"\n elif chinese == \"五\":\n return \"Fri\"\n elif chinese == \"六\":\n return \"Sat\"\n elif chinese == \"日\":\n return \"Sun\"\n\n\ndef clean(s):\n \"\"\"Returns version of s without undesired characters in it.\"\"\"\n wanted = \"0123456789-\"\n out = \"\"\n for c in s:\n if c in wanted:\n out += c\n return out\n\n\ndef make_jw_weeks(duration, w_type):\n\n du_list = duration.split(\"-\")\n if len(du_list) == 1:\n start = 0\n end = 18\n else:\n start = int(du_list[0])\n end = int(du_list[1])\n\n return_str = \"\"\n\n for x in range(start, end + 1):\n if w_type == \"all\" or x % 2 != (0 if w_type == \"odd\" else 1):\n return_str = return_str + \",\" + str(x)\n\n return return_str[1:]\n\n\ndef make_headers(referer=\"\"):\n dic = {}\n dic[\"User-Agent\"] = \"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/36.0.1985.143 Safari/537.36\"\n dic[\"Connection\"] = \"keep-alive\"\n dic[\"Accept-Language\"] = \"en-US,en;q=0.8,zh-CN;q=0.6,zh;q=0.4\"\n dic[\"Accept\"] = \"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8\"\n dic[\"Host\"] = \"222.201.132.114\"\n dic[\"Referer\"] = referer\n return dic\n\n\nclass LessonHandler(tornado.web.RequestHandler):\n @tornado.web.asynchronous\n @tornado.gen.engine\n def get(self):\n xh = self.get_argument(\"xh\")\n pw = self.get_argument(\"pw\")\n\n client = tornado.httpclient.AsyncHTTPClient()\n\n jw_url = 'http://222.201.132.114/default2.aspx'\n request = HTTPRequest(jw_url, \"GET\", follow_redirects=True)\n response = yield tornado.gen.Task(client.fetch,\n request)\n\n location = response.effective_url\n hash_string = location[location.find(\"(\"):location.find(\")\") + 1]\n login_url = \"%s%s%s\" % (\n \"http://222.201.132.114/\", hash_string, \"/default2.aspx\")\n\n page = etree.HTML(response.body)\n viewstate_input = page.xpath(\n \"/html/body/form/input[@name='__VIEWSTATE']\")[0]\n viewstate = viewstate_input.get(\"value\")\n\n params = {\"__VIEWSTATE\": viewstate, \"txtUserName\": xh,\n \"TextBox2\": pw, \"txtSecretCode\": \"\", \"Button1\": \"\"}\n\n request = HTTPRequest(\n login_url, \"POST\",\n None,\n body=urlencode(params),\n follow_redirects=True)\n response = yield tornado.gen.Task(client.fetch,\n request)\n\n main_url = response.effective_url\n page = etree.HTML(response.body)\n name_span = page.xpath(\n \"/html/body/div/div/div/form/div/ul/li/em/span[@id='xhxm']\")[0]\n name = name_span.text[-5:-2]\n\n lesson_url = \"%s%s%s%s%s%s%s\" % (\n \"http://222.201.132.114/\",\n hash_string,\n \"/xskbcx.aspx?xh=\",\n xh,\n \"&xm=\",\n make_jw_encode(name),\n \"&gnmkdm=N121603\")\n\n request = HTTPRequest(\n lesson_url,\n \"GET\",\n follow_redirects=True,\n headers=make_headers(main_url))\n response = yield tornado.gen.Task(client.fetch,\n request)\n\n page = etree.HTML(response.body)\n table_rows = page.xpath(\"/html/body/form/div/div/span/table[2]/tr\")\n\n lesson_list = []\n count = 0\n\n for row in table_rows[1:]:\n cells = row.getchildren()\n\n for cell in cells:\n\n cell_html = etree.tostring(cell, encoding=\"utf-8\")\n if b'{' in cell_html:\n cell_html = cell_html[cell_html.find(b\">\") + 1:-5]\n\n count = count + 1\n\n tiny_cell_list = cell_html.split(b\"
\")\n\n if len(tiny_cell_list) == 1:\n lesson_name = tiny_cell_list[0]\n lesson_teach_by = b\"\"\n lesson_classroom = b\"\"\n content = \"\"\n elif len(tiny_cell_list) == 4:\n lesson_name = tiny_cell_list[0]\n content = tiny_cell_list[1]\n lesson_teach_by = tiny_cell_list[2]\n lesson_classroom = tiny_cell_list[3]\n else:\n lesson_name = tiny_cell_list[0]\n lesson_teach_by = tiny_cell_list[2]\n content = tiny_cell_list[1]\n lesson_classroom = b\"\"\n\n content = content.decode(\n \"utf-8\")\n lesson_time = content[\n content.find(\"第\") + 1:content.find(\"节\")]\n\n lesson_day = make_jw_weekdays(content[\n content.find('周') + 1:content.find('第')])\n\n if \"单周\" in content:\n lesson_type = \"odd\"\n elif \"双周\" in content:\n lesson_type = \"even\"\n else:\n lesson_type = \"all\"\n\n lesson_week_du = clean(content[\n content.find(\"{\") + 1:content.find(\"}\")])\n\n lesson_weeks = make_jw_weeks(lesson_week_du, lesson_type)\n\n if '节/周' in content:\n continue\n\n lesson_dict = {\n \"lesson_day\": lesson_day,\n \"lesson_name\": lesson_name.decode(\"utf-8\"),\n \"lesson_teach_by\": lesson_teach_by.decode(\"utf-8\"),\n \"lesson_classroom\": lesson_classroom.decode(\"utf-8\"),\n \"lesson_time\": lesson_time,\n \"lesson_weeks\": lesson_weeks,\n \"lesson_type\": lesson_type\n }\n\n lesson_list.append(lesson_dict)\n\n return_dict = {\"count\": count, \"lessons\": lesson_list}\n json_data = json.dumps(return_dict, ensure_ascii=False)\n\n self.write(json_data)\n self.finish()\n\n def post(self):\n self.write(\"use get to enable cache\")\n\n def write_error(self, status_code, **kwargs):\n self.write(\"Gosh darnit, user! You caused a %d error.\" % status_code)\n\n\nclass ScoreHandler(tornado.web.RequestHandler):\n\n @tornado.web.asynchronous\n @tornado.gen.engine\n def get(self):\n xh = self.get_argument(\"xh\")\n pw = self.get_argument(\"pw\")\n if not xh or not pw:\n self.write(\"no enough parameter\")\n self.finish()\n\n client = tornado.httpclient.AsyncHTTPClient()\n\n jw_url = 'http://222.201.132.114/default2.aspx'\n request = HTTPRequest(jw_url, \"GET\", follow_redirects=True)\n response = yield tornado.gen.Task(client.fetch,\n request)\n\n location = response.effective_url\n hash_string = location[location.find(\"(\"):location.find(\")\") + 1]\n login_url = \"%s%s%s\" % (\n \"http://222.201.132.114/\", hash_string, \"/default2.aspx\")\n\n page = etree.HTML(response.body)\n viewstate_input = page.xpath(\n \"/html/body/form/input[@name='__VIEWSTATE']\")[0]\n viewstate = viewstate_input.get(\"value\")\n\n params = {\"__VIEWSTATE\": viewstate, \"txtUserName\": xh,\n \"TextBox2\": pw, \"txtSecretCode\": \"\", \"Button1\": \"\"}\n request = HTTPRequest(\n login_url,\n \"POST\",\n None,\n body=urlencode(params),\n follow_redirects=True,\n request_timeout=60)\n response = yield tornado.gen.Task(client.fetch,\n request)\n\n main_url = response.effective_url\n page = etree.HTML(response.body)\n name_span = page.xpath(\n \"/html/body/div/div/div/form/div/ul/li/em/span[@id='xhxm']\")[0]\n name = name_span.text[-5:-2]\n\n # critical\n score_url = \"%s%s%s%s%s%s%s\" % (\n \"http://222.201.132.114/\",\n hash_string,\n \"/xscjcx.aspx?xh=\",\n xh,\n \"&xm=\",\n make_jw_encode(name),\n \"&gnmkdm=N121605\")\n request = HTTPRequest(\n score_url,\n \"GET\",\n follow_redirects=True,\n headers=make_headers(main_url))\n response = yield tornado.gen.Task(client.fetch,\n request)\n\n page = etree.HTML(response.body)\n viewstate2_span = page.xpath(\n \"/html/body/form/input[@name='__VIEWSTATE']\")[0]\n viewstate2 = viewstate2_span.get(\"value\")\n\n # critical\n params = {\"__VIEWSTATE\": viewstate2, \"btn_zcj\": \"历年成绩\".encode(\"utf-8\")}\n request = HTTPRequest(\n score_url,\n \"POST\",\n body=urlencode(params),\n follow_redirects=True,\n headers=make_headers(score_url),\n request_timeout=60)\n response = yield tornado.gen.Task(client.fetch,\n request)\n response\n page = etree.HTML(response.body)\n score_table = page.xpath(\"//table[@id='Datagrid1']\")[0]\n\n count = 0\n score_list = []\n for score_row in score_table.getchildren()[1:]:\n cells = score_row.getchildren()\n\n score_lesson_from_to = cells[0].text\n score_term = cells[1].text\n score_lesson_code = cells[2].text\n score_name = cells[3].text\n score_lesson_kind = cells[4].text\n score_lesson_belongs_to = cells[5].text\n score_lesson_point = cells[6].text\n score_credit = cells[7].text\n score_value = cells[8].text\n score_reexam_value = cells[10].text\n score_restudy_value = cells[11].text\n score_lesson_issue_by = cells[12].text\n score_rank = cells[15].text\n\n score_dict = {\n 'id'\t\t\t\t\t\t: count\t\t\t\t\t,\n 'score_lesson_from_to'\t\t: score_lesson_from_to\t,\n 'score_term'\t\t\t\t: score_term\t\t\t\t,\n 'score_lesson_code'\t\t\t: score_lesson_code\t\t,\n 'score_name'\t\t\t\t: score_name\t\t\t\t,\n 'score_lesson_kind'\t\t\t: score_lesson_kind\t\t,\n 'score_lesson_belongs_to' \t: score_lesson_belongs_to,\n 'score_lesson_point' \t\t: score_lesson_point \t\t,\n 'score_credit'\t\t\t\t: score_credit\t\t\t,\n 'score_value'\t\t\t\t: score_value\t\t\t\t,\n 'score_reexam_value'\t\t: score_reexam_value\t,\n 'score_restudy_value'\t\t: score_restudy_value\t\t,\n 'score_lesson_issue_by'\t\t: score_lesson_issue_by\t,\n 'score_rank'\t\t\t\t: score_rank\t\t\t\t,\n }\n\n score_list.append(score_dict)\n\n count += 1\n\n name_dict = {\n 'name': name,\n 'count': count,\n\n }\n score_list.append(name_dict)\n json_data = json.dumps(score_list, ensure_ascii=False)\n\n self.set_status(200)\n self.write(json_data)\n\n self.finish()\n\n def post(self):\n self.write(\"use get to enable cache\")\n\n def write_error(self, status_code, **kwargs):\n self.write(\"Gosh darnit, user! You caused a %d error.\" % status_code)\n\n\nclass GpaHandler(tornado.web.RequestHandler):\n\n @tornado.web.asynchronous\n @tornado.gen.engine\n def get(self):\n xh = self.get_argument(\"xh\")\n pw = self.get_argument(\"pw\")\n if not xh or not pw:\n self.write(\"no enough parameter\")\n self.finish()\n\n client = tornado.httpclient.AsyncHTTPClient()\n\n jw_url = 'http://222.201.132.114/default2.aspx'\n request = HTTPRequest(jw_url, \"GET\", follow_redirects=True)\n response = yield tornado.gen.Task(client.fetch,\n request)\n\n location = response.effective_url\n hash_string = location[location.find(\"(\"):location.find(\")\") + 1]\n login_url = \"%s%s%s\" % (\n \"http://222.201.132.114/\", hash_string, \"/default2.aspx\")\n\n page = etree.HTML(response.body)\n viewstate_input = page.xpath(\n \"/html/body/form/input[@name='__VIEWSTATE']\")[0]\n viewstate = viewstate_input.get(\"value\")\n\n params = {\"__VIEWSTATE\": viewstate, \"txtUserName\": xh,\n \"TextBox2\": pw, \"txtSecretCode\": \"\", \"Button1\": \"\"}\n request = HTTPRequest(\n login_url,\n \"POST\",\n None,\n body=urlencode(params),\n follow_redirects=True)\n response = yield tornado.gen.Task(client.fetch,\n request)\n\n main_url = response.effective_url\n page = etree.HTML(response.body)\n name_span = page.xpath(\n \"/html/body/div/div/div/form/div/ul/li/em/span[@id='xhxm']\")[0]\n name = name_span.text[-5:-2]\n\n # critical\n score_url = \"%s%s%s%s%s%s%s\" % (\n \"http://222.201.132.114/\",\n hash_string,\n \"/xscjcx.aspx?xh=\",\n xh,\n \"&xm=\",\n make_jw_encode(name),\n \"&gnmkdm=N121605\")\n request = HTTPRequest(\n score_url,\n \"GET\",\n follow_redirects=True,\n headers=make_headers(main_url))\n response = yield tornado.gen.Task(client.fetch,\n request)\n\n page = etree.HTML(response.body)\n viewstate2_span = page.xpath(\n \"/html/body/form/input[@name='__VIEWSTATE']\")[0]\n viewstate2 = viewstate2_span.get(\"value\")\n\n # critical\n params = {\"__VIEWSTATE\": viewstate2, \"btn_zcj\": \"历年成绩\".encode(\"utf-8\")}\n request = HTTPRequest(\n score_url,\n \"POST\",\n body=urlencode(params),\n follow_redirects=True,\n headers=make_headers(score_url),\n request_timeout=60)\n response = yield tornado.gen.Task(client.fetch,\n request)\n\n page = etree.HTML(response.body)\n score_table = page.xpath(\"//table[@id='Datagrid1']\")[0]\n\n count = 0\n total_lesson_point = 0.0\n total_credit = 0.0\n result = {}\n\n for score_row in score_table.getchildren()[1:]:\n cells = score_row.getchildren()\n\n score_lesson_point = float(cells[6].text)\n score_credit = float(cells[7].text[1:])\n count = count + 1\n\n total_credit = total_credit + score_credit\n total_lesson_point = total_lesson_point + \\\n score_lesson_point * score_credit\n\n gpa = total_lesson_point / total_credit\n\n result[\"gpa\"] = gpa\n result[\"how_many_lessons\"] = count\n result[\"total_lesson_point\"] = total_lesson_point\n result[\"total_credit\"] = total_credit\n\n self.set_status(200)\n self.write(json.dumps(result))\n\n self.finish()\n\n def post(self):\n self.write(\"use get to enable cache\")\n\n def write_error(self, status_code, **kwargs):\n self.write(\"Gosh darnit, user! You caused a %d error.\" % status_code)\n\n\ndef getApplication():\n tornado.options.parse_command_line() # parse port\n app = tornado.web.Application(handlers=getHandlers())\n return app\n\n\ndef getHandlers():\n return [\n (r\"/Lesson/\", LessonHandler),\n (r\"/Score/\", ScoreHandler),\n (r\"/Gpa/\", GpaHandler),\n ]\n\n\nif __name__ == \"__main__\":\n tornado.options.parse_command_line() # parse port\n app = tornado.web.Application(handlers=getHandlers())\n http_server = tornado.httpserver.HTTPServer(app)\n http_server.listen(options.port)\n tornado.ioloop.IOLoop.instance().start()\n", "sub_path": "scuter/scuter.py", "file_name": "scuter.py", "file_ext": "py", "file_size_in_byte": 16739, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.exit", "line_number": 25, "usage_type": "call"}, {"api_name": "tornado.options.define", "line_number": 29, "usage_type": "call"}, {"api_name": "urllib.parse.quote", "line_number": 33, "usage_type": "call"}, {"api_name": "tornado.httpserver.web", "line_number": 93, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 93, "usage_type": "name"}, {"api_name": "tornado.httpserver.httpclient.AsyncHTTPClient", "line_number": 100, "usage_type": "call"}, {"api_name": "tornado.httpserver.httpclient", "line_number": 100, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 100, "usage_type": "name"}, {"api_name": "tornado.httpclient.HTTPRequest", "line_number": 103, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen.Task", "line_number": 104, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen", "line_number": 104, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 104, "usage_type": "name"}, {"api_name": "lxml.etree.HTML", "line_number": 112, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 112, "usage_type": "name"}, {"api_name": "tornado.httpclient.HTTPRequest", "line_number": 120, "usage_type": "call"}, {"api_name": "urllib.parse.urlencode", "line_number": 123, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen.Task", "line_number": 125, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen", "line_number": 125, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 125, "usage_type": "name"}, {"api_name": "lxml.etree.HTML", "line_number": 129, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 129, "usage_type": "name"}, {"api_name": "tornado.httpclient.HTTPRequest", "line_number": 143, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen.Task", "line_number": 148, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen", "line_number": 148, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 148, "usage_type": "name"}, {"api_name": "lxml.etree.HTML", "line_number": 151, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 151, "usage_type": "name"}, {"api_name": "lxml.etree.tostring", "line_number": 162, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 162, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 222, "usage_type": "call"}, {"api_name": "tornado.httpserver.web", "line_number": 94, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 94, "usage_type": "name"}, {"api_name": "tornado.httpserver.gen", "line_number": 95, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 95, "usage_type": "name"}, {"api_name": "tornado.httpserver.web", "line_number": 234, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 234, "usage_type": "name"}, {"api_name": "tornado.httpserver.httpclient.AsyncHTTPClient", "line_number": 245, "usage_type": "call"}, {"api_name": "tornado.httpserver.httpclient", "line_number": 245, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 245, "usage_type": "name"}, {"api_name": "tornado.httpclient.HTTPRequest", "line_number": 248, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen.Task", "line_number": 249, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen", "line_number": 249, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 249, "usage_type": "name"}, {"api_name": "lxml.etree.HTML", "line_number": 257, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 257, "usage_type": "name"}, {"api_name": "tornado.httpclient.HTTPRequest", "line_number": 264, "usage_type": "call"}, {"api_name": "urllib.parse.urlencode", "line_number": 268, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen.Task", "line_number": 271, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen", "line_number": 271, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 271, "usage_type": "name"}, {"api_name": "lxml.etree.HTML", "line_number": 275, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 275, "usage_type": "name"}, {"api_name": "tornado.httpclient.HTTPRequest", "line_number": 289, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen.Task", "line_number": 294, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen", "line_number": 294, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 294, "usage_type": "name"}, {"api_name": "lxml.etree.HTML", "line_number": 297, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 297, "usage_type": "name"}, {"api_name": "tornado.httpclient.HTTPRequest", "line_number": 304, "usage_type": "call"}, {"api_name": "urllib.parse.urlencode", "line_number": 307, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen.Task", "line_number": 311, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen", "line_number": 311, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 311, "usage_type": "name"}, {"api_name": "lxml.etree.HTML", "line_number": 314, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 314, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 363, "usage_type": "call"}, {"api_name": "tornado.httpserver.web", "line_number": 236, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 236, "usage_type": "name"}, {"api_name": "tornado.httpserver.gen", "line_number": 237, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 237, "usage_type": "name"}, {"api_name": "tornado.httpserver.web", "line_number": 377, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 377, "usage_type": "name"}, {"api_name": "tornado.httpserver.httpclient.AsyncHTTPClient", "line_number": 388, "usage_type": "call"}, {"api_name": "tornado.httpserver.httpclient", "line_number": 388, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 388, "usage_type": "name"}, {"api_name": "tornado.httpclient.HTTPRequest", "line_number": 391, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen.Task", "line_number": 392, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen", "line_number": 392, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 392, "usage_type": "name"}, {"api_name": "lxml.etree.HTML", "line_number": 400, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 400, "usage_type": "name"}, {"api_name": "tornado.httpclient.HTTPRequest", "line_number": 407, "usage_type": "call"}, {"api_name": "urllib.parse.urlencode", "line_number": 411, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen.Task", "line_number": 413, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen", "line_number": 413, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 413, "usage_type": "name"}, {"api_name": "lxml.etree.HTML", "line_number": 417, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 417, "usage_type": "name"}, {"api_name": "tornado.httpclient.HTTPRequest", "line_number": 431, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen.Task", "line_number": 436, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen", "line_number": 436, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 436, "usage_type": "name"}, {"api_name": "lxml.etree.HTML", "line_number": 439, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 439, "usage_type": "name"}, {"api_name": "tornado.httpclient.HTTPRequest", "line_number": 446, "usage_type": "call"}, {"api_name": "urllib.parse.urlencode", "line_number": 449, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen.Task", "line_number": 453, "usage_type": "call"}, {"api_name": "tornado.httpserver.gen", "line_number": 453, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 453, "usage_type": "name"}, {"api_name": "lxml.etree.HTML", "line_number": 456, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 456, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 483, "usage_type": "call"}, {"api_name": "tornado.httpserver.web", "line_number": 379, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 379, "usage_type": "name"}, {"api_name": "tornado.httpserver.gen", "line_number": 380, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 380, "usage_type": "name"}, {"api_name": "tornado.httpserver.options.parse_command_line", "line_number": 495, "usage_type": "call"}, {"api_name": "tornado.httpserver.options", "line_number": 495, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 495, "usage_type": "name"}, {"api_name": "tornado.httpserver.web.Application", "line_number": 496, "usage_type": "call"}, {"api_name": "tornado.httpserver.web", "line_number": 496, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 496, "usage_type": "name"}, {"api_name": "tornado.httpserver.options.parse_command_line", "line_number": 509, "usage_type": "call"}, {"api_name": "tornado.httpserver.options", "line_number": 509, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 509, "usage_type": "name"}, {"api_name": "tornado.httpserver.web.Application", "line_number": 510, "usage_type": "call"}, {"api_name": "tornado.httpserver.web", "line_number": 510, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 510, "usage_type": "name"}, {"api_name": "tornado.httpserver.httpserver.HTTPServer", "line_number": 511, "usage_type": "call"}, {"api_name": "tornado.httpserver.httpserver", "line_number": 511, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 511, "usage_type": "name"}, {"api_name": "tornado.options.options.port", "line_number": 512, "usage_type": "attribute"}, {"api_name": "tornado.options.options", "line_number": 512, "usage_type": "name"}, {"api_name": "tornado.httpserver.ioloop.IOLoop.instance", "line_number": 513, "usage_type": "call"}, {"api_name": "tornado.httpserver.ioloop", "line_number": 513, "usage_type": "attribute"}, {"api_name": "tornado.httpserver", "line_number": 513, "usage_type": "name"}]} +{"seq_id": "323501426", "text": "import numpy as np\nimport glob\nfrom PIL import Image\nfrom random import shuffle\nimport cv2\n\ntrain_X = []\ntrain_Y = []\ntrain_files = []\n\ntest_X = []\ntest_Y = []\ntest_files = []\n\nDATA_SET = \"found\"\n\n\ndef expand():\n\tfor path in test_files:\n\t\timage = cv2.imread(path)\n\t\texpanded = cv2.copyMakeBorder(src=image, dst=image, top=3, bottom=3, left=3, right=3, borderType=cv2.BORDER_CONSTANT, value=[0, 0, 0])\n\t\tcv2.imwrite(path, expanded)\n\n\tfor path in train_files:\n\t\timage = cv2.imread(path)\n\t\texpanded = cv2.copyMakeBorder(src=image, dst=image, top=3, bottom=3, left=3, right=3, borderType=cv2.BORDER_CONSTANT,\n\t\t value=[0, 0, 0])\n\t\tcv2.imwrite(path, expanded)\n\n\ndef preprocess():\n\tfor path in test_files:\n\t\timage = cv2.imread(path, 0)\n\t\timage = image[3:25, 3:25]\n\n\t\tblurred = cv2.fastNlMeansDenoising(image, h=7, templateWindowSize=7, searchWindowSize=21)\n\t\tthreshold = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 21, 7)\n\t\tthreshold = cv2.bitwise_not(threshold)\n\n\t\tcv2.imwrite(path, threshold)\n\n\tfor path in train_files:\n\t\timage = cv2.imread(path, 0)\n\n\t\timage = image[3:25, 3:25]\n\n\t\tblurred = cv2.fastNlMeansDenoising(image, h=7, templateWindowSize=7, searchWindowSize=21)\n\t\tthreshold = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 21, 7)\n\t\tthreshold = cv2.bitwise_not(threshold)\n\n\t\tcv2.imwrite(path, threshold)\n\n\ndef resize():\n\tfor path in test_files:\n\t\timage = cv2.imread(path)\n\t\timage = cv2.resize(image, (28, 28))\n\t\tcv2.imwrite(path, image)\n\n\ndef get_train_files():\n\tglobal train_files\n\ttrain_files = glob.glob(\"data/\" + DATA_SET + \"/training/*.jpg\")\n\n\tshuffle(train_files)\n\n\ndef get_test_files():\n\tglobal test_files\n\ttest_files = glob.glob(\"data/\" + DATA_SET + \"/testing/*.jpg\")\n\n\tshuffle(test_files)\n\n\ndef load_train_x():\n\tglobal train_X\n\n\tfor path in train_files:\n\t\timg = Image.open(path).convert(\"L\")\n\t\timgarr = np.array(img).astype(float).flatten()\n\n\t\ttrain_X.append(imgarr / 255)\n\n\ndef load_train_y():\n\tglobal train_Y\n\n\tfor i in range(len(train_files)):\n\t\ttrain_Y.append(np.array([0 for i in range(10)]).astype(float))\n\t\tidx = int(train_files[i].split(\"_\")[0][-1])\n\t\ttrain_Y[i][idx] = 1\n\n\ndef load_test_x():\n\tglobal test_X\n\n\tfor path in test_files:\n\t\timg = Image.open(path).convert(\"L\")\n\t\timgarr = np.array(img).astype(float).flatten()\n\n\t\ttest_X.append(imgarr / 255)\n\n\ndef load_test_y():\n\tglobal test_Y\n\n\tfor i in range(len(test_files)):\n\t\ttest_Y.append(np.array([0 for i in range(10)]).astype(float))\n\t\tidx = int(test_files[i].split(\"_\")[0][-1])\n\t\ttest_Y[i][idx] = 1\n\n\ndef serialize():\n\ttrain_x_file = open(\"data/\" + DATA_SET + \"/processed/training/X.npy\", \"wb\")\n\tnp.save(train_x_file, train_X)\n\n\ttrain_y_file = open(\"data/\" + DATA_SET + \"/processed/training/Y.npy\", \"wb\")\n\tnp.save(train_y_file, train_Y)\n\n\ttest_x_file = open(\"data/\" + DATA_SET + \"/processed/testing/X.npy\", \"wb\")\n\tnp.save(test_x_file, test_X)\n\n\ttest_y_file = open(\"data/\" + DATA_SET + \"/processed/testing/Y.npy\", \"wb\")\n\tnp.save(test_y_file, test_Y)\n\n\nif __name__ == \"__main__\":\n\tget_train_files()\n\tget_test_files()\n\n\t# expand()\n\n\t# preprocess()\n\n\tload_train_x()\n\tload_train_y()\n\n\tload_test_x()\n\tload_test_y()\n\n\tserialize()\n", "sub_path": "dataset-processing.py", "file_name": "dataset-processing.py", "file_ext": "py", "file_size_in_byte": 3200, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "cv2.imread", "line_number": 20, "usage_type": "call"}, {"api_name": "cv2.copyMakeBorder", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.BORDER_CONSTANT", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.copyMakeBorder", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.BORDER_CONSTANT", "line_number": 26, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.fastNlMeansDenoising", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.adaptiveThreshold", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.ADAPTIVE_THRESH_GAUSSIAN_C", "line_number": 37, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 37, "usage_type": "attribute"}, {"api_name": "cv2.bitwise_not", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 43, "usage_type": "call"}, {"api_name": "cv2.fastNlMeansDenoising", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.adaptiveThreshold", "line_number": 48, "usage_type": "call"}, {"api_name": "cv2.ADAPTIVE_THRESH_GAUSSIAN_C", "line_number": 48, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 48, "usage_type": "attribute"}, {"api_name": "cv2.bitwise_not", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 58, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 63, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 65, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 70, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 72, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 79, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 79, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 89, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 98, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 98, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 124, "usage_type": "call"}]} +{"seq_id": "432571539", "text": "from src.scripts import ZISweeper\nimport time\nfrom src.core import Script, Parameter, plotting\nfrom PySide.QtCore import Signal, QThread\nfrom collections import deque\nimport numpy as np\nfrom copy import deepcopy\n\nclass ZISweeperHighResolution(Script, QThread):\n updateProgress = Signal(int)\n\n _DEFAULT_SETTINGS = Parameter([\n Parameter('path', 'C:\\\\Users\\\\Experiment\\\\Desktop\\\\tmp_data\\\\fast', str, 'path to folder where data is saved'),\n Parameter('tag', 'some_name'),\n Parameter('save', True, bool,'check to automatically save data'),\n Parameter('high_res_df', 1000, float, 'frequency step of high res. scan'),\n Parameter('high_res_N', 21, int, 'number of data points of high res. scan'),\n ])\n\n _INSTRUMENTS = {}\n\n _SCRIPTS = {'zi sweep' : ZISweeper}\n\n def __init__(self, scripts, name = None, settings = None, log_output = None, timeout = 1000000000):\n self._recording = False\n self._timeout = timeout\n\n Script.__init__(self, name, settings, scripts = scripts, log_output = log_output)\n QThread.__init__(self)\n\n self.data = deque()\n\n # todo: clean this up! and plot data in gui!\n self._sweep_values = {'frequency' : [], 'x' : [], 'y' : [], 'phase': [], 'r':[]}.keys()\n\n\n def _receive_signal(self, progess_sub_script):\n # calculate progress of this script based on progress in subscript\n\n if self.current_subscript == 'quick scan':\n progress = int(self.weights['quick scan'] * progess_sub_script)\n elif self.current_subscript == 'high res scan':\n progress = int(self.weights['quick scan']*100 + self.weights['high res scan'] * progess_sub_script)\n else:\n progress = None\n # if calculated progress is 100 force it to 99, because we still have to save before script is finished\n if progress>= 100:\n progress = 99\n\n if progress is not None:\n self.updateProgress.emit(progress)\n\n if progess_sub_script == 100:\n self.current_subscript = None\n\n def _function(self):\n \"\"\"\n This is the actual function that will be executed. It uses only information that is provided in the settings property\n will be overwritten in the __init__\n \"\"\"\n\n\n\n def calculate_weights():\n \"\"\"\n calculate a weight inversely proportional to the expected to duration of the two steps in the\n script\n\n Returns: weights as a dictionary for the two steps\n\n \"\"\"\n weights = {}\n\n\n # estimate run time of step 1 (fast sweep)\n f_range = sweeper_script.settings['stop'] - sweeper_script.settings['start']\n N_samples = sweeper_script.settings['samplecount']\n df = f_range / N_samples\n\n t = N_samples / df\n\n weights['quick scan'] = t\n\n # estimate run time of step 2 (high res sweep)\n df = self.settings['high_res_df']\n N_samples = self.settings['high_res_N']\n\n t = N_samples / df\n\n weights['high res scan'] = t\n\n\n total_time = sum([v for k, v in weights.iteritems()])\n\n weights = {k: v/total_time for k, v in weights.iteritems()}\n\n print('weights',weights)\n\n return weights\n\n def run_scan(name):\n self.current_subscript = name\n sweeper_script.start()\n while self.current_subscript is name:\n time.sleep(0.1)\n\n def calc_new_range():\n\n\n df = self.settings['high_res_df']\n N = self.settings['high_res_N']\n\n r = sweeper_script.data[-1]['r']\n freq = sweeper_script.data[-1]['frequency']\n freq = freq[np.isfinite(r)]\n r = r[np.isfinite(r)]\n\n fo = freq[np.argmax(r)]\n\n f_start, f_end = fo - N/2 *df, fo + N/2 *df\n\n\n # make sure that we convert back to native python types (numpy file types don't pass the Parameter validation)\n return float(f_start), float(f_end), int(N)\n\n\n sweeper_script = self.scripts['zi sweep']\n #save initial settings, so that we can rest at the end of the script\n initial_settings = deepcopy(sweeper_script.settings)\n self.weights = calculate_weights()\n\n # take the signal from the subscript and route it to a function that takes care of it\n sweeper_script.updateProgress.connect(self._receive_signal)\n\n print('====== start quick scan ============')\n\n run_scan('quick scan')\n\n print('====== calculate new scan range ====')\n f_start, f_stop, N = calc_new_range()\n\n print('f_start, f_stop, N', f_start, f_stop, N)\n\n print('====== update sweeper ==============')\n sweeper_script.update({\n 'start' : f_start,\n 'stop' : f_stop,\n 'samplecount' : N\n })\n\n print('====== start high res scan =========')\n # print(sweeper_script.sweeper.finished())\n # print(sweeper_script.sweeper.progress())\n\n run_scan('high res scan')\n\n sweeper_script.updateProgress.disconnect()\n self.data = sweeper_script.data[-1]\n\n self._recording = False\n\n if self.settings['save']:\n self.save()\n\n # set the sweeper script back to initial settings\n sweeper_script.update(initial_settings)\n # make sure that progess is set 1o 100 because we check that in the old_gui\n self.updateProgress.emit(100)\n\n\n def plot(self, axes):\n if self.current_subscript == 'quick scan' and self.scripts['zi sweep'].data:\n self.scripts['zi sweep'].plot(axes)\n elif self.current_subscript in ('high res scan', None) and self.data:\n r = self.data['r']\n freq = self.data['frequency']\n freq = freq[np.isfinite(r)]\n r = r[np.isfinite(r)]\n plotting.plot_psd(freq, r, axes, False)\n\n", "sub_path": "src/scripts/zi_high_res_sweep.py", "file_name": "zi_high_res_sweep.py", "file_ext": "py", "file_size_in_byte": 5966, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "src.core.Script", "line_number": 9, "usage_type": "name"}, {"api_name": "PySide.QtCore.QThread", "line_number": 9, "usage_type": "name"}, {"api_name": "PySide.QtCore.Signal", "line_number": 10, "usage_type": "call"}, {"api_name": "src.core.Parameter", "line_number": 12, "usage_type": "call"}, {"api_name": "src.core.Parameter", "line_number": 13, "usage_type": "call"}, {"api_name": "src.core.Parameter", "line_number": 14, "usage_type": "call"}, {"api_name": "src.core.Parameter", "line_number": 15, "usage_type": "call"}, {"api_name": "src.core.Parameter", "line_number": 16, "usage_type": "call"}, {"api_name": "src.core.Parameter", "line_number": 17, "usage_type": "call"}, {"api_name": "src.scripts.ZISweeper", "line_number": 22, "usage_type": "name"}, {"api_name": "src.core.Script.__init__", "line_number": 28, "usage_type": "call"}, {"api_name": "src.core.Script", "line_number": 28, "usage_type": "name"}, {"api_name": "PySide.QtCore.QThread.__init__", "line_number": 29, "usage_type": "call"}, {"api_name": "PySide.QtCore.QThread", "line_number": 29, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 31, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.isfinite", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.isfinite", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 118, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.isfinite", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.isfinite", "line_number": 178, "usage_type": "call"}, {"api_name": "src.core.plotting.plot_psd", "line_number": 179, "usage_type": "call"}, {"api_name": "src.core.plotting", "line_number": 179, "usage_type": "name"}]} +{"seq_id": "16069429", "text": "import psycopg2 as ps\nimport csv\nimport math\n\nclass DBO:\n ##Custom database object, retains information about dbname, user and pw\n def __init__(self,dbname,user,pw):\n self.user = user\n self.pw = pw\n self.dbname = dbname\n self.admin = ps.connect(database='postgres', user=self.user, password=self.pw)\n self.admin.autocommit=True\n self.checkDBName()\n self.conn = ps.connect(database=self.dbname, user=self.user, password=self.pw)\n self.conn.autocommit=True\n \n\n def checkDBName(self):\n try:\n conn = ps.connect(database=self.dbname, user=self.user, password=self.pw)\n conn.close()\n except ps.OperationalError:\n print(\"Failed to find that database\")\n test = input(\"Would you like to create that database? (y/n)\")\n if test == 'y':\n self.createNewDB()\n else:\n print(\"There is no database\")\n \n def mapTypesToPostgres(self,dataset):\n ##Here we create data to help map python types to postgres types\n dic = {}\n tempD = {}\n for i,y in enumerate(dataset[0]):##Iterate through columns as headers\n if y!='':\n inconsistentWarning = False\n for k,x in enumerate(dataset):##Iterate through rows\n if k==0:##Set list for header\n header = x\n if k>0:\n try:\n test = tempD[self.checkHeader(header[i])]\n if test!=str(type(x[i])):\n if inconsistentWarning==True:\n print(\"Error: inconsistent type in {0}\".format(y))\n inconsistentWarning = True\n tempD[self.checkHeader(header[i])] = \"\"\n except KeyError:\n tempD[self.checkHeader(header[i])] = str(type(x[i]))\n return(tempD)\n\n def checkHeader(self,val):\n val = val.lower()\n exclude = [' ','\"',\"'\",'.',',','/','?',';',':','[',']','{','}','\\\\','|',\n '!','@','#','$','%','^','&','*','(',')','~','`','-','+','=']\n for e in exclude:\n val = val.replace(e,\"_\")\n return(val)\n \n def makeCreateTableStatement(self,dataset,mapping,tableName,cur):\n ##This creates a table based on the structure of the dataset\n typeDB = {\"\":'text',\n \"\":'integer',\n \"\":'numeric'}\n createTable = 'CREATE TABLE {0} ('.format(tableName)\n for k,var in enumerate(mapping):\n #print(var, mapping[var], typeDB[mapping[var]])\n var = self.checkHeader(var)\n createTable = createTable + '\"{0}\" {1}'.format(var, typeDB[mapping[var]])\n if k<(len(mapping)-1):\n createTable = createTable+', '\n createTable = createTable+') WITH (OIDS=FALSE); ALTER TABLE {0} OWNER TO postgres;'.format(tableName)\n cur.execute(createTable)\n #print(createTable)\n\n def checkForBlanks(self,header):\n newH = []\n for h in header:\n if h!='':\n newH.append(h)\n return(newH)\n\n def writeData(self,dataset,mapping,tableName,cur):\n ##This iterates through the data and creates statements to post to db\n header = dataset[0]\n header = self.checkForBlanks(header)\n data = dataset[1:len(dataset)]\n cmd = 'INSERT INTO {0} ('.format(tableName)\n for k, h in enumerate(header):\n cmd = cmd+self.checkHeader(h)\n if k < (len(header)-1):\n cmd = cmd+\", \"\n cmd = cmd+\") VALUES (\"\n for i, row in enumerate(data):\n for k, r in enumerate(header):\n r = row[k]\n if mapping[self.checkHeader(header[k])]==\"\":\n cmd = cmd+\"'{0}'\".format(r)\n else:\n cmd = cmd+\"{0}\".format(r)\n if k < (len(header)-1):\n cmd = cmd+\", \"\n if i < (len(data)-1):\n cmd = cmd+\"),(\"\n cmd = cmd+\");\"\n #print(cmd)\n #cur.execute(cmd)\n try:\n cur.execute(cmd)\n except ps.ProgrammingError:\n print('========================')\n print(cmd)\n print(len(header))\n for i in data:\n print(len(i))\n print(i)\n print('========================')\n cur.execute(cmd)\n\n## def writeData(self,dataset,mapping,tableName,cur):\n## ##This iterates through the data and creates statements to post to db\n## header = dataset[0]\n## header = self.checkForBlanks(header)\n## data = dataset[1:len(dataset)]\n## cmd = 'INSERT INTO {0} ('.format(tableName)\n## for k, h in enumerate(header):\n## cmd = cmd+self.checkHeader(h)\n## if k < (len(header)-1):\n## cmd = cmd+\", \"\n## cmd = cmd+\") VALUES (\"\n## for i, row in enumerate(data):\n## for k, r in enumerate(header):\n## r = row[k]\n## if mapping[self.checkHeader(header[k])]==\"\":\n## cmd = cmd+\"'{0}'\".format(r)\n## else:\n## cmd = cmd+\"{0}\".format(r)\n## if k < (len(header)-1):\n## cmd = cmd+\", \"\n## if i < (len(data)-1):\n## cmd = cmd+\"),(\"\n## cmd = cmd+\");\"\n## #print(cmd)\n## #cur.execute(cmd)\n## try:\n## cur.execute(cmd)\n## except ps.ProgrammingError:\n## print('========================')\n## print(cmd)\n## print(len(header))\n## for i in data:\n## print(len(i))\n## print(i)\n## print('========================')\n## cur.execute(cmd)\n\n## def copyFromCSV(self,fiName,tblName,delim=\",\",overwrite=False):\n## chunker = 20.\n## tblName = self.checkHeader(tblName)\n## cur = self.conn.cursor()\n## if overwrite==True:\n## self.dropFromPostgres(tblName, objType='TABLE')\n## with open(fiName) as fi:\n## reader = csv.reader(fi,delimiter=delim,dialect='excel')\n## for k,r in enumerate(reader):\n## #print(k)\n## data = []\n## ##print(k,((float(k)/500.)*500)==math.ceil(float(k)/500.)*500)\n## if k==0:\n## header = r\n## fullData = [header]\n## else:\n## for i in r:\n## data.append(getType(i))\n## #print(data)\n## fullData.append(data)\n## if k>0 and ((float(k)/chunker)*chunker)==math.ceil(float(k)/chunker)*chunker:\n## print(\"Time to write\")\n## print(k)\n## #print(fullData)\n## try:\n## x = mapping\n## except:\n## mapping = self.mapTypesToPostgres(fullData)\n## self.makeCreateTableStatement(fullData, mapping, tblName, cur)\n## self.writeData(fullData, mapping, tblName, cur)\n## fullData = [header]\n## if len(fullData)>1:\n## self.writeData(fullData,mapping,tblName,cur)##For final run through\n\n def copyFromCSV(self,fiName,tblName,delim=\",\",overwrite=False):\n print('Reading in data')\n data = readInData(fiName,delim)\n print('Finishesd reading in data')\n cur = self.conn.cursor()\n if overwrite==True:\n self.dropFromPostgres(tblName, objType='TABLE')\n mapping = self.mapTypesToPostgres(data)\n self.makeCreateTableStatement(data, mapping, tblName, cur)\n print(\"Writing data\")\n self.writeData(data, mapping, tblName,cur)\n print(\"Finished writing data\")\n\n def createNewDB(self):\n ##This should be done if no database exists\n cur = self.admin.cursor()\n cur.execute(\"CREATE DATABASE {0} WITH OWNER = {1} ENCODING = 'UTF8' TABLESPACE = pg_default LC_COLLATE = 'English_United States.1252' LC_CTYPE = 'English_United States.1252' CONNECTION LIMIT = -1;\".format(self.dbname.lower(),self.user))\n print(\"Created new empty database - {0}\".format(self.dbname))\n\n def dropFromPostgres(self, objName, objType='TABLE'):\n if objType=='TABLE':\n cur = self.conn.cursor()\n if objType=='DATABASE':\n if objName==self.dbname:\n self.conn.close()\n cur = self.admin.cursor()\n try:\n cur.execute(\"DROP {0} {1};\".format(objType, objName))\n print(\"{0} ({1}) dropped\".format(objType,objName))\n except:\n print(\"There was no {0} to drop\".format(objName))\n\n def adHoc(self,query):\n cur = self.conn.cursor()\n cur.execute(query)\n out = cur.fetchall()\n print(\"Query executed:\")\n print(query)\n return(out)\n\ndef getType(i):\n try:\n i = int(i)\n try:\n i = float(i)\n except ValueError:\n pass\n except ValueError:\n i = i.replace(\"'\",\"\")\n return(i)\n \n \ndef readInData(fiName, delim=','):\n data = []\n with open(fiName) as fi:\n reader = csv.reader(fi,delimiter=delim,dialect='excel')\n for r in reader:\n tempLi = []\n for i in r:\n try:\n i = int(i)\n try:\n i = float(i)\n except ValueError:\n pass\n except ValueError:\n i = i.replace(\"'\",\"\")\n tempLi.append(i)\n data.append(tempLi)\n\n return(data)\n \n \nfiName = 'C:/Users/jarrodanderin/Documents/_RWork/_Datasets/COW_Alliance_v3.03.csv'\ndbname = 'testing2'\ntblName = 'test'\nfiLi = open('fiNames.csv')\nreader = csv.reader(fiLi)\nsaveDic = {}\nfor r in reader:\n try:\n saveDic[r[2]].append(r)\n except KeyError:\n saveDic[r[2]] = [r]\nfor ty in saveDic:\n print(ty)\n for i in saveDic[ty]:\n if i[1]=='polity_four':\n print(\"==>\"+str(i[1]))\n db = DBO(ty,'postgres','pw')\n db.copyFromCSV(i[0],i[1],overwrite=True)\n##db = DBO(dbname,'postgres','pw')\n##db.copyFromCSV(fiName,tblName,overwrite=True)\n", "sub_path": "ConvertToDatabase.py", "file_name": "ConvertToDatabase.py", "file_ext": "py", "file_size_in_byte": 10590, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "psycopg2.connect", "line_number": 11, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 14, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 20, "usage_type": "call"}, {"api_name": "psycopg2.OperationalError", "line_number": 22, "usage_type": "attribute"}, {"api_name": "psycopg2.ProgrammingError", "line_number": 110, "usage_type": "attribute"}, {"api_name": "csv.reader", "line_number": 246, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 268, "usage_type": "call"}]} +{"seq_id": "607894115", "text": "import subprocess\nimport sys\nfrom datetime import datetime\n\n\ndef log(*args):\n sys.stdout.write(*args)\n sys.stdout.flush()\n with open(\"timeCmd.log\", \"a+\") as f:\n f.write(*args)\n\n\nif __name__ == \"__main__\":\n quotes = lambda text: text if ' ' not in text else '\"{}\"'.format(text)\n arguments = sys.argv\n cmd = \"\"\n for x in range(1, len(arguments)):\n cmd += quotes(arguments[x]) + \" \"\n log(\"\\nExecuting Command : {}\\n\".format(cmd))\n startTime = datetime.now()\n popen = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, universal_newlines=True)\n for stdout_line in iter(popen.stdout.readline, \"\"):\n log(\"\\t\" + stdout_line)\n popen.stdout.close()\n return_code = popen.wait()\n endTime = datetime.now()\n log(\"\\nTook {} seconds to execute\".format((endTime - startTime).total_seconds()))\n", "sub_path": "timeCmd/timeCmd.py", "file_name": "timeCmd.py", "file_ext": "py", "file_size_in_byte": 820, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.stdout.write", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 20, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 20, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 21, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 21, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "528280273", "text": "# https://www.youtube.com/watch?v=OMDn66kM9Qc\nimport torch\nfrom torch import nn\nfrom torch import optim\nfrom torch.utils import data\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data.dataset import random_split\nfrom torchvision import datasets, transforms\n\n# define the model\nmodel = nn.Sequential(\n nn.Linear(28 * 28, 64),\n nn.ReLU(),\n nn.Linear(64, 64),\n nn.ReLU(),\n nn.Linear(64, 10)\n).cuda()\n\n# define the optimizer\nparams = model.parameters() # under-the-hood\noptimizer = optim.SGD(params, lr=1e-2)\n\n# define the loss\nloss = nn.CrossEntropyLoss()\n\n# data, train and val split\ntrain_data = datasets.MNIST('data', train=True, download=True, transform=transforms.ToTensor())\ntrain, val = random_split(train_data, [55000, 5000])\ntrain_loader = DataLoader(train, batch_size=32)\nval_loader = DataLoader(val, batch_size=32)\n\n# training loop\nnb_epochs = 5\nfor epoch in range(nb_epochs):\n\n losses = [] # for logging\n\n for batch in train_loader:\n x, y = batch\n\n # x: b x 1 x 28 x 28\n b = x.size(0)\n x = x.view(b, -1).cuda()\n\n ### 5 steps for supervised learning ###\n # under-the-hood: gives the underlying idea, code will not work as is\n \n # 1 forward\n l = model(x) # logit\n \n # 2 compute the objective function\n J = loss(l, y.cuda())\n \n # 3 cleaning the gradient\n model.zero_grad()\n # under-the-hood:\n # params.grad._zero()\n \n # 4 accumulate the partial derivatives of J wrt params\n J.backward()\n # under-the-hood:\n # params.grad.add_(dJ/dparams)\n \n # 5 step in opposite direction of the gradient\n optimizer.step()\n # under-the-hood\n # with torch.no_grad(): params = params - eta * params.grad\n \n losses.append(J.item())\n\n print(f'Epoch {epoch+1}, training loss: {torch.tensor(losses).mean():.2f}')\n \n\n losses = [] # for logging\n\n for batch in val_loader:\n x, y = batch\n\n # x: b x 1 x 28 x 28\n b = x.size(0)\n x = x.view(b, -1).cuda()\n\n ### 5 steps for supervised learning ###\n # under-the-hood: gives the underlying idea, code will not work as is\n \n # 1 forward\n with torch.no_grad():\n l = model(x) # logit\n \n # 2 compute the objective function\n J = loss(l, y.cuda())\n \n losses.append(J.item())\n\n print(f'Epoch {epoch+1}, validation loss: {torch.tensor(losses).mean():.2f}')", "sub_path": "mnist-classifier-pytorch-basic-gpu.py", "file_name": "mnist-classifier-pytorch-basic-gpu.py", "file_ext": "py", "file_size_in_byte": 2531, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "torch.nn.Sequential", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 14, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torchvision.datasets.MNIST", "line_number": 27, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 27, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 27, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 27, "usage_type": "name"}, {"api_name": "torch.utils.data.dataset.random_split", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 95, "usage_type": "call"}]} +{"seq_id": "234957826", "text": "import torch\nimport torch.nn as nn\nimport numpy as np\n\n\nclass Model(object):\n def __init__(self):\n super(Model, self).__init__()\n\n self.model = nn.Sequential(\n nn.Linear(4, 64),\n nn.Sigmoid(),\n nn.Linear(64, 3)\n )\n self.loss = nn.CrossEntropyLoss()\n self.optimizer = torch.optim.Adam(params=self.model.parameters(), lr=0.001)\n\n\ndef read_data_bak(path: str):\n label2id = {'\"setosa\"': 0, '\"versicolor\"': 1, '\"virginica\"': 2}\n\n file = open(path, \"r\", encoding=\"utf-8\")\n lines = file.readlines()\n\n features, labels = [], []\n for i in range(1, len(lines)):\n line = lines[i].strip().split()\n length1 = float(line[1])\n width1 = float(line[2])\n length2 = float(line[3])\n width2 = float(line[4])\n label = label2id[line[5]]\n\n features.append([length1, width1, length2, width2])\n labels.append(label)\n return features, labels\n\n\ndef prepare_data(features: list, labels: list, ratio: float):\n assert len(features) == len(labels)\n\n data_size = len(features)\n idx = list(range(data_size))\n np.random.shuffle(idx)\n\n train_features = np.array([features[i] for i in idx[:int(ratio * data_size)]])\n train_labels = np.array([labels[i] for i in idx[:int(ratio * data_size)]])\n test_features = np.array([features[i] for i in idx[int(ratio * data_size):]])\n test_labels = np.array([labels[i] for i in idx[int(ratio * data_size):]])\n\n train_features, train_labels, test_features, test_labels = torch.from_numpy(train_features).float(), torch.from_numpy(train_labels).long(), torch.from_numpy(test_features).float(), torch.from_numpy(test_labels).long()\n return train_features, train_labels, test_features, test_labels\n\n\ndef eval(predictions: list, labels: torch.Tensor):\n labels = labels.data.tolist()\n truth = sum([1 if predictions[i] == labels[i] else 0 for i in range(len(labels))]) * 1.0 / len(labels)\n print(truth)\n\n\ndef read_data(file_path: str):\n label2id = {\n '\"setosa\"': 0,\n '\"versicolor\"': 1,\n '\"virginica\"': 2\n }\n\n iris_file = open(file_path, mode=\"r\", encoding=\"utf-8\")\n iris_data = iris_file.readlines()\n\n features, labels = [], []\n iris_data = iris_data[1:]\n for per_data in iris_data:\n per_data = per_data.split()\n\n length1 = float(per_data[1])\n width1 = float(per_data[2])\n length2 = float(per_data[3])\n width2 = float(per_data[4])\n\n label = per_data[5]\n labelid = label2id[label]\n\n features.append([length1, width1, length2, width2])\n labels.append(labelid)\n\n return features, labels\n\n\ndef shuffle_data(features: list, labels: list):\n np.random.seed(0)\n index = list(range(len(features)))\n np.random.shuffle(index)\n\n data_size = len(features)\n train_data_size = int(0.8 * data_size)\n\n train_features = [features[i] for i in index[:train_data_size]]\n train_labels = [labels[i] for i in index[:train_data_size]]\n\n test_features = [features[i] for i in index[train_data_size:]]\n test_labels = [labels[i] for i in index[train_data_size:]]\n\n return train_features, train_labels, test_features, test_labels\n\nif __name__ == '__main__':\n # definition\n label2labelid = {\n '\"setosa\"': 0,\n '\"versicolor\"': 1,\n '\"virginica\"': 2\n }\n\n # data pre-process\n features, labels = [], []\n file = open(\"data/iris.txt\", \"r\", encoding=\"utf-8\")\n total_lines = file.readlines()[1:]\n for each_line in total_lines:\n line = each_line.strip().split()\n\n id = line[0]\n length1 = float(line[1])\n width1 = float(line[2])\n length2 = float(line[3])\n width2 = float(line[4])\n label = line[5]\n labelid = label2labelid[label]\n\n features.append([length1, width1, length2, width2])\n labels.append(labelid)\n\n np.random.seed(0)\n index = list(range(len(features)))\n np.random.shuffle(index)\n\n train_data_size = int(len(features) * 0.8)\n test_data_size = int(len(features) * 0.2)\n\n train_features = [features[i] for i in index[:train_data_size]]\n train_labels = [labels[i] for i in index[:train_data_size]]\n test_features = [features[i] for i in index[train_data_size:]]\n test_labels = [labels[i] for i in index[train_data_size:]]\n\n train_features = torch.from_numpy(np.array(train_features)).float()\n train_labels = torch.from_numpy(np.array(train_labels)).long()\n test_features = torch.from_numpy(np.array(test_features)).float()\n test_labels = torch.from_numpy(np.array(test_labels)).long()\n\n model = nn.Sequential(\n nn.Linear(4, 128),\n nn.Sigmoid(),\n nn.Linear(128, 3)\n )\n loss = nn.CrossEntropyLoss()\n optimizer = torch.optim.Adam(params=model.parameters(), lr=0.001)\n\n epochs = 1000\n for each_epoch in range(epochs):\n optimizer.zero_grad()\n predictions = model(train_features)\n loss_value = loss(predictions, train_labels)\n loss_value.backward()\n optimizer.step()\n print(f\"epoch: {each_epoch + 1}, loss: {loss_value.data}\")\n\n model.eval()\n predictions = model(test_features).detach().numpy()\n predictions = np.argmax(predictions, axis=-1)\n eval(predictions, test_labels)\n\n \"\"\"\n features, labels = read_data(\"data/iris.txt\")\n train_features, train_labels, test_features, test_labels = shuffle_data(features, labels)\n train_features = torch.from_numpy(np.array(train_features)).float()\n test_features = torch.from_numpy(np.array(test_features)).float()\n train_labels = torch.from_numpy(np.array(train_labels)).long()\n test_labels = torch.from_numpy(np.array(test_labels)).long()\n\n model = nn.Sequential(\n nn.Linear(4, 256),\n nn.Sigmoid(),\n nn.Linear(256, 3)\n )\n loss = nn.CrossEntropyLoss()\n optimizer = torch.optim.Adam(params=model.parameters(), lr=0.001)\n\n predictions = model(test_features)\n\n epochs = 1000\n for each_epoch in range(epochs):\n optimizer.zero_grad()\n predictions = model(train_features)\n loss_value = loss(predictions, train_labels)\n loss_value.backward()\n optimizer.step()\n print(f\"epoch: {each_epoch}, loss: {loss_value.data}\")\n\n model.eval()\n predictions = model(test_features).detach().numpy()\n predictions = np.argmax(predictions, axis=-1).tolist()\n eval(predictions, test_labels)\n \"\"\"\n\n \"\"\"\n features, labels = read_data_bak(\"data/iris.txt\")\n train_features, train_labels, test_features, test_labels = prepare_data(features, labels, 0.8)\n\n model = Model()\n optimizer = model.optimizer\n epochs = 1000\n for epoch in range(epochs):\n optimizer.zero_grad()\n output = model.model(train_features)\n loss = model.loss(output, train_labels)\n loss.backward()\n optimizer.step()\n print(loss)\n\n predictions = []\n for i in range(test_features.shape[0]):\n feature = test_features[i]\n label = test_labels[i]\n model.model.eval()\n prediction = model.model(feature)\n prediction = np.argmax(prediction.detach().numpy(), axis=-1)\n predictions.append(prediction)\n eval(predictions, test_labels)\n \"\"\"\n\n\n", "sub_path": "nn_demo.py", "file_name": "nn_demo.py", "file_ext": "py", "file_size_in_byte": 7261, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "torch.nn.Sequential", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 15, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 15, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.random.shuffle", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 55, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 91, "usage_type": "attribute"}, {"api_name": "numpy.random.shuffle", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 93, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 132, "usage_type": "attribute"}, {"api_name": "numpy.random.shuffle", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 134, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 149, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 149, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 150, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 150, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 151, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 151, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 152, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 154, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 154, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 155, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 168, "usage_type": "call"}]} +{"seq_id": "444846099", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# __author__ = 'Arthur|http://wingedwhitetiger.com/'\n\n\nfrom PySide2 import QtWidgets, QtGui, QtCore\n\n\nclass OpenVDBExport(QtWidgets.QWidget):\n def __init__(self, parent=None):\n super(OpenVDBExport, self).__init__(parent)\n\n '''init data'''\n self.__time = [1.0, 240.0]\n self.__step = 1.0\n\n '''create layout'''\n main_layout = QtWidgets.QVBoxLayout(self)\n main_layout.setAlignment(QtCore.Qt.AlignTop)\n main_layout.setContentsMargins(0, 0, 0, 0)\n\n time_layout = QtWidgets.QHBoxLayout()\n time_layout.addSpacing(5)\n time_layout.setAlignment(QtCore.Qt.AlignLeft | QtCore.Qt.AlignTop)\n time_layout.setContentsMargins(0, 0, 0, 0)\n\n time_label_layout = QtWidgets.QVBoxLayout()\n time_label_layout.setAlignment(QtCore.Qt.AlignTop)\n time_label_layout.setContentsMargins(0, 0, 0, 0)\n\n time_options_layout = QtWidgets.QVBoxLayout()\n time_options_layout.setAlignment(QtCore.Qt.AlignTop)\n time_options_layout.setContentsMargins(0, 0, 0, 0)\n\n time_custom_layout = QtWidgets.QHBoxLayout()\n time_custom_layout.addSpacing(15)\n time_custom_layout.setAlignment(QtCore.Qt.AlignLeft | QtCore.Qt.AlignTop)\n time_custom_layout.setContentsMargins(0, 0, 0, 0)\n\n step_layout = QtWidgets.QHBoxLayout()\n step_layout.addSpacing(40)\n step_layout.setAlignment(QtCore.Qt.AlignLeft)\n step_layout.setContentsMargins(0, 0, 0, 0)\n\n '''create widget'''\n time_label = QtWidgets.QLabel('Frame Range:')\n self.__timeRange_btg = QtWidgets.QButtonGroup()\n self.__timeRange_btg.buttonClicked.connect(self.__toggle_time)\n current_rb = QtWidgets.QRadioButton('Current frame')\n current_rb.setObjectName('current')\n custom_rb = QtWidgets.QRadioButton('Start/End')\n custom_rb.setObjectName('custom')\n self.__timeRange_btg.addButton(current_rb)\n self.__timeRange_btg.addButton(custom_rb)\n custom_rb.setChecked(True)\n\n self.__time_custom_label = QtWidgets.QLabel('Start/End:')\n self.__custom_min = QtWidgets.QDoubleSpinBox()\n self.__custom_min.setMinimum(0.0)\n self.__custom_min.setMaximum(9998.9)\n self.__custom_min.setValue(self.__time[0])\n self.__custom_min.setDecimals(4)\n self.__custom_min.setSingleStep(1)\n self.__custom_min.valueChanged.connect(self.__set_time)\n\n self.__custom_max = QtWidgets.QDoubleSpinBox()\n self.__custom_max.setMinimum(1.0)\n self.__custom_max.setMaximum(9999.9)\n self.__custom_max.setValue(self.__time[1])\n self.__custom_max.setDecimals(4)\n self.__custom_max.setSingleStep(1)\n self.__custom_max.valueChanged.connect(self.__set_time)\n\n self.__step_label = QtWidgets.QLabel('Step:')\n self.__inc = QtWidgets.QDoubleSpinBox()\n self.__inc.setValue(1.0)\n self.__inc.setDecimals(4)\n self.__inc.setMinimum(0.01)\n self.__inc.setSingleStep(1)\n self.__inc.valueChanged.connect(self.__set_step)\n\n '''add layout'''\n main_layout.addLayout(time_layout)\n main_layout.addLayout(time_custom_layout)\n main_layout.addLayout(step_layout)\n\n time_layout.addLayout(time_label_layout)\n time_layout.addLayout(time_options_layout)\n\n '''add widget'''\n time_label_layout.addWidget(time_label)\n time_options_layout.addWidget(current_rb)\n time_options_layout.addWidget(custom_rb)\n\n time_custom_layout.addWidget(self.__time_custom_label)\n time_custom_layout.addWidget(self.__custom_min)\n time_custom_layout.addWidget(self.__custom_max)\n\n step_layout.addWidget(self.__step_label)\n step_layout.addWidget(self.__inc)\n\n def __toggle_time(self):\n option = self.__timeRange_btg.checkedButton().objectName()\n\n if option == 'custom':\n self.__time_custom_label.setEnabled(True)\n self.__custom_min.setEnabled(True)\n self.__custom_max.setEnabled(True)\n self.__step_label.setEnabled(True)\n self.__inc.setEnabled(True)\n self.__time = [self.__custom_min.value(), self.__custom_max.value()]\n else:\n self.__time_custom_label.setEnabled(False)\n self.__custom_min.setEnabled(False)\n self.__custom_max.setEnabled(False)\n self.__step_label.setEnabled(False)\n self.__inc.setEnabled(False)\n self.__time = []\n\n def __set_time(self):\n self.__time = [self.__custom_min.value(), self.__custom_max.value()]\n\n def __set_step(self):\n self.__step = self.__inc.value()\n\n def get_option(self):\n return {'time': self.__time,\n 'step': self.__step}\n\n\nif __name__ == '__main__':\n import sys\n\n app = QtWidgets.QApplication(sys.argv)\n\n repository = OpenVDBExport()\n repository.show()\n sys.exit(app.exec_())\n", "sub_path": "WitRepository/Houdini/16.0/scripts/python/repositoryLib/pysideLib/houdiniOpenVDBWidget.py", "file_name": "houdiniOpenVDBWidget.py", "file_ext": "py", "file_size_in_byte": 4989, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "PySide2.QtWidgets.QWidget", "line_number": 9, "usage_type": "attribute"}, {"api_name": "PySide2.QtWidgets", "line_number": 9, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QVBoxLayout", "line_number": 18, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 18, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 19, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 19, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QHBoxLayout", "line_number": 22, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 22, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 24, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 24, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QVBoxLayout", "line_number": 27, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 27, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 28, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 28, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QVBoxLayout", "line_number": 31, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 31, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 32, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 32, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QHBoxLayout", "line_number": 35, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 35, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 37, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 37, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QHBoxLayout", "line_number": 40, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 40, "usage_type": "name"}, {"api_name": "PySide2.QtCore.Qt", "line_number": 42, "usage_type": "attribute"}, {"api_name": "PySide2.QtCore", "line_number": 42, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QLabel", "line_number": 46, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 46, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QButtonGroup", "line_number": 47, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 47, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QRadioButton", "line_number": 49, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 49, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QRadioButton", "line_number": 51, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 51, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QLabel", "line_number": 57, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 57, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QDoubleSpinBox", "line_number": 58, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 58, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QDoubleSpinBox", "line_number": 66, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 66, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QLabel", "line_number": 74, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 74, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QDoubleSpinBox", "line_number": 75, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 75, "usage_type": "name"}, {"api_name": "PySide2.QtWidgets.QApplication", "line_number": 134, "usage_type": "call"}, {"api_name": "PySide2.QtWidgets", "line_number": 134, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 134, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 138, "usage_type": "call"}]} +{"seq_id": "218656986", "text": "import json\r\nfrom channels.generic.websocket import AsyncWebsocketConsumer\r\nfrom channels.db import database_sync_to_async\r\nfrom .models import Chat_room, Message\r\nfrom account.models import MyUser\r\n\r\nclass ChatConsumerA(AsyncWebsocketConsumer):\r\n async def connect(self):\r\n # print(self.scope['url_route']['kwargs']['room'])\r\n self.user = self.scope['user']\r\n self.room_name = self.scope['url_route']['kwargs']['room']\r\n self.room_group_name = 'chat_%s' % self.room_name\r\n print(self.room_group_name)\r\n # print(self.room_group_name) \r\n # Join room group\r\n await self.channel_layer.group_add(\r\n self.room_group_name,\r\n self.channel_name\r\n )\r\n\r\n await self.accept()\r\n\r\n async def receive(self,text_data):\r\n text_data_json = json.loads(text_data)\r\n message = text_data_json['message']\r\n sender = text_data_json['sender']\r\n # print(self)\r\n # call fx to save message to db\r\n await self.create_message(message,sender)\r\n \r\n # Send message to room group \r\n await self.channel_layer.group_send(\r\n self.room_group_name,\r\n {'type':'chat_message','message': message,'sender':sender}\r\n )\r\n \r\n async def chat_message(self,event):\r\n # print(event)\r\n message = event['message']\r\n sender = event['sender']\r\n # # Send message to WebSocket\r\n await self.send(text_data=json.dumps({\r\n 'room': self.room_name,\r\n 'message': message,\r\n 'sender':sender\r\n })) \r\n \r\n async def disconnect(self, close_code):\r\n # Leave room group\r\n await self.channel_layer.group_discard(\r\n self.room_group_name,\r\n self.channel_name\r\n )\r\n\r\n @database_sync_to_async\r\n def create_message(self,message,sender):\r\n room = Chat_room.objects.get(id=self.room_name)\r\n sender = MyUser.objects.get(username=sender)\r\n return Message.objects.create(room=room, sender=sender, content=message)\r\n\r\nclass ChatConsumerB(AsyncWebsocketConsumer):\r\n async def connect(self):\r\n await self.accept()\r\n await self.channel_layer.group_add('gossip',self.channel_name)\r\n # print(f' Added to gossip')\r\n \r\n async def disconnect(self,close_code):\r\n await self.channel_layer.group_discard('gossip',self.channel_name)\r\n # print(f' Removed from gossip')\r\n\r\n async def user_gossip(self, event):\r\n data = json.dumps(event)\r\n await self.send(data)\r\n # print(f' Got message on gossip')\r\n", "sub_path": "chat/consumers.py", "file_name": "consumers.py", "file_ext": "py", "file_size_in_byte": 2895, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "channels.generic.websocket.AsyncWebsocketConsumer", "line_number": 7, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 24, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 42, "usage_type": "call"}, {"api_name": "models.Chat_room.objects.get", "line_number": 57, "usage_type": "call"}, {"api_name": "models.Chat_room.objects", "line_number": 57, "usage_type": "attribute"}, {"api_name": "models.Chat_room", "line_number": 57, "usage_type": "name"}, {"api_name": "account.models.MyUser.objects.get", "line_number": 58, "usage_type": "call"}, {"api_name": "account.models.MyUser.objects", "line_number": 58, "usage_type": "attribute"}, {"api_name": "account.models.MyUser", "line_number": 58, "usage_type": "name"}, {"api_name": "models.Message.objects.create", "line_number": 59, "usage_type": "call"}, {"api_name": "models.Message.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "models.Message", "line_number": 59, "usage_type": "name"}, {"api_name": "channels.db.database_sync_to_async", "line_number": 55, "usage_type": "name"}, {"api_name": "channels.generic.websocket.AsyncWebsocketConsumer", "line_number": 61, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "488452939", "text": "#!/usr/bin/python2.7\n\nimport sys\nimport json\nimport time\nimport aol_api\nfrom data_file import report_book\nfrom mysql.connector import MySQLConnection, Error\nfrom python_dbconfig import read_db_config\n\ntodaytime = time.strftime(\"%Y-%m-%d %H:%M:%S\")\n\ndef connect():\n\n # \"\"\"Gets AOL Data and writes them to a MySQL table\"\"\"\n db = \"mysql_sa\"\n report_type = \"site_additions\"\n p_name = sys.argv[1]\n\n # Connect To DB:\n db_config = read_db_config(db)\n\n try:\n #print('Connecting to database...')\n conn = MySQLConnection(**db_config)\n\n if conn.is_connected():\n #print('Connection established')\n \n cursor = conn.cursor()\n\n sql = \"DROP TABLE IF EXISTS \" + p_name + \"_site_addition\"\n cursor.execute(sql)\n\n sql = \"CREATE TABLE \" + p_name + \"_site_addition (date varchar(50), media varchar(255), ad_revenue decimal(15, 5))\"\n cursor.execute(sql)\n\n # calls get_access_token function and starts script\n logintoken = aol_api.get_access_token(p_name)\n #print(logintoken)\n\n for report in report_book[report_type][p_name]:\n\n print(str(todaytime) + \" Running \" + p_name + \"_site addition with report # \" + str(report))\n result = aol_api.run_existing_report(logintoken, str(report))\n #print(result)\n\n for x in json.loads(result)['data']:\n date = x['row'][0]\n media = x['row'][1]\n ad_revenue = x['row'][2]\n\n list = (date, media, ad_revenue)\n #print(list)\n\n sql = \"\"\"INSERT INTO \"\"\" + p_name + \"\"\"_site_addition VALUES (\"%s\", \"%s\", \"%s\")\"\"\" % (date, media, ad_revenue)\n cursor.execute(sql)\n \n cursor.execute('commit')\n\n else:\n print('Connection failed.')\n \n except Error as error:\n print(error)\n\n finally:\n conn.close()\n #print('Connection closed.')\n\n\nif __name__ == '__main__':\n connect()\n\n", "sub_path": "Python/automated_processes/siteAdditions.py", "file_name": "siteAdditions.py", "file_ext": "py", "file_size_in_byte": 2091, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "time.strftime", "line_number": 11, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 18, "usage_type": "attribute"}, {"api_name": "python_dbconfig.read_db_config", "line_number": 21, "usage_type": "call"}, {"api_name": "mysql.connector.MySQLConnection", "line_number": 25, "usage_type": "call"}, {"api_name": "aol_api.get_access_token", "line_number": 39, "usage_type": "call"}, {"api_name": "data_file.report_book", "line_number": 42, "usage_type": "name"}, {"api_name": "aol_api.run_existing_report", "line_number": 45, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 48, "usage_type": "call"}, {"api_name": "mysql.connector.Error", "line_number": 64, "usage_type": "name"}]} +{"seq_id": "448789692", "text": "from flask import Flask, request\nimport os, requests, json\nimport ChatBot\n\napp = Flask(__name__)\nACCESS_TOKEN = os.environ['ACCESS_TOKEN']\nVERIFY_TOKEN = os.environ['VERIFY_TOKEN']\n\n@app.route('/', methods = [\"GET\", \"POST\"])\ndef ReciveMessage():\n\tif request.method == 'GET':\n\t\ttoken_sent = request.args.get(\"hub.verify_token\")\n\t\treturn VerifyFBToken(token_sent)\n\telse:\n\t\tdata = request.get_json()\n\t\tentry = data[\"entry\"][-1]\n\t\tmessaging_event = entry[\"messaging\"][-1]\n\n\t\tif messaging_event.get(\"message\"):\n\t\t\tsender_id = messaging_event[\"sender\"][\"id\"]\n\t\t\trecipient_id = messaging_event[\"recipient\"][\"id\"]\n\t\t\tmessagetext = messaging_event[\"message\"].get(\"text\")\n\t\t\tmessageattachment = messaging_event['message'].get('attachments')\n\n\t\t\tmessage = \"\"\n\t\t\tlatlong = False\n\t\t\tif messagetext:\n\t\t\t\tmessage = messagetext\n\t\t\telif messageattachment and messageattachment[0][\"type\"] == \"location\":\n\t\t\t\tmessage = [messageattachment[0]['payload']['coordinates'][\"lat\"], messageattachment[0]['payload']['coordinates'][\"long\"]]\n\t\t\t\tlatlong = True\n\n\t\t\tresponses = GetReply(sender_id, message, latlong)\n\t\t\tfor response in responses:\n\t\t\t\tSendMessage(sender_id, response)\n\n\t\t\tquick_replies = {\"text\":\"Send my location\",\"quick_replies\":[{\"content_type\":\"location\"}]}\n\t\t\tr = requests.post(\"https://graph.facebook.com/v2.6/me/messages?access_token=\" + ACCESS_TOKEN,\n\t\t\t\theaders = {\"Content-Type\": \"application/json\"}, \n\t\t\t\tdata = json.dumps({\"recipient\": {\"id\": recipient_id}, \"message\": quick_replies})\n\t\t\t\t)\n\treturn \"Message Processed\"\n\ndef VerifyFBToken(token_sent):\n\tif token_sent == VERIFY_TOKEN:\n\t\treturn request.args.get(\"hub.challenge\")\n\treturn \"Invalid verification token\"\n\ndef GetReply(recipient_id, message, latlong):\n\tCB = ChatBot.Chatbot()\n\tif latlong:\n\t\treturn CB.ProcessLatitudeLongitude(recipient_id, message)\n\treturn CB.ProcessMessage(recipient_id, message)\n\ndef SendMessage(recipient_id, response):\n\tr = requests.post(\"https://graph.facebook.com/v2.6/me/messages?access_token=\" + ACCESS_TOKEN,\n\t\theaders = {\"Content-Type\": \"application/json\"}, \n\t\tdata = json.dumps({\"recipient\": {\"id\": recipient_id}, \"message\": {\"text\": response}})\n\t\t)\n\treturn \"Success\"\n\nif __name__ == \"__main__\":\n\tapp.run()", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2188, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 7, "usage_type": "attribute"}, {"api_name": "flask.request.method", "line_number": 11, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 11, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 12, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 15, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 38, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 46, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 46, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 46, "usage_type": "name"}, {"api_name": "ChatBot.Chatbot", "line_number": 50, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 56, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 58, "usage_type": "call"}]} +{"seq_id": "210169290", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 13 21:32:08 2018\n\n@author: zkapach\n\"\"\"\nimport matplotlib\nmatplotlib.use(\"Agg\")\n\n\nimport _init_paths\nfrom core.train import get_training_roidb\nfrom core.config import cfg, cfg_from_file, cfg_from_list, get_output_dir, loadDatasetIndexDict,iconicImagesFileFormat\nfrom datasets.factory import get_repo_imdb\nfrom datasets.ds_utils import load_mixture_set,print_each_size,computeTotalAnnosFromAnnoCount,cropImageToAnnoRegion,roidbSampleHOG,roidbSampleImage\nimport os.path as osp\nimport datasets.imdb\nimport argparse\nimport pprint\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sys,os,cv2,pickle,uuid\n# pytorch imports\nfrom datasets.pytorch_roidb_loader import RoidbDataset\nfrom numpy import transpose as npt\nfrom ntd.hog_svm import plot_confusion_matrix, extract_pyroidb_features,appendHOGtoRoidb,split_data, scale_data,train_SVM,findMaxRegions, make_confusion_matrix\nfrom utils.misc import *\n\ndef parse_args():\n \"\"\"\n Parse input arguments\n \"\"\"\n parser = argparse.ArgumentParser(description='Test loading a mixture dataset')\n parser.add_argument('--cfg', dest='cfg_file',\n help='optional config file',\n default=None, type=str)\n parser.add_argument('--setID', dest='setID',\n help='which 8 digit ID to read from',\n default=['11111111'],nargs='*',type=str)\n parser.add_argument('--repeat', dest='repeat',\n help='which repeat to read from',\n default=[1],nargs='*',type=int)\n parser.add_argument('--size', dest='size',\n help='which size to read from',\n default=[250],nargs='*',type=int)\n parser.add_argument('--save', dest='save',\n help='save some samples with bboxes visualized?',\n action='store_true')\n parser.add_argument('--rand', dest='randomize',\n help='randomize (do not use a fixed seed)',\n action='store_true')\n parser.add_argument('--modelRaw', dest='modelRaw',\n help='give the path to a fit model',\n default=None, type=str)\n parser.add_argument('--modelCropped', dest='modelCropped',\n help='give the path to a fit model',\n default=None, type=str)\n\n if len(sys.argv) == 1:\n parser.print_help()\n sys.exit(1)\n\n args = parser.parse_args()\n return args\n\n\nif __name__ == '__main__':\n args = parse_args()\n\n print('Called with args:')\n print(args)\n if args.cfg_file is not None:\n cfg_from_file(args.cfg_file)\n if not args.randomize:\n np.random.seed(cfg.RNG_SEED)\n print('Using config:')\n pprint.pprint(cfg)\n\n ntdGameInfo = {}\n ntdGameInfo['trainSize'] = 5\n ntdGameInfo['testSize'] = 5\n\n cfg.DEBUG = False\n cfg.uuid = str(uuid.uuid4())\n\n setID_l = args.setID\n repeat_l = args.repeat\n size_l = args.size\n assert len(setID_l) == 1 and len(size_l) == 1,\\\n \"code only works for one size and size currently\"\n\n numEl = len(setID_l) * len(repeat_l) * len(size_l)\n rawMats = []\n croppedMats = []\n diffMats = []\n\n for setID in setID_l:\n for repeat in repeat_l:\n for size in size_l:\n ntdGameInfo['setID'] = setID\n ntdGameInfo['size'] = size\n ntdGameInfo['repeat'] = 222 # repeat\n\n \"\"\"\n convMat_fn = \"output/ntd/confMats_{}_{}_{}.pkl\".format(setID,repeat,size)\n convMat_fn = \"output/ntd/confMats_11111111_0_1000_4b142eec-0ae2-4ed5-9435-4440a8228b63.pkl\"\n convMat = pickle.load(open(convMat_fn,\"rb\"))\n cmRaw = convMat['raw']\n cmCropped = convMat['cropped']\n \"\"\"\n cmRaw = np.array([31,19,10,1,21,13,5,0,18,21,21,2,12,20,4,1,23,12,23,1,17,18,5,1,0,0,0,100,0,0,0,0,11,9,8,0,61,7,2,1,15,15,16,1,13,33,4,4,2,0,1,0,3,1,92,0,12,6,6,2,3,4,3,65]).reshape(8,8)\n cmCropped = np.array([20,14,18,9,8,12,8,11,12,20,20,8,9,14,7,11,16,19,19,8,10,13,7,9,8,8,8,49,6,6,8,8,8,10,10,4,42,12,5,8,19,16,15,7,8,17,7,11,12,8,11,18,9,7,26,9,11,13,12,9,14,11,9,21]).reshape(8,8)\n cmDiff = cmRaw - cmCropped\n plotNtdConfMats(cmRaw,cmCropped,cmDiff,ntdGameInfo)\n sys.exit()\n\n rawMats.append(cmRaw)\n croppedMats.append(cmCropped)\n diffMats.append(cmDiff)\n\n ntdGameInfo['ave'] = len(rawMats)\n ntdGameInfo['std'] = len(rawMats)\n\n rawMatsAve = np.array(rawMats).mean(axis=0)\n print(rawMatsAve.shape)\n croppedMatsAve = np.array(croppedMats).mean(axis=0)\n diffMatsAve = np.array(diffMats).mean(axis=0)\n\n plotNtdConfMats(rawMatsAve,croppedMatsAve,diffMatsAve,ntdGameInfo,\"ave\")\n\n rawMatsStd = np.array(rawMats).std(axis=0)\n croppedMatsStd = np.array(croppedMats).std(axis=0)\n diffMatsStd = np.array(diffMats).std(axis=0)\n\n plotNtdConfMats(rawMatsStd,croppedMatsStd,diffMatsStd,ntdGameInfo,\"std\")\n\n print(rawMatsAve.shape)\n print(\"\\n\\n -=-=-=- uuid: {} -=-=-=- \\n\\n\".format(cfg.uuid))\n\n \n\n\n\n\n", "sub_path": "tools/prettyConfMat.py", "file_name": "prettyConfMat.py", "file_ext": "py", "file_size_in_byte": 5256, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "matplotlib.use", "line_number": 9, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 34, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 60, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 62, "usage_type": "call"}, {"api_name": "core.config.cfg_from_file", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 76, "usage_type": "attribute"}, {"api_name": "core.config.cfg.RNG_SEED", "line_number": 76, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 76, "usage_type": "name"}, {"api_name": "pprint.pprint", "line_number": 78, "usage_type": "call"}, {"api_name": "core.config.cfg", "line_number": 78, "usage_type": "argument"}, {"api_name": "core.config.cfg.DEBUG", "line_number": 84, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 84, "usage_type": "name"}, {"api_name": "core.config.cfg.uuid", "line_number": 85, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 85, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 113, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 134, "usage_type": "call"}, {"api_name": "core.config.cfg.uuid", "line_number": 139, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 139, "usage_type": "name"}]} +{"seq_id": "401333562", "text": "'''\nThis script get the output of Sean's QGIS process and flattens the output in\norder to be runnable on other scripts\n'''\nimport argparse\nimport geopandas as gpd\n\n\ndef keep_rows_with_values(df, row_name):\n df_temp = df[['id', 'damagelevel', 'geometry', row_name]].dropna()\n df_temp.columns = ['id', 'damagelevel', 'geometry', 'image']\n return df_temp\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('input', help=\"name the geojson you want flat\")\nparser.add_argument('output', help=\"name what you want it called\")\nargs = parser.parse_args()\n\ndf = gpd.read_file('../data_training/boundingboxes/' + args.input)\n\ndates = ['files_0827', 'files_0828', 'files_0829', 'files_0830', 'files_0831',\n 'files_0901', 'files_0902', 'files_0903']\nflat_df = gpd.GeoDataFrame(columns=['id', 'damagelevel', 'geometry', 'image'])\nfor date in dates:\n temp_df = keep_rows_with_values(df, date)\n flat_df = flat_df.append(temp_df, ignore_index=True)\nflat_df.id = flat_df.index\n\nflat_df.to_file('../data_training/boundingboxes/' + args.output,\n driver='GeoJSON')\n", "sub_path": "scripts/pixel-conversion/flatten_geojson.py", "file_name": "flatten_geojson.py", "file_ext": "py", "file_size_in_byte": 1087, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 15, "usage_type": "call"}, {"api_name": "geopandas.read_file", "line_number": 20, "usage_type": "call"}, {"api_name": "geopandas.GeoDataFrame", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "506377781", "text": "\"\"\"\n抗功耗攻击评估模块。\nData类，用于读取存储明文、功耗数据\nEvalution类，攻击算法类。\n\"\"\"\n\nimport os\nimport numpy as np\nimport pandas as pd\nimport math\nimport struct\nimport matplotlib.pyplot as plt\n\n# from object_module import ObjISbox\n\n\"\"\"存储数据的类，用于读取存储于文件中的明文、功耗等数据。\n\"\"\"\n\n\nclass Data:\n \"\"\"读取并存储文件中的明文或功耗数据\n \"\"\"\n\n def __init__(self):\n \"\"\"data只读，存放数据，files只读，读取的文件名称列表\n \"\"\"\n self._data = np.array([])\n self._files = []\n\n def read_files(self, files, etype, ftype='c', columns=1):\n \"\"\"读入files列表中的文件，存储在columns列的数组中。\n Keyword Arguments:\n columns -- (default 1)\n etype -- (default )读取文件中数据的类型，如整形，浮点\n files -- (default [])\n ftype -- (default )读取文件的类型，如二进制（b），字符型（c）\n \"\"\"\n self._files = files\n data_dir = []\n\n for fname in files:\n if 'c' == ftype:\n with open(fname, 'r') as file:\n data_file = [etype(i) for i in file.read().split()]\n else:\n with open(fname, 'rb') as file:\n check = (1023, 1023, 1023, 1023, 1023, 1023, 1023,\n 1023, 1023, 1023, 1023, 1023, 0, 0, 0, 0)\n if check == struct.unpack('16H', file.read(32)):\n data = file.read()\n num_data = divmod(len(data), 2)[0]\n data_file = list(struct.unpack(''.join([str(num_data), 'H']), data))\n else:\n raise UserWarning(''.join(['文件', fname, '未通过较验']))\n\n data_dir.extend(data_file)\n\n num_total = len(data_dir)\n num_mod = divmod(num_total, columns)[1]\n \"\"\"将数据存成columns列n行的数组，多出的数据舍去。\n \"\"\"\n data_dir = np.array(data_dir)[0:(num_total - num_mod)]\n self._data = data_dir.reshape(-1, columns)\n\n\n def read_dir(self, dirname, etype, ftype, columns=1):\n \"\"\"读目录中的所有文件，存成columns列的数组。\n Keyword Arguments:\n dirname --\n columns -- (default -1)\n etype -- (default 'float')\n \"\"\"\n path_files = [''.join([dirname, x]) for x in os.listdir(dirname)]\n self.read_files(path_files, etype, ftype, columns)\n\n\n @property\n def data(self):\n \"\"\"设置变量_data为只读\n \"\"\"\n return self._data\n\n\n @property\n def files(self):\n \"\"\"设置变量_files为只读\n \"\"\"\n return self._files\n\n\nclass AttackCorr:\n \"\"\"抗相关系数攻击评估\n \"\"\"\n\n def __init__(self):\n \"\"\"result存放攻击结果数据。\n \"\"\"\n self._mat_corr = np.array([])\n self._result = []\n\n def do_attack(self, obj_module, plain, power, bits_key):\n \"\"\"对目标object抗攻击能力进行评估。\n Keyword Arguments:\n obj_module --\n data --\n power --\n \"\"\"\n\n self._mat_corr = np.array([self._corr_onekey(obj_module,\n power,\n plain,\n key)\n for key in range(2**bits_key)])\n\n abs_mat_corr = abs(self._mat_corr)\n self._result = np.where(abs_mat_corr == abs_mat_corr.max())[0]\n\n\n def plot_result(self, bits_key):\n \"\"\"输入key的数目，如8位密钥为256\n \"\"\"\n plt.plot(range(2**bits_key), self._mat_corr)\n\n @staticmethod\n def _corr_onekey(obj, power_data, plain_data, key):\n \"\"\"对key实施一轮攻击\n Keyword Arguments:\n obj_module -- 攻击目标\n plain -- 明文，为Data的实例\n key -- 某一个key,0-255\n \"\"\"\n\n \"\"\"明文对应某一密钥的输出密文的汉明距离\n \"\"\"\n\n def haming_distance(plain_data, obj, key):\n \"\"\"生成密钥key对应明文输出的汉明距离序列。\n \"\"\"\n cipher_current = 0\n for plain_text in plain_data:\n cipher_last = cipher_current\n cipher_current = obj.gen_cipher(plain_text, key)\n yield np.binary_repr(\n np.bitwise_xor(cipher_last, cipher_current)).count('1')\n\n haming = list(haming_distance(plain_data, obj, key))\n return np.corrcoef(haming, power_data.transpose())[0, 1:]\n\nclass EvaluationCorr:\n \"\"\"相关系数评估\n \"\"\"\n\n def __init__(self):\n \"docstring\"\n self._table = {'0.80': 0.84162123,\n '0.85': 1.03643339,\n '0.90': 1.28155157,\n '0.95': 1.64485363,\n '0.96': 1.75068607,\n '0.97': 1.88079361,\n '0.98': 2.05374891,\n '0.99': 2.32634787}\n\n self._result = {}\n\n def do_evaluation(self, mat_corr, truekey):\n \"\"\"\n Keyword Arguments:\n data_corr --\n truekey --\n \"\"\"\n self._result = dict([(k, self._evaluate(mat_corr, truekey, v))\n for (k, v) in self._table.items()])\n\n @staticmethod\n def _evaluate(mat_corr, truekey, z_alpha):\n \"\"\"\n Keyword Arguments:\n result_corr --\n truekey --\n alpha --\n \"\"\"\n p_truekey = abs(mat_corr[truekey]).max()\n return 3 + 8*((z_alpha/math.log((1 + p_truekey)/(1 - p_truekey)))**2)\n\n\n\nclass AttackMean:\n \"\"\"抗均值差攻击\n \"\"\"\n\n def __init__(self):\n \"\"\"result存放攻击结果数据。\n \"\"\"\n self._mat_mean = []\n self._result = []\n\n def do_attack(self, obj_module, plain, power, bits_key):\n \"\"\"对目标object抗攻击能力进行评估。\n Keyword Arguments:\n obj_module --\n data --\n power --\n \"\"\"\n\n self._mat_mean = np.array([self._mean_onekey(obj_module,\n power,\n plain,\n key,\n bits_key)\n for key in range(2**bits_key)])\n\n df_mat_mean = pd.DataFrame(self._mat_mean,\n index=(range(2**bits_key)),\n columns=range(bits_key))\n\n \"\"\"每个位对应的前五个最大值的密钥，共40个（8位，每位5个）。\n \"\"\"\n result_top5 = []\n for i in range(bits_key):\n result_top5.extend(list(df_mat_mean.sort(i, ascending=False).index[0:5]))\n\n \"\"\"将密钥由numpy.int64变为字符型，因为json不识别numpy.int64\n \"\"\"\n result_top5 = [str(i) for i in result_top5]\n\n \"\"\"统计密钥出现的次数\n \"\"\"\n result = {}\n for i in result_top5:\n result[i] = result.get(i, 0) + 1\n\n \"\"\"按密钥出现的次数进行排序\n \"\"\"\n self._result = sorted(result.items(), key=lambda d: d[1], reverse=True)\n\n # abs_mat_mean = abs(self._mat_mean)\n # self._result = np.array([np.where(i == i.max())[0] for i in self._mat_mean.transpose()]).reshape(bits_key)\n # self._result = np.array([np.where(i == i.max())[0] for i in df_mat_mean])\n\n\n def plot_result(self, bits_key):\n \"\"\"输入key的数目，如8位密钥为256\n \"\"\"\n plt.plot(range(2**bits_key), self._mat_mean[:, 2])\n\n @staticmethod\n def _mean_onekey(obj, power_data, plain_data, key, bits_key):\n \"\"\"对key实施一轮攻击\n Keyword Arguments:\n obj_module -- 攻击目标\n plain -- 明文，为Data的实例\n key -- 某一个key,0-255\n \"\"\"\n\n \"\"\"明文对应某一密钥的输出密文的汉明距离\n \"\"\"\n cipher_text = [np.binary_repr(obj.gen_cipher(plain_text, key), bits_key)\n for plain_text in plain_data]\n\n df_data = pd.DataFrame(power_data, index=cipher_text)\n\n all_cipher = [np.binary_repr(i, bits_key) for i in range(2**bits_key)]\n\n\n result = []\n for i in range(7, -1, -1):\n df_data_1 = df_data.loc[[x for x in all_cipher if x[i] == '1']]\n df_data_0 = df_data.loc[[x for x in all_cipher if x[i] == '0']]\n df_data_diff = np.array(df_data_1.mean() - df_data_0.mean())\n result.append(abs(df_data_diff).max())\n\n return np.array(result)\n\n\nif __name__ == '__main__':\n\n\n PLAIN = Data()\n PLAIN.read_dir('./data_repo/plain/', int, 'c', 1)\n\n POWER = Data()\n POWER.read_dir('./data_repo/power/', float, 'c', 400)\n\n OBJECTIVE = ObjISbox()\n\n ATTACKCORR = AttackCorr()\n ATTACKCORR.do_attack(OBJECTIVE, PLAIN.data[:1000], POWER.data[:1000, :], 8)\n EVALUATION = EvaluationCorr()\n EVALUATION.do_evaluation(ATTACKCORR._mat_corr, 198)\n ATTACKCORR.plot_result(8)\n\n\n ATTACKMEAN = AttackMean()\n ATTACKMEAN.do_attack(OBJECTIVE, PLAIN.data[:2000], POWER.data[:2000, :], 8)\n ATTACKMEAN.plot_result(8)\n\n plt.show()\n", "sub_path": "epar/epar.py", "file_name": "epar.py", "file_ext": "py", "file_size_in_byte": 9406, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 49, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 62, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 116, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "numpy.binary_repr", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.bitwise_xor", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.corrcoef", "line_number": 147, "usage_type": "call"}, {"api_name": "math.log", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 206, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 245, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 245, "usage_type": "name"}, {"api_name": "numpy.binary_repr", "line_number": 258, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.binary_repr", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 270, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 273, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 298, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 298, "usage_type": "name"}]} +{"seq_id": "642374310", "text": "import io\r\n\r\ndef get_traits(cur):\r\n trait_files = ['data/00_traits.txt', 'data/01_traits.txt',\r\n 'data/02_traits.txt', 'data/03_traits.txt']\r\n trait_id = 1\r\n for file in trait_files:\r\n trait_id = add_traits(file,cur,trait_id)\r\n\r\n\r\ndef add_traits(file,cur,trait_id):\r\n #open the file and add traits to the traitlookup relation\r\n with io.open(file,encoding=\"cp1252\") as f:\r\n for line in f.readlines():\r\n if line.find('=')!=-1 and line.find('{')!=-1 and line[0]!='\\t':\r\n name = line[0:line.find('=')-1]\r\n if name.find('#')!=-1: continue\r\n cur.execute('INSERT INTO traitlookup Values(?,?)',[trait_id,name])\r\n trait_id += 1\r\n return trait_id\r\n", "sub_path": "get_traits.py", "file_name": "get_traits.py", "file_ext": "py", "file_size_in_byte": 759, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "io.open", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "589564580", "text": "import torch\nimport math\nimport torch.nn as nn\nfrom torch.nn import functional as F\nimport numpy as np\n\n\nclass MDN(nn.Module):\n \"\"\"\n Mixture Density Network\n \"\"\"\n\n def __init__(self, input_size, num_mixtures):\n super().__init__()\n self.input_size = input_size\n self.num_mixtures = num_mixtures\n\n self.fc1 = nn.Linear(self.input_size, 6 * num_mixtures + 1)\n\n def forward(self, x):\n mid = self.fc1(x)\n return mid\n\n def sample(self, output, bias=5.):\n output = output.view(1, -1)\n w, mx, my, varx, vary, rho, end = self.to_dists(output, bias=bias)\n w = w.softmax(dim=-1).data.cpu().view(-1).numpy()\n idx = np.random.choice(range(self.num_mixtures), size=2, p=w)[0]\n varx = varx.view(-1)[idx].item()\n vary = vary.view(-1)[idx].item()\n mx = mx.view(-1)[idx].item()\n my = my.view(-1)[idx].item()\n rho = rho.view(-1)[idx].item()\n mean = torch.empty(2)\n mean[0], mean[1] = mx, my\n cov = torch.Tensor([[varx, rho*math.sqrt(varx*vary)],\n [rho*math.sqrt(varx*vary), vary]])\n\n z = torch.distributions.MultivariateNormal(loc=mean,\n covariance_matrix=cov)\n t = z.sample()\n\n end = torch.sigmoid(end).view(-1).item()\n e = 1 if torch.rand(1).item() < end else 0\n inp = torch.zeros(1, 1, 3)\n inp[0, 0, 0] = t[0]\n inp[0, 0, 1] = t[1]\n inp[0, 0, 2] = e\n return inp.view(1, 3)\n\n def to_dists(self, output, bias=0.):\n lens = [self.num_mixtures for i in range(6)]\n lens.append(1)\n w, mx, my, vx, vy, rho, end = output.split(lens, dim=-1)\n\n w = w * (1 + bias)\n w = w.log_softmax(dim=-1)\n varx = torch.nn.functional.softplus(vx-bias)\n vary = torch.nn.functional.softplus(vy-bias)\n rho = rho.tanh()\n return w, mx, my, varx, vary, rho, end\n\n def loss_fn(self, output, target):\n tx, ty, te = target.chunk(3, dim=-1)\n w, mx, my, varx, vary, rho, end = self.to_dists(output)\n\n ro2 = 1. - rho * rho\n dx = mx - tx\n dy = my - ty\n z = torch.pow(dx, 2) / varx\n z = z + torch.pow(dy, 2) / vary\n z = z - 2.0 * rho * dx * dy / torch.sqrt(varx*vary)\n\n llh = -z / 2.0 / ro2\n llh = llh - math.log(2*math.pi)\n llh = llh - 0.5 * (varx.log() + vary.log() + ro2.log())\n llh = torch.logsumexp(llh + w, dim=-1, keepdim=True)\n out = -llh + \\\n F.binary_cross_entropy_with_logits(end, te, reduction=\"none\")\n\n return out\n\n\nclass Decoder(nn.Module):\n def __init__(self, input_size=3, num_mixtures=20, rnn_hidden_size=128, num_rnn_layers=2, dropout=0.2):\n super().__init__()\n self.rnn_hidden_size = rnn_hidden_size\n self.num_rnn_layers = num_rnn_layers\n\n self.rnn = nn.LSTM(input_size=input_size, hidden_size=rnn_hidden_size,\n num_layers=num_rnn_layers, dropout=dropout)\n self.mdn = MDN(rnn_hidden_size, num_mixtures)\n\n def forward(self, x, hidden):\n mid, hidden = self.rnn(x, hidden)\n out = self.mdn(mid)\n return out, hidden\n\n def init_hidden_states(self, bs, device):\n state = (torch.ones(self.num_rnn_layers, bs, self.rnn_hidden_size, device=device).data,\n torch.ones(self.num_rnn_layers, bs, self.rnn_hidden_size, device=device).data)\n return state\n", "sub_path": "models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 3483, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "torch.nn.Module", "line_number": 8, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 8, "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": "numpy.random.choice", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.empty", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 36, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 36, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.distributions.MultivariateNormal", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.distributions", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.sigmoid", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn.functional.softplus", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.softplus", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "attribute"}, {"api_name": "torch.pow", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.sqrt", "line_number": 72, "usage_type": "call"}, {"api_name": "math.log", "line_number": 75, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 75, "usage_type": "attribute"}, {"api_name": "torch.logsumexp", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn.functional.binary_cross_entropy_with_logits", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 84, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "name"}, {"api_name": "torch.nn.LSTM", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 90, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 101, "usage_type": "call"}]} +{"seq_id": "33139935", "text": "# -*- coding: utf-8 -*-\n# file: train.py\n# author: yangheng \n# Copyright (C) 2020. All Rights Reserved.\n\nimport random\n\nimport numpy\nimport torch\nfrom torch.utils.data import DataLoader\nfrom transformers import BertModel, BertTokenizer\n\nfrom modules.models import LCA_BERT, SLIDE_LCF_BERT, LCF_BERT\nfrom modules.models import BERT_BASE, BERT_SPC\nfrom modules.utils.data_utils_for_inferring import Tokenizer4Bert, ABSADataset, parse_experiments\n\n\nclass Instructor:\n def __init__(self, opt):\n self.opt = opt\n # opt.learning_rate = 2e-5\n # Use any type of BERT to initialize your model.\n # The weights of loaded BERT will be covered after loading state_dict\n # self.bert = BertModel.from_pretrained('bert-base-uncased')\n self.bert = BertModel.from_pretrained(opt.pretrained_bert_name)\n self.bert_tokenizer = BertTokenizer.from_pretrained(opt.pretrained_bert_name, do_lower_case=True)\n tokenizer = Tokenizer4Bert(self.bert_tokenizer, opt.max_seq_len)\n self.model = opt.model_class(self.bert, opt).to(opt.device)\n\n self.model.load_state_dict(torch.load(opt.state_dict_path))\n infer_set = ABSADataset(opt.infer_data, tokenizer, opt)\n self.train_data_loader = DataLoader(dataset=infer_set, batch_size=1, shuffle=False)\n\n def _infer(self):\n sentiments = {0: 'Negative', 1: \"Neutral\", 2: 'Positive', -999: ''}\n Correct = {True: 'Correct', False: 'Wrong'}\n with torch.no_grad():\n self.model.eval()\n for _, sample in enumerate(self.train_data_loader):\n print(sample['text_raw'][0])\n\n inputs = [sample[col].to(self.opt.device) for col in self.opt.inputs_cols]\n self.model.eval()\n outputs = self.model(inputs)\n if 'lca' in self.opt.model_name:\n sen_logits, _, _ = outputs\n else:\n sen_logits = outputs\n t_probs = torch.softmax(sen_logits, dim=-1).cpu().numpy()\n sent = int(t_probs.argmax(axis=-1))\n real_sent = int(sample['polarity'])\n aspect = sample['aspect'][0]\n\n print('{} --> {}'.format(aspect, sentiments[sent])) if real_sent == -999 \\\n else print('{} --> {} Real Polarity: {} ({})'.format(aspect, sentiments[sent],\n sentiments[real_sent],\n Correct[sent == real_sent]))\n\n def run(self):\n\n _params = filter(lambda p: p.requires_grad, self.model.parameters())\n return self._infer()\n\n\ndef init_and_infer(opt):\n if opt.seed is not None:\n random.seed(opt.seed)\n numpy.random.seed(opt.seed)\n torch.manual_seed(opt.seed)\n torch.cuda.manual_seed(opt.seed)\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n\n model_classes = {\n 'bert_base': BERT_BASE,\n 'bert_spc': BERT_SPC,\n 'lca_bert': LCA_BERT,\n 'lcf_bert': LCF_BERT,\n 'lcfs_bert': LCF_BERT,\n 'slide_lcf_bert': SLIDE_LCF_BERT,\n 'slide_lcfs_bert': SLIDE_LCF_BERT,\n }\n\n initializers = {\n 'xavier_uniform_': torch.nn.init.xavier_uniform_,\n 'xavier_normal_': torch.nn.init.xavier_normal,\n 'orthogonal_': torch.nn.init.orthogonal_\n }\n\n opt.model_class = model_classes[opt.model_name]\n opt.inputs_cols = ABSADataset.input_colses[opt.model_name]\n opt.initializer = initializers[opt.initializer]\n\n ins = Instructor(opt)\n return ins.run() # _reset_params in every repeat\n\n\nif __name__ == '__main__':\n\n configs = parse_experiments('inferring_config.json')\n\n from modules.utils.Pytorch_GPUManager import GPUManager\n\n GM = GPUManager()\n gpu = GM.auto_choice()\n\n # only take the first config to infer each running\n opt = configs[0]\n opt.device = 'cuda:' + str(gpu)\n # config.device = 'cpu' # Uncomment this line to use CPU\n\n import os\n\n for file in os.listdir():\n if 'state_dict' in file:\n opt.state_dict_path = file\n if 'inferring.dat' in file:\n opt.infer_data = file\n if 'config.json' in file:\n opt.config = file\n if 'embedding' in file:\n opt.embedding = file.split('/')[-1]\n if 'tokenizer' in file:\n opt.tokenizer = file.split('/')[-1]\n\n print('*' * 80)\n print('Warning: Be sure the eval-config, eval-dataset, saved_state_dict, seed are compatible! ')\n print('*' * 80)\n opt.seed = int(opt.state_dict_path.split('seed')[1])\n init_and_infer(opt)\n", "sub_path": "batch_inferring/inferring.py", "file_name": "inferring.py", "file_ext": "py", "file_size_in_byte": 4725, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "transformers.BertModel.from_pretrained", "line_number": 25, "usage_type": "call"}, {"api_name": "transformers.BertModel", "line_number": 25, "usage_type": "name"}, {"api_name": "transformers.BertTokenizer.from_pretrained", "line_number": 26, "usage_type": "call"}, {"api_name": "transformers.BertTokenizer", "line_number": 26, "usage_type": "name"}, {"api_name": "modules.utils.data_utils_for_inferring.Tokenizer4Bert", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 30, "usage_type": "call"}, {"api_name": "modules.utils.data_utils_for_inferring.ABSADataset", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.softmax", "line_number": 49, "usage_type": "call"}, {"api_name": "random.seed", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 68, "usage_type": "attribute"}, {"api_name": "torch.manual_seed", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.cuda.manual_seed", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 70, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 71, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 72, "usage_type": "attribute"}, {"api_name": "modules.models.BERT_BASE", "line_number": 75, "usage_type": "name"}, {"api_name": "modules.models.BERT_SPC", "line_number": 76, "usage_type": "name"}, {"api_name": "modules.models.LCA_BERT", "line_number": 77, "usage_type": "name"}, {"api_name": "modules.models.LCF_BERT", "line_number": 78, "usage_type": "name"}, {"api_name": "modules.models.LCF_BERT", "line_number": 79, "usage_type": "name"}, {"api_name": "modules.models.SLIDE_LCF_BERT", "line_number": 80, "usage_type": "name"}, {"api_name": "modules.models.SLIDE_LCF_BERT", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.nn", "line_number": 85, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 86, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 87, "usage_type": "attribute"}, {"api_name": "modules.utils.data_utils_for_inferring.ABSADataset.input_colses", "line_number": 91, "usage_type": "attribute"}, {"api_name": "modules.utils.data_utils_for_inferring.ABSADataset", "line_number": 91, "usage_type": "name"}, {"api_name": "modules.utils.data_utils_for_inferring.parse_experiments", "line_number": 100, "usage_type": "call"}, {"api_name": "modules.utils.Pytorch_GPUManager.GPUManager", "line_number": 104, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 114, "usage_type": "call"}]} +{"seq_id": "372678383", "text": "import logging\nimport sys\nfrom datetime import date\n\nformatter = logging.Formatter('{pathname:s}:{lineno:d}:{levelname:s}: {message:s}', style='{')\n\nhandler = logging.StreamHandler(sys.stdout)\nhandler.setFormatter(formatter)\nhandler.setLevel(logging.DEBUG)\n\nroot = logging.getLogger()\nroot.addHandler(handler)\nroot.setLevel(logging.DEBUG)\n\nlogger = logging.getLogger(__name__)\n\nYEAR_FROM = 2000\nYEAR_TO = 3000\n\n\ndef parse(d):\n l = list(map(int, d.split('/')))\n\n possible_orderings = [\n (l[0], l[1], l[2]), # Y/m/d\n (l[2], l[1], l[0]), # d/m/Y\n (l[2], l[0], l[1]), # m/d/Y\n # Exotic formats\n (l[0], l[2], l[1]), # Y/d/m\n (l[1], l[0], l[2]), # m/Y/d\n (l[1], l[2], l[0]), # d/Y/m\n ]\n for data in possible_orderings:\n year, month, day = data\n if year + YEAR_FROM < YEAR_TO:\n year = year + YEAR_FROM\n try:\n return date(year, month, day).strftime('%Y-%m-%d')\n except ValueError as e:\n logger.debug(e)\n return '{} is illegal'.format(d)\n\n\ndef main():\n with open('data.txt') as f:\n data = f.readline()\n\n print(parse(data))\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "dateparser.py", "file_name": "dateparser.py", "file_ext": "py", "file_size_in_byte": 1198, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.Formatter", "line_number": 5, "usage_type": "call"}, {"api_name": "logging.StreamHandler", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 7, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 13, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "107466740", "text": "#\n# Arg parser factory class\n# Probably not needed at all...\n#\nfrom argparse import ArgumentParser\n\nclass ArgParserFactory:\n \"\"\" Class for creating an Arg Parser \"\"\"\n\n @staticmethod\n def create_arg_parser():\n \"\"\" creates a CMD line argument parser with possible options \"\"\"\n parser = ArgumentParser(description='Replaces Header section of Amgen standard .SAS files.')\n #group = parser.add_mutually_exclusive_group(required=True)\n\n parser.add_argument('-d', '--dir_file',\n help='Path of the [d]irectory paths .txt file, containing the paths to that need' +\n 'headers to be replaced',\n required=True)\n\n parser.add_argument('-r', '--replacement_file',\n help='Path of the .txt file containing the text that is used as the replacement content',\n required=True)\n\n parser.add_argument('-v', '--version_history',\n help='Wipe the [v]ersion history present in the header.',\n action='store_true')\n\n return parser\n", "sub_path": "header_replace_object_oriented/src/ArgParserFactory.py", "file_name": "ArgParserFactory.py", "file_ext": "py", "file_size_in_byte": 1155, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "562125317", "text": "import allure\r\nfrom page_locators.search_policy_locator import SearchLocator\r\nimport time\r\nfrom page_locators.menu_locator import MenuLocator\r\nfrom page_locators.client_detail_locator import ClientDetailLocator\r\nfrom testcase.mdes_base_case import MdesBaseCase\r\nfrom config import config\r\nfrom selenium import webdriver\r\n\r\n# 投保书审核/查询\r\nclass SearchPage(MdesBaseCase):\r\n temp_dict1 = {}\r\n\r\n @allure.step('查询已提交的投保书号并获取保单五要素')\r\n def search_policy_number(self):\r\n\r\n self.switch_frame_to_main()\r\n self.select_frame('frame_content')\r\n self.select_frame(0)\r\n self.select_frame('MainInfoFrame')\r\n # cont_no = '202010291707'\r\n cont_no = self.get_element_attribute(SearchLocator.proposal_cont_no, 'value') # 获取已提交的��保书号\r\n print(cont_no)\r\n self.switch_frame_to_main()\r\n print('开始点击投保书号查询，进入查询页面')\r\n # self.click_element(SearchLocator.search_cont_no) # 点击进入投保书号查询页面，方法一：通过定位元素,不稳定\r\n # self.click_element_by_js(SearchLocator.search_cont_no) # 点击进入投保书号查询页面，方法二：通过js定位，不稳定\r\n self.driver.get(config.get_config(\"MDES\", \"ip\") + SearchLocator.search_cont_no) # 点击进入投保书号查询页面，方法三：通过直接跳转链接\r\n self.switch_frame_to_main()\r\n self.select_frame('frame_content')\r\n # self.wait_element(SearchLocator.proposal_cont_no) --------------------------------\r\n self.input_text(SearchLocator.cont_no_input, cont_no) # 填入投保书号\r\n print('开始点击查询')\r\n self.click_element(SearchLocator.cont_no_searchBTN)\r\n time.sleep(3)\r\n try:\r\n self.click_element(SearchLocator.cont_no) # 点击投保书号进入保单详情页面\r\n except Exception as e:\r\n print('没有找到投保书号...')\r\n raise\r\n time.sleep(3)\r\n\r\n \"\"\" 采集留存信息 \"\"\"\r\n # temp_list = [] # 新建列表保存留存信息\r\n temp_risk_list = []\r\n\r\n self.switch_frame_to_main()\r\n self.select_frame('frame_content')\r\n self.select_frame(0)\r\n self.select_frame('MainInfoFrame')\r\n # proposal_cont_no = self.get_element_attribute(SearchLocator.proposal_cont_no, 'value') # 投保书号\r\n self.switch_frame_to_main()\r\n self.select_frame('frame_content')\r\n self.select_frame(0)\r\n self.select_frame('WorkFrame')\r\n\r\n \"\"\" 读取险种信息 \"\"\"\r\n tr_elements = self.get_elements(SearchLocator.tr_locator)\r\n for tr_element in tr_elements:\r\n temp_risk_list.append(tr_element.text)\r\n\r\n \"\"\" 将险种信息组合成字典\"\"\"\r\n\r\n temp_dict = {}\r\n\r\n # list2 = []\r\n # for i in range(len(temp_risk_list)):\r\n # list2 = temp_risk_list[i].split()\r\n # for j in range(len(list2)):\r\n # temp_dict[risk_keys_list[j] + \"_\" + str(i)] = list2[j]\r\n # print(risk_keys_list[j] + \"_\" + str(i))\r\n # print(temp_dict)\r\n\r\n risk_keys_list = ['risk_code', 'risk_name', 'year', 'pay_year', 'get_year', 'select_insured_or_premium',\r\n 'pay_frequency', 'each_premium']\r\n temp_dict = {(risk_keys_list[j] + \"_\" + str(i)): temp_risk_list[i].split()[j] for i in range(len(temp_risk_list)) for j in\r\n range(len(temp_risk_list[i].split()))}\r\n\r\n first_premium = self.get_element_text(SearchLocator.first_premium) # 合计首期保费\r\n temp_dict['first_premium'] = first_premium\r\n self.switch_frame_to_main()\r\n self.select_frame('frame_content')\r\n self.select_frame(0)\r\n self.select_frame('NavigationFrame')\r\n self.click_element(MenuLocator.tab_2) # 客户信息页\r\n self.switch_frame_to_main()\r\n self.select_frame('frame_content')\r\n self.select_frame(0)\r\n self.select_frame('MainInfoFrame')\r\n time.sleep(1)\r\n policy_number = self.get_element_attribute(SearchLocator.policy_number, 'value') # 保单号\r\n self.switch_frame_to_main()\r\n self.select_frame('frame_content')\r\n self.select_frame(0)\r\n self.select_frame('WorkFrame')\r\n # appnt_name = self.get_element_attribute(ClientDetailLocator.appnt_name, 'value') # 投保人姓名\r\n # appnt_sex = self.get_element_attribute(ClientDetailLocator.appnt_sex, 'value') # 投保人性别\r\n # appnt_nationality = self.get_element_attribute(ClientDetailLocator.appnt_nationality, 'value') # 投保人国籍\r\n # appnt_birthday = self.get_element_attribute(ClientDetailLocator.appnt_birthday, 'value') # 投保人生日\r\n # appnt_ID_type = self.get_element_attribute(ClientDetailLocator.appnt_id_type, 'value') # 投保人证件类型\r\n # appnt_ID = self.get_element_attribute(ClientDetailLocator.appnt_id, 'value') # 投保人证件号\r\n\r\n \"\"\"将保单信息&五要素组合成字典\"\"\"\r\n # temp_dict['proposal_cont_no'] = proposal_cont_no\r\n temp_dict['policy_number'] = policy_number\r\n # temp_dict['appnt_name'] = appnt_name\r\n # temp_dict['appnt_sex'] = appnt_sex\r\n # temp_dict['appnt_nationality'] = appnt_nationality\r\n # temp_dict['appnt_birthday'] = appnt_birthday\r\n # temp_dict['appnt_ID_type'] = appnt_ID_type\r\n # temp_dict['appnt_ID'] = appnt_ID\r\n\r\n return temp_dict\r\n\r\nif __name__ == \"__main__\":\r\n sp = SearchPage(webdriver.Ie())\r\n print(config.get_config('MDES', 'ip') + SearchLocator.search_cont_no)\r\n\r\n", "sub_path": "MetLife/page/search_policy_page.py", "file_name": "search_policy_page.py", "file_ext": "py", "file_size_in_byte": 5715, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "testcase.mdes_base_case.MdesBaseCase", "line_number": 11, "usage_type": "name"}, {"api_name": "page_locators.search_policy_locator.SearchLocator.proposal_cont_no", "line_number": 22, "usage_type": "attribute"}, {"api_name": "page_locators.search_policy_locator.SearchLocator", "line_number": 22, "usage_type": "name"}, {"api_name": "config.config.get_config", "line_number": 28, "usage_type": "call"}, {"api_name": "config.config", "line_number": 28, "usage_type": "name"}, {"api_name": "page_locators.search_policy_locator.SearchLocator.search_cont_no", "line_number": 28, "usage_type": "attribute"}, {"api_name": "page_locators.search_policy_locator.SearchLocator", "line_number": 28, "usage_type": "name"}, {"api_name": "page_locators.search_policy_locator.SearchLocator.cont_no_input", "line_number": 32, "usage_type": "attribute"}, {"api_name": "page_locators.search_policy_locator.SearchLocator", "line_number": 32, "usage_type": "name"}, {"api_name": "page_locators.search_policy_locator.SearchLocator.cont_no_searchBTN", "line_number": 34, "usage_type": "attribute"}, {"api_name": "page_locators.search_policy_locator.SearchLocator", "line_number": 34, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 35, "usage_type": "call"}, {"api_name": "page_locators.search_policy_locator.SearchLocator.cont_no", "line_number": 37, "usage_type": "attribute"}, {"api_name": "page_locators.search_policy_locator.SearchLocator", "line_number": 37, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 41, "usage_type": "call"}, {"api_name": "page_locators.search_policy_locator.SearchLocator.tr_locator", "line_number": 58, "usage_type": "attribute"}, {"api_name": "page_locators.search_policy_locator.SearchLocator", "line_number": 58, "usage_type": "name"}, {"api_name": "page_locators.search_policy_locator.SearchLocator.first_premium", "line_number": 79, "usage_type": "attribute"}, {"api_name": "page_locators.search_policy_locator.SearchLocator", "line_number": 79, "usage_type": "name"}, {"api_name": "page_locators.menu_locator.MenuLocator.tab_2", "line_number": 85, "usage_type": "attribute"}, {"api_name": "page_locators.menu_locator.MenuLocator", "line_number": 85, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 90, "usage_type": "call"}, {"api_name": "page_locators.search_policy_locator.SearchLocator.policy_number", "line_number": 91, "usage_type": "attribute"}, {"api_name": "page_locators.search_policy_locator.SearchLocator", "line_number": 91, "usage_type": "name"}, {"api_name": "allure.step", "line_number": 14, "usage_type": "call"}, {"api_name": "selenium.webdriver.Ie", "line_number": 116, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 116, "usage_type": "name"}, {"api_name": "config.config.get_config", "line_number": 117, "usage_type": "call"}, {"api_name": "config.config", "line_number": 117, "usage_type": "name"}, {"api_name": "page_locators.search_policy_locator.SearchLocator.search_cont_no", "line_number": 117, "usage_type": "attribute"}, {"api_name": "page_locators.search_policy_locator.SearchLocator", "line_number": 117, "usage_type": "name"}]} +{"seq_id": "37288587", "text": "\nfrom flask import Flask\nimport os\n\n\ndef create_app(test_config=None):\n # 创建实例\n # print('__name__:', __name__)\n app = Flask(__name__, instance_relative_config=True)\n app.config.from_mapping(SECRET_KEY='abc',\n DATABASE=os.path.join(app.instance_path, 'flaskr.sqlite'))\n # 从instance目录下找配置\n if test_config is None:\n app.config.from_pyfile('config.py', silent=True)\n else:\n app.config.form_mapping(test_config)\n\n # 确保实例目录存在\n try:\n # print('instance.app:', app.instance_path)\n os.makedirs(app.instance_path)\n except OSError:\n pass\n\n @app.route('/hello/') # 在这里，路径后必须要写反斜杠，404 error\n def hello():\n return \"hello world!\"\n\n from . import db\n db.init_app(app)\n\n from . import auth\n app.register_blueprint(auth.bp)\n\n from . import blog\n app.register_blueprint(blog.bp)\n app.add_url_rule('/', endpoint='index')\n\n print(app.url_map)\n\n return app\n\n#\n# if __name__ == '__main__':\n# app = create_app()\n# app.run()\n", "sub_path": "build/lib/flaskr/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1108, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "99029226", "text": "# coding = utf8\nimport logging\nimport multiprocessing\nimport subprocess\n\nimport pytest\nfrom airtest.core.api import *\nfrom poco.drivers.android.uiautomation import AndroidUiautomationPoco\n\nfrom config import install_app_necessary, SERIAL_NUMBER\nfrom page.fota.fota_page import Fota_Page\nfrom page.main_page import Main_Page\nfrom page.system.system import System\nfrom toolsbar.common import test_device\nfrom toolsbar.permissionGrant import grant_permission\n\nos.path.abspath(\".\")\n\n# 过滤airtest log只打印ERROR的Log\nlogger_airtest = logging.getLogger(\"airtest\")\nlogger_airtest.setLevel(logging.ERROR)\ncur_time = time.strftime(\"%Y%m%d_%H%M%S\")\n\"\"\"\n @File:run_test.py\n @Author:Bruce\n @Date:2020/12/15\n @Description:项目运行函数，存放测试和调试函数\n\"\"\"\n\n\"\"\"\n 单个设备poco、device不需要初始化\n 多个设备poco、device都需要创建新对象poco_item\n 后续将poco_item传入使用即可，airtest相关api，使用对应device_item进行调用\n case不需要重复写\n UI 进程和底部进程不要在同一个进程中容易出问题\n\"\"\"\n\n# 多机测试进程池:兼容单机和多机运行\n\"\"\"\n @description:多进程创建进行多台设备测试\n @tip:\n Pycharm调用adb缺陷，需要使用terminal输入charm来启动pycharm，以获得dash权限\n 执行case前，手动将pocoservice.apk的contniue安装好并将授权界面点掉，防止后续错误发生\n\"\"\"\n\n\ndef start_test():\n print(\"当前设备数量：\" + str(len(SERIAL_NUMBER)))\n if len(SERIAL_NUMBER) > 1:\n for i in test_device:\n install_app_necessary(i)\n grant_permission(i)\n else:\n install_app_necessary(test_device)\n grant_permission(test_device)\n test_pool = multiprocessing.Pool(len(SERIAL_NUMBER))\n for device_ in SERIAL_NUMBER:\n test_pool.apply_async(func=fota_test_area, args=(device_,))\n sleep(10)\n test_pool.close()\n test_pool.join()\n\n\n\"\"\"\n @description:Fota checklist测试函数执行区域\n @param:\n device_:设备序列号\n\"\"\"\n\n\ndef fota_test_area(device_):\n pytest.main([\"-v\", \"-s\", \"--cmdopt={}\".format(device_), \"{}\".format(\"./test_case/test_before_fota.py\"),\n \"--reruns={}\".format(1),\n \"--alluredir={}\".format(\"./temp/need_data[{}_{}]/\".format(cur_time, device_))])\n # 设置差异化\n subprocess.Popen(\n args=[\"allure\", \"generate\", \"./temp/need_data[{}_{}]/\".format(cur_time, device_), \"-o\",\n \"./report/test_report[{}_{}]/\".format(cur_time, device_),\n \"--clean\"],\n shell=False).communicate()[0]\n updatesw(device_)\n\n # subprocess.Popen(\n # \"allure generate ./temp/need_data[{}_{}] -o ./report/test_report[{}_{}]/ --clean\".format(cur_time, device_,\n # cur_time, device_),\n # shell=True).communicate()[0]\n\n\n\"\"\"\n @description:Fota checklist测试软件升级函数执行区域\n @param:\n device_:设备序列号\n\"\"\"\n\n\ndef updatesw(device_):\n print(\"开始新版本升级\")\n try:\n device_c = connect_device(\"Android:///{}\".format(device_))\n poco = AndroidUiautomationPoco(device=device_c, use_airtest_input=False,\n screenshot_each_action=False)\n main_page = Main_Page(device_c, poco)\n system = System(main_page)\n system.unlock_screen()\n fota_page = Fota_Page(main_page)\n fota_page.start_fota_page()\n fota_page.skip_guide()\n fota_page.updatesw()\n print(\"升级结果：\" + str(fota_page.check_update_result(device_)))\n print(\"Fota升级测试结束\")\n except Exception as ex:\n print(str(ex))\n\n\n\"\"\"\n @description:Fota checklist测试函数区域\n\"\"\"\n\n\ndef fota_checklist_test_module():\n start_test()\n\n\n\"\"\"\n @description:main函数，主要运行函数\n\"\"\"\nif __name__ == '__main__':\n print(\"脚本开始测试，Fota checklist模块测试正在运行中……\")\n for i in range(5):\n print(\"这是第{}次测试该脚本\".format(i))\n fota_checklist_test_module()\n print(\"This is {} times running and time is {}\".format(str(i), time.strftime(\"%Y%m%d_%H%M%S\")))\n print(\"脚本测试结束，请检查测试结果\")\n", "sub_path": "run_test.py", "file_name": "run_test.py", "file_ext": "py", "file_size_in_byte": 4342, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 20, "usage_type": "call"}, {"api_name": "logging.ERROR", "line_number": 21, "usage_type": "attribute"}, {"api_name": "config.SERIAL_NUMBER", "line_number": 48, "usage_type": "argument"}, {"api_name": "config.SERIAL_NUMBER", "line_number": 49, "usage_type": "argument"}, {"api_name": "toolsbar.common.test_device", "line_number": 50, "usage_type": "name"}, {"api_name": "config.install_app_necessary", "line_number": 51, "usage_type": "call"}, {"api_name": "toolsbar.permissionGrant.grant_permission", "line_number": 52, "usage_type": "call"}, {"api_name": "config.install_app_necessary", "line_number": 54, "usage_type": "call"}, {"api_name": "toolsbar.common.test_device", "line_number": 54, "usage_type": "argument"}, {"api_name": "toolsbar.permissionGrant.grant_permission", "line_number": 55, "usage_type": "call"}, {"api_name": "toolsbar.common.test_device", "line_number": 55, "usage_type": "argument"}, {"api_name": "multiprocessing.Pool", "line_number": 56, "usage_type": "call"}, {"api_name": "config.SERIAL_NUMBER", "line_number": 56, "usage_type": "argument"}, {"api_name": "config.SERIAL_NUMBER", "line_number": 57, "usage_type": "name"}, {"api_name": "pytest.main", "line_number": 72, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 76, "usage_type": "call"}, {"api_name": "poco.drivers.android.uiautomation", "line_number": 100, "usage_type": "name"}, {"api_name": "poco.drivers.android.uiautomation.AndroidUiautomationPoco", "line_number": 100, "usage_type": "call"}, {"api_name": "page.main_page.Main_Page", "line_number": 102, "usage_type": "call"}, {"api_name": "poco.drivers.android.uiautomation", "line_number": 102, "usage_type": "argument"}, {"api_name": "page.system.system.System", "line_number": 103, "usage_type": "call"}, {"api_name": "page.fota.fota_page.Fota_Page", "line_number": 105, "usage_type": "call"}]} +{"seq_id": "297002575", "text": "from multiprocessing import Process, Pool, Manager\r\n\r\nm1_file = 'matrix_1.txt'\r\nm2_file = 'matrix_2.txt'\r\n\r\nmatrix_1 = []\r\nmatrix_2 = []\r\n\r\n\r\ndef matrix_product(m1,m2,num_line,glob):\r\n result = glob.result\r\n dop = []\r\n sum = 0\r\n \r\n if len(m2) != len(m1[0]):\r\n print('Ошибка.Некорректные данные')\r\n return \"ошибка\"\r\n else:\r\n columns1 = len(m1[0])\r\n lines1 = len(m1)\r\n columns2 = len(m2[0])\r\n lines2 = len(m2)\r\n\t\t\r\n for line1 in range(num_line, num_line+1):\r\n for col2 in range(0,columns2):\r\n for col1 in range(0,columns1):\r\n sum += m1[line1][col1]*m2[col1][col2]\r\n dop.append(sum)\r\n sum = 0\r\n str_res = \"[\"\r\n for el in range(0,len(dop)-1):\r\n result[num_line*len(dop)+el] = dop[el]\r\n str_res+=f'{dop[el]},'\r\n result[num_line*len(dop)+(len(dop)-1)] = dop[len(dop)-1]\r\n str_res += f'{dop[len(dop)-1]}]'\r\n with open('result.txt','a') as f:\r\n f.write(str_res + '\\n')\r\n dop = []\r\n\r\n glob.result = result\r\n\r\nwith open('result.txt','w') as f:\r\n f.write('')\r\n\r\n\r\nwith open(m1_file,'r') as f:\r\n str = f.read()\r\n str = str.replace('[[','')\r\n str = str.replace(']]','')\r\n str = str.split('],[')\r\n for line in range(0,len(str)):\r\n matrix_1.append([])\r\n l = str[line].split(',')\r\n for el in range(0,len(l)):\r\n matrix_1[line].append(int(l[el]))\r\n\t\t\t\r\n\t\t\t\r\nwith open(m2_file,'r') as f:\r\n str = f.read()\r\n str = str.replace('[[','')\r\n str = str.replace(']]','')\r\n str = str.split('],[')\r\n for line in range(0,len(str)):\r\n matrix_2.append([])\r\n l = str[line].split(',')\r\n for el in range(0,len(l)):\r\n matrix_2[line].append(int(l[el]))\r\n\r\nprint('-----Результат произведения матриц:')\r\n\t\r\nmat = matrix_2\r\nif len(matrix_1) > len(matrix_2):\r\n mat = matrix_1\r\nif __name__ == '__main__':\r\n manager = Manager()\r\n Global = manager.Namespace()\r\n Global.result = [0 for i in range(0,len(mat)*len(mat[0]))]\r\n \r\n pool = Pool(len(matrix_1))\r\n\t\r\n pool.starmap(matrix_product,[(matrix_1, matrix_2, lin, Global) for lin in range(0,len(matrix_1))])\r\n result = Global.result\r\n n = 0\r\n for line in range(0,len(mat)):\r\n print('[',end='')\r\n for el in range(0,len(mat[line])-1):\r\n print(f'{result[n]},',end='')\r\n n += 1\r\n print(f'{result[n]}',end='')\r\n n += 1\r\n print(']\\n',end='')\r\n\r\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2645, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "multiprocessing.Manager", "line_number": 75, "usage_type": "call"}, {"api_name": "multiprocessing.Pool", "line_number": 79, "usage_type": "call"}]} +{"seq_id": "141262713", "text": "import numpy as np\nfrom config import cifar10_dir\n\n\ndef unpickle(file):\n import pickle\n with open(file, 'rb') as fo:\n dict = pickle.load(fo, encoding='bytes')\n return dict\n\n\ndef load_training_data():\n data = np.zeros((50000, 3, 32, 32), np.uint8)\n data[0 :10000, :] = unpickle(cifar10_dir + 'data_batch_1')[b'data'].reshape((-1, 3, 32, 32))\n data[10000:20000, :] = unpickle(cifar10_dir + 'data_batch_2')[b'data'].reshape((-1, 3, 32, 32))\n data[20000:30000, :] = unpickle(cifar10_dir + 'data_batch_3')[b'data'].reshape((-1, 3, 32, 32))\n data[30000:40000, :] = unpickle(cifar10_dir + 'data_batch_4')[b'data'].reshape((-1, 3, 32, 32))\n data[40000:50000, :] = unpickle(cifar10_dir + 'data_batch_5')[b'data'].reshape((-1, 3, 32, 32))\n data = np.swapaxes(data, 1, 3)\n data = np.swapaxes(data, 1, 2)\n\n mean_image = np.mean(data, axis=0)\n\n labels = np.zeros((50000,), np.uint8)\n labels[0 :10000] = np.array(unpickle(cifar10_dir + 'data_batch_1')[b'labels']).astype(np.uint8)\n labels[10000:20000] = np.array(unpickle(cifar10_dir + 'data_batch_2')[b'labels']).astype(np.uint8)\n labels[20000:30000] = np.array(unpickle(cifar10_dir + 'data_batch_3')[b'labels']).astype(np.uint8)\n labels[30000:40000] = np.array(unpickle(cifar10_dir + 'data_batch_4')[b'labels']).astype(np.uint8)\n labels[40000:50000] = np.array(unpickle(cifar10_dir + 'data_batch_5')[b'labels']).astype(np.uint8)\n\n np.save(cifar10_dir+'training_data.npy', data)\n np.save(cifar10_dir+'training_label.npy', labels)\n np.save(cifar10_dir+'mean_image.npy', mean_image)\n print(\"training data saved to npy format\")\n\n\ndef load_test_data():\n data = unpickle(cifar10_dir + 'test_batch')[b'data'].reshape((-1, 3, 32, 32))\n data = np.swapaxes(data, 1, 3)\n data = np.swapaxes(data, 1, 2)\n labels = np.array(unpickle(cifar10_dir + 'test_batch')[b'labels']).astype(np.uint8)\n np.save(cifar10_dir+'test_data.npy', data)\n np.save(cifar10_dir+'test_label.npy', labels)\n print(\"test data saved to npy format\")\n\n\ndef load_label_names():\n return unpickle(cifar10_dir + 'batches.meta')[b'label_names'] # list\n\n\ndef train_valid_split():\n data = np.load(cifar10_dir+'training_data.npy')\n train = data[1:45000]\n valid = data[45000:]\n np.save(cifar10_dir+'train_data.npy', train)\n np.save(cifar10_dir+'valid_data.npy', valid)\n\n label = np.load(cifar10_dir + 'training_label.npy')\n label_train = label[1:45000]\n label_valid = label[45000:]\n np.save(cifar10_dir+'train_label.npy', label_train)\n np.save(cifar10_dir+'valid_label.npy', label_valid)\n\n print(\"split done\")\n\n\nload_training_data()\nload_test_data()\ntrain_valid_split()", "sub_path": "Project/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 2691, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pickle.load", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 13, "usage_type": "attribute"}, {"api_name": "config.cifar10_dir", "line_number": 14, "usage_type": "name"}, {"api_name": "config.cifar10_dir", "line_number": 15, "usage_type": "name"}, {"api_name": "config.cifar10_dir", "line_number": 16, "usage_type": "name"}, {"api_name": "config.cifar10_dir", "line_number": 17, "usage_type": "name"}, {"api_name": "config.cifar10_dir", "line_number": 18, "usage_type": "name"}, {"api_name": "numpy.swapaxes", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "config.cifar10_dir", "line_number": 25, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 26, "usage_type": "call"}, {"api_name": "config.cifar10_dir", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 26, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "config.cifar10_dir", "line_number": 27, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 28, "usage_type": "call"}, {"api_name": "config.cifar10_dir", "line_number": 28, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 28, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 29, "usage_type": "call"}, {"api_name": "config.cifar10_dir", "line_number": 29, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 31, "usage_type": "call"}, {"api_name": "config.cifar10_dir", "line_number": 31, "usage_type": "name"}, {"api_name": "numpy.save", "line_number": 32, "usage_type": "call"}, {"api_name": "config.cifar10_dir", "line_number": 32, "usage_type": "name"}, {"api_name": "numpy.save", "line_number": 33, "usage_type": "call"}, {"api_name": "config.cifar10_dir", "line_number": 33, "usage_type": "name"}, {"api_name": "config.cifar10_dir", "line_number": 38, "usage_type": "name"}, {"api_name": "numpy.swapaxes", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.swapaxes", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "config.cifar10_dir", "line_number": 41, "usage_type": "name"}, {"api_name": "numpy.uint8", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 42, "usage_type": "call"}, {"api_name": "config.cifar10_dir", "line_number": 42, "usage_type": "name"}, {"api_name": "numpy.save", "line_number": 43, "usage_type": "call"}, {"api_name": "config.cifar10_dir", "line_number": 43, "usage_type": "name"}, {"api_name": "config.cifar10_dir", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 52, "usage_type": "call"}, {"api_name": "config.cifar10_dir", "line_number": 52, "usage_type": "name"}, {"api_name": "numpy.save", "line_number": 55, "usage_type": "call"}, {"api_name": "config.cifar10_dir", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.save", "line_number": 56, "usage_type": "call"}, {"api_name": "config.cifar10_dir", "line_number": 56, "usage_type": "name"}, {"api_name": "numpy.load", "line_number": 58, "usage_type": "call"}, {"api_name": "config.cifar10_dir", "line_number": 58, "usage_type": "name"}, {"api_name": "numpy.save", "line_number": 61, "usage_type": "call"}, {"api_name": "config.cifar10_dir", "line_number": 61, "usage_type": "name"}, {"api_name": "numpy.save", "line_number": 62, "usage_type": "call"}, {"api_name": "config.cifar10_dir", "line_number": 62, "usage_type": "name"}]} +{"seq_id": "358467254", "text": "import numpy as np\nfrom typing import List\nfrom learnt.regression import predict_outcome\n\n\ndef feature_derivative_ridge(errors, feature, weight, l2_penalty: float, feature_is_constant: bool):\n \"\"\"\n Computing the derivative of the regression cost function.\n Recall that the cost function is the sum over the data points of the squared difference between an observed output\n and a predicted output, plus the L2 penalty term.\n\n Parameters:\n ----------\n :param errors: ndarray\n :param feature: column of a feature.\n :param feature_is_constant: Set true when given column of a feature is a constant.\n :param weight: ndarray\n :param l2_penalty: (lambda) Regularization tuning parameter\n :return: derivation (ndarray)\n \"\"\"\n # (y-HW)ᵀ(y-HW) + λ |W|² is our cost function. to derive this; we'll get following:\n # -2Hᵀ(y-HW) + 2λW\n\n # IMPORTANT: We will not regularize the constant. Thus, in the case of the constant,\n # the derivative is just twice the sum of the errors (without the 2λw[0] term).\n # If feature_is_constant is True, derivative is twice the dot product of errors and feature\n errors = np.reshape(errors, [-1, 1]) # need error to be a n×1 vector\n derivative = np.float64(2 * np.dot(feature, errors)) # 1×n dot product n×1 gives us a scalar\n # simple form of code above:\n # derivative = feature * errors\n # derivative = 2 * sum(derivative)\n if not feature_is_constant:\n # Otherwise, derivative is twice the dot product plus 2*l2_penalty*weight\n derivative = derivative + 2 * (l2_penalty * weight)\n # Noticed omitted -1?! We are adding it at the updating weights term (at ridge gradient decent function).\n return derivative\n\n\n'''\n# To test your feature derivative function, run the following:\n\nimport pandas as pd\nfrom learnt.regression import get_numpy_data\nfrom learnt.regression import predict_outcome\n\ndtype_dict = {'bathrooms': float, 'waterfront': int, 'sqft_above': int, 'sqft_living15': float, 'grade': int,\n 'yr_renovated': int, 'price': float, 'bedrooms': float, 'zipcode': str, 'long': float,\n 'sqft_lot15': float, 'sqft_living': float, 'floors': float, 'condition': int, 'lat': float, 'date': str,\n 'sqft_basement': int, 'yr_built': int, 'id': str, 'sqft_lot': int, 'view': int}\n\ndf = pd.read_csv('kc_house_data.csv', dtype=dtype_dict)\nexample_features, example_output = get_numpy_data(df, ['sqft_living'], 'price')\nmy_weights = np.array([1., 10.])\ntest_predictions = predict_outcome(example_features, my_weights)\nerrors = test_predictions - example_output # prediction errors\n\n# next two lines should print the same values\nprint(feature_derivative_ridge(errors, example_features[:, 1], my_weights[1], 1, False))\nprint(np.sum(errors * example_features[:, 1]) * 2 + 20.)\nprint('')\n\n# next two lines should print the same values\nprint(feature_derivative_ridge(errors, example_features[:, 0], my_weights[0], 1, True))\nprint(np.sum(errors) * 2.)\n'''\n\n\ndef ridge_regression_gradient_descent(feature_matrix, output, initial_weights: List[float], step_size,\n l2_penalty: float,\n max_iterations: int = 100):\n # if type(initial_weights[0]) != float:\n # # make sure auto casting, (float to int) doesn't happen at updating weights[i].\n # raise Exception('initial_weights setted with an int number instead of a float')\n\n weights = np.array(initial_weights, dtype=np.float64) # make sure it's a numpy array\n while 0 < max_iterations: # while not reached maximum number of iterations:\n # compute the predictions using your predict_output() function\n predictions = predict_outcome(feature_matrix, weights)\n # compute the errors as predictions - output\n errors = predictions - output # predictions is n×1 so we need output to be n×1 too\n\n for i in range(len(weights)): # loop over each weight\n # Recall that feature_matrix[:,i] is the feature column associated with weights[i]\n is_constant: bool = False\n if i == 0: # when i is equal to 0, you are computing the derivative of the constant!\n is_constant = True\n\n # compute the derivative for weight[i]:\n derivative = feature_derivative_ridge(errors, feature_matrix[:, i], weights[i], l2_penalty, is_constant)\n # subtract the step size times the derivative from the current weight\n weights[i] = weights[i] - step_size * derivative\n max_iterations -= 1\n return weights.reshape(-1, 1)\n", "sub_path": "learnt/regression/ridge.py", "file_name": "ridge.py", "file_ext": "py", "file_size_in_byte": 4622, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.reshape", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 28, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 68, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 75, "usage_type": "attribute"}, {"api_name": "learnt.regression.predict_outcome", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "5187166", "text": "\n# coding: utf-8\n\n# In[1]:\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n\n# In[2]:\n\n\n# Create test data\n# Create const variance\na, b, c, d = [4, 2, 5, 7]\nnum_arr = 100\n\n# Create random values X1,X2,X3 and Y with Y = aX1 + bX2 + cX3 + dX4 with X4 of values is 1\nX1, X2, X3, X4 = [np.random.rand(100), np.random.rand(100), np.random.rand(100), np.ones(100)]\nY = a* X1 + b*X2 + c*X3 + np.random.rand(100)* X4\n\n# Import data into dataframe and transposing data\ndataFrame = pd.DataFrame({\n 'X1' : X1,\n 'X2' : X2,\n 'X3' : X3,\n 'X4' : X4,\n 'Y' : Y\n})\n\ndataFrame\n\n\n# In[3]:\n\n\n# Cai thien ham Training, su dung cong thuc nhan 2 matrix\n# Rows number in Training Data\nm = dataFrame.X1.count()\n# Columns number in Training Data\nn = len(dataFrame.columns) -1\n# Learning_rate\nlearning_rate = 0.02\n# Set random for theta\ntheta = np.random.rand(n)\n# To save values of cost function each loop\ncost_arr = []\n\ndef predict(datafrm, theta):\n col_start = 0\n col_end = 4\n # Get first 4 columns of table and transpose dimension\n get_trainData = datafrm.iloc[ : , col_start : col_end]\n return np.dot(theta, get_trainData.T)\n\ndef cost_update(datafrm, theta):\n yhat = predict(datafrm, theta)\n y = datafrm.Y\n return np.sum((yhat-y)**2)/(2*m)\n\ny = dataFrame.Y\n\nfor i in range(6000):\n yhat_train = predict(dataFrame, theta)\n for x in range(n):\n theta[x] = theta[x] - learning_rate*np.sum((yhat_train-y)*dataFrame.iloc[ : , x])/m\n cost_arr.append(cost_update(dataFrame, theta))\n \ncost_arr\nplt.plot(cost_arr)\n\n\n# In[4]:\n\n\ntheta\n\n", "sub_path": "01_Machine Learning/01_Liner Regression/03_Multi Variance.py", "file_name": "03_Multi Variance.py", "file_ext": "py", "file_size_in_byte": 1583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.random.rand", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}]} +{"seq_id": "314956956", "text": "# adapted code from Niklas\n\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport scipy.io\nfrom matplotlib_scalebar.scalebar import ScaleBar\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nfrom scipy import ndimage as ndi\nfrom scipy.optimize import curve_fit\nfrom skimage.feature import peak_local_max\nfrom skimage.filters import gaussian\nfrom skimage.morphology import flood\nfrom skimage.segmentation import watershed\nfrom skimage.transform import resize\n\nfrom util.inches import cm_to_inch\nfrom util.tum_jet import tum_jet\n\n\nclass MetalLuminescencePlotter:\n\n def __init__(self):\n mpl.rcParams['text.latex.preamble'] = [r'\\usepackage{amsmath}']\n self.min_v = 0.00e2\n self.max_v = 1.4e2\n self.map_c = tum_jet\n\n def plot(self):\n title_pad = 3\n interpolation = 'gaussian'\n fig, axs = plt.subplots(3, 3, gridspec_kw={'width_ratios': [1, 1, 0.2], 'wspace': 0.10, 'hspace': 0.3},\n figsize=(cm_to_inch(15) * 0.8, cm_to_inch(14)))\n plt.sca(axs[0, 0])\n nicer_ax2(plt.gca())\n d_ni = r'D$_\\text{Ni}$'\n d_pd = r'D$_\\text{Pd}$'\n\n d_uv = r'D$_\\text{UV}$'\n d_al_ox = r'D$_\\text{AlOx}$'\n plt.title(d_ni, fontsize=8, pad=title_pad)\n plt.tick_params(direction='inout', left=False, bottom=False)\n plt.xlim(0.5, 20)\n plt.ylim(0, 19.5)\n plt.gca().yaxis.set_ticks([])\n plt.gca().xaxis.set_ticks([])\n filepath = 'metal_luminescence/2019-06-06D45/scan.005.mat'\n mat = scipy.io.loadmat(filepath)\n x45 = mat['x'][0]\n y45 = mat['y'][0]\n z45 = mat['result'] / 1e3\n filepath = 'metal_luminescence/2019-06-08/scan.003.mat'\n mat = scipy.io.loadmat(filepath)\n x01b = mat['x'][0]\n y01b = mat['y'][0]\n z01b = mat['result'] / 1e3\n filepath = 'metal_luminescence/2019-06-12/scan.020.mat'\n mat = scipy.io.loadmat(filepath)\n x44 = mat['x'][0]\n y44 = mat['y'][0]\n z44 = mat['result'] / 1e3\n filepath = 'metal_luminescence/2019-07-9/scan.010.mat'\n mat = scipy.io.loadmat(filepath)\n x02c = mat['x'][0]\n y02c = mat['y'][0]\n z02c = mat['result'] / 1e3\n plt.imshow(z45.copy(), vmin=self.min_v, vmax=self.max_v, cmap=self.map_c, interpolation=interpolation,\n extent=[0, x45[-1] - x45[0], 0, y45[-1] - y45[0]])\n scalebar45 = ScaleBar(1E-6)\n plt.gca().add_artist(scalebar45)\n\n plt.sca(axs[0, 1])\n nicer_ax2(plt.gca())\n plt.title(d_pd, fontsize=8, pad=title_pad)\n plt.tick_params(direction='inout', right=True, top=True)\n plt.xlim(0.5, 20)\n plt.ylim(0, 19.5)\n plt.gca().yaxis.set_ticks([])\n plt.gca().xaxis.set_ticks([])\n plt.imshow(z44, vmin=self.min_v, vmax=self.max_v, cmap=self.map_c, interpolation=interpolation,\n extent=[0, x44[-1] - x44[0], 0, y44[-1] - y44[0]])\n scalebar44 = ScaleBar(1E-6)\n plt.gca().add_artist(scalebar44)\n\n plt.sca(axs[1, 0])\n nicer_ax2(plt.gca())\n plt.title(d_uv, fontsize=8, pad=title_pad)\n plt.tick_params(direction='inout', left=True, bottom=True)\n plt.xlim(0, 19.5)\n plt.ylim(0, 19.5)\n plt.gca().yaxis.set_ticks([])\n plt.gca().xaxis.set_ticks([])\n plt.imshow(z01b, vmin=self.min_v, vmax=self.max_v, cmap=self.map_c, interpolation=interpolation,\n extent=[0, x01b[-1] - x01b[0], 0, y01b[-1] - y01b[0]])\n scalebar01b = ScaleBar(1E-6)\n plt.gca().add_artist(scalebar01b)\n\n plt.sca(axs[1, 1])\n nicer_ax2(plt.gca())\n plt.title(d_al_ox, fontsize=8, pad=title_pad)\n plt.tick_params(direction='inout', right=True, top=True)\n plt.xlim(0.5, 20)\n plt.ylim(0, 19.5)\n plt.gca().yaxis.set_ticks([])\n plt.gca().xaxis.set_ticks([])\n plt.imshow(z02c, vmin=self.min_v, vmax=self.max_v, cmap=self.map_c, interpolation=interpolation,\n extent=[0, x02c[-1] - x02c[0], 0, y02c[-1] - y02c[0]])\n scalebar02c = ScaleBar(1E-6)\n plt.gca().add_artist(scalebar02c)\n\n plt.sca(axs[2, 0])\n imat = 1\n mats = [z45 - 15.29, z44 - 8.74, z01b - 10.16, z02c - 1.93]\n titles = ['Ni', 'Pd', 'UV', 'AlOx']\n thresholds = [50 - 15.29, 50 - 8.74, 40 - 10.16, 30 - 1.93]\n areas = [(4, 4), (4, 4), (4, 4), (4, 4)]\n hist_dat_mat = []\n for mat, title, threshold, area in zip(mats, titles, thresholds, areas):\n hist_dat_mat.append(find_peaks_simple(mat, threshold=threshold, area=area, sigma=1.5))\n wid = np.mean(hist_dat_mat[imat][0][:-1] - hist_dat_mat[imat][0][1:])\n plt.bar(hist_dat_mat[imat][0], hist_dat_mat[imat][1], wid, color='xkcd:gray')\n plt.plot(hist_dat_mat[imat][0], hist_dat_mat[imat][2], lw=0.7, color='xkcd:black')\n plt.plot(hist_dat_mat[imat][0], hist_dat_mat[imat][3], lw=0.7, color='#f5ea6a', ls='--')\n plt.plot(hist_dat_mat[imat][0], hist_dat_mat[imat][4], lw=0.7, color='#f5ea6a', ls='--')\n\n plt.gca().set_aspect(165 / 45)\n plt.xlim(45, 210)\n plt.ylim(0, 45)\n plt.xlabel(r'$I^{(k)}$ (kcps)')\n plt.ylabel('\\#')\n plt.annotate('bunched', xy=(150, 24), fontsize=6, ha='center', )\n plt.annotate(\"\", xy=(138, 15), xytext=(150, 23), arrowprops=dict(arrowstyle=\"->\"), fontsize=6)\n\n plt.annotate('individual', xy=(74, 33), xytext=(110, 38.5), fontsize=6, ha='left', va='center')\n plt.annotate(\"\", xy=(74, 36), xytext=(110, 39), arrowprops=dict(arrowstyle=\"->\"), fontsize=6)\n plt.title(d_pd, fontsize=8, pad=0)\n plt.sca(axs[2, 1])\n\n yield_val = [5.612, 5.016, 3.054, 2.888]\n brightness_val = [hist_dat_mat[i][5] for i in range(len(hist_dat_mat))]\n brightness_sigma = [hist_dat_mat[i][6] for i in range(len(hist_dat_mat))]\n yield_error = [0.423, 0.385, 0.248, 0.234]\n plt.errorbar(yield_val, brightness_val, xerr=yield_error, yerr=brightness_sigma, fmt='s', ms=2, lw=1, color='k')\n plt.annotate('Ni', (yield_val[0], brightness_val[0]), xytext=(yield_val[0] - 0.07, brightness_val[0] + 2),\n ha='right', fontsize=6, va='bottom')\n plt.annotate('Pd', (yield_val[1], brightness_val[1]), xytext=(yield_val[1] + 0.1, brightness_val[1] - 4),\n ha='left', fontsize=6, va='top')\n plt.annotate('UV', (yield_val[2], brightness_val[2]), xytext=(yield_val[2] + 0.08, brightness_val[2] + 2),\n ha='left', fontsize=6, va='bottom')\n plt.annotate('AlOx', (yield_val[3], brightness_val[3]), xytext=(yield_val[3] - 0.07, brightness_val[3] - 3),\n ha='right', fontsize=6, va='top')\n nicer_ax(plt.gca())\n plt.gca().set_aspect(4 / 70)\n axs[2, 1].set_xlim(1.9, 5.9)\n axs[2, 1].set_ylim(30, 100)\n axs[2, 1].set_xlabel(r'yield $\\eta$ (\\%)')\n axs[2, 1].set_ylabel(r'$I$ (kcps)')\n axs[2, 1].set_xticks([3, 5])\n axs[2, 1].set_yticks([50, 75])\n axs[2, 1].yaxis.set_label_position(\"right\")\n axs[2, 1].yaxis.tick_right()\n axs[2, 1].spines['left'].set_linewidth(0.5)\n axs[2, 1].spines['bottom'].set_linewidth(0.5)\n axs[2, 1].spines['left'].set_linewidth(0.5)\n axs[2, 1].spines['bottom'].set_linewidth(0.5)\n plt.grid(True, lw=0.5, ls=':')\n\n axs[1, 2].axis('off')\n axs[2, 2].axis('off')\n\n gs = axs[0, 2].get_gridspec()\n # remove the underlying axes\n for ax in axs[:2, 2]:\n ax.remove()\n axcolorbar = fig.add_subplot(gs[:2, 2])\n axcolorbar.axis('off')\n plt.sca(axcolorbar)\n divider = make_axes_locatable(axcolorbar)\n cax1 = divider.append_axes(\"left\", size=\"80%\", pad=0.00)\n norm = mpl.colors.Normalize(vmin=self.min_v, vmax=self.max_v)\n mpl.colorbar.ColorbarBase(cax1, cmap=tum_jet,\n norm=norm,\n orientation='vertical',\n ticks=np.arange(0, 150, 40))\n\n cax1.set_title('kcps', loc='left', fontsize=6.5)\n\n plt.rc('scalebar', sep=1)\n plt.rc('scalebar', frameon=False)\n plt.rc('scalebar', box_alpha=0.2)\n plt.rc('scalebar', border_pad=0)\n plt.rc('scalebar', length_fraction=0.3)\n plt.rc('scalebar', label_loc='top')\n plt.rc('scalebar', location='lower right')\n plt.rc('scalebar', color='w')\n\n plt.figtext(0.165, 0.895, 'a)', fontsize=8)\n plt.figtext(0.52, 0.895, 'b)', fontsize=8)\n plt.figtext(0.165, 0.623, 'c)', fontsize=8)\n plt.figtext(0.52, 0.623, 'd)', fontsize=8)\n plt.figtext(0.165, 0.345, 'e)', fontsize=8)\n plt.figtext(0.52, 0.345, 'f)', fontsize=8)\n\n fig.savefig('metal_luminescence' + '/confocal_pic.jpg', dpi=600, pad_inches=0, bbox_inches='tight')\n\n\ndef nicer_ax2(ax):\n ax.spines['top'].set_visible(True)\n ax.spines['right'].set_visible(True)\n ax.spines['left'].set_linewidth(0.8)\n ax.spines['bottom'].set_linewidth(0.8)\n ax.spines['left'].set_linewidth(0.8)\n ax.spines['bottom'].set_linewidth(0.8)\n ax.yaxis.tick_left()\n\n\ndef find_peaks_simple(mat_in, threshold=5., area=(4, 4), sigma=1., borders=None):\n if borders is None:\n borders = [0.305, 1.33489786, 1.42829031, 1.80438237, 2.79750851]\n local_max, local_max_val, marks, ws_area, flood_sections, flood_values = get_flood_array(mat_in, threshold, area)\n\n resize_factor = 3\n upsampled = resize(mat_in.copy(), np.array(mat_in.shape) * resize_factor, order=0)\n upsampled = gaussian(upsampled, sigma=sigma, cval=0)\n local_max_up, local_max_val_up, marks_up, ws_area_up, flood_sections_up, flood_values_up = get_flood_array(\n upsampled, threshold, np.array(area) * resize_factor)\n\n bins = 50\n values, bin_edges = np.histogram(local_max_val, bins=bins, density=False)\n bin_mid = (bin_edges[1:] + bin_edges[:-1]) / 2\n single_value = bin_mid[np.argmax(values)]\n\n values_up, binedges_up = np.histogram(local_max_val_up, bins=120, density=False)\n bin_mid_up = (binedges_up[1:] + binedges_up[:-1]) / 2\n single_value_up = bin_mid_up[np.argmax(values_up)]\n\n values_up, binedges_up = np.histogram(local_max_val_up / single_value_up, bins=bins, density=False, range=(0, 3))\n bin_mid_up = (binedges_up[1:] + binedges_up[:-1]) / 2\n\n p0 = [0.1, 1, 0.4, 0.21, 1.7, 0.5]\n bounds = ([0, 0, 0, 0, 1.3, .38], [np.inf, 1.3, 60, np.inf, np.inf, 20])\n try:\n coeff, b = curve_fit(double_peak, bin_mid_up, values_up, p0=p0, bounds=bounds)\n except:\n coeff = p0\n\n coeff_pd = pd.DataFrame()\n count_range = []\n count_range_up = []\n for ni, (i, j) in enumerate(\n [(borders[0], borders[1]), (borders[1], borders[2]), (borders[2], borders[3]), (borders[3], borders[4]),\n (borders[4], 1000)]):\n count_mask = (local_max_val > (i) * single_value) & (local_max_val <= (j) * single_value).astype('int')\n count_mask_up = (local_max_val_up > (i) * single_value_up) & (local_max_val_up <= (j) * single_value_up).astype(\n 'int')\n count_range.append(np.count_nonzero(count_mask) * (ni + 1))\n count_range_up.append(np.count_nonzero(count_mask_up) * (ni + 1))\n coeff_pd['CountRange'] = count_range\n coeff_pd['CountRange_up'] = count_range_up\n\n rng = bin_mid_up\n peak_d = double_peak(rng, *coeff)\n peak1, peak2 = double_peak_ind(rng, *coeff)\n\n return bin_mid_up * single_value_up, values_up, peak_d, peak1, peak2, coeff[1] * single_value_up, coeff[\n 2] * single_value_up / 2, len(local_max_val_up), len(local_max_val)\n\n\ndef nicer_ax(ax):\n ax.spines['top'].set_visible(True)\n ax.spines['right'].set_visible(True)\n ax.spines['left'].set_linewidth(0.8)\n ax.spines['bottom'].set_linewidth(0.8)\n ax.spines['left'].set_linewidth(0.8)\n ax.spines['bottom'].set_linewidth(0.8)\n ax.yaxis.tick_left()\n\n\ndef get_flood_array(matIn, threshold, area):\n local_maxi = peak_local_max(matIn, indices=False, footprint=np.ones(area), threshold_abs=threshold)\n markers = ndi.label(local_maxi)[0]\n labels = watershed(-matIn, markers)\n\n flood_sections = np.zeros_like(labels)\n flood_sections -= 1\n local_maxi_val = []\n for iii in range(1, np.max(markers) + 1):\n local_maxiS = np.argwhere(markers == iii)\n local_maxi_val.append(matIn[tuple(local_maxiS[0])])\n mask = (labels == iii) & flood(-matIn, tuple(local_maxiS[0]),\n tolerance=0.6 * (matIn[local_maxiS[0, 0], local_maxiS[0, 1]]))\n flood_sections[mask] = iii\n\n flood_seq_values = []\n for i in range(1, np.max(labels) + 1):\n val = np.sum(matIn[(flood_sections == i)])\n flood_seq_values.append(val)\n\n return local_maxi, local_maxi_val, markers, labels, flood_sections, flood_seq_values\n\n\ndef peak(x, *p):\n a1, b1, c1 = p\n return a1 * 1 / 2 / ((c1 / 2) ** 2 + (x - b1) ** 2)\n\n\ndef double_peak(x, *p):\n return peak(x, *p[:3]) + peak(x, *p[3:])\n\n\ndef double_peak_ind(x, *p):\n return peak(x, *p[:3]), peak(x, *p[3:])\n\n\nif __name__ == '__main__':\n plotter = MetalLuminescencePlotter()\n plotter.plot()\n", "sub_path": "images/chapter_6/metal_luminescence.py", "file_name": "metal_luminescence.py", "file_ext": "py", "file_size_in_byte": 13324, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "matplotlib.rcParams", "line_number": 25, "usage_type": "attribute"}, {"api_name": "util.tum_jet.tum_jet", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "util.inches.cm_to_inch", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.sca", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}, {"api_name": "scipy.io.io.loadmat", "line_number": 49, "usage_type": "call"}, {"api_name": "scipy.io.io", "line_number": 49, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 49, "usage_type": "name"}, {"api_name": "scipy.io.io.loadmat", "line_number": 54, "usage_type": "call"}, {"api_name": "scipy.io.io", "line_number": 54, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 54, "usage_type": "name"}, {"api_name": "scipy.io.io.loadmat", "line_number": 59, "usage_type": "call"}, {"api_name": "scipy.io.io", "line_number": 59, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 59, "usage_type": "name"}, {"api_name": "scipy.io.io.loadmat", "line_number": 64, "usage_type": "call"}, {"api_name": "scipy.io.io", "line_number": 64, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib_scalebar.scalebar.ScaleBar", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.sca", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "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.tick_params", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib_scalebar.scalebar.ScaleBar", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.sca", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 91, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 92, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib_scalebar.scalebar.ScaleBar", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.sca", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib_scalebar.scalebar.ScaleBar", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.sca", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.sca", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.errorbar", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.sca", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}, {"api_name": "mpl_toolkits.axes_grid1.make_axes_locatable", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.colors.Normalize", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 181, "usage_type": "attribute"}, {"api_name": "matplotlib.colorbar.ColorbarBase", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.colorbar", "line_number": 182, "usage_type": "attribute"}, {"api_name": "util.tum_jet.tum_jet", "line_number": 182, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 193, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rc", "line_number": 196, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 196, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figtext", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "skimage.transform.resize", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 224, "usage_type": "call"}, {"api_name": "skimage.filters.gaussian", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 242, "usage_type": "attribute"}, {"api_name": "scipy.optimize.curve_fit", "line_number": 244, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.count_nonzero", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.count_nonzero", "line_number": 258, "usage_type": "call"}, {"api_name": "skimage.feature.peak_local_max", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 281, "usage_type": "call"}, {"api_name": "scipy.ndimage.label", "line_number": 282, "usage_type": "call"}, {"api_name": "scipy.ndimage", "line_number": 282, "usage_type": "name"}, {"api_name": "skimage.segmentation.watershed", "line_number": 283, "usage_type": "call"}, {"api_name": "numpy.zeros_like", "line_number": 285, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.argwhere", "line_number": 289, "usage_type": "call"}, {"api_name": "skimage.morphology.flood", "line_number": 291, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 296, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 297, "usage_type": "call"}]} +{"seq_id": "643447917", "text": "from WindPy import *\nimport pandas as pd\nimport numpy as np\nfrom time import sleep\nimport os\nimport csv\nimport mysql.connector\n\n\npd.set_option('expand_frame_repr', False)\nclass NumpyMySQLConverter(mysql.connector.conversion.MySQLConverter):\n \"\"\" A mysql.connector Converter that handles Numpy types \"\"\"\n\n def _float32_to_mysql(self, value):\n return float(value)\n\n def _float64_to_mysql(self, value):\n return float(value)\n\n def _int32_to_mysql(self, value):\n return int(value)\n\n def _int64_to_mysql(self, value):\n return int(value)\n\n\nconfig = {\n 'user': 'wudilianghua',\n 'host': 'east2-mysql-instance1.cwj25hshjcl1.us-east-2.rds.amazonaws.com',\n 'password': 'nbwind123!',\n 'database': 'QuantDB'\n}\n\ndef calTime(original_datetime, delta):\n return (datetime.strptime(original_datetime, '%Y-%m-%d') + timedelta(days=delta)).strftime('%Y-%m-%d')\n\ndef calTime1(original_datetime, delta):\n return (datetime.strptime(original_datetime, '%Y-%m-%d %H-%M') + timedelta(hours=delta)).strftime('%Y-%m-%d %H-%M')\n\ndef getAllStock():\n allStock = w.wset(\"sectorconstituent\", \"sectorid=a001010100000000;field=wind_code\")\n fm = pd.DataFrame(allStock.Data, index=allStock.Fields)\n fm = fm.T # Transpose index and columns\n code_list = fm['wind_code'].values\n return code_list\n # return parseStock(code_list)\ndef parseStock(code_list):\n codes = ''\n ct = 1\n for code in code_list:\n if ct<=900:\n codes += code\n codes += ','\n ct = ct+1\n codes = codes[:len(codes) - 1]\n return codes\n\ndef conWSQData(indata1):\n fm = pd.DataFrame(indata1.Data, index=indata1.Fields, columns=indata1.Codes)\n fm = fm.T # Transpose index and columns\n fm['code'] = fm.index\n fm['datetime'] = indata1.Times[0]\n return fm\ndef getStockCategyMap(path):\n stockCategory = {}\n for root, dirs, files in os.walk(path):\n for file in files:\n # print (file)\n table = file.split(\".\")[0]\n category = table.replace(\"-\", '_')\n with open(path + file) as csvDataFile:\n\n csvReader = csv.reader(csvDataFile)\n for row in csvReader:\n stock_code = row[1]\n stockCategory.setdefault(stock_code, []).append(category)\n return stockCategory\n\nclass BanKuaiObj(object):\n zdf = 0\n zhanbi = 0\n code = \"\"\n\n\n\ndef SortByZhanbiasc(bankuaiList):\n return sorted(bankuaiList, key=lambda x: x.zhanbi, reverse=False)\n########################################################\ndef main():\n dir = \"C:/KeLiQuant/WindCategory/\"\n import logging\n logging.basicConfig(filename='Bankuai.log', level=logging.DEBUG)\n stockCategory = getStockCategyMap(dir)\n\n ##############################################\n w.start()\n codeList = getAllStock()\n print (len(codeList))\n w.stop()\n # codeLists = []\n # codeLists.append(codeList[:500])\n # codeLists.append(codeList[500:1000])\n # codeLists.append(codeList[1000:1500])\n # codeLists.append(codeList[1500:2000])\n # codeLists.append(codeList[2000:2500])\n # codeLists.append(codeList[2500:3000])\n # codeLists.append(codeList[3000:])\n\n codeLists = []\n\n eachListLength = 200\n for i in range(0, len(codeList), eachListLength):\n if i + eachListLength > len(codeList):\n codeLists.append(codeList[i:])\n else:\n codeLists.append(codeList[i:i + eachListLength])\n\n\n\n # conn = mysql.connector.connect(**config)\n # conn.set_converter_class(NumpyMySQLConverter)\n #\n # cur = conn.cursor()\n while (1):\n\n weekno = datetime.today().weekday()\n if weekno in [0, 1, 2, 3, 4]:\n curTime = datetime.today().strftime('%H-%M-%S')\n logging.debug(curTime)\n if (curTime >= '09-35-00' and curTime <= '09-35-59') or (curTime >= '10-00-00' and curTime <= '10-00-59')or (curTime >= '10-30-00' and curTime <= '10-30-59') \\\n or (curTime >= '11-00-00' and curTime <= '11-00-59')or (curTime >= '11-30-00' and curTime <= '11-30-59')or (curTime >= '13-00-00' and curTime <= '13-00-59') \\\n or (curTime >= '13-30-00' and curTime <= '13-30-59')or (curTime >= '14-00-00' and curTime <= '14-00-59')or (curTime >= '14-30-00' and curTime <= '14-30-59') \\\n or (curTime >= '15-00-00' and curTime <= '15-00-59'):\n w.start()\n conn = mysql.connector.connect(**config)\n conn.set_converter_class(NumpyMySQLConverter)\n\n\n categoryData = {}\n all_vol = 0\n curTime = datetime.today().strftime('%Y-%m-%d %H-%M')\n logging.debug(\"BanKuai--\" + curTime + \" is in processing\")\n\n for code_list in codeLists:\n parsedStocks = parseStock(code_list)\n data = conWSQData(w.wsq(parsedStocks, \"rt_vol,rt_pct_chg,rt_mkt_cap,rt_float_mkt_cap,rt_insti_activebuy_amt,rt_amt\"))\n # print (data)\n inserted = []\n ct = 1\n for row in data.itertuples():\n\n stock = row[0]\n if stock not in stockCategory:\n continue\n rt_vol = row[1]\n rt_pct_chg = row[2] * 100\n rt_mkt_cap = row[3]\n rt_float_mkt_cap = row[4]\n rt_insti_activebuy_amt = row[5]\n rt_amt = row[6]\n # zhan_bi = rt_vol/rt_float_mkt_cap\n inserted.append((curTime, stock, rt_pct_chg, rt_vol,rt_insti_activebuy_amt,rt_amt))\n\n all_vol = all_vol + rt_vol\n\n categories = stockCategory.get(stock)\n for category in categories:\n if category not in categoryData:\n categoryData[category] = [0,0,0,0,0] #rt_pct_chg*rt_float_mkt_cap(0), rt_float_mkt_cap(1), rt_pct_chg*rt_mkt_cap(2),rt_mkt_cap(3),rt_vol(4)\n catList = categoryData[category]\n catList[0] = catList[0] + (rt_pct_chg*rt_float_mkt_cap)\n catList[1] = catList[1] + rt_float_mkt_cap\n catList[2] = catList[2] + (rt_pct_chg * rt_mkt_cap)\n catList[3] = catList[3] + rt_mkt_cap\n catList[4] = catList[4] + rt_vol\n\n ct = ct+1\n # print (inserted)\n try:\n cur = conn.cursor()\n except:\n logging.debug(\"connection is lost 1\")\n conn = mysql.connector.connect(**config)\n conn.set_converter_class(NumpyMySQLConverter)\n cur = conn.cursor()\n query = \"\"\"INSERT INTO Stock_RT (DATA_DATETIME,STOCK_CODE,ZDF,RT_VOL,rt_insti_activebuy_amt,RT_AMT) VALUES (%s,%s,%s,%s,%s,%s)\"\"\"\n try:\n cur.executemany(query, inserted)\n except:\n # conn.commit()\n logging.debug('exception in loading')\n continue\n\n # print (str(ct),\" rows into stock_RT table\")\n conn.commit()\n\n bankuaiList = []\n ct=1\n inserted = []\n for category in categoryData.keys():\n ct = ct+1\n\n dataList = categoryData.get(category)\n zdf = dataList[0]/dataList[1]\n zhan_bi = dataList[4]/all_vol\n inserted.append((curTime, category, zdf, zhan_bi,dataList[1]))\n\n b = BanKuaiObj()\n b.code = category\n b.zhanbi = zhan_bi\n bankuaiList.append(b)\n query = \"\"\"INSERT INTO BanKuai_RT (DATA_DATETIME,BanKuai,ZDF,Volume_Zhan_Bi,RT_FLOAT_MKT_CAP) VALUES (%s,%s,%s,%s,%s)\"\"\"\n try:\n cur.executemany(query,inserted)\n except:\n # conn.commit()\n logging.debug('exception in loading bankuai')\n continue\n conn.commit()\n # print(str(ct), \" rows into BanKuai_RT table\")\n\n zhanbiSortedList = SortByZhanbiasc(bankuaiList)\n rank = 0\n ct = 1\n inserted = []\n for bankuai in zhanbiSortedList:\n ct = ct+1\n rank = rank + 1\n inserted.append((curTime, bankuai.code, rank))\n query = \"\"\"INSERT INTO BanKuai_Rank (DATA_DATETIME,BanKuai,Volume_Zhan_Bi) VALUES (%s,%s,%s)\"\"\"\n try:\n cur.executemany(query, inserted)\n except:\n # conn.commit()\n logging.debug('exception in loading bankuai ranking')\n continue\n # print(str(ct), \" rows into BanKuai_Rank table\")\n conn.commit()\n conn.close()\n w.stop()\n sleep(60)\n # conn.close()\n logging.debug (\"done processing\")\n\n\nif __name__ == \"__main__\": main()", "sub_path": "C009_realtime_ranking_v4.py", "file_name": "C009_realtime_ranking_v4.py", "file_ext": "py", "file_size_in_byte": 9417, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pandas.set_option", "line_number": 10, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 11, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 11, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 42, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 59, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 66, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 73, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 92, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 92, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 129, "usage_type": "call"}, {"api_name": "mysql.connector.connector.connect", "line_number": 135, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 135, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 135, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 142, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 182, "usage_type": "call"}, {"api_name": "mysql.connector.connector.connect", "line_number": 183, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 183, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 183, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 191, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 217, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 235, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 241, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 243, "usage_type": "call"}]} +{"seq_id": "255774730", "text": "\"\"\"\nCopyright 2017-present, Airbnb Inc.\n\nLicensed under the Apache License, Version 2.0 (the \"License\");\nyou may not use this file except in compliance with the License.\nYou may obtain a copy of the License at\n\n http://www.apache.org/licenses/LICENSE-2.0\n\nUnless required by applicable law or agreed to in writing, software\ndistributed under the License is distributed on an \"AS IS\" BASIS,\nWITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\nSee the License for the specific language governing permissions and\nlimitations under the License.\n\"\"\"\n# pylint: disable=abstract-class-instantiated,protected-access,no-self-use,abstract-method,attribute-defined-outside-init\nimport json\nfrom mock import Mock, mock_open, patch\n\nfrom boxsdk.exception import BoxException\nfrom nose.tools import assert_equal, assert_false, assert_items_equal, assert_true\nfrom requests.exceptions import ConnectionError, Timeout\n\nfrom app_integrations.apps.box import BoxApp\nfrom app_integrations.config import AppConfig\n\nfrom tests.unit.app_integrations.test_helpers import (\n get_valid_config_dict,\n MockSSMClient\n)\n\n@patch.object(BoxApp, 'type', Mock(return_value='type'))\n@patch.object(AppConfig, 'SSM_CLIENT', MockSSMClient())\nclass TestBoxApp(object):\n \"\"\"Test class for the BoxApp\"\"\"\n\n # Remove all abstractmethods so we can instantiate BoxApp for testing\n @patch.object(BoxApp, '__abstractmethods__', frozenset())\n def setup(self):\n \"\"\"Setup before each method\"\"\"\n self._app = BoxApp(AppConfig(get_valid_config_dict('box_admin_events')))\n\n def test_sleep(self):\n \"\"\"BoxApp - Sleep Seconds\"\"\"\n assert_equal(self._app._sleep_seconds(), 0)\n\n def test_required_auth_info(self):\n \"\"\"BoxApp - Required Auth Info\"\"\"\n assert_items_equal(self._app.required_auth_info().keys(), {'keyfile'})\n\n @patch('app_integrations.apps.box.JWTAuth.from_settings_dictionary',\n Mock(return_value=True))\n def test_keyfile_validator(self):\n \"\"\"BoxApp - Keyfile Validation, Success\"\"\"\n validation_function = self._app.required_auth_info()['keyfile']['format']\n data = {'test': 'keydata'}\n mocker = mock_open(read_data=json.dumps(data))\n with patch('__builtin__.open', mocker):\n loaded_keydata = validation_function('fakepath')\n assert_equal(loaded_keydata, data)\n\n @patch('app_integrations.apps.box.JWTAuth.from_settings_dictionary')\n def test_keyfile_validator_failure(self, cred_mock):\n \"\"\"BoxApp - Keyfile Validation, Failure\"\"\"\n validation_function = self._app.required_auth_info()['keyfile']['format']\n cred_mock.return_value = False\n mocker = mock_open(read_data=json.dumps({'test': 'keydata'}))\n with patch('__builtin__.open', mocker):\n assert_false(validation_function('fakepath'))\n cred_mock.assert_called()\n\n @patch('app_integrations.apps.box.JWTAuth.from_settings_dictionary')\n def test_keyfile_validator_bad_json(self, cred_mock):\n \"\"\"BoxApp - Keyfile Validation, Bad JSON\"\"\"\n validation_function = self._app.required_auth_info()['keyfile']['format']\n mocker = mock_open(read_data='invalid json')\n with patch('__builtin__.open', mocker):\n assert_false(validation_function('fakepath'))\n cred_mock.assert_not_called()\n\n @patch('app_integrations.apps.box.JWTAuth.from_settings_dictionary',\n Mock(return_value=True))\n def test_load_credentials(self):\n \"\"\"BoxApp - Load Auth, Success\"\"\"\n assert_true(self._app._load_auth('fakedata'))\n\n @patch('app_integrations.apps.box.JWTAuth.from_settings_dictionary')\n def test_load_credentials_bad(self, cred_mock):\n \"\"\"BoxApp - Load Auth, ValueError\"\"\"\n cred_mock.side_effect = ValueError('Bad things happened')\n assert_false(self._app._load_auth('fakedata'))\n\n @patch('app_integrations.apps.box.Client',\n Mock(return_value=True))\n @patch('app_integrations.apps.box.BoxApp._load_auth')\n def test_create_client(self, auth_mock):\n \"\"\"BoxApp - Create Client, Success\"\"\"\n assert_true(self._app._create_client())\n auth_mock.assert_called_with(self._app._config.auth['keyfile'])\n\n @patch('logging.Logger.debug')\n def test_create_client_exists(self, log_mock):\n \"\"\"BoxApp - Create Client, Exists\"\"\"\n self._app._client = True\n assert_true(self._app._create_client())\n log_mock.assert_called_with('Client already instantiated for %s', 'type')\n\n @patch('app_integrations.apps.box.BoxApp._load_auth',\n Mock(return_value=False))\n def test_create_client_fail_auth(self):\n \"\"\"BoxApp - Create Client, Auth Failure\"\"\"\n assert_false(self._app._create_client())\n\n def test_gather_logs(self):\n \"\"\"BoxApp - Gather Logs, Success\"\"\"\n with patch.object(self._app, '_client') as client_mock:\n payload = {\n 'chunk_size': 10,\n 'next_stream_position': '1152922976252290886',\n 'entries': self._get_sample_logs(10)\n }\n client_mock.make_request.return_value.json.return_value = payload\n\n assert_equal(len(self._app._gather_logs()), 10)\n assert_equal(self._app._last_timestamp, '2017-10-27T12:31:22-07:00')\n\n @patch('app_integrations.apps.box.BoxApp._create_client',\n Mock(return_value=True))\n @patch('logging.Logger.exception')\n def test_gather_logs_box_error(self, log_mock):\n \"\"\"BoxApp - Gather Logs, BoxException\"\"\"\n with patch.object(self._app, '_client') as client_mock:\n client_mock.make_request.side_effect = BoxException('bad error')\n assert_false(self._app._gather_logs())\n log_mock.assert_called_with('Failed to get events for %s', 'type')\n\n @patch('app_integrations.apps.box.BoxApp._create_client',\n Mock(return_value=True))\n @patch('logging.Logger.exception')\n def test_gather_logs_requests_error(self, log_mock):\n \"\"\"BoxApp - Gather Logs, ConnectionError\"\"\"\n with patch.object(self._app, '_client') as client_mock:\n self._app._next_stream_position = 10241040195019\n client_mock.make_request.side_effect = ConnectionError(response='bad error')\n assert_false(self._app._gather_logs())\n log_mock.assert_called_with('Bad response received from host, will retry once')\n\n @patch('app_integrations.apps.box.BoxApp._create_client',\n Mock(return_value=True))\n @patch('logging.Logger.exception')\n def test_gather_logs_requests_timeout(self, log_mock):\n \"\"\"BoxApp - Gather Logs, Timeout\"\"\"\n with patch.object(self._app, '_client') as client_mock:\n client_mock.make_request.side_effect = Timeout(response='request timed out')\n assert_false(self._app._gather_logs())\n log_mock.assert_called_with('Request timed out for %s', 'type')\n\n @patch('app_integrations.apps.box.BoxApp._load_auth',\n Mock(return_value=False))\n def test_gather_logs_no_client(self):\n \"\"\"BoxApp - Gather Logs, No Client\"\"\"\n with patch.object(self._app, '_client') as client_mock:\n self._app._client = False\n assert_false(self._app._gather_logs())\n client_mock.make_request.assert_not_called()\n\n @patch('app_integrations.apps.box.BoxApp._create_client',\n Mock(return_value=True))\n @patch('logging.Logger.error')\n def test_gather_logs_no_results(self, log_mock):\n \"\"\"BoxApp - Gather Logs, No Results From API\"\"\"\n with patch.object(self._app, '_client') as client_mock:\n client_mock.make_request.return_value.json.return_value = None\n assert_false(self._app._gather_logs())\n log_mock.assert_called_with('No results received from the Box API request for %s',\n 'type')\n\n @patch('app_integrations.apps.box.BoxApp._create_client',\n Mock(return_value=True))\n @patch('logging.Logger.info')\n def test_gather_logs_empty_items(self, log_mock):\n \"\"\"BoxApp - Gather Logs, Empty Entries List\"\"\"\n with patch.object(self._app, '_client') as client_mock:\n payload = {\n 'chunk_size': 0,\n 'next_stream_position': '1152922976252290886',\n 'entries': []\n }\n client_mock.make_request.return_value.json.return_value = payload\n assert_false(self._app._gather_logs())\n log_mock.assert_called_with('No events in response from the Box API request for %s',\n 'type')\n\n @staticmethod\n def _get_sample_logs(count):\n \"\"\"Helper function for returning sample Box admin event logs\"\"\"\n return [{\n 'additional_details': None,\n 'created_at': '2017-10-27T12:31:22-07:00',\n 'created_by': {\n 'id': '2710218233',\n 'login': 'testemail@email.com',\n 'name': 'User Name',\n 'type': 'user'\n },\n 'event_id': '0e0b8122-17ed-42ee-8a9d-d9a57bf8dd83',\n 'event_type': 'ADD_LOGIN_ACTIVITY_DEVICE',\n 'ip_address': '1.1.1.22',\n 'session_id': None,\n 'source': {\n 'id': '2710218233',\n 'login': 'testemail@email.com',\n 'name': 'User Name',\n 'type': 'user'\n },\n 'type': 'event'\n } for _ in range(count)]\n", "sub_path": "tests/unit/app_integrations/test_apps/test_box.py", "file_name": "test_box.py", "file_ext": "py", "file_size_in_byte": 9556, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "app_integrations.apps.box.BoxApp", "line_number": 41, "usage_type": "call"}, {"api_name": "app_integrations.config.AppConfig", "line_number": 41, "usage_type": "call"}, {"api_name": "tests.unit.app_integrations.test_helpers.get_valid_config_dict", "line_number": 41, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 38, "usage_type": "call"}, {"api_name": "app_integrations.apps.box.BoxApp", "line_number": 38, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 38, "usage_type": "name"}, {"api_name": "nose.tools.assert_equal", "line_number": 45, "usage_type": "call"}, {"api_name": "nose.tools.assert_items_equal", "line_number": 49, "usage_type": "call"}, {"api_name": "mock.mock_open", "line_number": 57, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 57, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 58, "usage_type": "call"}, {"api_name": "nose.tools.assert_equal", "line_number": 60, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 51, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 52, "usage_type": "call"}, {"api_name": "mock.mock_open", "line_number": 67, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 67, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 68, "usage_type": "call"}, {"api_name": "nose.tools.assert_false", "line_number": 69, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 62, "usage_type": "call"}, {"api_name": "mock.mock_open", "line_number": 76, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 77, "usage_type": "call"}, {"api_name": "nose.tools.assert_false", "line_number": 78, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 72, "usage_type": "call"}, {"api_name": "nose.tools.assert_true", "line_number": 85, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 81, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 82, "usage_type": "call"}, {"api_name": "nose.tools.assert_false", "line_number": 91, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 87, "usage_type": "call"}, {"api_name": "nose.tools.assert_true", "line_number": 98, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 93, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 94, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 95, "usage_type": "call"}, {"api_name": "nose.tools.assert_true", "line_number": 105, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 101, "usage_type": "call"}, {"api_name": "nose.tools.assert_false", "line_number": 112, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 108, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 109, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 116, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 116, "usage_type": "name"}, {"api_name": "nose.tools.assert_equal", "line_number": 124, "usage_type": "call"}, {"api_name": "nose.tools.assert_equal", "line_number": 125, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 132, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 132, "usage_type": "name"}, {"api_name": "boxsdk.exception.BoxException", "line_number": 133, "usage_type": "call"}, {"api_name": "nose.tools.assert_false", "line_number": 134, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 127, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 128, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 129, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 142, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 142, "usage_type": "name"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 144, "usage_type": "call"}, {"api_name": "nose.tools.assert_false", "line_number": 145, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 137, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 138, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 139, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 153, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 153, "usage_type": "name"}, {"api_name": "requests.exceptions.Timeout", "line_number": 154, "usage_type": "call"}, {"api_name": "nose.tools.assert_false", "line_number": 155, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 148, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 149, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 150, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 162, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 162, "usage_type": "name"}, {"api_name": "nose.tools.assert_false", "line_number": 164, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 158, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 159, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 172, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 172, "usage_type": "name"}, {"api_name": "nose.tools.assert_false", "line_number": 174, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 167, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 168, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 169, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 183, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 183, "usage_type": "name"}, {"api_name": "nose.tools.assert_false", "line_number": 190, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 178, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 179, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 180, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 32, "usage_type": "call"}, {"api_name": "app_integrations.apps.box.BoxApp", "line_number": 32, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 32, "usage_type": "name"}, {"api_name": "mock.Mock", "line_number": 32, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 33, "usage_type": "call"}, {"api_name": "app_integrations.config.AppConfig", "line_number": 33, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 33, "usage_type": "name"}, {"api_name": "tests.unit.app_integrations.test_helpers.MockSSMClient", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "36769207", "text": "from bs4 import BeautifulSoup\nfrom pyhanlp import *\nimport re\n\n\nclass HanlpUnit:\n def __init__(self):\n self.CustomDictionary = JClass(\"com.hankcs.hanlp.dictionary.CustomDictionary\")\n self.added_word_list = list()\n\n def add_word_list(self, word_list):\n \"\"\"\n 添加自定义词\n :param word_list:词列表，格式[{\"word\": \"\", \"mask\": \"\"}] word为词名，mask为词性\n :return:\n \"\"\"\n try:\n for item in word_list:\n result = self.CustomDictionary.add(item[\"word\"], item[\"mask\"])\n if result is False:\n self.added_word_list.append(item)\n return True\n except Exception:\n return False\n\n def cut(self, sentence):\n \"\"\"\n 分词\n :param sentence: 要分词的句子\n :return:\n \"\"\"\n cut_result = HanLP.segment(sentence)\n for i in range(len(cut_result)):\n cut_result[i] = str(cut_result[i])\n for item in self.added_word_list:\n if cut_result[i].split(\"/\")[0] == item[\"word\"]:\n cut_result[i] = item[\"word\"] + \"/\" + item[\"mask\"]\n break\n return cut_result\n\n @staticmethod\n def get_text_from_html(text):\n \"\"\"\n 从html内容中提取文本，主要是针对爬下来的带有html标签的新闻内容\n :param text:\n :return:\n \"\"\"\n text = \"
\" + text + \"
\"\n soup = BeautifulSoup(text, \"html.parser\")\n return soup.get_text()\n\n @staticmethod\n def split_paragraph(para):\n \"\"\"\n 将段落拆成句子\n :param para:\n :return:\n \"\"\"\n para = re.sub('([。！？\\?])([^”’])', r\"\\1\\n\\2\", para) # 单字符断句符\n para = re.sub('(\\.{6})([^”’])', r\"\\1\\n\\2\", para) # 英文省略号\n para = re.sub('(\\…{2})([^”’])', r\"\\1\\n\\2\", para) # 中文省略号\n para = re.sub('([。！？\\?][”’])([^，。！？\\?])', r'\\1\\n\\2', para)\n # 如果双引号前有终止符，那么双引号才是句子的终点，把分句符\\n放到双引号后，注意前面的几句都小心保留了双引号\n para = para.rstrip() # 段尾如果有多余的\\n就去掉它\n # 很多规则中会考虑分号;，但是这里我把它忽略不计，破折号、英文双引号等同样忽略，需要的再做些简单调整即可。\n return para.split(\"\\n\")\n\n def __del__(self):\n \"\"\"\n 消除对象时撤销已添加词汇\n :return:\n \"\"\"\n for item in self.added_word_list:\n self.CustomDictionary.remove(item[\"word\"])\n", "sub_path": "knowledge_map_system3.0/model/hanlpUnit.py", "file_name": "hanlpUnit.py", "file_ext": "py", "file_size_in_byte": 2682, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "bs4.BeautifulSoup", "line_number": 49, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 59, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 60, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 61, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 62, "usage_type": "call"}]} +{"seq_id": "217669112", "text": "import cv2\nimport numpy as np\n\nvideoLeftUp = cv2.VideoCapture('./res/2_003_013.mp4')\nvideoLeftDown = cv2.VideoCapture('./res/2_003_014.mp4')\nvideoRightUp = cv2.VideoCapture('./res/2_003_015.mp4')\nvideoRightDown = cv2.VideoCapture('./res/2_003_016.mp4')\n\nfps = videoLeftUp.get(cv2.CAP_PROP_FPS)\n\nwidth = (int(videoLeftUp.get(cv2.CAP_PROP_FRAME_WIDTH)))\nheight = (int(videoLeftUp.get(cv2.CAP_PROP_FRAME_HEIGHT)))\n\n#videoWriter = cv2.VideoWriter('./out/4in1.mp4', cv2.VideoWriter_fourcc('m', 'p', '4', 'v'), fps, (width, height))\n\nsuccessLeftUp, frameLeftUp = videoLeftUp.read()\nsuccessLeftDown , frameLeftDown = videoLeftDown.read()\nsuccessRightUp, frameRightUp = videoRightUp.read()\nsuccessRightDown, frameRightDown = videoRightDown.read()\n\nwhile successLeftUp and successLeftDown and successRightUp and successRightDown:\n frameLeftUp = cv2.resize(frameLeftUp, (int(width / 2), int(height / 2)), interpolation=cv2.INTER_CUBIC)\n frameLeftDown = cv2.resize(frameLeftDown, (int(width / 2), int(height / 2)), interpolation=cv2.INTER_CUBIC)\n frameRightUp = cv2.resize(frameRightUp, (int(width / 2), int(height / 2)), interpolation=cv2.INTER_CUBIC)\n frameRightDown = cv2.resize(frameRightDown, (int(width / 2), int(height / 2)), interpolation=cv2.INTER_CUBIC)\n\n frameUp = np.hstack((frameLeftUp, frameRightUp))\n frameDown = np.hstack((frameLeftDown, frameRightDown))\n frame = np.vstack((frameUp, frameDown))\n\n\n if cv2.waitKey(1) == 27:\n break\n cv2.imshow('Test camera', frame)\n successLeftUp, frameLeftUp = videoLeftUp.read()\n successLeftDown, frameLeftDown = videoLeftDown.read()\n successRightUp, frameRightUp = videoRightUp.read()\n successRightDown, frameRightDown = videoRightDown.read()\n\n#videoWriter.release()\nvideoLeftUp.release()\nvideoLeftDown.release()\nvideoRightUp.release()\nvideoRightDown.release()", "sub_path": "OpenCV/106.py", "file_name": "106.py", "file_ext": "py", "file_size_in_byte": 1847, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "cv2.VideoCapture", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.CAP_PROP_FPS", "line_number": 9, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_WIDTH", "line_number": 11, "usage_type": "attribute"}, {"api_name": "cv2.CAP_PROP_FRAME_HEIGHT", "line_number": 12, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 22, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 22, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 23, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.hstack", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 32, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "47575469", "text": "import concurrent.futures\nimport time\nimport mpu\nimport geoip2.database\nimport os\nimport datetime\n\nfrom haversine import haversine, Unit\n\nreader = geoip2.database.Reader('GeoLite2-City.mmdb')\n \ndnsiplist = [] \n\nfile_gs= \"gs.txt\" \n\nwith open(file_gs) as f:\n \n for line in f:\n dnsiplist.append(line.strip()) \n \nf.close()\n\nrfilename = \"100\"\nwfilename = \"100\" + \".dis\"\n\ntry:\n os.remove(wfilename)\nexcept:\n pass\n \nfor ip in dnsiplist:\n \n distance = 100000000000000\n nearest_ip = \"\"\n \n response = reader.city(ip)\n\n print(ip)\n print(\"open \" + rfilename)\n\n counter = 0\n with open(rfilename) as rf:\n \n for line2 in rf:\n\n #reader2 = geoip2.database.Reader('GeoLite2-City.mmdb')\n\n counter = counter + 1\n #print(counter)\n\n now = datetime.datetime.now() \n\n if counter % 100000 == 0:\n print(\"[\" + str(now) + \"] \" + str(counter) + \" lines done.\" )\n\n \n try:\n response2 = reader.city(line2.strip())\n except:\n continue\n \n try:\n\n paris = (response.location.latitude, response.location.longitude) # (lat, lon)\n lyon = (response2.location.latitude, response2.location.longitude) # (lat, lon)\n\n if float(distance) > float(haversine(lyon, paris)):\n #fw = open(wfilename, 'a')\n #fw.write(str(ip)+\",\"+str(line2.strip())+\",\"+str(haversine(lyon, paris))+\"\\n\")\n #fw.close() \n\n now = datetime.datetime.now() \n print(\"[\" + str(now) + \"] UPDATE:\"+str(ip)+\",\"+str(line2.strip())+\",\"+str(haversine(lyon, paris)))\n\n nearest_ip = line2.strip()\n distance = haversine(lyon, paris) \n \n \n except:\n continue\n\n #reader2.close()\n\n\n now = datetime.datetime.now() \n\n fw = open(wfilename, 'a')\n fw.write(str(ip)+\",\"+str(line2.strip())+\",\"+str(haversine(lyon, paris))+\"\\n\")\n fw.close()\n \n print(\"[\"+str(now)+\"]\"+\" COMMIT: \"+str(nearest_ip)+\",\"+str(line2.strip())+\",\"+str(haversine(lyon, paris)))\n \n print(\"close \" + rfilename)\n rf.close() \n\nreader.close()\n \n \n", "sub_path": "geoip/conc3.py", "file_name": "conc3.py", "file_ext": "py", "file_size_in_byte": 2399, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "geoip2.database.database.Reader", "line_number": 10, "usage_type": "call"}, {"api_name": "geoip2.database.database", "line_number": 10, "usage_type": "attribute"}, {"api_name": "geoip2.database", "line_number": 10, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 27, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 51, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 51, "usage_type": "attribute"}, {"api_name": "haversine.haversine", "line_number": 67, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 72, "usage_type": "attribute"}, {"api_name": "haversine.haversine", "line_number": 73, "usage_type": "call"}, {"api_name": "haversine.haversine", "line_number": 76, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 85, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 85, "usage_type": "attribute"}, {"api_name": "haversine.haversine", "line_number": 88, "usage_type": "call"}, {"api_name": "haversine.haversine", "line_number": 91, "usage_type": "call"}]} +{"seq_id": "630790671", "text": "#!/usr/bin/env python\n\nimport importlib\nimport json\nimport logging\nimport argparse\n\nfrom future.backports.urllib.parse import urlparse\n\nfrom oic.utils.authn.client import BearerHeader\nfrom oic.utils.keyio import build_keyjar\n\nfrom otest import ConfigurationError\nfrom otest import NotSupported\nfrom otest import exception_trace\nfrom otest.check import OK\nfrom otest.conversation import Conversation\nfrom otest.parse_cnf import parse_yaml_conf\n\nfrom oidctest.op import make_list\nfrom oidctest.common import make_client\nfrom oidctest.common import setup_logger\nfrom oidctest.common import run_flow\nfrom oidctest.common import Trace\nfrom oidctest.io import ClIO\nfrom oidctest.session import SessionHandler\nfrom oidctest.utils import get_check\n\nfrom requests.packages import urllib3\n\nurllib3.disable_warnings()\n\n__author__ = 'roland'\n\nlogger = logging.getLogger(\"\")\n\n\ndef get_claims(client):\n resp = {}\n for src in list(client.userinfo[\"_claim_names\"].values()):\n spec = client.userinfo[\"_claim_sources\"][src]\n ht_args = BearerHeader(client).construct(**spec)\n\n try:\n part = client.http_request(spec[\"endpoint\"], \"GET\", **ht_args)\n except Exception:\n raise\n resp.update(json.loads(part.content))\n\n return resp\n\n\ndef endpoint_support(client, endpoint):\n if endpoint in client.provider_info:\n return True\n else:\n return False\n\n\ndef run_func(spec, conv, req_args):\n if isinstance(spec, tuple):\n func, args = spec\n else:\n func = spec\n args = {}\n\n try:\n req_args = func(req_args, conv, args)\n except KeyError as err:\n conv.trace.error(\"function: %s failed\" % func)\n conv.trace.error(str(err))\n raise NotSupported\n except ConfigurationError:\n raise\n else:\n return req_args\n\n\ndef run_one(test_id, flows, profile, profiles, io, sh, **kw_args):\n try:\n redirs = kw_args[\"cinfo\"][\"client\"][\"redirect_uris\"]\n except KeyError:\n redirs = kw_args[\"cinfo\"][\"registered\"][\"redirect_uris\"]\n\n io = ClIO(flows=flows, profile=profile, **kw_args)\n sh = SessionHandler(None, profile, flows, **kw_args)\n\n _flow = flows[test_id]\n _cli = make_client(**kw_args)\n conversation = Conversation(_flow, _cli, kw_args[\"msg_factory\"],\n interaction=kw_args[\"conf\"].INTERACTION,\n trace_cls=Trace, callback_uris=redirs)\n # noinspection PyTypeChecker\n try:\n run_flow(profiles, conversation, test_id, kw_args[\"conf\"],\n profile, kw_args[\"check_factory\"], io, sh)\n except Exception as err:\n exception_trace(\"\", err, logger)\n print(conversation.trace)\n\n\ndef main(flows, profile, profiles, **kw_args):\n try:\n redirs = kw_args[\"cinfo\"][\"client\"][\"redirect_uris\"]\n except KeyError:\n redirs = kw_args[\"cinfo\"][\"registered\"][\"redirect_uris\"]\n\n test_list = make_list(flows, profile, **kw_args)\n\n for tid in test_list:\n io = ClIO(flows=flows, profile=profile, **kw_args)\n sh = SessionHandler(profile, flows, **kw_args)\n\n _flow = flows[tid]\n _cli, _cliconf = make_client(**kw_args)\n conversation = Conversation(_flow, _cli, kw_args[\"msg_factory\"],\n interaction=kw_args[\"conf\"].INTERACTION,\n trace_cls=Trace, callback_uris=redirs)\n\n _cli.conv = conversation\n # noinspection PyTypeChecker\n try:\n info = run_flow(profiles, conversation, tid, kw_args[\"conf\"],\n profile, kw_args[\"check_factory\"], io, sh)\n if info['status'] == OK:\n print('+{}'.format(tid))\n else:\n print('!{}'.format(tid))\n for ev in conversation.events:\n print(ev)\n break\n except Exception as err:\n exception_trace(\"\", err, logger)\n print(conversation.trace)\n break\n\n\nif __name__ == '__main__':\n from oidctest import profiles\n from oidctest import oper\n from oidctest import func\n from oic.oic.message import factory as oic_message_factory\n\n parser = argparse.ArgumentParser()\n parser.add_argument('-f', dest='flows')\n parser.add_argument('-l', dest=\"log_name\")\n parser.add_argument('-p', dest=\"profile\")\n parser.add_argument('-t', dest=\"testid\")\n parser.add_argument(dest=\"config\")\n cargs = parser.parse_args()\n\n fdef = {'Flows': {}, 'Order': [], 'Desc': []}\n cls_factories = {'': oper.factory}\n func_factory = func.factory\n\n spec = parse_yaml_conf(cargs.flows, cls_factories, func_factory)\n fdef['Flows'].update(spec['Flows'])\n for param in ['Order', 'Desc']:\n try:\n fdef[param].extend(spec[param])\n except KeyError:\n pass\n\n CONF = importlib.import_module(cargs.config)\n\n if cargs.log_name:\n setup_logger(logger, cargs.log_name)\n else:\n setup_logger(logger)\n\n # Add own keys for signing/encrypting JWTs\n jwks, keyjar, kidd = build_keyjar(CONF.keys)\n\n # export JWKS\n p = urlparse(CONF.KEY_EXPORT_URL)\n f = open(\".\" + p.path, \"w\")\n f.write(json.dumps(jwks))\n f.close()\n jwks_uri = p.geturl()\n\n kwargs = {\"base_url\": CONF.BASE, \"kidd\": kidd, \"keyjar\": keyjar,\n \"jwks_uri\": jwks_uri, \"flows\": fdef['Flows'], \"conf\": CONF,\n \"cinfo\": CONF.INFO, \"desc\": fdef['Desc'], 'order': fdef['Order'],\n \"profiles\": profiles, \"operations\": oper,\n \"profile\": cargs.profile, \"msg_factory\": oic_message_factory,\n 'check_factory': get_check}\n\n if cargs.testid:\n run_one(cargs.testid, **kwargs)\n else:\n main(**kwargs)\n", "sub_path": "test_tool/test_op/rp/cl/cloprp.py", "file_name": "cloprp.py", "file_ext": "py", "file_size_in_byte": 5769, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "requests.packages.urllib3.disable_warnings", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.packages.urllib3", "line_number": 31, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 35, "usage_type": "call"}, {"api_name": "oic.utils.authn.client.BearerHeader", "line_number": 42, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 48, "usage_type": "call"}, {"api_name": "otest.NotSupported", "line_number": 72, "usage_type": "name"}, {"api_name": "otest.ConfigurationError", "line_number": 73, "usage_type": "name"}, {"api_name": "oidctest.io.ClIO", "line_number": 85, "usage_type": "call"}, {"api_name": "oidctest.session.SessionHandler", "line_number": 86, "usage_type": "call"}, {"api_name": "oidctest.common.make_client", "line_number": 89, "usage_type": "call"}, {"api_name": "otest.conversation.Conversation", "line_number": 90, "usage_type": "call"}, {"api_name": "oidctest.common.Trace", "line_number": 92, "usage_type": "name"}, {"api_name": "oidctest.common.run_flow", "line_number": 95, "usage_type": "call"}, {"api_name": "otest.exception_trace", "line_number": 98, "usage_type": "call"}, {"api_name": "oidctest.op.make_list", "line_number": 108, "usage_type": "call"}, {"api_name": "oidctest.io.ClIO", "line_number": 111, "usage_type": "call"}, {"api_name": "oidctest.session.SessionHandler", "line_number": 112, "usage_type": "call"}, {"api_name": "oidctest.common.make_client", "line_number": 115, "usage_type": "call"}, {"api_name": "otest.conversation.Conversation", "line_number": 116, "usage_type": "call"}, {"api_name": "oidctest.common.Trace", "line_number": 118, "usage_type": "name"}, {"api_name": "oidctest.common.run_flow", "line_number": 123, "usage_type": "call"}, {"api_name": "otest.check.OK", "line_number": 125, "usage_type": "name"}, {"api_name": "otest.exception_trace", "line_number": 133, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 144, "usage_type": "call"}, {"api_name": "oidctest.oper.factory", "line_number": 153, "usage_type": "attribute"}, {"api_name": "oidctest.oper", "line_number": 153, "usage_type": "name"}, {"api_name": "oidctest.func.factory", "line_number": 154, "usage_type": "attribute"}, {"api_name": "oidctest.func", "line_number": 154, "usage_type": "name"}, {"api_name": "otest.parse_cnf.parse_yaml_conf", "line_number": 156, "usage_type": "call"}, {"api_name": "importlib.import_module", "line_number": 164, "usage_type": "call"}, {"api_name": "oidctest.common.setup_logger", "line_number": 167, "usage_type": "call"}, {"api_name": "oidctest.common.setup_logger", "line_number": 169, "usage_type": "call"}, {"api_name": "oic.utils.keyio.build_keyjar", "line_number": 172, "usage_type": "call"}, {"api_name": "future.backports.urllib.parse.urlparse", "line_number": 175, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 177, "usage_type": "call"}, {"api_name": "oidctest.profiles", "line_number": 184, "usage_type": "name"}, {"api_name": "oidctest.oper", "line_number": 184, "usage_type": "name"}, {"api_name": "oic.oic.message.factory", "line_number": 185, "usage_type": "name"}, {"api_name": "oidctest.utils.get_check", "line_number": 186, "usage_type": "name"}]} +{"seq_id": "598368986", "text": "import lib.helper_functions as helpers\nimport uuid\nimport datetime\nfrom datetime import timedelta\n\ndef getTransactionsByDirection(transactions, direction):\n res = [t for t in transactions if t['direction'] == direction]\n if res:\n return res\n else:\n return []\n\ndef setTransactionStartEnd(transaction, start, end):\n transaction['start'] = start\n transaction['end'] = end\n return transaction\n\ndef setNewTransactionBudget(transaction, new_budget):\n \"\"\"Returns updated transaction\"\"\"\n transaction['total_budget'] = new_budget\n num_of_payments = len(transaction['payments'])\n divided_budget = new_budget/num_of_payments\n for p in transaction['payments']:\n p['amount'] = divided_budget\n return transaction\n\ndef rescalePayments(transaction, num_of_payment_per_year):\n total_budget = transaction['total_budget']\n start_date = helpers.strToDate(transaction['start'])\n end_date = helpers.strToDate(transaction['end'])\n delta = end_date - start_date\n\n num_of_payments = round(delta.days/(int(365/num_of_payment_per_year)))\n num_of_payments = int(num_of_payments)\n if num_of_payments == 0:\n num_of_payments = 1\n\n budget_amount_per_payment = total_budget / num_of_payments\n\n interval = int(delta.days/num_of_payments)\n payments = []\n\n def generatePayments(x, add_days=0):\n p_date = start_date + timedelta(days=(interval*x)+add_days)\n return dict(\n date=p_date.strftime('%Y-%m-%d'),\n amount=budget_amount_per_payment,\n id=str(uuid.uuid4())\n )\n for x in reversed(range(num_of_payments,0,-1)):\n payments.append(generatePayments(x)) \n # if num_of_payment_per_year > 1: \n # for x in reversed(range(num_of_payments,0,-1)):\n # payments.append(generatePayments(x))\n # else:\n # for x in range(num_of_payments):\n # payments.append(generatePayments(x, add_days=35))\n \n transaction['payments'] = payments\n return transaction\n", "sub_path": "lib/transactions.py", "file_name": "transactions.py", "file_ext": "py", "file_size_in_byte": 2077, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "lib.helper_functions.strToDate", "line_number": 29, "usage_type": "call"}, {"api_name": "lib.helper_functions", "line_number": 29, "usage_type": "name"}, {"api_name": "lib.helper_functions.strToDate", "line_number": 30, "usage_type": "call"}, {"api_name": "lib.helper_functions", "line_number": 30, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 44, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "178875742", "text": "from django.views.generic import TemplateView\nfrom django.contrib.auth.decorators import login_required\nfrom django.utils.decorators import method_decorator\nfrom cv.forms import cvForm\nfrom cv.models import cv\nfrom django.shortcuts import render, redirect\nfrom django.utils import timezone\n\n# Create your views here.\n\ndef cv_view(request):\n content = cv.objects.order_by('-updated')[:1]\n return render(request, 'cv/cv.html', {'content' : content})\n\ndef cv_edit(request):\n form = cvForm() \n if request.method == \"POST\":\n form = cvForm(request.POST, request.user)\n if form.is_valid():\n new = form.save(commit=False)\n new.auther = request.user\n new.updated = timezone.now()\n new.save()\n return redirect('/cv/')\n\n return render(request, 'cv/cv_edit.html', {'form': form})\n else:\n if len(cv.objects.order_by('-updated')) != 0:\n content = cv.objects.order_by('-updated')[:1]\n form = cvForm(instance=content[0])\n else:\n form = cvForm()\n return render(request, 'cv/cv_edit.html', {'form': form})", "sub_path": "cv/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1133, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "cv.models.cv.objects.order_by", "line_number": 12, "usage_type": "call"}, {"api_name": "cv.models.cv.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "cv.models.cv", "line_number": 12, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 13, "usage_type": "call"}, {"api_name": "cv.forms.cvForm", "line_number": 16, "usage_type": "call"}, {"api_name": "cv.forms.cvForm", "line_number": 18, "usage_type": "call"}, {"api_name": "django.utils.timezone.now", "line_number": 22, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 22, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 24, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 26, "usage_type": "call"}, {"api_name": "cv.models.cv.objects.order_by", "line_number": 28, "usage_type": "call"}, {"api_name": "cv.models.cv.objects", "line_number": 28, "usage_type": "attribute"}, {"api_name": "cv.models.cv", "line_number": 28, "usage_type": "name"}, {"api_name": "cv.models.cv.objects.order_by", "line_number": 29, "usage_type": "call"}, {"api_name": "cv.models.cv.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "cv.models.cv", "line_number": 29, "usage_type": "name"}, {"api_name": "cv.forms.cvForm", "line_number": 30, "usage_type": "call"}, {"api_name": "cv.forms.cvForm", "line_number": 32, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "606935509", "text": "# HeatMay.py\r\n# Author: Dustin Wilson\r\n\r\nimport plotly.graph_objects as go\r\nimport csv\r\n\r\n# Creates a heat map of a given csv file\r\n# Path is the location of the csv file\r\ndef HeatMap(path):\r\n\r\n # Number of rows in the csv file\r\n numRow = 0\r\n # Number of cols\r\n numCol = 0\r\n\r\n # Opens the csv file and reads in each line\r\n # Adds each line to the array\r\n # First pass is to know how big to intialize the array\r\n with open(path, newline='') as File: \r\n reader = csv.reader(File)\r\n for row in reader:\r\n numCol = 0\r\n for indiv in row:\r\n numCol = numCol + 1\r\n numRow = numRow + 1\r\n \r\n # Intializes an array\r\n # Has zeros in all positions initially\r\n array = [[0 for i in range(numCol)] for j in range(numRow)]\r\n\r\n # Opens the csv file and reads in each line\r\n # Adds each line to the array\r\n # Second pass actually sets values\r\n with open(path, newline='') as File: \r\n reader = csv.reader(File)\r\n i = 0\r\n for row in reader:\r\n j = 0\r\n for indiv in row:\r\n array[i][j] = indiv\r\n j = j + 1\r\n i = i + 1\r\n\r\n # Creates the heat map based on the array which is built from csv file\r\n fig = go.Figure(data=go.Heatmap(z = array))\r\n # Displays the figure\r\n fig.show()", "sub_path": "Test/HeatMap.py", "file_name": "HeatMap.py", "file_ext": "py", "file_size_in_byte": 1285, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "csv.reader", "line_number": 20, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 35, "usage_type": "call"}, {"api_name": "plotly.graph_objects.Figure", "line_number": 45, "usage_type": "call"}, {"api_name": "plotly.graph_objects", "line_number": 45, "usage_type": "name"}, {"api_name": "plotly.graph_objects.Heatmap", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "435837809", "text": "import random\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom .rnncells import StackedLSTMCell, StackedGRUCell\nfrom .beam_search import Beam\nfrom .feedforward import FeedForward\nfrom utils import to_var, SOS_ID, UNK_ID, EOS_ID\nimport math\nimport pdb\nfrom queue import PriorityQueue\nimport operator\nimport numpy\n\n\n\nclass BeamSearchNode(object):\n def __init__(self, hiddenstate, previousNode, wordId, logProb, length):\n '''\n :param hiddenstate:\n :param previousNode:\n :param wordId:\n :param logProb:\n :param length:\n '''\n self.h = hiddenstate\n self.prevNode = previousNode\n self.wordid = wordId\n self.logp = logProb\n self.leng = length\n\n def eval(self, alpha=1.0):\n reward = 0\n # Add here a function for shaping a reward\n\n return self.logp / float(self.leng - 1 + 1e-6) + alpha * reward\n\nclass BaseRNNDecoder(nn.Module):\n def __init__(self):\n \"\"\"Base Decoder Class\"\"\"\n super(BaseRNNDecoder, self).__init__()\n\n @property\n def use_lstm(self):\n return isinstance(self.rnncell, StackedLSTMCell)\n\n def init_token(self, batch_size, SOS_ID=SOS_ID):\n \"\"\"Get Variable of Index (batch_size)\"\"\"\n x = to_var(torch.LongTensor([SOS_ID] * batch_size))\n return x\n\n def init_h(self, batch_size=None, zero=True, hidden=None):\n \"\"\"Return RNN initial state\"\"\"\n if hidden is not None:\n return hidden\n\n if self.use_lstm:\n # (h, c)\n return (to_var(torch.zeros(self.num_layers,\n batch_size,\n self.hidden_size)),\n to_var(torch.zeros(self.num_layers,\n batch_size,\n self.hidden_size)))\n else:\n # h\n return to_var(torch.zeros(self.num_layers,\n batch_size,\n self.hidden_size))\n\n def batch_size(self, inputs=None, h=None):\n \"\"\"\n inputs: [batch_size, seq_len]\n h: [num_layers, batch_size, hidden_size] (RNN/GRU)\n h_c: [2, num_layers, batch_size, hidden_size] (LSTMCell)\n \"\"\"\n if inputs is not None:\n batch_size = inputs.size(0)\n return batch_size\n\n else:\n if self.use_lstm:\n batch_size = h[0].size(1)\n else:\n batch_size = h.size(1)\n return batch_size\n\n def decode(self, out):\n \"\"\"\n Args:\n out: unnormalized word distribution [batch_size, vocab_size]\n Return:\n x: word_index [batch_size]\n \"\"\"\n\n # Sample next word from multinomial word distribution\n if self.sample:\n # x: [batch_size] - word index (next input)\n x = torch.multinomial(self.softmax(out / self.temperature), 1).view(-1)\n\n # Greedy sampling\n else:\n # x: [batch_size] - word index (next input)\n _, x = out.max(dim=1)\n return x\n\n def forward(self):\n \"\"\"Base forward function to inherit\"\"\"\n raise NotImplementedError\n\n def forward_step(self):\n \"\"\"Run RNN single step\"\"\"\n raise NotImplementedError\n\n def embed(self, x):\n \"\"\"word index: [batch_size] => word vectors: [batch_size, hidden_size]\"\"\"\n\n if self.training and self.word_drop > 0.0:\n if random.random() < self.word_drop:\n embed = self.embedding(to_var(x.data.new([UNK_ID] * x.size(0))))\n else:\n embed = self.embedding(x)\n else:\n embed = self.embedding(x)\n\n return embed\n'''\n def beam_decode(self,\n init_h=None,\n encoder_outputs=None, input_valid_length=None,\n decode=False):\n \"\"\"\n Args:\n encoder_outputs (Variable, FloatTensor): [batch_size, source_length, hidden_size]\n input_valid_length (Variable, LongTensor): [batch_size] (optional)\n init_h (variable, FloatTensor): [batch_size, hidden_size] (optional)\n Return:\n out : [batch_size, seq_len]\n \"\"\"\n batch_size = self.batch_size(h=init_h)\n\n # [batch_size x beam_size]\n x = self.init_token(batch_size * self.beam_size, SOS_ID)\n\n # [num_layers, batch_size x beam_size, hidden_size]\n h = self.init_h(batch_size, hidden=init_h).repeat(1, self.beam_size, 1)\n\n # batch_position [batch_size]\n # [0, beam_size, beam_size * 2, .., beam_size * (batch_size-1)]\n # Points where batch starts in [batch_size x beam_size] tensors\n # Ex. position_idx[5]: when 5-th batch starts\n batch_position = to_var(torch.arange(0, batch_size).long() * self.beam_size)\n\n # Initialize scores of sequence\n # [batch_size x beam_size]\n # Ex. batch_size: 5, beam_size: 3\n # [0, -inf, -inf, 0, -inf, -inf, 0, -inf, -inf, 0, -inf, -inf, 0, -inf, -inf]\n score = torch.ones(batch_size * self.beam_size) * -float('inf')\n score.index_fill_(0, torch.arange(0, batch_size).long() * self.beam_size, 0.0)\n score = to_var(score)\n\n # Initialize Beam that stores decisions for backtracking\n beam = Beam(\n batch_size,\n self.hidden_size,\n self.vocab_size,\n self.beam_size,\n self.max_unroll,\n batch_position)\n\n for i in range(self.max_unroll):\n\n # x: [batch_size x beam_size]; (token index)\n # =>\n # out: [batch_size x beam_size, vocab_size]\n # h: [num_layers, batch_size x beam_size, hidden_size]\n out, h = self.forward_step(x, h,\n encoder_outputs=encoder_outputs,\n input_valid_length=input_valid_length)\n # log_prob: [batch_size x beam_size, vocab_size]\n log_prob = F.log_softmax(out, dim=1)\n\n # [batch_size x beam_size]\n # => [batch_size x beam_size, vocab_size]\n score = score.view(-1, 1) + log_prob\n\n # Select `beam size` transitions out of `vocab size` combinations\n\n # [batch_size x beam_size, vocab_size]\n # => [batch_size, beam_size x vocab_size]\n # Cutoff and retain candidates with top-k scores\n # score: [batch_size, beam_size]\n # top_k_idx: [batch_size, beam_size]\n # each element of top_k_idx [0 ~ beam x vocab)\n\n score, top_k_idx = score.view(batch_size, -1).topk(self.beam_size, dim=1)\n\n\n # Get token ids with remainder after dividing by top_k_idx\n # Each element is among [0, vocab_size)\n # Ex. Index of token 3 in beam 4\n # (4 * vocab size) + 3 => 3\n # x: [batch_size x beam_size]\n x = (top_k_idx % self.vocab_size).view(-1)\n\n # top-k-pointer [batch_size x beam_size]\n # Points top-k beam that scored best at current step\n # Later used as back-pointer at backtracking\n # Each element is beam index: 0 ~ beam_size\n # + position index: 0 ~ beam_size x (batch_size-1)\n beam_idx = top_k_idx / self.vocab_size # [batch_size, beam_size]\n top_k_pointer = (beam_idx + batch_position.unsqueeze(1)).view(-1)\n\n\n # Select next h (size doesn't change)\n # [num_layers, batch_size * beam_size, hidden_size]\n h = h.index_select(1, top_k_pointer)\n\n # Update sequence scores at beam\n beam.update(score.clone(), top_k_pointer, x) # , h)\n\n # Erase scores for EOS so that they are not expanded\n # [batch_size, beam_size]\n eos_idx = x.data.eq(EOS_ID).view(batch_size, self.beam_size)\n if eos_idx.nonzero().dim() > 0:\n score.data.masked_fill_(eos_idx, -float('inf'))\n\n # prediction ([batch, k, max_unroll])\n # A list of Tensors containing predicted sequence\n # final_score [batch, k]\n # A list containing the final scores for all top-k sequences\n # length [batch, k]\n # A list specifying the length of each sequence in the top-k candidates\n # prediction, final_score, length = beam.backtrack()\n prediction, final_score, length = beam.backtrack()\n\n return prediction, final_score, length\n\n'''\nclass DecoderRNN(BaseRNNDecoder):\n def __init__(self, vocab_size, embedding_size,\n hidden_size, rnncell=StackedGRUCell, num_layers=1,\n dropout=0.0, word_drop=0.0,\n max_unroll=30, sample=True, temperature=1.0, beam_size=1):\n super(DecoderRNN, self).__init__()\n\n self.vocab_size = vocab_size\n self.embedding_size = embedding_size\n self.hidden_size = hidden_size\n self.num_layers = num_layers\n self.dropout = dropout\n self.temperature = temperature\n self.word_drop = word_drop\n self.max_unroll = max_unroll\n self.sample = sample\n self.beam_size = beam_size\n\n self.embedding = nn.Embedding(vocab_size, embedding_size)\n\n self.rnncell = rnncell(num_layers,\n embedding_size,\n hidden_size,\n dropout)\n self.out = nn.Linear(hidden_size, vocab_size)\n self.softmax = nn.Softmax(dim=1)\n\n def forward_step(self, x, h,\n encoder_outputs=None,\n input_valid_length=None):\n \"\"\"\n Single RNN Step\n 1. Input Embedding (vocab_size => hidden_size)\n 2. RNN Step (hidden_size => hidden_size)\n 3. Output Projection (hidden_size => vocab size)\n\n Args:\n x: [batch_size]\n h: [num_layers, batch_size, hidden_size] (h and c from all layers)\n\n Return:\n out: [batch_size,vocab_size] (Unnormalized word distribution)\n h: [num_layers, batch_size, hidden_size] (h and c from all layers)\n \"\"\"\n # x: [batch_size] => [batch_size, hidden_size]\n x = self.embed(x)\n # last_h: [batch_size, hidden_size] (h from Top RNN layer)\n # h: [num_layers, batch_size, hidden_size] (h and c from all layers)\n last_h, h = self.rnncell(x, h)\n\n if self.use_lstm:\n # last_h_c: [2, batch_size, hidden_size] (h from Top RNN layer)\n # h_c: [2, num_layers, batch_size, hidden_size] (h and c from all layers)\n last_h = last_h[0]\n\n # Unormalized word distribution\n # out: [batch_size, vocab_size]\n out = self.out(last_h)\n return out, h\n\n def forward(self, inputs, init_h=None, encoder_outputs=None, input_valid_length=None,\n decode=False, turn = None):\n \"\"\"\n Train (decode=False)\n Args:\n inputs (Variable, LongTensor): [batch_size, seq_len]\n init_h: (Variable, FloatTensor): [num_layers, batch_size, hidden_size]\n Return:\n out : [batch_size, seq_len, vocab_size]\n Test (decode=True)\n Args:\n inputs: None\n init_h: (Variable, FloatTensor): [num_layers, batch_size, hidden_size]\n Return:\n out : [batch_size, seq_len]\n \"\"\"\n batch_size = self.batch_size(inputs, init_h)\n\n # x: [batch_size]\n x = self.init_token(batch_size, SOS_ID)\n\n # h: [num_layers, batch_size, hidden_size]\n h = self.init_h(batch_size, hidden=init_h)\n\n\n if not decode:\n out_list = []\n seq_len = inputs.size(2)\n for i in range(seq_len):\n\n # x: [batch_size]\n # =>\n # out: [batch_size, vocab_size]\n # h: [num_layers, batch_size, hidden_size] (h and c from all layers)\n out, h = self.forward_step(x, h)\n\n out_list.append(out)\n x = inputs[:, turn, i]\n\n # [batch_size, max_target_len, vocab_size]\n return torch.stack(out_list, dim=1)\n\n elif decode == 'F1':\n x_list = []\n for i in range(self.max_unroll):\n # x: [batch_size]\n # =>\n # out: [batch_size, vocab_size]\n # h: [num_layers, batch_size, hidden_size] (h and c from all layers)\n out, h = self.decode_in_beam(x, h)\n log_prob, indexes = torch.topk(out, 1)\n decoded_t = torch.transpose(indexes, 0, 1)[0]\n # out: [batch_size, vocab_size]\n # => x: [batch_size]\n x_list.append(decoded_t)\n x = inputs[:, turn, i]\n return torch.stack(x_list, dim=1)\n\n elif decode == 'beam':\n x_list = []\n for i in range(self.max_unroll):\n # x: [batch_size]\n # =>\n # out: [batch_size, vocab_size]\n # h: [num_layers, batch_size, hidden_size] (h and c from all layers)\n out, h = self.decode_in_beam(x, h)\n log_prob, indexes = torch.topk(out, 1)\n decoded_t = torch.transpose(indexes, 0, 1)[0]\n # out: [batch_size, vocab_size]\n # => x: [batch_size]\n x_list.append(decoded_t)\n x = inputs[:, turn, i]\n return torch.stack(x_list, dim=1)\n\n else:\n x_list = []\n for i in range(self.max_unroll):\n\n # x: [batch_size]\n # =>\n # out: [batch_size, vocab_size]\n # h: [num_layers, batch_size, hidden_size] (h and c from all layers)\n out, h = self.forward_step(x, h)\n\n # out: [batch_size, vocab_size]\n # => x: [batch_size]\n x = self.decode(out)\n x_list.append(x)\n\n # [batch_size, max_target_len]\n return torch.stack(x_list, dim=1)\n\n def decode_in_beam(self, x, h, encoder_outputs=None):\n\n\n # x: [batch_size]\n # =>\n # out: [batch_size, vocab_size]\n # h: [num_layers, batch_size, hidden_size] (h and c from all layers)\n out, h = self.forward_step(x, h)\n out = F.log_softmax(out, dim=1)\n\n return out, h\n\n def beam_decode(self, inputs, decoder_hiddens=None, turn=None, encoder_outputs=None):\n '''\n :param target_tensor: target indexes tensor of shape [B, T] where B is the batch size and T is the maximum length of the output sentence\n :param decoder_hidden: input tensor of shape [1, B, H] for start of the decoding\n :param encoder_outputs: if you are using attention mechanism you can pass encoder outputs, [T, B, H] where T is the maximum length of input sentence\n :return: decoded_batch\n '''\n\n beam_width = self.beam_size\n topk = self.beam_size # how many sentence do you want to generate\n decoded_batch = []\n\n # decoding goes sentence by sentence\n seq_len = inputs.size(0)\n for idx in range(seq_len):\n if isinstance(decoder_hiddens, tuple): # LSTM case\n decoder_hidden = (\n decoder_hiddens[0][:, idx, :].unsqueeze(0), decoder_hiddens[1][:, idx, :].unsqueeze(0))\n else:\n decoder_hidden = decoder_hiddens[:, idx, :].unsqueeze(0)\n '''\n encoder_output = encoder_outputs[:, idx, :].unsqueeze(1)\n '''\n\n # Start with the start of the sentence token\n decoder_input = to_var(torch.LongTensor([[SOS_ID]]))\n\n # Number of sentence to generate\n endnodes = []\n number_required = topk\n\n # starting node - hidden vector, previous node, word id, logp, length\n node = BeamSearchNode(decoder_hidden, None, decoder_input, 0, 1)\n nodes = PriorityQueue()\n\n # start the queue\n nodes.put((-node.eval(), node))\n qsize = 1\n # start beam search\n while True:\n # give up when decoding takes too long\n if qsize > 999999: break\n nextnodes = []\n # fetch the best node\n for _ in range(nodes.qsize()):\n score, n = nodes.get()\n decoder_input = to_var(n.wordid[0])\n\n decoder_hidden = n.h\n\n if (n.wordid.item() == EOS_ID and n.prevNode != None) or n.leng >= self.max_unroll:\n endnodes.append((score, n))\n # if we reached maximum # of sentences required\n if len(endnodes) >= number_required:\n break\n\n # decode for one step using decoder\n decoder_output, decoder_hidden = self.decode_in_beam(decoder_input, decoder_hidden) #, encoder_output)\n\n # PUT HERE REAL BEAM SEARCH OF TOP\n log_prob, indexes = torch.topk(decoder_output, beam_width)\n #nextnodes = []\n for new_k in range(beam_width):\n decoded_t = indexes[0][new_k].view(1, -1)\n log_p = log_prob[0][new_k].item()\n\n node = BeamSearchNode(decoder_hidden, n, decoded_t, n.logp + log_p, n.leng + 1)\n score = -node.eval()\n nextnodes.append((score, node))\n\n if len(endnodes) >= number_required:\n break\n nextnodes = sorted(nextnodes, key=operator.itemgetter(0), reverse=True)\n length = min(len(nextnodes), beam_width)\n for i in range(length):\n score, nn = nextnodes[i]\n nodes.put((score, nn))\n # increase qsize\n\n qsize += len(nextnodes) - 1\n\n # choose nbest paths, back trace them\n '''\n if len(endnodes) <= beam_width:\n for _ in range(beam_width - len(endnodes)):\n score, n = nodes.get()\n endnodes.append((score, node))\n '''\n\n utterances = []\n for score, n in sorted(endnodes, key=operator.itemgetter(0)):\n utterance = []\n utterance.append(n.wordid.cpu().numpy()[0][0])\n # back trace\n while n.prevNode != None:\n n = n.prevNode\n utterance.append(n.wordid.cpu().numpy()[0][0])\n\n utterance = utterance[::-1]\n utterances.append(utterance)\n\n final_utterance = utterances[0]\n if len(utterances[0]) < self.max_unroll:\n final_utterance += [3 for _ in range(self.max_unroll-len(utterances[0]))]\n decoded_batch.append(final_utterance)\n\n return decoded_batch\n\n\n\n\n\n\n", "sub_path": "model/layers/decoder.py", "file_name": "decoder.py", "file_ext": "py", "file_size_in_byte": 19140, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "torch.nn.Module", "line_number": 38, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "name"}, {"api_name": "rnncells.StackedLSTMCell", "line_number": 45, "usage_type": "argument"}, {"api_name": "utils.SOS_ID", "line_number": 47, "usage_type": "name"}, {"api_name": "utils.to_var", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 49, "usage_type": "call"}, {"api_name": "utils.SOS_ID", "line_number": 49, "usage_type": "name"}, {"api_name": "utils.to_var", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 59, "usage_type": "call"}, {"api_name": "utils.to_var", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 62, "usage_type": "call"}, {"api_name": "utils.to_var", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.multinomial", "line_number": 99, "usage_type": "call"}, {"api_name": "random.random", "line_number": 119, "usage_type": "call"}, {"api_name": "utils.to_var", "line_number": 120, "usage_type": "call"}, {"api_name": "utils.UNK_ID", "line_number": 120, "usage_type": "name"}, {"api_name": "rnncells.StackedGRUCell", "line_number": 242, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 258, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 258, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 264, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 264, "usage_type": "name"}, {"api_name": "torch.nn.Softmax", "line_number": 265, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 265, "usage_type": "name"}, {"api_name": "utils.SOS_ID", "line_number": 319, "usage_type": "argument"}, {"api_name": "torch.stack", "line_number": 340, "usage_type": "call"}, {"api_name": "torch.topk", "line_number": 350, "usage_type": "call"}, {"api_name": "torch.transpose", "line_number": 351, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 356, "usage_type": "call"}, {"api_name": "torch.topk", "line_number": 366, "usage_type": "call"}, {"api_name": "torch.transpose", "line_number": 367, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 372, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 390, "usage_type": "call"}, {"api_name": "torch.nn.functional.log_softmax", "line_number": 400, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 400, "usage_type": "name"}, {"api_name": "utils.to_var", "line_number": 429, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 429, "usage_type": "call"}, {"api_name": "utils.SOS_ID", "line_number": 429, "usage_type": "name"}, {"api_name": "queue.PriorityQueue", "line_number": 437, "usage_type": "call"}, {"api_name": "utils.to_var", "line_number": 450, "usage_type": "call"}, {"api_name": "utils.EOS_ID", "line_number": 454, "usage_type": "name"}, {"api_name": "torch.topk", "line_number": 464, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 476, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 479, "usage_type": "name"}, {"api_name": "torch.nn", "line_number": 480, "usage_type": "name"}, {"api_name": "operator.itemgetter", "line_number": 494, "usage_type": "call"}]} +{"seq_id": "265220110", "text": "#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\"\"\"Small parser utils for smvk.\"\"\"\nimport re\nimport os\nimport pywikibot\n\nimport batchupload.common as common\nimport batchupload.helpers as helpers\n\ncleaner_pattern = None # to avoid repeated loads\n\n\ndef load_cleaner_patterns(filename=None):\n \"\"\"Load the cleaner patterns file if needed.\"\"\"\n if not filename:\n _filename = 'cleaner_patterns.json'\n filename = os.path.join(\n os.path.dirname(os.path.abspath(__file__)), _filename)\n\n global cleaner_pattern\n if not cleaner_pattern:\n cleaner_pattern = common.open_and_read_file(filename, as_json=True)\n return cleaner_pattern\n\n\ndef parse_external_id(ext_id):\n \"\"\"Match an external id to a Commons formating template.\"\"\"\n if ext_id.startswith('gnm/'):\n return gnm_parser(ext_id)\n elif ext_id.startswith('SMVK'): # same image in use in a sister collection\n return smvk_parser(ext_id)\n\n # if not caught by any of the above\n pywikibot.warning('{} is not a recognized external id'.format(ext_id))\n\n\ndef smvk_parser(ext_id, label_delimiter='!'):\n \"\"\"Parser for SMVK identifiers.\"\"\"\n # Not as sensitive as build_link_template nor is it validated\n museum, type, id = ext_id.split('/', 2)\n label = None\n if label_delimiter in id: # lable is added to the id during a merge\n id, _, label = id.partition(label_delimiter)\n prefix = ''\n if museum != 'SMVK-MM': # MM has prefix as part of id\n prefix = '|{}'.format(type)\n\n if label:\n return '{{%s-link%s|%s|%s}}' % (museum, prefix, id, label)\n return '{{%s-link%s|%s}}' % (museum, prefix, id)\n\n\ndef gnm_parser(ext_id):\n \"\"\"Parser for Gothenburgh Natural Museum identifiers.\"\"\"\n if not ext_id.startswith('gnm/photo/GNM'):\n pywikibot.warning(\n 'The GNM parser needs to be extended to handle {}'.format(ext_id))\n return '{{GNM-link|%s}}' % ext_id[len('gnm/photo/GNM'):]\n\n\ndef clean_uncertain(value, keep=False):\n \"\"\"\n Handle uncertain values in the data.\n\n Process any value containing a '[?]' string.\n\n :param value: the value or list of values to process\n :param keep: whether to keep the clean value or discard it\n \"\"\"\n was_list = isinstance(value, list)\n values = common.listify(value)\n new_list = []\n for val in values:\n if '[?]' in val:\n if keep:\n new_list.append(\n val.replace('[?]', '').replace(' ', ' ').strip())\n else:\n new_list.append(val)\n\n # return in same format as original\n if not was_list:\n if not new_list:\n return ''\n return new_list[0]\n return new_list\n\n\ndef get_last_year(date_text):\n \"\"\"Attempt to extract the last year in a wikitext date template.\"\"\"\n hits = re.findall('\\d\\d\\d\\d', date_text)\n if hits:\n return int(hits[-1])\n\n\ndef format_description_row(label, value, delimiter=','):\n \"\"\"Format a single description line.\"\"\"\n delimiter = '{} '.format(delimiter)\n return '
\\n{}: {}'.format(\n helpers.italicize(label),\n delimiter.join(common.listify(value)))\n\n\ndef replace_repeat_character(text, char_1, target, delimiter, char_2=None):\n \"\"\"\n Replace two characters by a single one.\n\n Replaces them even if separated by space or delimiter. Also merges any\n adjacent delimiters.\n\n If char_2 is not provided then it is assumed that char_1 is repeated\n \"\"\"\n char_2 = char_2 or char_1\n patterns = (\n char_1 + char_2,\n char_1 + delimiter + char_2,\n char_1 + ' ' + char_2)\n\n text = text.replace(delimiter * 2, delimiter)\n while any(text.find(pattern) > 0 for pattern in patterns):\n for pattern in patterns:\n text = text.replace(pattern, target + delimiter)\n text = text.replace(delimiter + ' ', delimiter)\n text = text.replace(delimiter * 2, delimiter)\n return text\n\n\ndef description_cleaner(text, structured=False):\n \"\"\"\n Attempt a cleanup of SMVK descriptions.\n\n The descriptions contain a lot of info which is more of internal notes\n character. This method contains an ugly list of such strings and attempts\n to get rid of them.\n\n Outsourced to the utils file because it is ugly.\n\n :param structured: if internal structure should be kept to facilitate\n diffs.\n \"\"\"\n delimiter = '¤'\n cleaner_patterns = load_cleaner_patterns()\n\n # anything found after one of these should be removed\n for test in cleaner_patterns.get('endings'):\n if text.find(test) >= 0:\n text = text[:text.find(test)]\n # anything found before one of these should be removed\n for test in cleaner_patterns.get('starts'):\n if text.find(test) >= 0:\n text = text[text.find(test) + len(test):]\n\n # remove these blocks from inside kept text\n for test in cleaner_patterns.get('middle'):\n while text.find(test) >= 0:\n start = text.find(test)\n end = start + len(test)\n text = text[:start].rstrip() + delimiter + text[end:].lstrip()\n\n # clean out any [...], there may be many\n while text.find('[') >= 0:\n start = text.find('[')\n end = text.find(']', start)\n if end < 0:\n break\n text = text[:start].rstrip() + delimiter + text[end + 1:].lstrip()\n\n # remove repeats, even if interspersed with delimiters\n repeats = (' ', ',', '.')\n for char in repeats:\n text = replace_repeat_character(text, char, char, delimiter)\n # special case .,\n text = replace_repeat_character(text, '.', '.', delimiter, char_2=',')\n\n # merge any remaining removed blocks\n while text.find(delimiter * 2) > 0:\n text = text.replace(delimiter * 2, delimiter)\n # ignore any removed block in the end\n text = text.strip(delimiter)\n\n if structured:\n return text.split(delimiter)\n else:\n no_space_before = (',', '.', ':', ';')\n for char in no_space_before:\n text = text.replace(delimiter + char, char)\n return text.replace(delimiter, ' ')\n\n\ndef clean_all_descriptions(filename):\n \"\"\"\n Clean all descriptions in a file.\n\n Load a file with one description per row, clean each and output a visible\n diff for on-wiki consumption.\n \"\"\"\n import os.path as path\n base, ext = path.splitext(filename)\n f_in = open(filename)\n f_out = open('{}_clean{}'.format(base, ext), 'w')\n\n intro = (\n 'Preview of description cleanup for SMVK.\\n'\n '{} text is discarded, {} text is '\n 'kept, {} indicates a description '\n 'which was completely discarded.\\n\\n----\\n\\n'.format(\n helpers.bolden('Black'),\n helpers.bolden('blue'),\n helpers.bolden('red')))\n f_out.write(intro)\n\n for l in f_in:\n if not l.strip():\n f_out.write('* {}'.format(l))\n continue\n cleaned = description_cleaner(l, structured=True)\n if not any(block.strip() for block in cleaned):\n f_out.write('* {}\\n'.format(\n l.rstrip()))\n else:\n end = 0\n clean_l = l\n for block in cleaned:\n block = block.strip()\n if not block:\n continue\n start = clean_l.find(block, end)\n end = start + len(block)\n clean_l = '{}{}{}'.format(\n clean_l[:start], block, clean_l[end:])\n end += len('')\n f_out.write('* {}'.format(clean_l))\n f_in.close()\n f_out.close()\n", "sub_path": "smvk/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 7727, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"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.abspath", "line_number": 19, "usage_type": "call"}, {"api_name": "batchupload.common.open_and_read_file", "line_number": 23, "usage_type": "call"}, {"api_name": "batchupload.common", "line_number": 23, "usage_type": "name"}, {"api_name": "pywikibot.warning", "line_number": 35, "usage_type": "call"}, {"api_name": "pywikibot.warning", "line_number": 57, "usage_type": "call"}, {"api_name": "batchupload.common.listify", "line_number": 72, "usage_type": "call"}, {"api_name": "batchupload.common", "line_number": 72, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 92, "usage_type": "call"}, {"api_name": "batchupload.helpers.italicize", "line_number": 101, "usage_type": "call"}, {"api_name": "batchupload.helpers", "line_number": 101, "usage_type": "name"}, {"api_name": "batchupload.common.listify", "line_number": 102, "usage_type": "call"}, {"api_name": "batchupload.common", "line_number": 102, "usage_type": "name"}, {"api_name": "os.path.splitext", "line_number": 199, "usage_type": "call"}, {"api_name": "os.path", "line_number": 199, "usage_type": "name"}, {"api_name": "batchupload.helpers.bolden", "line_number": 208, "usage_type": "call"}, {"api_name": "batchupload.helpers", "line_number": 208, "usage_type": "name"}, {"api_name": "batchupload.helpers.bolden", "line_number": 209, "usage_type": "call"}, {"api_name": "batchupload.helpers", "line_number": 209, "usage_type": "name"}, {"api_name": "batchupload.helpers.bolden", "line_number": 210, "usage_type": "call"}, {"api_name": "batchupload.helpers", "line_number": 210, "usage_type": "name"}]} +{"seq_id": "11950515", "text": "# -*- coding: utf-8 -*-\nimport asyncio\nimport requests\nfrom difflib import SequenceMatcher\nimport distutils.util\nimport json\nimport os\nimport platform\n\nfrom src.trackers.COMMON import COMMON\nfrom src.console import console \n\nclass STC():\n \"\"\"\n Edit for Tracker:\n Edit BASE.torrent with announce and source\n Check for duplicates\n Set type/category IDs\n Upload\n \"\"\"\n def __init__(self, config):\n self.config = config\n self.tracker = 'STC'\n self.source_flag = 'STC'\n self.upload_url = 'https://skipthecommericals.xyz/api/torrents/upload'\n self.search_url = 'https://skipthecommericals.xyz/api/torrents/filter'\n self.signature = '\\n[center][url=https://skipthecommericals.xyz/pages/1]Please Seed[/url][/center]'\n self.banned_groups = [\"\"]\n pass\n \n async def upload(self, meta):\n common = COMMON(config=self.config)\n await common.edit_torrent(meta, self.tracker, self.source_flag)\n await common.unit3d_edit_desc(meta, self.tracker, self.signature)\n cat_id = await self.get_cat_id(meta['category'])\n type_id = await self.get_type_id(meta['type'], meta.get('tv_pack', 0), meta.get('sd', 0), meta.get('category', \"\"))\n resolution_id = await self.get_res_id(meta['resolution'])\n stc_name = await self.edit_name(meta)\n if meta['anon'] == 0 and bool(distutils.util.strtobool(str(self.config['TRACKERS'][self.tracker].get('anon', \"False\")))) == False:\n anon = 0\n else:\n anon = 1\n if meta['bdinfo'] != None:\n mi_dump = None\n bd_dump = open(f\"{meta['base_dir']}/tmp/{meta['uuid']}/BD_SUMMARY_00.txt\", 'r', encoding='utf-8').read()\n else:\n mi_dump = open(f\"{meta['base_dir']}/tmp/{meta['uuid']}/MEDIAINFO.txt\", 'r', encoding='utf-8').read()\n bd_dump = None\n desc = open(f\"{meta['base_dir']}/tmp/{meta['uuid']}/[{self.tracker}]DESCRIPTION.txt\", 'r').read()\n open_torrent = open(f\"{meta['base_dir']}/tmp/{meta['uuid']}/[{self.tracker}]{meta['clean_name']}.torrent\", 'rb')\n files = {'torrent': open_torrent}\n data = {\n 'name' : stc_name,\n 'description' : desc,\n 'mediainfo' : mi_dump,\n 'bdinfo' : bd_dump, \n 'category_id' : cat_id,\n 'type_id' : type_id,\n 'resolution_id' : resolution_id,\n 'tmdb' : meta['tmdb'],\n 'imdb' : meta['imdb_id'].replace('tt', ''),\n 'tvdb' : meta['tvdb_id'],\n 'mal' : meta['mal_id'],\n 'igdb' : 0,\n 'anonymous' : anon,\n 'stream' : meta['stream'],\n 'sd' : meta['sd'],\n 'keywords' : meta['keywords'],\n 'personal_release' : int(meta.get('personalrelease', False)),\n 'internal' : 0,\n 'featured' : 0,\n 'free' : 0,\n 'doubleup' : 0,\n 'sticky' : 0,\n }\n # Internal\n if self.config['TRACKERS'][self.tracker].get('internal', False) == True:\n if meta['tag'] != \"\" and (meta['tag'][1:] in self.config['TRACKERS'][self.tracker].get('internal_groups', [])):\n data['internal'] = 1\n \n if meta.get('category') == \"TV\":\n data['season_number'] = meta.get('season_int', '0')\n data['episode_number'] = meta.get('episode_int', '0')\n headers = {\n 'User-Agent': f'Upload Assistant/2.1 ({platform.system()} {platform.release()})'\n }\n params = {\n 'api_token': self.config['TRACKERS'][self.tracker]['api_key'].strip()\n }\n \n if meta['debug'] == False:\n response = requests.post(url=self.upload_url, files=files, data=data, headers=headers, params=params)\n try:\n \n console.print(response.json())\n except:\n console.print(\"It may have uploaded, go check\")\n open_torrent.close()\n return \n else:\n console.print(f\"[cyan]Request Data:\")\n console.print(data)\n open_torrent.close()\n\n\n\n async def edit_name(self, meta):\n stc_name = meta.get('name')\n return stc_name\n\n async def get_cat_id(self, category_name):\n category_id = {\n 'MOVIE': '1', \n 'TV': '2', \n }.get(category_name, '0')\n return category_id\n\n async def get_type_id(self, type, tv_pack, sd, category):\n type_id = {\n 'DISC': '1', \n 'REMUX': '2',\n 'WEBDL': '4', \n 'WEBRIP': '5', \n 'HDTV': '6',\n 'ENCODE': '3'\n }.get(type, '0')\n if tv_pack == 1:\n if sd == 1:\n # Season SD\n type_id = '14'\n if type == \"ENCODE\":\n type_id = '18'\n if sd == 0:\n # Season HD\n type_id = '13'\n if type == \"ENCODE\":\n type_id = '18'\n if type == \"DISC\" and category == \"TV\":\n if sd == 1:\n # SD-RETAIL\n type_id = '17'\n if sd == 0:\n # HD-RETAIL\n type_id = '18'\n return type_id\n\n async def get_res_id(self, resolution):\n resolution_id = {\n '8640p':'10', \n '4320p': '1', \n '2160p': '2', \n '1440p' : '3',\n '1080p': '3',\n '1080i':'4', \n '720p': '5', \n '576p': '6', \n '576i': '7',\n '480p': '8', \n '480i': '9'\n }.get(resolution, '10')\n return resolution_id\n\n\n\n\n\n \n\n\n async def search_existing(self, meta):\n dupes = []\n console.print(\"[yellow]Searching for existing torrents on site...\")\n params = {\n 'api_token' : self.config['TRACKERS'][self.tracker]['api_key'].strip(),\n 'tmdbId' : meta['tmdb'],\n 'categories[]' : await self.get_cat_id(meta['category']),\n 'types[]' : await self.get_type_id(meta['type'], meta.get('tv_pack', 0), meta.get('sd', 0), meta.get('category', \"\")),\n 'resolutions[]' : await self.get_res_id(meta['resolution']),\n 'name' : \"\"\n }\n if meta['category'] == 'TV':\n params['name'] = f\"{meta.get('season', '')}{meta.get('episode', '')}\"\n if meta.get('edition', \"\") != \"\":\n params['name'] + meta['edition']\n try:\n response = requests.get(url=self.search_url, params=params)\n response = response.json()\n for each in response['data']:\n result = [each][0]['attributes']['name']\n dupes.append(result)\n except:\n console.print('[bold red]Unable to search for existing torrents on site. Either the site is down or your API key is incorrect')\n await asyncio.sleep(5)\n\n return dupes", "sub_path": "src/trackers/STC.py", "file_name": "STC.py", "file_ext": "py", "file_size_in_byte": 6985, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "src.trackers.COMMON.COMMON", "line_number": 32, "usage_type": "call"}, {"api_name": "distutils.util.util.strtobool", "line_number": 39, "usage_type": "call"}, {"api_name": "distutils.util.util", "line_number": 39, "usage_type": "attribute"}, {"api_name": "distutils.util", "line_number": 39, "usage_type": "name"}, {"api_name": "platform.system", "line_number": 85, "usage_type": "call"}, {"api_name": "platform.release", "line_number": 85, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 92, "usage_type": "call"}, {"api_name": "src.console.console.print", "line_number": 95, "usage_type": "call"}, {"api_name": "src.console.console", "line_number": 95, "usage_type": "name"}, {"api_name": "src.console.console.print", "line_number": 97, "usage_type": "call"}, {"api_name": "src.console.console", "line_number": 97, "usage_type": "name"}, {"api_name": "src.console.console.print", "line_number": 101, "usage_type": "call"}, {"api_name": "src.console.console", "line_number": 101, "usage_type": "name"}, {"api_name": "src.console.console.print", "line_number": 102, "usage_type": "call"}, {"api_name": "src.console.console", "line_number": 102, "usage_type": "name"}, {"api_name": "src.console.console.print", "line_number": 172, "usage_type": "call"}, {"api_name": "src.console.console", "line_number": 172, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 186, "usage_type": "call"}, {"api_name": "src.console.console.print", "line_number": 192, "usage_type": "call"}, {"api_name": "src.console.console", "line_number": 192, "usage_type": "name"}, {"api_name": "asyncio.sleep", "line_number": 193, "usage_type": "call"}]} +{"seq_id": "653531054", "text": "from flask import Blueprint, render_template, jsonify, request, abort\nfrom sqlalchemy.orm.exc import NoResultFound\n\nfrom config import (\n Session,\n)\n\n\nfrom models.models import Book\n\nquery = Blueprint('query', __name__)\n\n#Development tool to remove models from the database\n@query.route('/query', methods=['POST'])\ndef query_book():\n j = request.get_json()\n if 'title' not in j or 'author' not in j:\n abort(400)\n title = j['title']\n auth = j['author']\n session = Session()\n b = session.query(Book).filter(Book.title == title and Book.author == auth).all()\n if len(b) > 0:\n return jsonify({'exists': True})\n return jsonify({'exists': False})\n\n@query.route('/add', methods=['POST'])\ndef add_book():\n j = request.get_json()\n if 'title' not in j or 'author' not in j or 'owner' not in j:\n abort(400)\n title = j['title']\n author = j['author']\n owner = j['owner']\n session = Session()\n b = Book(title=title, author=author, owner_id=owner)\n session.add(b)\n session.commit()\n return jsonify({'success': True})\n", "sub_path": "src/api/query.py", "file_name": "query.py", "file_ext": "py", "file_size_in_byte": 1080, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Blueprint", "line_number": 11, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 18, "usage_type": "call"}, {"api_name": "config.Session", "line_number": 21, "usage_type": "call"}, {"api_name": "models.models.Book", "line_number": 22, "usage_type": "argument"}, {"api_name": "models.models.Book.title", "line_number": 22, "usage_type": "attribute"}, {"api_name": "models.models.Book.author", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.jsonify", "line_number": 24, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 31, "usage_type": "call"}, {"api_name": "config.Session", "line_number": 35, "usage_type": "call"}, {"api_name": "models.models.Book", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "358039690", "text": "import json\nfrom django.apps import apps\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.gis.geos import *\nfrom django.http import HttpResponseRedirect, HttpResponse, \\\n HttpResponseBadRequest, Http404\nfrom django.shortcuts import render, redirect\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.utils.translation import get_language\nfrom django.views.generic.list import ListView\nfrom django.views.generic.detail import DetailView\nfrom mezzanine.conf import settings\nfrom mezzanine.core.models import Displayable\nfrom mezzanine.pages.views import page\nfrom mezzanine.utils.cache import add_cache_bypass\nfrom mezzanine.utils.urls import home_slug\nfrom mezzanine.utils.views import paginate, render, set_cookie, is_spam\nfrom cities.models import Country, Region, City\nfrom directory.models import TagCategory, Tag, Main\nfrom directory.forms import SearchNarrowForm, SubmitMainForm, RatingForm\n\n\nclass MainListView(ListView):\n \"\"\"\n List View for Main objects (home page)\n \"\"\"\n model = Main\n template_name = \"pages/main_list.html\"\n paginate_by = 2\n\n def get_context_data(self, **kwargs):\n context = super(MainListView, self).get_context_data(**kwargs)\n if self.request.session.get('selected_city'):\n selected_city = self.request.session.get('selected_city')\n context['selected_city'] = City.objects \\\n .filter(name=selected_city)[:1].get()\n context['search_narrow_form'] = SearchNarrowForm()\n return context\n\nmain_list_view = MainListView.as_view()\n\n\nclass TagCategoryListView(ListView):\n \"\"\"\n List View for Tags\n \"\"\"\n model = TagCategory\n template_name = \"pages/tag_list.html\"\n paginate_by = 10\n\n def get_context_data(self, **kwargs):\n context = super(TagCategoryListView, self).get_context_data(**kwargs)\n if self.request.session.get('selected_city'):\n selected_city = self.request.session.get('selected_city')\n context['selected_city'] = City.objects \\\n .filter(name=selected_city)[:1].get()\n context['search_narrow_form'] = SearchNarrowForm()\n categories_qs = TagCategory.objects.order_by('title')\n # put categories, tags and counters in context\n categories = []\n for category in categories_qs:\n tags_dict = {}\n tags_objects = Tag.objects.filter(category=category).order_by(\n 'title')\n tags_dict_count = {}\n for tag in tags_objects:\n tags_count = Main.objects.filter(tag=tag).count()\n tags_dict_count = {tag: tags_count}\n tags_dict = {\n 'category': category, 'tags': tags_dict_count\n }\n categories.append(tags_dict)\n context['categories'] = categories\n return context\n\ntag_category_list_view = TagCategoryListView.as_view()\n\n\nclass TagDetailView(DetailView):\n \"\"\"\n Detail View for Tags\n \"\"\"\n model = Tag\n template_name = \"pages/tag.html\"\n\n def get_context_data(self, **kwargs):\n context = super(TagDetailView, self).get_context_data(**kwargs)\n if self.request.session.get('selected_city'):\n selected_city = self.request.session.get('selected_city')\n context['selected_city'] = City.objects \\\n .filter(name=selected_city)[:1].get()\n context['main_list'] = Main.objects.filter(\n address__city__name=selected_city, tag=self.object)\n else:\n context['main_list'] = Main.objects.filter(tag=self.object)\n context['search_narrow_form'] = SearchNarrowForm()\n return context\n\ntag_detail_view = TagDetailView.as_view()\n\n\n@login_required\ndef submit_main(request):\n \"\"\"\n View for submit new Main objects\n \"\"\"\n selected_city = None\n # check if selected city in session\n if request.session.get('selected_city'):\n selected_city = request.session.get('selected_city')\n selected_city = City.objects \\\n .filter(name=selected_city)[:1].get()\n search_narrow_form = SearchNarrowForm()\n form = SubmitMainForm(\n request.POST or None, request.FILES or None)\n if request.method == 'POST':\n if form.is_valid():\n title = form.cleaned_data['title']\n description = form.cleaned_data['description']\n address = form.cleaned_data['address']\n image = form.cleaned_data['image']\n new_main = Main(title=title, description=description,\n gen_description=False,\n address=address, image=image)\n new_main.save()\n return HttpResponseRedirect('/')\n return render(request, 'includes/submit_main.html',\\\n {'form': form, 'selected_city': selected_city,\\\n 'search_narrow_form': search_narrow_form})\n\n\ndef json_countries(request):\n \"\"\"\n Json view for Countries search\n \"\"\"\n q = request.GET.get('q')\n current_lang = get_language()[0:2]\n results = Country.objects.filter(alt_names__language=current_lang)\\\n .order_by('name')\n if q:\n results = results.filter(alt_names__name__icontains=q)\n flat_results = list(results.values_list('id', 'alt_names__name'))\n results = [{'id': item[0], 'text': item[1]} for item in flat_results]\n return HttpResponse(\n json.dumps(results, ensure_ascii=False),\n content_type='application/json; charset=utf-8')\n\n\ndef json_regions(request):\n \"\"\"\n Json view for Regions search\n \"\"\"\n q = request.GET.get('q')\n current_lang = get_language()[0:2]\n results = Region.objects.filter(alt_names__language=current_lang)\\\n .order_by('name')\n if q:\n results = results.filter(alt_names__name__icontains=q)\n flat_results = list(results.values_list('id', 'alt_names__name'))\n results = [{'id': item[0], 'text': item[1]} for item in flat_results]\n return HttpResponse(\n json.dumps(results, ensure_ascii=False),\n content_type='application/json; charset=utf-8')\n\n\ndef json_cities(request):\n \"\"\"\n Json view for Cities search\n \"\"\"\n q = request.GET.get('q')\n current_lang = get_language()[0:2]\n results = City.objects.filter(alt_names__language=current_lang)\\\n .order_by('name')\n if q:\n results = results.filter(alt_names__name__icontains=q)\n flat_results = list(results.values_list('id', 'alt_names__name'))\n results = [{'id': item[0], 'text': item[1]} for item in flat_results]\n return HttpResponse(\n json.dumps(results, ensure_ascii=False),\n content_type='application/json; charset=utf-8')\n\n\ndef search(request, template=\"search_results.html\", extra_context=None):\n \"\"\"\n Search view. Overrides Mezzanine's search, adding filters by Country,\n Region, City and writing the City to session if selected.\n \"\"\"\n query = request.GET.get(\"q\", \"\")\n page = request.GET.get(\"page\", 1)\n per_page = settings.SEARCH_PER_PAGE\n max_paging_links = settings.MAX_PAGING_LINKS\n country = request.GET.get(\"country\", \"\") or None\n region = request.GET.get(\"region\", \"\") or None\n city = request.GET.get(\"city\", \"\") or None\n if city:\n selected_city = City.objects.filter(id=city)[:1].get()\n request.session['selected_city'] = selected_city.name\n try:\n parts = request.GET.get(\"type\", \"\").split(\".\", 1)\n search_model = apps.get_model(*parts)\n search_model.objects.search # Attribute check\n except (ValueError, TypeError, LookupError, AttributeError):\n search_model = Displayable\n search_type = _(\"Everything\")\n else:\n search_type = search_model._meta.verbose_name_plural.capitalize()\n # default search results\n results = search_model.objects.search(query, for_user=request.user)\n # new filters, creating new list of results\n results_filtered = []\n if city or region or country:\n for i in results:\n # special filter of searching in Tags\n if isinstance(i, Tag):\n if (city and Main.objects.filter(address__city=city, tag__title=i.title).count() > 0) or\\\n (region and Main.objects.filter(address__city__region=region, tag__title=i.title).count() > 0) or\\\n (country and Main.objects.filter(address__city__country=country, tag__title=i.title).count() > 0):\n results_filtered.append(i)\n continue\n if city and i.address.filter(city__id=city).count() > 0:\n results_filtered.append(i)\n if i not in results_filtered and region and \\\n i.address.filter(city__region__id=region).count() > 0:\n results_filtered.append(i)\n if i not in results_filtered and country and \\\n i.address.filter(city__country__id=country).count() > 0:\n results_filtered.append(i)\n else:\n results_filtered = results\n paginated = paginate(results_filtered, page, per_page, max_paging_links)\n # put new list iof result in context\n context = {\"query\": query, \"results\": paginated,\n \"search_type\": search_type}\n context.update(extra_context or {})\n # add selected city to context\n if request.session.get('selected_city'):\n selected_city = request.session.get('selected_city')\n context['selected_city'] = City.objects \\\n .filter(name=selected_city)[:1].get()\n # add search form to context\n context['search_narrow_form'] = SearchNarrowForm()\n return render(request, template, context)\n\n\ndef initial_validation(request, prefix):\n \"\"\"\n Returns the related model instance and post data to use in the\n comment/rating views below.\n Both comments and ratings have a ``prefix_ACCOUNT_REQUIRED``\n setting. If this is ``True`` and the user is unauthenticated, we\n store their post data in their session, and redirect to login with\n the view's url (also defined by the prefix arg) as the ``next``\n param. We can then check the session data once they log in,\n and complete the action authenticated.\n On successful post, we pass the related object and post data back,\n which may have come from the session, for each of the comments and\n ratings view functions to deal with as needed.\n \"\"\"\n post_data = request.POST\n login_required_setting_name = prefix.upper() + \"S_ACCOUNT_REQUIRED\"\n posted_session_key = \"unauthenticated_\" + prefix\n redirect_url = \"\"\n if getattr(settings, login_required_setting_name, False):\n if not request.user.is_authenticated():\n request.session[posted_session_key] = request.POST\n error(request, _(\"You must be logged in. Please log in or \"\n \"sign up to complete this action.\"))\n redirect_url = \"%s?next=%s\" % (settings.LOGIN_URL, reverse(prefix))\n elif posted_session_key in request.session:\n post_data = request.session.pop(posted_session_key)\n if not redirect_url:\n model_data = post_data.get(\"content_type\", \"\").split(\".\", 1)\n if len(model_data) != 2:\n return HttpResponseBadRequest()\n try:\n model = apps.get_model(*model_data)\n obj = model.objects.get(id=post_data.get(\"object_pk\", None))\n except (TypeError, ObjectDoesNotExist, LookupError):\n redirect_url = \"/\"\n if redirect_url:\n if request.is_ajax():\n return HttpResponse(dumps({\"location\": redirect_url}))\n else:\n return redirect(redirect_url)\n return obj, post_data\n\n\ndef rating(request):\n \"\"\"\n Overrides Mezzanine's rating view.\n \"\"\"\n response = initial_validation(request, \"rating\")\n if isinstance(response, HttpResponse):\n return response\n obj, post_data = response\n url = add_cache_bypass(obj.get_absolute_url().split(\"#\")[0])\n response = redirect(url + \"#rating-%s\" % obj.id)\n rating_form = RatingForm(request, obj, post_data)\n if rating_form.is_valid():\n rating_form.save()\n if request.is_ajax():\n # Reload the object and return the rating fields as json.\n obj = obj.__class__.objects.get(id=obj.id)\n rating_name = obj.get_ratingfield_name()\n json = {}\n for f in (\"average\", \"count\", \"sum\"):\n json[\"rating_\" + f] = getattr(obj, \"%s_%s\" % (rating_name, f))\n response = HttpResponse(dumps(json))\n if rating_form.undoing:\n ratings = set(rating_form.previous) ^ set([rating_form.current])\n else:\n ratings = rating_form.previous + [rating_form.current]\n set_cookie(response, \"mezzanine-rating\", \",\".join(ratings))\n return response\n\ndef directory_page(request, slug, template=u\"pages/page.html\", extra_context=None):\n \"\"\"\n Overrides Mezzanine's page view, adding search form in context.\n \"\"\"\n included_search_form = {}\n included_search_form['search_narrow_form'] = SearchNarrowForm()\n return page(request, slug, template=u\"pages/page.html\", extra_context=included_search_form)\n", "sub_path": "directory/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 13152, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.views.generic.list.ListView", "line_number": 23, "usage_type": "name"}, {"api_name": "directory.models.Main", "line_number": 27, "usage_type": "name"}, {"api_name": "cities.models.City.objects.filter", "line_number": 35, "usage_type": "call"}, {"api_name": "cities.models.City.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "cities.models.City", "line_number": 35, "usage_type": "name"}, {"api_name": "directory.forms.SearchNarrowForm", "line_number": 37, "usage_type": "call"}, {"api_name": "django.views.generic.list.ListView", "line_number": 43, "usage_type": "name"}, {"api_name": "directory.models.TagCategory", "line_number": 47, "usage_type": "name"}, {"api_name": "cities.models.City.objects.filter", "line_number": 55, "usage_type": "call"}, {"api_name": "cities.models.City.objects", "line_number": 55, "usage_type": "attribute"}, {"api_name": "cities.models.City", "line_number": 55, "usage_type": "name"}, {"api_name": "directory.forms.SearchNarrowForm", "line_number": 57, "usage_type": "call"}, {"api_name": "directory.models.TagCategory.objects.order_by", "line_number": 58, "usage_type": "call"}, {"api_name": "directory.models.TagCategory.objects", "line_number": 58, "usage_type": "attribute"}, {"api_name": "directory.models.TagCategory", "line_number": 58, "usage_type": "name"}, {"api_name": "directory.models.Tag.objects.filter", "line_number": 63, "usage_type": "call"}, {"api_name": "directory.models.Tag.objects", "line_number": 63, "usage_type": "attribute"}, {"api_name": "directory.models.Tag", "line_number": 63, "usage_type": "name"}, {"api_name": "directory.models.Main.objects.filter", "line_number": 67, "usage_type": "call"}, {"api_name": "directory.models.Main.objects", "line_number": 67, "usage_type": "attribute"}, {"api_name": "directory.models.Main", "line_number": 67, "usage_type": "name"}, {"api_name": "django.views.generic.detail.DetailView", "line_number": 79, "usage_type": "name"}, {"api_name": "directory.models.Tag", "line_number": 83, "usage_type": "name"}, {"api_name": "cities.models.City.objects.filter", "line_number": 90, "usage_type": "call"}, {"api_name": "cities.models.City.objects", "line_number": 90, "usage_type": "attribute"}, {"api_name": "cities.models.City", "line_number": 90, "usage_type": "name"}, {"api_name": "directory.models.Main.objects.filter", "line_number": 92, "usage_type": "call"}, {"api_name": "directory.models.Main.objects", "line_number": 92, "usage_type": "attribute"}, {"api_name": "directory.models.Main", "line_number": 92, "usage_type": "name"}, {"api_name": "directory.models.Main.objects.filter", "line_number": 95, "usage_type": "call"}, {"api_name": "directory.models.Main.objects", "line_number": 95, "usage_type": "attribute"}, {"api_name": "directory.models.Main", "line_number": 95, "usage_type": "name"}, {"api_name": "directory.forms.SearchNarrowForm", "line_number": 96, "usage_type": "call"}, {"api_name": "cities.models.City.objects.filter", "line_number": 111, "usage_type": "call"}, {"api_name": "cities.models.City.objects", "line_number": 111, "usage_type": "attribute"}, {"api_name": "cities.models.City", "line_number": 111, "usage_type": "name"}, {"api_name": "directory.forms.SearchNarrowForm", "line_number": 113, "usage_type": "call"}, {"api_name": "directory.forms.SubmitMainForm", "line_number": 114, "usage_type": "call"}, {"api_name": "directory.models.Main", "line_number": 122, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 126, "usage_type": "call"}, {"api_name": "mezzanine.utils.views.render", "line_number": 127, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 102, "usage_type": "name"}, {"api_name": "django.utils.translation.get_language", "line_number": 137, "usage_type": "call"}, {"api_name": "cities.models.Country.objects.filter", "line_number": 138, "usage_type": "call"}, {"api_name": "cities.models.Country.objects", "line_number": 138, "usage_type": "attribute"}, {"api_name": "cities.models.Country", "line_number": 138, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 144, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 145, "usage_type": "call"}, {"api_name": "django.utils.translation.get_language", "line_number": 154, "usage_type": "call"}, {"api_name": "cities.models.Region.objects.filter", "line_number": 155, "usage_type": "call"}, {"api_name": "cities.models.Region.objects", "line_number": 155, "usage_type": "attribute"}, {"api_name": "cities.models.Region", "line_number": 155, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 161, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 162, "usage_type": "call"}, {"api_name": "django.utils.translation.get_language", "line_number": 171, "usage_type": "call"}, {"api_name": "cities.models.City.objects.filter", "line_number": 172, "usage_type": "call"}, {"api_name": "cities.models.City.objects", "line_number": 172, "usage_type": "attribute"}, {"api_name": "cities.models.City", "line_number": 172, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 178, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 179, "usage_type": "call"}, {"api_name": "mezzanine.pages.views.page", "line_number": 189, "usage_type": "name"}, {"api_name": "mezzanine.conf.settings.SEARCH_PER_PAGE", "line_number": 190, "usage_type": "attribute"}, {"api_name": "mezzanine.conf.settings", "line_number": 190, "usage_type": "name"}, {"api_name": "mezzanine.conf.settings.MAX_PAGING_LINKS", "line_number": 191, "usage_type": "attribute"}, {"api_name": "mezzanine.conf.settings", "line_number": 191, "usage_type": "name"}, {"api_name": "cities.models.City.objects.filter", "line_number": 196, "usage_type": "call"}, {"api_name": "cities.models.City.objects", "line_number": 196, "usage_type": "attribute"}, {"api_name": "cities.models.City", "line_number": 196, "usage_type": "name"}, {"api_name": "django.apps.apps.get_model", "line_number": 200, "usage_type": "call"}, {"api_name": "django.apps.apps", "line_number": 200, "usage_type": "name"}, {"api_name": "mezzanine.core.models.Displayable", "line_number": 203, "usage_type": "name"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 204, "usage_type": "call"}, {"api_name": "directory.models.Tag", "line_number": 214, "usage_type": "argument"}, {"api_name": "directory.models.Main.objects.filter", "line_number": 215, "usage_type": "call"}, {"api_name": "directory.models.Main.objects", "line_number": 215, "usage_type": "attribute"}, {"api_name": "directory.models.Main", "line_number": 215, "usage_type": "name"}, {"api_name": "directory.models.Main.objects.filter", "line_number": 216, "usage_type": "call"}, {"api_name": "directory.models.Main.objects", "line_number": 216, "usage_type": "attribute"}, {"api_name": "directory.models.Main", "line_number": 216, "usage_type": "name"}, {"api_name": "directory.models.Main.objects.filter", "line_number": 217, "usage_type": "call"}, {"api_name": "directory.models.Main.objects", "line_number": 217, "usage_type": "attribute"}, {"api_name": "directory.models.Main", "line_number": 217, "usage_type": "name"}, {"api_name": "mezzanine.utils.views.paginate", "line_number": 230, "usage_type": "call"}, {"api_name": "mezzanine.pages.views.page", "line_number": 230, "usage_type": "argument"}, {"api_name": "cities.models.City.objects.filter", "line_number": 238, "usage_type": "call"}, {"api_name": "cities.models.City.objects", "line_number": 238, "usage_type": "attribute"}, {"api_name": "cities.models.City", "line_number": 238, "usage_type": "name"}, {"api_name": "directory.forms.SearchNarrowForm", "line_number": 241, "usage_type": "call"}, {"api_name": "mezzanine.utils.views.render", "line_number": 242, "usage_type": "call"}, {"api_name": "mezzanine.conf.settings", "line_number": 263, "usage_type": "argument"}, {"api_name": "django.utils.translation.ugettext_lazy", "line_number": 266, "usage_type": "call"}, {"api_name": "mezzanine.conf.settings.LOGIN_URL", "line_number": 268, "usage_type": "attribute"}, {"api_name": "mezzanine.conf.settings", "line_number": 268, "usage_type": "name"}, {"api_name": "django.http.HttpResponseBadRequest", "line_number": 274, "usage_type": "call"}, {"api_name": "django.apps.apps.get_model", "line_number": 276, "usage_type": "call"}, {"api_name": "django.apps.apps", "line_number": 276, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 282, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 284, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 293, "usage_type": "argument"}, {"api_name": "mezzanine.utils.cache.add_cache_bypass", "line_number": 296, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 297, "usage_type": "call"}, {"api_name": "directory.forms.RatingForm", "line_number": 298, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 308, "usage_type": "call"}, {"api_name": "mezzanine.utils.views.set_cookie", "line_number": 313, "usage_type": "call"}, {"api_name": "directory.forms.SearchNarrowForm", "line_number": 321, "usage_type": "call"}, {"api_name": "mezzanine.pages.views.page", "line_number": 322, "usage_type": "call"}]} +{"seq_id": "349924864", "text": "#!/usr/bin/env python\n\nimport matplotlib\nmatplotlib.use('Agg')\n\nimport os, sys\nfrom importlib import import_module\nimport scipy as sp\nimport matplotlib.pyplot as pl\nfrom matplotlib import ticker, mlab, colors\nfrom matplotlib.cm import jet\nfrom mpl_toolkits.basemap.cm import sstanom\nimport g5lib.plotters as ptrs\nimport g5lib.domain as domain\nfrom g5lib import cmaps as g5cmaps\nfrom g5lib import g5dset\n\nvarname='S'\n# Read variable\nexp=g5dset.read_exp(sys.argv[1])\nexp.ctl=g5dset.Ctl(exp,'geosgcm_ocn3d')\n\nif exp.ctl.grid['lev'][-1] < 0.0:\n exp.ctl.grid['lev'][:]*=-1 \n\ndd=exp.ctl.domain\ndd['dates']=exp.dates\nind=domain.Domain(**dd)(exp.ctl.grid, exp.ctl.time)\nexp.am=exp.ctl.tave(varname,ind['tind']) \n\nexp.am.shiftgrid(30.)\n\nexp.lat_depth=exp.am.ave(3)\nexp.eq_depth=exp.am(lats=(-2.1,2.1)).ave(2)\n\nexp.lat_depth.name=exp.am.name+', Annual Mean'\nexp.eq_depth.name=exp.am.name+', Eq. Annual Mean'\n\n\n# Read experiment to compare\nexp1=g5dset.read_exp(exp.cmpexp)\nexp1.ctl=g5dset.Ctl(exp1,'geosgcm_ocn3d')\n\nif exp1.ctl.grid['lev'][-1] < 0.0:\n exp1.ctl.grid['lev'][:]*=-1 \n\ndd=exp1.ctl.domain\ndd['dates']=exp1.dates\nind=domain.Domain(**dd)(exp1.ctl.grid, exp1.ctl.time)\nexp1.am=exp1.ctl.tave(varname, ind['tind']) \n\nexp1.am.shiftgrid(30.)\n# If dimensions do not match, regrid\nif exp1.am.dims[2:] != exp.am.dims[2:]:\n exp1.am.regrid(exp.am.grid)\n\nexp1.lat_depth=exp1.am.ave(3)\nexp1.eq_depth=exp1.am(lats=(0,))\n\n# If levels do not match, interpolate\nif exp1.lat_depth.dims[1] != exp.lat_depth.dims[1]:\n exp1.lat_depth.vinterp(exp.lat_depth.grid,newmask=exp.lat_depth.data.mask)\n exp1.eq_depth.vinterp(exp.eq_depth.grid,newmask=exp.eq_depth.data.mask)\n\n# Read vaidation data and interpolate to exp grid\nobs=import_module('levitus')\n\nobs.am=obs.ctl('salt').ave(0)\nobs.am.shiftgrid(30.)\nobs.am.regrid(exp.am.grid)\n\nobs.lat_depth=obs.am.ave(3)\nobs.eq_depth=obs.am(lats=(0,))\n\nobs.lat_depth.vinterp(exp.lat_depth.grid,newmask=exp.lat_depth.data.mask)\nobs.eq_depth.vinterp(exp.eq_depth.grid,newmask=exp.eq_depth.data.mask)\n###################### Do plots #######################################################\n\nclevs=sp.arange(33.,36.1,0.2)\n\npath=exp.plot_path\ntry:\n os.makedirs(path)\nexcept OSError:\n pass\n \n\ndef plot_field(field, fig, clevs, cmap, fill_range=None):\n pl.figure(fig)\n pl.clf()\n n=colors.Normalize()\n n.autoscale(clevs)\n if fill_range is not None:\n m=g5cmaps.FilledCmap(cmap, fill_range=n(fill_range))\n else: \n m=cmap\n p=ptrs.Plotter2d(copts=dict(levels=clevs, cmap=m, norm=n))\n p(field)\n p.method=pl.contour\n p.copts=dict(levels=clevs[0::2], colors='black')\n p(field)\n ax=p.axis\n ax.set_ylabel('depth, m'); ax.invert_yaxis()\n return p\n\np=plot_field(exp.lat_depth, 1, clevs, jet)\nax=p.axis; ax.set_ylim(3000., 0.) \nax.xaxis.set_major_locator(ticker.MultipleLocator(30))\npl.grid(); pl.tight_layout(); pl.show()\npl.savefig(path+'/'+varname+'_lat_depth.png')\n\nclevs1=sp.arange(-2,2.1,0.2)\nfrange=(-0.2,0.2)\ndif=exp.lat_depth.subset(); dif.data-=exp1.lat_depth.data\ndif.name=exp.ctl.name+'-'+exp1.ctl.name+' '+varname+', Annual Mean'\np=plot_field(dif, 2, clevs1, sstanom, frange)\nax=p.axis; ax.set_ylim(3000., 0.)\nax.xaxis.set_major_locator(ticker.MultipleLocator(30))\npl.grid(); pl.tight_layout(); pl.show()\npl.savefig(path+'/'+varname+'_dif_lat_depth.png')\n\n\ndif=exp.lat_depth.subset(); dif.data-=obs.lat_depth.data\ndif.name=exp.ctl.name+'-'+obs.ctl.name+' '+varname+', Annual Mean'\np=plot_field(dif, 3, clevs1, sstanom, frange)\nax=p.axis; ax.set_ylim(3000., 0.)\nax.xaxis.set_major_locator(ticker.MultipleLocator(30))\npl.grid(); pl.tight_layout(); pl.show()\npl.savefig(path+'/'+varname+'-obs_lat_depth.png')\n\nclevs=sp.arange(34.,36.1,0.2)\n\np=plot_field(exp.eq_depth, 4, clevs, jet)\nax=p.axis; ax.set_ylim(500., 0.)\npl.grid(); pl.tight_layout(); pl.show()\npl.savefig(path+'/'+varname+'_eq_depth.png')\n\ndif=exp.eq_depth.subset(); dif.data-=exp1.eq_depth.data\ndif.name=exp.ctl.name+'-'+exp1.ctl.name+' '+varname+', Eq. Annual Mean'\np=plot_field(dif, 5, clevs1, sstanom, frange)\nax=p.axis; ax.set_ylim(500., 0.)\npl.grid(); pl.tight_layout(); pl.show()\npl.savefig(path+'/'+varname+'_dif_eq_depth.png')\n\ndif=exp.eq_depth.subset(); dif.data-=obs.eq_depth.data\ndif.name=exp.ctl.name+'-'+obs.ctl.name+' '+varname+', Eq. Annual Mean'\np=plot_field(dif, 6, clevs1, sstanom, frange)\nax=p.axis; ax.set_ylim(500., 0.)\npl.grid(); pl.tight_layout(); pl.show()\npl.savefig(path+'/'+varname+'-obs_eq_depth.png')\n\n", "sub_path": "GEOS_Util/coupled_diagnostics/analysis/clim/salt.py", "file_name": "salt.py", "file_ext": "py", "file_size_in_byte": 4477, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "matplotlib.use", "line_number": 4, "usage_type": "call"}, {"api_name": "g5lib.g5dset.read_exp", "line_number": 20, "usage_type": "call"}, {"api_name": "g5lib.g5dset", "line_number": 20, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "g5lib.g5dset.Ctl", "line_number": 21, "usage_type": "call"}, {"api_name": "g5lib.g5dset", "line_number": 21, "usage_type": "name"}, {"api_name": "g5lib.domain.Domain", "line_number": 28, "usage_type": "call"}, {"api_name": "g5lib.domain", "line_number": 28, "usage_type": "name"}, {"api_name": "g5lib.g5dset.read_exp", "line_number": 41, "usage_type": "call"}, {"api_name": "g5lib.g5dset", "line_number": 41, "usage_type": "name"}, {"api_name": "g5lib.g5dset.Ctl", "line_number": 42, "usage_type": "call"}, {"api_name": "g5lib.g5dset", "line_number": 42, "usage_type": "name"}, {"api_name": "g5lib.domain.Domain", "line_number": 49, "usage_type": "call"}, {"api_name": "g5lib.domain", "line_number": 49, "usage_type": "name"}, {"api_name": "importlib.import_module", "line_number": 66, "usage_type": "call"}, {"api_name": "scipy.arange", "line_number": 79, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.colors.Normalize", "line_number": 91, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 91, "usage_type": "name"}, {"api_name": "g5lib.cmaps.FilledCmap", "line_number": 94, "usage_type": "call"}, {"api_name": "g5lib.cmaps", "line_number": 94, "usage_type": "name"}, {"api_name": "g5lib.plotters.Plotter2d", "line_number": 97, "usage_type": "call"}, {"api_name": "g5lib.plotters", "line_number": 97, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.contour", "line_number": 99, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.cm.jet", "line_number": 106, "usage_type": "argument"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.ticker", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "scipy.arange", "line_number": 112, "usage_type": "call"}, {"api_name": "mpl_toolkits.basemap.cm.sstanom", "line_number": 116, "usage_type": "argument"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.ticker", "line_number": 118, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "mpl_toolkits.basemap.cm.sstanom", "line_number": 125, "usage_type": "argument"}, {"api_name": "matplotlib.ticker.MultipleLocator", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.ticker", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "scipy.arange", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.cm.jet", "line_number": 133, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 136, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 136, "usage_type": "name"}, {"api_name": "mpl_toolkits.basemap.cm.sstanom", "line_number": 140, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "mpl_toolkits.basemap.cm.sstanom", "line_number": 147, "usage_type": "argument"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 149, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 149, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}]} +{"seq_id": "509053017", "text": "# 1.\nclass Laptop:\n \"\"\"\n Make the class with composition.\n \"\"\"\n def __init__(self, laptop_manufacturer, hours_of_work):\n self.laptop_manufacturer = laptop_manufacturer\n self.battery = Battery(hours_of_work)\n\n def __str__(self):\n return f'{self.battery}'\n\nclass Battery:\n \"\"\"\n Make the class with composition.\n \"\"\"\n def __init__(self, hours_of_work):\n self.hours_of_work = hours_of_work\n\n def __str__(self):\n return f'{self.hours_of_work}'\n\n\nlaptop_1 = Laptop(\"Apple\", 7)\n\nprint(f'The laptop manufacturer is {laptop_1.laptop_manufacturer}, '\n f'and battery working hours are', laptop_1)\n\n\n# 2.\nclass Guitar:\n \"\"\"\n Make the class with aggregation\n \"\"\"\n def __init__(self, name, string):\n self.name = name\n self.string = string\n\n\nclass GuitarString:\n \"\"\"\n Make the class with aggregation\n \"\"\"\n def __init__(self, string_length):\n self.string_length = string_length\n \n def __str__(self):\n return f'{self.string_length}'\n\n\nstring = GuitarString(4)\nguitar = Guitar(\"Lennon\", string)\n\nprint(f'{guitar.name}, {guitar.string}')\n\n\n# 3\nclass Calc:\n \"\"\"\n Make class with one method \"add_nums\" with 3 parameters,\n which returns sum of these parameters.\n Note: this method should not take instance as first parameter.\n \"\"\"\n @staticmethod\n def add_nums(x, y, z):\n return x+y+z\n\n\nprint(Calc.add_nums(5, 3, 8))\n\n\n# 4\nclass Pasta:\n \"\"\"\n Make class which takes 1 parameter on init - list of ingredients\n and defines instance attribute ingredients.\n It should have 2 methods:\n carbonara (['forcemeat', 'tomatoes']) and bolognaise (['bacon', 'parmesan', 'eggs'])\n which should create Pasta instances with predefined list of ingredients.\n Example:\n pasta_1 = Pasta([\"tomato\", \"cucumber\"])\n pasta_1.ingredients will equal to [\"tomato\", \"cucumber\"]\n pasta_2 = Pasta.bolognaise()\n pasta_2.ingredients will equal to ['bacon', 'parmesan', 'eggs']\n \"\"\"\n\n CARBONARA = ['forcemeat', 'tomatoes']\n BOLOGNAISE = ['bacon', 'parmesan', 'eggs']\n\n def __init__(self, list_of_ingredients):\n self.list_of_ingredients = list_of_ingredients\n\n\n @classmethod\n def carbonara(cls):\n return Pasta(cls.CARBONARA)\n\n @classmethod\n def bolognaise(cls):\n return Pasta(cls.BOLOGNAISE)\n\n\npasta_1 = Pasta([\"tomato\", \"cucumber\"])\npasta_2 = Pasta.bolognaise()\npasta_3 = Pasta.carbonara()\n\nprint(pasta_1.list_of_ingredients)\nprint(pasta_2.list_of_ingredients)\nprint(pasta_3.list_of_ingredients)\n\n\n# 5*.\nclass Concert:\n \"\"\"\n Make class, which has max_visitors_num attribute and\n its instances will have visitors_count attribute.\n In case of setting visitors_count - max_visitors_num should be checked,\n if visitors_count value is bigger than max_visitors_num -\n visitors_count should be assigned with max_visitors_num.\n Example:\n Concert.max_visitor_num = 50\n concert = Concert()\n concert.visitors_count = 1000\n print(concert.visitors_count) # 50\n \"\"\"\n\n max_visitors_num = 22\n\n def __init__(self, visitors_count=0):\n self.visitors_count = visitors_count\n\n @property\n def visitors_count(self):\n return self._visitors_count\n\n @visitors_count.setter\n def visitors_count(self, x):\n if x < self.max_visitors_num:\n self._visitors_count = x\n else:\n self._visitors_count = self.max_visitors_num\n\n\nConcert.max_visitors_num = 50\nconcert = Concert(50)\nconcert.visitors_count = 1000\nprint(concert.visitors_count)\n\n\n#6.\nimport dataclasses\n\n\n@dataclasses.dataclass\nclass AddressBookDataClass:\n \"\"\"\n Create dataclass with 7 fields - key (int), name (str),\n phone_number (str), address (str), email (str), birthday (str), age (int)\n \"\"\"\n\n key: int\n name: str\n phone_number: str\n address: str\n email: str\n birthday: str\n age: int\n\n\ncontact_1 = AddressBookDataClass(1, 'Kiki', '8758181', 'City',\n 'jahjsHJAH@gmail.com', '11.09.1919', 101)\nprint(contact_1.address)\n\n\n# 7. Create the same class (6) but using NamedTuple\nimport collections\n\n\nAddressBookDataClass_1 = collections.namedtuple('AddressBookDataClass_1',\n ['key', 'name', 'phone_number', 'address',\n 'email', 'birthday', 'age'])\n\ncontact_1 = AddressBookDataClass_1(1, 'Kiki', '8758181', 'City',\n 'jahjsHJAH@gmail.com', '11.09.1919', 101)\n\nprint(contact_1[2])\n\n\n# 8.\nclass AddressBook:\n \"\"\"\n Create regular class taking 7 params on init - key, name, phone_number, address,\n email, birthday, age\n Make its str() representation the same as for AddressBookDataClass defined above.\n \"\"\"\n def __init__(self, key, name, phone_number, address, email, birthday, age):\n self.key = key\n self.name = name\n self.phone_number = phone_number\n self.address = address\n self.email = email\n self.birthday = birthday\n self.age = age\n\n def __str__(self):\n return f\"{__class__.__name__}(key={self.key}, name={self.name}, phone_number={self.phone_number}, \" \\\n f\"address={self.address}, email={self.email}, birthday={self.birthday}, age={self.age})\"\n\n\ncontact_2 = AddressBook(key=1, name='Pedro', phone_number='1717171', address='Tokyo',\n email='jahsgd@gmail.com', birthday='17.08.2004', age=16)\nprint(contact_2.name)\n\n\n# 9.\nclass Person:\n \"\"\"\n Change the value of the age property of the person object\n \"\"\"\n name = \"John\"\n age = 36\n country = \"USA\"\n\n\nperson_1 = Person()\n\nsetattr(person_1, 'age', '77')\n\nprint(f'{person_1.name} is {person_1.age}.')\n\n\n# 10.\nclass Student:\n \"\"\"\n Add an 'email' attribute of the object student and set its value\n Assign the new attribute to 'student_email' variable and print it by using getattr\n \"\"\"\n id = 0\n name = \"\"\n\n def __init__(self, id, name):\n self.id = id\n self.name = name\n\n\nstudent_1 = Student(1, \"Kiki\")\nsetattr(student_1, \"email\", \"student@gmail.com\")\nstudent_email = student_1.email\nprint(getattr(student_1, \"email\"))\nprint(student_email)\n\n\n#11*.\nclass Celsius:\n \"\"\"\n By using @property convert the celsius to fahrenheit\n Hint: (temperature * 1.8) + 32)\n \"\"\"\n def __init__(self, temperature=0):\n self._temperature = temperature\n\n @property\n def convert(self):\n return (self._temperature * 1.8) + 32\n\n\ncels_fahr = Celsius(5)\n\nprint(cels_fahr.convert)\n", "sub_path": "HW_5.py", "file_name": "HW_5.py", "file_ext": "py", "file_size_in_byte": 6625, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "dataclasses.dataclass", "line_number": 154, "usage_type": "attribute"}, {"api_name": "collections.namedtuple", "line_number": 179, "usage_type": "call"}]} +{"seq_id": "109614697", "text": "from django.contrib.auth.hashers import make_password\nfrom datetime import datetime, date\nfrom food_management.constants.constants import (\n DEFAULT_DATE_FORMAT, DEFAULT_TIME_FORMAT, BREAKFAST_START_TIME,\n BREAKFAST_END_TIME, LUNCH_START_TIME, LUNCH_END_TIME,\n DINNER_START_TIME, DINNER_END_TIME\n)\nfrom food_management.models.user import User\nfrom food_management.models.meal import Meal\nfrom food_management.models.user_rating import UserRating\nfrom food_management.models.user_feedback import UserFeedback\nfrom food_management.models.announcements import Announcements\nfrom food_management.models.items import Items\nfrom food_management.models.meal_course import MealCourse\nfrom food_management.dtos.dtos import (\n ItemAndQuantityDto, CustomeMealUpdateDto, ItemAndRatingDto,\n RatingDto, UpdateMealScheduleDto, ItemDetailsDto\n)\nfrom food_management.models.user_meal_status import UserMealStatus\nimport pytest\nfrom freezegun import freeze_time\nfrom django.utils import timezone\nfrom datetime import time\nfrom food_management.interactors.storages.dtos import (\n HomePageDto, MealCourseDto, ItemDto, MealDto, AnnouncementDtos,\n MealCourseCompleteDetailsDto, SetMealPreferenceDto, MealScheduleDto\n)\nfrom food_management.constants.enums import (\n TypeOfMeal, CategoryType, UnitType, CourseType\n)\n\n@pytest.fixture\ndef user_objs():\n\n user_dict = [\n {'username': 'user1', 'password': 'password1'},\n {'username': 'user2', 'password': 'password2'},\n {'username': 'user3', 'password': 'password3'}\n ]\n\n User.objects.bulk_create([\n User(username=user['username'], password=make_password(user['password'])\n ) for user in user_dict])\n\n@pytest.fixture\n@freeze_time('2020-02-12')\ndef meal_objs(item_objs):\n datetime_obj = datetime.now()\n meal_dict = [\n {\n 'meal_type': TypeOfMeal.breakfast.value,\n 'date': datetime_obj,\n 'from_time_string': BREAKFAST_START_TIME,\n 'to_time_string': BREAKFAST_END_TIME\n },\n {\n 'meal_type': TypeOfMeal.lunch.value,\n 'date': datetime_obj,\n 'from_time_string': LUNCH_START_TIME,\n 'to_time_string': LUNCH_END_TIME\n },\n {\n 'meal_type': TypeOfMeal.dinner.value,\n 'date': datetime_obj,\n 'from_time_string': DINNER_START_TIME,\n 'to_time_string': DINNER_END_TIME\n }\n ]\n\n Meal.objects.bulk_create([\n Meal(\n meal_type=meal['meal_type'], date=meal['date'],\n from_time_string=meal['from_time_string'],\n to_time_string=meal['to_time_string']\n )\n for meal in meal_dict\n ])\n\n\n\n@pytest.fixture\n@freeze_time('2020-02-12')\ndef meal_dtos(meal_objs, item_objs):\n meal_dtos_list = [\n MealDto(\n meal_id=1, meal_type=TypeOfMeal.breakfast.value,\n date=datetime.now(), from_time_string=BREAKFAST_START_TIME,\n to_time_string=BREAKFAST_END_TIME\n ),\n MealDto(\n meal_id=2, meal_type=TypeOfMeal.lunch.value,\n date=datetime.now(), from_time_string=LUNCH_START_TIME,\n to_time_string=LUNCH_END_TIME\n ),\n MealDto(\n meal_id=3, meal_type=TypeOfMeal.dinner.value,\n date=datetime.now(), from_time_string=DINNER_START_TIME,\n to_time_string=DINNER_END_TIME\n )\n ]\n return meal_dtos_list\n\n@pytest.fixture\ndef item_objs():\n items_dict = [\n {\n 'item': 'Idly', 'category': CategoryType.indian_bread.value,\n 'units': UnitType.pieces.value\n },\n {\n 'item': 'Poori', 'category': CategoryType.indian_bread.value,\n 'units': UnitType.pieces.value\n },\n {\n 'item': 'MasalaRice', 'category': CategoryType.rice.value,\n 'units': UnitType.laddles.value\n },\n {\n 'item': 'WhiteRice', 'category': CategoryType.rice.value,\n 'units': UnitType.laddles.value\n },\n {\n 'item': 'Dal', 'category': CategoryType.curry.value,\n 'units': UnitType.cups.value\n },\n {\n 'item': 'PotatoCurry', 'category': CategoryType.curry.value,\n 'units': UnitType.cups.value\n },\n {\n 'item': 'Curd', 'category': CategoryType.curry.value,\n 'units': UnitType.cups.value\n },\n {\n 'item': 'Roti', 'category': CategoryType.indian_bread.value,\n 'units': UnitType.pieces.value\n },\n {\n 'item': 'Rajma', 'category': CategoryType.curry.value,\n 'units': UnitType.cups.value\n },\n {\n 'item': 'FriedRice', 'category': CategoryType.rice.value,\n 'units': UnitType.laddles.value\n }\n ]\n Items.objects.bulk_create([\n Items(\n item=item['item'],\n category=item['category'],\n units=item['units']\n )\n for item in items_dict])\n\n@pytest.fixture\ndef item_dtos(item_objs, meal_objs):\n item_dtos_list = [\n ItemDto(\n item_id=1, item='Idly', category=CategoryType.indian_bread.value,\n units=UnitType.pieces.value, meal_id=1\n ),\n ItemDto(\n item_id=2, item='Poori', category=CategoryType.indian_bread.value,\n units=UnitType.pieces.value, meal_id=1\n ),\n ItemDto(\n item_id=3, item='MasalaRice', category=CategoryType.rice.value,\n units=UnitType.laddles.value, meal_id=1\n ),\n ItemDto(\n item_id=4, item='WhiteRice', category=CategoryType.rice.value,\n units=UnitType.laddles.value, meal_id=2\n ),\n ItemDto(\n item_id=5, item='Dal', category=CategoryType.curry.value,\n units=UnitType.cups.value, meal_id=2\n ),\n ItemDto(\n item_id=6, item='PotatoCurry', category=CategoryType.curry.value,\n units=UnitType.cups.value, meal_id=2\n ),\n ItemDto(\n item_id=7, item='Curd', category=CategoryType.curry.value,\n units=UnitType.cups.value, meal_id=2\n ),\n ItemDto(\n item_id=8, item='Roti', category=CategoryType.indian_bread.value,\n units=UnitType.pieces.value, meal_id=3\n ),\n ItemDto(\n item_id=9, item='Rajma', category=CategoryType.curry.value,\n units=UnitType.cups.value, meal_id=3\n ),\n ItemDto(\n item_id=10, item='FriedRice', category=CategoryType.rice.value,\n units=UnitType.laddles.value, meal_id=3\n )\n ]\n return item_dtos_list\n\n@pytest.fixture\ndef list_of_items_dtos(item_objs):\n\n items_list = [\n ItemDto(item_id=1, item='Idly', category='Indian-Bread', units='pieces', meal_id=None),\n ItemDto(item_id=2, item='Poori', category='Indian-Bread', units='pieces', meal_id=None),\n ItemDto(item_id=3, item='MasalaRice', category='Rice', units='laddles', meal_id=None),\n ItemDto(item_id=4, item='WhiteRice', category='Rice', units='laddles', meal_id=None),\n ItemDto(item_id=5, item='Dal', category='Curry', units='cups', meal_id=None),\n ItemDto(item_id=6, item='PotatoCurry', category='Curry', units='cups', meal_id=None),\n ItemDto(item_id=7, item='Curd', category='Curry', units='cups', meal_id=None),\n ItemDto(item_id=8, item='Roti', category='Indian-Bread', units='pieces', meal_id=None),\n ItemDto(item_id=9, item='Rajma', category='Curry', units='cups', meal_id=None),\n ItemDto(item_id=10, item='FriedRice', category='Rice', units='laddles', meal_id=None)\n ]\n return items_list\n\n\n@pytest.fixture\ndef meal_course_objs(item_objs, meal_objs):\n meal_course_list = [\n {\n 'item_id':1, 'meal_course': 'Half-meal', 'meal_id': 1, 'quantity':2\n },\n {\n 'item_id':2, 'meal_course': 'Half-meal', 'meal_id': 1, 'quantity':2\n },\n {\n 'item_id':3, 'meal_course': 'Half-meal', 'meal_id': 1, 'quantity':2\n },\n {\n 'item_id':4, 'meal_course': 'Half-meal', 'meal_id': 2, 'quantity':2\n },\n {\n 'item_id':5, 'meal_course': 'Half-meal', 'meal_id': 2, 'quantity':2\n },\n {\n 'item_id':6, 'meal_course': 'Half-meal', 'meal_id': 2, 'quantity':2\n },\n {\n 'item_id':7, 'meal_course': 'Half-meal', 'meal_id': 2, 'quantity':2\n },\n {\n 'item_id':8, 'meal_course': 'Full-meal', 'meal_id': 3, 'quantity':3\n },\n {\n 'item_id':9, 'meal_course': 'Full-meal', 'meal_id': 3, 'quantity':2\n },\n {\n 'item_id':10, 'meal_course': 'Full-meal', 'meal_id': 3, 'quantity':3\n },\n {\n 'item_id':1, 'meal_course': 'Full-meal', 'meal_id': 1, 'quantity':3\n },\n {\n 'item_id':2, 'meal_course': 'Full-meal', 'meal_id': 1, 'quantity':3\n },\n {\n 'item_id':3, 'meal_course': 'Full-meal', 'meal_id': 1, 'quantity':3\n }\n ]\n MealCourse.objects.bulk_create([\n MealCourse(\n meal_id=meal_course['meal_id'],\n item_id=meal_course['item_id'],\n meal_course=meal_course['meal_course'],\n quantity=meal_course['quantity']\n )\n for meal_course in meal_course_list\n ])\n\n\n@pytest.fixture\ndef user_meal_course_objs(meal_objs, user_objs, meal_course_objs):\n user_meal_course_dict=[\n {\n 'user_id': 1, 'meal_course_id': 1,\n 'meal_id': 1, 'item_id':1, 'custom_meal_quantity':2\n },\n {\n 'user_id': 1, 'meal_course_id': 2,\n 'meal_id': 1, 'item_id':2, 'custom_meal_quantity':2\n },\n {\n 'user_id': 1, 'meal_course_id': 3,\n 'meal_id': 1, 'item_id':3, 'custom_meal_quantity':2\n },\n {\n 'user_id': 1, 'meal_course_id': 4,\n 'meal_id': 2, 'item_id': 4, 'custom_meal_quantity':2\n },\n {\n 'user_id': 1, 'meal_course_id': 5,\n 'meal_id': 2, 'item_id':5, 'custom_meal_quantity':2\n },\n {\n 'user_id': 1, 'meal_course_id': 6,\n 'meal_id': 2, 'item_id':6, 'custom_meal_quantity':2\n },\n {\n 'user_id': 1, 'meal_course_id': 7,\n 'meal_id': 2, 'item_id':7, 'custom_meal_quantity':2\n },\n {\n 'user_id': 1, 'meal_course_id': 8,\n 'meal_id': 3, 'item_id':8, 'custom_meal_quantity':3\n },\n {\n 'user_id': 1, 'meal_course_id': 9,\n 'meal_id': 3, 'item_id':9, 'custom_meal_quantity':2\n },\n {\n 'user_id': 1, 'meal_course_id': 10,\n 'meal_id': 3, 'item_id':10, 'custom_meal_quantity':3\n }\n ]\n UserMealStatus.objects.bulk_create([\n UserMealStatus(\n user_id=user_meal['user_id'], meal_course_id=user_meal['meal_course_id'],\n meal_id=user_meal['meal_id'], item_id=user_meal['item_id'],\n custom_meal_quantity=user_meal['custom_meal_quaa']\n )\n for user_meal in user_meal_course_dict\n ])\n\n@pytest.fixture\ndef meal_course_dtos(user_meal_course_objs):\n meal_course_dtos_list = [\n MealCourseDto(\n meal_course=CourseType.half_meal.value,\n meal_id=1, meal_type=TypeOfMeal.breakfast.value\n ),\n MealCourseDto(\n meal_course=CourseType.half_meal.value,\n meal_id=2, meal_type=TypeOfMeal.lunch.value\n ),\n MealCourseDto(\n meal_course=CourseType.full_meal.value,\n meal_id=3, meal_type=TypeOfMeal.dinner.value\n )\n ]\n return meal_course_dtos_list\n\n@pytest.fixture\ndef home_page_dto(meal_course_dtos, meal_dtos, item_dtos):\n return HomePageDto(\n meal_course=meal_course_dtos,\n items=item_dtos,\n meal=meal_dtos\n )\n\n@pytest.fixture\n@freeze_time('2020-02-12')\ndef announcement_objs():\n announcement_list = [\n {\n 'title': 'Happy Birthday',\n 'subtitle': 'Birthday Special!',\n 'description':'Here are the newly added items on the occasion of our \\\n friends birthday',\n 'image':'https://www.google.co.in',\n 'date': datetime.now()\n }\n ]\n Announcements.objects.bulk_create([\n Announcements(\n title=announcement['title'],\n subtitle=announcement['subtitle'],\n description=announcement['description'],\n image=announcement['image'],\n date=announcement['date']\n )\n for announcement in announcement_list\n ])\n\n@pytest.fixture\ndef annoncement_dtos(announcement_objs):\n announcement_dtos_list = [\n AnnouncementDtos(\n title='Happy Birthday',\n subtitle='Birthday Special!',\n description='Here are the newly added items on the occasion of our \\\n friends birthday',\n image='https://www.google.co.in'\n )\n ]\n return announcement_dtos_list\n\n\n@pytest.fixture\ndef meal_items_dtos(item_objs):\n item_dtos_list = [\n ItemDto(\n item_id=1, item='Idly', category=CategoryType.indian_bread.value,\n units=UnitType.pieces.value, meal_id=1\n ),\n ItemDto(\n item_id=2, item='Poori', category=CategoryType.indian_bread.value,\n units=UnitType.pieces.value, meal_id=1\n ),\n ItemDto(\n item_id=3, item='MasalaRice', category=CategoryType.rice.value,\n units=UnitType.laddles.value, meal_id=1\n )\n ]\n return item_dtos_list\n\n\n@pytest.fixture\ndef meal_course_complete_details_dtos(item_objs, meal_course_objs):\n meal_course_details_list = [\n MealCourseCompleteDetailsDto(\n item_id=1, meal_course=CourseType.half_meal.value, quantity=2\n ),\n MealCourseCompleteDetailsDto(\n item_id=2, meal_course=CourseType.half_meal.value, quantity=2\n ),\n MealCourseCompleteDetailsDto(\n item_id=3, meal_course=CourseType.half_meal.value, quantity=2\n ),\n MealCourseCompleteDetailsDto(\n item_id=1, meal_course=CourseType.full_meal.value, quantity=3\n ),\n MealCourseCompleteDetailsDto(\n item_id=2, meal_course=CourseType.full_meal.value, quantity=3\n ),\n MealCourseCompleteDetailsDto(\n item_id=3, meal_course=CourseType.full_meal.value, quantity=3\n )\n ]\n return meal_course_details_list\n\n@pytest.fixture\ndef meal_data_dtos(meal_course_complete_details_dtos, meal_items_dtos):\n return SetMealPreferenceDto(\n meal_course=meal_course_complete_details_dtos,\n items=meal_items_dtos\n )\n\n@pytest.fixture\ndef custom_meal_objs(item_objs, meal_objs):\n meal_course_list = [\n {\n 'item_id':1, 'meal_course': 'Custom-meal', 'meal_id': 1, 'quantity':0\n },\n {\n 'item_id':2, 'meal_course': 'Custom-meal', 'meal_id': 1, 'quantity':0\n },\n {\n 'item_id':3, 'meal_course': 'Custom-meal', 'meal_id': 1, 'quantity':0\n }\n ]\n MealCourse.objects.bulk_create([\n MealCourse(\n meal_id=meal_course['meal_id'],\n item_id=meal_course['item_id'],\n meal_course=meal_course['meal_course'],\n quantity=meal_course['quantity']\n )\n for meal_course in meal_course_list\n ])\n\n@pytest.fixture\ndef item_and_quantity_dtos(item_objs):\n list_of_items = [\n ItemAndQuantityDto(\n item_id=1, quantity=2,\n ),\n ItemAndQuantityDto(\n item_id=2, quantity=1,\n ),\n ItemAndQuantityDto(\n item_id=3, quantity=1,\n )\n ]\n return list_of_items\n\n\n\n@pytest.fixture\n@freeze_time('2020-02-12')\ndef custom_meal_upadte_dto(user_objs, item_and_quantity_dtos, custom_meal_objs):\n return CustomeMealUpdateDto(\n user_id=1,\n meal_id = 1,\n meal_course = 'Custom-meal',\n items_and_quantities=item_and_quantity_dtos\n )\n\n@pytest.fixture\ndef user_rating_objs(user_objs, meal_objs):\n rating_list = [\n {\n 'user_id': 1, 'meal_id': 1, 'item_id': 1, 'taste': 4, 'quality': 3\n },\n {\n 'user_id': 1, 'meal_id': 1, 'item_id': 2, 'taste': 4, 'quality': 3\n },\n {\n 'user_id': 1, 'meal_id': 1, 'item_id': 3, 'taste': 4, 'quality': 3\n }\n ]\n UserRating.objects.bulk_create([\n UserRating(\n user_id=rating_obj['user_id'],\n meal_id=rating_obj['meal_id'],\n taste=rating_obj['taste'],\n quality=rating_obj['quality'],\n item_id=rating_obj['item_id']\n )\n for rating_obj in rating_list\n ])\n\n@pytest.fixture\ndef items_and_rating_dtos(item_objs, meal_objs):\n items_and_rating_dtos_list = [\n ItemAndRatingDto(item_id=1, quality=4, taste=4),\n ItemAndRatingDto(item_id=2, quality=4, taste=4),\n ItemAndRatingDto(item_id=3, quality=3, taste=4)\n ]\n return items_and_rating_dtos_list\n\n@pytest.fixture\ndef rating_dtos(items_and_rating_dtos, user_objs, meal_objs):\n return RatingDto(\n user_id=1,\n meal_id=1,\n description='',\n items_and_ratings=items_and_rating_dtos\n )\n\n@pytest.fixture\ndef user_feedback(user_objs, meal_objs):\n UserFeedback.objects.create(user_id=1, meal_id=1, description='')\n\n@pytest.fixture\ndef update_items_and_rating_dtos(item_objs, meal_objs):\n items_and_rating_dtos_list = [\n ItemAndRatingDto(item_id=1, quality=2, taste=2),\n ItemAndRatingDto(item_id=2, quality=2, taste=3),\n ItemAndRatingDto(item_id=3, quality=4, taste=5)\n ]\n return items_and_rating_dtos_list\n\n@pytest.fixture\ndef update_rating_dtos(\n update_items_and_rating_dtos, user_rating_objs,\n user_objs, meal_objs, user_feedback):\n return RatingDto(\n user_id=1,\n meal_id=1,\n description='',\n items_and_ratings=update_items_and_rating_dtos\n )\n\n@pytest.fixture\ndef breakfast_items():\n item_dtos = [\n ItemDto(\n item_id=1, item='Idly', category=CategoryType.indian_bread.value,\n units=UnitType.pieces.value, meal_id=1\n ),\n ItemDto(\n item_id=2,item='Poori', category=CategoryType.indian_bread.value,\n units=UnitType.pieces.value, meal_id=1\n ),\n ItemDto(\n item_id=3,item='MasalaRice', category=CategoryType.rice.value,\n units=UnitType.laddles.value, meal_id=1\n )\n ]\n return item_dtos\n\n@pytest.fixture\n@freeze_time('2020-02-12')\ndef meal_breakfast_dto():\n return MealDto(\n meal_id=1, meal_type=TypeOfMeal.breakfast.value,\n date=datetime.now(),\n from_time_string=BREAKFAST_START_TIME,\n to_time_string=BREAKFAST_END_TIME\n )\n\n@pytest.fixture\ndef meal_schedule_dto(breakfast_items, meal_breakfast_dto, meal_course_objs):\n return MealScheduleDto(\n items=breakfast_items,\n meal=meal_breakfast_dto\n )\n\n@pytest.fixture\ndef items_and_their_meal_course(item_objs):\n items_and_meal_course_list = [\n ItemDetailsDto(\n item_id=1,meal_course='Half-meal',quantity=2\n ),\n ItemDetailsDto(\n item_id=2,meal_course='Half-meal',quantity=2\n ),\n ItemDetailsDto(\n item_id=3,meal_course='Half-meal',quantity=1\n ),\n ItemDetailsDto(\n item_id=1,meal_course='Full-meal',quantity=3\n ),\n ItemDetailsDto(\n item_id=2,meal_course='Full-meal',quantity=3\n ),\n ItemDetailsDto(\n item_id=3,meal_course='Full-meal',quantity=2\n )\n ]\n return items_and_meal_course_list\n\n\n@pytest.fixture\n@freeze_time('2020-02-12')\ndef update_meal_schedule_dtos(items_and_their_meal_course, meal_course_objs):\n return UpdateMealScheduleDto(\n meal_type='Breakfast',\n date=date(2020,2,12),\n items=items_and_their_meal_course\n )\n\n@pytest.fixture\n@freeze_time('2020-02-12')\ndef create_meal_schedule(items_and_their_meal_course):\n return UpdateMealScheduleDto(\n meal_type='Breakfast',\n date=date(2020,2,12),\n items=items_and_their_meal_course\n )", "sub_path": "food_management/tests/storages/.~c9_invoke_wiL7Xo.py", "file_name": ".~c9_invoke_wiL7Xo.py", "file_ext": "py", "file_size_in_byte": 20348, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "food_management.models.user.User.objects.bulk_create", "line_number": 41, "usage_type": "call"}, {"api_name": "food_management.models.user.User.objects", "line_number": 41, "usage_type": "attribute"}, {"api_name": "food_management.models.user.User", "line_number": 41, "usage_type": "name"}, {"api_name": "food_management.models.user.User", "line_number": 42, "usage_type": "call"}, {"api_name": "django.contrib.auth.hashers.make_password", "line_number": 42, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 32, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 48, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 48, "usage_type": "name"}, {"api_name": "food_management.constants.enums.TypeOfMeal.breakfast", "line_number": 51, "usage_type": "attribute"}, {"api_name": "food_management.constants.enums.TypeOfMeal", "line_number": 51, "usage_type": "name"}, {"api_name": "food_management.constants.constants.BREAKFAST_START_TIME", "line_number": 53, "usage_type": "name"}, {"api_name": "food_management.constants.constants.BREAKFAST_END_TIME", "line_number": 54, "usage_type": "name"}, {"api_name": "food_management.constants.enums.TypeOfMeal.lunch", "line_number": 57, "usage_type": "attribute"}, {"api_name": "food_management.constants.enums.TypeOfMeal", "line_number": 57, "usage_type": "name"}, {"api_name": "food_management.constants.constants.LUNCH_START_TIME", "line_number": 59, "usage_type": "name"}, {"api_name": "food_management.constants.constants.LUNCH_END_TIME", "line_number": 60, "usage_type": "name"}, {"api_name": "food_management.constants.enums.TypeOfMeal.dinner", "line_number": 63, "usage_type": "attribute"}, {"api_name": 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"usage_type": "call"}, {"api_name": "food_management.constants.enums.CategoryType.rice", "line_number": 402, "usage_type": "attribute"}, {"api_name": "food_management.constants.enums.CategoryType", "line_number": 402, "usage_type": "name"}, {"api_name": "food_management.constants.enums.UnitType.laddles", "line_number": 403, "usage_type": "attribute"}, {"api_name": "food_management.constants.enums.UnitType", "line_number": 403, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 390, "usage_type": "attribute"}, {"api_name": "food_management.interactors.storages.dtos.MealCourseCompleteDetailsDto", "line_number": 412, "usage_type": "call"}, {"api_name": "food_management.constants.enums.CourseType.half_meal", "line_number": 413, "usage_type": "attribute"}, {"api_name": "food_management.constants.enums.CourseType", "line_number": 413, "usage_type": "name"}, {"api_name": "food_management.interactors.storages.dtos.MealCourseCompleteDetailsDto", "line_number": 415, "usage_type": "call"}, {"api_name": "food_management.constants.enums.CourseType.half_meal", "line_number": 416, "usage_type": "attribute"}, {"api_name": "food_management.constants.enums.CourseType", "line_number": 416, "usage_type": "name"}, {"api_name": "food_management.interactors.storages.dtos.MealCourseCompleteDetailsDto", "line_number": 418, "usage_type": "call"}, {"api_name": "food_management.constants.enums.CourseType.half_meal", "line_number": 419, "usage_type": "attribute"}, {"api_name": "food_management.constants.enums.CourseType", "line_number": 419, "usage_type": "name"}, {"api_name": "food_management.interactors.storages.dtos.MealCourseCompleteDetailsDto", "line_number": 421, "usage_type": "call"}, {"api_name": "food_management.constants.enums.CourseType.full_meal", "line_number": 422, "usage_type": "attribute"}, {"api_name": "food_management.constants.enums.CourseType", "line_number": 422, "usage_type": "name"}, {"api_name": "food_management.interactors.storages.dtos.MealCourseCompleteDetailsDto", "line_number": 424, "usage_type": "call"}, {"api_name": "food_management.constants.enums.CourseType.full_meal", "line_number": 425, "usage_type": "attribute"}, {"api_name": "food_management.constants.enums.CourseType", "line_number": 425, "usage_type": "name"}, {"api_name": "food_management.interactors.storages.dtos.MealCourseCompleteDetailsDto", "line_number": 427, "usage_type": "call"}, {"api_name": "food_management.constants.enums.CourseType.full_meal", "line_number": 428, "usage_type": "attribute"}, {"api_name": "food_management.constants.enums.CourseType", "line_number": 428, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 409, "usage_type": "attribute"}, {"api_name": "food_management.interactors.storages.dtos.SetMealPreferenceDto", "line_number": 435, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 433, "usage_type": "attribute"}, {"api_name": "food_management.models.meal_course.MealCourse.objects.bulk_create", "line_number": 453, "usage_type": "call"}, {"api_name": "food_management.models.meal_course.MealCourse.objects", "line_number": 453, "usage_type": "attribute"}, {"api_name": "food_management.models.meal_course.MealCourse", "line_number": 453, "usage_type": "name"}, {"api_name": "food_management.models.meal_course.MealCourse", "line_number": 454, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 440, "usage_type": "attribute"}, {"api_name": "food_management.dtos.dtos.ItemAndQuantityDto", "line_number": 466, "usage_type": "call"}, {"api_name": "food_management.dtos.dtos.ItemAndQuantityDto", "line_number": 469, "usage_type": "call"}, {"api_name": "food_management.dtos.dtos.ItemAndQuantityDto", "line_number": 472, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 463, "usage_type": "attribute"}, {"api_name": "food_management.dtos.dtos.CustomeMealUpdateDto", "line_number": 483, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 480, "usage_type": "attribute"}, {"api_name": "freezegun.freeze_time", "line_number": 481, "usage_type": "call"}, {"api_name": "food_management.models.user_rating.UserRating.objects.bulk_create", "line_number": 503, "usage_type": "call"}, {"api_name": "food_management.models.user_rating.UserRating.objects", "line_number": 503, "usage_type": "attribute"}, {"api_name": "food_management.models.user_rating.UserRating", "line_number": 503, "usage_type": "name"}, {"api_name": "food_management.models.user_rating.UserRating", "line_number": 504, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 490, "usage_type": "attribute"}, {"api_name": "food_management.dtos.dtos.ItemAndRatingDto", "line_number": 517, "usage_type": "call"}, {"api_name": "food_management.dtos.dtos.ItemAndRatingDto", "line_number": 518, "usage_type": "call"}, {"api_name": "food_management.dtos.dtos.ItemAndRatingDto", "line_number": 519, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 514, "usage_type": "attribute"}, {"api_name": "food_management.dtos.dtos.RatingDto", "line_number": 525, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 523, "usage_type": "attribute"}, {"api_name": "food_management.models.user_feedback.UserFeedback.objects.create", "line_number": 534, "usage_type": "call"}, {"api_name": "food_management.models.user_feedback.UserFeedback.objects", "line_number": 534, "usage_type": "attribute"}, {"api_name": "food_management.models.user_feedback.UserFeedback", "line_number": 534, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 532, "usage_type": "attribute"}, {"api_name": "food_management.dtos.dtos.ItemAndRatingDto", "line_number": 539, "usage_type": "call"}, {"api_name": "food_management.dtos.dtos.ItemAndRatingDto", "line_number": 540, "usage_type": "call"}, {"api_name": "food_management.dtos.dtos.ItemAndRatingDto", "line_number": 541, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 536, "usage_type": "attribute"}, {"api_name": "food_management.dtos.dtos.RatingDto", "line_number": 549, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 545, "usage_type": "attribute"}, {"api_name": "food_management.interactors.storages.dtos.ItemDto", "line_number": 559, "usage_type": "call"}, {"api_name": "food_management.constants.enums.CategoryType.indian_bread", "line_number": 560, "usage_type": "attribute"}, {"api_name": "food_management.constants.enums.CategoryType", "line_number": 560, "usage_type": "name"}, {"api_name": "food_management.constants.enums.UnitType.pieces", "line_number": 561, "usage_type": "attribute"}, {"api_name": "food_management.constants.enums.UnitType", "line_number": 561, "usage_type": "name"}, {"api_name": "food_management.interactors.storages.dtos.ItemDto", "line_number": 563, "usage_type": "call"}, {"api_name": "food_management.constants.enums.CategoryType.indian_bread", "line_number": 564, "usage_type": "attribute"}, {"api_name": "food_management.constants.enums.CategoryType", "line_number": 564, "usage_type": "name"}, {"api_name": "food_management.constants.enums.UnitType.pieces", "line_number": 565, "usage_type": "attribute"}, {"api_name": "food_management.constants.enums.UnitType", "line_number": 565, "usage_type": "name"}, {"api_name": "food_management.interactors.storages.dtos.ItemDto", "line_number": 567, "usage_type": "call"}, {"api_name": "food_management.constants.enums.CategoryType.rice", "line_number": 568, "usage_type": "attribute"}, {"api_name": "food_management.constants.enums.CategoryType", "line_number": 568, "usage_type": "name"}, {"api_name": "food_management.constants.enums.UnitType.laddles", "line_number": 569, "usage_type": "attribute"}, {"api_name": "food_management.constants.enums.UnitType", "line_number": 569, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 556, "usage_type": "attribute"}, {"api_name": "food_management.interactors.storages.dtos.MealDto", "line_number": 577, "usage_type": "call"}, {"api_name": "food_management.constants.enums.TypeOfMeal.breakfast", "line_number": 578, "usage_type": "attribute"}, {"api_name": "food_management.constants.enums.TypeOfMeal", "line_number": 578, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 579, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 579, "usage_type": "name"}, {"api_name": "food_management.constants.constants.BREAKFAST_START_TIME", "line_number": 580, "usage_type": "name"}, {"api_name": "food_management.constants.constants.BREAKFAST_END_TIME", "line_number": 581, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 574, "usage_type": "attribute"}, {"api_name": "freezegun.freeze_time", "line_number": 575, "usage_type": "call"}, {"api_name": "food_management.interactors.storages.dtos.MealScheduleDto", "line_number": 586, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 584, "usage_type": "attribute"}, {"api_name": "food_management.dtos.dtos.ItemDetailsDto", "line_number": 594, "usage_type": "call"}, {"api_name": "food_management.dtos.dtos.ItemDetailsDto", "line_number": 597, "usage_type": "call"}, {"api_name": "food_management.dtos.dtos.ItemDetailsDto", "line_number": 600, "usage_type": "call"}, {"api_name": "food_management.dtos.dtos.ItemDetailsDto", "line_number": 603, "usage_type": "call"}, {"api_name": "food_management.dtos.dtos.ItemDetailsDto", "line_number": 606, "usage_type": "call"}, {"api_name": "food_management.dtos.dtos.ItemDetailsDto", "line_number": 609, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 591, "usage_type": "attribute"}, {"api_name": "food_management.dtos.dtos.UpdateMealScheduleDto", "line_number": 619, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 621, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 616, "usage_type": "attribute"}, {"api_name": "freezegun.freeze_time", "line_number": 617, "usage_type": "call"}, {"api_name": "food_management.dtos.dtos.UpdateMealScheduleDto", "line_number": 628, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 630, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 625, "usage_type": "attribute"}, {"api_name": "freezegun.freeze_time", "line_number": 626, "usage_type": "call"}]} +{"seq_id": "314416472", "text": "\"\"\"\n\nUsage:\n exverify [( )] [options]\n exverify (-h | --help)\n exverify (-v | --version)\n\nOption:\n -h --help - Show this screen.\n -v --version - Show ExVerify version.\n --verbose - Print more text.\n\n - GDS file name.\n\n - Cell name to be extracted.\n - `ntrons` -> list nTron cells.\n - `jjs` -> list Josephson juction cells.\n - `vias` -> list VIA cells.\n\n - Process Data File name (.json).\n\n -s --select - Extrude selected cell to a new, seperate gds file.\n -p --plot - Only for debugging. Plot the LVS graphs.\n\n --filter - Disable the graph filtering algorithms.\n --combine - Disable combining mask graphs.\n --edges - Disable the graph edge generation. \n\n --model - Generate a 3D circuit model from the .gds file.\n --log=log - Generate a 3D circuit model from the .gds file.\n\n --viewer=all - Use the gdspy viewer for debugging.\n auron - View the geometry that will be send to the Auron package.\n ix - GDS file prepaired for InductEx.\n\n\"\"\"\n\n\nfrom docopt import docopt\nfrom itertools import count\n\nimport networkx as nx\n\nimport os\nimport sys\nimport yuna\nimport auron\nimport rikku\nimport gdspy\nimport pygmsh\n\nimport gdspy as gdscell\n\nfrom exverify import tools\nfrom exverify import version\nfrom exverify import convert\n\nfrom .tools import logging\n\nfrom termcolor import colored\nfrom collections import defaultdict\nfrom networkx.algorithms import isomorphism\n\n\n\"\"\"\nHacker: 1h3d*n\nFor: Volundr\nDocs: Algorithm 1\nDate: 31 April 2017\n\nDescription: Morph the moat layer and the wire layers.\n\n1) Get a list of all the polygons inside the GDS file.\n2) Send this list to the Clip library with the wiring\n layer number and the moat layer number as parameters.\n3) Get the union of all the wiring layer polygons that\n are connected. Update this to check for vias.\n4) Get the intersection of the moat layer with the\n wiring layer and save this in a new polygon.\n5) Get the difference of the moat layer with the\n wiring layer and save this in a new polygon.\n6) Join the intersected and difference polygons\n to form a list of atleast 3 polygons.\n7) We now know which part of the wiring layer\n goes over the moat is most probably mutually\n connected to wiring layer 2.\n8) Send this polygon structure to GMSH.\n\"\"\"\n\n\ndef _cell_accepted(args):\n \"\"\"\n Filter the unused cellreferences using the seleted cell\n as the new top-level cell.\n \"\"\"\n\n gds_file = os.getcwd() + '/' + args[''] + '.gds'\n gdsii = gdspy.GdsLibrary()\n gdsii.read_gds(gds_file, unit=1.0e-12)\n\n accept = True\n\n name = args['']\n\n if name is None:\n tools.list_layout_cells(gdsii)\n accept = False\n elif name == 'ntrons':\n tools.list_ntron_cells(gdsii)\n accept = False\n elif name == 'jjs':\n tools.list_jj_cells(gdsii)\n accept = False\n elif name == 'vias':\n tools.list_via_cells(gdsii)\n accept = False\n else:\n if name not in gdsii.cell_dict.keys():\n raise ValueError('not a valid cell name')\n\n if accept:\n gdspy.GdsLibrary(name='yuna_library')\n usercell = gdsii.extract(name)\n\n if args['--select']:\n cells = [usercell]\n for cell in usercell.get_dependencies(True):\n cells.append(cell)\n\n gdscell.write_gds(usercell.name + '.gds', \n cells,\n name='yuna_library',\n unit=1.0e-12)\n\n return gdsii, usercell\n return None, None\n\n\ndef _raise_phoenix(args):\n print('\\n----- ExVerify -----')\n\n args['--combine'] = not args['--combine']\n args['--filter'] = not args['--filter']\n args['--edges'] = not args['--edges']\n\n tools.parameter_print(args)\n\n tools.args = args\n\n if args['--log'] == 'debug':\n logging.basicConfig(level=logging.DEBUG)\n elif args['--log'] == 'info':\n logging.basicConfig(level=logging.INFO)\n\n print(colored('gdspy ', 'green'), end='')\n print('version - {}'.format(gdspy.__version__))\n print(colored('pygmsh ', 'green'), end='')\n print('version - {}'.format(pygmsh.__version__))\n print(colored('yuna ', 'green'), end='')\n print('version - {}'.format(yuna.__version__))\n print(colored('auron ', 'green'), end='')\n print('version - {}'.format(auron.__version__))\n print(colored('rikku ', 'green'), end='')\n print('version - {}'.format(rikku.__version__))\n\n\ndef phoenixdown():\n \"\"\"\n Main function of the Auron package.\n Generates a subgraph for each wirechain\n and then combines them into one graph network.\n \"\"\"\n\n args = docopt(__doc__, version=version.__version__)\n\n _raise_phoenix(args)\n\n gdsii, cell = _cell_accepted(args)\n\n if args[''] is not None:\n pdk_file = os.getcwd() + '/' + args[''] + '.json'\n\n json_devices = {'via': 1, 'ntron': 7}\n\n if cell is not None:\n yuna_geom = yuna.grand_summon(cell,\n pdk_file,\n json_devices)\n\n if args['--model']:\n model = rikku.mix(yuna_geom)\n print(model)\n else:\n layoutgraph = auron.bushido(yuna_geom,\n args['--combine'],\n args['--filter'],\n args['--edges'],\n args['--plot'])\n\n g1 = layoutgraph.g.copy()\n\n if g1 is None:\n print('... no graph was generated')\n else:\n convert.to_netlist(g1, args[''])\n g2 = convert.to_graph(args[''])\n\n GM = isomorphism.GraphMatcher(g1, g2)\n if GM.is_isomorphic():\n print('\\nYES - LN and SN matches :)\\n')\n else:\n print('\\nNO - LN & SN does not match :(\\n')\n", "sub_path": "exverify/overdrive.py", "file_name": "overdrive.py", "file_ext": "py", "file_size_in_byte": 6214, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "os.getcwd", "line_number": 97, "usage_type": "call"}, {"api_name": "gdspy.GdsLibrary", "line_number": 98, "usage_type": "call"}, {"api_name": "exverify.tools.list_layout_cells", "line_number": 106, "usage_type": "call"}, {"api_name": "exverify.tools", "line_number": 106, "usage_type": "name"}, {"api_name": "exverify.tools.list_ntron_cells", "line_number": 109, "usage_type": "call"}, {"api_name": "exverify.tools", "line_number": 109, "usage_type": "name"}, {"api_name": "exverify.tools.list_jj_cells", "line_number": 112, "usage_type": "call"}, {"api_name": "exverify.tools", "line_number": 112, "usage_type": "name"}, {"api_name": "exverify.tools.list_via_cells", "line_number": 115, "usage_type": "call"}, {"api_name": "exverify.tools", "line_number": 115, "usage_type": "name"}, {"api_name": "gdspy.GdsLibrary", "line_number": 122, "usage_type": "call"}, {"api_name": "gdspy.write_gds", "line_number": 130, "usage_type": "call"}, {"api_name": "exverify.tools.parameter_print", "line_number": 146, "usage_type": "call"}, {"api_name": "exverify.tools", "line_number": 146, "usage_type": "name"}, {"api_name": "exverify.tools.args", "line_number": 148, "usage_type": "attribute"}, {"api_name": "exverify.tools", "line_number": 148, "usage_type": "name"}, {"api_name": "tools.logging.basicConfig", "line_number": 151, "usage_type": "call"}, {"api_name": "tools.logging", "line_number": 151, "usage_type": "name"}, {"api_name": "tools.logging.DEBUG", "line_number": 151, "usage_type": "attribute"}, {"api_name": "tools.logging.basicConfig", "line_number": 153, "usage_type": "call"}, {"api_name": "tools.logging", "line_number": 153, "usage_type": "name"}, {"api_name": "tools.logging.INFO", "line_number": 153, "usage_type": "attribute"}, {"api_name": "termcolor.colored", "line_number": 155, "usage_type": "call"}, {"api_name": "gdspy.__version__", "line_number": 156, "usage_type": "attribute"}, {"api_name": "termcolor.colored", "line_number": 157, "usage_type": "call"}, {"api_name": "pygmsh.__version__", "line_number": 158, "usage_type": "attribute"}, {"api_name": "termcolor.colored", "line_number": 159, "usage_type": "call"}, {"api_name": "yuna.__version__", "line_number": 160, "usage_type": "attribute"}, {"api_name": "termcolor.colored", "line_number": 161, "usage_type": "call"}, {"api_name": "auron.__version__", "line_number": 162, "usage_type": "attribute"}, {"api_name": "termcolor.colored", "line_number": 163, "usage_type": "call"}, {"api_name": "rikku.__version__", "line_number": 164, "usage_type": "attribute"}, {"api_name": "docopt.docopt", "line_number": 174, "usage_type": "call"}, {"api_name": "exverify.version.__version__", "line_number": 174, "usage_type": "attribute"}, {"api_name": "exverify.version", "line_number": 174, "usage_type": "name"}, {"api_name": "os.getcwd", "line_number": 181, "usage_type": "call"}, {"api_name": "yuna.grand_summon", "line_number": 186, "usage_type": "call"}, {"api_name": "rikku.mix", "line_number": 191, "usage_type": "call"}, {"api_name": "auron.bushido", "line_number": 194, "usage_type": "call"}, {"api_name": "exverify.convert.to_netlist", "line_number": 205, "usage_type": "call"}, {"api_name": "exverify.convert", "line_number": 205, "usage_type": "name"}, {"api_name": "exverify.convert.to_graph", "line_number": 206, "usage_type": "call"}, {"api_name": "exverify.convert", "line_number": 206, "usage_type": "name"}, {"api_name": "networkx.algorithms.isomorphism.GraphMatcher", "line_number": 208, "usage_type": "call"}, {"api_name": "networkx.algorithms.isomorphism", "line_number": 208, "usage_type": "name"}]} +{"seq_id": "239730244", "text": "import pickle\nimport random\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom transformers import *\n\n\ndevice = 'cpu'\nif torch.cuda.is_available():\n device = 'cuda'\n\nSTART_TAG: str = ''\nSTOP_TAG: str = ''\n \n \nimport string\n\nchars = string.ascii_letters + string.digits + string.punctuation.strip()\n\nchar2idx = {\"\":0}\nfor c in chars:\n char2idx[c] = len(char2idx)\nidx2char = {v:k for k,v in char2idx.items()}\n\n\nclass BertTagger(torch.nn.Module):\n def __init__(self, bert_model_name, tag2idx, num_hidden_layers=2, max_sent_length=100, use_crf=True):\n super(BertTagger, self).__init__()\n \n if bert_model_name=='allenai/scibert_scivocab_uncased':\n self.bert = AutoModel.from_pretrained(bert_model_name)\n else:\n self.bert = BertModel.from_pretrained(bert_model_name, output_hidden_states=False, num_hidden_layers=num_hidden_layers, num_attention_heads=12)\n \n self.char_emb_dim = 50\n self.char_emb = torch.nn.Embedding(len(char2idx), self.char_emb_dim)\n self.conv1_padding = 0\n self.dilation = 1\n self.kernel_size = (3,1)\n self.stride = 1\n self.conv1 = torch.nn.Sequential(\n torch.nn.Conv1d(in_channels=1, out_channels=64, kernel_size=self.kernel_size,\n stride=self.stride, dilation=self.dilation,\n bias=True, padding=self.conv1_padding, padding_mode='rand'),\n torch.nn.ReLU(),\n \n )\n self.pool = torch.nn.MaxPool1d(kernel_size=3, stride=3)\n self.conv1_out_dim = (self.char_emb_dim+2*self.conv1_padding-self.dilation*(self.kernel_size[0]-1))//3\n print(\"char embedding dim: \", self.conv1_out_dim)\n self.linear = torch.nn.Linear(256*2, len(tag2idx))\n self.dropout = torch.nn.Dropout(0.1)\n self.rnn = torch.nn.LSTM(768+self.conv1_out_dim, 256, batch_first=True, bidirectional=True, num_layers=1)\n self.dropout2 = torch.nn.Dropout(0.1)\n \n if use_crf:\n self.crf = CRF(len(tag2idx), tag_dict=tag2idx)\n\n if torch.cuda.is_available():\n self.cuda()\n\n def forward(self, input, attention_mask, tokens_batch):\n self.zero_grad()\n out = self.bert(input, attention_mask=attention_mask)\n state = out[0] \n state = self.dropout(state)\n \n ############# char embedding ########\n batch_size, batch_sent_length = state.shape[0], state.shape[1]\n batch_char_embedding = torch.zeros(batch_size, batch_sent_length, self.conv1_out_dim).to(device)\n for i, tokens in enumerate(tokens_batch):\n sent_length = len(tokens)\n max_char_length = max(len(tk) if not tk.startswith('##') else len(tk)-2 for tk in tokens)\n \n onehot = [[char2idx[''] for _ in range(max_char_length)] for _ in range(sent_length)]\n for tid, tk in enumerate(tokens):\n if tk.startswith('##'):\n for cid in range(len(tk[2:])):\n onehot[tid][cid] = char2idx[tk[2+cid]]\n else:\n for cid in range(len(tk)):\n onehot[tid][cid] = char2idx[tk[cid]]\n \n onehot = torch.LongTensor(onehot).to(device)\n \n seq_char_embedding = self.char_emb(onehot)\n conv_seq = self.conv1(seq_char_embedding.unsqueeze(1))\n conv_seq = torch.mean(conv_seq, dim=1)\n pool_seq = F.max_pool1d(conv_seq, kernel_size=3)\n emb_seq = torch.max(pool_seq, dim=1)[0]\n \n batch_char_embedding[i, :sent_length] = emb_seq\n \n state = torch.cat((state, batch_char_embedding), dim=2)\n #####################################\n \n \n rnn_output, (final_hidden, _) = self.rnn(state)\n rnn_output = self.dropout2(rnn_output)\n logit = self.linear(rnn_output)\n \n return logit\n\n \n \nSTART_TAG: str = ''\nSTOP_TAG: str = ''\n\n\ndef to_scalar(var):\n return var.view(-1).data.tolist()[0]\n\n\ndef argmax(vec):\n _, idx = torch.max(vec, 1)\n return to_scalar(idx)\n\n\ndef log_sum_exp(vec):\n max_score = vec[0, argmax(vec)]\n max_score_broadcast = max_score.view(1, -1).expand(1, vec.size()[1])\n return max_score + \\\n torch.log(torch.sum(torch.exp(vec - max_score_broadcast)))\n\n\ndef argmax_batch(vecs):\n _, idx = torch.max(vecs, 1)\n return idx\n\n\ndef log_sum_exp_batch(vecs):\n maxi = torch.max(vecs, 1)[0]\n maxi_bc = maxi[:, None].repeat(1, vecs.shape[1])\n recti_ = torch.log(torch.sum(torch.exp(vecs - maxi_bc), 1))\n return maxi + recti_\n\n\ndef pad_tensors(tensor_list, type_=torch.FloatTensor):\n ml = max([x.shape[0] for x in tensor_list])\n shape = [len(tensor_list), ml] + list(tensor_list[0].shape[1:])\n template = type_(*shape)\n template.fill_(0)\n lens_ = [x.shape[0] for x in tensor_list]\n for i, tensor in enumerate(tensor_list):\n template[i, :lens_[i]] = tensor\n\n return template, lens_\n\nclass CRF(torch.nn.Module):\n def __init__(self, tagset_size, tag_dict):\n super(CRF, self).__init__()\n self.tagset_size = tagset_size\n self.tag_dict = tag_dict\n self.transitions = torch.nn.Parameter(torch.randn(self.tagset_size, self.tagset_size))\n self.transitions.data[self.tag_dict[START_TAG], :] = -10000.\n self.transitions.data[:, self.tag_dict[STOP_TAG]] = -10000.\n\n if torch.cuda.is_available():\n self.cuda()\n\n def neg_log_likelihood(self, rnn_out, tags, tags_prob, lengths):\n\n if torch.cuda.is_available():\n tags, _ = pad_tensors(tags, torch.cuda.LongTensor)\n else:\n tags, _ = pad_tensors(tags, torch.LongTensor)\n\n forward_score = self._forward_alg(rnn_out[:len(tags), :, :], lengths)\n gold_score = self._score_sentence(rnn_out[:len(tags), :, :], tags, tags_prob, lengths)\n\n score = torch.abs(forward_score - gold_score)\n\n return score.mean()\n\n def _forward_alg(self, feats, lens_):\n init_alphas = torch.Tensor(self.tagset_size).fill_(-10000.)\n init_alphas[self.tag_dict[START_TAG]] = 0.\n forward_var = torch.FloatTensor(feats.shape[0], feats.shape[1] + 1, feats.shape[2]).fill_(0)\n\n forward_var[:, 0, :] = init_alphas[None, :].repeat(feats.shape[0], 1)\n if torch.cuda.is_available():\n forward_var = forward_var.cuda()\n\n transitions = self.transitions.view(\n 1, self.transitions.shape[0], self.transitions.shape[1]\n ).repeat(feats.shape[0], 1, 1)\n\n for i in range(feats.shape[1]):\n emit_score = feats[:, i, :]\n tag_var = \\\n emit_score[:, :, None].repeat(1, 1, transitions.shape[2]) + \\\n transitions + \\\n forward_var[:, i, :][:, :, None].repeat(1, 1, transitions.shape[2]).transpose(2, 1)\n\n max_tag_var, _ = torch.max(tag_var, dim=2)\n tag_var = tag_var - max_tag_var[:, :, None].repeat(1, 1, transitions.shape[2])\n\n agg_ = torch.log(torch.sum(torch.exp(tag_var), dim=2))\n\n cloned = forward_var.clone()\n cloned[:, i + 1, :] = max_tag_var + agg_\n\n forward_var = cloned\n\n forward_var = forward_var[range(forward_var.shape[0]), lens_, :]\n terminal_var = forward_var + \\\n self.transitions[self.tag_dict[STOP_TAG]][None, :].repeat(forward_var.shape[0],\n 1)\n\n alpha = log_sum_exp_batch(terminal_var)\n return alpha\n\n def _score_sentence(self, feats, tags, tags_prob, lens_):\n \n start = torch.LongTensor([self.tag_dict[START_TAG]])\n start = start[None, :].repeat(tags.shape[0], 1)\n stop = torch.LongTensor([self.tag_dict[STOP_TAG]])\n stop = stop[None, :].repeat(tags.shape[0], 1)\n if torch.cuda.is_available():\n start = start.cuda()\n stop = stop.cuda()\n\n pad_start_tags = torch.cat([start, tags], 1)\n pad_stop_tags = torch.cat([tags, stop], 1)\n\n for i in range(len(lens_)):\n pad_stop_tags[i, lens_[i]:] = self.tag_dict[STOP_TAG]\n\n score = torch.FloatTensor(feats.shape[0])\n\n if torch.cuda.is_available():\n score = score.cuda()\n\n\n start_prob, end_prob = torch.Tensor([1.0]), torch.Tensor([1.0])\n if torch.cuda.is_available():\n start_prob = start_prob.cuda()\n end_prob = end_prob.cuda()\n\n\n for i in range(feats.shape[0]):\n r = torch.LongTensor(range(lens_[i]))\n if torch.cuda.is_available():\n r = r.cuda()\n \n if tags_prob:\n feats_prob = feats[i, r, tags[i, :lens_[i]]] * tags_prob[i][r, tags[i, :lens_[i]]]\n\n pad_start_tags_prob = torch.cat((start_prob, tags_prob[i][r, tags[i, :lens_[i]]]))\n pad_end_tags_prob = torch.cat((tags_prob[i][r, tags[i, :lens_[i]]], end_prob))\n\n score[i] = \\\n torch.sum(\n self.transitions[pad_stop_tags[i, :lens_[i] + 1], pad_start_tags[i, :lens_[i] + 1]] * pad_start_tags_prob * pad_end_tags_prob\n ) + torch.sum(feats[i,:lens_[i],:] * tags_prob[i])\n# torch.sum(feats_prob)\n \n else:\n score[i] = \\\n torch.sum(\n self.transitions[pad_stop_tags[i, :lens_[i] + 1], pad_start_tags[i, :lens_[i] + 1]]\n ) + \\\n torch.sum(feats[i, r, tags[i, :lens_[i]]])\n\n return score\n\n def viterbi_decode(self, feats):\n backpointers, backscores = [], []\n\n init_vvars = torch.Tensor(1, self.tagset_size).fill_(-10000.0)\n init_vvars[0][self.tag_dict[START_TAG]] = 0\n forward_var = init_vvars\n if torch.cuda.is_available():\n forward_var = forward_var.cuda()\n\n for feat in feats:\n next_tag_var = forward_var.view(1, -1).expand(self.tagset_size, self.tagset_size) + self.transitions\n _, bptrs_t = torch.max(next_tag_var, dim=1)\n # bptrs_t = bptrs_t.squeeze().data.cpu().numpy()\n # next_tag_var = next_tag_var.data.cpus().numpy()\n viterbivars_t = next_tag_var[range(len(bptrs_t)), bptrs_t]\n forward_var = viterbivars_t + feat\n backscores.append(forward_var)\n backpointers.append(bptrs_t)\n\n terminal_var = forward_var + self.transitions[self.tag_dict[STOP_TAG]]\n terminal_var.data[self.tag_dict[STOP_TAG]] = -10000.\n terminal_var.data[self.tag_dict[START_TAG]] = -10000.\n best_tag_id = argmax(terminal_var.unsqueeze(0))\n\n best_path = [best_tag_id]\n\n for bptrs_t in reversed(backpointers):\n best_tag_id = bptrs_t[best_tag_id]\n best_path.append(best_tag_id)\n best_scores = []\n for backscore in backscores:\n softmax = F.softmax(backscore, dim=0)\n _, idx = torch.max(backscore, 0)\n prediction = idx.item()\n best_scores.append(softmax[prediction].item())\n\n start = best_path.pop()\n assert start == self.tag_dict[START_TAG]\n best_path.reverse()\n return best_scores, best_path", "sub_path": "code-NCBI/tagger_models.py", "file_name": "tagger_models.py", "file_ext": "py", "file_size_in_byte": 11459, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "torch.cuda.is_available", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 11, "usage_type": "attribute"}, {"api_name": "string.ascii_letters", "line_number": 20, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 20, "usage_type": "attribute"}, {"api_name": "string.punctuation.strip", "line_number": 20, "usage_type": "call"}, {"api_name": "string.punctuation", "line_number": 20, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.nn.Embedding", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 38, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 43, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv1d", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 44, "usage_type": "attribute"}, {"api_name": "torch.nn.ReLU", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "attribute"}, {"api_name": "torch.nn.MaxPool1d", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "attribute"}, {"api_name": "torch.nn.Dropout", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "attribute"}, {"api_name": "torch.nn.LSTM", "line_number": 55, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 55, "usage_type": "attribute"}, {"api_name": "torch.nn.Dropout", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 61, "usage_type": "attribute"}, {"api_name": "torch.zeros", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.nn.functional.max_pool1d", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 91, "usage_type": "name"}, {"api_name": "torch.max", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 125, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 140, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 151, "usage_type": "attribute"}, {"api_name": "torch.nn.Parameter", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 156, "usage_type": "attribute"}, {"api_name": "torch.randn", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 160, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 160, "usage_type": "attribute"}, {"api_name": "torch.cuda.is_available", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 165, "usage_type": "attribute"}, {"api_name": "torch.cuda", "line_number": 166, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 168, "usage_type": "attribute"}, {"api_name": "torch.abs", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 178, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 180, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 183, "usage_type": "attribute"}, {"api_name": "torch.max", "line_number": 197, "usage_type": "call"}, {"api_name": "torch.log", "line_number": 200, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 200, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 200, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 217, "usage_type": "call"}, {"api_name": "torch.LongTensor", "line_number": 219, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 221, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 221, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 225, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 226, "usage_type": "call"}, {"api_name": "torch.FloatTensor", "line_number": 231, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 233, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 233, "usage_type": "attribute"}, {"api_name": "torch.Tensor", "line_number": 237, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 238, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 238, "usage_type": "attribute"}, {"api_name": "torch.LongTensor", "line_number": 244, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 245, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 245, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 251, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 252, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 255, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 257, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 262, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 265, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 272, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 275, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 275, "usage_type": "attribute"}, {"api_name": "torch.max", "line_number": 280, "usage_type": "call"}, {"api_name": "torch.nn.functional.softmax", "line_number": 300, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 300, "usage_type": "name"}, {"api_name": "torch.max", "line_number": 301, "usage_type": "call"}]} +{"seq_id": "413400841", "text": "from google.appengine.ext import db\nfrom datetime import date, datetime\nfrom dataFile import FileObject, dataObject\nfrom timeUtilities import GMT1, GMT2, UTC\nimport os, time\nimport cgi\nimport webapp2\n\n\nclass sensorsHandler(webapp2.RequestHandler):\n\n\tdef get(self):\n\t\tutc = datetime.utcnow()\n\t\tself.response.write(\"current date \" + utc.strftime(\"%A %d %B %Y %I:%M%p\") + '
')\n\n\t\tUKTime = datetime.fromtimestamp(time.mktime(utc.timetuple()), GMT1())\n\t\tRwandaTime = datetime.fromtimestamp(time.mktime(utc.timetuple()), GMT2())\n\n\t\tself.response.write(\"UK Time: \" + UKTime.ctime());\n\t\tself.response.write(\"
UK Timestamp \" + str(time.mktime(UKTime.timetuple())) )\n\t\tself.response.write(\"
Rwanda Time: \" + RwandaTime.ctime() + \"
\");\n\n\t\tdata_query = db.GqlQuery(\"SELECT * \"\n\t\t\t\t\t\t\t\t \"FROM dataObject \"\n\t\t\t\t\t\t\t\t \"ORDER BY tdate DESC LIMIT 1\"\n \t)\n\n\t\tself.response.out.write(\"
Number of rows: \" + str( data_query.count()) )\n\n\n\t\tnewObject = dataObject()\n\t\tnewObject.put()\n\n\t\tif data_query is not None:\n\n\t\t\tfor temp in data_query:\n\t\t\t\tself.response.write('
ACcurrent1 = %s
' % cgi.escape( str(temp.ac_current1) ) )\n\t\t\t\tself.response.write('
ACcurrent2 = %s
' % cgi.escape( str(temp.ac_current2) ) )\n\t\t\t\tself.response.write('
ACvoltage1 = %s
' % cgi.escape( str(temp.ac_voltage2) ) )\n\t\t\t\tself.response.write('
ACvoltage2 = %s
' % cgi.escape( str(temp.ac_voltage2) ) )\n\n\t\t\t\tself.response.write('
DCcurrent1 = %s
' % cgi.escape( str(temp.dc_current1) ) )\n\t\t\t\tself.response.write('
DCcurrent2 = %s
' % cgi.escape( str(temp.dc_current2) ) )\n\t\t\t\tself.response.write('
DCcurrent3 = %s
' % cgi.escape( str(temp.dc_current3) ) )\n\t\t\t\tself.response.write('
DCcurrent4 = %s
' % cgi.escape( str(temp.dc_current4) ) )\n\n\t\t\t\tself.response.out.write('
DCvoltage1 = %s
' % cgi.escape( str(temp.dc_voltage1) ) )\n\t\t\t\tself.response.out.write('
DCvoltage1 = %s
' % cgi.escape( str(temp.dc_voltage2) ) )\n\t\t\t\tself.response.out.write('
DCvoltage1 = %s
' % cgi.escape( str(temp.dc_voltage3) ) )\n\t\t\t\tself.response.out.write('
DCvoltage1 = %s
' % cgi.escape( str(temp.dc_voltage4) ) )\n\n\tdef post(self):\n\t\t# get data from mbed and write it to a buffer\n\t\tbuf = self.request.get('e.quinoxsensors') \n\n\t\t# convert data to an array by detecting whitespace\n\t\tarray = buf.split(' ')\n\n\t\tutc = datetime.utcnow()\n\t\tUKtime = datetime.fromtimestamp(time.mktime(utc.timetuple()), GMT1())\n\n\n\t\tdata_query = db.GqlQuery(\"SELECT * \"\n\t\t\t\t\t\t\t\t \"FROM dataObject \"\n\t\t\t\t\t\t\t\t)\n\t\tcount = data_query.count()\n\n\t\tif not (buf == ''):\n\t\t\ti = 0\n\n\t\t\tfor index, item in enumerate(array):\n\t\t\t\ti = i+1\n\n\t\t\t\tif( i%12 == 0):\n\t\t\t\t\tcount = count + 1\n\n\t\t\t\t\tnewObject = dataObject(tdate = UKtime)\n\t\t\t\t\tnewObject.sampleTime = int(array[index - 12])\n\t\t\t\t\tnewObject.ac_current1 = int(array[index - 11])\n\t\t\t\t\tnewObject.ac_current2 = int(array[index - 10])\n\t\t\t\t\tnewObject.ac_voltage1 = int(array[index - 9])\n\t\t\t\t\tnewObject.ac_voltage2 = int(array[index - 8])\n\n\t\t\t\t\tnewObject.dc_current1 = int(array[index - 7])\n\t\t\t\t\tnewObject.dc_current2 = int(array[index - 6])\n\t\t\t\t\tnewObject.dc_current3 = int(array[index - 5])\n\t\t\t\t\tnewObject.dc_current4 = int(array[index - 4])\n\n\t\t\t\t\tnewObject.dc_voltage1 = int(array[index - 3])\n\t\t\t\t\tnewObject.dc_voltage2 = int(array[index - 2])\n\t\t\t\t\tnewObject.dc_voltage3 = int(array[index - 1])\n\t\t\t\t\tnewObject.dc_voltage4 = int(array[index])\n\t\t\t\t\tnewObject.no = count\n\t\t\t\t\tnewObject.put()\n\n\t\t\ttmp = UKtime.strftime(\"%A %d %B %Y %I:%M%p\")\n\t\t\tself.response.write('Google App Engine Web server received file on ' + tmp+ ' (UKTime)')\n\nclass logHandler(webapp2.RequestHandler):\n\tdef get(self):\n\t\tquery = db.GqlQuery(\"SELECT * \"\n\t\t\t\t\t\t\t\"FROM FileObject \"\n\t\t\t\t\t\t\t\"ORDER BY tdate DESC LIMIT 1\"\n\t\t\t\t\t\t\t)\n\n\t\tif query is not None:\n\t\t\tfor tmp in query:\n\t\t\t\tself.response.write('log :
' + cgi.escape(tmp.text))\n\n\tdef post(self):\n\t\tbuf = self.request.get('e.quinoxlog')\n\n\t\tif not ( buf == ''):\n\t\t\tUKtime = datetime.fromtimestamp(time.mktime(datetime.utcnow().timetuple()), GMT1())\n\t\t\tnewObject = FileObject(text = buf, tdate = UKtime)\n\t\t\tnewObject.put()\n\n\t\t\t_time = UKtime.strftime(\"%A %d %B %Y %I:%M%p\")\n\t\t\tself.response.write('Google App Engine Web server received file on ' + _time + ' (UKTime)')\n\n\n\n\n\n\n", "sub_path": "2012-2013/Software/Google App Engine/datalogger2013Batima/modemUtilities.py", "file_name": "modemUtilities.py", "file_ext": "py", "file_size_in_byte": 4470, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "webapp2.RequestHandler", "line_number": 10, "usage_type": "attribute"}, {"api_name": "datetime.datetime.utcnow", "line_number": 13, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 13, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 16, "usage_type": "name"}, {"api_name": "time.mktime", "line_number": 16, "usage_type": "call"}, {"api_name": "timeUtilities.GMT1", "line_number": 16, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 17, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 17, "usage_type": "name"}, {"api_name": "time.mktime", "line_number": 17, "usage_type": "call"}, {"api_name": "timeUtilities.GMT2", "line_number": 17, "usage_type": "call"}, {"api_name": "time.mktime", "line_number": 20, "usage_type": "call"}, {"api_name": "google.appengine.ext.db.GqlQuery", "line_number": 23, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 23, "usage_type": "name"}, {"api_name": "dataFile.dataObject", "line_number": 31, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 37, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 38, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 39, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 40, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 42, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 43, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 44, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 45, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 47, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 48, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 49, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 50, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 59, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 59, "usage_type": "name"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 60, "usage_type": "name"}, {"api_name": "time.mktime", "line_number": 60, "usage_type": "call"}, {"api_name": "timeUtilities.GMT1", "line_number": 60, "usage_type": "call"}, {"api_name": "google.appengine.ext.db.GqlQuery", "line_number": 63, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 63, "usage_type": "name"}, {"api_name": "dataFile.dataObject", "line_number": 77, "usage_type": "call"}, {"api_name": "webapp2.RequestHandler", "line_number": 99, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.db.GqlQuery", "line_number": 101, "usage_type": "call"}, {"api_name": "google.appengine.ext.db", "line_number": 101, "usage_type": "name"}, {"api_name": "cgi.escape", "line_number": 108, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 114, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 114, "usage_type": "name"}, {"api_name": "time.mktime", "line_number": 114, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 114, "usage_type": "call"}, {"api_name": "timeUtilities.GMT1", "line_number": 114, "usage_type": "call"}, {"api_name": "dataFile.FileObject", "line_number": 115, "usage_type": "call"}]} +{"seq_id": "173273290", "text": "# -*- coding: utf-8 -*-\nimport scrapy\nfrom doubanSpider.items import DoubanspiderItem\n\nclass MoviespiderSpider(scrapy.Spider):\n name = 'movieSpider'\n allowed_domains = ['movie.douban.com']\n url = \"https://movie.douban.com/top250?start=\"\n offset = 0\n start_urls = [url+str(offset)]\n\n def parse(self, response):\n item = DoubanspiderItem()\n movies = response.xpath('//div[@class=\"item\"]')\n for each in movies:\n item[\"movie_name\"] = each.xpath('.//div[@class=\"hd\"]//a//span[1]//text()').extract()[0]\n item[\"movie_star\"] = each.xpath('.//div[@class=\"star\"]//span[2]//text()').extract()[0]\n item[\"movie_star_person\"] = each.xpath('.//div[@class=\"star\"]//span[4]//text()').extract()[0]\n item[\"movie_summary\"] = each.xpath('.//p[@class=\"quote\"]//span[1]//text()').extract()[0]\n item[\"movie_info_url\"] = each.xpath('.//div[@class=\"pic\"]//a//@href').extract()[0]\n item[\"movie_image\"] = each.xpath('.//div[@class=\"pic\"]//a//img//@src').extract()[0]\n\n yield item\n\n if self.offset < 250:\n self.offset += 25\n yield scrapy.Request(self.url+str(self.offset), callback=self.parse)", "sub_path": "doubanSpider/doubanSpider/spiders/movieSpider.py", "file_name": "movieSpider.py", "file_ext": "py", "file_size_in_byte": 1204, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "scrapy.Spider", "line_number": 5, "usage_type": "attribute"}, {"api_name": "doubanSpider.items.DoubanspiderItem", "line_number": 13, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "54339954", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import (absolute_import, division, print_function,\n unicode_literals)\n\nfrom rq import Connection, Queue, Worker\n\nlisten = ['queue1', 'queue2', 'queue3']\n\nif __name__ == '__main__':\n # Tell rq what Redis connection to use\n with Connection():\n q = Queue()\n Worker(map(q, listen), round_robin=True).work()\n", "sub_path": "examples/run_worker_round_robin.py", "file_name": "run_worker_round_robin.py", "file_ext": "py", "file_size_in_byte": 386, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "rq.Connection", "line_number": 11, "usage_type": "call"}, {"api_name": "rq.Queue", "line_number": 12, "usage_type": "call"}, {"api_name": "rq.Worker", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "274400385", "text": "import os\nimport luigi\nimport bingads\nimport yaml\nimport logging\nfrom suds.client import Client\nfrom datetime import date, timedelta, datetime\n\nfrom bingads.service_client import ServiceClient\nfrom bingads.authorization import *\nfrom bingads.v12.reporting import *\n\nfrom luigi.contrib.s3 import S3Client, S3Target\n\nimport bing_client_helper\n\nTHIS_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)))\nwith open(os.path.join(THIS_DIR, 'config.yml'), 'r') as yml_in:\n CONFIG = yaml.load(yml_in)\n\nREPORT_FILE_FORMAT = 'Csv'\n\nauthorization_data = None\n\nDATE = datetime.now().strftime(\"%Y_%m_%d\")\nFILE_NAME = \"BingAds_Report_{0}.csv\".format(DATE)\n\n\nclass PullCampaignReportsFromAPI(luigi.Task):\n\n def parse_report_and_append(self, input_file):\n file_in = open(input_file, \"r\")\n lines = file_in.readlines()\n\n row_count = lines[8][7:-3]\n rows = lines[10: 11 + int(row_count)]\n\n with self.output().open(\"r\") as mem_file:\n mem_lines = mem_file.read()\n\n with self.output().open(\"w\") as file_out:\n file_out.write(mem_lines)\n for line in rows[1:]:\n row = line.split(\",\")\n\n # Strip quotes from numeric fields\n for i in range(4, 10):\n row[i] = row[i][1:-1]\n\n file_out.write(','.join(row))\n\n file_in.close()\n os.remove(input_file)\n\n def get_campaign_performance_report_request(self, account_id, campaign_ids):\n \"\"\"\"\n Build a campaign performance report request, including Format, ReportName,\n Time, and Columns.\n \"\"\"\n reporting_service = ServiceClient(\n 'ReportingService',\n version=12,\n authorization_data=authorization_data,\n environment=CONFIG['api']['environment'],\n )\n\n report_request = reporting_service.factory.create('CampaignPerformanceReportRequest')\n report_request.Format = REPORT_FILE_FORMAT\n report_request.ReportName = 'My Campaign Performance Report'\n report_request.ReturnOnlyCompleteData = False\n report_request.Aggregation = 'Daily'\n report_request.Language = 'English'\n\n scope = reporting_service.factory.create('AccountThroughCampaignReportScope')\n if campaign_ids is None:\n scope.AccountIds = {'long': account_id}\n scope.Campaigns = None\n else:\n scope.AccountIds = None\n campaigns = reporting_service.factory.create('ArrayOfCampaignReportScope')\n for campaign_id in campaign_ids['long']:\n campaign_report_scope = reporting_service.factory.create('CampaignReportScope')\n campaign_report_scope.AccountId = authorization_data.account_id\n campaign_report_scope.CampaignId = campaign_id\n campaigns.CampaignReportScope.append(campaign_report_scope)\n scope.Campaigns = campaigns\n\n report_request.Scope = scope\n\n # You may either use a custom date range or predefined time.\n report_time = reporting_service.factory.create('ReportTime')\n report_time.PredefinedTime = 'Yesterday'\n report_time.ReportTimeZone = 'EasternTimeUSCanada'\n report_request.Time = report_time\n\n # Specify columns to include in campaign performance report\n # column names are pulled from config.yml file\n report_columns = reporting_service.factory.create('ArrayOfCampaignPerformanceReportColumn')\n column_list = []\n\n for field in CONFIG['output_report_fields']:\n column_list.append(field)\n\n report_columns.CampaignPerformanceReportColumn.append(column_list)\n report_request.Columns = report_columns\n\n return report_request\n\n def get_reports_for_accounts(self, accounts):\n global authorization_data\n\n with self.output().open(\"w\") as output_file:\n # Pull column header names from config.yml file\n column_list = []\n for field in CONFIG['output_report_fields']:\n column_list.append(field)\n\n # Write column headers to the BingAds Report file\n output_file.write(\", \".join(column_list))\n output_file.write('\\n')\n\n for account in accounts['AdvertiserAccount']:\n\n print(account.Name + ' ' + str(account.Id))\n raw_file_name_string = 'RAW_BingAds_' + str(account.Id) + '_' + DATE\n\n report_request = self.get_campaign_performance_report_request(account.Id, None)\n\n reporting_service_manager = ReportingServiceManager(\n authorization_data=authorization_data,\n poll_interval_in_milliseconds=5000,\n environment=CONFIG['api']['environment'],\n )\n\n staging_directory = os.getcwd()\n reporting_download_parameters = ReportingDownloadParameters(\n report_request=report_request,\n result_file_directory=staging_directory,\n result_file_name=raw_file_name_string,\n overwrite_result_file=True, # Set this value true if you want to overwrite the same file.\n timeout_in_milliseconds=3600000\n )\n\n result_file_path = reporting_service_manager.download_file(reporting_download_parameters)\n\n if result_file_path is not None:\n self.parse_report_and_append(raw_file_name_string)\n\n def run(self):\n # Pulls refresh token from 'refresh.txt'\n # If token doesnt exist, user is prompted to initiate manual OAuth flow\n global authorization_data\n authorization_data = bing_client_helper.authenticate_with_oauth()\n\n customer_service = ServiceClient(\n 'CustomerManagementService',\n version=12,\n authorization_data=authorization_data,\n environment=CONFIG['api']['environment'],\n )\n\n user = customer_service.GetUser(None).User\n account_list = bing_client_helper.search_accounts_by_user_id(user.Id)\n self.get_reports_for_accounts(account_list)\n\n def output(self):\n return luigi.LocalTarget(FILE_NAME)\n\n\nclass PushCampaignReportToS3(luigi.Task):\n s3_client = S3Client()\n bucket = luigi.Parameter()\n\n def requires(self):\n return PullCampaignReportsFromAPI()\n\n def run(self):\n self.s3_client.put(FILE_NAME, self.s3_key_path)\n\n def output(self):\n s3_uri_template = \"{bucket}/{prefix}/{filename}\"\n s3_prefix = CONFIG[\"s3\"][\"path\"]\n self.s3_key_path = s3_uri_template.format(bucket=self.bucket,\n prefix=s3_prefix,\n filename=FILE_NAME)\n\n return S3Target(self.s3_key_path, client=self.s3_client)\n\n\nif __name__ == '__main__':\n luigi.run(main_task_cls=PushCampaignReportToS3)\n\n", "sub_path": "pull_daily_campaign_reports.py", "file_name": "pull_daily_campaign_reports.py", "file_ext": "py", "file_size_in_byte": 6890, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "name"}, {"api_name": "luigi.Task", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 53, "usage_type": "call"}, {"api_name": "bingads.service_client.ServiceClient", "line_number": 60, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 135, "usage_type": "call"}, {"api_name": "bing_client_helper.authenticate_with_oauth", "line_number": 153, "usage_type": "call"}, {"api_name": "bingads.service_client.ServiceClient", "line_number": 155, "usage_type": "call"}, {"api_name": "bing_client_helper.search_accounts_by_user_id", "line_number": 163, "usage_type": "call"}, {"api_name": "luigi.LocalTarget", "line_number": 167, "usage_type": "call"}, {"api_name": "luigi.Task", "line_number": 170, "usage_type": "attribute"}, {"api_name": "luigi.contrib.s3.S3Client", "line_number": 171, "usage_type": "call"}, {"api_name": "luigi.Parameter", "line_number": 172, "usage_type": "call"}, {"api_name": "luigi.contrib.s3.S3Target", "line_number": 187, "usage_type": "call"}, {"api_name": "luigi.run", "line_number": 191, "usage_type": "call"}]} +{"seq_id": "496719559", "text": "import logging\n\nfrom pylons import request, response, session, tmpl_context as c, url\nfrom pylons.controllers.util import abort, redirect\n\nfrom kazhal.lib.base import BaseController, render\nfrom kazhal.lib import helpers as h\n\nfrom pylons.decorators.rest import restrict\n\nfrom kazhal.model import Session, Group, Permission\nfrom repoze.what.predicates import not_anonymous, has_permission,is_anonymous,in_group\nfrom repoze.what.plugins.pylonshq import ActionProtector\n\nimport formencode\nfrom formencode import htmlfill\nfrom pylons.decorators import validate\n\nfrom schemas import NewGroupForm,EditGroupForm\n\nlog = logging.getLogger(__name__)\nfrom pylons.i18n import set_lang,get_lang,_\n\nclass AddgroupController(BaseController):\n def __before__(self):\n h.setMenuItems(_('menus.dat'))\n self.menu_items = session[_('menus.dat')]\n\n @ActionProtector(in_group('admin'))\n def new(self): \n c.permissions = Session.query(Permission)\n c.menu_items = h.top_menu(self.menu_items,_('Customers'))\n return render('/derived/group/addgroup.html')\n \n \n @restrict('POST')\n @ActionProtector(in_group('admin'))\n @validate(schema=NewGroupForm(), form='new')\n def create(self): \n #if Session.query(Group).filter_by(group=request.POST['group']).one() != None:\n #abort(404)\n newgroup = Group(request.POST['group'])\n newgroup.permissions = self.form_result['permissions'] \n Session.add(newgroup)\n Session.commit()\n h.flash(_('Group successfully Created.'))\n redirect(url(controller='addgroup', action='list'))\n \n @ActionProtector(in_group('admin'))\n def edit(self,id): \n values={}\n group = Session.query(Group).filter_by(id=id).one()\n values['group']= group.group\n\n c.permissions = Session.query(Permission).all()\n for i,perm in enumerate(c.permissions):\n for permission in group.permissions:\n if permission.name == perm.name:\n values['permissions-%i.%i'%(i,perm.id)]= 1 \n\n c.menu_items = h.top_menu(self.menu_items,_('Customers'))\n html = render('/derived/group/edit.html')\n return htmlfill.render(html, defaults=values)\n\n \n @restrict('POST')\n @ActionProtector(in_group('admin'))\n @validate(schema=EditGroupForm(), form='edit') \n def save(self,id):\n if id is None:\n abort(404)\n group = self.form_result['group']\n del self.form_result['group']\n for k,v in self.form_result.items():\n if getattr(group, k) != v:\n setattr(group, k, v) \n Session.add(group)\n Session.commit()\n response.status_int = 302\n response.headers['location'] = url(controller='addgroup', action='list')\n return \"Moved temporarily\"\n\n @ActionProtector(in_group('admin'))\n def delete(self,id):\n if id is None:\n abort(404)\n group = Session.query(Group).filter_by(id=id).one()\n if group is None:\n abort(404)\n h.flash(_('Group successfully deleted.'))\n\n Session.delete(group)\n Session.commit()\n redirect(url(controller='addgroup', action='list'))\n return \"Group Deleted\"\n \n @ActionProtector(in_group('admin'))\n def list(self):\n groups = Session.query(Group).all()\n c.groups = groups\n c.menu_items = h.top_menu(self.menu_items,_('Customers'))\n return render('/derived/group/list.html')\n\n def view(self,id):\n pass\n ", "sub_path": "kazhal/controllers/addgroup.py", "file_name": "addgroup.py", "file_ext": "py", "file_size_in_byte": 3598, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 21, "usage_type": "call"}, {"api_name": "kazhal.lib.base.BaseController", "line_number": 24, "usage_type": "name"}, {"api_name": "kazhal.lib.helpers.setMenuItems", "line_number": 26, "usage_type": "call"}, {"api_name": "kazhal.lib.helpers", "line_number": 26, "usage_type": "name"}, {"api_name": "pylons.i18n._", "line_number": 26, "usage_type": "call"}, {"api_name": "pylons.session", "line_number": 27, "usage_type": "name"}, {"api_name": "pylons.i18n._", "line_number": 27, "usage_type": "call"}, {"api_name": "pylons.tmpl_context.permissions", "line_number": 31, "usage_type": "attribute"}, {"api_name": "pylons.tmpl_context", "line_number": 31, "usage_type": "name"}, {"api_name": "kazhal.model.Session.query", "line_number": 31, "usage_type": "call"}, {"api_name": "kazhal.model.Permission", "line_number": 31, "usage_type": "argument"}, {"api_name": "kazhal.model.Session", "line_number": 31, "usage_type": "name"}, {"api_name": "pylons.tmpl_context.menu_items", "line_number": 32, "usage_type": "attribute"}, {"api_name": "pylons.tmpl_context", "line_number": 32, "usage_type": "name"}, {"api_name": "kazhal.lib.helpers.top_menu", "line_number": 32, "usage_type": "call"}, {"api_name": "kazhal.lib.helpers", "line_number": 32, "usage_type": "name"}, {"api_name": "pylons.i18n._", "line_number": 32, "usage_type": "call"}, {"api_name": "kazhal.lib.base.render", "line_number": 33, "usage_type": "call"}, {"api_name": "repoze.what.plugins.pylonshq.ActionProtector", "line_number": 29, "usage_type": "call"}, {"api_name": "repoze.what.predicates.in_group", "line_number": 29, "usage_type": "call"}, {"api_name": "kazhal.model.Group", "line_number": 42, "usage_type": "call"}, {"api_name": "pylons.request.POST", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pylons.request", "line_number": 42, "usage_type": "name"}, {"api_name": "kazhal.model.Session.add", "line_number": 44, "usage_type": "call"}, {"api_name": "kazhal.model.Session", "line_number": 44, "usage_type": "name"}, {"api_name": "kazhal.model.Session.commit", "line_number": 45, "usage_type": "call"}, {"api_name": "kazhal.model.Session", "line_number": 45, "usage_type": "name"}, {"api_name": "kazhal.lib.helpers.flash", "line_number": 46, "usage_type": "call"}, {"api_name": "kazhal.lib.helpers", "line_number": 46, "usage_type": "name"}, {"api_name": "pylons.i18n._", "line_number": 46, "usage_type": "call"}, {"api_name": "pylons.controllers.util.redirect", "line_number": 47, "usage_type": "call"}, {"api_name": "pylons.url", "line_number": 47, "usage_type": "call"}, {"api_name": "pylons.decorators.rest.restrict", "line_number": 36, "usage_type": "call"}, {"api_name": "repoze.what.plugins.pylonshq.ActionProtector", "line_number": 37, "usage_type": "call"}, {"api_name": "repoze.what.predicates.in_group", "line_number": 37, "usage_type": "call"}, {"api_name": "pylons.decorators.validate", "line_number": 38, "usage_type": "call"}, {"api_name": "schemas.NewGroupForm", "line_number": 38, "usage_type": "call"}, {"api_name": "kazhal.model.Session.query", "line_number": 52, "usage_type": "call"}, {"api_name": "kazhal.model.Group", "line_number": 52, "usage_type": "argument"}, {"api_name": "kazhal.model.Session", "line_number": 52, "usage_type": "name"}, {"api_name": "pylons.tmpl_context.permissions", "line_number": 55, "usage_type": "attribute"}, {"api_name": "pylons.tmpl_context", "line_number": 55, "usage_type": "name"}, {"api_name": "kazhal.model.Session.query", "line_number": 55, "usage_type": "call"}, {"api_name": "kazhal.model.Permission", "line_number": 55, "usage_type": "argument"}, {"api_name": "kazhal.model.Session", "line_number": 55, "usage_type": "name"}, {"api_name": "pylons.tmpl_context.permissions", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pylons.tmpl_context", "line_number": 56, "usage_type": "name"}, {"api_name": "pylons.tmpl_context.menu_items", "line_number": 61, "usage_type": "attribute"}, {"api_name": "pylons.tmpl_context", "line_number": 61, "usage_type": "name"}, {"api_name": "kazhal.lib.helpers.top_menu", "line_number": 61, "usage_type": "call"}, {"api_name": "kazhal.lib.helpers", "line_number": 61, "usage_type": "name"}, {"api_name": "pylons.i18n._", "line_number": 61, "usage_type": "call"}, {"api_name": "kazhal.lib.base.render", "line_number": 62, "usage_type": "call"}, {"api_name": "formencode.htmlfill.render", "line_number": 63, "usage_type": "call"}, {"api_name": "formencode.htmlfill", "line_number": 63, "usage_type": "name"}, {"api_name": "repoze.what.plugins.pylonshq.ActionProtector", "line_number": 49, "usage_type": "call"}, {"api_name": "repoze.what.predicates.in_group", "line_number": 49, "usage_type": "call"}, {"api_name": "pylons.controllers.util.abort", "line_number": 71, "usage_type": "call"}, {"api_name": "kazhal.model.Session.add", "line_number": 77, "usage_type": "call"}, {"api_name": "kazhal.model.Session", "line_number": 77, "usage_type": "name"}, {"api_name": "kazhal.model.Session.commit", "line_number": 78, "usage_type": "call"}, {"api_name": "kazhal.model.Session", "line_number": 78, "usage_type": "name"}, {"api_name": "pylons.response.status_int", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pylons.response", "line_number": 79, "usage_type": "name"}, {"api_name": "pylons.response.headers", "line_number": 80, "usage_type": "attribute"}, {"api_name": "pylons.response", "line_number": 80, "usage_type": "name"}, {"api_name": "pylons.url", "line_number": 80, "usage_type": "call"}, {"api_name": "pylons.decorators.rest.restrict", "line_number": 66, "usage_type": "call"}, {"api_name": "repoze.what.plugins.pylonshq.ActionProtector", "line_number": 67, "usage_type": "call"}, {"api_name": "repoze.what.predicates.in_group", "line_number": 67, "usage_type": "call"}, {"api_name": "pylons.decorators.validate", "line_number": 68, "usage_type": "call"}, {"api_name": "schemas.EditGroupForm", "line_number": 68, "usage_type": "call"}, {"api_name": "pylons.controllers.util.abort", "line_number": 86, "usage_type": "call"}, {"api_name": "kazhal.model.Session.query", "line_number": 87, "usage_type": "call"}, {"api_name": "kazhal.model.Group", "line_number": 87, "usage_type": "argument"}, {"api_name": "kazhal.model.Session", "line_number": 87, "usage_type": "name"}, {"api_name": "pylons.controllers.util.abort", "line_number": 89, "usage_type": "call"}, {"api_name": "kazhal.lib.helpers.flash", "line_number": 90, "usage_type": "call"}, {"api_name": "kazhal.lib.helpers", "line_number": 90, "usage_type": "name"}, {"api_name": "pylons.i18n._", "line_number": 90, "usage_type": "call"}, {"api_name": "kazhal.model.Session.delete", "line_number": 92, "usage_type": "call"}, {"api_name": "kazhal.model.Session", "line_number": 92, "usage_type": "name"}, {"api_name": "kazhal.model.Session.commit", "line_number": 93, "usage_type": "call"}, {"api_name": "kazhal.model.Session", "line_number": 93, "usage_type": "name"}, {"api_name": "pylons.controllers.util.redirect", "line_number": 94, "usage_type": "call"}, {"api_name": "pylons.url", "line_number": 94, "usage_type": "call"}, {"api_name": "repoze.what.plugins.pylonshq.ActionProtector", "line_number": 83, "usage_type": "call"}, {"api_name": "repoze.what.predicates.in_group", "line_number": 83, "usage_type": "call"}, {"api_name": "kazhal.model.Session.query", "line_number": 99, "usage_type": "call"}, {"api_name": "kazhal.model.Group", "line_number": 99, "usage_type": "argument"}, {"api_name": "kazhal.model.Session", "line_number": 99, "usage_type": "name"}, {"api_name": "pylons.tmpl_context.groups", "line_number": 100, "usage_type": "attribute"}, {"api_name": "pylons.tmpl_context", "line_number": 100, "usage_type": "name"}, {"api_name": "pylons.tmpl_context.menu_items", "line_number": 101, "usage_type": "attribute"}, {"api_name": "pylons.tmpl_context", "line_number": 101, "usage_type": "name"}, {"api_name": "kazhal.lib.helpers.top_menu", "line_number": 101, "usage_type": "call"}, {"api_name": "kazhal.lib.helpers", "line_number": 101, "usage_type": "name"}, {"api_name": "pylons.i18n._", "line_number": 101, "usage_type": "call"}, {"api_name": "kazhal.lib.base.render", "line_number": 102, "usage_type": "call"}, {"api_name": "repoze.what.plugins.pylonshq.ActionProtector", "line_number": 97, "usage_type": "call"}, {"api_name": "repoze.what.predicates.in_group", "line_number": 97, "usage_type": "call"}]} +{"seq_id": "118003632", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jun 8 15:57:59 2019\n\n@author: hankui\n\"\"\"\n\n# https://practice.geeksforgeeks.org/problems/find-sum-of-different-corresponding-bits-for-all-pairs/0\n\n#%%\narr = [1, 3, 5] \nn = len(arr) \n\n\n#%% sum of bit differences among all pairs\n# source: \n\ndef sumBitDifferences(arr, n): \n \n ans = 0 # Initialize result \n \n # traverse over all bits \n for i in range(0, 32): \n \n # count number of elements with i'th bit set \n count = 0\n for j in range(0,n): \n if ( (arr[j] & (1 << i)) ): \n count += 1\n \n # Add \"count * (n - count) * 2\" to the answer \n ans += (count * (n - count) * 2); \n \n return ans \n\n\n#%%\nfrom itertools import permutations\nimport numpy as np\ndef BitDiff(arr): \n \n # initialised answer\n count = 0\n \n # the length of the array\n n = len(arr)\n \n # obtain the binary representation for every number in the array\n br = [] # initialise an empty list \n for i in range(0,n):\n br_i = str(bin(arr[i]))[2:]\n br.append(br_i)\n \n # get all combinations of length 2\n comb = list(permutations(list(range(0,n)), 2))\n \n for ii in comb:\n \n str_a = br[ii[0]];\n str_b = br[ii[1]];\n # maximum length of the two strings being compared\n max_len = max(len(str_a), len(str_b))\n \n # minimum length of the two strings being compared \n min_len = min(len(str_a), len(str_b))\n \n # append zeros to the end of the shorter string\n ind = np.argmin([len(str_a), len(str_b)])\n str_short = br[ii[ind]]\n str_short = str_short.ljust(max_len, '0')\n \n for l in range(max_len):\n if str_short[l] != br[ii[1-ind]][l]:\n count += 1\n \n \n return count\n\n\n#%% one correct submission\nfor _ in range(int(input())):\n n=int(input())\n arr=list(map(int,input().split()))\n ans = 0 # Initialize result \n \n # traverse over all bits \n for i in range(0, 32): \n \n # count number of elements with i'th bit set \n count = 0\n for j in range(0,n): \n if ( (arr[j] & (1 << i)) ): \n count+=1\n \n # Add \"count * (n - count) * 2\" to the answer \n ans =(ans+ (count * (n - count) * 2))%(pow(10,9)+7); \n print(ans)\n\n\n#%%\nt=int(input())\nres = BitDiff(arr)\n \n#%%\nprint(sumBitDifferences(arr, n)) \n", "sub_path": "General/SolvedFirstTime/SumOfBits.py", "file_name": "SumOfBits.py", "file_ext": "py", "file_size_in_byte": 2467, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "itertools.permutations", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.argmin", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "619615366", "text": "import math\nfrom datetime import datetime\nfrom multiprocessing import Pool\nfrom pathlib import Path\nfrom typing import Optional\n\nimport pandas as pd\nfrom dateutil import tz\nfrom sqlalchemy.types import Date, DateTime, Float, Integer, String, Text\n\nfrom config import config\nfrom lib import db, helpers, transformers\n\n# French timezone\nFRA = tz.gettz(\"Europe/Paris\")\n\n\ndef create_table_cis_atc():\n source = {\"pattern\": \"CIS-ATC_2021-01-04.xlsx\"}\n read_excel_config = {\n \"dtype\": {\"cis\": str},\n \"index_col\": \"cis\",\n \"usecols\": [\"cis\", \"atc\", \"nom_atc\"],\n \"names\": [\"cis\", \"atc\", \"nom_atc\"],\n }\n to_sql_config = {\n \"name\": \"specialite_atc\",\n \"index\": True,\n \"if_exists\": \"replace\",\n \"dtype\": {\"cis\": String(16)},\n }\n _path = helpers.get_path_from_source(source)\n if isinstance(_path, Path):\n df = helpers.load_excel_to_df(read_excel_config, _path)\n db.create_table_from_df(df, to_sql_config)\n else:\n print(f'file with pattern {source[\"pattern\"]} not found')\n\n\ndef create_table_bdpm_cis():\n \"\"\"\n Table specialite\n \"\"\"\n source = config[\"bdpm_cis_url\"]\n tmp_path = Path(config[\"tmp_folder\"]).joinpath(\"BDPM_CIS.txt\")\n custom_date_parser = lambda x: datetime.strptime(x, \"%d/%m/%Y\")\n read_csv_config = {\n \"sep\": \"\\t\",\n \"encoding\": \"latin1\",\n \"names\": [\n \"cis\",\n \"nom\",\n \"forme_pharma\",\n \"voie_admin\",\n \"statut_amm\",\n \"type_amm\",\n \"etat_commercialisation\",\n \"date_amm\",\n \"statut_bdpm\",\n \"num_autorisation\",\n \"titulaires\",\n \"surveillance_renforcee\",\n ],\n \"header\": None,\n \"index_col\": \"cis\",\n \"parse_dates\": [\"date_amm\"],\n \"date_parser\": custom_date_parser,\n \"dtype\": {\"cis\": str},\n }\n to_sql_config = {\n \"name\": \"specialite\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\n \"cis\": String(16),\n \"nom\": Text,\n \"forme_pharma\": Text,\n \"voie_admin\": Text,\n \"statut_amm\": Text,\n \"type_amm\": Text,\n \"etat_commercialisation\": Text,\n \"date_amm\": Date,\n \"statut_bdpm\": Text,\n \"num_autorisation\": Text,\n \"titulaires\": Text,\n \"surveillance_renforcee\": Text,\n },\n }\n _path = helpers.download_file_from_url(source, tmp_path)\n if isinstance(_path, Path):\n df = helpers.load_csv_to_df(read_csv_config, _path)\n # cleaning\n helpers.serie_to_lowercase(df, read_csv_config[\"names\"][1:])\n db.create_table_from_df(df, to_sql_config)\n else:\n print(f\"tmp file {tmp_path} not found\")\n\n\ndef create_tables_rsp_compo():\n \"\"\"\n Table substance\n \"\"\"\n source = config[\"rsp_compo_url\"]\n tmp_path = Path(config[\"tmp_folder\"]).joinpath(\"COMPO.txt\")\n read_csv_config = {\n \"sep\": \"\\t\",\n \"encoding\": \"latin1\",\n \"names\": [\n \"cis\",\n \"elem_pharma\",\n \"code\",\n \"nom\",\n \"dosage\",\n \"ref_dosage\",\n \"nature_composant\",\n \"num_lien\",\n \"v\",\n ],\n \"header\": None,\n \"index_col\": \"code\",\n \"dtype\": {\"cis\": str, \"code\": str},\n }\n to_sql_config = {\n \"name\": \"substance\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\n \"code\": String(16),\n \"nom\": Text,\n },\n }\n _path = helpers.download_file_from_url(source, tmp_path)\n if isinstance(_path, Path):\n df = helpers.load_csv_to_df(read_csv_config, _path)\n # cleaning\n df = df[df.nature_composant == \"SA\"]\n df = df[[\"nom\"]]\n df = df[~df.index.duplicated(keep=\"first\")]\n helpers.serie_to_lowercase(df, [\"nom\"])\n db.create_table_from_df(df, to_sql_config)\n\n # table specialite_substance\n read_csv_config[\"index_col\"] = \"cis\"\n to_sql_config = {\n \"name\": \"specialite_substance\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\n \"cis\": String(16),\n \"code_substance\": String(16),\n \"elem_pharma\": Text,\n \"dosage\": Text,\n \"ref_dosage\": Text,\n },\n }\n df = helpers.load_csv_to_df(read_csv_config, path=_path)\n # cleaning\n df = df[df.nature_composant == \"SA\"]\n df = df[[\"code\", \"elem_pharma\", \"dosage\", \"ref_dosage\"]]\n df = df.rename(columns={\"code\": \"code_substance\"})\n db.create_table_from_df(df, to_sql_config)\n else:\n print(f'file with pattern {source[\"pattern\"]} not found')\n\n\ndef create_table_atc():\n source = {\"pattern\": \"atc_names.json\"}\n to_sql_config = {\n \"name\": \"classes_atc\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\"code\": String(16)},\n }\n _path = helpers.get_path_from_source(source)\n if isinstance(_path, Path):\n df = helpers.load_to_df_atc(_path)\n db.create_table_from_df(df, to_sql_config)\n else:\n print(f'file with pattern {source[\"pattern\"]} not found')\n\n\ndef create_table_cis_cip_bdpm():\n \"\"\"\n Table presentation\n \"\"\"\n source = config[\"cis_cip_url\"]\n tmp_path = Path(config[\"tmp_folder\"]).joinpath(\"CIS_CIP_bdpm.txt\")\n\n read_csv_config = {\n \"sep\": \"\\t\",\n \"encoding\": \"latin1\",\n \"names\": [\n \"cis\",\n \"cip7\",\n \"libelle_presentation\",\n \"statut_admin_presentation\",\n \"etat_commercialisation\",\n \"date_declaration_commercialisation\",\n \"cip13\",\n \"agrement_collectivites\",\n \"taux_remboursement\",\n \"prix_medicament_euro\",\n \"nb_1\",\n \"nb_2\",\n \"indications_remboursement\",\n ],\n \"index_col\": 0,\n \"header\": None,\n \"dtype\": {\"cis\": str, \"cip13\": str},\n \"parse_dates\": [\"date_declaration_commercialisation\"],\n }\n to_sql_config = {\n \"name\": \"presentation\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\"cis\": String(16), \"cip13\": String(16)},\n }\n _path = helpers.download_file_from_url(source, tmp_path)\n if isinstance(_path, Path):\n df = helpers.load_csv_to_df(read_csv_config, _path)\n # cleaning\n df = df.drop(\n [\n \"prix_medicament_euro\",\n \"nb_1\",\n \"nb_2\",\n \"indications_remboursement\",\n ],\n axis=1,\n )\n df = df.where(pd.notnull(df), None)\n db.create_table_from_df(df, to_sql_config)\n else:\n print(f'file with pattern {source[\"pattern\"]} not found.')\n\n\n# # ORDEI\n\n\ndef round_small_values(conso_value: int) -> Optional[int]:\n if conso_value <= 10:\n return None\n if 10 < conso_value < 50:\n return 50\n elif 50 <= conso_value < 95:\n return 100\n else:\n return round(conso_value, -int(math.log10(conso_value)))\n\n\ndef create_open_medic_tables():\n source = {\"pattern\": \"open_medic2014_2018_cis_agg.csv\"}\n read_csv_config = {\n \"sep\": \";\",\n \"dtype\": {\"cis\": str},\n \"usecols\": [\"cis\", \"age\", \"conso\", \"n_conso_an\", \"sexe\"],\n \"index_col\": \"cis\",\n \"header\": 0,\n \"names\": [\"index\", \"cis\", \"sexe\", \"age\", \"conso\", \"n_conso_an\", \"SEXE\"],\n }\n _path = helpers.get_path_from_source(source)\n if isinstance(_path, Path):\n df = helpers.load_csv_to_df(read_csv_config, _path)\n create_spe_exposition_table(df)\n create_spe_patients_sexe_table(df)\n create_spe_patients_age_table(df)\n else:\n print(f'file with pattern {source[\"pattern\"]} not found.')\n\n\ndef create_spe_exposition_table(df: pd.DataFrame):\n to_sql_config = {\n \"name\": \"specialite_exposition\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\n \"cis\": String(16),\n \"conso_an_trunc\": Integer,\n \"exposition\": Integer,\n },\n }\n df = df.groupby(\"cis\").agg(n_conso_an=(\"n_conso_an\", \"sum\"), conso=(\"conso\", \"sum\"))\n df[\"exposition\"] = df[\"n_conso_an\"].apply(\n helpers.get_exposition_level, type=\"specialite\"\n )\n df[\"conso_an_trunc\"] = df.n_conso_an.apply(round_small_values)\n df = df[[\"conso_an_trunc\", \"exposition\"]]\n db.create_table_from_df(df, to_sql_config)\n\n\ndef create_spe_patients_sexe_table(df: pd.DataFrame):\n to_sql_config = {\n \"name\": \"specialite_patient_sexe_ordei\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\n \"cis\": String(16),\n \"sexe\": Integer,\n \"conso\": Integer,\n \"pourcentage_patients\": Float,\n },\n }\n conso = df.groupby([\"cis\", \"sexe\"]).conso.sum().rename(\"conso\")\n conso_pct = (\n conso.groupby(level=0)\n .apply(lambda x: x / x.sum() * 100)\n .rename(\"pourcentage_patients\")\n )\n final_df = pd.merge(conso, conso_pct, on=[\"cis\", \"sexe\"])\n final_df.drop([\"conso\"], axis=1, inplace=True)\n final_df.reset_index(inplace=True, level=[\"sexe\"])\n db.create_table_from_df(final_df, to_sql_config)\n\n\ndef create_spe_patients_age_table(df: pd.DataFrame):\n to_sql_config = {\n \"name\": \"specialite_patient_age_ordei\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\n \"cis\": String(16),\n \"age\": Text,\n \"conso\": Integer,\n \"pourcentage_patients\": Float,\n },\n }\n conso = df.groupby([\"cis\", \"age\"]).conso.sum().rename(\"conso\")\n conso_pct = (\n conso.groupby(level=0)\n .apply(lambda x: x / x.sum() * 100)\n .rename(\"pourcentage_patients\")\n )\n final_df = pd.merge(conso, conso_pct, on=[\"cis\", \"age\"])\n final_df.drop([\"conso\"], axis=1, inplace=True)\n final_df.reset_index(inplace=True, level=[\"age\"])\n db.create_table_from_df(final_df, to_sql_config)\n\n\ndef create_substance_tables():\n source = {\"pattern\": \"bnpv_open_medic1418_sa_codex.csv\"}\n read_csv_config = {\n \"sep\": \";\",\n \"encoding\": \"ISO-8859-1\",\n \"dtype\": {\"code\": str},\n \"usecols\": [\n \"annee\",\n \"sexe\",\n \"age\",\n \"substance\",\n \"code\",\n \"conso\",\n \"cas\",\n ],\n \"index_col\": \"code\",\n \"header\": 0,\n \"names\": [\n \"index\",\n \"annee\",\n \"sexe\",\n \"age\",\n \"substance\",\n \"code\",\n \"cas\",\n \"conso\",\n ],\n }\n _path = helpers.get_path_from_source(source)\n if isinstance(_path, Path):\n df = helpers.load_csv_to_df(read_csv_config, _path)\n create_substance_exposition_table(df)\n create_substance_patients_sexe_table(df)\n create_substance_patients_age_table(df)\n create_substance_cas_sexe_table(df)\n create_substance_cas_age_table(df)\n else:\n print(f'file with pattern {source[\"pattern\"]} not found')\n\n\ndef create_substance_exposition_table(df: pd.DataFrame):\n to_sql_config = {\n \"name\": \"substance_exposition\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\n \"code\": String(16),\n \"exposition\": Integer,\n \"cas\": Integer,\n \"taux_cas\": Float,\n \"annee\": Integer,\n \"conso_annee\": Integer,\n \"cas_annee\": Integer,\n \"conso_an_trunc\": Integer,\n },\n }\n df_by_years = df.groupby([\"code\", \"annee\"]).agg(\n conso_annee=(\"conso\", \"sum\"), cas_annee=(\"cas\", \"sum\")\n )\n df_by_code = df_by_years.groupby(\"code\").agg(\n conso=(\"conso_annee\", \"sum\"),\n cas=(\"cas_annee\", \"sum\"),\n exposition=(\n \"conso_annee\",\n lambda x: helpers.get_total_exposition_level(x, \"substance\"),\n ),\n )\n final_df = df_by_years.join(df_by_code, on=\"code\")\n final_df = helpers.filter_df_on_low_values(final_df, [\"cas\", \"cas_annee\"])\n final_df[\"taux_cas\"] = final_df.apply(\n axis=1,\n func=lambda x: x.cas * 100000 / x.conso if 10 < x.cas <= x.conso else None,\n )\n final_df[\"conso_an_trunc\"] = final_df.conso.apply(\n lambda x: round_small_values(x / 5)\n )\n\n final_df.drop([\"conso\"], inplace=True, axis=1)\n final_df.reset_index(inplace=True, level=[\"annee\"])\n db.create_table_from_df(final_df[final_df.cas.notnull()], to_sql_config)\n\n\ndef create_substance_patients_sexe_table(df: pd.DataFrame):\n to_sql_config = {\n \"name\": \"substance_patient_sexe_ordei\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\n \"code\": String(16),\n \"sexe\": Integer,\n \"conso\": Integer,\n \"pourcentage_patients\": Float,\n },\n }\n df_copy = df.copy(deep=True)\n df_copy.sexe = df_copy.sexe.apply(lambda x: helpers.mapSexeToCode(x))\n conso = df_copy.groupby([\"code\", \"sexe\"]).conso.sum().rename(\"conso\")\n conso = helpers.filter_serie_on_low_values(conso)\n conso_pct = (\n conso.groupby(level=0)\n .apply(lambda x: x / x.sum() * 100 if x is not None else None)\n .rename(\"pourcentage_patients\")\n )\n df_final = pd.merge(conso, conso_pct, on=[\"code\", \"sexe\"])\n df_final.pourcentage_patients = df_final.apply(\n lambda x: x.pourcentage_patients\n if not df_final.loc[x.name[0]].pourcentage_patients.isnull().values.any()\n else None,\n axis=1,\n )\n df_final.drop([\"conso\"], inplace=True, axis=1)\n df_final.reset_index(inplace=True, level=[\"sexe\"])\n db.create_table_from_df(df_final, to_sql_config)\n\n\ndef create_substance_patients_age_table(df: pd.DataFrame):\n to_sql_config = {\n \"name\": \"substance_patient_age_ordei\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\n \"code\": String(16),\n \"age\": Text,\n \"conso\": Integer,\n \"pourcentage_patients\": Float,\n },\n }\n conso = df.groupby([\"code\", \"age\"]).conso.sum().rename(\"conso\")\n conso = helpers.filter_serie_on_low_values(conso)\n conso_pct = (\n conso.groupby(level=0)\n .apply(lambda x: x / x.sum() * 100 if x is not None else None)\n .rename(\"pourcentage_patients\")\n )\n final_df = pd.merge(conso, conso_pct, on=[\"code\", \"age\"])\n final_df.pourcentage_patients = final_df.apply(\n lambda x: x.pourcentage_patients\n if not final_df.loc[x.name[0]].pourcentage_patients.isnull().values.any()\n else None,\n axis=1,\n )\n final_df.reset_index(inplace=True, level=[\"age\"])\n final_df.drop([\"conso\"], inplace=True, axis=1)\n db.create_table_from_df(final_df, to_sql_config)\n\n\ndef create_substance_cas_sexe_table(df: pd.DataFrame):\n to_sql_config = {\n \"name\": \"substance_cas_sexe_ordei\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\n \"code\": String(16),\n \"sexe\": Integer,\n \"pourcentage_cas\": Float,\n },\n }\n df_copy = df.copy(deep=True)\n df_copy.sexe = df_copy.sexe.apply(lambda x: helpers.mapSexeToCode(x))\n cas = df_copy.groupby([\"code\", \"sexe\"]).cas.sum().rename(\"cas\")\n cas = helpers.filter_serie_on_low_values(cas)\n cas_pct = (\n cas.groupby(level=0)\n .apply(lambda x: x / x.sum() * 100 if cas is not None else None)\n .rename(\"pourcentage_cas\")\n )\n df_final = pd.merge(cas, cas_pct, on=[\"code\", \"sexe\"])\n df_final.pourcentage_cas = df_final.apply(\n lambda x: x.pourcentage_cas\n if not df_final.loc[x.name[0]].pourcentage_cas.isnull().values.any()\n else None,\n axis=1,\n )\n df_final.drop([\"cas\"], axis=1, inplace=True)\n df_final.reset_index(inplace=True, level=[\"sexe\"])\n db.create_table_from_df(df_final[df_final.pourcentage_cas.notnull()], to_sql_config)\n\n\ndef create_substance_cas_age_table(df: pd.DataFrame):\n to_sql_config = {\n \"name\": \"substance_cas_age_ordei\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\n \"code\": String(16),\n \"age\": Text,\n \"pourcentage_cas\": Float,\n },\n }\n cas = df.groupby([\"code\", \"age\"])[\"cas\"].sum().rename(\"cas\")\n cas = helpers.filter_serie_on_low_values(cas)\n cas_pct = (\n cas.groupby(level=0)\n .apply(lambda x: x / x.sum() * 100 if cas is not None else None)\n .rename(\"pourcentage_cas\")\n )\n final_df = pd.merge(cas, cas_pct, on=[\"code\", \"age\"])\n final_df.pourcentage_cas = final_df.apply(\n lambda x: x.pourcentage_cas\n if not final_df.loc[x.name[0]].pourcentage_cas.isnull().values.any()\n else None,\n axis=1,\n )\n final_df.drop([\"cas\"], axis=1, inplace=True)\n final_df.reset_index(inplace=True, level=[\"age\"])\n db.create_table_from_df(final_df[final_df.pourcentage_cas.notnull()], to_sql_config)\n\n\ndef create_notificateurs_table():\n source = {\"pattern\": \"bnpv_notif_sa_codex_snds.csv\"}\n read_csv_config = {\n \"encoding\": \"ISO-8859-1\",\n \"sep\": \";\",\n \"dtype\": {\"code\": str},\n \"usecols\": [\n \"notificateur\",\n \"substance_active\",\n \"code\",\n \"age\",\n \"sexe\",\n \"n_decla\",\n \"n_cas\",\n ],\n \"index_col\": \"code\",\n \"header\": 0,\n \"names\": [\n \"index\",\n \"notificateur\",\n \"substance_active\",\n \"code\",\n \"age\",\n \"sexe\",\n \"n_decla\",\n \"n_cas\",\n ],\n }\n to_sql_config = {\n \"name\": \"substance_notif_ordei\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\n \"code\": String(16),\n \"notificateur\": Text,\n \"pourcentage_notif\": Float,\n },\n }\n _path = helpers.get_path_from_source(source)\n if isinstance(_path, Path):\n df = helpers.load_csv_to_df(read_csv_config, _path)\n decla = df.groupby([\"code\", \"notificateur\"]).n_decla.sum()\n decla_pct = (\n decla.groupby(level=0)\n .apply(lambda x: x / x.sum() * 100)\n .rename(\"pourcentage_notif\")\n )\n final_df = pd.merge(decla, decla_pct, on=[\"code\", \"notificateur\"])\n final_df.pourcentage_notif = final_df.apply(\n lambda x: x.pourcentage_notif if x.n_decla > 10 else None, axis=1\n )\n final_df.drop([\"n_decla\"], axis=1, inplace=True)\n final_df.reset_index(inplace=True, level=[\"notificateur\"])\n db.create_table_from_df(\n final_df[final_df.pourcentage_notif.notnull()], to_sql_config\n )\n else:\n print(f'file with pattern {source[\"pattern\"]} not found')\n\n\ndef create_substance_soclong_and_hlt_tables():\n source = {\"pattern\": \"bnpv_eff_soclong_sa_codex_snds.csv\"}\n read_csv_config = {\n \"encoding\": \"ISO-8859-1\",\n \"sep\": \";\",\n \"dtype\": {\"code\": str},\n \"usecols\": [\n \"substance_active\",\n \"code\",\n \"soc_long\",\n \"age\",\n \"sexe\",\n \"n_decla_eff\",\n \"n_cas\",\n ],\n \"index_col\": \"code\",\n \"header\": 0,\n \"names\": [\n \"index\",\n \"substance_active\",\n \"code\",\n \"soc_long\",\n \"age\",\n \"sexe\",\n \"n_decla_eff\",\n \"n_cas\",\n ],\n }\n _path = helpers.get_path_from_source(source)\n if isinstance(_path, Path):\n df_soclong = helpers.load_csv_to_df(read_csv_config, _path)\n create_substance_soclong_table(df_soclong)\n create_hlt_table(df_soclong)\n else:\n print(f'file with pattern {source[\"pattern\"]} not found')\n\n\ndef create_substance_soclong_table(df: pd.DataFrame):\n to_sql_config = {\n \"name\": \"substance_soclong_ordei\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\"code\": String(16), \"soc_long\": Text, \"pourcentage_cas\": Float},\n }\n total_case_per_sex_and_age = df.groupby([\"code\", \"sexe\", \"age\"]).agg(\n {\"n_cas\": \"max\"}\n )\n total_case = total_case_per_sex_and_age.groupby(\"code\").agg({\"n_cas\": \"sum\"})\n decla_eff = (\n df.groupby([\"code\", \"soc_long\"]).n_decla_eff.sum().reset_index(level=\"soc_long\")\n )\n final_df = pd.merge(total_case, decla_eff, left_index=True, right_on=[\"code\"])\n final_df = helpers.filter_df_on_low_values(final_df, [\"n_decla_eff\", \"n_cas\"])\n final_df[\"pourcentage_cas\"] = final_df.apply(\n lambda x: x.n_decla_eff / x.n_cas * 100 if x.n_decla_eff and x.n_cas else None,\n axis=1,\n result_type=\"expand\",\n )\n final_df = final_df.rename(columns={\"n_decla_eff\": \"n_cas_effet\"})\n final_df.drop(\"n_cas\", inplace=True, axis=1)\n db.create_table_from_df(final_df[final_df.pourcentage_cas.notnull()], to_sql_config)\n\n\ndef create_hlt_table(df_soclong: pd.DataFrame):\n source = {\"pattern\": \"bnpv_eff_hlt_soclong_sa_codex_snds.csv\"}\n read_csv_config = {\n \"encoding\": \"ISO-8859-1\",\n \"sep\": \";\",\n \"dtype\": {\"code\": str},\n \"usecols\": [\n \"subtance_active\",\n \"code\",\n \"age\",\n \"sexe\",\n \"effet_hlt\",\n \"soc_long\",\n \"n_decla_eff_hlt\",\n ],\n \"index_col\": \"code\",\n \"header\": 0,\n \"names\": [\n \"index\",\n \"subtance_active\",\n \"code\",\n \"age\",\n \"sexe\",\n \"effet_hlt\",\n \"soc_long\",\n \"n_decla_eff_hlt\",\n ],\n }\n to_sql_config = {\n \"name\": \"substance_hlt_ordei\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\n \"code\": String(16),\n \"soc_long\": String(255),\n \"effet_hlt\": String(255),\n \"pourcentage_cas\": Float,\n },\n }\n _path = helpers.get_path_from_source(source)\n if isinstance(_path, Path):\n df = helpers.load_csv_to_df(read_csv_config, _path)\n decla_eff = df_soclong.groupby([\"code\", \"soc_long\"]).agg({\"n_decla_eff\": \"sum\"})\n hlt = df.groupby([\"code\", \"soc_long\", \"effet_hlt\"]).agg(\n {\"n_decla_eff_hlt\": \"sum\"}\n )\n hlt.reset_index([\"effet_hlt\"], inplace=True)\n tmp_df = pd.merge(decla_eff, hlt, left_index=True, right_index=True)\n soclong_hlt = (\n tmp_df.groupby([\"code\", \"soc_long\"])\n .n_decla_eff_hlt.sum()\n .rename(\"n_decla_eff_soclong\")\n )\n final_df = pd.merge(tmp_df, soclong_hlt, left_index=True, right_index=True)\n\n final_df[\"pourcentage_cas\"] = final_df.apply(\n lambda x: x.n_decla_eff_hlt / x.n_decla_eff_soclong * 100,\n axis=1,\n result_type=\"expand\",\n )\n final_df.pourcentage_cas = final_df.apply(\n lambda x: x.pourcentage_cas if x.n_decla_eff_hlt > 10 else None, axis=1\n )\n final_df.reset_index([\"soc_long\"], inplace=True)\n final_df.drop(\n [\"n_decla_eff_soclong\", \"n_decla_eff_hlt\", \"n_decla_eff\"],\n inplace=True,\n axis=1,\n )\n db.create_table_from_df(\n final_df[final_df.pourcentage_cas.notnull()], to_sql_config\n )\n else:\n print(f'file with pattern {source[\"pattern\"]} not found')\n\n\ndef check_threshold(df: pd.DataFrame, x: pd.Series):\n dfx = df.loc[x.name]\n if (\n dfx[dfx.grave == \"oui\"].cas.values[0] > 10\n and dfx[dfx.grave == \"non\"].cas.values[0] > 10\n ):\n return x.cas\n else:\n return None\n\n\ndef create_cas_grave_table():\n source = {\"pattern\": \"bnpv_cas_grave_sa_codex_snds.csv\"}\n read_csv_config = {\n \"encoding\": \"ISO-8859-1\",\n \"sep\": \";\",\n \"dtype\": {\"code\": str},\n \"usecols\": [\n \"grave\",\n \"code\",\n \"cas\",\n ],\n \"header\": 0,\n \"names\": [\n \"grave\",\n \"subtance_active\",\n \"code\",\n \"cas\",\n ],\n }\n to_sql_config = {\n \"name\": \"substance_cas_grave_ordei\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\n \"code\": String(16),\n \"grave\": String(16),\n \"pourcentage_cas\": Float,\n },\n }\n _path = helpers.get_path_from_source(source)\n if isinstance(_path, Path):\n df = helpers.load_csv_to_df(read_csv_config, _path)\n df.grave = df.grave.str.lower()\n df = df.set_index(\"code\")\n df.cas = df.apply(lambda x: check_threshold(df, x), axis=1)\n df.grave = df.grave.apply(lambda x: \"Grave\" if x == \"oui\" else \"Non grave\")\n df = df.where(pd.notnull(df), None)\n df = df.sort_index()\n db.create_table_from_df(df[df.cas.notnull()], to_sql_config)\n else:\n print(f'file with pattern {source[\"pattern\"]} not found')\n\n\ndef create_table_emed():\n source = {\"pattern\": \"RqHackathon_20220225.xlsx\"}\n read_excel_config = {\n \"usecols\": \"E:L,N:Q\",\n \"names\": [\n \"lieu_erreur\",\n \"initial_erreur\",\n \"nature_erreur\",\n \"cause_erreur\",\n \"population_erreur\",\n \"qualif_erreur\",\n \"effet_indesirable\",\n \"gravite\",\n \"denomination\",\n \"dci\",\n \"atc\",\n \"voie\",\n ],\n }\n to_sql_config = {\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\"cis\": String(16)},\n }\n tables = {\n # \"lieu\": \"lieu_erreur\",\n # \"cause\": \"cause_erreur\",\n \"population\": \"population_erreur\",\n \"initial\": \"initial_erreur\",\n \"nature\": \"nature_erreur\",\n \"effet_indesirable\": \"effet_indesirable\",\n \"gravite\": \"gravite\",\n }\n\n _path = helpers.get_path_from_source(source)\n if isinstance(_path, Path):\n df = helpers.load_excel_to_df(read_excel_config, _path)\n df = transformers.erreurs_med.clean_emed_df(df)\n\n df_spe = db.create_df_from_table(\"specialite\")\n df_spe = df_spe.set_index(\"cis\")\n\n # Create 'erreur_med_cis_denomination' corresp table\n df_corresp = transformers.erreurs_med.get_corresp_df(df, df_spe)\n args_corresp = {\n **{\"name\": \"erreur_med_cis_denomination\"},\n **to_sql_config,\n }\n db.create_table_from_df(df_corresp, args_corresp)\n\n # Create all tables\n for table_name, table_column in tables.items():\n df_table = transformers.erreurs_med.get_table_df(df, df_spe, table_column)\n args = {\n **{\"name\": \"erreur_med_{}\".format(table_name)},\n **to_sql_config,\n }\n db.create_table_from_df(df_table, args)\n else:\n print(f'file with pattern {source[\"pattern\"]} not found')\n\n\ndef create_table_ruptures():\n # Load 2 dependencies tables\n df_spe = db.create_df_from_table(\"specialite\").reset_index()\n df_pres = db.create_df_from_table(\"presentation\")\n\n # Old ruptures file (<= 03/05/2021)\n source = {\"pattern\": \"ListeDesRuptures_2022_3_110_59_37.xlsx\"}\n read_excel_config = {\n \"header\": 0,\n \"parse_dates\": [\n \"Date Signalement\",\n ],\n \"usecols\": [\n \"Signalement\",\n \"Date Signalement\",\n \"Laboratoire\",\n \"Spécialité\",\n \"Rupture\",\n \"Etat dossier\",\n \"ATC\",\n \"DCI\",\n \"Origine_Cause_RS\",\n ],\n }\n # Old ruptures file is not full at this time\n _path = helpers.get_path_from_source(source)\n if isinstance(_path, Path):\n df_old = helpers.load_excel_to_df(read_excel_config, _path)\n df_old = transformers.ruptures.clean_old_ruptures_df(df_old, df_spe)\n\n # Create table signalement\n _create_table_causes(df_old[[\"numero\", \"cause\"]])\n\n df_old = df_old.drop([\"cause\"], axis=1)\n\n # New ruptures file (>= 04/05/2021)\n source = {\"pattern\": \"Dossier_de_rupture_100322.xlsx\"}\n read_excel_config = {\n \"header\": 0,\n \"dtype\": {\"numero\": str, \"cip13\": str},\n \"usecols\": [\n \"Numéro\",\n \"État\",\n \"Date de déclaration\",\n \"Classification\",\n \"Nom Laboratoire\",\n \"CIP\",\n \"Nom\",\n \"DCI\",\n \"Code ATC\",\n \"Presentation\",\n \"Classe Therapeutique\",\n ],\n }\n to_sql_config = {\n \"name\": \"ruptures\",\n \"if_exists\": \"replace\",\n \"dtype\": {\n \"annee\": String,\n \"cip13\": String,\n \"date\": Date,\n },\n }\n _path = helpers.get_path_from_source(source)\n if isinstance(_path, Path):\n df_new = helpers.load_excel_to_df(read_excel_config, _path)\n df_new = transformers.ruptures.clean_new_ruptures_df(df_new, df_pres)\n\n df_ruptures_final = transformers.ruptures.merge_new_and_old_ruptures_df(\n df_old, df_new\n )\n\n # Create table signalement\n _create_table_signalements(df_new, df_pres)\n\n # Create table ruptures\n df_ruptures_final = df_ruptures_final.set_index(\"numero\")\n db.create_table_from_df(df_ruptures_final, to_sql_config)\n else:\n print(f'file with pattern {source[\"pattern\"]} not found')\n\n\ndef _create_table_causes(df_old: pd.DataFrame):\n df_old = transformers.causes_ruptures.clean_old_causes_df(df_old)\n\n source = {\"pattern\": \"causes_100322.xlsx\"}\n read_excel_config = {\n \"header\": 0,\n \"dtype\": {\"numero\": str, \"cip13\": str},\n \"usecols\": [\"rst_rpt_numero\", \"lov_label\"],\n }\n to_sql_config = {\"name\": \"causes\", \"if_exists\": \"replace\"}\n\n _path = helpers.get_path_from_source(source)\n if isinstance(_path, Path):\n df_new = helpers.load_excel_to_df(read_excel_config, _path)\n df_new = transformers.causes_ruptures.clean_new_causes_df(df_new)\n\n df_causes = transformers.causes_ruptures.merge_new_and_old_causes_df(\n df_old, df_new\n )\n\n # We remove \"Stock inférieur au stock défini par le décret n°2021-349 du 30 mars 2021\"\n # because it is not a rupture cause\n df_causes = df_causes[\n df_causes.cause\n != \"Stock inférieur au stock défini par le décret n°2021-349 du 30 mars 2021\"\n ]\n db.create_table_from_df(df_causes, to_sql_config)\n\n\ndef _create_table_signalements(df: pd.DataFrame, df_pres: pd.DataFrame):\n # Load 2 dependencies tables\n df_classes_atc = db.create_df_from_table(\"classes_atc\")\n df_spe_atc = db.create_df_from_table(\"specialite_atc\").sort_values(by=\"atc\")\n\n to_sql_config = {\n \"name\": \"signalements\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\n \"annee\": String,\n \"nb_signalements\": Integer,\n \"nb_presentations\": Integer,\n },\n }\n\n # Create df_pres_atc from df_pres and (df_classes_atc & df_spe_atc)\n df_spe_atc[\"atc1\"] = df_spe_atc.atc.apply(lambda x: x[:1])\n df_spe_atc = df_spe_atc.merge(\n df_classes_atc, left_on=\"atc1\", right_on=\"code\", how=\"left\"\n )\n df_pres = df_pres.merge(df_spe_atc[[\"cis\", \"atc1\", \"label\"]], on=\"cis\", how=\"left\")\n df_pres_atc = (\n df_pres.groupby(\"label\").agg(nb_presentations=(\"cip13\", \"count\")).reset_index()\n )\n\n # Create new df_sig from df, df_classes_atc and df_pres_atc\n df = df.merge(df_classes_atc, left_on=\"atc1\", right_on=\"code\", how=\"left\")\n df = df.drop_duplicates(subset=[\"numero\", \"cis\"], keep=\"first\")\n\n # Exclude décret rows\n df = df[df.classification.isin([\"rupture\", \"risque\"])]\n\n # Compute number of signalings, per year, per atc class\n df[\"annee\"] = df.date.dt.year\n\n # Remove bad data (year = NaN)\n df.annee = df.annee.fillna(0)\n df = df[df.annee != 0]\n\n # Force type to integer\n df.annee = df.annee.astype(int)\n\n df_sig = (\n df.groupby([\"annee\", \"label\"])\n .agg(nb_signalements=(\"numero\", \"count\"))\n .reset_index()\n .sort_values(by=\"nb_signalements\", ascending=False)\n )\n\n # Add number of presentations by atc class\n df_sig = df_sig.merge(df_pres_atc, on=\"label\", how=\"left\").sort_values(\n by=[\"annee\", \"nb_signalements\"], ascending=False\n )\n\n df_sig = df_sig.set_index(\"annee\")\n\n db.create_table_from_df(df_sig, to_sql_config)\n\n\ndef create_table_mesures():\n # Load dependency table\n try:\n df_ruptures = db.create_df_from_table(\"ruptures\")\n except Exception:\n print(\"Unable to import dataframe from table ruptures\")\n return\n\n source = {\"pattern\": \"Mesure_100322.xlsx\"}\n read_excel_config = {\n \"header\": 0,\n \"usecols\": [\n \"Etat\",\n \"Numéro Rupture\",\n \"Identifiant\",\n \"Description\",\n \"Nom Produit\",\n \"Demande de mise en place\",\n \"Date mise en place\",\n \"Date de fin prévisionnelle\",\n \"Date de clotûre\",\n \"Justification\",\n ],\n }\n to_sql_config = {\n \"name\": \"mesures\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\n \"annee\": String,\n \"date_demande\": Date,\n \"date_mise_en_place\": Date,\n \"date_previ_fin\": Date,\n \"date_cloture\": Date,\n },\n }\n\n _path = helpers.get_path_from_source(source)\n if isinstance(_path, Path):\n df = helpers.load_excel_to_df(read_excel_config, _path)\n df = transformers.mesures.prepare_df_mesures(df, df_ruptures)\n db.create_table_from_df(df, to_sql_config)\n else:\n print(f'file with pattern {source[\"pattern\"]} not found')\n\n\ndef create_table_icones():\n # Load table dependency\n df_spe = db.create_df_from_table(\"specialite\")\n\n to_sql_config = {\n \"name\": \"icones\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\n \"cis\": String(16),\n \"forme_pharma\": Text,\n \"icone\": Text,\n },\n }\n df_icones = transformers.icones.prepare_df_icones(df_spe)\n db.create_table_from_df(df_icones, to_sql_config)\n\n\ndef create_table_mesusage():\n # Load dependency tables\n try:\n df_spe = db.create_df_from_table(\"specialite\")\n except Exception:\n print(\"Unable to create df from table `specialite`\")\n\n source = {\"pattern\": \"20210104 - YAuffray - Mésusages depuis 2015.xlsx\"}\n read_excel_config = {\n \"sheet_name\": \"Complet\",\n \"usecols\": [\n \"Cas CRPV\",\n \"Mode Recueil\",\n \"Typ Décl\",\n \"Typ Cas\",\n \"Typ Notif\",\n \"Cadre Notif\",\n \"Sex\",\n \"Age\",\n \"Grave\",\n \"Décès\",\n \"Notif\",\n \"Médicaments\",\n \"Voie\",\n \"Début TT\",\n \"Fin TT\",\n \"Durée\",\n \"Début EI\",\n \"Fin EI\",\n \"HLT\",\n \"HLGT\",\n \"SOC long\",\n \"Evolution\",\n \"Indication\",\n ],\n }\n tables = {\n \"mesusage_global_sexe\": \"sexe\",\n \"mesusage_global_age\": \"age\",\n \"mesusage_global_gravite\": \"gravite\",\n \"mesusage_global_declarant\": \"notificateur\",\n \"mesusage_global_annee\": \"annee\",\n \"mesusage_specialite_sexe\": [\"cis\", \"sexe\"],\n \"mesusage_specialite_age\": [\"cis\", \"age\"],\n \"mesusage_specialite_soc\": [\"cis\", \"soc_long\"],\n }\n to_sql_config = {\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\"cis\": String(16)},\n }\n _path = helpers.get_path_from_source(source)\n if isinstance(_path, Path):\n df = helpers.load_excel_to_df(read_excel_config, _path)\n df_mesusage = transformers.mesusage.prepare_df_mesusage(df, df_spe)\n for table_name, table_columns in tables.items():\n df_mesusage_final = transformers.mesusage.get_proporition_df(\n df_mesusage, table_columns\n )\n if not table_name.startswith(\"mesusage_global\"):\n df_mesusage_final = df_mesusage_final.set_index(\"cis\")\n db.create_table_from_df(\n df_mesusage_final, {**{\"name\": table_name}, **to_sql_config}\n )\n else:\n print(f'file with pattern {source[\"pattern\"]} not found')\n\n\ndef create_table_pv():\n \"\"\"\n Nombre de cas déclarés dans la BNPV chaque année\n \"\"\"\n to_sql_config = {\n \"name\": \"cas_pv\",\n \"if_exists\": \"replace\",\n \"index\": True,\n }\n df = pd.DataFrame(transformers.pv.NB_CAS_AN)\n db.create_table_from_df(df, to_sql_config)\n\n\ndef create_scrapping_tables():\n to_sql_description = {\n \"name\": \"description\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\"cis\": String(16)},\n }\n to_sql_publications = {\n \"name\": \"publications\",\n \"if_exists\": \"replace\",\n \"index\": True,\n \"dtype\": {\"cis\": String(16)},\n }\n df_cis = pd.read_sql(\"specialite\", db.connect_db())\n cis_list = list(df_cis.cis.unique())\n\n flatten = lambda t: [item for sublist in t for item in sublist]\n\n with Pool(10) as p:\n print(\"start scrapping BDPM...\")\n print(\"count cis:\", len(cis_list))\n all_scraps = p.map(transformers.bdpm.scrap_bdpm, cis_list)\n\n # Table descriptions\n print(\"start creating descriptions table...\")\n descriptions_list = list(map(lambda x: x[0], all_scraps))\n df_descriptions = pd.DataFrame(descriptions_list, columns=[\"cis\", \"description\"])\n df_descriptions = df_descriptions.set_index(\"cis\").sort_index()\n db.create_table_from_df(df_descriptions, to_sql_description)\n\n # Table publications\n print(\"start creating publications table...\")\n publications_list = flatten(list(map(lambda x: x[1], all_scraps)))\n df_publications = pd.DataFrame(\n publications_list, columns=[\"cis\", \"title\", \"type\", \"link\"]\n )\n df_publications = df_publications.set_index(\"cis\").sort_index()\n db.create_table_from_df(df_publications, to_sql_publications)\n\n\ndef create_global_dec_table():\n print(\"start creating global indicators table...\")\n source = {\"pattern\": \"global_indic_v3.xlsx\"}\n read_excel_config = {\n \"header\": 0,\n \"sheet_name\": \"data\",\n \"dtype\": {\"label\": str, \"N\": int, \"pct\": float, \"commentaire\": str},\n }\n to_sql_config = {\n \"name\": \"global_ei_indicators\",\n \"if_exists\": \"replace\",\n \"dtype\": {\"label\": String, \"N\": Integer, \"pct\": Float, \"commentaire\": String},\n }\n\n _path = helpers.get_path_from_source(source)\n if isinstance(_path, Path):\n df = helpers.load_excel_to_df(read_excel_config, _path)\n\n df = df.where(pd.notnull(df), None)\n\n db.create_table_from_df(df, to_sql_config)\n\n\ndef create_table_config():\n print(\"start creating config table...\")\n to_sql_config = {\n \"name\": \"config\",\n \"if_exists\": \"replace\",\n \"dtype\": {\"populate_last_update\": DateTime},\n \"index\": False,\n }\n last_update = datetime.now(tz=FRA)\n df = pd.DataFrame([last_update], columns=[\"populate_last_update\"])\n\n db.create_table_from_df(df, to_sql_config)\n\n\nprint(\"start to populate db...\")\n\ncreate_table_bdpm_cis()\ncreate_tables_rsp_compo()\ncreate_table_cis_cip_bdpm()\ncreate_table_atc()\ncreate_table_cis_atc()\n\n# Scrapping\ncreate_scrapping_tables()\n\n# Ordei\ncreate_open_medic_tables()\ncreate_substance_tables()\ncreate_notificateurs_table()\ncreate_substance_soclong_and_hlt_tables()\ncreate_cas_grave_table()\ncreate_global_dec_table()\n\n# Erreurs médicamenteuses\ncreate_table_emed()\n\n# Ruptures\ncreate_table_ruptures()\ncreate_table_mesures()\n\n# Logos\ncreate_table_icones()\n\n# Mésusage\n# create_table_mesusage()\n\n# Pharmacovigilance\ncreate_table_pv()\n\n# Config\ncreate_table_config()\n\nprint(\"end populate db...\")\n", "sub_path": "datamed/populate/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 39996, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "dateutil.tz.gettz", "line_number": 15, "usage_type": "call"}, {"api_name": "dateutil.tz", "line_number": 15, "usage_type": "name"}, {"api_name": "sqlalchemy.types.String", "line_number": 30, "usage_type": "call"}, {"api_name": "lib.helpers.get_path_from_source", "line_number": 32, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 32, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 33, "usage_type": "argument"}, {"api_name": "lib.helpers.load_excel_to_df", "line_number": 34, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 34, "usage_type": "name"}, {"api_name": "lib.db.create_table_from_df", "line_number": 35, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 35, "usage_type": "name"}, {"api_name": "config.config", "line_number": 44, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 45, "usage_type": "call"}, {"api_name": "config.config", "line_number": 45, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 46, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 46, "usage_type": "name"}, {"api_name": "sqlalchemy.types.String", "line_number": 75, "usage_type": "call"}, {"api_name": "sqlalchemy.types.Text", "line_number": 76, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Text", "line_number": 77, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Text", "line_number": 78, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Text", "line_number": 79, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Text", "line_number": 80, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Text", "line_number": 81, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Date", "line_number": 82, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Text", "line_number": 83, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Text", "line_number": 84, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Text", "line_number": 85, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Text", "line_number": 86, "usage_type": "name"}, {"api_name": "lib.helpers.download_file_from_url", "line_number": 89, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 89, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 90, "usage_type": "argument"}, {"api_name": "lib.helpers.load_csv_to_df", "line_number": 91, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 91, "usage_type": "name"}, {"api_name": "lib.helpers.serie_to_lowercase", "line_number": 93, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 93, "usage_type": "name"}, {"api_name": "lib.db.create_table_from_df", "line_number": 94, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 94, "usage_type": "name"}, {"api_name": "config.config", "line_number": 103, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 104, "usage_type": "call"}, {"api_name": "config.config", "line_number": 104, "usage_type": "name"}, {"api_name": "sqlalchemy.types.String", "line_number": 128, "usage_type": "call"}, {"api_name": "sqlalchemy.types.Text", "line_number": 129, "usage_type": "name"}, {"api_name": "lib.helpers.download_file_from_url", "line_number": 132, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 132, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 133, "usage_type": "argument"}, {"api_name": "lib.helpers.load_csv_to_df", "line_number": 134, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 134, "usage_type": "name"}, {"api_name": "lib.helpers.serie_to_lowercase", "line_number": 139, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 139, "usage_type": "name"}, {"api_name": "lib.db.create_table_from_df", "line_number": 140, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 140, "usage_type": "name"}, {"api_name": "sqlalchemy.types.String", "line_number": 149, "usage_type": "call"}, {"api_name": "sqlalchemy.types.String", "line_number": 150, "usage_type": "call"}, {"api_name": "sqlalchemy.types.Text", "line_number": 151, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Text", "line_number": 152, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Text", "line_number": 153, "usage_type": "name"}, {"api_name": "lib.helpers.load_csv_to_df", "line_number": 156, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 156, "usage_type": "name"}, {"api_name": "lib.db.create_table_from_df", "line_number": 161, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 161, "usage_type": "name"}, {"api_name": "sqlalchemy.types.String", "line_number": 172, "usage_type": "call"}, {"api_name": "lib.helpers.get_path_from_source", "line_number": 174, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 174, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 175, "usage_type": "argument"}, {"api_name": "lib.helpers.load_to_df_atc", "line_number": 176, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 176, "usage_type": "name"}, {"api_name": "lib.db.create_table_from_df", "line_number": 177, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 177, "usage_type": "name"}, {"api_name": "config.config", "line_number": 186, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 187, "usage_type": "call"}, {"api_name": "config.config", "line_number": 187, "usage_type": "name"}, {"api_name": "sqlalchemy.types.String", "line_number": 216, "usage_type": "call"}, {"api_name": "lib.helpers.download_file_from_url", "line_number": 218, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 218, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 219, "usage_type": "argument"}, {"api_name": "lib.helpers.load_csv_to_df", "line_number": 220, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 220, "usage_type": "name"}, {"api_name": "pandas.notnull", "line_number": 231, "usage_type": "call"}, {"api_name": "lib.db.create_table_from_df", "line_number": 232, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 232, "usage_type": "name"}, {"api_name": "math.log10", "line_number": 248, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 240, "usage_type": "name"}, {"api_name": "lib.helpers.get_path_from_source", "line_number": 261, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 261, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 262, "usage_type": "argument"}, {"api_name": "lib.helpers.load_csv_to_df", "line_number": 263, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 263, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 271, "usage_type": "attribute"}, {"api_name": "sqlalchemy.types.String", "line_number": 277, "usage_type": "call"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 278, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 279, "usage_type": "name"}, {"api_name": "lib.helpers.get_exposition_level", "line_number": 284, "usage_type": "attribute"}, {"api_name": "lib.helpers", "line_number": 284, "usage_type": "name"}, {"api_name": "lib.db.create_table_from_df", "line_number": 288, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 288, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 291, "usage_type": "attribute"}, {"api_name": "sqlalchemy.types.String", "line_number": 297, "usage_type": "call"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 298, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 299, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Float", "line_number": 300, "usage_type": "name"}, {"api_name": "pandas.merge", "line_number": 309, "usage_type": "call"}, {"api_name": "lib.db.create_table_from_df", "line_number": 312, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 312, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 315, "usage_type": "attribute"}, {"api_name": "sqlalchemy.types.String", "line_number": 321, "usage_type": "call"}, {"api_name": "sqlalchemy.types.Text", "line_number": 322, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 323, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Float", "line_number": 324, "usage_type": "name"}, {"api_name": "pandas.merge", "line_number": 333, "usage_type": "call"}, {"api_name": "lib.db.create_table_from_df", "line_number": 336, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 336, "usage_type": "name"}, {"api_name": "lib.helpers.get_path_from_source", "line_number": 367, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 367, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 368, "usage_type": "argument"}, {"api_name": "lib.helpers.load_csv_to_df", "line_number": 369, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 369, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 379, "usage_type": "attribute"}, {"api_name": "sqlalchemy.types.String", "line_number": 385, "usage_type": "call"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 386, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 387, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Float", "line_number": 388, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 389, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 390, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 391, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 392, "usage_type": "name"}, {"api_name": "lib.helpers.get_total_exposition_level", "line_number": 403, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 403, "usage_type": "name"}, {"api_name": "lib.helpers.filter_df_on_low_values", "line_number": 407, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 407, "usage_type": "name"}, {"api_name": "lib.db.create_table_from_df", "line_number": 418, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 418, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 421, "usage_type": "attribute"}, {"api_name": "sqlalchemy.types.String", "line_number": 427, "usage_type": "call"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 428, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 429, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Float", "line_number": 430, "usage_type": "name"}, {"api_name": "lib.helpers.mapSexeToCode", "line_number": 434, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 434, "usage_type": "name"}, {"api_name": "lib.helpers.filter_serie_on_low_values", "line_number": 436, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 436, "usage_type": "name"}, {"api_name": "pandas.merge", "line_number": 442, "usage_type": "call"}, {"api_name": "lib.db.create_table_from_df", "line_number": 451, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 451, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 454, "usage_type": "attribute"}, {"api_name": "sqlalchemy.types.String", "line_number": 460, "usage_type": "call"}, {"api_name": "sqlalchemy.types.Text", "line_number": 461, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 462, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Float", "line_number": 463, "usage_type": "name"}, {"api_name": "lib.helpers.filter_serie_on_low_values", "line_number": 467, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 467, "usage_type": "name"}, {"api_name": "pandas.merge", "line_number": 473, "usage_type": "call"}, {"api_name": "lib.db.create_table_from_df", "line_number": 482, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 482, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 485, "usage_type": "attribute"}, {"api_name": "sqlalchemy.types.String", "line_number": 491, "usage_type": "call"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 492, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Float", "line_number": 493, "usage_type": "name"}, {"api_name": "lib.helpers.mapSexeToCode", "line_number": 497, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 497, "usage_type": "name"}, {"api_name": "lib.helpers.filter_serie_on_low_values", "line_number": 499, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 499, "usage_type": "name"}, {"api_name": "pandas.merge", "line_number": 505, "usage_type": "call"}, {"api_name": "lib.db.create_table_from_df", "line_number": 514, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 514, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 517, "usage_type": "attribute"}, {"api_name": "sqlalchemy.types.String", "line_number": 523, "usage_type": "call"}, {"api_name": "sqlalchemy.types.Text", "line_number": 524, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Float", "line_number": 525, "usage_type": "name"}, {"api_name": "lib.helpers.filter_serie_on_low_values", "line_number": 529, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 529, "usage_type": "name"}, {"api_name": "pandas.merge", "line_number": 535, "usage_type": "call"}, {"api_name": "lib.db.create_table_from_df", "line_number": 544, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 544, "usage_type": "name"}, {"api_name": "sqlalchemy.types.String", "line_number": 580, "usage_type": "call"}, {"api_name": "sqlalchemy.types.Text", "line_number": 581, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Float", "line_number": 582, "usage_type": "name"}, {"api_name": "lib.helpers.get_path_from_source", "line_number": 585, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 585, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 586, "usage_type": "argument"}, {"api_name": "lib.helpers.load_csv_to_df", "line_number": 587, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 587, "usage_type": "name"}, {"api_name": "pandas.merge", "line_number": 594, "usage_type": "call"}, {"api_name": "lib.db.create_table_from_df", "line_number": 600, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 600, "usage_type": "name"}, {"api_name": "lib.helpers.get_path_from_source", "line_number": 635, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 635, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 636, "usage_type": "argument"}, {"api_name": "lib.helpers.load_csv_to_df", "line_number": 637, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 637, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 644, "usage_type": "attribute"}, {"api_name": "sqlalchemy.types.String", "line_number": 649, "usage_type": "call"}, {"api_name": "sqlalchemy.types.Text", "line_number": 649, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Float", "line_number": 649, "usage_type": "name"}, {"api_name": "pandas.merge", "line_number": 658, "usage_type": "call"}, {"api_name": "lib.helpers.filter_df_on_low_values", "line_number": 659, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 659, "usage_type": "name"}, {"api_name": "lib.db.create_table_from_df", "line_number": 667, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 667, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 670, "usage_type": "attribute"}, {"api_name": "sqlalchemy.types.String", "line_number": 703, "usage_type": "call"}, {"api_name": "sqlalchemy.types.String", "line_number": 704, "usage_type": "call"}, {"api_name": "sqlalchemy.types.String", "line_number": 705, "usage_type": "call"}, {"api_name": "sqlalchemy.types.Float", "line_number": 706, "usage_type": "name"}, {"api_name": "lib.helpers.get_path_from_source", "line_number": 709, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 709, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 710, "usage_type": "argument"}, {"api_name": "lib.helpers.load_csv_to_df", "line_number": 711, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 711, "usage_type": "name"}, {"api_name": "pandas.merge", "line_number": 717, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 723, "usage_type": "call"}, {"api_name": "lib.db.create_table_from_df", "line_number": 739, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 739, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 746, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 746, "usage_type": "attribute"}, {"api_name": "sqlalchemy.types.String", "line_number": 781, "usage_type": "call"}, {"api_name": "sqlalchemy.types.String", "line_number": 782, "usage_type": "call"}, {"api_name": "sqlalchemy.types.Float", "line_number": 783, "usage_type": "name"}, {"api_name": "lib.helpers.get_path_from_source", "line_number": 786, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 786, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 787, "usage_type": "argument"}, {"api_name": "lib.helpers.load_csv_to_df", "line_number": 788, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 788, "usage_type": "name"}, {"api_name": "pandas.notnull", "line_number": 793, "usage_type": "call"}, {"api_name": "lib.db.create_table_from_df", "line_number": 795, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 795, "usage_type": "name"}, {"api_name": "sqlalchemy.types.String", "line_number": 822, "usage_type": "call"}, {"api_name": "lib.helpers.get_path_from_source", "line_number": 834, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 834, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 835, "usage_type": "argument"}, {"api_name": "lib.helpers.load_excel_to_df", "line_number": 836, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 836, "usage_type": "name"}, {"api_name": "lib.transformers.erreurs_med.clean_emed_df", "line_number": 837, "usage_type": "call"}, {"api_name": "lib.transformers.erreurs_med", "line_number": 837, "usage_type": "attribute"}, {"api_name": "lib.transformers", "line_number": 837, "usage_type": "name"}, {"api_name": "lib.db.create_df_from_table", "line_number": 839, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 839, "usage_type": "name"}, {"api_name": "lib.transformers.erreurs_med.get_corresp_df", "line_number": 843, "usage_type": "call"}, {"api_name": "lib.transformers.erreurs_med", "line_number": 843, "usage_type": "attribute"}, {"api_name": "lib.transformers", "line_number": 843, "usage_type": "name"}, {"api_name": "lib.db.create_table_from_df", "line_number": 848, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 848, "usage_type": "name"}, {"api_name": "lib.transformers.erreurs_med.get_table_df", "line_number": 852, "usage_type": "call"}, {"api_name": "lib.transformers.erreurs_med", "line_number": 852, "usage_type": "attribute"}, {"api_name": "lib.transformers", "line_number": 852, "usage_type": "name"}, {"api_name": "lib.db.create_table_from_df", "line_number": 857, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 857, "usage_type": "name"}, {"api_name": "lib.db.create_df_from_table", "line_number": 864, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 864, "usage_type": "name"}, {"api_name": "lib.db.create_df_from_table", "line_number": 865, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 865, "usage_type": "name"}, {"api_name": "lib.helpers.get_path_from_source", "line_number": 887, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 887, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 888, "usage_type": "argument"}, {"api_name": "lib.helpers.load_excel_to_df", "line_number": 889, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 889, "usage_type": "name"}, {"api_name": "lib.transformers.ruptures.clean_old_ruptures_df", "line_number": 890, "usage_type": "call"}, {"api_name": "lib.transformers.ruptures", "line_number": 890, "usage_type": "attribute"}, {"api_name": "lib.transformers", "line_number": 890, "usage_type": "name"}, {"api_name": "sqlalchemy.types.String", "line_number": 920, "usage_type": "name"}, {"api_name": "sqlalchemy.types.String", "line_number": 921, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Date", "line_number": 922, "usage_type": "name"}, {"api_name": "lib.helpers.get_path_from_source", "line_number": 925, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 925, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 926, "usage_type": "argument"}, {"api_name": "lib.helpers.load_excel_to_df", "line_number": 927, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 927, "usage_type": "name"}, {"api_name": "lib.transformers.ruptures.clean_new_ruptures_df", "line_number": 928, "usage_type": "call"}, {"api_name": "lib.transformers.ruptures", "line_number": 928, "usage_type": "attribute"}, {"api_name": "lib.transformers", "line_number": 928, "usage_type": "name"}, {"api_name": "lib.transformers.ruptures.merge_new_and_old_ruptures_df", "line_number": 930, "usage_type": "call"}, {"api_name": "lib.transformers.ruptures", "line_number": 930, "usage_type": "attribute"}, {"api_name": "lib.transformers", "line_number": 930, "usage_type": "name"}, {"api_name": "lib.db.create_table_from_df", "line_number": 939, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 939, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 944, "usage_type": "attribute"}, {"api_name": "lib.transformers.causes_ruptures.clean_old_causes_df", "line_number": 945, "usage_type": "call"}, {"api_name": "lib.transformers.causes_ruptures", "line_number": 945, "usage_type": "attribute"}, {"api_name": "lib.transformers", "line_number": 945, "usage_type": "name"}, {"api_name": "lib.helpers.get_path_from_source", "line_number": 955, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 955, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 956, "usage_type": "argument"}, {"api_name": "lib.helpers.load_excel_to_df", "line_number": 957, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 957, "usage_type": "name"}, {"api_name": "lib.transformers.causes_ruptures.clean_new_causes_df", "line_number": 958, "usage_type": "call"}, {"api_name": "lib.transformers.causes_ruptures", "line_number": 958, "usage_type": "attribute"}, {"api_name": "lib.transformers", "line_number": 958, "usage_type": "name"}, {"api_name": "lib.transformers.causes_ruptures.merge_new_and_old_causes_df", "line_number": 960, "usage_type": "call"}, {"api_name": "lib.transformers.causes_ruptures", "line_number": 960, "usage_type": "attribute"}, {"api_name": "lib.transformers", "line_number": 960, "usage_type": "name"}, {"api_name": "lib.db.create_table_from_df", "line_number": 970, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 970, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 973, "usage_type": "attribute"}, {"api_name": "lib.db.create_df_from_table", "line_number": 975, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 975, "usage_type": "name"}, {"api_name": "lib.db.create_df_from_table", "line_number": 976, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 976, "usage_type": "name"}, {"api_name": "sqlalchemy.types.String", "line_number": 983, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 984, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 985, "usage_type": "name"}, {"api_name": "lib.db.create_table_from_df", "line_number": 1030, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 1030, "usage_type": "name"}, {"api_name": "lib.db.create_df_from_table", "line_number": 1036, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 1036, "usage_type": "name"}, {"api_name": "sqlalchemy.types.String", "line_number": 1062, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Date", "line_number": 1063, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Date", "line_number": 1064, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Date", "line_number": 1065, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Date", "line_number": 1066, "usage_type": "name"}, {"api_name": "lib.helpers.get_path_from_source", "line_number": 1070, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 1070, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 1071, "usage_type": "argument"}, {"api_name": "lib.helpers.load_excel_to_df", "line_number": 1072, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 1072, "usage_type": "name"}, {"api_name": "lib.transformers.mesures.prepare_df_mesures", "line_number": 1073, "usage_type": "call"}, {"api_name": "lib.transformers.mesures", "line_number": 1073, "usage_type": "attribute"}, {"api_name": "lib.transformers", "line_number": 1073, "usage_type": "name"}, {"api_name": "lib.db.create_table_from_df", "line_number": 1074, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 1074, "usage_type": "name"}, {"api_name": "lib.db.create_df_from_table", "line_number": 1081, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 1081, "usage_type": "name"}, {"api_name": "sqlalchemy.types.String", "line_number": 1088, "usage_type": "call"}, {"api_name": "sqlalchemy.types.Text", "line_number": 1089, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Text", "line_number": 1090, "usage_type": "name"}, {"api_name": "lib.transformers.icones.prepare_df_icones", "line_number": 1093, "usage_type": "call"}, {"api_name": "lib.transformers.icones", "line_number": 1093, "usage_type": "attribute"}, {"api_name": "lib.transformers", "line_number": 1093, "usage_type": "name"}, {"api_name": "lib.db.create_table_from_df", "line_number": 1094, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 1094, "usage_type": "name"}, {"api_name": "lib.db.create_df_from_table", "line_number": 1100, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 1100, "usage_type": "name"}, {"api_name": "sqlalchemy.types.String", "line_number": 1146, "usage_type": "call"}, {"api_name": "lib.helpers.get_path_from_source", "line_number": 1148, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 1148, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 1149, "usage_type": "argument"}, {"api_name": "lib.helpers.load_excel_to_df", "line_number": 1150, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 1150, "usage_type": "name"}, {"api_name": "lib.transformers.mesusage.prepare_df_mesusage", "line_number": 1151, "usage_type": "call"}, {"api_name": "lib.transformers.mesusage", "line_number": 1151, "usage_type": "attribute"}, {"api_name": "lib.transformers", "line_number": 1151, "usage_type": "name"}, {"api_name": "lib.transformers.mesusage.get_proporition_df", "line_number": 1153, "usage_type": "call"}, {"api_name": "lib.transformers.mesusage", "line_number": 1153, "usage_type": "attribute"}, {"api_name": "lib.transformers", "line_number": 1153, "usage_type": "name"}, {"api_name": "lib.db.create_table_from_df", "line_number": 1158, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 1158, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 1174, "usage_type": "call"}, {"api_name": "lib.transformers.pv", "line_number": 1174, "usage_type": "attribute"}, {"api_name": "lib.transformers", "line_number": 1174, "usage_type": "name"}, {"api_name": "lib.db.create_table_from_df", "line_number": 1175, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 1175, "usage_type": "name"}, {"api_name": "sqlalchemy.types.String", "line_number": 1183, "usage_type": "call"}, {"api_name": "sqlalchemy.types.String", "line_number": 1189, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 1191, "usage_type": "call"}, {"api_name": "lib.db.connect_db", "line_number": 1191, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 1191, "usage_type": "name"}, {"api_name": "multiprocessing.Pool", "line_number": 1196, "usage_type": "call"}, {"api_name": "lib.transformers.bdpm", "line_number": 1199, "usage_type": "attribute"}, {"api_name": "lib.transformers", "line_number": 1199, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 1204, "usage_type": "call"}, {"api_name": "lib.db.create_table_from_df", "line_number": 1206, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 1206, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 1211, "usage_type": "call"}, {"api_name": "lib.db.create_table_from_df", "line_number": 1215, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 1215, "usage_type": "name"}, {"api_name": "sqlalchemy.types.String", "line_number": 1229, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Integer", "line_number": 1229, "usage_type": "name"}, {"api_name": "sqlalchemy.types.Float", "line_number": 1229, "usage_type": "name"}, {"api_name": "lib.helpers.get_path_from_source", "line_number": 1232, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 1232, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 1233, "usage_type": "argument"}, {"api_name": "lib.helpers.load_excel_to_df", "line_number": 1234, "usage_type": "call"}, {"api_name": "lib.helpers", "line_number": 1234, "usage_type": "name"}, {"api_name": "pandas.notnull", "line_number": 1236, "usage_type": "call"}, {"api_name": "lib.db.create_table_from_df", "line_number": 1238, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 1238, "usage_type": "name"}, {"api_name": "sqlalchemy.types.DateTime", "line_number": 1246, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 1249, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 1249, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 1250, "usage_type": "call"}, {"api_name": "lib.db.create_table_from_df", "line_number": 1252, "usage_type": "call"}, {"api_name": "lib.db", "line_number": 1252, "usage_type": "name"}]} +{"seq_id": "181589299", "text": "from threading import Thread, RLock\nfrom random import randint\nimport pygame as pg\nimport json\n\nclass task_recipe_checker(Thread):\n def __init__(self, task_timer, level, score):\n self.score = score\n self.kebab_content = []\n self.target_list = []\n self.timer = task_timer\n self.player_level = level\n self.lock = RLock()\n self.end_recipe = False\n self.recipe_value = [pg.image.load(\"images/indicators/good.png\"), pg.image.load(\"images/indicators/bad.png\")]\n self.image_recipe_value = None\n #Graphical recipes\n self.grecipes = {\n \"Kebab_meat_Tomato_Salad_Cheese_Onions\" : [\"Fromage\", \"Viande\", \"Onions\", \"Salade\", \"Tomate\"],\n \"Tomato_Salad_Onions_Cheese\" : [\"Fromage\", \"Onions\", \"Salade\", \"Tomate\"],\n \"Kebab_meat_Tomato_Salad_Onions\" : [\"Viande\", \"Onions\", \"Salade\", \"Tomate\"],\n \"Tomato_Salad_Onions\" : [\"Onions\", \"Salade\", \"Tomate\"],\n \"Kebab_meat_Tomato_Cheese_Salad\" : [\"Fromage\", \"Viande\", \"Salade\", \"Tomate\"],\n \"Tomato_Cheese_Salad\" : [\"Fromage\", \"Salade\", \"Tomate\"],\n \"Kebab_meat_Tomato_Salad\" : [\"Viande\", \"Salade\", \"Tomate\"],\n \"Tomato_Salad\" : [\"Salade\", \"Tomate\"],\n \"Kebab_meat_Tomato_Cheese_Onions\" : [\"Fromage\", \"Viande\", \"Onions\", \"Tomate\"],\n \"Tomato_Cheese_Onions\" : [\"Fromage\", \"Onions\", \"Tomate\"],\n \"Kebab_meat_Tomato_Onions\" : [\"Viande\", \"Onions\", \"Tomate\"],\n \"Tomato_Onions\" : [\"Onions\", \"Tomate\"],\n \"Kebab_meat_Tomato_Cheese\" : [\"Fromage\", \"Viande\", \"Tomate\"],\n \"Tomato_Cheese\" : [\"Fromage\", \"Tomate\"],\n \"Kebab_meat_Tomato\" : [\"Viande\", \"Tomate\"],\n \"Tomato\" : [\"Tomate\"],\n \"Kebab_meat_Cheese_Onions_Salad\" : [\"Fromage\", \"Viande\", \"Onions\", \"Salade\"],\n \"Salad_Cheese_Onions\" : [\"Fromage\", \"Onions\", \"Salade\"],\n \"Kebab_meat_Onions_Salad\" : [\"Viande\", \"Onions\", \"Salade\"],\n \"Salad_Cheese\" : [\"Fromage\", \"Salade\"],\n \"Salad_Onions\" : [\"Onions\", \"Salade\"],\n \"Salad_Kebab_meat\" : [\"Viande\", \"Salade\"],\n \"Salad\" : [\"Salade\"],\n \"Kebab_meat_Cheese_Onions\" : [\"Fromage\", \"Viande\", \"Onions\"],\n \"Onions_Cheese\" : [\"Fromage\", \"Onions\"],\n \"Kebab_meat_Onions\" : [\"Viande\", \"Onions\"],\n \"Onions\" : [\"Onions\"],\n \"Kebab_meat_Cheese\" : [\"Fromage\", \"Viande\"],\n \"Cheese\" : [\"Fromage\"],\n \"Kebab_meat\" : [\"Viande\"],\n \"Kebab_meat_Salad_Cheese\" : [\"Fromage\", \"Viande\", \"Salade\"]\n }\n\n try:\n f = open(\"ressources/items.json\")\n except IOError as e:\n print(e)\n\n with f as json_file:\n self.items_list = json.load(json_file)\n\n Thread.__init__(self)\n\n def get_target_list(self):\n with self.lock:\n return self.target_list\n\n def add_kebab_item(self, i):\n with self.lock:\n self.kebab_content.append(i)\n\n def get_kebab_content(self):\n with self.lock:\n return self.kebab_content\n\n def random_item(self):\n\n try:\n items_index_level = [key for key in self.items_list\n if int(key) <= self.player_level]\n random_level = items_index_level[randint(0,len(items_index_level)-1)]\n selected_item_list = [item for item in self.items_list[random_level]]\n items_index_list = [key for key in selected_item_list[0]]\n selected_item = selected_item_list[0][\n items_index_list[randint(0, len(items_index_list)-1)]\n ]\n except:\n selected_item = 0\n\n return selected_item\n\n def new_recipe(self):\n self.target_list = []\n item_number = randint(2, 7)\n for i in range(0, item_number):\n random_item = self.random_item()\n if random_item != 0:\n self.target_list.append(random_item[\"name\"])\n else:\n pass\n\n #Remove duplicates items\n self.target_list = list(set(self.target_list))\n\n def get_state_image(self):\n with self.lock:\n return self.image_recipe_value\n\n def recipe_is_done(self):\n with self.lock:\n return self.end_recipe\n\n def run(self):\n self.new_recipe()\n recipe_len = len(self.target_list)\n target_in_kebab = []\n\n while self.timer.get_actual_time() > 0:\n for item in set(self.target_list).intersection(self.kebab_content):\n target_in_kebab.append(item)\n del self.target_list[self.target_list.index(item)]\n\n if len(self.kebab_content) == recipe_len:\n if len(target_in_kebab) == recipe_len:\n self.score.update_actual_score(5)\n self.image_recipe_value = self.recipe_value[0]\n else:\n self.score.update_actual_score(-5)\n self.image_recipe_value = self.recipe_value[1]\n\n self.end_recipe = True\n time_ending = self.timer.get_actual_time()\n self.new_recipe()\n recipe_len = len(self.target_list)\n target_in_kebab = []\n self.kebab_content = []\n\n #Unset value image\n if \"time_ending\" in locals():\n if self.timer.get_actual_time() <= time_ending - 2 and self.end_recipe == True:\n self.end_recipe = False\n", "sub_path": "_class/task/recipe.py", "file_name": "recipe.py", "file_ext": "py", "file_size_in_byte": 6155, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "threading.Thread", "line_number": 6, "usage_type": "name"}, {"api_name": "threading.RLock", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 15, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 58, "usage_type": "call"}, {"api_name": "threading.Thread.__init__", "line_number": 60, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 60, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 79, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 83, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "547206775", "text": "\"\"\"\nevent_number_model_training.py\n\nThis script is used to train a clasiffier aimed to identify the number of audio events in an audio frame level\nTraining features values are DirAC diffuseness calculated by generate_diffuseness_and_source_count.by\nThe output of the model is 0, 1 or 2 events\nSeveral models are evaluated using a simple pipeline and unique gridsearch for each algorithm\nSklearn modules are used as first aproach: Random Forest, Gradient Boosting and Support Vector Classifiers\nIf GB is chosen, XGBoost or Light XGB will be eventually implemented since they optimize computation (paralelization)\nThe trained model is stored as a joblib file in the folder ....\n\n\"\"\"\n\nfrom baseline import parameter\nimport os\nimport pandas as pd\nimport sklearn\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.metrics import accuracy_score,confusion_matrix\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier\nfrom sklearn.svm import SVC\nimport joblib\n# import xgboost as xgb\nimport numpy as np\nimport scipy\n\nif os.environ.get('USER') == 'ribanez':\n user = 'FAIK'\n import xgboost as xgb\n\n\nparams = parameter.get_params()\ndata_input_path = os.path.join(params['dataset_dir'],'models/event_number/input_data' ) # path to folders\nmodel_output_path = os.path.join(params['dataset_dir'], 'models/event_number/') # path to arrays\n\n# Import data and parse in pandas dataframes\ndff_x=pd.read_pickle(os.path.join(data_input_path,'training_x_event_number.pkl'))\ndff_y=pd.read_pickle(os.path.join(data_input_path,'training_y_event_number.pkl'))\n\ndff_y_binary=dff_y.astype(int)\ndff_y_binary=dff_y_binary.astype(str)\ndff_y_binary['target'] = pd.Categorical(dff_y_binary['target'])\n#dff_y_binary['target'].replace({'2': '1'}, inplace=True)\nprint(dff_y_binary.describe())\nprint(dff_y['target'].value_counts())\n#df=pd.DataFrame(columns=)\narray_dff_x=dff_x.values\ndff_x['mean']=np.mean(array_dff_x, axis=1)\ndff_x['min']=np.min(array_dff_x, axis=1)\ndff_x['max']=np.max(array_dff_x, axis=1)\ndff_x['mean']=np.mean(array_dff_x, axis=1)\ndff_x['median']=np.median(array_dff_x, axis=1)\ndff_x['mode']=np.percentile(array_dff_x,50,axis=1)\ndff_x['range']=np.ptp(array_dff_x, axis=1)\ndff_x['p10']=np.percentile(array_dff_x,10,axis=1)\ndff_x['p20']=np.percentile(array_dff_x,20,axis=1)\ndff_x['p25']=np.percentile(array_dff_x,25,axis=1)\ndff_x['p30']=np.percentile(array_dff_x,30,axis=1)\ndff_x['p40']=np.percentile(array_dff_x,40,axis=1)\ndff_x['p60']=np.percentile(array_dff_x,60,axis=1)\ndff_x['p70']=np.percentile(array_dff_x,70,axis=1)\ndff_x['p75']=np.percentile(array_dff_x,75,axis=1)\ndff_x['p80']=np.percentile(array_dff_x,80,axis=1)\ndff_x['p90']=np.percentile(array_dff_x,90,axis=1)\ndff_x['iqr']=scipy.stats.iqr(array_dff_x, axis=1,rng=(25,75),interpolation='lower')\ndff_x['std']=np.std(array_dff_x, axis=1)\ndff_x['var']=np.var(array_dff_x, axis=1)\ndff_x['skew']=scipy.stats.skew(array_dff_x, axis=1)\ndff_x['kurt']=scipy.stats.kurtosis(array_dff_x, axis=1)\n\ncolumns=['mean','min','max','median','mode','range','p10','p20','p25','p30','p40','p60','p70','p75','p80','p90','iqr','std','var','skew','kurt']\ndff_x=dff_x[columns]\n\nprint(\"Dimensiones dataset \")\nprint(dff_x.shape)\n\n'''\nn=20\ni=0\nj=0\nk=0\ndfaux=dff_x\n#dfaux = dff_x\nlista=[]\nfor column in range(len(dff_x.columns)):\n i+=1\n k+=1\n if i==n:\n j+=1\n lista.append('v' + str(j))\n dfaux['v'+str(j)]=dff_x['v'+str(k)]\n i=0\ndfaux=dfaux[lista]\nprint(len(dfaux.columns))\ndff_x=dfaux\n'''\n# Defining some pipelines. GB, RF and SVC\n\npipe_rf = Pipeline([('reg', RandomForestClassifier(random_state=42))])\n\npipe_gb = Pipeline([('reg', GradientBoostingClassifier(random_state=42))])\n\npipe_svr = Pipeline([('reg', SVC())])\n\npipe_XGB = Pipeline([('reg',xgb.XGBClassifier(random_state=42))])\n\n# Defining some Grids\n\ngrid_params_rf = [{'reg__n_estimators': [100],\n 'reg__max_depth': [8],\n 'reg__max_features': [\"sqrt\"],\n 'reg__min_samples_split': [4]}]\n\ngrid_params_gb = [{'reg__learning_rate': [0.01,0.02,0.03],\n 'reg__n_estimators' : [100,500,1000],\n 'reg__max_depth' : [4,6,8]}]\n\ngrid_params_svr = [{'reg__kernel': ['rbf'],\n 'reg__gamma': [1e-8,0.9],\n 'reg__C': [1, 10000000]}]\n\ngrid_params_XGB = [{'reg__colsample_bytree': [0.1,0.9],\n \"reg__learning_rate\": [0.01,0.5], # default 0.1\n \"reg__max_depth\": [3], # default 3\n \"reg__n_estimators\": [100,200]}]\n\n# Defining some grid searches\njobs=-1\n\ngs_rf = GridSearchCV(estimator=pipe_rf,param_grid=grid_params_rf,scoring='accuracy',cv=2,verbose=10,n_jobs=-1)\n\ngs_gb = GridSearchCV(estimator=pipe_gb,param_grid=grid_params_gb,scoring='accuracy',cv=5,verbose=10,n_jobs=1)\n\ngs_svr = GridSearchCV(estimator=pipe_svr,param_grid=grid_params_svr,scoring='accuracy',cv=2,verbose=10,n_jobs=-1)\n\ngs_XGB = GridSearchCV(estimator=pipe_XGB,param_grid=grid_params_XGB,scoring='accuracy',cv=4,verbose=10,n_jobs=-1)\n\ngrids = [gs_rf]\n\ngrid_dict = {0: 'xgb'}\n\n# Split train and test\n\ntrain_x, test_x, train_y, test_y = train_test_split(dff_x, dff_y_binary['target'], test_size=0.10, random_state=42)\n\n# Train\n\nbest_acc = 0\nbest_cls = 0\nbest_gs = ''\nfor idx, gs in enumerate(grids):\n print('\\nEstimator: %s' % grid_dict[idx])\n # Fit grid search\n\n gs.fit(train_x,train_y)\n # Best params\n print('Best params: %s' % gs.best_params_)\n # Best training data r2\n print('Best training accuracy: %.3f' % gs.best_score_)\n # Prediction using best model\n y_pred = gs.predict(test_x)\n print('Prediction accuracy for best params: %.3f ' % accuracy_score(test_y, y_pred))\n print('Prediction accuracy for best params:')\n print(confusion_matrix(test_y,y_pred))\n # Track best (highest test accuracy) model\n if accuracy_score(test_y, y_pred) > best_acc:\n best_acc = accuracy_score(test_y, y_pred)\n best_gs = gs\n best_cls = idx\nprint('\\n Classifier with best score: %s' % grid_dict[best_cls])\n\n# Save model (local)\njoblib.dump(best_gs.best_estimator_, model_output_path+'/model.joblib')\njoblib.dump(best_gs.best_params_, model_output_path+'/params.joblib')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n", "sub_path": "APRI/event_number_model_training.py", "file_name": "event_number_model_training.py", "file_ext": "py", "file_size_in_byte": 6401, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.environ.get", "line_number": 31, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 31, "usage_type": "attribute"}, {"api_name": "baseline.parameter.get_params", "line_number": 36, "usage_type": "call"}, {"api_name": "baseline.parameter", "line_number": 36, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pandas.read_pickle", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pandas.read_pickle", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pandas.Categorical", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.median", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.ptp", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 68, "usage_type": "call"}, {"api_name": "scipy.stats.iqr", "line_number": 69, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 69, "usage_type": "attribute"}, {"api_name": "numpy.std", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.var", "line_number": 71, "usage_type": "call"}, {"api_name": "scipy.stats.skew", "line_number": 72, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 72, "usage_type": "attribute"}, {"api_name": "scipy.stats.kurtosis", "line_number": 73, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 73, "usage_type": "attribute"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 103, "usage_type": "call"}, {"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 103, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 105, "usage_type": "call"}, {"api_name": "sklearn.ensemble.GradientBoostingClassifier", "line_number": 105, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 107, "usage_type": "call"}, {"api_name": "sklearn.svm.SVC", "line_number": 107, "usage_type": "call"}, {"api_name": "sklearn.pipeline.Pipeline", "line_number": 109, "usage_type": "call"}, {"api_name": "xgboost.XGBClassifier", "line_number": 109, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 134, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 136, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 138, "usage_type": "call"}, {"api_name": "sklearn.model_selection.GridSearchCV", "line_number": 140, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 148, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 166, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 168, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 170, "usage_type": "call"}, {"api_name": "sklearn.metrics.accuracy_score", "line_number": 171, "usage_type": "call"}, {"api_name": "joblib.dump", "line_number": 177, "usage_type": "call"}, {"api_name": "joblib.dump", "line_number": 178, "usage_type": "call"}]} +{"seq_id": "422363639", "text": "from setuptools import setup, find_packages\nfrom os.path import join\n\nversion = '1.0b1'\nreadme = open(\"README.txt\").read()\nhistory = open('HISTORY.txt').read()\n\nsetup(name='ely.croppableimagefield',\n version=version,\n description=\"CroppableImageField is a drop-in replacement for the Archetype field ImageField\",\n long_description = readme + '\\n' + history,\n classifiers=[\n \"Framework :: Plone\",\n 'License :: OSI Approved :: GNU General Public License (GPL)',\n ],\n author = 'Michael Dunstan',\n author_email = 'michael@elyt.com',\n url = 'http://pypi.python.org/pypi/ely.croppableimagefield',\n license = 'GPL',\n packages=find_packages(),\n namespace_packages=['ely'],\n include_package_data=True,\n zip_safe=False,\n install_requires=[\n 'setuptools',\n ],\n )\n", "sub_path": "pypi_install_script/ely.croppableimagefield-1.0b1.tar/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 867, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "setuptools.setup", "line_number": 8, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "309979882", "text": "import jpype\nfrom jpype.types import *\nimport common\n\n\nclass CollectionTestCase(common.JPypeTestCase):\n\n def setUp(self):\n super(CollectionTestCase, self).setUp()\n\n def testCollection(self):\n collection = jpype.java.util.ArrayList()\n collection.add(1)\n collection.add(2)\n self.assertEqual([1, 2], [i for i in collection])\n\n def testIterateHashmap(self):\n collection = jpype.java.util.HashMap()\n collection.put('A', 1)\n collection.put('B', 2)\n asdict = dict()\n for x in collection.entrySet():\n asdict[str(x.getKey())] = x.getValue().longValue()\n self.assertEqual(asdict, {'A': 1, 'B': 2})\n\n def testEnumMap(self):\n enumclass = jpype.JClass('jpype.collection.TestEnum')\n enummap = jpype.java.util.EnumMap(enumclass)\n enummap.put(enumclass.A, 'ABC')\n enummap.put(enumclass.B, 'DEF')\n asdict = dict()\n for x in enummap.entrySet():\n asdict[str(x.getKey())] = x.getValue()\n self.assertEqual({'A': 'ABC', 'B': 'DEF'}, asdict)\n\n def testMapPut(self):\n jmap = jpype.JClass(\"java.util.HashMap\")()\n jmap[\"a\"] = 1\n self.assertEqual(jmap[\"a\"], 1)\n\n def testMapPutAll(self):\n jmap = jpype.JClass(\"java.util.HashMap\")()\n dic = {\"a\": \"1\", \"b\": \"2\", \"c\": \"3\"}\n jmap.putAll(dic)\n self.assertEqual(jmap[\"a\"], \"1\")\n self.assertEqual(jmap[\"b\"], \"2\")\n self.assertEqual(jmap[\"c\"], \"3\")\n with self.assertRaises(TypeError):\n jmap.putAll([1, 2, 3])\n\n def testListGet(self):\n jlist = jpype.JClass(\"java.util.ArrayList\")()\n jlist.addAll([1, 2, 3, 4])\n self.assertEqual(jlist[0], 1)\n self.assertEqual(jlist[3], 4)\n self.assertEqual(jlist[-1], 4)\n self.assertEqual(jlist[-4], 1)\n\n def testListSlice(self):\n jlist = jpype.JClass(\"java.util.ArrayList\")()\n jlist.addAll([1, 2, 3, 4])\n jlist[1:3] = [5, 6]\n self.assertEqual(jlist[1], 5)\n self.assertEqual(jlist[2], 6)\n\n def testListDel(self):\n jlist = jpype.JClass(\"java.util.ArrayList\")()\n jlist.addAll([1, 2, 3, 4])\n del jlist[0]\n self.assertEqual(len(jlist), 3)\n self.assertEqual(jlist[0], 2)\n\n def testCollectionAddAll(self):\n l = [1, 2, 3, 4]\n l2 = ['a', 'b']\n jlist = jpype.JClass(\"java.util.ArrayList\")()\n jlist.addAll(l)\n jcollection = jpype.JObject(jlist, jpype.java.util.Collection)\n jcollection.addAll(l2)\n l.extend(l2)\n self.assertEqual(l, list(jcollection))\n\n def testListSetItemNeg(self):\n l = [1, 2, 3, 4]\n jlist = jpype.JClass(\"java.util.ArrayList\")()\n jlist.addAll([1, 2, 3, 4])\n jlist[-1] = 5\n l[-1] = 5\n self.assertEqual(l, list(jlist))\n jlist[-2] = 6\n l[-2] = 6\n self.assertEqual(l, list(jlist))\n with self.assertRaises(IndexError):\n jlist[-5] = 6\n\n def testMapKeyError(self):\n hm = JClass('java.util.HashMap')()\n with self.assertRaises(KeyError):\n hm['foo']\n hm['foo'] = None\n self.assertEqual(hm['foo'], None)\n\n def testHashMapEntryIter(self):\n hm = JClass('java.util.HashMap')()\n hm['alice'] = 'alice'\n hm['betty'] = 'betty'\n hm['catty'] = 'catty'\n for p, v in hm.entrySet():\n self.assertEqual(p, v)\n\n def testTreeMapEntryIter(self):\n hm = JClass('java.util.TreeMap')()\n hm['alice'] = 'alice'\n hm['betty'] = 'betty'\n hm['catty'] = 'catty'\n for p, v in hm.entrySet():\n self.assertEqual(p, v)\n\n def testSetDelItem(self):\n hs = JClass('java.util.HashSet')()\n hs.add('a')\n hs.add('b')\n hs.add('c')\n self.assertIn('a', hs)\n del hs['a']\n self.assertNotIn('a', hs)\n\n def testMapEntry(self):\n hm = JClass('java.util.TreeMap')()\n hm['alice'] = 'alice'\n h = hm.entrySet()\n self.assertEqual(len(h.iterator().next()), 2)\n\n def testListIter(self):\n ls = JClass('java.util.ArrayList')([0, 1, 2, 3])\n for i, j in enumerate(ls):\n self.assertEqual(i, j)\n\n def testEnumeration(self):\n st = JClass('java.util.StringTokenizer')(\"this is a test\")\n out = []\n for i in st:\n out.append(str(i))\n self.assertEqual(len(i), 4)\n self.assertEqual(\" \".join(out), \"this is a test\")\n\n def testCollectionDelItem(self):\n ja = JClass('java.util.ArrayList')(['1', '2', '3'])\n jc = JObject(ja, 'java.util.Collection')\n with self.assertRaisesRegex(TypeError, 'remove'):\n del jc[1]\n\n def testHashMapCtor(self):\n HashMap = JClass('java.util.HashMap')\n dc = dict()\n dc['fred'] = 1\n dc['george'] = 2\n dc['paul'] = 3\n hm = HashMap(dc)\n for p, v in dc.items():\n self.assertEqual(hm[p], v)\n\n def testHashMapPutAll(self):\n HashMap = JClass('java.util.HashMap')\n hm = HashMap()\n dc = dict()\n dc['fred'] = 1\n dc['george'] = 2\n dc['paul'] = 3\n hm.putAll(dc)\n for p, v in dc.items():\n self.assertEqual(hm[p], v)\n\n def testHashMapConvert(self):\n HashMap = JClass('java.util.HashMap')\n hm = HashMap()\n hm['fred'] = 1\n hm['george'] = 2\n hm['paul'] = 3\n dc = dict(hm)\n for p, v in hm.items():\n self.assertEqual(dc[p], v)\n\n def testMapABC(self):\n from collections.abc import Mapping, Sized, Iterable, Container\n hm = JClass('java.util.HashMap')()\n self.assertIsInstance(hm, Sized)\n self.assertIsInstance(hm, Iterable)\n self.assertIsInstance(hm, Container)\n self.assertIsInstance(hm, Mapping)\n\n def testUnmodifiableNext(self):\n ArrayList = JClass('java.util.ArrayList')\n Collections = JClass('java.util.Collections')\n a = ArrayList()\n a.add(\"first\")\n a.add(\"second\")\n a.add(\"third\")\n for i in a:\n pass\n\n for i in Collections.unmodifiableList(a):\n pass\n", "sub_path": "test/jpypetest/test_collection.py", "file_name": "test_collection.py", "file_ext": "py", "file_size_in_byte": 6202, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "common.JPypeTestCase", "line_number": 6, "usage_type": "attribute"}, {"api_name": "jpype.java.util.ArrayList", "line_number": 12, "usage_type": "call"}, {"api_name": "jpype.java", "line_number": 12, "usage_type": "attribute"}, {"api_name": "jpype.java.util.HashMap", "line_number": 18, "usage_type": "call"}, {"api_name": "jpype.java", "line_number": 18, "usage_type": "attribute"}, {"api_name": "jpype.JClass", "line_number": 27, "usage_type": "call"}, {"api_name": "jpype.java.util.EnumMap", "line_number": 28, "usage_type": "call"}, {"api_name": "jpype.java", "line_number": 28, "usage_type": "attribute"}, {"api_name": "jpype.JClass", "line_number": 37, "usage_type": "call"}, {"api_name": "jpype.JClass", "line_number": 42, "usage_type": "call"}, {"api_name": "jpype.JClass", "line_number": 52, "usage_type": "call"}, {"api_name": "jpype.JClass", "line_number": 60, "usage_type": "call"}, {"api_name": "jpype.JClass", "line_number": 67, "usage_type": "call"}, {"api_name": "jpype.JClass", "line_number": 76, "usage_type": "call"}, {"api_name": "jpype.JObject", "line_number": 78, "usage_type": "call"}, {"api_name": "jpype.java", "line_number": 78, "usage_type": "attribute"}, {"api_name": "jpype.JClass", "line_number": 85, "usage_type": "call"}, {"api_name": "collections.abc.Sized", "line_number": 187, "usage_type": "name"}, {"api_name": "collections.abc.Iterable", "line_number": 188, "usage_type": "name"}, {"api_name": "collections.abc.Container", "line_number": 189, "usage_type": "name"}, {"api_name": "collections.abc.Mapping", "line_number": 190, "usage_type": "name"}]} +{"seq_id": "261043955", "text": "# -*- coding: utf-8 -*-\nimport argparse\nimport logging\nimport sys\n\nfrom cronq.backends.mysql import Storage\nfrom cronq.models.event import Event\nfrom cronq.models.job import Job\n\nfrom sqlalchemy import between\nfrom sqlalchemy.sql.expression import asc\nfrom sqlalchemy.sql.expression import desc\n\nlogger = logging.getLogger(__name__)\n\n\ndef prune_record(event_id, max_event_id, event_range, storage):\n last = event_id + event_range\n if last > max_event_id:\n last = max_event_id\n\n stmt = Event.__table__.delete().where(between(Event.id, event_id, last))\n storage._engine.execute(stmt)\n logger.info('Pruning {0} - {1}'.format(event_id, last))\n storage.session.commit()\n return last\n\n\ndef prune(first, last, interval):\n storage = Storage(isolation_level=None)\n\n event_id = first\n while event_id <= last:\n try:\n event_id = prune_record(event_id, last, interval, storage)\n except (KeyboardInterrupt, SystemExit):\n storage.session.commit()\n return\n except Exception as e:\n logger.warning(e)\n return\n if event_id == last:\n break\n\n storage.session.commit()\n\n\ndef prune_keep_record(job_id, keep, storage):\n event = storage.session.query(Event).filter_by(job_id=job_id).\\\n order_by(asc(Event.id)).limit(1).first()\n\n min_id = None\n if event is not None:\n min_id = event.id\n\n events = storage.session.query(Event).filter_by(job_id=job_id).\\\n order_by(desc(Event.id)).limit(keep)\n event_ids = [e.id for e in events]\n if len(event_ids) == 0:\n logger.info('No events for {0}'.format(job_id))\n return\n\n max_id = min(event_ids)\n if min_id == max_id:\n logger.info('Min and max event ids for {0} are the same: {1} - {2}'.format( # noqa\n job_id, min_id, max_id))\n return\n\n if min_id > max_id:\n logger.info('Min event id for {0} is larger than max event id: {1} - {2}'.format( # noqa\n job_id, min_id, max_id))\n return\n\n logger.info('Job ID {0}, Pruning events {1} - {2}'.format(\n job_id, min_id, max_id))\n\n stmt = Event.__table__.delete()\\\n .where(between(Event.id, min_id, max_id))\\\n .where(Event.job_id == job_id)\n storage._engine.execute(stmt)\n storage.session.commit()\n\n\ndef prune_keep(keep):\n storage = Storage(isolation_level=None)\n jobs = storage.session.query(Job).order_by(asc(Job.id))\n for job in jobs:\n prune_keep_record(job.id, keep, storage)\n\n\ndef prune_type(args):\n is_keep = args['keep'] is not None\n is_interval = True\n interval_args = []\n for k in ['first', 'last']:\n if args[k] is None:\n is_interval = False\n interval_args.append(False)\n else:\n interval_args.append(True)\n\n type_ = None\n error = None\n if not is_interval and True in interval_args:\n error = 'If any \"range\" args are specified, all must be specified'\n\n if is_keep:\n if is_interval:\n error = 'Cannot specify both \"keep\" arg and \"range\" args'\n else:\n type_ = 'keep'\n else:\n if not is_interval:\n error = 'Must specify either \"keep\" arg or \"range\" args'\n else:\n type_ = 'range'\n\n return type_, error\n\n\ndef main():\n parser = argparse.ArgumentParser(description='Prunes the cronq datastore')\n parser.add_argument('--keep',\n type=int,\n default=None,\n help='number of event entries to keep')\n parser.add_argument('--first',\n type=int,\n default=None,\n help='first entry to prune')\n parser.add_argument('--interval',\n type=int,\n default=100,\n help='interval to delete by')\n parser.add_argument('--last',\n type=int,\n default=None,\n help='last entry to prune')\n args = parser.parse_args()\n args = vars(args)\n\n type_, error = prune_type(args)\n if error is not None:\n logger.warning(error)\n sys.exit(1)\n\n if type_ == 'keep':\n prune_keep(args['keep'])\n else:\n prune(args['first'], args['last'], args['range'])\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "cronq/pruner.py", "file_name": "pruner.py", "file_ext": "py", "file_size_in_byte": 4421, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "cronq.models.event.Event.__table__.delete", "line_number": 22, "usage_type": "call"}, {"api_name": "cronq.models.event.Event.__table__", "line_number": 22, "usage_type": "attribute"}, {"api_name": "cronq.models.event.Event", "line_number": 22, "usage_type": "name"}, {"api_name": "sqlalchemy.between", "line_number": 22, "usage_type": "call"}, {"api_name": "cronq.models.event.Event.id", "line_number": 22, "usage_type": "attribute"}, {"api_name": "cronq.backends.mysql.Storage", "line_number": 30, "usage_type": "call"}, {"api_name": "cronq.models.event.Event", "line_number": 49, "usage_type": "argument"}, {"api_name": "sqlalchemy.sql.expression.asc", "line_number": 50, "usage_type": "call"}, {"api_name": "cronq.models.event.Event.id", "line_number": 50, "usage_type": "attribute"}, {"api_name": "cronq.models.event.Event", "line_number": 50, "usage_type": "name"}, {"api_name": "cronq.models.event.Event", "line_number": 56, "usage_type": "argument"}, {"api_name": "sqlalchemy.sql.expression.desc", "line_number": 57, "usage_type": "call"}, {"api_name": "cronq.models.event.Event.id", "line_number": 57, "usage_type": "attribute"}, {"api_name": "cronq.models.event.Event", "line_number": 57, "usage_type": "name"}, {"api_name": "cronq.models.event.Event.__table__.delete", "line_number": 77, "usage_type": "call"}, {"api_name": "cronq.models.event.Event.__table__", "line_number": 77, "usage_type": "attribute"}, {"api_name": "cronq.models.event.Event", "line_number": 77, "usage_type": "name"}, {"api_name": "sqlalchemy.between", "line_number": 78, "usage_type": "call"}, {"api_name": "cronq.models.event.Event.id", "line_number": 78, "usage_type": "attribute"}, {"api_name": "cronq.models.event.Event", "line_number": 78, "usage_type": "name"}, {"api_name": "cronq.models.event.Event.job_id", "line_number": 79, "usage_type": "attribute"}, {"api_name": "cronq.models.event.Event", "line_number": 79, "usage_type": "name"}, {"api_name": "cronq.backends.mysql.Storage", "line_number": 85, "usage_type": "call"}, {"api_name": "cronq.models.job.Job", "line_number": 86, "usage_type": "argument"}, {"api_name": "sqlalchemy.sql.expression.asc", "line_number": 86, "usage_type": "call"}, {"api_name": "cronq.models.job.Job.id", "line_number": 86, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 122, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 145, "usage_type": "call"}]} +{"seq_id": "445691845", "text": "#!/usr/bin/env python\n\nimport os\nimport re\nimport sys\nimport gzip\nimport pandas as pd\nimport logging\n\nlogging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s: %(message)s')\n\n\ngtf, assembly_report = sys.argv[1:]\n\n# read in the chromosome names...\nchromosomes = pd.read_csv(assembly_report, delimiter='\\t', comment='#', header=None)\nchromosomes.columns = ['sequence_name', 'sequence_role', 'assigned_molecule', 'assigned_molecule_location_type', 'genbank', 'relationship', 'refseq', 'assembly_unit', 'sequence_length', 'ucsc']\nchromosome_translations = {row['refseq']: row['ucsc'] for index, row in chromosomes.iterrows()}\n\n# for each item in the GTF:\n# convert chromosome name\n# check the source. if it's Curated Genomic or BestRefSeq, keep\n# keep genes \n# as the name of the feature -- use 'gene' attribute\n\nHEADER = re.compile(\"^#\")\n\nwith gzip.open(gtf, 'rt') as f:\n for line in f:\n if HEADER.match(line):\n continue\n chrom, source, feature_type, start, end, score, strand, phase, info = line.rstrip().split('\\t')\n if feature_type != 'gene':\n continue\n chrom = chromosome_translations[chrom]\n info = [i.split(' ') for i in info.rstrip(';').split('\"; ')]\n info = {i[0]: i[1].replace('\"', '') for i in info}\n gene_name = info['gene']\n if chrom == 'na':\n logging.info('Skipping {} (no chromosome translation)'.format(gene_name))\n continue\n print('{chrom}\\t{start}\\t{end}\\t{gene_name}\\t.\\t{strand}'.format(**locals()))\n\n\n", "sub_path": "bin/refseq-annotation-and-report-to-genic-regions.py", "file_name": "refseq-annotation-and-report-to-genic-regions.py", "file_ext": "py", "file_size_in_byte": 1548, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "logging.basicConfig", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 16, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 26, "usage_type": "call"}, {"api_name": "gzip.open", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 40, "usage_type": "call"}]} +{"seq_id": "180043216", "text": "\"\"\"\r\nInitialize Flask app\r\n\r\n\"\"\"\r\nfrom flask import Flask, jsonify, request, make_response\r\nimport os, sys\r\nimport traceback\r\nfrom flask_debugtoolbar import DebugToolbarExtension\r\nfrom werkzeug.debug import DebuggedApplication\r\nfrom flask_sqlalchemy import SQLAlchemy\r\nfrom models import Plant, Supplier, update_model, GrowWeek\r\nfrom standard_models import DBEntry\r\n\r\nfrom google.appengine.api.taskqueue import taskqueue\r\n\r\n\r\napp = Flask('application')\r\n\r\napp.config['SQLALCHEMY_DATABASE_URI'] = DBEntry.get_connection_string('Datawarehouse')\r\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\r\ndb = SQLAlchemy(app)\r\n\r\nimport database\r\n\r\nif os.getenv('FLASK_CONF') == 'TEST':\r\n app.config.from_object('application.settings.Testing')\r\n\r\nelif 'SERVER_SOFTWARE' in os.environ and os.environ['SERVER_SOFTWARE'].startswith('Dev'):\r\n # Development settings\r\n app.config.from_object('application.settings.Development')\r\n # Flask-DebugToolbar\r\n toolbar = DebugToolbarExtension(app)\r\n\r\n # Google app engine mini profiler\r\n # https://github.com/kamens/gae_mini_profiler\r\n app.wsgi_app = DebuggedApplication(app.wsgi_app, evalex=True)\r\n _temp = __import__('gae_mini_profiler', globals(), locals(), ['profiler', 'templatetags'], -1)\r\n profiler = _temp.profiler\r\n templatetags = _temp.templatetags\r\n #from gae_mini_profiler import profiler, templatetags\r\n #from flasext.gae_mini_profiler import profiler\r\n\r\n @app.context_processor\r\n def inject_profiler():\r\n return dict(profiler_includes=templatetags.profiler_includes())\r\n app.wsgi_app = profiler.ProfilerWSGIMiddleware(app.wsgi_app)\r\n \r\n Plant.dev_get_create()\r\n Supplier.dev_get_create()\r\nelse:\r\n app.config.from_object('application.settings.Production')\r\n\r\n# Enable jinja2 loop controls extension\r\napp.jinja_env.add_extension('jinja2.ext.loopcontrols')\r\n\r\n# Pull in URL dispatch routes\r\nimport urls\r\nimport restful\r\n\r\n@app.route('/rest')\r\n@app.route('/rest//',methods=['DELETE', 'GET', 'GET_METADATA', 'POST', 'PUT'])\r\ndef rest_impl(path):\r\n return restful.process_rest_request(path, request,make_response())\r\n\r\n\r\n@app.route('/notes/save/',methods=['POST'])\r\ndef save_note(pg_key):\r\n #jin = request.form\r\n jin = request.get_json(force=True)\r\n return restful.notes_wrapper(plantgrow_key=pg_key, note = jin['note'], method='save')\r\n\r\n@app.route('/notes/get/',methods=['GET'])\r\ndef get_notes(pg_key):\r\n return restful.notes_wrapper(plantgrow_key=pg_key, method='get')\r\n\r\n@app.route('/notes/delete/',methods=['GET'])\r\ndef delete_note(nt_key):\r\n return restful.notes_wrapper(note_key=nt_key, method='delete')\r\n\r\n@app.route('/week_summary//', methods=['GET'])\r\ndef get_summary(year, week_num):\r\n return restful.get_week_summary(year, week_num)\r\n\r\n\r\n@app.route('/plantgrow/availability/', methods=['GET'])\r\ndef get_availability(plantgrow):\r\n try:\r\n avail = restful.get_availability(plantgrow)\r\n return jsonify({'availability': avail})\r\n except:\r\n traceback.print_exc(file=sys.stdout)\r\n print(\"Unexpected error:\", sys.exc_info()[0])\r\n \r\n return jsonify({'availability':0})\r\n\r\n@app.route('/plantgrow/update/',methods=['GET','POST'])\r\ndef upd_plantgrow():\r\n try:\r\n jpg = request.get_json()\r\n return restful.update_plant_grow(jpg['plant'], jpg['week'], jpg['actual'])\r\n except Exception:\r\n return traceback.format_exc()\r\n\r\n@app.route('/supplier_plants/update/',methods=['GET','POST'])\r\ndef update_supplier_plants():\r\n try:\r\n uJson = request.get_json()\r\n return restful.update_plantweek_entry(uJson)\r\n except Exception:\r\n msg = traceback.format_exc()\r\n print(msg)\r\n return {'status':'failed','msg': msg}\r\n \r\n@app.route('/customer_reserve/update/',methods=['GET','POST'])\r\ndef update_customer_reserve():\r\n try:\r\n uJson = request.get_json()\r\n return restful.update_plantweek_entry(uJson)\r\n except Exception:\r\n return traceback.format_exc() \r\n\r\n@app.route('/update_info//',methods=['DELETE', 'GET', 'GET_METADATA', 'POST', 'PUT'])\r\ndef get_update_info(path):\r\n return restful.get_update_info(path)\r\n\r\n@app.route('/options//',methods=['GET'])\r\ndef get_options(path):\r\n r = restful.get_option_field(path, request.values)\r\n return jsonify(r)\r\n\r\n@app.route('/log_message',methods=['GET','POST'])\r\ndef process_add_log_message():\r\n uJson = request.get_json()\r\n return restful.add_logging_message(uJson['message'],uJson['msg_type'])\r\n\r\n@app.route(\"/test_email\", methods=['GET'])\r\ndef send_test_email():\r\n return restful.send_test_email()\r\n\r\n@app.route('/push_dw',methods=['GET','POST'])\r\ndef process_dw_task():\r\n process_task = request.values.get(\"task\")\r\n process_step = request.values.get('process')\r\n task = taskqueue.add(\r\n url='/run_dw_task',\r\n target='worker',\r\n params={'task':process_task,'process':process_step})\r\n \r\n return jsonify({'task_name':task.name,'task_eta':task.eta})\r\n\r\n@app.route('/run_dw_task',methods=['POST','GET'])\r\ndef run_dw_task():\r\n runtask = request.values.get('task')\r\n process = request.values.get(\"process\") # either prep or run\r\n \r\n try:\r\n if runtask == 'newprop':\r\n update_model(process)\r\n elif runtask == 'fix_dates':\r\n GrowWeek.set_mondays()\r\n elif runtask == 'date':\r\n if process == 'prep':\r\n database.set_date()\r\n else:\r\n database.get_date()\r\n elif runtask == 'supply':\r\n if process == 'prep':\r\n database.set_supply()\r\n else:\r\n database.get_supply()\r\n elif runtask == 'reserve':\r\n if process == 'prep':\r\n database.set_reserves()\r\n else:\r\n database.get_reserves()\r\n elif runtask == 'summary':\r\n if process == 'prep':\r\n database.set_summary()\r\n else:\r\n database.get_summary()\r\n elif runtask == 'all':\r\n if process == 'prep':\r\n database.set_date()\r\n database.set_supply()\r\n database.set_reserves()\r\n database.set_summary()\r\n else:\r\n database.get_date()\r\n database.get_supply()\r\n database.get_reserves()\r\n database.get_summary()\r\n else:\r\n print(\"Got {}, not sure what to do!!1\".format(runtask))\r\n return jsonify({\"status\":\"success\"})\r\n except:\r\n traceback.print_exc(file=sys.stdout)\r\n print(\"Unexpected error:\", sys.exc_info()[0])\r\n return jsonify({\"status\":\"failed\"})\r\n \r\nif __name__ == \"__main__\":\n app.run()", "sub_path": "sales-inv-corchids/application/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 6846, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Flask", "line_number": 17, "usage_type": "call"}, {"api_name": "standard_models.DBEntry.get_connection_string", "line_number": 19, "usage_type": "call"}, {"api_name": "standard_models.DBEntry", "line_number": 19, "usage_type": "name"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 21, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 25, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask_debugtoolbar.DebugToolbarExtension", "line_number": 32, "usage_type": "call"}, {"api_name": "werkzeug.debug.DebuggedApplication", "line_number": 36, "usage_type": "call"}, {"api_name": "models.Plant.dev_get_create", "line_number": 48, "usage_type": "call"}, {"api_name": "models.Plant", "line_number": 48, "usage_type": "name"}, {"api_name": "models.Supplier.dev_get_create", "line_number": 49, "usage_type": "call"}, {"api_name": "models.Supplier", "line_number": 49, "usage_type": "name"}, {"api_name": "restful.process_rest_request", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 63, "usage_type": "argument"}, {"api_name": "flask.make_response", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 69, "usage_type": "name"}, {"api_name": "restful.notes_wrapper", "line_number": 70, "usage_type": "call"}, {"api_name": "restful.notes_wrapper", "line_number": 74, "usage_type": "call"}, {"api_name": "restful.notes_wrapper", "line_number": 78, "usage_type": "call"}, {"api_name": "restful.get_week_summary", "line_number": 82, "usage_type": "call"}, {"api_name": "restful.get_availability", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 89, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 91, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 91, "usage_type": "attribute"}, {"api_name": "sys.exc_info", "line_number": 92, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 94, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 99, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 99, "usage_type": "name"}, {"api_name": "restful.update_plant_grow", "line_number": 100, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 102, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 107, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 107, "usage_type": "name"}, {"api_name": "restful.update_plantweek_entry", "line_number": 108, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 110, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 117, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 117, "usage_type": "name"}, {"api_name": "restful.update_plantweek_entry", "line_number": 118, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 120, "usage_type": "call"}, {"api_name": "restful.get_update_info", "line_number": 124, "usage_type": "call"}, {"api_name": "restful.get_option_field", "line_number": 128, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 128, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 128, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 129, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 133, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 133, "usage_type": "name"}, {"api_name": "restful.add_logging_message", "line_number": 134, "usage_type": "call"}, {"api_name": "restful.send_test_email", "line_number": 138, "usage_type": "call"}, {"api_name": "flask.request.values.get", "line_number": 142, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 142, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 142, "usage_type": "name"}, {"api_name": "flask.request.values.get", "line_number": 143, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 143, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 143, "usage_type": "name"}, {"api_name": "google.appengine.api.taskqueue.taskqueue.add", "line_number": 144, "usage_type": "call"}, {"api_name": "google.appengine.api.taskqueue.taskqueue", "line_number": 144, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 149, "usage_type": "call"}, {"api_name": "flask.request.values.get", "line_number": 153, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 153, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 153, "usage_type": "name"}, {"api_name": "flask.request.values.get", "line_number": 154, "usage_type": "call"}, {"api_name": "flask.request.values", "line_number": 154, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 154, "usage_type": "name"}, {"api_name": "models.update_model", "line_number": 158, "usage_type": "call"}, {"api_name": "models.GrowWeek.set_mondays", "line_number": 160, "usage_type": "call"}, {"api_name": "models.GrowWeek", "line_number": 160, "usage_type": "name"}, {"api_name": "database.set_date", "line_number": 163, "usage_type": "call"}, {"api_name": "database.get_date", "line_number": 165, "usage_type": "call"}, {"api_name": "database.set_supply", "line_number": 168, "usage_type": "call"}, {"api_name": "database.get_supply", "line_number": 170, "usage_type": "call"}, {"api_name": "database.set_reserves", "line_number": 173, "usage_type": "call"}, {"api_name": "database.get_reserves", "line_number": 175, "usage_type": "call"}, {"api_name": "database.set_summary", "line_number": 178, "usage_type": "call"}, {"api_name": "database.get_summary", "line_number": 180, "usage_type": "call"}, {"api_name": "database.set_date", "line_number": 183, "usage_type": "call"}, {"api_name": "database.set_supply", "line_number": 184, "usage_type": "call"}, {"api_name": "database.set_reserves", "line_number": 185, "usage_type": "call"}, {"api_name": "database.set_summary", "line_number": 186, "usage_type": "call"}, {"api_name": "database.get_date", "line_number": 188, "usage_type": "call"}, {"api_name": "database.get_supply", "line_number": 189, "usage_type": "call"}, {"api_name": "database.get_reserves", "line_number": 190, "usage_type": "call"}, {"api_name": "database.get_summary", "line_number": 191, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 194, "usage_type": "call"}, {"api_name": "traceback.print_exc", "line_number": 196, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 196, "usage_type": "attribute"}, {"api_name": "sys.exc_info", "line_number": 197, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 198, "usage_type": "call"}]} +{"seq_id": "329019373", "text": "import datetime\nfrom django.shortcuts import render, get_object_or_404\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\n\n# Create your views here.\n\nfrom .models import Item, Price\n\n\ndef list(request, page=1):\n items = Item.objects.all()\n paginator = Paginator(items, 25)\n items = paginator.get_page(page)\n return render(request, \"pricecheck/list.html\", {\"items\": items})\n\n\ndef main(request, id):\n item = get_object_or_404(Item, pk=id)\n start_from = datetime.date.today() - datetime.timedelta(days=7)\n price_last = Price.objects.filter(item=item,\n date__gte=start_from).order_by(\"date\")\n js = []\n for price in price_last:\n js.append(\n ['new Date(\"{}T00:00\")'.format(price.date.isoformat()), price.price]\n )\n js = str(js).replace(\"'\", \"\")\n return render(request, \"pricecheck/main.html\",\n {\"item\": item, \"price\": js})\n", "sub_path": "pricecheck/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 951, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "models.Item.objects.all", "line_number": 11, "usage_type": "call"}, {"api_name": "models.Item.objects", "line_number": 11, "usage_type": "attribute"}, {"api_name": "models.Item", "line_number": 11, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 12, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 14, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 18, "usage_type": "call"}, {"api_name": "models.Item", "line_number": 18, "usage_type": "argument"}, {"api_name": "datetime.date.today", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 19, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Price.objects.filter", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Price.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.Price", "line_number": 20, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "369545360", "text": "# Standard Library\nimport gzip\nimport logging\nfrom typing import Any, Iterable, List, Mapping, Tuple\n\n# Third Party Imports\nimport jsonschema\nfrom cityhash import CityHash64\n\n# Local Imports\nimport bel.edge.edges\nimport bel.lang.belobj\nfrom bel.Config import config\nfrom bel.utils import http_client\n\nlog = logging.getLogger(__name__)\n\n\n# TODO is this code being used? We also have bel.nanopub.validate.validate(nanopub, error_level) for validation\n\n\nclass Nanopub(object):\n \"\"\"Nanopub object to manage Nanopub processing\"\"\"\n\n def __init__(self, endpoint: str = config.get(\"api\", \"\")) -> None:\n \"\"\" Initialize Nanopub\n\n Args:\n endpoint (str): BEL.bio API endpoint uri, e.g. https://api.bel.bio/v1, default read from config\n \"\"\"\n self.endpoint = endpoint\n\n def validate(self, nanopub: Mapping[str, Any]) -> Tuple[bool, List[Tuple[str, str]]]:\n \"\"\"Validates using the nanopub schema\n\n Args:\n nanopub (Mapping[str, Any]): nanopub dict\n\n Returns:\n Tuple[bool, List[Tuple[str, str]]]:\n bool: Is valid? Yes = True, No = False\n List[Tuple[str, str]]: Validation issues, empty if valid, tuple is ('ERROR|WARNING', msg)\n e.g. [('WARNING', \"Context ID not found\")] \"\"\"\n\n # Validate nanopub\n (is_valid, messages) = validate_to_schema(nanopub, self.nanopub_schema)\n if not is_valid:\n return messages\n\n # Extract BEL Version\n if nanopub[\"nanopub\"][\"type\"][\"name\"].upper() == \"BEL\":\n bel_version = nanopub[\"nanopub\"][\"type\"][\"version\"]\n else:\n is_valid = False\n return (\n is_valid,\n f\"Not a BEL Nanopub according to nanopub.type.name: {nanopub['nanopub']['type']['name']}\",\n )\n\n all_messages = []\n # Validate BEL Statements\n bel_obj = bel.lang.belobj.BEL(bel_version, self.endpoint)\n for edge in nanopub[\"nanopub\"][\"edges\"]:\n bel_statement = f\"{edge['subject']} {edge['relation']} {edge['object']}\"\n parse_obj = bel_obj.parse(bel_statement)\n if not parse_obj.valid:\n all_messages.extend(\n (\n \"ERROR\",\n f\"BEL statement parse error {parse_obj.error}, {parse_obj.err_visual}\",\n )\n )\n\n # Validate nanopub.context\n for context in nanopub[\"nanopub\"][\"context\"]:\n (is_valid, messages) = self.validate_context(context)\n all_messages.extend(messages)\n\n is_valid = True\n for _type, msg in all_messages:\n if _type == \"ERROR\":\n is_valid = False\n\n return (is_valid, all_messages)\n\n def validate_context(self, context: Mapping[str, Any]) -> Tuple[bool, List[Tuple[str, str]]]:\n \"\"\" Validate context\n\n Args:\n context (Mapping[str, Any]): context dictionary of type, id and label\n\n Returns:\n Tuple[bool, List[Tuple[str, str]]]:\n bool: Is valid? Yes = True, No = False\n List[Tuple[str, str]]: Validation issues, empty if valid, tuple is ('ERROR|WARNING', msg)\n e.g. [('WARNING', \"Context ID not found\")]\n \"\"\"\n\n url = f'{self.endpoint}/terms/{context[\"id\"]}'\n\n res = http_client.get(url)\n if res.status_code == 200:\n return (True, [])\n else:\n return (False, [(\"WARNING\", f'Context {context[\"id\"]} not found at {url}')])\n\n def bel_edges(\n self,\n nanopub: Mapping[str, Any],\n namespace_targets: Mapping[str, List[str]] = {},\n rules: List[str] = [],\n orthologize_target: str = None,\n ) -> List[Mapping[str, Any]]:\n \"\"\"Create BEL Edges from BEL nanopub\n\n Args:\n nanopub (Mapping[str, Any]): bel nanopub\n namespace_targets (Mapping[str, List[str]]): what namespaces to canonicalize\n rules (List[str]): which computed edge rules to process, default is all,\n look at BEL Specification yaml file for computed edge signature keys,\n e.g. degradation, if any rule in list is 'skip', then skip computing edges\n just return primary_edge\n orthologize_target (str): species to convert BEL into, e.g. TAX:10090 for mouse, default option does not orthologize\n\n Returns:\n List[Mapping[str, Any]]: edge list with edge attributes (e.g. context)\n \"\"\"\n\n edges = bel.edge.edges.create_edges(\n nanopub,\n self.endpoint,\n namespace_targets=namespace_targets,\n rules=rules,\n orthologize_target=orthologize_target,\n )\n\n return edges\n\n\ndef validate_to_schema(nanopub, schema) -> Tuple[bool, List[Tuple[str, str]]]:\n \"\"\"Validate nanopub against jsonschema for nanopub\n\n Args:\n nanopub (Mapping[str, Any]): nanopub dict\n schema (Mapping[str, Any]): nanopub schema\n\n Returns:\n Tuple[bool, List[str]]:\n bool: Is valid? Yes = True, No = False\n List[Tuple[str, str]]: Validation issues, empty if valid, tuple is ('Error|Warning', msg)\n e.g. [('ERROR', \"'subject' is a required property\")]\n \"\"\"\n\n v = jsonschema.Draft4Validator(schema)\n messages = []\n errors = sorted(v.iter_errors(nanopub), key=lambda e: e.path)\n for error in errors:\n for suberror in sorted(error.context, key=lambda e: e.schema_path):\n print(list(suberror.schema_path), suberror.message, sep=\", \")\n messages.append((\"ERROR\", suberror.message))\n\n is_valid = True\n if errors:\n is_valid = False\n\n return (is_valid, messages)\n\n\n# Following is used in nanopub-tools codebase\ndef hash_nanopub(nanopub: Mapping[str, Any]) -> str:\n \"\"\"Create CityHash64 from nanopub for duplicate check\n\n TODO - check that this hash value is consistent between C# and Python running on\n laptop and server\n\n Build string to hash\n\n Collect flat array of (all values.strip()):\n nanopub.type.name\n nanopub.type.version\n\n One of:\n nanopub.citation.database.name\n nanopub.citation.database.id\n\n OR\n\n nanopub.citation.database.uri\n\n OR\n\n nanopub.citation.database.reference\n\n Extend with sorted list of assertions (SRO as single string with space between S, R and O)\n\n Extend with sorted list of annotations (nanopub.annotations.type + ' ' + nanopub.annotations.id)\n\n Convert array to string by joining array elements separated by a space\n\n Create CityHash64(str) and return\n\n \"\"\"\n\n hash_list = []\n\n # Type\n hash_list.append(nanopub[\"nanopub\"][\"type\"].get(\"name\", \"\").strip())\n hash_list.append(nanopub[\"nanopub\"][\"type\"].get(\"version\", \"\").strip())\n\n # Citation\n if nanopub[\"nanopub\"][\"citation\"].get(\"database\", False):\n hash_list.append(nanopub[\"nanopub\"][\"citation\"][\"database\"].get(\"name\", \"\").strip())\n hash_list.append(nanopub[\"nanopub\"][\"citation\"][\"database\"].get(\"id\", \"\").strip())\n elif nanopub[\"nanopub\"][\"citation\"].get(\"uri\", False):\n hash_list.append(nanopub[\"nanopub\"][\"citation\"].get(\"uri\", \"\").strip())\n elif nanopub[\"nanopub\"][\"citation\"].get(\"reference\", False):\n hash_list.append(nanopub[\"nanopub\"][\"citation\"].get(\"reference\", \"\").strip())\n\n # Assertions\n assertions = []\n for assertion in nanopub[\"nanopub\"][\"assertions\"]:\n if assertion.get(\"relation\") is None:\n assertion[\"relation\"] = \"\"\n if assertion.get(\"object\") is None:\n assertion[\"object\"] = \"\"\n assertions.append(\n \" \".join(\n (\n assertion[\"subject\"].strip(),\n assertion.get(\"relation\", \"\").strip(),\n assertion.get(\"object\", \"\").strip(),\n )\n ).strip()\n )\n assertions = sorted(assertions)\n hash_list.extend(assertions)\n\n # Annotations\n annotations = []\n\n for anno in nanopub[\"nanopub\"][\"annotations\"]:\n annotations.append(\n \" \".join((anno.get(\"type\", \"\").strip(), anno.get(\"id\", \"\").strip())).strip()\n )\n\n annotations = sorted(annotations)\n hash_list.extend(annotations)\n\n np_string = \" \".join([l.lower() for l in hash_list])\n\n return \"{:x}\".format(CityHash64(np_string))\n", "sub_path": "bel/nanopub/nanopubs.py", "file_name": "nanopubs.py", "file_ext": "py", "file_size_in_byte": 8476, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "bel.Config.config.get", "line_number": 25, "usage_type": "call"}, {"api_name": "bel.Config.config", "line_number": 25, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 33, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 33, "usage_type": "name"}, {"api_name": "bel.edge.edges.lang.belobj.BEL", "line_number": 62, "usage_type": "call"}, {"api_name": "bel.edge.edges.lang", "line_number": 62, "usage_type": "attribute"}, {"api_name": "bel.edge.edges", "line_number": 62, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 33, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 33, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 86, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 86, "usage_type": "name"}, {"api_name": "bel.utils.http_client.get", "line_number": 101, "usage_type": "call"}, {"api_name": "bel.utils.http_client", "line_number": 101, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 86, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 86, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 109, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 109, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 110, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 110, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 111, "usage_type": "name"}, {"api_name": "bel.edge.edges.edge.edges.create_edges", "line_number": 129, "usage_type": "call"}, {"api_name": "bel.edge.edges.edge", "line_number": 129, "usage_type": "attribute"}, {"api_name": "bel.edge.edges", "line_number": 129, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 113, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 113, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 113, "usage_type": "name"}, {"api_name": "jsonschema.Draft4Validator", "line_number": 154, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 140, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 140, "usage_type": "name"}, {"api_name": "typing.Mapping", "line_number": 170, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 170, "usage_type": "name"}, {"api_name": "cityhash.CityHash64", "line_number": 251, "usage_type": "call"}]} +{"seq_id": "583966673", "text": "import sys\nfrom pyknp import Juman\nfrom argparse import ArgumentParser\n\n\ndef parse_args():\n p = ArgumentParser(description='add label(hinshi)')\n p.add_argument('source', help='[in] input fileaname')\n p.add_argument('target', help='[out] output fileaname')\n args = p.parse_args()\n return args\n\n\n# 進捗バーを出力\ndef printProgressInfo(end, now):\n MAX_LEN = 50\n progress = 1.0 if now == end-1 else 1.0 * now / end\n BAR_LEN = MAX_LEN if now == end-1 else int(MAX_LEN * progress)\n progressbar_str = ('[' + '=' * BAR_LEN +\n ('>' if BAR_LEN < MAX_LEN else '=') +\n ' ' * (MAX_LEN - BAR_LEN) +\n '] %.1f%% (%d/%d)' % (progress * 100., now, end))\n sys.stderr.write('\\r' + progressbar_str)\n sys.stderr.flush()\n\n\nif __name__ == '__main__':\n args = parse_args()\n # 出力ファイルのリセット\n fp = open(args.target, \"w\")\n fp.close()\n\n n_line = sum(1 for line in open(args.source, \"r\"))\n\n with open(args.target, \"a\") as target_file:\n for i, line in enumerate(open(args.source, \"r\")):\n juman = Juman()\n input_sentence = line.replace(\" \", \"\")\n res = juman.analysis(input_sentence)\n\n sentence_l = [mrph.midasi+\"/\"+mrph.hinsi for mrph in res.mrph_list()]\n sentence = \" \".join(sentence_l)\n\n target_file.write(sentence+\"\\n\")\n printProgressInfo(n_line, i+1)\n\n print(\"end...\")\n", "sub_path": "src/script/addLabel.py", "file_name": "addLabel.py", "file_ext": "py", "file_size_in_byte": 1477, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 23, "usage_type": "attribute"}, {"api_name": "sys.stderr.flush", "line_number": 24, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 24, "usage_type": "attribute"}, {"api_name": "pyknp.Juman", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "173941825", "text": "from flask import render_template, redirect, url_for, flash, send_file, make_response, Blueprint, request\nfrom flask_login import current_user\nimport flask_login\nfrom flask_login.utils import login_required\nfrom trial import db\nfrom trial.generalforms.forms import DefectReportForm, LeaveForm\nfrom trial.models import Leave, Post\nimport pdfkit\n\n\ngeneralforms = Blueprint('generalforms', __name__) \n\n\n#Create a route for defect form\n@generalforms.route('/defect', methods=['GET', 'POST'])\ndef defect():\n form = DefectReportForm()\n if form.validate_on_submit():\n flash(f'Defect Report Form submitted successfully', 'success') \n return redirect(url_for('generalforms.defect'))\n posts = Post.query.order_by(Post.id.desc()).all()\n return render_template('generalforms/defect_rep.html', title='Road Defects Report', form=form, posts=posts)\n\n#Create a route for leave Form \n@generalforms.route('/leave', methods=['GET', 'POST'])\n@login_required\ndef leave():\n form = LeaveForm()\n if form.validate_on_submit():\n name=request.form.get('name')\n le_ave = Leave(name=name, rank=form.rank.data, section=form.section.data, date_app=form.date_app.data,\n tele_no=form.tele_no.data, leave_cat=form.leave_cat.data, no_of_days=form.no_of_days.data, \n start_date=form.start_date.data, end_date=form.end_date.data, supp_info=form.supp_info.data,\n address=form.address.data, mobile_no=form.mobile_no.data, email=form.email.data, \n days_proceed=form.days_proceed.data, effec_date=form.effec_date.data, resump_date=form.resump_date.data,\n outs_days=form.outs_days.data, author=current_user)\n db.session.add(le_ave)\n db.session.commit()\n flash(f\"Leave form submitted successfully\", 'success')\n return redirect(url_for('generalforms.view_form', post_id=le_ave.id))\n posts = Post.query.order_by(Post.id.desc()).all() \n return render_template('generalforms/leave_form.html', title='Leave Form Report', form=form, posts=posts)\n\n\n\n#Route to view the Leave form\n@generalforms.route('/post/')\n@login_required\ndef post(post_id):\n post = Leave.query.get_or_404(post_id)\n posts = Post.query.order_by(Post.id.desc()).all()\n return render_template('generalforms/render_form.html', post=post, posts=posts)\n\n#Route for View form \n@generalforms.route('/view_form/', methods=['GET', 'POST'])\n@login_required\ndef view_form(post_id):\n post = Leave.query.get_or_404(post_id)\n posts = Post.query.order_by(Post.id.desc()).all()\n \n return render_template('generalforms/view_lv_form.html', title='Leave', post=post, posts=posts) \n\n#Generate pdf from the Leave Form\n@generalforms.route('/get_pdf/', methods=['GET','POST'])\n@login_required\ndef get_pdf(post_id):\n\n post = Leave.query.get_or_404(post_id)\n posts = Post.query.order_by(Post.id.desc()).all()\n rendered= render_template('generalforms/render_form.html', title=current_user.username, post=post, posts=posts)\n css = ['trial/static/css/bootstrap.min.css', 'trial/static/css/style.css']\n\n options = {'enable-local-file-access': None}\n pdf = pdfkit.from_string(rendered, False, options=options, css=css)\n response = make_response(pdf)\n \n response.headers['content-Type'] = 'application/pdf'\n response.headers['content-Disposition'] = 'inline; filename=output.pdf'\n\n return response\n\n#Render pdf format of the Leave Form\n@generalforms.route('/render/', methods=['GET', 'POST'])\ndef render(post_id):\n post = Leave.query.get_or_404(post_id)\n posts = Post.query.order_by(Post.id.desc()).all()\n return render_template('generalforms/render_form.html', post=post, title=current_user.username, posts=posts)\n\n\n#Route to view other forms \n@generalforms.route('/other_forms', methods=['GET', 'POST'])\n@login_required\ndef others():\n posts = Post.query.order_by(Post.id.desc()).all()\n return render_template('generalforms/other_forms.html', title='Other Forms', posts=posts)\n\n#Route to download Hospital Form\n@generalforms.route('/download_hospital-form')\n@login_required\ndef download_hosp():\n p = './static/other_forms/GHANA HIGHWAY AUTHORITY (HOSPITAL FORM).pdf'\n\n return send_file(p, as_attachment=True)\n\n#Route to download Accomodation Form\n@generalforms.route('/download_accomodation-form')\n@login_required \ndef download_accom():\n p = './static/other_forms/GHANA HIGHWAY AUTHORITY (REQUEST FOR ACCOMODATION).pdf'\n\n return send_file(p, as_attachment=True)", "sub_path": "trial/generalforms/routes.py", "file_name": "routes.py", "file_ext": "py", "file_size_in_byte": 4567, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "flask.Blueprint", "line_number": 11, "usage_type": "call"}, {"api_name": "trial.generalforms.forms.DefectReportForm", "line_number": 17, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 20, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 20, "usage_type": "call"}, {"api_name": "trial.models.Post.query.order_by", "line_number": 21, "usage_type": "call"}, {"api_name": "trial.models.Post.query", "line_number": 21, "usage_type": "attribute"}, {"api_name": "trial.models.Post", "line_number": 21, "usage_type": "name"}, {"api_name": "trial.models.Post.id.desc", "line_number": 21, "usage_type": "call"}, {"api_name": "trial.models.Post.id", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 22, "usage_type": "call"}, {"api_name": "trial.generalforms.forms.LeaveForm", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.request.form.get", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "trial.models.Leave", "line_number": 31, "usage_type": "call"}, {"api_name": "flask_login.current_user", "line_number": 36, "usage_type": "name"}, {"api_name": "trial.db.session.add", "line_number": 37, "usage_type": "call"}, {"api_name": "trial.db.session", "line_number": 37, "usage_type": "attribute"}, {"api_name": "trial.db", "line_number": 37, "usage_type": "name"}, {"api_name": "trial.db.session.commit", "line_number": 38, "usage_type": "call"}, {"api_name": "trial.db.session", "line_number": 38, "usage_type": "attribute"}, {"api_name": "trial.db", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 40, "usage_type": "call"}, {"api_name": "trial.models.Post.query.order_by", "line_number": 41, "usage_type": "call"}, {"api_name": "trial.models.Post.query", "line_number": 41, "usage_type": "attribute"}, {"api_name": "trial.models.Post", "line_number": 41, "usage_type": "name"}, {"api_name": "trial.models.Post.id.desc", "line_number": 41, "usage_type": "call"}, {"api_name": "trial.models.Post.id", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 42, "usage_type": "call"}, {"api_name": "flask_login.utils.login_required", "line_number": 26, "usage_type": "name"}, {"api_name": "trial.models.Leave.query.get_or_404", "line_number": 50, "usage_type": "call"}, {"api_name": "trial.models.Leave.query", "line_number": 50, "usage_type": "attribute"}, {"api_name": "trial.models.Leave", "line_number": 50, "usage_type": "name"}, {"api_name": "trial.models.Post.query.order_by", "line_number": 51, "usage_type": "call"}, {"api_name": "trial.models.Post.query", "line_number": 51, "usage_type": "attribute"}, {"api_name": "trial.models.Post", "line_number": 51, "usage_type": "name"}, {"api_name": "trial.models.Post.id.desc", "line_number": 51, "usage_type": "call"}, {"api_name": "trial.models.Post.id", "line_number": 51, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 52, "usage_type": "call"}, {"api_name": "flask_login.utils.login_required", "line_number": 48, "usage_type": "name"}, {"api_name": "trial.models.Leave.query.get_or_404", "line_number": 58, "usage_type": "call"}, {"api_name": "trial.models.Leave.query", "line_number": 58, "usage_type": "attribute"}, {"api_name": "trial.models.Leave", "line_number": 58, "usage_type": "name"}, {"api_name": "trial.models.Post.query.order_by", "line_number": 59, "usage_type": "call"}, {"api_name": "trial.models.Post.query", "line_number": 59, "usage_type": "attribute"}, {"api_name": "trial.models.Post", "line_number": 59, "usage_type": "name"}, {"api_name": "trial.models.Post.id.desc", "line_number": 59, "usage_type": "call"}, {"api_name": "trial.models.Post.id", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 61, "usage_type": "call"}, {"api_name": "flask_login.utils.login_required", "line_number": 56, "usage_type": "name"}, {"api_name": "trial.models.Leave.query.get_or_404", "line_number": 68, "usage_type": "call"}, {"api_name": "trial.models.Leave.query", "line_number": 68, "usage_type": "attribute"}, {"api_name": "trial.models.Leave", "line_number": 68, "usage_type": "name"}, {"api_name": "trial.models.Post.query.order_by", "line_number": 69, "usage_type": "call"}, {"api_name": "trial.models.Post.query", "line_number": 69, "usage_type": "attribute"}, {"api_name": "trial.models.Post", "line_number": 69, "usage_type": "name"}, {"api_name": "trial.models.Post.id.desc", "line_number": 69, "usage_type": "call"}, {"api_name": "trial.models.Post.id", "line_number": 69, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 70, "usage_type": "call"}, {"api_name": "flask_login.current_user.username", "line_number": 70, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 70, "usage_type": "name"}, {"api_name": "pdfkit.from_string", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 75, "usage_type": "call"}, {"api_name": "flask_login.utils.login_required", "line_number": 65, "usage_type": "name"}, {"api_name": "trial.models.Leave.query.get_or_404", "line_number": 85, "usage_type": "call"}, {"api_name": "trial.models.Leave.query", "line_number": 85, "usage_type": "attribute"}, {"api_name": "trial.models.Leave", "line_number": 85, "usage_type": "name"}, {"api_name": "trial.models.Post.query.order_by", "line_number": 86, "usage_type": "call"}, {"api_name": "trial.models.Post.query", "line_number": 86, "usage_type": "attribute"}, {"api_name": "trial.models.Post", "line_number": 86, "usage_type": "name"}, {"api_name": "trial.models.Post.id.desc", "line_number": 86, "usage_type": "call"}, {"api_name": "trial.models.Post.id", "line_number": 86, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 87, "usage_type": "call"}, {"api_name": "flask_login.current_user.username", "line_number": 87, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 87, "usage_type": "name"}, {"api_name": "trial.models.Post.query.order_by", "line_number": 94, "usage_type": "call"}, {"api_name": "trial.models.Post.query", "line_number": 94, "usage_type": "attribute"}, {"api_name": "trial.models.Post", "line_number": 94, "usage_type": "name"}, {"api_name": "trial.models.Post.id.desc", "line_number": 94, "usage_type": "call"}, {"api_name": "trial.models.Post.id", "line_number": 94, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 95, "usage_type": "call"}, {"api_name": "flask_login.utils.login_required", "line_number": 92, "usage_type": "name"}, {"api_name": "flask.send_file", "line_number": 103, "usage_type": "call"}, {"api_name": "flask_login.utils.login_required", "line_number": 99, "usage_type": "name"}, {"api_name": "flask.send_file", "line_number": 111, "usage_type": "call"}, {"api_name": "flask_login.utils.login_required", "line_number": 107, "usage_type": "name"}]} +{"seq_id": "633389609", "text": "import requests\nimport datetime\nfrom bs4 import BeautifulSoup\nimport google_calendar_lib as GCar\n\nclass Chouseisan(object):\n def __init__(self):\n calendar_id = '4hrfgm5memn5sdfgiahhsjsme8@group.calendar.google.com'\n days = 7\n self.holidays = GCar.get_holidays(calendar_id, days)\n self.week = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']\n\n def _get_token(self):\n url_for_token = 'https://chouseisan.com'\n response = requests.get(url_for_token)\n response.raise_for_status()\n\n soup = BeautifulSoup(response.text, 'html.parser')\n\n return soup.find(id='chousei_token').get('value')\n\n def create_schedule(self, name='No_title', comment='No_comment', kouho='No_option'):\n create_url = 'https://chouseisan.com/schedule/newEvent/create'\n payload = { 'name' : name, 'comment' : comment, 'kouho' : kouho, 'chousei_token' : self._get_token() }\n response = requests.post(create_url, data=payload, allow_redirects=False)\n\n complete_url = response.headers['location']\n new_schedule_url = 'https://chouseisan.com/s?' + complete_url.split('?')[1]\n return new_schedule_url\n\n def get_total(self, url, number=3):\n response = requests.get(url)\n response.raise_for_status()\n soup = BeautifulSoup(response.text, 'html.parser')\n\n tables = soup.find(id='nittei')\n simple_table = []\n table_list = [table for table in tables] # faster than `tables.contents`\n table_list = table_list[1::2] # remove blank line\n\n for table in table_list:\n row = []\n for t in table:\n if \"\" == str(type(t)):\n elements = [e for e in t]\n row.append(elements[0].string if 1 == len(elements) else elements[1].contents[0].string)\n simple_table.append(row)\n\n simple_table.pop()\n simple_table.sort(key=lambda l: l.count('○'))\n simple_table.reverse()\n names = simple_table.pop()\n names.pop(0) # remove '日程'\n\n results = []\n\n for t in simple_table[0:number]:\n date = t.pop(0)\n result_row = [date]\n for i, e in enumerate(t):\n if '○' == e:\n result_row.append(names[i])\n results.append(result_row)\n\n s = ''\n\n for result in results:\n s += result[0] + '\\n'\n s += ' '.join(result[1:]) + '\\n\\n'\n\n return s[:-2]\n\n def get_days(self, num=7):\n dt_now = datetime.datetime.now()\n result = []\n for i in range(num):\n result.append((dt_now + datetime.timedelta(days = i)).date().isoformat())\n return result\n\n def get_options_str(self):\n result = ''\n for i, date in enumerate(self.get_days()):\n if date in self.holidays:\n result += date[5:] + ' (' + self.week[i] + '.) ' + 'AM' + '\\n'\n result += date[5:] + ' (' + self.week[i] + '.) ' + 'PM' + '\\n'\n else:\n result += date[5:] + ' (' + self.week[i] + '.) ' + '4限' + '\\n'\n result += date[5:] + ' (' + self.week[i] + '.) ' + '5限' + '\\n'\n result += date[5:] + ' (' + self.week[i] + '.) ' + '6限' + '\\n'\n return result\n\nif __name__ == '__main__':\n cho = Chouseisan()\n test = cho.get_options_str()\n print(cho.holidays)\n print(test)\n\n", "sub_path": "chouseisanlib.py", "file_name": "chouseisanlib.py", "file_ext": "py", "file_size_in_byte": 3450, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "google_calendar_lib.get_holidays", "line_number": 10, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 18, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 25, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 32, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 34, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 74, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 74, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "190397065", "text": "import task\n\n\nclass TaskList():\n\n def __init__(self, name, task_counter=0):\n \"\"\"Initialiserar classvariabler.\n \n self.task_counter räknar antalet uppgifter i listan\n self.task_list är listan där uppgifter lagras\n self.name är namnet på den specifika listan\n \"\"\"\n self.task_counter = task_counter\n self.task_list = []\n self.name = name\n\n def create_task(self, description):\n \"\"\"Lägger till en ny uppgift i listan task_list.\n \n namges genom description som tas in som argument\n skriver ut namn på uppgift och namn på listan'\n \"\"\"\n self.task_counter += 1\n self.task_list.append(task.Task(self.task_counter, description, False))\n print(\"Du har lagt till {} i {}!\".format(description, self.name))\n\n def mark_done(self, task_id):\n \"\"\"Markerar en uppgift som klar baserat på argumentet task_id.\n\n kallar på funktionen mark_done()\n returnerar en boolean\n \"\"\"\n for current_task in self.task_list:\n if int(task_id) == current_task.task_id:\n current_task.mark_done()\n return True\n return False\n\n def __str__(self):\n \"\"\"Skriver ut alla task i task_list som en sträng.\"\"\"\n if self.task_counter == 0:\n return \"Det finns inga uppgifter!!!\"\n task_print = \"\"\n for task in self.task_list:\n task_print += (str(task) + \"\\n\")\n return task_print\n \n\n\n \n", "sub_path": "task_list.py", "file_name": "task_list.py", "file_ext": "py", "file_size_in_byte": 1526, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "task.Task", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "62346906", "text": "# Transformer-Based Sentiment Analysis Utils\nimport pandas as pd\nimport numpy as np\nimport tensorflow as tf\n\nfrom transformers import BertTokenizer\nfrom transformers import TFBertForSequenceClassification, TFTrainer, TFTrainingArguments, BertConfig\n\ndef load_distilbert_model(model_path='./tf_model.h5', config_path='./config.json'):\n from transformers import DistilBertConfig\n from transformers import TFDistilBertForSequenceClassification\n\n config = DistilBertConfig.from_json_file(config_path)\n model_reloaded = TFDistilBertForSequenceClassification.from_pretrained(model_path, config=config)\n return model_reloaded\n\ndef read_data(csv_path, tweet_col='text', label_col=None, shuffle=True):\n if label_col:\n df = pd.read_csv(csv_path, usecols=[tweet_col, label_col])\n if shuffle:\n df = df.sample(frac=1)\n X = df['text'].to_list()\n y = df[label_col].to_list()\n else:\n df = pd.read_csv(csv_path, usecols=[tweet_col])\n if shuffle:\n df = df.sample(frac=1)\n X = df['text'].to_list()\n y=None\n\n return df, X, y\n\ndef preprocess_data_distilbert(X, y=None, orig_checkpoint='distilbert-base-uncased'):\n from transformers import DistilBertTokenizerFast\n tokenizer = DistilBertTokenizerFast.from_pretrained(orig_checkpoint, num_labels=3)\n encodings = tokenizer(X, truncation=True, padding=True)\n print(len(encodings))\n if not y:\n y = np.zeros(len(X))\n dataset = tf.data.Dataset.from_tensor_slices((dict(encodings), y))\n dataset_batched = dataset.batch(16)\n\n return dataset_batched\n\n", "sub_path": "SentimentUtils.py", "file_name": "SentimentUtils.py", "file_ext": "py", "file_size_in_byte": 1603, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "transformers.DistilBertConfig.from_json_file", "line_number": 13, "usage_type": "call"}, {"api_name": "transformers.DistilBertConfig", "line_number": 13, "usage_type": "name"}, {"api_name": "transformers.TFDistilBertForSequenceClassification.from_pretrained", "line_number": 14, "usage_type": "call"}, {"api_name": "transformers.TFDistilBertForSequenceClassification", "line_number": 14, "usage_type": "name"}, {"api_name": "pandas.read_csv", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 25, "usage_type": "call"}, {"api_name": "transformers.DistilBertTokenizerFast.from_pretrained", "line_number": 35, "usage_type": "call"}, {"api_name": "transformers.DistilBertTokenizerFast", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 39, "usage_type": "call"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 40, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 40, "usage_type": "attribute"}]} +{"seq_id": "508337024", "text": "from flask import (\n render_template,\n request,\n current_app,\n redirect,\n url_for,\n flash,\n)\nfrom flask_login import login_required, current_user\n\nfrom ...models import (\n Post,\n Category,\n Comment,\n)\nfrom ...forms import (\n PostForm,\n CategoryForm,\n SettingForm,\n)\nfrom . import admin_bp\nfrom ...extensions import db\nfrom ...utils import redirect_back\n\n\n@admin_bp.before_request\n@login_required\ndef login_protect():\n '''\n 为 admin 蓝本下的所有视图函数添加保护\n '''\n pass\n\n\n@admin_bp.route('/posts/manage')\ndef manage_posts():\n '''\n 管理文章视图 \n '''\n page = request.args.get('page', 1, type=int)\n pagination = Post.query.order_by(Post.timestamp.desc()).paginate(\n page, per_page=current_app.config['BLOG_MANAGE_POST_PER_PAGE'])\n posts = pagination.items\n return render_template('admin/manage_posts.html', pagination=pagination, posts=posts)\n\n\n@admin_bp.route('/post/new', methods=['GET', 'POST'])\ndef new_post():\n form = PostForm()\n if form.validate_on_submit():\n title = form.title.data\n body = form.body.data\n category = Category.query.get(form.category.data)\n post = Post(title=title, body=body, category=category)\n db.session.add(post)\n db.session.commit()\n flash('发表成功.', 'success')\n return redirect(url_for('blog.show_post', post_id=post.id))\n return render_template('admin/new_post.html', form=form)\n\n\n@admin_bp.route('/post/edit/', methods=['GET', 'POST'])\ndef edit_post(post_id):\n form = PostForm()\n post = Post.query.get_or_404(post_id)\n if form.validate_on_submit():\n post.title = form.title.data\n post.body = form.body.data\n post.category = Category.query.get(form.category.data)\n db.session.commit()\n flash('文章更新成功.', 'success')\n return redirect(url_for('blog.show_post', post_id=post_id))\n form.title.data = post.title\n form.body.data = post.body\n form.category.data = post.category_id\n return render_template('admin/edit_post.html', form=form)\n\n\n@admin_bp.route('/post/delete/', methods=['POST'])\ndef delete_post(post_id):\n post = Post.query.get_or_404(post_id)\n db.session.delete(post)\n db.session.commit()\n flash('删除文章成功', 'success')\n return redirect_back()\n\n\n@admin_bp.route('/comments/manage')\ndef manage_comments():\n filter_rule = request.args.get('filter', 'all') # 从查询字符串获取过滤规则\n page = request.args.get('page', 1, type=int)\n per_page = current_app.config['BLOG_MANAGE_COMMENT_PER_PAGE']\n if filter_rule == 'unread':\n filtered_comments = Comment.query.filter_by(reviewed=False)\n elif filter_rule == 'admin':\n filtered_comments = Comment.query.filter_by(from_admin=True)\n else:\n filtered_comments = Comment.query\n\n pagination = filtered_comments.order_by(Comment.timestamp.desc()).paginate(\n page, per_page=per_page)\n comments = pagination.items\n return render_template('admin/manage_comments.html', comments=comments,\n pagination=pagination)\n\n\n@admin_bp.route('/comment/delete/', methods=['POST'])\ndef delete_comment(comment_id):\n comment = Comment.query.get_or_404(comment_id)\n db.session.delete(comment)\n db.session.commit()\n flash('删除评论成功', 'success')\n return redirect_back()\n\n\n@admin_bp.route('/set-comment/', methods=['POST'])\ndef set_comment(post_id):\n post = Post.query.get_or_404(post_id)\n if post.can_comment:\n post.can_comment = False\n flash('当前文章设置为禁止评论', 'info')\n else:\n post.can_comment = True\n flash('当前文章设置为允许评论', 'info')\n db.session.commit()\n return redirect(url_for('blog.show_post', post_id=post_id))\n\n\n@admin_bp.route('/approve_comment/', methods=['POST'])\ndef approve_comment(comment_id):\n comment = Comment.query.get_or_404(comment_id)\n comment.reviewed = True\n db.session.commit()\n flash('评论审核通过', 'success')\n return redirect_back()\n\n\n@admin_bp.route('/categories/manage')\ndef manage_categories():\n return render_template('admin/manage_categories.html')\n\n\n@admin_bp.route('/category/new', methods=['GET', 'POST'])\ndef new_category():\n form = CategoryForm()\n if form.validate_on_submit():\n name = form.name.data\n category = Category(name=name)\n db.session.add(category)\n db.session.commit()\n flash('分类创建成功', 'success')\n return redirect_back()\n return render_template('admin/new_category.html', form=form)\n\n\n@admin_bp.route('/category/edit/', methods=['GET', 'POST'])\ndef edit_category(category_id):\n form = CategoryForm()\n category = Category.query.get_or_404(category_id)\n if form.validate_on_submit():\n category.name = form.name.data\n db.session.commit()\n flash('分类修改成功', 'success')\n return redirect_back()\n form.name.data = category.name\n return render_template('admin/edit_category.html', form=form)\n\n\n@admin_bp.route('/category/delete/', methods=['POST'])\ndef delete_category(category_id):\n category = Category.query.get_or_404(category_id)\n if category.id == 1:\n flash('不能删除默认分类', 'warning')\n return redirect_back()\n category.delete()\n flash('删除成功', 'success')\n return redirect_back()\n\n\n@admin_bp.route('/settings', methods=['GET', 'POST'])\ndef settings():\n form = SettingForm()\n if form.validate_on_submit():\n print('yes')\n current_user.name = form.name.data\n current_user.blog_title = form.blog_title.data\n current_user.blog_sub_title = form.blog_sub_title.data\n current_user.about = form.about.data\n db.session.commit()\n flash('博客信息更新成功', 'success')\n return redirect(url_for('blog.index'))\n form.name.data = current_user.name\n form.blog_title.data = current_user.blog_title\n form.blog_sub_title.data = current_user.blog_sub_title\n form.about.data = current_user.about\n return render_template('admin/settings.html', form=form)", "sub_path": "blog/blueprints/admin/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 6269, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "flask_login.login_required", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 40, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 40, "usage_type": "name"}, {"api_name": "models.Post.query.order_by", "line_number": 41, "usage_type": "call"}, {"api_name": "models.Post.query", "line_number": 41, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 41, "usage_type": "name"}, {"api_name": "models.Post.timestamp.desc", "line_number": 41, "usage_type": "call"}, {"api_name": "models.Post.timestamp", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.current_app.config", "line_number": 42, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 42, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 44, "usage_type": "call"}, {"api_name": "forms.PostForm", "line_number": 49, "usage_type": "call"}, {"api_name": "models.Category.query.get", "line_number": 53, "usage_type": "call"}, {"api_name": "models.Category.query", "line_number": 53, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 53, "usage_type": "name"}, {"api_name": "models.Post", "line_number": 54, "usage_type": "call"}, {"api_name": "extensions.db.session.add", "line_number": 55, "usage_type": "call"}, {"api_name": "extensions.db.session", "line_number": 55, "usage_type": "attribute"}, {"api_name": "extensions.db", "line_number": 55, "usage_type": "name"}, {"api_name": "extensions.db.session.commit", "line_number": 56, "usage_type": "call"}, {"api_name": "extensions.db.session", "line_number": 56, "usage_type": "attribute"}, {"api_name": "extensions.db", "line_number": 56, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 58, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 59, "usage_type": "call"}, {"api_name": "forms.PostForm", "line_number": 64, "usage_type": "call"}, {"api_name": "models.Post.query.get_or_404", "line_number": 65, "usage_type": "call"}, {"api_name": "models.Post.query", "line_number": 65, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 65, "usage_type": "name"}, {"api_name": "models.Category.query.get", "line_number": 69, "usage_type": "call"}, {"api_name": "models.Category.query", "line_number": 69, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 69, "usage_type": "name"}, {"api_name": "extensions.db.session.commit", "line_number": 70, "usage_type": "call"}, {"api_name": "extensions.db.session", "line_number": 70, "usage_type": "attribute"}, {"api_name": "extensions.db", "line_number": 70, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 72, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 72, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 76, "usage_type": "call"}, {"api_name": "models.Post.query.get_or_404", "line_number": 81, "usage_type": "call"}, {"api_name": "models.Post.query", "line_number": 81, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 81, "usage_type": "name"}, {"api_name": "extensions.db.session.delete", "line_number": 82, "usage_type": "call"}, {"api_name": "extensions.db.session", "line_number": 82, "usage_type": "attribute"}, {"api_name": "extensions.db", "line_number": 82, "usage_type": "name"}, {"api_name": "extensions.db.session.commit", "line_number": 83, "usage_type": "call"}, {"api_name": "extensions.db.session", "line_number": 83, "usage_type": "attribute"}, {"api_name": "extensions.db", "line_number": 83, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 84, "usage_type": "call"}, {"api_name": "utils.redirect_back", "line_number": 85, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 90, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 90, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 90, "usage_type": "name"}, {"api_name": "flask.request.args.get", "line_number": 91, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 91, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 91, "usage_type": "name"}, {"api_name": "flask.current_app.config", "line_number": 92, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 92, "usage_type": "name"}, {"api_name": "models.Comment.query.filter_by", "line_number": 94, "usage_type": "call"}, {"api_name": "models.Comment.query", "line_number": 94, "usage_type": "attribute"}, {"api_name": "models.Comment", "line_number": 94, "usage_type": "name"}, {"api_name": "models.Comment.query.filter_by", "line_number": 96, "usage_type": "call"}, {"api_name": "models.Comment.query", "line_number": 96, "usage_type": "attribute"}, {"api_name": "models.Comment", "line_number": 96, "usage_type": "name"}, {"api_name": "models.Comment.query", "line_number": 98, "usage_type": "attribute"}, {"api_name": "models.Comment", "line_number": 98, "usage_type": "name"}, {"api_name": "models.Comment.timestamp.desc", "line_number": 100, "usage_type": "call"}, {"api_name": "models.Comment.timestamp", "line_number": 100, "usage_type": "attribute"}, {"api_name": "models.Comment", "line_number": 100, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 103, "usage_type": "call"}, {"api_name": "models.Comment.query.get_or_404", "line_number": 109, "usage_type": "call"}, {"api_name": "models.Comment.query", "line_number": 109, "usage_type": "attribute"}, {"api_name": "models.Comment", "line_number": 109, "usage_type": "name"}, {"api_name": "extensions.db.session.delete", "line_number": 110, "usage_type": "call"}, {"api_name": "extensions.db.session", "line_number": 110, "usage_type": "attribute"}, {"api_name": "extensions.db", "line_number": 110, "usage_type": "name"}, {"api_name": "extensions.db.session.commit", "line_number": 111, "usage_type": "call"}, {"api_name": "extensions.db.session", "line_number": 111, "usage_type": "attribute"}, {"api_name": "extensions.db", "line_number": 111, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 112, "usage_type": "call"}, {"api_name": "utils.redirect_back", "line_number": 113, "usage_type": "call"}, {"api_name": "models.Post.query.get_or_404", "line_number": 118, "usage_type": "call"}, {"api_name": "models.Post.query", "line_number": 118, "usage_type": "attribute"}, {"api_name": "models.Post", "line_number": 118, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 121, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 124, "usage_type": "call"}, {"api_name": "extensions.db.session.commit", "line_number": 125, "usage_type": "call"}, {"api_name": "extensions.db.session", "line_number": 125, "usage_type": "attribute"}, {"api_name": "extensions.db", "line_number": 125, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 126, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 126, "usage_type": "call"}, {"api_name": "models.Comment.query.get_or_404", "line_number": 131, "usage_type": "call"}, {"api_name": "models.Comment.query", "line_number": 131, "usage_type": "attribute"}, {"api_name": "models.Comment", "line_number": 131, "usage_type": "name"}, {"api_name": "extensions.db.session.commit", "line_number": 133, "usage_type": "call"}, {"api_name": "extensions.db.session", "line_number": 133, "usage_type": "attribute"}, {"api_name": "extensions.db", "line_number": 133, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 134, "usage_type": "call"}, {"api_name": "utils.redirect_back", "line_number": 135, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 140, "usage_type": "call"}, {"api_name": "forms.CategoryForm", "line_number": 145, "usage_type": "call"}, {"api_name": "models.Category", "line_number": 148, "usage_type": "call"}, {"api_name": "extensions.db.session.add", "line_number": 149, "usage_type": "call"}, {"api_name": "extensions.db.session", "line_number": 149, "usage_type": "attribute"}, {"api_name": "extensions.db", "line_number": 149, "usage_type": "name"}, {"api_name": "extensions.db.session.commit", "line_number": 150, "usage_type": "call"}, {"api_name": "extensions.db.session", "line_number": 150, "usage_type": "attribute"}, {"api_name": "extensions.db", "line_number": 150, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 151, "usage_type": "call"}, {"api_name": "utils.redirect_back", "line_number": 152, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 153, "usage_type": "call"}, {"api_name": "forms.CategoryForm", "line_number": 158, "usage_type": "call"}, {"api_name": "models.Category.query.get_or_404", "line_number": 159, "usage_type": "call"}, {"api_name": "models.Category.query", "line_number": 159, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 159, "usage_type": "name"}, {"api_name": "extensions.db.session.commit", "line_number": 162, "usage_type": "call"}, {"api_name": "extensions.db.session", "line_number": 162, "usage_type": "attribute"}, {"api_name": "extensions.db", "line_number": 162, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 163, "usage_type": "call"}, {"api_name": "utils.redirect_back", "line_number": 164, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 166, "usage_type": "call"}, {"api_name": "models.Category.query.get_or_404", "line_number": 171, "usage_type": "call"}, {"api_name": "models.Category.query", "line_number": 171, "usage_type": "attribute"}, {"api_name": "models.Category", "line_number": 171, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 173, "usage_type": "call"}, {"api_name": "utils.redirect_back", "line_number": 174, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 176, "usage_type": "call"}, {"api_name": "utils.redirect_back", "line_number": 177, "usage_type": "call"}, {"api_name": "forms.SettingForm", "line_number": 182, "usage_type": "call"}, {"api_name": "flask_login.current_user.name", "line_number": 185, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 185, "usage_type": "name"}, {"api_name": "flask_login.current_user.blog_title", "line_number": 186, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 186, "usage_type": "name"}, {"api_name": "flask_login.current_user.blog_sub_title", "line_number": 187, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 187, "usage_type": "name"}, {"api_name": "flask_login.current_user.about", "line_number": 188, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 188, "usage_type": "name"}, {"api_name": "extensions.db.session.commit", "line_number": 189, "usage_type": "call"}, {"api_name": "extensions.db.session", "line_number": 189, "usage_type": "attribute"}, {"api_name": "extensions.db", "line_number": 189, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 190, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 191, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 191, "usage_type": "call"}, {"api_name": "flask_login.current_user.name", "line_number": 192, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 192, "usage_type": "name"}, {"api_name": "flask_login.current_user.blog_title", "line_number": 193, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 193, "usage_type": "name"}, {"api_name": "flask_login.current_user.blog_sub_title", "line_number": 194, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 194, "usage_type": "name"}, {"api_name": "flask_login.current_user.about", "line_number": 195, "usage_type": "attribute"}, {"api_name": "flask_login.current_user", "line_number": 195, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 196, "usage_type": "call"}]} +{"seq_id": "411169132", "text": "import os\nimport datetime\nimport pandas as pd\n\n# for OSI-450 validation\nYEARS_OF_INTEREST = range(1972, 2016)\n# for OSI-401 validation\n# YEARS_OF_INTEREST = [1996]\nVALIDATION_ID = 'OSI450'\nCSV_HEADER = ['reference_time', 'run_time', 'total_bias', 'ice_bias',\n 'water_bias', 'total_stddev', 'ice_stddev', 'water_stddev',\n 'within_10pct', 'within_20pct']\n\nSTART_YEAR = min(YEARS_OF_INTEREST)\nEND_YEAR = max(YEARS_OF_INTEREST)\nif END_YEAR == START_YEAR:\n END_YEAR += 1\n\nBASE_PATH = os.path.join(os.path.expanduser('~/'), 'validation', 'data')\nINPUT_DIR = os.path.join(BASE_PATH, 'input')\n# for OSI-409 validation\nOUTPUT_DIR = os.path.join(BASE_PATH, 'output')\n# for OSI-401 validation\n# OUTPUT_DIR = os.path.join(BASE_PATH, 'output', 'OSI-401-a')\nTMP_DIR = os.path.join(BASE_PATH, 'input', 'tmp')\n\nAREAS = 'etc/areas.cfg'\nDESCRIPTION = 'Comparison of NIC ice charts and OSI-450 products for {0}' \\\n' hemisphere'\nSHORT_DESCRIPTION = 'OSI450_validation_{0}_{1}' # hemisphere, date\nPICKLED_DATA = 'OSI450_val_data.hdf5'\n\n# for OSI-409 validation\nMETNO_DOWNL = {\n 'protocol': 'ftp://',\n 'host': 'osisaf.met.no',\n 'remote_dir_f_pattern': 'reprocessed/ice/conc/v1p2/*/*/*ease*.nc.gz',\n 'remote_date_pattern': (r'\\d{12}', '%Y%m%d%H%M'),\n 'glob_file': os.path.join(TMP_DIR, 'metno_files.json')\n}\n\n# for OSI-401 validation\n# METNO_DOWNL = {\n# 'protocol': 'ftp://',\n# 'host': 'osisaf.met.no',\n# 'remote_dir_f_pattern': 'archive/ice/conc/*/*/*_polstere-100_multi_*.nc',\n# 'remote_date_pattern': (r'\\d{12}', '%Y%m%d%H%M'),\n# 'glob_file': os.path.join(TMP_DIR, 'metno_files.json')\n# }\n\n\nNIC_BIN_DOWNL = {\n 'protocol': 'ftp://',\n 'host': 'sidads.colorado.edu',\n 'remote_dir_f_pattern':\n 'pub/DATASETS/NOAA/G02172/weekly/nic_weekly_*_tot.v0.bin',\n 'remote_date_pattern': (r'\\d{4}_\\d{2}_\\d{2}', '%Y_%m_%d'),\n 'glob_file': os.path.join(TMP_DIR, 'nic_bin_files.json')\n}\n\nNIC_SIG_DOWNL = {\n 'protocol': 'http://',\n 'host': 'wdc.aari.ru',\n 'remote_dir_f_pattern': 'datasets/d0001/south/nic/*/*.sig',\n 'remote_date_pattern': (r'\\d{6}', '%Y%W'),\n 'glob_file': os.path.join(TMP_DIR, 'nic_sig_files.json'),\n}\n\nNIC_SHP_DOWNL = {\n 'scrape': True,\n 'protocol': 'http://',\n 'host': 'www.natice.noaa.gov',\n 'remote_html_path': {\n 'nh':\n 'products/weekly_products.html?oldarea=Arctic&area=Arctic&'\n 'oldformat=Shapefiles&format=Shapefiles&month0=Jan&day0=01&'\n 'year0=2006&month1=Jan&day1=01&year1={0}&subareas='\n 'Hemispheric'.format(datetime.datetime.now().year + 1),\n 'sh':\n 'products/weekly_products.html?oldarea=Antarctic&area=Antarctic&'\n 'oldformat=Shapefiles&format=Shapefiles&month0=Jan&day0=01&'\n 'year0=2006&month1=Jan&day1=01&year1={0}&subareas='\n 'Hemispheric'.format(datetime.datetime.now().year + 1)\n },\n 'remote_file_pattern': {\n 'nh': 'pub/weekly/arctic/{0}/shapefiles/hemispheric/arctic{1}.zip',\n 'sh': 'pub/weekly/antarctic/{0}/shapefiles/hemispheric/antarc{1}.zip'\n },\n 'remote_link_pattern': {\n 'nh': r'href\\s?=\\s?\".*arctic\\d{6}.zip',\n 'sh': r'href\\s?=\\s?\".*antarc\\d{6}.zip',\n },\n 'remote_date_pattern': (r'\\d{6}', '%y%m%d'),\n 'glob_file': os.path.join(TMP_DIR, 'nic_shp_files.json')\n}\n\n# http://thredds.met.no/thredds/dodsC/osisaf/met.no/ice/conc/2016/09/ice_conc_sh_polstere-100_multi_201609211200.nc\nMETNO_THREDDS_DOWNL = {\n 'generate':\n pd.date_range('1/1/{0} 12:00'.format(START_YEAR),\n '1/1/{0} 12:00'.format(END_YEAR), freq='D'),\n 'protocol': 'http://',\n 'host': 'thredds.met.no',\n 'remote_dir_f_pattern':\n 'thredds/dodsC/metusers/sicci_shared/v2.0draftC/{0}/{1}/' \\\n 'ice_conc_{2}h_ease2-250_cdr-v2p0_{3}1200.nc',\n 'remote_date_pattern': (r'\\d{12}', '%Y%m%d%H%M'),\n 'glob_file': os.path.join(TMP_DIR, 'metno_thredds_files.json')\n}\n\n\nif not os.path.exists(INPUT_DIR):\n os.system('mkdir -p {0}'.format(INPUT_DIR))\nif not os.path.exists(OUTPUT_DIR):\n os.system('mkdir -p {0}'.format(OUTPUT_DIR))\nif not os.path.exists(TMP_DIR):\n os.system('mkdir -p {0}'.format(TMP_DIR))\n\ntry:\n import apt\nexcept Exception as e:\n print(\"No python-apt installed, I wont check for gdal-bin and lftp...\")\nelse:\n cache = apt.Cache()\n if not cache['gdal-bin'].is_installed:\n raise Exception('You have to have \"gdal-bin\" installed. Do \"apt-get '\n 'install gdal-bin\"!')\n if not cache['lftp'].is_installed:\n raise Exception('You have to have \"lftp\" installed. Do \"apt-get '\n 'install lftp\"!')\n", "sub_path": "trollvalidation/validations/ice_conc_configuration.py", "file_name": "ice_conc_configuration.py", "file_ext": "py", "file_size_in_byte": 4662, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 19, "usage_type": "call"}, {"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": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "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.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"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": 66, "usage_type": "call"}, {"api_name": "os.path", "line_number": 66, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 78, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 78, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 83, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 83, "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": "pandas.date_range", "line_number": 100, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path", "line_number": 114, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 117, "usage_type": "call"}, {"api_name": "apt.Cache", "line_number": 124, "usage_type": "call"}]} +{"seq_id": "294862319", "text": "# In MADDPG, each agent has its own actor and its own critic\n# original [Paper](http://arxiv.org/abs/1706.02275)\n\n# responsible for\n# - action taking\n# - model updates (policy + critic)\n# see ddpg.py for other details of one DDPG agent\n\n# hyper-parameters\n# - [policies are parameterized by a 2-layer ReLU MLP with 64 units per layer]o.p.\n# - [2 hidden layers with 400 and 300 units respectively]ddpg.p.\n# - [actions were not included until the 2nd hidden layer of Q]ddpg.p.\n\n# Features:\n# - For Critic, use Huber-loss (less sensitive to outliers than the squared error loss)\n# - quadratic for small values of [target-estimate], and linear for large values\n\nfrom ddpg import DDPGAgent\nimport torch\nfrom utilities import soft_update, transpose_to_tensor\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n# device = 'cpu'\n\n\nclass MADDPG:\n def __init__(self, discount_factor=0.95, tau=0.01):\n super(MADDPG, self).__init__()\n\n # args = in_actor, hidden_in_actor, hidden_out_actor, out_actor, in_critic, hidden_in_critic, hidden_out_critic\n # critic input = obs_full + actions = 2*24+2+2=52\n self.maddpg_agent = [DDPGAgent(24, 400, 300, 2, 52, 400, 300),\n DDPGAgent(24, 400, 300, 2, 52, 400, 300)]\n # DDPGAgent(24, 16, 8, 2, 52, 32, 16)]\n\n self.discount_factor = discount_factor\n self.tau = tau\n self.iter = 0\n\n def act(self, obs_all_agents, noise=0.0):\n \"\"\"get local network actions from all agents\"\"\"\n actions = [agent.act(obs, noise) for agent, obs in zip(self.maddpg_agent, obs_all_agents)]\n return actions\n\n def target_act(self, obs_all_agents, noise=0.0):\n \"\"\"get target network actions from all the agents in the MADDPG object \"\"\"\n target_actions = [agent.target_act(obs, noise) for agent, obs in zip(self.maddpg_agent, obs_all_agents)]\n return target_actions\n\n def update(self, samples, agent_number, logger):\n \"\"\"\n update critic and actor nets of one given agent\n :param samples: a list, representing the batch and containing 7 elements\n samples = [obs, obs_full, action, reward, next_obs, next_obs_full, done]\n with e.g. done = [[False, False], [False, False], False, False]] for batch_size = 3\n :param agent_number: int -- in [0, 1]\n :param logger: writer object for tensorboard\n :return: -\n \"\"\"\n # figure out whether GPU or CPU is used\n # print(\"completing Update() with device = {}\".format(device))\n\n # need to transpose each element of the samples\n # to flip obs[parallel_agent][agent_number] to\n # obs[agent_number][parallel_agent]\n obs, obs_full, action, reward, next_obs, next_obs_full, done = map(transpose_to_tensor, samples)\n obs_full = torch.stack(obs_full).to(device)\n next_obs_full = torch.stack(next_obs_full).to(device)\n\n # only one agent it updated\n agent = self.maddpg_agent[agent_number]\n\n # ---------------------------- update local critic ---------------------------- #\n agent.critic_optimizer.zero_grad()\n # critic loss = batch-mean of [y - Q(s,a) from local network]^2\n # y = reward of this timestep + discount * Q(st+1, at+1) from target network\n\n # get predicted next-state actions and Q values from target models\n target_actions = self.target_act(next_obs)\n target_actions = torch.cat(target_actions, dim=1)\n\n # feed the concatenated (states + actions) directly into the *input* layer of the critic\n target_critic_input = torch.cat((next_obs_full.t(), target_actions), dim=1).to(device)\n with torch.no_grad():\n q_next = agent.target_critic(target_critic_input)\n\n # compute the TD-target\n y = reward[agent_number].view(-1, 1).to(device) + self.discount_factor * q_next * \\\n (1 - done[agent_number].view(-1, 1).to(device))\n\n # compute the TD-estimate\n # action = torch.cat(action, dim=1)\n action = torch.cat(action, dim=1).to(device)\n critic_input = torch.cat((obs_full.t(), action), dim=1).to(device)\n q_estimate = agent.local_critic(critic_input)\n # print(\"q_estimate = {}\".format(q_estimate))\n\n # compute loss on [TD-target - TD-estimate]^2\n huber_loss = torch.nn.SmoothL1Loss()\n critic_loss = huber_loss(q_estimate, y.detach()) # y.detach() to prevent grads back\n\n # minimize the loss: 1)perform a backward pass and 2)update the weights\n # use autograd to compute the backward pass\n critic_loss.backward()\n\n # torch.nn.utils.clip_grad_norm_(agent.local_critic.parameters(), 0.5)\n # update the weights\n agent.critic_optimizer.step()\n\n # ---------------------------- update local actor ---------------------------- #\n # update actor local network using policy gradient\n agent.actor_optimizer.zero_grad()\n\n # each local actor (#1 and #2) gives its actions\n actions_predict = [self.maddpg_agent[i].local_actor(ob.to(device)).to(device) if i == agent_number else\n self.maddpg_agent[i].local_actor(ob.to(device)).detach().to(device) # detach() prevents grads back\n for i, ob in enumerate(obs)]\n actions_predict = torch.cat(actions_predict, dim=1).to(device)\n\n # combine all the actions and observations for input to local critic\n # many of the obs are redundant, and obs[1] contains all useful information already\n critic_input = torch.cat((obs_full.t(), actions_predict), dim=1)\n\n # use samples to estimate the expectation of gradient. Hence mean()\n # Deterministic Gradient Policy Theorem: gradient = expectation[Q-values]\n # pytorch by default does gradient DESCENT. Hence minus term for ASCENT\n actor_loss = -agent.local_critic(critic_input).mean()\n\n actor_loss.backward()\n # torch.nn.utils.clip_grad_norm_(agent.local_actor.parameters(),0.5)\n agent.actor_optimizer.step()\n\n # ---------------------------- Logging ---------------------------- #\n # torch.Tensor.item() to get a Python number from a tensor containing a single value\n a_l = actor_loss.cpu().detach().item() # prevent grads back\n c_l = critic_loss.cpu().detach().item()\n logger.add_scalars('agent%i/losses' % agent_number,\n {'local_critic_loss': c_l,\n 'local_actor_loss': a_l},\n self.iter) # number of network updates (local -> target)\n\n def update_targets(self):\n \"\"\"soft update of critic and actor target networks for all agents\"\"\"\n self.iter += 1\n for ddpg_agent in self.maddpg_agent:\n soft_update(ddpg_agent.target_actor, ddpg_agent.local_actor, self.tau)\n soft_update(ddpg_agent.target_critic, ddpg_agent.local_critic, self.tau)\n\n def reset(self):\n for ddpg_agent in self.maddpg_agent:\n ddpg_agent.reset()\n\n def save(self, saving_name):\n for i, ddpg_agent in enumerate(self.maddpg_agent):\n torch.save(ddpg_agent.local_actor.state_dict(), saving_name + '-' + str(i) + '.actor.pth')\n torch.save(ddpg_agent.local_critic.state_dict(), saving_name + '-' + str(i) + '.critic.pth')\n\n def load(self, loading_name):\n for i, ddpg_agent in enumerate(self.maddpg_agent):\n actor_file = torch.load(loading_name + '-' + str(i) + '.actor.pth', map_location='cpu')\n critic_file = torch.load(loading_name + '-' + str(i) + '.critic.pth', map_location='cpu')\n # same config for locals and targets\n ddpg_agent.local_actor.load_state_dict(actor_file)\n ddpg_agent.target_actor.load_state_dict(actor_file)\n ddpg_agent.local_critic.load_state_dict(critic_file)\n ddpg_agent.target_critic.load_state_dict(critic_file)\n print('Loaded: {}.actor.pth'.format(loading_name))\n print('Loaded: {}.critic.pth'.format(loading_name))\n", "sub_path": "p3_collab-compet/src_draft_maddpg/maddpg.py", "file_name": "maddpg.py", "file_ext": "py", "file_size_in_byte": 8084, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "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": "ddpg.DDPGAgent", "line_number": 32, "usage_type": "call"}, {"api_name": "ddpg.DDPGAgent", "line_number": 33, "usage_type": "call"}, {"api_name": "utilities.transpose_to_tensor", "line_number": 66, "usage_type": "argument"}, {"api_name": "torch.stack", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.stack", "line_number": 68, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 93, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn.SmoothL1Loss", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 99, "usage_type": "attribute"}, {"api_name": "torch.cat", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 122, "usage_type": "call"}, {"api_name": "utilities.soft_update", "line_number": 146, "usage_type": "call"}, {"api_name": "utilities.soft_update", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 160, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 161, "usage_type": "call"}]} +{"seq_id": "595480011", "text": "\"\"\"\nThis is a script for collecting truth catalog from cosmos deep debiasing run\n#TODO: \n1. filenames here need to be changed for a better structure. \n2. the data products here needs more description \n\"\"\"\nimport sys\nsys.path.append(\"../\")\nfrom filesystem import LegacySimData \nimport subprocess\nfrom glob import glob\nimport astropy.io.fits as fits\nfrom astropy.table import Table\nfrom SurveySource import BaseSource\nimport os\nimport numpy as np\nimport glob \nimport os\nfrom astropy.table import vstack,Table\nfrom SurveySource import BaseSource\nfrom astropy.coordinates import SkyCoord\nfrom astropy import units as u\n#keys to be collected to generate sweep files\nsweep_keys= ['BRICKNAME','RA','DEC','TYPE','OBJID','EBV','FLUX_G','FLUX_R','FLUX_Z','FLUX_W1','FLUX_W2','FLUX_IVAR_G','FLUX_IVAR_R','FLUX_IVAR_Z','FLUX_IVAR_W1','FLUX_IVAR_W2','MW_TRANSMISSION_G','MW_TRANSMISSION_R','MW_TRANSMISSION_Z','MW_TRANSMISSION_W1','MW_TRANSMISSION_W2','NOBS_G','NOBS_R','NOBS_Z','NOBS_W1','NOBS_W2','SHAPE_R','SHAPE_E1','SHAPE_E2','FIBERFLUX_G','FIBERFLUX_R','FIBERFLUX_Z','MASKBITS','SERSIC','DCHISQ','PSFSIZE_G','PSFSIZE_R','PSFSIZE_Z','PSFDEPTH_G','PSFDEPTH_R','PSFDEPTH_Z','GALDEPTH_G','GALDEPTH_R','GALDEPTH_Z','WISEMASK_W1','WISEMASK_W2','ANYMASK_G','ANYMASK_R','ANYMASK_Z','BX','BY','GAIA_PHOT_G_MEAN_MAG','FIBERTOTFLUX_Z']\n\nclass CosmosDeep(object):\n def __init__(self):\n self.origin_outdir = '/global/project/projectdirs/cosmo/work/legacysurvey/dr9.1.1/'\n self.dr9_outdir = '/global/project/projectdirs/cosmo/work/legacysurvey/dr9/south/'\n self.survey_dir = self.dr9_outdir\n self.outdir = self.origin_outdir\n self.savedir = '/global/cscratch1/sd/huikong/Obiwan/dr9_LRG/obiwan_out/cosmos_deep/'\n self.bricklist = self._get_bricklist()\n self.ccdnum_fn = os.path.join(self.savedir,'ccd_num.fits')\n self.cut_bricklist = np.loadtxt(os.path.join(self.savedir,'bricklist_cutted.txt'),dtype = np.str)\n def _get_bricklist(self,write=True):\n #get list of deep bricks by finding bricknames in coadd files\n if os.path.isfile(os.path.join(self.savedir,'bricklist.txt')):\n bricklist = np.loadtxt(os.path.join(self.savedir,'bricklist.txt'),dtype = np.str)\n return bricklist\n fns = glob.glob(os.path.join(self.outdir, 'coadd','*','*'))\n bricklist = []\n for fn in fns:\n bricklist.append(os.path.basename(fn))\n bricklist = np.array(bricklist, dtype = np.str)\n if write:\n np.savetxt(os.path.join(self.savedir,'bricklist.txt'), bricklist, fmt=\"%s\")\n return bricklist\n def get_ccd_num(self):\n ccd1 = []\n ccd2 = []\n depthz1=[]\n depthz2=[]\n for brickname in self.bricklist:\n ccd_fn = os.path.join(self.origin_outdir, 'coadd', brickname[:3], brickname, 'legacysurvey-%s-ccds.fits'%brickname)\n depthz_fn = os.path.join(self.origin_outdir, 'coadd', brickname[:3], brickname, 'legacysurvey-%s-depth-z.fits.fz'%brickname)\n ccd_num1 = len(fits.getdata(ccd_fn))\n depthz_median = np.median(fits.getdata(depthz_fn).ravel())\n \n ccd_fn_dr9 = os.path.join(self.dr9_outdir, 'coadd', brickname[:3], brickname, 'legacysurvey-%s-ccds.fits'%brickname)\n depthz_fn_dr9 = os.path.join(self.dr9_outdir, 'coadd', brickname[:3], brickname, 'legacysurvey-%s-depth-z.fits.fz'%brickname)\n ccd_num2 = len(fits.getdata(ccd_fn_dr9))\n depthz_dr9_median = np.median(fits.getdata(depthz_fn_dr9).ravel())\n \n ccd1.append(ccd_num1)\n ccd2.append(ccd_num2)\n depthz1.append(depthz_median)\n depthz2.append(depthz_dr9_median)\n ccd1 = np.array(ccd1)\n ccd2 = np.array(ccd2)\n depthz1 = np.array(depthz1)\n depthz2 = np.array(depthz2)\n T = Table()\n T['brickname'] = self.bricklist\n T['ccd_deep'] = ccd1\n T['ccd_dr9'] = ccd2\n T['depthz_deep'] = depthz1\n T['depthz_dr9'] = depthz2\n T.write(os.path.join(self.savedir,'ccd_num.fits'), overwrite = True)\n def cut_bricks(self, scale = None, depthz_cut = None, write=True):\n #cut the bricks that does not have enough ccds, so deep_ccd_num>dr9_ccd_num*scale or using median galdepth cut, this is a rough cut, I use galdepth_z>300\n t = fits.getdata(self.ccdnum_fn)\n print(t['ccd_dr9'].max(),t['ccd_dr9'].min())\n if scale is not None:\n sel = t['ccd_deep']>scale*t['ccd_dr9']\n else:\n sel = t['depthz_deep']>depthz_cut\n print('total ccd: %d, after cut: %d'%(len(sel), sel.sum()))\n if write:\n np.savetxt(os.path.join(self.savedir,'bricklist_cutted.txt'),t[sel]['brickname'], fmt=\"%s\")\n self.cut_bricklist = t[sel]['brickname']\n def get_cosmos_repeats_lists(self):\n #return a list of cosmos repeats bricks\n self.reference_outdir = '/global/cscratch1/sd/dstn/dr9-cosmos-subs/'\n fns = glob.glob(os.path.join(self.reference_outdir,'80','coadd','*','*'))\n bricklist = []\n for fn in fns:\n bricklist.append(os.path.basename(fn))\n self.repeat_bricklist = bricklist\n return bricklist\n def make_truth(self,TYPE='deep'):\n #make truth inputs from cosmos deep region\n #deep is collecting deep data, dr9 is corresponding dr9 data, both using self.cut_bricklist defined previously \n assert(TYPE in [\"deep\",\"dr9\"])\n if TYPE == \"deep\":\n output_fn = \"truth.fits\"\n if TYPE == \"dr9\":\n output_fn = \"dr9_mirror.fits\"\n tab = None\n for brickname in self.cut_bricklist:\n print(brickname)\n if TYPE == \"deep\":\n self.catalog = LegacySimData(survey_dir=self.survey_dir, outdir=self.outdir, brick=brickname)\n elif TYPE == \"dr9\":\n self.catalog = LegacySimData(survey_dir=self.survey_dir, outdir=self.dr9_outdir, brick=brickname)\n else:\n raise\n tractor_fn = self.catalog.find_file('tractor')\n dat_i = Table.read(tractor_fn)\n tab_i = Table()\n for key in sweep_keys:\n tab_i[key.lower()] = dat_i[key.lower()]\n if tab is None:\n tab = tab_i\n else:\n tab = vstack((tab, tab_i))\n tab.write(self.savedir+output_fn,overwrite=True)\n print(\"saved %s\"%(self.savedir+output_fn))\n def add_cards(self, filetype, obj):\n #only add it to dr9 since w1 in deep does not work\n assert(filetype in [\"cosmos_deep_dr9\",\"cosmos_deep\"])\n assert(obj in [\"LRG_sv3_like\",\"LRG_sv3\"])\n catalog_i = BaseSource(filetype=filetype, survey_dir=self.survey_dir, outdir=self.outdir,force_construct=True)\n card = catalog_i.target_selection(obj)\n t_truth = Table.read(catalog_i.source_fn)\n t_truth[obj]=card\n t_truth.write(catalog_i.source_fn, overwrite=True)\n print(catalog_i.source_fn)\n def match(self, filetype1, filetype2):\n if filetype1 == \"cosmos_deep\" and filetype2 == \"cosmos_deep_dr9\":\n #match dr9 to truth\n prefix = \"dr9\"\n elif filetype1 == \"cosmos_deep_dr9\" and filetype2 == \"cosmos_deep\":\n #match truth to dr9\n prefix = \"truth\"\n catalog_1 = BaseSource(filetype=filetype1, survey_dir=self.survey_dir, outdir=self.outdir,force_construct=True)\n catalog_2 = BaseSource(filetype=filetype2, survey_dir=self.survey_dir, outdir=self.outdir,force_construct=True)\n cat1, cat2, matched = catalog_1.match_catalog(catalog_1.source, catalog_2.source)\n T = Table()\n T['matched'] = matched\n input_sweep_keys = sweep_keys.copy()\n input_sweep_keys.extend(['LRG_sv3_like','LRG_sv3'])\n for key in input_sweep_keys:\n T[key.lower()] = cat1[key.lower()]\n T[\"%s_%s\"%(prefix, key.lower())] = cat2[key.lower()]\n names = catalog_1.source.columns.names\n for name in names:\n if name not in input_sweep_keys:\n T[key.lower()] = cat1[key.lower()]\n T.write(catalog_1.source_fn,overwrite=True)\n def truth_match_to_cosmos(self):\n bricknames = self.get_cosmos_repeats_lists()\n catalog_1 = BaseSource(filetype='cosmos_deep', survey_dir=self.survey_dir, outdir=self.outdir,force_construct=True)\n cat1_all = catalog_1.source\n sels = np.zeros(len(cat1_all),dtype=np.bool)\n \n for brickname in bricknames:\n sel = (cat1_all['brickname']==brickname)\n sels+=sel\n cat1 = cat1_all[sels]\n tot_sets = np.arange(0,10)\n topdir = '/global/cscratch1/sd/huikong/Obiwan/dr9_LRG/obiwan_out/cosmos_subsets/cosmos_all_stacked/'\n T = Table()\n for one_set in tot_sets:\n print(one_set)\n cat2 = fits.getdata('/global/cscratch1/sd/huikong/Obiwan/dr9_LRG/obiwan_out/cosmos_subsets/cosmos_all_stacked/cosmos_set%d.fits'%one_set)\n c1 = SkyCoord(ra=cat1['ra']*u.degree, dec=cat1['dec']*u.degree)\n c2 = SkyCoord(ra=cat2['ra']*u.degree, dec=cat2['dec']*u.degree)\n idx, d2d, d3d = c1.match_to_catalog_sky(c2)\n matched = d2d.value <= 1./3600\n distance = d2d.value\n cat2 = cat2[idx]\n T['set_%d_matched'%one_set] = matched\n T['set_%d_sv3_lrg'%one_set] = cat2['set_%d_lrg_sv3'%one_set]\n T['set_%d_sv3_elg_hip'%one_set] = cat2['set_%d_elg_sv3_hip'%one_set]\n for key in sweep_keys:\n T[key.lower()] = cat1[key.lower()]\n T[\"set%d_%s\"%(one_set, key.lower())] = cat2[key.lower()]\n T['lrg_sv3'] = cat1['lrg_sv3']\n T['lrg_sv3_like'] = cat1['lrg_sv3_like']\n T.write(self.savedir+\"/truth_cosmos_repeats.fits\",overwrite=True)\n def run(self):\n cd = self\n \n cd.get_ccd_num()\n cd.cut_bricks(depthz_cut = 300,write=True)\n print(\"make_truth1\")\n cd.make_truth(TYPE = \"deep\")\n print(\"make_truth2\")\n cd.make_truth(TYPE=\"dr9\")\n print(\"add_cards1\")\n cd.add_cards(filetype=\"cosmos_deep_dr9\",obj='LRG_sv3_like')\n print(\"add_cards2\")\n cd.add_cards(filetype=\"cosmos_deep_dr9\",obj='LRG_sv3')\n print(\"add_cards3\")\n cd.add_cards(filetype=\"cosmos_deep\",obj='LRG_sv3_like')\n print(\"add_cards4\")\n cd.add_cards(filetype=\"cosmos_deep\",obj='LRG_sv3')\n print(\"match1\")\n cd.match(filetype1 = \"cosmos_deep\", filetype2=\"cosmos_deep_dr9\")\n print(\"match2\")\n cd.match(filetype1 = \"cosmos_deep_dr9\", filetype2=\"cosmos_deep\")\n print(\"truth_match_to_cosmos\")\n cd.truth_match_to_cosmos()\n def split(self, filetype, topdir, target):\n #split the files into per brick file needed for obiwan run\n assert(filetype in [\"cosmos_deep_dr9\",\"cosmos_deep\"])\n assert(target in [\"LRG_sv3_like\",\"LRG_sv3\"])\n catalog_i = BaseSource(filetype=filetype, survey_dir=self.survey_dir, outdir=self.outdir,force_construct=True)\n source = catalog_i.source\n ids = np.arange(len(source))\n for brickname in self.cut_bricklist:\n \n print(brickname)\n sel = (source['brickname']==brickname)&(source[target])\n print(sel.sum())\n T = Table()\n T['ra'] = source[sel]['ra']\n T['dec'] = source[sel]['dec']\n T['e1'] = source[sel]['shape_e1']\n T['e2'] = source[sel]['shape_e2']\n T['n'] = source[sel]['sersic']\n #some g band is nan, set it to a high mag\n T['g'] = 22.5 - 2.5*np.log10(source[sel]['flux_g']/source[sel]['mw_transmission_g'])\n idx = np.where(source[sel]['flux_g']<=0)\n T['g'][idx] = 30\n T['r'] = 22.5 - 2.5*np.log10(source[sel]['flux_r']/source[sel]['mw_transmission_r'])\n T['z'] = 22.5 - 2.5*np.log10(source[sel]['flux_r']/source[sel]['mw_transmission_z'])\n T['w1'] = 22.5 - 2.5*np.log10(source[sel]['flux_w1']/source[sel]['mw_transmission_w1'])\n T['w2'] = np.clip(0,30,22.5 - 2.5*np.log10(source[sel]['flux_w2']/source[sel]['mw_transmission_w2']))\n T['rhalf'] = source[sel]['shape_r']\n T['id'] = ids[sel]\n T.write(topdir+'/brick_%s.fits'%brickname,overwrite=True)\n \n def split_elgs(self, topdir):\n fn = \"/global/cscratch1/sd/adematti/legacysim/dr9/cosmos/merged/truth_ELG_HIP.fits\"\n source = fits.getdata(fn)\n ids = np.arange(len(source))\n for brickname in self.cut_bricklist:\n \n print(brickname)\n sel = (source['brickname']==brickname)\n print(sel.sum())\n T = Table()\n T['ra'] = source[sel]['ra']\n T['dec'] = source[sel]['dec']\n T['e1'] = source[sel]['shape_e1']\n T['e2'] = source[sel]['shape_e2']\n T['n'] = source[sel]['sersic']\n T['g'] = 22.5 - 2.5*np.log10(source[sel]['flux_g']/source[sel]['mw_transmission_g'])\n T['r'] = 22.5 - 2.5*np.log10(source[sel]['flux_r']/source[sel]['mw_transmission_r'])\n T['z'] = 22.5 - 2.5*np.log10(source[sel]['flux_r']/source[sel]['mw_transmission_z'])\n T['w1'] = 22.5*np.ones(sel.sum())\n T['w2'] = 22.5*np.ones(sel.sum())\n T['rhalf'] = source[sel]['shape_r']\n T['id'] = ids[sel]\n T.write(topdir+'/brick_%s.fits'%brickname,overwrite=True)\n def collect_tracers(self, tracer):\n assert(tracer in ['elg','lrg'])\n all_fn = \"/global/cscratch1/sd/huikong/Obiwan/dr9_LRG/obiwan_out/sim_deep/output/rs0/tractor/*/*\"\n all_fns = glob.glob(all_fn)\n elg_fns = []\n lrg_fns = []\n for fn in all_fns:\n if 'elg' in fn:\n elg_fns.append(fn)\n else:\n lrg_fns.append(fn)\n if tracer == 'elg':\n fns = elg_fns\n else:\n fns = lrg_fns\n \n samp = None\n for fn in fns:\n tt = fits.getdata(fn)\n if 'current_gflux' in tt.columns.names:\n tracer_i = Table.read(fn)\n if samp is None:\n samp = tracer_i\n else:\n samp = vstack((samp,tracer_i))\n samp['bad'] = np.zeros(len(samp),dtype = np.bool)\n sel = (samp['n']==-999)\n samp['bad'][sel] = np.ones(sel.sum(),dtype = np.bool)\n samp['n'] = np.clip(samp['n'],0.2,7)\n samp.write(\"/global/cscratch1/sd/huikong/Obiwan/dr9_LRG/obiwan_out/sim_deep/%s_v2.fits\"%tracer,overwrite = True)\n print(\"written /global/cscratch1/sd/huikong/Obiwan/dr9_LRG/obiwan_out/sim_deep/%s_v2.fits\"%tracer)\n \n if tracer=='elg':\n topdir = '/global/cscratch1/sd/huikong/Obiwan/dr9_LRG/obiwan_out/sim_deep/divided_randoms_elg/'\n else:\n topdir = '/global/cscratch1/sd/huikong/Obiwan/dr9_LRG/obiwan_out/sim_deep/divided_randoms/'\n final = None\n for fn in fns:\n tracer_i = Table.read(fn)\n brickname = fn[-13:-5]\n randoms_i = Table.read(topdir+\"brick_%s.fits\"%brickname)\n assert(len(tracer_i)==len(randoms_i))\n if len(tracer_i)>0:\n tracer_i['bad'] = np.zeros(len(tracer_i),dtype = np.bool)\n sel = (tracer_i['n']==-999)\n tracer_i['bad'][sel] = np.ones(sel.sum(),dtype = np.bool)\n sel = (~tracer_i['fitted'])|(tracer_i['bad'])\n randoms_i['resampled_e1'] = tracer_i['e1']\n randoms_i['resampled_e1'][sel] = randoms_i['e1'][sel]\n \n randoms_i['resampled_e2'] = tracer_i['e2']\n randoms_i['resampled_e2'][sel] = randoms_i['e2'][sel]\n \n randoms_i['resampled_rhalf'] = tracer_i['rhalf']\n randoms_i['resampled_rhalf'][sel] = randoms_i['rhalf'][sel]\n \n randoms_i['resampled_n'] = tracer_i['n']\n randoms_i['resampled_n'][sel] = randoms_i['n'][sel]\n \n randoms_i['brickname'] = np.array([brickname]*len(randoms_i),dtype=np.str)\n \n randoms_i['bad'] = tracer_i['bad']\n \n \n if final is None:\n final = randoms_i\n else:\n final = vstack((final,randoms_i))\n final.write(\"/global/cscratch1/sd/huikong/Obiwan/dr9_LRG/obiwan_out/sim_deep/%s_final_v2.fits\"%tracer,overwrite=True)\n print(\"written /global/cscratch1/sd/huikong/Obiwan/dr9_LRG/obiwan_out/sim_deep/%s_final_v2.fits\"%tracer)\n \n if tracer == 'lrg':\n seed_fn = '/global/cscratch1/sd/huikong/Obiwan/dr9_LRG/obiwan_out/cosmos_deep/truth.fits'\n if tracer == 'elg':\n seed_fn = \"/global/cscratch1/sd/adematti/legacysim/dr9/cosmos/merged/truth_ELG_HIP.fits\"\n \n fn1 = \"/global/cscratch1/sd/huikong/Obiwan/dr9_LRG/obiwan_out/sim_deep/%s_final.fits\"%tracer\n fn2 = seed_fn\n dat1 = fits.getdata(fn1)\n dat2 = fits.getdata(fn2)\n print(\"bad: %d\"%dat1['bad'].sum())\n from astropy.coordinates import SkyCoord\n from astropy import units as u\n c2 = SkyCoord(ra=dat1['ra']*u.degree, dec=dat1['dec']*u.degree)\n c1 = SkyCoord(ra=np.array(dat2['ra'])*u.degree, dec=np.array(dat2['dec'])*u.degree)\n idx, d2d, d3d = c1.match_to_catalog_sky(c2)\n matched = (d2d.value<1./3600)\n truth = Table.read(fn2)\n truth['matched'] = matched\n truth['resampled_e1'] = dat1['resampled_e1'][idx]\n truth['resampled_e2'] = dat1['resampled_e2'][idx]\n truth['resampled_rhalf'] = dat1['resampled_rhalf'][idx]\n truth['resampled_n'] = dat1['resampled_n'][idx]\n truth['bad'] = dat1['bad'][idx]\n\n truth.write(\"/global/cscratch1/sd/huikong/Obiwan/dr9_LRG/obiwan_out/sim_deep/%s_truth_v2.fits\"%tracer,overwrite = True)\n print(\"written /global/cscratch1/sd/huikong/Obiwan/dr9_LRG/obiwan_out/sim_deep/%s_truth_v2.fits\"%tracer)\n \n def make_seed(self):\n fn = \"/global/cscratch1/sd/huikong/Obiwan/dr9_LRG/obiwan_out/sim_deep/lrg_truth.fits\"\n dat = fits.getdata(fn)\n sel = dat['LRG_sv3_like']\n print(\"matched: %d/%d\"%((dat['matched']&sel).sum(),sel.sum()))\n sel = dat['matched']&dat['LRG_sv3_like']&(dat['galdepth_z']>1000)&(~dat['bad'])\n T = Table()\n T['ra'] = dat[sel]['ra']\n T['dec'] = dat[sel]['dec']\n gmag = 22.5 - 2.5*np.log10(dat[sel]['flux_g']/dat[sel]['mw_transmission_g'])\n g_sel = ~((gmag>0)&(gmag<30))\n gmag[g_sel] = 30\n rmag = 22.5 - 2.5*np.log10(dat[sel]['flux_r']/dat[sel]['mw_transmission_r'])\n zmag = 22.5 - 2.5*np.log10(dat[sel]['flux_z']/dat[sel]['mw_transmission_z'])\n w1mag = 22.5 - 2.5*np.log10(dat[sel]['flux_w1']/dat[sel]['mw_transmission_w1'])\n w2mag = 22.5 - 2.5*np.log10(dat[sel]['flux_w2']/dat[sel]['mw_transmission_w2'])\n T['g'] = gmag\n T['r'] = rmag\n T['z'] = zmag\n T['w1'] = w1mag\n T['w2'] = w2mag\n T['e1'] = dat[sel]['resampled_e1']\n T['e2'] = dat[sel]['resampled_e2']\n T['n'] = np.clip(dat[sel]['resampled_n'],0.2,7)\n T['rhalf'] = dat[sel]['resampled_rhalf']\n T['id_sample'] = np.arange(sel.sum())\n T.write(\"/global/cscratch1/sd/huikong/Obiwan/dr9_LRG/obiwan_out/cosmos_deep/seed.fits\",overwrite=True)\n \n \n \n \n \n \nif __name__ == '__main__':\n cd = CosmosDeep()\n cd.run()\n topdir = '/global/cscratch1/sd/huikong/Obiwan/dr9_LRG/obiwan_out/sim_deep/divided_randoms/'\n cd.split(filetype = \"cosmos_deep\", topdir=topdir, target=\"LRG_sv3_like\")\n topdir = '/global/cscratch1/sd/huikong/Obiwan/dr9_LRG/obiwan_out/sim_deep/divided_randoms_elg/'\n cd.split_elgs(topdir)\n cd.collect_tracers('lrg')\n cd.make_seed()\n \n \n \n", "sub_path": "bin/cosmos_deep.py", "file_name": "cosmos_deep.py", "file_ext": "py", "file_size_in_byte": 20020, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.loadtxt", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.str", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.str", "line_number": 39, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.str", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.savetxt", "line_number": 47, "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": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path", "line_number": 56, "usage_type": "attribute"}, {"api_name": "astropy.io.fits.getdata", "line_number": 57, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 57, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 58, "usage_type": "call"}, {"api_name": "astropy.io.fits.getdata", "line_number": 58, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 58, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "astropy.io.fits.getdata", "line_number": 62, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 62, "usage_type": "name"}, {"api_name": "numpy.median", "line_number": 63, "usage_type": "call"}, {"api_name": "astropy.io.fits.getdata", "line_number": 63, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 63, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 72, "usage_type": "call"}, {"api_name": "astropy.table.Table", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "astropy.io.fits.getdata", "line_number": 82, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 82, "usage_type": "name"}, {"api_name": "numpy.savetxt", "line_number": 90, "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": "glob.glob", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "filesystem.LegacySimData", "line_number": 113, "usage_type": "call"}, {"api_name": "filesystem.LegacySimData", "line_number": 115, "usage_type": "call"}, {"api_name": "astropy.table.Table.read", "line_number": 119, "usage_type": "call"}, {"api_name": "astropy.table.Table", "line_number": 119, "usage_type": "name"}, {"api_name": "astropy.table.Table", "line_number": 120, "usage_type": "call"}, {"api_name": "astropy.table.vstack", "line_number": 126, "usage_type": "call"}, {"api_name": "SurveySource.BaseSource", "line_number": 133, "usage_type": "call"}, {"api_name": "astropy.table.Table.read", "line_number": 135, "usage_type": "call"}, {"api_name": "astropy.table.Table", "line_number": 135, "usage_type": "name"}, {"api_name": "SurveySource.BaseSource", "line_number": 146, "usage_type": "call"}, {"api_name": "SurveySource.BaseSource", "line_number": 147, "usage_type": "call"}, {"api_name": "astropy.table.Table", "line_number": 149, "usage_type": "call"}, {"api_name": "SurveySource.BaseSource", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 165, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 165, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 171, "usage_type": "call"}, {"api_name": "astropy.table.Table", "line_number": 173, "usage_type": "call"}, {"api_name": "astropy.io.fits.getdata", "line_number": 176, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 176, "usage_type": "name"}, {"api_name": "astropy.coordinates.SkyCoord", "line_number": 177, "usage_type": "call"}, {"api_name": "astropy.units.degree", "line_number": 177, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 177, "usage_type": "name"}, {"api_name": "astropy.coordinates.SkyCoord", "line_number": 178, "usage_type": "call"}, {"api_name": "astropy.units.degree", "line_number": 178, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 178, "usage_type": "name"}, {"api_name": "SurveySource.BaseSource", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 221, "usage_type": "call"}, {"api_name": "astropy.table.Table", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 237, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 240, "usage_type": "call"}, {"api_name": "astropy.io.fits.getdata", "line_number": 247, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 247, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 248, "usage_type": "call"}, {"api_name": "astropy.table.Table", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 260, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 261, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 262, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 263, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 264, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 271, "usage_type": "call"}, {"api_name": "astropy.io.fits.getdata", "line_number": 286, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 286, "usage_type": "name"}, {"api_name": "astropy.table.Table.read", "line_number": 288, "usage_type": "call"}, {"api_name": "astropy.table.Table", "line_number": 288, "usage_type": "name"}, {"api_name": "astropy.table.vstack", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 293, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 295, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 296, "usage_type": "call"}, {"api_name": "astropy.table.Table.read", "line_number": 306, "usage_type": "call"}, {"api_name": "astropy.table.Table", "line_number": 306, "usage_type": "name"}, {"api_name": "astropy.table.Table.read", "line_number": 308, "usage_type": "call"}, {"api_name": "astropy.table.Table", "line_number": 308, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 311, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 313, "usage_type": "call"}, {"api_name": "numpy.bool", "line_number": 313, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 327, "usage_type": "call"}, {"api_name": "numpy.str", "line_number": 327, "usage_type": "attribute"}, {"api_name": "astropy.table.vstack", "line_number": 335, "usage_type": "call"}, {"api_name": "astropy.io.fits.getdata", "line_number": 346, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 346, "usage_type": "name"}, {"api_name": "astropy.io.fits.getdata", "line_number": 347, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 347, "usage_type": "name"}, {"api_name": "astropy.coordinates.SkyCoord", "line_number": 351, "usage_type": "call"}, {"api_name": "astropy.units.degree", "line_number": 351, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 351, "usage_type": "name"}, {"api_name": "astropy.coordinates.SkyCoord", "line_number": 352, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 352, "usage_type": "call"}, {"api_name": "astropy.units.degree", "line_number": 352, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 352, "usage_type": "name"}, {"api_name": "astropy.table.Table.read", "line_number": 355, "usage_type": "call"}, {"api_name": "astropy.table.Table", "line_number": 355, "usage_type": "name"}, {"api_name": "astropy.io.fits.getdata", "line_number": 368, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 368, "usage_type": "name"}, {"api_name": "astropy.table.Table", "line_number": 372, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 375, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 378, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 379, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 380, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 381, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 389, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 391, "usage_type": "call"}, {"api_name": "{'SkyCoord': 'astropy.coordinates.SkyCoord', 'u': 'astropy.units'}", "line_number": 400, "usage_type": "call"}]} +{"seq_id": "503342080", "text": "import flask\r\nfrom flask import request, jsonify\r\nfrom google.cloud import bigquery\r\nfrom google.oauth2 import service_account\r\nimport pandas as pd\r\nimport os.path\r\nimport ssl\r\nimport json\r\nfrom s2 import s2\r\napp = flask.Flask(__name__)\r\napp.config[\"DEBUG\"] = True\r\n\r\ngcp_project = \"weighty-flag-307702\"\r\nclass Gcp_connect:\r\n def __init__(self):\r\n my_path = os.path.abspath(os.path.dirname(__file__))\r\n path = os.path.join(my_path, \"../Config/weighty-flag-307702-26125390e609.json\")\r\n print(path)\r\n #self.credentials = service_account.Credentials.from_service_account_file('Config\\weighty-flag-307702-26125390e609.json')\r\n self.credentials = service_account.Credentials.from_service_account_file(path)\r\n self.client = bigquery.Client(credentials=self.credentials, project=gcp_project)\r\n def get_client(self):\r\n return self.client\r\n\r\n#Get GCP Client\r\ndef get_gcp_client():\r\n return Gcp_connect().get_client()\r\n\r\ngcp_client=get_gcp_client()\r\n\r\n@app.route('/', methods=['GET'])\r\ndef home():\r\n return \"

Gojek Taxi API.

\"\r\n\r\n#\r\n@app.route('/total_trips', methods=['GET'])\r\ndef total_trips():\r\n query_parameters = request.args\r\n start_date= query_parameters.get('start')\r\n end_date = query_parameters.get('end')\r\n print(start_date)\r\n print(end_date)\r\n query_res = gcp_client.query(\"\"\"select date, total_trips from (\r\n SELECT CAST(DATE(trip_start_timestamp) as DATE) as date, count(*) as total_trips from\r\n `bigquery-public-data.chicago_taxi_trips.taxi_trips` where CAST(DATE(trip_start_timestamp) as DATE) between\r\n '\"\"\"+start_date+\"' and '\"+end_date+\"' group by CAST(DATE(trip_start_timestamp) as DATE))a order by date\"\"\")\r\n # to store results in dataframe\r\n results = [] # empty dataframe\r\n for row in query_res:\r\n results.append({\"date\":str(row.date),\"total_trips\":row.total_trips})\r\n return {\"data\": results}\r\n\r\n@app.route('/avg_speed_24hrs', methods=['GET'])\r\n\r\ndef avg_speed_24hrs():\r\n\r\n query_parameters = request.args\r\n date= query_parameters.get('date')\r\n\r\n query_res=gcp_client.query(\"\"\"select \r\n avg(trip_miles / (TIMESTAMP_DIFF(trip_end_timestamp, trip_start_timestamp, minute) / 60)) as average_speed\r\n from `bigquery-public-data.chicago_taxi_trips.taxi_trips` where\r\n abs(DATE_DIFF(DATE '\"\"\"+date+\"', CAST(DATE(trip_end_timestamp) AS DATE), DAY)) < 1 and \" \\\r\n \"trip_end_timestamp != trip_start_timestamp\"\"\")\r\n\r\n results = [] # empty dataframe\r\n for row in query_res:\r\n results.append({\"average_speed\":row.average_speed})\r\n return {\"data\": results}\r\n\r\n@app.route('/average_fare_heatmap', methods=['GET'])\r\n\r\ndef avg_fare_heatmap():\r\n query_parameters = request.args\r\n date= query_parameters.get('date')\r\n query_res = gcp_client.query(\"\"\"select pickup_location,avg(fare) as avg_fare from `bigquery-public-data.chicago_taxi_trips.taxi_trips`\r\n where pickup_location is not null and CAST(DATE(trip_start_timestamp) AS DATE) = '\"\"\"+date+\"' group by pickup_location\"\"\")\r\n\r\n results = [] # empty dataframe\r\n for row in query_res:\r\n results.append({\"s2id\":row.pickup_location,\"fare\":row.avg_fare})\r\n return {\"data\": results}\r\n\r\ndef calculate_s2id(point,radius ):\r\n latlong = s2.LatLngFromDegrees(point.Latitude, point.Longitude)\r\n s2Point = s2.PointFromLatLng(latlong)\r\n EarthRadiusInMeter=10\r\n angle = s2.Angle(radius / EarthRadiusInMeter)\r\n sphereCap = s2.CapFromCenterAngle(s2Point, angle)\r\n region = s2.Region(sphereCap)\r\n rc = s2.RegionCoverer(MaxLevel=16, MinLevel=16)\r\n cellUnion = rc.Covering(region)\r\n stringCellIDs=[]\r\n for cellID in cellUnion:\r\n stringCellIDs.append(int(cellID))\r\n return stringCellIDs\r\n\r\n@app.errorhandler(404)\r\ndef page_not_found(e):\r\n return \"

404

The resource could not be found.

\", 404\r\n\r\nif __name__ == \"__main__\":\r\n app.run()", "sub_path": "webapp/Source/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 3965, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Flask", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.path.abspath", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 16, "usage_type": "name"}, {"api_name": "os.path.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 17, "usage_type": "name"}, {"api_name": "google.oauth2.service_account.Credentials.from_service_account_file", "line_number": 20, "usage_type": "call"}, {"api_name": "google.oauth2.service_account.Credentials", "line_number": 20, "usage_type": "attribute"}, {"api_name": "google.oauth2.service_account", "line_number": 20, "usage_type": "name"}, {"api_name": "google.cloud.bigquery.Client", "line_number": 21, "usage_type": "call"}, {"api_name": "google.cloud.bigquery", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.request.args", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.request.args", "line_number": 57, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 57, "usage_type": "name"}, {"api_name": "flask.request.args", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 74, "usage_type": "name"}, {"api_name": "s2.s2.LatLngFromDegrees", "line_number": 85, "usage_type": "call"}, {"api_name": "s2.s2", "line_number": 85, "usage_type": "name"}, {"api_name": "s2.s2.PointFromLatLng", "line_number": 86, "usage_type": "call"}, {"api_name": "s2.s2", "line_number": 86, "usage_type": "name"}, {"api_name": "s2.s2.Angle", "line_number": 88, "usage_type": "call"}, {"api_name": "s2.s2", "line_number": 88, "usage_type": "name"}, {"api_name": "s2.s2.CapFromCenterAngle", "line_number": 89, "usage_type": "call"}, {"api_name": "s2.s2", "line_number": 89, "usage_type": "name"}, {"api_name": "s2.s2.Region", "line_number": 90, "usage_type": "call"}, {"api_name": "s2.s2", "line_number": 90, "usage_type": "name"}, {"api_name": "s2.s2.RegionCoverer", "line_number": 91, "usage_type": "call"}, {"api_name": "s2.s2", "line_number": 91, "usage_type": "name"}]} +{"seq_id": "201173631", "text": "import time\nimport logging\nimport threading\nimport sys\nimport traceback\nimport platform\n\nimport graphsignal\nfrom graphsignal import statistics\nfrom graphsignal.uploader import Uploader\nfrom graphsignal.predictions import Prediction\nfrom graphsignal.windows import Window, Model, Metric, Event\n\nlogger = logging.getLogger('graphsignal')\n\nMAX_TAGS = 10\nMAX_EVENTS = 50\n\nMIN_WINDOW_SIZE = 50\nMIN_WINDOW_DURATION = 120\nMAX_WINDOW_DURATION = 600\n\n_session_index = {}\n_session_index_lock = threading.Lock()\n\n\nclass Session(object):\n __slots__ = [\n '_deployment_name',\n '_tags',\n '_prediction_window',\n '_event_window',\n '_window_start_time',\n '_window_size',\n '_update_lock',\n '_is_updated',\n '_upload_timer'\n ]\n\n def __init__(self, deployment_name):\n self._deployment_name = deployment_name\n self._tags = {}\n self._update_lock = threading.Lock()\n self._reset_window()\n\n def _reset_window(self):\n self._prediction_window = []\n self._event_window = []\n self._window_start_time = time.time()\n self._window_size = 0\n self._is_updated = False\n\n def _set_updated(self):\n self._is_updated = True\n if self._upload_window():\n graphsignal._get_uploader().flush_in_thread()\n\n def __enter__(self):\n return self\n\n def __exit__(self, exc_type, exc_val, exc_tb):\n if exc_type and exc_val and exc_tb:\n message = traceback.format_exception_only(exc_type, exc_val)\n stack_trace = traceback.format_tb(exc_tb)\n\n attributes = {}\n if isinstance(message, list) and len(message) > 0:\n attributes['Message'] = str('\\n'.join(message))\n if isinstance(stack_trace, list) and len(stack_trace) > 0:\n attributes['Stack trace'] = str('\\n'.join(stack_trace))\n\n self.log_event(\n description='Prediction exception',\n attributes=attributes,\n is_error=True\n )\n\n def set_tag(self, name=None, value=None):\n '''\n Set model deployment tags.\n\n Args:\n name (:obj:`str`):\n Tag name.\n value (:obj:`int` or :obj:`float`):\n Tag value.\n '''\n\n if not isinstance(name, str) or len(name) > 250:\n logger.error('invalid tag name format')\n return\n if not isinstance(value, (str, int, float)) or len(str(value)) > 2500:\n logger.error(\n 'invalid tag value format for name: {0}'.format(name))\n return\n\n if len(self._tags) >= MAX_TAGS:\n logger.error(\n 'too many tags, max={0}'.format(MAX_TAGS))\n return\n\n self._tags[name] = value\n\n def log_prediction(\n self,\n input_data=None,\n output_data=None,\n actual_timestamp=None):\n '''\n Log single or batch model prediction.\n\n See `Supported Data Formats `_\n for detailed description of data types and formats.\n\n Computed data statistics are uploaded at certain intervals and on process exit. No raw data is uploaded.\n\n Args:\n input_data (:obj:`list` or :obj:`dict` or :obj:`numpy.ndarray` or :obj:`pandas.DataFrame`, optional):\n Input data instances.\n output_data (:obj:`list` or :obj:`dict` or :obj:`numpy.ndarray` or :obj:`pandas.DataFrame`, optional):\n Output data instances.\n actual_timestamp (:obj:`int`, optional, default is current timestamp):\n Actual timestamp of the measurement, when different from current timestamp.\n '''\n\n self._window_size += max(\n statistics.estimate_size(input_data),\n statistics.estimate_size(output_data))\n\n with self._update_lock:\n self._prediction_window.append(Prediction(\n input_data=input_data,\n output_data=output_data,\n timestamp=actual_timestamp))\n\n self._set_updated()\n\n def log_event(\n self,\n description=None,\n attributes=None,\n is_error=False,\n actual_timestamp=None):\n '''\n Log arbitrary event or exception.\n\n Args:\n description (:obj:`str`):\n Event description.\n attributes (:obj:`dict`, optional):\n Event attributes.\n is_error (:obj:`bool`, optional):\n Set error type.\n actual_timestamp (:obj:`int`, optional, default is current timestamp):\n Actual timestamp of the measurement, when different from current timestamp.\n '''\n\n if not description or not isinstance(\n description, str) or len(description) > 250:\n logger.error('invalid format for description')\n return\n\n if attributes is not None:\n if isinstance(attributes, dict):\n for name, value in attributes.items():\n if not isinstance(name, str) or len(name) > 250:\n logger.error('invalid attribute name format')\n return\n if not isinstance(value, (str, int, float)):\n logger.error(\n 'invalid attribute value format for attribute name {0}'.format(name))\n return\n else:\n logger.error('invalid attributes format, expecting dict')\n return\n\n if len(self._event_window) >= MAX_EVENTS:\n logger.error('too many events, max={0}'.format(MAX_EVENTS))\n return\n\n type_name = Event.TYPE_INFO\n if is_error:\n type_name = Event.TYPE_ERROR\n event_name = Event.NAME_ERROR\n\n with self._update_lock:\n event = Event(\n type=type_name,\n name=event_name,\n description=description,\n timestamp=actual_timestamp)\n for name, value in attributes.items():\n event.add_attribute(name, value)\n self._event_window.append(event)\n\n self._set_updated()\n\n def _upload_window(self, force=False):\n if not self._is_updated:\n return False\n\n # check if current window should be uploaded\n if not force:\n window_duration = time.time() - self._window_start_time\n if window_duration < MIN_WINDOW_DURATION:\n return False\n if (self._window_size < MIN_WINDOW_SIZE and\n window_duration < MAX_WINDOW_DURATION):\n return False\n\n # reset\n with self._update_lock:\n prediction_window = self._prediction_window\n events_window = self._event_window\n self._reset_window()\n\n # initialize window object\n window = Window()\n\n # set model\n window.model = Model(\n deployment=self._deployment_name)\n if self._tags is not None:\n for name, value in self._tags.items():\n window.model.add_tag(name, value)\n\n # add prediction count metric\n last_timestamp = max([p.timestamp for p in prediction_window if p]) if len(\n prediction_window) > 0 else None\n prediction_count_metric = Metric(\n dataset='model_statistics',\n name='prediction_count',\n aggregation=Metric.AGGREGATION_SUM,\n timestamp=last_timestamp)\n prediction_count_metric.set_gauge(len(prediction_window))\n window.add_metric(prediction_count_metric)\n\n # add computed data metrics\n try:\n data_metrics = statistics.compute_metrics(\n prediction_window)\n if data_metrics is not None and len(data_metrics) > 0:\n for metric in data_metrics:\n window.add_metric(metric)\n except Exception:\n logger.error(\n 'Unable to compute data statistics', exc_info=True)\n\n # add events\n for event in events_window:\n window.add_event(event)\n\n if logger.isEnabledFor(logging.DEBUG):\n logger.debug('Uploading window:')\n logger.debug(window)\n\n graphsignal._get_uploader().upload_window(window.to_dict())\n return True\n\n\ndef get_session(deployment_name):\n if not deployment_name or len(deployment_name) > 250:\n raise ValueError('invalid deployment_name format')\n\n with _session_index_lock:\n if deployment_name in _session_index:\n return _session_index[deployment_name]\n else:\n sess = Session(deployment_name)\n _session_index[deployment_name] = sess\n return sess\n\n\ndef reset_all():\n with _session_index_lock:\n _session_index.clear()\n\n\ndef upload_all(force=False):\n session_list = None\n with _session_index_lock:\n session_list = _session_index.values()\n\n uploaded = False\n for session in session_list:\n if session._upload_window(force=force):\n uploaded = True\n\n return uploaded\n", "sub_path": "graphsignal/sessions.py", "file_name": "sessions.py", "file_ext": "py", "file_size_in_byte": 9286, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 24, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 43, "usage_type": "call"}, {"api_name": "time.time", "line_number": 49, "usage_type": "call"}, {"api_name": "graphsignal._get_uploader", "line_number": 56, "usage_type": "call"}, {"api_name": "traceback.format_exception_only", "line_number": 63, "usage_type": "call"}, {"api_name": "traceback.format_tb", "line_number": 64, "usage_type": "call"}, {"api_name": "graphsignal.statistics.estimate_size", "line_number": 127, "usage_type": "call"}, {"api_name": "graphsignal.statistics", "line_number": 127, "usage_type": "name"}, {"api_name": "graphsignal.statistics.estimate_size", "line_number": 128, "usage_type": "call"}, {"api_name": "graphsignal.statistics", "line_number": 128, "usage_type": "name"}, {"api_name": "graphsignal.predictions.Prediction", "line_number": 131, "usage_type": "call"}, {"api_name": "graphsignal.windows.Event.TYPE_INFO", "line_number": 181, "usage_type": "attribute"}, {"api_name": "graphsignal.windows.Event", "line_number": 181, "usage_type": "name"}, {"api_name": "graphsignal.windows.Event.TYPE_ERROR", "line_number": 183, "usage_type": "attribute"}, {"api_name": "graphsignal.windows.Event", "line_number": 183, "usage_type": "name"}, {"api_name": "graphsignal.windows.Event.NAME_ERROR", "line_number": 184, "usage_type": "attribute"}, {"api_name": "graphsignal.windows.Event", "line_number": 184, "usage_type": "name"}, {"api_name": "graphsignal.windows.Event", "line_number": 187, "usage_type": "call"}, {"api_name": "time.time", "line_number": 204, "usage_type": "call"}, {"api_name": "graphsignal.windows.Window", "line_number": 218, "usage_type": "call"}, {"api_name": "graphsignal.windows.Model", "line_number": 221, "usage_type": "call"}, {"api_name": "graphsignal.windows.Metric", "line_number": 230, "usage_type": "call"}, {"api_name": "graphsignal.windows.Metric.AGGREGATION_SUM", "line_number": 233, "usage_type": "attribute"}, {"api_name": "graphsignal.windows.Metric", "line_number": 233, "usage_type": "name"}, {"api_name": "graphsignal.statistics.compute_metrics", "line_number": 240, "usage_type": "call"}, {"api_name": "graphsignal.statistics", "line_number": 240, "usage_type": "name"}, {"api_name": "logging.DEBUG", "line_number": 253, "usage_type": "attribute"}, {"api_name": "graphsignal._get_uploader", "line_number": 257, "usage_type": "call"}]} +{"seq_id": "335137291", "text": "from jpype import java, javax, JProxy\n\nclass AbstractAction(object):\n ACCELERATOR_KEY = javax.swing.Action.ACCELERATOR_KEY\n ACTION_COMMAND_KEY = javax.swing.Action.ACTION_COMMAND_KEY\n DEFAULT = javax.swing.Action.DEFAULT\n LONG_DESCRIPTION = javax.swing.Action.LONG_DESCRIPTION\n MNEMONIC_KEY = javax.swing.Action.MNEMONIC_KEY\n NAME = javax.swing.Action.NAME\n SHORT_DESCRIPTION = javax.swing.Action.SHORT_DESCRIPTION\n SMALL_ICON = javax.swing.Action.SMALL_ICON\n\n def __init__(self, cb, name=None, icon=None):\n object.__init__(self)\n\n self.__proxy = JProxy(javax.swing.Action, inst=self)\n self.__values = {}\n self.__cb = cb\n self.__listeners = []\n self.__enabled = True\n\n if name is not None:\n self.putValue(AbstractAction.NAME, name)\n\n if icon is not None:\n self.putValue(AbstractAction.SMALL_ICON, icon)\n\n proxy = property(lambda self: self.__proxy)\n\n def addPropertyChangeListener(self, listener):\n self.__listeners.append(listener)\n\n def getValue(self, key):\n return self.__values.get(key, None)\n\n def isEnabled(self):\n if self.__enabled:\n return True\n return False\n\n def putValue(self, key, value):\n oldVal = self.__values.get(key, None)\n if oldVal != value:\n self.__values[key] = value\n self.__notify(key, oldVal, value)\n\n def removePropertyChangeListener(self, listener):\n self.__listeners.remove(listener)\n\n def setEnabled(self, b):\n if (b and not self.__enabled) or (not b and self.__enabled):\n self.__enabled = b\n self.__notify(\"enabled\", java.lang.Boolean(not b),\n java.lang.Boolean(b))\n\n def actionPerformed(self, ev):\n self.__cb(ev)\n\n def __notify(self, k, oldVal, newVal):\n ev = java.beans.PropertyChangeEvent(self.__proxy, k, oldVal, newVal)\n for i in self.__listeners:\n i.propertyChange(ev)\n", "sub_path": "jpypex/swing/AbstractAction.py", "file_name": "AbstractAction.py", "file_ext": "py", "file_size_in_byte": 2049, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "jpype.javax.swing", "line_number": 4, "usage_type": "attribute"}, {"api_name": "jpype.javax", "line_number": 4, "usage_type": "name"}, {"api_name": "jpype.javax.swing", "line_number": 5, "usage_type": "attribute"}, {"api_name": "jpype.javax", "line_number": 5, "usage_type": "name"}, {"api_name": "jpype.javax.swing", "line_number": 6, "usage_type": "attribute"}, {"api_name": "jpype.javax", "line_number": 6, "usage_type": "name"}, {"api_name": "jpype.javax.swing", "line_number": 7, "usage_type": "attribute"}, {"api_name": "jpype.javax", "line_number": 7, "usage_type": "name"}, {"api_name": "jpype.javax.swing", "line_number": 8, "usage_type": "attribute"}, {"api_name": "jpype.javax", "line_number": 8, "usage_type": "name"}, {"api_name": "jpype.javax.swing", "line_number": 9, "usage_type": "attribute"}, {"api_name": "jpype.javax", "line_number": 9, "usage_type": "name"}, {"api_name": "jpype.javax.swing", "line_number": 10, "usage_type": "attribute"}, {"api_name": "jpype.javax", "line_number": 10, "usage_type": "name"}, {"api_name": "jpype.javax.swing", "line_number": 11, "usage_type": "attribute"}, {"api_name": "jpype.javax", "line_number": 11, "usage_type": "name"}, {"api_name": "jpype.JProxy", "line_number": 16, "usage_type": "call"}, {"api_name": "jpype.javax.swing", "line_number": 16, "usage_type": "attribute"}, {"api_name": "jpype.javax", "line_number": 16, "usage_type": "name"}, {"api_name": "jpype.java.lang.Boolean", "line_number": 53, "usage_type": "call"}, {"api_name": "jpype.java.lang", "line_number": 53, "usage_type": "attribute"}, {"api_name": "jpype.java", "line_number": 53, "usage_type": "name"}, {"api_name": "jpype.java.lang.Boolean", "line_number": 54, "usage_type": "call"}, {"api_name": "jpype.java.lang", "line_number": 54, "usage_type": "attribute"}, {"api_name": "jpype.java", "line_number": 54, "usage_type": "name"}, {"api_name": "jpype.java.beans.PropertyChangeEvent", "line_number": 60, "usage_type": "call"}, {"api_name": "jpype.java.beans", "line_number": 60, "usage_type": "attribute"}, {"api_name": "jpype.java", "line_number": 60, "usage_type": "name"}]} +{"seq_id": "600735086", "text": "import os\nimport sys\nimport logging\nimport numpy as np\nimport utils as ut\nimport configparser\nfrom . import local_computations as local\n\nCONFIG_FILE = 'dkm_config.cfg'\nDEFAULT_data_file = 'data.txt'\nDEFAULT_k = 5\nDEFAULT_shuffle = True\nDEFAULT_learning_rate = 0.001\nDEFAULT_optimization = 'lloyd'\n\n\ndef dkm_local_noop(args, **kwargs):\n \"\"\"\n # Description:\n Nooperation\n\n # PREVIOUS PHASE:\n NA\n\n # INPUT:\n\n # OUTPUT:\n\n # NEXT PHASE:\n remote_init_env\n \"\"\"\n computation_output = dict(output=dict(computation_phase=\"dkm_local_noop\"),\n )\n return computation_output\n\n\ndef dkm_local_init_env(args,\n config_file=CONFIG_FILE,\n k=DEFAULT_k,\n optimization=DEFAULT_optimization,\n shuffle=DEFAULT_shuffle,\n learning_rate=DEFAULT_learning_rate,\n **kwargs):\n \"\"\"\n # Description:\n Initialize the local environment, creating the config file.\n\n # PREVIOUS PHASE:\n remote_init_env\n\n # INPUT:\n\n | name | type | default |\n | --- | --- | --- |\n | config_file | str | config.cfg |\n | k | int | 5 |\n | optimization | str | lloyd |\n | shuffle | bool | False |\n | data_file | str | data.txt |\n | learning_rate | float | 0.001 |\n\n # OUTPUT:\n - config file written to disk\n\n # NEXT PHASE:\n local_init_centroids\n \"\"\"\n state, inputs, cache = ut.resolve_args(args)\n data_file = ut.resolve_input('all_windows', cache)\n ut.log('LOCAL: Initializing remote environment', state)\n config_path = os.path.join(state['outputDirectory'], config_file)\n cache['config_file'] = config_path\n config = configparser.ConfigParser()\n config['LOCAL'] = dict(k=k,\n optimization=optimization,\n shuffle=shuffle,\n data_file=data_file,\n learning_rate=learning_rate)\n with open(config_path, 'w') as file:\n config.write(file)\n # output\n computation_output = dict(\n output=dict(\n config_file=config_path,\n computation_phase=\"dkm_local_init_env\"),\n state=state,\n cache=cache\n )\n return computation_output\n\n\ndef dkm_local_init_centroids(args,\n config_file=CONFIG_FILE,\n **kwargs):\n \"\"\"\n # Description:\n Initialize K centroids from own data.\n\n # PREVIOUS PHASE:\n local_init_env\n\n # INPUT:\n\n | name | type | default |\n | --- | --- | --- |\n | config_file | str | config.cfg |\n\n # OUTPUT:\n - centroids: list of numpy arrays\n\n # NEXT PHASE:\n remote_init_centroids\n \"\"\"\n state, inputs, cache = ut.resolve_args(args)\n config_file = ut.resolve_input('config_file', cache)\n ut.log('LOCAL: Initializing centroids', state)\n config = configparser.ConfigParser()\n config.read(config_file)\n data = np.load(config['LOCAL']['data_file'])\n centroids = local.initialize_own_centroids(data, int(config['LOCAL']['k']))\n np.save(os.path.join(state['outputDirectory'], 'initial_centroids'), 'centroids')\n ut.log('Local centroids looks like %s' % (str(type(centroids))), state)\n # output\n cache['local_centroids'] = centroids\n computation_output = dict(output=dict(\n config_file=config_file,\n local_centroids=centroids,\n computation_phase=\"dkm_local_init_env\"),\n state=state,\n cache=cache\n )\n return computation_output\n\n\ndef dkm_local_compute_clustering(args,\n config_file=CONFIG_FILE,\n **kwargs):\n \"\"\"\n # Description:\n Assign data instances to clusters.\n\n # PREVIOUS PHASE:\n remote_init_centroids (on first run only)\n remote_cehck_convergence\n\n # INPUT:\n\n | name | type | default |\n | --- | --- | --- |\n | config_file | str | config.cfg |\n | remote_centroids | list | None |\n | computation_phase | list | None |\n\n # OUTPUT:\n - centroids: list of numpy arrays\n\n # NEXT PHASE:\n remote_init_centroids\n \"\"\"\n state, inputs, cache = ut.resolve_args(args)\n config_file = ut.resolve_input('config_file', cache)\n remote_centroids = ut.resolve_input('remote_centroids', inputs)\n computation_phase = ut.resolve_input('computation_phase', inputs)\n ut.log('LOCAL: computing clustering', state)\n if remote_centroids is None:\n raise ValueError(\n \"LOCAL: at local_compute_clustering - remote_centroids not passed correctly\"\n )\n if computation_phase is None:\n raise ValueError(\n \"LOCAL: at local_compute_clustering - computation_phase not passed correctly\"\n )\n config = configparser.ConfigParser()\n config.read(config_file)\n ut.log('Config file is %s, with keys %s' % (config_file, str(dict(config))), state)\n\n data = np.load(config['LOCAL']['data_file'])\n\n cluster_labels = local.compute_clustering(data, remote_centroids)\n\n new_comp_phase = \"dkm_local_compute_clustering\"\n if computation_phase == \"dkm_remote_optimization_step\":\n new_comp_phase = \"dkm_local_compute_clustering_2\"\n\n computation_output = ut.default_computation_output(args)\n cache['cluster_labels'] = cluster_labels\n cache['remote_centroids'] = remote_centroids\n computation_output['output'] = dict(\n computation_phase=new_comp_phase,\n remote_centroids=remote_centroids,\n cluster_labels=cluster_labels\n )\n computation_output['cache'] = cache\n return computation_output\n\n\ndef dkm_local_compute_optimizer(args,\n config_file=CONFIG_FILE,\n **kwargs):\n \"\"\"\n # Description:\n Compute local optimizers with local data.\n\n # PREVIOUS PHASE:\n local_compute_clustering\n\n # INPUT:\n\n | name | type | default |\n | --- | --- | --- |\n | config_file | str | config.cfg |\n | remote_centroids | list | None |\n | cluster_labels | list | None |\n\n # OUTPUT:\n - centroids: list of numpy arrays\n\n # NEXT PHASE:\n remote_init_centroids\n \"\"\"\n state, inputs, cache = ut.resolve_args(args)\n config_file = ut.resolve_input('config_file', cache)\n remote_centroids = ut.resolve_input('remote_centroids', inputs, cache)\n cluster_labels = ut.resolve_input('cluster_labels', inputs, cache)\n if remote_centroids is None:\n raise ValueError(\n \"LOCAL: at local_compute_clustering - remote_centroids not passed correctly\"\n )\n if cluster_labels is None:\n raise ValueError(\n \"LOCAL: at local_compute_clustering - cluster_labels not passed correctly\"\n )\n ut.log('LOCAL: computing optimizers', state)\n config = configparser.ConfigParser()\n config.read(config_file)\n data = np.load(config['LOCAL']['data_file'])\n k = int(config['LOCAL']['k'])\n learning_rate = config['LOCAL']['learning_rate']\n optimization = config['LOCAL']['optimization']\n if optimization == 'lloyd':\n local_optimizer = local.compute_mean(data, cluster_labels, k)\n elif optimization == 'gradient':\n # Gradient descent has sites compute gradients locally\n local_optimizer = local.compute_gradient(data, cluster_labels[i], remote_centroids, learning_rate)\n\n outdir = state['outputDirectory']\n np.save(os.path.join(outdir, 'local_optimizer.npy'), local_optimizer)\n np.save(os.path.join(outdir, 'local_cluster_labels.npy'), cluster_labels)\n\n \"\"\" Debugged by AK \"\"\"\n local_optimizer = [l.tolist() if isinstance(l, np.ndarray) else l for l in local_optimizer]\n computation_output = dict(output=dict(\n local_optimizer=local_optimizer,\n computation_phase=\"dkm_local_compute_optimizer\"),\n state=state\n )\n return computation_output\n", "sub_path": "local.py", "file_name": "local.py", "file_ext": "py", "file_size_in_byte": 8778, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "utils.resolve_args", "line_number": 68, "usage_type": "call"}, {"api_name": "utils.resolve_input", "line_number": 69, "usage_type": "call"}, {"api_name": "utils.log", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 73, "usage_type": "call"}, {"api_name": "utils.resolve_args", "line_number": 114, "usage_type": "call"}, {"api_name": "utils.resolve_input", "line_number": 115, "usage_type": "call"}, {"api_name": "utils.log", "line_number": 116, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 119, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 121, "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": "utils.log", "line_number": 122, "usage_type": "call"}, {"api_name": "utils.resolve_args", "line_number": 160, "usage_type": "call"}, {"api_name": "utils.resolve_input", "line_number": 161, "usage_type": "call"}, {"api_name": "utils.resolve_input", "line_number": 162, "usage_type": "call"}, {"api_name": "utils.resolve_input", "line_number": 163, "usage_type": "call"}, {"api_name": "utils.log", "line_number": 164, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 173, "usage_type": "call"}, {"api_name": "utils.log", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 177, "usage_type": "call"}, {"api_name": "utils.default_computation_output", "line_number": 185, "usage_type": "call"}, {"api_name": "utils.resolve_args", "line_number": 221, "usage_type": "call"}, {"api_name": "utils.resolve_input", "line_number": 222, "usage_type": "call"}, {"api_name": "utils.resolve_input", "line_number": 223, "usage_type": "call"}, {"api_name": "utils.resolve_input", "line_number": 224, "usage_type": "call"}, {"api_name": "utils.log", "line_number": 233, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 236, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 247, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 247, "usage_type": "call"}, {"api_name": "os.path", "line_number": 247, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 248, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 248, "usage_type": "call"}, {"api_name": "os.path", "line_number": 248, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 251, "usage_type": "attribute"}]} +{"seq_id": "442744504", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nfrom face2face.models import MeshModel\nfrom face2face.utils.opengl import Render\nimport face2face.optimize.image as opt\nfrom face2face.utils.mesh import generateFace, generateTexture\n\nimport dlib\nimport cv2\nimport numpy as np\nfrom scipy.optimize import least_squares\nfrom skimage import img_as_float, img_as_ubyte\n\nimport os\nimport glob\nimport argparse\nimport time\nimport pandas as pd\nfrom tqdm import tqdm\n\n\n\ndef getFaceKeypoints(img, detector, predictor, maxImgSizeForDetection=320):\n imgScale = 1\n scaledImg = img\n if max(img.shape) > maxImgSizeForDetection:\n imgScale = maxImgSizeForDetection / float(max(img.shape))\n scaledImg = cv2.resize(img, (int(img.shape[1] * imgScale), int(img.shape[0] * imgScale)))\n\n dets = detector(scaledImg, 1)\n\n if len(dets) == 0:\n return None\n\n shapes2D = []\n for det in dets:\n faceRectangle = dlib.rectangle(int(det.left() / imgScale), int(det.top() / imgScale), int(det.right() / imgScale), int(det.bottom() / imgScale))\n dlibShape = predictor(img, faceRectangle)\n shape2D = np.array([[p.x, p.y] for p in dlibShape.parts()])\n shape2D = shape2D.T\n shapes2D.append(shape2D)\n\n return shapes2D\n\n\ndef loadOpenFaceKeypoints(frame_cnt, openFace_landmarks):\n shapes2D = []\n frame = openFace_landmarks[openFace_landmarks['frame'] == frame_cnt]\n\n for i in range(0, 68):\n x = frame[' x_' + str(i)].values[0]\n y = frame[' y_' + str(i)].values[0]\n shapes2D.append([x, y])\n\n return shapes2D\n\n\ndef saveImage(path, img):\n b,g,r = cv2.split(img)\n img = cv2.merge([r,g,b])\n img = img_as_ubyte(img)\n cv2.imwrite(path, img)\n\n\ndef main():\n # Set weights for the 3DMM RGB color shape, landmark shape, and regularization terms\n max_iterations = 9\n wCol = 1\n # old\n # wLan = 2.5e-5\n # wRegS = 1.25e-4\n \n # init\n # wLan = 2.9e-5\n # wRegS = 0.25e-5\n\n # dlib\n # wLan = 1.25e-5\n # wRegS = 0.25e-5\n wLan = 0\n wRegS = 2.5e-5\n\n # openFace - Test\n # wLan = 1.3e-5\n # wRegS = 0.6e-4\n\n # lsmr is numerically stable and faster\n tr_solver = 'lsmr'\n\n\n # Change directory to the folder that holds the VRN data, OpenPose landmarks, and original images (frames) from the source video\n os.chdir('./data')\n \n # Load 3DMM\n m = MeshModel('../models/bfm2017.npz')\n \n # Set an orthographic projection for the camera matrix\n cam = 'orthographic'\n\n # Landmark detector\n if FLAGS.openFace_landmarks is None:\n print('Using dlib landmarks...')\n predictor_path = \"../models/shape_predictor_68_face_landmarks.dat\"\n detector = dlib.get_frontal_face_detector()\n predictor = dlib.shape_predictor(predictor_path)\n else:\n print('Using openFace landmarks...')\n openFaceData = pd.read_csv(FLAGS.openFace_landmarks)\n\n # apply mask on faces if supplied\n if FLAGS.face_mask is not None:\n mask_id = np.load(FLAGS.face_mask)\n m.face = np.delete(m.face, mask_id, axis = 0)\n m.vertex2face = np.array([np.where(np.isin(m.face.T, vertexInd).any(axis = 0))[0] for vertexInd in range(m.numVertices)])\n\n # Load parameters\n all_param = np.load(FLAGS.parameters)\n texCoef = all_param[:m.numTex]\n shCoef = all_param[m.numTex: m.numTex + 27]\n param = all_param[m.numTex + 27:]\n idCoef = param[:m.numId]\n expCoef = param[m.numId : m.numId + m.numExp]\n\n vertexImgColor = None\n if FLAGS.img_texture is not None:\n vertexImgColor = np.load(os.path.join(FLAGS.img_texture))\n\n data_path = os.path.join(FLAGS.input_dir, '*.png')\n keyframes = glob.glob(data_path)\n\n start = time.time()\n\n for i in tqdm(range(FLAGS.start_frame, len(keyframes))):\n fNameImgOrig = os.path.join(FLAGS.input_dir, str(i) + '.png')\n\n # Load the source video frame and convert to 64-bit float\n b,g,r = cv2.split(cv2.imread(fNameImgOrig))\n img_org = cv2.merge([r,g,b])\n img_org = cv2.GaussianBlur(img_org, (5, 5), 0)\n img = img_as_float(img_org)\n\n if FLAGS.openFace_landmarks is None:\n shape2D = getFaceKeypoints(img_org, detector, predictor)\n shape2D = np.asarray(shape2D)[0].T\n else:\n shape2D = loadOpenFaceKeypoints(i + 1, openFaceData)\n shape2D = np.asarray(shape2D)\n\n lm = shape2D[m.targetLMInd, :2]\n\n if i == FLAGS.start_frame:\n vertexCoords = generateFace(np.r_[param[:-1], 0, param[-1]], m)\n # Rendering of initial 3DMM shape with mean texture model\n texParam = np.r_[texCoef, shCoef.flatten()]\n meshData = np.r_[vertexCoords.T, m.texMean.T]\n renderObj = Render(img.shape[1], img.shape[0], meshData, m.face)\n\n # Adjust Landmarks to be consistent across segments\n p1_id = 27 # nose\n p2_id = 8 # jaw\n x2 = lm[p1_id, 0]\n x1 = lm[p2_id, 0]\n y2 = lm[p1_id, 1]\n y1 = lm[p2_id, 1]\n nosejaw_dist = ((x2 - x1)**2 + (y2 - y1)**2)**(1/2)\n wLan = wLan * (225.0 / nosejaw_dist)\n\n\n # \"\"\"\n # Optimization over all experssion & SH\n # \"\"\"\n # LSMR is numerically stable combared to the default option (Exact)\n initFit = least_squares(opt.denseJointExpResiduals, np.r_[shCoef, param[m.numId:]], tr_solver = tr_solver, max_nfev = max_iterations, jac = opt.denseJointExpJacobian, args = (idCoef, texCoef, img, lm, m, renderObj, (wCol, wLan, wRegS), vertexImgColor), verbose = 0, x_scale = 'jac')\n shCoef = initFit['x'][:27]\n expCoef = initFit['x'][27:]\n param = np.r_[idCoef, expCoef]\n\n # # Generate 3DMM vertices from shape and similarity transform parameters\n vertexCoords = generateFace(np.r_[param[:-1], 0, param[-1]], m)\n\n # Generate the texture at the 3DMM vertices from the learned texture coefficients\n texParam = np.r_[texCoef, shCoef.flatten()]\n texture = generateTexture(vertexCoords, texParam, m, vertexImgColor)\n\n # Render the 3DMM\n renderObj.updateVertexBuffer(np.r_[vertexCoords.T, texture.T])\n renderObj.resetFramebufferObject()\n renderObj.render()\n rendering = renderObj.grabRendering()\n\n saveImage(os.path.join(FLAGS.output_dir, str(i) + \".png\"), rendering)\n np.save(os.path.join(FLAGS.output_dir, str(i) + \"_params\"), np.r_[shCoef, param])\n\n elapsed = time.time() - start\n print(time.strftime(\"%H:%M:%S\", time.gmtime(elapsed)))\n\n\nif __name__ == \"__main__\":\n\n parser = argparse.ArgumentParser(description = 'Initialize Identity & Texture from multiple frames')\n parser.add_argument('--input_dir', help = 'Path to frames')\n parser.add_argument('--parameters', help = 'Path to parameters to start tracking')\n parser.add_argument('--output_dir', help = 'Output directory')\n parser.add_argument('--openFace_landmarks', help = 'Path to openface landmarks otherwise dlib will be used (optional)')\n parser.add_argument('--img_texture', help = 'Path to texture (vertex space) instead of PCA model (optional)')\n parser.add_argument('--face_mask', help = 'Path to face ids to mask as eyes (optional)')\n parser.add_argument('--start_frame', help = 'Frame to start tracking from',type = int, default = 0)\n\n FLAGS, unparsed = parser.parse_known_args()\n\n main()\n", "sub_path": "cli/tracker.py", "file_name": "tracker.py", "file_ext": "py", "file_size_in_byte": 7404, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "cv2.resize", "line_number": 28, "usage_type": "call"}, {"api_name": "dlib.rectangle", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.split", "line_number": 59, "usage_type": "call"}, {"api_name": "cv2.merge", "line_number": 60, "usage_type": "call"}, {"api_name": "skimage.img_as_ubyte", "line_number": 61, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 62, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 92, "usage_type": "call"}, {"api_name": "face2face.models.MeshModel", "line_number": 95, "usage_type": "call"}, {"api_name": "dlib.get_frontal_face_detector", "line_number": 104, "usage_type": "call"}, {"api_name": "dlib.shape_predictor", "line_number": 105, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.delete", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.isin", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 126, "usage_type": "call"}, {"api_name": "os.path", "line_number": 126, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 129, "usage_type": "call"}, {"api_name": "time.time", "line_number": 131, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "cv2.split", "line_number": 137, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 137, "usage_type": "call"}, {"api_name": "cv2.merge", "line_number": 138, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 139, "usage_type": "call"}, {"api_name": "skimage.img_as_float", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 144, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 147, "usage_type": "call"}, {"api_name": "face2face.utils.mesh.generateFace", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.r_", "line_number": 152, "usage_type": "attribute"}, {"api_name": "numpy.r_", "line_number": 154, "usage_type": "attribute"}, {"api_name": "numpy.r_", "line_number": 155, "usage_type": "attribute"}, {"api_name": "face2face.utils.opengl.Render", "line_number": 156, "usage_type": "call"}, {"api_name": "scipy.optimize.least_squares", "line_number": 173, "usage_type": "call"}, {"api_name": "face2face.optimize.image.denseJointExpResiduals", "line_number": 173, "usage_type": "attribute"}, {"api_name": "face2face.optimize.image", "line_number": 173, "usage_type": "name"}, {"api_name": "numpy.r_", "line_number": 173, "usage_type": "attribute"}, {"api_name": "face2face.optimize.image.denseJointExpJacobian", "line_number": 173, "usage_type": "attribute"}, {"api_name": "numpy.r_", "line_number": 176, "usage_type": "attribute"}, {"api_name": "face2face.utils.mesh.generateFace", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.r_", "line_number": 179, "usage_type": "attribute"}, {"api_name": "numpy.r_", "line_number": 182, "usage_type": "attribute"}, {"api_name": "face2face.utils.mesh.generateTexture", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.r_", "line_number": 186, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 191, "usage_type": "call"}, {"api_name": "os.path", "line_number": 191, "usage_type": "attribute"}, {"api_name": "numpy.save", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path", "line_number": 192, "usage_type": "attribute"}, {"api_name": "numpy.r_", "line_number": 192, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 194, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 195, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 195, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 200, "usage_type": "call"}]} +{"seq_id": "581001264", "text": "from __future__ import print_function\nfrom warnings import filterwarnings\nfilterwarnings('ignore', module='IPython.html.widgets')\n\nimport Tkinter as tk\nimport ttk\n\ntry:\n import cPickle as pickle \nexcept:\n import pickle\n\nimport threading, tkFileDialog\nfrom serial import Serial\nfrom time import sleep, time, strftime\nfrom math import pi\n\ntry:\n from tecancavrotest.models import XCaliburD\n from tecancavrotest.transport import TecanAPISerial, TecanAPINode\nexcept ImportError: # Support direct import from package\n import sys\n import os\n dirn = os.path.dirname\n LOCAL_DIR = os.path.dirname(os.path.realpath(__file__))\n sys.path.append(dirn(dirn(LOCAL_DIR)))\n from tecancavrotest.models import XCaliburD\n from tecancavrotest.transport import TecanAPISerial, TecanAPINode\n\n\n########################################################################################################################\n\nclass AppWidget(tk.Frame):\n def __init__(self, parent, title=\"App Widget\", scrolled=False):\n tk.Frame.__init__(self, parent)\n \n self.parent = parent\n self.title = title\n self.scrolled = scrolled\n \n self.wrapperframe = tk.Frame(self.parent, bg=self.parent.parent.framecolor)\n \n self.titleframe = tk.Frame(self.wrapperframe, bg=self.parent.parent.headcolor)\n self.title = tk.Label(self.titleframe, text=self.title, bg=self.parent.parent.headcolor)\n self.title.config(font=self.parent.parent.headfont, fg=self.parent.parent.headfc)\n self.title.pack()\n self.titleframe.grid(row=0, column=0, ipadx=5, sticky='we')\n\n self.bodyframe = tk.Frame(self.wrapperframe, bg=self.parent.parent.framecolor)\n self.bodyframe.grid(row=1, column=0, sticky='we')\n \n self.wrapperframe.pack(padx=10, pady=10)\n \n########################################################################################################################\n\nclass MainMenu(tk.Menu):\n\n def __init__(self, parent, root, *args, **kwargs):\n tk.Menu.__init__(self, parent)\n\n self.parent = parent\n self.root = root\n\n self.menubar = tk.Menu(self.root)\n\n self.filemenu = tk.Menu(self.menubar, tearoff=0)\n# self.filemenu.add_command(label=\"New protocol\", command=self.parent.protocol.newProtocol)\n self.filemenu.add_command(label=\"Open protocol...\", command=self.parent.protocol.loadProtocol)\n self.filemenu.add_command(label=\"Save protocol...\", command=self.parent.protocol.saveProtocol)\n self.filemenu.add_separator()\n self.filemenu.add_command(label=\"Exit\", command=self.root.destroy)\n\n self.helpmenu = tk.Menu(self.menubar, tearoff=0)\n self.helpmenu.add_command(label=\"About...\")\n self.helpmenu.add_command(label=\"Manual...\")\n\n self.menubar.add_cascade(menu=self.filemenu, label=\"File\")\n self.menubar.add_cascade(menu=self.helpmenu, label=\"Help\")\n \n self.filemenu.config(bg=self.parent.headcolor, fg=self.parent.headfc, bd=1, activebackground=self.parent.buttoncolor, activeforeground=self.parent.bodyfc)\n self.helpmenu.config(bg=self.parent.headcolor, fg=self.parent.headfc, bd=1, activebackground=self.parent.buttoncolor, activeforeground=self.parent.bodyfc)\n\n self.root.config(menu=self.menubar)\n \n########################################################################################################################\n\nclass Log(tk.Frame):\n\n def __init__(self, parent, *args, **kwargs):\n tk.Frame.__init__(self, parent)\n\n self.parent = parent\n\n self.config(bg=self.parent.wrapcolor)\n\n self.logframe = AppWidget(self, \"Log\", False)\n\n self.renderLog()\n\n\n def addRecord(self, text):\n self.log.configure(state=\"normal\")\n formattedtext = self.format(text)+\"\\n\"\n self.log.insert(\"end\", formattedtext)\n self.log.configure(state=\"disabled\")\n self.log.see(\"end\")\n\n\n def format(self, text):\n formattedtime = strftime('%d %b %H:%M:%S')\n formattedtext = str(formattedtime)+\" - \"+text\n \n return formattedtext\n\n\n def renderLog(self):\n self.log = tk.Text(self.logframe.bodyframe, height=\"15\", width=\"120\", bg=self.parent.lightercolor, bd=0, fg=self.parent.bodyfc, highlightthickness=0)\n self.log.pack(padx=10, pady=10, fill=\"both\", expand=1)\n self.log.configure(state=\"disabled\")\n\n########################################################################################################################\n\nclass ControlPanel(tk.Frame):\n def __init__(self, parent, *args, **kwargs):\n tk.Frame.__init__(self, parent)\n\n self.parent = parent\n \n self.config(bg=self.parent.wrapcolor)\n\n self.primingframe = AppWidget(self, \"Port Priming\", False)\n\n self.calibframe = AppWidget(self, \"Output Calibration\", False)\n\n self.cmdframe = AppWidget(self, \"Control Panel\", False)\n \n self.cycletimeframe = AppWidget(self, \"Cycle Duration\", False)\n\n self.renderCP()\n\n\n def renderCP(self):\n pframe = tk.Frame(self.primingframe.bodyframe, bg=self.parent.framecolor)\n pframe.pack()\n\n lframe = tk.Frame(pframe, bg=self.parent.darkercolor)\n lframe.grid(row=0, column=0)\n\n rframe = tk.Frame(pframe, bg=self.parent.lightercolor)\n rframe.grid(row=0, column=1)\n\n tk.Label(lframe, text=\"Port\", bg=self.parent.darkercolor, fg=self.parent.bodyfc).grid(row=0, column=0, columnspan=2)\n tk.Label(rframe, text=\"Tubing Type\", bg=self.parent.lightercolor, fg=self.parent.bodyfc).grid(row=0, column=0, columnspan=2)\n# tk.Label(self.priminglf, text=\"Length(in)\").grid(row=0, column=2)\n\n self.portstoprime = []\n self.tubingtypes = []\n# self.lengths = []\n\n for i in range(1, 10):\n port = tk.DoubleVar()\n tk.Checkbutton(lframe, variable=port, bg=self.parent.darkercolor, highlightthickness=0).grid(row=i, column=0)\n tk.Label(lframe, text=str(i), bg=self.parent.darkercolor, fg=self.parent.bodyfc).grid(row=i, column=1)\n self.portstoprime.append(port)\n\n tubingtype = tk.IntVar()\n tk.Radiobutton(rframe, text=\"PEEK\", variable=tubingtype, value=1, indicator=0, offrelief='flat', bg=self.parent.lightercolor, fg=self.parent.bodyfc, selectcolor=self.parent.buttoncolor, highlightthickness=0).grid(row=i, column=0)\n #tk.Label(rframe, text=\"PEEK\", bg=self.parent.lightercolor, fg=self.parent.bodyfc).grid(row=i, column=1)\n tk.Radiobutton(rframe, text=\"non-PEEK\", variable=tubingtype, value=2, indicator=0, offrelief='flat', bg=self.parent.lightercolor, fg=self.parent.bodyfc, selectcolor=self.parent.buttoncolor, highlightthickness=0).grid(row=i, column=1)\n #tk.Label(rframe, text=\"non-PEEK\", bg=self.parent.lightercolor, fg=self.parent.bodyfc).grid(row=i, column=3)\n \n self.tubingtypes.append(tubingtype)\n\n# length = tk.DoubleVar()\n# tk.Entry(self.primingll, width=4, textvariable=length).grid(row=i, column=2)\n# self.lengths.append(length)\n\n pf = tk.Frame(pframe, bg=self.parent.framecolor)\n pf.grid(row=len(self.portstoprime)+2, column=0, columnspan=3, padx=5, pady=5)\n\n self.volume = tk.Spinbox(pf, from_=500, to=1000, width=4, bg=self.parent.entrycolor, bd=0)\n self.volume.pack(side='left', padx=5)\n\n #ttk.Separator(pframe, orient='horizontal').grid(row=len(self.portstoprime)+1, column=0, columnspan=3, sticky='ew')\n\n tk.Button(pf, text=\"Prime Ports\", command=lambda: self.parent.protocol.primePorts(self.portstoprime, self.volume, self.tubingtypes), relief='flat', bg=self.parent.buttoncolor, activebackground=self.parent.buttoncolor, activeforeground=self.parent.bodyfc, fg=self.parent.buttonfc, highlightthickness=0).pack(padx=5, side='left')\n\n #ttk.Separator(pframe, orient='horizontal').grid(row=len(self.portstoprime)+3, column=0, columnspan=3, sticky='ew')\n\n tk.Button(pframe, text=\"Return Port Contents\", command=lambda: self.parent.protocol.returnPortContents(self.portstoprime), relief='flat', bg=self.parent.buttoncolor, activebackground=self.parent.buttoncolor, activeforeground=self.parent.bodyfc, fg=self.parent.buttonfc, highlightthickness=0).grid(row=len(self.portstoprime)+3, column=0, columnspan=2, padx=5, pady=5)\n\n ########################################\n cf = tk.Frame(self.calibframe.bodyframe, bg=self.parent.framecolor)\n cf.pack(padx=5, pady=5)\n\n self.calibvolume = tk.Spinbox(cf, from_=0, to=1000, width=4, bg=self.parent.entrycolor, bd=0)\n self.calibvolume.pack(padx=5, side='left')\n tk.Button(cf, text=\"Calibrate\", command=lambda: self.parent.protocol.calibrateOutput(self.calibvolume), relief='flat', bg=self.parent.buttoncolor, activebackground=self.parent.buttoncolor, activeforeground=self.parent.bodyfc, fg=self.parent.buttonfc, highlightthickness=0).pack(padx=5, side='left') \n\n ########################################\n cpframe = tk.Frame(self.cmdframe.bodyframe, bg=self.parent.framecolor)\n cpframe.pack()\n\n tk.Button(cpframe, text=\"Execute Cycle\", command=self.parent.protocol.executeCycle, width=15, relief='flat', bg=self.parent.buttoncolor, activebackground=self.parent.buttoncolor, activeforeground=self.parent.bodyfc, fg=self.parent.buttonfc, highlightthickness=0).grid(row=0, column=0, padx=5, pady=5)\n tk.Button(cpframe, text=\"Update Protocol\", command=self.parent.protocol.updateProtocol, width=15, relief='flat', bg=self.parent.buttoncolor, activebackground=self.parent.buttoncolor, activeforeground=self.parent.bodyfc, fg=self.parent.buttonfc, highlightthickness=0).grid(row=1, column=0, padx=5, pady=5)\n tk.Button(cpframe, text=\"Add Command\", command=self.parent.protocol.addCommand, width=15, relief='flat', bg=self.parent.buttoncolor, activebackground=self.parent.buttoncolor, activeforeground=self.parent.bodyfc, fg=self.parent.buttonfc, highlightthickness=0).grid(row=2, column=0, padx=5, pady=5)\n tk.Button(cpframe, text=\"Calculate Cycle Times\", command=self.parent.protocol.renderCycleTimes, width=15, relief='flat', bg=self.parent.buttoncolor, activebackground=self.parent.buttoncolor, activeforeground=self.parent.bodyfc, fg=self.parent.buttonfc, highlightthickness=0).grid(row=6, column=0, padx=5, pady=5)\n\n #ttk.Separator(cpframe, orient='horizontal').grid(row=3, column=0, sticky='ew')\n# pumps = ['/dev/ttyUSb0']\n pumps = [x[0] for x in self.parent.protocol.devices]\n self.selectedpump = tk.StringVar()\n self.selectedpump.set(pumps[0])\n option = ttk.Combobox(cpframe, textvariable=self.selectedpump, state='readonly')\n option['values'] = pumps\n option.grid(row=4, column=0, padx=5, pady=5)\n \n tk.Button(cpframe, text=\"Reset Pump\", command=self.parent.protocol.resetPump, width=15, relief='flat', bg=self.parent.buttoncolor, activebackground=self.parent.buttoncolor, activeforeground=self.parent.bodyfc, fg=self.parent.buttonfc, highlightthickness=0).grid(row=5, column=0, padx=5, pady=5)\n\n ########################################\n self.ctframe = tk.Frame(self.cycletimeframe.bodyframe, bg=self.parent.framecolor)\n self.ctframe.pack()\n \n tk.Label(self.ctframe, text=\"Cycle\", bg=self.parent.darkercolor, fg=self.parent.bodyfc).grid(row=0, column=0, sticky='we')\n tk.Label(self.ctframe, text=\"Duration\", bg=self.parent.lightercolor, fg=self.parent.bodyfc).grid(row=0, column=1, sticky='we')\n\n########################################################################################################################\n\nclass Protocol(tk.Frame):\n\n def __init__(self, parent, *args, **kwargs):\n tk.Frame.__init__(self, parent)\n\n self.parent = parent\n\n self.config(bg=self.parent.wrapcolor)\n\n self.devices = self.getSerialPumps()\n self.device_dict = dict(self.devices)\n\n if not self.device_dict:\n print(\"There is no pump connected. Please connect one and try again.\")\n sys.exit()\n else:\n print(\"Device dict: \" + str(self.device_dict))\n\n self.protocol = []\n\n self.protocol.append({})\n\n self.cmdnumbers = []\n self.cycles = []\n self.names = []\n self.pumpports = []\n self.fromports = []\n self.toports = []\n self.volumes = []\n self.speeds = []\n self.waitmins = []\n self.waitsecs = []\n self.wasteornots = []\n self.statuses = []\n\n self.cyclecounter = 0\n self.cmdcounter = 0\n\n self.calibrationvolume = 0\n\n self.protocolframe = AppWidget(self, 'Protocol', False)\n \n self.frame = VerticalScrolledFrame(self.protocolframe.bodyframe, bg=self.parent.framecolor)\n self.frame.pack()\n\n self.renderProtocol()\n\n\n def updateProtocol(self):\n for i in range(len(self.protocol)):\n self.protocol[i] = {\n 'cmdnumber': int(self.cmdnumbers[i]),\n 'cycle': int(self.cycles[i].get()),\n 'name': self.names[i].get(),\n 'pump': self.pumpports[i].get(),\n 'fromport': int(self.fromports[i].get()),\n 'toport': int(self.toports[i].get()),\n 'volume': int(self.volumes[i].get()),\n 'speed': int(self.speeds[i].get()),\n 'waitmins': int(self.waitmins[i].get()),\n 'waitsecs': int(self.waitsecs[i].get()),\n 'waste': int(self.wasteornots[i].get()), \n }\n print(\"Updated command list: \" + str(self.protocol))\n\n\n def resetProtocol(self):\n self.protocol = []\n self.cmdcounter = 0\n self.cyclecounter = 0\n\n self.cmdnumbers = []\n self.cycles = []\n self.names = []\n self.pumpports = []\n self.fromports = []\n self.toports = []\n self.volumes = []\n self.speeds = []\n self.waitmins = []\n self.waitsecs = []\n self.wasteornots = []\n self.statuses = []\n\n# self.renderProtocol()\n\n\n def saveProtocol(self):\n self.updateProtocol()\n filename = tkFileDialog.asksaveasfilename(defaultextension='.pkl')\n if filename is None:\n return\n file = open(filename, 'wb')\n pickle.dump(self.protocol, file)\n file.close()\n\n\n def loadProtocol(self):\n filename = tkFileDialog.askopenfilename(filetypes=[('Pickled protocols', '*.pkl')])\n if filename is None:\n return\n file = open(filename, 'rb')\n protocol = pickle.load(file)\n file.close()\n print(\"Loaded protocol: \" + str(protocol))\n# self.protocol = self.cmdnumbers = self.cycles = self.names = self.pumpports = self.fromports = self.toports = self.volumes = self.speeds = self.waitmins = self.waitsecs = self.wasteornots = self.statuses = []\n self.resetProtocol()\n self.protocol = protocol\n print(\"Native protocol: \" + str(self.protocol))\n self.renderProtocol()\n #self.insertFieldValues()\n\n\n def addCommand(self):\n print(\"Previous command list: \"+str(self.protocol))\n self.protocol.append({})\n print(\"Appended command list: \"+str(self.protocol))\n\n self.renderCommand(len(self.protocol)-1)\n\n# self.update_idletasks()\n\n\n def renderCommand(self, i):\n if i%2 == 0:\n color = self.parent.lightercolor\n else:\n color = self.parent.darkercolor\n\n cframe = tk.Frame(self.frame.interior, bg=color)\n frame = tk.Frame(cframe, bg=color)\n\n tk.Label(frame, text=str(i+1)+\") \", bg=color, width=3, fg=self.parent.bodyfc).grid(row=0, column=0)\n\n self.cmdnumbers.append(i)\n\n tk.Label(frame, text=\"Cycle: \", bg=color, fg=self.parent.bodyfc).grid(row=0, column=1)\n sb_cycle = tk.Spinbox(frame, from_=0, to=99, width=2, bg=self.parent.entrycolor, bd=0, fg=self.parent.entryfc)\n sb_cycle.grid(row=0, column=2)\n\n self.cycles.append(sb_cycle)\n\n if self.protocol[i].get('cycle') is not None:\n sb_cycle['value'] = self.protocol[i].get('cycle') \n\n tk.Label(frame, text=\"Name: \", bg=color, fg=self.parent.bodyfc).grid(row=0, column=3)\n commandname = tk.StringVar()\n en_name = tk.Entry(frame, textvariable=commandname, bg=self.parent.entrycolor, width=20, bd=0)\n en_name.grid(row=0, column=4)\n\n self.names.append(commandname)\n\n if self.protocol[i].get('name') is not None:\n self.names[i].set(self.protocol[i].get('name'))\n\n tk.Label(frame, text=\"Pump: \", bg=color, fg=self.parent.bodyfc).grid(row=0, column=5)\n# pumps = ['/dev/ttyUSB0', '/dev/ttyUSB1']\n pumps = [x[0] for x in self.devices]\n #print(\"List of pumps: \" + str(pumps))\n selectedpump = tk.StringVar()\n selectedpump.set(pumps[0])\n #self.parent.setPump(self.selectedpump.get())\n #print(\"Selected pump: \" + str(self.selectedpump.get()))\n #option = tk.OptionMenu(frame, selectedpump, *pumps)\n option = ttk.Combobox(frame, textvariable=selectedpump, state='readonly', width=15)\n option['values'] = pumps\n option.grid(row=0, column=6)\n\n self.pumpports.append(selectedpump)\n\n if self.protocol[i].get('pump') is not None:\n self.pumpports[i].set(self.protocol[i].get('pump'))\n\n tk.Label(frame, text=\"From Port: \", bg=color, fg=self.parent.bodyfc).grid(row=0, column=7)\n sb_fromport = tk.Spinbox(frame, from_=1, to=9, width=2, bg=self.parent.entrycolor, bd=0, fg=self.parent.entryfc)\n sb_fromport.grid(row=0, column=8)\n\n self.fromports.append(sb_fromport)\n\n if self.protocol[i].get('fromport') is not None:\n self.fromports[i]['value'] = self.protocol[i].get('fromport')\n\n tk.Label(frame, text=\"To Port: \", bg=color, fg=self.parent.bodyfc).grid(row=0, column=9)\n sb_toport = tk.Spinbox(frame, from_=1, to=9, width=2, bg=self.parent.entrycolor, bd=0, fg=self.parent.entryfc)\n sb_toport.grid(row=0, column=10)\n\n self.toports.append(sb_toport)\n\n if self.protocol[i].get('toport') is not None:\n self.toports[i]['value'] = self.protocol[i].get('toport')\n\n tk.Label(frame, text=\"Volume(uL): \", bg=color, fg=self.parent.bodyfc).grid(row=0, column=11)\n sb_volume = tk.Spinbox(frame, from_=1, to=1000, width=4, bg=self.parent.entrycolor, bd=0, fg=self.parent.entryfc)\n sb_volume.grid(row=0, column=12)\n\n self.volumes.append(sb_volume)\n\n if self.protocol[i].get('volume') is not None:\n self.volumes[i]['value'] = self.protocol[i].get('volume')\n\n tk.Label(frame, text=\"Speed(0-40): \", bg=color, fg=self.parent.bodyfc).grid(row=0, column=13)\n sb_speed = tk.Spinbox(frame, from_=0, to=40, width=2, bg=self.parent.entrycolor, bd=0, fg=self.parent.entryfc)\n sb_speed.grid(row=0, column=14)\n\n self.speeds.append(sb_speed)\n\n if self.protocol[i].get('speed') is not None:\n self.speeds[i]['value'] = self.protocol[i].get('speed')\n\n tk.Label(frame, text=\"Leave for: \", bg=color, fg=self.parent.bodyfc).grid(row=0, column=15)\n sb_timemin = tk.Spinbox(frame, from_=0, to=600, width=3, bg=self.parent.entrycolor, bd=0, fg=self.parent.entryfc)\n sb_timemin.grid(row=0, column=16)\n tk.Label(frame, text=\"min\", bg=color, fg=self.parent.bodyfc).grid(row=0, column=17)\n sb_timesec = tk.Spinbox(frame, from_=5, to=59, width=2, bg=self.parent.entrycolor, bd=0, fg=self.parent.entryfc)\n sb_timesec.grid(row=0, column=18) \n tk.Label(frame, text=\"sec\", bg=color, fg=self.parent.bodyfc).grid(row=0, column=19)\n\n self.waitmins.append(sb_timemin)\n self.waitsecs.append(sb_timesec)\n\n if self.protocol[i].get('waitmins') is not None:\n self.waitmins[i]['value'] = self.protocol[i].get('waitmins')\n if self.protocol[i].get('waitsecs') is not None:\n self.waitsecs[i]['value'] = self.protocol[i].get('waitsecs')\n\n waste = tk.IntVar()\n tk.Radiobutton(frame, text=\"Return\", variable=waste, value=0, bg=color, highlightthickness=0, fg=self.parent.logofc).grid(row=0, column=20)\n tk.Radiobutton(frame, text=\"Waste\", variable=waste, value=1, bg=color, highlightthickness=0, fg=self.parent.logofc).grid(row=0, column=21)\n\n self.wasteornots.append(waste)\n\n if self.protocol[i].get('waste') is not None:\n self.wasteornots[i].set(self.protocol[i].get('waste'))\n\n tk.Label(frame, text=\" - \", bg=color, fg=self.parent.bodyfc).grid(row=0, column=22)\n status = tk.Label(frame, text=\"Not complete\", bg=color, fg=self.parent.bodyfc)\n status.grid(row=0, column=23)\n\n self.statuses.append(status)\n\n frame.pack(pady=5, fill='x')\n cframe.grid(row=i, column=0, sticky='we')\n \n# self.parent.update_idletasks()\n\n\n def renderProtocol(self):\n for i in range(len(self.protocol)):\n self.renderCommand(i)\n\n\n def renderCycleTimes(self):\n #for i in len(set(self.protocol.get('cycle'))):\n times = {16: 18, 30: 88}\n \n numcycles = len({v['cycle']:v for v in self.protocol}.values())\n print(\"# cycles: \"+str(numcycles))\n \n for i in range(numcycles):\n print(\"cycle: \"+str(i))\n time = 0\n for cmd in self.protocol:\n if cmd.get('cycle') == i:\n speed = cmd.get('speed')\n #print(\"speed: \"+str(speed))\n volume = int(cmd.get('volume'))\n #print(\"volume: \"+str(volume))\n vratio = volume/1000.0\n #print(\"vratio: \"+str(vratio))\n \n t1 = times.get(speed)*vratio\n #print(str(t1))\n t2 = times.get(30)*vratio\n execdelay = 8*4\n wait = int(cmd.get('waitmins'))*60+int(cmd.get('waitsecs'))\n #print(str(t2))\n delay = execdelay+wait\n tt = 3*t1+t2+delay\n #print(str(tt))\n time += tt\n tk.Label(self.parent.cp.ctframe, text=i, bg=self.parent.darkercolor, fg=self.parent.bodyfc).grid(row=i+1, column=0, sticky='we')\n tk.Label(self.parent.cp.ctframe, text=str(time)+\"sec\", bg=self.parent.lightercolor, fg=self.parent.bodyfc).grid(row=i+1, column=1, sticky='we')\n \n def primePorts(self, portstoprime, volume, tubingtypes):\n pump = self.device_dict.get('/dev/ttyUSB0')\n\n buffertime = 2\n speed = 14\n\n for i in range(len(portstoprime)):\n if portstoprime[i].get() == 1:\n if tubingtypes[i].get() == 1:\n speed = 28\n elif tubingtypes[i].get() == 2:\n speed = 14\n v = int(volume.get()) \n port = i+1\n\n print(\"Priming port \"+str(port)+\" with \"+str(v)+\"uL at speed: \"+str(speed))\n self.parent.log.addRecord(\"Priming port \"+str(port)+\" with \"+str(v)+\"uL at speed: \"+str(speed))\n\n pump.primePort(port, v, speed, port)\n\n sleep(buffertime)\n\n print(\"Washing valve\")\n self.parent.log.addRecord(\"Washing valve\")\n pump.primePort(5, 750, speed, 9)\n\n sleep(buffertime)\n\n\n def returnPortContents(self, portstoprime):\n pump = self.device_dict.get('/dev/ttyUSB0')\n\n volume = 500\n speed = 14\n buffertime = 2\n\n pump.setSpeed(speed)\n sleep(1)\n\n for i in range(len(portstoprime)):\n if portstoprime[i].get() == 1:\n port = i+1\n \n print(\"Returning contents of port \" +str(port))\n self.parent.log.addRecord(\"Returning contents of port \" +str(port))\n esttime = pump.extract(7, volume, execute=True)\n sleep(esttime+buffertime)\n esttime = pump.dispense(port, volume, execute=True)\n sleep(esttime+buffertime)\n\n\n def calibrateOutput(self, volume):\n self.calibrationvolume = int(volume.get())\n self.parent.log.addRecord(\"Output port has been calibrated for additional \" + str(self.calibrationvolume) + \"uL \\n\")\n\n\n def executeCycle(self):\n self.updateProtocol()\n\n for cmd in self.protocol:\n \n if cmd['cycle'] == self.cyclecounter:\n self.parent.log.addRecord(\"Carrying out cycle: \" + str(self.cyclecounter))\n print(\"Found cmd with appropriate cycle\")\n self.executeCommand(self.cmdcounter)\n self.cmdcounter += 1\n \n self.cyclecounter += 1\n\n\n def executeCommand(self, index):\n self.updateProtocol()\n \n i = index\n\n status = self.statuses[i]\n status.config(bg=\"yellow\", fg=\"black\")\n status['text'] = 'Processing'\n\n pump = self.device_dict.get(self.protocol[i].get('pump'))\n\n name = str(self.protocol[i].get('name'))\n toport = int(self.protocol[i].get('toport'))\n fromport = int(self.protocol[i].get('fromport'))\n volume = int(self.protocol[i].get('volume')) + self.calibrationvolume\n speed = int(self.protocol[i].get('speed'))\n waitmins = int(self.protocol[i].get('waitmins'))\n waitsecs = int(self.protocol[i].get('waitsecs'))\n\n timebuffer = 6\n\n timewait = waitmins*60 + waitsecs\n\n waste = int(self.protocol[i].get('waste'))\n\n self.parent.log.addRecord(\"Starting command '\" + name+ \"'\")\n self.parent.log.addRecord(\"Selected pump for this command is: \" + str(pump)) \n self.parent.log.addRecord(\"Setting pump speed to \" + str(speed))\n pump.setSpeed(speed)\n sleep(2)\n \n self.parent.log.addRecord(\"Extracting \" + str(volume) + \"uL from port \" + str(fromport))\n esttime = pump.extract(fromport, volume, execute=True)\n self.parent.log.addRecord(\"Estimated time: \" + str(esttime) + \"sec\")\n sleep(esttime+timebuffer)\n\n self.parent.log.addRecord(\"Dispensing \" + str(volume) + \"uL to port \" + str(toport))\n esttime = pump.dispense(toport, volume, execute=True)\n sleep(esttime+timebuffer)\n\n self.parent.log.addRecord(\"Waiting for \" + str(waitmins) + \"min \" + str(waitsecs) + \"sec before extraction\")\n sleep(timewait)\n\n self.parent.log.addRecord(\"Setting pump speed to 30 for extraction\")\n pump.setSpeed(30)\n sleep(2)\n self.parent.log.addRecord(\"Extracting \" + str(volume) + \"uL from port \" + str(toport))\n esttime = pump.extract(toport, volume, execute=True)\n sleep(esttime+timebuffer)\n self.parent.log.addRecord(\"Setting pump speed back to \" + str(speed))\n pump.setSpeed(speed)\n sleep(2)\n if waste == 0:\n self.parent.log.addRecord(\"Dispensing \" + str(volume) + \"uL back to port \" + str(fromport))\n esttime = pump.dispense(fromport, volume, execute=True)\n sleep(esttime+timebuffer)\n elif waste == 1:\n self.parent.log.addRecord(\"Dispensing \" + str(volume) + \"uL to waste port 9\")\n esttime = pump.dispense(9, volume, execute=True)\n sleep(esttime+timebuffer)\n\n self.parent.log.addRecord(\"Command '\" + name + \"' is finished \\n\")\n\n status['text'] = 'Complete'\n status.config(bg='green', fg=\"black\")\n\n\n #if (self.cmdcounter+1) == len(self.protocol):\n # print(\"Resetting cmdcounter to 0\")\n # self.cmdcounter = 0\n # self.resetStatuses()\n #else:\n # print(\"Incrementing cmdcounter\")\n # self.cmdcounter += 1\n\n\n def resetStatuses(self):\n for i in range(len(self.protocol)):\n status = self.statuses[i]\n status['text'] = 'Not complete'\n status.config(fg='black')\n\n\n def resetPump(self):\n port = self.parent.cp.selectedpump.get()\n pump = self.device_dict.get(port)\n self.parent.log.addRecord(\"Resetting pump \" + str(pump) + \"\\n\")\n pump.init()\n\n\n def findSerialPumps(self):\n return TecanAPISerial.findSerialPumps()\n\n\n def getSerialPumps(self):\n pump_list = self.findSerialPumps()\n return [(ser_port, XCaliburD(com_link=TecanAPISerial(0,ser_port, 9600))) for ser_port, _, _ in pump_list]\n\n########################################################################################################################\n\nclass SerialPort():\n\n def __init__(self, parent, port, baud, timeout):\n self.parent = parent\n self.portname = port\n self.baudrate = baud\n self.timeout = timeout\n\n self.read = True\n\n self.port = Serial(self.portname, self.baudrate, timeout=self.timeout, writeTimeout=0)\n\n\n def readPort(self, bits):\n while self.read:\n cmd = self.port.read(bits)\n print(\"Cmd: \"+cmd)\n if len(cmd) > 0:\n self.parent.log.addRecord(\"Received serial command: \" + str(cmd))\n if cmd == \"pump\":\n self.parent.protocol.executeCycle()\n\n########################################################################################################################\n\nclass SerialThread(threading.Thread):\n def __init__(self, parent, port, baud, timeout):\n threading.Thread.__init__(self)\n\n self.parent = parent\n self.port = port\n self.baud = baud\n self.timeout = timeout\n\n\n def run(self):\n s = Serial(self.port, self.baud, timeout=self.timeout)\n while True:\n# if s.inWaiting():\n cmd = s.read(5)\n print('Cmd: '+str(cmd))\n if len(cmd) > 0:\n self.parent.log.addRecord(\"Received serial command '\" + str(cmd) + \"'\")\n if cmd == 'pump':\n self.parent.protocol.executeCycle() \n\n########################################################################################################################\n\nclass VerticalScrolledFrame(tk.Frame):\n def __init__(self, parent, *args, **kwargs):\n tk.Frame.__init__(self, parent, *args, **kwargs)\n\n self.parent = parent\n\n vscrollbar = tk.Scrollbar(self, orient='vertical', relief='flat')\n vscrollbar.pack(fill='y', side='right', expand=False)\n\n canvas = tk.Canvas(self, bd=0, highlightthickness=0, yscrollcommand=vscrollbar.set)\n canvas.pack(side='left', fill='both', expand=True)\n canvas.config(height=300)\n \n vscrollbar.config(command=canvas.yview)\n\n canvas.xview_moveto(0)\n canvas.yview_moveto(0)\n\n self.interior = interior = tk.Frame(canvas)\n interior_id = canvas.create_window(0, 0, window=interior, anchor='nw')\n\n def _configure_interior(event):\n size = (interior.winfo_reqwidth(), interior.winfo_reqheight())\n canvas.config(scrollregion=\"0 0 %s %s\" % size)\n if interior.winfo_reqwidth() != canvas.winfo_width():\n canvas.config(width=interior.winfo_reqwidth())\n interior.bind('', _configure_interior)\n\n def _configure_canvas(event):\n if interior.winfo_reqwidth() != canvas.winfo_width():\n canvas.itemconfigure(interior_id, width=canvas.winfo_width())\n canvas.bind('', _configure_canvas)\n\n########################################################################################################################\n\n#--------------------------- @30 1000uL = 88sec\n#--------------------------- @31 1000uL = 102sec\n#--------------------------- @32 1000uL = 122sec\n#--------------------------- @33 1000uL = 152sec\n\nif __name__ == \"__main__\":\n\n class App(tk.Frame):\n def __init__(self, parent, *args, **kwargs):\n tk.Frame.__init__(self, parent, *args, **kwargs)\n\n self.headfont = ('arial', 16, 'normal')\n\n self.logofc = \"#468ef2\"\n self.headcolor = \"#707070\"\n self.headfc = \"#e5e5e5\"\n self.wrapcolor = \"#212121\"\n self.framecolor = \"#4f4f4f\"\n self.buttoncolor = \"#468ef2\"\n self.buttonfc = \"#e5e5e5\"\n self.entrycolor = \"#e5e5e5\"\n self.entryfc = \"#1c1c1c\"\n self.bodyfc = \"#e5e5e5\"\n self.lightercolor = \"#595959\"\n self.darkercolor = \"#4a4a4a\"\n\n self.config(bg=self.wrapcolor)\n\n self.parent = parent\n\n self.c1 = tk.Frame(self, bg=self.wrapcolor).grid(row=0, column=0)\n self.c2 = tk.Frame(self, bg=self.wrapcolor).grid(row=0, column=1)\n self.c3 = tk.Frame(self, bg=self.wrapcolor).grid(row=0, column=2)\n\n tk.Label(self, text=\"CASPA\", bg=self.wrapcolor, fg=self.logofc, font=('arial', 24, 'normal')).grid(row=0, column=1)\n\n self.protocol = Protocol(self)\n self.protocol.grid(row=1, column=1, sticky='n')\n\n self.menu = MainMenu(self, self.parent)\n\n self.cp = ControlPanel(self)\n self.cp.grid(row=1, column=0, rowspan=2)\n\n self.log = Log(self)\n self.log.grid(row=2, column=1, sticky='n')\n\n thread = SerialThread(self, '/dev/ttyAMA0', 9600, 1)\n thread.start()\n\n# self.processSerial()\n\n# self.serial = SerialPort(self, \"/dev/ttyAMA0\", 9600, 1)\n\n# thread = threading.Thread(target=self.serial.readPort, args=(20,)) \n# thread.start()\n\n\n def processSerial(self):\n while self.queue.qsize():\n try:\n cmd = self.queue.get()\n print(\"Cmd: \" + str(cmd))\n if cmd == 'pump':\n self.protocol.executeCycle()\n except Queue.Empty:\n pass\n self.after(100, self.processSerial)\n\n\n root = tk.Tk()\n root.wm_title(\"Computer-Aided Syringe Pump Automation\")\n\n app = App(root)\n app.pack(padx=5, pady=5)\n\n root.config(bg=app.wrapcolor)\n\n root.mainloop()\n", "sub_path": "old/example/ipythonnb/caspa2p5.py", "file_name": "caspa2p5.py", "file_ext": "py", "file_size_in_byte": 34344, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "warnings.filterwarnings", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 25, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "Tkinter.Frame", "line_number": 33, "usage_type": "attribute"}, {"api_name": "Tkinter.Frame.__init__", "line_number": 35, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 35, "usage_type": "attribute"}, {"api_name": "Tkinter.Frame", "line_number": 41, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 43, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 44, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 49, "usage_type": "call"}, {"api_name": "Tkinter.Menu", "line_number": 56, "usage_type": "attribute"}, {"api_name": "Tkinter.Menu.__init__", "line_number": 59, "usage_type": "call"}, {"api_name": "Tkinter.Menu", "line_number": 59, "usage_type": "attribute"}, {"api_name": "Tkinter.Menu", "line_number": 64, "usage_type": "call"}, {"api_name": "Tkinter.Menu", "line_number": 66, "usage_type": "call"}, {"api_name": "Tkinter.Menu", "line_number": 73, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 87, "usage_type": "attribute"}, {"api_name": "Tkinter.Frame.__init__", "line_number": 90, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 90, "usage_type": "attribute"}, {"api_name": "time.strftime", "line_number": 110, "usage_type": "call"}, {"api_name": "Tkinter.Text", "line_number": 117, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 123, "usage_type": "attribute"}, {"api_name": "Tkinter.Frame.__init__", "line_number": 125, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 125, "usage_type": "attribute"}, {"api_name": "Tkinter.Frame", "line_number": 143, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 146, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 149, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 152, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 153, "usage_type": "call"}, {"api_name": "Tkinter.DoubleVar", "line_number": 161, "usage_type": "call"}, {"api_name": "Tkinter.Checkbutton", "line_number": 162, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 163, "usage_type": "call"}, {"api_name": "Tkinter.IntVar", "line_number": 166, "usage_type": "call"}, {"api_name": "Tkinter.Radiobutton", "line_number": 167, "usage_type": "call"}, {"api_name": "Tkinter.Radiobutton", "line_number": 169, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 178, "usage_type": "call"}, {"api_name": "Tkinter.Spinbox", "line_number": 181, "usage_type": "call"}, {"api_name": "Tkinter.Button", "line_number": 186, "usage_type": "call"}, {"api_name": "Tkinter.Button", "line_number": 190, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 193, "usage_type": "call"}, {"api_name": "Tkinter.Spinbox", "line_number": 196, "usage_type": "call"}, {"api_name": "Tkinter.Button", "line_number": 198, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 201, "usage_type": "call"}, {"api_name": "Tkinter.Button", "line_number": 204, "usage_type": "call"}, {"api_name": "Tkinter.Button", "line_number": 205, "usage_type": "call"}, {"api_name": "Tkinter.Button", "line_number": 206, "usage_type": "call"}, {"api_name": "Tkinter.Button", "line_number": 207, "usage_type": "call"}, {"api_name": "Tkinter.StringVar", "line_number": 212, "usage_type": "call"}, {"api_name": "ttk.Combobox", "line_number": 214, "usage_type": "call"}, {"api_name": "Tkinter.Button", "line_number": 218, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 221, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 224, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 225, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 229, "usage_type": "attribute"}, {"api_name": "Tkinter.Frame.__init__", "line_number": 232, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 232, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 243, "usage_type": "call"}, {"api_name": "tkFileDialog.asksaveasfilename", "line_number": 318, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 322, "usage_type": "call"}, {"api_name": "tkFileDialog.askopenfilename", "line_number": 327, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 331, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 358, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 359, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 361, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 365, "usage_type": "call"}, {"api_name": "Tkinter.Spinbox", "line_number": 366, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 374, "usage_type": "call"}, {"api_name": "Tkinter.StringVar", "line_number": 375, "usage_type": "call"}, {"api_name": "Tkinter.Entry", "line_number": 376, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 384, "usage_type": "call"}, {"api_name": "Tkinter.StringVar", "line_number": 388, "usage_type": "call"}, {"api_name": "ttk.Combobox", "line_number": 393, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 402, "usage_type": "call"}, {"api_name": "Tkinter.Spinbox", "line_number": 403, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 411, "usage_type": "call"}, {"api_name": "Tkinter.Spinbox", "line_number": 412, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 420, "usage_type": "call"}, {"api_name": "Tkinter.Spinbox", "line_number": 421, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 429, "usage_type": "call"}, {"api_name": "Tkinter.Spinbox", "line_number": 430, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 438, "usage_type": "call"}, {"api_name": "Tkinter.Spinbox", "line_number": 439, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 441, "usage_type": "call"}, {"api_name": "Tkinter.Spinbox", "line_number": 442, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 444, "usage_type": "call"}, {"api_name": "Tkinter.IntVar", "line_number": 454, "usage_type": "call"}, {"api_name": "Tkinter.Radiobutton", "line_number": 455, "usage_type": "call"}, {"api_name": "Tkinter.Radiobutton", "line_number": 456, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 463, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 464, "usage_type": "call"}, {"api_name": "time.time", "line_number": 489, "usage_type": "name"}, {"api_name": "time.time", "line_number": 508, "usage_type": "name"}, {"api_name": "Tkinter.Label", "line_number": 509, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 510, "usage_type": "call"}, {"api_name": "time.time", "line_number": 510, "usage_type": "argument"}, {"api_name": "time.sleep", "line_number": 532, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 538, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 549, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 558, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 560, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 611, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 616, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 620, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 623, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 627, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 630, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 633, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 637, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 641, "usage_type": "call"}, {"api_name": "tecancavrotest.transport.TecanAPISerial.findSerialPumps", "line_number": 673, "usage_type": "call"}, {"api_name": "tecancavrotest.transport.TecanAPISerial", "line_number": 673, "usage_type": "name"}, {"api_name": "tecancavrotest.models.XCaliburD", "line_number": 678, "usage_type": "call"}, {"api_name": "tecancavrotest.transport.TecanAPISerial", "line_number": 678, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 692, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 706, "usage_type": "attribute"}, {"api_name": "threading.Thread.__init__", "line_number": 708, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 708, "usage_type": "attribute"}, {"api_name": "serial.Serial", "line_number": 717, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 729, "usage_type": "attribute"}, {"api_name": "Tkinter.Frame.__init__", "line_number": 731, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 731, "usage_type": "attribute"}, {"api_name": "Tkinter.Scrollbar", "line_number": 735, "usage_type": "call"}, {"api_name": "Tkinter.Canvas", "line_number": 738, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 747, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 771, "usage_type": "attribute"}, {"api_name": "Tkinter.Frame.__init__", "line_number": 773, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 773, "usage_type": "attribute"}, {"api_name": "Tkinter.Frame", "line_number": 794, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 795, "usage_type": "call"}, {"api_name": "Tkinter.Frame", "line_number": 796, "usage_type": "call"}, {"api_name": "Tkinter.Label", "line_number": 798, "usage_type": "call"}, {"api_name": "Tkinter.Tk", "line_number": 834, "usage_type": "call"}]} +{"seq_id": "562231332", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Dec 12 20:02:13 2018\n\n@author: jagtarsingh\n\"\"\"\n\n\n\n\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\nimport torchvision\nfrom torchvision import datasets\nfrom torchvision import transforms\nfrom torchvision.utils import save_image\nimport torchvision.utils as vutils\n\nfrom random import randint\nfrom matplotlib import pyplot as plt\nfrom IPython.display import Image\nfrom IPython.core.display import Image, display\n\nget_ipython().run_line_magic('load_ext', 'autoreload')\nget_ipython().run_line_magic('autoreload', '2')\n\n\n\n# Device configuration\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\ndevice\n\n\nbs = 32\n\n\n\n\n# Load Data\ndataset = datasets.ImageFolder(root='/Users/jagtarsingh/OneDrive/UPenn/CIS680/VAE/cufs', transform=transforms.Compose([\n transforms.Resize(64),\n transforms.ToTensor(), \n]))\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=bs, shuffle=False)\nlen(dataset.imgs), len(dataloader)\n\n\n\nfixed_x, _ = next(iter(dataloader))\nsave_image(fixed_x, 'real_image.png')\n\nImage('real_image.png')\n\n\n\nclass Flatten(nn.Module):\n def forward(self, input):\n return input.view(input.size(0), -1)\n\n\n\nclass UnFlatten(nn.Module):\n def forward(self, input, size=1024):\n return input.view(input.size(0), size, 1, 1)\n\n\n\nclass VAE(nn.Module):\n def __init__(self, channels=3, h_dim=1024, latent_dim=64):\n super(VAE, self).__init__()\n self.encoder = nn.Sequential(\n nn.Conv2d(channels, 32, kernel_size=4, stride=2),\n nn.BatchNorm2d(32),\n nn.ReLU(),\n nn.Conv2d(32, 64, kernel_size=4, stride=2),\n nn.BatchNorm2d(64),\n nn.ReLU(),\n nn.Conv2d(64, 128, kernel_size=4, stride=2),\n nn.BatchNorm2d(128),\n nn.ReLU(),\n nn.Conv2d(128, 256, kernel_size=4, stride=2),\n nn.BatchNorm2d(256),\n nn.ReLU(),\n Flatten()\n )\n \n self.fc1 = nn.Linear(h_dim, latent_dim)\n# self.fc2 = nn.Linear(h_dim, latent_dim)\n self.fc3 = nn.Linear(latent_dim, h_dim)\n \n self.decoder = nn.Sequential(\n UnFlatten(),\n nn.ConvTranspose2d(h_dim, 128, kernel_size=5, stride=2),\n nn.ReLU(),\n nn.ConvTranspose2d(128, 64, kernel_size=5, stride=2),\n nn.ReLU(),\n nn.ConvTranspose2d(64, 32, kernel_size=6, stride=2),\n nn.ReLU(),\n nn.ConvTranspose2d(32, channels, kernel_size=6, stride=2),\n nn.Sigmoid(),\n )\n \n \n\n def forward(self, x):\n encoded_images = self.encoder(x)\n latent_space = self.fc1(encoded_images)\n \n z = self.fc3(latent_space)\n z = self.decoder(z)\n return z\n \n\n\n\n\nchannels = fixed_x.size(1)\nprint(channels)\n\n\n\nmodel = VAE(channels=channels).to(device)\n\noptimizer = torch.optim.Adam(model.parameters(), lr=1e-3) \n\n\n\ndef loss_fn(recon_x, x):\n Recon_loss = F.mse_loss(recon_x, x, size_average=False)\n return Recon_loss \n\n\n\nepochs = 100\n\n\n\nitera = 0\nloss_all = []\nfor epoch in range(epochs):\n for idx, (images, _) in enumerate(dataloader):\n if idx!=5:\n \n recon_images = model(images)\n loss= loss_fn(recon_images, images)\n batch_s = images.size(0)\n \n # loss = loss_fn(recon_images, images, mu, log_sig)\n loss_all.append(loss.data[0]/batch_s)\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n itera+=1\n\n \n if idx == 4:\n n = min(images.size(0), 8)\n recon_images = model(images)\n comparison = torch.cat([images[:n],\n recon_images[:n]])\n save_image(comparison.data.cpu(),\n './reconstructed_AE_train/reconstruction_' + str(epoch) + '.png', nrow=n)\n print(\"Epoch[{}/{}] Loss: {:.3f} {:.3f} {:.3f}\".format(epoch+1, \n epochs, loss.data[0]/bs, Recon_loss.data[0]/bs, KL_loss.data[0]/bs))\n if idx == 5:\n n = min(images.size(0), 8)\n recon_images = model(images)\n comparison = torch.cat([images[:n],\n recon_images[:n]])\n save_image(comparison.data.cpu(),\n './reconstructed_AE_test/reconstruction_' + str(epoch) + '.png', nrow=n)\n print(\"Epoch[{}/{}] Loss: {:.3f} {:.3f} {:.3f}\".format(epoch+1, \n epochs, loss.data[0]/bs, Recon_loss.data[0]/bs, KL_loss.data[0]/bs))\n \n \n \n\nplt.plot(loss_all)\nplt.xlabel('Iterations')\nplt.ylabel('Loss')\nplt.title('Loss Vs Iteration for CUFS dataset')\nplt.savefig('AE_loss.png')\ntorch.save(model.state_dict(), 'AE.torch')\n\n#recon_images= model(fixed_x)\n#comparison = torch.cat([fixed_x[:],recon_images[:]])\n#save_image(comparison.data.cpu(),\n# './reconstructed_AE/reconstruction_last' + str(epoch) + '.png', nrow=n)\n\n", "sub_path": "Autoencoder.py", "file_name": "Autoencoder.py", "file_ext": "py", "file_size_in_byte": 5165, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "torch.device", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.ImageFolder", "line_number": 45, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 45, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 45, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 45, "usage_type": "name"}, {"api_name": "torchvision.transforms.Resize", "line_number": 46, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 46, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 47, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 49, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 49, "usage_type": "attribute"}, {"api_name": "torchvision.utils.save_image", "line_number": 55, "usage_type": "call"}, {"api_name": "IPython.core.display.Image", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 61, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 67, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 67, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 73, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 76, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 82, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 82, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 83, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 83, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 85, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 85, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 86, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 87, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 88, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 92, "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.Sequential", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 96, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 98, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 99, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 99, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 100, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 101, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 101, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 102, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 102, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 103, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 103, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 104, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 105, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 129, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 129, "usage_type": "attribute"}, {"api_name": "torch.nn.functional.mse_loss", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 134, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 164, "usage_type": "call"}, {"api_name": "torchvision.utils.save_image", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 173, "usage_type": "call"}, {"api_name": "torchvision.utils.save_image", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 187, "usage_type": "name"}, {"api_name": "torch.save", "line_number": 188, "usage_type": "call"}]} +{"seq_id": "256665717", "text": "from collections import deque\n\nn,m = map(int,input().split())\ngraph = [ [] for _ in range(n+1) ]\nin_degree = [0] * (n+1)\n\ndef topological_sort():\n res = []\n for i in range(1,n+1):\n if in_degree[i] == 0:\n que.append(i)\n\n while que:\n p = que.popleft()\n res.append(p)\n for adj in graph[p]:\n in_degree[adj] -= 1\n if in_degree[adj] == 0:\n que.append(adj)\n\n print(*res)\n\nfor _ in range(m):\n a, b = map(int,input().split())\n graph[a].append(b)\n in_degree[b] += 1\n\nque = deque() \n\n\n\ntopological_sort()\n\n\n", "sub_path": "3.beakjoon/jungle/week3/2252_줄세우기.py", "file_name": "2252_줄세우기.py", "file_ext": "py", "file_size_in_byte": 593, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "collections.deque", "line_number": 28, "usage_type": "call"}]} +{"seq_id": "193960752", "text": "import numpy as np\nimport tables as tb\nimport calendar as cd\nimport datetime\nimport pandas as pd\nimport time\n\n\"\"\"Data types for pytables\"\"\"\nB_dtype = np.dtype([('Ycp','float32'),('ls','float32'),('rs','float32')])\nCP_dtype = np.dtype([('cp1','float32'),('cp2','float32')])\ndmonths_dtype = np.dtype([('heat','int8'),('cool','int8')])\nmodels_dtype = np.dtype([('B',B_dtype),('CP',CP_dtype),('RMSE','float32'),('Rsqr','float32'),('CV_RMSE','float32')])\ndata_dtype = np.dtype([('Result','S4'),('dmonths',dmonths_dtype)])\nt_dtype = np.dtype([('Result','S4'),('t1','float32'),('t2','float32')])\ntest_dtype = np.dtype([('shape_test','S4'),('data',data_dtype),('T',t_dtype),('Result','S4')])\nallModels_dtype = np.dtype([('enddate','int32'),('mtype','int8'),('Ycp','float32'),('ls','float32'),('rs','float32'),('cp1','float32'),('cp2','float32'),('RMSE','float32'),('Rsqr','float32'),('CV_RMSE','float32'),('shape_test','S4'),('data_test','S4'),('heat_months','int8'),('cool_months','int8'),('t_test','S4'),('t_ls','float32'),('t_rs','float32'),('model_result','S4')])\ntimeseries_dtype = np.dtype([('enddate','int32'),('OAT','float32'),('usage','float32')])\nraw_dtype = np.dtype([('start_date','int32'),('end_date','int32'),('elec','float32'),('fuel','float32'),('OAT','float32'),('prepost','int8')])\n\n\"Test Files\"\nenergy = [\"/Volumes/MAC 1/Documents/CUNY EDL/BEMA-Project/BEMA/Data/Woodhull_Seperate.csv\",\"/Volumes/MAC 1/Documents/CUNY EDL/BEMA-Project/BEMA/Data/Woodhull_Combined.csv\"]\nweather = [\"/Volumes/MAC 1/Documents/CUNY EDL/BEMA-Project/BEMA/Data/LGAAverageDailyTemperature.csv\",\"/Volumes/MAC 1/Documents/CUNY EDL/BEMA-Project/BEMA/Data/LGAAverageDailyTemperature.csv\"]\nfac = [\"Woodhull Seperate\",\"Woodhull Combined\"]\n\nclass createHDF5(object):\n \n def __init__(self, energy, weather, fac, filename):\n \n self.fac = fac\n self.energy = energy\n self.weather = weather\n self.table = None\n self.h5_filename = filename\n \n \"\"\"Create HDF5 File\"\"\"\n self.h5 = tb.openFile(self.h5_filename,'w')\n \n def runAllFiles(self):\n \n #Create PyTable group for each facility\n for index,fac in enumerate(self.fac):\n self.h5.create_group(self.h5.root,fac,fac)\n self.h5.flush()\n \n #Process data\n self.runFile(self.energy[index],self.weather[index],fac)\n \n #Close h5\n self.h5.close()\n \n def runFile(self,energy,weather,fac):\n #Open up energy and average temperature files as panda dataframes\n energy_file = pd.read_csv(energy)\n temps = pd.read_csv(weather)\n \n #Store data into PyTable\n self.table = self.h5.createTable(\"/\"+fac,'rawdata', raw_dtype,\"Raw Data\")\n reading = self.table.row\n temps.index = pd.to_datetime(temps.pop('Date'))\n \n for row in energy_file.iterrows():\n reading['start_date'] = int(time.mktime(time.strptime(row[1]['Start_Date'], \"%m/%d/%y\")))\n reading['end_date'] = int(time.mktime(time.strptime(row[1]['End_Date'], \"%m/%d/%y\")))\n reading['elec'] = row[1]['Elec']\n reading['fuel'] = row[1]['Fuel']\n temperatures = temps.loc[row[1]['Start_Date']:row[1]['End_Date'],'Temp']\n reading['OAT'] = np.mean(temperatures)\n reading['prepost'] = row[1]['PrePost']\n reading.append()\n \n self.table.flush()\n self.h5.flush()\n \n #Delete panda dataframes\n del energy_file\n del temps\n \n #Electricity\n dates = np.array([x['end_date'] for x in self.table.where(\"\"\"(elec > 0)\"\"\")])\n if len(dates) > 0:\n \n last_row = dates[-1] #Last date in set\n \n #Create group in hdf5 file to store tables containing model coefficients for each model year\n self.h5.create_group(\"/\"+fac,\"elec_models\",\"Elec Models\")\n \n for reading in dates:\n startdate = datetime.datetime.fromtimestamp(reading)\n month = startdate.month\n year = startdate.year + 1\n day = startdate.day - 5\n if day < 1:\n month -= 1\n if month < 1:\n month = 12\n year = year - 1\n day = cd.monthrange(year,month)[-1]\n enddate = datetime.datetime(year,month,day)\n mcheck = month - 1\n if mcheck < 1:\n mcheck = 12\n year = year - 1\n if day > cd.monthrange(year,mcheck)[-1]:\n day = day - cd.monthrange(year,mcheck)[-1]\n mcheck += 1\n if mcheck > 12:\n mcheck = 1\n year = year + 1\n check = datetime.datetime(year,mcheck,day)\n startdate = int(startdate.strftime('%s'))\n enddate = int(enddate.strftime('%s'))\n check = int(check.strftime('%s'))\n if check > last_row:\n break\n else:\n x_elec = np.array([x['OAT'] for x in self.table.iterrows() if x['elec'] > 0 and startdate <= x['end_date'] < enddate])\n y_elec = np.array([x['elec'] for x in self.table.iterrows() if x['elec'] > 0 and startdate <= x['end_date'] < enddate])\n dates_elec = np.array([x['end_date'] for x in self.table.iterrows() if x['elec'] > 0 and startdate <= x['end_date'] < enddate])\n self.runAllModels(x_elec,y_elec,dates_elec,fac,\"elec\")\n\n #Fuel\n dates = np.array([x['end_date'] for x in self.table.where(\"\"\"(fuel > 0)\"\"\")])\n if len(dates) > 0:\n \n last_row = dates[-1] #Last date in set\n \n #Create group in hdf5 file to store tables containing model coefficients for each model year\n self.h5.create_group(\"/\"+fac,\"fuel_models\",\"Fuel Models\")\n \n for reading in dates:\n startdate = datetime.datetime.fromtimestamp(reading)\n month = startdate.month\n year = startdate.year + 1\n day = startdate.day - 5\n if day < 1:\n month -= 1\n if month < 1:\n month = 12\n year = year - 1\n day = cd.monthrange(year,month)[-1]\n enddate = datetime.datetime(year,month,day)\n mcheck = month - 1\n if mcheck < 1:\n mcheck = 12\n year = year - 1\n if day > cd.monthrange(year,mcheck)[-1]:\n day = day - cd.monthrange(year,mcheck)[-1]\n mcheck += 1\n if mcheck > 12:\n mcheck = 1\n year = year + 1\n check = datetime.datetime(year,mcheck,day)\n startdate = int(startdate.strftime('%s'))\n enddate = int(enddate.strftime('%s'))\n check = int(check.strftime('%s'))\n if check > last_row:\n break\n else:\n x_fuel = np.array([x['OAT'] for x in self.table.iterrows() if x['fuel'] > 0 and startdate <= x['end_date'] < enddate])\n y_fuel = np.array([x['fuel'] for x in self.table.iterrows() if x['fuel'] > 0 and startdate <= x['end_date'] < enddate])\n dates_fuel = np.array([x['end_date'] for x in self.table.iterrows() if x['fuel'] > 0 and startdate <= x['end_date'] < enddate])\n self.runAllModels(x_fuel,y_fuel,dates_fuel,fac,\"fuel\")\n \n \"\"\"Creates X Matrix for Least Squares Equation given array of outside air temps,\n change point(s) and model type\"\"\"\n def createXMatrix(self,mtype,x,cp1 = 0.0,cp2 = 0.0):\n c1 = np.array(np.ones(len(x)))\n if mtype == 5 or mtype == 6:\n c2 = np.array(np.where(cp1 < x,0.0,x - cp1))\n if mtype == 5:\n c3 = np.array(np.where(cp1 > x,0.0,x - cp1))\n else:\n c3 = np.array(np.where(cp2 > x,0.0,x - cp2))\n X = np.mat(np.column_stack((c1,c2,c3)))\n elif mtype == 4:\n c2 = np.array(np.where(cp1 < x,0.0,x - cp1))\n X = np.mat(np.column_stack((c1,c2))) \n else:\n c2 = np.array(np.where(cp1 > x,0.0,x - cp1))\n X = np.mat(np.column_stack((c1,c2)))\n return X\n \n \"\"\"Calculates least square estimates of the model coefficients\"\"\"\n def leastSquares(self,X,Y):\n Xtrans = X.transpose()\n A = np.dot(Xtrans,X)\n G = np.dot(Xtrans,Y)\n Ainv = np.linalg.inv(A)\n B = np.dot(Ainv,G)\n return B\n \n \"\"\"Calculates RMSE of the model\"\"\"\n def stats(self,mtype,X,Y,B):\n Ytrans = Y.transpose()\n Xtrans = X.transpose()\n Btrans = B.transpose()\n A = float(np.dot(Ytrans,Y))\n V = np.dot(Xtrans,Y)\n V = float(np.dot(Btrans,V))\n SSE = float(A - V)\n Y = np.squeeze(np.asarray(Y))\n Ysst = Y - np.average(Y)\n Yssttrans = Ysst.transpose()\n SST = float(np.dot(Yssttrans,Ysst))\n if mtype == 2:\n params = 2\n elif mtype < 5:\n params = 3\n else:\n params = mtype - 1\n MSE = float(SSE / (len(Y)-params))\n Rsquared = 1 - (SSE*(len(Y)-1))/(SST*(len(Y)-params))\n RMSE = np.sqrt(MSE)\n CV_RMSE = RMSE / np.average(Y) * 100\n return [RMSE, Rsquared, CV_RMSE]\n \n \"\"\"Loops through possible change points stores the coefficients, change point(s),\n and RMSE into a structured array then returns the model with the \n lowest RMSE\"\"\"\n def createModel(self,mtype,x,y):\n Y = np.mat(y).transpose()\n RMSE1 = 0\n RMSE2 = 0\n if mtype == 2:\n models = np.zeros(1,dtype = models_dtype)\n B_temp = np.zeros(1,B_dtype)\n CP = np.zeros(1,CP_dtype)\n X = self.createXMatrix(mtype,x)\n B = self.leastSquares(X,Y)\n Perform = self.stats(mtype,X,Y,B)\n CP[0] = (0.0,0.0)\n if B[1] < 0:\n B_temp[0] = (B[0],B[1],0.0)\n else:\n B_temp[0] = (B[0],0.0,B[1])\n models[0] = (B_temp[0],CP[0],Perform[0],Perform[1],Perform[2]) \n model = models[0]\n return model\n else:\n x_sorted = np.sort(x)\n lowT = x_sorted[len(x)/4 - 1]\n maxT = x_sorted[(len(x)-len(x)/4)-1]\n step = 0.25\n models = np.zeros(1,dtype = models_dtype)\n B_temp = np.zeros(1,B_dtype)\n CP = np.zeros(1,CP_dtype)\n for cp1 in np.arange(lowT,maxT,step):\n if mtype == 6:\n models2 = np.zeros(1,dtype = models_dtype)\n for cp2 in np.arange(cp1,maxT,step):\n X = self.createXMatrix(mtype,x,cp1,cp2)\n B = self.leastSquares(X,Y)\n Perform = self.stats(mtype,X,Y,B)\n CP[0] = (cp1,cp2)\n B_temp[0] = (B[0],B[1],B[2])\n if Perform[0] < RMSE2 or RMSE2 == 0:\n models2[0] = (B_temp[0],CP[0],Perform[0],Perform[1],Perform[2])\n RMSE2 = Perform[0]\n cp2 = models2['CP']['cp2']\n else:\n cp2 = 0.0\n X = self.createXMatrix(mtype,x,cp1,cp2)\n B = self.leastSquares(X,Y)\n Perform = self.stats(mtype,X,Y,B)\n CP[0] = (cp1,cp2)\n if mtype < 5:\n if mtype == 3:\n B_temp[0] = (B[0],0.0,B[1])\n else:\n B_temp[0] = (B[0],B[1],0.0)\n else:\n B_temp[0] = (B[0],B[1],B[2])\n if Perform[0] < RMSE1 or RMSE1 == 0:\n models[0] = (B_temp[0],CP[0],Perform[0],Perform[1],Perform[2])\n RMSE1 = Perform[0]\n return models[0]\n \n \"\"\"Performs shape test on model (Based off of Texas A&M algorithm)\"\"\"\n def shapeTest(self,mtype,ls,rs):\n test = \"Fail\"\n if mtype == 3:\n if rs > 0.0:\n test = \"Pass\"\n elif mtype == 4:\n if ls < 0.0:\n test = \"Pass\" \n elif mtype == 5:\n if ls < 0.0 and rs > 0.0:\n test = \"Pass\"\n elif ls < 0.0 and rs < 0.0:\n test = \"Pass\"\n elif ls > 0.0 and rs > 0.0:\n test = \"Pass\"\n elif mtype == 6:\n if ls < 0.0 and rs > 0.0:\n test = \"Pass\" \n return test\n \n \"\"\"Performs data population test on model (Based off of Texas A&M algorithm)\"\"\"\n def datapopTest(self,mtype,x,cp1,cp2):\n test = \"Fail\"\n region1 = 0\n region2 = 0\n region3 = 0\n end = len(x)\n dmonths = np.zeros(1,dmonths_dtype)\n if mtype == 6:\n for i in range(end):\n if x[i] <= cp1:\n region1 += 1\n elif x[i] > cp1 and x[i] <= cp2:\n region2 += 1\n elif x[i] > cp2:\n region3 += 1\n if region1 > 2 and region2 > 2 and region3 > 2:\n test = \"Pass\"\n dmonths[0]['cool'] = region3\n dmonths[0]['heat'] = region1\n else:\n for i in range(end):\n if x[i] <= cp1:\n region1 += 1\n elif x[i] > cp1:\n region2 += 1\n if region1 > 2 and region2 > 2:\n test = \"Pass\"\n if mtype == 4:\n dmonths[0]['heat'] = region1\n elif mtype == 5:\n dmonths[0]['heat'] = region1\n dmonths[0]['cool'] = region2\n else:\n dmonths[0]['cool'] = region2\n return (test, dmonths[0])\n \n \"\"\"Calulates t-statistic. (equation from \"A Second Course in Statistics\n Regression Analysis\")\"\"\"\n def calcT(self,b,RMSE,XXinverse,loc):\n c = XXinverse[(loc,loc)]\n tstat = b / (RMSE * np.sqrt(c))\n return tstat\n \n \"\"\"Checks to see if t-statistic is within the acceptable range for ls and rs\"\"\"\n def tTest(self,mtype,x,cp1,cp2,ls,rs,RMSE):\n test = \"Fail\"\n value = 2.0\n tstat = 0.0\n tstat2 = 0.0\n X = self.createXMatrix(mtype,x,cp1,cp2)\n Xtrans = X.transpose()\n XtransX = np.dot(Xtrans,X)\n XXinverse = np.linalg.inv(XtransX)\n if mtype == 3:\n tstat2 = self.calcT(rs,RMSE,XXinverse,1)\n if tstat2 > value or tstat2 < (-1 * value):\n test = \"Pass\"\n elif mtype == 4: \n tstat = self.calcT(ls,RMSE,XXinverse,1)\n if tstat > value or tstat < (-1 * value):\n test = \"Pass\"\n elif mtype > 4: \n tstat = self.calcT(ls,RMSE,XXinverse,1)\n tstat2 = self.calcT(rs,RMSE,XXinverse,2)\n if (tstat > value or tstat < (-1 * value)) and (tstat2 > value or tstat2 < (-1 * value)):\n test = \"Pass\"\n return (test,tstat,tstat2)\n \n \"\"\"Checks to see if model passes all the test\n (Includes test for 2P model looking at the RMSE)\"\"\"\n def testModel(self,x,y,mtype,model):\n test_values = np.zeros(1,test_dtype)\n test_values['Result'] = \"Fail\"\n ls = model['B']['ls']\n rs = model['B']['rs']\n cp1 = model['CP']['cp1']\n cp2 = model['CP']['cp2']\n Rsqr = model['Rsqr']\n RMSE = model['RMSE']\n test_count = 0\n if mtype == 2:\n if Rsqr > .75:\n test_values[0]['Result'] = \"Pass\"\n if ls == 0:\n test_values[0]['data']['dmonths']['cool'] = len(y)\n else:\n test_values[0]['data']['dmonths']['heat'] = len(y)\n else:\n shape_test = self.shapeTest(mtype,ls,rs)\n test_values[0]['shape_test'] = str(shape_test)\n if shape_test == \"Pass\":\n test_count = test_count + 1\n dTest = self.datapopTest(mtype,x,cp1,cp2)\n test_values[0]['data']['Result'] = dTest[0]\n test_values[0]['data']['dmonths']['heat'] = dTest[1]['heat']\n test_values[0]['data']['dmonths']['cool'] = dTest[1]['cool']\n if dTest[0] == \"Pass\":\n test_count = test_count + 1\n t_Test = self.tTest(mtype,x,cp1,cp2,ls,rs,RMSE)\n test_values[0]['T']['Result'] = t_Test[0]\n test_values[0]['T']['t1'] = t_Test[1]\n test_values[0]['T']['t2'] = t_Test[2]\n if t_Test[0] == \"Pass\":\n test_count = test_count + 1\n if test_count == 3:\n test_values[0]['Result'] = \"Pass\"\n return test_values[0]\n \n \"\"\"Stores the resulting model from the createModel() function and test result\n from the testModel() function for each model type (2,3C,3H,4,5) in a PyTable\n for that year\"\"\"\n def runAllModels(self,x,y,dates,name,etype):\n enddate = datetime.datetime.fromtimestamp(dates[-1])\n enddate_str =enddate.strftime('%m_%d_%y')\n model = self.createModel(2,x,y)\n test_values = self.testModel(x,y,2,model)\n \n #Create group containing tables for all models and timeseries data\n self.h5.createGroup(\"/\"+name+\"/\"+etype+\"_models\",enddate_str, name + \" \" + enddate_str)\n \n #Table containing model coefficients and test results\n modeltable = self.h5.createTable(\"/\"+name+\"/\"+etype+\"_models\"+\"/\"+enddate_str,\"Models\", allModels_dtype,name + \" \" + enddate_str+\" Models\")\n models = modeltable.row\n models['enddate'] = int(enddate.strftime('%s'))\n models['mtype'] = 2\n models['Ycp'] = model['B']['Ycp']\n models['ls'] = model['B']['ls']\n models['rs'] = model['B']['rs']\n models['cp1'] = model['CP']['cp1']\n models['cp2'] = model['CP']['cp2']\n models['RMSE'] = model['RMSE']\n models['Rsqr'] = model['Rsqr']\n models['CV_RMSE'] = model['CV_RMSE']\n models['shape_test'] = test_values['shape_test']\n models['data_test'] = test_values['data']['Result']\n models['heat_months'] = test_values['data']['dmonths']['heat']\n models['cool_months'] = test_values['data']['dmonths']['cool']\n models['t_test'] = test_values['T']['Result']\n models['t_ls'] = test_values['T']['t1']\n models['t_rs'] = test_values['T']['t2']\n models['model_result'] = test_values['Result']\n models.append()\n for mtype in range(3,7):\n model = self.createModel(mtype,x,y)\n test_values = self.testModel(x,y,mtype,model)\n models['enddate'] = int(enddate.strftime('%s'))\n models['mtype'] = mtype\n models['Ycp'] = model['B']['Ycp']\n models['ls'] = model['B']['ls']\n models['rs'] = model['B']['rs']\n models['cp1'] = model['CP']['cp1']\n models['cp2'] = model['CP']['cp2']\n models['RMSE'] = model['RMSE']\n models['Rsqr'] = model['Rsqr']\n models['CV_RMSE'] = model['CV_RMSE']\n models['shape_test'] = test_values['shape_test']\n models['data_test'] = test_values['data']['Result']\n models['heat_months'] = test_values['data']['dmonths']['heat']\n models['cool_months'] = test_values['data']['dmonths']['cool']\n models['t_test'] = test_values['T']['Result']\n models['t_ls'] = test_values['T']['t1']\n models['t_rs'] = test_values['T']['t2']\n models['model_result'] = test_values['Result']\n models.append()\n modeltable.flush()\n \n #Table containing timeseries data\n timetable = self.h5.createTable(\"/\"+name+\"/\"+etype+\"_models\"+\"/\"+enddate_str,\"Time_Series\",timeseries_dtype,name + \" \" + enddate_str+\" Time Series\")\n reading = timetable.row\n for enddate,x,y in zip(dates,x,y):\n reading['enddate'] = enddate\n reading['OAT'] = x\n reading['usage'] = y\n reading.append()\n timetable.flush()\n \n self.h5.flush()", "sub_path": "Python_Files/Old Files/changepoint_regression_pytables.py", "file_name": "changepoint_regression_pytables.py", "file_ext": "py", "file_size_in_byte": 20473, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.dtype", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 18, "usage_type": "call"}, {"api_name": "tables.openFile", "line_number": 36, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 54, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 59, "usage_type": "call"}, {"api_name": "time.mktime", "line_number": 62, "usage_type": "call"}, {"api_name": "time.strptime", "line_number": 62, "usage_type": "call"}, {"api_name": "time.mktime", "line_number": 63, "usage_type": "call"}, {"api_name": "time.strptime", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 79, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 88, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 88, "usage_type": "attribute"}, {"api_name": "calendar.monthrange", "line_number": 97, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 98, "usage_type": "call"}, {"api_name": "calendar.monthrange", "line_number": 103, "usage_type": "call"}, {"api_name": "calendar.monthrange", "line_number": 104, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 122, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 131, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 131, "usage_type": "attribute"}, {"api_name": "calendar.monthrange", "line_number": 140, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 141, "usage_type": "call"}, {"api_name": "calendar.monthrange", "line_number": 146, "usage_type": "call"}, {"api_name": "calendar.monthrange", "line_number": 147, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 159, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 161, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 171, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.mat", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.mat", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.mat", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 187, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 188, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 197, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.average", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.mat", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 244, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 245, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 340, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 351, "usage_type": "call"}, {"api_name": "numpy.linalg.inv", "line_number": 352, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 352, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 371, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 412, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 412, "usage_type": "attribute"}]} +{"seq_id": "563120809", "text": "# coding:utf-8\n# 获取excel单元格中的内容,获取行数据，以及数据值判断\nfrom tool.operation_excel import OperationExcel\nfrom operation_data.data_config import DataConfig\nfrom tool.operation_json import OperationJson\nfrom tool.connect_db import OperationMysql\nfrom tool.common_util import CommonUtil\nimport json\n\n\nclass GetData:\n def __init__(self, file_path, sheet_id):\n self.opera_excel = OperationExcel(file_path, sheet_id)\n self.data_config = DataConfig()\n self.com_util = CommonUtil()\n\n def get_case_lines(self):\n \"\"\"\n 去获取excel行数，就是case的个数\n :return:\n \"\"\"\n return self.opera_excel.get_lines()\n\n def get_is_run(self, row):\n \"\"\"\n 获取是否可行\n :param row:\n :return:\n \"\"\"\n flag = None\n col = int(DataConfig.get_is_run())\n run_model = self.opera_excel.get_cell_value(row, col)\n if run_model == 'yes':\n flag = True\n else:\n flag = False\n return flag\n\n def is_header(self, row):\n \"\"\"\n 是否携带header\n :param row:\n :return:\n \"\"\"\n col = int(DataConfig.get_header())\n header = self.opera_excel.get_cell_value(row, col)\n if header != '':\n return header\n else:\n return None\n\n def get_run_method(self, row):\n \"\"\"\n 获取请求方式\n :param row:\n :return:\n \"\"\"\n col = int(DataConfig.get_run_method())\n request_method = self.opera_excel.get_cell_value(row, col)\n return request_method\n\n def get_request_url(self, row):\n \"\"\"\n 获取url\n :param row:\n :return:\n \"\"\"\n col = int(DataConfig.get_url())\n url = self.opera_excel.get_cell_value(row, col)\n return url\n\n def get_is_depend(self, row):\n \"\"\"\n 判断是否有case依赖,并返回caseid\n :param row:\n :return:\n \"\"\"\n col = int(DataConfig.get_case_depend())\n depend_case_id = self.opera_excel.get_cell_value(row, col)\n if depend_case_id == \"\":\n return None\n return depend_case_id\n\n def get_depend_key(self, row): # invalid func\n \"\"\"\n 获取依赖数据的key\n :param row:\n :return:\n \"\"\"\n col = int(DataConfig.get_depend_key())\n depend_key = self.opera_excel.get_cell_value(row, col)\n if depend_key == \"\":\n return None\n return depend_key\n\n def get_current_key(self, row): # invalid func\n \"\"\"\n 获取当前请求的key\n :param row:\n :return:\n \"\"\"\n col = int(DataConfig.get_current_key())\n current_key = self.opera_excel.get_cell_value(row, col)\n if current_key == '':\n return None\n return current_key\n\n def get_other_depend_key(self, row): # invalid func\n \"\"\"\n 获取其他依赖字段，不从接口中直接得到的依赖字段\n :param row:\n :return:\n \"\"\"\n col = int(DataConfig.get_other_depend_key())\n other_depend_key = self.opera_excel.get_cell_value(row, col)\n if other_depend_key == '':\n return None\n return other_depend_key\n\n def _get_other_depend_key_sql(self, row): # invalid func\n \"\"\"\n 获取查询other_depend_key的sql\n :param row:\n :return:\n \"\"\"\n col = int(DataConfig.get_other_depend_key_sql())\n other_depend_key_sql = self.opera_excel.get_cell_value(row, col)\n if other_depend_key_sql == '':\n return None\n return other_depend_key_sql\n\n def get_other_depend_key_value_from_db(self, row): # invalid func\n \"\"\"\n 获取需要依赖DB查询值的other_depend_key\n :param row:\n :return: return a sql query result\n \"\"\"\n op_mysql = OperationMysql()\n # op_mysql.connect_db()\n sql = self._get_other_depend_key_sql(row)\n result = op_mysql.get_one(op_mysql.execute_sql(sql))\n op_mysql.close_db()\n return result\n\n def get_request_data(self, row):\n \"\"\"\n 获取请求数据\n :param row:\n :return:\n \"\"\"\n col = int(DataConfig.get_request_data())\n data = self.opera_excel.get_cell_value(row, col)\n if data == '':\n return None\n return data # type is str\n\n def get_file_path(self, row):\n \"\"\"\n 获取上传文件的路径\n :param row:\n :return:\n \"\"\"\n col = int(DataConfig.get_file_path())\n file_path = self.opera_excel.get_cell_value(row, col)\n if file_path == '':\n return None\n return file_path\n\n def get_key_need_randomstr(self, row):\n \"\"\"\n 获取需要使用随机字符串的key\n :param row:\n :return:\n \"\"\"\n col = int(DataConfig.get_key_need_randomstr())\n key_need_randomstr = self.opera_excel.get_cell_value(row, col)\n if key_need_randomstr == '':\n return None\n return key_need_randomstr\n\n def get_random_str(self, row):\n \"\"\"\n 获取随机字符串\n :param row:\n :return:\n \"\"\"\n col = int(DataConfig.get_random_str())\n random_str = self.opera_excel.get_cell_value(row, col)\n if random_str == '':\n return None\n return random_str\n\n def update_key_with_randomstr(self, row):\n \"\"\"\n 使用随机字符串random_str复制给request_data中需要使用随机字符串的key，\n 如果key_need_randomstr为空，返回的是未经处理的request_data\n :param row:\n :return: 替换过key值的request_data\n \"\"\"\n request_data = self.get_request_data(row)\n request_data = json.loads(request_data, encoding='UTF-8')\n key_need_randomstr = self.get_key_need_randomstr(row)\n if key_need_randomstr == '':\n return request_data\n random_str = self.get_random_str(row)\n request_data[key_need_randomstr] = random_str\n return request_data\n\n def get_request_param(self, row): # invalid func\n \"\"\"\n 获取需要依赖DB查询值的request_data中的字段\n :param row:\n :return:\n \"\"\"\n col = int(DataConfig.get_request_param())\n request_param = self.opera_excel.get_cell_value(row, col)\n if request_param == '':\n return None\n return request_param\n\n def _get_request_param_sql(self, row): # invalid func\n \"\"\"\n 获取查询request_param值的sql\n :return:\n \"\"\"\n col = int(DataConfig.get_request_param_sql())\n request_param_sql = self.opera_excel.get_cell_value(row, col)\n if request_param_sql == '':\n return None\n return request_param_sql\n\n def get_request_param_value_from_db(self, row): # invalid func\n \"\"\"\n 查询sql返回request_param的值\n :param row:\n :return: return a sql query result\n \"\"\"\n op_mysql = OperationMysql()\n # op_mysql.connect_db()\n sql = self._get_request_param_sql(row)\n result = op_mysql.get_one(op_mysql.execute_sql(sql))\n op_mysql.close_db()\n return result\n\n def get_expect_as_fixed_value(self, row):\n \"\"\"\n 获取完整及固定值的期望结果\n :param row:\n :return:\n \"\"\"\n col = int(DataConfig.get_expect_as_fixed_value())\n expect_fixed_value = self.opera_excel.get_cell_value(row, col)\n if expect_fixed_value == '':\n return None\n return expect_fixed_value\n\n def get_expect_code(self, row):\n \"\"\"\n 获取期望的code值\n :param row:\n :return:\n \"\"\"\n col = int(DataConfig.get_expect_code())\n expect_code = self.opera_excel.get_cell_value(row, col)\n if expect_code == '':\n return None\n return int(expect_code)\n\n def get_expect_msg(self, row):\n \"\"\"\n 获取期望的接口json返回值中的msg值\n :param row:\n :return:\n \"\"\"\n col = int(DataConfig.get_expect_msg())\n expect_msg = self.opera_excel.get_cell_value(row, col)\n if expect_msg == '':\n return None\n return expect_msg\n\n def get_expect_data(self, row):\n \"\"\"\n 获取期望的接口json返回值中的data部分的关键key\n :param row:\n :return:\n \"\"\"\n col = int(DataConfig.get_expect_data())\n expect_key = self.opera_excel.get_cell_value(row, col)\n if expect_key == '':\n return None\n return expect_key\n\n def get_expect_key_sql(self, row):\n \"\"\"\n 获取sql\n :param row:\n :return:\n \"\"\"\n col = int(DataConfig.get_expect_key_sql())\n sql = self.opera_excel.get_cell_value(row, col)\n if sql == '':\n return None\n return sql\n\n def get_expect_key_value_from_db(self, row): # 无用函数\n \"\"\"\n 通过sql获取预期data中的key值\n :param row:\n :return:\n \"\"\"\n op_mysql = OperationMysql()\n # op_mysql.connect_db()\n sql = self.get_expect_key_sql(row)\n result = op_mysql.get_one(op_mysql.execute_sql(sql))\n op_mysql.close_db()\n return result\n\n def write_api_assertion_result(self, row, value):\n \"\"\"\n 将执行结果pass/fail写入excel\n :param row:\n :param value:\n :return:\n \"\"\"\n col = int(DataConfig.get_api_assertion_result())\n self.opera_excel.write_value(row, col, value)\n\n def write_response(self, row, res):\n \"\"\"\n 将接口返回写入excel\n :param row:\n :param res:\n :return:\n \"\"\"\n col = int(DataConfig.get_response_result())\n self.opera_excel.write_value(row, col, res)\n\n def get_response_result(self, row):\n col = int(DataConfig.get_response_result())\n response_result = self.opera_excel.get_cell_value(row, col)\n if response_result == '':\n return None\n return response_result\n\n def is_set_global_vars(self, row):\n \"\"\"\n 返回set_global_vars\n :param row:\n :return:\n \"\"\"\n col= int(DataConfig.is_set_global_vars())\n global_vars = self.opera_excel.get_cell_value(row, col)\n if global_vars == '':\n return None\n return global_vars\n\n\n # def get_field_name_for_assert_from_db(self, row):\n # col = int(GlobalVar.get_field_name_for_assert_from_db())\n # data = self.opera_excel.get_cell_value(row, col)\n # if data == \"\":\n # return None\n # else:\n # return data\n #\n # def get_field_name_for_assert_from_response(self, row):\n # col = int(GlobalVar.get_field_name_for_assert_from_response())\n # data = self.opera_excel.get_cell_value(row, col)\n # if data == \"\":\n # return None\n # else:\n # return data\n\n # def get_target_value_for_assert_from_db(self, row):\n # field_for_assert = self.get_field_name_for_assert_from_db(row)\n # expect_result_from_db = self.get_expect_data_from_mysql(row)\n # target_value_from_expect_result = []\n # self.com_util.get_target_value(field_for_assert, expect_result_from_db, target_value_from_expect_result)\n # return target_value_from_expect_result # return a list\n #\n # def get_target_value_for_assert_from_response(self, row, response):\n # field_for_assert = self.get_field_name_for_assert_from_response(row)\n # target_value_from_response = []\n # self.com_util.get_target_value(field_for_assert, response, target_value_from_response)\n # return target_value_from_response # return a list\n\n\n # 获取依赖字段是否与当前请求的字段同名\n # def is_same_field_name(self, row):\n # col = int(GlobalVar.get_is_same_field_name())\n # current_field = self.opera_excel.get_cell_value(row, col)\n # if current_field == \"\":\n # return None\n # else:\n # return current_field\n # 通过获取关键字拿到data数据 先从excel指定行中得到请求数据作为key去json文件里面读取到该key对应的value\n # value可能是登录后获取的cookie参数\n\n # def get_data_from_json(self, row): # ???没用的函数???\n # opera_json = OperationJson()\n # request_data = opera_json.get_data(self.get_request_data(row))\n # return request_data\n\n\nif __name__ == '__main__':\n demoGetData = GetData('../dataconfig/case3.xls', 0)\n # print(demoGetData.get_case_lines())\n # print(demoGetData.get_is_run(3))\n # print(demoGetData.is_header(1))\n # print(demoGetData.get_run_method(3))\n # print(demoGetData.get_request_url(2))\n # print(demoGetData.get_is_depend(3))\n # print(demoGetData.get_depend_key(3))\n # print(demoGetData.get_current_key(3))\n # print(demoGetData.get_other_depend_key(3))\n # print(demoGetData.get_other_depend_key_sql(3))\n # print(demoGetData.get_other_depend_key_value_from_db(3))\n print('-------------------------------------------分割线-----------------------------------------------------------')\n # print(demoGetData.get_request_data(3))\n print('-------------------------------------------分割线-----------------------------------------------------------')\n # print(demoGetData.get_file_path(5))\n # print(demoGetData.get_key_need_randomstr(4))\n # print(demoGetData.get_random_str(4))\n # print(demoGetData.update_key_with_randomstr(4))\n # print(demoGetData.get_request_param(4))\n # print(demoGetData.get_request_param_sql(4))\n # print(demoGetData.get_request_param_value_from_db(4))\n # print(demoGetData.get_expect_as_fixed_value(5))\n # print(demoGetData.get_expect_code(5))\n # print(demoGetData.get_expect_msg(5))\n # print(demoGetData.get_expect_data(5))\n # print(demoGetData.get_expect_key_value_from_db(5))\n # value = '{\"code\":0,\"msg\":\"iot switch off\",\"data\":null}'\n # demoGetData.write_response(5, value)\n # print(demoGetData.get_response_result(5))\n # demoGetData.write_api_assertion_result(5, 'pass')\n # temp = demoGetData.update_key_with_randomstr(4)\n # print(temp)\n # file_path = demoGetData.get_file_path(5)\n # print(file_path)\n # temp = demoGetData.get_expect_key_sql(1)\n # print(temp)\n # print(demoGetData.get_expect_code(1))\n # print(demoGetData.is_set_global_vars(1))\n\n\n temp = demoGetData.get_request_data(5)\n print(type(temp))\n temp2 = json.loads(temp)\n print(temp2)\n print(type(temp2))\n\n\n\n", "sub_path": "operation_data/get_data.py", "file_name": "get_data.py", "file_ext": "py", "file_size_in_byte": 14760, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "tool.operation_excel.OperationExcel", "line_number": 13, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 14, "usage_type": "call"}, {"api_name": "tool.common_util.CommonUtil", "line_number": 15, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig.get_is_run", "line_number": 31, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 31, "usage_type": "name"}, {"api_name": "operation_data.data_config.DataConfig.get_header", "line_number": 45, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 45, "usage_type": "name"}, {"api_name": "operation_data.data_config.DataConfig.get_run_method", "line_number": 58, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 58, "usage_type": "name"}, {"api_name": "operation_data.data_config.DataConfig.get_url", "line_number": 68, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 68, "usage_type": "name"}, {"api_name": "operation_data.data_config.DataConfig.get_case_depend", "line_number": 78, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 78, "usage_type": "name"}, {"api_name": "operation_data.data_config.DataConfig.get_depend_key", "line_number": 90, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 90, "usage_type": "name"}, {"api_name": "operation_data.data_config.DataConfig.get_current_key", "line_number": 102, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 102, "usage_type": "name"}, {"api_name": "operation_data.data_config.DataConfig.get_other_depend_key", "line_number": 114, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 114, "usage_type": "name"}, {"api_name": "operation_data.data_config.DataConfig.get_other_depend_key_sql", "line_number": 126, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 126, "usage_type": "name"}, {"api_name": "tool.connect_db.OperationMysql", "line_number": 138, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig.get_request_data", "line_number": 151, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 151, "usage_type": "name"}, {"api_name": "operation_data.data_config.DataConfig.get_file_path", "line_number": 163, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 163, "usage_type": "name"}, {"api_name": "operation_data.data_config.DataConfig.get_key_need_randomstr", "line_number": 175, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 175, "usage_type": "name"}, {"api_name": "operation_data.data_config.DataConfig.get_random_str", "line_number": 187, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 187, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 201, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig.get_request_param", "line_number": 215, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 215, "usage_type": "name"}, {"api_name": "operation_data.data_config.DataConfig.get_request_param_sql", "line_number": 226, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 226, "usage_type": "name"}, {"api_name": "tool.connect_db.OperationMysql", "line_number": 238, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig.get_expect_as_fixed_value", "line_number": 251, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 251, "usage_type": "name"}, {"api_name": "operation_data.data_config.DataConfig.get_expect_code", "line_number": 263, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 263, "usage_type": "name"}, {"api_name": "operation_data.data_config.DataConfig.get_expect_msg", "line_number": 275, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 275, "usage_type": "name"}, {"api_name": "operation_data.data_config.DataConfig.get_expect_data", "line_number": 287, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 287, "usage_type": "name"}, {"api_name": "operation_data.data_config.DataConfig.get_expect_key_sql", "line_number": 299, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 299, "usage_type": "name"}, {"api_name": "tool.connect_db.OperationMysql", "line_number": 311, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig.get_api_assertion_result", "line_number": 325, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 325, "usage_type": "name"}, {"api_name": "operation_data.data_config.DataConfig.get_response_result", "line_number": 335, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 335, "usage_type": "name"}, {"api_name": "operation_data.data_config.DataConfig.get_response_result", "line_number": 339, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 339, "usage_type": "name"}, {"api_name": "operation_data.data_config.DataConfig.is_set_global_vars", "line_number": 351, "usage_type": "call"}, {"api_name": "operation_data.data_config.DataConfig", "line_number": 351, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 449, "usage_type": "call"}]} +{"seq_id": "257907189", "text": "#!/usr/bin/env python\n\n#\n# Licensed to the Apache Software Foundation (ASF) under one or more\n# contributor license agreements. See the NOTICE file distributed with\n# this work for additional information regarding copyright ownership.\n# The ASF licenses this file to You under the Apache License, Version 2.0\n# (the \"License\"); you may not use this file except in compliance with\n# 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\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 json\n\nfrom apache_ranger.model.ranger_base import RangerBase\n\n\nclass RangerSecurityZoneService:\n def __init__(self, resources=None):\n self.resources = resources if resources is not None else []\n\n def __repr__(self):\n return json.dumps(self, default=lambda x: x.__dict__, sort_keys=True, indent=4)\n\n\nclass RangerSecurityZone(RangerBase):\n def __init__(self, id=None, guid=None, createdBy=None, updatedBy=None, createTime=None, updateTime=None,\n version=None, isEnabled=None, name=None, services=None, tagServices=None, adminUsers=None,\n adminUserGroups=None, auditUsers=None, auditUserGroups=None, description=None):\n super().__init__(id, guid, createdBy, updatedBy, createTime, updateTime, version, isEnabled)\n self.name = name\n self.services = services if services is not None else {}\n self.tagServices = tagServices if tagServices is not None else []\n self.adminUsers = adminUsers if adminUsers is not None else []\n self.adminUserGroups = adminUserGroups if adminUserGroups is not None else []\n self.auditUsers = auditUsers if auditUsers is not None else []\n self.auditUserGroups = auditUserGroups if auditUserGroups is not None else []\n self.description = description\n return\n\n def __repr__(self):\n return json.dumps(self, default=lambda x: x.__dict__, sort_keys=True, indent=4)\n", "sub_path": "intg/src/main/python/apache_ranger/model/ranger_security_zone.py", "file_name": "ranger_security_zone.py", "file_ext": "py", "file_size_in_byte": 2227, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "json.dumps", "line_number": 29, "usage_type": "call"}, {"api_name": "apache_ranger.model.ranger_base.RangerBase", "line_number": 32, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "469078008", "text": "def ncdump(nc_fid, verb=True):\n '''\n ncdump outputs dimensions, variables and their attribute information.\n The information is similar to that of NCAR's ncdump utility.\n ncdump requires a valid instance of Dataset.\n\n Parameters\n ----------\n nc_fid : netCDF4.Dataset\n A netCDF4 dateset object\n verb : Boolean\n whether or not nc_attrs, nc_dims, and nc_vars are printed\n\n Returns\n -------\n nc_attrs : list\n A Python list of the NetCDF file global attributes\n nc_dims : list\n A Python list of the NetCDF file dimensions\n nc_vars : list\n A Python list of the NetCDF file variables\n '''\n def print_ncattr(key):\n \"\"\"\n Prints the NetCDF file attributes for a given key\n\n Parameters\n ----------\n key : unicode\n a valid netCDF4.Dataset.variables key\n \"\"\"\n try:\n print(\"\\t\\ttype:\", repr(nc_fid.variables[key].dtype))\n for ncattr in nc_fid.variables[key].ncattrs():\n print('\\t\\t%s:' % ncattr,\\\n repr(nc_fid.variables[key].getncattr(ncattr)))\n except KeyError:\n print(\"\\t\\tWARNING: %s does not contain variable attributes\" % key)\n\n # NetCDF global attributes\n nc_attrs = nc_fid.ncattrs()\n if verb:\n print(\"NetCDF Global Attributes:\")\n for nc_attr in nc_attrs:\n print('\\t%s:' % nc_attr, repr(nc_fid.getncattr(nc_attr)))\n nc_dims = [dim for dim in nc_fid.dimensions] # list of nc dimensions\n # Dimension shape information.\n if verb:\n print(\"NetCDF dimension information:\")\n for dim in nc_dims:\n print(\"\\tName:\", dim)\n print(\"\\t\\tsize:\", len(nc_fid.dimensions[dim]))\n print_ncattr(dim)\n # Variable information.\n nc_vars = [var for var in nc_fid.variables] # list of nc variables\n if verb:\n print(\"NetCDF variable information:\")\n for var in nc_vars:\n if var not in nc_dims:\n print('\\tName:', var)\n print(\"\\t\\tdimensions:\", nc_fid.variables[var].dimensions)\n print(\"\\t\\tsize:\", nc_fid.variables[var].size)\n print_ncattr(var)\n return nc_attrs, nc_dims, nc_vars\n\n\ndef findScaleOffset(nc_fid, var, scaleKey='SCale', offsetKey='offset',\n print_info=False):\n '''\n findScaleOffset searchs for scale and offset in the attributes, return thems\n if found, otherwise returns 1 and 0 respectively\n\n Parameters\n ----------\n nc_fid : netCDF4.Dataset\n A netCDF4 dateset object\n var : string\n var to search (tas, t2m, etc)\n scaleKey : string\n string to search for (case insensitive)\n offsetKey : string\n string to search for (case insensitive)\n\n Returns\n -------\n Found : boolean\n True if any of the scaling factors is found\n scale_factor : depends on the NetCDF (double, float, etc)\n The value of the scale factor\n add_offset : depends on the NetCDF (double, float, etc)\n The value of the offset\n '''\n\n # scale var\n found = False\n scale_factor = 1\n add_offset = 0\n\n try:\n var_attrs = nc_fid.variables[var].ncattrs()\n if print_info:\n print(\"%s attributes : %s\" % (var, var_attrs))\n # print(type(tas_attrs)) #list\n for attr in var_attrs:\n if scaleKey.lower() in attr.lower(): # lower just to make it case insensitive\n scale_factor = nc_fid.variables[var].getncattr(attr)\n print(\"Found %s: %f\" % (attr, scale_factor))\n found = True\n if offsetKey.lower() in attr.lower():\n add_offset = nc_fid.variables[var].getncattr(attr)\n print(\"Found %s: %f\" % (attr, add_offset))\n found = True\n except:\n print(\"Error, leaving to default vals\")\n # scale var\n found = False\n scale_factor = 1\n add_offset = 0\n return found, scale_factor, add_offset\n\n\ndef convertTime(cdo, nc_file_in, nc_file_out):\n cdo.setreftime(\"1850-01-01,00:00:00\", input=\"-setcalendar,standard \"+nc_file_in, output=nc_file_out)\n\n\ndef convertTemp(cdo, nc_file_in, nc_file_out):\n import os\n nc_file_out_aux = \"convertTemp_aux.nc\"\n cdo.subc(\"273.15\", input=nc_file_in, output=nc_file_out_aux)\n cdo.chunit(\"K,C\", input=nc_file_out_aux, output=nc_file_out)\n os.remove(nc_file_out_aux)\n\n\ndef convertPrecip(cdo, nc_file_in, nc_file_out):\n import os\n nc_file_out_aux = \"convertPrecip_aux.nc\"\n # 1 kg of rain water spread over 1 square meter of surface is 1 mm in thickness\n # there are 60X60X24=86400 seconds in one day.\n # Therefore, 1 kg/m2/s = 86400 mm/day.\n cdo.mulc(\"86400\", input=nc_file_in, output=nc_file_out_aux)\n cdo.chunit(\"'kg m-2 s-1','mm day-1'\", input=nc_file_out_aux, output=nc_file_out)\n os.remove(nc_file_out_aux)\n\n\ndef draw_map(m, scale=1):\n from itertools import chain\n import numpy as np\n # draw a shaded-relief image\n m.shadedrelief(scale=scale)\n\n # Add Coastlines, States, and Country Boundaries\n# m.drawcoastlines()\n# m.drawstates()\n m.drawcountries()\n\n # lats and longs are returned as a dictionary\n lats = m.drawparallels(np.linspace(-90, 90, 100))\n lons = m.drawmeridians(np.linspace(-180, 180, 300))\n\n # keys contain the plt.Line2D instances\n lat_lines = chain(*(tup[1][0] for tup in lats.items()))\n lon_lines = chain(*(tup[1][0] for tup in lons.items()))\n all_lines = chain(lat_lines, lon_lines)\n\n # cycle through these lines and set the desired style\n for line in all_lines:\n line.set(linestyle='-', alpha=0.3, color='b')\n\n\ndef get_subdirs(a_dir):\n '''\n Return (me da los nombres de cada directorio) the names of the directories inside the input folder,\n and the full path of each directory.\n '''\n import os\n return [[name, os.path.join(a_dir, name)] for name in os.listdir(a_dir)\n if os.path.isdir(os.path.join(a_dir, name))]\n\n\ndef get_subfiles(a_dir):\n '''\n Return the names of the files inside the input folder,\n and the full path of each file.\n '''\n import os\n return [[name, os.path.join(a_dir, name)] for name in os.listdir(a_dir)\n if os.path.isfile(os.path.join(a_dir, name))]\n\n\ndef check(dir_in):\n import os\n if os.path.exists(dir_in):\n return True\n else:\n return False\n\n\ndef check_and_create(dir_in):\n '''\n Check if a dir exists, if not, create it\n '''\n import os\n if os.path.exists(dir_in):\n pass # does nothing\n # print(\"Dir already exists\", dir_in)\n else:\n print(\"Create dir\", dir_in)\n os.mkdir(dir_in)\n\n\ndef plotRavel(file_path, param):\n '''\n flaten array to 1D and plot it\n '''\n\n from netCDF4 import Dataset\n import numpy as np\n\n import matplotlib.pyplot as plt\n\n fh = Dataset(file_path, mode='r') # file handle\n\n pr = fh.variables[param][:, :, :]\n fh.close()\n\n plt.plot(np.ravel(pr))\n plt.title(file_path)\n plt.show()\n\n\ndef moving_average(arr, win=3):\n ''' calculates non weigthed moving moving_average of array\n '''\n\n import numpy as np\n if win == 0:\n return arr\n ret = np.cumsum(arr, dtype=float)\n ret[win:] = ret[win:] - ret[:-win]\n return ret[win - 1:] / win\n\n\ndef reorderLegend(ax=None, order=None, unique=False):\n '''\n # Returns tuple of handles, labels for axis ax, after reordering them to\n conform to the label order `order`, and if unique is True,\n after removing entries with duplicate labels\n\n From https://gitlab.com/cpbl/cpblUtilities/blob/master/mathgraph.py\n '''\n import matplotlib.pyplot as plt\n import numpy as np\n\n if ax is None:\n ax = plt.gca()\n handles, labels = ax.get_legend_handles_labels()\n labels, handles = zip(*sorted(zip(labels, handles), key=lambda t: t[0])) # sort both labels and handles by labels\n if order is not None: # Sort according to a given list (not necessarily complete)\n keys = dict(zip(order, range(len(order))))\n labels, handles = zip(*sorted(zip(labels, handles), key=lambda t, keys=keys: keys.get(t[0], np.inf)))\n if unique:\n labels, handles = zip(*unique_everseen(zip(labels, handles), key=labels)) # Keep only the first of each handle\n # ax.legend(handles, labels)\n return(handles, labels)\n\n\ndef unique_everseen(seq, key=None):\n seen = set()\n seen_add = seen.add\n return [x for x, k in zip(seq, key) if not (k in seen or seen_add(k))]\n\n\ndef draw_screen_poly(box_in, m):\n '''\n '''\n import numpy as np\n from matplotlib.patches import Polygon\n\n import matplotlib.pyplot as plt\n\n # to draw polygon\n lon0 = box_in[0]-360\n lon1 = box_in[1]-360\n lat0 = box_in[2]\n lat1 = box_in[3]\n resolution = 10\n lats_r = np.hstack((np.linspace(lat0, lat1, resolution),\n np.linspace(lat1, lat0, resolution)))\n\n lons_r = np.hstack((np.linspace(lon0, lon0, resolution),\n np.linspace(lon1, lon1, resolution)))\n\n x, y = m(lons_r, lats_r)\n xy = zip(x, y)\n poly = Polygon(list(xy), fc=(1, 0, 0, 0.0), ec=(0.8, 0, 0, 1), lw=2)\n plt.gca().add_patch(poly)\n\n\ndef plot_basemap_regions(nc_in, png_name_in, param_in, region_in, title_in, cdo, bounds_in, colors_in, over_in, under_in, poly_in=False):\n '''\n\n '''\n from netCDF4 import Dataset\n import numpy as np\n\n import matplotlib.pyplot as plt\n import matplotlib as mpl\n from mpl_toolkits.basemap import Basemap\n\n boxDict = {\n \"Andes\": [283-1, 288+1, 0, 8.5+1], # long1, long2, lat1, lat2\n \"Alpine\": [5-1, 14+1, 44.5-1, 48.5+1]\n }\n box_in = boxDict[region_in]\n box = \"%d,%d,%d,%d\" % (box_in[0], box_in[1], box_in[2], box_in[3]) # box of Cdo\n\n print(box)\n print(nc_in)\n print(param_in)\n\n fh = Dataset(nc_in, 'r')\n\n lons = fh.variables['lon'][:]\n lats = fh.variables['lat'][:]\n param = fh.variables[param_in][0:, :, :]\n\n param_units = fh.variables[param_in].units\n param_name = fh.variables[param_in].long_name\n # close file\n fh.close()\n\n # Get some parameters for the Stereographic Projection\n # lon_0 = lons.mean()\n # lat_0 = lats.mean()\n\n # m = Basemap(projection='moll',lon_0=0,resolution='l')\n # m = Basemap(width=50000, height=10000,\n # resolution='l', projection='moll',\\\n # lat_ts=40, lat_0=lat_0, lon_0=lon_0) # stere=stereographic projection\n #\n # m = Basemap(projection='ortho', lat_0=5, lon_0=-60, resolution='l')\n m = Basemap(projection='cass', llcrnrlat=box_in[2]-2, urcrnrlat=box_in[3]+2,\n llcrnrlon=box_in[0]-2, urcrnrlon=box_in[1]+2, resolution='l',\n lon_0=box_in[0]+3, lat_0=box_in[2]+4)\n\n lons_dim = len(lons.shape)\n if 2 == lons_dim:\n lon = lons\n lat = lats\n elif 1 == lons_dim:\n lon, lat = np.meshgrid(lons, lats)\n else:\n print(\"Error in lon lat array dimension: %d\" % lons_dim)\n\n xi, yi = m(lon, lat)\n\n # Plot Data\n # cmap = plt.get_cmap('terrain')'' 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0\n cmap = mpl.colors.ListedColormap(colors_in)\n cmap.set_over(over_in)\n cmap.set_under(under_in)\n\n norm = mpl.colors.BoundaryNorm(bounds_in, cmap.N)\n # cb3 = mpl.colorbar.ColorbarBase(ax, cmap=cmap,\n # norm=norm,\n # boundaries=[-10] + bounds + [10],\n # extend='both',\n # extendfrac='auto',\n # ticks=bounds,\n # spacing='uniform',\n # orientation='horizontal')\n\n cs = m.pcolor(xi, yi, np.squeeze(param), alpha=0.7, cmap=cmap, norm=norm)\n\n # Add Grid Lines\n m.drawparallels(np.arange(-80., 81., 10.), labels=[1, 0, 0, 0], fontsize=10)\n m.drawmeridians(np.arange(-180., 181., 10.), labels=[0, 0, 0, 1], fontsize=10)\n\n # Add Coastlines, States, and Country Boundaries\n m.drawcoastlines()\n # m.drawstates()\n m.drawcountries()\n m.shadedrelief()\n\n # Add Colorbar\n cbar = m.colorbar(cs, location='bottom', pad=\"10%\")\n cbar.set_label(\"%s (%s)\" % (param_name, param_units))\n\n if (poly_in):\n draw_screen_poly(box_in, m)\n\n # Add Title\n title_region = (title_in)\n plt.title(title_region)\n plt.savefig(png_name_in, dpi=200)\n # plt.show()\n plt.close()\n\n\ndef plot_time_series(file_path_in_array, png_name_in=None, param_in=None, region=None, h_line=None):\n '''\n plot_time_series ...\n '''\n import pathlib\n from useful_functions import findScaleOffset\n from netcdftime import utime\n import matplotlib.pyplot as plt\n import matplotlib.dates as date_plt\n from useful_functions import moving_average\n from netCDF4 import Dataset # http://code.google.com/p/netcdf4-python/\n import datetime as dt # Python standard library datetime module\n from bisect import bisect_left\n import logging\n\n logger = logging.getLogger('root')\n FORMAT = \"[%(filename)s:%(lineno)s - %(funcName)20s() ] %(message)s\"\n logging.basicConfig(format=FORMAT)\n logger.setLevel(logging.debug())\n\n plot_each = True\n\n # Read time and param vars\n if param_in is None:\n return\n\n date_fill = None\n rcp45_p25_fill = None\n rcp45_p75_fill = None\n\n rcp85_p25_fill = None\n rcp85_p75_fill = None\n\n histo_date_fill = None\n histo_rcp45_p25_fill = None\n histo_rcp45_p75_fill = None\n\n histo_rcp85_p25_fill = None\n histo_rcp85_p75_fill = None\n\n for file_path_in in file_path_in_array:\n\n plt.figure(region+' '+param_in, figsize=(15, 6))\n logger.debug(\"plot_time_series \"+file_path_in)\n\n data_in = Dataset(file_path_in, mode='r')\n\n time = data_in.variables['time'][:]\n param = data_in.variables[param_in][:]\n # Scale var\n [scal_req, scale_factor, add_offset] = findScaleOffset(data_in, param_in)\n param_scaled = (scale_factor*param)+add_offset\n\n # create time vector\n time_uni = data_in.variables['time'].units\n time_cal = data_in.variables['time'].calendar\n\n cdftime = utime(time_uni, calendar=time_cal)\n date = [cdftime.num2date(t) for t in time]\n\n # ############# A plot of Maximum precipitation ##############\n\n # plt.plot(date, param_scaled[:, 0, 0], label=model)\n\n days_2006 = 57160.5 # 2006 value in time:units = \"days since 1850-1-1 00:00:00\" ; time:calendar = \"standard\" ;'\n index_2006 = bisect_left(time, days_2006)\n\n half_window = 60 # 60-5\n window = half_window * 2\n date_start = half_window\n date_end = half_window - 1 # date [x:-y], where x+y = window - 1\n\n param_scaled_smoothed = moving_average(arr=param_scaled[:, 0, 0], win=window)\n\n if \"25\" in file_path_in and \"rcp45\" in file_path_in:\n rcp45_p25_fill = param_scaled_smoothed[index_2006-1-date_end-1:]\n histo_rcp45_p25_fill = param_scaled_smoothed[:index_2006-date_start]\n\n # plt.plot(date[index_2006-1:-date_end], param_scaled_smoothed[index_2006-1-date_end-1:], 'g--', label=pathlib.Path(file_path_in).stem.split(\"45\")[0])#.split(\"_histo\")[0])\n elif \"75\" in file_path_in and \"rcp45\" in file_path_in:\n rcp45_p75_fill = param_scaled_smoothed[index_2006-1-date_end-1:]\n date_fill = date[index_2006-1:-date_end]\n histo_rcp45_p75_fill = param_scaled_smoothed[:index_2006-date_start]\n histo_date_fill = date[date_start:index_2006]\n # plt.plot(date[index_2006-1:-date_end], param_scaled_smoothed[index_2006-1-date_end-1:], 'g--', label=pathlib.Path(file_path_in).stem.split(\"45\")[0])#.split(\"_histo\")[0])\n elif \"rcp45\" in file_path_in:\n plt.plot(date[date_start : index_2006], param_scaled_smoothed[:index_2006-date_start], 'k') # label=pathlib.Path(file_path_in).stem.split(\"45\")[0])#.split(\"_histo\")[0])\n plt.plot(date[index_2006-1 : -date_end], param_scaled_smoothed[index_2006-1-date_end-1:], 'g', label=pathlib.Path(file_path_in).stem.split(\"45\")[0])#.split(\"_histo\")[0])\n\n\n if \"25\" in file_path_in and \"rcp85\" in file_path_in:\n rcp85_p25_fill = param_scaled_smoothed[index_2006-1-date_end-1:]\n histo_rcp85_p25_fill = param_scaled_smoothed[:index_2006-date_start]\n # plt.plot(date[index_2006-1:-date_end], param_scaled_smoothed[index_2006-1-date_end-1:], 'r--', label=pathlib.Path(file_path_in).stem.split(\"45\")[0])#.split(\"_histo\")[0])\n elif \"75\" in file_path_in and \"rcp85\" in file_path_in:\n rcp85_p75_fill = param_scaled_smoothed[index_2006-1-date_end-1:]\n date_fill = date[index_2006-1:-date_end]\n histo_rcp85_p75_fill = param_scaled_smoothed[:index_2006-date_start]\n histo_date_fill = date[date_start:index_2006]\n # plt.plot(date[index_2006-1:-date_end], param_scaled_smoothed[index_2006-1-date_end-1:], 'r--', label=pathlib.Path(file_path_in).stem.split(\"45\")[0])#.split(\"_histo\")[0])\n elif \"rcp85\" in file_path_in:\n plt.plot(date[index_2006-1:-date_end], param_scaled_smoothed[index_2006-1-date_end-1:], 'r', label=pathlib.Path(file_path_in).stem.split(\"45\")[0])#.split(\"_histo\")[0])\n plt.plot(date[date_start:index_2006], param_scaled_smoothed[:index_2006-date_start], 'k') # label=pathlib.Path(file_path_in).stem.split(\"45\")[0])#.split(\"_histo\")[0])\n\n # plt.ylabel(\"%s Anomaly (%s)\" % (data_in.variables[param_in].long_name,\n # data_in.variables[param_in].units))\n #plt.ylabel(\"Exceedance rate (%)\") # TX90P\n plt.ylabel( data_in.variables[param_in].units) # R95P, SDII, RX5DAY, SDII\n #plt.ylabel(\"Days\") # FD\n\n plt.ticklabel_format(useOffset=False, axis='y')\n plt.xlabel(\"Year\")\n plt.title('Annual '+data_in.variables[param_in].long_name+' Anomaly '+'in the ' + region + ' region (smoothed)', fontweight='bold')\n data_in.close()\n\n ############## TO PLOT each model ############\n # if plot_each:\n # plt.legend()#loc=(0, 0), fontsize=7, frameon=True, ncol=11, bbox_to_anchor=(0, -0.5)) # Legend for smoothed\n # plt.tight_layout(rect=[0, 0, 1, 1])\n #\n # # add horizontal line at y=0\n # if h_line is not None:\n # plt.axhline(y=h_line, color='k')\n # # highligth 1961 to 1990 range\n # plt.axvspan(dt.datetime(1961, 1, 1), dt.datetime(1990, 12, 30), color='b', alpha=0.1)\n #\n # plt.grid(b=True, linestyle='--', linewidth=1)\n # plt.show()\n\n if rcp45_p25_fill is not None:\n plt.fill_between(date_fill, rcp45_p25_fill, rcp45_p75_fill,\n facecolor=\"g\", # The fill color\n # color='', # The outline color\n alpha=0.2) # Transparency of the fill\n\n if rcp85_p25_fill is not None:\n plt.fill_between(date_fill, rcp85_p25_fill, rcp85_p75_fill,\n facecolor=\"r\", # The fill color\n # color='', # The outline color\n alpha=0.2) # Transparency of the fill\n\n if histo_rcp45_p25_fill is not None:\n plt.fill_between(histo_date_fill, histo_rcp45_p25_fill, histo_rcp45_p75_fill,\n facecolor=\"k\", # The fill color\n # color='', # The outline color\n alpha=0.1)\n\n if histo_rcp85_p25_fill is not None:\n plt.fill_between(histo_date_fill, histo_rcp85_p25_fill, histo_rcp85_p75_fill,\n facecolor=\"k\", # The fill color\n # color='', # The outline color\n alpha=0.1) # Transparency of the fill\n\n # plt.legend(loc=(0, 0), fontsize=7, frameon=True, ncol=11, bbox_to_anchor=(0, -0.5)) # Legend for smoothed\n plt.tight_layout(rect=[0, 0, 1, 1])\n\n # add horizontal line at y=0\n if h_line is not None:\n plt.axhline(y=h_line, color='b', alpha=0.5, linestyle='--')\n # logger.debug(clean((clean((list(plt.yticks()[0]))))))\n # plt.yticks(list(plt.yticks()[0]) + [h_line])\n # highligth 1961 to 1990 range\n plt.axvspan(dt.datetime(1961, 1, 1), dt.datetime(1990, 12, 30), color='b', alpha=0.1)\n\n plt.grid(b=True, linestyle='--', linewidth=1)\n\n # cdftime = utime(time_uni, calendar=time_cal)\n # date = [cdftime.num2date(time[140])]\n # dates = [dt.datetime(1861,1,1),\n # dt.datetime(1890,1,1),\n # dt.datetime(1961,1,1),\n # dt.datetime(1990,1,1),\n # dt.datetime(2006,1,1),\n # dt.datetime(2061,1,1),\n # dt.datetime(2090,1,1)]\n #\n # dates_plot = [date_plt.date2num(date) for date in dates]\n # plt.xticks(dates_plot)\n\n # plt.show()\n\n if png_name_in is None:\n plt.show()\n else:\n logger.debug((png_name_in))\n plt.savefig(png_name_in, dpi=150)\n", "sub_path": "python3/useful_functions.py", "file_name": "useful_functions.py", "file_ext": "py", "file_size_in_byte": 21189, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.remove", "line_number": 131, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 142, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 158, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 161, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 162, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 163, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path", "line_number": 176, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 177, "usage_type": "call"}, {"api_name": "os.path", "line_number": 177, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 177, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 186, "usage_type": "call"}, {"api_name": "os.path", "line_number": 186, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 186, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 187, "usage_type": "call"}, {"api_name": "os.path", "line_number": 187, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 187, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path", "line_number": 192, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 203, "usage_type": "call"}, {"api_name": "os.path", "line_number": 203, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 208, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 221, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 226, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 226, "usage_type": "name"}, {"api_name": "numpy.ravel", "line_number": 226, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}, {"api_name": "numpy.cumsum", "line_number": 238, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 255, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 255, "usage_type": "name"}, {"api_name": "numpy.inf", "line_number": 260, "usage_type": "attribute"}, {"api_name": "numpy.hstack", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 290, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 290, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 291, "usage_type": "call"}, {"api_name": "matplotlib.patches.Polygon", "line_number": 295, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 296, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 296, "usage_type": "name"}, {"api_name": "netCDF4.Dataset", "line_number": 321, "usage_type": "call"}, {"api_name": "mpl_toolkits.basemap.Basemap", "line_number": 342, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 351, "usage_type": "call"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 359, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 359, "usage_type": "attribute"}, {"api_name": "matplotlib.colors.BoundaryNorm", "line_number": 363, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 363, "usage_type": "attribute"}, {"api_name": "numpy.squeeze", "line_number": 373, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 376, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 377, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 394, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 394, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 395, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 395, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 397, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 397, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 415, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 417, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 418, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 442, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 442, "usage_type": "name"}, {"api_name": "netCDF4.Dataset", "line_number": 445, "usage_type": "call"}, {"api_name": "useful_functions.findScaleOffset", "line_number": 450, "usage_type": "call"}, {"api_name": "netcdftime.utime", "line_number": 457, "usage_type": "call"}, {"api_name": "bisect.bisect_left", "line_number": 465, "usage_type": "call"}, {"api_name": "useful_functions.moving_average", "line_number": 472, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 486, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 486, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 487, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 487, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 487, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 501, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 501, "usage_type": "name"}, {"api_name": "pathlib.Path", "line_number": 501, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 502, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 502, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 507, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 507, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ticklabel_format", "line_number": 510, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 510, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 511, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 511, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 512, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 512, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 530, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 530, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 536, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 536, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 542, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 542, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 548, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 548, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 554, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 554, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 558, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 558, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axvspan", "line_number": 562, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 562, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 562, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 564, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 564, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 582, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 582, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 585, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 585, "usage_type": "name"}]} +{"seq_id": "167366868", "text": "#!/usr/bin/env python\n# coding: utf-8\n\nfrom bs4 import BeautifulSoup as bs\nimport requests \nimport pymongo\nimport pandas as pd\nimport re\nimport os\nimport csv\nimport time\nimport json\nimport ast\nimport pprint\nfrom datetime import datetime, timedelta\nfrom sqlalchemy import create_engine\n\nDATABASE_URI = os.environ.get('DATABASE_URL', '') or \"postgresql://postgres:postgres@localhost:5432/corona_db\"\nprint(DATABASE_URI)\n\nengine = create_engine(DATABASE_URI)\n# connection_string = \"postgres:postgres@localhost:5432/corona_db\"\n\ndef create_geojson(date, df_merged):\n df_countries_by_day = df_merged[(df_merged.date==date)]\n df_data = df_countries_by_day.groupby(['country','country_2']).agg({'conf_count':'sum', 'cured_count':'sum', 'dead_count':'sum'}).reset_index()\n df_data = df_data.set_index('country')\n \n json_df = pd.read_json('static/world_geojsons/world.json', encoding='UTF-8')\n json_df_feat = pd.DataFrame(json_df.features)\n\n geo_string = \"\"\n\n for index, row in json_df_feat.iterrows():\n# print(row['features']['properties']['ADMIN'])\n # print(index)\n try:\n str_feat_1 = (\"{\\\"type\\\": \\\"Feature\\\",\")\n\n prop_name = row['features']['properties']['ADMIN']\n prop_american_name = df_data.loc[prop_name,\"country_2\"]\n prop_confirmedCount = df_data.loc[prop_name,\"conf_count\"]\n prop_suspectedCount = 0\n prop_curedCount = df_data.loc[prop_name,\"cured_count\"]\n prop_deadCount = df_data.loc[prop_name,\"dead_count\"] \n prop_date = date\n\n str_prop_double_quotes = str(row['features']['properties'])\n str_prop_double_quotes = str_prop_double_quotes.replace(\"\\'\",\"\\\"\")\n\n str_prop_1 = (\"\\\"properties\\\" : \" + str_prop_double_quotes + \"\\\",\")[:-3]\n str_prop_2 = (\",\\\"american_name\\\" : \\\"\" + prop_american_name + \"\\\",\")\n str_prop_3 = (\"\\\"confirmedCount\\\" : \\\"\" + str(prop_confirmedCount) + \"\\\",\")\n str_prop_4 = (\"\\\"suspectedCount\\\" : \\\"\" + str(prop_suspectedCount) + \"\\\",\")\n str_prop_5 = (\"\\\"curedCount\\\" : \\\"\" + str(prop_curedCount) + \"\\\",\")\n str_prop_6 = (\"\\\"deadCount\\\" : \\\"\" + str(prop_deadCount) + \"\\\",\")\n str_prop_7 = (\"\\\"date\\\" : \\\"\" + prop_date + \"\\\"},\")\n\n str_prop_all = str_prop_1 + str_prop_2 + str_prop_3 + str_prop_4 + str_prop_5 + str_prop_6 + str_prop_7\n\n str_geom_1 = (\"\\\"geometry\\\":\" + str(row['features']['geometry']) + \"},\")\n str_geom_1 = str_geom_1.replace(\"\\'\",\"\\\"\")\n\n str_for_each_province = (str_feat_1)+(str_prop_all)+(str_geom_1)\n geo_string = geo_string + (str_for_each_province)\n except:\n geo_string = geo_string\n \n pre_fix = \"{\\\"type\\\": \\\"FeatureCollection\\\", \\\"features\\\": [\"\n post_fix = \"]}\"\n\n total_string = pre_fix + geo_string[:-1] + post_fix\n\n output_path = os.path.join(\"static/world_geojsons\", date + \".json\")\n with open(output_path, \"w\", encoding='UTF-8') as text_file:\n text_file.write(total_string)\n text_file.close()\n\ndef save_world_to_database(df_world):\n # connection_string = \"postgres:postgres@localhost:5432/corona_db\"\n # engine = create_engine(f'postgresql://{connection_string}')\n df_world.to_sql(name='daily_stats_world', con=engine, if_exists='append', index=False)\n\ndef load_new():\n\n confirmed_url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Confirmed.csv'\n df_confirmed = pd.read_csv(confirmed_url)\n cured_url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Recovered.csv'\n df_cured = pd.read_csv(cured_url)\n death_url = 'https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_19-covid-Deaths.csv'\n df_death = pd.read_csv(death_url)\n\n df_confirmed_long = df_confirmed.melt(id_vars=[\"Province/State\", \"Country/Region\",\"Lat\",\"Long\"], \n var_name=\"Date\", \n value_name=\"Value\")\n df_confirmed_long.head()\n output_path = os.path.join(\"static/world_data\", \"df_world_confirmed_original.csv\")\n df_confirmed.to_csv(output_path)\n\n df_cured_long = df_cured.melt(id_vars=[\"Province/State\", \"Country/Region\",\"Lat\",\"Long\"], \n var_name=\"Date\", \n value_name=\"Value\")\n df_cured_long.head()\n output_path = os.path.join(\"static/world_data\", \"df_world_cured_original.csv\")\n df_cured.to_csv(output_path)\n\n df_death_long = df_death.melt(id_vars=[\"Province/State\", \"Country/Region\",\"Lat\",\"Long\"], \n var_name=\"Date\", \n value_name=\"Value\")\n\n output_path = os.path.join(\"static/world_data\", \"df_world_death_original.csv\")\n df_death.to_csv(output_path)\n\n df_confirmed_long[\"new_date\"] = pd.to_datetime(df_confirmed_long['Date'])\n\n df_confirmed_long[\"Province/State\"].fillna(df_confirmed_long[\"Country/Region\"], inplace=True)\n df_confirmed_long = df_confirmed_long.rename(columns={\"Province/State\":\"american_name\", \"Country/Region\":\"country\", \"Lat\":\"lat\",\"Long\":\"long\", \"Date\":\"date\", \"Value\": \"conf_count\"})\n\n df_cured_long[\"Province/State\"].fillna(df_cured_long[\"Country/Region\"], inplace=True)\n df_cured_long = df_cured_long.rename(columns={\"Province/State\":\"american_name\", \"Country/Region\":\"country\", \"Lat\":\"lat\",\"Long\":\"long\",\"Date\":\"date\", \"Value\": \"cured_count\"})\n\n df_death_long[\"Province/State\"].fillna(df_death_long[\"Country/Region\"], inplace=True)\n df_death_long = df_death_long.rename(columns={\"Province/State\":\"american_name\", \"Country/Region\":\"country\", \"Lat\":\"lat\",\"Long\":\"long\",\"Date\":\"date\", \"Value\": \"dead_count\"})\n\n df_confirmed_long.loc[(df_confirmed_long.country == 'Mainland China'),'country']='China'\n df_confirmed_long.loc[(df_confirmed_long.country == 'US'),'country']='United States of America'\n df_confirmed_long = df_confirmed_long.set_index(['date', 'american_name'])\n\n df_cured_long.loc[(df_cured_long.country == 'Mainland China'),'country']='China'\n df_cured_long.loc[(df_cured_long.country == 'US'),'country']='United States of America'\n df_cured_long = df_cured_long.set_index(['date', 'american_name'])\n\n df_death_long.loc[(df_death_long.country == 'Mainland China'),'country']='China'\n df_death_long.loc[(df_death_long.country == 'US'),'country']='United States of America'\n df_death_long = df_death_long.set_index(['date', 'american_name'])\n\n query_str = open('static/sql/world_max_date.sql')\n query_text = \"\"\n for text in query_str:\n query_text = query_text + text\n\n # connection_string = \"postgres:postgres@localhost:5432/corona_db\"\n # engine = create_engine(f'postgresql://{connection_string}')\n # connection = engine.connect()\n rs = engine.execute(query_text)\n\n # rs = connection.execute(query_text)\n for i in rs:\n last_date = (i[0])\n print(last_date)\n # connection.close()\n\n df_merged = pd.merge(df_confirmed_long, df_cured_long, how='left', on=['date', 'american_name', 'country','lat','long'])\n\n df_merged = pd.merge(df_merged, df_death_long,how='left', on=['date', 'american_name', 'country','lat','long'])\n df_merged = df_merged.reset_index()\n df_merged = df_merged.drop(['date'], axis=1)\n df_merged['country_2'] = df_merged['country']\n\n df_merged = df_merged.rename(columns={\"new_date\":\"date\"})\n\n df_merged_new = df_merged[df_merged['date'] > last_date]\n\n output_path = os.path.join(\"static/world_data\", \"df_world_new.csv\")\n df_merged_new.to_csv(output_path)\n df_merged_new = df_merged_new[df_merged['conf_count']!=0]\n\n current_date = datetime.now()\n print(\"Current Date \",current_date)\n yesterday = current_date+ timedelta(days=0)\n print(\"Yesterday: \",yesterday)\n\n start_date = datetime.strptime(last_date, \"%Y-%m-%d\")\n print(\"Start Date\", start_date)\n\n while yesterday > start_date:\n pass_date = start_date.strftime(\"%Y-%m-%d\")\n print(pass_date)\n create_geojson(pass_date, df_merged)\n start_date = start_date + timedelta(days=1)\n\n url_world = \"static/world_data/df_world_new.csv\"\n df_world = pd.read_csv(url_world)\n df_world = df_world.drop(['country_2'], axis=1)\n df_world.head()\n \n save_world_to_database(df_world)\n\n query_str = open('static/sql/world_top_10.sql')\n query_text = \"\"\n for text in query_str:\n query_text = query_text + text\n\n # connection_string = \"postgres:postgres@localhost:5432/corona_db\"\n # engine = create_engine(f'postgresql://{connection_string}') \n\n df_query = pd.read_sql_query(query_text, con=engine)\n print(df_query.head())", "sub_path": "load_db.py", "file_name": "load_db.py", "file_ext": "py", "file_size_in_byte": 8780, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.environ.get", "line_number": 18, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sqlalchemy.create_engine", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.read_json", "line_number": 29, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 87, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 89, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "pandas.to_datetime", "line_number": 114, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 153, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path", "line_number": 164, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 168, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 168, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 170, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 173, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 173, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 180, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 183, "usage_type": "call"}, {"api_name": "pandas.read_sql_query", "line_number": 197, "usage_type": "call"}]} +{"seq_id": "579682073", "text": "\"\"\"Tests for citrine.informatics.predictors serialization.\"\"\"\nfrom copy import deepcopy\nfrom uuid import UUID\n\nimport pytest\n\nfrom citrine.informatics.descriptors import RealDescriptor\nfrom citrine.informatics.predictors import ExpressionPredictor, GeneralizedMeanPropertyPredictor, \\\n GraphPredictor, Predictor, SimpleMLPredictor, IngredientsToSimpleMixturePredictor, \\\n LabelFractionsPredictor, SimpleMixturePredictor, IngredientFractionsPredictor, DeprecatedExpressionPredictor\n\n\ndef valid_serialization_output(data):\n \"\"\"Remove fields that are not preserved by serialization.\"\"\"\n return {x: y for x, y in data.items() if x not in {'status', 'status_info'}}\n\n\ndef test_simple_legacy_deserialization(valid_simple_ml_predictor_data):\n \"\"\"Ensure that a deserialized SimplePredictor looks sane.\"\"\"\n predictor: SimpleMLPredictor = SimpleMLPredictor.build(valid_simple_ml_predictor_data)\n assert predictor.name == 'ML predictor'\n assert predictor.description == 'Predicts z from input x and latent variable y'\n assert len(predictor.inputs) == 1\n assert predictor.inputs[0] == RealDescriptor(\"x\", 0, 100, \"\")\n assert len(predictor.outputs) == 1\n assert predictor.outputs[0] == RealDescriptor(\"z\", 0, 100, \"\")\n assert len(predictor.latent_variables) == 1\n assert predictor.latent_variables[0] == RealDescriptor(\"y\", 0, 100, \"\")\n assert len(predictor.training_data) == 1\n assert predictor.training_data[0].table_id == UUID('e5c51369-8e71-4ec6-b027-1f92bdc14762')\n\n\ndef test_polymorphic_legacy_deserialization(valid_simple_ml_predictor_data):\n \"\"\"Ensure that a polymorphically deserialized SimplePredictor looks sane.\"\"\"\n predictor: SimpleMLPredictor = Predictor.build(valid_simple_ml_predictor_data)\n assert predictor.name == 'ML predictor'\n assert predictor.description == 'Predicts z from input x and latent variable y'\n assert len(predictor.inputs) == 1\n assert predictor.inputs[0] == RealDescriptor(\"x\", 0, 100, \"\")\n assert len(predictor.outputs) == 1\n assert predictor.outputs[0] == RealDescriptor(\"z\", 0, 100, \"\")\n assert len(predictor.latent_variables) == 1\n assert predictor.latent_variables[0] == RealDescriptor(\"y\", 0, 100, \"\")\n assert len(predictor.training_data) == 1\n assert predictor.training_data[0].table_id == UUID('e5c51369-8e71-4ec6-b027-1f92bdc14762')\n\n\ndef test_legacy_serialization(valid_simple_ml_predictor_data):\n \"\"\"Ensure that a serialized SimplePredictor looks sane.\"\"\"\n predictor = SimpleMLPredictor.build(valid_simple_ml_predictor_data)\n serialized = predictor.dump()\n serialized['id'] = valid_simple_ml_predictor_data['id']\n assert serialized == valid_serialization_output(valid_simple_ml_predictor_data)\n\n\ndef test_graph_serialization(valid_graph_predictor_data):\n \"\"\"Ensure that a serialized GraphPredictor looks sane.\"\"\"\n graph_data_copy = deepcopy(valid_graph_predictor_data)\n predictor = GraphPredictor.build(valid_graph_predictor_data)\n serialized = predictor.dump()\n serialized['id'] = graph_data_copy['id']\n assert serialized['config']['predictors'] == graph_data_copy['config']['predictors']\n assert serialized == valid_serialization_output(graph_data_copy)\n\n\ndef test_deprecated_expression_serialization(valid_deprecated_expression_predictor_data):\n \"\"\"Ensure that a serialized DeprecatedExpressionPredictor looks sane.\"\"\"\n predictor = DeprecatedExpressionPredictor.build(valid_deprecated_expression_predictor_data)\n serialized = predictor.dump()\n serialized['id'] = valid_deprecated_expression_predictor_data['id']\n assert serialized == valid_serialization_output(valid_deprecated_expression_predictor_data)\n\n\ndef test_expression_serialization(valid_expression_predictor_data):\n \"\"\"Ensure that a serialized ExpressionPredictor looks sane.\"\"\"\n predictor = ExpressionPredictor.build(valid_expression_predictor_data)\n serialized = predictor.dump()\n serialized['id'] = valid_expression_predictor_data['id']\n assert serialized == valid_serialization_output(valid_expression_predictor_data)\n\n\ndef test_ing_to_simple_mixture_serialization(valid_ing_to_simple_mixture_predictor_data):\n \"\"\"Ensure that a serialized IngredientsToSimpleMixturePredictor looks sane.\"\"\"\n predictor = IngredientsToSimpleMixturePredictor.build(valid_ing_to_simple_mixture_predictor_data)\n serialized = predictor.dump()\n serialized['id'] = valid_ing_to_simple_mixture_predictor_data['id']\n assert serialized == valid_serialization_output(valid_ing_to_simple_mixture_predictor_data)\n\n\ndef test_generalized_mean_property_serialization(valid_generalized_mean_property_predictor_data):\n \"\"\"Ensure that a serialized GeneralizedMeanPropertyPredictor looks sane.\"\"\"\n predictor = GeneralizedMeanPropertyPredictor.build(valid_generalized_mean_property_predictor_data)\n serialized = predictor.dump()\n serialized['id'] = valid_generalized_mean_property_predictor_data['id']\n assert serialized == valid_serialization_output(valid_generalized_mean_property_predictor_data)\n\n\ndef test_simple_mixture_predictor_serialization(valid_simple_mixture_predictor_data):\n predictor = SimpleMixturePredictor.build(valid_simple_mixture_predictor_data)\n serialized = predictor.dump()\n serialized['id'] = valid_simple_mixture_predictor_data['id']\n assert serialized == valid_serialization_output(valid_simple_mixture_predictor_data)\n\n\ndef test_label_fractions_serialization(valid_label_fractions_predictor_data):\n \"\"\"Ensure that a serialized LabelFractionPredictor looks sane.\"\"\"\n predictor = LabelFractionsPredictor.build(valid_label_fractions_predictor_data)\n serialized = predictor.dump()\n serialized['id'] = valid_label_fractions_predictor_data['id']\n assert serialized == valid_serialization_output(valid_label_fractions_predictor_data)\n\n\ndef test_ingredient_fractions_serialization(valid_ingredient_fractions_predictor_data):\n \"\"\"\"Ensure that a serialized IngredientsFractionsPredictor looks sane.\"\"\"\n predictor = IngredientFractionsPredictor.build(valid_ingredient_fractions_predictor_data)\n serialized = predictor.dump()\n serialized[\"id\"] = valid_ingredient_fractions_predictor_data['id']\n assert serialized == valid_serialization_output(valid_ingredient_fractions_predictor_data)\n\n\ndef test_invalid_predictor_type(invalid_predictor_data):\n \"\"\"Ensures we raise proper exception when an invalid type is used.\"\"\"\n with pytest.raises(ValueError):\n Predictor.build(invalid_predictor_data)\n", "sub_path": "tests/serialization/test_predictors.py", "file_name": "test_predictors.py", "file_ext": "py", "file_size_in_byte": 6503, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "citrine.informatics.predictors.SimpleMLPredictor", "line_number": 20, "usage_type": "name"}, {"api_name": "citrine.informatics.predictors.SimpleMLPredictor.build", "line_number": 20, "usage_type": "call"}, {"api_name": "citrine.informatics.descriptors.RealDescriptor", "line_number": 24, "usage_type": "call"}, {"api_name": "citrine.informatics.descriptors.RealDescriptor", "line_number": 26, "usage_type": "call"}, {"api_name": "citrine.informatics.descriptors.RealDescriptor", "line_number": 28, "usage_type": "call"}, {"api_name": "uuid.UUID", "line_number": 30, "usage_type": "call"}, {"api_name": "citrine.informatics.predictors.SimpleMLPredictor", "line_number": 35, "usage_type": "name"}, {"api_name": "citrine.informatics.predictors.Predictor.build", "line_number": 35, "usage_type": "call"}, {"api_name": "citrine.informatics.predictors.Predictor", "line_number": 35, "usage_type": "name"}, {"api_name": "citrine.informatics.descriptors.RealDescriptor", "line_number": 39, "usage_type": "call"}, {"api_name": "citrine.informatics.descriptors.RealDescriptor", "line_number": 41, "usage_type": "call"}, {"api_name": "citrine.informatics.descriptors.RealDescriptor", "line_number": 43, "usage_type": "call"}, {"api_name": "uuid.UUID", "line_number": 45, "usage_type": "call"}, {"api_name": "citrine.informatics.predictors.SimpleMLPredictor.build", "line_number": 50, "usage_type": "call"}, {"api_name": "citrine.informatics.predictors.SimpleMLPredictor", "line_number": 50, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 58, "usage_type": "call"}, {"api_name": "citrine.informatics.predictors.GraphPredictor.build", "line_number": 59, "usage_type": "call"}, {"api_name": "citrine.informatics.predictors.GraphPredictor", "line_number": 59, "usage_type": "name"}, {"api_name": "citrine.informatics.predictors.DeprecatedExpressionPredictor.build", "line_number": 68, "usage_type": "call"}, {"api_name": "citrine.informatics.predictors.DeprecatedExpressionPredictor", "line_number": 68, "usage_type": "name"}, {"api_name": "citrine.informatics.predictors.ExpressionPredictor.build", "line_number": 76, "usage_type": "call"}, {"api_name": "citrine.informatics.predictors.ExpressionPredictor", "line_number": 76, "usage_type": "name"}, {"api_name": "citrine.informatics.predictors.IngredientsToSimpleMixturePredictor.build", "line_number": 84, "usage_type": "call"}, {"api_name": "citrine.informatics.predictors.IngredientsToSimpleMixturePredictor", "line_number": 84, "usage_type": "name"}, {"api_name": "citrine.informatics.predictors.GeneralizedMeanPropertyPredictor.build", "line_number": 92, "usage_type": "call"}, {"api_name": "citrine.informatics.predictors.GeneralizedMeanPropertyPredictor", "line_number": 92, "usage_type": "name"}, {"api_name": "citrine.informatics.predictors.SimpleMixturePredictor.build", "line_number": 99, "usage_type": "call"}, {"api_name": "citrine.informatics.predictors.SimpleMixturePredictor", "line_number": 99, "usage_type": "name"}, {"api_name": "citrine.informatics.predictors.LabelFractionsPredictor.build", "line_number": 107, "usage_type": "call"}, {"api_name": "citrine.informatics.predictors.LabelFractionsPredictor", "line_number": 107, "usage_type": "name"}, {"api_name": "citrine.informatics.predictors.IngredientFractionsPredictor.build", "line_number": 115, "usage_type": "call"}, {"api_name": "citrine.informatics.predictors.IngredientFractionsPredictor", "line_number": 115, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 123, "usage_type": "call"}, {"api_name": "citrine.informatics.predictors.Predictor.build", "line_number": 124, "usage_type": "call"}, {"api_name": "citrine.informatics.predictors.Predictor", "line_number": 124, "usage_type": "name"}]} +{"seq_id": "527234468", "text": "import math\nimport numpy as np\nfrom abc import ABCMeta, abstractmethod\nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\n\nclass NonLinearMethod(metaclass=ABCMeta):\n\n def __init__(self, function):\n self.function = function\n\n # 収束かどうかを判定するための値\n self.errorRangeLimit = 0.00001\n\n\nclass SteepestDecentMethod(NonLinearMethod):\n def __init__(self, function, dif_func, x0):\n super().__init__(function)\n self.dif_func = dif_func\n self.x = x0\n self.transition_x0 = []\n self.transition_x1 = []\n\n def backtrack(self, alpha=0.5, beta=0.8):\n while True:\n self.transition_x0.append(self.x[0])\n self.transition_x1.append(self.x[1])\n dx = 1.0\n while True:\n next_x = self.x - dx * self.dif_func(self.x)\n armijo_rule = self.function(next_x) - self.function(self.x) + alpha * dx * pow(np.linalg.norm(self.dif_func(self.x)), 2)\n if armijo_rule <= 0:\n break\n else:\n dx *= beta\n self.nextX = next_x\n\n errorRange = math.fabs(np.linalg.norm(self.nextX - self.x))\n\n # 収束したかどうかの判定\n if errorRange > self.errorRangeLimit:\n self.x = self.nextX\n else:\n break\n\n def getAnswer(self, filename=\"\"):\n if filename != \"\":\n # グラフの描画\n plt.figure()\n plt.title(\"Back Tracking Line Search\")\n plt.xlabel(\"x1\")\n plt.ylabel(\"x2\")\n plt.xlim([-5,5])\n plt.ylim([-5,5])\n plt.plot(self.transition_x0, self.transition_x1)\n plt.savefig(filename)\n optX = self.nextX\n optA = self.function(optX)\n return optX, optA\n\n# 問題として与えられた関数\ndef f(x):\n return 10.0 * pow(x[0], 2) + pow(x[1], 2)\n\n# 与えられた関数の１階微分\ndef dif_f(x):\n return np.array([20.0 * x[0], 2.0 * x[1]])\n\nif __name__ == \"__main__\":\n # 初期値\n x0 = np.array([1.0,5.0])\n\n method = SteepestDecentMethod(f,dif_f,x0)\n method.backtrack()\n x,a = method.getAnswer(filename=\"./backtrack.png\")\n print(\"最適解: \", x)\n print(\"最適値: \", a)\n", "sub_path": "assignments/5.py", "file_name": "5.py", "file_ext": "py", "file_size_in_byte": 2325, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "matplotlib.use", "line_number": 5, "usage_type": "call"}, {"api_name": "abc.ABCMeta", "line_number": 8, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 32, "usage_type": "attribute"}, {"api_name": "math.fabs", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 39, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 56, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 57, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "186172225", "text": "# This Python file uses the following encoding: utf-8\n\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 import os\n return open(os.path.join(os.path.dirname(__file__), fname)).read()\n\nfrom setuptools import setup, find_packages\n\npackage_name='cdb_query'\nsetup(\n name = package_name,\n version = \"1.3\",\n packages=[package_name],\n package_dir = {package_name: 'lib'},\n#\n# # metadata for upload to PyPI\n author = \"F. B. Laliberté, P. J. Kushner\",\n author_email = \"frederic.laliberte@utoronto.ca\",\n description = \"Simple tools to query and retrieve data from the ESGF's CMIP5 and CORDEX projects.\",\n license = \"BSD\",\n keywords = \"atmosphere climate\",\n url = \"http://proj.badc.rl.ac.uk/exarch\", # project home page, if any\n classifiers=[\n \"Development Status :: 4 - Beta\",\n \"Intended Audience :: Science/Research\",\n \"Natural Language :: English\",\n \"License :: OSI Approved :: BSD License\",\n \"Programming Language :: Python :: 2.7\",\n \"Programming Language :: Fortran\",\n \"Topic :: Scientific/Engineering :: Atmospheric Science\",\n \"Topic :: Scientific/Engineering :: Mathematics\"\n ],\n long_description=read('README'),\n install_requires = ['numpy','h5py','netCDF4','sqlalchemy','esgf-pyclient','timeaxis'],\n zip_safe=False,\n # other arguments here...\n #package_data = {package_name : ['lib/*.sh']},\n entry_points = {\n 'console_scripts': [\n 'cdb_query_CMIP5 = '+package_name+'.cdb_query_archive:main_CMIP5',\n 'cdb_query_CORDEX = '+package_name+'.cdb_query_archive:main_CORDEX',\n 'cdb_query_NMME = '+package_name+'.cdb_query_archive:main_NMME',\n 'cdb_query_LRFTIP = '+package_name+'.cdb_query_archive:main_LRFTIP'\n ],\n }\n )\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 2213, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "47144867", "text": "#!/usr/bin/python\n#--------------------------------------\n# ___ ___ _ ____\n# / _ \\/ _ \\(_) __/__ __ __\n# / , _/ ___/ /\\ \\/ _ \\/ // /\n# /_/|_/_/ /_/___/ .__/\\_, /\n# /_/ /___/\n#\n# I2C OLED Slideshow\n#\n# This script creates a slideshow of images\n# on an I2C OLED Display using the Adafruit\n# python library.\n#\n# It shows images found in the same directory\n# as this script.\n#\n# Please see https://www.raspberrypi-spy.co.uk/\n# for more information.\n#\n# Author : Matt Hawkins\n# Date : 12/03/2018\n#\n#--------------------------------------\nimport os\nimport sys\nimport time\nimport Adafruit_SSD1306\nfrom PIL import Image\n\n# Default delay of 1 second unless provided\n# via command line parameter\ndelay=1\nif len(sys.argv)==2:\n if sys.argv[1].isdigit():\n delay=int(sys.argv[1])\n\nprint(\"Using \"+str(delay)+\" second delay\")\n\n# List of image file extensions to look for\nimageExtensions=[\".jpg\",\".pgm\",\".ppm\",\".pbm\",\".bmp\",\".png\"]\n\n# 128x32 display with hardware I2C:\n#disp = Adafruit_SSD1306.SSD1306_128_32(rst=None)\n\n# 128x64 display with hardware I2C:\ndisp = Adafruit_SSD1306.SSD1306_128_64(rst=None)\n\n# Initialize library.\ndisp.begin()\n\n# Clear display.\ndisp.clear()\ndisp.display()\n\nwhile True:\n\n # Find all files in current directory\n for root, dirs, files in os.walk(\".\"):\n for filename in files:\n # Split filename to find file extension\n head, ext=os.path.splitext(filename)\n\n # Check if file has suitable image extension\n if ext in imageExtensions:\n\n print(filename)\n\n # Open image file\n image = Image.open(filename)\n\n # Check if image size matches display size\n if image.size==(disp.width, disp.height):\n # Convert image to 1-bit colour\n image=image.convert('1')\n else:\n # Resize to match display and convert to 1-bit colour\n image=image.resize((disp.width, disp.height), Image.ANTIALIAS).convert('1')\n\n # Display image.\n disp.image(image)\n disp.display()\n\n # Wait before showing next image\n time.sleep(delay)\n\n", "sub_path": "i2c/demo.py", "file_name": "demo.py", "file_ext": "py", "file_size_in_byte": 2079, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.argv", "line_number": 34, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 35, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 36, "usage_type": "attribute"}, {"api_name": "Adafruit_SSD1306.SSD1306_128_64", "line_number": 47, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 70, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 70, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 78, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 78, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "78008683", "text": "## -*- coding: utf-8 -*-\n##----------------------------------------------------------------------\n## ProbeConfig model\n##----------------------------------------------------------------------\n## Copyright (C) 2007-2014 The NOC Project\n## See LICENSE for details\n##----------------------------------------------------------------------\n\n## Python modules\nfrom collections import defaultdict\nimport datetime\nimport logging\nimport random\n## Django modules\nimport django.db.models.signals\n## Third-party modules\nimport mongoengine.signals\nfrom mongoengine.document import Document, EmbeddedDocument\nfrom mongoengine.fields import (\n StringField, IntField, DictField, DateTimeField, FloatField,\n ListField, EmbeddedDocumentField)\n## NOC Modules\nfrom noc import settings\n\nlogger = logging.getLogger(__name__)\n\n\nclass CollectorAddress(EmbeddedDocument):\n proto = StringField()\n address = StringField()\n port = IntField()\n\n\nclass MetricCollectors(EmbeddedDocument):\n policy = StringField(default=\"prio\")\n write_concern = IntField(default=1)\n collectors = ListField(EmbeddedDocumentField(CollectorAddress))\n\n\nclass ProbeConfigMetric(EmbeddedDocument):\n metric = StringField()\n metric_type = StringField()\n thresholds = ListField()\n convert = StringField()\n scale = FloatField(default=1.0)\n collectors = EmbeddedDocumentField(MetricCollectors)\n\n\nclass ProbeConfig(Document):\n meta = {\n \"collection\": \"noc.pm.probeconfig\",\n \"indexes\": [(\"model_id\", \"object_id\"),\n (\"probe_id\", \"instance_id\"),\n (\"probe_id\", \"instance_id\", \"expire\"),\n \"uuid\", \"expire\", \"changed\", \"metrics.metric\"]\n }\n\n # Reference to model or document, like sa.ManagedObject\n model_id = StringField()\n # Object id, converted to string\n object_id = StringField()\n #\n probe_id = StringField()\n instance_id = IntField()\n #\n managed_object = IntField(required=False)\n #\n uuid = StringField()\n #\n changed = DateTimeField(default=datetime.datetime.now)\n expire = DateTimeField()\n # Configuration section\n handler = StringField()\n interval = IntField()\n config = DictField()\n metrics = ListField(EmbeddedDocumentField(ProbeConfigMetric))\n\n PROFILES = defaultdict(list) # model -> [(model, field), ...]\n MODELS = []\n TTL = settings.config.getint(\"pm\", \"config_ttl\")\n TTL_JITTER = settings.config.getfloat(\"pm\", \"config_ttl_jitter\")\n TJL = int(TTL - TTL_JITTER * TTL)\n TJH = int(TTL + TTL_JITTER * TTL)\n\n DELETE_DATE = datetime.datetime(2030, 1, 1)\n\n def __unicode__(self):\n return unicode(self.uuid)\n\n @property\n def is_deleted(self):\n return (self.changed == self.expire and\n self.expire == self.DELETE_DATE)\n\n @property\n def is_expired(self):\n return self.expire <= datetime.datetime.now()\n\n @classmethod\n def get_model_id(cls, object):\n if isinstance(object._meta, dict):\n # Document\n return u\"%s.%s\" % (object.__module__.split(\".\")[1],\n object.__class__.__name__)\n else:\n # Model\n return u\"%s.%s\" % (object._meta.app_label,\n object._meta.object_name)\n\n def get_object(self):\n return MetricSettings(\n model_id=self.model_id,\n object_id=self.object_id\n ).get_object()\n\n @classmethod\n def install(cls):\n mongoengine.signals.class_prepared.connect(cls.on_new_document)\n django.db.models.signals.class_prepared.connect(cls.on_new_model)\n\n @classmethod\n def on_new_model(cls, sender, *args, **kwargs):\n if hasattr(sender, \"get_probe_config\"):\n cls.MODELS += [sender]\n django.db.models.signals.post_save.connect(\n cls.on_change_model, sender=sender)\n django.db.models.signals.pre_delete.connect(\n cls.on_delete_model, sender=sender)\n p_field = getattr(sender, \"PROFILE_LINK\", None)\n if p_field:\n for f in sender._meta.fields:\n if f.name == p_field:\n pm = f.rel.to\n cls.PROFILES[pm] += [(sender, p_field)]\n break\n\n @classmethod\n def on_new_document(cls, sender, *args, **kwargs):\n if hasattr(sender, \"get_probe_config\"):\n cls.MODELS += [sender]\n mongoengine.signals.post_save.connect(\n cls.on_change_document, sender=sender)\n mongoengine.signals.pre_delete.connect(\n cls.on_delete_document, sender=sender)\n p_field = getattr(sender, \"PROFILE_LINK\", None)\n if p_field:\n pm = sender._fields[p_field].document_type_obj\n cls.PROFILES[pm] += [(sender, p_field)]\n\n @classmethod\n def _delete_object(cls, object):\n model_id = cls.get_model_id(object)\n object_id = str(object.id)\n # Mark probeconfig as deleted\n logger.debug(\"Marking ProbeConfig as deleted: %s:%s\",\n model_id, object_id)\n cls._get_collection().update({\n \"model_id\": model_id,\n \"object_id\": object_id\n },\n {\n \"$set\": {\n \"changed\": cls.DELETE_DATE,\n \"expire\": cls.DELETE_DATE\n }\n },\n multi=True\n )\n # wipe out metricsettings\n logger.debug(\"Deleting MetricSettings: %s:%s\",\n model_id, object_id)\n MetricSettings._get_collection().remove({\n \"model_id\": model_id,\n \"object_id\": object_id\n })\n\n @classmethod\n def get_ttl(cls):\n if not cls.TTL_JITTER:\n return cls.TTL\n else:\n return random.randint(cls.TJL, cls.TJH)\n\n @classmethod\n def _refresh_object(cls, object):\n def get_collectors(es):\n c = collectors.get(es.probe.id)\n if c:\n return c\n c = es.probe.storage.default_collector\n collectors[es.probe.id] = c\n return c\n\n def get_instance(probe, uuid):\n ni = probe.n_instances\n if ni < 1:\n return 0\n else:\n return int(str(uuid)[:8], 16) % ni\n\n def get_refresh_ops(bulk, o):\n model_id = cls.get_model_id(o)\n logger.debug(\"Bulk refresh %s %s\", model_id, o)\n # Cleanup\n bulk.find(\n {\n \"model_id\": model_id,\n \"object_id\": str(o.id)\n }\n ).update(\n {\n \"$set\": {\n \"changed\": cls.DELETE_DATE,\n \"expire\": cls.DELETE_DATE\n }\n }\n )\n for es in MetricSettings.get_effective_settings(o):\n if es.managed_object:\n mo = es.managed_object.id\n else:\n mo = None\n bulk.find(\n {\n \"uuid\": es.uuid\n }\n ).upsert().update(\n {\n \"$set\": {\n \"model_id\": es.model_id,\n \"object_id\": str(es.object.id) if es.object else None,\n \"changed\": now,\n \"expire\": now + datetime.timedelta(seconds=cls.get_ttl()),\n \"handler\": es.handler,\n \"interval\": es.interval,\n \"probe_id\": str(es.probe.id),\n \"instance_id\": get_instance(es.probe, es.uuid),\n \"config\": es.config,\n \"managed_object\": mo,\n \"metrics\": [{\n \"metric\": m.metric,\n \"metric_type\": m.metric_type.name,\n \"thresholds\": m.thresholds,\n \"convert\": m.convert,\n \"scale\": m.scale,\n \"collectors\": get_collectors(es)\n } for m in es.metrics]\n }\n }\n )\n for m, n in cls.PROFILES[o.__class__]:\n for obj in m.objects.filter(**{n: o.id}):\n get_refresh_ops(bulk, obj)\n\n logger.debug(\"Refresh object %s\", object)\n collectors = {} # Storage rule -> collector url\n # @todo: Make configurable\n now = datetime.datetime.now()\n bulk = cls._get_collection().initialize_ordered_bulk_op()\n get_refresh_ops(bulk, object)\n bulk.execute()\n\n @classmethod\n def _refresh_config(cls, object):\n def get_collectors(es):\n c = collectors.get(es.probe.id)\n if c:\n return c\n c = es.probe.storage.default_collector\n collectors[es.probe.id] = c\n return c\n\n def get_instance(probe, uuid):\n ni = probe.n_instances\n if ni < 1:\n return 0\n else:\n return int(str(uuid)[:8], 16) % ni\n\n def get_refresh_ops(bulk, o):\n model_id = cls.get_model_id(o)\n logger.debug(\"Bulk refresh %s %s\", model_id, o)\n # Cleanup\n bulk.find(\n {\n \"model_id\": \"pm.MetricConfig\",\n \"object_id\": str(o.id)\n }\n ).update(\n {\n \"$set\": {\n \"changed\": cls.DELETE_DATE,\n \"expire\": cls.DELETE_DATE\n }\n }\n )\n for es in o.get_effective_settings():\n bulk.find(\n {\n \"uuid\": es.uuid\n }\n ).upsert().update(\n {\n \"$set\": {\n \"model_id\": \"pm.MetricConfig\",\n \"object_id\": str(o.id),\n \"changed\": now,\n \"expire\": now + datetime.timedelta(seconds=cls.get_ttl()),\n \"handler\": es.handler,\n \"interval\": es.interval,\n \"probe_id\": str(es.probe.id),\n \"instance_id\": get_instance(es.probe, es.uuid),\n \"config\": es.config,\n \"metrics\": [{\n \"metric\": m.metric,\n \"metric_type\": m.metric_type.name,\n \"thresholds\": m.thresholds,\n \"convert\": m.convert,\n \"scale\": m.scale,\n \"collectors\": get_collectors(es)\n } for m in es.metrics]\n }\n }\n )\n\n logger.debug(\"Refresh metric config %s\", object.name)\n collectors = {} # Storage rule -> collector url\n # @todo: Make configurable\n now = datetime.datetime.now()\n bulk = cls._get_collection().initialize_ordered_bulk_op()\n get_refresh_ops(bulk, object)\n bulk.execute()\n\n @classmethod\n def on_change_model(cls, sender, instance, *args, **kwargs):\n cls._refresh_object(instance)\n\n @classmethod\n def on_change_document(cls, sender, document=None, *args, **kwargs):\n cls._refresh_object(document)\n\n @classmethod\n def on_delete_model(cls, sender, instance, *args, **kwargs):\n cls._delete_object(instance)\n # Rebuild configs for related objects\n for m, n in cls.PROFILES[sender]:\n for obj in m.objects.filter(**{n: instance.id}):\n cls._refresh_object(obj)\n\n @classmethod\n def on_delete_document(cls, sender, document, *args, **kwargs):\n cls._delete_object(document)\n # Rebuild configs for related objects\n for m, n in cls.PROFILES[sender]:\n for obj in m.objects.filter(**{n: document.id}):\n cls._refresh_object(obj)\n\n @classmethod\n def on_change_storage(cls, sender, document=None, *args, **kwargs):\n logger.debug(\"Apply changed storage '%s'\", document.name)\n for p in Probe.objects.filter(storage=document):\n logger.info(\"Applying changes to Probe '%s'\", p.name)\n for pc in ProbeConfig.objects.filter(probe_id=str(p.id)):\n pc.refresh()\n\n @classmethod\n def on_change_metric_settings(cls, sender, document=None, *args, **kwargs):\n object = document.get_object()\n logger.debug(\"Apply changed MetricSettings for '%s'\", object)\n cls._refresh_object(object)\n if not document.metric_sets:\n logger.debug(\"Delete empty MetricSettings for %s\", object)\n document.delete()\n\n @classmethod\n def on_delete_metric_settings(cls, sender, document, *args, **kwargs):\n object = document.get_object()\n logger.debug(\"Apply deleted MetricSettings for '%s'\", object)\n cls._refresh_object(object)\n\n @classmethod\n def on_change_metric_config(cls, sender, document=None, *args, **kwargs):\n logger.debug(\"Apply changed MetricConfig for '%s'\", document.name)\n cls._refresh_config(document)\n\n @classmethod\n def on_delete_metric_config(cls, sender, document, *args, **kwargs):\n logger.debug(\"Apply deleted MetricConfig for '%s'\", document.name)\n cls._delete_object(document)\n\n @classmethod\n def on_change_metric_set(cls, sender, document=None, *args, **kwargs):\n logger.info(\"Applying changes to MetricSet '%s'\", document.name)\n # Find all affected metric settings\n for ms in MetricSettings.objects.filter(\n metric_sets__metric_set=document.id):\n cls._refresh_object(ms.get_object())\n\n @classmethod\n def on_delete_metric_set(cls, sender, document, *args, **kwargs):\n logger.info(\"Deleting MetricSet '%s'\", document.name)\n for ms in MetricSettings.objects.filter(\n metric_sets__metric_set=document.id\n ):\n ms.metric_sets = [s for s in ms.metric_sets\n if s.metric_set.id != document.id]\n ms.save() # Triggers refresh_object\n\n @classmethod\n def on_change_probe(cls, sender, document=None, *args, **kwargs):\n logger.info(\"Applying changes to Probe '%s'\", document.name)\n for pc in ProbeConfig.objects.filter(probe_id=str(document.id)):\n pc.refresh()\n\n @classmethod\n def on_change_auth_profile(cls, sender, instance, *args, **kwargs):\n logger.info(\"Applying changes to AuthProfile '%s'\" % instance.name)\n for mo in instance.managedobject_set.all():\n cls._refresh_object(mo)\n\n @classmethod\n def on_change_object_caps(cls, sender, document=None, *args, **kwargs):\n logger.info(\"Applying changes to object capabilities '%s'\", document.object.name)\n cls.on_change_model(document.object, document.object)\n\n def refresh(self):\n logger.debug(\"Refreshing %s\", self.uuid)\n o = self.get_object()\n if not o:\n return\n if self.model_id == \"pm.MetricConfig\":\n self._refresh_config(o)\n else:\n self._refresh_object(o)\n\n @classmethod\n def rebuild(cls, model_id=None):\n pass\n\n##\nfrom metricset import MetricSet\nfrom metricsettings import MetricSettings\nfrom metricconfig import MetricConfig\nfrom probe import Probe", "sub_path": "pm/models/probeconfig.py", "file_name": "probeconfig.py", "file_ext": "py", "file_size_in_byte": 15822, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 25, "usage_type": "call"}, {"api_name": "mongoengine.document.EmbeddedDocument", "line_number": 28, "usage_type": "name"}, {"api_name": "mongoengine.fields.StringField", "line_number": 29, "usage_type": "call"}, {"api_name": "mongoengine.fields.StringField", "line_number": 30, "usage_type": "call"}, {"api_name": "mongoengine.fields.IntField", "line_number": 31, "usage_type": "call"}, {"api_name": "mongoengine.document.EmbeddedDocument", "line_number": 34, "usage_type": "name"}, {"api_name": "mongoengine.fields.StringField", "line_number": 35, "usage_type": "call"}, {"api_name": "mongoengine.fields.IntField", "line_number": 36, "usage_type": "call"}, {"api_name": "mongoengine.fields.ListField", "line_number": 37, "usage_type": "call"}, {"api_name": "mongoengine.fields.EmbeddedDocumentField", "line_number": 37, "usage_type": "call"}, {"api_name": "mongoengine.document.EmbeddedDocument", "line_number": 40, "usage_type": "name"}, {"api_name": "mongoengine.fields.StringField", "line_number": 41, "usage_type": "call"}, {"api_name": "mongoengine.fields.StringField", "line_number": 42, "usage_type": "call"}, {"api_name": "mongoengine.fields.ListField", "line_number": 43, "usage_type": "call"}, {"api_name": "mongoengine.fields.StringField", "line_number": 44, "usage_type": "call"}, {"api_name": "mongoengine.fields.FloatField", "line_number": 45, "usage_type": "call"}, {"api_name": "mongoengine.fields.EmbeddedDocumentField", "line_number": 46, "usage_type": "call"}, {"api_name": "mongoengine.document.Document", "line_number": 49, "usage_type": "name"}, {"api_name": "mongoengine.fields.StringField", "line_number": 59, "usage_type": "call"}, {"api_name": "mongoengine.fields.StringField", "line_number": 61, "usage_type": "call"}, {"api_name": "mongoengine.fields.StringField", "line_number": 63, "usage_type": "call"}, {"api_name": "mongoengine.fields.IntField", "line_number": 64, "usage_type": "call"}, {"api_name": "mongoengine.fields.IntField", "line_number": 66, "usage_type": "call"}, {"api_name": "mongoengine.fields.StringField", "line_number": 68, "usage_type": "call"}, {"api_name": "mongoengine.fields.DateTimeField", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 70, "usage_type": "attribute"}, {"api_name": "mongoengine.fields.DateTimeField", "line_number": 71, "usage_type": "call"}, {"api_name": "mongoengine.fields.StringField", "line_number": 73, "usage_type": "call"}, {"api_name": "mongoengine.fields.IntField", "line_number": 74, "usage_type": "call"}, {"api_name": "mongoengine.fields.DictField", "line_number": 75, "usage_type": "call"}, {"api_name": "mongoengine.fields.ListField", "line_number": 76, "usage_type": "call"}, {"api_name": "mongoengine.fields.EmbeddedDocumentField", "line_number": 76, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 78, "usage_type": "call"}, {"api_name": "noc.settings.config.getint", "line_number": 80, "usage_type": "call"}, {"api_name": "noc.settings.config", "line_number": 80, "usage_type": "attribute"}, {"api_name": "noc.settings", "line_number": 80, "usage_type": "name"}, {"api_name": "noc.settings.config.getfloat", "line_number": 81, "usage_type": "call"}, {"api_name": "noc.settings.config", "line_number": 81, "usage_type": "attribute"}, {"api_name": "noc.settings", "line_number": 81, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 85, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 97, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 97, "usage_type": "attribute"}, {"api_name": "mongoengine.signals.signals.class_prepared.connect", "line_number": 118, "usage_type": "call"}, {"api_name": "mongoengine.signals.signals", "line_number": 118, "usage_type": "attribute"}, {"api_name": "mongoengine.signals", "line_number": 118, "usage_type": "name"}, {"api_name": "django.db.models.signals.db.models.signals.class_prepared.connect", "line_number": 119, "usage_type": "call"}, {"api_name": "django.db.models.signals.db", "line_number": 119, "usage_type": "attribute"}, {"api_name": "django.db.models.signals", "line_number": 119, "usage_type": "name"}, {"api_name": "django.db.models.signals.db.models.signals.post_save.connect", "line_number": 125, "usage_type": "call"}, {"api_name": "django.db.models.signals.db", "line_number": 125, "usage_type": "attribute"}, {"api_name": "django.db.models.signals", "line_number": 125, "usage_type": "name"}, {"api_name": "django.db.models.signals.db.models.signals.pre_delete.connect", "line_number": 127, "usage_type": "call"}, {"api_name": "django.db.models.signals.db", "line_number": 127, "usage_type": "attribute"}, {"api_name": "django.db.models.signals", "line_number": 127, "usage_type": "name"}, {"api_name": "mongoengine.signals.signals.post_save.connect", "line_number": 141, "usage_type": "call"}, {"api_name": "mongoengine.signals.signals", "line_number": 141, "usage_type": "attribute"}, {"api_name": "mongoengine.signals", "line_number": 141, "usage_type": "name"}, {"api_name": "mongoengine.signals.signals.pre_delete.connect", "line_number": 143, "usage_type": "call"}, {"api_name": "mongoengine.signals.signals", "line_number": 143, "usage_type": "attribute"}, {"api_name": "mongoengine.signals", "line_number": 143, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 182, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 233, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 258, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 258, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 308, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 329, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 329, "usage_type": "attribute"}]} +{"seq_id": "639314534", "text": "import os\nimport requests\nfrom subprocess import call\n\n\n# yandex translation api key\napi_key = 'get your yandex api key here: https://tech.yandex.ru/translate/'\napp_name = 'notify-send-translate-selection'\n\n\ndef notify(title, message):\n call(['notify-send', title, message, '-h', 'string:x-canonical-private-synchronous:' + app_name])\n\n\nerror_code_descriptions = {\n 401: 'Invalid API key',\n 402: 'Blocked API key',\n 404: 'Exceeded the daily limit on the amount of translated text',\n 413: 'Exceeded the maximum text size',\n 422: 'The text cannot be translated',\n 501: 'The specified translation direction is not supported'\n}\n\n\nif __name__ == '__main__':\n text = os.popen('xsel').read()\n payload = {'key': api_key, 'text': text, 'lang': 'ru', 'format': 'plain'}\n url = 'https://translate.yandex.net/api/v1.5/tr.json/translate'\n response = requests.get(url, params=payload)\n if response.status_code == 200:\n notify(text, response.json().get('text')[0])\n else:\n notify('Невозможно перевести', error_code_descriptions.get(response.status_code, 'Неизвестная проблема'))\n", "sub_path": "trsel.py", "file_name": "trsel.py", "file_ext": "py", "file_size_in_byte": 1155, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "subprocess.call", "line_number": 12, "usage_type": "call"}, {"api_name": "os.popen", "line_number": 26, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "60572823", "text": "from django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.contrib.auth.models import User\nfrom django.conf import settings\nfrom django.http import JsonResponse, Http404\nfrom django.views import View\nfrom django.views.generic.base import TemplateResponseMixin\n\nimport logging\nimport requests\nfrom requests_saml import HTTPSAMLAuth\nfrom requests_kerberos import HTTPKerberosAuth\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass SendThroughIdP(LoginRequiredMixin, TemplateResponseMixin, View):\n \"\"\" allow the user to POST some data; then the render SP's response to it\n\n data will be exchanged between IdP and SP based on user POST:\n 1. user POST to IdP\n 2. IdP perform POST to SP\n 3. SP perform GET to IdP for more data\n 4. IdP return GET request\n 5. SP return POST request\n 6. user response\n \"\"\"\n SP_ENDPOINT = '{}/endpoint/'\n template_name = 'endpoint.html'\n extra_context = {\n \"known_sp_ids\": [x for x in settings.SAML_IDP_SPCONFIG],\n }\n\n def post(self, request, *args, **kwargs):\n ''' this is where we start data exchange '''\n url = self.SP_ENDPOINT.format('http://localhost:9000')\n\n user_data = request.POST\n logger.debug(f'received POST from user {request.user!r}')\n\n # do we have to set cookies?\n cookies = request.COOKIES\n\n with requests.Session() as session:\n logger.debug('performing requests lib auth config')\n k = HTTPKerberosAuth()\n s = HTTPSAMLAuth(chained_auth=k)\n\n logger.debug('starting transaction with service provider')\n response = session.post(\n url, data=user_data, cookies=cookies, auth=s)\n\n logger.debug(f'SP response status: {response.status_code}')\n\n return self.render_to_response({\n 'last_stop': True,\n 'sp_response': {\n 'header': response.headers,\n 'status_code': response.status_code,\n # 'content': str(response.text),\n 'content': response.text,\n # 'content': response.content,\n },\n })\n\n def get(self, request, *args, **kwargs):\n return self.render_to_response(self.extra_context)\n\n\nclass ProvideInfo(LoginRequiredMixin, View):\n http_method_names = ['get']\n\n def get(self, request, *args, **kwargs):\n logger.debug('providing data to SP')\n u = request.user\n logger.debug(f'user object: {u}')\n\n return JsonResponse({\n 'username': u.username if u.is_authenticated else 'ANONYMOUS',\n 'authenticated': u.is_authenticated,\n # 'META': dict(**request.META),\n # 'headers': str(request.headers),\n })\n\n\n# Views bellow are related to the alternate protocol:\n# 1. user fills a form and POST to service provider\n# 2. SP perform POST to IdP for more data\n# 3. IdP return POST request\n# 4. user response\n\nclass PostToSP(LoginRequiredMixin, TemplateResponseMixin, View):\n ''' allow user to POST directly to service provider '''\n SP_ENDPOINT = '{}/endpoint/direct/'\n template_name = 'send_to_sp.html'\n extra_context = {\n \"known_sp_ids\": [x for x in settings.SAML_IDP_SPCONFIG],\n \"sp_url\": SP_ENDPOINT.format('http://localhost:9000'),\n }\n\n def get(self, request, *args, **kwargs):\n return self.render_to_response(self.extra_context)\n\n\nclass AlternateProvideInfo(LoginRequiredMixin, View):\n \"\"\" provide some info based on correct data\n\n example: provide user's last login date if POST data\n (username, date_joined) are correct\n \"\"\"\n http_method_names = ['post']\n\n def post(self, request, *args, **kwargs):\n ''' will provide JSON data if POST parameters exist in DB '''\n data = request.POST\n print(data)\n try:\n user = User.objects.get(username=data.get('username'))\n except User.DoesNotExist:\n raise Http404\n\n return JsonResponse({\n 'id': user.pk,\n 'last_login': user.last_login,\n 'date_joined': user.date_joined,\n 'is_authenticated': user.is_authenticated,\n })\n", "sub_path": "idp/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4219, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 17, "usage_type": "name"}, {"api_name": "django.views.generic.base.TemplateResponseMixin", "line_number": 17, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 17, "usage_type": "name"}, {"api_name": "django.conf.settings.SAML_IDP_SPCONFIG", "line_number": 31, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 31, "usage_type": "name"}, {"api_name": "requests.Session", "line_number": 44, "usage_type": "call"}, {"api_name": "requests_kerberos.HTTPKerberosAuth", "line_number": 46, "usage_type": "call"}, {"api_name": "requests_saml.HTTPSAMLAuth", "line_number": 47, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 70, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 70, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 78, "usage_type": "call"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 92, "usage_type": "name"}, {"api_name": "django.views.generic.base.TemplateResponseMixin", "line_number": 92, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 92, "usage_type": "name"}, {"api_name": "django.conf.settings.SAML_IDP_SPCONFIG", "line_number": 97, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 97, "usage_type": "name"}, {"api_name": "django.contrib.auth.mixins.LoginRequiredMixin", "line_number": 105, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 105, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 118, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 118, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 118, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.DoesNotExist", "line_number": 119, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 119, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 120, "usage_type": "name"}, {"api_name": "django.http.JsonResponse", "line_number": 122, "usage_type": "call"}]} +{"seq_id": "320655117", "text": "'''\n Script to send data to FarmBeats using azure-eventhub v5.1.0\n It is uses fake data to test the connection with FarmBeats.\n Once a connection is stablished, you can just modify\n the code to send the data obtained from your sensors.\n '''\n\nimport asyncio, json\nfrom azure.eventhub.aio import EventHubProducerClient\nfrom azure.eventhub import EventData\n\nEVENTHUBCONNECTIONSTRING = \"\"\nEVENTHUBNAME = \"\"\n\nmessage = {\n \"deviceid\":\"\",\n \"timestamp\":\"2020-05-25T12:52:32.3155488Z\",\n \"version\":\"1\",\n \"sensors\":[\n {\n \"id\":\"\",\n \"sensordata\":[\n {\n \"timestamp\":\"2020-05-25T12:52:32.3155488Z\",\n \"capacitive_soil_moisture\":79.0\n }\n ]\n },\n {\n \"id\":\"\",\n \"sensordata\":[\n {\n \"timestamp\":\"2020-05-25T12:52:32.3155488Z\",\n \"grove_light_sensor\":90.0\n }\n ]\n },\n {\n \"id\":\"\",\n \"sensordata\":[\n {\n \"timestamp\":\"2020-05-25T12:52:32.3155488Z\",\n \"grove_temperature\":18.0\n },\n {\n \"timestamp\":\"2020-05-25T12:52:32.3155488Z\",\n \"grove_humidity\":56.0\n },\n {\n \"timestamp\":\"2020-05-25T12:52:32.3155488Z\",\n \"grove_barometer\":97.0\n }\n ]\n }\n ]\n}\nmessage = json.dumps(message)\n\nasync def run():\n # Create a producer client to send messages to the event hub.\n # Specify a connection string to your event hubs namespace and\n \t # the event hub name.\n producer = EventHubProducerClient.from_connection_string(conn_str=EVENTHUBCONNECTIONSTRING, eventhub_name=EVENTHUBNAME)\n async with producer:\n # Create a batch.\n event_data_batch = await producer.create_batch()\n\n # Add events to the batch.\n event_data_batch.add(EventData(message))\n\n # Send the batch of events to the event hub.\n await producer.send_batch(event_data_batch)\n print(\"Message sent.\")\n\n\n# python3.7 or newer\nasyncio.run(run())\n\n# python3.6\n# loop = asyncio.get_event_loop()\n# loop.run_until_complete(run())\n", "sub_path": "community-members/Imperial College London/Scenario-Learning-Modules/Aware_Kits/Azure_FarmBeats_Integration/Python/client5_1_0.py", "file_name": "client5_1_0.py", "file_ext": "py", "file_size_in_byte": 2463, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "json.dumps", "line_number": 57, "usage_type": "call"}, {"api_name": "azure.eventhub.aio.EventHubProducerClient.from_connection_string", "line_number": 63, "usage_type": "call"}, {"api_name": "azure.eventhub.aio.EventHubProducerClient", "line_number": 63, "usage_type": "name"}, {"api_name": "azure.eventhub.EventData", "line_number": 69, "usage_type": "call"}, {"api_name": "asyncio.run", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "569905777", "text": "import numpy as np \nimport cv2\n\ncanvas = np.zeros((400,400,3), dtype = \"uint8\")\n\nblue = (255,0,0)\nline = cv2.line(canvas, (0,0), (400, 200), blue)\n\nteal = (255,255,0)\nline = cv2.line(canvas, (200,400), (400, 200), teal, 10)\n\npurple = (255,0,255)\ncv2.rectangle(canvas, (150, 150), (250,250), purple, -1)\n\ncanvas = np.zeros((400,400,3), dtype = \"uint8\")\n(x, y) = (canvas.shape[1]//2, canvas.shape[0]//2)\nfor r in range(0,255, 5):\n cv2.circle(canvas, (x,y), r, (r,0,0))\n\ncanvas = np.zeros((400,400,3), dtype = \"uint8\")\nfor i in range(0,25,1):\n rand_radius = np.random.randint(0, high=200)\n rand_color = np.random.randint(0, high=256, size=(3,)).tolist()\n rand_point = np.random.randint(0, high=400, size=(2,))\n\n cv2.circle(canvas, tuple(rand_point), rand_radius, rand_color, -1)\n\n\n\ncv2.imshow(\"Art!\", canvas)\ncv2.waitKey(0)", "sub_path": "ch.5/drawing.py", "file_name": "drawing.py", "file_ext": "py", "file_size_in_byte": 835, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.zeros", "line_number": 4, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.circle", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 23, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 24, "usage_type": "attribute"}, {"api_name": "cv2.circle", "line_number": 26, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "503747457", "text": "# Copyright 2021 Google LLC\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\"\"\"Tests for fedjax.experimental.metrics.\"\"\"\n\nfrom absl.testing import absltest\nfrom absl.testing import parameterized\n\nfrom fedjax.experimental import metrics\n\nimport jax.numpy as jnp\nimport numpy.testing as npt\n\n\nclass MeanStatTest(absltest.TestCase):\n\n def test_str(self):\n stat = metrics.MeanStat.new(2, 4)\n self.assertEqual(\n 'MeanStat(accum=DeviceArray(2, dtype=int32), weight=DeviceArray(4, dtype=int32)) => 0.5',\n str(stat))\n\n def test_new(self):\n stat = metrics.MeanStat.new(jnp.array([2, 3, 1]), jnp.array([1, 0, 1]))\n npt.assert_array_equal(stat.accum, [2, 0, 1])\n npt.assert_array_equal(stat.weight, [1, 0, 1])\n\n def test_result(self):\n stat = metrics.MeanStat.new(2, 5)\n self.assertEqual(stat.result(), 0.4)\n\n def test_merge(self):\n stat_0 = metrics.MeanStat.new(1, 2)\n stat_1 = metrics.MeanStat.new(2, 3)\n merged_stat = stat_0.merge(stat_1)\n self.assertEqual(merged_stat.accum, 3)\n self.assertEqual(merged_stat.weight, 5)\n\n def test_reduce(self):\n stat = metrics.MeanStat.new(jnp.array([1, 2, 4]), jnp.array([1, 1, 0]))\n reduced_stat = stat.reduce()\n self.assertEqual(reduced_stat.accum, 3)\n self.assertEqual(reduced_stat.weight, 2)\n\n\nclass SumStatTest(absltest.TestCase):\n\n def test_str(self):\n stat = metrics.SumStat.new(2)\n self.assertEqual('SumStat(accum=DeviceArray(2, dtype=int32)) => 2',\n str(stat))\n\n def test_result(self):\n stat = metrics.SumStat.new(2)\n self.assertEqual(stat.result(), 2)\n\n def test_merge(self):\n stat_0 = metrics.SumStat.new(1)\n stat_1 = metrics.SumStat.new(2)\n merged_stat = stat_0.merge(stat_1)\n self.assertEqual(merged_stat.accum, 3)\n\n def test_reduce(self):\n stat = metrics.SumStat.new(jnp.array([1, 2, 1]))\n reduced_stat = stat.reduce()\n self.assertEqual(reduced_stat.accum, 4)\n\n\nclass MetricsTest(parameterized.TestCase):\n\n def test_cross_entropy_loss(self):\n example = {'y': jnp.array(1)}\n prediction = jnp.array([1.2, 0.4])\n metric = metrics.CrossEntropyLoss()\n loss = metric.evaluate_example(example, prediction)\n self.assertAlmostEqual(loss.result(), 1.1711007)\n\n @parameterized.named_parameters(\n {\n 'testcase_name': 'correct',\n 'target': 2,\n 'prediction': [0, 0, 1],\n 'expected_result': 1.,\n }, {\n 'testcase_name': 'incorrect',\n 'target': 1,\n 'prediction': [1, 0, 0],\n 'expected_result': 0.,\n })\n def test_accuracy(self, target, prediction, expected_result):\n example = {'y': jnp.array(target)}\n prediction = jnp.array(prediction)\n metric = metrics.Accuracy()\n accuracy = metric.evaluate_example(example, prediction)\n self.assertEqual(accuracy.result(), expected_result)\n\n def test_sequence_token_cross_entropy_loss(self):\n example = {'y': jnp.array([1, 0, 1])}\n prediction = jnp.array([[1.2, 0.4], [2.3, 0.1], [0.3, 3.2]])\n metric = metrics.SequenceTokenCrossEntropyLoss()\n loss = metric.evaluate_example(example, prediction)\n self.assertAlmostEqual(loss.result(), 0.612331725)\n\n def test_sequence_cross_entropy_loss(self):\n example = {'y': jnp.array([1, 0, 1])}\n prediction = jnp.array([[1.2, 0.4], [2.3, 0.1], [0.3, 3.2]])\n metric = metrics.SequenceCrossEntropyLoss()\n loss = metric.evaluate_example(example, prediction)\n self.assertAlmostEqual(loss.result(), 1.2246635)\n\n def test_sequence_token_accuracy(self):\n example = {'y': jnp.array([1, 2, 2, 1, 0])}\n # prediction = [1, 0, 2, 1, 0].\n prediction = jnp.array([[0, 1, 0], [1, 0, 0], [0, 0, 1], [0, 1, 0],\n [1, 0, 0]])\n metric = metrics.SequenceTokenAccuracy()\n accuracy = metric.evaluate_example(example, prediction)\n self.assertEqual(accuracy.result(), 0.75) # 3 / 4.\n\n def test_sequence_token_count(self):\n example = {'y': jnp.array([1, 2, 2, 3, 4, 0, 0])}\n prediction = jnp.array([]) # Unused.\n metric = metrics.SequenceTokenCount(masked_target_values=(0, 2))\n count = metric.evaluate_example(example, prediction)\n self.assertEqual(count.result(), 3)\n\n @parameterized.named_parameters(\n {\n 'testcase_name': 'untruncated',\n 'target': [1, 2, 2, 3, 4, 0, 0],\n 'expected_result': 0.,\n }, {\n 'testcase_name': 'truncated',\n 'target': [1, 2, 2, 3, 3, 3, 3],\n 'expected_result': 1.,\n })\n def test_sequence_truncation_rate(self, target, expected_result):\n example = {'y': jnp.array(target)}\n prediction = jnp.array([]) # Unused.\n metric = metrics.SequenceTruncationRate(eos_target_value=4)\n truncation_rate = metric.evaluate_example(example, prediction)\n self.assertEqual(truncation_rate.result(), expected_result)\n\n def test_sequence_token_oov_rate(self):\n example = {'y': jnp.array([1, 2, 2, 3, 4, 0, 0])}\n prediction = jnp.array([]) # Unused.\n metric = metrics.SequenceTokenOOVRate(oov_target_values=(2,))\n oov_rate = metric.evaluate_example(example, prediction)\n self.assertEqual(oov_rate.result(), 0.4) # 2 / 5.\n\n def test_sequence_length(self):\n example = {'y': jnp.array([1, 2, 3, 4, 0, 0])}\n prediction = jnp.array([]) # Unused.\n metric = metrics.SequenceLength()\n sequence_length = metric.evaluate_example(example, prediction)\n self.assertEqual(sequence_length.result(), 4.0)\n\n\nif __name__ == '__main__':\n absltest.main()\n", "sub_path": "fedjax/experimental/metrics_test.py", "file_name": "metrics_test.py", "file_ext": "py", "file_size_in_byte": 5975, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "absl.testing.absltest.TestCase", "line_number": 25, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 25, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.MeanStat.new", "line_number": 28, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics.MeanStat", "line_number": 28, "usage_type": "attribute"}, {"api_name": "fedjax.experimental.metrics", "line_number": 28, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.MeanStat.new", "line_number": 34, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics.MeanStat", "line_number": 34, "usage_type": "attribute"}, {"api_name": "fedjax.experimental.metrics", "line_number": 34, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 35, "usage_type": "name"}, {"api_name": "numpy.testing.assert_array_equal", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.testing", "line_number": 36, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.MeanStat.new", "line_number": 39, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics.MeanStat", "line_number": 39, "usage_type": "attribute"}, {"api_name": "fedjax.experimental.metrics", "line_number": 39, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.MeanStat.new", "line_number": 43, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics.MeanStat", "line_number": 43, "usage_type": "attribute"}, {"api_name": "fedjax.experimental.metrics", "line_number": 43, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.MeanStat.new", "line_number": 44, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics.MeanStat", "line_number": 44, "usage_type": "attribute"}, {"api_name": "fedjax.experimental.metrics", "line_number": 44, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.MeanStat.new", "line_number": 50, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics.MeanStat", "line_number": 50, "usage_type": "attribute"}, {"api_name": "fedjax.experimental.metrics", "line_number": 50, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 50, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 50, "usage_type": "name"}, {"api_name": "absl.testing.absltest.TestCase", "line_number": 56, "usage_type": "attribute"}, {"api_name": "absl.testing.absltest", "line_number": 56, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.SumStat.new", "line_number": 59, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics.SumStat", "line_number": 59, "usage_type": "attribute"}, {"api_name": "fedjax.experimental.metrics", "line_number": 59, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.SumStat.new", "line_number": 64, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics.SumStat", "line_number": 64, "usage_type": "attribute"}, {"api_name": "fedjax.experimental.metrics", "line_number": 64, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.SumStat.new", "line_number": 68, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics.SumStat", "line_number": 68, "usage_type": "attribute"}, {"api_name": "fedjax.experimental.metrics", "line_number": 68, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.SumStat.new", "line_number": 69, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics.SumStat", "line_number": 69, "usage_type": "attribute"}, {"api_name": "fedjax.experimental.metrics", "line_number": 69, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.SumStat.new", "line_number": 74, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics.SumStat", "line_number": 74, "usage_type": "attribute"}, {"api_name": "fedjax.experimental.metrics", "line_number": 74, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 74, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 74, "usage_type": "name"}, {"api_name": "absl.testing.parameterized.TestCase", "line_number": 79, "usage_type": "attribute"}, {"api_name": "absl.testing.parameterized", "line_number": 79, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 82, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 82, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 83, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 83, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.CrossEntropyLoss", "line_number": 84, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics", "line_number": 84, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 101, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 101, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 102, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 102, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.Accuracy", "line_number": 103, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics", "line_number": 103, "usage_type": "name"}, {"api_name": "absl.testing.parameterized.named_parameters", "line_number": 88, "usage_type": "call"}, {"api_name": "absl.testing.parameterized", "line_number": 88, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 108, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 108, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 109, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 109, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.SequenceTokenCrossEntropyLoss", "line_number": 110, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics", "line_number": 110, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 115, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 115, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 116, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 116, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.SequenceCrossEntropyLoss", "line_number": 117, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics", "line_number": 117, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 122, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 122, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 124, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 124, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.SequenceTokenAccuracy", "line_number": 126, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics", "line_number": 126, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 131, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 131, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 132, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 132, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.SequenceTokenCount", "line_number": 133, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics", "line_number": 133, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 148, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 148, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 149, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 149, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.SequenceTruncationRate", "line_number": 150, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics", "line_number": 150, "usage_type": "name"}, {"api_name": "absl.testing.parameterized.named_parameters", "line_number": 137, "usage_type": "call"}, {"api_name": "absl.testing.parameterized", "line_number": 137, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 155, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 155, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 156, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 156, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.SequenceTokenOOVRate", "line_number": 157, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics", "line_number": 157, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 162, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 162, "usage_type": "name"}, {"api_name": "jax.numpy.array", "line_number": 163, "usage_type": "call"}, {"api_name": "jax.numpy", "line_number": 163, "usage_type": "name"}, {"api_name": "fedjax.experimental.metrics.SequenceLength", "line_number": 164, "usage_type": "call"}, {"api_name": "fedjax.experimental.metrics", "line_number": 164, "usage_type": "name"}, {"api_name": "absl.testing.absltest.main", "line_number": 170, "usage_type": "call"}, {"api_name": "absl.testing.absltest", "line_number": 170, "usage_type": "name"}]} +{"seq_id": "33482135", "text": "'''\nAuthor : Oguzhan Gencoglu\nContact : oguzhan.gencoglu@tut.fi\nCreated : 26.07.2014\nLatest Version : 26.07.2014\n'''\n\n# import required packages\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib import rcParams\n\ndef plot_pie(percentages, labels):\n # Plot pie chart\n number_of_per = percentages.shape[0]\n plt.figure(figsize=(11,11), facecolor='white')\n # The slices will be ordered and plotted counter-clockwise.\n explode = np.ones(number_of_per)*0.1\n cs = plt.cm.Set1(np.arange(number_of_per)/float(number_of_per))\n plt.pie(percentages, explode=explode, labels=labels, autopct='%1.2f%%',\n colors=cs, shadow=True, startangle=90, labeldistance=1.15)\n \n plt.title('Used Currencies', fontsize=30, y=1.04)\n rcParams['font.size'] = 25.0\n rcParams['lines.linewidth'] = 1.8\n plt.show()\n return None", "sub_path": "Python/Plotting/plot_pie.py", "file_name": "plot_pie.py", "file_ext": "py", "file_size_in_byte": 883, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm.Set1", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 19, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pie", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.rcParams", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.rcParams", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "575670622", "text": "import sys\nimport os\nimport time\nimport censys.certificates\nfrom censys.ipv4 import CensysIPv4\nimport censys\nimport csv\n\n\n# Function finds subdomain related to domain\ndef subdomain_search(domain, api_id, api_secret, subdomain_fields):\n subdomain = censys.certificates.CensysCertificates(api_id, api_secret)\n results = subdomain.search(domain, fields=subdomain_fields)\n subdomain_list = list(results)\n\n res_list = []\n for i in range(len(subdomain_list)): # Remove duplicate domains and append in new list\n if subdomain_list[i] not in subdomain_list[i + 1:]:\n res_list.append(subdomain_list[i])\n\n with open(domain + '.domains.csv', 'w', newline='') as f: # Write results pulled via API to CSV file\n writer = csv.DictWriter(f, subdomain_fields)\n writer.writerows(res_list)\n print('[+] Results written to csv file: ' + domain + '.domains.csv')\n\n\n# Function finds ipv4 information related to domain\ndef ipv4_search(domain, api_id, api_secret, ipv4_fields):\n ipv4 = CensysIPv4(api_id, api_secret)\n results = ipv4.search(domain, fields=ipv4_fields)\n ipv4_list = list(results)\n\n res_list = []\n for i in range(len(ipv4_list)): # Remove duplicate domains and append in new list\n if ipv4_list[i] not in ipv4_list[i + 1:]:\n res_list.append(ipv4_list[i])\n\n with open(domain + '.ipv4.csv', 'w', newline='') as f:\n writer = csv.DictWriter(f, ipv4_fields)\n writer.writerows(res_list)\n print('[+] Results written to csv file: ' + domain + '.ipv4.csv')\n\n\n# Function initialises values for searches and runs the search functions\ndef main(domain, api_id, api_secret):\n subdomain_fields = ['parsed.names']\n ipv4_fields = [\n \"ip\",\n \"location.city\",\n \"location.country\",\n \"location.country_code\",\n \"location.postal_code\",\n \"autonomous_system.name\",\n \"autonomous_system.organization\"\n ]\n print(\"[+] Finding subdomains of %s \" % domain)\n subdomain_search(domain, api_id, api_secret, subdomain_fields)\n\n print(\"[+] Finding related ipv4 of %s \" % domain)\n ipv4_search(domain, api_id, api_secret, ipv4_fields)\n\n\nif __name__ == \"__main__\":\n api_id = input(\"Enter Censys API id: \")\n api_secret = input(\"Enter Censys secret: \")\n\n # api_id = 'a5d9d1dd-c9f7-4217-a342-f4f94c1993d2'\n # api_secret = 'BByXL2VEnCTulDoZyToNwrK3Nla0zo77'\n\n valid_domain = ['.com', '.net', '.org', '.co.uk']\n domain_input = input(\"Enter the domain: \")\n domain = domain_input.split()\n print(\"Checking %s is valid \" % domain)\n\n for domain in domain: # For domain present after user input,\n if domain.endswith(tuple(valid_domain)): # check domain is valid before attempting to\n print(\"[+] %s is valid \" % domain) # query censys api\n main(domain, api_id, api_secret)\n else:\n print('[-] Invalid domain ')\n print('[-] exiting ....')\n exit(1)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2958, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "censys.certificates.CensysCertificates", "line_number": 12, "usage_type": "call"}, {"api_name": "censys.certificates", "line_number": 12, "usage_type": "attribute"}, {"api_name": "csv.DictWriter", "line_number": 22, "usage_type": "call"}, {"api_name": "censys.ipv4.CensysIPv4", "line_number": 29, "usage_type": "call"}, {"api_name": "csv.DictWriter", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "233017784", "text": "from pathlib import Path\n\nfrom setuptools import setup\n\nreqs_file = Path('requirements.in')\nreqs = reqs_file.read_text().splitlines()\n\ndev_reqs_file = Path('requirements-dev.in')\ndev_reqs = dev_reqs_file.read_text().splitlines()\n\nsetup(\n name='curlit',\n version='0.1',\n packages=['curlit'],\n url='https://github.com/shivdhar/curlit',\n license='MIT',\n author='Shiv Dhar',\n author_email='shiv.dhar@gmail.com',\n description='Curl your HTTP!',\n\n install_requires=reqs,\n extras_require={\n 'dev': dev_reqs,\n }\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 546, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pathlib.Path", "line_number": 5, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 8, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "372463824", "text": "\"OrderPortal: Order information files.\"\n\nfrom __future__ import print_function, absolute_import\n\nimport logging\nfrom collections import OrderedDict as OD\nimport re\nimport base64\n\nimport tornado.web\n\nfrom orderportal.requesthandler import RequestHandler, ApiV1Mixin\nfrom orderportal.order import *\n\n\nclass NscOrderPkgV1(ApiV1Mixin, OrderApiV1Mixin, Order):\n \"\"\"Order package; JSON.\n\n Returns a complete representation of the order, suitable for\n importing into LIMS.\n\n See project_data_package.py in int repository for a prototype\n script.\n \"\"\"\n\n @tornado.web.authenticated\n def get(self, iuid):\n order = self.get_entity(iuid, doctype=constants.ORDER)\n data = OD()\n data['type'] = 'order'\n data = self.get_json(order,\n names=self.get_account_names([order['owner']]),\n item=data)\n\n data['fields'] = order['fields']\n data['invalid'] = order['invalid']\n data['files'] = []\n for filename in order.get(\"_attachments\", []):\n stub = order['_attachments'][filename]\n file_content = self.db.get_attachment(order, filename).read()\n data['files'].append(dict(filename=filename,\n size=stub['length'],\n content_type=stub['content_type'],\n data=base64.b64encode(file_content)))\n\n ## data['owner'] = self.get_account(order['owner'])\n self.set_header('Content-Type', \"text/json\")\n filename = \"{0}.order\".format(order['title'])\n self.set_header('Content-Disposition', 'attachment; filename=\"{0}\"'.format(filename))\n self.write(data)\n\n", "sub_path": "orderportal/nsc_order_package.py", "file_name": "nsc_order_package.py", "file_ext": "py", "file_size_in_byte": 1707, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "orderportal.requesthandler.ApiV1Mixin", "line_number": 16, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 29, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 44, "usage_type": "call"}, {"api_name": "tornado.web.web", "line_number": 26, "usage_type": "attribute"}, {"api_name": "tornado.web", "line_number": 26, "usage_type": "name"}]} +{"seq_id": "142950861", "text": "import psycopg2\nimport math\n\nconn = psycopg2.connect(\"dbname=cnssankey user=xiya\")\ncur = conn.cursor()\n\n# for i in range(10):\n # cur.execute(\"INSERT INTO test (num, data) VALUES (%s, %s)\", (i,math.sin(i)))\n\ndef make_data_point(first_name, last_name, gender, cohort_year):\n return {\n \"first_name\": first_name,\n \"last_name\": last_name,\n \"gender\": gender,\n \"cohort_year\": cohort_year\n }\n\nproto_data = [\n [\"Mary\", \"Chen\", \"F\", 2001],\n [\"Edward\", \"Tock\", \"M\", 1997],\n [\"Seth\", \"Tan\", \"M\", 2006],\n [\"Xiya\", \"Yang\", \"M\", 2015],\n [\"Yolanda\", \"Yeo\", \"F\", 2013] \n]\n\ndata = [make_data_point(*d) for d in proto_data]\n\nfor d in data:\n cur.execute(\"INSERT INTO student_flow_dashboard_person (first_name, last_name, gender, cohort_year) VALUES (%s, %s, %s, %s);\", (d[\"first_name\"],d[\"last_name\"],d[\"gender\"],d[\"cohort_year\"]))\n\n\nconn.commit()\ncur.close()\nconn.close()\n \n", "sub_path": "src/processing/connect.py", "file_name": "connect.py", "file_ext": "py", "file_size_in_byte": 876, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "psycopg2.connect", "line_number": 4, "usage_type": "call"}]} +{"seq_id": "494066372", "text": "\"\"\" Utility functions for dealing with PRAW Reddit instances. \"\"\"\n\nfrom urllib.parse import urljoin\n\nfrom flask import url_for\nfrom praw import Reddit\n\nfrom app import CUBERS_APP\nfrom app.persistence.comp_manager import get_comp_event_by_id\nfrom app.util.times_util import convert_centiseconds_to_friendly_time\nfrom app.persistence.user_manager import get_user_by_username\n\n# -------------------------------------------------------------------------------------------------\n\nREDIRECT = CUBERS_APP.config['REDDIT_REDIRECT_URI']\nCLIENT_ID = CUBERS_APP.config['REDDIT_CLIENT_ID']\nCLIENT_SECRET = CUBERS_APP.config['REDDIT_CLIENT_SECRET']\nAPP_URL = CUBERS_APP.config['APP_URL']\nTARGET_SUBREDDIT = CUBERS_APP.config['TARGET_SUBREDDIT']\nIS_DEVO = CUBERS_APP.config['IS_DEVO']\nUSER_AGENT = 'web:rcubersComps:v0.01 by /u/euphwes'\n\nPROD_CUBERSIO_ACCT = CUBERS_APP.config['PROD_CUBERSIO_ACCT']\nDEVO_CUBERSIO_ACCT = CUBERS_APP.config['DEVO_CUBERSIO_ACCT']\n\nCOMMENT_FOOTER_TEMPLATE = '\\n'.join([\n '',\n '----',\n '^(Check out) [^(my profile)]({}) ^at [^(cubers.io)]({})^(!)',\n])\n\nREDDIT_URL_ROOT = 'http://www.reddit.com'\n\n# -------------------------------------------------------------------------------------------------\n\ndef build_comment_source_from_events_results(events_results, username):\n \"\"\" Builds the source of a Reddit comment that meets the formatting requirements of the\n /r/cubers weekly competition scoring script. \"\"\"\n\n comment_source = ''\n event_line_template = '**{}: {}** = {}\\n{}'\n\n for results in events_results:\n comp_event = get_comp_event_by_id(results.comp_event_id)\n event_name = comp_event.Event.name\n is_fmc = event_name == 'FMC'\n times_string = results.times_string\n comment = '\\n' if not results.comment else build_user_comment(results.comment)\n\n if is_fmc:\n event_result = int(results.average) / 100\n if event_result == int(event_result):\n event_result = int(event_result)\n else:\n event_result = convert_centiseconds_to_friendly_time(results.result)\n\n line = event_line_template.format(event_name, event_result, times_string, comment)\n comment_source += line\n\n if not events_results:\n comment_source += \"*Nothing complete at the moment...*\\n\"\n\n profile_url = urljoin(APP_URL, url_for('profile', username=username))\n footer = COMMENT_FOOTER_TEMPLATE.format(profile_url, APP_URL)\n comment_source += footer\n\n return comment_source\n\n\ndef build_user_comment(comment_body):\n \"\"\" Builds up the user's comment text into the format expected by Reddit 'quotations'. \"\"\"\n\n reddit_comment_body = \"\"\n for line in comment_body.splitlines():\n line = line.replace('#', r'\\#') # escape '#' signs so they are not interpreted as headings\n reddit_comment_body += '>' + line + \"\\n\\n\"\n\n return reddit_comment_body\n\n\ndef get_new_reddit():\n \"\"\" Returns a new, unauthenticated Reddit instance. \"\"\"\n return Reddit(client_id=CLIENT_ID, client_secret=CLIENT_SECRET, redirect_uri=REDIRECT,\n user_agent=USER_AGENT)\n\n\ndef get_authed_reddit_for_cubersio_acct():\n \"\"\" Returns a PRAW instance for the Reddit account to post the competition under. \"\"\"\n\n if IS_DEVO:\n token = get_user_by_username(DEVO_CUBERSIO_ACCT).refresh_token\n else:\n token = get_user_by_username(PROD_CUBERSIO_ACCT).refresh_token\n\n return Reddit(client_id=CLIENT_ID, client_secret=CLIENT_SECRET,\n refresh_token=token, user_agent=USER_AGENT)\n\n\ndef get_authed_reddit_for_user(user):\n \"\"\" Returns a PRAW instance for this user using their refresh token. \"\"\"\n return Reddit(client_id=CLIENT_ID, client_secret=CLIENT_SECRET,\n refresh_token=user.refresh_token, user_agent=USER_AGENT)\n\n\ndef get_non_user_reddit():\n \"\"\" Returns a PRAW instance for cases where we do not need to be authed as a user. \"\"\"\n return Reddit(client_id=CLIENT_ID, client_secret=CLIENT_SECRET, redirect_uri=REDIRECT,\n user_agent=USER_AGENT)\n\n\ndef submit_comment_for_user(user, reddit_thread_id, comment_body):\n \"\"\" Submits the comment with the specified body on behalf of the user and returns a URL\n for the comment. \"\"\"\n comp_submission = get_authed_reddit_for_user(user).submission(id=reddit_thread_id)\n comment = comp_submission.reply(comment_body)\n return (REDDIT_URL_ROOT + comment.permalink), comment.id\n\n\ndef update_comment_for_user(user, comment_thread_id, comment_body):\n \"\"\" Updates the comment with the specified body on behalf of the user and returns a URL\n for the comment. \"\"\"\n r = get_authed_reddit_for_user(user)\n comment = r.comment(id=comment_thread_id)\n comment.edit(comment_body)\n return (REDDIT_URL_ROOT + comment.permalink), comment.id\n\n\ndef get_permalink_for_user_and_comment(user, comment_thread_id):\n \"\"\" Returns a full URL for the specified user's comment. \"\"\"\n r = get_authed_reddit_for_user(user)\n comment = r.comment(id=comment_thread_id)\n return REDDIT_URL_ROOT + comment.permalink\n\n\ndef submit_competition_post(title, post_body):\n \"\"\" Posts a Submission for the competition, and returns a Reddit submission ID. \"\"\"\n r = get_authed_reddit_for_cubersio_acct()\n cubers = r.subreddit(TARGET_SUBREDDIT)\n return cubers.submit(title=title, selftext=post_body, send_replies=False).id\n\n\ndef update_results_thread(post_body, thread_id):\n \"\"\" Updates a results thread with the given post_body. \"\"\"\n r = get_authed_reddit_for_cubersio_acct()\n submission = r.submission(id=thread_id)\n submission.edit(post_body)\n return submission.id\n\n\ndef get_permalink_for_comp_thread(reddit_thread_id):\n \"\"\" Returns the permalink for the competition thread specified by the ID above. \"\"\"\n try:\n comp = get_non_user_reddit().submission(id=reddit_thread_id)\n return REDDIT_URL_ROOT + comp.permalink\n except:\n return \"Oops, no thread exists with that ID.\"\n\n\ndef get_submission_with_id(reddit_thread_id):\n \"\"\" Returns the Submission object for a given Reddit thread ID. \"\"\"\n return get_non_user_reddit().submission(id=reddit_thread_id)\n\n\ndef get_username_refresh_token_from_code(code):\n \"\"\" Returns the username and current refresh token for a given Reddit auth code. \"\"\"\n reddit = get_new_reddit()\n refresh_token = reddit.auth.authorize(code)\n username = reddit.user.me().name\n return username, refresh_token\n\n\ndef get_user_auth_url(state='...'):\n \"\"\" Returns a url for authenticating with Reddit. \"\"\"\n return get_new_reddit().auth.url(['identity', 'read', 'submit', 'edit'], state, 'permanent')\n", "sub_path": "app/util/reddit_util.py", "file_name": "reddit_util.py", "file_ext": "py", "file_size_in_byte": 6698, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "app.CUBERS_APP.config", "line_number": 15, "usage_type": "attribute"}, {"api_name": "app.CUBERS_APP", "line_number": 15, "usage_type": "name"}, {"api_name": "app.CUBERS_APP.config", "line_number": 16, "usage_type": "attribute"}, {"api_name": "app.CUBERS_APP", "line_number": 16, "usage_type": "name"}, {"api_name": "app.CUBERS_APP.config", "line_number": 17, "usage_type": "attribute"}, {"api_name": "app.CUBERS_APP", "line_number": 17, "usage_type": "name"}, {"api_name": "app.CUBERS_APP.config", "line_number": 18, "usage_type": "attribute"}, {"api_name": "app.CUBERS_APP", "line_number": 18, "usage_type": "name"}, {"api_name": "app.CUBERS_APP.config", "line_number": 19, "usage_type": "attribute"}, {"api_name": "app.CUBERS_APP", "line_number": 19, "usage_type": "name"}, {"api_name": "app.CUBERS_APP.config", "line_number": 20, "usage_type": "attribute"}, {"api_name": "app.CUBERS_APP", "line_number": 20, "usage_type": "name"}, {"api_name": "app.CUBERS_APP.config", "line_number": 23, "usage_type": "attribute"}, {"api_name": "app.CUBERS_APP", "line_number": 23, "usage_type": "name"}, {"api_name": "app.CUBERS_APP.config", "line_number": 24, "usage_type": "attribute"}, {"api_name": "app.CUBERS_APP", "line_number": 24, "usage_type": "name"}, {"api_name": "app.persistence.comp_manager.get_comp_event_by_id", "line_number": 44, "usage_type": "call"}, {"api_name": "app.util.times_util.convert_centiseconds_to_friendly_time", "line_number": 55, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 63, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 63, "usage_type": "call"}, {"api_name": "praw.Reddit", "line_number": 83, "usage_type": "call"}, {"api_name": "app.persistence.user_manager.get_user_by_username", "line_number": 91, "usage_type": "call"}, {"api_name": "app.persistence.user_manager.get_user_by_username", "line_number": 93, "usage_type": "call"}, {"api_name": "praw.Reddit", "line_number": 95, "usage_type": "call"}, {"api_name": "praw.Reddit", "line_number": 101, "usage_type": "call"}, {"api_name": "praw.Reddit", "line_number": 107, "usage_type": "call"}]} +{"seq_id": "68064822", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on 2020-09-17 17:21\n\n@author: a002028\n\n\"\"\"\nimport os\n\nimport numpy as np\n\nfrom bawsvis.session import Session\nfrom bawsvis.readers.text import np_txt_reader\nfrom bawsvis.plotting import PlotMap, PlotIceMap\nimport matplotlib.pyplot as plt\nimport cmocean\n\n\nif __name__ == \"__main__\":\n\n s = Session()\n\n lat = np_txt_reader('..\\\\proj\\\\havgem\\\\Johannes_Johansson\\\\N_FIX\\\\python_process_data\\\\lat_baws.txt')\n lon = np_txt_reader('..\\\\proj\\\\havgem\\\\Johannes_Johansson\\\\N_FIX\\\\python_process_data\\\\lon_baws.txt')\n\n # lat = np_txt_reader('E:\\\\Johannes_exjobb\\\\import_data\\\\lat_small.txt')\n # lon = np_txt_reader('E:\\\\Johannes_exjobb\\\\import_data\\\\lon_small.txt')\n\n # wd_data = 'E:\\\\Johannes_exjobb\\\\MODIS_data\\\\outdata\\\\monthly_seasonally_cummulative_and_FCA_data\\\\Cumulative\\\\annual\\\\Cumu_%s.txt'\n\n # for year in range(2019, 2021):\n # year = str(year)\n #\n # file = wd_data % year\n # data = np_txt_reader(file)\n #\n # map_frame = {'lat_min': 52., 'lat_max': 66.,\n # 'lon_min': 7., 'lon_max': 37.5}\n #\n # plot = PlotMap(data_mat=data.astype(float),\n # lat_mat=lat,\n # lon_mat=lon,\n # cbar_label='Number of bloom days',\n # cmap_step=5,\n # max_tick=20,\n # min_tick=0,\n # use_frame=True,\n # p_color=True,\n # map_frame=map_frame,\n # resolution='h',\n # fig_title='Cyanobacterial bloom %s' % year,\n # fig_name='aggregation_%s.png' % year,\n # save_fig=True,\n # clear_fig=True,\n # )\n #\n # plot._draw_map()\n # plot._draw_mesh(p_color=True)\n # plot._save_figure(''.join((s.setting.export_directory, 'aggregation_baws_modis_%s.png' % year)))\n data = np_txt_reader('C:\\\\Utveckling\\\\BAWS-vis\\\\bawsvis\\\\export\\\\modis_aggregation_2002-2020.txt')\n data = np.where(data==0, np.nan, data)\n data = data/19.\n\n # mask = np_txt_reader('...N_FIX\\\\Result\\\\MASK_BP_GoF_GoB.txt')\n\n # map_frame = {'lat_min': 52., 'lat_max': 66.,\n # 'lon_min': 7., 'lon_max': 37.5}\n\n map_frame = {'lat_min': 52.5, 'lat_max': 66.,\n 'lon_min': 9., 'lon_max': 36.8}\n\n plot = PlotIceMap(data_mat=data.astype(float),\n lat_mat=lat,\n lon_mat=lon,\n cbar_label='Average number of bloom days per year',\n cmap=cmocean.cm.haline,\n cmap_step=2,\n max_tick=10,\n min_tick=0,\n use_frame=True,\n p_color=True,\n map_frame=map_frame,\n resolution='f',\n fig_title='Cyanobacterial bloom 2002-2020',\n fig_name='aggregation_2002_2020_2.png',\n save_fig=True,\n clear_fig=True,\n )\n\n plot._draw_map()\n plot._draw_mesh(p_color=True)\n\n save_dir = r'..\\proj\\havgem\\Johannes_Johansson\\coclime_figures\\map'\n f_name = 'aggregation_2002_2020_v4'\n plt.savefig(os.path.join(save_dir, f_name) + '.png', format='png', dpi=500)\n # plt.savefig(os.path.join(save_dir, f_name) + '.eps', format='eps')\n plt.savefig(os.path.join(save_dir, f_name) + '.pdf', format='pdf')\n # plot._save_figure(''.join((s.setting.export_directory, 'aggregation_2002_2020_3.png')))\n", "sub_path": "bawsvis/examples/baws_plot_map_modis.py", "file_name": "baws_plot_map_modis.py", "file_ext": "py", "file_size_in_byte": 3600, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "bawsvis.session.Session", "line_number": 21, "usage_type": "call"}, {"api_name": "bawsvis.readers.text.np_txt_reader", "line_number": 23, "usage_type": "call"}, {"api_name": "bawsvis.readers.text.np_txt_reader", "line_number": 24, "usage_type": "call"}, {"api_name": "bawsvis.readers.text.np_txt_reader", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 61, "usage_type": "attribute"}, {"api_name": "bawsvis.plotting.PlotIceMap", "line_number": 72, "usage_type": "call"}, {"api_name": "cmocean.cm", "line_number": 76, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path", "line_number": 95, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}]} +{"seq_id": "291149851", "text": "import os, sys\n\ncurrentdir = os.path.dirname(os.path.realpath(__file__))\nparentdir = os.path.dirname(currentdir)\nsys.path.append(parentdir) # PYTHON > 3.3 does not allow relative referencing\n\nPYCHARM_EXEC = os.getenv('PYCHARM_EXEC') == 'True'\n\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard, EarlyStopping\nimport voxelmorph as vxm\nimport neurite as ne\nimport h5py\nfrom datetime import datetime\n\nimport DeepDeformationMapRegistration.utils.constants as C\nfrom DeepDeformationMapRegistration.data_generator import DataGeneratorManager2D\nfrom DeepDeformationMapRegistration.utils.misc import try_mkdir\nfrom DeepDeformationMapRegistration.losses import HausdorffDistanceErosion\nfrom DeepDeformationMapRegistration.layers import UncertaintyWeighting\n\n\nos.environ['CUDA_DEVICE_ORDER'] = C.DEV_ORDER\nos.environ['CUDA_VISIBLE_DEVICES'] = '1' # const.GPU_NUM # Check availability before running using 'nvidia-smi'\n\nC.TRAINING_DATASET = '/mnt/EncryptedData1/Users/javier/vessel_registration/ov_dataset/training'\nC.BATCH_SIZE = 256\nC.LIMIT_NUM_SAMPLES = None\nC.EPOCHS = 10000\n\nif PYCHARM_EXEC:\n path_prefix = os.path.join('scripts', 'tf')\nelse:\n path_prefix = ''\n\n# Load data\n# Build data generator\nsample_list = [os.path.join(C.TRAINING_DATASET, f) for f in os.listdir(C.TRAINING_DATASET) if\n f.startswith('sample')]\nsample_list.sort()\n\ndata_generator = DataGeneratorManager2D(sample_list[:C.LIMIT_NUM_SAMPLES],\n C.BATCH_SIZE, C.TRAINING_PERC,\n (64, 64, 1),\n fix_img_tag='dilated/input/fix',\n mov_img_tag='dilated/input/mov',\n multi_loss=True,\n )\n\n# Build model\nin_shape_img, in_shape_grad = data_generator.train_generator.input_shape\nenc_features = [32, 32, 32, 32, 32, 32] # const.ENCODER_FILTERS\ndec_features = [32, 32, 32, 32, 32, 32, 32, 16] # const.ENCODER_FILTERS[::-1]\nnb_features = [enc_features, dec_features]\nvxm_model = vxm.networks.VxmDense(inshape=in_shape_img[:-1], nb_unet_features=nb_features, int_steps=0)\n\n#moving = tf.keras.Input(shape=in_shape_img, name='multiLoss_moving_input', dtype=tf.float32)\n#fixed = tf.keras.Input(shape=in_shape_img, name='multiLoss_fixed_input', dtype=tf.float32)\ngrad = tf.keras.Input(shape=(*in_shape_img[:-1], 2), name='multiLoss_grad_input', dtype=tf.float32)\n\ndef dice_loss(y_true, y_pred):\n # Dice().loss returns -Dice score\n return 1 + vxm.losses.Dice().loss(y_true, y_pred)\n\n#fixed_pred, dm_pred = vxm_model([moving, fixed])\nmultiLoss = UncertaintyWeighting(num_loss_fns=2,\n num_reg_fns=1,\n loss_fns=[HausdorffDistanceErosion(2, 2).loss, dice_loss],\n reg_fns=[vxm.losses.Grad('l2').loss],\n prior_loss_w=[1., 1.],\n prior_reg_w=[0.01],\n name='MultiLossLayer')\nloss = multiLoss([vxm_model.inputs[1], vxm_model.inputs[1], vxm_model.references.y_source, vxm_model.references.y_source, grad, vxm_model.references.pos_flow])\n\nfull_model = tf.keras.Model(inputs=vxm_model.inputs + [grad], outputs=vxm_model.outputs + [loss])\n\n# Compile the model\nfull_model.compile(optimizer=tf.keras.optimizers.Adam(lr=1e-4), loss=None)\n\n# Train\noutput_folder = os.path.join('train_2d_multiloss_haussdorf_dice_grad' + datetime.now().strftime(\"%H%M%S-%d%m%Y\"))\ntry_mkdir(output_folder)\ntry_mkdir(os.path.join(output_folder, 'checkpoints'))\ntry_mkdir(os.path.join(output_folder, 'tensorboard'))\nmy_callbacks = [\n # EarlyStopping(patience=const.EARLY_STOP_PATIENCE, monitor='dice', mode='max', verbose=1),\n ModelCheckpoint(os.path.join(output_folder, 'checkpoints', 'best_model.h5'),\n save_best_only=True, monitor='val_loss', verbose=0, mode='min'),\n ModelCheckpoint(os.path.join(output_folder, 'checkpoints', 'weights.{epoch:05d}-{val_loss:.2f}.h5'),\n save_best_only=True, save_weights_only=True, monitor='val_loss', verbose=0, mode='min'),\n # CSVLogger(train_log_name, ';'),\n # UpdateLossweights([haus_weight, dice_weight], [const.MODEL+'_resampler_seg', const.MODEL+'_resampler_seg'])\n TensorBoard(log_dir=os.path.join(output_folder, 'tensorboard'),\n batch_size=C.BATCH_SIZE, write_images=True, histogram_freq=10, update_freq='epoch',\n write_grads=True),\n EarlyStopping(monitor='val_loss', verbose=1, patience=50, min_delta=0.0001)\n]\nhist = full_model.fit_generator(data_generator.train_generator,\n epochs=C.EPOCHS,\n validation_data=data_generator.validation_generator,\n verbose=2,\n callbacks=my_callbacks)\n", "sub_path": "TrainingScripts/Train_2d_uncertaintyWeighting.py", "file_name": "Train_2d_uncertaintyWeighting.py", "file_ext": "py", "file_size_in_byte": 4961, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.dirname", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path", "line_number": 3, "usage_type": "attribute"}, {"api_name": "os.path.realpath", "line_number": 3, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 4, "usage_type": "call"}, {"api_name": "os.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 5, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 7, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 24, "usage_type": "attribute"}, {"api_name": "DeepDeformationMapRegistration.utils.constants.DEV_ORDER", "line_number": 24, "usage_type": "attribute"}, {"api_name": "DeepDeformationMapRegistration.utils.constants", "line_number": 24, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 25, "usage_type": "attribute"}, {"api_name": "DeepDeformationMapRegistration.utils.constants.TRAINING_DATASET", "line_number": 27, "usage_type": "attribute"}, {"api_name": "DeepDeformationMapRegistration.utils.constants", "line_number": 27, "usage_type": "name"}, {"api_name": "DeepDeformationMapRegistration.utils.constants.BATCH_SIZE", "line_number": 28, "usage_type": "attribute"}, {"api_name": "DeepDeformationMapRegistration.utils.constants", "line_number": 28, "usage_type": "name"}, {"api_name": "DeepDeformationMapRegistration.utils.constants.LIMIT_NUM_SAMPLES", "line_number": 29, "usage_type": "attribute"}, {"api_name": "DeepDeformationMapRegistration.utils.constants", "line_number": 29, "usage_type": "name"}, {"api_name": "DeepDeformationMapRegistration.utils.constants.EPOCHS", "line_number": 30, "usage_type": "attribute"}, {"api_name": "DeepDeformationMapRegistration.utils.constants", "line_number": 30, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "DeepDeformationMapRegistration.utils.constants.TRAINING_DATASET", "line_number": 39, "usage_type": "attribute"}, {"api_name": "DeepDeformationMapRegistration.utils.constants", "line_number": 39, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 39, "usage_type": "call"}, {"api_name": "DeepDeformationMapRegistration.data_generator.DataGeneratorManager2D", "line_number": 43, "usage_type": "call"}, {"api_name": "DeepDeformationMapRegistration.utils.constants.LIMIT_NUM_SAMPLES", "line_number": 43, "usage_type": "attribute"}, {"api_name": "DeepDeformationMapRegistration.utils.constants", "line_number": 43, "usage_type": "name"}, {"api_name": "DeepDeformationMapRegistration.utils.constants.BATCH_SIZE", "line_number": 44, "usage_type": "attribute"}, {"api_name": "DeepDeformationMapRegistration.utils.constants", "line_number": 44, "usage_type": "name"}, {"api_name": "DeepDeformationMapRegistration.utils.constants.TRAINING_PERC", "line_number": 44, "usage_type": "attribute"}, {"api_name": "voxelmorph.networks.VxmDense", "line_number": 56, "usage_type": "call"}, {"api_name": "voxelmorph.networks", "line_number": 56, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.Input", "line_number": 60, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 60, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 60, "usage_type": "attribute"}, {"api_name": "voxelmorph.losses.Dice", "line_number": 64, "usage_type": "call"}, {"api_name": "voxelmorph.losses", "line_number": 64, "usage_type": "attribute"}, {"api_name": "DeepDeformationMapRegistration.layers.UncertaintyWeighting", "line_number": 67, "usage_type": "call"}, {"api_name": "DeepDeformationMapRegistration.losses.HausdorffDistanceErosion", "line_number": 69, "usage_type": "call"}, {"api_name": "voxelmorph.losses.Grad", "line_number": 70, "usage_type": "call"}, {"api_name": "voxelmorph.losses", "line_number": 70, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.Model", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.optimizers.Adam", "line_number": 79, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 82, "usage_type": "name"}, {"api_name": "DeepDeformationMapRegistration.utils.misc.try_mkdir", "line_number": 83, "usage_type": "call"}, {"api_name": "DeepDeformationMapRegistration.utils.misc.try_mkdir", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "DeepDeformationMapRegistration.utils.misc.try_mkdir", "line_number": 85, "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": "tensorflow.keras.callbacks.ModelCheckpoint", "line_number": 88, "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": "tensorflow.keras.callbacks.ModelCheckpoint", "line_number": 90, "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": "tensorflow.keras.callbacks.TensorBoard", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path", "line_number": 94, "usage_type": "attribute"}, {"api_name": "DeepDeformationMapRegistration.utils.constants.BATCH_SIZE", "line_number": 95, "usage_type": "attribute"}, {"api_name": "DeepDeformationMapRegistration.utils.constants", "line_number": 95, "usage_type": "name"}, {"api_name": "tensorflow.keras.callbacks.EarlyStopping", "line_number": 97, "usage_type": "call"}, {"api_name": "DeepDeformationMapRegistration.utils.constants.EPOCHS", "line_number": 100, "usage_type": "attribute"}, {"api_name": "DeepDeformationMapRegistration.utils.constants", "line_number": 100, "usage_type": "name"}]} +{"seq_id": "184085246", "text": "\"\"\"NVDM Tensorflow implementation by Yishu Miao, adapted to work with the Dirichlet distribution by Sophie Burkhardt\"\"\"\nfrom __future__ import print_function\n\nimport numpy as np\nimport tensorflow as tf\nimport math\nimport os\nimport utils as utils\nimport sys\nimport argparse\nimport pickle\n\nnp.random.seed(0)\ntf.set_random_seed(0)\n\nflags = tf.app.flags\nflags.DEFINE_integer('batch_size', 200, 'Batch size.')\nflags.DEFINE_integer('n_hidden', 100, 'Size of each hidden layer.')\nflags.DEFINE_boolean('test', True, 'Process test data.')\nflags.DEFINE_string('non_linearity', 'relu', 'Non-linearity of the MLP.')\nflags.DEFINE_string('summaries_dir','summaries','where to save the summaries')\nFLAGS = flags.FLAGS\n\nclass NVDM(object):\n \"\"\" Neural Variational Document Model -- BOW VAE.\n \"\"\"\n def __init__(self, \n vocab_size,\n n_hidden,\n n_topic,\n learning_rate, \n batch_size,\n non_linearity,\n adam_beta1,\n adam_beta2,\n dir_prior):\n tf.reset_default_graph()\n self.vocab_size = vocab_size\n self.n_hidden = n_hidden\n self.n_topic = n_topic\n self.n_sample = 1#n_sample\n self.non_linearity = non_linearity\n self.learning_rate = learning_rate\n self.batch_size = batch_size\n\n lda=False\n self.x = tf.placeholder(tf.float32, [None, vocab_size], name='input')\n self.mask = tf.placeholder(tf.float32, [None], name='mask') # mask paddings\n self.warm_up = tf.placeholder(tf.float32, (), name='warm_up') # warm up\n self.adam_beta1=adam_beta1\n self.adam_beta2=adam_beta2\n self.keep_prob = tf.placeholder(tf.float32, name='keep_prob')\n self.min_alpha = tf.placeholder(tf.float32,(), name='min_alpha')\n # encoder\n with tf.variable_scope('encoder'): \n self.enc_vec = utils.mlp(self.x, [self.n_hidden], self.non_linearity)\n self.enc_vec = tf.nn.dropout(self.enc_vec,self.keep_prob)\n self.mean = tf.contrib.layers.batch_norm(utils.linear(self.enc_vec, self.n_topic, scope='mean'))\n self.alpha = tf.maximum(self.min_alpha,tf.log(1.+tf.exp(self.mean)))\n #Dirichlet prior alpha0\n self.prior = tf.ones((batch_size,self.n_topic), dtype=tf.float32, name='prior')*dir_prior\n \n \n self.analytical_kld = tf.lgamma(tf.reduce_sum(self.alpha,axis=1))-tf.lgamma(tf.reduce_sum(self.prior,axis=1))\n self.analytical_kld-=tf.reduce_sum(tf.lgamma(self.alpha),axis=1)\n self.analytical_kld+=tf.reduce_sum(tf.lgamma(self.prior),axis=1)\n minus = self.alpha-self.prior\n test = tf.reduce_sum(tf.multiply(minus,tf.digamma(self.alpha)-tf.reshape(tf.digamma(tf.reduce_sum(self.alpha,1)),(batch_size,1))),1)\n self.analytical_kld+=test\n self.analytical_kld = self.mask*self.analytical_kld # mask paddings\n max_kld = tf.argmax(self.analytical_kld,0)\n\n with tf.variable_scope('decoder'):\n if self.n_sample ==1: # single sample\n u = tf.random_uniform((batch_size,self.n_topic))\n with tf.variable_scope('prob'):\n #CDF transform\n self.doc_vec = tf.pow(u*self.alpha*tf.exp(tf.lgamma(self.alpha)),1./self.alpha)\n #normalize\n self.doc_vec = tf.div(self.doc_vec,tf.reshape(tf.reduce_sum(self.doc_vec,1), (-1, 1)))\n self.doc_vec.set_shape(self.alpha.get_shape())\n #reconstruction\n if lda:\n logits = tf.log(tf.clip_by_value(utils.linear_LDA(self.doc_vec, self.vocab_size, scope='projection',no_bias=True),1e-10,1.0))\n else:\n logits = tf.nn.log_softmax(tf.contrib.layers.batch_norm(utils.linear(self.doc_vec, self.vocab_size, scope='projection',no_bias=True)))\n self.recons_loss = -tf.reduce_sum(tf.multiply(logits, self.x), 1)\n \n dir1=tf.contrib.distributions.Dirichlet(self.prior)\n dir2=tf.contrib.distributions.Dirichlet(self.alpha)\n self.kld = dir2.log_prob(self.doc_vec)-dir1.log_prob(self.doc_vec)\n max_kld_sampled = tf.arg_max(self.kld,0)\n # multiple samples\n #not implemented\n \n self.objective = self.recons_loss + self.warm_up*self.analytical_kld\n self.true_objective = self.recons_loss + self.kld\n \n self.analytical_objective = self.recons_loss+self.analytical_kld\n \n fullvars = tf.trainable_variables()\n\n enc_vars = utils.variable_parser(fullvars, 'encoder')\n dec_vars = utils.variable_parser(fullvars, 'decoder')\n \n #this is the standard gradient for the reconstruction network\n dec_grads = tf.gradients(self.objective, dec_vars)\n \n \n #####################################################\n #Now calculate the gradient for the encoding network#\n #####################################################\n \n \n kl_grad = tf.gradients(self.analytical_kld,enc_vars)\n \n g_rep = tf.gradients(self.recons_loss,enc_vars)\n \n enc_grads = [g_r+self.warm_up*g_e for g_r,g_e in zip(g_rep,kl_grad)]\n \n \n \n optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate,beta1=self.adam_beta1,beta2=self.adam_beta2)#,beta1=0.99\n self.optim_enc = optimizer.apply_gradients(zip(enc_grads, enc_vars))\n self.optim_dec = optimizer.apply_gradients(zip(dec_grads, dec_vars))\n self.optim_all = optimizer.apply_gradients(list(zip(enc_grads, enc_vars))+list(zip(dec_grads, dec_vars)))\n \n\n\ndef train(sess, model, \n train_url, \n test_url, \n batch_size, \n vocab_size,\n alternate_epochs=1,#10\n lexicon=[],\n result_file='test.txt',\n B=1,\n warm_up_period=100):\n \"\"\"train nvdm model.\"\"\"\n train_set, train_count = utils.data_set(train_url)\n test_set, test_count = utils.data_set(test_url)\n # hold-out development dataset\n train_size=len(train_set)\n validation_size=int(train_size*0.1)\n dev_set = train_set[:validation_size]\n dev_count = train_count[:validation_size]\n train_set = train_set[validation_size:]\n train_count = train_count[validation_size:]\n #print('sizes',train_size,validation_size,len(dev_set),len(train_set))\n optimize_jointly = True\n dev_batches = utils.create_batches(len(dev_set), batch_size, shuffle=False)\n test_batches = utils.create_batches(len(test_set), batch_size, shuffle=False)\n warm_up = 0\n min_alpha = 0.00001#\n curr_B=B\n\n best_print_ana_ppx=1e10\n early_stopping_iters=30\n no_improvement_iters=0\n stopped=False\n epoch=-1\n #for epoch in range(training_epochs):\n while not stopped:\n epoch+=1\n train_batches = utils.create_batches(len(train_set), batch_size, shuffle=True)\n if warm_up<1.:\n warm_up += 1./warm_up_period\n else:\n warm_up=1.\n \n print('B',curr_B)\n #-------------------------------\n # train\n #for switch in range(0, 2):\n if optimize_jointly:\n optim = model.optim_all\n print_mode = 'updating encoder and decoder'\n elif switch == 0:\n optim = model.optim_dec\n print_mode = 'updating decoder'\n else:\n optim = model.optim_enc\n print_mode = 'updating encoder'\n for i in range(alternate_epochs):\n loss_sum = 0.0\n ana_loss_sum = 0.0\n ppx_sum = 0.0\n kld_sum_train = 0.0\n ana_kld_sum_train = 0.0\n word_count = 0\n doc_count = 0\n recon_sum=0.0\n for idx_batch in train_batches:\n data_batch, count_batch, mask = utils.fetch_data(\n train_set, train_count, idx_batch, vocab_size)\n input_feed = {model.x.name: data_batch, model.mask.name: mask,model.keep_prob.name: 0.75,model.warm_up.name: warm_up,model.min_alpha.name:min_alpha}\n _, (loss,recon, kld_train,ana_loss,ana_kld_train) = sess.run((optim, \n [model.true_objective, model.recons_loss, model.kld,model.analytical_objective,model.analytical_kld]),\n input_feed)\n loss_sum += np.sum(loss)\n ana_loss_sum += np.sum(ana_loss)\n kld_sum_train += np.sum(kld_train) / np.sum(mask) \n ana_kld_sum_train += np.sum(ana_kld_train) / np.sum(mask)\n word_count += np.sum(count_batch)\n # to avoid nan error\n count_batch = np.add(count_batch, 1e-12)\n # per document loss\n ppx_sum += np.sum(np.divide(loss, count_batch)) \n doc_count += np.sum(mask)\n recon_sum+=np.sum(recon)\n print_loss = recon_sum/len(train_batches)\n dec_vars = utils.variable_parser(tf.trainable_variables(), 'decoder')\n phi = dec_vars[0]\n phi = sess.run(phi)\n utils.print_top_words(phi, lexicon,result_file=None)\n print_ppx = np.exp(loss_sum / word_count)\n print_ana_ppx = np.exp(ana_loss_sum / word_count)\n print_ppx_perdoc = np.exp(ppx_sum / doc_count)\n print_kld_train = kld_sum_train/len(train_batches)\n print_ana_kld_train = ana_kld_sum_train/len(train_batches)\n print('| Epoch train: {:d} |'.format(epoch+1), \n print_mode, '{:d}'.format(i),\n '| Corpus ppx: {:.5f}'.format(print_ppx), # perplexity for all docs\n '| Per doc ppx: {:.5f}'.format(print_ppx_perdoc), # perplexity for per doc\n '| KLD: {:.5}'.format(print_kld_train),\n '| Loss: {:.5}'.format(print_loss),\n '| ppx anal.: {:.5f}'.format(print_ana_ppx),\n '|KLD anal.: {:.5f}'.format(print_ana_kld_train))\n \n \n #-------------------------------\n # dev\n loss_sum = 0.0\n kld_sum_dev = 0.0\n ppx_sum = 0.0\n word_count = 0\n doc_count = 0\n recon_sum=0.0\n print_ana_ppx = 0.0\n ana_loss_sum = 0.0\n for idx_batch in dev_batches:\n data_batch, count_batch, mask = utils.fetch_data(\n dev_set, dev_count, idx_batch, vocab_size)\n input_feed = {model.x.name: data_batch, model.mask.name: mask,model.keep_prob.name: 1.0,model.warm_up.name: 1.0,model.min_alpha.name:min_alpha}\n loss,recon, kld_dev,ana_kld,ana_loss = sess.run([model.objective, model.recons_loss,model.kld, model.analytical_kld,model.analytical_objective],\n input_feed)\n loss_sum += np.sum(loss)\n ana_loss_sum += np.sum(ana_loss)\n kld_sum_dev += np.sum(kld_dev) / np.sum(mask) \n word_count += np.sum(count_batch)\n count_batch = np.add(count_batch, 1e-12)\n ppx_sum += np.sum(np.divide(loss, count_batch))\n doc_count += np.sum(mask) \n recon_sum+=np.sum(recon)\n print_ana_ppx = np.exp(ana_loss_sum / word_count)\n print_ppx = np.exp(loss_sum / word_count)\n print_ppx_perdoc = np.exp(ppx_sum / doc_count)\n print_kld_dev = kld_sum_dev/len(dev_batches)\n print_loss = recon_sum/len(dev_batches)\n if print_ppx save improved model\n \n tf.train.Saver().save(sess, 'models/improved_model') \n \n else:\n no_improvement_iters+=1\n print('no_improvement_iters',no_improvement_iters,'best ppx',best_print_ana_ppx)\n if no_improvement_iters>=early_stopping_iters:\n #if model has not improved for 30 iterations, stop training\n ###########STOP TRAINING############\n stopped=True\n print('stop training after',epoch,'iterations,no_improvement_iters',no_improvement_iters)\n ###########LOAD BEST MODEL##########\n print('load stored model')\n tf.train.Saver().restore(sess,'models/improved_model')\n print('| Epoch dev: {:d} |'.format(epoch+1), \n '| Perplexity: {:.9f}'.format(print_ppx),\n '| Per doc ppx: {:.5f}'.format(print_ppx_perdoc),\n '| KLD: {:.5}'.format(print_kld_dev) ,\n '| Loss: {:.5}'.format(print_loss)) \n\n #-------------------------------\n # test\n if FLAGS.test:\n \n loss_sum = 0.0\n kld_sum_test = 0.0\n ppx_sum = 0.0\n word_count = 0\n doc_count = 0\n recon_sum = 0.0\n ana_loss_sum = 0.0\n ana_kld_sum_test = 0.0\n for idx_batch in test_batches:\n data_batch, count_batch, mask = utils.fetch_data(\n test_set, test_count, idx_batch, vocab_size)\n input_feed = {model.x.name: data_batch, model.mask.name: mask,model.keep_prob.name: 1.0,model.warm_up.name: 1.0,model.min_alpha.name:min_alpha}\n loss, recon,kld_test,ana_loss,ana_kld_test = sess.run([model.objective, model.recons_loss,model.kld,model.analytical_objective,model.analytical_kld],\n input_feed)\n loss_sum += np.sum(loss)\n kld_sum_test += np.sum(kld_test)/np.sum(mask) \n ana_loss_sum += np.sum(ana_loss)\n ana_kld_sum_test += np.sum(ana_kld_test) / np.sum(mask)\n word_count += np.sum(count_batch)\n count_batch = np.add(count_batch, 1e-12)\n ppx_sum += np.sum(np.divide(loss, count_batch))\n doc_count += np.sum(mask) \n recon_sum+=np.sum(recon)\n print_loss = recon_sum/len(test_batches)\n print_ppx = np.exp(loss_sum / word_count)\n print_ppx_perdoc = np.exp(ppx_sum / doc_count)\n print_kld_test = kld_sum_test/len(test_batches)\n print_ana_ppx = np.exp(ana_loss_sum / word_count)\n print_ana_kld_test = ana_kld_sum_test/len(train_batches)\n print('| Epoch test: {:d} |'.format(epoch+1), \n '| Perplexity: {:.9f}'.format(print_ppx),\n '| Per doc ppx: {:.5f}'.format(print_ppx_perdoc),\n '| KLD: {:.5}'.format(print_kld_test),\n '| Loss: {:.5}'.format(print_loss),\n '| ppx anal.: {:.5f}'.format(print_ana_ppx),\n '|KLD anal.: {:.5f}'.format(print_ana_kld_test)) \n if stopped:#epoch==training_epochs-1:\n #only do it once in the end\n print('calculate topic coherence (might take a few minutes)')\n coherence=utils.topic_coherence(test_set,phi, lexicon)\n print('topic coherence',str(coherence))\n \n \ndef myrelu(features):\n return tf.maximum(features, 0.0)\n\ndef parseArgs():\n #get line from config file\n args = sys.argv\n linum = int(args[1])\n argstring=''\n configname = 'tfconfig'\n with open(configname,'r') as rf:\n for i,line in enumerate(rf):\n #print i,line\n argstring = line\n if i+1==linum:\n print(line)\n break\n argparser = argparse.ArgumentParser()\n #define arguments\n argparser.add_argument('--adam_beta1',default=0.9, type=float)\n argparser.add_argument('--adam_beta2',default=0.999, type=float)\n argparser.add_argument('--learning_rate',default=1e-3, type=float)\n argparser.add_argument('--dir_prior',default=0.1, type=float)\n argparser.add_argument('--n_topic',default=50, type=int)\n argparser.add_argument('--n_sample',default=1, type=int)\n argparser.add_argument('--warm_up_period',default=100, type=int)\n argparser.add_argument('--data_dir',default='data/20news', type=str)\n return argparser.parse_args(argstring.split())\n\ndef main(argv=None):\n if FLAGS.non_linearity == 'tanh':\n non_linearity = tf.nn.tanh\n elif FLAGS.non_linearity == 'sigmoid':\n non_linearity = tf.nn.sigmoid\n else:\n non_linearity = myrelu#max(features, 1.1)#tf.nn.relu\n \n args = parseArgs()\n adam_beta1 = args.adam_beta1\n adam_beta2 = args.adam_beta2\n learning_rate = args.learning_rate\n dir_prior = args.dir_prior\n warm_up_period = args.warm_up_period\n n_sample = args.n_sample\n n_topic = args.n_topic\n lexicon=[]\n vocab_path = os.path.join(args.data_dir, 'vocab.new')\n with open(vocab_path,'r') as rf:\n for line in rf:\n word = line.split()[0]\n lexicon.append(word)\n vocab_size=len(lexicon)\n \n nvdm = NVDM(vocab_size=vocab_size,\n n_hidden=FLAGS.n_hidden,\n n_topic=n_topic, \n learning_rate=learning_rate, \n batch_size=FLAGS.batch_size,\n non_linearity=non_linearity,\n adam_beta1=adam_beta1,\n adam_beta2=adam_beta2,\n dir_prior=dir_prior)\n sess = tf.Session()\n init = tf.global_variables_initializer()\n result = sess.run(init)\n train_url = os.path.join(args.data_dir, 'train.feat')\n test_url = os.path.join(args.data_dir, 'test.feat')\n \n train(sess, nvdm, train_url, test_url, FLAGS.batch_size,vocab_size,lexicon=lexicon,\n result_file=None,\n warm_up_period = warm_up_period)\n\nif __name__ == '__main__':\n tf.app.run()\n", "sub_path": "nvdm_dirichlet_invCDF.py", "file_name": "nvdm_dirichlet_invCDF.py", "file_ext": "py", "file_size_in_byte": 16752, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.random.seed", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 13, "usage_type": "attribute"}, {"api_name": "tensorflow.set_random_seed", "line_number": 14, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 16, "usage_type": "attribute"}, {"api_name": "tensorflow.reset_default_graph", "line_number": 37, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 47, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 47, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 48, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 49, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 52, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 52, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 53, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 53, "usage_type": "attribute"}, {"api_name": "tensorflow.variable_scope", "line_number": 55, "usage_type": "call"}, {"api_name": "utils.mlp", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.nn.dropout", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 57, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.layers.batch_norm", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 58, "usage_type": "attribute"}, {"api_name": "utils.linear", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.maximum", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.log", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.exp", "line_number": 59, "usage_type": "call"}, {"api_name": "tensorflow.ones", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 61, "usage_type": "attribute"}, {"api_name": "tensorflow.lgamma", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.lgamma", "line_number": 65, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.lgamma", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.multiply", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.digamma", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 68, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 71, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 73, "usage_type": "call"}, {"api_name": "tensorflow.random_uniform", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.variable_scope", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.pow", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.exp", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.lgamma", "line_number": 78, "usage_type": "call"}, {"api_name": "tensorflow.div", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.reshape", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.log", "line_number": 84, "usage_type": "call"}, {"api_name": "tensorflow.clip_by_value", "line_number": 84, "usage_type": "call"}, {"api_name": "utils.linear_LDA", "line_number": 84, "usage_type": "call"}, {"api_name": "tensorflow.nn.log_softmax", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 86, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.layers.batch_norm", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 86, "usage_type": "attribute"}, {"api_name": "utils.linear", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.reduce_sum", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.multiply", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.contrib.distributions.Dirichlet", "line_number": 89, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 89, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.distributions.Dirichlet", "line_number": 90, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 90, "usage_type": "attribute"}, {"api_name": "tensorflow.arg_max", "line_number": 92, "usage_type": "call"}, {"api_name": "tensorflow.trainable_variables", "line_number": 101, "usage_type": "call"}, {"api_name": "utils.variable_parser", "line_number": 103, "usage_type": "call"}, {"api_name": "utils.variable_parser", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorflow.gradients", "line_number": 107, "usage_type": "call"}, {"api_name": "tensorflow.gradients", "line_number": 115, "usage_type": "call"}, {"api_name": "tensorflow.gradients", "line_number": 117, "usage_type": "call"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 123, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 123, "usage_type": "attribute"}, {"api_name": "utils.data_set", "line_number": 141, "usage_type": "call"}, {"api_name": "utils.data_set", "line_number": 142, "usage_type": "call"}, {"api_name": "utils.create_batches", "line_number": 152, "usage_type": "call"}, {"api_name": "utils.create_batches", "line_number": 153, "usage_type": "call"}, {"api_name": "utils.create_batches", "line_number": 166, "usage_type": "call"}, {"api_name": "utils.fetch_data", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 202, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 211, "usage_type": "call"}, {"api_name": "utils.variable_parser", "line_number": 213, "usage_type": "call"}, {"api_name": "tensorflow.trainable_variables", "line_number": 213, "usage_type": "call"}, {"api_name": "utils.print_top_words", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 219, "usage_type": "call"}, {"api_name": "utils.fetch_data", "line_number": 243, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 258, "usage_type": "call"}, {"api_name": "tensorflow.train.Saver", "line_number": 266, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 266, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 278, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 278, "usage_type": "attribute"}, {"api_name": "utils.fetch_data", "line_number": 298, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.add", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 309, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 309, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 310, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 313, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 316, "usage_type": "call"}, {"api_name": "utils.topic_coherence", "line_number": 328, "usage_type": "call"}, {"api_name": "tensorflow.maximum", "line_number": 333, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 337, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 348, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 362, "usage_type": "attribute"}, {"api_name": "tensorflow.nn", "line_number": 364, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 377, "usage_type": "call"}, {"api_name": "os.path", "line_number": 377, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 393, "usage_type": "call"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 394, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 396, "usage_type": "call"}, {"api_name": "os.path", "line_number": 396, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 397, "usage_type": "call"}, {"api_name": "os.path", "line_number": 397, "usage_type": "attribute"}, {"api_name": "tensorflow.app.run", "line_number": 404, "usage_type": "call"}, {"api_name": "tensorflow.app", "line_number": 404, "usage_type": "attribute"}]} +{"seq_id": "280961601", "text": "import sys, os, traceback, time\nfrom functools import lru_cache\nfrom math import sin, cos, sqrt, radians\nfrom OpenGL.GL import *\nfrom OpenGL.GLU import *\nfrom OpenGL.GLUT import *\nfrom PyQt5 import QtGui\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtWidgets import QHBoxLayout, QFrame, QSplitter, QTabWidget\nfrom PyQt5.QtWidgets import QRadioButton, QButtonGroup\nfrom PyQt5 import QtCore as Qt\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtOpenGL import *\nfrom stl import mesh\nfrom dxf_loader import DXF_Loader\nfrom stl_loader import STL_loader\nfrom drawer import drawPolygon, drawText, drawText_3D, drawAxis, create_vbo, draw_vbo\nimport ctypes\nimport numpy as np\nfrom math import sqrt, pi, exp, floor\n\nwindow_title = \"Cloth SIM@PyQt5 v.0.5\"\nscreen_size = [600, 500]\nto_models = \"/Users/ryotaro/py_projects/pygame_sim/model\"\n\n##################### DXF ANALYSATION #####################\nextensor = \"model/extensor_hood_test002.dxf\"\n## スケーリング済\nextensor_reduced_scale = 1/1500\nExtensor = DXF_Loader(extensor, extensor_reduced_scale, -0.65, 2, -1.)\nstop_points_3d, particle_points_3d, poly_lines_3d = Extensor.ver_col_ind()\n## オリジナルスケール\norgExtensor = DXF_Loader(extensor, 1, 0, 0, 0, integer=True)\norg_sp_3d, org_pp_3d, org_pl_3d = orgExtensor.ver_col_ind()\n\nconst_set = []\nfor set in poly_lines_3d:\n for i in range(len(set)-1):\n const_set.append([set[i].tolist(), set[i+1].tolist()])\n\n##################### LOAD 3D MODEL #####################\nfinger = \"/Index\"\nnames_list = [\"/Metacarpal3_01.stl\",\n \"/Proximal_Phalanx3_01_org.stl\",\n \"/Middle_Phalanxh3_01_org.stl\",\n \"/Distal_Phalanxh3_01_org.stl\"]\n\nfile_name = [to_models+finger+names_list[0],\n to_models+finger+names_list[1],\n to_models+finger+names_list[2],\n to_models+finger+names_list[3]]\nbone_reduced_scale = 1/15\nMetacarpal3 = STL_loader(file_name[0], bone_reduced_scale)\nProximal_Phalanx3 = STL_loader(file_name[1], bone_reduced_scale)\nMiddle_Phalanxh3 = STL_loader(file_name[2], bone_reduced_scale)\nDistal_Phalanxh3 = STL_loader(file_name[3], bone_reduced_scale)\n\n## 頂点座標, カラー, 構成インデックス\nMeta_ver, Meta_col, Meta_ind = Metacarpal3.ver_col_ind()\nProP_ver, ProP_col, ProP_ind = Proximal_Phalanx3.ver_col_ind()\nMidP_ver, MidP_col, MidP_ind = Middle_Phalanxh3.ver_col_ind()\nDisP_ver, DisP_col, DisP_ind = Distal_Phalanxh3.ver_col_ind()\n\n## フレーム用カラー\nMeta_Frame_col = Metacarpal3.color(Meta_ver, _r=0, _g=0, _b=0)\nProP_Frame_col = Metacarpal3.color(ProP_ver, _r=0, _g=0, _b=0)\nMidP_Frame_col = Metacarpal3.color(MidP_ver, _r=0, _g=0, _b=0)\nDisP_Frame_col = Metacarpal3.color(DisP_ver, _r=0, _g=0, _b=0)\n\n\"\"\" 各モデルの座標の最大値(Y軸) \"\"\"\nMeta_max_index = np.argmax(np.array(Metacarpal3.all_mesh_particle)[:,1])\nMeta_max_cood = Metacarpal3.all_mesh_particle[Meta_max_index]\nProP_max_index = np.argmax(np.array(Proximal_Phalanx3.all_mesh_particle)[:,1])\nProP_max_cood = Metacarpal3.all_mesh_particle[ProP_max_index]\nMidP_max_index = np.argmax(np.array(Middle_Phalanxh3.all_mesh_particle)[:,1])\nMidP_max_cood = Metacarpal3.all_mesh_particle[MidP_max_index]\n\n###############################################################\n\ndef gaussian_function(sigma, mu, x, A=1.25):\n return A*(1/sqrt(2*pi*sigma) * exp(-1/(2*sigma*sigma)*(x-mu)**2))\n\ndef super_gaussian_function(sigma, mu, lmd, x, A=1.25):\n return A*exp(-(1/2*sigma*sigma*(x-mu)**2)**lmd)\n\ndef subtract(vec1,vec2):\n return [vec1[i]-vec2[i] for i in [0,1,2]]\n\ndef get_length(vec):\n return sum([vec[i]*vec[i] for i in [0,1,2]])**0.5\n\nBLACK = (0, 0, 0)\nWHITE = (1, 1, 1)\nRED = (1, 0, 0)\nGREEN = (0, 1, 0)\n\ndelta_t = 0.2\nNUM_ITER = 10\n\nclass Particle:\n def __init__(self, x, y, z, m=1.0):\n self.m = m\n self.init_x, self.init_y, self.init_z = x, y, z\n self.x, self.y, self.z = x, y, z\n self.oldx, self.oldy, self.oldz = x, y, z\n self.newx, self.newy, self.newz = x, y, z\n self.ax = 0\n self.ay = 0#-9.8 #0\n self.az = 0\n\n self.fixed = False\n\n def when_move(self, x, y, z):\n self.x, self.y, self.z = x, y, z\n\n def update(self, delta_t):\n if self.fixed == False:\n # Verlet Integration\n # (https://www.watanabe-lab.jp/blog/archives/1993)\n self.newx = 2.0 * self.x - self.oldx + self.ax * delta_t**2\n self.newy = 2.0 * self.y - self.oldy + self.ay * delta_t**2\n self.newz = 2.0 * self.z - self.oldz + self.az * delta_t**2\n self.oldx = self.x\n self.oldy = self.y\n self.oldz = self.z\n self.x = self.newx\n self.y = self.newy\n self.z = self.newz\n\n def set_pos(self, pos):\n self.x, self.y, self.z = pos\n\n def draw_sp(self):\n color = GREEN\n glColor3f(*color);\n glPointSize(10);\n glBegin(GL_POINTS);\n glVertex3fv(tuple((self.x, self.y, self.z)));\n glEnd();\n\n drawText_3D(str(self.x)+\", \"+str(self.y)+\", \"+str(self.z),\n self.x, self.y, self.z)\n\n def draw(self):\n DisP_cood_y = 10.61333\n MidP_cood_y = 8.88667\n #if self.fixed == True:\n #else:\n color = RED\n glColor3f(*color);\n glPointSize(10);\n glBegin(GL_POINTS);\n glVertex3fv(tuple((self.x, self.y, self.z)));\n glEnd();\n\n# パーティクルへの拘束条件\nclass Constraint:\n def __init__(self, index0, index1):\n self.index0 = index0\n self.index1 = index1\n delta_x = particles[index0].x - particles[index1].x\n delta_y = particles[index0].y - particles[index1].y\n delta_z = particles[index0].z - particles[index1].z\n self.restLength = sqrt(delta_x**2 + delta_y**2 + delta_z**2)\n self.init_d = 0\n self.d = 0\n\n def update(self):\n delta_x = particles[self.index1].x - particles[self.index0].x\n delta_y = particles[self.index1].y - particles[self.index0].y\n delta_z = particles[self.index1].z - particles[self.index0].z\n deltaLength = sqrt(delta_x**2 + delta_y**2 + delta_z**2)\n diff = (deltaLength - self.restLength)/(deltaLength+0.001)\n\n le = 0.5\n if particles[self.index0].fixed == False:\n particles[self.index0].x += le * diff * delta_x\n particles[self.index0].y += le * diff * delta_y\n particles[self.index0].z += le * diff * delta_z\n if particles[self.index1].fixed == False:\n particles[self.index1].x -= le * diff * delta_x\n particles[self.index1].y -= le * diff * delta_y\n particles[self.index1].z -= le * diff * delta_z\n\n def draw(self):\n ## 初期位置からパーティクル間の距離を計算\n f_x0 = particles[self.index0].init_x\n f_y0 = particles[self.index0].init_y\n f_z0 = particles[self.index0].init_z\n f_x1 = particles[self.index1].init_x\n f_y1 = particles[self.index1].init_y\n f_z1 = particles[self.index1].init_z\n self.init_d = sqrt((f_x0-f_x1)**2+(f_y0-f_y1)**2+(f_z0-f_z1)**2)\n\n x0 = particles[self.index0].x\n y0 = particles[self.index0].y\n z0 = particles[self.index0].z\n x1 = particles[self.index1].x\n y1 = particles[self.index1].y\n z1 = particles[self.index1].z\n self.d = sqrt((x0-x1)**2+(y0-y1)**2++(z0-z1)**2)\n\n #pygame.draw.line(surf, rgb(d, minimum=init_d, maximum=init_d*1.25),\n # (int(x0), int(y0)), (int(x1), int(y1)), size)\n glColor3f(1, 0, 1)\n glBegin(GL_LINES)\n glVertex3fv(tuple((x0, y0, z0)))\n glVertex3fv(tuple((x1, y1, z1)))\n glEnd()\n\n###stop_points_3d, particle_points_3d, poly_lines_3d\nparticles = []\nfor p_point in particle_points_3d:\n p = Particle(p_point[0], p_point[1], p_point[2])\n particles.append(p)\n\n### DISTAL PHALANX SP ancs[8, 1, 7] -> particles[60, 21, 59] ###\n### MIDDLE PHALANX SP ancs[6, 0, 5] -> particles[58, 22, 57] ###\nfor sp in stop_points_3d:\n try:\n anc_idx = particle_points_3d.tolist().index(sp.tolist())\n particles[anc_idx].fixed = True\n except:\n print(\"sp error : \", sp)\n\ndef flooring(x, n=2):\n return floor(x*10**n) / (10**n)\n\nconstraints = []\nfor pl in poly_lines_3d:\n top_count = len(pl)\n pl = pl.tolist()\n for i in range(top_count-1):\n try:\n if pl[i][1] == 5.8583:index0 = 31\n else:index0 = particle_points_3d.tolist().index(pl[i])\n\n if pl[i+1][1] == 5.8583:index1 = 31\n else:index1 = particle_points_3d.tolist().index(pl[i+1])\n c = Constraint(index0, index1)\n constraints.append(c)\n except:\n print(\"pl error : \",pl[i], pl[i+1])\n\nMeta_angle, Meta_AbdAdd_angle, ProP_angle, MidP_angle, DisP_angle = 0., 0., 0., 0., 0.\nMeta, PrxPh, MddPh, DisPh = False, False, False, False\nDisP_1, DisP_2, DisP_3 = [0,0,0], [0,0,0], [0,0,0]\nMidP_1, MidP_2, MidP_3 = [0,0,0], [0,0,0], [0,0,0]\nserect = None\nclass DrawWidget(QGLWidget):\n Meta_buff=np.array([None])\n ProP_buff=np.array([None])\n MidP_buff=np.array([None])\n DisP_buff=np.array([None])\n\n outMeta_buff=np.array([None])\n outProP_buff=np.array([None])\n outMidP_buff=np.array([None])\n outDisP_buff=np.array([None])\n def __init__(self, parent):\n QGLWidget.__init__(self, parent)\n self.setMinimumSize(*screen_size)\n self.camera_rot = [70,23]\n self.camera_radius = 2.5\n self.camera_center = [0.5,0.5,0.5]\n self.camera_cood = [[0.],[0.],[0.]]\n self.camera_wide_angle = 60\n self.angle_x, self.angle_y, self.angle_z = 0., 0., 0.\n self.vias_x, self.vias_y, self.vias_z = 0.,0.,0.\n self.bool_vias_x, self.bool_vias_y, self.bool_vias_z = False, False, False\n self.org = tuple((0,0,0))\n self.org_points = [[tuple((0, 0, 0)), tuple((5, 0, 0))],\n [tuple((0, 0, 0)), tuple((0, 5, 0))],\n [tuple((0, 0, 0)), tuple((0, 0, 5))]]\n self.bRGB = [.0, .0, .0]\n self.Meta, self.PrxPh, self.MddPh, self.DisPh = False, False, False, False\n self.keys_list = []\n self.all_camera_status = []\n #self.Meta_angle, self.ProP_angle, self.MidP_angle, self.DisP_angle = 0., 0., 0., 0.\n\n def mode_sp(self, mode):\n global serect\n if mode==0:serect=\"DisP_1\";\n elif mode==1:serect=\"DisP_2\";\n elif mode==2:serect=\"DisP_3\";\n elif mode==3:serect=\"MidP_1\";\n elif mode==4:serect=\"MidP_2\";\n elif mode==5:serect=\"MidP_3\";\n\n def sp_slide_listener(self, axis, val):\n global DisP_1, DisP_2, DisP_3, MidP_1, MidP_2, MidP_3\n if serect==\"DisP_1\":\n if axis==\"X\":DisP_1[0]=val\n elif axis==\"Y\":DisP_1[1]=val\n elif axis==\"Z\":DisP_1[2]=val\n print(serect, DisP_1)\n elif serect==\"DisP_2\":\n if axis==\"X\":DisP_2[0]=val\n elif axis==\"Y\":DisP_2[1]=val\n elif axis==\"Z\":DisP_2[2]=val\n print(serect, DisP_2)\n elif serect==\"DisP_3\":\n if axis==\"X\":DisP_3[0]=val\n elif axis==\"Y\":DisP_3[1]=val\n elif axis==\"Z\":DisP_3[2]=val\n print(serect, DisP_3)\n elif serect==\"MidP_1\":\n if axis==\"X\":MidP_1[0]=val\n elif axis==\"Y\":MidP_1[1]=val\n elif axis==\"Z\":MidP_1[2]=val\n print(serect, MidP_1)\n elif serect==\"MidP_2\":\n if axis==\"X\":MidP_2[0]=val\n elif axis==\"Y\":MidP_2[1]=val\n elif axis==\"Z\":MidP_2[2]=val\n print(serect, MidP_2)\n elif serect==\"MidP_3\":\n if axis==\"X\":MidP_3[0]=val\n elif axis==\"Y\":MidP_3[1]=val\n elif axis==\"Z\":MidP_3[2]=val\n print(serect, MidP_3)\n else:print(\"serect is None\")\n\n def joint_listener(self, typ, val):\n global Meta_angle, Meta_AbdAdd_angle, ProP_angle, MidP_angle, DisP_angle\n if typ==\"Meta\":Meta_angle=val\n elif typ==\"Meta_AbdAdd\":Meta_AbdAdd_angle=val\n elif typ==\"ProP\":ProP_angle=val\n elif typ==\"MidP\":MidP_angle=val\n elif typ==\"DisP\":DisP_angle=val\n\n def box_listener(self, bool_list):\n global Meta, PrxPh, MddPh, DisPh\n Meta, PrxPh, MddPh, DisPh = bool_list\n\n def key_listener(self, event):\n key = event.key()\n move_pix = 0.5\n if event.modifiers() & Qt.ShiftModifier:\n if key==Qt.Key_X : self.camera_cood[0][0] -= 0.05\n elif key==Qt.Key_Y : self.camera_cood[1][0] -= 0.05\n elif key==Qt.Key_Z : self.camera_cood[2][0] -= 0.05\n\n elif key == Qt.Key_Up : self.angle_x += move_pix\n elif key == Qt.Key_Down : self.angle_x -= move_pix\n\n elif key==Qt.Key_X : self.camera_cood[0][0] += 0.05\n elif key==Qt.Key_Y : self.camera_cood[1][0] += 0.05\n elif key==Qt.Key_Z : self.camera_cood[2][0] += 0.05\n\n elif key==Qt.Key_Left : self.angle_y += move_pix\n elif key==Qt.Key_Right : self.angle_y -= move_pix\n elif key==Qt.Key_Up : self.angle_z += move_pix\n elif key==Qt.Key_Down : self.angle_z -= move_pix\n\n def mouse_listener(self, type, event, mv_cood=[0, 0]):\n ## LEFT, MIDDLE, RIGHT, WHEEL, MOVE\n move_pix = 0.5\n if type == 'MOVE':\n self.camera_rot[0] += mv_cood[0]\n self.camera_rot[1] += mv_cood[1]\n if type == 'WHEEL':\n if event.angleDelta().y() == 120 : self.camera_radius -= move_pix\n elif event.angleDelta().y() == -120 : self.camera_radius += move_pix\n\n def cameraRESET(self):\n self.camera_rot = [70,23]\n self.camera_radius = 2.5\n self.camera_center = [0.5,0.5,0.5]\n self.camera_cood = [[0.],[0.],[0.]]\n self.angle_x, self.angle_y, self.angle_z = 0., 0., 0.\n self.vias_x, self.vias_y, self.vias_z = 0.,0.,0.\n\n def paintGL(self):\n glClearColor(*self.bRGB, 0.0)\n glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)\n\n glLoadIdentity()\n camera_pos = [self.camera_center[0]+self.camera_radius*cos(radians(self.camera_rot[0]))*cos(radians(self.camera_rot[1])),\n self.camera_center[1]+self.camera_radius*sin(radians(self.camera_rot[1])),\n self.camera_center[2]+self.camera_radius*sin(radians(self.camera_rot[0]))*cos(radians(self.camera_rot[1]))]\n gluLookAt(camera_pos[0], camera_pos[1], camera_pos[2],\n self.camera_center[0], self.camera_center[1], self.camera_center[2], 0,1,0)\n\n ## 指定軸に対して回転\n glRotated(self.angle_x, 1.0, 0.0, 0.0)\n glRotated(self.angle_y, 0.0, 1.0, 0.0)\n glRotated(self.angle_z, 0.0, 0.0, 1.0)\n ## 指定軸に対して平行移動\n glTranslatef(-self.camera_cood[0][0], -self.camera_cood[1][0], -self.camera_cood[2][0])\n\n drawText_3D(\"X\", 3., 0., 0.)\n drawText_3D(\"Y\", 0., 3., 0.)\n drawText_3D(\"Z\", 0., 0., 3.)\n drawAxis()\n\n ########################## SET EXTENSOR HOOD ##########################\n\n for i in range(len(particles)):\n particles[i].update(delta_t)\n\n for i in range(NUM_ITER):\n for ii in range(len(constraints)):\n constraints[ii].update()\n\n ############################## DRAW BONES ##############################\n glPushMatrix();\n ## 中手骨の描画\n if not Meta:\n global Meta_buff, outMeta_buff\n if self.Meta_buff.all()==None:\n Meta_buff = create_vbo(self.Meta_buff, Meta_ver, Meta_col, Meta_ind)\n outMeta_buff = create_vbo(self.outMeta_buff, Meta_ver, Meta_Frame_col, Meta_ind)\n draw_vbo(Meta_buff, Meta_ind)\n draw_vbo(outMeta_buff, Meta_ind, mode_front=GL_LINE, mode_back=GL_LINE)\n\n ## 基節骨の描画\n pos_proP = (2.4, (Meta_max_cood[1]-0.2)-Meta_angle*0.01, Meta_angle*0.002)\n glTranslatef(*pos_proP)\n if not PrxPh:\n global ProP_buff, outProP_buff\n if self.ProP_buff.all()==None:\n ProP_buff = create_vbo(self.ProP_buff, ProP_ver, ProP_col, ProP_ind)\n outProP_buff = create_vbo(self.outProP_buff, ProP_ver, ProP_Frame_col, ProP_ind)\n glRotatef(Meta_angle, 1, 0, 0)\n glTranslatef(-1.2,0,0)\n glRotated(Meta_AbdAdd_angle, 0, 0, 1)\n glTranslatef(-1.2-Meta_AbdAdd_angle*0.003,0,0)\n draw_vbo(ProP_buff, ProP_ind)\n draw_vbo(outProP_buff, ProP_ind, mode_front=GL_LINE)\n\n ## 中節骨の描画\n mddp_vias = gaussian_function(sigma=20, mu=60, x=ProP_angle, A=1.7)\n pos_midP = (0, (1.462+1.8)-ProP_angle*0.008, -ProP_angle*0.001+mddp_vias)\n glTranslatef(*pos_midP)\n if not MddPh:\n global MidP_buff, outMidP_buff\n if self.MidP_buff.all()==None:\n MidP_buff = create_vbo(self.MidP_buff, MidP_ver, MidP_col, MidP_ind)\n outMidP_buff = create_vbo(self.outMidP_buff, MidP_ver, MidP_Frame_col, MidP_ind)\n glRotatef(ProP_angle+3, 1, 0, 0)\n draw_vbo(MidP_buff, MidP_ind)\n draw_vbo(outMidP_buff, MidP_ind, mode_front=GL_LINE)\n\n #print(pos_proP[1]+pos_midP[1])\n glTranslatef(0,pos_proP[1]+pos_midP[1],0)\n particles[58].draw_sp()\n particles[58].when_move(0, (1.462+1.8)-ProP_angle*0.008, -ProP_angle*0.001+mddp_vias)\n particles[22].draw_sp()\n particles[22].when_move(0, (1.462+1.8)-ProP_angle*0.008, -ProP_angle*0.001+mddp_vias)\n particles[57].draw_sp()\n particles[57].when_move(0, (1.462+1.8)-ProP_angle*0.008, -ProP_angle*0.001+mddp_vias)\n glTranslatef(0,0,0)\n\n\n ## 末節骨の描画\n disp_vias = gaussian_function(sigma=25, mu=70, x=MidP_angle, A=1.9)\n pos_disP = (0, (2.906-0.95)-MidP_angle*0.009, -MidP_angle*0.005+disp_vias)\n glTranslatef(*pos_disP)\n if not DisPh:\n global DisP_buff, outDisP_buff\n if self.DisP_buff.all()==None:\n DisP_buff = create_vbo(self.DisP_buff, DisP_ver, DisP_col, DisP_ind)\n outDisP_buff = create_vbo(self.outDisP_buff, DisP_ver, DisP_Frame_col, DisP_ind)\n glRotatef(MidP_angle+3, 1, 0, 0)\n draw_vbo(DisP_buff, DisP_ind)\n draw_vbo(outDisP_buff, DisP_ind, mode_front=GL_LINE)\n\n glTranslatef(0,pos_proP[1]+pos_midP[1]+pos_disP[1],0)\n particles[60].draw_sp()\n particles[21].draw_sp()\n particles[59].draw_sp()\n glTranslatef(0,0,0)\n\n glPopMatrix();\n ##########################################################################\n\n ## 座標の表示 -self.camera_cood[0][0], -self.camera_cood[1][0], -self.camera_cood[2][0]\n drawText(\"Camera Pos : \"+str(round(camera_pos[0], 2))+\", \"\\\n +str(round(camera_pos[1], 2))+\", \"\\\n +str(round(camera_pos[2], 2)), 2, 12, *screen_size)\n\n drawText(\"Camera Axe : \"+str(round(self.camera_cood[0][0], 2))+\", \"\\\n +str(round(self.camera_cood[1][0], 2))+\", \"\\\n +str(round(self.camera_cood[2][0], 2)), 2, 2, *screen_size)\n ## 関節角度の表示\n drawText(\"Meta Angle : \" +str(float(Meta_angle))+\"°\"+\" | \"\n +\"Meta Abd & Add Angle : \"+str(float(Meta_AbdAdd_angle))+\"°\",2, screen_size[1]-10, *screen_size)\n drawText(\"ProP Angle : \" +str(float(ProP_angle))+\"°\", 2, screen_size[1]-20, *screen_size)\n drawText(\"MidP Angle : \" +str(float(MidP_angle))+\"°\", 2, screen_size[1]-30, *screen_size)\n drawText(\"DisP Angle : \"+str(float(DisP_angle))+\"°\", 2, screen_size[1]-40, *screen_size)\n\n ########################## DRAW EXTENSOR HOOD ##########################\n #\"\"\"\n glPushMatrix();\n for i in range(len(particles)):\n particles[i].draw()\n\n for i in range(len(constraints)):\n constraints[i].draw()\n glPopMatrix();\n #\"\"\"\n ##########################################################################\n ## 原点の描画\n glColor3f(1, 1, 0)\n glPointSize(30)\n glBegin(GL_POINTS)\n glVertex3fv(self.org)\n glEnd()\n\n glFlush()\n\n def resizeGL(self, w, h):\n glViewport(0, 0, w, h)\n glMatrixMode(GL_PROJECTION)\n glLoadIdentity()\n gluPerspective(30.0, w/h, 1.0, 100.0)\n glMatrixMode(GL_MODELVIEW)\n\n def initializeGL(self):\n glutInitDisplayMode(GLUT_RGBA | GLUT_DOUBLE | GLUT_DEPTH)\n\n glClearColor(*self.bRGB, 1.0)\n glClearDepth(1.0)\n\n glMatrixMode(GL_PROJECTION)\n glLoadIdentity()\n gluPerspective(40.0, 1.0, .1, 100.0)\n\n##### http://penguinitis.g1.xrea.com/computer/programming/Python/PyQt5/PyQt5-memo/PyQt5-memo.html\nclass Joint_Slider(QWidget):\n def __init__(self, parent=None):\n QWidget.__init__(self)\n self.gl = DrawWidget(self)\n self.Meta_lab = QLabel(\"0\")\n self.ProP_lab = QLabel(\"0\")\n self.MidP_lab = QLabel(\"0\")\n self.DisP_lab = QLabel(\"0\")\n\n self.Meta_lab.setFont(QtGui.QFont(\"Sanserif\", 10))\n self.ProP_lab.setFont(QtGui.QFont(\"Sanserif\", 10))\n self.MidP_lab.setFont(QtGui.QFont(\"Sanserif\", 10))\n self.DisP_lab.setFont(QtGui.QFont(\"Sanserif\", 10))\n\n self.initUI()\n\n def initUI(self):\n splitter1 = QSplitter(Qt.Vertical)\n splitter2 = QSplitter(Qt.Vertical)\n splitter3 = QSplitter(Qt.Vertical)\n ## Meta,ProP,MidP,DisP\n Meta_slider = QSlider(Qt.Horizontal)\n Meta_AbdAdd_slider = QSlider(Qt.Horizontal)\n ProP_slider = QSlider(Qt.Horizontal)\n MidP_slider = QSlider(Qt.Horizontal)\n DisP_slider = QSlider(Qt.Horizontal)\n\n label_Meta = QLabel(\"Angle of the Meta (Flexion / extension)　/ (abduction / adduction)\")\n label_ProP = QLabel(\"Angle of the ProP (Flexion / extension)\")\n label_MidP = QLabel(\"Angle of the MidP (Flexion / extension)\")\n ## 屈曲 / 伸展\n Meta_slider.setMinimum(-10)\n Meta_slider.setMaximum(90)\n Meta_slider.valueChanged.connect(lambda val: self.gl.joint_listener(\"Meta\", val))\n ## 外転 / 内転\n Meta_AbdAdd_slider.setMinimum(-20)\n Meta_AbdAdd_slider.setMaximum(20)\n Meta_AbdAdd_slider.valueChanged.connect(lambda val: self.gl.joint_listener(\"Meta_AbdAdd\",val))\n splitter1.addWidget(label_Meta)\n splitter1.addWidget(Meta_slider)\n splitter1.addWidget(Meta_AbdAdd_slider)\n splitter1.setFrameShape(QFrame.Panel)\n\n ## 屈曲 / 伸展\n ProP_slider.setMinimum(-10)\n ProP_slider.setMaximum(90)\n ProP_slider.valueChanged.connect(lambda val: self.gl.joint_listener(\"ProP\",val))\n splitter2.addWidget(label_ProP)\n splitter2.addWidget(ProP_slider)\n splitter2.setFrameShape(QFrame.Panel)\n\n ## 屈曲 / 伸展\n MidP_slider.setMinimum(-10)\n MidP_slider.setMaximum(90)\n MidP_slider.valueChanged.connect(lambda val: self.gl.joint_listener(\"MidP\",val))\n splitter3.addWidget(label_MidP)\n splitter3.addWidget(MidP_slider)\n splitter3.setFrameShape(QFrame.Panel)\n\n layout = QVBoxLayout()\n layout.addWidget(splitter1)\n layout.addWidget(splitter2)\n layout.addWidget(splitter3)\n\n self.setLayout(layout)\n\nclass Coordination_slider(QFrame):\n def __init__(self, parent=None):\n super().__init__(parent)\n self.gl = DrawWidget(self)\n ## パーティクルの移動\n slider_x = QSlider(Qt.Horizontal)\n slider_x.setMinimum(-10)\n slider_x.setMaximum(10)\n slider_x.setTickInterval(.1)\n slider_x.valueChanged.connect(lambda val: self.gl.sp_slide_listener(\"X\", val))\n\n slider_y = QSlider(Qt.Horizontal)\n slider_y.setMinimum(-10)\n slider_y.setMaximum(10)\n slider_y.setTickInterval(.1)\n slider_y.valueChanged.connect(lambda val: self.gl.sp_slide_listener(\"Y\", val))\n\n slider_z = QSlider(Qt.Horizontal)\n slider_z.setMinimum(-10)\n slider_z.setMaximum(10)\n slider_z.setTickInterval(.1)\n slider_z.valueChanged.connect(lambda val: self.gl.sp_slide_listener(\"Z\", val))\n\n label_x = QLabel(\"Coordination X\")\n label_y = QLabel(\"Coordination Y\")\n label_z = QLabel(\"Coordination Z\")\n\n layout = QVBoxLayout()\n\n layout.addWidget(label_x)\n layout.addWidget(slider_x)\n layout.addWidget(label_y)\n layout.addWidget(slider_y)\n layout.addWidget(label_z)\n layout.addWidget(slider_z)\n\n self.setLayout(layout)\n\nclass Particle_cBox(QFrame):\n def __init__(self, parent=None):\n super().__init__(parent)\n self.gl = DrawWidget(self)\n ## パーティクルの選択\n cBox_label = QLabel(\"Select Stop Particle\")\n self.combo = QComboBox(self)\n self.combo.addItem(\"DisP_1\")\n self.combo.addItem(\"DisP_2\")\n self.combo.addItem(\"DisP_3\")\n self.combo.addItem(\"MidP_1\")\n self.combo.addItem(\"MidP_2\")\n self.combo.addItem(\"MidP_3\")\n\n button = QPushButton(\"Check\")\n button.clicked.connect(self.buttonClicked)\n\n layout = QVBoxLayout()\n layout.addWidget(cBox_label)\n layout.addWidget(self.combo)\n layout.addWidget(button)\n\n self.setLayout(layout)\n\n def buttonClicked(self):\n self.gl.mode_sp(self.combo.currentIndex())\n\nclass AnchorPoint_Slider(QWidget):\n def __init__(self, parent=None):\n super().__init__(parent)\n self.gl = DrawWidget(self)\n\n self.initUI()\n\n def initUI(self):\n frame1 = Particle_cBox(self)\n frame1.setFrameShape(QFrame.Panel)\n\n frame2 = Coordination_slider(self)\n frame2.setFrameShape(QFrame.Panel)\n\n splitter = QSplitter(Qt.Horizontal)\n splitter.addWidget(frame1)\n splitter.addWidget(frame2)\n splitter.setHandleWidth(10)\n\n layout = QHBoxLayout()\n layout.addWidget(splitter)\n self.setLayout(layout)\n\nclass Bone_CheckBox(QWidget):\n def __init__(self, parent=None):\n super().__init__(parent)\n self.bool_list = [True, True, True, True]\n self.listCheckBox = names_list\n self.listLabel = []\n self.layout = QGridLayout()\n for label in range(len(self.listCheckBox)):\n self.listLabel.append(\"\")\n self.gl = DrawWidget(self)\n self.initUI()\n\n def initUI(self):\n for i, v in enumerate(self.listCheckBox):\n self.listCheckBox[i] = QCheckBox(v)\n self.listLabel[i] = QLabel()\n self.layout.addWidget(self.listCheckBox[i], i+10, 0)\n self.layout.addWidget(self.listLabel[i], i+10, 1)\n\n sc_button = QPushButton(\"Show / Clear\")\n sc_button.clicked.connect(self.check_checkbox)\n self.layout.addWidget(sc_button, 20, 0)\n\n #rst_button = QPushButton(\"RESER CAMERA VIEW\")\n #rst_button.clicked.connect(self.gl.cameraRESET)\n #layout.addWidget(rst_button, 30, 0)\n self.setLayout(self.layout)\n\n def check_checkbox(self):\n for i, v in enumerate(self.listCheckBox):\n if v.checkState():\n self.bool_list[i]=True\n else:\n self.bool_list[i]=False\n self.gl.box_listener(self.bool_list)\n\nclass QTWidget(QWidget):\n def __init__(self):\n QWidget.__init__(self)\n self.clicked_points = [0, 0]\n self.gl = DrawWidget(self)\n self.initUI()\n\n timer = QTimer(self)\n timer.setInterval(20) # period, in milliseconds\n timer.timeout.connect(self.gl.updateGL)\n timer.start()\n\n def initUI(self):\n gui_layout = QGridLayout()\n self.setLayout(gui_layout)\n gui_layout.addWidget(self.gl)\n\n widget1 = Joint_Slider(self)\n widget2 = Bone_CheckBox(self)\n widget3 = AnchorPoint_Slider(self)\n\n tab = QTabWidget()\n tab.addTab(widget1, \"Joint slider\")\n tab.addTab(widget2, \"Check Box (Bone)\")\n tab.addTab(widget3, \"Stop Particle slider\")\n\n rst_button = QPushButton(\"RESER CAMERA VIEW\")\n rst_button.clicked.connect(self.gl.cameraRESET)\n widget2.layout.addWidget(rst_button, 30, 0)\n\n gui_layout.addWidget(tab)\n self.setLayout(gui_layout)\n\n def keyPressEvent(self, event):\n self.gl.key_listener(event)\n\n ## LEFT, MIDDLE, RIGHT, WHEEL, MOVE\n def mouseButtonKind(self, buttons):\n if buttons & Qt.LeftButton : self.gl.mouse_listener(\"LEFT\", None)\n if buttons & Qt.MidButton : self.gl.mouse_listener(\"MIDDLE\", None)\n if buttons & Qt.RightButton : self.gl.mouse_listener(\"RIGHT\", None)\n\n def mousePressEvent(self, e):\n self.mouseButtonKind(e.buttons())\n self.clicked_points = [e.pos().x(), e.pos().y()]\n\n def mouseReleaseEvent(self, e):\n self.mouseButtonKind(e.buttons())\n\n def wheelEvent(self, e):\n self.gl.mouse_listener(\"WHEEL\", e)\n\n def mouseMoveEvent(self, e):\n # マウスの相対移動座標\n mvX, mvY = self.clicked_points[0]-e.x(), self.clicked_points[1]-e.y()\n self.gl.mouse_listener(\"MOVE\", e, mv_cood=[-mvX*0.2, -mvY*0.2])\n self.update()\n\nif __name__=='__main__':\n app = QApplication(sys.argv)\n w = QTWidget()\n w.setWindowTitle(window_title)\n w.show()\n\n sys.exit(app.exec_())\n", "sub_path": "cloth_test_opengl_3D_05.py", "file_name": "cloth_test_opengl_3D_05.py", "file_ext": "py", "file_size_in_byte": 29680, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "dxf_loader.DXF_Loader", "line_number": 30, "usage_type": "call"}, {"api_name": "dxf_loader.DXF_Loader", "line_number": 33, "usage_type": "call"}, {"api_name": "stl_loader.STL_loader", "line_number": 53, "usage_type": "call"}, {"api_name": "stl_loader.STL_loader", "line_number": 54, "usage_type": "call"}, {"api_name": "stl_loader.STL_loader", "line_number": 55, "usage_type": "call"}, {"api_name": "stl_loader.STL_loader", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 75, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 81, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 81, "usage_type": "name"}, {"api_name": "math.exp", "line_number": 81, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 84, "usage_type": "call"}, {"api_name": "drawer.drawText_3D", "line_number": 141, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 164, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 172, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 193, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 201, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 257, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 258, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 259, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.ShiftModifier", "line_number": 339, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 339, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Key_X", "line_number": 340, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 340, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Key_Y", "line_number": 341, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 341, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Key_Z", "line_number": 342, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 342, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Key_Up", "line_number": 344, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 344, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Key_Down", "line_number": 345, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 345, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Key_X", "line_number": 347, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 347, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Key_Y", "line_number": 348, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 348, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Key_Z", "line_number": 349, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 349, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Key_Left", "line_number": 351, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 351, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Key_Right", "line_number": 352, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 352, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Key_Up", "line_number": 353, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 353, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Key_Down", "line_number": 354, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 354, "usage_type": "name"}, {"api_name": "math.cos", "line_number": 379, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 379, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 380, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 380, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 381, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 381, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 381, "usage_type": "call"}, {"api_name": "drawer.drawText_3D", "line_number": 392, "usage_type": "call"}, {"api_name": "drawer.drawText_3D", "line_number": 393, "usage_type": "call"}, {"api_name": "drawer.drawText_3D", "line_number": 394, "usage_type": "call"}, {"api_name": "drawer.drawAxis", "line_number": 395, "usage_type": "call"}, {"api_name": "drawer.create_vbo", "line_number": 412, "usage_type": "call"}, {"api_name": "drawer.create_vbo", "line_number": 413, "usage_type": "call"}, {"api_name": "drawer.draw_vbo", "line_number": 414, "usage_type": "call"}, {"api_name": "drawer.draw_vbo", "line_number": 415, "usage_type": "call"}, {"api_name": "drawer.create_vbo", "line_number": 423, "usage_type": "call"}, {"api_name": "drawer.create_vbo", "line_number": 424, "usage_type": "call"}, {"api_name": "drawer.draw_vbo", "line_number": 429, "usage_type": "call"}, {"api_name": "drawer.draw_vbo", "line_number": 430, "usage_type": "call"}, {"api_name": "drawer.create_vbo", "line_number": 439, "usage_type": "call"}, {"api_name": "drawer.create_vbo", "line_number": 440, "usage_type": "call"}, {"api_name": "drawer.draw_vbo", "line_number": 442, "usage_type": "call"}, {"api_name": "drawer.draw_vbo", "line_number": 443, "usage_type": "call"}, {"api_name": "drawer.create_vbo", "line_number": 463, "usage_type": "call"}, {"api_name": "drawer.create_vbo", "line_number": 464, "usage_type": "call"}, {"api_name": "drawer.draw_vbo", "line_number": 466, "usage_type": "call"}, {"api_name": "drawer.draw_vbo", "line_number": 467, "usage_type": "call"}, {"api_name": "drawer.drawText", "line_number": 479, "usage_type": "call"}, {"api_name": "drawer.drawText", "line_number": 483, "usage_type": "call"}, {"api_name": "drawer.drawText", "line_number": 487, "usage_type": "call"}, {"api_name": "drawer.drawText", "line_number": 489, "usage_type": "call"}, {"api_name": "drawer.drawText", "line_number": 490, "usage_type": "call"}, {"api_name": "drawer.drawText", "line_number": 491, "usage_type": "call"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 540, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 540, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 541, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 541, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 542, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 542, "usage_type": "name"}, {"api_name": "PyQt5.QtGui.QFont", "line_number": 543, "usage_type": "call"}, {"api_name": "PyQt5.QtGui", "line_number": 543, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSplitter", "line_number": 548, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Vertical", "line_number": 548, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 548, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSplitter", "line_number": 549, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Vertical", "line_number": 549, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 549, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSplitter", "line_number": 550, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Vertical", "line_number": 550, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 550, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Horizontal", "line_number": 552, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 552, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Horizontal", "line_number": 553, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 553, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Horizontal", "line_number": 554, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 554, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Horizontal", "line_number": 555, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 555, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Horizontal", "line_number": 556, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 556, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame.Panel", "line_number": 572, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 572, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame.Panel", "line_number": 580, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 580, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame.Panel", "line_number": 588, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 588, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 597, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Horizontal", "line_number": 602, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 602, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Horizontal", "line_number": 608, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 608, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Horizontal", "line_number": 614, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 614, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 635, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame.Panel", "line_number": 671, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 671, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame.Panel", "line_number": 674, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 674, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QSplitter", "line_number": 676, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Horizontal", "line_number": 676, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 676, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 681, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTabWidget", "line_number": 742, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.LeftButton", "line_number": 759, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 759, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.MidButton", "line_number": 760, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 760, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.RightButton", "line_number": 761, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 761, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 780, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 785, "usage_type": "call"}]} +{"seq_id": "60099993", "text": "import csv\nimport numpy as np\nfrom numba import jit, f8, i8\nimport matplotlib.pyplot as plt\n\ndef open_csv(name):\n\tmat = []\n\twith open(\"data/\" + name + \".csv\", newline = \"\") as f:\n\t\treader = csv.reader(f, delimiter = \",\", quotechar = '\"')\n\n\t\tfor row in reader:\n\t\t\tmat.append([])\n\t\t\tfor col in row:\n\t\t\t\tmat[-1].append(float(col))\n\n\treturn np.array(mat)\n\ndef plot_heatmap(x, y, fs, level, name):\n#def plot_heatmap(x, y, fs, name):\n\tdef _imp(i):\n\t\tplt.figure()\n\t\tcontour = plt.contourf(x, y, fs[i], level)\n\t\t#contour = plt.contourf(x, y, fs[i], 100)\n\t\tplt.colorbar(contour)\n\t\tplt.savefig(\"magnetic/{}/{}_figure{}.png\".format(name, name, i))\n\n\treturn _imp\n\ndef plot_vector(x, y, fxs, fys):\n\tdef _imp(i):\t\n\t\tplt.figure()\n\t\tplt.quiver(x, y, fxs[i], fys[i], color = \"blue\", angles = \"xy\", scale_units = \"xy\")\n\t\tplt.savefig(\"magnetic/density/density_figure{}.png\".format(i))\n\treturn _imp\n\n@jit(f8[:,:](f8[:],i8,i8))\ndef get_f(vec, W, H):\n\tf = np.reshape(vec, (H, W))\n\tf_return = np.array([[0] * (W + 2)]) \n\t\n\tfor f_i in f:\n\t\tf_i = np.append(f_i, 0)\n\t\tf_i = np.insert(f_i, 0, 0)\n\t\tf_return = np.append(f_return, [f_i], axis = 0)\n\n\tf_return = np.append(f_return, [[0] * (W + 2)], axis = 0)\n\t\n\treturn f_return\n\n\nif __name__ == \"__main__\":\n\tmat = open_csv(\"mag\")\n\tmat1 = open_csv(\"jx\")\n\tmat2 = open_csv(\"jy\")\n\n\tW = 50\n\tH = 50\n\tX = 0.1\n\tY = 0.1\n\tx, y = np.meshgrid(np.linspace(0, X, W + 2), np.linspace(0, Y, H + 2))\n\tfs = []\n\tfxs = []\n\tfys = []\n\n\t\n\tfor m in mat:\n\t\tfs.append(get_f(m, W, H))\n\tv_max = np.asarray(fs).max()\n\tv_min = np.asarray(fs).min()\n\tlevel = np.linspace(v_min, v_max, 1000)\n\t\n\tanimation_f = plot_heatmap(x, y, fs, level, \"magnetic\")\n\t#animation_f = plot_heatmap(x, y, fs, \"magnetic\")\n\t#animation_f = plot_vector(x, y, fxs, fys)\n\t\n\tfor t in range(len(mat)):\n\t\tanimation_f(t)\n\t\tprint(t)\n\t\n\t\n\tfor m1, m2 in zip(mat1, mat2):\n\t\tfxs.append(get_f(m1, W, H))\n\t\tfys.append(get_f(m2, W, H))\n\t\n\t#animation_f = plot_heatmap(x, y, fs, level, \"magnetic\")\n\t#animation_f = plot_heatmap(x, y, fs, \"magnetic\")\n\tanimation_f = plot_vector(x, y, fxs, fys)\n\tfor t in range(len(mat1)):\n\t\tanimation_f(t)\n\t\tprint(t)\n", "sub_path": "poisson_equation/visualize_magnetic.py", "file_name": "visualize_magnetic.py", "file_ext": "py", "file_size_in_byte": 2096, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "csv.reader", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.quiver", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.reshape", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.insert", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 46, "usage_type": "call"}, {"api_name": "numba.jit", "line_number": 36, "usage_type": "call"}, {"api_name": "numba.i8", "line_number": 36, "usage_type": "argument"}, {"api_name": "numba.f8", "line_number": 36, "usage_type": "name"}, {"api_name": "numpy.meshgrid", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 70, "usage_type": "call"}]} +{"seq_id": "287306791", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('examen', '0005_auto_20150712_1244'),\n ]\n\n operations = [\n migrations.AlterModelOptions(\n name='capitulo',\n options={'verbose_name': 'Capitulo', 'ordering': ['capitulo'], 'verbose_name_plural': 'Capitulos'},\n ),\n migrations.AlterField(\n model_name='examen',\n name='fecha',\n field=models.DateField(auto_now=True),\n ),\n ]\n", "sub_path": "examen/migrations/0006_auto_20160524_1933.py", "file_name": "0006_auto_20160524_1933.py", "file_ext": "py", "file_size_in_byte": 590, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterModelOptions", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}]} +{"seq_id": "355617175", "text": "from enum import unique\nfrom flask import Flask,render_template,url_for,redirect,request\nfrom flask_sqlalchemy import SQLAlchemy\n\n\n\n\napp = Flask(__name__)\n\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:////Users/afaru/OneDrive/Masaüstü/hatim2/hatimson.db'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\ndb = SQLAlchemy(app)\n\n\n@app.route(\"/\")\ndef index():\n return render_template(\"index.html\")\n\n@app.route(\"/add\",methods=[\"POST\",\"GET\"])\ndef add():\n if request.method=='POST':\n service1=request.form['service1']\n service=request.form['service']\n service3=request.form['service3']\n service4=request.form['service4']\n new = users(mintika=service1,kurum=service,hatim=service3,adet=service4)\n db.session.add(new)\n db.session.commit()\n \n \n return redirect(url_for(\"index\")) \n\n\n\nclass users(db.Model):\n _id= db.Column(db.Integer,primary_key=True)\n mintika = db.Column(db.String(80))\n kurum = db.Column(db.String(120))\n hatim = db.Column(db.String(30))\n adet = db.Column(db.Integer)\ndef __init__(self, mintika, kurum, hatim,adet):\n self.mintika = mintika\n self.kurum = kurum\n self.hatim = hatim\n self.adet = adet\n \n\n\n\nif __name__==\"__main__\":\n db.create_all()\n app.run(debug=True) \n\n \n \n\n\n ", "sub_path": "index.py", "file_name": "index.py", "file_ext": "py", "file_size_in_byte": 1308, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 17, "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.form", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 25, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 25, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "614929912", "text": "# Copyright 2014 - Mirantis, 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\nimport mock\nfrom oslo.config import cfg\n\nfrom mistral.actions.openstack import actions\nfrom mistral import context as auth_context\nfrom mistral.db.v1 import api as db_api\nfrom mistral import engine\nfrom mistral.engine.drivers.default import engine as concrete_engine\nfrom mistral.engine.drivers.default import executor\nfrom mistral.engine import states\nfrom mistral.openstack.common import log as logging\nfrom mistral.tests import base\n\n\nLOG = logging.getLogger(__name__)\n\n# Use the set_default method to set value otherwise in certain test cases\n# the change in value is not permanent.\ncfg.CONF.set_default('auth_enable', False, group='pecan')\n\n\ndef create_workbook(definition_path):\n return db_api.workbook_create({\n 'name': 'my_workbook',\n 'definition': base.get_resource(definition_path)\n })\n\n\n@mock.patch.object(\n engine.EngineClient, 'start_workflow_execution',\n mock.MagicMock(side_effect=base.EngineTestCase.mock_start_workflow))\n@mock.patch.object(\n engine.EngineClient, 'convey_task_result',\n mock.MagicMock(side_effect=base.EngineTestCase.mock_task_result))\n@mock.patch.object(\n concrete_engine.DefaultEngine, '_run_task',\n mock.MagicMock(side_effect=base.EngineTestCase.mock_run_task))\nclass OpenStackActionsEngineTest(base.EngineTestCase):\n @mock.patch.object(actions.GlanceAction, 'run',\n mock.Mock(return_value=\"images\"))\n def test_glance_action(self):\n context = {}\n wb = create_workbook('openstack/glance.yaml')\n task_name = 'glance_image_list'\n execution = self.engine.start_workflow_execution(wb['name'],\n task_name,\n context)\n\n # We have to reread execution to get its latest version.\n execution = db_api.execution_get(execution['id'])\n\n self.assertEqual(states.SUCCESS, execution['state'])\n\n tasks = db_api.tasks_get(workbook_name=wb['name'],\n execution_id=execution['id'])\n\n self.assertEqual(1, len(tasks))\n\n task = self._assert_single_item(tasks, name=task_name)\n\n self.assertEqual(states.SUCCESS, task['state'])\n self.assertEqual(\"images\", task['output']['task'][task_name])\n\n @mock.patch.object(actions.KeystoneAction, 'run',\n mock.Mock(return_value=\"users\"))\n def test_keystone_action(self):\n context = {}\n wb = create_workbook('openstack/keystone.yaml')\n task_name = 'keystone_user_list'\n execution = self.engine.start_workflow_execution(wb['name'],\n task_name,\n context)\n\n # We have to reread execution to get its latest version.\n execution = db_api.execution_get(execution['id'])\n\n self.assertEqual(states.SUCCESS, execution['state'])\n\n tasks = db_api.tasks_get(workbook_name=wb['name'],\n execution_id=execution['id'])\n\n self.assertEqual(1, len(tasks))\n\n task = self._assert_single_item(tasks, name=task_name)\n\n self.assertEqual(states.SUCCESS, task['state'])\n self.assertEqual(\"users\", task['output']['task'][task_name])\n\n @mock.patch.object(actions.NovaAction, 'run',\n mock.Mock(return_value=\"servers\"))\n @mock.patch.object(executor.DefaultExecutor, \"handle_task\",\n mock.MagicMock())\n def test_nova_action(self):\n context = {}\n task_name = 'nova_server_findall'\n task_params = {'status': 'ACTIVE', 'tenant_id': '8e44eb2ce32'}\n wb = create_workbook('openstack/nova.yaml')\n execution = self.engine.start_workflow_execution(wb['name'],\n task_name,\n context)\n\n tasks = db_api.tasks_get(workbook_name=wb['name'],\n execution_id=execution['id'])\n\n self.assertEqual(1, len(tasks))\n task = self._assert_single_item(tasks, name=task_name)\n\n executor.DefaultExecutor.handle_task.assert_called_once_with(\n auth_context.ctx(),\n params=task_params,\n task_id=task['id'],\n action_name=\"nova.servers_findall\"\n )\n\n self.engine.convey_task_result(task['id'],\n states.SUCCESS,\n \"servers\")\n\n # We have to reread execution to get its latest version.\n execution = db_api.execution_get(execution['id'])\n\n self.assertEqual(states.SUCCESS, execution['state'])\n\n tasks = db_api.tasks_get(workbook_name=wb['name'],\n execution_id=execution['id'])\n\n self.assertEqual(1, len(tasks))\n\n task = self._assert_single_item(tasks, name=task_name)\n\n self.assertEqual(states.SUCCESS, task['state'])\n self.assertEqual(\"servers\", task['output']['task'][task_name])\n\n @mock.patch.object(actions.HeatAction, 'run',\n mock.Mock(return_value=\"stacks\"))\n def test_heat_action(self):\n context = {}\n wb = create_workbook('openstack/heat.yaml')\n task_name = 'heat_stack_list'\n execution = self.engine.start_workflow_execution(wb['name'],\n task_name,\n context)\n\n # We have to reread execution to get its latest version.\n execution = db_api.execution_get(execution['id'])\n\n self.assertEqual(states.SUCCESS, execution['state'])\n\n tasks = db_api.tasks_get(workbook_name=wb['name'],\n execution_id=execution['id'])\n\n self.assertEqual(1, len(tasks))\n\n task = self._assert_single_item(tasks, name=task_name)\n\n self.assertEqual(states.SUCCESS, task['state'])\n self.assertEqual(\"stacks\", task['output']['task'][task_name])\n\n @mock.patch.object(actions.NeutronAction, 'run',\n mock.Mock(return_value=\"networks\"))\n def test_neutron_action(self):\n context = {}\n wb = create_workbook('openstack_tasks/neutron.yaml')\n task_name = 'neutron_list_networks'\n execution = self.engine.start_workflow_execution(wb['name'],\n task_name,\n context)\n\n # We have to reread execution to get its latest version.\n execution = db_api.execution_get(execution['id'])\n\n self.assertEqual(states.SUCCESS, execution['state'])\n\n tasks = db_api.tasks_get(workbook_name=wb['name'],\n execution_id=execution['id'])\n\n self.assertEqual(1, len(tasks))\n\n task = self._assert_single_item(tasks, name=task_name)\n\n self.assertEqual(states.SUCCESS, task['state'])\n self.assertEqual(\"networks\", task['output']['task'][task_name])\n", "sub_path": "mistral/tests/unit/engine/test_openstack_actions.py", "file_name": "test_openstack_actions.py", "file_ext": "py", "file_size_in_byte": 7709, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "mistral.openstack.common.log.getLogger", "line_number": 29, "usage_type": "call"}, {"api_name": "mistral.openstack.common.log", "line_number": 29, "usage_type": "name"}, {"api_name": "oslo.config.cfg.CONF.set_default", "line_number": 33, "usage_type": "call"}, {"api_name": "oslo.config.cfg.CONF", "line_number": 33, "usage_type": "attribute"}, {"api_name": "oslo.config.cfg", "line_number": 33, "usage_type": "name"}, {"api_name": "mistral.db.v1.api.workbook_create", "line_number": 37, "usage_type": "call"}, {"api_name": "mistral.db.v1.api", "line_number": 37, "usage_type": "name"}, {"api_name": "mistral.tests.base.get_resource", "line_number": 39, "usage_type": "call"}, {"api_name": "mistral.tests.base", "line_number": 39, "usage_type": "name"}, {"api_name": "mistral.tests.base.EngineTestCase", "line_number": 52, "usage_type": "attribute"}, {"api_name": "mistral.tests.base", "line_number": 52, "usage_type": "name"}, {"api_name": "mistral.db.v1.api.execution_get", "line_number": 64, "usage_type": "call"}, {"api_name": "mistral.db.v1.api", "line_number": 64, "usage_type": "name"}, {"api_name": "mistral.engine.states.SUCCESS", "line_number": 66, "usage_type": "attribute"}, {"api_name": "mistral.engine.states", "line_number": 66, "usage_type": "name"}, {"api_name": "mistral.db.v1.api.tasks_get", "line_number": 68, "usage_type": "call"}, {"api_name": "mistral.db.v1.api", "line_number": 68, "usage_type": "name"}, {"api_name": "mistral.engine.states.SUCCESS", "line_number": 75, "usage_type": "attribute"}, {"api_name": "mistral.engine.states", "line_number": 75, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 53, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 53, "usage_type": "attribute"}, {"api_name": "mistral.actions.openstack.actions.GlanceAction", "line_number": 53, "usage_type": "attribute"}, {"api_name": "mistral.actions.openstack.actions", "line_number": 53, "usage_type": "name"}, {"api_name": "mock.Mock", "line_number": 54, "usage_type": "call"}, {"api_name": "mistral.db.v1.api.execution_get", "line_number": 89, "usage_type": "call"}, {"api_name": "mistral.db.v1.api", "line_number": 89, "usage_type": "name"}, {"api_name": "mistral.engine.states.SUCCESS", "line_number": 91, "usage_type": "attribute"}, {"api_name": "mistral.engine.states", "line_number": 91, "usage_type": "name"}, {"api_name": "mistral.db.v1.api.tasks_get", "line_number": 93, "usage_type": "call"}, {"api_name": "mistral.db.v1.api", "line_number": 93, "usage_type": "name"}, {"api_name": "mistral.engine.states.SUCCESS", "line_number": 100, "usage_type": "attribute"}, {"api_name": "mistral.engine.states", "line_number": 100, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 78, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 78, "usage_type": "attribute"}, {"api_name": "mistral.actions.openstack.actions.KeystoneAction", "line_number": 78, "usage_type": "attribute"}, {"api_name": "mistral.actions.openstack.actions", "line_number": 78, "usage_type": "name"}, {"api_name": "mock.Mock", "line_number": 79, "usage_type": "call"}, {"api_name": "mistral.db.v1.api.tasks_get", "line_number": 116, "usage_type": "call"}, {"api_name": "mistral.db.v1.api", "line_number": 116, "usage_type": "name"}, {"api_name": "mistral.engine.drivers.default.executor.DefaultExecutor.handle_task.assert_called_once_with", "line_number": 122, "usage_type": "call"}, {"api_name": "mistral.engine.drivers.default.executor.DefaultExecutor", "line_number": 122, "usage_type": "attribute"}, {"api_name": "mistral.engine.drivers.default.executor", "line_number": 122, "usage_type": "name"}, {"api_name": "mistral.context.ctx", "line_number": 123, "usage_type": "call"}, {"api_name": "mistral.context", "line_number": 123, "usage_type": "name"}, {"api_name": "mistral.engine.states.SUCCESS", "line_number": 130, "usage_type": "attribute"}, {"api_name": "mistral.engine.states", "line_number": 130, "usage_type": "name"}, {"api_name": "mistral.db.v1.api.execution_get", "line_number": 134, "usage_type": "call"}, {"api_name": "mistral.db.v1.api", "line_number": 134, "usage_type": "name"}, {"api_name": "mistral.engine.states.SUCCESS", "line_number": 136, "usage_type": "attribute"}, {"api_name": "mistral.engine.states", "line_number": 136, "usage_type": "name"}, {"api_name": "mistral.db.v1.api.tasks_get", "line_number": 138, "usage_type": "call"}, {"api_name": "mistral.db.v1.api", "line_number": 138, "usage_type": "name"}, {"api_name": "mistral.engine.states.SUCCESS", "line_number": 145, "usage_type": "attribute"}, {"api_name": "mistral.engine.states", "line_number": 145, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 103, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 103, "usage_type": "attribute"}, {"api_name": "mistral.actions.openstack.actions.NovaAction", "line_number": 103, "usage_type": "attribute"}, {"api_name": "mistral.actions.openstack.actions", "line_number": 103, "usage_type": "name"}, {"api_name": "mock.Mock", "line_number": 104, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 105, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 105, "usage_type": "attribute"}, {"api_name": "mistral.engine.drivers.default.executor.DefaultExecutor", "line_number": 105, "usage_type": "attribute"}, {"api_name": "mistral.engine.drivers.default.executor", "line_number": 105, "usage_type": "name"}, {"api_name": "mock.MagicMock", "line_number": 106, "usage_type": "call"}, {"api_name": "mistral.db.v1.api.execution_get", "line_number": 159, "usage_type": "call"}, {"api_name": "mistral.db.v1.api", "line_number": 159, "usage_type": "name"}, {"api_name": "mistral.engine.states.SUCCESS", "line_number": 161, "usage_type": "attribute"}, {"api_name": "mistral.engine.states", "line_number": 161, "usage_type": "name"}, {"api_name": "mistral.db.v1.api.tasks_get", "line_number": 163, "usage_type": "call"}, {"api_name": "mistral.db.v1.api", "line_number": 163, "usage_type": "name"}, {"api_name": "mistral.engine.states.SUCCESS", "line_number": 170, "usage_type": "attribute"}, {"api_name": "mistral.engine.states", "line_number": 170, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 148, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 148, "usage_type": "attribute"}, {"api_name": "mistral.actions.openstack.actions.HeatAction", "line_number": 148, "usage_type": "attribute"}, {"api_name": "mistral.actions.openstack.actions", "line_number": 148, "usage_type": "name"}, {"api_name": "mock.Mock", "line_number": 149, "usage_type": "call"}, {"api_name": "mistral.db.v1.api.execution_get", "line_number": 184, "usage_type": "call"}, {"api_name": "mistral.db.v1.api", "line_number": 184, "usage_type": "name"}, {"api_name": "mistral.engine.states.SUCCESS", "line_number": 186, "usage_type": "attribute"}, {"api_name": "mistral.engine.states", "line_number": 186, "usage_type": "name"}, {"api_name": "mistral.db.v1.api.tasks_get", "line_number": 188, "usage_type": "call"}, {"api_name": "mistral.db.v1.api", "line_number": 188, "usage_type": "name"}, {"api_name": "mistral.engine.states.SUCCESS", "line_number": 195, "usage_type": "attribute"}, {"api_name": "mistral.engine.states", "line_number": 195, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 173, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 173, "usage_type": "attribute"}, {"api_name": "mistral.actions.openstack.actions.NeutronAction", "line_number": 173, "usage_type": "attribute"}, {"api_name": "mistral.actions.openstack.actions", "line_number": 173, "usage_type": "name"}, {"api_name": "mock.Mock", "line_number": 174, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 43, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 43, "usage_type": "attribute"}, {"api_name": "mistral.engine.EngineClient", "line_number": 44, "usage_type": "attribute"}, {"api_name": "mistral.engine", "line_number": 44, "usage_type": "name"}, {"api_name": "mock.MagicMock", "line_number": 45, "usage_type": "call"}, {"api_name": "mistral.tests.base.EngineTestCase", "line_number": 45, "usage_type": "attribute"}, {"api_name": "mistral.tests.base", "line_number": 45, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 46, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 46, "usage_type": "attribute"}, {"api_name": "mistral.engine.EngineClient", "line_number": 47, "usage_type": "attribute"}, {"api_name": "mistral.engine", "line_number": 47, "usage_type": "name"}, {"api_name": "mock.MagicMock", "line_number": 48, "usage_type": "call"}, {"api_name": "mistral.tests.base.EngineTestCase", "line_number": 48, "usage_type": "attribute"}, {"api_name": "mistral.tests.base", "line_number": 48, "usage_type": "name"}, {"api_name": "mock.patch.object", "line_number": 49, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 49, "usage_type": "attribute"}, {"api_name": "mistral.engine.drivers.default.engine.DefaultEngine", "line_number": 50, "usage_type": "attribute"}, {"api_name": "mistral.engine.drivers.default.engine", "line_number": 50, "usage_type": "name"}, {"api_name": "mock.MagicMock", "line_number": 51, "usage_type": "call"}, {"api_name": "mistral.tests.base.EngineTestCase", "line_number": 51, "usage_type": "attribute"}, {"api_name": "mistral.tests.base", "line_number": 51, "usage_type": "name"}]} +{"seq_id": "432861103", "text": "import boto3\nimport sys\nimport string\nimport time\nfrom biz_util import get_biz_data\nfrom ec2_network_util import GetEc2Module, get_region, write_region_conf\n\n\nclass CreateEc2(object):\n\n def __init__(self):\n self.ec2 = boto3.resource('ec2')\n\n def create_instance(self, **kwargs):\n instances = self.ec2.create_instances(\n # 接口测试\n DryRun = False,\n ImageId = ami_id,\n # 实例类型,c5.large 2C4G\n InstanceType = instance_type,\n KeyName = 'nearme_keke_nso',\n # 最大启动实例数量\n MaxCount = instance_num,\n # 最小启动实例数量\n MinCount = instance_num,\n # 是否开启实例监控，另外收费，不建议开启\n Monitoring = {'Enabled': False},\n SubnetId = subnet_id,\n SecurityGroupIds = security_group_ids,\n # 实例控制台删除保护\n DisableApiTermination = True,\n # 启动 EBS 优化实例\n EbsOptimized = True,\n # IAM 角色，EMR 集群需要添加，其他不用\n #IamInstanceProfile = {'Arn': 'arn:aws:iam::406329597408:instance-profile/EMR_EC2_DefaultRole'},\n # 开启容量预留实例开关\n CapacityReservationSpecification = {\n 'CapacityReservationPreference': 'open'\n },\n BlockDeviceMappings = disk_list,\n TagSpecifications = [\n {\n 'ResourceType': 'instance',\n 'Tags': [\n {\n 'Key': 'biz',\n 'Value': biz\n },\n {\n 'Key': 'biz_id',\n 'Value': biz_id\n },\n {\n 'Key': 'department',\n 'Value': department\n },\n {\n 'Key': 'department_id',\n 'Value': department_id\n }\n ]\n },\n {\n 'ResourceType': 'volume',\n 'Tags': [\n {\n 'Key': 'biz',\n 'Value': biz\n },\n {\n 'Key': 'biz_id',\n 'Value': biz_id\n },\n {\n 'Key': 'department',\n 'Value': department\n },\n {\n 'Key': 'department_id',\n 'Value': department_id\n }\n ]\n }\n ]\n )\n return instances\n\n def get_ec2_info(self, instances):\n self.ec2 = ec2\n ec2_infos = []\n for instance in instances:\n ec2_info = {}\n ip = instance.private_ip_address\n ec2_id = instance.instance_id\n ec2_info['ip'] = ip\n ec2_info['ec2_id'] = ec2_id\n ec2_infos.append(ec2_info)\n return ec2_infos\n\nclass Docker(object):\n\n def __init__(self):\n self.client = boto3.client('ec2')\n\n def create_ni(self, **kwargs):\n response = self.client.create_network_interface(\n Description = ip,\n Groups = security_group_ids,\n SecondaryPrivateIpAddressCount = sub_network_ip_num,\n SubnetId = sub_subnet_id)\n return response['NetworkInterface']['NetworkInterfaceId']\n\n def attach_ni(self, **kwargs):\n response = self.client.attach_network_interface(\n DeviceIndex = device_index,\n InstanceId = ec2_id,\n NetworkInterfaceId = network_interface_id)\n\n\nif __name__ == '__main__':\n region = get_region()\n write_region_conf(region)\n module = GetEc2Module()\n vpc_id = module.get_vpcs()\n ami_id = module.get_amis()\n availab_zone = module.get_azs()\n subnet_id = module.get_subnets(vpc_id, availab_zone)\n security_group_ids = [module.get_security_group(vpc_id)]\n instance_type = module.get_model()\n biz = str(input('请输入一级业务分类 P：')).strip().lower()\n biz_id, department, department_id = get_biz_data(biz)\n instance_num = int(input('请输入需要创建的实例数量：').strip().lower())\n is_data_disk = str(input('请输入是否需要数据磁盘，Y/N：')).strip().upper()\n if is_data_disk == 'Y':\n disk_list = module.get_disks()\n elif is_data_disk == 'N':\n disk_list = []\n sub_network_num = int(input('请输入辅助网卡数量：').strip())\n sub_network_ip_num = int(input('请输入辅助网卡IP数量：').strip())\n ec2 = CreateEc2()\n paras = {}\n paras2 = {}\n paras3 = {}\n paras['ami_id'] = ami_id\n paras['instance_type'] = instance_type\n paras['instance_num'] = instance_num\n paras['subnet_id'] = subnet_id\n paras['security_group_ids'] = security_group_ids\n paras['disk_list'] = disk_list\n paras['biz'] = biz\n paras['biz_id'] = biz_id\n paras['department'] = department\n paras['department_id'] = department_id\n instances = ec2.create_instance(**paras)\n ec2_infos = ec2.get_ec2_info(instances)\n docker = Docker()\n sub_subnet_id = module.get_subnets(vpc_id, availab_zone)\n time.sleep(100)\n for ec2_info in ec2_infos:\n ip = ec2_info['ip']\n ec2_id = ec2_info['ec2_id']\n for device_index in range(1,sub_network_num):\n paras2['ip'] = ip\n paras2['sub_subnet_id'] = sub_subnet_id\n paras2['security_group_ids'] = security_group_ids\n paras2['sub_network_ip_num'] = sub_network_ip_num\n network_interface_id = docker.create_ni(**paras2)\n time.sleep(3)\n\n paras3['device_index'] = device_index\n paras3['ec2_id'] = ec2_id\n paras3['network_interface_id'] = network_interface_id\n docker.attach_ni(**paras3)\n print(ip)\n", "sub_path": "create_ec2_for_k8s.py", "file_name": "create_ec2_for_k8s.py", "file_ext": "py", "file_size_in_byte": 6214, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "boto3.resource", "line_number": 12, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 103, "usage_type": "call"}, {"api_name": "ec2_network_util.get_region", "line_number": 121, "usage_type": "call"}, {"api_name": "ec2_network_util.write_region_conf", "line_number": 122, "usage_type": "call"}, {"api_name": "ec2_network_util.GetEc2Module", "line_number": 123, "usage_type": "call"}, {"api_name": "biz_util.get_biz_data", "line_number": 131, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 158, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 168, "usage_type": "call"}]} +{"seq_id": "220552651", "text": "from pathlib import Path\nimport xarray as xr\nfrom dash import Input, Output, State, Patch\nfrom dash.exceptions import PreventUpdate\n\nfrom pyxro.webapp.defaults import (\n TEMPLATE_REFLECTIVITY,\n OUT_FOLDER,\n)\n\ndef get_calculation_data(samplefolder, calculation_type):\n outfolder = Path(samplefolder) / OUT_FOLDER\n if calculation_type == 'reflectivity':\n outfile = outfolder / f'{TEMPLATE_REFLECTIVITY}.nc'\n data = xr.load_dataarray(outfile, engine='h5netcdf')\n return data.to_dict()\n return None\n\ndef register_calculation(app):\n ###\n # Reflectivity\n ###\n @app.callback(\n Output('calc-figure-reflectivity', 'figure', allow_duplicate=True),\n Input('calc-figure-options-log', 'value'),\n prevent_initial_call=True,\n )\n def switch_figure_options(switch_values):\n \"\"\"\n Update the graph depending on settings\n \"\"\"\n patch = Patch()\n if 'log' in switch_values:\n patch['layout']['yaxis']['type'] = 'log'\n else:\n patch['layout']['yaxis']['type'] = 'linear'\n return patch\n", "sub_path": "pyxro/webapp/callbacks/calculations.py", "file_name": "calculations.py", "file_ext": "py", "file_size_in_byte": 1105, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pathlib.Path", "line_number": 12, "usage_type": "call"}, {"api_name": "pyxro.webapp.defaults.OUT_FOLDER", "line_number": 12, "usage_type": "name"}, {"api_name": "pyxro.webapp.defaults.TEMPLATE_REFLECTIVITY", "line_number": 14, "usage_type": "name"}, {"api_name": "xarray.load_dataarray", "line_number": 15, "usage_type": "call"}, {"api_name": "dash.Patch", "line_number": 32, "usage_type": "call"}, {"api_name": "dash.Output", "line_number": 24, "usage_type": "call"}, {"api_name": "dash.Input", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "126545488", "text": "#!/bin/env python3\n\nimport sys\nimport re\nfrom itertools import chain\nfrom collections import defaultdict\n\ndef solution(filename):\n\tdata = []\n\twith open(filename) as f:\n\t\tfor x in f:\n\t\t\tline = re.split(r' \\(contains ',x)\n\t\t\tallergens = line[1].strip()[:-1].split(', ')\n\t\t\tdata.append((allergens,line[0].split()))\n\treturn countRemaining(determineAllergens(data))\n\ndef countRemaining(data):\n\t# count the remaining ingredients\n\ttotal = 0\n\tfor d in data:\n\t\ttotal += len(d[1])\n\treturn total\n\ndef removeAllergen(allergen, name, data):\n\t# remove all occurrences of allergen from the dictionary\n\tfor d in data:\n\t\t# remove allergens \n\t\tif allergen in d[0]:\n\t\t\td[0].remove(allergen)\n\t\t# remove names \n\t\tif name in d[1]:\n\t\t\td[1].remove(name)\n\treturn data\n\ndef determineAllergens(data):\n\tallergen_map = {}\n\t# get list of allergens\n\tallergens = list(set(list(chain.from_iterable([[a for a in k[0]] for k in data]))))\n\t# for each allergen: get all ingredients that have that allergen and see which is common to all\n\twhile len(allergen_map.keys()) < len(allergens):\n\t\tto_find = [al for al in allergens if al not in allergen_map.keys()]\n\t\tfor a in to_find:\n\t\t\tn = getCommonAllergens(a, data)\n\t\t\t# if only one common to all, assign as name\n\t\t\tif len(n) == 1: \n\t\t\t\tallergen_map[a] = n[0]\n\t\t\t\tdata = removeAllergen(a,n[0],data)\n\treturn data\n\ndef getCommonAllergens(allergen, data):\n\t# find common name in all ingredients listed with allergen\n\ting_lists = [a[1] for a in data if allergen in a[0]]\n\tcommon = set(ing_lists[0])\n\tfor ing in ing_lists[1:]:\n\t\tcommon.intersection_update(ing)\n\treturn list(common)\n\nif __name__ == '__main__':\n\tif len(sys.argv) < 2:\n\t\tprint(\"Usage: python3 day21code.py \")\n\t\tsys.exit(1)\n\tingredients = solution(sys.argv[1])\n\tprint(\"Frequency of ingredients that cannot possibly contain any of the allergens: {}\".format(ingredients))\n", "sub_path": "day21/day21code.py", "file_name": "day21code.py", "file_ext": "py", "file_size_in_byte": 1848, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "re.split", "line_number": 12, "usage_type": "call"}, {"api_name": "itertools.chain.from_iterable", "line_number": 38, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 38, "usage_type": "name"}, {"api_name": "sys.argv", "line_number": 59, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 61, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 62, "usage_type": "attribute"}]} +{"seq_id": "548955890", "text": "# -*- coding: utf-8 -*-\n__author__ = 'victor'\n\nimport os\nimport sys\nreload(sys)\nsys.setdefaultencoding('utf-8')\nsys.path.append(os.path.join(os.path.split(os.path.realpath(__file__))[0], '..'))\nsys.path.append(os.path.join(os.path.split(os.path.realpath(__file__))[0], '../..'))\nsys.path.append(os.path.join(os.path.split(os.path.realpath(__file__))[0], '../../util'))\n\nimport loghelper\nimport config as tsbconfig\nimport templates\nfrom common import dbutil\n\nfrom elasticsearch import Elasticsearch\n\n\n# logger\nloghelper.init_logger(\"interior\", stream=True)\nlogger_is = loghelper.get_logger(\"interior\")\n\n\nclass InteriorSearchClient(object):\n\n global logger_is\n logger = logger_is\n\n def __init__(self, es=None):\n\n if not es:\n host, port = tsbconfig.get_es_config()\n self.es = Elasticsearch([{'host': host, 'port': port}])\n else:\n self.es = es\n\n def search(self, key, actives=None, start=0, size=10):\n\n if not actives:\n actives = ['Y', 'A', 'P']\n query = templates.get_new_completion(key, actives)\n hits = self.es.search(index=\"xiniudata\", doc_type=\"interior\",\n body={\"query\": query, \"from\": start, \"size\": size})\n\n # result success check\n count = hits['hits'].get('total', 0)\n if ('error' in hits) or hits.get('time_out'):\n return {'name': list(hits), 'count': count, 'status': 'failed'}\n hits = hits['hits']['hits']\n if len(hits) == 0 or (not hits):\n return {'name': list(hits), 'count': count}\n\n hits = map(lambda x: {'code': x['_source']['code'], 'name': x['_source']['name'],\n 'active': x['_source']['active']},\n filter(lambda item: '_source' in item, hits))\n return {'name': list(hits), 'count': count}", "sub_path": "data/search/interior/interior_client.py", "file_name": "interior_client.py", "file_ext": "py", "file_size_in_byte": 1842, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.setdefaultencoding", "line_number": 7, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 8, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"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.split", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path.append", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.realpath", "line_number": 10, "usage_type": "call"}, {"api_name": "loghelper.init_logger", "line_number": 21, "usage_type": "call"}, {"api_name": "loghelper.get_logger", "line_number": 22, "usage_type": "call"}, {"api_name": "config.get_es_config", "line_number": 33, "usage_type": "call"}, {"api_name": "elasticsearch.Elasticsearch", "line_number": 34, "usage_type": "call"}, {"api_name": "templates.get_new_completion", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "417692556", "text": "from django.conf.urls import (url, handler400, handler403, handler404, handler500 )\nfrom . import views\nfrom django.views.generic import TemplateView\n\nhandler400 = 'adminapp.views.bad_request'\nhandler403 = 'adminapp.views.permission_denied'\nhandler404 = 'adminapp.views.page_not_found'\nhandler500 = 'adminapp.views.server_error'\n\nurlpatterns = [\n\turl(r'^$', views.index, name='index'),\n\turl(r'^alumnos/$', views.alumnosxgrado, name='alumnosxgrado'),\n\turl(r'^alumnos/all/$', views.alumnos_sinmat, name='alumnos_sinmat'),\n\turl(r'^alumnos/grado/(?P\\d+)/$', views.all_students, name='all_students'),\n\turl(r'^alumnos/new/$', views.new_student, name='new_student'),\n\turl(r'^alumnos/new/add/$', views.new_student_add, name='new_student_add'),\n\turl(r'^alumnos/familiar/(?P\\d+)/all/$', views.all_familiar, name='all_familiar'),\n\turl(r'^alumnos/familiar/(?P\\d+)/$', views.new_familiar, name='new_familiar'),\n\turl(r'^alumnos/familiar/add/$', views.new_familiar_add, name='new_familiar_add'),\n\turl(r'^promotores/$', views.all_promotores, name='all_promotores'),\n\turl(r'^promotores/new/$', views.new_promotor, name='new_promotor'),\n\turl(r'^promotores/new/add/$', views.new_promotor_add, name='new_promotor_add'),\n\turl(r'^promotores/edit/(?P\\d+)/$', views.edit_promotores, name='edit_promotores'),\n\turl(r'^promotores/delete/(?P\\d+)/$', views.delete_promotor, name='delete_promotor'),\n\turl(r'^facilitador/$', views.all_facilitador, name='all_facilitador'),\n\turl(r'^facilitador/new/$', views.new_facilitador, name='new_facilitador'),\n\turl(r'^facilitador/new/add/$', views.new_facilitador_add, name='new_facilitador_add'),\n\turl(r'^facilitador/delete/(?P\\d+)/$', views.delete_facilitador, name='delete_facilitador'),\n\turl(r'^facilitador/edit/(?P\\d+)/$', views.edit_facilitador, name='edit_facilitador'),\n\turl(r'^grados/$', views.all_grados, name='all_grados'),\n\turl(r'^grados/new/$', views.new_grado, name='new_grado'),\n\turl(r'^grados/new/add/$', views.new_grado_add, name='new_grado_add'),\n\turl(r'^grados/delete/(?P\\d+)/$', views.delete_grado, name='delete_grado'),\n\turl(r'^grados/edit/(?P\\d+)/$', views.edit_grado, name='edit_grado'),\n\turl(r'^centros/$', views.all_centros, name='all_centros'),\n\t\n\turl(r'^enroll/new/$', views.new_enroll, name='new_enroll'),\n\turl(r'^enroll/massive/$', views.enroll_massive, name='enroll_massive'),\n\turl(r'^enroll/massive/add/$', views.enroll_massive_add, name='enroll_massive_add'),\n\turl(r'^enroll/all/$', views.all_enroll, name='all_enroll'),\n\turl(r'^enroll/new/add/$', views.new_enroll_add, name='new_enroll_add'),\n\turl(r'^enroll/new/alumno/(?P\\d+)/$', views.matricularxalumno, name='matricularxalumno'),\n\turl(r'^enroll/new/alumno/add/$', views.matricularxalumno_add, name='matricularxalumno_add'),\n\turl(r'^reports/$', views.all_reports, name='all_reports'),\n\turl(r'^reports/graphics$', views.all_graphics, name='all_graphics'),\n\turl(r'^reports/fisico/$', views.reportes ,name=\"reportes\"),\n\turl(r'^notas/$', views.notas ,name=\"notas\"),\n\turl(r'^descargas/$', views.descargas ,name=\"descargas\"),\n\turl(r'^tombola/$', views.tombola ,name=\"tombola\"),\n\n]", "sub_path": "adminapp/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 3107, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.conf.urls.handler400", "line_number": 5, "usage_type": "name"}, {"api_name": "django.conf.urls.handler403", "line_number": 6, "usage_type": "name"}, {"api_name": "django.conf.urls.handler404", "line_number": 7, "usage_type": "name"}, {"api_name": "django.conf.urls.handler500", "line_number": 8, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.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": 24, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 28, "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": 31, "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": 34, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 35, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 37, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 38, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 39, "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": 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.url", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "12607150", "text": "# coding: utf-8\n# Copyright (c) Max-Planck-Institut für Eisenforschung GmbH - Computational Materials Design (CM) Department\n# Distributed under the terms of \"New BSD License\", see the LICENSE file.\n\nimport numpy as np\nfrom pyiron_base import Settings\nfrom sklearn.cluster import AgglomerativeClustering\nfrom scipy.sparse import coo_matrix\nfrom scipy.spatial import Voronoi\nfrom pyiron_atomistics.atomistics.structure.pyscal import get_steinhardt_parameter_structure, analyse_cna_adaptive, \\\n analyse_centro_symmetry, analyse_diamond_structure, analyse_voronoi_volume\n\n__author__ = \"Joerg Neugebauer, Sam Waseda\"\n__copyright__ = (\n \"Copyright 2021, Max-Planck-Institut für Eisenforschung GmbH - \"\n \"Computational Materials Design (CM) Department\"\n)\n__version__ = \"1.0\"\n__maintainer__ = \"Sam Waseda\"\n__email__ = \"waseda@mpie.de\"\n__status__ = \"production\"\n__date__ = \"Sep 1, 2017\"\n\ns = Settings()\n\ndef get_average_of_unique_labels(labels, values):\n \"\"\"\n\n This function returns the average values of those elements, which share the same labels\n\n Example:\n\n >>> labels = [0, 1, 0, 2]\n >>> values = [0, 1, 2, 3]\n >>> print(get_average_of_unique_labels(labels, values))\n array([1, 1, 3])\n\n \"\"\"\n labels = np.unique(labels, return_inverse=True)[1]\n unique_labels = np.unique(labels)\n mat = coo_matrix((np.ones_like(labels), (labels, np.arange(len(labels)))))\n mean_values = np.asarray(mat.dot(np.asarray(values).reshape(len(labels), -1))/mat.sum(axis=1))\n if np.prod(mean_values.shape).astype(int)==len(unique_labels):\n return mean_values.flatten()\n return mean_values\n\nclass Analyse:\n \"\"\" Class to analyse atom structure. \"\"\"\n def __init__(self, structure):\n \"\"\"\n Args:\n structure (:class:`pyiron.atomistics.structure.atoms.Atoms`): reference Atom structure.\n \"\"\"\n self._structure = structure\n\n def get_layers(self, distance_threshold=0.01, id_list=None, wrap_atoms=True, planes=None):\n \"\"\"\n Get an array of layer numbers.\n\n Args:\n distance_threshold (float): Distance below which two points are\n considered to belong to the same layer. For detailed\n description: sklearn.cluster.AgglomerativeClustering\n id_list (list/numpy.ndarray): List of atoms for which the layers\n should be considered.\n planes (list/numpy.ndarray): Planes along which the layers are calculated. Planes are\n given in vectors, i.e. [1, 0, 0] gives the layers along the x-axis. Default planes\n are orthogonal unit vectors: [[1, 0, 0], [0, 1, 0], [0, 0, 1]]. If you have a\n tilted box and want to calculate the layers along the directions of the cell\n vectors, use `planes=np.linalg.inv(structure.cell).T`. Whatever values are\n inserted, they are internally normalized, so whether [1, 0, 0] is entered or\n [2, 0, 0], the results will be the same.\n\n Returns: Array of layer numbers (same shape as structure.positions)\n\n Example I - how to get the number of layers in each direction:\n\n >>> structure = Project('.').create_structure('Fe', 'bcc', 2.83).repeat(5)\n >>> print('Numbers of layers:', np.max(structure.analyse.get_layers(), axis=0)+1)\n\n Example II - get layers of only one species:\n\n >>> print('Iron layers:', structure.analyse.get_layers(\n ... id_list=structure.select_index('Fe')))\n \"\"\"\n if distance_threshold <= 0:\n raise ValueError('distance_threshold must be a positive float')\n if id_list is not None and len(id_list)==0:\n raise ValueError('id_list must contain at least one id')\n if wrap_atoms and planes is None:\n positions, indices = self._structure.get_extended_positions(\n width=distance_threshold, return_indices=True\n )\n if id_list is not None:\n id_list = np.arange(len(self._structure))[np.array(id_list)]\n id_list = np.any(id_list[:,np.newaxis]==indices[np.newaxis,:], axis=0)\n positions = positions[id_list]\n indices = indices[id_list]\n else:\n positions = self._structure.positions\n if id_list is not None:\n positions = positions[id_list]\n if wrap_atoms:\n positions = self._structure.get_wrapped_coordinates(positions)\n if planes is not None:\n mat = np.asarray(planes).reshape(-1, 3)\n positions = np.einsum('ij,i,nj->ni', mat, 1/np.linalg.norm(mat, axis=-1), positions)\n layers = []\n for ii,x in enumerate(positions.T):\n cluster = AgglomerativeClustering(\n linkage='complete',\n n_clusters=None,\n distance_threshold=distance_threshold\n ).fit(x.reshape(-1,1))\n first_occurrences = np.unique(cluster.labels_, return_index=True)[1]\n permutation = x[first_occurrences].argsort().argsort()\n labels = permutation[cluster.labels_]\n if wrap_atoms and planes is None and self._structure.pbc[ii]:\n mean_positions = get_average_of_unique_labels(labels, positions)\n scaled_positions = np.einsum(\n 'ji,nj->ni', np.linalg.inv(self._structure.cell), mean_positions\n )\n unique_inside_box = np.all(np.absolute(scaled_positions-0.5+1.0e-8)<0.5, axis=-1)\n arr_inside_box = np.any(\n labels[:,None]==np.unique(labels)[unique_inside_box][None,:], axis=-1\n )\n first_occurences = np.unique(indices[arr_inside_box], return_index=True)[1]\n labels = labels[arr_inside_box]\n labels -= np.min(labels)\n labels = labels[first_occurences]\n layers.append(labels)\n if planes is not None and len(np.asarray(planes).shape)==1:\n return np.asarray(layers).flatten()\n return np.vstack(layers).T\n\n def pyscal_steinhardt_parameter(self, neighbor_method=\"cutoff\", cutoff=0, n_clusters=2,\n q=(4, 6), averaged=False, clustering=True):\n \"\"\"\n Calculate Steinhardts parameters\n\n Args:\n neighbor_method (str) : can be ['cutoff', 'voronoi']\n cutoff (float) : can be 0 for adaptive cutoff or any other value\n n_clusters (int) : number of clusters for K means clustering\n q (list) : can be from 2-12, the required q values to be calculated\n averaged (bool) : If True, calculates the averaged versions of the parameter\n clustering (bool) : If True, cluster based on the q values\n\n Returns:\n list: calculated q parameters\n\n \"\"\"\n return get_steinhardt_parameter_structure(\n self._structure, neighbor_method=neighbor_method, cutoff=cutoff, n_clusters=n_clusters,\n q=q, averaged=averaged, clustering=clustering\n )\n\n def pyscal_cna_adaptive(self, mode=\"total\", ovito_compatibility=False):\n \"\"\"\n Use common neighbor analysis\n\n Args:\n mode (\"total\"/\"numeric\"/\"str\"): Controls the style and level\n of detail of the output.\n - total : return number of atoms belonging to each structure\n - numeric : return a per atom list of numbers- 0 for unknown,\n 1 fcc, 2 hcp, 3 bcc and 4 icosa\n - str : return a per atom string of sructures\n ovito_compatibility(bool): use ovito compatiblity mode\n\n Returns:\n (depends on `mode`)\n \"\"\"\n return analyse_cna_adaptive(atoms=self._structure, mode=mode, ovito_compatibility=ovito_compatibility)\n\n def pyscal_centro_symmetry(self, num_neighbors=12):\n \"\"\"\n Analyse centrosymmetry parameter\n\n Args:\n num_neighbors (int) : number of neighbors\n\n Returns:\n list: list of centrosymmetry parameter\n \"\"\"\n return analyse_centro_symmetry(atoms=self._structure, num_neighbors=num_neighbors)\n\n def pyscal_diamond_structure(self, mode=\"total\", ovito_compatibility=False):\n \"\"\"\n Analyse diamond structure\n\n Args:\n mode (\"total\"/\"numeric\"/\"str\"): Controls the style and level\n of detail of the output.\n - total : return number of atoms belonging to each structure\n - numeric : return a per atom list of numbers- 0 for unknown,\n 1 fcc, 2 hcp, 3 bcc and 4 icosa\n - str : return a per atom string of sructures\n ovito_compatibility(bool): use ovito compatiblity mode\n\n Returns:\n (depends on `mode`)\n \"\"\"\n return analyse_diamond_structure(atoms=self._structure, mode=mode, ovito_compatibility=ovito_compatibility)\n\n def pyscal_voronoi_volume(self):\n \"\"\" Calculate the Voronoi volume of atoms \"\"\"\n return analyse_voronoi_volume(atoms=self._structure)\n\n def get_voronoi_vertices(self, epsilon=2.5e-4, distance_threshold=0, width_buffer=10):\n \"\"\"\n Get voronoi vertices of the box.\n\n Args:\n epsilon (float): displacement to add to avoid wrapping of atoms at borders\n distance_threshold (float): distance below which two vertices are considered as one.\n Agglomerative clustering algorith (sklearn) is employed. Final positions are given\n as the average positions of clusters.\n width_buffer (float): width of the layer to be added to account for pbc.\n\n Returns:\n numpy.ndarray: 3d-array of vertices\n\n This function detect octahedral and tetrahedral sites in fcc; in bcc it detects tetrahedral\n sites. In defects (e.g. vacancy, dislocation, grain boundary etc.), it gives a list of\n positions interstitial atoms might want to occupy. In order for this to be more successful,\n it might make sense to look at the distance between the voronoi vertices and their nearest\n neighboring atoms via:\n\n >>> voronoi_vertices = structure_of_your_choice.analyse.get_voronoi_vertices()\n >>> neigh = structure_of_your_choice.get_neighborhood(voronoi_vertices)\n >>> print(neigh.distances.min(axis=-1))\n\n \"\"\"\n voro = Voronoi(self._structure.get_extended_positions(width_buffer)+epsilon)\n xx = voro.vertices\n if distance_threshold > 0:\n cluster = AgglomerativeClustering(\n linkage='single',\n distance_threshold=distance_threshold,\n n_clusters=None\n )\n cluster.fit(xx)\n xx = get_average_of_unique_labels(cluster.labels_, xx)\n xx = xx[np.linalg.norm(xx-self._structure.get_wrapped_coordinates(xx, epsilon=0), axis=-1) metric_val))\n\n def save(self, iteration, epoch, model, optimizer, lr_scheduler,\n device, metric_val, covar_list=''):\n \"\"\"If this iteration corresponds to a save iteration, save model parameters to disk.\n\n Args:\n iteration: Iteration that just finished.\n epoch: epoch to stamp on the checkpoint\n model: Model to save.\n optimizer: Optimizer for model parameters.\n lr_scheduler: Learning rate scheduler for optimizer.\n device: Device where the model/optimizer parameters belong.\n metric_val: Value for determining whether checkpoint is best so far.\n \"\"\"\n if iteration % self.iters_per_save != 0:\n return\n\n ckpt_dict = {\n 'ckpt_info': {'epoch': epoch, 'iteration': iteration, self.metric_name: metric_val},\n 'model_name': model.module.__class__.__name__,\n 'task_sequence': model.module.task_sequence,\n 'model_state': model.to('cpu').state_dict(),\n 'optimizer': optimizer.state_dict(),\n 'lr_scheduler': None if lr_scheduler is None else lr_scheduler.state_dict(),\n 'covar_list': covar_list,\n }\n model.to(device)\n\n ckpt_path = os.path.join(self.save_dir, 'iter_{}_{}_{:.2f}.pth.tar'.format(iteration, self.metric_name, metric_val))\n torch.save(ckpt_dict, ckpt_path)\n\n if self._is_best(metric_val):\n # Save the best model\n print(f\"Saving the model based on metric={self.metric_name} and \\\n maximize={self.maximize_metric} with value={metric_val}\")\n self.best_metric_val = metric_val\n best_path = os.path.join(self.save_dir, 'best.pth.tar')\n shutil.copy(ckpt_path, best_path)\n\n # Add checkpoint path to priority queue (lower priority order gets removed first\n if not self.keep_topk:\n priority_order = iteration\n elif self.maximize_metric:\n priority_order = metric_val\n else:\n priority_order = -metric_val\n\n self.ckpt_paths.put((priority_order, ckpt_path))\n\n # Remove a checkpoint if more than max_ckpts ckpts saved\n if self.ckpt_paths.qsize() > self.max_ckpts:\n _, oldest_ckpt = self.ckpt_paths.get()\n try:\n os.remove(oldest_ckpt)\n except OSError:\n pass\n\n @classmethod\n def load_model(cls, ckpt_path, gpu_ids, model_args, data_args):\n \"\"\"Load model parameters from disk.\n\n Args:\n ckpt_path: Path to checkpoint to load.\n gpu_ids: GPU IDs for DataParallel.\n\n Returns:\n Model loaded from checkpoint, dict of additional checkpoint info (e.g. epoch, metric).\n \"\"\"\n device = 'cuda:{}'.format(gpu_ids[0]) if len(gpu_ids) > 0 else 'cpu'\n ckpt_dict = torch.load(ckpt_path, map_location=device)\n\n # Build model, load parameters\n model_fn = models.__dict__[ckpt_dict['model_name']]\n original_task_sequence = ckpt_dict['task_sequence']\n task_sequence = TASK_SEQUENCES[data_args.task_sequence] if data_args.task_sequence else original_task_sequence\n\n model = model_fn(task_sequence, model_args)\n\n # Transform classifier if task_sequence for current task is\n # different than the pretrained model.\n # if model_args.transform_classifier:\n num_orign_classes = (len(original_task_sequence)\n if 'task_sequence' in ckpt_dict else model_args.n_orig_classes)\n num_origin_covars = (len(ckpt_dict['covar_list'].split(';'))\n if 'covar_list' in ckpt_dict and len(ckpt_dict['covar_list']) > 0 else 0)\n model.transform_model_shape(num_orign_classes, num_origin_covars)\n\n model = nn.DataParallel(model, gpu_ids)\n model.load_state_dict(ckpt_dict['model_state'])\n \n num_covars = len(model_args.covar_list.split(';')) if len(model_args.covar_list) > 0 else 0\n if num_origin_covars == 0:\n model.module.transform_model_shape(len(task_sequence), num_covars)\n\n return model, ckpt_dict['ckpt_info']\n\n @classmethod\n def load_ensemble(cls, ckpt_paths, gpu_ids, model_args, data_args):\n \"\"\"Load multiple models from disk.\n Args:\n ckpt_paths: List of checkpoint paths to load.\n gpu_ids: GPU IDs for DataParallel.\n Returns:\n Ensemble Model loaded from checkpoint, list of dicts of additional\n checkpoint info (e.g. iters, metric).\n \"\"\"\n individual_models = []\n ckpt_dicts = []\n for ckpt_path in ckpt_paths:\n model, ckpt_info = cls.load_model(ckpt_path, gpu_ids, model_args, data_args)\n individual_models.append(model)\n ckpt_dicts.append(ckpt_info)\n\n ensemble_model = EnsembleClassifier(individual_models)\n return ensemble_model, ckpt_dicts\n\n\n @classmethod\n def load_optimizer(cls, ckpt_path, gpu_ids, optimizer, lr_scheduler=None):\n \"\"\"Load optimizer and LR scheduler state from disk.\n\n Args:\n ckpt_path: Path to checkpoint to load.\n gpu_ids: GPU IDs for loading the state dict.\n optimizer: Optimizer to initialize with parameters from the checkpoint.\n lr_scheduler: Optional learning rate scheduler to initialize with parameters from the checkpoint.\n \"\"\"\n device = 'cuda:{}'.format(gpu_ids[0]) if len(gpu_ids) > 0 else 'cpu'\n ckpt_dict = torch.load(ckpt_path, map_location=device)\n optimizer.load_state_dict(ckpt_dict['optimizer'])\n if lr_scheduler is not None:\n lr_scheduler.load_state_dict(ckpt_dict['lr_scheduler'])\n", "sub_path": "saver/model_saver.py", "file_name": "model_saver.py", "file_ext": "py", "file_size_in_byte": 7423, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "queue.PriorityQueue", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 72, "usage_type": "call"}, {"api_name": "os.path", "line_number": 72, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "shutil.copy", "line_number": 81, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 97, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 113, "usage_type": "call"}, {"api_name": "models.__dict__", "line_number": 116, "usage_type": "attribute"}, {"api_name": "dataset.TASK_SEQUENCES", "line_number": 118, "usage_type": "name"}, {"api_name": "torch.nn.DataParallel", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 131, "usage_type": "name"}, {"api_name": "models.EnsembleClassifier", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 172, "usage_type": "call"}]} +{"seq_id": "489212474", "text": "import sys\r\nsys.settrace\r\n\r\n# Standard\r\nfrom pathlib import Path\r\nfrom datetime import timedelta, datetime\r\nimport os\r\n\r\nimport pandas as pd\r\n\r\n\r\nclass config():\r\n # Prefect flow options\r\n PREFECT_ENV = os.environ.get('PREFECT_ENV') or 'prod'\r\n \r\n # Directory options\r\n if os.environ.get('ENV_TEST') == 'true':\r\n NOAA_TEMP_CSV_DIR = Path.home() / 'github' / 'NOAA-Global-Temp-Data-Processing' / 'test' / 'data_downloads'/ 'noaa_daily_avg_temps'\r\n else: \r\n #NOAA_TEMP_CSV_DIR = os.environ.get('NOAA_TEMP_CSV_DIR') or Path('/') / 'mnt' / 'sda1' / 'data_downloads' / 'noaa_daily_avg_temps'\r\n NOAA_TEMP_CSV_DIR = os.environ.get('NOAA_TEMP_CSV_DIR') or Path('/') / 'media' / 'share' / 'store_240a' / 'data_downloads' / 'noaa_daily_avg_temps'\r\n\r\nlocal_config = config\r\nprint(local_config.NOAA_TEMP_CSV_DIR)\r\nlocal_config.NOAA_TEMP_CSV_DIR = Path('/mnt/c/Users/Ben/Documents/working_datasets/noaa_global_temps')\r\n\r\n# PyPI\r\nfrom prefect import task, Flow, Parameter\r\n# from prefect.tasks.postgres import PostgresExecute, PostgresFetch, PostgresExecuteMany\r\nfrom prefect.schedules import IntervalSchedule\r\nfrom prefect.tasks.secrets import PrefectSecret\r\nfrom prefect.engine.signals import LOOP\r\n#from prefect.engine.executors import LocalDaskExecutor\r\n#from prefect.executors import LocalDaskExecutor\r\nfrom prefect.executors.dask import LocalDaskExecutor\r\n# import psycopg2 as pg\r\n# from psycopg2.errors import UniqueViolation, InvalidTextRepresentation # pylint: disable=no-name-in-module\r\n\r\n# url = 'https://www.ncei.noaa.gov/data/global-summary-of-the-day/access/'\r\n# export PREFECT__CONTEXT__SECRETS__MY_KEY=\"MY_VALUE\"\r\n# export PREFECT__ENGINE__EXECUTOR__DEFAULT_CLASS=\"prefect.engine.executors.LocalDaskExecutor\"\r\n# prefect agent start --name dask_test\r\n# prefect register flow --file psql_sample.py --name psql_test_v2 --project \r\n# ? add_default_labels=False\r\n\r\n# local testing: export NOAA_TEMP_CSV_DIR=$PWD/test/data_downloads/noaa_daily_avg_temps\r\n\r\n\r\ndef unique_values_only_one(column: str):\r\n value_l = column.unique()\r\n if len(value_l) > 1:\r\n return 'X'\r\n return value_l[0]\r\n\r\n\r\n@task(log_stdout=True)\r\ndef list_year_folders():\r\n return os.listdir(local_config.NOAA_TEMP_CSV_DIR)\r\n\r\n\r\n@task(log_stdout=True) # pylint: disable=no-value-for-parameter\r\ndef calculate_year_csv(year_folder):\r\n folder_dir_path = local_config.NOAA_TEMP_CSV_DIR\r\n with open(f'results_{year_folder}.csv', 'w', newline='') as f:\r\n sites_in_folder = os.listdir(folder_dir_path / year_folder)\r\n f.write('SITE_NUMBER,LATITUDE,LONGITUDE,ELEVATION,AVERAGE_TEMP\\n')\r\n for site in sites_in_folder:\r\n df1 = pd.read_csv(folder_dir_path / year_folder / site)\r\n average_temp = df1['TEMP'].mean()\r\n site_number = unique_values_only_one(df1['STATION'])\r\n latitude = unique_values_only_one(df1['LATITUDE'])\r\n longitude = unique_values_only_one(df1['LONGITUDE'])\r\n elevation = unique_values_only_one(df1['ELEVATION'])\r\n if site_number == 'X':\r\n print(f'Non-unique SITE_NUMBER: {folder_dir_path}/{year_folder}/{site}')\r\n if latitude == 'X':\r\n print(f'Non-unique LATITUDE: {folder_dir_path}/{year_folder}/{site}')\r\n if longitude == 'X':\r\n print(f'Non-unique LONGITUDE: {folder_dir_path}/{year_folder}/{site}')\r\n if elevation == 'X':\r\n print(f'Non-unique ELEVATION: {folder_dir_path}/{year_folder}/{site}')\r\n # if site_number == 'X' \\\r\n # or latitude == 'X' \\\r\n # or longitude == 'X' \\\r\n # or elevation == 'X':\r\n # print(f'Non-unique column:', folder_dir_path, year_folder, site)\r\n f.write(f'{site_number},{latitude},{longitude},{elevation},{average_temp}\\n') \r\n\r\n\r\nschedule = IntervalSchedule(\r\n start_date=datetime.utcnow() + timedelta(seconds=1),\r\n interval=timedelta(seconds=10),\r\n)\r\n\r\n#schedule = IntervalSchedule(interval=timedelta(minutes=2))\r\nexecutor=LocalDaskExecutor(scheduler=\"processes\", num_workers=10)#, local_processes=True)\r\nwith Flow(name=\"NOAA Temps: Process CSVs\", executor=executor, schedule=schedule) as flow:\r\n # folder_path_flow = Parameter('folder_path_flow', default=os.environ.get('NOAA_TEMP_CSV_DIR') or Path('/') / 'media' / 'share' / 'store_240a' / 'data_downloads' / 'noaa_daily_avg_temps')\r\n # job_size = Parameter('JOB_SIZE', default=200)\r\n folders = list_year_folders()\r\n calculate_year_csv.map(year_folder=folders)#list_of_tuples=t3_records)#, waiting_for=t4_stations)\r\n\r\n\r\nif __name__ == '__main__':\r\n #flow.register(project_name=\"Global Warming Data\")\r\n state = flow.run()#executor=LocalDaskExecutor(scheduler=\"processes\", num_workers=6))#, local_processes=True)\r\n assert state.is_successful()\r\n", "sub_path": "noaa_calc_avg_yearly_temp.py", "file_name": "noaa_calc_avg_yearly_temp.py", "file_ext": "py", "file_size_in_byte": 4880, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.settrace", "line_number": 2, "usage_type": "attribute"}, {"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": 17, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pathlib.Path.home", "line_number": 18, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 18, "usage_type": "name"}, {"api_name": "os.environ.get", "line_number": 21, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 21, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 21, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 25, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 58, "usage_type": "call"}, {"api_name": "prefect.task", "line_number": 56, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 65, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 68, "usage_type": "call"}, {"api_name": "prefect.task", "line_number": 61, "usage_type": "call"}, {"api_name": "prefect.schedules.IntervalSchedule", "line_number": 90, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 91, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 91, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 91, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 92, "usage_type": "call"}, {"api_name": "prefect.executors.dask.LocalDaskExecutor", "line_number": 96, "usage_type": "call"}, {"api_name": "prefect.Flow", "line_number": 97, "usage_type": "call"}]} +{"seq_id": "585940784", "text": "from bs4 import BeautifulSoup\nimport requests\nimport sys\n\nipaddr = sys.argv[1]\ntry:\n req = requests.get(\"http://www.ipsorgu.com/?ip=\" + str(ipaddr) + \"#sorgu\")\n soup = BeautifulSoup(req.text, 'html.parser')\n googlemaps = soup.find(\"iframe\")\n googlemaps = str(googlemaps).split(\"src=\")\n googlemaps = str(googlemaps[1]).split(\"&\")\n googlemaps = str(googlemaps[0]).replace('\"', \"\")\n googlemaps = str(googlemaps).split(\"=\")\n info = soup.find_all(\"em\")\n info1 = str(info).replace('', '')\n info1 = str(info1).replace('', '')\n info1 = info1.split(\", \")\n print(\"Location : \" + str(googlemaps[1]))\n print(\"Country : \" + str(info1[0]).replace(\"[\", \"\"))\n print(\"Region : \" + str(info1[1]))\n print(\"Host : \" + str(info1[3]))\n req = requests.get(\"https://whatismyipaddress.com/ip/\" + str(ipaddr))\n soup = BeautifulSoup(req.text, 'html.parser')\n info2 = soup.find_all(\"td\")\n info3 = str(info2).replace('', '')\n info3 = str(info3).replace('', '')\n info3 = str(info3).replace('[', '')\n info3 = info3.split(\", \")\n print(\"Decimal : \" + str(info3[1]))\n print(\"ASN : \" + str(info3[3]))\n print(\"ISP : \" + str(info3[4]))\n print(\"Postal Code : \" + str(info3[16]).replace(\"]\", \"\"))\n print(\"continent : \" + str(info3[10]))\nexcept:\n pass\n", "sub_path": "ipsorgu.py", "file_name": "ipsorgu.py", "file_ext": "py", "file_size_in_byte": 1334, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.argv", "line_number": 5, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 7, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 8, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 22, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "376700858", "text": "from django.shortcuts import render, redirect\nfrom django.contrib.auth.models import User\nfrom .models import questoes, provas\nfrom .forms import questoesForm, provaForm\n\nfrom django.http import HttpResponseRedirect\nfrom django.views.generic import TemplateView\n\n\nfrom reportlab.pdfgen import canvas\n\n# Create your views here.\n\n'''\nuser = User.objects.all()\nprint(user)\n'''\n'''\n Sistema CBV\n'''\n\n#CBV Index\nclass IndexPage(TemplateView):\n template_name = 'index/index.html'\n\n\n#função das questões\ndef questao(request):\n q = questoes.objects.all() # Busca pela tabela\n\n questForm = questoesForm(request.POST or None)\n\n if request.method == 'POST':\n if questForm.is_valid():\n print(questForm.cleaned_data['codigo'])\n print(questForm.cleaned_data['descricao'])\n print(questForm.cleaned_data['disciplina'])\n\n questForm.save()\n return redirect('questao')\n print(q)\n \n return render(request, 'questao/questoes.html', {'questForm': questForm})\n\n\ndef prova(request):\n p = provas.objects.all()\n\n pForm = provaForm(request.POST or None)\n\n if request.method == 'POST':\n if pForm.is_valid():\n print(pForm.cleaned_data['questoes'])\n\n pForm.save()\n return redirect('questao')\n print(p)\n\n def main(args):\n \n \n cnv = canvas.Canvas(pdfProva, pagesize=A4)\n \n #desenha o perímetro do painel\n desenhaPerimetro(cnv,offsetX ,offsetY,\n comprimento,largura)\n \n \n #escreve os textos no painel\n cnv.setFont('Times-Bold',16)\n escreveTextoCentralizadoX(cnv, inicioTexto1Y, texto1)\n \n cnv.setFont('Times-Bold',14)\n escreveTextoCentralizadoX(cnv, inicioTexto2Y, texto2) \n \n \n #desenha os furos para o autofalante\n desenhaAutofalante(cnv)\n \n \n #desenha o furo e textos da chave de ganho\n chaveSeletoraGanho(cnv) \n \n \n cnv.save()\n return 0\n \n return render(request, 'prova/provas.html', {'pForm': pForm})\n\n\n\n\n", "sub_path": "geracao_prova/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2150, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.views.generic.TemplateView", "line_number": 23, "usage_type": "name"}, {"api_name": "models.questoes.objects.all", "line_number": 29, "usage_type": "call"}, {"api_name": "models.questoes.objects", "line_number": 29, "usage_type": "attribute"}, {"api_name": "models.questoes", "line_number": 29, "usage_type": "name"}, {"api_name": "forms.questoesForm", "line_number": 31, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 40, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 43, "usage_type": "call"}, {"api_name": "models.provas.objects.all", "line_number": 47, "usage_type": "call"}, {"api_name": "models.provas.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "models.provas", "line_number": 47, "usage_type": "name"}, {"api_name": "forms.provaForm", "line_number": 49, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 56, "usage_type": "call"}, {"api_name": "reportlab.pdfgen.canvas.Canvas", "line_number": 62, "usage_type": "call"}, {"api_name": "reportlab.pdfgen.canvas", "line_number": 62, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 88, "usage_type": "call"}]} +{"seq_id": "311950316", "text": "import time\nfrom rpi_ws281x import *\nimport argparse\nimport RPi.GPIO as GPIO\n\n# LED strip configuration:\nLED_COUNT = 3 # Number of LED pixels.\nLED_PIN = 12 # GPIO pin connected to the pixels (18 uses PWM!).\n#LED_PIN = 10 # GPIO pin connected to the pixels (10 uses SPI /dev/spidev0.0).\nLED_FREQ_HZ = 800000 # LED signal frequency in hertz (usually 800khz)\nLED_DMA = 10 # DMA channel to use for generating signal (try 10)\nLED_BRIGHTNESS = 255 # Set to 0 for darkest and 255 for brightest\nLED_INVERT = False # True to invert the signal (when using NPN transistor level shift)\nLED_CHANNEL = 0 # set to '1' for GPIOs 13, 19, 41, 45 or 53\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-c', '--clear', action='store_true', help='clear the display on exit')\nargs = parser.parse_args()\n\n# Create NeoPixel object with appropriate configuration.\nstrip = Adafruit_NeoPixel(LED_COUNT, LED_PIN, LED_FREQ_HZ, LED_DMA, LED_INVERT, LED_BRIGHTNESS, LED_CHANNEL)\n# Intialize the library (must be called once before other functions).\nstrip.begin()\n\n# Define functions which animate LEDs in various ways.\ndef colorWipe( R, G, B):\n \"\"\"Wipe color across display a pixel at a time.\"\"\"\n color = Color(R,G,B)\n for i in range(strip.numPixels()):\n strip.setPixelColor(i, color)\n strip.show()\n\ndef run():\n strip.setPixelColor(0, Color(0, 0, 255)) # No. 1 light is blue.\n strip.setPixelColor(1, Color(0, 255, 0)) # No. 2 light is green.\n strip.setPixelColor(2, Color(255, 0, 0)) # No. 3 light is red.\n strip.show()\n time.sleep(2)\n colorWipe(0, 0, 0) # All lights off.\n time.sleep(1)\n\nif __name__ == '__main__':\n try:\n while True:\n run()\n except KeyboardInterrupt:\n colorWipe(0, 0, 0)\n\n\n", "sub_path": "RobotHAT/04_ws2812.py", "file_name": "04_ws2812.py", "file_ext": "py", "file_size_in_byte": 1809, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 17, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 39, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "187555629", "text": "import cherrypy\n\nfrom pagebase import PageBase\nfrom components.xmlcomponent import XmlComponent\nfrom lib.xmlhelper import XmlHelper\nfrom model.useraccount import UserAccount\n\nclass UIPageBase(PageBase):\n \n def prepare(self):\n super(UIPageBase, self).prepare()\n self.add_stylesheet('ui.css')\n self.add_page_component(self.__create_user_display())\n self.add_page_component(self.__create_main_nav())\n self.add_page_component(self.create_page_content())\n self.add_page_component(self.__create_footer())\n \n @property \n def pageName(self):\n return self.get_page_name()\n \n def get_page_name(self):\n return 'Not Specified'\n \n def create_page_content(self):\n return XmlComponent(tagname='div', attributes={'id': 'mainAreaContainer'})\n \n def __create_user_display(self):\n container = XmlComponent(tagname='div', attributes={'id': 'userDisplayContainer'})\n \n XmlHelper.create_subelement(container.root, tagname='span', text='Logged in as:')\n \n user = UserAccount(cherrypy.session.get('user-id'))\n XmlHelper.create_subelement(container.root, tagname='span', text=user.get_display_name())\n \n # Product selection container\n a = XmlHelper.create_subelement(container.root, tagname='div', attributes={'id': 'productSelectionContainer'})\n XmlHelper.create_subelement(a, tagname='span', text='Current Product:')\n sel = XmlHelper.create_subelement(a, tagname='select', attributes={'id': 'productSelection'})\n XmlHelper.create_subelement(sel, tagname='option')\n \n return container\n \n def __create_main_nav(self):\n data = [\n ('Product Backlog', '/'),\n ('Sprints', '/sprints'),\n ('Charts', '/charts')]\n \n nav = XmlComponent(tagname='nav', attributes={'id': 'mainNav'}) \n for i in data:\n link = XmlHelper.create_subelement(nav.root, tagname='a')\n XmlHelper.set_attribute(link, 'href', i[1])\n XmlHelper.set_text(link, i[0])\n \n if self.pageName == i[0]:\n XmlHelper.add_class(link, 'selected')\n \n return nav\n \n def __create_footer(self):\n footer = XmlComponent(tagname='div', attributes={'id': 'footerContainer'})\n \n d = XmlHelper.create_subelement(footer.root, tagname='div')\n XmlHelper.set_attribute(d, 'id', 'poweredByContainer')\n XmlHelper.create_subelement(d, tagname='span', text='Powered by')\n XmlHelper.create_subelement(d, tagname='img',\n attributes={'id': 'cherrypyLogo','src': '/img/cplogo.gif', 'alt': 'CherryPy'})\n \n return footer", "sub_path": "src/www/views/uipagebase.py", "file_name": "uipagebase.py", "file_ext": "py", "file_size_in_byte": 2765, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pagebase.PageBase", "line_number": 8, "usage_type": "name"}, {"api_name": "components.xmlcomponent.XmlComponent", "line_number": 26, "usage_type": "call"}, {"api_name": "components.xmlcomponent.XmlComponent", "line_number": 29, "usage_type": "call"}, {"api_name": "lib.xmlhelper.XmlHelper.create_subelement", "line_number": 31, "usage_type": "call"}, {"api_name": "lib.xmlhelper.XmlHelper", "line_number": 31, "usage_type": "name"}, {"api_name": "model.useraccount.UserAccount", "line_number": 33, "usage_type": "call"}, {"api_name": "cherrypy.session.get", "line_number": 33, "usage_type": "call"}, {"api_name": "cherrypy.session", "line_number": 33, "usage_type": "attribute"}, {"api_name": "lib.xmlhelper.XmlHelper.create_subelement", "line_number": 34, "usage_type": "call"}, {"api_name": "lib.xmlhelper.XmlHelper", "line_number": 34, "usage_type": "name"}, {"api_name": "lib.xmlhelper.XmlHelper.create_subelement", "line_number": 37, "usage_type": "call"}, {"api_name": "lib.xmlhelper.XmlHelper", "line_number": 37, "usage_type": "name"}, {"api_name": "lib.xmlhelper.XmlHelper.create_subelement", "line_number": 38, "usage_type": "call"}, {"api_name": "lib.xmlhelper.XmlHelper", "line_number": 38, "usage_type": "name"}, {"api_name": "lib.xmlhelper.XmlHelper.create_subelement", "line_number": 39, "usage_type": "call"}, {"api_name": "lib.xmlhelper.XmlHelper", "line_number": 39, "usage_type": "name"}, {"api_name": "lib.xmlhelper.XmlHelper.create_subelement", "line_number": 40, "usage_type": "call"}, {"api_name": "lib.xmlhelper.XmlHelper", "line_number": 40, "usage_type": "name"}, {"api_name": "components.xmlcomponent.XmlComponent", "line_number": 50, "usage_type": "call"}, {"api_name": "lib.xmlhelper.XmlHelper.create_subelement", "line_number": 52, "usage_type": "call"}, {"api_name": "lib.xmlhelper.XmlHelper", "line_number": 52, "usage_type": "name"}, {"api_name": "lib.xmlhelper.XmlHelper.set_attribute", "line_number": 53, "usage_type": "call"}, {"api_name": "lib.xmlhelper.XmlHelper", "line_number": 53, "usage_type": "name"}, {"api_name": "lib.xmlhelper.XmlHelper.set_text", "line_number": 54, "usage_type": "call"}, {"api_name": "lib.xmlhelper.XmlHelper", "line_number": 54, "usage_type": "name"}, {"api_name": "lib.xmlhelper.XmlHelper.add_class", "line_number": 57, "usage_type": "call"}, {"api_name": "lib.xmlhelper.XmlHelper", "line_number": 57, "usage_type": "name"}, {"api_name": "components.xmlcomponent.XmlComponent", "line_number": 62, "usage_type": "call"}, {"api_name": "lib.xmlhelper.XmlHelper.create_subelement", "line_number": 64, "usage_type": "call"}, {"api_name": "lib.xmlhelper.XmlHelper", "line_number": 64, "usage_type": "name"}, {"api_name": "lib.xmlhelper.XmlHelper.set_attribute", "line_number": 65, "usage_type": "call"}, {"api_name": "lib.xmlhelper.XmlHelper", "line_number": 65, "usage_type": "name"}, {"api_name": "lib.xmlhelper.XmlHelper.create_subelement", "line_number": 66, "usage_type": "call"}, {"api_name": "lib.xmlhelper.XmlHelper", "line_number": 66, "usage_type": "name"}, {"api_name": "lib.xmlhelper.XmlHelper.create_subelement", "line_number": 67, "usage_type": "call"}, {"api_name": "lib.xmlhelper.XmlHelper", "line_number": 67, "usage_type": "name"}]} +{"seq_id": "319673266", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Oct 10 17:04:54 2018\n\n@author: 朱诚锐\n\"\"\"\nimport jieba\nimport jieba.posseg as pseg\nimport re\nfrom openpyxl import load_workbook\n\njieba.load_userdict(r\"User_Dict.txt\")\n\n\ndef Cut_byAnatomy(Content):\n '''\n 将描述原文根据解剖位置切分成段落\n 判断\n 一个set为（、）（并列关系）（位置词）解剖部位 \n 识别解剖部位，若其前面不是顿号、位置词或解剖部位，则进行分段\n '''\n if not isinstance(Content,str):\n raise TypeError(\"not string type\")\n Content = Content.replace(\" \",\"\")\n outp = []#由切片组成，短语的slic表示\n slic = \"\"#由jieba分词结果组成\n \n result = pseg.cut(Content, HMM=False)\n r_list = []\n #将分词结果存为list[[word,flag],]\n for w in result:\n r_list.append([w.word,w.flag])\n \n for i in range(len(r_list)):\n w = r_list[i]\n w_before = r_list[i-1]\n try:\n w_after = r_list[i+1]\n except:\n w_after = [\"\",\"\"]\n if i==0:\n w_before=[\"\",\"\"]\n #如果该词是 解剖 或 位置，对其前置词进行分析\n if w[1] in [\"jp\"]:\n #若其前面不是顿号、位置词或解剖部位，则将slic移入outp，从该词新建slic\n if (w_before[1] not in [\"jp\",\"wz\",\"blgx\"]) and w_before[0] not in[\"、\",\"-\"]:\n outp.append(slic)\n slic = w[0]\n else:\n slic += w[0]\n elif w[1] in [\"wz\"]:\n if w_after[1] not in ['jp',\"wz\",\"blgx\"]:\n slic += w[0]\n else:\n if (w_before[1] not in [\"jp\",\"wz\",\"blgx\"]) and w_before[0] not in[\"、\",\"-\"]:\n outp.append(slic)\n slic=w[0]\n else:\n slic +=w[0] \n else:\n slic+= w[0]\n #End Slic\n outp.append(slic)\n while \"\" in outp:\n outp.remove(\"\")\n \n return outp\n\ndef Cut_byXHMC(Text):\n '''\n 处理的结果，提取其中的信号描述\n '''\n result = pseg.cut(Text,HMM=False)\n #将flag和word分别存为list\n flags = []\n words = []\n for w in result:\n flags.append(w.flag)\n words.append(w.word)\n if \"xhmc\" in flags:\n pass\n \ndef WriteToCell(Cell,Value,quto=\"、\",override=False):\n \"\"\"\n 将内容写入单元格，去重，中间用quto分隔\n \"\"\"\n if override == True:\n Cell.value = Value\n else:\n if Cell.value ==None or Cell.value ==\"\":\n Cell.value = Value\n else:\n if Value not in Cell.value:\n Cell.value += (quto+Value)\n \nclass Word:\n def __init__(self,text,flag,index):\n self.word = text\n self.flag = flag\n self.index = index\nclass Sentence:\n def __init__(self,index):\n self.index = index\n self.result = []\n self.words = []\n self.flags = []\n def add(self,each_Word):\n self.result.append(each_Word)\n self.words.append(each_Word.word)\n self.flags.append(each_Word.flag)\n\n def before(self,a_Word):\n idx = a_Word.idnex\n if idx ==0:\n return None\n else:\n return Sentence(self.index,self.result[:idx],self.words[:idx],self.flags[:idx])\n \nclass ParsedContent:\n def __init__(self,raw_content):\n # 将内容按照标点符号分隔\n if raw_content == None:\n self.contents = None\n else:\n self.contents=re.split(r\",|，|。|、|;|；|/\",raw_content.strip())\n self.sentences=[]\n for content in self.contents:\n result = pseg.cut(content,HMM=False)\n s_idx = 0\n w_idx = 0\n sentence = Sentence(index=s_idx)\n for w in result: \n word=Word(w.word,w.flag,w_idx)\n sentence.add(word)\n w_idx += 1\n self.sentences.append(sentence)\n s_idx +=1 \n \nclass ParsedDiagnosis:\n def __init__(self,raw_diagnosis):\n if raw_diagnosis ==None:\n self.diagnosis = [\"\"]\n else:\n self.diagnosis = re.split(r\"\\n\",raw_diagnosis.strip())\n def if_occupied(self,disease,write_to_cell,key_word = \"占位\"):\n for each_dig in self.diagnosis:\n if disease in each_dig:\n if key_word in each_dig:\n write_to_cell.value = \"有\"\n def parse_jp(self, disease,write_to_cell):\n #处理诊断中的���剖位置\n for each_dig in self.diagnosis:\n if disease in each_dig:\n result = pseg.cut(each_dig,HMM=False)\n for w in result:\n if w.flag == \"jp\":\n WriteToCell(write_to_cell,w.word,\"、\")\n \n \n'''\nTODO：单位句子中存在两个及以上信号名称如何处理，难点-长T1，T1低信号，顺序问题\n'''\n\n \ndef main():\n path = r\"C:\\Users\\朱诚锐\\OneDrive\\DEEPWISE\\问诊平台\\数据\\征象提取\"\n #文件名\n filename = r\"\\1022-颅内海绵状血管瘤.xlsx\"\n #输出文件名\n out_filename = r\"\\1022处理后.xlsx\"\n dirc = path+filename\n\n book = load_workbook(dirc)\n sheet = book[\"Sheet1\"]\n \n #初始化信号位置\n content_col=13 #空白列，作为存储提取描述内容的容器\n raw_content_col = 5 #原始诊断内容\n diagnosis_col = 6\n jp_col = 14\n xhxz_col = 28\n zqsm_col = 20\n zqsmlx_col = 21\n zw_col = 31\n disease=\"海绵\" #疾病关键字\n T1_col = 16\n T2_col=17\n Flair_col=18\n DWI_col = 19\n SWI_col = 24\n df_col = 15\n ADC_col = 23\n \n #遍历数据行,\n for row in range(2,sheet.max_row+1):\n #加载目标单元格\n diagnosis_cell = sheet.cell(row=row,column=diagnosis_col)\n content_cell = sheet.cell(row=row,column=content_col)\n raw_content = sheet.cell(row=row,column=raw_content_col).value\n xhxz_cell = sheet.cell(row=row,column=xhxz_col)\n zqsm_cell = sheet.cell(row=row,column=zqsm_col)\n zqsmlx_cell = sheet.cell(row=row,column=zqsmlx_col)\n jp_cell = sheet.cell(row=row,column=jp_col)\n zw_cell = sheet.cell(row=row,column=zw_col)\n T1_cell=sheet.cell(row=row,column=T1_col)\n T2_cell=sheet.cell(row=row,column=T2_col)\n Flair_cell=sheet.cell(row=row,column=Flair_col)\n SWI_cell=sheet.cell(row=row,column=SWI_col)\n DWI_cell=sheet.cell(row=row,column=DWI_col)\n df_cell = sheet.cell(row=row,column=df_col)\n ADC_cell = sheet.cell(row=row,column=ADC_col)\n print(\"row:{}\\nraw_content:{}\\n{}\".format(row,raw_content,\"+\"*30))\n \n \n #处理诊断，\n diagnosis = ParsedDiagnosis(diagnosis_cell.value)\n #识别疾病所在的诊断内容，提取解剖位置，写入jp_cell\n diagnosis.parse_jp(disease,jp_cell)\n #识别疾病所在的诊断内容，提取是否占位，写入zw_cell\n diagnosis.if_occupied(disease,zw_cell)\n #识别疾病所在的诊断内容，提取是否多发，写入df_cell\n diagnosis.if_occupied(disease,df_cell,\"多发\")\n \n \n #处理描述，将疾病部位所在的段落提取出\n try:\n jp = jp_cell.value.split(\"、\")\n print(\"jp:\",jp)\n for each_jp in jp:\n for sentence in (Cut_byAnatomy(raw_content)):\n if each_jp in sentence:\n WriteToCell(content_cell,sentence,quto=\"。\")\n except:\n content_cell.value = raw_content\n \n\n content = sheet.cell(row=row,column=content_col).value\n print(\"content:\",content)\n\n #根据标点符号拆分内容\n class_content = ParsedContent(content)\n if class_content.contents == None:\n continue\n else:\n for sentence in class_content.sentences:\n result = sentence.result\n flags = sentence.flags\n words = sentence.words\n if sentence ==None or words == None:\n continue\n print(words)\n try:\n #对增强扫描进行处理\n if \"zqsm\" in flags:\n #print(\"flags:{} \\nwords:{}\".format(flags,words))\n #如果存在zqsm，同时有存在性负的提示词，则记为不强化\n if \"czxf\" in flags and \"xhmc\" not in flags:\n WriteToCell(zqsm_cell,\"不强化\")\n elif \"czxz\" in flags and\"czxf\" not in flags:\n WriteToCell(zqsm_cell,words[flags.index(\"czxz\")]+\"强化\")\n if \"xhxr\" in flags:\n WriteToCell(zqsmlx_cell,words[flags.index(\"xhxr\")]+\"强化\")\n #对信号形状进行处理：如果信号形状和解剖或信号名称同时出现\n elif \"xhxz\" in flags and (\"jp\" in flags or \"xhmc\" in flags):\n WriteToCell(xhxz_cell,words[flags.index(\"xhxz\")])\n #对信号进行提取\n except:\n pass\n \n #对信号强度进行提取\n if \"xhqd\" in flags:\n if flags.count(\"xhmc\")==1:\n \n if \"T1\" in words or \"T1WI\" in words:\n to_cell = T1_cell\n elif \"T2\" in words or \"T2WI\" in words:\n to_cell = T2_cell\n elif \"DWI\" in words:\n to_cell = DWI_cell\n elif \"SWI\" in words:\n to_cell = SWI_cell\n elif \"FLAIR\" in words:\n to_cell = Flair_cell\n elif \"ADC\" in words:\n to_cell = ADC_cell\n else:\n continue\n if \"czxf\" in flags:\n WriteToCell(to_cell,\"未见\"+ words[flags.index(\"xhqd\")]+\"信号\")\n else:\n WriteToCell(to_cell,words[flags.index(\"xhqd\")]+\"信号\")\n \n print(\"\\n\")\n #保存\n book.save(path+out_filename)\n print(\"\\nfinished\")\nmain()", "sub_path": "DW/NEW-提取描述.py", "file_name": "NEW-提取描述.py", "file_ext": "py", "file_size_in_byte": 10569, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "jieba.load_userdict", "line_number": 12, "usage_type": "call"}, {"api_name": "jieba.posseg.cut", "line_number": 28, "usage_type": "call"}, {"api_name": "jieba.posseg", "line_number": 28, "usage_type": "name"}, {"api_name": "jieba.posseg.cut", "line_number": 73, "usage_type": "call"}, {"api_name": "jieba.posseg", "line_number": 73, "usage_type": "name"}, {"api_name": "re.split", "line_number": 125, "usage_type": "call"}, {"api_name": "jieba.posseg.cut", "line_number": 128, "usage_type": "call"}, {"api_name": "jieba.posseg", "line_number": 128, "usage_type": "name"}, {"api_name": "re.split", "line_number": 144, "usage_type": "call"}, {"api_name": "jieba.posseg.cut", "line_number": 154, "usage_type": "call"}, {"api_name": "jieba.posseg", "line_number": 154, "usage_type": "name"}, {"api_name": "openpyxl.load_workbook", "line_number": 173, "usage_type": "call"}]} +{"seq_id": "161744536", "text": "import torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.utils.data import DataLoader\n\nfrom dataloader import dataset\nfrom model_zoo.cnn import baseCNN\nfrom utils.trainer import Trainer\n\ntorch.manual_seed(31415)\n\nclass Args():\n train_seq_len = 30\n val_seq_len = 7\n batch_size = 16\n conv_out_channels = 8\n conv_kernel_size = 4\n max_pool_kernel_size = 2\n learning_rate = 1e-4\n weight_decay = 0.01\n num_epochs = 100\n device = torch.device('cuda:0' if torch.cuda. is_available() else 'cpu') \n\n\nargs = Args()\n\nargs.train_dl = DataLoader(dataset('data/energy_daily_train.csv', args), batch_size=args.batch_size)\nargs.val_dl = DataLoader(dataset('data/energy_daily_val.csv', args), batch_size=args.batch_size)\nargs.test_dl = DataLoader(dataset('data/energy_daily_test.csv', args), batch_size=args.batch_size)\n\nmodel = baseCNN(args).to(args.device)\n\ncriterion = nn.MSELoss()\noptimizer = optim.SGD(model.parameters(), lr=0.01, weight_decay=0.01)\n\ntrainer = Trainer(model, criterion, optimizer, args)\n\ntrainer.train()\n\n\nfrom ax import optimize\n\n\ndef trainer_funk(params):\n class Args():\n train_seq_len = params['train_seq_len']\n val_seq_len = params['val_seq_len']\n conv_out_channels = params['conv_out_channels']\n conv_kernel_size = params['conv_kernel_size']\n max_pool_kernel_size = params['max_pool_kernel_size']\n lr = params['lr']\n wd = params['wd']\n \n args = Args() \n args.num_epochs = 100\n args.batch_size = 16\n args.device = torch.device('cuda:0' if torch.cuda. is_available() else 'cpu') \n args.train_dl = DataLoader(dataset('data/energy_daily_train.csv', args), batch_size=args.batch_size)\n args.val_dl = DataLoader(dataset('data/energy_daily_val.csv', args), batch_size=args.batch_size)\n args.test_dl = DataLoader(dataset('data/energy_daily_test.csv', args), batch_size=args.batch_size)\n\n model = baseCNN(args).to(args.device)\n criterion = nn.MSELoss()\n optimizer = optim.SGD(model.parameters(), lr=args.lr, weight_decay=args.wd)\n trainer = Trainer(model, criterion, optimizer, args)\n \n return trainer.train()\n\n\nparams = {'train_seq_len':30, \n 'val_seq_len': 7,\n 'conv_out_channels': 8,\n 'conv_kernel_size': 4,\n 'max_pool_kernel_size': 2,\n 'lr': 1e-2,\n 'wd': 0.01\n }\n\ntrainer_funk(params)\n\nbest_parameters, best_values, _, _ = optimize(\n parameters=[{'name': 'train_seq_len', 'type': 'choice', 'values': [14, 21, 28, 42, 63], 'value_type': 'int'},\n {'name': 'val_seq_len', 'type': 'choice', 'values': [7, 14, 21], 'value_type': 'int'},\n {'name': 'conv_out_channels', 'type': 'choice', 'values': [8, 16, 32, 64], 'value_type': 'int'},\n {'name': 'conv_kernel_size', 'type': 'choice', 'values': [3, 5, 7], 'value_type': 'int'},\n {'name': 'max_pool_kernel_size', 'type': 'choice', 'values': [3, 5, 7], 'value_type': 'int'},\n {'name': 'lr', 'type': 'range', 'bounds': [0.0001, 0.1], 'value_type': 'float'},\n {'name': 'wd', 'type': 'range', 'bounds': [0.01, 10], 'value_type': 'float'}],\n evaluation_function=trainer_funk,\n total_trials=50,\n minimize=True)\n\nprint(best_parameters)\n\nbest_values\n\n\n", "sub_path": "PyTorch/param_opt.py", "file_name": "param_opt.py", "file_ext": "py", "file_size_in_byte": 3305, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "torch.manual_seed", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 27, "usage_type": "call"}, {"api_name": "dataloader.dataset", "line_number": 27, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 28, "usage_type": "call"}, {"api_name": "dataloader.dataset", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 29, "usage_type": "call"}, {"api_name": "dataloader.dataset", "line_number": 29, "usage_type": "call"}, {"api_name": "model_zoo.cnn.baseCNN", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 34, "usage_type": "name"}, {"api_name": "utils.trainer.Trainer", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 57, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 58, "usage_type": "call"}, {"api_name": "dataloader.dataset", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 59, "usage_type": "call"}, {"api_name": "dataloader.dataset", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 60, "usage_type": "call"}, {"api_name": "dataloader.dataset", "line_number": 60, "usage_type": "call"}, {"api_name": "model_zoo.cnn.baseCNN", "line_number": 62, "usage_type": "call"}, {"api_name": "torch.nn.MSELoss", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 63, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 64, "usage_type": "name"}, {"api_name": "utils.trainer.Trainer", "line_number": 65, "usage_type": "call"}, {"api_name": "ax.optimize", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "479937054", "text": "\nimport os.path as path\nimport os\nimport json\nfrom random import randint\nimport time\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom IPython import display\n\nfrom skimage.draw import polygon\nimport skimage.io as sio\nimport tracking.tracking_config as tc\nfrom PIL import Image\n\ndef showAnns(anns, imgid):\n \"\"\"\n Display the specified annotations.\n :param anns (array of object): annotations to display\n :return: None\n \"\"\"\n from matplotlib.collections import PatchCollection\n\n ax = plt.gca()\n ax.set_autoscale_on(False)\n polygons = []\n color = []\n np.random.seed(1)\n color_coeffs = np.random.random((31, 3))\n for ann_idx, ann in enumerate(anns):\n if int(ann['image_id']) < int(imgid):\n continue\n if int(ann['image_id']) > int(imgid):\n break\n c_assoc = ann['track_id'] * 97 % 31\n c = (color_coeffs[c_assoc:c_assoc+1, :]*0.6+0.4).tolist()[0]\n if 'keypoints' in ann and type(ann['keypoints']) == list:\n # turn skeleton into zero-based index\n # sks = np.array(coco.loadCats(ann['category_id'])[0]['skeleton'])-1\n kp = np.array(ann['keypoints'])\n x = kp[0::3]\n y = kp[1::3]\n v = kp[2::3]\n # for sk in sks:\n # if np.all(v[sk]>0):\n # plt.plot(x[sk],y[sk], linewidth=3, color=c)\n plt.plot(x[v>0], y[v>0],'o',markersize=8, markerfacecolor=c, markeredgecolor='k',markeredgewidth=2)\n plt.plot(x[v>1], y[v>1],'o',markersize=8, markerfacecolor=c, markeredgecolor=c, markeredgewidth=2)\n p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)\n ax.add_collection(p)\n p = PatchCollection(polygons, facecolor='none', edgecolors=color, linewidths=2)\n ax.add_collection(p)\n\n\ndef visualize(json_file):\n with open(json_file, 'r') as f:\n file = json.load(f)\n\n file_pathes = {}\n img_ids = []\n for img_file in file['images']:\n file_path = img_file['file_name']\n file_pathes[img_file['id']] = file_path\n for ann in file['annotations']:\n inst = ann['image_id']\n img_ids.append(inst)\n anns = file[\"annotations\"]\n\n # for idx, imgid in enumerate(img_ids):\n for idx, imgid in enumerate(file_pathes):\n video_file_name = json_file.split('/')[-1].split('.')[0]\n save_img_path = tc.config.TRACKING.SAVE_IMAGE_PATH\n queue_len_path = os.path.join(save_img_path,\"Qlen\" + str(tc.config.TRACKING.QUEUE_LEN))\n if not os.path.exists(queue_len_path):\n os.mkdir(queue_len_path)\n dir_name = os.path.join(queue_len_path, video_file_name)\n if not os.path.exists(dir_name):\n os.mkdir(dir_name)\n vis_folder = os.path.join(dir_name,'vis')\n if not os.path.exists(vis_folder):\n os.mkdir(vis_folder)\n print(idx+1,\"/\",len(file_pathes))\n file_name = file_pathes[str(imgid)]\n root = tc.config.TRACKING.ROOT\n file_path = os.path.join(root, file_name)\n im = Image.open(file_path)\n width, height = im.size\n fig = plt.figure(figsize=[width*0.01, height*0.01])\n img = sio.imread(file_path)\n\n # Display.\n plt.clf()\n plt.axis('off')\n plt.imshow(img)\n\n # Visualize keypoints.\n showAnns(anns, imgid)\n # If you want to save the visualizations somewhere:\n\n output_file_name = file_name.split(\"/\")[-1].split(\".\")[0]\n plt.savefig(\"{}/vis_{}.png\".format(vis_folder,output_file_name))\n # Frame updates.\n display.clear_output(wait=True)\n display.display(plt.gcf())\n time.sleep(1. / 10.)\n # If you want to just look at the first image, uncomment:\n # break\n plt.close()", "sub_path": "tracking/visualization.py", "file_name": "visualization.py", "file_ext": "py", "file_size_in_byte": 3774, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "matplotlib.pyplot.gca", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 25, "usage_type": "name"}, {"api_name": "numpy.random.seed", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 29, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 41, "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": "matplotlib.pyplot.plot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.collections.PatchCollection", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.collections.PatchCollection", "line_number": 52, "usage_type": "call"}, {"api_name": "json.load", "line_number": 58, "usage_type": "call"}, {"api_name": "tracking.tracking_config.config", "line_number": 73, "usage_type": "attribute"}, {"api_name": "tracking.tracking_config", "line_number": 73, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path", "line_number": 74, "usage_type": "attribute"}, {"api_name": "tracking.tracking_config.config", "line_number": 74, "usage_type": "attribute"}, {"api_name": "tracking.tracking_config", "line_number": 74, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path", "line_number": 75, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 78, "usage_type": "call"}, {"api_name": "os.path", "line_number": 78, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 82, "usage_type": "call"}, {"api_name": "tracking.tracking_config.config", "line_number": 85, "usage_type": "attribute"}, {"api_name": "tracking.tracking_config", "line_number": 85, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "PIL.Image.open", "line_number": 87, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "skimage.io.imread", "line_number": 90, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "IPython.display.clear_output", "line_number": 104, "usage_type": "call"}, {"api_name": "IPython.display", "line_number": 104, "usage_type": "name"}, {"api_name": "IPython.display.display", "line_number": 105, "usage_type": "call"}, {"api_name": "IPython.display", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gcf", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}]} +{"seq_id": "6430036", "text": "import seplis\nimport logging\nseplis.config_load()\n\nindexer = seplis.Show_indexer(\n url=seplis.config['client']['api_url'], \n access_token=seplis.config['client']['access_token']\n)\n\nshows = indexer.get('/shows?sort=id&per_page=500')\nfor show in shows.all():\n indexer.update_show(show['id'])\n logging.info('updated show {}'.format(show['id']))", "sub_path": "examples/updateallshows.py", "file_name": "updateallshows.py", "file_ext": "py", "file_size_in_byte": 353, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "seplis.config_load", "line_number": 3, "usage_type": "call"}, {"api_name": "seplis.Show_indexer", "line_number": 5, "usage_type": "call"}, {"api_name": "seplis.config", "line_number": 6, "usage_type": "attribute"}, {"api_name": "seplis.config", "line_number": 7, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "399330481", "text": "# encoding: utf-8\n\nimport json\n\nfrom google.appengine.api import users\n\nfrom django.shortcuts import get_object_or_404\n# from django.utils.decorators import method_decorator\n# from django.views.decorators.csrf import ensure_csrf_cookie\n\nfrom todos.utils import JsonResponse\nfrom todos.models import TodoList, TodoItem, LABEL_COLORS\n\nfrom django.views.generic import TemplateView\n\n\nclass IndexView(TemplateView):\n template_name = 'index.html'\n\n # @method_decorator(ensure_csrf_cookie)\n # def dispatch(self, *args, **kwargs):\n # super(IndexView, self).dispatch(*args, **kwargs)\n\n\nclass TodoListView(TemplateView):\n\n user = users.get_current_user().user_id()\n\n def get(self, request, todo_list_id=None):\n \"\"\" Fetch todo_list(s) \"\"\"\n\n if todo_list_id:\n # Get all todo lists\n todo_list_id = int(todo_list_id)\n todo_lists = [get_object_or_404(TodoList, pk=todo_list_id, user=self.user)]\n else:\n # Get one todo list\n todo_lists = TodoList.objects.filter(user=self.user)\n\n return JsonResponse([todo_list.to_dict() for todo_list in todo_lists])\n\n def put(self, request, todo_list_id=None):\n \"\"\" Create / update todo_list \"\"\"\n\n data = json.loads(request.body)\n title = data.get('title')\n created = data.get('created')\n\n if todo_list_id:\n # Update todo list\n todo_list_id = int(todo_list_id)\n todo_list = get_object_or_404(TodoList, pk=todo_list_id, user=self.user)\n todo_list = TodoList(\n pk=todo_list.pk,\n title=title,\n user=self.user,\n )\n else:\n # Create todo list\n todo_list = TodoList(\n title=title,\n user=self.user,\n created=float(created),\n )\n\n todo_list.save()\n return JsonResponse(todo_list.to_dict())\n\n def delete(self, request, todo_list_id):\n \"\"\" Delete todo_list \"\"\"\n\n todo_list_id = int(todo_list_id)\n todo_list = get_object_or_404(TodoList, pk=todo_list_id, user=self.user)\n\n for todo_item in todo_list.todo_items.all():\n # Delete todo items\n todo_item.delete()\n # Delete todo list\n todo_list.delete()\n\n return JsonResponse({'id': todo_list_id})\n\n\nclass TodoItemView(TemplateView):\n\n user = users.get_current_user().user_id()\n\n def put(self, request, todo_list_id, todo_item_id=None):\n \"\"\" Create / update todo_item \"\"\"\n\n data = json.loads(request.body)\n text = data.get('text')\n done = data.get('done')\n created = data.get('created')\n\n if not text:\n text = ''\n\n if not done:\n done = False\n\n todo_list_id = int(todo_list_id)\n todo_list = get_object_or_404(TodoList, pk=todo_list_id, user=self.user)\n\n if todo_item_id:\n todo_item_id = int(todo_item_id)\n get_object_or_404(TodoItem, pk=todo_item_id, todo_list=todo_list)\n todo_item = TodoItem(\n pk=todo_item_id,\n text=text,\n done=done,\n todo_list=todo_list,\n )\n else:\n todo_item = TodoItem(\n text=text,\n done=done,\n todo_list=todo_list,\n created=float(created)\n )\n\n todo_item.save()\n return JsonResponse(todo_item.to_dict())\n\n def delete(self, request, todo_list_id, todo_item_id):\n \"\"\" Delete todo_item \"\"\"\n\n todo_list_id = int(todo_list_id)\n todo_list = get_object_or_404(TodoList, pk=todo_list_id, user=self.user)\n\n todo_item_id = int(todo_item_id)\n todo_item = get_object_or_404(TodoItem, pk=todo_item_id, todo_list=todo_list)\n\n todo_item.delete()\n return JsonResponse({'id': todo_item_id})\n", "sub_path": "todos/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3920, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.views.generic.TemplateView", "line_number": 17, "usage_type": "name"}, {"api_name": "django.views.generic.TemplateView", "line_number": 25, "usage_type": "name"}, {"api_name": "google.appengine.api.users.get_current_user", "line_number": 27, "usage_type": "call"}, {"api_name": "google.appengine.api.users", "line_number": 27, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 35, "usage_type": "call"}, {"api_name": "todos.models.TodoList", "line_number": 35, "usage_type": "argument"}, {"api_name": "todos.models.TodoList.objects.filter", "line_number": 38, "usage_type": "call"}, {"api_name": "todos.models.TodoList.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "todos.models.TodoList", "line_number": 38, "usage_type": "name"}, {"api_name": "todos.utils.JsonResponse", "line_number": 40, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 45, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 52, "usage_type": "call"}, {"api_name": "todos.models.TodoList", "line_number": 52, "usage_type": "argument"}, {"api_name": "todos.models.TodoList", "line_number": 53, "usage_type": "call"}, {"api_name": "todos.models.TodoList", "line_number": 60, "usage_type": "call"}, {"api_name": "todos.utils.JsonResponse", "line_number": 67, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 73, "usage_type": "call"}, {"api_name": "todos.models.TodoList", "line_number": 73, "usage_type": "argument"}, {"api_name": "todos.utils.JsonResponse", "line_number": 81, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 84, "usage_type": "name"}, {"api_name": "google.appengine.api.users.get_current_user", "line_number": 86, "usage_type": "call"}, {"api_name": "google.appengine.api.users", "line_number": 86, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 91, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 103, "usage_type": "call"}, {"api_name": "todos.models.TodoList", "line_number": 103, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 107, "usage_type": "call"}, {"api_name": "todos.models.TodoItem", "line_number": 107, "usage_type": "argument"}, {"api_name": "todos.models.TodoItem", "line_number": 108, "usage_type": "call"}, {"api_name": "todos.models.TodoItem", "line_number": 115, "usage_type": "call"}, {"api_name": "todos.utils.JsonResponse", "line_number": 123, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 129, "usage_type": "call"}, {"api_name": "todos.models.TodoList", "line_number": 129, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 132, "usage_type": "call"}, {"api_name": "todos.models.TodoItem", "line_number": 132, "usage_type": "argument"}, {"api_name": "todos.utils.JsonResponse", "line_number": 135, "usage_type": "call"}]} +{"seq_id": "286295276", "text": "from pymongo import MongoClient\nimport rdflib\n\n\ndef getMongoData():\n # db connection\n client = MongoClient('mongo', 27021)\n db = client['mantistable']\n\n # main dict to contain mongodb documents\n index_link = {\"id\": {}, \"sub_id\": {}, \"links\": {}, \"sub_links\": {}, \"attr_id\": {}, \"attrs\": {}}\n\n # get infotable\n collection = db['mantistable_infotable']\n docs_iterable = collection.find({})\n for document in docs_iterable:\n data = document['ne_cols']\n for num in data:\n if not (\"rel\" in num):\n if num[\"type\"] is not None and num[\"type\"] != \"\":\n index_link[\"id\"][num[\"index\"]] = num[\"type\"]\n index_link[\"links\"][num[\"index\"]] = list()\n else:\n if num[\"rel\"] is not None and num[\"rel\"] != \"\":\n index_link[\"sub_id\"][num[\"index\"]] = num[\"rel\"]\n index_link[\"sub_links\"][num[\"index\"]] = list()\n\n data = document['lit_cols']\n for num in data:\n if num[\"rel\"] is not None:\n index_link[\"attr_id\"][num[\"index\"]] = num[\"rel\"]\n index_link[\"attrs\"][num[\"index\"]] = list()\n\n data = document['no_ann_cols']\n for num in data:\n if num[\"header\"] is not None:\n index_link[\"attr_id\"][num[\"index\"]] = num[\"header\"]\n index_link[\"attrs\"][num[\"index\"]] = list()\n\n # get datatable\n collection = db['mantistable_tabledata']\n docs_iterable = collection.find({})\n for document in docs_iterable:\n data = document['data']\n for index in index_link[\"id\"].keys():\n for i in range(len(data[index])):\n if \"linked_entity\" in data[index][i] and data[index][i][\"linked_entity\"] is not None and data[index][i][\"linked_entity\"] != \"null\":\n index_link[\"links\"][index].append({\"id\": i, \"value\": data[index][i][\"linked_entity\"]})\n\n for index in index_link[\"sub_id\"].keys():\n for i in range(len(data[index])):\n if \"linked_entity\" in data[index][i] and data[index][i][\"linked_entity\"] is not None and data[index][i][\"linked_entity\"] != \"null\":\n index_link[\"sub_links\"][index].append({\"id\": i, \"value\": data[index][i][\"linked_entity\"]})\n\n for index in index_link[\"attr_id\"].keys():\n for i in range(len(data[index])):\n if \"value_original\" in data[index][i] and data[index][i][\"value_original\"] is not None and data[index][i][\"value_original\"] != \"null\":\n index_link[\"attrs\"][index].append({\"id\": i, \"value\": data[index][i][\"value_original\"]})\n\n return index_link\n\n\ndef mongo2rdf(index_link):\n # load rdf header and footer\n # NOTE: use Assets().get_asset(\"export/intro.txt\") instead\n with open(\"mantistable/private/export/intro.txt\", \"r\", encoding=\"utf-8\", newline=\"\\r\\n\") as f:\n intro = f.read()\n with open(\"mantistable/private/export/outro.txt\", \"r\", encoding=\"utf-8\", newline=\"\\r\\n\") as f:\n outro = f.read()\n\n # data scan and rdf string build\n rdf = intro\n for index in index_link[\"id\"].keys():\n for item in index_link[\"links\"][index]:\n rdf += '\\r\\n '\n rdf += '\\r\\n '\n rdf += '\\r\\n ' + str(\n index) + ''\n rdf += '\\r\\n ' + str(\n item[\"id\"]) + ''\n for sub_id in index_link[\"sub_id\"].keys():\n rdf += '\\r\\n '\n for attr_id in index_link[\"attr_id\"].keys():\n if index_link[\"attr_id\"][attr_id] != \"\":\n rdf += '\\r\\n '\n rdf += '\\r\\n '\n rdf += outro\n\n # debug output\n '''\n with open(\"out.xml\", \"w+\", encoding=\"utf-8\", newline=\"\\n\") as f:\n f.write(rdf)\n '''\n\n # build rdf graph\n graph = rdflib.Graph()\n graph.parse(data=rdf, format='xml')\n\n return graph\n\n\ndef exportXml(graph):\n # with open(\"conversions/xml.xml\", \"w+\", encoding=\"utf-8\", newline=\"\\r\\n\") as f:\n # f.write(graph.serialize(format='xml').decode(\"utf-8\"))\n return graph.serialize(format='xml').decode(\"utf-8\")\n\n\ndef exportNt(graph):\n # with open(\"conversions/nt.nt\", \"w+\", encoding=\"utf-8\", newline=\"\\r\\n\") as f:\n # f.write(graph.serialize(format='nt').decode(\"utf-8\"))\n return graph.serialize(format='nt').decode(\"utf-8\")\n\n\ndef exportN3(graph):\n # with open(\"conversions/n3.n3\", \"w+\", encoding=\"utf-8\", newline=\"\\r\\n\") as f:\n # f.write(graph.serialize(format='n3').decode(\"utf-8\"))\n return graph.serialize(format='n3').decode(\"utf-8\")\n\n\ndef exportTurtle(graph):\n # with open(\"conversions/turtle.ttl\", \"w+\", encoding=\"utf-8\", newline=\"\\r\\n\") as f:\n # f.write(graph.serialize(format='turtle').decode(\"utf-8\"))\n return graph.serialize(format='turtle').decode(\"utf-8\")\n\n\ndef exportJsonLd(graph):\n # with open(\"conversions/json-ld.jsonld\", \"w+\", encoding=\"utf-8\", newline=\"\\r\\n\") as f:\n # f.write(graph.serialize(format='json-ld').decode(\"utf-8\"))\n return graph.serialize(format='json-ld').decode(\"utf-8\")\n\n\ndef exportRdf(exportType):\n mongoData = getMongoData()\n graph = mongo2rdf(mongoData)\n if exportType == 'xml':\n return exportXml(graph)\n elif exportType == 'nt':\n return exportNt(graph)\n elif exportType == 'n3':\n return exportN3(graph)\n elif exportType == 'turtle':\n return exportTurtle(graph)\n elif exportType == 'jsonld':\n return exportJsonLd(graph)\n", "sub_path": "mantistable/process/utils/export/rdf.py", "file_name": "rdf.py", "file_ext": "py", "file_size_in_byte": 7662, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pymongo.MongoClient", "line_number": 7, "usage_type": "call"}, {"api_name": "rdflib.Graph", "line_number": 117, "usage_type": "call"}]} +{"seq_id": "207101183", "text": "import argparse\nimport time\nimport collections\nimport os\nimport sys\nimport torch\nimport torch.nn\nfrom torch.autograd import Variable\nimport torch.nn as nn\nimport numpy\nnp = numpy\n\n# NOTE ==============================================\n# This is where your models are imported\nfrom models import RNN, GRU \n\n# Use the GPU if you have one\nif torch.cuda.is_available():\n\tprint(\"Using the GPU\")\n\tdevice = torch.device(\"cuda\") \nelse:\n\tprint(\"WARNING: You are about to run on cpu, and this will likely run out \\\n\t\tof memory. \\n You can try setting batch_size=1 to reduce memory usage\")\n\tdevice = torch.device(\"cpu\")\n\t\n\t\n###############################################################################\n#\n# \n# DATA LOADING & PROCESSING\n#\n###############################################################################\n\n# HELPER FUNCTIONS\ndef _read_words(filename):\n with open(filename, \"r\") as f:\n return f.read().replace(\"\\n\", \"\").split()\n\ndef _build_vocab(filename):\n data = _read_words(filename)\n\n counter = collections.Counter(data)\n count_pairs = sorted(counter.items(), key=lambda x: (-x[1], x[0]))\n\n words, _ = list(zip(*count_pairs))\n word_to_id = dict(zip(words, range(len(words))))\n id_to_word = dict((v, k) for k, v in word_to_id.items())\n\n return word_to_id, id_to_word\n\ndef _file_to_word_ids(filename, word_to_id):\n data = _read_words(filename)\n return [word_to_id[word] for word in data if word in word_to_id]\n\n# Processes the raw data from text files\ndef ptb_raw_data(data_path=None, prefix=\"ptb\"):\n train_path = os.path.join(data_path, prefix + \".train.txt\")\n valid_path = os.path.join(data_path, prefix + \".valid.txt\")\n test_path = os.path.join(data_path, prefix + \".test.txt\")\n\n word_to_id, id_2_word = _build_vocab(train_path)\n train_data = _file_to_word_ids(train_path, word_to_id)\n valid_data = _file_to_word_ids(valid_path, word_to_id)\n test_data = _file_to_word_ids(test_path, word_to_id)\n return train_data, valid_data, test_data, word_to_id, id_2_word\n\n\ndata='data'\n# LOAD DATA\nprint('Loading data from '+data)\nraw_data = ptb_raw_data(data_path=data)\ntrain_data, valid_data, test_data, word_to_id, id_2_word = raw_data\nvocab_size = len(word_to_id)\nprint(' vocabulary size: {}'.format(vocab_size))\n\n\nembSize=[200,200]\nbatchSize=[20,20]\ndropOut=[0.35,0.35]\nhiddenSize=[1500,1500]\nmodel_types=['RNN','GRU']\nnumLayers=[2,2]\nseqLen=[35,35]\nseq_len=[35,70]\nsamples=10\npath=['best_params_RNN.pt','best_params_GRU.pt']\n\n\n\n\nfor m in range(len(model_types)):\n\tfor s in range(len(seq_len)):\n\t\tprint('Processing model: '+model_types[m]+' seq_len: '+str(seq_len[s])+'\\n')\n\t\tif model_types[m]=='RNN':\n\t\t\tmodel = RNN(emb_size=embSize[m], hidden_size=hiddenSize[m], \n\t\t\t\t\tseq_len=seqLen[m], batch_size=batchSize[m],\n\t\t\t\t\tvocab_size=vocab_size, num_layers=numLayers[m], \n\t\t\t\t\tdp_keep_prob=dropOut[m])\n\t\telse:\n\t\t\tmodel =GRU(emb_size=embSize[m], hidden_size=hiddenSize[m], \n\t\t\t\t\tseq_len=seqLen[m], batch_size=batchSize[m],\n\t\t\t\t\tvocab_size=vocab_size, num_layers=numLayers[m], \n\t\t\t\t\tdp_keep_prob=dropOut[m])\n\t\tmodel.load_state_dict(torch.load(path[m]))\n\t\tmodel = model.to(device)\n\t\thidden = nn.Parameter(torch.zeros(numLayers[m],samples,hiddenSize[m])).to(device)\n\t\tinput=torch.ones(10000)*1/1000\n\t\tinput=torch.multinomial(input,samples).to(device)\n\t\tmodel.eval()\n\t\toutput=model.generate(input, hidden, seq_len[s])\n\t\tprint('Saving generated samples')\n\t\tfid=open(model_types[m]+'_' +str(seq_len[s])+'.txt','w')\n\t\tfor i in range(samples):\n\t\t\tfor j in range(seq_len[s]):\n\t\t\t\tfid.write(id_2_word.get(output[j,i].item())+' ')\n\t\t\tfid.write('\\n')\n\t\tfid.close()", "sub_path": "assignment2/Seq_Generation/generateSequences.py", "file_name": "generateSequences.py", "file_ext": "py", "file_size_in_byte": 3612, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "torch.cuda.is_available", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 18, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 24, "usage_type": "call"}, {"api_name": "collections.Counter", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "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": "models.RNN", "line_number": 95, "usage_type": "call"}, {"api_name": "models.GRU", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 106, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 107, "usage_type": "call"}, {"api_name": "torch.multinomial", "line_number": 108, "usage_type": "call"}]} +{"seq_id": "604322284", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Dec 29 13:02:50 2016\n\n@author: Renato Barros Arantes\n\"\"\"\nimport time\nimport sys\n\nimport numpy as np\n\nfrom heapq import heappush, heappop\nfrom xml.etree import cElementTree as ElementTree\nfrom xml.etree.ElementTree import Element, SubElement\nfrom xml.dom import minidom\n\ndef prettify(elem):\n \"\"\"Return a pretty-printed XML string for the Element.\n \"\"\"\n rough_string = ElementTree.tostring(elem, 'utf-8')\n reparsed = minidom.parseString(rough_string)\n return reparsed.toprettyxml(indent=\" \", encoding='utf-8')\n\n# record program start execution time...\nstart_time = time.time()\n\nidx_cnt = 0\nidx = dict()\ndoc = dict()\n\ndef get_key(w1, w2):\n global idx, idx_cnt\n if not w1 in idx:\n idx[w1] = idx_cnt\n doc[idx_cnt] = w1\n idx_cnt = idx_cnt + 1\n if not w2 in idx:\n idx[w2] = idx_cnt\n doc[idx_cnt] = w2\n idx_cnt = idx_cnt + 1\n return idx[w1], idx[w2]\n\n# load the cossine matrix...\nf = open(sys.argv[1], 'r')\ncos = np.zeros((60000, 60000))\nfor l in f:\n t = l.rstrip('\\n').split(',')\n w1, w2 = get_key(t[0], t[1])\n cos[w1][w2] = -float(t[2])\n if idx_cnt%1000 == 0:\n print('idx_cnt = ',idx_cnt)\n\nf.close()\n\nN = 10 # for each movie list N most relevants correlations\ncnt = 0 # number of movies\nfnd = 0 # number of cossine(w, w') found\nnfn = 0 # number of cossine(w, w') not found\nkwf = 0 # number of keywords found\nxml = ElementTree.parse('C:\\Desenv\\IMDb\\data\\IMDb.clean.xml').getroot()\nmovies = Element('movies')\nfor e in xml.iter():\n if e.tag == 'code':\n movie = SubElement(movies, 'movie')\n code = SubElement(movie, e.tag)\n code.text = e.text\n cnt = cnt + 1\n if cnt % 1000 == 0:\n print(cnt)\n elif e.tag == 'language_codes':\n lang = SubElement(movie, e.tag)\n lang.text = e.text\n elif e.tag in ['title', 'plot_outline', 'plot']:\n tag = SubElement(movie, e.tag)\n tag.text = e.text\n if e.text is None:\n continue\n # keep the correlated words...\n s = set([])\n q = [] # priority queue\n l = e.text.split()\n for i in range(len(l)):\n for j in range(i+1, len(l)):\n w1, w2 = get_key(l[i], l[j])\n t1 = (w1, w2)\n t2 = (w2, w1)\n # avoid duplicates...\n if t1 in s or t2 in s:\n continue\n s.add(t1)\n s.add(t2)\n if cos[w1][w2] != 0.0:\n fnd = fnd + 1\n heappush(q, (cos[w1][w2], w1, w2))\n elif cos[w2][w1] != 0.0:\n fnd = fnd + 1\n heappush(q, (cos[w2][w1], w2, w1))\n else:\n nfn = nfn + 1\n # keep only the n most relevants...\n while len(q) > N:\n heappop(q)\n elif e.tag in ['keywords']:\n tag = SubElement(movie, e.tag)\n tag.text = e.text\n # convert keywords text to a set so we can search on it...\n s = set([])\n if not e.text is None:\n s = set(e.text.split())\n # list only the n most relevants...\n sim = SubElement(movie, 'similarities')\n while len(q) > 0:\n pqi = heappop(q) # priority queue item...\n w1 = doc[pqi[1]]\n w2 = doc[pqi[2]]\n if w1 in s:\n att = { 'w1' : w1, 'w2' : w2, 'cos' : str(-pqi[0])}\n sml = SubElement(sim, 'similarity', att)\n sml.text = 'keyword'\n kwf = kwf + 1\n elif w2 in s:\n att = { 'w1' : w2, 'w2' : w1, 'cos' : str(-pqi[0])}\n sml = SubElement(sim, 'similarity', att)\n sml.text = 'keyword'\n kwf = kwf + 1\n else:\n att = { 'w1' : w1, 'w2' : w2, 'cos' : str(-pqi[0])}\n sml = SubElement(sim, 'similarity', att)\n\nout = open(sys.argv[1]+'.cos.xml', \"wb\")\nout.write(prettify(movies))\n\nprint('Movies={}, Words_Found={}, Words_Not_Found={}, Keywords_Found={}'.\n format(cnt, fnd, nfn, kwf))\n\nprint(\"--- %s seconds ---\" % (time.time() - start_time))", "sub_path": "IMDbSimilarity.py", "file_name": "IMDbSimilarity.py", "file_ext": "py", "file_size_in_byte": 4210, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "xml.etree.cElementTree.tostring", "line_number": 20, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 20, "usage_type": "name"}, {"api_name": "xml.dom.minidom.parseString", "line_number": 21, "usage_type": "call"}, {"api_name": "xml.dom.minidom", "line_number": 21, "usage_type": "name"}, {"api_name": "time.time", "line_number": 25, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 45, "usage_type": "call"}, {"api_name": "xml.etree", "line_number": 60, "usage_type": "name"}, {"api_name": "xml.etree.cElementTree.parse", "line_number": 60, "usage_type": "call"}, {"api_name": "xml.etree.cElementTree", "line_number": 60, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 61, "usage_type": "call"}, {"api_name": "xml.etree.iter", "line_number": 62, "usage_type": "call"}, {"api_name": "xml.etree", "line_number": 62, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 64, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 65, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 71, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 74, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 94, "usage_type": "call"}, {"api_name": "heapq.heappush", "line_number": 97, "usage_type": "call"}, {"api_name": "heapq.heappop", "line_number": 102, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 104, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 111, "usage_type": "call"}, {"api_name": "heapq.heappop", "line_number": 113, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 118, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 123, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.SubElement", "line_number": 128, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 130, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 136, "usage_type": "call"}]} +{"seq_id": "317920415", "text": "# !/usr/bin/env python\n# -*- coding: utf-8 -*-\n# This file is part of the project\n# https://github.com/flaminhoe21/corporate-knowledge-managment-vum.git\n# author Iva Tsaneva. All rights reserved.\n\nfrom django.urls import path\nfrom . import views\n\n\nurlpatterns = [\n path('mine/',\n views.PublisherPostList.as_view(), name='posts_list_merge'),\n path('create/',\n views.PublisherPostView.as_view(), name='post_create'),\n path('/edit/',\n views.PublisherPostUpdate.as_view(), name='post_edit'),\n path('/delete/',\n views.PublisherPostDelete.as_view(), name='post_delete'),\n path('/module/', views.PostUpdateViewModel.as_view(), name='post_module_update'),\n\n path('module//content//create/',\n views.PostAddContent.as_view(),\n name='module_content_create'),\n\n path('module//content///',\n views.PostAddContent.as_view(),\n name='module_content_update'),\n\n path('content//delete/', views.PostContentDelete.as_view(), name='module_content_delete'),\n\n path('module//', views.PostContentListing.as_view(), name='inner_post_list'),\n\n path('category//', views.CategoryListing.as_view(), name='categories_listing'),\n path('/', views.PostDetailsText.as_view(), name='post_details'),\n\n]\n", "sub_path": "ckmsProject/publications/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1377, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "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"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "246355848", "text": "from django.views.generic.base import TemplateView\nimport xlwt\nfrom django.shortcuts import render\nfrom django.views.generic import View, TemplateView\nfrom django.contrib.auth import authenticate, login\nfrom django.shortcuts import render,redirect\nfrom django.http import HttpResponse\nfrom django.contrib.auth.decorators import login_required\nfrom datetime import datetime\nfrom django.contrib.auth import logout\nfrom .forms import AddUserForm, AddEmpForm, UserUpdateForm, EmpUpdateForm\nfrom django.contrib.auth.models import User\nfrom .models import EmpDetails\n\nclass LoginView(View):\n\ttemplate_name = 'index.html'\n\tdef get(self,request):\n\t\tform = AddUserForm\n\t\treturn render(request,self.template_name,{'form':form})\n\n\tdef post(self,request):\n\t\tusername = request.POST['username']\n\t\tpassword = request.POST['password1']\n\t\tuser = authenticate(request, username=username, password=password)\n\t\tif user is not None:\n\t\t\tlogin(request, user)\n\t\t\treturn redirect ('home')\n\t\telse: return redirect('login')\n\nclass HomeView(View):\n\ttemplate_name = 'home_1.html'\n\tdef get(self,request):\n\t\tif not request.user.is_authenticated:\n\t\t\treturn redirect('login')\n\t\treturn render (request,self.template_name)\n\n\ndef logout_view(request):\n\trequest.session.flush()\n\tlogout(request)\n\t# Redirect to a success page.\n\treturn redirect(\"login\")\n\n\nclass EmpAdd(View):\n\ttemplate_name = 'services.html'\n\tform_class = AddUserForm\n\tcnt_class = AddEmpForm\n\n\tdef get(self, request):\n\t\tif request.user.is_superuser:\n\t\t\tform1 = self.form_class()\n\t\t\tform2 = AddEmpForm()\n\t\t\treturn render(request,self.template_name,{'form1':form1, 'form2':form2})\n\t\telse:\n\t\t\treturn HttpResponse('invalid user')\n\n\tdef post(self,request):\n\t\tform1 = self.form_class(request.POST)\n\t\tform2 = AddEmpForm(request.POST, request.FILES)\n\t\tif form1.is_valid() and form2.is_valid():\n\t\t\tusr_d = User.objects.create_user(\n\t\t\t\tusername = request.POST.get('username'),\n\t\t\t\tfirst_name = request.POST.get('first_name'),\n\t\t\t\tlast_name = request.POST.get('last_name'),\n\t\t\t\temail = request.POST.get('email'),\n\t\t\t\tpassword = request.POST.get('password1'))\n\t\t\tusr_d.is_staff=True\n\t\t\tusr_d.save()\n\t\t\temp_details = EmpDetails.objects.create(\n\t\t\t\tuser_key = usr_d,\n\t\t\t\tuser_name = usr_d.first_name + \" \" +usr_d.last_name,\n\t\t\t\tuser_email = request.POST.get('email'),\n\t\t\t\tuser_password = request.POST.get('password1'),\n\t\t\t\tuser_phone = request.POST.get('user_phone'),\n\t\t\t\tuser_address = request.POST.get('user_address'),\n\t\t\t\tuser_image = request.FILES.get('user_image'),\n\t\t\t\t)\n\t\t\temp_details.save()\n\n\t\t\treturn redirect('emplist')\n\n\t\telse:\n\t\t\tprint('not validated')\n\t\t\tprint(form1.errors)\n\t\t\tprint(form2.errors)\n\t\t\tform1 = self.form_class()\n\t\t\tform2 = AddEmpForm()\n\t\t\treturn render(request,self.template_name,{'form1':form1,'form2':form2})\n\nclass EmpList(View):\n\ttemplate_name='portfolio.html'\n\tdef get(self,request):\n\t\tif request.user.is_staff:\n\t\t\tobj = EmpDetails.objects.all()\n\t\t\tcontext = {\n\t\t\t# 'user_list':obj1,\n\t\t\t'doc_list':obj\n\t\t\t}\n\t\t\treturn render(request,self.template_name,context)\n\t\telse:\n\t\t\treturn HttpResponse('invalid user')\n\n\nclass EmpUpdateView(View):\n\ttemplate_name = 'empupdate.html'\n\tdef get(self,request,pk):\n\t\tif request.user.is_superuser:\n\t\t\tuser_objd = User.objects.get(id=pk)\n\t\t\t\t\n\t\t\tprint(user_objd)\n\t\t\tform1 = UserUpdateForm(initial = \n\t\t\t\t{'username':user_objd.username,\n\t\t\t\t'first_name':user_objd.first_name,\n\t\t\t\t'last_name':user_objd.last_name,\n\t\t\t\t'email':user_objd.email}\n\t\t\t\t)\n\t\t\tdoc = EmpDetails.objects.get(user_key=user_objd)\n\t\t\n\t\t\tprint(doc)\n\t\t\tform2 = EmpUpdateForm(initial={\n\t\t\t\t\t 'user_name':doc.user_name, \n\t\t\t\t\t 'user_phone':doc.user_phone,\n\t\t\t\t\t 'user_address':doc.user_address,\n\t\t\t\t\t 'user_image':doc.user_image})\n\t\t\treturn render (request,self.template_name,{\"form1\":form1, \"form2\":form2})\n\t\telse:\n\t\t\treturn HttpResponse('invalid user')\n\tdef post(self,request,pk):\n\t\tform1 = UserUpdateForm(request.POST)\n\t\tform2 = EmpUpdateForm(request.POST)\n\t\tif form1.is_valid() and form2.is_valid():\n\t\t\t\n\n\t\t\tusr_d = User.objects.create(\n\t\t\t\tusername = request.POST.get('username'),\n\t\t\t\tfirst_name = request.POST.get('first_name'),\n\t\t\t\tlast_name = request.POST.get('last_name'),\n\t\t\t\temail = request.POST.get('email'))\n\t\t\t\t# password = request.POST.get('password1'))\n\t\t\tusr_d.is_staff = True\n\t\t\tusr_d.save()\n\t\t\tusr_doc = EmpDetails.objects.create(\n\t\t\t\tuser_key = usr_d,\n\t\t\t\tuser_name = request.POST.get('first_name') + ' '+ request.POST.get('last_name'),\n\t\t\t\tuser_email = request.POST.get('email'),\n\t\t\t\tuser_phone = request.POST.get('user_phone'),\n\t\t\t\tuser_address = request.POST.get('user_address'),\n\t\t\t\tuser_image = request.POST.get('user_image'),\n\t\t\t\t)\n\t\t\tusr_doc.save()\n\t\t\t# messages.success(request, _('Your profile was successfully updated!'))\n\t\t\treturn redirect('emplist')\n\n\t\telse:\n\t\t\tform1 = UserUpdateForm()\n\t\t\tform2 = EmpUpdateForm()\n\t\t\treturn render(request,self.template_name,{'form1':form1, 'form2' :form2})\n\n\nclass ContactView(TemplateView):\n\ttemplate_name='contact.html'", "sub_path": "emp/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4931, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.views.generic.View", "line_number": 15, "usage_type": "name"}, {"api_name": "forms.AddUserForm", "line_number": 18, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 19, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 24, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 26, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 27, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 28, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 30, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 34, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 35, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 40, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 42, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 45, "usage_type": "name"}, {"api_name": "forms.AddUserForm", "line_number": 47, "usage_type": "name"}, {"api_name": "forms.AddEmpForm", "line_number": 48, "usage_type": "name"}, {"api_name": "forms.AddEmpForm", "line_number": 53, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 54, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 56, "usage_type": "call"}, {"api_name": "forms.AddEmpForm", "line_number": 60, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.create_user", "line_number": 62, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 62, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 62, "usage_type": "name"}, {"api_name": "models.EmpDetails.objects.create", "line_number": 70, "usage_type": "call"}, {"api_name": "models.EmpDetails.objects", "line_number": 70, "usage_type": "attribute"}, {"api_name": "models.EmpDetails", "line_number": 70, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 81, "usage_type": "call"}, {"api_name": "forms.AddEmpForm", "line_number": 88, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 89, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 91, "usage_type": "name"}, {"api_name": "models.EmpDetails.objects.all", "line_number": 95, "usage_type": "call"}, {"api_name": "models.EmpDetails.objects", "line_number": 95, "usage_type": "attribute"}, {"api_name": "models.EmpDetails", "line_number": 95, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 100, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 102, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 105, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User.objects.get", "line_number": 109, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 109, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 109, "usage_type": "name"}, {"api_name": "forms.UserUpdateForm", "line_number": 112, "usage_type": "call"}, {"api_name": "models.EmpDetails.objects.get", "line_number": 118, "usage_type": "call"}, {"api_name": "models.EmpDetails.objects", "line_number": 118, "usage_type": "attribute"}, {"api_name": "models.EmpDetails", "line_number": 118, "usage_type": "name"}, {"api_name": "forms.EmpUpdateForm", "line_number": 121, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 126, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 128, "usage_type": "call"}, {"api_name": "forms.UserUpdateForm", "line_number": 130, "usage_type": "call"}, {"api_name": "forms.EmpUpdateForm", "line_number": 131, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.create", "line_number": 135, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 135, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 135, "usage_type": "name"}, {"api_name": "models.EmpDetails.objects.create", "line_number": 143, "usage_type": "call"}, {"api_name": "models.EmpDetails.objects", "line_number": 143, "usage_type": "attribute"}, {"api_name": "models.EmpDetails", "line_number": 143, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 153, "usage_type": "call"}, {"api_name": "forms.UserUpdateForm", "line_number": 156, "usage_type": "call"}, {"api_name": "forms.EmpUpdateForm", "line_number": 157, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 158, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 161, "usage_type": "name"}]} +{"seq_id": "74224586", "text": "import random\nfrom django.db import models\nfrom course.models import Subject\nfrom user.models import Student, Teacher\n\n# Create your models here.\n\nDIFF_CHOICES = {\n ('Dễ', 'Dễ'),\n ('Trung bình', 'Trung bình'),\n ('Khó', 'Khó')\n}\n\nRESULT_CHOICES = {\n (\"Có\", 'Có'),\n (\"Không\", 'Không')\n}\n\nclass Quiz(models.Model):\n subject = models.ForeignKey(Subject, on_delete=models.CASCADE, related_name='quizzes')\n teacher = models.ForeignKey(Teacher, on_delete=models.CASCADE, related_name='quizzes')\n name = models.CharField(max_length=255)\n number_of_question = models.IntegerField(default=0)\n time = models.IntegerField(help_text=\"Thời gian làm bài\")\n required_score_to_pass = models.IntegerField(help_text=\"Điểm cần đạt\")\n difficulity = models.CharField(max_length=10, choices=DIFF_CHOICES)\n show_result = models.CharField(max_length=10, choices=RESULT_CHOICES, default=\"Có\")\n docx = models.FileField(upload_to='quiz', default='', blank=True, null=True)\n created_at = models.DateTimeField(auto_now_add=True)\n\n def __str__(self):\n return f\"{self.name}\"\n\n def search(self):\n return f\"{self.name} - {self.subject.name}\" \n\n def get_question(self):\n questions = list(self.questions.all())\n random.shuffle(questions)\n return questions[:self.number_of_question]\n\n\nclass Question(models.Model):\n quiz = models.ForeignKey(Quiz, on_delete=models.CASCADE, related_name='questions')\n text = models.CharField(max_length=255)\n\n def __str__(self):\n return str(self.text)\n\n def get_answers(self):\n answers = list(self.answers.all()) # = return self.answer_set.all()\n random.shuffle(answers)\n return answers \n\n\nclass Answer(models.Model):\n text = models.CharField(max_length=255)\n correct = models.BooleanField(default=False)\n question = models.ForeignKey(Question, on_delete=models.CASCADE, related_name='answers')\n\n def __str__(self):\n return f\"question: {self.question.text}, answer: {self.text}, correct: {self.correct}\"\n\n\nclass StudentAnswer(models.Model):\n quiz = models.ForeignKey(Quiz, on_delete=models.CASCADE, related_name='quiz_result')\n student = models.ForeignKey(Student, on_delete=models.CASCADE, related_name='quiz_result')\n score = models.FloatField()\n\n def __str__(self):\n return f\"{self.quiz.name} - {self.student.name}\"", "sub_path": "quiz/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 2412, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"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": "course.models.Subject", "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": "user.models.Teacher", "line_number": 21, "usage_type": "argument"}, {"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.CharField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"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.db.models.IntegerField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.FileField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "random.shuffle", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 43, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 44, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 44, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 44, "usage_type": "attribute"}, {"api_name": "django.db.models.CharField", "line_number": 45, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 45, "usage_type": "name"}, {"api_name": "random.shuffle", "line_number": 52, "usage_type": "call"}, {"api_name": "django.db.models.Model", "line_number": 56, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 56, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 57, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 57, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 58, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 58, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 59, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 59, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 59, "usage_type": "attribute"}, {"api_name": "django.db.models.Model", "line_number": 65, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 65, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 66, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 66, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 66, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 67, "usage_type": "call"}, {"api_name": "user.models.Student", "line_number": 67, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 67, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 67, "usage_type": "attribute"}, {"api_name": "django.db.models.FloatField", "line_number": 68, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 68, "usage_type": "name"}]} +{"seq_id": "569408117", "text": "from rest_framework import serializers\nfrom .models import Project, Team, Submit\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth import authenticate\nfrom rest_framework.serializers import SerializerMethodField\n\nclass LoginSerializer(serializers.ModelSerializer):\n access = serializers.BooleanField(read_only=True)\n type_user = serializers.CharField(read_only=True)\n username = serializers.CharField(write_only=True)\n\n class Meta:\n model = User\n fields = ('id', 'username', 'password', 'access', 'type_user')\n extra_kwargs = {\n 'username': {'write_only': True},\n 'password': {'write_only': True}\n }\n\n def validate_username(self, value):\n return value\n\n def validate(self, data):\n username = data.get(\"username\")\n password = data.get(\"password\")\n user = authenticate(username=username, password=password)\n\n if user is not None:\n data['access'] = True\n data['id'] = user.id\n if user.is_superuser:\n data['type_user'] = 'admin'\n else:\n data['type_user'] = 'supervisor'\n else:\n data['access'] = False\n data['type_user'] = ''\n return data\n\nclass UserSerializer(serializers.HyperlinkedModelSerializer):\n class Meta:\n model = User\n fields = ('id','url','username', 'password','email', 'is_superuser')\n extra_kwargs = {\n \"password\": {\"write_only\": True},\n \"is_staff\": {\"read_only\": True},\n \"is_superuser\": {\"read_only\": True},\n }\n\n def create(self, validated_data):\n user = User(**validated_data)\n user.set_password(validated_data['password'])\n user.is_active = True\n user.save()\n return user\n\nclass SupervisorSerializer(serializers.ModelSerializer):\n class Meta:\n model = User\n fields = ('id','username', 'is_staff')\n extra_kwargs = {\n \"is_staff\": {\"read_only\": True},\n }\n\nclass Team_Project_Serializer(serializers.ModelSerializer):\n\n class Meta:\n model = Team\n fields = ('id','name','note',)\n\nclass ProjectSerializer(serializers.ModelSerializer):\n supervisor = SupervisorSerializer(many=False)\n team = Team_Project_Serializer(many=True)\n\n class Meta:\n model = Project\n fields = ('id','name', 'contruction_name', 'address', 'construction_items','investor','position', 'supervisor', 'team')\n extra_kwargs = {\n \"supervisor\": {\"read_only\": True}\n }\n\nclass Project_Team_Serializer(serializers.HyperlinkedModelSerializer):\n class Meta:\n model = Project\n fields = ('id','name', 'contruction_name', 'address','investor' ,'construction_items','position')\n\nclass CreateProjectSerializer(serializers.ModelSerializer):\n team = serializers.ListField(\n child=serializers.IntegerField(min_value=1, required=False)\n )\n class Meta:\n model = Project\n fields = ('id','name', 'contruction_name', 'address', 'construction_items','position', 'investor', 'supervisor', 'team')\n\n def create(self, validated_data):\n project = Project()\n project.name = validated_data['name']\n project.contruction_name = validated_data['contruction_name']\n project.address = validated_data['address']\n project.construction_items = validated_data['construction_items']\n project.position = validated_data['position']\n project.investor = validated_data['investor']\n project.supervisor = validated_data['supervisor']\n project.save()\n teams = Team.objects.filter(pk__in=validated_data['team'])\n for team in teams:\n team.project = project\n team.save()\n return project\n\n def update(self, instance, validated_data):\n instance.name = validated_data.get('name', instance.name)\n instance.contruction_name = validated_data.get('contruction_name', instance.contruction_name)\n instance.address = validated_data.get('address', instance.address)\n instance.construction_items = validated_data.get('construction_items', instance.construction_items)\n instance.position = validated_data.get('position', instance.position)\n instance.investor = validated_data.get('investor', instance.investor)\n instance.supervisor = validated_data.get('supervisor', instance.supervisor)\n instance.save()\n new_teams = Team.objects.filter(pk__in=validated_data['team'])\n old_teams = Team.objects.filter(project=instance)\n for old_team in old_teams:\n if old_team not in new_teams:\n old_team.project = None\n old_team.save()\n for new_team in new_teams:\n new_team.project = instance\n new_team.save()\n return instance\n\n\nclass CreateTeamSerializer(serializers.ModelSerializer):\n\n class Meta:\n model = Team\n fields = ('id','name','note',)\n\nclass TeamSubmitSerializer(serializers.ModelSerializer):\n class Meta:\n model = Submit\n fields = ('id','projects', 'team', 'date', 'task_name','worker_number','process', 'content', 'proposed_materials', 'job_tomorrow')\n\nclass TeamSerializer(serializers.ModelSerializer):\n project = Project_Team_Serializer(many=False)\n submits = TeamSubmitSerializer(many=True)\n class Meta:\n model = Team\n fields = ('id','name','note','project', 'submits')\n\nclass SubmitTeamSerializer(serializers.ModelSerializer):\n class Meta:\n model = Team\n fields = ('id','name','note')\n\nclass SubmitSerializer(serializers.ModelSerializer):\n team = SubmitTeamSerializer(many=False)\n class Meta:\n model = Submit\n fields = ('id','projects', 'team', 'date', 'task_name','worker_number','process', 'content', 'proposed_materials', 'job_tomorrow')\n\nclass CreateSubmitSerializer(serializers.ModelSerializer):\n class Meta:\n model = Submit\n fields = ('id','projects', 'team', 'date', 'task_name','worker_number','process', 'content', 'proposed_materials', 'job_tomorrow')", "sub_path": "backend/Construction/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 6147, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 7, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 7, "usage_type": "name"}, {"api_name": "rest_framework.serializers.BooleanField", "line_number": 8, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 8, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 9, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 9, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 10, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 10, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User", "line_number": 13, "usage_type": "name"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 26, "usage_type": "call"}, {"api_name": "rest_framework.serializers.HyperlinkedModelSerializer", "line_number": 40, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 40, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User", "line_number": 42, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User", "line_number": 51, "usage_type": "call"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 57, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 57, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User", "line_number": 59, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 65, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 65, "usage_type": "name"}, {"api_name": "models.Team", "line_number": 68, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 71, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 71, "usage_type": "name"}, {"api_name": "models.Project", "line_number": 76, "usage_type": "name"}, {"api_name": "rest_framework.serializers.HyperlinkedModelSerializer", "line_number": 82, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 82, "usage_type": "name"}, {"api_name": "models.Project", "line_number": 84, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 87, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 87, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ListField", "line_number": 88, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 88, "usage_type": "name"}, {"api_name": "rest_framework.serializers.IntegerField", "line_number": 89, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 89, "usage_type": "name"}, {"api_name": "models.Project", "line_number": 92, "usage_type": "name"}, {"api_name": "models.Project", "line_number": 96, "usage_type": "call"}, {"api_name": "models.Team.objects.filter", "line_number": 105, "usage_type": "call"}, {"api_name": "models.Team.objects", "line_number": 105, "usage_type": "attribute"}, {"api_name": "models.Team", "line_number": 105, "usage_type": "name"}, {"api_name": "models.Team.objects.filter", "line_number": 120, "usage_type": "call"}, {"api_name": "models.Team.objects", "line_number": 120, "usage_type": "attribute"}, {"api_name": "models.Team", "line_number": 120, "usage_type": "name"}, {"api_name": "models.Team.objects.filter", "line_number": 121, "usage_type": "call"}, {"api_name": "models.Team.objects", "line_number": 121, "usage_type": "attribute"}, {"api_name": "models.Team", "line_number": 121, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 132, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 132, "usage_type": "name"}, {"api_name": "models.Team", "line_number": 135, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 138, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 138, "usage_type": "name"}, {"api_name": "models.Submit", "line_number": 140, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 143, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 143, "usage_type": "name"}, {"api_name": "models.Team", "line_number": 147, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 150, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 150, "usage_type": "name"}, {"api_name": "models.Team", "line_number": 152, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 155, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 155, "usage_type": "name"}, {"api_name": "models.Submit", "line_number": 158, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 161, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 161, "usage_type": "name"}, {"api_name": "models.Submit", "line_number": 163, "usage_type": "name"}]} +{"seq_id": "594426425", "text": "# uncompyle6 version 3.6.7\n# Python bytecode 3.6 (3379)\n# Decompiled from: Python 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)]\n# Embedded file name: build/bdist.macosx-10.7-x86_64/egg/calibration_client/tests/modules/device_type_test.py\n# Compiled at: 2018-03-12 09:48:15\n# Size of source mod 2**32: 4597 bytes\n__doc__ = 'DeviceTypeTest class'\nimport unittest\nfrom calibration_client.calibration_client import CalibrationClient\nfrom .module_base import ModuleBase\nfrom ..common.config_test import *\nfrom ..common.generators import Generators\nfrom ..common.secrets import *\nfrom ...modules.device_type import DeviceType\nMODULE_NAME = DEVICE_TYPE\n\nclass DeviceTypeTest(ModuleBase, unittest.TestCase):\n\n def setUp(self):\n self.cal_client = CalibrationClient(client_id=(CLIENT_OAUTH2_INFO['CLIENT_ID']),\n client_secret=(CLIENT_OAUTH2_INFO['CLIENT_SECRET']),\n token_url=(CLIENT_OAUTH2_INFO['TOKEN_URL']),\n refresh_url=(CLIENT_OAUTH2_INFO['REFRESH_URL']),\n auth_url=(CLIENT_OAUTH2_INFO['AUTH_URL']),\n scope=(CLIENT_OAUTH2_INFO['SCOPE']),\n user_email=(CLIENT_OAUTH2_INFO['EMAIL']),\n base_api_url=BASE_API_URL)\n _DeviceTypeTest__unique_name1 = Generators.generate_unique_name('DeviceType01')\n self.dev_typ_01 = {'name':_DeviceTypeTest__unique_name1, \n 'flg_available':'true', \n 'description':'desc 01'}\n _DeviceTypeTest__unique_name_upd = Generators.generate_unique_name('DeviceTypeUpd01')\n self.dev_typ_01_upd = {'name':_DeviceTypeTest__unique_name_upd, \n 'flg_available':'false', \n 'description':'desc 01 Updated!'}\n\n def test_create_device_type(self):\n dev_typ_01 = DeviceType(calibration_client=(self.cal_client), name=(self.dev_typ_01['name']),\n flg_available=(self.dev_typ_01['flg_available']),\n description=(self.dev_typ_01['description']))\n result1 = dev_typ_01.create()\n self.assert_create_success(MODULE_NAME, result1, self.dev_typ_01)\n device_type = result1['data']\n device_type_id = result1['data']['id']\n device_type_name = result1['data']['name']\n dev_typ_01_dup = dev_typ_01\n result2 = dev_typ_01_dup.create()\n expect_app_info = {'name': ['has already been taken']}\n self.assert_create_error(MODULE_NAME, result2, expect_app_info)\n result3 = DeviceType.get_by_name(self.cal_client, device_type_name)\n self.assert_find_success(MODULE_NAME, result3, self.dev_typ_01)\n result4 = DeviceType.get_by_id(self.cal_client, device_type_id)\n self.assert_find_success(MODULE_NAME, result4, self.dev_typ_01)\n result5 = DeviceType.get_by_id(self.cal_client, -666)\n self.assert_find_error(MODULE_NAME, result5, RESOURCE_NOT_FOUND)\n dev_typ_01.name = self.dev_typ_01_upd['name']\n dev_typ_01.flg_available = self.dev_typ_01_upd['flg_available']\n dev_typ_01.description = self.dev_typ_01_upd['description']\n result6 = dev_typ_01.update()\n self.assert_update_success(MODULE_NAME, result6, self.dev_typ_01_upd)\n dev_typ_01.name = '__THIS_NAME_IS_1_CHARACTERS_LONGER_THAN_THE_ALLOWED_MAX_NUM__'\n dev_typ_01.flg_available = self.dev_typ_01_upd['flg_available']\n dev_typ_01.description = self.dev_typ_01_upd['description']\n result7 = dev_typ_01.update()\n expect_app_info = {'name': ['is too long (maximum is 60 characters)']}\n self.assert_update_error(MODULE_NAME, result7, expect_app_info)\n result8 = dev_typ_01.delete()\n self.assert_delete_success(MODULE_NAME, result8)\n result9 = dev_typ_01.delete()\n self.assert_delete_error(MODULE_NAME, result9, RESOURCE_NOT_FOUND)\n\n def fields_validation(self, receive, expect):\n self.assert_eq_hfield(receive, expect, 'name', STRING)\n self.assert_eq_hfield(receive, expect, 'flg_available', BOOLEAN)\n self.assert_eq_hfield(receive, expect, 'description', STRING)\n\n\nif __name__ == '__main__':\n unittest.main()", "sub_path": "pycfiles/calibraxis-0.2.0-py2.py3-none-any/device_type_test.cpython-36.py", "file_name": "device_type_test.cpython-36.py", "file_ext": "py", "file_size_in_byte": 4065, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "module_base.ModuleBase", "line_number": 17, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 17, "usage_type": "attribute"}, {"api_name": "calibration_client.calibration_client.CalibrationClient", "line_number": 20, "usage_type": "call"}, {"api_name": "common.generators.Generators.generate_unique_name", "line_number": 28, "usage_type": "call"}, {"api_name": "common.generators.Generators", "line_number": 28, "usage_type": "name"}, {"api_name": "common.generators.Generators.generate_unique_name", "line_number": 32, "usage_type": "call"}, {"api_name": "common.generators.Generators", "line_number": 32, "usage_type": "name"}, {"api_name": "modules.device_type.DeviceType", "line_number": 38, "usage_type": "call"}, {"api_name": "modules.device_type.DeviceType.get_by_name", "line_number": 50, "usage_type": "call"}, {"api_name": "modules.device_type.DeviceType", "line_number": 50, "usage_type": "name"}, {"api_name": "modules.device_type.DeviceType.get_by_id", "line_number": 52, "usage_type": "call"}, {"api_name": "modules.device_type.DeviceType", "line_number": 52, "usage_type": "name"}, {"api_name": "modules.device_type.DeviceType.get_by_id", "line_number": 54, "usage_type": "call"}, {"api_name": "modules.device_type.DeviceType", "line_number": 54, "usage_type": "name"}, {"api_name": "unittest.main", "line_number": 79, "usage_type": "call"}]} +{"seq_id": "634634195", "text": "from django.shortcuts import redirect, render_to_response\r\nfrom django.template import RequestContext\r\nfrom maxsite.admin.forms import TemplateForm\r\nfrom maxsite.models import AppTemplate\r\nfrom datetime import date\r\n\r\ndef index(request):\r\n form = TemplateForm()\r\n templates = AppTemplate.objects.all()\r\n response_dictionary = {\"contents\": {\"pagename\": \"Template\", \"h_templates\":\"active\", \"year\": date.today().year}, 'form': form, 'templates': templates}\r\n return render_to_response('myadmin/template-admin.html', response_dictionary, context_instance=RequestContext(request))\r\n\r\ndef update(request):\r\n if request.method == \"POST\":\r\n form = TemplateForm(request.POST)\r\n if form.is_valid():\r\n cd = form.cleaned_data\r\n id = cd[\"id\"]\r\n if id:\r\n template = AppTemplate.objects.filter(id=long(id))[0]\r\n else:\r\n template = AppTemplate()\r\n \r\n# template.module = cd['module']\r\n template.name = cd['name']\r\n template.content = cd['content']\r\n# template.pagename = cd['pagename']\r\n# template.caption = cd['caption']\r\n\r\n template.save()\r\n\r\n return redirect (\"/myadmin/templates\")\r\n else:\r\n id = request.GET.get('id')\r\n if id:\r\n template = AppTemplate.objects.filter(id=long(id))[0]\r\n data = {'id': id,\r\n# 'module': template.module,\r\n# 'view': template.view,\r\n 'name': template.name,\r\n# 'caption': template.caption,\r\n 'content': template.content}\r\n form = TemplateForm(data)\r\n \r\n response_dictionary = {\"contents\": {\"pagename\": \"Template\", \"h_templates\":\"active\", \"year\": date.today().year}, 'form': form}\r\n return render_to_response('myadmin/template-admin.html', response_dictionary, context_instance=RequestContext(request))\r\n\r\n", "sub_path": "maksudsite/src/maksudsite-hrd/maxsite/admin/templates/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1951, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "maxsite.admin.forms.TemplateForm", "line_number": 8, "usage_type": "call"}, {"api_name": "maxsite.models.AppTemplate.objects.all", "line_number": 9, "usage_type": "call"}, {"api_name": "maxsite.models.AppTemplate.objects", "line_number": 9, "usage_type": "attribute"}, {"api_name": "maxsite.models.AppTemplate", "line_number": 9, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 10, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 10, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 11, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 11, "usage_type": "call"}, {"api_name": "maxsite.admin.forms.TemplateForm", "line_number": 15, "usage_type": "call"}, {"api_name": "maxsite.models.AppTemplate.objects.filter", "line_number": 20, "usage_type": "call"}, {"api_name": "maxsite.models.AppTemplate.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "maxsite.models.AppTemplate", "line_number": 20, "usage_type": "name"}, {"api_name": "maxsite.models.AppTemplate", "line_number": 22, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 32, "usage_type": "call"}, {"api_name": "maxsite.models.AppTemplate.objects.filter", "line_number": 36, "usage_type": "call"}, {"api_name": "maxsite.models.AppTemplate.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "maxsite.models.AppTemplate", "line_number": 36, "usage_type": "name"}, {"api_name": "maxsite.admin.forms.TemplateForm", "line_number": 43, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 45, "usage_type": "name"}, {"api_name": "django.shortcuts.render_to_response", "line_number": 46, "usage_type": "call"}, {"api_name": "django.template.RequestContext", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "578414022", "text": "import json\nfrom unittest import mock\n\nfrom app.main.service.authorization_service import AuthorizationResponse\nfrom test.base.base_testcase import BaseTestCase\n\n\nclass AuthorizationControllerTests(BaseTestCase):\n def test_login_empty(self):\n \"\"\" Test an empty login request \"\"\"\n response = self.client.post('/api/authorization/login', content_type='application/json')\n\n self.assert400(response)\n\n def test_login_partial(self):\n \"\"\" Test a partial login request \"\"\"\n response = self.client.post('/api/authorization/login', content_type='application/json',\n data=json.dumps(dict(email='test@example.com')))\n\n self.assert400(response)\n\n @mock.patch('app.main.service.authorization_service.login')\n def test_login_invalid(self, mock_login):\n \"\"\" Test a login request with an invalid wrong credentials response \"\"\"\n mock_login.return_value = dict(success=False,\n message='Invalid credentials.'), AuthorizationResponse.InvalidCredentials\n\n response = self.client.post('/api/authorization/login', content_type='application/json',\n data=json.dumps(self.get_sample_credentials()))\n\n self.assertEqual(response.status_code, AuthorizationResponse.InvalidCredentials)\n self.assertFalse(response.json['success'])\n\n @mock.patch('app.main.service.authorization_service.login')\n def test_login_valid(self, mock_login):\n \"\"\" Test a login request with a valid response \"\"\"\n token = 'example token'\n mock_login.return_value = dict(success=True,\n token=token), AuthorizationResponse.Success\n\n response = self.client.post('/api/authorization/login', content_type='application/json',\n data=json.dumps(self.get_sample_credentials()))\n\n self.assertEqual(response.status_code, AuthorizationResponse.Success)\n self.assertEqual(response.json['token'], token)\n self.assertTrue(response.json['success'])\n\n @mock.patch('app.main.service.authorization_service.logout')\n def test_logout_with_token(self, mock_logout):\n \"\"\" Test a logout request with a token \"\"\"\n mock_logout.return_value = dict(success=True), AuthorizationResponse.Success\n\n response = self.client.get('/api/authorization/logout', headers={'Authorization': 'Test'})\n\n self.assertEqual(response.status_code, AuthorizationResponse.Success)\n self.assertTrue(response.json['success'])\n\n @mock.patch('app.main.service.authorization_service.logout')\n def test_logout_no_token(self, mock_logout):\n \"\"\" Test a logout request without a token \"\"\"\n mock_logout.return_value = dict(success=True), AuthorizationResponse.Success\n\n response = self.client.get('/api/authorization/logout')\n\n self.assertEqual(response.status_code, AuthorizationResponse.Success)\n self.assertTrue(response.json['success'])\n", "sub_path": "test/controller/authorization_controller_tests.py", "file_name": "authorization_controller_tests.py", "file_ext": "py", "file_size_in_byte": 3030, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "test.base.base_testcase.BaseTestCase", "line_number": 8, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 18, "usage_type": "call"}, {"api_name": "app.main.service.authorization_service.AuthorizationResponse.InvalidCredentials", "line_number": 26, "usage_type": "attribute"}, {"api_name": "app.main.service.authorization_service.AuthorizationResponse", "line_number": 26, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 29, "usage_type": "call"}, {"api_name": "app.main.service.authorization_service.AuthorizationResponse.InvalidCredentials", "line_number": 31, "usage_type": "attribute"}, {"api_name": "app.main.service.authorization_service.AuthorizationResponse", "line_number": 31, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 22, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 22, "usage_type": "name"}, {"api_name": "app.main.service.authorization_service.AuthorizationResponse.Success", "line_number": 39, "usage_type": "attribute"}, {"api_name": "app.main.service.authorization_service.AuthorizationResponse", "line_number": 39, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 42, "usage_type": "call"}, {"api_name": "app.main.service.authorization_service.AuthorizationResponse.Success", "line_number": 44, "usage_type": "attribute"}, {"api_name": "app.main.service.authorization_service.AuthorizationResponse", "line_number": 44, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 34, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 34, "usage_type": "name"}, {"api_name": "app.main.service.authorization_service.AuthorizationResponse.Success", "line_number": 51, "usage_type": "attribute"}, {"api_name": "app.main.service.authorization_service.AuthorizationResponse", "line_number": 51, "usage_type": "name"}, {"api_name": "app.main.service.authorization_service.AuthorizationResponse.Success", "line_number": 55, "usage_type": "attribute"}, {"api_name": "app.main.service.authorization_service.AuthorizationResponse", "line_number": 55, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 48, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 48, "usage_type": "name"}, {"api_name": "app.main.service.authorization_service.AuthorizationResponse.Success", "line_number": 61, "usage_type": "attribute"}, {"api_name": "app.main.service.authorization_service.AuthorizationResponse", "line_number": 61, "usage_type": "name"}, {"api_name": "app.main.service.authorization_service.AuthorizationResponse.Success", "line_number": 65, "usage_type": "attribute"}, {"api_name": "app.main.service.authorization_service.AuthorizationResponse", "line_number": 65, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 58, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 58, "usage_type": "name"}]} +{"seq_id": "588030179", "text": "import sys #to import from command line\nimport matplotlib.pyplot as plt #to plot\nimport math as mt\n\n#Usage message\ntry:\n n = sys.argv[1] #read command line input\nexcept:\n print(\"Usage: give one of the following command line arguments:\")\n print(\"1: f(x) = x\")\n print(\"2: f(x) = x**2\")\n print(\"3: f(x) = x**3\")\n print(\"4: f(x) = sin(x)\")\n print(\"5: f(x) = cos(x)\")\n print(\"6: f(x) = tan(x)\")\n print(\"7: f(x) = exp(x)\")\n print(\"8: f(x) = sqrt(abs(x))\")\n sys.exit()\n \n#initialize list\nxval = [i*0.1 for i in range(-30,31)]\n\n#implement function\nif n == \"1\":\n yval = [x for x in xval]\nelif n == \"2\":\n yval = [x**2 for x in xval]\nelif n == \"3\":\n yval = [x**3 for x in xval]\nelif n == \"4\":\n yval = [mt.sin(x) for x in xval]\nelif n == \"5\":\n yval = [mt.cos(x) for x in xval]\nelif n == \"6\":\n yval = [mt.tan(x) for x in xval]\nelif n == \"7\":\n yval = [mt.exp(x) for x in xval]\nelif n == \"8\":\n yval = [mt.sqrt(mt.fabs(x)) for x in xval]\nelse:\n print(\"Invalid argument\")\n sys.exit()\n \n#plot\nplt.figure()\nplt.plot(xval,yval)\nplt.show()\n\n", "sub_path": "assignment1.py", "file_name": "assignment1.py", "file_ext": "py", "file_size_in_byte": 1089, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.argv", "line_number": 7, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 18, "usage_type": "call"}, {"api_name": "math.sin", "line_number": 31, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 33, "usage_type": "call"}, {"api_name": "math.tan", "line_number": 35, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 37, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 39, "usage_type": "call"}, {"api_name": "math.fabs", "line_number": 39, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 47, "usage_type": "name"}]} +{"seq_id": "283443243", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nColour - Analysis\n=================\n\nDefines various objects that typically output the geometry as JSON to be\nloaded by \"Three.js\".\n\"\"\"\n\nfrom __future__ import division\n\nimport json\nimport numpy as np\nimport os\nimport re\nfrom collections import OrderedDict\nfrom werkzeug.contrib.cache import SimpleCache\n\nfrom colour import (ILLUMINANTS, Lab_to_XYZ, LCHab_to_Lab, LOG_DECODING_CURVES,\n POINTER_GAMUT_DATA, POINTER_GAMUT_ILLUMINANT,\n OETFS_REVERSE, RGB_COLOURSPACES, RGB_to_RGB, RGB_to_XYZ,\n XYZ_to_RGB, XYZ_to_JzAzBz, XYZ_to_OSA_UCS,\n is_within_pointer_gamut, read_image)\nfrom colour.models import (XYZ_to_colourspace_model, function_gamma,\n function_linear)\nfrom colour.plotting import filter_cmfs, filter_RGB_colourspaces\nfrom colour.utilities import (CaseInsensitiveMapping, first_item,\n normalise_maximum, tsplit, tstack)\nfrom colour.volume import XYZ_outer_surface\n\n__author__ = 'Colour Developers'\n__copyright__ = 'Copyright (C) 2018 - Colour Developers'\n__license__ = 'New BSD License - http://opensource.org/licenses/BSD-3-Clause'\n__maintainer__ = 'Colour Developers'\n__email__ = 'colour-science@googlegroups.com'\n__status__ = 'Production'\n\n__all__ = [\n 'LINEAR_FILE_FORMATS', 'DTYPE_MAPPING', 'POSITION_DTYPE', 'COLOUR_DTYPE',\n 'COLOURSPACE_MODELS', 'COLOURSPACE_MODELS_LABELS', 'DECODING_CCTFS',\n 'PRIMARY_COLOURSPACE', 'SECONDARY_COLOURSPACE', 'IMAGE_COLOURSPACE',\n 'IMAGE_DECODING_CCTF', 'COLOURSPACE_MODEL', 'IMAGE_CACHE', 'load_image',\n 'XYZ_to_colourspace_model_normalised', 'colourspace_model_axis_reorder',\n 'colourspace_model_faces_reorder', 'decoding_cctfs', 'colourspace_models',\n 'RGB_colourspaces', 'buffer_geometry', 'create_plane', 'create_box',\n 'image_data', 'RGB_colourspace_volume_visual', 'spectral_locus_visual',\n 'RGB_image_scatter_visual', 'pointer_gamut_visual',\n 'visible_spectrum_visual'\n]\n\nLINEAR_FILE_FORMATS = ('.exr', '.hdr')\n\"\"\"\nAssumed linear image formats.\n\nLINEAR_IMAGE_FORMATS : tuple\n\"\"\"\n\nDTYPE_MAPPING = CaseInsensitiveMapping({\n 'Float16': np.float16,\n 'Float32': np.float32,\n 'Float64': np.float64,\n})\n\"\"\"\nDtype mapping.\n\nDTYPE_MAPPING : CaseInsensitiveMapping\n **{'Float16', 'Float32', 'Float64'}**\n\"\"\"\n\nPOSITION_DTYPE = DTYPE_MAPPING.get(\n os.environ.get('COLOUR_ANALYSIS_POSITION_DTYPE', 'Float32'))\n\"\"\"\nDefault floating point number dtype for visual data except colour. Float32 is\nusually chosen over Float16 or Float64 as a good compromise between precision\nand data size.\n\nPOSITION_DTYPE : type\n\"\"\"\n\nCOLOUR_DTYPE = DTYPE_MAPPING.get(\n os.environ.get('COLOUR_ANALYSIS_COLOUR_DTYPE', 'Float16'))\n\"\"\"\nDefault floating point number dtype for visual colour and image data. Float16\nis usually chosen over Float32 and Float64 because it is lighter and thus more\nadapted to send data from the server to client.\n\nCOLOUR_DTYPE : type\n\"\"\"\n\nCOLOURSPACE_MODELS = ('CIE XYZ', 'CIE xyY', 'CIE Lab', 'CIE Luv', 'CIE UCS',\n 'CIE UVW', 'DIN 99', 'Hunter Lab', 'Hunter Rdab', 'IPT',\n 'JzAzBz', 'OSA UCS', 'hdr-CIELAB', 'hdr-IPT')\n\"\"\"\nReference colourspace models defining available colour transformations from\nCIE XYZ tristimulus values.\n\nCOLOURSPACE_MODELS : tuple\n **{'CIE XYZ', 'CIE xyY', 'CIE Lab', 'CIE Luv', 'CIE UCS', 'CIE UVW',\n 'DIN 99', 'Hunter Lab', 'Hunter Rdab', 'IPT', 'JzAzBz', 'OSA UCS',\n 'hdr-CIELAB', 'hdr-IPT'}**\n\"\"\"\n\nCOLOURSPACE_MODELS_LABELS = {\n 'CIE XYZ': ('X', 'Y', 'Z'),\n 'CIE xyY': ('x', 'Y', 'y'),\n 'CIE Lab': ('a*', 'L*', 'b*'),\n 'CIE Luv': ('u*', 'L*', 'v*'),\n 'CIE UCS': ('U', 'W', 'V'),\n 'CIE UVW': ('U*', 'W*', 'V*'),\n 'DIN 99': ('a99', 'L99', 'b99'),\n 'Hunter Lab': ('a', 'L', 'b'),\n 'Hunter Rdab': ('a', 'Rd', 'b'),\n 'IPT': ('P', 'I', 'T'),\n 'JzAzBz': ('Az', 'Jz', 'Bz'),\n 'OSA UCS': ('j', 'J', 'g'),\n 'hdr-CIELAB': ('a hdr', 'L hdr', 'b hdr'),\n 'hdr-IPT': ('P hdr', 'I hdr', 'T hdr')\n}\n\"\"\"\nReference colourspace models axes labels, ordered so that luminance is on *Y*\naxis.\n\nCOLOURSPACE_MODELS : dict\n **{'CIE XYZ', 'CIE xyY', 'CIE Lab', 'CIE Luv', 'CIE UCS', 'CIE UVW',\n 'DIN 99', 'Hunter Lab', 'Hunter Rdab', 'IPT', 'JzAzBz', 'OSA UCS',\n 'hdr-CIELAB', 'hdr-IPT'}**\n\"\"\"\n\nDECODING_CCTFS = OrderedDict()\nDECODING_CCTFS.update(\n sorted({\n 'Gamma 2.2': lambda x: function_gamma(x, 2.2),\n 'Gamma 2.4': lambda x: function_gamma(x, 2.4),\n 'Gamma 2.6': lambda x: function_gamma(x, 2.6),\n 'Linear': function_linear,\n }.items()))\nDECODING_CCTFS.update(sorted(OETFS_REVERSE.items()))\nDECODING_CCTFS.update(sorted(LOG_DECODING_CURVES.items()))\n\"\"\"\nDecoding colour component transfer functions.\n\nDECODING_CCTFS : OrderedDict\n\"\"\"\n\nPRIMARY_COLOURSPACE = 'sRGB'\n\"\"\"\nPrimary analysis RGB colourspace.\n\nPRIMARY_COLOURSPACE : unicode\n\"\"\"\n\nSECONDARY_COLOURSPACE = 'DCI-P3'\n\"\"\"\nSecondary analysis RGB colourspace.\n\nSECONDARY_COLOURSPACE : unicode\n\"\"\"\n\nIMAGE_COLOURSPACE = 'Primary'\n\"\"\"\nAnalysed image RGB colourspace either *Primary* or *Secondary*.\n\nIMAGE_COLOURSPACE : unicode\n\"\"\"\n\nIMAGE_DECODING_CCTF = 'sRGB'\n\"\"\"\nAnalysed image RGB colourspace decoding colour component transfer function.\n\nIMAGE_DECODING_CCTF : unicode\n\"\"\"\n\nCOLOURSPACE_MODEL = 'CIE xyY'\n\"\"\"\nAnalysis colour model.\n\nCOLOURSPACE_MODEL : unicode\n **{'CIE XYZ', 'CIE xyY', 'CIE Lab', 'CIE Luv', 'CIE UCS', 'CIE UVW',\n 'DIN 99', 'Hunter Lab', 'Hunter Rdab', 'IPT', 'JzAzBz', 'OSA UCS',\n 'hdr-CIELAB', 'hdr-IPT'}**\n\"\"\"\n\nPOINTER_GAMUT_DATA = Lab_to_XYZ(\n LCHab_to_Lab(POINTER_GAMUT_DATA), POINTER_GAMUT_ILLUMINANT)\n\"\"\"\nPointer's Gamut data converted to *CIE XYZ* tristimulus values.\n\nPOINTER_GAMUT_DATA : ndarray\n\"\"\"\n\nIMAGE_CACHE = SimpleCache(default_timeout=60 * 24 * 7)\n\"\"\"\nServer side cache for images.\n\nIMAGE_CACHE : SimpleCache\n\"\"\"\n\n\ndef load_image(path, decoding_cctf='sRGB'):\n \"\"\"\n Loads the image at given path and caches it in `IMAGE_CACHE` cache. If the\n image is already cached, it is returned directly.\n\n Parameters\n ----------\n path : unicode\n Image path.\n decoding_cctf : unicode, optional\n Decoding colour component transfer function (Decoding CCTF) /\n electro-optical transfer function (EOTF / EOCF) that maps an\n :math:`R'G'B'` video component signal value to tristimulus values at\n the display.\n\n Returns\n -------\n ndarray\n Image as a ndarray.\n \"\"\"\n\n is_linear_image = os.path.splitext(path)[-1].lower() in LINEAR_FILE_FORMATS\n\n key = path if is_linear_image else '{0}-{1}'.format(path, decoding_cctf)\n\n RGB = IMAGE_CACHE.get(key)\n if RGB is None:\n RGB = read_image(path)\n\n if not is_linear_image:\n RGB = DECODING_CCTFS[decoding_cctf](RGB)\n\n IMAGE_CACHE.set(key, RGB)\n\n return RGB\n\n\ndef XYZ_to_colourspace_model_normalised(XYZ, illuminant, model, **kwargs):\n \"\"\"\n Converts from *CIE XYZ* tristimulus values to given colourspace model while\n normalising for visual convenience some of the models.\n\n Parameters\n ----------\n XYZ : array_like\n *CIE XYZ* tristimulus values.\n illuminant : array_like\n *CIE XYZ* tristimulus values *illuminant* *xy* chromaticity\n coordinates.\n model : unicode\n **{'CIE XYZ', 'CIE xyY', 'CIE xy', 'CIE Lab', 'CIE LCHab', 'CIE Luv',\n 'CIE Luv uv', 'CIE LCHuv', 'CIE UCS', 'CIE UCS uv', 'CIE UVW',\n 'DIN 99', 'Hunter Lab', 'Hunter Rdab', 'IPT', 'JzAzBz, 'OSA UCS',\n 'hdr-CIELAB', 'hdr-IPT'}**,\n Colourspace model to convert the *CIE XYZ* tristimulus values to.\n\n Other Parameters\n ----------------\n \\**kwargs : dict, optional\n Keywords arguments.\n\n Returns\n -------\n ndarray\n Colourspace model values.\n \"\"\"\n\n ijk = XYZ_to_colourspace_model(XYZ, illuminant, model, **kwargs)\n\n if model == 'JzAzBz':\n ijk /= XYZ_to_JzAzBz([1, 1, 1])[0]\n if model == 'OSA UCS':\n ijk /= XYZ_to_OSA_UCS([1, 1, 1])[0]\n\n return ijk\n\n\ndef colourspace_model_axis_reorder(a, model=None):\n \"\"\"\n Reorder the axes of given colourspace model :math:`a` array so that\n luminance is on *Y* axis.\n\n Parameters\n ----------\n a : array_like\n Colourspace model :math:`a` array.\n model : unicode, optional\n **{'CIE XYZ', 'CIE xyY', 'CIE Lab', 'CIE Luv', 'CIE UCS', 'CIE UVW',\n 'DIN 99', 'Hunter Lab', 'Hunter Rdab', 'IPT', 'JzAzBz', 'OSA UCS',\n 'hdr-CIELAB', 'hdr-IPT'}**\n Colourspace model.\n\n Returns\n -------\n ndarray\n Reordered colourspace model :math:`a` array.\n \"\"\"\n\n i, j, k = tsplit(a)\n if model in ('CIE XYZ', ):\n a = tstack((k, j, i))\n elif model in ('CIE UCS', 'CIE UVW', 'CIE xyY'):\n a = tstack((j, k, i))\n elif model in ('CIE Lab', 'CIE LCHab', 'CIE Luv', 'CIE LCHuv', 'DIN 99',\n 'Hunter Lab', 'Hunter Rdab', 'IPT', 'JzAzBz', 'OSA UCS',\n 'hdr-CIELAB', 'hdr-IPT'):\n a = tstack((k, i, j))\n\n return a\n\n\ndef colourspace_model_faces_reorder(a, model=None):\n \"\"\"\n Reorder the faces of given colourspace model :math:`a` array.\n\n Parameters\n ----------\n a : array_like\n Colourspace model :math:`a` array.\n model : unicode, optional\n **{'CIE XYZ', 'CIE xyY', 'CIE Lab', 'CIE Luv', 'CIE UCS', 'CIE UVW',\n 'DIN 99', 'Hunter Lab', 'Hunter Rdab', 'IPT', 'JzAzBz', 'OSA UCS',\n 'hdr-CIELAB', 'hdr-IPT'}**\n Colourspace model.\n\n Returns\n -------\n Figure\n Reordered colourspace model :math:`a` array.\n \"\"\"\n\n if model in ('CIE XYZ', ):\n a = a[::-1]\n\n return a\n\n\ndef decoding_cctfs():\n \"\"\"\n Returns the decoding colour component transfer functions formatted as\n *JSON*.\n\n Returns\n -------\n unicode\n Decoding colour component transfer functions formatted as *JSON*.\n \"\"\"\n\n return json.dumps(DECODING_CCTFS.keys())\n\n\ndef colourspace_models():\n \"\"\"\n Returns the colourspace models formatted as *JSON*.\n\n Returns\n -------\n unicode\n Colourspace models formatted as *JSON*.\n \"\"\"\n\n return json.dumps(COLOURSPACE_MODELS_LABELS)\n\n\ndef RGB_colourspaces():\n \"\"\"\n Returns the RGB colourspaces formatted as *JSON*.\n\n Returns\n -------\n unicode\n RGB colourspaces formatted as *JSON*.\n \"\"\"\n\n return json.dumps(RGB_COLOURSPACES.keys())\n\n\ndef buffer_geometry(**kwargs):\n \"\"\"\n Returns given geometry formatted as *JSON* compatible with *Three.js*\n `BufferGeometryLoader `_.\n\n Other Parameters\n ----------------\n \\**kwargs : dict, optional\n Valid attributes from `BufferGeometryLoader `_.\n\n Returns\n -------\n unicode\n Geometry formatted as *JSON*.\n \"\"\"\n\n data = {\n 'metadata': {\n 'version': 4,\n 'type': 'BufferGeometry',\n 'generator': 'colour-three'\n },\n 'data': {\n 'attributes': {}\n }\n }\n\n data_types_conversion = {\n 'float16': 'Float32Array', # Unsupported, casted up.\n 'float32': 'Float32Array',\n 'float64': 'Float32Array', # Unsupported, casted down.\n 'uint16': 'Uint16Array',\n 'uint32': 'Uint32Array',\n 'uint64': 'Uint32Array', # Unsupported, casted down.\n }\n\n for attribute, values in kwargs.items():\n values = np.asarray(values)\n shape = values.shape\n dtype = values.dtype.name\n\n values = np.ravel(values)\n\n if 'float' in dtype:\n dtype = (COLOUR_DTYPE if attribute == 'color' else POSITION_DTYPE)\n values = np.around(values, np.finfo(dtype).precision)\n values = np.nan_to_num(values)\n dtype = np.dtype(dtype).name\n\n data['data']['attributes'][attribute] = {\n 'itemSize': shape[-1],\n 'type': data_types_conversion[dtype],\n 'array': values.tolist()\n }\n\n return json.dumps(data)\n\n\ndef create_plane(width=1,\n height=1,\n width_segments=1,\n height_segments=1,\n direction='+z'):\n \"\"\"\n Generates vertices and indices for a filled and outlined plane.\n\n Parameters\n ----------\n width : float, optional\n Plane width.\n height : float, optional\n Plane height.\n width_segments : int, optional\n Plane segments count along the width.\n height_segments : float, optional\n Plane segments count along the height.\n direction: unicode, optional\n ``{'-x', '+x', '-y', '+y', '-z', '+z'}``\n Direction the plane will be facing.\n\n Returns\n -------\n vertices : array\n Array of vertices suitable for use as a VertexBuffer.\n faces : array\n Indices to use to produce a filled plane.\n outline : array\n Indices to use to produce an outline of the plane.\n\n References\n ----------\n .. [1] Cabello, R. (n.d.). PlaneBufferGeometry.js. Retrieved May 12, 2015,\n from http://git.io/vU1Fh\n \"\"\"\n\n x_grid = width_segments\n y_grid = height_segments\n\n x_grid1 = x_grid + 1\n y_grid1 = y_grid + 1\n\n # Positions, normals and uvs.\n positions = np.zeros(x_grid1 * y_grid1 * 3)\n normals = np.zeros(x_grid1 * y_grid1 * 3)\n uvs = np.zeros(x_grid1 * y_grid1 * 2)\n\n y = np.arange(y_grid1) * height / y_grid - height / 2\n x = np.arange(x_grid1) * width / x_grid - width / 2\n\n positions[::3] = np.tile(x, y_grid1)\n positions[1::3] = -np.repeat(y, x_grid1)\n\n normals[2::3] = 1\n\n uvs[::2] = np.tile(np.arange(x_grid1) / x_grid, y_grid1)\n uvs[1::2] = np.repeat(1 - np.arange(y_grid1) / y_grid, x_grid1)\n\n # Faces and outline.\n faces, outline = [], []\n for i_y in range(y_grid):\n for i_x in range(x_grid):\n a = i_x + x_grid1 * i_y\n b = i_x + x_grid1 * (i_y + 1)\n c = (i_x + 1) + x_grid1 * (i_y + 1)\n d = (i_x + 1) + x_grid1 * i_y\n\n faces.extend(((a, b, d), (b, c, d)))\n outline.extend(((a, b), (b, c), (c, d), (d, a)))\n\n positions = np.reshape(positions, (-1, 3))\n uvs = np.reshape(uvs, (-1, 2))\n normals = np.reshape(normals, (-1, 3))\n\n faces = np.reshape(faces, (-1, 3)).astype(np.uint32)\n outline = np.reshape(outline, (-1, 2)).astype(np.uint32)\n\n direction = direction.lower()\n if direction in ('-x', '+x'):\n shift, neutral_axis = 1, 0\n elif direction in ('-y', '+y'):\n shift, neutral_axis = -1, 1\n elif direction in ('-z', '+z'):\n shift, neutral_axis = 0, 2\n\n sign = -1 if '-' in direction else 1\n\n positions = np.roll(positions, shift, -1)\n normals = np.roll(normals, shift, -1) * sign\n colors = np.ravel(positions)\n colors = np.hstack((np.reshape(\n np.interp(colors, (np.min(colors), np.max(colors)), (0, 1)),\n positions.shape), np.ones((positions.shape[0], 1))))\n colors[..., neutral_axis] = 0\n\n vertices = np.zeros(positions.shape[0],\n [('position', np.float32, 3), ('uv', np.float32, 2),\n ('normal', np.float32, 3), ('colour', np.float32, 4)])\n\n vertices['position'] = positions\n vertices['uv'] = uvs\n vertices['normal'] = normals\n vertices['colour'] = colors\n\n return vertices, faces, outline\n\n\ndef create_box(width=1,\n height=1,\n depth=1,\n width_segments=1,\n height_segments=1,\n depth_segments=1,\n planes=None):\n \"\"\"\n Generates vertices and indices for a filled and outlined box.\n\n Parameters\n ----------\n width : float, optional\n Box width.\n height : float, optional\n Box height.\n depth : float, optional\n Box depth.\n width_segments : int, optional\n Box segments count along the width.\n height_segments : float, optional\n Box segments count along the height.\n depth_segments : float, optional\n Box segments count along the depth.\n planes: array_like, optional\n Any combination of ``{'-x', '+x', '-y', '+y', '-z', '+z'}``\n Included planes in the box construction.\n\n Returns\n -------\n vertices : array\n Array of vertices suitable for use as a VertexBuffer.\n faces : array\n Indices to use to produce a filled box.\n outline : array\n Indices to use to produce an outline of the box.\n \"\"\"\n\n planes = (('+x', '-x', '+y', '-y', '+z', '-z')\n if planes is None else [d.lower() for d in planes])\n\n w_s, h_s, d_s = width_segments, height_segments, depth_segments\n\n planes_m = []\n if '-z' in planes:\n planes_m.append(list(create_plane(width, depth, w_s, d_s, '-z')))\n planes_m[-1][0]['position'][..., 2] -= height / 2\n planes_m[-1][1] = np.fliplr(planes_m[-1][1])\n if '+z' in planes:\n planes_m.append(list(create_plane(width, depth, w_s, d_s, '+z')))\n planes_m[-1][0]['position'][..., 2] += height / 2\n\n if '-y' in planes:\n planes_m.append(list(create_plane(height, width, h_s, w_s, '-y')))\n planes_m[-1][0]['position'][..., 1] -= depth / 2\n planes_m[-1][1] = np.fliplr(planes_m[-1][1])\n if '+y' in planes:\n planes_m.append(list(create_plane(height, width, h_s, w_s, '+y')))\n planes_m[-1][0]['position'][..., 1] += depth / 2\n\n if '-x' in planes:\n planes_m.append(list(create_plane(depth, height, d_s, h_s, '-x')))\n planes_m[-1][0]['position'][..., 0] -= width / 2\n planes_m[-1][1] = np.fliplr(planes_m[-1][1])\n if '+x' in planes:\n planes_m.append(list(create_plane(depth, height, d_s, h_s, '+x')))\n planes_m[-1][0]['position'][..., 0] += width / 2\n\n positions = np.zeros((0, 3), dtype=np.float32)\n uvs = np.zeros((0, 2), dtype=np.float32)\n normals = np.zeros((0, 3), dtype=np.float32)\n\n faces = np.zeros((0, 3), dtype=np.uint32)\n outline = np.zeros((0, 2), dtype=np.uint32)\n\n offset = 0\n for vertices_p, faces_p, outline_p in planes_m:\n positions = np.vstack((positions, vertices_p['position']))\n uvs = np.vstack((uvs, vertices_p['uv']))\n normals = np.vstack((normals, vertices_p['normal']))\n\n faces = np.vstack((faces, faces_p + offset))\n outline = np.vstack((outline, outline_p + offset))\n offset += vertices_p['position'].shape[0]\n\n vertices = np.zeros(positions.shape[0],\n [('position', np.float32, 3), ('uv', np.float32, 2),\n ('normal', np.float32, 3), ('colour', np.float32, 4)])\n\n colors = np.ravel(positions)\n colors = np.hstack((np.reshape(\n np.interp(colors, (np.min(colors), np.max(colors)), (0, 1)),\n positions.shape), np.ones((positions.shape[0], 1))))\n\n vertices['position'] = positions\n vertices['uv'] = uvs\n vertices['normal'] = normals\n vertices['colour'] = colors\n\n return vertices, faces, outline\n\n\ndef image_data(path,\n primary_colourspace=PRIMARY_COLOURSPACE,\n secondary_colourspace=SECONDARY_COLOURSPACE,\n image_colourspace=IMAGE_COLOURSPACE,\n image_decoding_cctf=IMAGE_DECODING_CCTF,\n out_of_primary_colourspace_gamut=False,\n out_of_secondary_colourspace_gamut=False,\n out_of_pointer_gamut=False,\n saturate=False):\n \"\"\"\n Returns given image RGB data or its out of gamut values formatted as\n *JSON*.\n\n Parameters\n ----------\n path : unicode\n Server side path of the image to read.\n primary_colourspace : unicode, optional\n Primary RGB colourspace used to generate out of gamut values.\n secondary_colourspace: unicode, optional\n Secondary RGB colourspace used to generate out of gamut values.\n image_colourspace: unicode, optional\n **{'Primary', 'Secondary'}**,\n Analysed image RGB colourspace.\n image_decoding_cctf : unicode, optional\n Analysed image decoding colour component transfer function\n (Decoding CCTF) / electro-optical transfer function (EOTF / EOCF) that\n maps an :math:`R'G'B'` video component signal value to tristimulus\n values at the display.\n out_of_primary_colourspace_gamut : bool, optional\n Whether to only generate the out of primary RGB colourspace gamut\n values.\n out_of_secondary_colourspace_gamut : bool, optional\n Whether to only generate the out of secondary RGB colourspace gamut\n values.\n out_of_pointer_gamut : bool, optional\n Whether to only generate the out of *Pointer's Gamut* values.\n saturate : bool, optional\n Whether to clip the image in domain [0, 1].\n\n Returns\n -------\n unicode\n RGB image data or its out of gamut values formatted as *JSON*.\n \"\"\"\n\n primary_colourspace = first_item(\n filter_RGB_colourspaces('^{0}$'.format(\n re.escape(primary_colourspace))))\n secondary_colourspace = first_item(\n filter_RGB_colourspaces('^{0}$'.format(\n re.escape(secondary_colourspace))))\n\n colourspace = (primary_colourspace if image_colourspace == 'Primary' else\n secondary_colourspace)\n\n RGB = load_image(path, image_decoding_cctf)\n\n if saturate:\n RGB = np.clip(RGB, 0, 1)\n\n if out_of_primary_colourspace_gamut:\n if image_colourspace == 'Secondary':\n RGB = RGB_to_RGB(RGB, secondary_colourspace, primary_colourspace)\n\n RGB[np.logical_and(RGB >= 0, RGB <= 1)] = 0\n RGB[RGB != 0] = 1\n RGB[np.any(RGB == 1, axis=-1)] = 1\n\n if out_of_secondary_colourspace_gamut:\n if image_colourspace == 'Primary':\n RGB = RGB_to_RGB(RGB, primary_colourspace, secondary_colourspace)\n\n RGB[np.logical_and(RGB >= 0, RGB <= 1)] = 0\n RGB[RGB != 0] = 1\n RGB[np.any(RGB == 1, axis=-1)] = 1\n\n if out_of_pointer_gamut:\n O_PG = is_within_pointer_gamut(\n RGB_to_XYZ(RGB, colourspace.whitepoint, colourspace.whitepoint,\n colourspace.RGB_to_XYZ_matrix)).astype(np.int_)\n O_PG = 1 - O_PG\n RGB[O_PG != 1] = 0\n RGB[O_PG == 1] = 1\n\n shape = RGB.shape\n RGB = np.ravel(RGB[..., 0:3].reshape(-1, 3))\n RGB = np.around(RGB, np.finfo(COLOUR_DTYPE).precision)\n\n return json.dumps({\n 'width': shape[1],\n 'height': shape[0],\n 'data': RGB.tolist()\n })\n\n\ndef RGB_colourspace_volume_visual(colourspace=PRIMARY_COLOURSPACE,\n colourspace_model=COLOURSPACE_MODEL,\n segments=16,\n wireframe=False):\n \"\"\"\n Returns a RGB colourspace volume visual geometry formatted as *JSON*.\n\n Parameters\n ----------\n colourspace : unicode, optional\n RGB colourspace used to generate the visual geometry.\n colourspace_model : unicode, optional\n Colourspace model used to generate the visual geometry.\n segments : int, optional\n Segments count per side of the *box* used to generate the visual\n geometry.\n wireframe : bool, optional\n Whether the visual geometry must represent a wireframe visual.\n\n Returns\n -------\n unicode\n RGB colourspace volume visual geometry formatted as *JSON*.\n \"\"\"\n\n colourspace = first_item(\n filter_RGB_colourspaces('^{0}$'.format(re.escape(colourspace))))\n\n cube = create_box(\n width_segments=segments,\n height_segments=segments,\n depth_segments=segments)\n\n vertices = cube[0]['position'] + 0.5\n faces = colourspace_model_faces_reorder(\n np.reshape(cube[1], (-1, 1)), colourspace_model)\n RGB = cube[0]['colour']\n\n XYZ = RGB_to_XYZ(vertices, colourspace.whitepoint, colourspace.whitepoint,\n colourspace.RGB_to_XYZ_matrix)\n vertices = colourspace_model_axis_reorder(\n XYZ_to_colourspace_model_normalised(\n XYZ, colourspace.whitepoint, colourspace_model), colourspace_model)\n\n return buffer_geometry(position=vertices, color=RGB, index=faces)\n\n\ndef RGB_image_scatter_visual(path,\n primary_colourspace=PRIMARY_COLOURSPACE,\n secondary_colourspace=SECONDARY_COLOURSPACE,\n image_colourspace=IMAGE_COLOURSPACE,\n image_decoding_cctf=IMAGE_DECODING_CCTF,\n colourspace_model=COLOURSPACE_MODEL,\n out_of_primary_colourspace_gamut=False,\n out_of_secondary_colourspace_gamut=False,\n out_of_pointer_gamut=False,\n sub_sampling=25,\n saturate=False):\n \"\"\"\n Returns a RGB image scatter visual geometry formatted as *JSON* for\n given image.\n\n Parameters\n ----------\n path : unicode\n Server side path of the image to read to generate the scatter points.\n primary_colourspace : unicode, optional\n Primary RGB colourspace used to generate the visual geometry.\n secondary_colourspace: unicode, optional\n Secondary RGB colourspace used to generate the visual geometry.\n image_colourspace: unicode, optional\n **{'Primary', 'Secondary'}**,\n Analysed image RGB colourspace.\n image_decoding_cctf : unicode, optional\n Analysed image decoding colour component transfer function\n (Decoding CCTF) / electro-optical transfer function (EOTF / EOCF) that\n maps an :math:`R'G'B'` video component signal value to tristimulus\n values at the display.\n colourspace_model : unicode, optional\n Colourspace model used to generate the visual geometry.\n out_of_primary_colourspace_gamut : bool, optional\n Whether to only generate the out of primary RGB colourspace gamut\n visual geometry.\n out_of_secondary_colourspace_gamut : bool, optional\n Whether to only generate the out of secondary RGB colourspace gamut\n visual geometry.\n out_of_pointer_gamut : bool, optional\n Whether to only generate the out of *Pointer's Gamut* visual geometry.\n sub_sampling : int, optional\n Consider every ``sub_sampling`` pixels of the image to generate the\n visual geometry. Using a low number will yield a large quantity of\n points, e.g. *1* yields *2073600* points for a *1080p* image.\n saturate : bool, optional\n Whether to clip the image in domain [0, 1].\n\n Returns\n -------\n unicode\n RGB image scatter visual geometry formatted as *JSON*.\n \"\"\"\n\n primary_colourspace = first_item(\n filter_RGB_colourspaces('^{0}$'.format(\n re.escape(primary_colourspace))))\n secondary_colourspace = first_item(\n filter_RGB_colourspaces('^{0}$'.format(\n re.escape(secondary_colourspace))))\n\n colourspace = (primary_colourspace if image_colourspace == 'Primary' else\n secondary_colourspace)\n\n RGB = load_image(path, image_decoding_cctf)\n\n if saturate:\n RGB = np.clip(RGB, 0, 1)\n\n RGB = RGB[..., 0:3].reshape(-1, 3)[::sub_sampling]\n\n if out_of_primary_colourspace_gamut:\n RGB_c = np.copy(RGB)\n\n if image_colourspace == 'Secondary':\n RGB_c = RGB_to_RGB(RGB, secondary_colourspace, primary_colourspace)\n\n RGB = RGB[np.any(np.logical_or(RGB_c < 0, RGB_c > 1), axis=-1)]\n\n if out_of_secondary_colourspace_gamut:\n RGB_c = np.copy(RGB)\n\n if image_colourspace == 'Primary':\n RGB_c = RGB_to_RGB(RGB, primary_colourspace, secondary_colourspace)\n\n RGB = RGB[np.any(np.logical_or(RGB_c < 0, RGB_c > 1), axis=-1)]\n\n if out_of_pointer_gamut:\n O_PG = is_within_pointer_gamut(\n RGB_to_XYZ(RGB, colourspace.whitepoint, colourspace.whitepoint,\n colourspace.RGB_to_XYZ_matrix)).astype(np.int_)\n O_PG = 1 - O_PG\n RGB = RGB[O_PG == 1]\n\n XYZ = RGB_to_XYZ(RGB, colourspace.whitepoint, colourspace.whitepoint,\n colourspace.RGB_to_XYZ_matrix)\n\n vertices = colourspace_model_axis_reorder(\n XYZ_to_colourspace_model_normalised(\n XYZ, colourspace.whitepoint, colourspace_model), colourspace_model)\n\n if (out_of_primary_colourspace_gamut or\n out_of_secondary_colourspace_gamut or out_of_pointer_gamut):\n RGB = np.ones(RGB.shape)\n\n return buffer_geometry(position=vertices, color=RGB)\n\n\ndef spectral_locus_visual(colourspace=PRIMARY_COLOURSPACE,\n colourspace_model=COLOURSPACE_MODEL,\n cmfs='CIE 1931 2 Degree Standard Observer'):\n \"\"\"\n Returns the spectral locus visual geometry formatted as *JSON*.\n\n Parameters\n ----------\n colourspace : unicode, optional\n RGB colourspace used to generate the visual geometry.\n colourspace_model : unicode, optional\n Colourspace model used to generate the visual geometry.\n cmfs : unicode, optional\n Standard observer colour matching functions used to draw the spectral\n locus.\n\n Returns\n -------\n unicode\n Spectral locus visual geometry formatted as *JSON*.\n \"\"\"\n\n colourspace = first_item(\n filter_RGB_colourspaces('^{0}$'.format(re.escape(colourspace))))\n\n cmfs = first_item(filter_cmfs(cmfs))\n XYZ = cmfs.values\n\n XYZ = np.vstack((XYZ, XYZ[0, ...]))\n\n vertices = colourspace_model_axis_reorder(\n XYZ_to_colourspace_model_normalised(\n XYZ, colourspace.whitepoint, colourspace_model), colourspace_model)\n\n RGB = normalise_maximum(\n XYZ_to_RGB(XYZ, colourspace.whitepoint, colourspace.whitepoint,\n colourspace.XYZ_to_RGB_matrix),\n axis=-1)\n\n return buffer_geometry(position=vertices, color=RGB)\n\n\ndef pointer_gamut_visual(colourspace_model='CIE xyY'):\n \"\"\"\n Returns the *Pointer's Gamut* visual geometry formatted as *JSON*.\n\n Parameters\n ----------\n colourspace_model : unicode, optional\n Colourspace model used to generate the visual geometry.\n\n Returns\n -------\n unicode\n *Pointer's Gamut* visual geometry formatted as *JSON*.\n \"\"\"\n\n pointer_gamut_data = np.reshape(POINTER_GAMUT_DATA, (16, -1, 3))\n vertices = []\n for i in range(16):\n section = colourspace_model_axis_reorder(\n XYZ_to_colourspace_model_normalised(\n np.vstack((pointer_gamut_data[i],\n pointer_gamut_data[i][0, ...])),\n POINTER_GAMUT_ILLUMINANT, colourspace_model),\n colourspace_model)\n\n vertices.append(np.array(zip(section, section[1:])))\n\n vertices = np.asarray(vertices)\n\n return buffer_geometry(position=vertices)\n\n\ndef visible_spectrum_visual(colourspace_model='CIE xyY'):\n \"\"\"\n Returns the visible spectrum visual geometry formatted as *JSON*.\n\n Parameters\n ----------\n colourspace_model : unicode, optional\n Colourspace model used to generate the visual geometry.\n\n Returns\n -------\n unicode\n Visible spectrum visual geometry formatted as *JSON*.\n \"\"\"\n\n XYZ = XYZ_outer_surface()\n vertices = colourspace_model_axis_reorder(\n XYZ_to_colourspace_model_normalised(\n XYZ, ILLUMINANTS['CIE 1931 2 Degree Standard Observer']['E'],\n colourspace_model), colourspace_model)\n\n vertices = np.array(zip(vertices, vertices[1:]))\n\n return buffer_geometry(position=vertices)\n", "sub_path": "colour_analysis.py", "file_name": "colour_analysis.py", "file_ext": "py", "file_size_in_byte": 31631, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "colour.utilities.CaseInsensitiveMapping", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.float16", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 60, "usage_type": "attribute"}, {"api_name": "numpy.float64", "line_number": 61, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 71, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 71, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 81, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 81, "usage_type": "attribute"}, {"api_name": "collections.OrderedDict", "line_number": 129, "usage_type": "call"}, {"api_name": "colour.models.function_gamma", "line_number": 132, "usage_type": "call"}, {"api_name": "colour.models.function_gamma", "line_number": 133, "usage_type": "call"}, {"api_name": "colour.models.function_gamma", "line_number": 134, "usage_type": "call"}, {"api_name": "colour.models.function_linear", "line_number": 135, "usage_type": "name"}, {"api_name": "colour.OETFS_REVERSE.items", "line_number": 137, "usage_type": "call"}, {"api_name": "colour.OETFS_REVERSE", "line_number": 137, "usage_type": "name"}, {"api_name": "colour.LOG_DECODING_CURVES.items", "line_number": 138, "usage_type": "call"}, {"api_name": "colour.LOG_DECODING_CURVES", "line_number": 138, "usage_type": "name"}, {"api_name": "colour.POINTER_GAMUT_DATA", "line_number": 183, "usage_type": "name"}, {"api_name": "colour.Lab_to_XYZ", "line_number": 183, "usage_type": "call"}, {"api_name": "colour.POINTER_GAMUT_ILLUMINANT", "line_number": 184, "usage_type": "argument"}, {"api_name": "colour.LCHab_to_Lab", "line_number": 184, "usage_type": "call"}, {"api_name": "colour.POINTER_GAMUT_DATA", "line_number": 184, "usage_type": "argument"}, {"api_name": "werkzeug.contrib.cache.SimpleCache", "line_number": 191, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 220, "usage_type": "call"}, {"api_name": "os.path", "line_number": 220, "usage_type": "attribute"}, {"api_name": "colour.read_image", "line_number": 226, "usage_type": "call"}, {"api_name": "colour.models.XYZ_to_colourspace_model", "line_number": 266, "usage_type": "call"}, {"api_name": "colour.XYZ_to_JzAzBz", "line_number": 269, "usage_type": "call"}, {"api_name": "colour.XYZ_to_OSA_UCS", "line_number": 271, "usage_type": "call"}, {"api_name": "colour.utilities.tsplit", "line_number": 297, "usage_type": "call"}, {"api_name": "colour.utilities.tstack", "line_number": 299, "usage_type": "call"}, {"api_name": "colour.utilities.tstack", "line_number": 301, "usage_type": "call"}, {"api_name": "colour.utilities.tstack", "line_number": 305, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 347, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 360, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 373, "usage_type": "call"}, {"api_name": "colour.RGB_COLOURSPACES.keys", "line_number": 373, "usage_type": "call"}, {"api_name": "colour.RGB_COLOURSPACES", "line_number": 373, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 415, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 419, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 423, "usage_type": "call"}, {"api_name": "numpy.finfo", "line_number": 423, "usage_type": "call"}, {"api_name": "numpy.nan_to_num", "line_number": 424, "usage_type": "call"}, {"api_name": "numpy.dtype", "line_number": 425, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 433, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 480, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 481, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 482, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 484, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 485, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 487, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 488, "usage_type": "call"}, {"api_name": "numpy.tile", "line_number": 492, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 492, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 493, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 493, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 507, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 508, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 509, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 511, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 511, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 512, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 512, "usage_type": "attribute"}, {"api_name": "numpy.roll", "line_number": 524, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 525, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 526, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 527, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 527, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 528, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 528, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 528, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 529, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 532, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 533, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 534, "usage_type": "attribute"}, {"api_name": "numpy.fliplr", "line_number": 591, "usage_type": "call"}, {"api_name": "numpy.fliplr", "line_number": 599, "usage_type": "call"}, {"api_name": "numpy.fliplr", "line_number": 607, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 612, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 612, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 613, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 613, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 614, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 614, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 616, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 616, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 617, "usage_type": "call"}, {"api_name": "numpy.uint32", "line_number": 617, "usage_type": "attribute"}, {"api_name": "numpy.vstack", "line_number": 621, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 622, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 623, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 625, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 626, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 629, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 630, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 631, "usage_type": "attribute"}, {"api_name": "numpy.ravel", "line_number": 633, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 634, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 634, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 635, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 635, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 635, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 636, "usage_type": "call"}, {"api_name": "colour.utilities.first_item", "line_number": 692, "usage_type": "call"}, {"api_name": "colour.plotting.filter_RGB_colourspaces", "line_number": 693, "usage_type": "call"}, {"api_name": "re.escape", "line_number": 694, "usage_type": "call"}, {"api_name": "colour.utilities.first_item", "line_number": 695, "usage_type": "call"}, {"api_name": "colour.plotting.filter_RGB_colourspaces", "line_number": 696, "usage_type": "call"}, {"api_name": "re.escape", "line_number": 697, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 705, "usage_type": "call"}, {"api_name": "colour.RGB_to_RGB", "line_number": 709, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 711, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 713, "usage_type": "call"}, {"api_name": "colour.RGB_to_RGB", "line_number": 717, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 719, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 721, "usage_type": "call"}, {"api_name": "colour.is_within_pointer_gamut", "line_number": 724, "usage_type": "call"}, {"api_name": "colour.RGB_to_XYZ", "line_number": 725, "usage_type": "call"}, {"api_name": "numpy.int_", "line_number": 726, "usage_type": "attribute"}, {"api_name": "numpy.ravel", "line_number": 732, "usage_type": "call"}, {"api_name": "numpy.around", "line_number": 733, "usage_type": "call"}, {"api_name": "numpy.finfo", "line_number": 733, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 735, "usage_type": "call"}, {"api_name": "colour.utilities.first_item", "line_number": 767, "usage_type": "call"}, {"api_name": "colour.plotting.filter_RGB_colourspaces", "line_number": 768, "usage_type": "call"}, {"api_name": "re.escape", "line_number": 768, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 777, "usage_type": "call"}, {"api_name": "colour.RGB_to_XYZ", "line_number": 780, "usage_type": "call"}, {"api_name": "colour.utilities.first_item", "line_number": 843, "usage_type": "call"}, {"api_name": "colour.plotting.filter_RGB_colourspaces", "line_number": 844, "usage_type": "call"}, {"api_name": "re.escape", "line_number": 845, "usage_type": "call"}, {"api_name": "colour.utilities.first_item", "line_number": 846, "usage_type": "call"}, {"api_name": "colour.plotting.filter_RGB_colourspaces", "line_number": 847, "usage_type": "call"}, {"api_name": "re.escape", "line_number": 848, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 856, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 861, "usage_type": "call"}, {"api_name": "colour.RGB_to_RGB", "line_number": 864, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 866, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 866, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 869, "usage_type": "call"}, {"api_name": "colour.RGB_to_RGB", "line_number": 872, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 874, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 874, "usage_type": "call"}, {"api_name": "colour.is_within_pointer_gamut", "line_number": 877, "usage_type": "call"}, {"api_name": "colour.RGB_to_XYZ", "line_number": 878, "usage_type": "call"}, {"api_name": "numpy.int_", "line_number": 879, "usage_type": "attribute"}, {"api_name": "colour.RGB_to_XYZ", "line_number": 883, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 892, "usage_type": "call"}, {"api_name": "colour.utilities.first_item", "line_number": 919, "usage_type": "call"}, {"api_name": "colour.plotting.filter_RGB_colourspaces", "line_number": 920, "usage_type": "call"}, {"api_name": "re.escape", "line_number": 920, "usage_type": "call"}, {"api_name": "colour.utilities.first_item", "line_number": 922, "usage_type": "call"}, {"api_name": "colour.plotting.filter_cmfs", "line_number": 922, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 925, "usage_type": "call"}, {"api_name": "colour.utilities.normalise_maximum", "line_number": 931, "usage_type": "call"}, {"api_name": "colour.XYZ_to_RGB", "line_number": 932, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 954, "usage_type": "call"}, {"api_name": "colour.POINTER_GAMUT_DATA", "line_number": 954, "usage_type": "argument"}, {"api_name": "colour.POINTER_GAMUT_ILLUMINANT", "line_number": 961, "usage_type": "argument"}, {"api_name": "numpy.vstack", "line_number": 959, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 964, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 966, "usage_type": "call"}, {"api_name": "colour.volume.XYZ_outer_surface", "line_number": 986, "usage_type": "call"}, {"api_name": "colour.ILLUMINANTS", "line_number": 989, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 992, "usage_type": "call"}]} +{"seq_id": "587511478", "text": "from configparser import ConfigParser\nimport fnmatch\nimport os\nimport pickle\nimport csv\nfrom os.path import join\n\nimport numpy as np\nfrom eqep.data.eqdata import EQData\nfrom eqep.shakemap.shakemap_factory import create_shakemap\nfrom obspy.core import read\nfrom obspy.core.utcdatetime import UTCDateTime\nfrom obspy.core.stream import Stream\n\n\n\"\"\"\nKeywords for the configuration file\n\"\"\"\nSTATIONS_CSV = 'station_locations'\nSCALE = 'scale'\nALGORITHM = 'algorithm'\nACCURACY = 'accuracy'\nSTYLE = 'style'\nSIZE = 'size'\nLAT_MIN = 'lat_min'\nLAT_MAX = 'lat_max'\nLONG_MIN = 'long_min'\nLONG_MAX = 'long_max'\nBEGIN_TIME = 'time_begin'\nEND_TIME = 'time_end'\nTIME_INTERVAL = 'time_interval'\nINPUT_DIR = 'in_dir'\nSTATION_CODES = 'stations'\nDEPTH = 'depth'\nWIDTH = 'width'\nHEIGHT = 'height'\nOUT_DIR = 'out_dir'\n\nSAVE_ANIMATION = 'animation'\nSAVE_CSV_IN = 'save_csv_in'\nSAVE_CSV_OUT = 'save_csv_out'\nSAVE_KML = 'save_kml'\nSAVE_KMZ = 'save_kmz'\n\nMSEED_SUFFIX = 'mseed'\n\n\nclass StationReader(object):\n def __init__(self, path_to_file='stations.csv'):\n \"\"\"\n Constructs a new StationReader that reads the given csv file, and\n initializes an internal dictionary for storing the information of the\n stations.\n\n :param path_to_file\n \"\"\"\n self.file_location = path_to_file\n\n def read_csv(self):\n \"\"\"\n Reads a CSV file in the following format:\n\n station_name_1, longitude_1, latitude_1,\n station_name_2, longitude_2, latitude_2,\n ...\n station_name_n, longitude_n, latitude_n\n\n :return dictionary: code of the station\n \"\"\"\n ret = {}\n with open(self.file_location, 'r') as f:\n # ignoring the lines starting with a # symbol, as they are comments\n for row in csv.reader(filter(lambda r: r[0] != '#', f)):\n ret[row[0]] = (row[1], row[2])\n self.dict = ret\n\n def get_coordinate(self, station_code) -> (float, float):\n \"\"\" Returns the coordinate of the given station as a tuple (\n longitude, latitude).\n\n If this station code is not known, a LookError will be raised,\n therefore a the method contains_station should be called first.\n\n :param basestring station_code: the station code\n :return float longitude: the longitude in degrees\n :return float latitude: the latitude in degrees\n \"\"\"\n\n if self.contains_station(station_code):\n return float(self.dict[station_code][0]), float(\n self.dict[station_code][1])\n else:\n raise LookupError(\n \"The given station %s is not contained in the given csv file\"\n % station_code)\n\n def contains_station(self, station_code) -> bool:\n \"\"\"\n Checks whether the given station code is contained in the internal\n dictionary (retrieved from the csv file) or not.\n\n :param basestring station_code: the station code to check\n :return: whether its contained or not\n \"\"\"\n return station_code in self.dict\n\n\nclass DataInventory(object):\n \"\"\"The DataInventory class manages a whole directory (recursively) by\n saving the meta data in a meta data file, which will be a pickle\n file.\n\n It uses the obspy.core.trace.Stats container for managing the\n header files.\n\n \"\"\"\n\n def __init__(self, metafile):\n \"\"\"Initializes a new data inventory that keeps track all the meta\n information of the Miniseed files.\n\n :metafile: the filename of the Pickle file.\n \"\"\"\n self.metafile = metafile\n self.working_stream = Stream()\n\n def unpickle_data(self):\n data = {}\n open(self.metafile, 'a').close()\n try:\n with open(self.metafile, 'rb') as file:\n unpickler = pickle.Unpickler(file)\n data = unpickler.load()\n if not isinstance(data, dict):\n data = {}\n except EOFError:\n return {}\n return data\n\n\n def scan(self, input_root, force=False):\n \"\"\"\n\n :force: with the force flag being set, it will force the\n scanner to scan the files regardless of being already scanned.\n\n \"\"\"\n\n # http://stackoverflow.com/questions/7100125/storing-python-dictionaries\n\n self.data = self.unpickle_data()\n for root, dirnames, filenames in os.walk(input_root):\n for filename in fnmatch.filter(filenames, '*.%s' % MSEED_SUFFIX):\n f = join(input_root, root, filename)\n if f in self.data and not force:\n continue\n st = read(f, headonly=True)\n self.data[f] = []\n for tr in st:\n self.data[f].append((tr.stats.starttime, tr.stats.endtime,\n tr.stats.station))\n\n pickle.dump(self.data, open(self.metafile, 'wb'))\n\n def get_traces(self, begin_time, end_time, station_code=None):\n \"\"\"Returns in O(n) whether a stream with the given criteria exists.\n\n :return: the traces \n \"\"\"\n\n st = Stream()\n for tr in self.working_stream:\n if tr.stats.starttime <= begin_time \\\n <= end_time <= tr.stats.endtime \\\n and tr.stats.station == station_code:\n st += tr\n return st\n\n def get_data(self, begin_time, end_time, station_code):\n r = self.get_traces(begin_time, end_time, station_code)\n if r:\n return r\n\n for f in self.data:\n for (l, r, station) in self.data[f]:\n if l <= begin_time <= end_time <= r \\\n and station == station_code:\n self.working_stream += read(f)\n break\n\n return self.get_traces(begin_time, end_time, station_code)\n\n\nclass ConfigReader(object):\n def __init__(self, metafile='.metafile', cfg_str=None, cfg_source=None,\n root_dir=None):\n \"\"\"After initialization either read_file or read_string must be\n called, otherwise the config parser will not know anything\n about the configuration file.\n\n \"\"\"\n self.cfg_parser = ConfigParser()\n self.data_inventory = DataInventory(metafile)\n self.mdict = {}\n self.cfg_source = cfg_source\n self.cfg_str = cfg_str\n self.root_dir = root_dir\n\n def __getitem__(self, item):\n return self.mdict[item]\n\n def read(self):\n if self.cfg_source:\n self.read_file(self.cfg_source)\n elif self.cfg_str:\n self.read_string(self.cfg_str)\n\n def read_file(self, source=None):\n \"\"\"\n Takes the source file that is given and parses it appropriately. If a\n string should be read then the read_string method should be called.\n\n :param source: the source of the configuration file\n \"\"\"\n self.cfg_parser.read_file(source)\n\n def read_string(self, string=None):\n \"\"\"\n Reads the string and parses it appropriately.\n\n The shakemaps can be read afterwards (if the configuration file\n and the input files are set up properly).\n\n :param string: the string to parse\n \"\"\"\n self.cfg_parser.read_string(string)\n\n def find_peak(self, station_code, tbeg, tend):\n \"\"\"Finds the peak ground velocity with the given criteria.\n\n :param station_code: the code of the station\n :param tbeg: begin time\n :param tend: end time\n :return: the peak ground velocity that match the given criteria\n\n \"\"\"\n stream = self.data_inventory.get_data(tbeg, tend, station_code)\n stream = stream.slice(tbeg, tend)\n return max(stream.max())\n\n def read_shakemaps(self, section_name=None):\n \"\"\"Retrieves the preinitialized ShakeMaps from the given section\n name. It uses the the configuration parser to retrieve the respective\n information.\n\n The configurations inside a section will have the highest\n priorities i.e. overrides the value in the DEFAULT section.\n\n The ShakeMap objects can then be calculated by calling the\n corresponding .calculate() function.\n\n It also starts scanning the data inventory, which can take a\n while depending on the size of the directory.\n\n TODO: This can somehow be configured as an iterator. This\n would optimize the memory. \n\n :param section_name: the name of the section\n\n \"\"\"\n\n sm = []\n\n if section_name is None:\n section_name = self.sections()[0]\n\n self.mdict = self.cfg_parser[section_name]\n\n # Initializing the data inventory\n self.data_inventory.scan(os.path.join(self.root_dir, self[INPUT_DIR]))\n\n # Read the boundaries of the ShakeMap\n long_min = int(self[LONG_MIN])\n long_max = int(self[LONG_MAX])\n lat_min = int(self[LAT_MIN])\n lat_max = int(self[LAT_MAX])\n bounds = (long_min, long_max, lat_min, lat_max)\n\n # Read the stations, their coordinates and more\n station_reader = StationReader(path_to_file=os.path.join(\n self.root_dir, self[STATIONS_CSV]))\n\n station_reader.read_csv()\n station_codes = self[STATION_CODES].split(',')\n coords_x, coords_y = [], []\n for station_code in station_codes:\n # TODO: the coordinates are switched in EQData\n y, x = station_reader.get_coordinate(station_code)\n coords_x.append(x)\n coords_y.append(y)\n\n # Determine the time ineterval\n tbeg = UTCDateTime(self[BEGIN_TIME])\n tend = UTCDateTime(self[END_TIME])\n tdelta = int(self[TIME_INTERVAL])\n tcur = tbeg\n\n # We create the ShakeMaps now for each time interval according to the\n # configurations that have been read just above.\n while tcur + tdelta <= tend:\n pgvs = []\n for sc in station_codes:\n pgvs.append(self.find_peak(sc, tcur, tcur + tdelta))\n\n eqdata = EQData(np.array(pgvs),\n np.array(coords_x),\n np.array(coords_y),\n np.array(station_codes))\n\n shakemap = create_shakemap(eqdata, self[ALGORITHM], self[SCALE],\n self[STYLE], (self[DEPTH], self[DEPTH]),\n bounds, (float(self[WIDTH]), float(self[HEIGHT])))\n\n shakemap.tbeg = tcur.strftime('%Y-%m-%dT%H:%M:%S')\n sm.append(shakemap)\n\n tcur += tdelta\n\n return sm\n\n def sections(self):\n \"\"\"\n The sections that have been read by the config parser will be\n returned as a list of strings.\n :return: the list of sections that are read in the given\n configuration file\n \"\"\"\n return self.cfg_parser.sections()\n", "sub_path": "service/cfgreader.py", "file_name": "cfgreader.py", "file_ext": "py", "file_size_in_byte": 10943, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "csv.reader", "line_number": 73, "usage_type": "call"}, {"api_name": "obspy.core.stream.Stream", "line_number": 125, "usage_type": "call"}, {"api_name": "pickle.Unpickler", "line_number": 132, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 152, "usage_type": "call"}, {"api_name": "fnmatch.filter", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 154, "usage_type": "call"}, {"api_name": "obspy.core.read", "line_number": 157, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 163, "usage_type": "call"}, {"api_name": "obspy.core.stream.Stream", "line_number": 171, "usage_type": "call"}, {"api_name": "obspy.core.read", "line_number": 188, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 202, "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.path.join", "line_number": 290, "usage_type": "call"}, {"api_name": "os.path", "line_number": 290, "usage_type": "attribute"}, {"api_name": "obspy.core.utcdatetime.UTCDateTime", "line_number": 303, "usage_type": "call"}, {"api_name": "obspy.core.utcdatetime.UTCDateTime", "line_number": 304, "usage_type": "call"}, {"api_name": "eqep.data.eqdata.EQData", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 316, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 317, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 318, "usage_type": "call"}, {"api_name": "eqep.shakemap.shakemap_factory.create_shakemap", "line_number": 320, "usage_type": "call"}]} +{"seq_id": "518129818", "text": "\"\"\"ashinamo URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n https://docs.djangoproject.com/en/1.8/topics/http/urls/\nExamples:\nFunction views\n 1. Add an import: from my_app import views\n 2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')\nClass-based views\n 1. Add an import: from other_app.views import Home\n 2. Add a URL to urlpatterns: url(r'^$', Home.as_view(), name='home')\nIncluding another URLconf\n 1. Add an import: from blog import urls as blog_urls\n 2. Add a URL to urlpatterns: url(r'^blog/', include(blog_urls))\n\"\"\"\nfrom django.conf.urls import include, url\nfrom django.contrib import admin\nimport os\nfrom settings import BASE_DIR \n\nurlpatterns = [\n url(r'^statics/(?P.*)$', 'django.views.static.serve',{\"document_root\":os.path.join(BASE_DIR, \"./static\").replace(\"\\\\\",\"/\")}),\n\n url(r'^admin/', include(admin.site.urls)),\n url(r'^$', 'apphome.views.index', name=\"index\"),\n url(r'^cpu/$', 'apphome.views.cpu', name=\"cpu\"),\n url(r'^mem/$', 'apphome.views.mem', name=\"mem\"),\n url(r'^io/$', 'apphome.views.io', name=\"io\"),\n url(r'^net/$', 'apphome.views.net', name=\"net\"),\n\n\n url(r'^data/cpu/$', 'appdata.views.getcpu', name='datacpu'),\n url(r'^data/mem/$', 'appdata.views.getmem', name='datamem'),\n url(r'^data/io/$', 'appdata.views.getio', name=\"dataio\"),\n url(r'^data/net/$', 'appdata.views.getnet', name=\"datanet\"),\n]\n", "sub_path": "ashinamo/ashinamo/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1457, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.conf.urls.url", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "settings.BASE_DIR", "line_number": 22, "usage_type": "argument"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.conf.urls.url", "line_number": 24, "usage_type": "call"}, {"api_name": "django.conf.urls.include", "line_number": 24, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 24, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 24, "usage_type": "name"}, {"api_name": "django.conf.urls.url", "line_number": 25, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 26, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 28, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 29, "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": 34, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "472103307", "text": "from kafka import KafkaConsumer\r\nfrom datetime import datetime\r\nfrom elasticsearch import Elasticsearch\r\nimport time\r\nimport json\r\n\r\nes = Elasticsearch([{'host': '54.187.19.224', 'port': 9200}])\r\n\r\n# To consume messages\r\nconsumer = KafkaConsumer('packetbeats-topic', group_id=\"teamb\",\r\n auto_commit_enable=True,\r\n auto_commit_interval_ms=30 * 1000,\r\n auto_offset_reset='smallest',\r\n bootstrap_servers=['ec2-99-79-7-20.ca-central-1.compute.amazonaws.com:9092'])\r\nesid = 0\r\n\r\nfor message in consumer:\r\n print(\"next\")\r\n esid += 1\r\n if esid % 1000 == 0:\r\n print(esid)\r\n\r\n msg={}\r\n schema = json.loads(message.value)\r\n # for (k,v) in schema.items():\r\n # if (k=='@timestamp')\r\n # ts = v\r\n # msg['timestamp'] = ts\r\n msg[\"index\"] = \"team-b\"\r\n msg[\"schema\"] = schema\r\n\r\n if not 'index' in msg:\r\n print(\"you must specify the index name in the json wrapper\")\r\n sys.exit(-1)\r\n\r\n index = msg['index']\r\n\r\n try:\r\n es.index(index=index, doc_type= msg['doc_type'], id=esid, body=msg['body'])\r\n except:\r\n continue\r\n\r\n ", "sub_path": "kafka.py", "file_name": "kafka.py", "file_ext": "py", "file_size_in_byte": 1189, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "elasticsearch.Elasticsearch", "line_number": 7, "usage_type": "call"}, {"api_name": "kafka.KafkaConsumer", "line_number": 10, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "95614815", "text": "import argparse # Args\nimport socket, select # Networking\nimport multiprocessing, subprocess # Multiprocessing and signals\nimport traceback, sys, os, tempfile, ast, time # Misc\nimport binascii\n\n\n\narg_parser = argparse.ArgumentParser()\narg_parser.add_argument(\n \"--listen_ip\", type=str, default=\"0.0.0.0\",\n help=\"IP address of a local interface to listen on.\"\n)\narg_parser.add_argument(\n \"--listen_port\", type=int, default=1414,\n help=\"TCP port to listen for incoming connections on.\"\n)\narg_parser.add_argument(\n \"--max_connections\", type=int, default=5,\n help=\"Max number of simultaneous connections supported.\"\n)\narg_parser.add_argument(\n \"--read_size\", type=int, default=4096,\n help=\"Number of bytes read from wire before forwarding.\"\n)\narg_parser.add_argument(\n \"--server_ip\", type=str, default=\"172.17.0.2\",\n help=\"IP address of the server your traffic is ultimately bound for.\"\n)\narg_parser.add_argument(\n \"--server_port\", type=int, default=1414,\n help=\"TCP port on remote server to send traffic to; probably same as listen_port.\"\n)\narg_parser.add_argument(\n \"--client_color\", type=int, default=13,\n help=\"8-bit color code for client-sent text.\"\n)\narg_parser.add_argument(\n \"--server_color\", type=int, default=14,\n help=\"8-bit color code for server-sent text.\"\n)\narg_parser.add_argument(\n \"--error_color\", type=int, default=9,\n help=\"8-bit color code for error messages from alsanna.\"\n)\narg_parser.add_argument(\n \"--editor\", type=str, default=\"nano\",\n help=\"Command to use for launching editor.\"\n)\narg_parser.add_argument(\n \"--intercept_client\", action=\"store_true\", # Will be False unless supplied on CLI\n help=\"Whether to intercept client-sent data for editing.\"\n)\narg_parser.add_argument(\n \"--intercept_server\", action=\"store_true\", # Will be False unless supplied on CLI\n help=\"Whether to intercept server-sent data for editing.\"\n)\narg_parser.add_argument(\n \"--edit_delay\", type=int, default=1,\n help=\"Number of seconds to wait before sending edited messages.\"\n)\n\nargs = arg_parser.parse_args()\n\ndef format_error(error_message):\n return \"\\033[38;5;\" + str(args.error_color) + \"m\" + error_message + \"\\033[0m\"\n\ndef process_messages(preprocessing_q):\n \"\"\"\n Perform synchronous message processing steps, one message at a time. Lets us reason\n about our messages in order without giving up the ability to handle multiple\n connections.\n \"\"\"\n postprocessing_queues = {}\n while True:\n # Die if orphaned\n if os.getppid() == 1:\n return\n\n # Get a message\n try:\n connection_id, message = preprocessing_q.get()\n except:\n print(format_error(\"Failed to get message\\n\" + traceback.format_exc()),\n file=sys.stderr)\n continue\n\n # Act on special messages\n if connection_id == \"Err\":\n print(format_error(message), file=sys.stderr)\n continue\n if connection_id == \"Kill\":\n del postprocessing_queues[message] # Destroy reference to a now-dead queue.\n continue\n if connection_id not in postprocessing_queues.keys(): # Register new queue\n postprocessing_queues[connection_id] = message # \"message# is Queue object\n continue\n\n # Colorize text\n if connection_id[-1] == \"c\":\n color = args.client_color\n elif connection_id[-1] == \"s\":\n color = args.server_color\n else:\n color = 8 # If this ever gets used something's gone wrong.\n\n\n if (connection_id[-1] == \"c\"):\n message_hex = binascii.hexlify(message)\n #reduce the FAP level to 9\n if (message_hex.find(b'49442020') > -1):\n message_hex = message_hex.replace(b'494420200d', b'494420200c')\n message_hex = message_hex.replace(b'494420200e', b'494420200c')\n message_hex = message_hex.replace(b'494420200f', b'494420200c')\n message_hex = message_hex.replace(b'4944202010', b'494420200c')\n message_hex = message_hex.replace(b'4944202011', b'494420200c')\n if (message_hex.find(b'43415554') > -1):\n print(str(binascii.unhexlify(message_hex[message_hex.find(b'43415554'):])))\n message = binascii.unhexlify(message_hex)\n\n\n\n\n message = str(message)\n colorful = \"\\033[38;5;\" + str(color) + \"m\" + message + \"\\033[0m\"\n #print(colorful) # Print received message\n\n # Open the message in an editor if required\n\n try:\n if (connection_id[-1] == \"c\" and args.intercept_client) \\\n or (connection_id[-1] == \"s\" and args.intercept_server):\n with tempfile.NamedTemporaryFile(mode=\"w+\") as tmpfile:\n tmpfile.write(message)\n tmpfile.flush()\n time.sleep(args.edit_delay)\n subprocess.run([args.editor, tmpfile.name])\n tmpfile.seek(0)\n message = tmpfile.read()\n except:\n print(\n format_error(\"Error reading message from disk.\\n\"\n + traceback.format_exc()),\n file=sys.stderr\n )\n\n # Send the last-good message version (original if the modification failed)\n finally:\n try:\n postprocessing_queues[connection_id].put(message)\n except:\n pass # If a subprocess died and its queue is gone, just continue\n\ndef forward(receive_sock, send_sock, processing_q, result_q, connection_id):\n \"\"\"\n Handles one direction of communication in a connection. For processing messages\n in order, see the process_messages() function. This implementation assumes the\n server is finicky and will close the connection if a message is not promptly sent\n after connection - to allow you editing time, we therefore wait to open\n the TCP connection until after editing. Assumes the client is patient.\n \"\"\"\n return_sock = False\n # If we haven't opened the forwarding connection yet, send_sock is None. So we have\n # to write our select() call to avoid passing None to it. This never gets called\n # with receive_sock as None so we don't worry about it.\n readable, writable, exception = select.select(\n [receive_sock], # rlist\n [send_sock] if send_sock is not None else [], # wlist\n [receive_sock, send_sock] if send_sock is not None else [receive_sock], # xlist\n 0 # timeout disabled - don't block\n )\n\n if len(exception) > 0:\n processing_q.put((\"Err\", \"Exception in socket(s): \" + str(exception)))\n return True # Something bad happened, close the sockets.\n\n if receive_sock in readable and (send_sock in writable or send_sock is None):\n data_bytes = receive_sock.recv(args.read_size)\n if len(data_bytes) == 0: # Signifies connection is closed on the remote end.\n return True\n processing_q.put((connection_id, data_bytes))\n try:\n data_string = result_q.get() # Blocks until processed message available\n\n if send_sock is None: # We need to open a connection\n try:\n return_sock = True\n send_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM, 0)\n send_sock.connect((args.server_ip, args.server_port))\n send_sock.setblocking(False)\n except:\n processing_q.put((\"Err\", \"Error setting up forwarder.\\n\"\n + traceback.format_exc()))\n return True # Can't send data without this, so give up.\n\n try:\n data_bytes = ast.literal_eval(data_string) # Convert from string to bytes\n except:\n processing_q.put((\"Err\", \"Error parsing processed message.\\n\"\n + traceback.format_exc()))\n finally:\n try:\n sent = 0\n while sent < len(data_bytes):\n sent += send_sock.send(data_bytes[sent:])\n except:\n processing_q.put((\"Err\", \"Error forwarding message to destination.\\n\"\n + traceback.format_exc()))\n send_sock.close() # Might need to do this here if it failed immediately\n return True # Probably want to give up if sending failed somehow.\n if return_sock: # Disgusting overloading of return value type\n return send_sock\n else:\n return False # Keep connection alive.\n\n\ndef manage_connections(listen_sock, processing_q, connection_id):\n \"\"\"\n Handle logic required to set up and maintain connections. The ability to hack this\n is the main selling point of alsanna - for instance if you need to send a plaintext\n message from the server to the client prior to TLS negotiation, you can do it here.\n\n So that we can have all the protocol logic in one place, we handle both directions\n of traffic here. A more specialized proxy might benefit in modularity from having\n a child process for each direction and using blocking sockets for each.\n\n The forward() method handles setting up the socket we use to forward data to the\n server. This is ugly as heck, but handles the common case where the server kills\n your connection if you take too long editing something.\n \"\"\"\n\n ###########################################################\n # Initialize queues and set up TLS on listener if need be #\n ###########################################################\n\n q_manager = multiprocessing.Manager()\n c_result_q = q_manager.Queue()\n processing_q.put((str(connection_id) + \"c\", c_result_q))\n s_result_q = q_manager.Queue()\n processing_q.put((str(connection_id) + \"s\", s_result_q))\n\n with listen_sock:\n forward_sock = None\n\n ############################################################################\n # Shuffle bytes back and forth, setting up forwarder on first sent message #\n ############################################################################\n client_done, server_done = False, False\n try:\n # We check forward_sock to handle the situation where we never update\n # server_done because we errored out trying to send from client to server\n while not client_done and (not server_done or forward_sock is None):\n # Die if orphaned\n if os.getppid() == 1:\n return\n # Send try to forward data in each direction\n if not client_done:\n try:\n client_done = forward(listen_sock, forward_sock,\n processing_q, c_result_q,\n str(connection_id) + \"c\")\n if isinstance(client_done, socket.socket):\n forward_sock = client_done\n client_done = False\n except:\n processing_q.put((\"Err\", \"Error forwarding data to server.\\n\"\n + traceback.format_exc()))\n break\n if not server_done and forward_sock is not None:\n try:\n server_done = forward(forward_sock, listen_sock,\n processing_q, s_result_q,\n str(connection_id) + \"s\")\n except:\n processing_q.put((\"Err\", \"Error forwarding data to client.\\n\"\n + traceback.format_exc()))\n break\n finally: # No context manager because it's None sometimes; manual cleanup\n if forward_sock is not None:\n forward_sock.close()\n\n\ndef main():\n \"\"\"\n Highest-level server logic. Sets up the synchronous message processor, sets up\n connections, and spins up a subprocess to handle each connection.\n \"\"\"\n processing_q = multiprocessing.Queue()\n\n message_processor = multiprocessing.Process(target=process_messages,\n args=(processing_q,))\n message_processor.daemon = True\n message_processor.start()\n\n connection_id = 0\n connections = {}\n\n l_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM, 0)\n # Allow socket to be reused quickly after quitting.\n l_sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n l_sock.bind((args.listen_ip, args.listen_port))\n l_sock.listen(args.max_connections)\n with l_sock:\n while True:\n try:\n listen_sock, addr = l_sock.accept()\n\n connections[connection_id] = multiprocessing.Process(\n target=manage_connections,\n args=(listen_sock,\n processing_q,\n connection_id))\n connections[connection_id].start()\n connection_id += 1\n except:\n processing_q.put((\"Err\", \"Parent process dying, exiting alsanna.\\n\"\n + traceback.format_exc()))\n break\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "my_boyfriend.py", "file_name": "my_boyfriend.py", "file_ext": "py", "file_size_in_byte": 13649, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 9, "usage_type": "call"}, {"api_name": "os.getppid", "line_number": 77, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 84, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 85, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 90, "usage_type": "attribute"}, {"api_name": "binascii.hexlify", "line_number": 109, "usage_type": "call"}, {"api_name": "binascii.unhexlify", "line_number": 118, "usage_type": "call"}, {"api_name": "binascii.unhexlify", "line_number": 119, "usage_type": "call"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 133, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 136, "usage_type": "call"}, {"api_name": "subprocess.run", "line_number": 137, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 143, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 144, "usage_type": "attribute"}, {"api_name": "select.select", "line_number": 166, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 188, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 188, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 188, "usage_type": "attribute"}, {"api_name": "traceback.format_exc", "line_number": 193, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 197, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 200, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 208, "usage_type": "call"}, {"api_name": "multiprocessing.Manager", "line_number": 236, "usage_type": "call"}, {"api_name": "os.getppid", "line_number": 254, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 262, "usage_type": "attribute"}, {"api_name": "traceback.format_exc", "line_number": 267, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 276, "usage_type": "call"}, {"api_name": "multiprocessing.Queue", "line_number": 288, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 290, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 298, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 298, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 298, "usage_type": "attribute"}, {"api_name": "socket.SOL_SOCKET", "line_number": 300, "usage_type": "attribute"}, {"api_name": "socket.SO_REUSEADDR", "line_number": 300, "usage_type": "attribute"}, {"api_name": "multiprocessing.Process", "line_number": 308, "usage_type": "call"}, {"api_name": "traceback.format_exc", "line_number": 317, "usage_type": "call"}]} +{"seq_id": "194630860", "text": "#CSV to database / SQLite3 version / 17-10-2014 13:03 / Author: Angelo\n#version 3.4.0\n\nimport csv\nimport sqlite3\n\nfrom tkinter import ttk\nfrom tkinter import *\n\nimport library\n\nclass csv_to_db():\n\tdef __init__(self, dir, file_name):\n\t\t# This will 1) fix the database name, 2) delete a library of same name in the directory,\n\t\t# 3) read the csv file data, 4) call the create_database function with the above arguments. \t\n\t\tprint(\"Inside read_csv.py csv_to_db.__init__\") # DEBUG LINE\n\t\t\n\t\tdatabase_name = dir+library.create_db_name(file_name)\n\t\tlibrary.delete_db(database_name)\n\t\tdata = csv.reader(open(file_name))\n\t\tself.create_database(dir, file_name, database_name, data)\n\t\t\n\n\tdef create_database(self, dir, file_name, database_name, data):\n\t\t# This will create the database and pass it the csv data.\n\t\tprint(\"Inside read_csv.py csv_to_db.create_databse\") # DEBUG LINE\n\t\t\t\t\n\t\tconn = sqlite3.connect(database_name)\n\t\tc = conn.cursor()\n\t\t\n\t\tcol_names = next(data, None)\n\t\t\n\t\ttable_string = self.create_table_string(col_names)\n\t\tinsert_string = self.create_insert_string(len(col_names))\n\t\tc.execute(\"CREATE TABLE maint (%s)\" % (table_string))\n\t\t\n\t\tfor row in data:\n\t\t\tcount = 0\n\t\t\ttmp_list = []\n\t\t\tfor item in col_names:\n\t\t\t\ttmp_el = row[count]\n\t\t\t\ttry:\n\t\t\t\t\ttmp_el = float(tmp_el)\n\t\t\t\t\tif (tmp_el).is_integer():\n\t\t\t\t\t\ttmp_el = int(tmp_el)\n\t\t\t\texcept:\n\t\t\t\t\ttmp_el = None\n\n\t\t\t\ttmp_list.append(tmp_el)\n\t\t\t\tcount += 1\n\n\t\t\tc.execute(insert_string, (tmp_list))\n\t\t\tdel tmp_list[:]\n\t\t\n\t\tconn.commit()\n\t\tconn.close()\n\t\t\n\tdef create_insert_string(self, num):\n\t\t# This will create the string that the create_database function will use to pass the data into the database.\n\t\t#print(\"Inside read_csv.py csv_to_db.create_insert_string\") # DEBUG LINE\n\t\ts = \"INSERT INTO maint VALUES (\"\n\t\tfor i in range(num):\n\t\t\tif i+1 != num:\n\t\t\t\ts = s+'?,'\n\t\t\telse:\n\t\t\t\ts = s+'?)'\n\t\treturn s\n\n\tdef create_table_string(self, col_names):\n\t\t# This will create the string that the create_database will use to create the columns in the database.\n\t\tprint(\"Inside read_csv.py csv_to_db.create_table_string\") # DEBUG LINE\n\t\ttable_list = [] \n\t\tfor i in range(len(col_names)):\n\t\t\tcolumn_name = library.create_col_name(col_names[i])\n\t\t\ttable_list.append(column_name)\n\t\treturn ', '.join(table_list)\t\n", "sub_path": "SQLite3/src/csv_to_db.py", "file_name": "csv_to_db.py", "file_ext": "py", "file_size_in_byte": 2256, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "library.create_db_name", "line_number": 18, "usage_type": "call"}, {"api_name": "library.delete_db", "line_number": 19, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 28, "usage_type": "call"}, {"api_name": "library.create_col_name", "line_number": 74, "usage_type": "call"}]} +{"seq_id": "208492698", "text": "from django.urls import path\n\nfrom . import views\n\napp_name = 'oauth2access'\n\nurlpatterns = [\n path('authorize/', views.authorize, name='authorize'),\n path('token/', views.token, name='token'),\n path('info/', views.infomation, name='info')\n]\n\n", "sub_path": "serverAuth/oauth2access/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 252, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "315142653", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Oct 10 16:32:39 2016\n\n@author: imchugh\n\"\"\"\n\n# Standard modules\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# My modules\nimport respiration_photosynthesis_run as rp_run\nimport utility_funcs\n\nreload(rp_run)\n \nf = '/home/imchugh/Code/Python/Config_files/Whroo_master_configs.txt'\nvar_list = ['Fc', 'Fc_Sc', 'Fc_Sc_u*']\nstor_list = [False, True, True]\nustar_list = [0,\n 0, \n {'2011': 0.31,\n '2012': 0.30,\n '2013': 0.32,\n '2014': 0.32}]\n\n# Get the uncorrected data and gap-fill Fc\ndf = pd.DataFrame()\nfor i, var in enumerate(var_list):\n \n temp_dict = rp_run.main(use_storage = stor_list[i],\n storage_var = 'Fc_storage_obs',\n ustar_threshold = ustar_list[i],\n config_file = f,\n do_light_response = True)[0]\n temp_dict['NEE_est'] = temp_dict['Re'] + temp_dict['GPP']\n temp_dict['NEE_filled'] = temp_dict['NEE_series']\n temp_dict['NEE_filled'][np.isnan(temp_dict['NEE_filled'])] = \\\n temp_dict['NEE_est'][np.isnan(temp_dict['NEE_filled'])]\n df[var] = temp_dict['NEE_filled']\n if i == 0:\n df.index = temp_dict['date_time']\n \n# Do calculations of means for all groups\ndiurnal_df = df.groupby([lambda x: x.hour, lambda y: y.minute]).mean()\ndiurnal_df.index = np.linspace(0, 23.5, 48)\n\n# Set plot iterables\nvars_dict = {1: ['Fc', 'Fc_Sc'],\n 2: ['Fc_Sc', 'Fc_Sc_u*']}\n \nnames_dict = {1: ['$F_c$', '$F_c\\/+\\/S_c$'],\n 2: ['$F_c\\/+\\/S_c$', '$(F_c\\/+\\/S_c)_{u_*corr}$']}\n\nlines_dict = {'Fc': ':',\n 'Fc_Sc': '--',\n 'Fc_Sc_u*': '-'}\n\n# Instantiate plot\nfig, (ax1, ax2) = plt.subplots(1, 2, figsize = (16, 6), sharex = True)\nfig.patch.set_facecolor('white')\nfig_labels = ['a)', 'b)', 'c)', 'd)']\n\nfor i, ax in enumerate((ax1, ax2)):\n\n counter = i + 1\n ax.set_xlim([0, 24])\n ax.set_ylim([-10, 4])\n ax.set_xticks([0,4,8,12,16,20,24])\n ax.xaxis.set_ticks_position('bottom')\n ax.yaxis.set_ticks_position('left')\n ax.spines['right'].set_visible(False)\n ax.spines['top'].set_visible(False)\n ax.set_xlabel(r'$Time\\/(hours)$', fontsize = 18)\n if counter % 2 != 0:\n ax.set_ylabel(r'$NEE\\/(\\mu mol\\/CO_2\\/m^{-2}\\/s^{-1})$', fontsize = 18)\n x = diurnal_df.index\n var1 = vars_dict[counter][0]\n var2 = vars_dict[counter][1]\n y1 = diurnal_df[var1]\n y2 = diurnal_df[var2]\n ax.plot(x, y1, color = 'black', linestyle = lines_dict[var1], \n linewidth = 2, label = names_dict[counter][0])\n ax.plot(x, y2, color = 'black', linestyle = lines_dict[var2], \n linewidth = 2, label = names_dict[counter][1])\n ax.fill_between(x, y1, y2, where=y2>=y1, facecolor='0.8', \n edgecolor='None',interpolate=True)\n ax.fill_between(x, y1, y2, where=y1>=y2, facecolor='0.8', \n edgecolor='None',interpolate=True)\n\n ax.legend(fontsize = 18, loc = 'lower right', frameon = False)\n ax.axhline(y = 0, color = 'black', linestyle = '-')\n ax.text(-2.6, 3.8, fig_labels[i], fontsize = 12)\n\n#fig.savefig(utility_funcs.set_output_path('diurnal_NEE_effects_of_storage_correction.png'),\n# bbox_inches='tight',\n# dpi = 300) \nplt.show()", "sub_path": "plot_diel_effect_of_storage.py", "file_name": "plot_diel_effect_of_storage.py", "file_ext": "py", "file_size_in_byte": 3367, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pandas.DataFrame", "line_number": 30, "usage_type": "call"}, {"api_name": "respiration_photosynthesis_run.main", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}]} +{"seq_id": "166969161", "text": "from PySide import QtCore\nimport logging\n# quickmamba\nfrom quickmamba.models import QObjectListModel\n# gui\nfrom buttleofx.gui.graph.node import NodeWrapper\nfrom buttleofx.gui.graph.connection import ConnectionWrapper\n\n\nclass GraphWrapper(QtCore.QObject):\n \"\"\"\n Class GraphWrapper defined by:\n - _view : to have the view object\n - _rootObject : to have the root object\n\n - _nodeWrappers : list of node wrappers (the python objects we use to communicate with the QML)\n - _connectionWrappers : list of connections wrappers (the python objects we use to communicate with the QML)\n\n - _zMax : to manage the depth of the graph (in QML)\n\n - _graph : the name of the graph mapped by the instance of this class.\n\n This class is a view (= a map) of a graph.\n \"\"\"\n\n def __init__(self, graph, view):\n super(GraphWrapper, self).__init__(view)\n\n self._view = view\n self._rootObject = view.rootObject()\n\n self._nodeWrappers = QObjectListModel(self)\n self._connectionWrappers = QObjectListModel(self)\n\n self._zMax = 2\n\n self._graph = graph\n\n # links core signals to wrapper layer\n self._graph.nodesChanged.connect(self.updateNodeWrappers)\n self._graph.connectionsCoordChanged.connect(self.updateConnectionsCoord)\n self._graph.connectionsChanged.connect(self.updateConnectionWrappers)\n\n logging.info(\"Gui : GraphWrapper created\")\n\n def __str__(self):\n \"\"\"\n Displays on terminal some data.\n Usefull to debug the class.\n \"\"\"\n str_list = []\n\n str_list.append(\"=== Graph Buttle Wrapper === \\n\")\n str_list.append(\"---- all nodeWrappers ---- \\n\")\n\n for nodeWrapper in self._nodeWrappers:\n str_list.append(nodeWrapper.__str__())\n str_list.append(\"\\n\")\n\n str_list.append(\"---- all connectionWrappers ---- \\n\")\n for con in self._connectionWrappers:\n str_list.append(con.__str__())\n str_list.append(\"\\n\")\n\n str_list.append((self.getGraphMapped()).__str__())\n\n return ''.join(str_list)\n\n ################################################## ACCESSORS ##################################################\n\n #################### getters ####################\n\n def getGraphMapped(self):\n \"\"\"\n Returns the graph (the node list and the connection list), mapped by this graphWrapper.\n \"\"\"\n return self._graph\n\n def getNodeWrappers(self):\n \"\"\"\n Returns the nodeWrapper list.\n \"\"\"\n return self._nodeWrappers\n\n def getNodeWrapper(self, nodeName):\n \"\"\"\n Returns the right nodeWrapper, identified with its nodeName.\n \"\"\"\n for nodeWrapper in self._nodeWrappers:\n if nodeWrapper.getName() == nodeName:\n return nodeWrapper\n return None # QtCore.QObject(self)\n\n def getConnectionWrappers(self):\n \"\"\"\n Returns the connectionWrapper list.\n \"\"\"\n return self._connectionWrappers\n\n def getConnectionWrapper(self, connectionId):\n \"\"\"\n Returns a connectionWrapper given a connection id.\n \"\"\"\n for connection in self._connectionWrappers:\n if connection.getId() == connectionId:\n return connection\n return None\n\n @QtCore.Slot(result=QtCore.QObject)\n def getLastCreatedNodeWrapper(self):\n \"\"\"\n Returns the wrapper of the last node created.\n \"\"\"\n return self._nodeWrappers[-1]\n\n def getZMax(self):\n \"\"\"\n Returns the depth of the QML graph\n \"\"\"\n return self._zMax\n\n def getPositionClip(self, nodeName, clipName, clipIndex):\n \"\"\"\n Function called when a new idClip is created.\n Returns the position of the clip.\n The calculation is the same as in the QML file (Node.qml).\n \"\"\"\n node = self.getNodeWrapper(nodeName)\n\n nodeCoord = node.getCoord()\n widthNode = node.getWidth()\n clipSpacing = node.getClipSpacing()\n clipSize = node.getClipSize()\n inputTopMargin = node.getInputTopMargin()\n outputTopMargin = node.getOutputTopMargin()\n\n if (clipName == \"Output\"):\n xClip = nodeCoord.x() + widthNode + clipSize / 2\n yClip = nodeCoord.y() + outputTopMargin + clipSize / 2\n else:\n xClip = nodeCoord.x() - clipSize / 2\n yClip = nodeCoord.y() + inputTopMargin + int(clipIndex) * (clipSpacing + clipSize) + clipSize / 2\n return (xClip, yClip)\n\n @QtCore.Slot(str, str, int, result=QtCore.QPointF)\n def getPointClip(self, nodeName, clipName, clipIndex):\n \"\"\"\n Returns the position of the clip as a QPointF.\n Usefull in QML to have access to x and y.\n \"\"\"\n pos = self.getPositionClip(nodeName, clipName, clipIndex)\n return QtCore.QPointF(pos[0], pos[1])\n\n #################### setters ####################\n\n def setZMax(self, zMax):\n \"\"\"\n Sets the depth of the QML graph\n \"\"\"\n self._zMax = zMax\n\n ################################################## CREATIONS ##################################################\n\n def createNodeWrapper(self, nodeName):\n \"\"\"\n Creates a node wrapper and add it to the nodeWrappers list.\n \"\"\"\n # we search the right node in the node list\n node = self._graph.getNode(nodeName)\n if node:\n nodeWrapper = NodeWrapper(node, self._view)\n self._nodeWrappers.append(nodeWrapper)\n\n def createConnectionWrapper(self, connection):\n \"\"\"\n Creates a connection wrapper and add it to the connectionWrappers list.\n \"\"\"\n conWrapper = ConnectionWrapper(connection, self._view)\n self._connectionWrappers.append(conWrapper)\n\n ################################################ UPDATE WRAPPER LAYER ################################################\n\n def updateNodeWrappers(self):\n \"\"\"\n Updates the nodeWrappers when the signal nodesChanged has been emitted.\n \"\"\"\n # we clear the list\n self.getNodeWrappers().clear()\n # and we fill with the new data\n for node in self._graph.getNodes():\n self.createNodeWrapper(node.getName())\n\n def updateConnectionWrappers(self):\n \"\"\"\n Updates the connectionWrappers when the signal connectionsChanged has been emitted.\n \"\"\"\n # we clear the list\n self.getConnectionWrappers().clear()\n # and we fill with the new data\n for connection in self._graph.getConnections():\n self.createConnectionWrapper(connection)\n\n @QtCore.Slot(QtCore.QObject)\n def updateConnectionsCoord(self, node):\n \"\"\"\n Updates the coordinates of the connections when a node is beeing moved.\n This update just affects the connections of the moving node.\n \"\"\"\n # for each connection of the graph\n for connection in self._graph.getConnections():\n # if the connection concerns the node we're moving\n if node.getName() in connection.getConcernedNodes():\n clipOut = connection.getClipOut()\n clipIn = connection.getClipIn()\n if self.getNodeWrapper(clipOut.getNodeName()) is not None:\n # update clipOut coords\n clipOut.setCoord(self.getPositionClip(clipOut.getNodeName(), clipOut.getClipName(), clipOut.getClipIndex()))\n if self.getNodeWrapper(clipIn.getNodeName()) is not None:\n # update clipIn coords\n clipIn.setCoord(self.getPositionClip(clipIn.getNodeName(), clipIn.getClipName(), clipIn.getClipIndex()))\n self.updateConnectionWrappers()\n\n ################################################## DATA EXPOSED TO QML ##################################################\n\n # nodeWrappers and connectionWrappers\n nodeWrappers = QtCore.Property(QtCore.QObject, getNodeWrappers, constant=True)\n connectionWrappers = QtCore.Property(QtCore.QObject, getConnectionWrappers, constant=True)\n\n # z index for QML (good superposition of nodes in the graph)\n zMaxChanged = QtCore.Signal()\n zMax = QtCore.Property(int, getZMax, setZMax, notify=zMaxChanged)\n", "sub_path": "buttleofx/gui/graph/graphWrapper.py", "file_name": "graphWrapper.py", "file_ext": "py", "file_size_in_byte": 8449, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "PySide.QtCore.QObject", "line_number": 10, "usage_type": "attribute"}, {"api_name": "PySide.QtCore", "line_number": 10, "usage_type": "name"}, {"api_name": "quickmamba.models.QObjectListModel", "line_number": 32, "usage_type": "call"}, {"api_name": "quickmamba.models.QObjectListModel", "line_number": 33, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 44, "usage_type": "call"}, {"api_name": "PySide.QtCore.Slot", "line_number": 109, "usage_type": "call"}, {"api_name": "PySide.QtCore", "line_number": 109, "usage_type": "name"}, {"api_name": "PySide.QtCore.QObject", "line_number": 109, "usage_type": "attribute"}, {"api_name": "PySide.QtCore.QPointF", "line_number": 152, "usage_type": "call"}, {"api_name": "PySide.QtCore", "line_number": 152, "usage_type": "name"}, {"api_name": "PySide.QtCore.Slot", "line_number": 145, "usage_type": "call"}, {"api_name": "PySide.QtCore", "line_number": 145, "usage_type": "name"}, {"api_name": "PySide.QtCore.QPointF", "line_number": 145, "usage_type": "attribute"}, {"api_name": "buttleofx.gui.graph.node.NodeWrapper", "line_number": 171, "usage_type": "call"}, {"api_name": "buttleofx.gui.graph.connection.ConnectionWrapper", "line_number": 178, "usage_type": "call"}, {"api_name": "PySide.QtCore.Slot", "line_number": 203, "usage_type": "call"}, {"api_name": "PySide.QtCore", "line_number": 203, "usage_type": "name"}, {"api_name": "PySide.QtCore.QObject", "line_number": 203, "usage_type": "attribute"}, {"api_name": "PySide.QtCore.Property", "line_number": 226, "usage_type": "call"}, {"api_name": "PySide.QtCore", "line_number": 226, "usage_type": "name"}, {"api_name": "PySide.QtCore.QObject", "line_number": 226, "usage_type": "attribute"}, {"api_name": "PySide.QtCore.Property", "line_number": 227, "usage_type": "call"}, {"api_name": "PySide.QtCore", "line_number": 227, "usage_type": "name"}, {"api_name": "PySide.QtCore.QObject", "line_number": 227, "usage_type": "attribute"}, {"api_name": "PySide.QtCore.Signal", "line_number": 230, "usage_type": "call"}, {"api_name": "PySide.QtCore", "line_number": 230, "usage_type": "name"}, {"api_name": "PySide.QtCore.Property", "line_number": 231, "usage_type": "call"}, {"api_name": "PySide.QtCore", "line_number": 231, "usage_type": "name"}]} +{"seq_id": "592265289", "text": "import qubovert\nimport pyxis as px\nimport numpy as np\nfrom sklearn.metrics.pairwise import cosine_distances\n\nfrom qubo_nn.data import LMDBDataLoader\n\n\nQUBO_SIZE = 9\n\n\ndef solve_qubo2(item):\n Q = qubovert.utils.matrix_to_qubo(item.reshape(QUBO_SIZE, QUBO_SIZE))\n sol = Q.solve_bruteforce(all_solutions=False)\n sol_ = [0 for _ in range(QUBO_SIZE)]\n for k, v in sol.items():\n sol_[k] = v\n return sol_\n\n\ncfg = {\n 'dataset_id': 'a3dbg_all',\n 'use_big': False,\n 'model': {\n 'batch_size': 1,\n 'train_eval_split': 1.0,\n 'shuffle_data': False\n }\n}\n\nlmdb_loader = LMDBDataLoader(cfg)\nloader = lmdb_loader.train_data_loader\ndata = list(loader)\n\nlabels = [d[1][0].numpy() for d in data]\ndata = [d[0][0].numpy() for d in data]\ndata_copy = [d.copy() for d in data]\n\nsols = [np.array([solve_qubo2(d) for d in curr_data]) for curr_data in data]\nprob = [\"NP\", \"MC\", \"MVC\", \"SP\", \"M2SAT\", \"SPP\", \"QA\", \"QK\", \"M3SAT\", \"TSP\", \"MCQ\"]\n\n\ndef make_similar(sols_all, factors=.1):\n for i in range(11):\n if i % 2 == 0:\n if i == 10:\n diff2 = (data_copy[i] - data[i-1]) * factors[i]\n data[i] += diff1\n check(i, data[i], data_copy[i], sols_all[i])\n else:\n diff1 = (data_copy[i+1] - data[i]) * factors[i]\n diff2 = (data_copy[i] - data[i+1]) * factors[i]\n data[i] += diff1\n data[i+1] += diff2\n check(i, data[i], data_copy[i], sols_all[i])\n check(i+1, data[i+1], data_copy[i+1], sols_all[i+1])\n\n\ndef check(i, our_data, cached_data, sols):\n total_len = QUBO_SIZE * len(our_data)\n new_sol = np.array([solve_qubo2(d) for d in our_data])\n # print(abs(new_sol - sols).sum())\n binary_err = 0\n real_err = 0\n total_val = 0\n for k, (new_sol_, sol) in enumerate(zip(new_sol, sols)):\n best_val = sol.T @ cached_data[k] @ sol\n new_val = new_sol_.T @ cached_data[k] @ new_sol_\n\n binary_err += min(\n abs(new_sol_ - sol).sum(),\n abs((1 - new_sol_) - sol).sum()\n )\n real_err += abs(best_val - new_val)\n total_val += abs(best_val)\n # print(prob[i], binary_err, binary_err / total_len, real_err, real_err / total_val)\n print(prob[i], \"\\t\", binary_err / total_len)\n return our_data\n\n\n# factors = [.5, .5, .5, .5, .5, .5, .5, .5, .5, .5, .5]\n# factors = [.4, .4, .4, .4, .4, .4, .4, .4, .4, .4, .4]\n# factors = [.3, .3, .3, .3, .3, .3, .3, .3, .3, .3, .3]\n# factors = [.2, .2, .2, .2, .2, .2, .2, .2, .2, .2, .2]\n# factors = [.6, .4, .6, .4, .6, .4, .6, .4, .6, .4, .6]\n# factors = [.5, .4, .5, .4, .5, .4, .5, .4, .5, .4, .5]\n# factors = [.4, .3, .4, .3, .4, .3, .4, .3, .4, .3, .4]\n# factors = [.6, .4, .6, .4, .6, .4, .6, .4, .6, .4, .6]\n# factors = [0.] * 11\n# factors = [0.05] * 11\nfactors = [0.6] * 11\nmake_similar(sols, factors)\n\ndata_flattened = [d.flatten() for d in data]\nprint(\"COS DIST\")\nprint(cosine_distances(data_flattened).round(2))\n\n\ndef save(data, labels, cfg_id):\n data_new = []\n for d in data:\n data_new.extend(d)\n data = np.array(data_new)\n labels_new = []\n for label in labels:\n labels_new.extend(label)\n labels = np.array(labels_new)\n dirpath = 'datasets/%s/'\n db = px.Writer(\n dirpath=dirpath % cfg_id,\n map_size_limit=60000,\n ram_gb_limit=60\n )\n db.put_samples('input', data, 'target', labels)\n db.close()\n\n\ncfg_id = \"sim_pair_%d_%d\" % tuple(int(100 * f) for f in factors[:2])\nsave(data, labels, cfg_id)\nprint(cfg_id)\n", "sub_path": "qubo_nn/noisy_similarity_algo_pairwise.py", "file_name": "noisy_similarity_algo_pairwise.py", "file_ext": "py", "file_size_in_byte": 3575, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "qubovert.utils.matrix_to_qubo", "line_number": 13, "usage_type": "call"}, {"api_name": "qubovert.utils", "line_number": 13, "usage_type": "attribute"}, {"api_name": "qubo_nn.data.LMDBDataLoader", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 61, "usage_type": "call"}, {"api_name": "sklearn.metrics.pairwise.cosine_distances", "line_number": 96, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 107, "usage_type": "call"}, {"api_name": "pyxis.Writer", "line_number": 109, "usage_type": "call"}]} +{"seq_id": "351208198", "text": "#!/usr/bin/env python\n#\n# Copyright 2007 Google 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#\nimport webapp2\nfrom caesar import encrypt # used to encrypt data\nimport cgi # used to escape bad input from user\n\nform=\"\"\"\n
\n\n
\n
\n\n
\n
\n\n\n
\n\"\"\" # ends variable named form\nclass MainPage(webapp2.RequestHandler): #generic req handler from google\n\n def formwrite(self, rotate=\"\", raw=\"\"): # sets var default values to \"\"\n replacements = {\"rotate\" : rotate, \"raw\" : raw} # dictionary - replace \"\" w/user input\n self.response.write(form % replacements)\n\n#create write form function\n#should accept form , and rotate and text as parameters with empty default values\n\n def get(self):\n self.formwrite()\n\n def post(self):\n rotate = self.request.get(\"rotate\")\n raw = self.request.get(\"raw\")\n\n if not rotate.isdigit(): #validate rotate as number\n self.response.write (\"Rotate must be a number\")\n return\n rotate = int(rotate)\n\n raw = cgi.escape(raw) #any bad data entered into the textarea called raw\n #will be \"escaped\"(turned into plain text)\n result = encrypt(raw,rotate) # this calls the function encrypt,\n #the data entered into the form fields raw,rotate\n #are put into the required parameters for the function\n #and returns encrypted text into the text area called raw\n self.formwrite(rotate, result)\n\n#get text from text area\n#escape text area text ie text_area_text = cgi.escape(text_area_text)\n#call encrypt using rotate\n#call write form function with rotate and encrypted text passed in\n# self.response.out.write(rotate)\n\n\napp = webapp2.WSGIApplication([('/', MainPage) ],# 1 URL '/', that maps to handler called MainPage\n debug=True) #this is the URL mapping section.\n", "sub_path": "main_final.py", "file_name": "main_final.py", "file_ext": "py", "file_size_in_byte": 2745, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "webapp2.RequestHandler", "line_number": 36, "usage_type": "attribute"}, {"api_name": "cgi.escape", "line_number": 57, "usage_type": "call"}, {"api_name": "caesar.encrypt", "line_number": 59, "usage_type": "call"}, {"api_name": "webapp2.WSGIApplication", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "307254006", "text": "import re\nimport os\nimport requests\nfrom PIL import Image\nfrom io import BytesIO\n\ndef getHTML(url):\n try:\n r = requests.get(url)\n r.raise_for_status()\n r.encoding = r.apparent_encoding\n return r.text\n except:\n return 'error'\n\ndef getImage(html): # 获取真实的URL（被加密）\n reg = r'\"objURL\":\"(.*?)\"' # 最小匹配\n imgre = re.compile(reg) # 预编译\n imglist = re.findall(imgre, html) #list\n length = len(imglist)\n print(length)\n return imglist\n\ndef url_decoded(url_): # 对URL解密\n s = ''\n f = { \"w\": \"a\", \"k\": \"b\", \"v\": \"c\", \"1\": \"d\", \"j\": \"e\", \"u\": \"f\", \"2\": \"g\",\\\n \"i\": \"h\", \"t\": \"i\", \"3\": \"j\", \"h\": \"k\", \"s\": \"l\", \"4\": \"m\", \"g\": \"n\",\\\n \"5\": \"o\", \"r\": \"p\", \"q\": \"q\", \"6\": \"r\", \"f\": \"s\", \"p\": \"t\", \"7\": \"u\",\\\n \"e\": \"v\", \"o\": \"w\", \"8\": \"1\", \"d\": \"2\", \"n\": \"3\", \"9\": \"4\", \"c\": \"5\",\\\n \"m\": \"6\", \"0\": \"7\", \"b\": \"8\", \"l\": \"9\", \"a\": \"0\", \"_z2C$q\": \":\",\\\n \"_z&e3B\": \".\", \"AzdH3F\": \"/\" }\n \n #lst = re.findall(r'[a-w\\d]|-|_z2C\\$q|_z&e3B|AzdH3F', url_) 不能满足需求\n lst = re.findall(r'AzdH3F|_z2C\\$q|_z&e3B|AzdH3F|[a-z\\d]|[-_=?]', url_, re.I)\n for i in lst:\n if i in f:\n s += f[i]\n else:\n s += i\n return s\n\ndef downLoad(urls, n, ini_url): # 下载并保存图片到本地(经测试，有 .jpeg 和 .jpg 的格式\n hd = {'user-agent':'chrome/10'}\n for url in urls:\n try:\n rq = requests.get(url, headers=hd)\n rq.raise_for_status()\n img_stream = rq.content\n im = Image.open(BytesIO(img_stream))\n # 经测试 im.format 返回的是大写\n \n picture_format = im.format.lower()\n \n file_path = 'D:/test/pic_8/img_' + str(n) + '.' + picture_format\n x, y = im.size\n if x < 1366 or y < 768: #对图片进行筛选\n continue\n with open(file_path, 'ab') as f:\n f.write(img_stream)\n \n except:\n print('error', url)\n continue\n n += 1\n return n\n\ndef main(word, file_num): #word: 搜索关键字； file_num: 文件数量 (每个文件有30张图片)\n \n initial_url = 'https://image.baidu.com/search/acjson?tn=resultjson_com&ipn=rj&ct=201326592&is=&fp=result&queryWord={word}&cl=2&lm=-1&ie=utf-8&oe=utf-8&adpicid=&st=-1&z=&ic=0&word={word}&s=&se=&tab=&width=&height=&face=0&istype=2&qc=&nc=1&fr=&pn='\n \n keyword_url = initial_url.format(word=word)\n \n n = 1\n for i in range(file_num):\n url = keyword_url + str(30*i)\n \n \n data = getHTML(url)\n url_list = getImage(data)\n L = []\n for x in url_list:\n L.append(url_decoded(x))\n n = downLoad(L, n, url_list)\n\nmain('1366x768高清壁纸护眼', 3)\n \n \n \n \n \n", "sub_path": "get_baidu_picture_2.py", "file_name": "get_baidu_picture_2.py", "file_ext": "py", "file_size_in_byte": 2866, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "requests.get", "line_number": 9, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 18, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 19, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 34, "usage_type": "call"}, {"api_name": "re.I", "line_number": 34, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 46, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 49, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 49, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "583915080", "text": "import collections\nimport functools\nimport logging\nimport re\nimport string\n\nfrom . import base\n\n\nlogger = logging.getLogger(__name__)\n\n\nASCII_TRANSLATION_TABLE = str.maketrans(\n string.ascii_uppercase, string.ascii_lowercase)\n\nRFC1459_TRANSLATION_TABLE = str.maketrans(\n string.ascii_uppercase + '{}|', string.ascii_lowercase + '[]\\\\')\n\nSTRICT_RFC1459_TRANSLATION_TABLE = str.maketrans(\n string.ascii_uppercase + '{}|~', string.ascii_lowercase + '[]\\\\^')\n\n\nCASEFOLDERS = {\n 'ascii': lambda s: s.translate(ASCII_TRANSLATION_TABLE),\n 'rfc1459': lambda s: s.translate(RFC1459_TRANSLATION_TABLE),\n 'strict-rfc1459': lambda s: s.translate(STRICT_RFC1459_TRANSLATION_TABLE),\n 'rfc3454': str.casefold,\n}\n\n\nclass Feature(base.Feature):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.isupport = {}\n\n async def irc_005_received(self, msg):\n for param in msg.args[1:-1]:\n name, equal, value = param.partition('=')\n if equal != '=':\n value = None\n self.isupport[name] = getattr(\n self, '_isupport_convert_' + name.lower(), lambda x: x)(value)\n\n if 'CASEMAPPING' in self.isupport:\n self.casefold = CASEFOLDERS.get(\n self.isupport['CASEMAPPING'].lower(), CASEFOLDERS['ascii'])\n\n @staticmethod\n def _isupport_convert_prefix(value):\n match = re.match(r'\\((?P.*?)\\)(?P.*)', value)\n return dict(zip(match.group('modes'), match.group('prefixes')))\n\n _isupport_convert_chantypes = staticmethod(set)\n\n ChanModes = collections.namedtuple('ChanModes',\n ['modes_with_nick',\n 'modes_with_param',\n 'modes_with_param_when_set',\n 'modes_without_param'])\n\n @classmethod\n def _isupport_convert_chanmodes(cls, value):\n return cls.ChanModes(*(set(v) for v in value.split(',')))\n\n _isupport_convert_modes = staticmethod(int)\n\n @staticmethod\n def _isupport_convert_chanlimit(value):\n chanlimit = {}\n\n for spec in value.split(','):\n sigils, limit = spec.split(':', 1)\n for sigil in sigils:\n chanlimit[sigil] = int(limit)\n\n return chanlimit\n\n _isupport_convert_maxchannels = staticmethod(int)\n _isupport_convert_nicklen = staticmethod(int)\n _isupport_convert_maxbans = staticmethod(int)\n\n @staticmethod\n def _isupport_convert_maxlist(value):\n maxlist = {}\n\n for spec in value.split(','):\n modes, limit = spec.split(':', 1)\n for mode in modes:\n maxlist[mode] = int(limit)\n\n return maxlist\n\n _isupport_convert_statusmsg = staticmethod(set)\n _isupport_convert_elist = staticmethod(set)\n _isupport_convert_topiclen = staticmethod(int)\n _isupport_convert_kicklen = staticmethod(int)\n _isupport_convert_channellen = staticmethod(int)\n _isupport_convert_childlen = staticmethod(int)\n\n @classmethod\n def _isupport_convert_idchan(value):\n idchan = {}\n\n for spec in value.split(','):\n sigils, limit = spec.split(':', 1)\n for sigil in sigils:\n idchan[sigil] = int(limit)\n\n return idchan\n\n @staticmethod\n def _isupport_convert_silence(value):\n if value is None:\n return value\n return int(value)\n\n _isupport_convert_awaylen = staticmethod(int)\n _isupport_convert_maxnicklen = staticmethod(int)\n _isupport_convert_maxtargets = staticmethod(int)\n _isupport_convert_watch = staticmethod(int)\n _isupport_convert_monitor = staticmethod(int)\n\n @staticmethod\n def _isupport_convert_targmax(value):\n targmax = {}\n\n for targ in value.split(','):\n command, num = targ.split(':')\n num = int(num) if num else None\n targmax[command] = num\n\n return targmax\n\n ExtBan = collections.namedtuple('ExtBan', ['prefix', 'flags'])\n\n @classmethod\n def _isupport_convert_extban(cls, value):\n prefix, flags = value.split(',', 1)\n return cls.ExtBan(prefix, set(flags))\n", "sub_path": "aidle/features/isupport.py", "file_name": "isupport.py", "file_ext": "py", "file_size_in_byte": 4231, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "string.ascii_uppercase", "line_number": 14, "usage_type": "attribute"}, {"api_name": "string.ascii_lowercase", "line_number": 14, "usage_type": "attribute"}, {"api_name": "string.ascii_uppercase", "line_number": 17, "usage_type": "attribute"}, {"api_name": "string.ascii_lowercase", "line_number": 17, "usage_type": "attribute"}, {"api_name": "string.ascii_uppercase", "line_number": 20, "usage_type": "attribute"}, {"api_name": "string.ascii_lowercase", "line_number": 20, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 50, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 55, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 134, "usage_type": "call"}]} +{"seq_id": "21250289", "text": "'''\nDo something when the bot is ready to use.\n\nLast update: 24/04/19\n'''\n# Dependancies\nimport discord, asyncio, time\nfrom discord.ext import commands\nfrom discord.ext.commands import Cog\n\nfrom configuration.global_config import V_MAJ,V_MED,V_MIN,V_PHASE\n\nclass On_Ready(Cog):\n def __init__(self, client):\n self.client = client\n \n @Cog.listener()\n async def on_ready(self):\n '''\n Displays a message in the terminal when the bot is ready to use.\n '''\n execution_time = time.strftime('%d/%m/%y - %H:%M', time.gmtime())\n bot_name = self.client.user.name\n console_message = '\\n\\n{} v{}.{}.{} - {}\\n\\n{}\\n----------------------------------------'.format(bot_name, V_MAJ, V_MED, V_MIN, V_PHASE, execution_time)\n\n print(console_message)\n \ndef setup(client):\n client.add_cog(On_Ready(client))", "sub_path": "cogs/event/on_ready_event.py", "file_name": "on_ready_event.py", "file_ext": "py", "file_size_in_byte": 863, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 13, "usage_type": "name"}, {"api_name": "time.strftime", "line_number": 22, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 22, "usage_type": "call"}, {"api_name": "configuration.global_config.V_MAJ", "line_number": 24, "usage_type": "argument"}, {"api_name": "configuration.global_config.V_MED", "line_number": 24, "usage_type": "argument"}, {"api_name": "configuration.global_config.V_MIN", "line_number": 24, "usage_type": "argument"}, {"api_name": "configuration.global_config.V_PHASE", "line_number": 24, "usage_type": "argument"}, {"api_name": "discord.ext.commands.Cog.listener", "line_number": 17, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 17, "usage_type": "name"}]} +{"seq_id": "65364132", "text": "from datetime import datetime, timedelta\nimport requests\nfrom django.conf import settings\nfrom django.utils import translation\nfrom .models import *\nfrom django.shortcuts import render\nfrom django.http import Http404, HttpResponseRedirect, HttpResponse\nfrom django.contrib.auth import authenticate, login\n\nfrom pprint import pprint\n\n\ndef index(request):\n return render(request, 'index.html')\n\n\ndef auth(request):\n if request.method == 'POST':\n user = authenticate(request, username=request.POST['login'], password=request.POST['password'])\n if user is not None:\n login(request, user)\n return HttpResponseRedirect(request.POST['next'])\n\n\ndef races(request):\n try:\n if str(request.user.groups.all()[0]) == \"sportsmen\":\n if request.method == \"POST\":\n try:\n Race.objects.get(sportsmen=request.user.username, distance=int(request.POST[\"distance\"]))\n except:\n Race(sportsmen=request.user.username, distance=int(request.POST[\"distance\"])).save()\n return HttpResponseRedirect(\"/races\")\n else:\n return render(request, \"races0.html\", {\"races\": Race.objects.filter(sportsmen=request.user.username)})\n\n if str(request.user.groups.all()[0]) == \"referee\":\n if request.method == \"POST\":\n stime = datetime.strptime(request.POST[\"stime\"], \"%H:%M\")\n temp_time = datetime.strptime(request.POST[\"btime\"], \"%M:%S\")\n btime = timedelta(minutes=temp_time.minute, seconds=temp_time.second)\n tracks = int(request.POST[\"tracks\"]) - 1\n\n time = stime - btime\n number = 0\n\n for d in [\"100\", \"400\", \"1000\"]:\n track = -1\n time += btime\n number += 1\n for race in Race.objects.filter(distance=d):\n if track < tracks:\n track += 1\n Race.objects.filter(id=race.id).update(track=track + 1, number=number,\n time=time.strftime(\"%H:%M:%S\"))\n else:\n number += 1\n time += btime\n track = 0\n Race.objects.filter(id=race.id).update(track=track + 1, number=number,\n time=time.strftime(\"%H:%M:%S\"))\n return HttpResponseRedirect(\"/races\")\n\n return render(request, \"races1.html\", {\"races\": Race.objects.order_by(\"time\")})\n except:\n return HttpResponseRedirect(\"/\")\n\n\ndef results(request):\n try:\n if str(request.user.groups.all()[0]) == \"referee\":\n if request.method == \"POST\":\n if request.POST[\"but\"] == \"add\":\n Race.objects.filter(sportsmen=request.POST[\"sportsmen\"], distance=request.POST[\"distance\"]).\\\n update(result_time=request.POST[\"result_time\"])\n if request.POST[\"but\"] == \"sort\":\n for d in [\"100\", \"400\", \"1000\"]:\n i = 0\n for race in Race.objects.filter(distance=d).order_by(\"result_time\"):\n i += 1\n Race.objects.filter(id=race.id).update(place=i)\n return HttpResponseRedirect(\"/results\")\n else:\n return render(request, \"result1.html\", {\"races\": Race.objects.order_by(\"time\", \"track\")})\n\n if str(request.user.groups.all()[0]) == \"sportsmen\":\n return render(request, \"result0.html\", {\"races\": Race.objects.order_by(\"time\", \"track\")})\n except:\n return HttpResponseRedirect(\"/\")\n", "sub_path": "app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3856, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.shortcuts.render", "line_number": 14, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 19, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 21, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 22, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 33, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 39, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 40, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 41, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 62, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 64, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 66, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 82, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 84, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 87, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 89, "usage_type": "call"}]} +{"seq_id": "588765726", "text": "\"\"\"create users table\n\nRevision ID: 2713aad37476\nRevises: None\nCreate Date: 2013-09-24 06:51:16.987042\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = '2713aad37476'\ndown_revision = None\n\nfrom alembic import op\nfrom sqlalchemy import *\nfrom sqlalchemy.dialects.mysql import INTEGER as Integer\n\n\ndef upgrade():\n op.create_table(\n 'users',\n Column('user_id', Integer(unsigned=True), primary_key=True, nullable=False),\n Column('email_address', String(length=255), unique=True, index=True, nullable=False),\n Column('created_at', DateTime, nullable=False),\n Column('updated_at', DateTime, nullable=False),\n mysql_engine='InnoDB',\n mysql_charset='utf8'\n )\n\ndef downgrade():\n op.drop_table('users')\n", "sub_path": "db/migrations/versions/2713aad37476_create_users_table.py", "file_name": "2713aad37476_create_users_table.py", "file_ext": "py", "file_size_in_byte": 760, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "alembic.op.create_table", "line_number": 19, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 19, "usage_type": "name"}, {"api_name": "sqlalchemy.dialects.mysql.INTEGER", "line_number": 21, "usage_type": "call"}, {"api_name": "alembic.op.drop_table", "line_number": 30, "usage_type": "call"}, {"api_name": "alembic.op", "line_number": 30, "usage_type": "name"}]} +{"seq_id": "420880014", "text": "\"\"\"\n루트로부터 bfs를 하여 가중치의 합이 가장 큰 마지막 노드를 찾고,\n그 노드로 부터 또 bfs를 하여 가중치 합이 가장큰 마지막 노드를 찾는다.\n그 길이가 지름.\n\"\"\"\n\nfrom collections import deque\n\nn = int(input())\n\ngraph = {}\nfor i in range(n-1):\n p,c,m=list(map(int,input().split()))\n if graph.get(p) is None :\n graph[p] = [(c,m)]\n else :\n graph[p].append((c,m))\n if graph.get(c) is None :\n graph[c] = [(p,m)]\n else :\n graph[c].append((p,m))\n\ndef bfs(graph,startNum):\n q = deque()\n q.extend(graph[startNum])\n visited = [startNum]\n sumT = (0,-1)\n while q:\n num,m = q.popleft()\n visited.append(num)\n graph[num]\n if len(graph[num]) == 1 and sumT[1] < m:\n sumT = (num,m)\n for (num2,m2) in graph[num]:\n if num2 in visited:\n continue\n q.append((num2,m2+m))\n return sumT\nif n == 1 :\n print(0)\nelse :\n first = bfs(graph,1)\n print(bfs(graph,first[0])[1])\n \n\n\n\n# def dfs(graph,startNum):\n# stack = graph[startNum]\n# visited = [1]\n# while stack:\n# num,m = stack.pop()\n# visited.append(num)\n# for (num2,m) in graph[num]:\n# if num2 in visited:\n# continue\n# stack.append((num2,m))\n# print(visited)", "sub_path": "baeckjoon/1967.py", "file_name": "1967.py", "file_ext": "py", "file_size_in_byte": 1239, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "collections.deque", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "280462440", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n ]\n\n operations = [\n migrations.CreateModel(\n name='Item',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=200)),\n ('description', models.TextField(default='', blank=True)),\n ('link', models.CharField(default='', max_length=200, blank=True)),\n ('added_date', models.DateTimeField(auto_now_add=True)),\n ('edited_date', models.DateTimeField(auto_now=True)),\n ],\n options={\n 'ordering': ('edited_date',),\n },\n ),\n migrations.CreateModel(\n name='WishList',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('owner', models.ForeignKey(to=settings.AUTH_USER_MODEL, related_name='wish_list')),\n ('subscriptions', models.ManyToManyField(related_name='subscriptions_rel_+', to='WishListManager.WishList')),\n ],\n ),\n migrations.AddField(\n model_name='item',\n name='wish_list',\n field=models.ForeignKey(to='WishListManager.WishList', related_name='items'),\n ),\n ]\n", "sub_path": "WishListManager/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 1606, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "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": 15, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 33, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "575817649", "text": "import asyncio\nfrom logging import exception\nfrom nodewire import Message\nfrom socket_link import SocketLink\nfrom web_link import WebLink\nfrom mqtt_link import MqttLink\nfrom execution_context import ExecutionContext\nfrom execution_engine import ExecutionEngine\nimport sizeof as sizeof\nimport json\nfrom bson.objectid import ObjectId\nimport time\n\nfrom datetime import datetime, timedelta\nfrom config import mongo_client\n\n\nclass CommandProcessor:\n def __init__(self):\n self.sys_db = mongo_client.nodewire\n self.clients = []\n self.subscriptions = []\n self.messages = asyncio.Queue()\n\n self.socket = SocketLink(self.messages)\n self.socket.new = self.client_added\n self.socket.client_done = self.client_closed\n\n self.pipe = SocketLink(self.messages, port=9001)\n self.pipe.new = self.client_added\n self.pipe.client_done = self.client_closed\n self.pipe.safe = True\n\n self.web = WebLink(self.messages)\n self.web.new = self.client_added\n self.web.client_done = self.client_closed\n\n self.mqtt = MqttLink(self.messages)\n self.mqtt.new = self.client_added\n self.mqtt.client_done = self.client_closed\n\n self.send_queues = []\n for _ in range(4): # 5 tasks for return message\n q = asyncio.Queue()\n self.send_queues.append(q)\n asyncio.Task(self.msg_sender(q))\n self.sq_index = 0\n\n self.execution_contexts = {} # one per gateway\n self.when_dos = [] # universal whendo list\n self.auto_execed = []\n self.exec_engine = ExecutionEngine(self.when_dos, self.execution_contexts)\n self.exec_engine.start()\n\n async def start_execution(self, client, n):\n if not client.safe and n == 0 and client.gateway not in self.execution_contexts:\n gw = await self.sys_db.instances.find_one({'instance_id': client.gateway})\n users = [u['user_instance_and_node_name'].split(':')[1] for u in gw['users'] if u['admin'] == True]\n if users:\n user = users[0]\n self.execution_contexts[client.gateway] = ExecutionContext(client.gateway, self.when_dos,\n self.exec_engine, self.messages)\n if client.gateway not in self.auto_execed:\n kk = await self.execution_contexts[client.gateway].engine_process([\"exec('auto')\"], user)\n if kk == '':\n self.auto_execed.append(client.gateway)\n await self.execution_contexts[client.gateway].engine_process([\"kill('auto')\"], user)\n await self.sys_db.live_gateways.replace_one({'instance': client.gateway},\n {'instance': client.gateway, 'count': n + 1})\n\n def client_added(self, client):\n if client in self.clients:\n self.client_closed(client)\n n = len([c for c in self.clients if c.gateway == client.gateway])\n client.seqn = n\n if n < 20:\n self.sq_index = (self.sq_index + 1) % len(self.send_queues)\n client.send_queue = self.send_queues[self.sq_index]\n asyncio.create_task(self.start_execution(client, n))\n self.clients.append(client)\n if client.type == 'mqtt':\n node = client.nodes[-1]\n if node not in self.execution_contexts[client.gateway].pvs and node != 'ee' and node != 'nwscript':\n self.execution_contexts[client.gateway].add_node(node, client.gateway)\n return True\n return False\n\n def client_closed(self, client):\n try:\n clients = [c for c in self.clients if c == client]\n if len(clients) != 0:\n client = clients[0]\n self.terminate_connections(clients)\n # update number of connected gateways\n n = len([c for c in self.clients if c.gateway == client.gateway])\n n_g = {'instance': client.gateway, 'count': n}\n\n asyncio.get_event_loop().call_soon(self.sys_db.live_gateways.replace_one, {'instance': client.gateway},\n n_g)\n except Exception as ex:\n print('pass 7'),\n print(ex)\n\n async def scavenger(self):\n while True:\n await asyncio.sleep(300) # wait 5 minutes\n for client in [c for c in self.clients if not c.safe if c.type!='mqtt']:\n try:\n if time.time() - client.last_seen > 900: # older than 15 minutes\n self.terminate_connections([client])\n except Exception as ex:\n print('error in scavanger')\n print(ex)\n await asyncio.sleep(0)\n\n def terminate_connections(self, clients):\n for client in clients:\n if client.type!= 'mqtt':\n client.task.cancel()\n if client in self.clients:\n self.clients.remove(client)\n for node in client.nodes:\n if client.gateway is not None and node in self.execution_contexts[client.gateway].pvs:\n try:\n self.execution_contexts[client.gateway].pvs.remove(node)\n thenode = [n for n in self.execution_contexts[client.gateway].variables['nodes'] if\n n.name == node]\n if len(thenode) != 0:\n self.execution_contexts[client.gateway].variables['nodes'].remove(thenode[0])\n self.exec_engine.pending_signals.put_nowait(['nodes', client.gateway])\n except Exception as ex:\n print(\"{}. couldn't remove node: {}\".format(str(ex), node))\n if client.type == 'web' and len(client.nodes)!=0:\n self.exec_engine.pending_signals.put_nowait(('session', client.nodes[0], client.session, client.gateway, client.session))\n client.close()\n\n async def check_id(self, node, gateway, Sender, id, task):\n try:\n client = None\n clients = [client for client in self.clients if client.gateway == gateway and node in client.ghosts]\n if len(clients) != 1:\n print(f'{len(clients)} other instances present')\n for c in clients:\n if node in c.nodes and c.task != task:\n print(f'{node} terminate')\n self.terminate_connections([c])\n elif node in c.ghosts and c.task == task:\n print(f'{node} select')\n if client is None or client.last_seen < c.last_seen:\n client = c\n else:\n client = clients[0]\n\n if not client is None: # and node in client['nodes']:\n d_instance = await self.sys_db.instances.find_one(\n {'instance_id': gateway}) # , {'registered_nodes', 1})\n if client.type == 'web' or len(\n [rn for rn in d_instance['registered_nodes'] if rn['name'] == node and rn['id'] == id]) != 0:\n if node in client.ghosts: client.ghosts.remove(node)\n client.nodes.append(node)\n await client.send((Sender + ' ack cp'))\n self.messages.put_nowait(('{}:{} get ports {}:ee'.format(gateway, node, gateway), None))\n if node not in self.execution_contexts[gateway].pvs and node != 'ee' and node != 'nwscript':\n self.execution_contexts[gateway].add_node(node, gateway)\n # self.messages.put_nowait(('{}:{} get ports {}:ee'.format(gateway, node, gateway), None))\n else:\n pass\n else:\n await client.send('{}:{} not_registered {}:ee'.format(gateway, node, gateway))\n\n else:\n print(\"shouldn't happen\")\n except ConnectionResetError:\n clients = [client for client in self.clients if task == client.task]\n self.terminate_connections(clients)\n except Exception as ex:\n print('checkid error', ex)\n # self.messages.put_nowait((Sender + ' error ' + str(ex).split() + ' cp', None))\n\n def is_auth(self, gateway, nodename, task):\n clients = [client for client in self.clients if task == client.task]\n if clients and clients[0].safe: return True\n if nodename == 'ee' or nodename == 'cp' or (\n gateway in self.execution_contexts and nodename in self.execution_contexts[gateway].apps): return True\n theyre = [cc for cc in self.clients if cc.gateway == gateway and nodename in cc.nodes]\n return len(theyre) != 0\n\n async def handle_subscriptions(self, msg):\n subscribers = [s for s in self.subscriptions if s['command'] == msg.command and s['target'] == msg.sender_full]\n for subscriber in subscribers:\n if subscriber['client'] is None or (subscriber['client'].type!='mqtt' and subscriber['client'].task._state == 'FINISHED'):\n self.subscriptions.remove(subscriber)\n elif msg.address_full != subscriber['subscriber']:\n raw = '{} {} {} {}'.format(subscriber['subscriber'], msg.command, ' '.join(p for p in msg.params), msg.sender_full)\n if not subscriber['client']:\n self.messages.put_nowait((raw, None))\n else:\n try:\n await subscriber['client'].send(raw)\n except:\n self.terminate_connections([subscriber['client']])\n self.subscriptions.remove(subscriber)\n\n async def handle_val(self, msg, task):\n try:\n varname = msg.sender\n for nn in [p for p in self.execution_contexts[msg.sender_instance].variables['nodes'] if p.name == varname]:\n try:\n val = json.loads(msg.params[1])\n except ValueError:\n bc = {'app': msg.address, 'module': self.execution_contexts[msg.sender_instance].themodule, 'variables': {}}\n val = await self.execution_contexts[msg.sender_instance].evaluate(msg.params[1], bc)\n if val != nn[msg.params[0]]:\n nn.set(msg.params[0], val)\n self.exec_engine.pending_signals.put_nowait([varname + '.' + msg.params[0], msg.sender_instance])\n if not nn.discovery_complete:\n nullports = [pp for pp in nn.ports if nn[pp] is None and pp!=msg.params[0]]\n if nullports == []:\n nn.discovery_complete = True\n self.execution_contexts[msg.sender_instance].anounce(nn.name, nn.gateway)\n self.messages.put_nowait(('{}:{} get type {}:ee'.format(msg.sender_instance, nn.name, msg.sender_instance), None))\n for p in nullports:\n response = '{}:{} get {} ee'.format(msg.sender_instance, nn.name, p)\n self.messages.put_nowait((response, None))\n break\n if varname == 'cp':\n self.execution_contexts[msg.sender_instance].variables[msg.params]['cp'].set(msg.params[0],json.loads(msg.params[1]))\n self.exec_engine.pending_signals.put_nowait([varname + '.' + msg.params[0], msg.sender_instance])\n if '@' in varname and '.' in varname:\n try:\n val = json.loads(msg.params[1])\n except:\n bc = {'app': msg.address, 'module': self.execution_contexts[msg.sender_instance].themodule, 'variables': {}}\n val = await self.execution_contexts[msg.sender_instance].evaluate(msg.params[1], bc)\n cs = [c for c in self.clients if c['task'] == task]\n signal = ('me.' + msg.params[0], varname, val, msg.sender, cs[0].session)\n self.exec_engine.pending_signals.put_nowait(signal)\n if varname == 'db':\n self.execution_contexts[msg.sender_instance].variables['_id'] = msg.params[1]\n except ValueError:\n print('pass 6')\n except Exception:\n print('pass 7')\n\n async def handle_ee(self, msg, task):\n gateway = msg.sender_instance\n if msg.command == 'val':\n await self.handle_val(msg, task)\n elif msg.command == 'type':\n nodename = msg.sender\n try:\n thenode = [p for p in self.execution_contexts[gateway].variables['nodes'] if p.name == nodename][0]\n thenode.settype(msg.params[0])\n except:\n pass\n elif msg.command == 'set':\n if msg.params[0] == 'scriptlet':\n # list = json.loads(Params[1])\n line = msg.params[1][1:-1]\n result = []\n try:\n if line == 'reset':\n s = 'cleared'\n self.execution_contexts[gateway].reset('')\n elif line == 'debug':\n s = None\n l_no = 0\n user =msg.sender\n for whendo in [wd for wd in self.when_dos if wd.instance==gateway and wd.app==self.execution_contexts[gateway].theapp[user]]:\n l_no+=1\n if whendo.errors != []:\n result.append('Rule {} -> {}'.format(l_no, json.dumps(whendo.errors)))\n whendo.errors = []\n else:\n user =msg.sender\n s = await self.execution_contexts[gateway].engine_process(line.splitlines(), user)\n if s != None: result.append(str(s))\n except Exception as ex:\n result.append(str(ex))\n self.messages.put_nowait(('{} val script {} {}:ee'.format(msg.sender_full, json.dumps(result), gateway), None))\n # print(result)\n elif msg.params[0] == 'script':\n result = []\n try:\n #self.execution_contexts[gateway].reset()\n user = msg.sender\n if user in self.execution_contexts[gateway].theapp:\n # Params[1] = Params[1].replace(\"'\", '\"')\n lines = msg.params[1][1:-1].splitlines()\n await self.execution_contexts[gateway].engine_process_file(lines, user)\n result.append('Running {}:{}'.format(self.execution_contexts[gateway].theapp[user], self.execution_contexts[gateway].themodule))\n except Exception as ex:\n result.append(str(ex))\n self.messages.put_nowait(('{} val script {} {}:ee'.format(msg.sender_full,json.dumps(result),gateway),None))\n elif msg.params[0].split('.')[0] in self.execution_contexts[gateway].variables[msg.address]['inputs'] or msg.params[0].split('[')[0] in self.execution_contexts[gateway].variables[Address]['inputs']:\n bc = {'app': msg.address,'module': self.execution_contexts[gateway].themodule, 'variables': {}}\n await self.execution_contexts[gateway].evaluate(msg.address + '.' + msg.params[0] + '=' + msg.params[1], bc)\n signal = [msg.params[0], gateway, msg.sender]\n if not task is None:\n cs = [c for c in self.clients if c.task==task]\n if cs[0].session is not None:\n signal.append(cs[0].session)\n self.exec_engine.pending_signals.put_nowait(signal)\n if msg.address != 'ee':\n self.messages.put_nowait(('{} val {} {} {}:{}'.format(msg.sender_full, msg.params[0], msg.params[1], gateway, msg.address_full), None))\n elif msg.command == 'nodes':\n gw = msg.sender_instance\n self.execution_contexts[gateway].nodes = [n.split(':')[1] for n in msg.params]\n self.execution_contexts[gateway].pvs = []\n self.execution_contexts[gateway].variables['nodes'] = []\n for node in self.execution_contexts[gateway].nodes:\n if node not in self.execution_contexts[gateway].pvs and node != 'ee' and node != 'nwscript':\n self.messages.put_nowait(('{}:cp subscribe {} val {}:ee'.format(gw,node,gateway),None))\n # self.messages.put_nowait(('{}:{} get ports {}:ee'.format(gw, node, gateway), None))\n self.execution_contexts[gateway].add_node(node, gateway)\n elif msg.command == 'ports':\n varname = msg.sender\n gw = msg.sender_instance\n if varname not in self.execution_contexts[gateway].pvs and varname != 'ee' and varname != 'nwscript':\n self.messages.put_nowait(('{}:cp subscribe {} val {}:ee'.format(gw, varname, gateway), None))\n self.execution_contexts[gateway].add_node(varname, gateway)\n nn = [p for p in self.execution_contexts[gateway].variables['nodes'] if p.name == varname][0]\n if len(msg.params) == 0:\n nn.discovery_complete = True\n self.execution_contexts[msg.sender_instance].anounce(nn.name, nn.gateway)\n else:\n nn.discovery_complete = False\n self.messages.put_nowait(('{} get {} {}:ee'.format(msg.sender_full, msg.params[0], gateway), None))\n for port in msg.params:\n nn.set(port, None)\n # self.messages.put_nowait(('{} get {} {}:ee'.format(Sender, port, gateway), None))\n elif msg.command == 'get':\n bc = {'app': msg.address,'module': self.execution_contexts[gateway].themodule, 'variables': {}}\n if msg.params[0] == 'ports':\n ports = [p for p in self.execution_contexts[gateway].variables[msg.address]['inputs']] # todo MUMT\n for p in self.execution_contexts[gateway].variables[msg.address]['outputs']:\n if p not in ports: ports.append(p)\n self.messages.put_nowait(('{} ports {} {}:{}'.format(msg.sender, ' '.join(p for p in ports), gateway, msg.address), None))\n elif self.execution_contexts[gateway].is_defined(msg.params[0], bc) and (\n msg.params[0] in self.execution_contexts[gateway].variables[msg.address]['inputs'] or\n msg.params[0] in self.execution_contexts[gateway].variables[msg.address]['outputs']):\n theval = self.execution_contexts[gateway].get_val(msg.params[0], bc) # todo MUMT\n self.messages.put_nowait(('{} val {} {} {}:{}'.format(msg.sender, msg.params[0],\n '\"' + theval + '\"' if isinstance(theval,str) else str(theval), gateway, msg.address), None))\n elif msg.params[0] == 'status':\n for whendo in [wd for wd in self.when_dos if wd.instance == gateway and wd.app == msg.address]:\n self.messages.put_nowait(('{} val status {} {}:ee'.format(msg.sender_full, json.dumps(whendo.errors), gateway), None))\n elif '.' in msg.params[0]:\n var = msg.params[0].split(':')[0]\n if self.execution_contexts[gateway].is_defined(var, bc) and (\n var in self.execution_contexts[gateway].variables[msg.address]['inputs'] or\n var in self.execution_contexts[gateway].variables[msg.address]['outputs']):\n theval = self.execution_contexts[gateway].get_val(msg.params[0], bc)\n self.messages.put_nowait(('{} val {} {} {}:{}'.format(msg.sender_full, var,'\"' + theval + '\"' if isinstance(theval,str) else str(theval), gateway, msg.address_full), None))\n\n async def handle_db(self, msg):\n gateway = msg.address_instance\n db = mongo_client[gateway]\n collection = db[msg.params[0]]\n if msg.command == 'set':\n if msg.params[1] == 'drop':\n await collection.drop()\n elif msg.params[1] == 'remove':\n query = json.loads(msg.params[2])\n if '_id' in query: query['_id'] = ObjectId(query['_id'])\n await collection.delete_many(query)\n elif msg.params[1] == 'index':\n keys = json.loads(msg.params[2])\n options = json.loads(msg.params[3]) if len(msg.params)>=4 else None\n if isinstance(keys, dict):\n keys = [(k, keys[k]) for k in keys]\n if options:\n await collection.create_index(keys, background=True, **options)\n else:\n await collection.create_index(keys, background=True)\n print('index')\n else:\n if len(msg.params) == 3:\n query = json.loads(msg.params[1])\n if '_id' in query: query['_id'] = ObjectId(query['_id'])\n if msg.params[2] == 'remove':\n await collection.delete_many(query)\n elif msg.params[2] == 'removeindex':\n await collection.drop_index(query)\n elif msg.params[2] == 'index':\n keys = query\n if isinstance(keys, dict):\n keys = [(k, keys[k]) for k in keys]\n await collection.create_index(keys, background=True)\n else:\n doc = json.loads(msg.params[2])\n if isinstance(doc, list) or '$set' in doc:\n id = await collection.update_many(query, doc) # update_many\n else:\n id = await collection.replace_one(query, doc) # replace one\n response = msg.sender + ' val ' + msg.params[0] + '_id \\\"' + str(id.modified_count) + \"\\\" db\"\n elif len(msg.params) == 4:\n if msg.params[3] == 'index':\n keys = json.loads(msg.params[1])\n options = json.loads(msg.params[2])\n if isinstance(keys, dict):\n keys = [(k, keys[k]) for k in keys]\n await collection.create_index(keys, background=True, **options)\n else:\n docs = json.loads(msg.params[1])\n if type(docs) is dict:\n docs = [docs]\n for doc in docs:\n if '_id' in doc:\n doc['_id'] = ObjectId(doc['_id'])\n await collection.replace_one({'_id': doc['_id']}, doc)\n id = doc['_id']\n else:\n result = (await collection.insert_one(doc))\n id = result.inserted_id\n response = msg.sender_full + ' val ' + msg.params[0] + '_id \\\"' + str(id) + \"\\\" db\"\n elif msg.command == 'get':\n if msg.params[0] == 'ports':\n collections = await db.collection_names(include_system_collections=False)\n response = msg.sender_full + ' ports ' + ' '.join(c for c in collections) + ' db'\n else:\n query = json.loads(msg.params[1])\n if type(query) is list:\n pipeline = query if type(query) is list else json.loads(msg.params[2])\n # if '_id' in query: query['_id'] = ObjectId(query['_id'])\n results = await collection.aggregate(pipeline).to_list(None)\n rs = []\n try:\n for result in results:\n if '_id' in result: result['_id'] = str(result['_id'])\n rs.append(result)\n response = msg.sender_full + ' val ' + msg.params[0] + ' ' + json.dumps(rs) + ' db'\n except Exception as ex:\n print('pass 4')\n else:\n if '_id' in query: query['_id'] = ObjectId(query['_id'])\n try:\n if len(msg.params) >= 4:\n options = json.loads(msg.params[3])\n sort = options['$sort'] if '$sort' in options else {}\n limit = options['$limit'] if '$limit' in options else {}\n skip = options['$skip'] if '$skip' in options else 0\n if sort!={} and type(sort) == dict:\n sort = list(sort.items())\n if sort and limit:\n results = await collection.find(query, json.loads(msg.params[2])).skip(skip).sort(sort).limit(limit).to_list(None)\n elif sort:\n results = await collection.find(query, json.loads(msg.params[2])).skip(skip).sort(sort).to_list(None)\n elif limit:\n results = await collection.find(query, json.loads(msg.params[2])).skip(skip).limit(limit).to_list(None)\n else:\n results = await collection.find(query, json.loads(msg.params[2])).skip(skip).to_list(None)\n elif len(msg.params)>=3:\n results = await collection.find(query, json.loads(msg.params[2])).to_list(None)\n else:\n results = await collection.find(query).to_list(None)\n rs = []\n for result in results:\n if '_id' in result: result['_id'] = str(result['_id'])\n rs.append(result)\n response = msg.sender_full + ' val ' + msg.params[0] + ' ' + json.dumps(rs) + ' db'\n except Exception as ex:\n print('pass 5')\n self.messages.put_nowait((response, None))\n\n def app_permission(self, app, context):\n if 'permission' in context.variables[app]:\n return {'name': app, 'access_permission': context.variables[app]['permission']}\n else:\n return {'name': app, 'access_permission': [2, 2, 0]}\n\n async def access_allowed(self, user, node, command):\n gu, u = user.split(':')\n g, n = node.split(':')\n\n if u in ['cp', 'ee', 'remote'] and gu == g: return True\n\n n_gateway = await self.sys_db.instances.find_one({'instance_id': g})\n d_user = await self.sys_db.users.find_one({'email': u})\n # u_gateway = await self.sys_db.instances.find_one({'instance_id': gu})\n if d_user is None:\n try:\n client = [client for client in self.clients if client.gateway == gu and u in client.nodes]\n if client != []:\n d_user = client[0].user\n elif u in self.execution_contexts[g].apps:\n email = self.execution_contexts[g].owners[u]\n d_user = await self.sys_db.users.find_one({'email': email})\n else:\n raise Exception('Node or User \"{}\" does not exist'.format(u))\n except Exception as ex:\n raise Exception('Node or User \"{}\" does not exist'.format(u))\n\n if n_gateway['owner'] == d_user['_id']: # super user\n return True\n\n i_user = [u1 for u1 in n_gateway['users'] if u1['user_instance_and_node_name'] == user]\n if i_user == []:\n userclass = 2 # 'others, unregistered users\n else:\n i_user = i_user[0]\n if i_user['admin']:\n userclass = 0 # 'admin'\n else:\n userclass = 1 # 'user'\n\n if n in self.execution_contexts[g].apps:\n i_nodes = [self.app_permission(n, self.execution_contexts[g])]\n else:\n i_nodes = [n1 for n1 in (n_gateway['registered_nodes'] +\n [\n {'name': 'cp', 'access_permission': [2, 2, 0]},\n {'name': 'ee', 'access_permission': [2, 1, 0]},\n {'name': 'db', 'access_permission': [2, 0, 0]}\n ]) if n1['name'] == n]\n if len(i_nodes) != 0:\n i_node = i_nodes[0]\n node_permission = i_node['access_permission'][userclass]\n if command == 'set' and node_permission == 2:\n return True\n elif command != 'set' and node_permission >= 1:\n return True\n return False\n \n async def handle(self, message: Message, task):\n if message.command == 'ThisIs':\n try:\n clients = [client for client in self.clients if task == client.task]\n client = clients[0]\n node = message.sender\n '''\n nodes ghosts\n --------------------------\n in in 1 shouldn't happen\n in not in 2 remove node from clients\n not in in 3 replace ghost if new tcp connection\n not in not in 4 handle\n '''\n if node not in client.ghosts and node not in client.nodes: # case 4\n # add to ghosts\n client.ghosts.append(node)\n if client.type == 'web':\n await self.check_id(node, message.sender_instance, message.sender, message.params[0], task)\n await client.send(message.sender + ' ack cp')\n else:\n if message.params != []:\n await self.check_id(node, message.sender_instance, message.sender, message.params[0], task)\n else:\n await client.send(message.sender + ' ack cp')\n await client.send(message.sender + ' get id cp')\n elif node in client.nodes: # and len(clients)==1: # case 2\n # pass\n if time.time() - client.last_seen > 20:\n client.nodes.remove(node) # = [n for n in client['nodes'] if n != node]\n else: # case 3\n if client.type == 'tcp':\n # terminate previous connections\n other_ghosts = [client for client in self.clients if\n node in client.ghosts and client.task != task]\n if len(other_ghosts) > 1:\n self.terminate_connections(other_ghosts)\n if message.params != []:\n await self.check_id(node, message.sender_instance, message.sender, message.params[0], task)\n else:\n await client.send(message.sender + ' ack cp')\n await client.send(message.sender + ' get id cp')\n elif client.type == 'web':\n await client.send(message.sender + ' ack cp')\n except ConnectionResetError:\n clients = [client for client in self.clients if task == client.task]\n self.terminate_connections(clients)\n except Exception as ex:\n print(f'pass 2: {ex}, node is {message.sender}')\n elif message.command == 'id':\n node = message.sender\n await self.check_id(node, message.sender_instance, message.sender, message.params[0], task)\n elif message.command == 'keepalive':\n client = [client for client in self.clients if task == client.task][0]\n if client:\n try:\n await client.send(message.sender + ' ack cp')\n varname = message.sender\n if varname not in self.execution_contexts[message.sender_instance].pvs and varname not in ['ee','nwscript','cp','db']:\n self.terminate_connections([client])\n except ConnectionResetError:\n self.terminate_connections([client])\n else:\n print(f'CLIENT NOT CONNECTED => {message}')\n elif self.is_auth(message.sender_instance, message.sender, task):\n if message.command == 'get':\n if message.params[0] == 'nodes':\n nodeses = [[n for n in client.nodes] for client in self.clients if\n client.gateway == message.sender_instance and (\n client.type == 'tcp' or client.type == 'mqtt')]\n nodes_web = [[n for n in client.nodes[1:]] for client in self.clients if\n client.gateway == message.sender_instance and client.type == 'web']\n nodes = self.execution_contexts[\n message.sender_instance].apps if message.sender_instance in self.execution_contexts else []\n for nodegroup in nodeses: nodes = nodes + nodegroup\n for nodegroup in nodes_web: nodes = nodes + nodegroup\n response = '{} nodes {} {}:cp'.format(message.sender, ' '.join(nodes), message.sender_instance)\n self.messages.put_nowait((response, None))\n elif message.params[0] == 'ghosts':\n nodeses = [[n for n in client.ghosts] for client in self.clients if\n client.gateway == message.sender_instance]\n nodes = []\n for nodegroup in nodeses: nodes = nodes + nodegroup\n response = '{} ghosts {} {}:cp'.format(message.sender, ' '.join(nodes), message.sender_instance)\n self.messages.put_nowait((response, None))\n elif message.params[0] == 'gateways':\n def fdate(t):\n d = datetime(1970, 1, 1) + timedelta(seconds=t)\n return d.strftime('%X %x')\n\n gw = [{'gateway': c.gateway, 'lastseen': fdate(c['last_seen']), 'nodes': c.nodes} for c in\n self.clients]\n response = f'{message.sender} val gateways {json.dumps(gw)} cp'\n self.messages.put_nowait((response, None))\n elif message.params[0] == 'users':\n users = [' '.join(u) for u in [c.nodes for c in self.clients if\n c['type'] == 'web' and c.gateway == message.sender_instance]]\n response = f'{message.sender} val users {json.dumps(users)} cp'\n self.messages.put_nowait((response, None))\n elif message.params[0] == 'connections':\n response = f'{message.sender} val connections {len(self.clients)} cp'\n self.messages.put_nowait((response, None))\n elif message.params[0] == 'mem':\n m = sizeof.deep_getsizeof(self.execution_contexts[message.sender_instance].variables, set())\n cc = [c for c in self.clients if c.gateway == message.sender_instance]\n n = sizeof.deep_getsizeof(cc[0], set())\n print(m, n)\n response = f'{message.sender} val mem ' + '{\"clients:\":' + str(n) + ', \"context\":' + str(\n m) + '} cp'\n self.messages.put_nowait((response, None))\n elif message.command == 'set':\n if message.params[0] == 'id': # cp set id node_name id_code new_name sender\n clients = [c for c in self.clients if message.params[1] in c.ghosts and c.gateway == message.sender_instance]\n if clients:\n client = clients[0]\n client.ghosts.remove(message.params[1])\n try:\n await client.send('{} set id {} cp'.format(message.params[1], message.params[2]))\n await client.send('{} set name {} cp'.format(message.params[1], message.params[3]))\n except:\n self.terminate_connections(clients)\n elif message.params[0] == 'reset': # reset all conections from this instance\n clients = [c for c in self.clients if c.gateway == message.sender_instance]\n self.terminate_connections(clients)\n elif message.command == 'register':\n try:\n # cp register node id pwd=password user\n d_instance = await self.sys_db.instances.find_one({'instance_id': message.sender_instance})\n i_user = \\\n [u for u in d_instance['users'] if u['user_instance_and_node_name'] == message.sender_full][0]\n if i_user['admin']: # d_user['password'] == Params[2].split('=')[1] and i_user['admin']:\n node = {'name': message.params[0], 'id': message.params[1], 'access_permission': [2, 2, 1]}\n if not node in [n['name'] for n in d_instance['registered_nodes']]:\n d_instance['registered_nodes'].append(node)\n await self.sys_db.instances.replace_one({'_id': d_instance['_id']}, d_instance)\n except Exception as ex:\n print('pass 3')\n elif message.command == 'getnode':\n nodename = message.params[0].split(':')[1] if ':' in message.params[0] else message.params[0]\n nodeinstance = message.params[0].split(':')[0] if ':' in message.params[0] else message.sender_instance\n if await self.access_allowed(message.sender_full, nodeinstance+':'+nodename, 'get'):\n nodes = [n for n in self.execution_contexts[nodeinstance].variables['nodes'] if n['name'] == nodename]\n if len(nodes) != 0:\n dnode = nodes[0]\n nodename = dnode.name\n nodebody = dnode.json()\n if nodes[0].type != None:\n response = '{} node {} {} {} {} cp'.format(message.sender_full, nodebody, nodename, nodes[0].gateway, nodes[0].type)\n else:\n response = '{} node {} {} {} cp'.format(message.sender_full, nodebody, nodename, nodes[0].gateway)\n self.messages.put_nowait((response, None))\n else:\n nodes = [n for n in self.execution_contexts[nodeinstance].apps if n == nodename]\n if len(nodes) != 0:\n ports = [p for p in\n self.execution_contexts[nodeinstance].variables[nodename]['inputs']]\n for p in self.execution_contexts[nodeinstance].variables[nodename]['outputs']:\n if p not in ports: ports.append(p)\n bc = {'app': nodename,'module': self.execution_contexts[nodeinstance].themodule, 'variables': {}}\n content = {}\n for port in ports:\n try:\n content[port] = self.execution_contexts[nodeinstance].get_val(port, bc)\n except:\n content[port] = None\n response = '{} node {} {} {} {} cp'.format(message.sender_full, json.dumps(content), nodename, nodeinstance, nodename)\n self.messages.put_nowait((response, None))\n else:\n #node not online\n self.execution_contexts[nodeinstance].nodewaiters.append({'nodename': nodename, 'waiter': message.sender_full})\n\n elif message.command == 'erase':\n # cp erase node user\n d_instance = await self.sys_db.instances.find_one({'instance_id': message.sender_instance})\n i_user = [u for u in d_instance['users'] if u['user_instance_and_node_name'] == message.sender][0]\n if i_user['admin']:\n client = \\\n [c for c in self.clients if message.params[0] in c.ghosts and c.gateway == message.sender_instance][\n 0]\n if client['type'] == 'tcp':\n client.send('{} set reset cp'.format(message.params[0]))\n else:\n await client.send('{} set reset cp'.format(message.params[0]))\n\n elif message.command == 'subscribe':\n # cp subscribe node cmd sender\n if message.sender != message.params[0]:\n clients = [client for client in self.clients if task == client.task]\n client = clients[0] if len(clients) != 0 else None\n target = message.params[0] if ':' in message.params[0] else message.sender_instance + ':' + message.params[0]\n if len([s for s in self.subscriptions if s['target'] == target and s['command'] == message.params[1] and s['subscriber'] == message.sender_full and s['client'].task==client.task]) == 0:\n self.subscriptions.append({'target': target, 'command': message.params[1], 'subscriber': message.sender_full, 'client': client})\n else:\n client = [c for c in self.clients if message.sender in c.ghosts and c.gateway == message.sender_instance]\n if client:\n await client[0].send(message.sender + ' auth_error cp')\n\n async def msg_sender(self, dqueue):\n while True:\n raw_msg, client = await dqueue.get()\n try:\n await client.send(raw_msg)\n except Exception as ex:\n print('message sender error', ex)\n self.terminate_connections([client])\n\n async def process(self):\n while True:\n #try:\n raw, sender = await self.messages.get()\n # print(f'received:{raw}<<')\n message = Message(raw)\n if message.command == 'error':\n continue\n asyncio.Task(self.handle_subscriptions(message))\n if message.address == 'cp':\n try:\n await self.handle(message, sender)\n except Exception as ex:\n print('error while handling message', message, ex)\n elif (message.command in ['get', 'set'] and (await self.access_allowed(message.sender_full, message.address_full, message.command)))\\\n or (not message.command in ['get', 'set'] and await self.access_allowed(message.address_full, message.sender_full, message.command)):\n if message.address == 'db':\n await self.handle_db(message)\n elif message.address == 'ee' or message.address in self.execution_contexts[message.address_instance].apps:\n try:\n await self.handle_ee(message, sender)\n except Exception as ex:\n print('error in ee while handling message', message, ex)\n else:\n if 'session' in message.named_params:\n clients = [c for c in self.clients if\n message.address in c.nodes and c.instance == message.address_instance and c.session ==\n message.named_params['session']]\n else:\n clients = [c for c in self.clients if\n message.address in c.nodes and c.gateway == message.address_instance] + \\\n [c for c in self.clients if message.address_full in c.nodes and c.safe]\n for client in clients:\n client.send_queue.put_nowait((raw, client))\n if message.command == 'val' and message.sender!='ee':\n await self.handle_val(message, sender)\n self.messages.task_done()\n #except Exception as ex:\n # print('pass unknowns', ex)\n\n async def run_async(self):\n await asyncio.gather(\n asyncio.ensure_future(self.socket.start()),\n asyncio.ensure_future(self.pipe.start()),\n asyncio.ensure_future(self.web.start()),\n asyncio.ensure_future(self.mqtt.start()),\n asyncio.ensure_future(self.process()),\n asyncio.ensure_future(self.scavenger())\n )\n\n def run(self):\n loop = asyncio.get_event_loop()\n try:\n loop.run_until_complete(self.run_async())\n loop.run_forever()\n except KeyboardInterrupt:\n loop.run_until_complete(loop.shutdown_asyncgens())\n\n\nif __name__ == \"__main__\":\n the_cp = CommandProcessor()\n the_cp.run()\n", "sub_path": "cp/cp.py", "file_name": "cp.py", "file_ext": "py", "file_size_in_byte": 45650, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "config.mongo_client.nodewire", "line_number": 20, "usage_type": "attribute"}, {"api_name": "config.mongo_client", "line_number": 20, "usage_type": "name"}, {"api_name": "asyncio.Queue", "line_number": 23, "usage_type": "call"}, {"api_name": "socket_link.SocketLink", "line_number": 25, "usage_type": "call"}, {"api_name": "socket_link.SocketLink", "line_number": 29, "usage_type": "call"}, {"api_name": "web_link.WebLink", "line_number": 34, "usage_type": "call"}, {"api_name": "mqtt_link.MqttLink", "line_number": 38, "usage_type": "call"}, {"api_name": "asyncio.Queue", "line_number": 44, "usage_type": "call"}, {"api_name": "asyncio.Task", "line_number": 46, "usage_type": "call"}, {"api_name": "execution_engine.ExecutionEngine", "line_number": 52, "usage_type": "call"}, {"api_name": "execution_context.ExecutionContext", "line_number": 61, "usage_type": "call"}, {"api_name": "asyncio.create_task", "line_number": 79, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 98, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 106, "usage_type": "call"}, {"api_name": "time.time", "line_number": 109, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 114, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 209, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 227, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 231, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 272, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 280, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 294, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 347, "usage_type": "call"}, {"api_name": "config.mongo_client", "line_number": 358, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 364, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 365, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 368, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 369, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 379, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 380, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 391, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 399, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 400, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 405, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 410, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 422, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 424, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 432, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 436, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 439, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 446, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 448, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 450, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 452, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 454, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 461, "usage_type": "call"}, {"api_name": "nodewire.Message", "line_number": 525, "usage_type": "name"}, {"api_name": "time.time", "line_number": 553, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 612, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 612, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 617, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 622, "usage_type": "call"}, {"api_name": "sizeof.deep_getsizeof", "line_number": 628, "usage_type": "call"}, {"api_name": "sizeof.deep_getsizeof", "line_number": 630, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 690, "usage_type": "call"}, {"api_name": "nodewire.Message", "line_number": 736, "usage_type": "call"}, {"api_name": "asyncio.Task", "line_number": 739, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 772, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 773, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 774, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 775, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 776, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 777, "usage_type": "call"}, {"api_name": "asyncio.ensure_future", "line_number": 778, "usage_type": "call"}, {"api_name": "asyncio.get_event_loop", "line_number": 782, "usage_type": "call"}]} +{"seq_id": "8162448", "text": "import requests, openpyxl,json, re, traceback, logging, time\n\n\n#========================== OPEN EXCEL======================================\n\ndef readexcel(Excel_Location, Excel_Sheet_Name, Module_Name):\n data = {}\n print(\"Opening Excel\")\n try:\n Excel_WorkBook = openpyxl.load_workbook(Excel_Location)\n print(\"Opened Excel\")\n except Exception as error:\n print(\"Error in opening API Excel File\")\n traceback.print_stack()\n\n#========================== GET SHEET FROM EXCEL======================================\n\ndef find_sheet_name(Excel_Sheet_Name,Module_Name,Excel_WorkBook):\n print(\"inside sheet name\")\n for sheetname in Excel_WorkBook.worksheets:\n if sheetname.title == Excel_Sheet_Name:\n ActiveWorkSheet = Excel_WorkBook[sheetname.title]\n print(\"Title of Sheet = \" + ActiveWorkSheet.title)\n print(\"Sheetname =\" + sheetname.title)\n return sheetname\n else:\n continue\n\n#========================== FIND API NAME FROM EXCEL SHEET ======================================\n\ndef find_module_name(Sheetname,Module_Name):\n RowLength = Sheetname.max_row\n ColumnLength = Sheetname.max_column\n for i in range(1, RowLength + 2):\n RowContents = Sheetname.cell(row=i, column=1)\n print(\"Row \" + str(RowContents.row) + \" = \" + str(RowContents.value))\n if ((RowContents.value) == Module_Name):\n print(\"Module Found : \" + str(RowContents.value) + \" in Row - \" + str(RowContents.row) + \" of Worksheet - \" + Sheetname.title)\n return RowContents\n else:\n continue\n try:\n print(str((Sheetname[\"A\" + str(RowContents.row)]).value) + \"\\n That API name section\")\n except Exception as error:\n print(\"Error in getting API Name\")\n traceback.print_stack()\n break\n \n\n # for j in range(1, ColumnLength + 1):\n # Response = dict()\n # ColumnContents = SheetName.cell(row=RowContents.row, column=j)\n # print(ColumnContents.value, end=\"\" + \"\\n\")\n\n#========================== GET THE HTTP METHOD FROM THE API OF THE EXCEL SHEET ======================================\n# \ndef read_method(Sheetname,Rowcontents,Excel_WorkBook):\n try:\n HTTP_Method = (Sheetname[\"B\" + str(Rowcontents.row)].value)\n print(\"ROw\" + str(Rowcontents.row))\n print(HTTP_Method.value)\n return str(HTTP_Method.value)\n except Exception as error:\n print(\"Error in reading HTTP Method from Excel File\")\n print(error)\n Excel_WorkBook.close()\n return 0\n \n#========================== GET PROTOCOL FROM THE API OF THE EXCEL SHEET ======================================\n\ndef read_protocol(Sheetname,RowContents,Excel_WorkBook):\n try:\n Request_Protocol = (Sheetname[\"C\" + str(RowContents.row)])\n print(Request_Protocol.value)\n return str(Request_Protocol.value)\n except Exception as error:\n print(\"Error in reading Request Protocol from Excel File\")\n Excel_WorkBook.close() \n \n\n\n#========================== GET BASE URL FROM THE API OF THE EXCEL SHEET ======================================\n\ndef read_base_url(Sheetname,RowContents,Excel_WorkBook):\n try:\n Request_BaseURL = (Sheetname[\"D\" + str(RowContents.row)])\n print(\"BaseURL = \" + str(Request_BaseURL.value) + \"\\n\")\n return str(Request_BaseURL.value)\n \n except:\n print(\"Error in reading Request Base URL from Excel File\")\n Excel_WorkBook.close() \n\n#========================== GET RELATIVE URL FROM THE API OF THE EXCEL SHEET ======================================\n\ndef read_relative_url(Sheetname,RowContents,Excel_WorkBook): \n try:\n Request_RelativeURL = (Sheetname[\"E\" + str(RowContents.row)])\n print(\"RelativeURL = \" + str(Request_RelativeURL.value) + \"\\n\")\n return str(Request_RelativeURL.value)\n except:\n print(\"Error in reading Request Relative URL from Excel File\")\n Excel_WorkBook.close()\n\n\n # SEt URL\n # try:\n # data['URL'] = str(data['Protocol']) + \"://\" + str(data['BaseURL']) + str(data['RelativeURL'])\n # print(data['URL'])\n # except Exception as error:\n # print(error)\n # traceback.print_stack()\n # Excel_WorkBook.close()\n# break\n\n \n#========================== GET BODY FROM THE API OF THE EXCEL SHEET ======================================\n\ndef read_body(Sheetname,RowContents,Excel_WorkBook):\n try:\n Body_Row = json.loads(str((Sheetname[\"F\" + str(RowContents.row)]).value))\n print(\"Body ROW\",Body_Row)\n return Body_Row\n except:\n print(\"Error in reading Request Body from Excel File\")\n Excel_WorkBook.close()\n#data['Body'] = json.loads(str((SheetName[\"F\" + str(RowContents.row)]).value))\n#Body_Row=(Sheetname[\"F\" + str(RowContents.row)]) \n\n#========================== GET HEADER FROM THE API OF THE EXCEL SHEET ======================================\n\ndef read_header(Sheetname,RowContents,Excel_WorkBook):\n try:\n Header_Row = json.loads(str((Sheetname[\"G\" + str(RowContents.row)]).value))\n return Header_Row\n except:\n print(\"Error in reading Request Header from Excel File\")\n Excel_WorkBook.close()\n#Header_Row = (Sheetname[\"G\" + str(RowContents.row)])\n#data['Header'] = json.loads(str((SheetName[\"G\" + str(RowContents.row)]).value)) try:\n\n#========================== GET COOKIE FROM THE API OF THE EXCEL SHEET ======================================\n\ndef read_cookie(Sheetname,RowContents,Excel_WorkBook):\n try:\n Cookie_Row = json.loads(str((Sheetname[\"H\" + str(RowContents.row)]).value))\n return Cookie_Row\n except:\n print(\"Error in reading Request Cookie from Excel File\")\n Excel_WorkBook.close()\n \n# Cookie_Row = (Sheetname[\"H\" + str(RowContents.row)])\n# data['Cookie'] = json.loads(str((SheetName[\"H\" + str(RowContents.row)]).value))\n\n#========================== GET ALL API DETAILS TO SEND THE REQUEST ======================================\n \ndef find_api(Excel_Location, Excel_Sheet_Name, Module_Name):\n try:\n data = {}\n Excel_WorkBook=readexcel(Excel_Location, Excel_Sheet_Name, Module_Name)\n print(\"Outside read excel\")\n Sheetname=find_sheet_name(Excel_Sheet_Name,Module_Name,Excel_WorkBook)\n print(\"Out of sheet name\" + Sheetname.title)\n Rowcontents=find_module_name(Sheetname,Module_Name)\n print(\"Outside row\")\n if(read_method(Sheetname,Rowcontents,Excel_WorkBook)):\n method_name=read_method(Sheetname,Rowcontents,Excel_WorkBook)\n data['HTTPMethod'] = method_name\n print(data['HTTPMethod'])\n else:\n raise Error(\"HTTP Method not recognised\")\n protocol_name=read_protocol(Sheetname,Rowcontents,Excel_WorkBook)\n data['Protocol'] = protocol_name\n print(data['Protocol'] + \"\\n\")\n base_url=read_base_url(Sheetname,Rowcontents,Excel_WorkBook)\n relative_url=read_relative_url(Sheetname,Rowcontents,Excel_WorkBook)\n try:\n data['URL'] = str(data['Protocol']) + \"://\" + base_url + relative_url\n print(data['URL'])\n except:\n print(\"Error in concatenating URL\")\n #Excel_WorkBook.close()\n body=read_body(Sheetname,Rowcontents,Excel_WorkBook)\n print(\"Body = \" + body + \"\\n\")\n data['Body'] = body\n print(\"Fdata Body\")\n print(data['Body'])\n header=read_header(Sheetname,Rowcontents,Excel_WorkBook)\n print(\"Header = \" + header + \"\\n\")\n data['Header'] = json.loads(header)\n print(data['Header'])\n cookie=read_cookie(Sheetname,Rowcontents,Excel_WorkBook) \n print(\"Cookie = \" + cookie + \"\\n\")\n data['Cookie'] = json.loads(cookie)\n print(data['Cookie'])\n print(data)\n except:\n print(\"Error in reading contents\")\n\n return data\n\n\n#========================== IDENTIFY THE $ PARAMETERS TO BE ATTACKED ======================================\n\ndef find_vulnerable_parameters(result):\n parameter = {}\n try:\n Method = str(result['HTTPMethod'])\n parameter['HTTPMethod'] = Method\n print(\"HTTP Method: \" + parameter['HTTPMethod'])\n\n Protocol = str(result['Protocol'])\n parameter['Protocol'] = Protocol\n print(\"Protocol: \" + parameter['Protocol'])\n\n parameter['URL'] = str(result['URL'])\n print(\"URL: \" + str(parameter['URL']))\n\n parameter['Body']=result['Body']\n print(\"Body: \" + str(parameter['Body']))\n\n parameter['Header']=result['Header']\n print(\"Header: \" + str(parameter['Header']))\n\n parameter['Cookie']=result['Cookie']\n print(\"Cookie: \" + str(parameter['Cookie']))\n\n except:\n\n print(\"Error in Reading API Contents\")\n\n try:\n parameter['URL_Parameter'] = re.findall(r'\\$(.*?)\\$', parameter['URL'])\n if(parameter['URL_Parameter']):\n print(\"Parameter found in URL\")\n print(parameter['URL_Parameter'])\n else:\n print(\"No Parameter found in URL\")\n # parameter['URL_Parameter'] = parameter['URL']\n\n parameter['Body_Parameter'] = re.findall(r'\\$(.*?)\\$', str(parameter['Body']))\n if(parameter['Body_Parameter']):\n print(\"Parameter found in Body\")\n print(parameter['Body_Parameter'])\n else:\n print(\"No Parameter found in Body\")\n parameter['Body_Parameter'] = parameter['Body']\n\n parameter['Header_Parameter'] = re.findall(r'\\$(.*?)\\$', str(parameter['Header']))\n if (parameter['Header_Parameter']):\n print(\"Parameter found in Header\")\n print(parameter['Header_Parameter'])\n else:\n print(\"No Parameter found in Header\")\n #parameter['Header_Parameter'] = parameter['Header']\n\n parameter['Cookie_Parameter'] = re.findall(r'\\$(.*?)\\$', str(parameter['Cookie']))\n if (parameter['Cookie_Parameter']):\n print(\"Parameter found in Cookie\")\n print(parameter['Cookie_Parameter'])\n else:\n print(\"No Parameter found in Cookie\")\n #parameter['Cookie_Parameter'] = parameter['Cookie']\n except:\n print(\"Error in fetching Parameters\")\n\n return parameter\n\n\n#========================== PERFORM ATTACK ON THE $ PARAMETERS AND GET RESPONSE ======================================\n\ndef perform_attack(Area,METhod,Any_Parameter,Payload_RowLength,Payload_SheetName,URL,Body,Header,Cookie):\n result = []\n if(Any_Parameter):\n print(\"Parameter found in = \" + Area)\n for i in range(2, Payload_RowLength + 1):\n Payload_RowContents = Payload_SheetName.cell(row=i, column=1)\n print(\"Row \" + str(Payload_RowContents.row - 1) + \" = \" + str(Payload_RowContents.value), end=\"\" + \"\\n\")\n for key in Any_Parameter:\n if(Area=='URL'):\n print(\"Area is = \" + Area)\n AttackURL = URL.replace(key, str(Payload_RowContents.value))\n AttackURL = AttackURL.replace(\"$\", \"\")\n print(\"Attack url : \" + str(AttackURL))\n AttackBody = str(Body).replace(\"$\", \"\")\n print(\"Body : \" + str(AttackBody))\n AttackHeader = str(Header).replace(\"$\", \"\")\n print(\"Header : \" + str(AttackHeader))\n AttackCookie = str(Cookie).replace(\"$\", \"\")\n print(\"Cookie : \" + AttackCookie)\n elif(Area == 'Body'):\n print(\"Area is = \" + Area)\n AttackBody = Body.replace(key, str(Payload_RowContents.value))\n print(\"Original BOdy ===============\" + AttackBody)\n print(\"for2\", i+1)\n AttackURL = URL\n print(\"URL : \" + str(AttackURL))\n AttackBody = str(AttackBody).replace(\"$\", \"\")\n print(\"AttackBody : \" + str(AttackBody))\n AttackHeader = str(Header)\n print(\"Header : \" + str(AttackHeader))\n AttackCookie = str(Cookie)\n print(\"Cookie : \" + AttackCookie)\n elif(Area == 'Header'):\n print(\"Area is = \" + Area)\n AttackHeader = Header.replace(key, str(Payload_RowContents.value))\n AttackURL = AttackURL.replace(\"$\", \"\")\n print(AttackURL)\n AttackBody = str(Body).replace(\"$\", \"\")\n print(AttackBody)\n AttackHeader = str(AttackHeader).replace(\"$\", \"\")\n print(AttackHeader)\n AttackCookie = str(Cookie).replace(\"$\", \"\")\n print(AttackCookie)\n elif(Area == 'Cookie'):\n print(\"Area is = \" + Area)\n AttackCookie = Cookie.replace(key, str(Payload_RowContents.value))\n AttackURL = AttackURL.replace(\"$\", \"\")\n print(AttackURL)\n AttackBody = str(Body).replace(\"$\", \"\")\n print(AttackBody)\n AttackHeader = str(Header).replace(\"$\", \"\")\n print(AttackHeader)\n AttackCookie = str(AttackCookie).replace(\"$\", \"\")\n print(AttackCookie) \n try:\n if (METhod == 'GET'):\n print(\"Method found in attack = \" + METhod)\n response = requests.get(AttackURL, data=AttackBody, headers=AttackHeader)\n elif(METhod == 'POST'):\n print(\"Method found in attack = \" + METhod)\n response = requests.post(AttackURL, data=AttackBody, headers=AttackHeader)\n print(\"Got ============ response\")\n elif(METhod == 'PUT'):\n response = requests.put(AttackURL, data=AttackBody, headers=AttackHeader)\n elif(METhod == 'DELETE'):\n response = requests.delete(AttackURL, data=AttackBody, headers=AttackHeader)\n StatusCode = str(response.status_code)\n Response_Body = str(response.text)\n print(\"Response Status Code : \" + str(StatusCode) + \"\\n\")\n print(\"Response Body : \" + str(Response_Body) + \"\\n\")\n result.append(StatusCode)\n print(result)\n time.sleep(10)\n except:\n print(traceback)\n print(\"Error in executing: \" + str(AttackURL))\n StatusCode = '500'\n result.append(StatusCode)\n print(result)\n time.sleep(10)\n print(result)\n else:\n print(\"No Parameter choosen in the API\")\n return result\n \n\n#========================== MAIN FUNCTION WHICH IS CALLED BY API.ROBOT ======================================\n\ndef CORS(Excel_Location, Excel_Sheet_Name, Module_Name):\n result = {}\n try:\n returnvalue = readexcel(Excel_Location, Excel_Sheet_Name, Module_Name)\n print(\"Data from find_vulnerable_parameters \")\n print(result)\n except Exception as error:\n print(error)\n traceback.print_stack()\n\n try:\n API = returnvalue['API']\n except Exception as error:\n print(error)\n traceback.print_stack()\n\n try:\n Method = returnvalue['HTTPMethod']\n except Exception as error:\n print(error)\n traceback.print_stack()\n\n try:\n Protocol = returnvalue['Protocol']\n except Exception as error:\n print(error)\n traceback.print_stack()\n\n try:\n BaseURL = returnvalue['BaseURL']\n except Exception as error:\n print(error)\n traceback.print_stack()\n\n try:\n RelativeURL = returnvalue['RelativeURL']\n except Exception as error:\n print(error)\n traceback.print_stack()\n\n try:\n URL = returnvalue['URL']\n except Exception as error:\n print(error)\n traceback.print_stack()\n\n try:\n Body = returnvalue['Body']\n except Exception as error:\n print(error)\n traceback.print_stack()\n\n try:\n Header = returnvalue['Header']\n except Exception as error:\n print(error)\n traceback.print_stack()\n\n\n try:\n Cookie = returnvalue['Cookie']\n except Exception as error:\n print(error)\n traceback.print_stack()\n\n try:\n Check_BaseURL = re.findall(r'\\$(.*?)\\$', str(BaseURL))\n if (Check_BaseURL != \"\"):\n for key in Check_BaseURL:\n BaseURL = BaseURL.replace(\"$\", \"\")\n print(URL)\n else:\n print(\"No Change in Base URL\")\n except Exception as error:\n print(error)\n traceback.print_stack()\n\n try:\n Check_RelativeURL = re.findall(r'\\$(.*?)\\$', str(RelativeURL))\n if (Check_RelativeURL != \"\"):\n for key in Check_RelativeURL:\n RelativeURL = RelativeURL.replace(\"$\", \"\")\n print(RelativeURL)\n else:\n print(\"No Change in Relative URL\")\n except Exception as error:\n print(\"Error in reading Relative URL\")\n traceback.print_stack()\n\n try:\n Check_Method = re.findall(r'\\$(.*?)\\$', str(Method))\n if (Check_Method != \"\"):\n for key in Check_Method:\n Method = Method.replace(\"$\", \"\")\n print(Method)\n else:\n print(\"No Change in Method\")\n except Exception as error:\n print(\"Error in finding Method\")\n traceback.print_stack()\n\n try:\n Check_Protocol = re.findall(r'\\$(.*?)\\$', str(Protocol))\n if (Check_Protocol != \"\"):\n for key in Check_Protocol:\n Protocol = Protocol.replace(\"$\", \"\")\n print(Protocol)\n else:\n print(\"No Change in Protocol\")\n except Exception as error:\n print(\"Error in finding Protocol\")\n traceback.print_stack()\n\n try:\n Check_URL = re.findall(r'\\$(.*?)\\$', str(URL))\n if(Check_URL != \"\"):\n for key in Check_URL:\n URL = URL.replace(\"$\",\"\")\n print(URL)\n else:\n print(\"No Change in URL\")\n except Exception as error:\n print(error)\n traceback.print_stack()\n\n try:\n Check_Body = re.findall(r'\\$(.*?)\\$', str(Body))\n if (Check_Body != \"\"):\n for key in Check_Body:\n Body = Body.replace(\"$\", \"\")\n print(Body)\n else:\n print(\"No Change in Body\")\n print(Body)\n except Exception as error:\n print(error)\n traceback.print_stack()\n\n\n try:\n Check_Header = re.findall(r'\\$(.*?)\\$', str(Header))\n if (Check_Header != \"\"):\n for key in Check_Header:\n Header = Header.replace(\"$\", \"\")\n print(Header)\n else:\n print(\"No Change in Header\")\n print(Header)\n except Exception as error:\n print(error)\n traceback.print_stack()\n\n try:\n Check_Cookie = re.findall(r'\\$(.*?)\\$', str(Cookie))\n if (Check_Cookie != \"\"):\n for key in Check_Cookie:\n Cookie = Cookie.replace(\"$\", \"\")\n print(Cookie)\n else:\n print(\"No Change in Cookie\")\n print(Cookie)\n except Exception as error:\n print(error)\n traceback.print_stack()\n\n\n try:\n StatusCode = {}\n Origin = {'Origin':'www.geeksforgeeks.org'}\n print(Origin)\n\n if(Method == 'GET'):\n GET = requests.get(URL, data=Body, headers=Origin)\n result['GET StatusCode'] = str(GET.status_code)\n\n elif(Method == 'POST'):\n POST = requests.post(URL, data=Body, headers=Origin)\n result['GET StatusCode'] = str(POST.status_code)\n\n elif (Method == 'PUT'):\n PUT = requests.put(URL, data=Body, headers=Origin)\n result['HOST StatusCode'] = str(PUT.status_code)\n\n elif (Method == 'DELETE'):\n DELETE = requests.delete(URL, data=Body, headers=Origin)\n result['GET StatusCode'] = str(DELETE.status_code)\n\n except Exception as error:\n print(\"Error in executing HOST Injection\")\n traceback.print_stack()\n\n return result", "sub_path": "MethodFiles/Cross_Origin_Resource_Sharing.py", "file_name": "Cross_Origin_Resource_Sharing.py", "file_ext": "py", "file_size_in_byte": 21028, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "openpyxl.load_workbook", "line_number": 10, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 14, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 46, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 121, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 134, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 146, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 190, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 194, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 233, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 241, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 249, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 257, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 329, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 332, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 335, "usage_type": "call"}, {"api_name": "requests.delete", "line_number": 337, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 344, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 351, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 368, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 374, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 380, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 386, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 392, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 398, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 404, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 410, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 416, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 423, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 426, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 435, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 438, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 447, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 450, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 459, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 462, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 471, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 474, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 483, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 486, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 496, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 500, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 510, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 513, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 523, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 532, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 536, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 540, "usage_type": "call"}, {"api_name": "requests.delete", "line_number": 544, "usage_type": "call"}, {"api_name": "traceback.print_stack", "line_number": 549, "usage_type": "call"}]} +{"seq_id": "257343643", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nui class for the BUILD toolset\n\"\"\"\n#==============================================================================\n# imports\n#==============================================================================\nimport sys, os, warnings, tempfile, logging, configparser, datetime, time\nimport os.path\nfrom shutil import copyfile\n\n#PyQt\nfrom PyQt5 import uic, QtWidgets\nfrom PyQt5.QtWidgets import QAction, QFileDialog, QListWidget, QTableWidgetItem\n\n#===============================================================================\n# from qgis.PyQt.QtCore import QSettings, QTranslator, QCoreApplication, QObject \n# from qgis.PyQt.QtGui import QIcon\n#===============================================================================\n\n\nfrom qgis.core import *\nfrom qgis.analysis import *\nimport qgis.utils\nimport processing\nfrom processing.core.Processing import Processing\n\n\nimport resources\n\nimport pandas as pd\nimport numpy as np #Im assuming if pandas is fine, numpy will be fine\n\n\n#==============================================================================\n# custom imports\n#==============================================================================\n\nfrom build.rsamp import Rsamp\nfrom build.lisamp import LikeSampler\nfrom build.oth_rfda import RFDAconv\n\n\n\nfrom hlpr.plug import *\nfrom hlpr.Q import *\nfrom hlpr.basic import *\n\n# This loads your .ui file so that PyQt can populate your plugin with the elements from Qt Designer\nui_fp = os.path.join(os.path.dirname(__file__), 'build.ui')\nassert os.path.exists(ui_fp)\nFORM_CLASS, _ = uic.loadUiType(ui_fp)\n\n\nclass DataPrep_Dialog(QtWidgets.QDialog, FORM_CLASS, QprojPlug):\n \n event_name_set = [] #event names\n \n \n \n def __init__(self, iface, parent=None):\n \"\"\"these will only ini tthe first baseclass (QtWidgets.QDialog)\n \n required\"\"\"\n super(DataPrep_Dialog, self).__init__(parent)\n #super(DataPrep_Dialog, self).__init__(parent)\n self.setupUi(self)\n \n # Set up the user interface from Designer through FORM_CLASS.\n # After self.setupUi() you can access any designer object by doing\n # self., and you can use autoconnect slots - see\n # http://qt-project.org/doc/qt-4.8/designer-using-a-ui-file.html\n # #widgets-and-dialogs-with-auto-connect\n\n self.ras = []\n self.ras_dict = {}\n self.vec = None\n\n self.iface = iface\n \n self.qproj_setup()\n \n \n self.connect_slots()\n \n \n self.logger.info('DataPrep_Dialog initilized')\n \n\n def connect_slots(self):\n log = self.logger.getChild('connect_slots')\n #self.testit()\n #======================================================================\n # pull project data\n #======================================================================\n #pull layer info from project\n rlays_d = dict()\n vlays_d = dict()\n for layname, layer in QgsProject.instance().mapLayers().items():\n if isinstance(layer, QgsVectorLayer):\n vlays_d[layname] = layer\n elif isinstance(layer, QgsRasterLayer):\n rlays_d[layname] = layer\n else:\n self.logger.debug('%s not filtered'%layname)\n \n #=======================================================================\n # general----------------\n #=======================================================================\n #=======================================================================\n # def test():\n # self.logger.push('test button pushed')\n # \n # for i in range(10):\n # time.sleep(.5)\n # self.progressBar.setValue(i + 1)\n # \n # self.logger.push('finished')\n #=======================================================================\n #ok/cancel buttons\n self.buttonBox.accepted.connect(self.reject)\n self.buttonBox.rejected.connect(self.reject)\n \n \n #connect to status label\n \"\"\"\n this could be moved onto the feedback object...\n but would be a lot of work to move it off the logger\n and not sure what the benefit would be\n \n see hlpr.plug.logger._loghlp()\n \"\"\"\n self.logger.statusQlab=self.progressText\n self.logger.statusQlab.setText('BuildDialog initialized')\n \n #======================================================================\n # setup tab----------\n #======================================================================\n #populate guis\n self.comboBox_vec.setFilters(QgsMapLayerProxyModel.VectorLayer) #SS. Inventory Layer: Drop down\n self.comboBox_aoi.setFilters(QgsMapLayerProxyModel.PolygonLayer) #SS. Project AOI\n self.comboBox_SSelv.addItems(['datum', 'ground']) #ss elevation type\n \n self.comboBox_aoi.setCurrentIndex(-1) #by default, lets have this be blank\n \n #Working Directory browse\n def browse_wd():\n return self.browse_button(self.lineEdit_wd, prompt='Select Working Directory',\n qfd = QFileDialog.getExistingDirectory)\n \n self.pushButton_wd.clicked.connect(browse_wd) # SS. Working Dir. Browse\n \n #WD force open\n def open_wd():\n force_open_dir(self.lineEdit_wd.text())\n \n self.pushButton_wd_open.clicked.connect(open_wd)\n \n #======================================================================\n # #Inventory Vector Layer\n #======================================================================\n #change the 'cid' display when the finv selection changes\n def upd_cid():\n return self.mfcb_connect(\n self.mFieldComboBox_cid, self.comboBox_vec.currentLayer(),\n fn_str = 'xid' )\n \n self.comboBox_vec.layerChanged.connect(upd_cid) #SS inventory vector layer\n \n #find a good layer\n try:\n for layname, vlay in vlays_d.items():\n if layname.startswith('finv'):\n break\n \n self.logger.info('setting comboBox_vec = %s'%vlay.name())\n self.comboBox_vec.setLayer(vlay)\n except Exception as e:\n self.logger.warning('failed to set inventory layer w: \\n %s'%e)\n \n #Vulnerability Curve Set\n def browse_curves():\n return self.browse_button(self.lineEdit_curve, prompt='Select Curve Set',\n qfd = QFileDialog.getOpenFileName)\n \n self.pushButton_SScurves.clicked.connect(browse_curves)# SS. Vuln Curve Set. Browse\n \n #program controls\n self.checkBox_SSoverwrite.stateChanged.connect(self.set_overwrite) #SS overwrite data files\n \n #generate new control file \n self.pushButton_generate.clicked.connect(self.build_scenario) #SS. generate\n \n #CanFlood Control File\n def browse_cf():\n return self.browse_button(self.lineEdit_cf_fp, prompt='Select CanFlood control file',\n qfd=QFileDialog.getOpenFileName)\n \n self.pushButton_cf.clicked.connect(browse_cf)# SS. Model Control File. Browse\n \n #======================================================================\n # hazard sampler---------\n #======================================================================\n # Set GUI elements\n self.comboBox_ras.setFilters(QgsMapLayerProxyModel.RasterLayer)\n \"\"\"\n todo: swap this out with better selection widget\n \"\"\"\n #selection \n self.pushButton_remove.clicked.connect(self.remove_text_edit)\n self.pushButton_clear.clicked.connect(self.clear_text_edit)\n self.pushButton_add_all.clicked.connect(self.add_all_text_edit)\n \n self.comboBox_ras.currentTextChanged.connect(self.add_ras)\n \n #=======================================================================\n # inundation\n #=======================================================================\n #connect dtm layer name to display box\n def upd_dtmlayname():\n vlay = self.comboBox_dtm.currentLayer()\n if isinstance(vlay,QgsVectorLayer):\n self.label_HS_dtmln.setText(vlay.name())\n \n self.comboBox_dtm.layerChanged.connect(upd_dtmlayname)\n \n\n #=======================================================================\n # #complex\n #=======================================================================\n #display the gtype when the finv changes\n def upd_gtype():\n vlay = self.comboBox_vec.currentLayer()\n if isinstance(vlay,QgsVectorLayer):\n gtype = QgsWkbTypes().displayString(vlay.wkbType())\n self.label_HS_finvgtype.setText(gtype)\n \n self.comboBox_vec.layerChanged.connect(upd_gtype) #SS inventory vector layer\n \n #display sampling stats options to user \n def upd_stat():\n vlay = self.comboBox_vec.currentLayer()\n if isinstance(vlay,QgsVectorLayer):\n gtype = QgsWkbTypes().displayString(vlay.wkbType())\n self.comboBox_HS_stat.setCurrentIndex(-1)\n \n if 'Polygon' in gtype:\n self.comboBox_HS_stat.addItems(\n ['Mean','Median','Min','Max'])\n \n self.comboBox_vec.layerChanged.connect(upd_stat) #SS inventory vector layer\n \n \n #=======================================================================\n # #execute\n #=======================================================================\n self.pushButton_HSgenerate.clicked.connect(self.run_rsamp)\n \n #======================================================================\n # event likelihoods\n #======================================================================\n self.pushButton_ELstore.clicked.connect(self.set_event_vals)\n \n \"\"\"dev button\n self.pushButton_ELdev.clicked.connect(self._pop_el_table)\"\"\"\n \n \n #======================================================================\n # Conditional Probabilities-----------\n #======================================================================\n \"\"\"todo: rename the buttons so they align w/ the set labels\n \n todo: automatically populate the first column of boxes w/ those layers\n sampled w/ rsamp\n \"\"\"\n #list of combo box names on the likelihood sampler tab\n self.ls_cb_d = { #set {hazard raster : lpol}\n 1: (self.MLCB_LS1_event_3, self.MLCB_LS1_lpol_3),\n 2: (self.MLCB_LS1_event_4, self.MLCB_LS1_lpol_4),\n 3: (self.MLCB_LS1_event_5, self.MLCB_LS1_lpol_5),\n 4: (self.MLCB_LS1_event, self.MLCB_LS1_lpol),\n 5: (self.MLCB_LS1_event_6, self.MLCB_LS1_lpol_6),\n 6: (self.MLCB_LS1_event_7, self.MLCB_LS1_lpol_7),\n 7: (self.MLCB_LS1_event_2, self.MLCB_LS1_lpol_2),\n 8: (self.MLCB_LS1_event_8, self.MLCB_LS1_lpol_8)\n }\n \n #loop and set filteres\n first = True\n for sname, (mlcb_haz, mlcb_lpol) in self.ls_cb_d.items():\n #set drop down filters on hazard bars\n mlcb_haz.setFilters(QgsMapLayerProxyModel.RasterLayer)\n mlcb_haz.setAllowEmptyLayer(True)\n mlcb_haz.setCurrentIndex(-1) #set selection to none\n \n #on polygon bars\n mlcb_lpol.setFilters(QgsMapLayerProxyModel.PolygonLayer)\n mlcb_lpol.setAllowEmptyLayer(True)\n mlcb_lpol.setCurrentIndex(-1) #set selection to none\n \n if first:\n mlcb_lpol_1 = mlcb_lpol\n first = False\n\n \n #connect to update the field name box (based on the first layer)\n def upd_lfield(): #updating the field box\n return self.mfcb_connect(\n self.mFieldComboBox_LSfn, mlcb_lpol_1.currentLayer(),\n fn_str = 'fail' )\n \n \n mlcb_lpol_1.layerChanged.connect(upd_lfield)\n \n \n #connect execute\n self.pushButton_LSsample.clicked.connect(self.run_lisamp)\n \n #======================================================================\n # DTM sampler---------\n #======================================================================\n self.comboBox_dtm.setFilters(QgsMapLayerProxyModel.RasterLayer)\n self.pushButton_DTMsamp.clicked.connect(self.run_dsamp)\n \n #======================================================================\n # validator-----------\n #======================================================================\n self.pushButton_Validate.clicked.connect(self.run_validate)\n \n #======================================================================\n # other------------\n #======================================================================\n #Vulnerability Curve Set\n def browse_rfda_crv():\n return self.browse_button(self.lineEdit_wd_OthRf_cv, prompt='Select RFDA curve .xls',\n qfd = QFileDialog.getOpenFileName)\n \n self.pushButton_wd_OthRf_cv.clicked.connect(browse_rfda_crv)\n \n self.mMapLayerComboBox_OthR_rinv.setFilters(QgsMapLayerProxyModel.PointLayer)\n \n self.pushButton_OthRfda.clicked.connect(self.run_rfda)\n\n\n\n \n #======================================================================\n # defaults-----------\n #======================================================================\n \"\"\"\"\n to speed up testing.. manually configure the project\n \"\"\"\n\n debug_dir =os.path.join(os.path.expanduser('~'), 'CanFlood', 'build')\n self.lineEdit_cf_fp.setText(os.path.join(debug_dir, 'CanFlood_scenario1.txt'))\n self.lineEdit_wd.setText(debug_dir)\n \n if not os.path.exists(debug_dir):\n log.info('builg directory: %s'%debug_dir)\n os.makedirs(debug_dir)\n \n #=======================================================================\n # wrap\n #=======================================================================\n \n \n \n \n \n \n\n\n #==========================================================================\n # Layer Loading---------------\n #==========================================================================\n def add_ras(self):\n x = [str(self.listWidget_ras.item(i).text()) for i in range(self.listWidget_ras.count())]\n self.ras_dict.update({ (self.comboBox_ras.currentText()) : (self.comboBox_ras.currentLayer()) })\n if (self.comboBox_ras.currentText()) not in x:\n self.listWidget_ras.addItem(self.comboBox_ras.currentText())\n self.ras_dict.update({ (self.comboBox_ras.currentText()) : (self.comboBox_ras.currentLayer()) })\n \n def clear_text_edit(self):\n if len(self.ras_dict) > 0:\n self.listWidget_ras.clear()\n self.ras_dict = {}\n \n def remove_text_edit(self):\n if (self.listWidget_ras.currentItem()) is not None:\n value = self.listWidget_ras.currentItem().text()\n item = self.listWidget_ras.takeItem(self.listWidget_ras.currentRow())\n item = None\n for k in list(self.ras_dict):\n if k == value:\n self.ras_dict.pop(value)\n\n def add_all_text_edit(self):\n layers = self.iface.mapCanvas().layers()\n #layers_vec = [layer for layer in layers if layer.type() == QgsMapLayer.VectorLayer]\n layers_ras = [layer for layer in layers if layer.type() == QgsMapLayer.RasterLayer]\n x = [str(self.listWidget_ras.item(i).text()) for i in range(self.listWidget_ras.count())]\n for layer in layers_ras:\n if (layer.name()) not in x:\n self.ras_dict.update( { layer.name() : layer} )\n self.listWidget_ras.addItem(str(layer.name()))\n\n #===========================================================================\n # common methods----------\n #===========================================================================\n def slice_aoi(self, vlay):\n \n aoi_vlay = self.comboBox_aoi.currentLayer()\n log = self.logger.getChild('slice_aoi')\n \n \n #=======================================================================\n # selection\n #=======================================================================\n if self.checkBox_sels.isChecked():\n if not aoi_vlay is None: \n raise Error('only one method of aoi selection is allowed')\n \n log.info('slicing finv \\'%s\\' w/ %i selected feats'%(\n vlay.name(), vlay.selectedFeatureCount()))\n \n res_vlay = self.saveselectedfeatures(vlay, logger=log)\n #=======================================================================\n # check for no selection\n #=======================================================================\n elif aoi_vlay is None:\n log.debug('no aoi selected... not slicing')\n return vlay\n\n #=======================================================================\n # slice\n #=======================================================================\n else:\n vlay.removeSelection()\n log.info('slicing finv \\'%s\\' and %i feats w/ aoi \\'%s\\''%(\n vlay.name(),vlay.dataProvider().featureCount(), aoi_vlay.name()))\n \n res_vlay = self.selectbylocation(vlay, aoi_vlay, result_type='layer', logger=log)\n \n assert isinstance(res_vlay, QgsVectorLayer)\n \n vlay.removeSelection()\n \n #=======================================================================\n # wrap\n #=======================================================================\n if self.checkBox_loadres.isChecked():\n self.qproj.addMapLayer(res_vlay)\n self.logger.info('added \\'%s\\' to canvas'%res_vlay.name())\n \n \n \n return res_vlay\n \n \n\n\n def build_scenario(self): #'Generate' on the setup tab\n \"\"\"\n Generate a CanFlood project from scratch\n \n This tab facilitates the creation of a Control File from user specified parameters and inventory, \n as well as providing general file control variables for the other tools in the toolset.\n \n \n \n \"\"\"\n log = self.logger.getChild('build_scenario')\n log.info('build_scenario started')\n self.tag = self.linEdit_ScenTag.text() #set the secnario tag from user provided name\n \"\"\"\n todo: make a fresh pull of this for each tool\n \"\"\"\n \n cid = self.mFieldComboBox_cid.currentField() #user selected field\n \n self.wd = self.lineEdit_wd.text() #pull the wd filepath from the user provided in 'Browse'\n \n finv_raw = self.comboBox_vec.currentLayer()\n \n\n \n #=======================================================================\n # prechecks\n #=======================================================================\n assert isinstance(self.wd, str)\n \n assert isinstance(self.tag, str)\n assert isinstance(finv_raw, QgsVectorLayer), 'must select a VectorLayer'\n \n \n #check cid\n assert isinstance(cid, str)\n if cid == '' or cid in self.invalid_cids:\n raise Error('user selected invalid cid \\'%s\\''%cid) \n \n assert cid in [field.name() for field in finv_raw.fields()]\n \n if not os.path.exists(self.wd):\n os.makedirs(self.wd)\n log.info('built working directory: %s'%self.wd)\n \n #=======================================================================\n # aoi slice\n #=======================================================================\n finv = self.slice_aoi(finv_raw)\n \n \n #=======================================================================\n # convert finv\n #=======================================================================\n self.feedback.upd_prog(10)\n finv_fp = self.convert_finv(finv, cid) #convert the finv to csv and write to file\n #======================================================================\n # build the control file\n #======================================================================\n \n assert os.path.exists(finv_fp)\n self.feedback.upd_prog(50)\n \n #called by build_scenario()\n dirname = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n \n #get the default template from the program files\n cf_src = os.path.join(dirname, '_pars/CanFlood_control_01.txt')\n assert os.path.exists(cf_src)\n\n \n\n \n #get control file name from user provided tag\n cf_fn = 'CanFlood_%s.txt'%self.tag\n cf_path = os.path.join(self.wd, cf_fn)\n\n \n #see if this exists\n if os.path.exists(cf_path):\n msg = 'generated control file already exists. overwrite=%s \\n %s'%(\n self.overwrite, cf_path)\n if self.overwrite:\n log.warning(msg)\n else:\n raise Error(msg)\n \n \n #copy over the default template\n copyfile(cf_src, cf_path)\n \n\n self.feedback.upd_prog(75)\n #======================================================================\n # update the control file\n #======================================================================\n \"\"\"todo: switch over to helper function\"\"\"\n pars = configparser.ConfigParser(allow_no_value=True)\n _ = pars.read(cf_path) #read it from the new location\n \n #parameters\n pars.set('parameters', 'cid', cid) #user selected field\n pars.set('parameters', 'name', self.tag) #user selected field\n pars.set('parameters', 'felv', self.comboBox_SSelv.currentText()) #user selected field\n \n #filepaths\n pars.set('dmg_fps', 'curves', self.lineEdit_curve.text())\n pars.set('dmg_fps', 'finv', finv_fp)\n \n \n #set note\n pars.set('parameters', '#control file template created from \\'scenario setup\\' on %s'%(\n datetime.datetime.now().strftime('%Y-%m-%d %H.%M.%S')\n ))\n \n #write the config file \n with open(cf_path, 'w') as configfile:\n pars.write(configfile)\n \n log.info(\"default CanFlood model config file created :\\n %s\"%cf_path)\n \n \"\"\"NO. should only populate this automatically from ModelControlFile.Browse\n self.lineEdit_curve.setText(os.path.normpath(os.path.join(self.wd, 'CanFlood - curve set 01.xls')))\"\"\"\n \n \"\"\"TODO:\n write aoi filepath to scratch file\n \"\"\"\n self.feedback.upd_prog(95)\n #======================================================================\n # wrap\n #======================================================================\n \n #display the control file in the dialog\n self.lineEdit_cf_fp.setText(cf_path)\n \n \"\"\"not sure what this is\n self.lineEdit_control_2.setText(os.path.normpath(os.path.join(self.wd, 'CanFlood_control_01.txt')))\"\"\"\n \n log.push('control file created for \"\\'%s\\''%self.tag)\n self.feedback.upd_prog(None) #set the progress bar back down to zero\n\n \n def convert_finv(self, #convert the finv vector to csv file\n vlay, \n cid): \n log = self.logger.getChild('convert_finv')\n #======================================================================\n # prechecks\n #======================================================================\n log.info('on \\'%s\\' w/ %i feats'%(\n vlay.name(), vlay.dataProvider().featureCount()))\n \n #extract data\n df = vlay_get_fdf(vlay, feedback=self.feedback)\n \n #drop geometery indexes\n for gindx in self.invalid_cids: \n df = df.drop(gindx, axis=1, errors='ignore')\n \n if not cid in df.columns:\n raise Error('cid not found in finv_df')\n \n assert df[cid].is_unique\n assert 'int' in df[cid].dtypes.name\n \n #write it as a csv\n out_fp = os.path.join(self.wd, 'finv_%s_%s.csv'%(self.tag, vlay.name()))\n df.to_csv(out_fp, index=False) \n \n log.info(\"inventory csv written to file:\\n %s\"%out_fp)\n \n return out_fp\n \n \n\n \n def run_rsamp(self): #execute rsamp\n log = self.logger.getChild('run_rsamp')\n\n log.info('user pressed \\'pushButton_HSgenerate\\'')\n \n #=======================================================================\n # assemble/prepare inputs\n #=======================================================================\n finv_raw = self.comboBox_vec.currentLayer()\n rlay_l = list(self.ras_dict.values())\n \n crs = self.qproj.crs()\n\n cf_fp = self.get_cf_fp()\n out_dir = self.lineEdit_wd.text()\n \n\n #update some parameters\n cid = self.mFieldComboBox_cid.currentField() #user selected field\n psmp_stat = self.comboBox_HS_stat.currentText()\n \n #inundation\n as_inun = self.checkBox_HS_in.isChecked()\n \n if as_inun:\n dthresh = self.mQgsDoubleSpinBox_HS.value()\n dtm_rlay=self.comboBox_dtm.currentLayer()\n \n assert isinstance(dthresh, float)\n assert isinstance(dtm_rlay, QgsRasterLayer)\n \n else:\n dthresh, dtm_rlay = None, None\n \n \n #=======================================================================\n # slice aoi\n #=======================================================================\n finv = self.slice_aoi(finv_raw)\n\n \n \n\n #======================================================================\n # precheck\n #======================================================================\n if finv is None:\n raise Error('got nothing for finv')\n if not isinstance(finv, QgsVectorLayer):\n raise Error('did not get a vector layer for finv')\n \n for rlay in rlay_l:\n if not isinstance(rlay, QgsRasterLayer):\n raise Error('unexpected type on raster layer')\n \n if not os.path.exists(out_dir):\n raise Error('working directory does not exist: %s'%out_dir)\n \n if cid is None or cid=='':\n raise Error('need to select a cid')\n \n if not cid in [field.name() for field in finv.fields()]:\n raise Error('requested cid field \\'%s\\' not found on the finv_raw'%cid)\n \n\n assert os.path.exists(cf_fp), 'bad control file specified'\n #======================================================================\n # execute\n #======================================================================\n\n #build the sample\n wrkr = Rsamp(logger=self.logger, \n tag = self.tag, #set by build_scenario() \n feedback = self.feedback, #let the instance build its own feedback worker\n cid=cid,crs = crs,\n out_dir = out_dir\n )\n \n \"\"\"try just passing the Dialog's feedback\n #connect the status bar to the worker's feedback\n wrkr.feedback.progressChanged.connect(self.upd_prog)\"\"\"\n \n \n \n #execute the tool\n res_vlay = wrkr.run(rlay_l, finv,\n psmp_stat=psmp_stat,\n as_inun=as_inun, dtm_rlay=dtm_rlay, dthresh=dthresh)\n \n #check it\n wrkr.check()\n \n #save csv results to file\n wrkr.write_res(res_vlay, )\n \n #update ocntrol file\n wrkr.upd_cf(cf_fp)\n \n #======================================================================\n # post---------\n #======================================================================\n \"\"\"\n the hazard sampler sets up a lot of the other tools\n \"\"\"\n #======================================================================\n # add to map\n #======================================================================\n if self.checkBox_loadres.isChecked():\n self.qproj.addMapLayer(res_vlay)\n self.logger.info('added \\'%s\\' to canvas'%res_vlay.name())\n \n #======================================================================\n # update event names\n #======================================================================\n self.event_name_set = [lay.name() for lay in rlay_l]\n \n log.info('set %i event names: \\n %s'%(len(self.event_name_set), \n self.event_name_set))\n \n #======================================================================\n # populate Event Likelihoods table\n #======================================================================\n l = self.event_name_set\n for tbl in [self.fieldsTable_EL]:\n\n tbl.setRowCount(len(l)) #add this many rows\n \n for rindx, ename in enumerate(l):\n tbl.setItem(rindx, 0, QTableWidgetItem(ename))\n \n log.info('populated tables with event names')\n \n #======================================================================\n # populate lisamp\n #======================================================================\n \n #get the mlcb\n try:\n rlay_d = {indxr: rlay for indxr, rlay in enumerate(rlay_l)}\n \n for indxr, (sname, (mlcb_h, mlcb_v)) in enumerate(self.ls_cb_d.items()):\n if indxr in rlay_d:\n mlcb_h.setLayer(rlay_l[indxr])\n \n else:\n \"\"\"\n todo: clear the remaining comboboxes\n \"\"\"\n break\n\n\n except Exception as e:\n log.error('failed to populate lisamp fields w/\\n %s'%e)\n \n \n #======================================================================\n # wrap\n #======================================================================\n self.feedback.upd_prog(None) #set the progress bar back down to zero\n\n log.push('Rsamp finished')\n \n return\n \n def run_dsamp(self): #sample dtm raster\n \n self.logger.info('user pressed \\'pushButton_DTMsamp\\'')\n\n \n #=======================================================================\n # assemble/prepare inputs\n #=======================================================================\n \n finv_raw = self.comboBox_vec.currentLayer()\n rlay = self.comboBox_dtm.currentLayer()\n \n crs = self.qproj.crs()\n\n cf_fp = self.get_cf_fp()\n out_dir = self.lineEdit_wd.text()\n \n\n #update some parameters\n cid = self.mFieldComboBox_cid.currentField() #user selected field\n psmp_stat = self.comboBox_HS_stat.currentText()\n \n\n #======================================================================\n # aoi slice\n #======================================================================\n finv = self.slice_aoi(finv_raw)\n \n\n #======================================================================\n # precheck\n #======================================================================\n \n if finv is None:\n raise Error('got nothing for finv')\n if not isinstance(finv, QgsVectorLayer):\n raise Error('did not get a vector layer for finv')\n \n\n if not isinstance(rlay, QgsRasterLayer):\n raise Error('unexpected type on raster layer')\n \n if not os.path.exists(out_dir):\n raise Error('working directory does not exist: %s'%out_dir)\n \n if cid is None or cid=='':\n raise Error('need to select a cid')\n \n if not cid in [field.name() for field in finv.fields()]:\n raise Error('requested cid field \\'%s\\' not found on the finv_raw'%cid)\n \n \n #======================================================================\n # execute\n #======================================================================\n\n #build the sample\n wrkr = Rsamp(logger=self.logger, \n tag=self.tag, #set by build_scenario() \n feedback = self.feedback, #needs to be connected to progress bar\n cid=cid,crs=crs, \n out_dir = out_dir, fname='gels'\n )\n \n \n #connect the status bar\n #wrkr.feedback.progressChanged.connect(self.upd_prog)\n \n res_vlay = wrkr.run([rlay], finv, psmp_stat=psmp_stat)\n \n #check it\n wrkr.dtm_check(res_vlay)\n \n #save csv results to file\n wrkr.write_res(res_vlay, out_dir = out_dir)\n \n #update ocntrol file\n wrkr.update_cf({\n 'dmg_fps':(\n {'gels':wrkr.out_fp},\n '#\\'gels\\' file path set from rsamp.py at %s'%(datetime.datetime.now().strftime('%Y-%m-%d %H.%M.%S')),\n ),\n 'parameters':(\n {'felv':'ground'}, \n )\n \n },cf_fp)\n \n #======================================================================\n # add to map\n #======================================================================\n if self.checkBox_loadres.isChecked():\n self.qproj.addMapLayer(finv)\n self.logger.info('added \\'%s\\' to canvas'%finv.name())\n \n self.feedback.upd_prog(None) #set the progress bar back down to zero\n self.logger.push('dsamp finished') \n \n def run_lisamp(self): #sample dtm raster\n \n self.logger.info('user pressed \\'pushButton_DTMsamp\\'')\n\n \n #=======================================================================\n # assemble/prepare inputs\n #=======================================================================\n finv_raw = self.comboBox_vec.currentLayer()\n crs = self.qproj.crs()\n cf_fp = self.get_cf_fp()\n out_dir = self.lineEdit_wd.text()\n cid = self.mFieldComboBox_cid.currentField() #user selected field\n \n lfield = self.mFieldComboBox_LSfn.currentField()\n \n #collect lpols\n lpol_d = dict()\n for sname, (mlcb_haz, mlcb_lpol) in self.ls_cb_d.items():\n hlay = mlcb_haz.currentLayer()\n \n if not isinstance(hlay, QgsRasterLayer):\n continue\n \n lpol_vlay = mlcb_lpol.currentLayer()\n \n if not isinstance(lpol_vlay, QgsVectorLayer):\n raise Error('must provide a matching VectorLayer for set %s'%sname)\n\n lpol_d[hlay.name()] = lpol_vlay \n \n #======================================================================\n # aoi slice\n #======================================================================\n finv = self.slice_aoi(finv_raw)\n \n\n #======================================================================\n # precheck\n #======================================================================\n \n if finv is None:\n raise Error('got nothing for finv')\n if not isinstance(finv, QgsVectorLayer):\n raise Error('did not get a vector layer for finv')\n \n if not os.path.exists(out_dir):\n raise Error('working directory does not exist: %s'%out_dir)\n \n if cid is None or cid=='':\n raise Error('need to select a cid')\n \n if lfield is None or lfield=='':\n raise Error('must select a valid lfield')\n \n if not cid in [field.name() for field in finv.fields()]:\n raise Error('requested cid field \\'%s\\' not found on the finv_raw'%cid)\n \n \n \n #======================================================================\n # execute\n #======================================================================\n\n #build the sample\n wrkr = LikeSampler(logger=self.logger, \n tag=self.tag, #set by build_scenario() \n feedback = self.feedback, #needs to be connected to progress bar\n crs = crs,\n )\n \n #connect the status bar\n #wrkr.feedback.progressChanged.connect(self.upd_prog)\n \n res_df = wrkr.run(finv, lpol_d, cid=cid, lfield=lfield)\n \n #check it\n wrkr.check()\n \n #save csv results to file\n wrkr.write_res(res_df, out_dir = out_dir)\n \n #update ocntrol file\n wrkr.upd_cf(cf_fp)\n \n #======================================================================\n # add to map\n #======================================================================\n if self.checkBox_loadres.isChecked():\n res_vlay = wrkr.vectorize(res_df)\n self.qproj.addMapLayer(res_vlay)\n self.logger.info('added \\'%s\\' to canvas'%finv.name())\n \n self.feedback.upd_prog(None) #set the progress bar back down to zero\n self.logger.push('lisamp finished') \n \n return\n \n def _pop_el_table(self): #developing the table widget\n \n\n l = ['e1', 'e2', 'e3']\n tbl = self.fieldsTable_EL\n tbl.setRowCount(len(l)) #add this many rows\n \n for rindx, ename in enumerate(l):\n tbl.setItem(rindx, 0, QTableWidgetItem(ename))\n \n self.logger.push('populated likelihoods table with event names')\n \n \n \n def set_event_vals(self): #saving the event likelihoods table to file\n \"\"\"store user specified event variables into the 'evals' dataset\n \n \n \"\"\"\n log = self.logger.getChild('set_event_vals')\n log.info('user pushed \\'pushButton_ELstore\\'')\n \n\n #======================================================================\n # collect variables\n #======================================================================\n #get displayed control file path\n cf_fp = self.get_cf_fp()\n out_dir = self.lineEdit_wd.text()\n \n #likelihood paramter\n if self.radioButton_ELari.isChecked():\n event_probs = 'ari'\n else:\n event_probs = 'aep'\n self.logger.info('\\'event_probs\\' set to \\'%s\\''%event_probs)\n \n \n #======================================================================\n # collcet table data\n #======================================================================\n\n df = qtbl_get_df(self.fieldsTable_EL)\n \n self.logger.info('extracted data w/ %s \\n%s'%(str(df.shape), df))\n \n # check it\n if df.iloc[:, 1].isna().any():\n raise Error('got %i nulls in the likelihood column'%df.iloc[:,1].isna().sum())\n \n miss_l = set(self.event_name_set).symmetric_difference(df.iloc[:,0].values)\n if len(miss_l)>0:\n raise Error('event name mismatch')\n \n \n #======================================================================\n # clean it\n #======================================================================\n aep_df = df.set_index(df.columns[0]).iloc[:,0].to_frame().T\n \n\n \n #======================================================================\n # #write to file\n #======================================================================\n ofn = os.path.join(self.lineEdit_wd.text(), 'evals_%i_%s.csv'%(len(aep_df.columns), self.tag))\n \n from hlpr.Q import Qcoms\n #build a shell worker for these taxks\n wrkr = Qcoms(logger=log, tag=self.tag, feedback=self.feedback, out_dir=out_dir)\n \n eaep_fp = wrkr.output_df(aep_df, ofn, \n overwrite=self.overwrite, write_index=False)\n \n \n \n #======================================================================\n # update the control file\n #======================================================================\n wrkr.update_cf(\n {\n 'parameters':({'event_probs':event_probs},),\n 'risk_fps':({'evals':eaep_fp}, \n '#evals file path set from %s.py at %s'%(\n __name__, datetime.datetime.now().strftime('%Y-%m-%d %H.%M.%S')))\n \n },\n cf_fp = cf_fp\n )\n \n \n \n self.logger.push('generated \\'aeps\\' and set \\'event_probs\\' to control file')\n \n def run_validate(self):\n #raise Error('broken')\n \"\"\"\n a lot of this is duplicated in model.scripts_.setup_pars\n \n TODO: consolidate with setup_pars\n \n \"\"\"\n log = self.logger.getChild('valid')\n log.info('user pressed \\'pushButton_Validate\\'')\n \n #======================================================================\n # load the control file\n #======================================================================\n #get the control file path\n cf_fp = self.get_cf_fp()\n \n #build/run theparser\n log.info('validating control file: \\n %s'%cf_fp)\n pars = configparser.ConfigParser(inline_comment_prefixes='#', allow_no_value=True)\n _ = pars.read(cf_fp) #read it\n \n self.feedback.upd_prog(10)\n #======================================================================\n # assemble the validation parameters\n #======================================================================\n #import the class objects\n from model.dmg2 import Dmg2\n from model.risk2 import Risk2\n from model.risk1 import Risk1\n \n #populate all possible test parameters\n \"\"\"\n todo: finish this\n \"\"\"\n vpars_pos_d = {\n 'risk1':(self.checkBox_Vr1, Risk1),\n 'dmg2':(self.checkBox_Vi2, Dmg2),\n 'risk2':(self.checkBox_Vr2, Risk2),\n #'risk3':(self.checkBox_Vr3, (None, None, None)),\n }\n \n #select based on user check boxes\n vpars_d = dict()\n \n for vtag, (checkBox, model) in vpars_pos_d.items():\n \n if checkBox.isChecked():\n vpars_d[vtag] = model\n \n if len(vpars_d) == 0:\n raise Error('no validation options selected!')\n \n log.info('user selected %i validation parameter sets'%len(vpars_d))\n \n #======================================================================\n # validate\n #======================================================================\n\n \n vflag_d = dict()\n for vtag, model in vpars_d.items():\n self.feedback.upd_prog(80/len(vpars_d), method='append')\n\n \"\"\"needto play with init sequences to get this to work\"\"\"\n\n \n #==================================================================\n # set validation flag\n #==================================================================\n vflag_d[model.valid_par] = 'True'\n \n #======================================================================\n # update control file\n #======================================================================\n self.update_cf(\n {'validation':(vflag_d, )\n },\n cf_fp = cf_fp\n )\n self.feedback.upd_prog(100)\n \n log.push('completed %i validations'%len(vpars_d))\n \n self.feedback.upd_prog(None)\n return\n \n def run_rfda(self): #Other.Rfda tab\n log = self.logger.getChild('run_rfda')\n \n #======================================================================\n # collect from ui\n #======================================================================\n rinv_vlay = self.mMapLayerComboBox_OthR_rinv.currentLayer()\n crv_fp = self.lineEdit_wd_OthRf_cv.text()\n bsmt_ht = self.lineEdit_OthRf_bht.text()\n #cid = self.mFieldComboBox_cid.currentField() #user selected field\n \n crs = self.qproj.crs()\n out_dir = self.lineEdit_wd.text()\n \n try:\n bsmt_ht = float(bsmt_ht)\n except Exception as e:\n raise Error('failed to convert bsmt_ht to float w/ \\n %s'%e)\n \n \n #======================================================================\n # input checks\n #======================================================================\n #======================================================================\n # if cid is None or cid=='':\n # raise Error('need to select a cid')\n #======================================================================\n \n wrkr = RFDAconv(logger=self.logger, out_dir=out_dir, tag=self.tag, bsmt_ht = bsmt_ht)\n #======================================================================\n # invnentory convert\n #======================================================================\n if isinstance(rinv_vlay, QgsVectorLayer):\n \n \n finv_vlay = wrkr.to_finv(rinv_vlay)\n \n self.qproj.addMapLayer(finv_vlay)\n log.info('added \\'%s\\' to canvas'%finv_vlay.name())\n \n #======================================================================\n # curve convert\n #======================================================================\n if os.path.exists(crv_fp):\n df_raw = pd.read_excel(crv_fp, header=None)\n \n df_d = wrkr.to_curveset(df_raw, logger=log)\n \n basefn = os.path.splitext(os.path.split(crv_fp)[1])[0]\n \n ofp = wrkr.output(df_d, basefn=basefn)\n \n else:\n log.info('no valid crv_fp provided')\n \n #======================================================================\n # wrap\n #======================================================================\n self.logger.push('finished')\n \n\n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n \n \n \n\n \n \n \n ", "sub_path": "canflood/build/BuildDialog.py", "file_name": "BuildDialog.py", "file_ext": "py", "file_size_in_byte": 48672, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"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.dirname", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path", "line_number": 51, "usage_type": "attribute"}, {"api_name": "PyQt5.uic.loadUiType", "line_number": 52, "usage_type": "call"}, {"api_name": "PyQt5.uic", "line_number": 52, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDialog", "line_number": 55, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 55, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getExistingDirectory", "line_number": 149, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 149, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 184, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 184, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 197, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 197, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFileDialog.getOpenFileName", "line_number": 336, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QFileDialog", "line_number": 336, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 354, "usage_type": "call"}, {"api_name": "os.path", "line_number": 354, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 354, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 355, "usage_type": "call"}, {"api_name": "os.path", "line_number": 355, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 358, "usage_type": "call"}, {"api_name": "os.path", "line_number": 358, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 360, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 503, "usage_type": "call"}, {"api_name": "os.path", "line_number": 503, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 504, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 522, "usage_type": "call"}, {"api_name": "os.path", "line_number": 522, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 526, "usage_type": "call"}, {"api_name": "os.path", "line_number": 526, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 526, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 529, "usage_type": "call"}, {"api_name": "os.path", "line_number": 529, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 530, "usage_type": "call"}, {"api_name": "os.path", "line_number": 530, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 537, "usage_type": "call"}, {"api_name": "os.path", "line_number": 537, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 541, "usage_type": "call"}, {"api_name": "os.path", "line_number": 541, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 551, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 559, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 574, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 574, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 628, "usage_type": "call"}, {"api_name": "os.path", "line_number": 628, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 693, "usage_type": "call"}, {"api_name": "os.path", "line_number": 693, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 703, "usage_type": "call"}, {"api_name": "os.path", "line_number": 703, "usage_type": "attribute"}, {"api_name": "build.rsamp.Rsamp", "line_number": 709, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 766, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 844, "usage_type": "call"}, {"api_name": "os.path", "line_number": 844, "usage_type": "attribute"}, {"api_name": "build.rsamp.Rsamp", "line_number": 859, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 882, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 882, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 946, "usage_type": "call"}, {"api_name": "os.path", "line_number": 946, "usage_type": "attribute"}, {"api_name": "build.lisamp.LikeSampler", "line_number": 965, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableWidgetItem", "line_number": 1006, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 1063, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1063, "usage_type": "attribute"}, {"api_name": "hlpr.Q.Qcoms", "line_number": 1067, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 1082, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 1082, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 1111, "usage_type": "call"}, {"api_name": "model.risk1.Risk1", "line_number": 1128, "usage_type": "name"}, {"api_name": "model.dmg2.Dmg2", "line_number": 1129, "usage_type": "name"}, {"api_name": "model.risk2.Risk2", "line_number": 1130, "usage_type": "name"}, {"api_name": "model.dmg2", "line_number": 1137, "usage_type": "name"}, {"api_name": "model.dmg2", "line_number": 1140, "usage_type": "name"}, {"api_name": "model.dmg2", "line_number": 1153, "usage_type": "name"}, {"api_name": "model.dmg2.valid_par", "line_number": 1162, "usage_type": "attribute"}, {"api_name": "model.dmg2", "line_number": 1162, "usage_type": "name"}, {"api_name": "build.oth_rfda.RFDAconv", "line_number": 1207, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 1222, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1222, "usage_type": "attribute"}, {"api_name": "pandas.read_excel", "line_number": 1223, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 1227, "usage_type": "call"}, {"api_name": "os.path", "line_number": 1227, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 1227, "usage_type": "call"}]} +{"seq_id": "130097648", "text": "# uncompyle6 version 3.6.7\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)]\n# Embedded file name: /home/c24b/projets/crawtext/newspaper/settings.py\n# Compiled at: 2014-11-06 08:50:33\n__doc__ = '\\nUnlike configuration.py, this file is meant for static, entire project\\nencompassing settings, like memoization and caching file directories.\\n'\n__title__ = 'newspaper'\n__author__ = 'Lucas Ou-Yang'\n__license__ = 'MIT'\n__copyright__ = 'Copyright 2014, Lucas Ou-Yang'\nimport logging, os\nfrom cookielib import CookieJar as cj\nfrom .version import __version__\nlog = logging.getLogger(__name__)\nPARENT_DIRECTORY = os.path.dirname(os.path.abspath(__file__))\nPOPULAR_URLS = os.path.join(PARENT_DIRECTORY, 'resources/misc/popular_sources.txt')\nUSERAGENTS = os.path.join(PARENT_DIRECTORY, 'resources/misc/useragents.txt')\nSTOPWORDS_DIR = os.path.join(PARENT_DIRECTORY, 'resources/text')\nNLP_STOPWORDS_EN = os.path.join(PARENT_DIRECTORY, 'resources/misc/stopwords-nlp-en.txt')\nDATA_DIRECTORY = '.newspaper_scraper'\nTOP_DIRECTORY = os.path.join(os.path.expanduser('~'), DATA_DIRECTORY)\nif not os.path.exists(TOP_DIRECTORY):\n os.mkdir(TOP_DIRECTORY)\nLOGFILE = os.path.join(TOP_DIRECTORY, 'newspaper_errors_%s.log' % __version__)\nMONITOR_LOGFILE = os.path.join(TOP_DIRECTORY, 'newspaper_monitors_%s.log' % __version__)\nMEMO_FILE = 'memoized'\nMEMO_DIR = os.path.join(TOP_DIRECTORY, MEMO_FILE)\nif not os.path.exists(MEMO_DIR):\n os.mkdir(MEMO_DIR)\nCF_CACHE_DIRECTORY = 'feed_category_cache'\nANCHOR_DIRECTORY = os.path.join(TOP_DIRECTORY, CF_CACHE_DIRECTORY)\nif not os.path.exists(ANCHOR_DIRECTORY):\n os.mkdir(ANCHOR_DIRECTORY)\nTRENDING_URL = 'http://www.google.com/trends/hottrends/atom/feed?pn=p1'", "sub_path": "pycfiles/craynn-0.1.4-py3-none-any/settings.py", "file_name": "settings.py", "file_ext": "py", "file_size_in_byte": 1773, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 15, "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": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.expanduser", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "version.__version__", "line_number": 24, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "version.__version__", "line_number": 25, "usage_type": "name"}, {"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.exists", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "114513266", "text": "\nimport logging\nfrom getpass import getpass\nfrom steamctl import __appname__\nfrom steamctl.utils.storage import UserDataFile, UserDataDirectory\nfrom steamctl.utils.prompt import pmt_confirmation, pmt_input\nfrom steamctl.utils.web import make_requests_session\nfrom steamctl.utils.format import print_table, fmt_datetime\nfrom steam import webapi, webauth\nfrom steam.guard import SteamAuthenticator, SteamAuthenticatorError\n\n# patch session method\nwebapi._make_requests_session = make_requests_session\n\n_LOG = logging.getLogger(__name__)\n\nclass BetterMWA(webauth.MobileWebAuth):\n def __init__(self, username):\n webauth.MobileWebAuth.__init__(self, username)\n\n def bcli_login(self, password=None, sa_instance=None):\n email_code = twofactor_code = ''\n\n while True:\n try:\n if not password:\n raise webauth.LoginIncorrect\n return self.login(password, captcha, email_code, twofactor_code)\n except (webauth.LoginIncorrect, webauth.CaptchaRequired) as exp:\n email_code = twofactor_code = ''\n\n if isinstance(exp, webauth.LoginIncorrect):\n prompt = (\"Enter password for %s: \" if not password else\n \"Invalid password for %s. Enter password: \")\n password = getpass(prompt % repr(self.username))\n if isinstance(exp, webauth.CaptchaRequired):\n if captcha:\n print(\"Login error: %s\" % str(exp))\n if not pmt_confirmation(\"Try again?\", default_yes=True):\n raise EOFError\n self.refresh_captcha()\n\n if self.captcha_url:\n prompt = \"Solve CAPTCHA at %s\\nCAPTCHA code: \" % self.captcha_url\n captcha = input(prompt)\n continue\n\n captcha = ''\n except webauth.EmailCodeRequired:\n prompt = (\"Enter email code: \" if not email_code else\n \"Incorrect code. Enter email code: \")\n email_code, twofactor_code = input(prompt), ''\n except webauth.TwoFactorCodeRequired as exp:\n if not sa_instance:\n prompt = (\"Enter 2FA code: \" if not twofactor_code else\n \"Incorrect code. Enter 2FA code: \")\n email_code, twofactor_code = '', input(prompt)\n else:\n if twofactor_code:\n print(\"Login error: %s\" % str(exp))\n if not pmt_confirmation(\"Try again?\", default_yes=True):\n raise EOFError\n\n email_code, twofactor_code = '', sa_instance.get_code()\n\n\ndef cmd_authenticator_add(args):\n account = args.account.lower().strip()\n secrets_file = UserDataFile('authenticator/{}.json'.format(account))\n\n if secrets_file.exists():\n print(\"There is already an authenticator for that account\")\n return 1 # error\n\n print(\"To add an authenticator, first we need to login to Steam\")\n print(\"Account name:\", account)\n\n wa = BetterMWA(account)\n try:\n wa.bcli_login()\n except (KeyboardInterrupt, EOFError):\n print(\"Login interrupted\")\n return 1 # error\n\n print(\"Login successful. Checking pre-conditions...\")\n\n sa = SteamAuthenticator(backend=wa)\n\n # check phone number, and add one if its missing\n if not sa.has_phone_number():\n print(\"No phone number on this account. This is required.\")\n\n if pmt_confirmation(\"Do you want to add a phone number?\", default_yes=True):\n print(\"Phone number need to include country code and no spaces.\")\n\n while True:\n phnum = pmt_input(\"Enter phone number:\", regex=r'^(\\+|00)[0-9]+$')\n\n resp = sa.validate_phone_number(phnum)\n _LOG.debug(\"Phone number validation for %r: %s\", phnum, resp)\n\n if not resp.get('is_valid', False):\n print(\"That number is not valid for Steam.\")\n continue\n\n if not sa.add_phone_number(phnum):\n print(\"Failed to add phone number!\")\n continue\n\n print(\"Phone number added. Confirmation SMS sent.\")\n\n while not sa.confirm_phone_number(pmt_input(\"Enter SMS code:\", regex='^[0-9]+$')):\n print(\"Code was incorrect. Try again.\")\n\n break\n else:\n # user declined adding a phone number, we cant proceed\n return 1 # error\n\n # being adding authenticator setup\n sa.add()\n\n _LOG.debug(\"Authenticator secrets obtained. Saving to disk\")\n\n secrets_file.write_json(sa.secrets)\n\n print(\"Authenticator secrets obtained. SMS code for finalization sent.\")\n\n while True:\n code = pmt_input(\"Enter SMS code:\", regex='^[0-9]+$')\n try:\n sa.finalize(code)\n except SteamAuthenticatorError as exp:\n print(\"Finalization error: %s\", exp)\n continue\n else:\n break\n\n # finish line\n print(\"Authenticator added successfully!\")\n print(\"To get a code run: {} authenticator code {}\".format(__appname__, account))\n\n\ndef cmd_authenticator_remove(args):\n account = args.account.lower().strip()\n secrets_file = UserDataFile('authenticator/{}.json'.format(account))\n secrets = secrets_file.read_json()\n\n if not secrets:\n print(\"No authenticator found for %r\" % account)\n return 1 #error\n\n if args.force:\n secrets_file.remove()\n print(\"Forceful removal of %r successful\" % account)\n return\n\n print(\"To remove an authenticator, first we need to login to Steam\")\n print(\"Account name:\", account)\n\n wa = BetterMWA(account)\n sa = SteamAuthenticator(secrets, backend=wa)\n\n try:\n wa.bcli_login(sa_instance=sa)\n except (KeyboardInterrupt, EOFError):\n print(\"Login interrupted\")\n return 1 # error\n\n print(\"Login successful.\")\n\n while True:\n if not pmt_confirmation(\"Proceed with removing Steam Authenticator?\"):\n break\n else:\n try:\n sa.remove()\n except SteamAuthenticatorError as exp:\n print(\"Removal error: %s\" % exp)\n continue\n except (EOFError, KeyboardInterrupt):\n break\n else:\n secrets_file.remove()\n print(\"Removal successfu!\")\n return\n\n print(\"Removal cancelled.\")\n\ndef cmd_authenticator_list(args):\n rows = []\n\n for secrets_file in UserDataDirectory('authenticator').iter_files('*.json'):\n secrets = secrets_file.read_json()\n rows.append([\n secrets['account_name'],\n secrets['token_gid'],\n fmt_datetime(int(secrets['server_time']), utc=args.utc),\n ])\n\n if rows:\n print_table(rows,\n ['Account', 'Token GID', 'Created'],\n )\n else:\n print(\"No authenticators found\")\n\ndef cmd_authenticator_code(args):\n account = args.account.lower().strip()\n secrets = UserDataFile('authenticator/{}.json'.format(account)).read_json()\n\n if not secrets:\n print(\"No authenticator for %r\" % account)\n return 1 # error\n\n print(SteamAuthenticator(secrets).get_code())\n\n", "sub_path": "steamctl/commands/authenticator/cmds.py", "file_name": "cmds.py", "file_ext": "py", "file_size_in_byte": 7477, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "steam.webapi._make_requests_session", "line_number": 13, "usage_type": "attribute"}, {"api_name": "steam.webapi", "line_number": 13, "usage_type": "name"}, {"api_name": "steamctl.utils.web.make_requests_session", "line_number": 13, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "steam.webauth.MobileWebAuth", "line_number": 17, "usage_type": "attribute"}, {"api_name": "steam.webauth", "line_number": 17, "usage_type": "name"}, {"api_name": "steam.webauth.MobileWebAuth.__init__", "line_number": 19, "usage_type": "call"}, {"api_name": "steam.webauth.MobileWebAuth", "line_number": 19, "usage_type": "attribute"}, {"api_name": "steam.webauth", "line_number": 19, "usage_type": "name"}, {"api_name": "steam.webauth.LoginIncorrect", "line_number": 27, "usage_type": "attribute"}, {"api_name": "steam.webauth", "line_number": 27, "usage_type": "name"}, {"api_name": "steam.webauth.LoginIncorrect", "line_number": 29, "usage_type": "attribute"}, {"api_name": "steam.webauth", "line_number": 29, "usage_type": "name"}, {"api_name": "steam.webauth.CaptchaRequired", "line_number": 29, "usage_type": "attribute"}, {"api_name": "steam.webauth.LoginIncorrect", "line_number": 32, "usage_type": "attribute"}, {"api_name": "steam.webauth", "line_number": 32, "usage_type": "name"}, {"api_name": "getpass.getpass", "line_number": 35, "usage_type": "call"}, {"api_name": "steam.webauth.CaptchaRequired", "line_number": 36, "usage_type": "attribute"}, {"api_name": "steam.webauth", "line_number": 36, "usage_type": "name"}, {"api_name": "steamctl.utils.prompt.pmt_confirmation", "line_number": 39, "usage_type": "call"}, {"api_name": "steam.webauth.EmailCodeRequired", "line_number": 49, "usage_type": "attribute"}, {"api_name": "steam.webauth", "line_number": 49, "usage_type": "name"}, {"api_name": "steam.webauth.TwoFactorCodeRequired", "line_number": 53, "usage_type": "attribute"}, {"api_name": "steam.webauth", "line_number": 53, "usage_type": "name"}, {"api_name": "steamctl.utils.prompt.pmt_confirmation", "line_number": 61, "usage_type": "call"}, {"api_name": "steamctl.utils.storage.UserDataFile", "line_number": 69, "usage_type": "call"}, {"api_name": "steam.guard.SteamAuthenticator", "line_number": 87, "usage_type": "call"}, {"api_name": "steamctl.utils.prompt.pmt_confirmation", "line_number": 93, "usage_type": "call"}, {"api_name": "steamctl.utils.prompt.pmt_input", "line_number": 97, "usage_type": "call"}, {"api_name": "steamctl.utils.prompt.pmt_input", "line_number": 112, "usage_type": "call"}, {"api_name": "steamctl.utils.prompt.pmt_input", "line_number": 130, "usage_type": "call"}, {"api_name": "steam.guard.SteamAuthenticatorError", "line_number": 133, "usage_type": "name"}, {"api_name": "steamctl.__appname__", "line_number": 141, "usage_type": "argument"}, {"api_name": "steamctl.utils.storage.UserDataFile", "line_number": 146, "usage_type": "call"}, {"api_name": "steam.guard.SteamAuthenticator", "line_number": 162, "usage_type": "call"}, {"api_name": "steamctl.utils.prompt.pmt_confirmation", "line_number": 173, "usage_type": "call"}, {"api_name": "steam.guard.SteamAuthenticatorError", "line_number": 178, "usage_type": "name"}, {"api_name": "steamctl.utils.storage.UserDataDirectory", "line_number": 193, "usage_type": "call"}, {"api_name": "steamctl.utils.format.fmt_datetime", "line_number": 198, "usage_type": "call"}, {"api_name": "steamctl.utils.format.print_table", "line_number": 202, "usage_type": "call"}, {"api_name": "steamctl.utils.storage.UserDataFile", "line_number": 210, "usage_type": "call"}, {"api_name": "steam.guard.SteamAuthenticator", "line_number": 216, "usage_type": "call"}]} +{"seq_id": "435428124", "text": "import os\nfrom setuptools import setup\n\ndef read(fname):\n if os.path.exists(fname):\n return open(os.path.join(os.path.dirname(__file__), fname)).read()\n\n\nsrcdir = os.path.join(os.path.dirname(__file__), 'src')\n\nsetup(\n name = \"mjsrpc2\",\n version = \"0.0.7\",\n author = \"Marian Neagul\",\n author_email = \"marian@ieat.ro\",\n description = \"mjsrpc2 is a extension of jsonrpc2 providing introspection and argument type validation\",\n license = \"APL\",\n keywords = \"jsonrpc2 rpc\",\n url = \"http://developers.mosaic-cloud.eu\",\n package_dir = {'':srcdir},\n packages = [\"mjsrpc2\", \"mjsrpc2.ui\"],\n long_description = read('README.rst'),\n classifiers = [\n \"Intended Audience :: Developers\",\n \"Development Status :: 3 - Alpha\",\n \"Topic :: Software Development :: Libraries :: Python Modules\",\n \"License :: OSI Approved :: Apache Software License\",\n ],\n entry_points = {\n 'console_scripts': [\n 'mjsrpc2-cli = mjsrpc2.ui.app:main',\n ]\n },\n setup_requires = [\"setuptools_webdav\", ],\n install_requires = ['jsonrpc2', \"pyyaml>=3.0\"]\n)\n", "sub_path": "pypi_install_script/mjsrpc2-0.0.7.tar/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1222, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.exists", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path", "line_number": 5, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 6, "usage_type": "call"}, {"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": "470734143", "text": "#%%\nimport os\nGPU = \"0,1,2\"\nos.environ[\"CUDA_DEVICE_ORDER\"]=\"PCI_BUS_ID\"\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=GPU\n\nimport random\nimport config as cfg\nfrom PIL import Image\nimport numpy as np\nfrom utils import *\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\n\n# gpus = tf.config.experimental.list_physical_devices('GPU')\n# if gpus:\n# try:\n# # Currently, memory growth needs to be the same across GPUs\n# for gpu in gpus:\n# tf.config.experimental.set_memory_growth(gpu, True)\n# logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n# print(len(gpus), \"Physical GPUs,\", len(logical_gpus), \"Logical GPUs\")\n# except RuntimeError as e:\n# # Memory growth must be set before GPUs have been initialized\n# print(e)\n\nclass Generator(object):\n \n def _single_input_generator(self, video):\n # video = self.id_list[index]\n print(\"video: \", video)\n selected_person = random.choice(range( len(self.annotation[video]['p_l']))) # select person from all persons\n # video = self.listen_class[idx]['video']\n # selected_person = self.listen_class[idx]['selected_person']\n label = self._label_generator(video, selected_person)\n # label= np.zeros(80)\n # while label[36]!=1:\n # selected_person = random.choice(range( len(self.annotation[video]['p_l']))) # select person from all persons\n # label = self._label_generator(video, selected_person)\n # frame_list, org_list = self._frame_list_generator (video, selected_person)\n frame_list, org_list = self._new_image_generator (video, selected_person)\n # mask_list = self._mask_list_generator (video, selected_person)\n \n # Imm = np.zeros((frame_list.shape), dtype = np.uint8)#.shape[1],frame_list.shape[2],frame_list.shape[3]\n # for jj in range(frame_list.shape[0]):\n # for kk in range(3):\n # A = normaliseTouint8(frame_list[jj,:,:,kk])\n # B = normaliseTouint8(mask_list[jj,:,:])//42\n # Imm[jj,:,:,kk] = A*B\n # # plt.figure(jj+1)\n # # plt.imshow(Imm)\n # final_input = np.concatenate([Imm/255, org_list], axis=-1)\n final_input = np.concatenate([frame_list, org_list], axis=-1)\n return final_input, label\n \n def _frame_list_generator(self, video, selected_person):\n frame_list = []\n org_list = []\n frame_id_list =[0,4,9,14,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,44,49,54,59]#np.array(np.arange(60))\n for frame_id in frame_id_list:\n frame = self.annotation[video]['f_l'][frame_id]\n bbox = self.annotation[video]['p_l'][selected_person][\"bb_l\"][frame_id]\n v_id = self.annotation[video]['v_id']\n path = os.path.join(cfg.VIDEOS_DATASET_PATH, v_id, frame+'.png' )\n try:\n img = read_image(path)\n except:\n img = Image.new('RGB', (cfg.WIDTH, cfg.HEIGHT), color = (0, 0, 0))\n # sng_p, width, height = img_tranfrom(img, bbox)\n sng_p, width, height = img_tranfrom_8_points(img, bbox)\n imgx = imm_resize(sng_p)\n org_img = imm_resize(np.array(img))\n \n org_list.append(org_img)\n frame_list.append(imgx)\n\n frame_list = np.array(frame_list)\n org_list = np.array(org_list)\n return frame_list, org_list# np.reshape(frame_list,(3,224,224,60))\n \n def _mask_list_generator(self, video, selected_person):\n mask_list = []\n mask_id_list =[0,4,9,14,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,44,49,54,59]#np.array(np.arange(60))\n for mask_id in mask_id_list:\n mask = self.annotation[video]['f_l'][mask_id]\n bbox = self.annotation[video]['p_l'][selected_person][\"bb_l\"][mask_id]\n p_id = self.annotation[video]['p_l'][selected_person]['p_id']\n v_id = self.annotation[video]['v_id']\n path = os.path.join(cfg.SEGMENTS_DATASET_PATH, v_id, mask+'_'+str(p_id)+'.png' )\n try:\n img = read_image(path)\n except:\n img = Image.new('L', (cfg.WIDTH, cfg.HEIGHT), color=0)\n # sng_p, width, height = img_tranfrom(img, bbox)\n imgx = mask_resize(img)\n mask_list.append(imgx)\n\n mask_list = np.array(mask_list)\n return mask_list# np.reshape(mask_list,(3,224,224,60))\n \n def _new_image_generator(self, video, selected_person):\n frame_list = []\n org_list = []\n frame_id_list =[0,4,9,14,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,44,49,54,59]#np.array(np.arange(60))\n for frame_id in frame_id_list:\n frame = self.annotation[video]['f_l'][frame_id]\n bbox = self.annotation[video]['p_l'][selected_person][\"bb_l\"][frame_id]\n v_id = self.annotation[video]['v_id']\n p_id = self.annotation[video]['p_l'][selected_person]['p_id']\n \n path = os.path.join(cfg.VIDEOS_DATASET_PATH, v_id, frame+'.png' )\n mask_path = os.path.join(cfg.SEGMENTS_DATASET_PATH, v_id, frame+'_'+str(p_id)+'.png' )\n try:\n img = read_image(path)\n except:\n img = Image.new('RGB', (cfg.WIDTH, cfg.HEIGHT), color = (0, 0, 0))\n \n try:\n mask_img = read_image(mask_path)\n except:\n mask_img = Image.new('L', (cfg.WIDTH, cfg.HEIGHT), color=0)\n \n \n # sng_p, width, height = img_tranfrom(img, bbox)\n sng_p, width, height = img_tranfrom_8_points(img, bbox)\n imgx = imm_resize_changed(sng_p, mask_img)\n org_img = imm_resize(np.array(img))\n \n org_list.append(org_img)\n frame_list.append(imgx)\n\n frame_list = np.array(frame_list)\n org_list = np.array(org_list)\n return frame_list, org_list#\n \n \n\n def _label_generator(self, video, selected_person):\n action_list = self.annotation[video]['p_l'][selected_person]['a_l']\n action_list = list(set(action_list))\n label= np.zeros(80)\n for action in action_list:\n # if action==37 or action==4:\n label[action-1]=1\n return label\n\n\nclass Data_Loader(Generator):\n def __init__(self):\n self.annotation = file_reader(cfg.ANNOTATION_PATH)\n # self.listen_class = file_reader(\"class_4_37.json\")\n ## self.total_list = list(self.listen_class.keys())\n self.train_list, self.val_list = Data_Loader.split_dataset(len(self.annotation))\n self.train_ds = self.initilize_ds(self.train_list)\n self.val_ds = self.initilize_ds(self.val_list)\n\n\n\n @classmethod\n def split_dataset(cls, ds_size):\n # total_list = np.arange(ds_size)\n # total_list =np.arange(10)\n np.random.seed(seed=1717)\n \n np.random.shuffle(total_list)\n # print(\"total_list: \", total_list[:3])\n # total_list = total_list[:cfg.DATASET_SIZE]\n # divider =round(cfg.DATASET_SIZE*cfg.SPLIT_RATIO)\n divider =round(ds_size*cfg.SPLIT_RATIO)\n return total_list[:divider-6], total_list[divider+1:]\n\n @classmethod\n def input_generator(cls, id_list):\n for idx in range(len(id_list)):\n yield id_list[idx]\n\n\n \n def read_transform(self, idx):\n [frame_list, label] = tf.py_function(self._single_input_generator, [idx], [tf.float32, tf.int32])\n return frame_list, label\n\n \n def initilize_ds(self, list_ids):\n ds = tf.data.Dataset.from_generator(Data_Loader.input_generator , args= [list_ids], output_types= (tf.int32))\n ds = ds.map(self.read_transform, num_parallel_calls=tf.data.experimental.AUTOTUNE)\n # ds =ds.cache()\n ds = ds.prefetch(tf.data.experimental.AUTOTUNE)\n return ds\n\n\nds = Data_Loader()\ntrain_ds = ds.train_ds\n# val_ds =ds.val_ds\n\nfor [f, l] in train_ds.take(3):\n print(f.shape)\n print(l)\n plt.imshow(f[15][:, :, :3].numpy())\n \n plt.show()\n plt.imshow(f[15][:, :, 3:].numpy())\n plt.show()\n \n# np.save(f\"./results/one_input.npy\",f)\n# np.save(f\"./results/one_input_mask.npy\",m)\n# for [f, m, l] in val_ds.take(10):\n# print(f.shape)\n# print(m.shape)\n# print(l)\n# plt.imshow(f[0].numpy())\n# plt.show()\n# plt.imshow(m[0].numpy())\n# plt.show()\n#[ 3055 48388 65973]\n# %%\n", "sub_path": "data_loader.py", "file_name": "data_loader.py", "file_ext": "py", "file_size_in_byte": 8515, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.environ", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 5, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "config.VIDEOS_DATASET_PATH", "line_number": 65, "usage_type": "attribute"}, {"api_name": "PIL.Image.new", "line_number": 69, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 69, "usage_type": "name"}, {"api_name": "config.WIDTH", "line_number": 69, "usage_type": "attribute"}, {"api_name": "config.HEIGHT", "line_number": 69, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 79, "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": "config.SEGMENTS_DATASET_PATH", "line_number": 90, "usage_type": "attribute"}, {"api_name": "PIL.Image.new", "line_number": 94, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 94, "usage_type": "name"}, {"api_name": "config.WIDTH", "line_number": 94, "usage_type": "attribute"}, {"api_name": "config.HEIGHT", "line_number": 94, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 112, "usage_type": "call"}, {"api_name": "os.path", "line_number": 112, "usage_type": "attribute"}, {"api_name": "config.VIDEOS_DATASET_PATH", "line_number": 112, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 113, "usage_type": "call"}, {"api_name": "os.path", "line_number": 113, "usage_type": "attribute"}, {"api_name": "config.SEGMENTS_DATASET_PATH", "line_number": 113, "usage_type": "attribute"}, {"api_name": "PIL.Image.new", "line_number": 117, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 117, "usage_type": "name"}, {"api_name": "config.WIDTH", "line_number": 117, "usage_type": "attribute"}, {"api_name": "config.HEIGHT", "line_number": 117, "usage_type": "attribute"}, {"api_name": "PIL.Image.new", "line_number": 122, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 122, "usage_type": "name"}, {"api_name": "config.WIDTH", "line_number": 122, "usage_type": "attribute"}, {"api_name": "config.HEIGHT", "line_number": 122, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 142, "usage_type": "call"}, {"api_name": "config.ANNOTATION_PATH", "line_number": 151, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 164, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 164, "usage_type": "attribute"}, {"api_name": "numpy.random.shuffle", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 166, "usage_type": "attribute"}, {"api_name": "config.SPLIT_RATIO", "line_number": 170, "usage_type": "attribute"}, {"api_name": "tensorflow.py_function", "line_number": 181, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 181, "usage_type": "attribute"}, {"api_name": "tensorflow.int32", "line_number": 181, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.from_generator", "line_number": 186, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 186, "usage_type": "attribute"}, {"api_name": "tensorflow.int32", "line_number": 186, "usage_type": "attribute"}, {"api_name": "tensorflow.data", "line_number": 187, "usage_type": "attribute"}, {"api_name": "tensorflow.data", "line_number": 189, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}]} +{"seq_id": "590052719", "text": "from collections import namedtuple\nfrom functools import partial\nfrom hashlib import sha256\nimport hmac\nfrom datetime import datetime\nimport os\nfrom urllib.parse import urlencode, urlsplit, quote\nfrom uuid import uuid4\n\nimport apsw\nimport httpx\n\n\ndef sqlite_s3_query(sql, url, params=(), get_credentials=lambda: (\n os.environ['AWS_DEFAULT_REGION'],\n os.environ['AWS_ACCESS_KEY_ID'],\n os.environ['AWS_SECRET_ACCESS_KEY'],\n os.environ.get('AWS_SESSION_TOKEN'), # Only needed for temporary credentials\n), get_http_client=lambda: httpx.Client()):\n vfs_name = 's3-' + str(uuid4())\n file_name = 's3-' + str(uuid4())\n body_hash = sha256(b'').hexdigest()\n scheme, netloc, path, _, _ = urlsplit(url)\n\n class S3VFS(apsw.VFS):\n def __init__(self, size, get_range):\n self.size = size\n self.get_range = get_range\n super().__init__(vfs_name)\n\n def xOpen(self, _, __):\n return S3VFSFile(size, get_range)\n\n def xFullPathname(self, p):\n return p\n\n class S3VFSFile():\n def __init__(self, size, get_range):\n self.size = size\n self.get_range = get_range\n\n def xRead(self, amount, offset):\n return get_range(offset, offset + amount - 1)\n\n def xFileSize(self):\n return self.size\n\n def xClose(self):\n pass\n\n def xFileControl(self, _, __):\n return False\n\n def make_auth_request(http_client, method, params, headers):\n region, access_key_id, secret_access_key, session_token = get_credentials()\n to_auth_headers = headers + (\n (('x-amz-security-token', session_token),) if session_token is not None else \\\n ()\n )\n request_headers = aws_sigv4_headers(\n access_key_id, secret_access_key, region, 'GET', to_auth_headers, params,\n )\n url = f'{scheme}://{netloc}{path}?{urlencode(params)}'\n response = http_client.get(url, headers=request_headers)\n response.raise_for_status()\n return response\n\n def aws_sigv4_headers(\n access_key_id, secret_access_key, region, method, to_auth_headers, params,\n ):\n algorithm = 'AWS4-HMAC-SHA256'\n\n now = datetime.utcnow()\n amzdate = now.strftime('%Y%m%dT%H%M%SZ')\n datestamp = now.strftime('%Y%m%d')\n credential_scope = f'{datestamp}/{region}/s3/aws4_request'\n\n to_auth_headers_lower = tuple((\n (header_key.lower(), ' '.join(header_value.split()))\n for header_key, header_value in to_auth_headers\n ))\n required_headers = (\n ('host', netloc),\n ('x-amz-content-sha256', body_hash),\n ('x-amz-date', amzdate),\n )\n headers = sorted(to_auth_headers_lower + required_headers)\n signed_headers = ';'.join(key for key, _ in headers)\n\n def signature():\n def canonical_request():\n canonical_uri = quote(path, safe='/~')\n quoted_params = sorted(\n (quote(key, safe='~'), quote(value, safe='~'))\n for key, value in params\n )\n canonical_querystring = '&'.join(f'{key}={value}' for key, value in quoted_params)\n canonical_headers = ''.join(f'{key}:{value}\\n' for key, value in headers)\n\n return f'{method}\\n{canonical_uri}\\n{canonical_querystring}\\n' + \\\n f'{canonical_headers}\\n{signed_headers}\\n{body_hash}'\n\n def sign(key, msg):\n return hmac.new(key, msg.encode('ascii'), sha256).digest()\n\n string_to_sign = f'{algorithm}\\n{amzdate}\\n{credential_scope}\\n' + \\\n sha256(canonical_request().encode('ascii')).hexdigest()\n\n date_key = sign(('AWS4' + secret_access_key).encode('ascii'), datestamp)\n region_key = sign(date_key, region)\n service_key = sign(region_key, 's3')\n request_key = sign(service_key, 'aws4_request')\n return sign(request_key, string_to_sign).hex()\n\n return (\n (b'authorization', (\n f'{algorithm} Credential={access_key_id}/{credential_scope}, '\n f'SignedHeaders={signed_headers}, Signature=' + signature()).encode('ascii')\n ),\n (b'x-amz-date', amzdate.encode('ascii')),\n (b'x-amz-content-sha256', body_hash.encode('ascii')),\n ) + to_auth_headers\n\n with get_http_client() as http_client:\n head_headers = make_auth_request(http_client, 'HEAD', (), ()).headers\n version_id = head_headers['x-amz-version-id']\n size = int(head_headers['content-length'])\n get_range = lambda bytes_from, bytes_to: \\\n make_auth_request(http_client, 'HEAD',\n (('versionId', version_id),),\n (('range', f'bytes={bytes_from}-{bytes_to}'),)\n ).content\n vfs = S3VFS(size, get_range)\n\n with apsw.Connection(f'file:/{file_name}?immutable=1',\n flags=apsw.SQLITE_OPEN_READONLY | apsw.SQLITE_OPEN_URI,\n vfs=vfs_name,\n ) as conn:\n cursor = conn.cursor()\n cursor.execute(sql, params)\n row_constructor = namedtuple('Row', tuple(name for name, type in cursor.getdescription()))\n\n for row in cursor:\n yield row_constructor(*row)\n\n vfs.unregister()\n", "sub_path": "sqlite_s3_query.py", "file_name": "sqlite_s3_query.py", "file_ext": "py", "file_size_in_byte": 5436, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 18, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 18, "usage_type": "attribute"}, {"api_name": "httpx.Client", "line_number": 19, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 20, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 21, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 22, "usage_type": "call"}, {"api_name": "urllib.parse.urlsplit", "line_number": 23, "usage_type": "call"}, {"api_name": "apsw.VFS", "line_number": 25, "usage_type": "attribute"}, {"api_name": "urllib.parse.urlencode", "line_number": 63, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 73, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 73, "usage_type": "name"}, {"api_name": "urllib.parse.quote", "line_number": 92, "usage_type": "call"}, {"api_name": "urllib.parse.quote", "line_number": 94, "usage_type": "call"}, {"api_name": "hmac.new", "line_number": 104, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 104, "usage_type": "argument"}, {"api_name": "hashlib.sha256", "line_number": 107, "usage_type": "call"}, {"api_name": "apsw.Connection", "line_number": 135, "usage_type": "call"}, {"api_name": "apsw.SQLITE_OPEN_READONLY", "line_number": 136, "usage_type": "attribute"}, {"api_name": "apsw.SQLITE_OPEN_URI", "line_number": 136, "usage_type": "attribute"}, {"api_name": "collections.namedtuple", "line_number": 141, "usage_type": "call"}]} +{"seq_id": "112532369", "text": "import re\nfrom .matchers.basic_matchers.double_asterisk_matcher import DoubleAsteriskMatcher\nfrom .matchers.basic_matchers.component_matcher import CompMatcher\nfrom .matchers.path_matcher import PathMatcher\nfrom typing import List\n\n\ndef gitignore_parser(text: str) -> List[PathMatcher]:\n \"\"\"\n Git ingnore rule will be fed into this function as text/string.\n This function will parse them and return a list of PathMatcher.\n That list of path matchers will further be used for matching or rejecting paths.\n :param text: gitignore rules text\n :return:\n \"\"\"\n _lines = re.split(r'\\n\\r|\\n|\\r', text)\n path_matchers = []\n # Put a backslash (\"\\\") in front of the first hash for patterns that begin with a hash.\n for line in _lines:\n line_original = line\n is_negative = False\n only_directories = False\n is_root_relative = True\n path_comps = ()\n # A blank line matches no files, so it can serve as a separator for readability.\n line = line.strip()\n if not line:\n continue\n if line == \"/\":\n \"Invalid rule. A single forward slash as rule is just invalid/outside the repo, so ignore it\"\n \"bail out\"\n # That was the answer for: \"/\" will return empty path comps? DONE: have a closer look and test.\n continue\n # A line starting with # serves as a comment.\n if line.startswith('#'):\n continue\n # An optional prefix \"!\" which negates the pattern; any matching file excluded by a previous pattern will become\n # included again.\n if line.startswith('!'):\n is_negative = True\n line = line[1:]\n\n if line.startswith('\\\\'):\n # Put a backslash (\"\\\") in front of the first hash for patterns that begin with a hash.\n if line.startswith(r'\\#'):\n line = line[1:]\n # At the Put a backslash in front of the ! put \\ to make it literal\n if line.startswith(r'\\!'):\n line = line[1:]\n\n # now replace the consecutive seps |: not in the rule though\n line = re.sub(r'/+', r'/', line)\n # If there is a separator at the end of the pattern then the pattern will only match directories, otherwise\n # the pattern can match both files and directories.\n if line.endswith('/'):\n only_directories = True\n line = line[:-1]\n # If there is a separator at the beginning or middle (or both) of the pattern, then the pattern is relative to\n # the directory level of the particular .gitignore file itself. Otherwise the pattern may also match at\n # any level below the .gitignore level.\n if '\\\\' in line: # backward `\\` slash is just invalid in git (?) (tested true on linux,\n # TODO: gotta test on windows)\n \"bail out\"\n continue\n path_comps = line.split('/')\n if path_comps[0] == '':\n del path_comps[0]\n else:\n \"The first char is not / (path_comps[0]='' after split by /), so if there is only one comp, then...\"\n if len(path_comps) == 1:\n is_root_relative = False\n\n _path_comps = []\n # reduce consecutive double asterisks\n prev_d_asterisks = False\n for comp in path_comps:\n if comp == '**':\n if prev_d_asterisks:\n # drop this one\n continue\n else:\n _path_comps.append(comp)\n prev_d_asterisks = True\n else:\n _path_comps.append(comp)\n prev_d_asterisks = False\n path_comps = _path_comps\n\n # build the match objects\n matcher_objects = []\n\n idx = 0\n while idx < len(path_comps):\n comp = path_comps[idx]\n idx += 1\n\n if comp == '**':\n matcher = DoubleAsteriskMatcher(comp)\n # Eliminate consecutive double asterisks # TODO: write test case for this.\n while idx < len(path_comps):\n _next_comp = path_comps[idx]\n if _next_comp == '**':\n idx += 1\n else:\n break\n else:\n matcher = CompMatcher(comp)\n matcher_objects.append(matcher)\n path_matchers.append(\n PathMatcher(matcher_objects, line_original, is_negative, only_directories, is_root_relative)\n )\n\n return path_matchers\n", "sub_path": "src/ContentFS/pathmatch/rules_parser.py", "file_name": "rules_parser.py", "file_ext": "py", "file_size_in_byte": 4573, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "re.split", "line_number": 16, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 52, "usage_type": "call"}, {"api_name": "matchers.basic_matchers.double_asterisk_matcher.DoubleAsteriskMatcher", "line_number": 98, "usage_type": "call"}, {"api_name": "matchers.basic_matchers.component_matcher.CompMatcher", "line_number": 107, "usage_type": "call"}, {"api_name": "matchers.path_matcher.PathMatcher", "line_number": 110, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 8, "usage_type": "name"}, {"api_name": "matchers.path_matcher.PathMatcher", "line_number": 8, "usage_type": "name"}]} +{"seq_id": "56571318", "text": "# native imports\nimport glob\nimport os\n\n# opensource imports\nimport torch\nimport torchvision.models as models\nimport imageio\n\n# local imports\nfrom utils import util\nfrom option import args #COMMAND LINE ARGUMENTS VIEW option.py file\n\n########################################################################\n\nHRPATH = \"../../../data/DIV2K_train_HR\"\nLRPATH = \"../../../data/DIV2K_train_LR_bicubic/X4\"\nLR_PATHS = glob.glob(os.path.join(LRPATH,\"*\"))\nHR_PATHS = glob.glob(os.path.join(HRPATH,\"*\"))\nLR_PATHS.sort()\nHR_PATHS.sort()\nVGG = models.vgg19(pretrained=True)\nVGG.to(args.device)\n\n# forward function using vgg19 network just to get latent vectors\ndef forward(x,VGG=VGG):\n VGG.eval()\n with torch.no_grad():\n x = VGG.features(x)\n x = VGG.avgpool(x)\n x = torch.flatten(x,1)\n return x\n\n# run feature extraction on all low resolution image patches\ndata = []\nlabel = []\nindices = list(range(len(HR_PATHS)))\nfor n, idx in enumerate(indices):\n hr_path = HR_PATHS[idx]\n lr_path = LR_PATHS[idx]\n\n LR = imageio.imread(lr_path)\n HR = imageio.imread(hr_path)\n\n LR,HR,_ = util.getTrainingPatches(LR,HR,args)\n patch_ids = list(range(len(LR)))\n for i in range(0,len(LR),32):\n labels = torch.Tensor(patch_ids[i:i+32]).long()\n\n batch = LR[labels,:,:,:]\n batch = batch.to(args.device)\n features = forward(batch)\n\n", "sub_path": "trainM/analysis/analyze.py", "file_name": "analyze.py", "file_ext": "py", "file_size_in_byte": 1382, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "glob.glob", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"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": "torchvision.models.vgg19", "line_number": 22, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 22, "usage_type": "name"}, {"api_name": "option.args.device", "line_number": 23, "usage_type": "attribute"}, {"api_name": "option.args", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.no_grad", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.flatten", "line_number": 31, "usage_type": "call"}, {"api_name": "imageio.imread", "line_number": 42, "usage_type": "call"}, {"api_name": "imageio.imread", "line_number": 43, "usage_type": "call"}, {"api_name": "utils.util.getTrainingPatches", "line_number": 45, "usage_type": "call"}, {"api_name": "option.args", "line_number": 45, "usage_type": "argument"}, {"api_name": "utils.util", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 48, "usage_type": "call"}, {"api_name": "option.args.device", "line_number": 51, "usage_type": "attribute"}, {"api_name": "option.args", "line_number": 51, "usage_type": "name"}]} +{"seq_id": "347800208", "text": "from __future__ import (absolute_import, division, print_function)\n__metaclass__ = type\n\nDOCUMENTATION = \"\"\"\n lookup: cops_registry\n author: kiorky\n version_added: \"2.5\"\n short_description: Assemble vars under a common prefix under a namespaced dict of those vars\n description:\n - Assemble vars under a common prefix under a namespaced dict of those vars\n options:\n _terms:\n description: The strings to render\n required: True\n default:\n description:\n - What to return if a variable is undefined.\n - If no default is set, it will result in an error if any of the variables is undefined.\n\"\"\"\n\nEXAMPLES = \"\"\"\n- name: Show value of 'variablename'\n debug: msg=\"{{ lookup('vars', 'common_prefix_')}}\n\"\"\"\n\nRETURN = \"\"\"\n_value:\n description:\n - value of the variables requested.\n\"\"\"\n\nfrom ansible.errors import AnsibleError, AnsibleUndefinedVariable\nfrom ansible.module_utils.six import string_types\nfrom ansible.plugins.lookup import LookupBase\nfrom ansible.module_utils import six\n\nfrom collections import OrderedDict\nfrom copsf_api import __funcs__, REGISTRY_DEFAULT_SUFFIX\n\n\nclass LookupModule(LookupBase):\n\n def run(self, terms, variables=None, **kwargs):\n if variables is not None:\n self._templar.available_variables = variables\n\n self.set_options(direct=kwargs)\n default = self.get_option('default')\n gs = kwargs.get('global_scope', True)\n kwargs['global_scope'] = False\n\n ret = []\n for value in terms:\n try:\n val = __funcs__['copsf_registry'](\n self._templar.available_variables, value, **kwargs)\n registry = self._templar.template(self._templar.template(val[0], fail_on_undefined=True))\n defaults_vals_reg ='__{0}{1}'.format(value, REGISTRY_DEFAULT_SUFFIX)\n rval = OrderedDict()\n if gs:\n rval[defaults_vals_reg] = val[1].get(defaults_vals_reg)\n for v, ival in six.iteritems(registry):\n rval['{0}{1}'.format(value, v)] = ival\n rval['{0}vars'.format(value)] = registry\n else:\n rval = registry\n ret.append(rval)\n except AnsibleUndefinedVariable:\n if default is not None:\n ret.append(default)\n else:\n raise\n\n return ret\n", "sub_path": "ansible_plugins/lookup_plugins/cops_registry.py", "file_name": "cops_registry.py", "file_ext": "py", "file_size_in_byte": 2479, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "ansible.plugins.lookup.LookupBase", "line_number": 41, "usage_type": "name"}, {"api_name": "copsf_api.__funcs__", "line_number": 55, "usage_type": "name"}, {"api_name": "copsf_api.REGISTRY_DEFAULT_SUFFIX", "line_number": 58, "usage_type": "argument"}, {"api_name": "collections.OrderedDict", "line_number": 59, "usage_type": "call"}, {"api_name": "ansible.module_utils.six.iteritems", "line_number": 62, "usage_type": "call"}, {"api_name": "ansible.module_utils.six", "line_number": 62, "usage_type": "name"}, {"api_name": "ansible.errors.AnsibleUndefinedVariable", "line_number": 68, "usage_type": "name"}]} +{"seq_id": "502567085", "text": "from rest_framework import serializers\nfrom collections import OrderedDict\nfrom rest_framework.relations import PKOnlyObject\nfrom drf_queryfields import QueryFieldsMixin\nfrom performance.models import EPSA, Variable, Indicator, VariableReport, IndicatorMeasurement\n\nclass CustomModelSerializer(QueryFieldsMixin,serializers.ModelSerializer):\n def to_representation(self,instance):\n fields = self._readable_fields\n ret = OrderedDict()\n for field in fields:\n try:\n attribute = field.get_attribute(instance)\n except SkipField:\n continue\n if attribute in [None, '', []]:\n continue\n check_for_none = attribute.pk if isinstance(attribute, PKOnlyObject) else attribute\n if check_for_none is None:\n ret[field.field_name] = None\n else:\n ret[field.field_name] = field.to_representation(attribute)\n return ret\n\nclass CustomListModelSerializer(serializers.ListSerializer):\n def is_valid(self,raise_exception=False):\n if not hasattr(self, '_validated_data'):\n self._validated_data = self.initial_data\n self._errors = {}\n return True\n\ndef bulk_create_or_update(model,data,unique_together=[]):\n ret = []\n for props in data:\n if not set(unique_together) <= set(props.keys()):\n ret.append({'ignorado':{'no_identificable':'No se proporcionaron todos los campos necesarios para identificar la instancia de manera única.'}})\n continue\n key_vals = [props.get(key_prop) for key_prop in unique_together]\n if not any(key_vals):\n ret.append({'ignorado':{'objeto_en_blanco':'Todas las propiedades clave de este objeto estan en blanco.'}})\n continue\n qs = model.objects.filter(**{k:v for k,v in zip(unique_together,key_vals)})\n if qs.count() > 0:\n qs.update(**props)\n ret_key = 'actualizado'\n else:\n e,created = model.objects.get_or_create(**props)\n if created:\n ret_key = 'creado'\n else:\n ret_key = 'ignorado'\n ret.append({ret_key: props})\n return ret\n\nclass EPSAListSerializer(CustomListModelSerializer):\n def create(self, validated_data):\n unique_together = ['code',]\n return bulk_create_or_update(EPSA,validated_data,unique_together)\nclass EPSASerializer(CustomModelSerializer):\n class Meta:\n model = EPSA\n fields = '__all__'\n list_serializer_class = EPSAListSerializer\n\nclass VariableListSerializer(CustomListModelSerializer):\n def create(self, validated_data):\n unique_together = ['code',]\n return bulk_create_or_update(Variable,validated_data,unique_together)\nclass VariableSerializer(CustomModelSerializer):\n class Meta:\n model = Variable\n fields = '__all__'\n list_serializer_class = VariableListSerializer\n\nclass IndicatorListSerializer(CustomListModelSerializer):\n def create(self, validated_data):\n unique_together = ['code',]\n return bulk_create_or_update(Indicator,validated_data,unique_together)\nclass IndicatorSerializer(QueryFieldsMixin, serializers.ModelSerializer):\n class Meta:\n model = Indicator\n fields = '__all__'\n list_serializer_class = IndicatorListSerializer\n\nclass VariableReportListSerializer(CustomListModelSerializer):\n def create(self, validated_data):\n unique_together = ['epsa','year','month',]\n return bulk_create_or_update(VariableReport,validated_data,unique_together)\nclass VariableReportSerializer(QueryFieldsMixin, serializers.ModelSerializer):\n # epsa = serializers.CharField(allow_blank=True,required=False)\n class Meta:\n model = VariableReport\n fields = '__all__'\n list_serializer_class = VariableReportListSerializer\n # def create(self, validated_data):\n # epsa_code = validated_data.pop('epsa',None)\n\n # if epsa_code:\n # epsa_tuple = EPSA.objects.get_or_create(code=epsa_code)\n # measurement = IndicatorMeasurement.objects.create(epsa=epsa_tuple[0], **validated_data)\n # else:\n # measurement = IndicatorMeasurement.objects.create(**validated_data)\n\n # return measurement\n\nclass IndicatorMeasurementListSerializer(CustomListModelSerializer):\n def create(self, validated_data):\n unique_together = ['epsa','year','month',]\n return bulk_create_or_update(IndicatorMeasurement,validated_data,unique_together)\nclass IndicatorMeasurementSerializer(QueryFieldsMixin, serializers.ModelSerializer):\n # epsa = serializers.CharField(allow_blank=True,required=False)\n class Meta:\n model = IndicatorMeasurement\n fields = '__all__'\n list_serializer_class = IndicatorMeasurementListSerializer\n \n # def create(self, validated_data):\n # epsa_code = validated_data.pop('epsa',None)\n\n # if epsa_code:\n # epsa_tuple = EPSA.objects.get_or_create(code=epsa_code)\n # measurement = IndicatorMeasurement.objects.create(epsa=epsa_tuple[0], **validated_data)\n # else:\n # measurement = IndicatorMeasurement.objects.create(**validated_data)\n\n # return measurement\n \n\n", "sub_path": "performance/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 5296, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "drf_queryfields.QueryFieldsMixin", "line_number": 7, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 7, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 7, "usage_type": "name"}, {"api_name": "collections.OrderedDict", "line_number": 10, "usage_type": "call"}, {"api_name": "rest_framework.relations.PKOnlyObject", "line_number": 18, "usage_type": "argument"}, {"api_name": "rest_framework.serializers.ListSerializer", "line_number": 25, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 25, "usage_type": "name"}, {"api_name": "performance.models.EPSA", "line_number": 58, "usage_type": "argument"}, {"api_name": "performance.models.EPSA", "line_number": 61, "usage_type": "name"}, {"api_name": "performance.models.Variable", "line_number": 68, "usage_type": "argument"}, {"api_name": "performance.models.Variable", "line_number": 71, "usage_type": "name"}, {"api_name": "performance.models.Indicator", "line_number": 78, "usage_type": "argument"}, {"api_name": "drf_queryfields.QueryFieldsMixin", "line_number": 79, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 79, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 79, "usage_type": "name"}, {"api_name": "performance.models.Indicator", "line_number": 81, "usage_type": "name"}, {"api_name": "performance.models.VariableReport", "line_number": 88, "usage_type": "argument"}, {"api_name": "drf_queryfields.QueryFieldsMixin", "line_number": 89, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 89, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 89, "usage_type": "name"}, {"api_name": "performance.models.VariableReport", "line_number": 92, "usage_type": "name"}, {"api_name": "performance.models.IndicatorMeasurement", "line_number": 109, "usage_type": "argument"}, {"api_name": "drf_queryfields.QueryFieldsMixin", "line_number": 110, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 110, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 110, "usage_type": "name"}, {"api_name": "performance.models.IndicatorMeasurement", "line_number": 113, "usage_type": "name"}]} +{"seq_id": "183390288", "text": "import urllib\nfrom bs4 import BeautifulSoup\nimport re\n\n# import pandas as pd\n# import numpy as np\n\nclass Scraper:\n def __init__(self, url):\n self.url = url\n self.html = urllib.request.urlopen(self.url)\n self.soup = BeautifulSoup(self.html, 'html.parser')\n\nclass URLScraper(Scraper):\n def __init__(self, url):\n super().__init__(url)\n self.url_list = self.get_urls(self.soup)\n # for l in self.url_list:\n # print(l)\n \n def get_urls(self, parsed):\n \"\"\"Returns a list of all box score URLs\"\"\"\n links = parsed.find_all('a', attrs={'class': 'link'})\n url_list = list()\n for link in links:\n if 'Box Score' in link.get('aria-label'):\n url_list.append('http://athletics.cmu.edu' + link.get('href'))\n return url_list\n\nclass PBPScraper(Scraper):\n def __init__(self, url):\n super().__init__(url)\n self.events = self.get_events(self.soup) # dict\n self.players = self.get_players(self.events)\n self.home_score, self.visitor_score = self.get_final(self.soup)\n self.pms = self.get_pm(self.events, self.home_score, self.visitor_score)\n \n def get_events(self, parsed):\n \"\"\"Returns a dictionary mapping player names to lists of their in-game actions\"\"\"\n home_rows = parsed.find_all('tr', attrs={'class': 'row home'})\n events_list = list()\n events_dict = dict()\n for row in home_rows:\n event = row.find('span', attrs={'class': 'text'}).text.strip().replace('\\n', '')\n event = ' '.join(event.split())\n time = row.find('td', attrs={'class': 'time'}).text.strip()\n home_score = row.find('span', attrs={'class': 'h-score'}).text.strip()\n visitor_score = row.find('span', attrs={'class': 'v-score'}).text.strip()\n m = re.match(r'([A-Z]*?[a-z ]*)([A-Z]+,[A-Z]+)', event)\n if m:\n player = m.group(2)\n if player in events_dict:\n events_dict[player].append((time, event.replace(player, ''), home_score, visitor_score))\n # time, event, home score, visitor score\n else:\n events_dict[player] = [(time, event.replace(player, ''), home_score, visitor_score)]\n return events_dict\n \n def get_final(self, parsed):\n \"\"\"Returns the final home and visitor scores\"\"\"\n home_final = parsed.find('div', attrs={'class': 'team-score home'}).text.strip()\n visitor_final = parsed.find('div', attrs={'class': 'team-score visitor'}).text.strip()\n return home_final, visitor_final\n \n def get_pm(self, events, home_score, visitor_score):\n \"\"\"Returns a dictionary mapping player names to their plus-minuses\"\"\"\n # Dependencies: phrasing 'goes to the bench' and 'enters the game'\n # Assumes that no player plays the entire game (seems reasonable)\n plus_minuses = dict()\n for player in events:\n in_game = False\n pm = 0\n score = (0,0)\n event_list = events[player]\n for event in event_list:\n if 'enters the game' in event[1]:\n score = (int(event[2]), int(event[3]))\n in_game = True\n elif 'goes to the bench' in event[1]:\n pm += (int(event[2]) - int(event[3])) - (int(score[0]) - int(score[1]))\n in_game = False\n if in_game:\n pm += (int(home_score) - int(visitor_score)) - (int(score[0] - int(score[1])))\n plus_minuses[player] = pm\n return plus_minuses\n \n def get_players(self, events):\n return list(self.events.keys())\n \nclass BSScraper(Scraper):\n def __init__(self, url):\n super().__init__(url)\n self.is_home = self.is_home(self.soup)\n self.totals = self.get_totals(self.soup, self.is_home) # dict\n self.rm_stats = self.get_player_stats(self.soup, 'Ryan Maha')\n self.get_usage(self.rm_stats, self.totals)\n \n def is_home(self, parsed):\n \"\"\"Returns if CMU is the home team\"\"\"\n # Dependencies: requires that 'at Carnegie Mellon' is in the

header for\n # home games and not for away games\n heads = parsed.find_all('div', attrs={'class': 'head'})\n for head in heads:\n game = head.find('h1')\n if game != None:\n break\n game = game.text.strip()\n game = ' '.join(game.split())\n if 'at Carnegie Mellon' in game:\n return True\n else:\n return False\n \n def get_totals(self, parsed, is_home):\n \"\"\"Returns the box score totals for CMU\"\"\"\n # Dependencies: the exact box score organization---the mapping from numbers to\n # statistics is hard-coded; away team is the top box, home team is the bottom box\n all_totals = parsed.find_all('tr', attrs={'class': 'totals'})\n if is_home:\n idx = 2\n else:\n idx = 0\n totals = all_totals[idx].text.split()[1:]\n labels = ['FGM-A', '3PM-A', 'FTM-A', 'OREB', 'DREB', 'REB', 'AST', 'STL',\n 'BLK', 'TO', 'PF', 'PTS']\n totals_dict = dict()\n for i in range(len(labels)):\n key = labels[i]\n value = totals[i]\n totals_dict[key] = value\n return totals_dict\n \n def get_player_stats(self, parsed, player):\n \"\"\"Returns the box score statistics of the given player\"\"\"\n # Dependencies: the exact box score organization---player number, dash, \n # first name, last name, dash, position must all be there in that order\n # before the rest of the statistics (which also must be in their specific order)\n # Requires that the player name takes the example form 'Ryan Maha'\n trs = parsed.find_all('tr')\n player_tr = None\n for tr in trs:\n temp = tr.find('a', attrs={'class': 'player-name'})\n if temp != None:\n if temp.text.strip() == player:\n player_tr = tr\n break\n idx = 6\n player_stats = player_tr.text.strip().split()[idx:]\n labels = ['MIN', 'FGM-A', '3PM-A', 'FTM-A', 'OREB', 'DREB', 'REB', 'AST', 'STL',\n 'BLK', 'TO', 'PF', 'PTS']\n player_stats_dict = dict()\n for i in range(len(labels)):\n key = labels[i]\n value = player_stats[i]\n player_stats_dict[key] = value\n return player_stats_dict\n \n def get_usage(self, stats_dt, totals_dt):\n # Usage rate = 100 * [(Team Minutes)/(5*(Player Minutes))]*\n # [(Field Goal Attempts)+0.44*(Free Throw Attempts)+(Turnovers)]/\n # [(Team Field Goal Attempts)+0.44*(Team Free Throw Attempts)+Team Turnovers)]\n team_min = 40 # set at 2 20-min halves\n player_min = int(stats_dt['MIN'])\n fga = int(stats_dt['FGM-A'].split('-')[1])\n fta = int(stats_dt['FTM-A'].split('-')[1])\n turnovers = int(stats_dt['TO'])\n team_fga = int(totals_dt['FGM-A'].split('-')[1])\n team_fta = int(totals_dt['FTM-A'].split('-')[1])\n team_turnovers = int(totals_dt['TO'])\n usg = 100 * (team_min/(5*player_min)) * (fga+0.44*fta+turnovers) \\\n / (team_fga+0.44*team_fta+team_turnovers)\n return usg\n \nclass ADVSTATS:\n def __init__(self, url):\n self.url = url\n pbp_suffix = '?view=plays'\n bs_suffix = '?view=boxscore'\n self.pbps = PBPScraper(url + pbp_suffix)\n self.bss = BSScraper(url + bs_suffix)\n \nif __name__ == '__main__': \n url_scraper = URLScraper('http://athletics.cmu.edu/sports/mbkb/2017-18/schedule')\n advstats = ADVSTATS(url_scraper.url_list[0])", "sub_path": "scrape.py", "file_name": "scrape.py", "file_ext": "py", "file_size_in_byte": 7895, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "urllib.request.urlopen", "line_number": 11, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 11, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 12, "usage_type": "call"}, {"api_name": "re.match", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "137642437", "text": "import logging\nimport os\nfrom threading import Thread\nfrom crawler.common.download import VideoDownloadManager\n\nlogger = logging.getLogger(__name__)\n\n\ndef _get_url_file_name(path):\n if u'?' in path:\n path = path.split('?')[0]\n name = path.split('/')[-1]\n logger.debug(name)\n return name\n\n\ndef run(parameter):\n path = os.path.join(os.getcwd(), os.pardir, 'video')\n\n manager = VideoDownloadManager({'Accept': '*/*',\n 'Accept-Encoding': 'gzip, deflate, sdch, br'})\n \n infos = [('https://redirector.googlevideo.com', 'ipz-773.mp4')]\n \n for info in infos:\n if isinstance(info, tuple):\n url, name = info\n else:\n url = info\n name = _get_url_file_name(url)\n manager.download(url, os.path.join(path, name))\n", "sub_path": "Crawler/crawler/video/jobs/download_url.py", "file_name": "download_url.py", "file_ext": "py", "file_size_in_byte": 824, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 18, "usage_type": "call"}, {"api_name": "os.pardir", "line_number": 18, "usage_type": "attribute"}, {"api_name": "crawler.common.download.VideoDownloadManager", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}]} +{"seq_id": "270593452", "text": "import os\r\nimport sys\r\nimport glob\r\nimport serial\r\nimport winsound\r\nimport matplotlib\r\nimport numpy as np\r\nmatplotlib.use(\"QT5Agg\")\r\nfrom time import sleep\r\nfrom multiprocessing import Process\r\nfrom matplotlib.figure import Figure\r\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\r\nfrom matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar\r\nfrom PyQt5.QtWidgets import QApplication , QMainWindow, QVBoxLayout, QDial, QSlider\r\nfrom PyQt5 import uic, QtCore, QtGui\r\n\r\n\r\n\r\nForm = uic.loadUiType(os.path.join(os.getcwd(),'gui.ui',))[0]\r\n\r\n\r\nclass PulseWindow(QMainWindow, Form):\r\n\r\n def __init__(self):\r\n Form.__init__(self)\r\n QMainWindow.__init__(self)\r\n self.setupUi(self)\r\n\r\n\r\n self.fig = Figure(frameon=False)\r\n self.ax1 = self.fig.add_axes([0.05, 0.55, 0.9, 0.4])\r\n self.ax2 = self.fig.add_axes([0.05, 0.05, 0.9, 0.45])\r\n self.ax1.set_facecolor('white')\r\n self.x = np.linspace(0,10,2000)\r\n self.line1,=self.ax1.plot(self.x,np.zeros(2000),linewidth=1.5,color='red')\r\n self.line2,=self.ax2.plot(self.x,np.zeros(2000),linewidth=1.5,color='blue')\r\n self.ax1.set_ylim([-1,7])\r\n self.ax1.set_xlim([0,10])\r\n self.ax2.set_ylim([-1,7])\r\n self.ax2.set_xlim([0,10]) \r\n self.ax1.grid(True)\r\n self.ax2.grid(True)\r\n\r\n\r\n self.canvas = FigureCanvas(self.fig)\r\n self.navi = NavigationToolbar(self.canvas, self)\r\n \r\n self.status = True\r\n self.gridflag = True\r\n\r\n self.bkclrbox = ['white','yellow','cyan','black']\r\n self.linclrbox = ['white', 'yellow', 'cyan', 'black', 'red', 'green', 'brown']\r\n self.gridlinclrbox = ['white', 'yellow', 'cyan', 'black', 'red', 'green', 'brown']\r\n\r\n self.height = 0\r\n self.ax_bkclr = 0\r\n self.ax_hight = 0\r\n self.ax_length = 0\r\n self.ax_linclr = 4\r\n self.ax_gridlinclr = 0\r\n\r\n\r\n l=QVBoxLayout(self.matplotlib_widget)\r\n l.addWidget(self.canvas)\r\n l.addWidget(self.navi)\r\n\r\n\r\n self.start_pushButton.clicked.connect(self.start)\r\n self.timeSlider.sliderMoved.connect(self.time1)\r\n self.timeSlider2.sliderMoved.connect(self.time2)\r\n self.voltSlider.sliderMoved.connect(self.volt1) \r\n self.voltSlider2.sliderMoved.connect(self.volt2) \r\n self.bkgndclr_pushButton.clicked.connect(self.bkgndclr_change)\r\n self.gridlinescolor_pushButton.clicked.connect(self.gridlinclr_change) \r\n self.SaveFig_pushButton.clicked.connect(self.SaveFig)\r\n\r\n self.SerialUpdate = serialupdate()\r\n self.SerialUpdate.update_trigger.connect(self.update_plot)\r\n self.SerialUpdate.start()\r\n\r\n\r\n\r\n def start(self):\r\n self.status = not self.status\r\n if self.status==False:\r\n self.start_pushButton.setText('Start')\r\n self.SerialUpdate.stop()\r\n else:\r\n self.start_pushButton.setText('Hold')\r\n self.SerialUpdate.start()\r\n \r\n \r\n\r\n def volt1(self):\r\n self.height = self.height + 1\r\n if self.height == 2:\r\n self.ax1.set_ylim([-1,3])\r\n self.fig.canvas.draw()\r\n elif self.height == 3:\r\n self.height = 0\r\n self.ax1.set_ylim([-1,8])\r\n self.fig.canvas.draw()\r\n elif self.height == 1:\r\n self.ax1.set_ylim([-1,5])\r\n self.fig.canvas.draw()\r\n \r\n\r\n def time1(self):\r\n if self.ax_length==0:\r\n self.ax_length=1\r\n self.ax1.set_xlim([4,8])\r\n self.fig.canvas.draw()\r\n elif self.ax_length==1:\r\n self.ax_length=2\r\n self.ax1.set_xlim([6,8])\r\n self.fig.canvas.draw()\r\n elif self.ax_length==2:\r\n self.ax_length=3\r\n self.ax1.set_xlim([7,8])\r\n self.fig.canvas.draw()\r\n else:\r\n self.ax_length=0\r\n self.ax1.set_xlim([0,8])\r\n self.fig.canvas.draw()\r\n\r\n\r\n def volt2(self):\r\n self.height = self.height + 1\r\n if self.height == 2:\r\n self.ax2.set_ylim([-1,3])\r\n self.fig.canvas.draw()\r\n elif self.height == 3:\r\n self.height = 0\r\n self.ax2.set_ylim([-1,8])\r\n self.fig.canvas.draw()\r\n elif self.height == 1:\r\n self.ax2.set_ylim([-1,5])\r\n self.fig.canvas.draw()\r\n \r\n\r\n def time2(self):\r\n if self.ax_length==0:\r\n self.ax_length=1\r\n self.ax2.set_xlim([4,8])\r\n self.fig.canvas.draw()\r\n elif self.ax_length==1:\r\n self.ax_length=2\r\n self.ax2.set_xlim([6,8])\r\n self.fig.canvas.draw()\r\n elif self.ax_length==2:\r\n self.ax_length=3\r\n self.ax2.set_xlim([7,8])\r\n else:\r\n self.ax_length=0\r\n self.ax1.set_xlim([0,8])\r\n self.fig.canvas.draw()\r\n\r\n\r\n def bkgndclr_change(self):\r\n self.ax_bkclr+=1\r\n if self.ax_bkclr==4:\r\n self.ax_bkclr=0\r\n while self.ax_bkclr==self.ax_linclr or (self.ax_bkclr==self.ax_gridlinclr and self.gridflag==True):\r\n self.ax_bkclr+=1\r\n if self.ax_bkclr==4:\r\n self.ax_bkclr=0\r\n self.ax1.set_axis_bgcolor(self.bkclrbox[self.ax_bkclr])\r\n self.ax2.set_axis_bgcolor(self.bkclrbox[self.ax_bkclr])\r\n self.fig.canvas.draw()\r\n\r\n\r\n def gridlinclr_change(self):\r\n self.ax_gridlinclr+=1\r\n if self.ax_gridlinclr==7:\r\n self.ax_gridlinclr=0\r\n\r\n if self.ax_gridlinclr==self.ax_bkclr:\r\n self.ax_gridlinclr+=1\r\n for xgridlin in self.ax1.xaxis.get_gridlines():\r\n xgridlin.set_color(self.gridlinclrbox[self.ax_gridlinclr])\r\n for ygridlin in self.ax1.yaxis.get_gridlines():\r\n ygridlin.set_color(self.gridlinclrbox[self.ax_gridlinclr])\r\n for xtick in self.ax1.xaxis.get_ticklines():\r\n xtick.set_color(self.gridlinclrbox[self.ax_gridlinclr])\r\n for ytick in self.ax1.yaxis.get_ticklines():\r\n ytick.set_color(self.gridlinclrbox[self.ax_gridlinclr])\r\n self.fig.canvas.draw()\r\n\r\n\r\n def update_plot(self, y1, y2):\r\n try:\r\n self.line1.set_ydata(y1)\r\n self.line2.set_ydata(y2)\r\n self.fig.canvas.draw()\r\n\r\n except:\r\n pass\r\n \r\n \r\n def SaveFig(self):\r\n self.fig.save(\"ppg.pdf\")\r\n pass\r\n\r\n\r\nclass serialupdate(QtCore.QThread):\r\n\r\n update_trigger=QtCore.pyqtSignal(list, list)\r\n\r\n def __init__(self):\r\n QtCore.QThread.__init__(self)\r\n\r\n self.ser=serial.Serial()\r\n self.ser.port=serial_ports()[0]\r\n self.ser.baudrate=115200\r\n self.ser.open()\r\n self.ser.reset_input_buffer()\r\n self.ser.readline()\r\n \r\n self.y1=[0 for _ in range(2000)]\r\n self.y2=[0 for _ in range(2000)]\r\n self.list1=[]\r\n self.list2=[]\r\n\r\n self._is_running=True\r\n\r\n\t\r\n def run(self):\r\n\r\n self.ser.readline()\r\n self._is_running=True\r\n\r\n while (self._is_running):\r\n del self.y1[0:40]\r\n del self.y2[0:40]\r\n for j in range (40):\r\n try:\r\n if self._is_running==True:\r\n\r\n ak = self.ser.readline()\r\n sk = str(ak)\r\n pk = sk.split(',')[0]\r\n pk2 = sk.split(',')[1]\r\n gh = pk.split(\"'\")\r\n gh2 = pk2.split(\"\\\\n\")\r\n\r\n input1 = (float(gh[1])*(5/4096))\r\n input2 = (float(gh2[0])*(5/4096))\r\n\r\n print(float(gh[1]))\r\n print(float(gh2[0]))\r\n\r\n else:\r\n input=3\r\n\r\n self.list1.append(input1)\r\n self.list2.append(input2)\r\n\r\n except:\r\n self.list1.append(3)\r\n self.list2.append(3)\r\n\r\n\r\n self.list1 = moveave(self.list1,11)\r\n self.y1.extend(self.list1)\r\n\r\n self.list2 = moveave(self.list2,11)\r\n self.y2.extend(self.list2)\r\n \r\n self.list1=[]\r\n self.list2=[]\r\n\r\n self.update_trigger.emit(self.y1, self.y2)\r\n \r\n sleep(0.035)\r\n \r\n\r\n\r\n def stop(self):\r\n\r\n self._is_running=False\r\n self.ser.reset_input_buffer()\r\n \r\n\r\n def __del__(self):\r\n\r\n self.ser.reset_input_buffer()\r\n self.ser.close()\r\n\r\n\r\n\r\ndef serial_ports():\r\n\r\n if sys.platform.startswith('win'):\r\n ports = ['COM%s' % (i + 1) for i in range(256)]\r\n elif sys.platform.startswith('linux') or sys.platform.startswith('cygwin'):\r\n ports = glob.glob('/dev/tty[A-Za-z]*')\r\n elif sys.platform.startswith('darwin'):\r\n ports = glob.glob('/dev/tty.*')\r\n else:\r\n raise EnvironmentError('Unsupported platform')\r\n\r\n result = []\r\n for port in ports:\r\n try:\r\n s = serial.Serial(port)\r\n s.close()\r\n result.append(port)\r\n except (OSError, serial.SerialException):\r\n pass\r\n return result\r\n\r\n\r\n\r\ndef moveave(input_list,windowsize):\r\n tempnparray=np.array(input_list)\r\n oneslist=np.ones(windowsize)\r\n\r\n tempnparray=np.convolve(tempnparray,oneslist)/windowsize\r\n \r\n templist=list(tempnparray)\r\n\r\n for i in range(int(windowsize/2)):\r\n del templist[0]\r\n\r\n for j in range(int(windowsize/2)):\r\n del templist[len(input_list)+int(windowsize/2)-j-1]\r\n \r\n for i in range(int(windowsize/2)):\r\n templist[i]=templist[i]*windowsize/(int(windowsize/2)+1+i)\r\n \r\n for j in range(int(windowsize/2)):\r\n templist[len(input_list)-j-1]=templist[len(input_list)-j-1]*windowsize/(int(windowsize/2)+1+j)\r\n \r\n return templist\r\n\r\n\r\n\r\n\r\n\r\napp=QApplication(sys.argv)\r\napp.setStyle(\"Fusion\")\r\nwindow=PulseWindow()\r\nwindow.show()\r\nsys.exit(app.exec_())", "sub_path": "GUI/gui.py", "file_name": "gui.py", "file_ext": "py", "file_size_in_byte": 10146, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "matplotlib.use", "line_number": 8, "usage_type": "call"}, {"api_name": "PyQt5.uic.loadUiType", "line_number": 19, "usage_type": "call"}, {"api_name": "PyQt5.uic", "line_number": 19, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 19, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 22, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMainWindow.__init__", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.figure.Figure", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.backends.backend_qt5agg.FigureCanvasQTAgg", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.backends.backend_qt5agg.NavigationToolbar2QT", "line_number": 46, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 63, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QThread", "line_number": 205, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 205, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.pyqtSignal", "line_number": 207, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 207, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QThread.__init__", "line_number": 210, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QThread", "line_number": 210, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 210, "usage_type": "name"}, {"api_name": "serial.Serial", "line_number": 212, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 274, "usage_type": "call"}, {"api_name": "sys.platform.startswith", "line_number": 293, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 293, "usage_type": "attribute"}, {"api_name": "sys.platform.startswith", "line_number": 295, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 295, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 296, "usage_type": "call"}, {"api_name": "sys.platform.startswith", "line_number": 297, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 297, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 298, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 305, "usage_type": "call"}, {"api_name": "serial.SerialException", "line_number": 308, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 316, "usage_type": "call"}, {"api_name": "numpy.convolve", "line_number": 318, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 340, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 340, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 344, "usage_type": "call"}]} +{"seq_id": "339201047", "text": "import os\nfrom requests import get\nfrom requests.exceptions import RequestException\nfrom contextlib import closing\nfrom bs4 import BeautifulSoup\n\n\ndef is_good_response(resp):\n content_type = resp.headers['Content-Type'].lower()\n return (resp.status_code == 200\n and content_type is not None\n and content_type.find('html') > -1)\n\n\ndef simple_get(url):\n try:\n with closing(get(url, stream=True)) as resp:\n if is_good_response(resp):\n return resp.content\n else:\n return None\n\n except RequestException:\n return None\n\n\ndef parse_domains(page=1):\n url = 'https://park.io/domains/index/io/page:%s' % (page,)\n response = simple_get(url)\n domains = []\n if response is not None:\n html = BeautifulSoup(response, 'html.parser')\n tds = html.select('td[data-label=\"Domain Name\"]')\n for td in tds:\n for a in td.select('a'):\n print(' - {}'.format(a.text))\n domains.append(a.text)\n\n if len(tds) > 0:\n domains = domains + parse_domains(page + 1)\n \n return domains\n\n\ndef filter_thesaurus(domains):\n filtered = []\n for domain in domains:\n print(' - {}'.format(domain))\n stripped = domain.replace('.io', '')\n url = 'http://www.thesaurus.com/browse/%s' % (stripped,)\n response = simple_get(url)\n if response is not None:\n html = BeautifulSoup(response, 'html.parser')\n not_found = False\n for div in html.select('div'):\n if div.text == 'no thesaurus results':\n not_found = True\n if not_found is False:\n filtered.append(domain)\n return filtered\n\n\nif __name__ == '__main__':\n\n if os.path.isfile('parkio_scrapper.txt') is False:\n print('Scraping the list of domains')\n domains = sorted(parse_domains())\n with open('parkio_scrapper.txt', 'w') as file_handler:\n for item in domains:\n file_handler.write(\"{}\\n\".format(item))\n else:\n print('Reading the domain file')\n with open('parkio_scrapper.txt') as file_handler:\n domains = file_handler.read().splitlines()\n\n if os.path.isfile('saurus.txt') is False:\n print('Filtering word domains')\n saurus = filter_thesaurus(domains)\n with open('parkio_scrapper_saurus.txt', 'w') as file_handler:\n for item in saurus:\n file_handler.write(\"{}\\n\".format(item))\n else:\n print('Reading the word domain file')\n with open('parkio_scrapper_saurus.txt') as file_handler:\n saurus = file_handler.read().splitlines()\n", "sub_path": "python/parkio_scrapper.py", "file_name": "parkio_scrapper.py", "file_ext": "py", "file_size_in_byte": 2708, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "contextlib.closing", "line_number": 17, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 17, "usage_type": "call"}, {"api_name": "requests.exceptions.RequestException", "line_number": 23, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 32, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path", "line_number": 76, "usage_type": "attribute"}]} +{"seq_id": "395132644", "text": "\"\"\"\nThe cat face data comes from https://sites.google.com/site/catdatacollection/data\n\"\"\"\n\nimport pickle\nimport math\nimport os\nimport random\nimport cv2\nimport numpy as np\nfrom bs4 import BeautifulSoup\nfrom PIL import Image\nfrom typing import Union, List\n\n\n\ndef imread(path, shape=None, bw=False, rgba=False, dtype=np.float32):\n # type: (str, tuple, bool, bool) -> np.ndarray\n \"\"\"\n\n :param path: path to the image\n :param shape: (Height, width)\n :param bw: Whether the image is black and white.\n :param rgba: Whether the image is in rgba format.\n :return: np array with shape (height, width, num_color(1, 3, or 4))\n \"\"\"\n assert not (bw and rgba)\n if bw:\n convert_format = 'L'\n elif rgba:\n convert_format = 'RGBA'\n else:\n convert_format = 'RGB'\n\n if shape is None:\n return np.asarray(Image.open(path).convert(convert_format), dtype)\n else:\n return np.asarray(Image.open(path).convert(convert_format).resize((shape[1], shape[0]), resample=Image.ANTIALIAS), dtype)\n\n\ndef read_and_resize_images(dirs, height=None, width=None, bw=False, rgba=False):\n # type: (Union[str,List[str]], Union[int,None], Union[int,None], bool, bool) -> Union[np.ndarray,List[np.ndarray]]\n \"\"\"\n\n :param dirs: a single string or a list of strings of paths to images.\n :param height: height of outputted images. If height and width are both None, then the image is not resized.\n :param width: width of outputted images. If height and width are both None, then the image is not resized.\n :param bw: Whether the image is black and white\n :param rgba: Whether the image is in rgba format.\n :return: images resized to the specific height or width supplied. It is either a numpy array or a list of numpy\n arrays\n \"\"\"\n if isinstance(dirs, list):\n images = [read_and_resize_images(d, height, width) for d in dirs]\n return images\n elif isinstance(dirs, str):\n image_1 = imread(dirs)\n # If there is no width and height, we automatically take the first image's width and height and apply to all the\n # other ones.\n if width is not None:\n if height is not None:\n target_shape = (height, width)\n else:\n target_shape = (int(math.floor(float(image_1.shape[0]) /\n image_1.shape[1] * width)), width)\n else:\n if height is not None:\n target_shape = (height, int(math.floor(float(image_1.shape[1]) /\n image_1.shape[0] * height)))\n else:\n target_shape = (image_1.shape[0], image_1.shape[1])\n return imread(dirs, shape=target_shape, bw=bw, rgba=rgba)\n\ndef get_all_image_paths_in_dir(directory):\n # type: (str) -> List[str]\n \"\"\"\n\n :param directory: The parent directory of the images.\n :return: A sorted list of paths to images in the directory as well as all of its subdirectories.\n \"\"\"\n _allowed_extensions = ['.jpg', '.png', '.JPG', '.PNG']\n if not directory.endswith('/'):\n raise AssertionError('The directory must end with a /')\n content_dirs = []\n for path, subdirs, files in os.walk(directory):\n for name in files:\n full_file_path = os.path.join(path, name)\n base, ext = os.path.splitext(full_file_path)\n if ext in _allowed_extensions:\n content_dirs.append(full_file_path)\n if len(content_dirs) == 0:\n raise AssertionError('There is no image in directory %s' % directory)\n content_dirs = sorted(content_dirs)\n return content_dirs\n\ndef resize_images(image_arrays, size=[32, 32]):\n # convert float type to integer \n image_arrays = (image_arrays * 255).astype('uint8')\n \n resized_image_arrays = np.zeros([image_arrays.shape[0]]+size)\n for i, image_array in enumerate(image_arrays):\n image = Image.fromarray(image_array)\n resized_image = image.resize(size=size, resample=Image.ANTIALIAS)\n \n resized_image_arrays[i] = np.asarray(resized_image)\n \n return np.expand_dims(resized_image_arrays, 3) \n\ndef save_pickle(data, path):\n with open(path, 'wb') as f:\n try:\n pickle.dump(data, f, pickle.HIGHEST_PROTOCOL)\n except:\n pickle.dump(data, f)\n print ('Saved %s..' %path)\n\ndef get_category_name(image_path, category_subdir_index = -3):\n \"\"\"\n Assume the category name is the dir that contains the image\n :param image_path:\n :return:\n \"\"\"\n # image_dir = os.path.dirname(image_path).strip('/')\n # image_dir.strip('face').strip('/')\n # category_name = image_dir[image_dir.rfind(\"/\")+1:]\n category_name = image_path.split('/')[category_subdir_index]\n return category_name\n\ndef load_cat_and_dog(hw, parent_dir = 'cat_and_dog', save_dir='cnd'):\n # train = load_cat_and_dog_face_from_list(hw, parent_dir=parent_dir,is_trainval=True)\n train, test = load_cat_and_dog_face_from_list(hw, parent_dir=parent_dir)\n\n # for i in range(train['X'].shape[0]):\n #\n # cv2.imshow('Hint', cv2.cvtColor(train['X'][i].astype(np.uint8), cv2.COLOR_RGB2BGR))\n # cv2.waitKey(0)\n\n if not os.path.exists(save_dir):\n os.mkdir(save_dir)\n\n save_pickle(train, os.path.join(save_dir,'train.pkl'))\n save_pickle(test, os.path.join(save_dir,'test.pkl'))\n\ndef load_cat_and_dog_face_from_list(hw, parent_dir=\"cat_and_dog\", train_split = 0.9):\n # file_path = os.path.join(parent_dir,\"annotations/trainval.txt\") if is_trainval else os.path.join(parent_dir,\"annotations/test.txt\")\n # with open(file_path) as trainval_file:\n # lines = trainval_file.readlines()\n # file_names = [l.split(\" \")[0] for l in lines]\n\n file_names = []\n for path, subdirs, files in os.walk(os.path.join(parent_dir,\"annotations/xmls\")):\n for name in files:\n full_file_path = os.path.join(path, name)\n base, ext = os.path.splitext(full_file_path)\n _, file_name = os.path.split(base)\n if ext == \".xml\":\n file_names.append(file_name)\n\n file_names = sorted(file_names)\n file_names = [name for name in file_names if (\n os.path.isfile(os.path.join(parent_dir, \"annotations/xmls\", name+'.xml'))\n and os.path.isfile(os.path.join(parent_dir, \"images\", name+'.jpg')))]\n\n xml = [load_annotation(os.path.join(parent_dir, \"annotations/xmls\", p+'.xml')) for p in file_names]\n crop_coordinates = [(int(current_xml.annotation.object.bndbox.xmin.contents[0]),\n int(current_xml.annotation.object.bndbox.ymin.contents[0]),\n int(current_xml.annotation.object.bndbox.xmax.contents[0]),\n int(current_xml.annotation.object.bndbox.ymax.contents[0])) for current_xml in xml]\n categories = [current_xml.annotation.filename.contents[0].split(\"_\")[0] for current_xml in xml]\n unique_categories = sorted(list(set(categories)))\n print(\"Number of unique categories for cat and dog dataset: %d.\" %(len(unique_categories)))\n unique_categories_dict = {unique_categories[i]:i for i in range(len(unique_categories))}\n\n labels = np.array([unique_categories_dict[d] for d in categories])\n\n image_paths = [os.path.join(parent_dir, \"images\", p+'.jpg') for p in file_names]\n cropped_images = np.array([np.asarray(\n Image.open(path).convert(\"RGB\").crop(crop_coordinates[path_i]).resize((hw,hw), Image.ANTIALIAS),np.uint8)\n for path_i, path in enumerate(image_paths)]) # Should I use np.float32??\n\n num_train = int(cropped_images.shape[0] * train_split) # Select 90% of data as training data.\n train_images = cropped_images[:num_train]\n test_images = cropped_images[num_train:]\n train_labels = labels[:num_train]\n test_labels = labels[num_train:]\n\n return {'X': train_images, 'y': train_labels}, {'X': test_images, 'y': test_labels}\n\n\ndef load_annotation(path):\n \"\"\"\n Load annotation file for a given image.\n Args:\n img_name (string): string of the image name, relative to\n the image directory.\n Returns:\n BeautifulSoup structure: the annotation labels loaded as a\n BeautifulSoup data structure\n \"\"\"\n with open(path) as f:\n xml = f.readlines()\n xml = ''.join([line.strip('\\t') for line in xml])\n return BeautifulSoup(xml)\n\ndef main():\n # This is for when I manually separated train and test\n # cat_train_dirs = get_all_image_paths_in_dir('CatOpen/train/')\n # cats_train = np.array(read_and_resize_images(cat_train_dirs))\n #\n # train = {'X': cats_train}\n #\n # cat_test_dirs = get_all_image_paths_in_dir('CatOpen/test/')\n # cats_test = np.array(read_and_resize_images(cat_test_dirs))\n # test = {'X': cats_test}\n #\n # if not os.path.exists('cat/'):\n # os.mkdir('cat/')\n #\n # save_pickle(train, 'cat/train.pkl')\n # save_pickle(test, 'cat/test.pkl')\n\n # This one won't work because profile data is gray scale..\n # cat_front_dirs = get_all_image_paths_in_dir('CatOpen/')\n # cat_front_images = np.array(read_and_resize_images(cat_front_dirs))\n # cat_left_dirs = get_all_image_paths_in_dir('ProfileData/')\n # cat_left_images = np.array(read_and_resize_images(cat_left_dirs))\n # cat_right_images = cat_left_images[:,:,::-1,:] # flip\n #\n # cat_all_images = np.random.shuffle(np.concatenate((cat_front_images,cat_left_images,cat_right_images)))\n # num_train = cat_all_images.shape[0] * 90 / 100 # Select 90% of data as training data.\n # cat_train_images = cat_all_images[:num_train]\n # cat_test_images = cat_all_images[num_train:]\n #\n # train = {'X': cat_train_images}\n # test = {'X': cat_test_images}\n #\n # if not os.path.exists('cat/'):\n # os.mkdir('cat/')\n #\n # save_pickle(train, 'cat/train.pkl')\n # save_pickle(test, 'cat/test.pkl')\n\n # The following is for datasets that has all images in subdirectories representing their categories.\n # Human dataset\n # rootdir = '/mnt/data_drive/home/ubuntu/PycharmProjects/facescrub/resized_224/' # '/mnt/data_drive/home/ubuntu/PycharmProjects/facescrub/download/' # 'facescrub/'\n # save_dir = '/mnt/data_drive/home/ubuntu/datasets/human_224/'\n # hw = 224\n #\n #\n # face_dirs = [d for d in get_all_image_paths_in_dir(rootdir) if d.split('/')[-2] == \"face\"]\n # face_categories = [get_category_name(d) for d in face_dirs]\n # face_unique_categories = sorted(list(set(face_categories)))\n # # assert len(face_unique_categories) == 530 # Uncomment this for facescrub dataset sanity check.\n # print(\"Number of unique categories for human face: %d. Number of images %d\" %(len(face_unique_categories), len(face_dirs)))\n # assert face_unique_categories > 0 and face_dirs > 0\n # face_unique_categories_dict = {face_unique_categories[i]:i for i in range(len(face_unique_categories))}\n #\n # random_index = list(range(len(face_dirs)))\n # random.shuffle(random_index)\n # num_train = len(face_dirs) * 90 / 100 # Select 90% of data as training data.\n # train_index = random_index[:num_train]\n # test_index = random_index[num_train:]\n #\n # face_train_dirs = [face_dirs[i] for i in train_index]\n # face_train = np.array(read_and_resize_images(face_train_dirs, height=hw, width=hw), dtype=np.uint8)\n # assert face_train.shape[1] == hw and face_train.shape[2] == hw and face_train.shape[3] == 3\n # face_train_label = np.array([face_unique_categories_dict[d] for d in [face_categories[i] for i in train_index]], dtype=np.uint8)\n # train = {'X': face_train, 'y': face_train_label}\n #\n # face_test_dirs = [face_dirs[i] for i in test_index]\n # face_test = np.array(read_and_resize_images(face_test_dirs, height=hw, width=hw), dtype=np.uint8)\n # assert face_test.shape[1] == hw and face_test.shape[2] == hw and face_test.shape[3] == 3\n # face_test_label = np.array([face_unique_categories_dict[d] for d in [face_categories[i] for i in test_index]], dtype=np.uint8)\n # test = {'X': face_test, 'y': face_test_label}\n #\n # if not os.path.exists(save_dir):\n # os.mkdir(save_dir)\n # save_pickle(train, os.path.join(save_dir,'train.pkl'))\n # save_pickle(test, os.path.join(save_dir,'test.pkl'))\n\n\n # The following is for datasets that has all images in subdirectories representing their categories.\n # IT also separates male actors from female ones.\n # Human dataset\n\n for s in ['male','female']:\n rootdir = '/mnt/tf_drive/home/ubuntu/PycharmProjects/facescrub/download/' # 'facescrub/'\n hw = 128\n save_dir = '/mnt/data_drive/home/ubuntu/datasets/human_%d_%s/' %(hw,s)\n with open('facescrub_actors_names.txt' if s == 'male' else 'facescrub_actresses_names.txt', 'r') as f:\n current_sex_actors = set([name.strip('\\n') for name in f.readlines()])\n\n\n face_dirs = [d for d in get_all_image_paths_in_dir(rootdir) if d.split('/')[-2] == \"face\" and d.split('/')[-3] in current_sex_actors]\n face_categories = [get_category_name(d) for d in face_dirs]\n face_unique_categories = sorted(list(set(face_categories)))\n assert len(face_unique_categories) == 530 / 2 # Uncomment this for facescrub dataset sanity check.\n print(\"Number of unique categories for human face: %d. Number of images %d\" %(len(face_unique_categories), len(face_dirs)))\n assert face_unique_categories > 0 and face_dirs > 0\n face_unique_categories_dict = {face_unique_categories[i]:i for i in range(len(face_unique_categories))}\n\n random_index = list(range(len(face_dirs)))\n random.shuffle(random_index)\n num_train = len(face_dirs) * 90 / 100 # Select 90% of data as training data.\n train_index = random_index[:num_train]\n test_index = random_index[num_train:]\n\n face_train_dirs = [face_dirs[i] for i in train_index]\n face_train = np.array(read_and_resize_images(face_train_dirs, height=hw, width=hw), dtype=np.uint8)\n assert face_train.shape[1] == hw and face_train.shape[2] == hw and face_train.shape[3] == 3\n face_train_label = np.array([face_unique_categories_dict[d] for d in [face_categories[i] for i in train_index]], dtype=np.uint8)\n train = {'X': face_train, 'y': face_train_label}\n\n face_test_dirs = [face_dirs[i] for i in test_index]\n face_test = np.array(read_and_resize_images(face_test_dirs, height=hw, width=hw), dtype=np.uint8)\n assert face_test.shape[1] == hw and face_test.shape[2] == hw and face_test.shape[3] == 3\n face_test_label = np.array([face_unique_categories_dict[d] for d in [face_categories[i] for i in test_index]], dtype=np.uint8)\n test = {'X': face_test, 'y': face_test_label}\n\n if not os.path.exists(save_dir):\n os.mkdir(save_dir)\n save_pickle(train, os.path.join(save_dir,'train.pkl'))\n save_pickle(test, os.path.join(save_dir,'test.pkl'))\n #\n # # Anime face\n # # rootdir = '/mnt/data_drive/home/ubuntu/datasets/animeface-character-dataset/thumb/' # 'facescrub/'\n # # save_dir = '/mnt/data_drive/home/ubuntu/datasets/anime_face_128/'\n # # hw = 128\n # rootdir = '/mnt/data_drive/home/ubuntu/datasets/animeface-character-dataset/thumb/' # 'facescrub/'\n # # save_dir = '/mnt/data_drive/home/ubuntu/datasets/anime_face_128/'\n # # hw = 128\n # for hw in [224]:\n #\n # save_dir = '/mnt/data_drive/home/ubuntu/datasets/anime_face_%d/' %hw\n # face_dirs = [d for d in get_all_image_paths_in_dir(rootdir)]\n # face_categories = [get_category_name(d,category_subdir_index=-2) for d in face_dirs]\n # face_unique_categories = sorted(list(set(face_categories)))\n # print(\"Number of unique categories for human face: %d. Number of images %d. They are: %s\" %(len(face_unique_categories), len(face_dirs), str(face_unique_categories)))\n # assert face_unique_categories > 0 and face_dirs > 0\n # face_unique_categories_dict = {face_unique_categories[i]:i for i in range(len(face_unique_categories))}\n #\n # random_index = list(range(len(face_dirs)))\n # random.shuffle(random_index)\n # num_train = len(face_dirs) * 90 / 100 # Select 90% of data as training data.\n # train_index = random_index[:num_train]\n # test_index = random_index[num_train:]\n #\n # face_train_dirs = [face_dirs[i] for i in train_index]\n # face_train = np.array(read_and_resize_images(face_train_dirs, height=hw, width=hw), dtype=np.uint8)\n # assert face_train.shape[1] == hw and face_train.shape[2] == hw and face_train.shape[3] == 3\n # face_train_label = np.array([face_unique_categories_dict[d] for d in [face_categories[i] for i in train_index]], dtype=np.uint8)\n # train = {'X': face_train, 'y': face_train_label}\n #\n # face_test_dirs = [face_dirs[i] for i in test_index]\n # face_test = np.array(read_and_resize_images(face_test_dirs, height=hw, width=hw), dtype=np.uint8)\n # assert face_test.shape[1] == hw and face_test.shape[2] == hw and face_test.shape[3] == 3\n # face_test_label = np.array([face_unique_categories_dict[d] for d in [face_categories[i] for i in test_index]], dtype=np.uint8)\n # test = {'X': face_test, 'y': face_test_label}\n #\n # if not os.path.exists(save_dir):\n # os.mkdir(save_dir)\n # save_pickle(train, os.path.join(save_dir,'train.pkl'))\n # save_pickle(test, os.path.join(save_dir,'test.pkl'))\n # #\n # # # load_cat_and_dog(32, save_dir=\"cnd_32\")\n # # # load_cat_and_dog(128, save_dir=\"cnd_128\")\n # pass\n\n \nif __name__ == \"__main__\":\n main()\n ", "sub_path": "working_code/prepro_cat.py", "file_name": "prepro_cat.py", "file_ext": "py", "file_size_in_byte": 17728, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.float32", "line_number": 17, "usage_type": "attribute"}, {"api_name": "numpy.asarray", "line_number": 36, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 36, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 36, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 38, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 38, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 38, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 38, "usage_type": "attribute"}, {"api_name": "math.floor", "line_number": 64, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 68, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 100, "usage_type": "call"}, {"api_name": "PIL.Image.fromarray", "line_number": 102, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 102, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 103, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 103, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 107, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 112, "usage_type": "call"}, {"api_name": "pickle.HIGHEST_PROTOCOL", "line_number": 112, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 114, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path", "line_number": 138, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path", "line_number": 142, "usage_type": "attribute"}, {"api_name": "os.walk", "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": 153, "usage_type": "call"}, {"api_name": "os.path", "line_number": 153, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path", "line_number": 154, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path", "line_number": 155, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path", "line_number": 161, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 162, "usage_type": "call"}, {"api_name": "os.path", "line_number": 162, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 162, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path", "line_number": 164, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 174, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path", "line_number": 176, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 177, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 178, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 178, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 178, "usage_type": "attribute"}, {"api_name": "numpy.uint8", "line_number": 178, "usage_type": "attribute"}, {"api_name": "bs4.BeautifulSoup", "line_number": 203, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 309, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 309, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 311, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 315, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 315, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 317, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 317, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 320, "usage_type": "call"}, {"api_name": "os.path", "line_number": 320, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 321, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 322, "usage_type": "call"}, {"api_name": "os.path", "line_number": 322, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 323, "usage_type": "call"}, {"api_name": "os.path", "line_number": 323, "usage_type": "attribute"}]} +{"seq_id": "650283820", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef sigmoid(Z):\n\n A = 1/(1+np.exp(-Z))\n cache = Z\n\n return A, cache\n\ndef relu(Z):\n\n A = np.maximum(0,Z)\n\n assert(A.shape == Z.shape)\n\n cache = Z\n return A, cache\n\n\ndef initialize_parameters_he(layers_dims):\n\n np.random.seed(4)\n parameters = {}\n L = len(layers_dims) - 1 # integer representing the number of layers\n\n for l in range(1, L + 1):\n\n parameters['W' + str(l)] = np.random.randn(layers_dims[l], layers_dims[l-1]) * (np.sqrt(2. / layers_dims[l-1]))\n parameters['b' + str(l)] = np.zeros((layers_dims[l], 1))\n\n return parameters\n\n\n\ndef initialize_parameters(layers_dims):\n\n np.random.seed(4)\n parameters = {}\n L = len(layers_dims) - 1 # integer representing the number of layers\n\n for l in range(1, L + 1):\n\n parameters['W' + str(l)] = np.random.randn(layers_dims[l], layers_dims[l-1]) * 0.01\n parameters['b' + str(l)] = np.zeros((layers_dims[l], 1))\n\n return parameters\n\n\n\n\ndef activation_forward(A_prev, W, b, activation):\n\n if activation == \"sigmoid\":\n\n Z = np.dot(W, A_prev) + b\n\n assert(Z.shape == (W.shape[0], A_prev.shape[1]))\n\n linear_cache = (A_prev, W, b)\n\n A, activation_cache = sigmoid(Z)\n\n\n elif activation == \"relu\":\n\n Z = np.dot(W, A_prev) + b\n\n assert(Z.shape == (W.shape[0], A_prev.shape[1]))\n\n linear_cache = (A_prev, W, b)\n\n A, activation_cache = relu(Z)\n\n assert (A.shape == (W.shape[0], A_prev.shape[1]))\n cache = (linear_cache, activation_cache)\n\n return A, cache\n\n\n\ndef activation_forward_with_dropout(A_prev, W, b, activation, keep_prob):\n\n np.random.seed(1)\n\n Z = np.dot(W, A_prev) + b\n\n assert(Z.shape == (W.shape[0], A_prev.shape[1]))\n\n linear_cache = (A_prev, W, b)\n\n if activation == \"sigmoid\":\n\n A, activation_cache = sigmoid(Z)\n\n cache = (linear_cache, activation_cache)\n\n elif activation == \"relu\":\n\n A, activation_cache = relu(Z)\n\n D = np.random.rand(A.shape[0], A.shape[1])\n D = D < keep_prob\n A = np.multiply(A, D)\n A /= keep_prob\n\n cache = (linear_cache, activation_cache, D)\n\n assert (A.shape == (W.shape[0], A_prev.shape[1]))\n\n return A, cache\n\n\n\ndef model_forward(X, parameters):\n\n caches = []\n A = X\n L = len(parameters) // 2\n\n for l in range(1, L):\n A_prev = A\n\n A, cache = activation_forward(A_prev,\n parameters['W' + str(l)],\n parameters['b' + str(l)],\n activation='relu')\n caches.append(cache)\n\n AL, cache = activation_forward(A,\n parameters['W' + str(L)],\n parameters['b' + str(L)],\n activation='sigmoid')\n caches.append(cache)\n\n assert(AL.shape == (1, X.shape[1]))\n\n return AL, caches\n\n\ndef model_forward_with_dropout(X, parameters, keep_prob=0.5):\n\n caches = []\n A = X\n L = len(parameters) // 2\n\n for l in range(1, L):\n A_prev = A\n\n A, cache = activation_forward_with_dropout(A_prev,\n parameters['W' + str(l)],\n parameters['b' + str(l)],\n activation='relu',\n keep_prob = keep_prob)\n caches.append(cache)\n\n AL, cache = activation_forward_with_dropout(A,\n parameters['W' + str(L)],\n parameters['b' + str(L)],\n activation='sigmoid',\n keep_prob = keep_prob)\n caches.append(cache)\n\n assert(AL.shape == (1, X.shape[1]))\n\n return AL, caches\n\n\ndef compute_cost(AL, Y):\n\n m = Y.shape[1]\n\n #cost = (-1 / m) * (np.dot(Y, np.log(AL).T) + np.dot(1 - Y, np.log(1 - AL).T))\n\n #cost = np.squeeze(cost)\n\n # deal with 0 derivative\n logprobs = np.multiply(-np.log(AL), Y) + np.multiply(-np.log(1-AL), 1 - Y)\n cost = 1./m * np.nansum(logprobs)\n\n assert(cost.shape == ())\n\n return cost\n\n\ndef compute_cost_with_regularization(AL, Y, parameters, lambd):\n\n m = Y.shape[1]\n cross_entropy_cost = compute_cost(AL, Y)\n L2_regularization_cost = 0\n\n for l in range(len(parameters)/2):\n L2_regularization_cost += np.sum(np.square(parameters[\"W\" + str(l+1)]))\n\n L2_regularization_cost = L2_regularization_cost * (1. / m) * (lambd / 2)\n\n cost = cross_entropy_cost + L2_regularization_cost\n\n return cost\n\n\ndef relu_backward(dA, cache):\n\n Z = cache\n\n dZ = np.array(dA, copy=True)\n dZ = np.multiply(dZ, np.int64(Z >= 0))\n\n assert (dZ.shape == Z.shape)\n\n return dZ\n\ndef sigmoid_backward(dA, cache):\n\n Z = cache\n\n s = 1/(1+np.exp(-Z))\n dZ = dA * s * (1-s)\n\n assert (dZ.shape == Z.shape)\n\n return dZ\n\ndef activation_backward(dA, cache, activation):\n\n linear_cache, activation_cache = cache\n\n if activation == \"relu\":\n\n dZ = relu_backward(dA, activation_cache)\n\n elif activation == \"sigmoid\":\n\n dZ = sigmoid_backward(dA, activation_cache)\n\n\n A_prev, W, b = linear_cache\n m = A_prev.shape[1]\n\n dW = (1. / m) * np.dot(dZ, A_prev.T)\n db = (1. / m) * np.sum(dZ, axis=1, keepdims=True)\n dA_prev = np.dot(W.T, dZ)\n\n assert (dA_prev.shape == A_prev.shape)\n assert (dW.shape == W.shape)\n assert (db.shape == b.shape)\n\n return dA_prev, dW, db\n\n\ndef activation_backward_with_regularization(dA, cache, activation, lambd):\n\n linear_cache, activation_cache = cache\n\n if activation == \"relu\":\n\n dZ = relu_backward(dA, activation_cache)\n\n\n elif activation == \"sigmoid\":\n\n dZ = sigmoid_backward(dA, activation_cache)\n\n A_prev, W, b = linear_cache\n m = A_prev.shape[1]\n\n dW = (1. / m) * (np.dot(dZ, A_prev.T) + lambd * W)\n db = (1. / m) * np.sum(dZ, axis=1, keepdims=True)\n dA_prev = np.dot(W.T, dZ)\n\n assert (dA_prev.shape == A_prev.shape)\n assert (dW.shape == W.shape)\n assert (db.shape == b.shape)\n\n return dA_prev, dW, db\n\n\n\ndef activation_backward_with_dropout(dA, cache, activation, keep_prob):\n\n linear_cache, activation_cache, D = cache\n\n A_prev, W, b = linear_cache\n\n if activation == \"relu\":\n\n dA = np.multiply(dA, D)\n dA /= keep_prob\n\n dZ = relu_backward(dA, activation_cache)\n\n elif activation == \"sigmoid\":\n\n dZ = sigmoid_backward(dA, activation_cache)\n\n m = A_prev.shape[1]\n\n dW = (1. / m) * np.dot(dZ, A_prev.T)\n db = (1. / m) * np.sum(dZ, axis=1, keepdims=True)\n dA_prev = np.dot(W.T, dZ)\n\n assert (dA_prev.shape == A_prev.shape)\n assert (dW.shape == W.shape)\n assert (db.shape == b.shape)\n\n return dA_prev, dW, db\n\ndef nan_divide(a, b):\n\n with np.errstate(divide='ignore', invalid='ignore'):\n c = np.true_divide(a,b)\n c[c == np.inf] = 0\n c = np.nan_to_num(c)\n return c\n\n\ndef model_backward(AL, Y, caches):\n\n grads = {}\n L = len(caches)\n m = AL.shape[1]\n Y = Y.reshape(AL.shape)\n\n dAL = - (nan_divide(Y, AL) - nan_divide(1 - Y, 1 - AL))\n\n current_cache = caches[-1]\n grads[\"dA\" + str(L)], grads[\"dW\" + str(L)], grads[\"db\" + str(L)] = activation_backward(dAL, current_cache, activation=\"sigmoid\")\n\n for l in reversed(range(L-1)):\n\n current_cache = caches[l]\n\n dA_prev_temp, dW_temp, db_temp = activation_backward(grads[\"dA\" + str(l + 2)], current_cache, activation=\"relu\")\n grads[\"dA\" + str(l + 1)] = dA_prev_temp\n grads[\"dW\" + str(l + 1)] = dW_temp\n grads[\"db\" + str(l + 1)] = db_temp\n\n return grads\n\ndef model_backward_with_regularization(AL, Y, caches, lambd):\n\n grads = {}\n L = len(caches)\n m = AL.shape[1]\n Y = Y.reshape(AL.shape)\n\n dAL = - (nan_divide(Y, AL) - nan_divide(1 - Y, 1 - AL))\n\n current_cache = caches[-1]\n grads[\"dA\" + str(L)], grads[\"dW\" + str(L)], grads[\"db\" + str(L)] = activation_backward_with_regularization(dAL, current_cache, activation=\"sigmoid\", lambd = lambd)\n\n for l in reversed(range(L-1)):\n\n current_cache = caches[l]\n\n dA_prev_temp, dW_temp, db_temp = activation_backward_with_regularization(grads[\"dA\" + str(l + 2)], current_cache, activation=\"relu\", lambd = lambd)\n grads[\"dA\" + str(l + 1)] = dA_prev_temp\n grads[\"dW\" + str(l + 1)] = dW_temp\n grads[\"db\" + str(l + 1)] = db_temp\n\n return grads\n\n\ndef model_backward_with_dropout(AL, Y, caches, keep_prob):\n\n grads = {}\n L = len(caches)\n m = AL.shape[1]\n Y = Y.reshape(AL.shape)\n\n dAL = - (nan_divide(Y, AL) - nan_divide(1 - Y, 1 - AL))\n\n current_cache = caches[-1]\n grads[\"dA\" + str(L)], grads[\"dW\" + str(L)], grads[\"db\" + str(L)] = activation_backward(dAL, current_cache, activation=\"sigmoid\")\n\n for l in reversed(range(L-1)):\n\n current_cache = caches[l]\n\n dA_prev_temp, dW_temp, db_temp = activation_backward_with_dropout(grads[\"dA\" + str(l + 2)], current_cache, activation=\"relu\", keep_prob = keep_prob)\n grads[\"dA\" + str(l + 1)] = dA_prev_temp\n grads[\"dW\" + str(l + 1)] = dW_temp\n grads[\"db\" + str(l + 1)] = db_temp\n\n return grads\n\n\n\ndef update_parameters(parameters, grads, learning_rate):\n\n L = len(parameters) // 2\n\n for l in range(L):\n parameters[\"W\" + str(l + 1)] = parameters[\"W\" + str(l + 1)] - learning_rate * grads[\"dW\" + str(l + 1)]\n parameters[\"b\" + str(l + 1)] = parameters[\"b\" + str(l + 1)] - learning_rate * grads[\"db\" + str(l + 1)]\n\n return parameters\n\ndef predict(X, y, parameters):\n\n m = X.shape[1]\n n = len(parameters) // 2\n p = np.zeros((1,m))\n\n probs, caches = model_forward(X, parameters)\n\n for i in range(0, probs.shape[1]):\n if probs[0,i] > 0.5:\n p[0,i] = 1\n else:\n p[0,i] = 0\n\n accuracy = np.sum((p == y)/m)\n\n print(\"Accuracy: \" + str(accuracy))\n\n return accuracy\n\n\n\ndef model_stopping(X, Y, valid_x, valid_y, layers_dims, learning_rate = 0.0075, num_iterations = 10000, print_cost=False, print_size=100):\n\n costs = []\n\n accuracy_prev = 0\n\n parameters = initialize_parameters(layers_dims)\n\n for i in range(0, num_iterations):\n\n AL, caches = model_forward(X, parameters)\n\n cost = compute_cost(AL, Y)\n\n grads = model_backward(AL, Y, caches)\n\n cache = parameters\n\n parameters = update_parameters(parameters, grads, learning_rate=learning_rate)\n\n if print_cost and i % print_size == 0:\n print (\"Cost after iteration %i: %f\" %(i, cost))\n\n if print_cost and i % 100 == 0:\n accuracy = predict(valid_x, valid_y, parameters)\n costs.append(cost)\n\n if accuracy < accuracy_prev:\n parameters = cache\n print (\"Early stopping at iteration {} to prevent overfitting\".format(i))\n break\n else:\n accuracy_prev = accuracy\n\n plt.plot(np.squeeze(costs))\n plt.ylabel('cost')\n plt.xlabel('iterations (per tens)')\n plt.title(\"Learning rate =\" + str(learning_rate))\n plt.show()\n\n return parameters\n\n\n\ndef deep_model(X, Y, layers_dims, learning_rate = 0.0075, num_iterations = 10000, print_cost=False, print_size=100, lambd=0, keep_prob=1, continue_train = False, initial_parameters = 0):\n\n Y = Y.reshape(1, X.shape[1])\n \n costs = []\n\n accuracy_prev = 0\n\n if continue_train == False:\n parameters = initialize_parameters(layers_dims)\n else:\n parameters = initial_parameters\n\n for i in range(0, num_iterations):\n\n if keep_prob == 1:\n AL, caches = model_forward(X, parameters)\n else:\n AL, caches = model_forward_with_dropout(X, parameters, keep_prob)\n\n if lambd == 0:\n cost = compute_cost(AL, Y)\n else:\n cost = compute_cost_with_regularization(AL, Y, parameters, lambd)\n\n # one or the other\n assert(lambd==0 or keep_prob==1)\n\n if lambd == 0 and keep_prob == 1:\n grads = model_backward(AL, Y, caches)\n elif lambd != 0:\n grads = model_backward_with_regularization(AL, Y, caches, lambd)\n elif keep_prob <1:\n grads = model_backward_with_dropout(AL, Y, caches, keep_prob)\n\n cache = parameters\n\n parameters = update_parameters(parameters, grads, learning_rate=learning_rate)\n\n if print_cost and i % print_size == 0:\n print (\"Cost after iteration %i: %f\" %(i, cost))\n\n if print_cost and i % 100 == 0:\n costs.append(cost)\n\n plt.plot(np.squeeze(costs))\n plt.ylabel('cost')\n plt.xlabel('iterations (per tens)')\n plt.title(\"Learning rate =\" + str(learning_rate))\n plt.show()\n\n np.save(\"param_\" + str(len(layers_dims)-1) + \"layer_\" + str(i), parameters)\n\n return parameters\n\n\n# Prediction for binary outcomes\ndef predict_dec(parameters, X):\n\n # Predict using forward propagation and a classification threshold of 0.5\n a3, cache = model_forward(X, parameters)\n\n predictions = (a3>0.5)\n return predictions\n", "sub_path": "DL_np.py", "file_name": "DL_np.py", "file_ext": "py", "file_size_in_byte": 13124, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.exp", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.maximum", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 30, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 85, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 87, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 103, "usage_type": "attribute"}, {"api_name": "numpy.multiply", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.nansum", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.square", "line_number": 194, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 207, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.int64", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 243, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 268, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 269, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 270, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 299, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 300, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 301, "usage_type": "call"}, {"api_name": "numpy.errstate", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.true_divide", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.inf", "line_number": 313, "usage_type": "attribute"}, {"api_name": "numpy.nan_to_num", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 404, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 414, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 456, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 456, "usage_type": "name"}, {"api_name": "numpy.squeeze", "line_number": 456, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 457, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 457, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 458, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 458, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 459, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 459, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 460, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 460, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 511, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 511, "usage_type": "name"}, {"api_name": "numpy.squeeze", "line_number": 511, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 512, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 512, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 513, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 513, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 514, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 514, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 515, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 515, "usage_type": "name"}, {"api_name": "numpy.save", "line_number": 517, "usage_type": "call"}]} +{"seq_id": "330272843", "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 ]\n\n operations = [\n migrations.CreateModel(\n name='Cyclist',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('firstname', models.CharField(max_length=50)),\n ('lastname', models.CharField(max_length=50)),\n ('secondlastname', models.CharField(max_length=50)),\n ('email', models.EmailField(max_length=254)),\n ('age', models.PositiveIntegerField()),\n ('birthday', models.DateField()),\n ('created', models.DateField(auto_now_add=True)),\n ('phone', models.CharField(max_length=50)),\n ('category', models.CharField(default=b'P', max_length=1, choices=[(b'P', b'Principiante'), (b'I', b'Intermedia'), (b'A', b'Avanzada')])),\n ('alias', models.CharField(max_length=50)),\n ('club', models.CharField(max_length=50)),\n ('emergency_phone', models.CharField(max_length=50)),\n ('contact_name', models.CharField(max_length=50)),\n ('contact_phone', models.CharField(max_length=50)),\n ],\n ),\n migrations.CreateModel(\n name='Event',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=50)),\n ('created', models.DateField(auto_now_add=True)),\n ('date', models.DateField(auto_now_add=True)),\n ('cost', models.DecimalField(max_digits=10, decimal_places=3)),\n ('distance', models.DecimalField(max_digits=10, decimal_places=2)),\n ('category', models.CharField(default=b'P', max_length=1, choices=[(b'P', b'Principiante'), (b'I', b'Intermedia'), (b'A', b'Avanzada')])),\n ('limit', models.PositiveIntegerField(default=0)),\n ],\n ),\n migrations.CreateModel(\n name='Jersey',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('total', models.PositiveIntegerField(default=0)),\n ('amount', models.PositiveIntegerField(default=0)),\n ('event', models.ForeignKey(to='core.Event')),\n ],\n ),\n migrations.CreateModel(\n name='Medal',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('total', models.PositiveIntegerField(default=0)),\n ('amount', models.PositiveIntegerField(default=0)),\n ('event', models.ForeignKey(to='core.Event')),\n ],\n ),\n migrations.CreateModel(\n name='Suscription',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('number', models.PositiveIntegerField()),\n ('jersey', models.BooleanField(default=False)),\n ('medal', models.BooleanField(default=False)),\n ('ride', models.BooleanField(default=False)),\n ('size', models.CharField(default=b'L', max_length=1, choices=[(b'S', b'Small'), (b'M', b'Medium'), (b'L', b'Large'), (b'XL', b'X Large'), (b'XXL', b'XX Large')])),\n ('package', models.CharField(default=b'U', max_length=1, choices=[(b'D', b'Entregado'), (b'U', b'Pendiente')])),\n ('status', models.CharField(default=b'U', max_length=1, choices=[(b'A', b'Accepted'), (b'P', b'In Process')])),\n ('cyclist', models.ForeignKey(to='core.Cyclist')),\n ('event', models.ForeignKey(to='core.Event')),\n ('user', models.ForeignKey(to=settings.AUTH_USER_MODEL)),\n ],\n ),\n ]\n", "sub_path": "bicia_2/apps/core/migrations/0001_initial.py", "file_name": "0001_initial.py", "file_ext": "py", "file_size_in_byte": 4239, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "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": 15, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 21, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 21, "usage_type": "name"}, {"api_name": "django.db.models.EmailField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 28, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 28, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 29, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 29, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 38, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 38, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 40, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 42, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 42, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 44, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 44, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 45, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 45, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 48, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 48, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 51, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 51, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 52, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 52, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 53, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 53, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 54, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 54, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 57, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 57, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 60, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 60, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 61, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 61, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 62, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 62, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 63, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 63, "usage_type": "name"}, {"api_name": "django.db.migrations.CreateModel", "line_number": 66, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 66, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 69, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 69, "usage_type": "name"}, {"api_name": "django.db.models.PositiveIntegerField", "line_number": 70, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 70, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 71, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 71, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 72, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 72, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 73, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 73, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 74, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 74, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 75, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 75, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 76, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 76, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 77, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 77, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 78, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 78, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 79, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 79, "usage_type": "name"}, {"api_name": "django.conf.settings.AUTH_USER_MODEL", "line_number": 79, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 79, "usage_type": "name"}]} +{"seq_id": "588091221", "text": "#!/usr/bin/env python\n#\n# Copyright 2007 Google LLC\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\n\n\n\"\"\"Generated protocol buffer code.\"\"\"\nfrom google.protobuf import descriptor as _descriptor\nfrom google.protobuf import message as _message\nfrom google.protobuf import reflection as _reflection\nfrom google.protobuf import symbol_database as _symbol_database\n\n\n_sym_db = _symbol_database.Default()\n\n\n\n\nDESCRIPTOR = _descriptor.FileDescriptor(\n name='google/appengine/base/capabilities.proto',\n package='google.appengine',\n syntax='proto2',\n serialized_options=b'\\n%com.google.appengine.api.capabilitiesB\\016CapabilitiesPb\\370\\001\\001',\n create_key=_descriptor._internal_create_key,\n serialized_pb=b'\\n(google/appengine/base/capabilities.proto\\x12\\x10google.appengine\\\"\\x86\\x01\\n\\x14\\x43\\x61pabilityConfigList\\x12\\x32\\n\\x06\\x63onfig\\x18\\x01 \\x03(\\x0b\\x32\\\".google.appengine.CapabilityConfig\\x12:\\n\\x0e\\x64\\x65\\x66\\x61ult_config\\x18\\x02 \\x01(\\x0b\\x32\\\".google.appengine.CapabilityConfig\\\"\\xa9\\x02\\n\\x10\\x43\\x61pabilityConfig\\x12\\x0f\\n\\x07package\\x18\\x01 \\x02(\\t\\x12\\x12\\n\\ncapability\\x18\\x02 \\x02(\\t\\x12\\x42\\n\\x06status\\x18\\x03 \\x01(\\x0e\\x32).google.appengine.CapabilityConfig.Status:\\x07UNKNOWN\\x12\\x16\\n\\x0escheduled_time\\x18\\x07 \\x01(\\t\\x12\\x18\\n\\x10internal_message\\x18\\x04 \\x01(\\t\\x12\\x15\\n\\radmin_message\\x18\\x05 \\x01(\\t\\x12\\x15\\n\\rerror_message\\x18\\x06 \\x01(\\t\\\"L\\n\\x06Status\\x12\\x0b\\n\\x07\\x44\\x45\\x46\\x41ULT\\x10\\x00\\x12\\x0b\\n\\x07\\x45NABLED\\x10\\x01\\x12\\r\\n\\tSCHEDULED\\x10\\x02\\x12\\x0c\\n\\x08\\x44ISABLED\\x10\\x03\\x12\\x0b\\n\\x07UNKNOWN\\x10\\x04\\x42:\\n%com.google.appengine.api.capabilitiesB\\x0e\\x43\\x61pabilitiesPb\\xf8\\x01\\x01'\n)\n\n\n\n_CAPABILITYCONFIG_STATUS = _descriptor.EnumDescriptor(\n name='Status',\n full_name='google.appengine.CapabilityConfig.Status',\n filename=None,\n file=DESCRIPTOR,\n create_key=_descriptor._internal_create_key,\n values=[\n _descriptor.EnumValueDescriptor(\n name='DEFAULT', index=0, number=0,\n serialized_options=None,\n type=None,\n create_key=_descriptor._internal_create_key),\n _descriptor.EnumValueDescriptor(\n name='ENABLED', index=1, number=1,\n serialized_options=None,\n type=None,\n create_key=_descriptor._internal_create_key),\n _descriptor.EnumValueDescriptor(\n name='SCHEDULED', index=2, number=2,\n serialized_options=None,\n type=None,\n create_key=_descriptor._internal_create_key),\n _descriptor.EnumValueDescriptor(\n name='DISABLED', index=3, number=3,\n serialized_options=None,\n type=None,\n create_key=_descriptor._internal_create_key),\n _descriptor.EnumValueDescriptor(\n name='UNKNOWN', index=4, number=4,\n serialized_options=None,\n type=None,\n create_key=_descriptor._internal_create_key),\n ],\n containing_type=None,\n serialized_options=None,\n serialized_start=421,\n serialized_end=497,\n)\n_sym_db.RegisterEnumDescriptor(_CAPABILITYCONFIG_STATUS)\n\n\n_CAPABILITYCONFIGLIST = _descriptor.Descriptor(\n name='CapabilityConfigList',\n full_name='google.appengine.CapabilityConfigList',\n filename=None,\n file=DESCRIPTOR,\n containing_type=None,\n create_key=_descriptor._internal_create_key,\n fields=[\n _descriptor.FieldDescriptor(\n name='config', full_name='google.appengine.CapabilityConfigList.config', index=0,\n number=1, type=11, cpp_type=10, label=3,\n has_default_value=False, default_value=[],\n message_type=None, enum_type=None, containing_type=None,\n is_extension=False, extension_scope=None,\n serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),\n _descriptor.FieldDescriptor(\n name='default_config', full_name='google.appengine.CapabilityConfigList.default_config', index=1,\n number=2, type=11, cpp_type=10, label=1,\n has_default_value=False, default_value=None,\n message_type=None, enum_type=None, containing_type=None,\n is_extension=False, extension_scope=None,\n serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),\n ],\n extensions=[\n ],\n nested_types=[],\n enum_types=[\n ],\n serialized_options=None,\n is_extendable=False,\n syntax='proto2',\n extension_ranges=[],\n oneofs=[\n ],\n serialized_start=63,\n serialized_end=197,\n)\n\n\n_CAPABILITYCONFIG = _descriptor.Descriptor(\n name='CapabilityConfig',\n full_name='google.appengine.CapabilityConfig',\n filename=None,\n file=DESCRIPTOR,\n containing_type=None,\n create_key=_descriptor._internal_create_key,\n fields=[\n _descriptor.FieldDescriptor(\n name='package', full_name='google.appengine.CapabilityConfig.package', index=0,\n number=1, type=9, cpp_type=9, label=2,\n has_default_value=False, default_value=b\"\".decode('utf-8'),\n message_type=None, enum_type=None, containing_type=None,\n is_extension=False, extension_scope=None,\n serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),\n _descriptor.FieldDescriptor(\n name='capability', full_name='google.appengine.CapabilityConfig.capability', index=1,\n number=2, type=9, cpp_type=9, label=2,\n has_default_value=False, default_value=b\"\".decode('utf-8'),\n message_type=None, enum_type=None, containing_type=None,\n is_extension=False, extension_scope=None,\n serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),\n _descriptor.FieldDescriptor(\n name='status', full_name='google.appengine.CapabilityConfig.status', index=2,\n number=3, type=14, cpp_type=8, label=1,\n has_default_value=True, default_value=4,\n message_type=None, enum_type=None, containing_type=None,\n is_extension=False, extension_scope=None,\n serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),\n _descriptor.FieldDescriptor(\n name='scheduled_time', full_name='google.appengine.CapabilityConfig.scheduled_time', index=3,\n number=7, type=9, cpp_type=9, label=1,\n has_default_value=False, default_value=b\"\".decode('utf-8'),\n message_type=None, enum_type=None, containing_type=None,\n is_extension=False, extension_scope=None,\n serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),\n _descriptor.FieldDescriptor(\n name='internal_message', full_name='google.appengine.CapabilityConfig.internal_message', index=4,\n number=4, type=9, cpp_type=9, label=1,\n has_default_value=False, default_value=b\"\".decode('utf-8'),\n message_type=None, enum_type=None, containing_type=None,\n is_extension=False, extension_scope=None,\n serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),\n _descriptor.FieldDescriptor(\n name='admin_message', full_name='google.appengine.CapabilityConfig.admin_message', index=5,\n number=5, type=9, cpp_type=9, label=1,\n has_default_value=False, default_value=b\"\".decode('utf-8'),\n message_type=None, enum_type=None, containing_type=None,\n is_extension=False, extension_scope=None,\n serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),\n _descriptor.FieldDescriptor(\n name='error_message', full_name='google.appengine.CapabilityConfig.error_message', index=6,\n number=6, type=9, cpp_type=9, label=1,\n has_default_value=False, default_value=b\"\".decode('utf-8'),\n message_type=None, enum_type=None, containing_type=None,\n is_extension=False, extension_scope=None,\n serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key),\n ],\n extensions=[\n ],\n nested_types=[],\n enum_types=[\n _CAPABILITYCONFIG_STATUS,\n ],\n serialized_options=None,\n is_extendable=False,\n syntax='proto2',\n extension_ranges=[],\n oneofs=[\n ],\n serialized_start=200,\n serialized_end=497,\n)\n\n_CAPABILITYCONFIGLIST.fields_by_name['config'].message_type = _CAPABILITYCONFIG\n_CAPABILITYCONFIGLIST.fields_by_name['default_config'].message_type = _CAPABILITYCONFIG\n_CAPABILITYCONFIG.fields_by_name['status'].enum_type = _CAPABILITYCONFIG_STATUS\n_CAPABILITYCONFIG_STATUS.containing_type = _CAPABILITYCONFIG\nDESCRIPTOR.message_types_by_name['CapabilityConfigList'] = _CAPABILITYCONFIGLIST\nDESCRIPTOR.message_types_by_name['CapabilityConfig'] = _CAPABILITYCONFIG\n_sym_db.RegisterFileDescriptor(DESCRIPTOR)\n\nCapabilityConfigList = _reflection.GeneratedProtocolMessageType('CapabilityConfigList', (_message.Message,), {\n 'DESCRIPTOR' : _CAPABILITYCONFIGLIST,\n '__module__' : 'google.appengine.base.capabilities_pb2'\n\n })\n_sym_db.RegisterMessage(CapabilityConfigList)\n\nCapabilityConfig = _reflection.GeneratedProtocolMessageType('CapabilityConfig', (_message.Message,), {\n 'DESCRIPTOR' : _CAPABILITYCONFIG,\n '__module__' : 'google.appengine.base.capabilities_pb2'\n\n })\n_sym_db.RegisterMessage(CapabilityConfig)\n\n\nDESCRIPTOR._options = None\n\n", "sub_path": "src/google/appengine/base/capabilities_pb2.py", "file_name": "capabilities_pb2.py", "file_ext": "py", "file_size_in_byte": 9379, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "google.protobuf.symbol_database.Default", "line_number": 27, "usage_type": "call"}, {"api_name": "google.protobuf.symbol_database", "line_number": 27, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor.FileDescriptor", "line_number": 32, "usage_type": "call"}, {"api_name": "google.protobuf.descriptor", "line_number": 32, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor._internal_create_key", "line_number": 37, "usage_type": "attribute"}, {"api_name": "google.protobuf.descriptor", "line_number": 37, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor.EnumDescriptor", "line_number": 43, "usage_type": "call"}, {"api_name": "google.protobuf.descriptor", "line_number": 43, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor._internal_create_key", "line_number": 48, "usage_type": "attribute"}, {"api_name": "google.protobuf.descriptor", "line_number": 48, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor.EnumValueDescriptor", "line_number": 50, "usage_type": "call"}, {"api_name": "google.protobuf.descriptor", "line_number": 50, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor._internal_create_key", "line_number": 54, "usage_type": "attribute"}, {"api_name": "google.protobuf.descriptor", "line_number": 54, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor.EnumValueDescriptor", "line_number": 55, "usage_type": "call"}, {"api_name": "google.protobuf.descriptor", "line_number": 55, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor._internal_create_key", "line_number": 59, "usage_type": "attribute"}, {"api_name": "google.protobuf.descriptor", "line_number": 59, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor.EnumValueDescriptor", "line_number": 60, "usage_type": "call"}, {"api_name": "google.protobuf.descriptor", "line_number": 60, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor._internal_create_key", "line_number": 64, "usage_type": "attribute"}, {"api_name": "google.protobuf.descriptor", "line_number": 64, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor.EnumValueDescriptor", "line_number": 65, "usage_type": "call"}, {"api_name": "google.protobuf.descriptor", "line_number": 65, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor._internal_create_key", "line_number": 69, "usage_type": "attribute"}, {"api_name": "google.protobuf.descriptor", "line_number": 69, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor.EnumValueDescriptor", "line_number": 70, "usage_type": "call"}, {"api_name": "google.protobuf.descriptor", "line_number": 70, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor._internal_create_key", "line_number": 74, "usage_type": "attribute"}, {"api_name": "google.protobuf.descriptor", "line_number": 74, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor.Descriptor", "line_number": 84, "usage_type": "call"}, {"api_name": "google.protobuf.descriptor", "line_number": 84, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor._internal_create_key", "line_number": 90, "usage_type": "attribute"}, {"api_name": "google.protobuf.descriptor", "line_number": 90, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor.FieldDescriptor", "line_number": 92, "usage_type": "call"}, {"api_name": "google.protobuf.descriptor", "line_number": 92, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor._internal_create_key", "line_number": 98, "usage_type": "attribute"}, {"api_name": "google.protobuf.descriptor", "line_number": 98, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor.FieldDescriptor", "line_number": 99, "usage_type": "call"}, {"api_name": "google.protobuf.descriptor", "line_number": 99, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor._internal_create_key", "line_number": 105, "usage_type": "attribute"}, {"api_name": "google.protobuf.descriptor", "line_number": 105, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor.Descriptor", "line_number": 123, "usage_type": "call"}, {"api_name": "google.protobuf.descriptor", "line_number": 123, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor._internal_create_key", "line_number": 129, "usage_type": "attribute"}, {"api_name": "google.protobuf.descriptor", "line_number": 129, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor.FieldDescriptor", "line_number": 131, "usage_type": "call"}, {"api_name": "google.protobuf.descriptor", "line_number": 131, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor._internal_create_key", "line_number": 137, "usage_type": "attribute"}, {"api_name": "google.protobuf.descriptor", "line_number": 137, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor.FieldDescriptor", "line_number": 138, "usage_type": "call"}, {"api_name": "google.protobuf.descriptor", "line_number": 138, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor._internal_create_key", "line_number": 144, "usage_type": "attribute"}, {"api_name": "google.protobuf.descriptor", "line_number": 144, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor.FieldDescriptor", "line_number": 145, "usage_type": "call"}, {"api_name": "google.protobuf.descriptor", "line_number": 145, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor._internal_create_key", "line_number": 151, "usage_type": "attribute"}, {"api_name": "google.protobuf.descriptor", "line_number": 151, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor.FieldDescriptor", "line_number": 152, "usage_type": "call"}, {"api_name": "google.protobuf.descriptor", "line_number": 152, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor._internal_create_key", "line_number": 158, "usage_type": "attribute"}, {"api_name": "google.protobuf.descriptor", "line_number": 158, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor.FieldDescriptor", "line_number": 159, "usage_type": "call"}, {"api_name": "google.protobuf.descriptor", "line_number": 159, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor._internal_create_key", "line_number": 165, "usage_type": "attribute"}, {"api_name": "google.protobuf.descriptor", "line_number": 165, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor.FieldDescriptor", "line_number": 166, "usage_type": "call"}, {"api_name": "google.protobuf.descriptor", "line_number": 166, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor._internal_create_key", "line_number": 172, "usage_type": "attribute"}, {"api_name": "google.protobuf.descriptor", "line_number": 172, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor.FieldDescriptor", "line_number": 173, "usage_type": "call"}, {"api_name": "google.protobuf.descriptor", "line_number": 173, "usage_type": "name"}, {"api_name": "google.protobuf.descriptor._internal_create_key", "line_number": 179, "usage_type": "attribute"}, {"api_name": "google.protobuf.descriptor", "line_number": 179, "usage_type": "name"}, {"api_name": "google.protobuf.reflection.GeneratedProtocolMessageType", "line_number": 205, "usage_type": "call"}, {"api_name": "google.protobuf.reflection", "line_number": 205, "usage_type": "name"}, {"api_name": "google.protobuf.message.Message", "line_number": 205, "usage_type": "attribute"}, {"api_name": "google.protobuf.message", "line_number": 205, "usage_type": "name"}, {"api_name": "google.protobuf.reflection.GeneratedProtocolMessageType", "line_number": 212, "usage_type": "call"}, {"api_name": "google.protobuf.reflection", "line_number": 212, "usage_type": "name"}, {"api_name": "google.protobuf.message.Message", "line_number": 212, "usage_type": "attribute"}, {"api_name": "google.protobuf.message", "line_number": 212, "usage_type": "name"}]} +{"seq_id": "129303112", "text": "from datetime import datetime, timezone\n\nfrom django.contrib.auth import get_user_model\nfrom django.core.cache import caches\n\nfrom .settings import app_settings\nfrom .utils import SAMLError, SAMLSettingsError\n\n\ndef get_clean_map(user_map: dict, saml_data: dict) -> dict:\n final_map = dict()\n strict_mapping = app_settings.SAML_USERS_STRICT_MAPPING\n for usr_k, usr_v in user_map.items():\n if strict_mapping and isinstance(usr_v, dict):\n if \"default\" in usr_v.keys():\n raise SAMLSettingsError(\n \"A default value is set for key %s in SAML_USER_MAP \\\n while SAML_USERS_STRICT_MAPPING is activated\"\n % usr_k\n )\n\n index = 0\n val = usr_v\n default = None\n if isinstance(usr_v, dict):\n index = usr_v.get(\"index\", 0)\n val = usr_v.get(\"key\", usr_k)\n default = usr_v.get(\"default\", None)\n\n attr = saml_data.get(val, default)\n if isinstance(attr, list):\n attr = attr[index]\n\n if attr is None:\n if strict_mapping:\n raise SAMLError(\n \"Response missing attribute %s while SAML_USERS_STRICT_MAPPING is activated\"\n % usr_k\n )\n\n continue\n\n final_map[usr_k] = attr\n\n return final_map\n\n\nclass Backend: # pragma: no cover\n def user_can_authenticate(self, user):\n \"\"\"\n Reject users with is_active=False. Custom user models that don't have\n that attribute are allowed.\n \"\"\"\n is_active = getattr(user, \"is_active\", None)\n return is_active or is_active is None\n\n def authenticate(self, request, saml_auth=None, user_map=dict()):\n if not saml_auth:\n return None\n\n assertion_id = saml_auth.get_last_assertion_id()\n not_on_or_after = datetime.fromtimestamp(\n saml_auth.get_last_assertion_not_on_or_after(), tz=timezone.utc\n )\n assertion_timeout = not_on_or_after - datetime.now(tz=timezone.utc)\n\n if app_settings.SAML_REPLAY_PROTECTION:\n # Store the assertion id in cache so we can ensure only once\n # processing during validity period\n cache = caches[app_settings.SAML_CACHE]\n if not cache.add(\n assertion_id, assertion_id, timeout=assertion_timeout.seconds\n ):\n # Check if adding the key worked, if the return is false the key already exists\n # so we fail auth. This should let us only process an assertion ID once\n return None\n\n UserModel = get_user_model()\n\n final_map = get_clean_map(user_map, saml_auth.get_attributes())\n\n lookup_attr = app_settings.SAML_USERS_LOOKUP_ATTRIBUTE\n lookup_map = dict()\n if isinstance(lookup_attr, str):\n lookup_map = {lookup_attr: saml_auth.get_nameid()}\n elif isinstance(lookup_attr, (tuple, list)):\n if lookup_attr[1] == \"NameId\":\n lookup_map = {lookup_attr[0]: saml_auth.get_nameid()}\n else:\n lookup_map = {lookup_attr[0]: final_map[lookup_attr[1]]}\n else:\n raise SAMLSettingsError(\n \"The value of SAML_USERS_LOOKUP_ATTRIBUTE must be a str, tuple, or list\"\n )\n\n sync_attributes = app_settings.SAML_USERS_SYNC_ATTRIBUTES\n create_users = app_settings.SAML_AUTO_CREATE_USERS\n\n try:\n if create_users and sync_attributes:\n user, _ = UserModel._default_manager.update_or_create(\n defaults=final_map, **lookup_map\n )\n elif create_users:\n user, _ = UserModel._default_manager.get_or_create(\n defaults=final_map, **lookup_map\n )\n else:\n user = UserModel._default_manager.get(**lookup_map)\n if sync_attributes:\n try:\n for key, val in final_map.items():\n setattr(user, key, val)\n user.save()\n except Exception:\n pass\n except Exception as err:\n return None\n\n if self.user_can_authenticate(user):\n return user\n\n def get_user(self, user_id):\n UserModel = get_user_model()\n try:\n user = UserModel._default_manager.get(pk=user_id)\n except UserModel.DoesNotExist:\n return None\n\n return user if self.user_can_authenticate(user) else None\n\n\nSamlBackend = Backend\n", "sub_path": "src/saml2_pro_auth/auth.py", "file_name": "auth.py", "file_ext": "py", "file_size_in_byte": 4643, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "settings.app_settings.SAML_USERS_STRICT_MAPPING", "line_number": 12, "usage_type": "attribute"}, {"api_name": "settings.app_settings", "line_number": 12, "usage_type": "name"}, {"api_name": "utils.SAMLSettingsError", "line_number": 16, "usage_type": "call"}, {"api_name": "utils.SAMLError", "line_number": 36, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 62, "usage_type": "name"}, {"api_name": "datetime.timezone.utc", "line_number": 63, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 63, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 65, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 65, "usage_type": "name"}, {"api_name": "datetime.timezone.utc", "line_number": 65, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 65, "usage_type": "name"}, {"api_name": "settings.app_settings.SAML_REPLAY_PROTECTION", "line_number": 67, "usage_type": "attribute"}, {"api_name": "settings.app_settings", "line_number": 67, "usage_type": "name"}, {"api_name": "django.core.cache.caches", "line_number": 70, "usage_type": "name"}, {"api_name": "settings.app_settings.SAML_CACHE", "line_number": 70, "usage_type": "attribute"}, {"api_name": "settings.app_settings", "line_number": 70, "usage_type": "name"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 78, "usage_type": "call"}, {"api_name": "settings.app_settings.SAML_USERS_LOOKUP_ATTRIBUTE", "line_number": 82, "usage_type": "attribute"}, {"api_name": "settings.app_settings", "line_number": 82, "usage_type": "name"}, {"api_name": "utils.SAMLSettingsError", "line_number": 92, "usage_type": "call"}, {"api_name": "settings.app_settings.SAML_USERS_SYNC_ATTRIBUTES", "line_number": 96, "usage_type": "attribute"}, {"api_name": "settings.app_settings", "line_number": 96, "usage_type": "name"}, {"api_name": "settings.app_settings.SAML_AUTO_CREATE_USERS", "line_number": 97, "usage_type": "attribute"}, {"api_name": "settings.app_settings", "line_number": 97, "usage_type": "name"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 124, "usage_type": "call"}]} +{"seq_id": "564306715", "text": "##############################################################################\n#\n# Copyright (c) 2007 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\"\"\"Javascript Form Framework AJAX Framework.\n\n$Id: $\n\"\"\"\n__docformat__ = \"reStructuredText\"\nimport sys\nimport cjson\nimport inspect\nimport zope.component\nimport zope.interface\nfrom zope.publisher.interfaces import NotFound\nfrom zope.publisher.browser import BrowserPage\nfrom zope.publisher.publish import mapply\nfrom zope.traversing.api import getParents\nfrom z3c.traverser import traverser\nfrom z3c.form.util import SelectionManager, createCSSId\nfrom z3c.traverser.interfaces import ITraverserPlugin\nfrom z3c.formjs import interfaces\n\n\ndef getUniquePrefixer(n=2, prefix='form'):\n def createPrefix(form):\n parents = getParents(form)\n return prefix + ''.join(\n [createCSSId(getattr(obj, '__name__', None)\n or obj.__class__.__name__)\n for obj in parents[:n]])\n return createPrefix\n\n\nclass AJAXHandlers(SelectionManager):\n \"\"\"A selection manager for handling AJAX request handlers.\"\"\"\n zope.interface.implements(interfaces.IAJAXHandlers)\n managerInterface = interfaces.IAJAXHandlers\n\n def __init__(self, *args):\n handlers = []\n for arg in args:\n if self.managerInterface.providedBy(arg):\n handlers += arg.items()\n elif interfaces.IAJAXHandler.providedBy(arg):\n handlers.append((arg.func.__name__, arg))\n else:\n raise TypeError(\"Unrecognized argument type\", arg)\n keys = []\n seq = []\n byname = {}\n for name, handler in handlers:\n keys.append(name)\n seq.append(handler)\n byname[name] = handler\n\n self._data_keys = keys\n self._data_values = seq\n self._data = byname\n\n def addHandler(self, name, handler):\n self._data_keys.append(name)\n self._data_values.append(handler)\n self._data[name] = handler\n\n def __repr__(self):\n return \"<%s %r>\" % (self.__class__.__name__, self.keys())\n\n\nclass AJAXHandler(object):\n zope.interface.implements(interfaces.IAJAXHandler)\n\n def __init__(self, func):\n self.func = func\n\n def __call__(self, view):\n result = mapply(self.func, (view,), view.request)\n if type(result) != str and type(result) != unicode:\n return cjson.encode(result)\n return result\n\n def __repr__(self):\n return \"<%s %r>\" % (self.__class__.__name__, self.func.__name__)\n\n\nclass JSONHandler(AJAXHandler):\n def __call__(self, view):\n args = [cjson.decode(view.request[name])\n for name in inspect.getargspec(self.func)[0][1:]]\n result = self.func(view, *args)\n return cjson.encode(result)\n\n\nclass AJAXRequestHandler(object):\n \"\"\"Mix-in class for forms to support AJAX calls.\"\"\"\n zope.interface.implements(interfaces.IAJAXRequestHandler,\n interfaces.IFormTraverser)\n\n ajaxRequestHandlers = AJAXHandlers()\n\n\ndef handler(func):\n \"\"\"A decorator for defining an AJAX request handler.\"\"\"\n handler = AJAXHandler(func)\n frame = sys._getframe(1)\n f_locals = frame.f_locals\n handlers = f_locals.setdefault('ajaxRequestHandlers', AJAXHandlers())\n handlers.addHandler(func.__name__, handler)\n return handler\n\n\ndef json(func):\n \"\"\"A decorator for defining JSONHandler objects.\n\n This AJAX request handlers does JSON pre and post processing.\n \"\"\"\n handler = JSONHandler(func)\n frame = sys._getframe(1)\n f_locals = frame.f_locals\n handlers = f_locals.setdefault('ajaxRequestHandlers', AJAXHandlers())\n handlers.addHandler(func.__name__, handler)\n return handler\n\n\nclass AJAXView(BrowserPage):\n \"\"\"A wrapper class around AJAX handler to allow it to be publishable.\"\"\"\n\n def __init__(self, handler, request, view):\n self.context = self.handler = handler\n self.request = request\n self.__parent__ = self.view = view\n\n def __call__(self):\n return self.handler(self.view)\n\n\nclass AJAXRequestTraverserPlugin(object):\n \"\"\"Allow access to methods registered as an ajax request handler.\"\"\"\n\n zope.interface.implements(ITraverserPlugin)\n\n def __init__(self, context, request):\n self.context = context\n self.request = request\n\n def publishTraverse(self, request, name):\n handler = self.context.ajaxRequestHandlers.get(name)\n if handler is None:\n raise NotFound(self.context, name, request)\n\n return AJAXView(handler, self.request, self.context)\n", "sub_path": "z3c.formjs/branches/pcardune-client-notify-r87806/src/z3c/formjs/ajax.py", "file_name": "ajax.py", "file_ext": "py", "file_size_in_byte": 5109, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "zope.traversing.api.getParents", "line_number": 36, "usage_type": "call"}, {"api_name": "z3c.form.util.createCSSId", "line_number": 38, "usage_type": "call"}, {"api_name": "z3c.form.util.SelectionManager", "line_number": 44, "usage_type": "name"}, {"api_name": "zope.component.interface.implements", "line_number": 46, "usage_type": "call"}, {"api_name": "zope.component.interface", "line_number": 46, "usage_type": "attribute"}, {"api_name": "zope.component", "line_number": 46, "usage_type": "name"}, {"api_name": "z3c.formjs.interfaces.IAJAXHandlers", "line_number": 46, "usage_type": "attribute"}, {"api_name": "z3c.formjs.interfaces", "line_number": 46, "usage_type": "name"}, {"api_name": "z3c.formjs.interfaces.IAJAXHandlers", "line_number": 47, "usage_type": "attribute"}, {"api_name": "z3c.formjs.interfaces", "line_number": 47, "usage_type": "name"}, {"api_name": "z3c.formjs.interfaces.IAJAXHandler.providedBy", "line_number": 54, "usage_type": "call"}, {"api_name": "z3c.formjs.interfaces.IAJAXHandler", "line_number": 54, "usage_type": "attribute"}, {"api_name": "z3c.formjs.interfaces", "line_number": 54, "usage_type": "name"}, {"api_name": "zope.component.interface.implements", "line_number": 80, "usage_type": "call"}, {"api_name": "zope.component.interface", "line_number": 80, "usage_type": "attribute"}, {"api_name": "zope.component", "line_number": 80, "usage_type": "name"}, {"api_name": "z3c.formjs.interfaces.IAJAXHandler", "line_number": 80, "usage_type": "attribute"}, {"api_name": "z3c.formjs.interfaces", "line_number": 80, "usage_type": "name"}, {"api_name": "zope.publisher.publish.mapply", "line_number": 86, "usage_type": "call"}, {"api_name": "cjson.encode", "line_number": 88, "usage_type": "call"}, {"api_name": "cjson.decode", "line_number": 97, "usage_type": "call"}, {"api_name": "inspect.getargspec", "line_number": 98, "usage_type": "call"}, {"api_name": "cjson.encode", "line_number": 100, "usage_type": "call"}, {"api_name": "zope.component.interface.implements", "line_number": 105, "usage_type": "call"}, {"api_name": "zope.component.interface", "line_number": 105, "usage_type": "attribute"}, {"api_name": "zope.component", "line_number": 105, "usage_type": "name"}, {"api_name": "z3c.formjs.interfaces.IAJAXRequestHandler", "line_number": 105, "usage_type": "attribute"}, {"api_name": "z3c.formjs.interfaces", "line_number": 105, "usage_type": "name"}, {"api_name": "z3c.formjs.interfaces.IFormTraverser", "line_number": 106, "usage_type": "attribute"}, {"api_name": "z3c.formjs.interfaces", "line_number": 106, "usage_type": "name"}, {"api_name": "sys._getframe", "line_number": 114, "usage_type": "call"}, {"api_name": "sys._getframe", "line_number": 127, "usage_type": "call"}, {"api_name": "zope.publisher.browser.BrowserPage", "line_number": 134, "usage_type": "name"}, {"api_name": "zope.component.interface.implements", "line_number": 149, "usage_type": "call"}, {"api_name": "z3c.traverser.interfaces.ITraverserPlugin", "line_number": 149, "usage_type": "argument"}, {"api_name": "zope.component.interface", "line_number": 149, "usage_type": "attribute"}, {"api_name": "zope.component", "line_number": 149, "usage_type": "name"}, {"api_name": "zope.publisher.interfaces.NotFound", "line_number": 158, "usage_type": "call"}]} +{"seq_id": "65624378", "text": "# Application Configuration\nimport os\nimport yaml\nfrom base.utils.data_utils import json_encoder\n\n# CeNDR Version\nAPP_CONFIG, CENDR_VERSION = os.environ['GAE_VERSION'].split(\"-\", 1)\nif APP_CONFIG not in ['development', 'master']:\n APP_CONFIG = 'development'\nCENDR_VERSION = CENDR_VERSION.replace(\"-\", '.')\n\n# BUILDS AND RELEASES\n# The first release is the current release\n# (RELEASE, ANNOTATION_GENOME)\nRELEASES = [(\"20200815\", \"WS276\"),\n (\"20180527\", \"WS263\"),\n (\"20170531\", \"WS258\"),\n (\"20160408\", \"WS245\")]\n\n# The most recent release\nDATASET_RELEASE, WORMBASE_VERSION = RELEASES[0]\n\n# SQLITE DATABASE\nSQLITE_PATH = f\"base/cendr.{DATASET_RELEASE}.{WORMBASE_VERSION}.db\"\n\n\ndef load_yaml(path):\n return yaml.load(open(path), Loader=yaml.SafeLoader)\n\n\n# CONFIG\ndef get_config(APP_CONFIG):\n \"\"\"Load all configuration information including\n constants defined above.\n\n (BASE_VARS are the same regardless of whether we are debugging or in production)\n \"\"\"\n config = dict()\n BASE_VARS = load_yaml(\"env_config/base.yaml\")\n APP_CONFIG_VARS = load_yaml(f\"env_config/{APP_CONFIG}.yaml\")\n config.update(BASE_VARS)\n config.update(APP_CONFIG_VARS)\n # Add configuration variables\n # Remove base prefix for SQLAlchemy as it is loaded\n # from application folder\n config[\"SQLALCHEMY_DATABASE_URI\"] = f\"sqlite:///{SQLITE_PATH}\".replace(\"base/\", \"\")\n config['json_encoder'] = json_encoder\n config.update({\"CENDR_VERSION\": CENDR_VERSION,\n \"APP_CONFIG\": APP_CONFIG,\n \"DATASET_RELEASE\": DATASET_RELEASE,\n \"WORMBASE_VERSION\": WORMBASE_VERSION,\n \"RELEASES\": RELEASES})\n return config\n\n\n# Generate the configuration\nconfig = get_config(APP_CONFIG)\n", "sub_path": "base/config.py", "file_name": "config.py", "file_ext": "py", "file_size_in_byte": 1782, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.environ", "line_number": 7, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 28, "usage_type": "call"}, {"api_name": "yaml.SafeLoader", "line_number": 28, "usage_type": "attribute"}, {"api_name": "base.utils.data_utils.json_encoder", "line_number": 47, "usage_type": "name"}]} +{"seq_id": "250090710", "text": "#!/usr/bin/env python\n#version 2.1\n\nfrom PyQt5 import Qt\nfrom PyQt5 import QtCore\nfrom functools import partial\nimport math\n\nclass slew_frame(Qt.QFrame):\n def __init__(self, parent=None):\n super(slew_frame, self).__init__()\n self.parent = parent\n self.az_speed = 9\n self.el_speed = 9\n self.initUI()\n\n def initUI(self):\n self.setFrameShape(Qt.QFrame.StyledPanel)\n self.initWidgets()\n self.connect_signals()\n\n def initWidgets(self):\n pixmap = Qt.QPixmap(\"./icons/arrow.png\")\n base = pixmap.transformed(Qt.QTransform().rotate(-90))\n\n arrow_u = Qt.QIcon(base)\n arrow_r = Qt.QIcon(base.transformed(Qt.QTransform().rotate(90)))\n arrow_l = Qt.QIcon(base.transformed(Qt.QTransform().rotate(270)))\n arrow_d = Qt.QIcon(base.transformed(Qt.QTransform().rotate(180)))\n\n arrow_ur = base.transformed(Qt.QTransform().rotate(45))\n x = (arrow_ur.width() - base.width()) / 2\n y = (arrow_ur.height() - base.height()) / 2\n arrow_ur = Qt.QIcon(arrow_ur.copy(x,y,base.width(), base.height()))\n\n arrow_dr = Qt.QIcon(base.transformed(Qt.QTransform().rotate(135)).copy(x,y,base.width(), base.height()))\n arrow_dl = Qt.QIcon(base.transformed(Qt.QTransform().rotate(225)).copy(x,y,base.width(), base.height()))\n arrow_ul = Qt.QIcon(base.transformed(Qt.QTransform().rotate(315)).copy(x,y,base.width(), base.height()))\n\n btn_size = 20\n\n frame_lbl = Qt.QLabel(\"Slew Controls:\")\n frame_lbl.setAlignment(Qt.Qt.AlignLeft|Qt.Qt.AlignVCenter)\n frame_lbl.setStyleSheet(\"QLabel {font:12pt; font-weight:bold; text-decoration: underline; color:rgb(255,0,0);}\")\n frame_lbl.setFixedWidth(200)\n frame_lbl.setFixedHeight(20)\n\n #Top Row of buttons\n self.ulButton = Qt.QPushButton(self)\n self.ulButton.setIcon(arrow_ul)\n self.ulButton.setIconSize(Qt.QSize(btn_size,btn_size))\n self.ulButton.setStyleSheet(\"QPushButton { background-color:rgb(200,0,0); }\")\n self.uButton = Qt.QPushButton(self)\n self.uButton.setIcon(arrow_u)\n self.uButton.setIconSize(Qt.QSize(btn_size,btn_size))\n self.uButton.setStyleSheet(\"QPushButton { background-color:rgb(200,0,0); }\")\n self.urButton = Qt.QPushButton(self)\n self.urButton.setIcon(arrow_ur)\n self.urButton.setIconSize(Qt.QSize(btn_size,btn_size))\n self.urButton.setStyleSheet(\"QPushButton { background-color:rgb(200,0,0); }\")\n top_hbox = Qt.QHBoxLayout()\n top_hbox.addWidget(self.ulButton)\n top_hbox.addWidget(self.uButton)\n top_hbox.addWidget(self.urButton)\n\n #Middle Row of buttons\n self.lButton = Qt.QPushButton(self)\n self.lButton.setIcon(arrow_l)\n self.lButton.setIconSize(Qt.QSize(btn_size,btn_size))\n self.lButton.setStyleSheet(\"QPushButton { background-color:rgb(200,0,0); }\")\n self.stopButton = Qt.QPushButton(self)\n pixmap = Qt.QPixmap(\"./icons/stop.png\")\n self.stopButton.setIcon(Qt.QIcon(pixmap))\n self.stopButton.setIconSize(Qt.QSize(btn_size,btn_size))\n self.stopButton.setStyleSheet(\"QPushButton { background-color:rgb(200,0,0); }\")\n self.rButton = Qt.QPushButton(self)\n self.rButton.setIcon(arrow_r)\n self.rButton.setIconSize(Qt.QSize(btn_size,btn_size))\n self.rButton.setStyleSheet(\"QPushButton { background-color:rgb(200,0,0); }\")\n middle_hbox = Qt.QHBoxLayout()\n middle_hbox.addWidget(self.lButton)\n middle_hbox.addWidget(self.stopButton)\n middle_hbox.addWidget(self.rButton)\n\n #Bottom Row of Buttons\n self.dlButton = Qt.QPushButton(self)\n self.dlButton.setIcon(arrow_dl)\n self.dlButton.setIconSize(Qt.QSize(btn_size,btn_size))\n self.dlButton.setStyleSheet(\"QPushButton { background-color:rgb(200,0,0); }\")\n self.dButton = Qt.QPushButton(self)\n self.dButton.setIcon(arrow_d)\n self.dButton.setIconSize(Qt.QSize(btn_size,btn_size))\n self.dButton.setStyleSheet(\"QPushButton { background-color:rgb(200,0,0); }\")\n self.drButton = Qt.QPushButton(self)\n self.drButton.setIcon(arrow_dr)\n self.drButton.setIconSize(Qt.QSize(btn_size,btn_size))\n self.drButton.setStyleSheet(\"QPushButton { background-color:rgb(200,0,0); }\")\n bottom_hbox = Qt.QHBoxLayout()\n bottom_hbox.addWidget(self.dlButton)\n bottom_hbox.addWidget(self.dButton)\n bottom_hbox.addWidget(self.drButton)\n\n #Controls for Azimuth Motor Speed\n lbl = Qt.QLabel(\"Azimuth Speed:\")\n lbl.setAlignment(Qt.Qt.AlignRight|Qt.Qt.AlignVCenter)\n lbl.setStyleSheet(\"QLabel {font:10pt; color:rgb(255,0,0);}\")\n lbl.setFixedWidth(100)\n self.slewAzSpeedSlider = Qt.QSlider(QtCore.Qt.Horizontal)\n self.slewAzSpeedSlider.setMaximum(9)\n self.slewAzSpeedSlider.setMinimum(1)\n self.slewAzSpeedSlider.setValue(9)\n self.slewAzSpeedSlider.setStyleSheet(\"QSlider {background-color:rgb(0,0,0); color:rgb(200,0,0)}\")\n self.slewAzLabel = Qt.QLabel(\"{:d}\".format(self.az_speed))\n self.slewAzLabel.setAlignment(QtCore.Qt.AlignLeft|QtCore.Qt.AlignVCenter)\n self.slewAzLabel.setStyleSheet(\"QLabel {color:rgb(255,0,0);}\")\n az_hbox = Qt.QHBoxLayout()\n az_hbox.addWidget(lbl)\n az_hbox.addWidget(self.slewAzSpeedSlider)\n az_hbox.addWidget(self.slewAzLabel)\n\n #Controls for Elevation Motor Speed\n lbl = Qt.QLabel(\"Elevation Speed:\")\n lbl.setAlignment(Qt.Qt.AlignRight|Qt.Qt.AlignVCenter)\n lbl.setStyleSheet(\"QLabel { font:10pt; color:rgb(255,0,0);}\")\n lbl.setFixedWidth(100)\n self.slewElSpeedSlider = Qt.QSlider(QtCore.Qt.Horizontal)\n self.slewElSpeedSlider.setMaximum(9)\n self.slewElSpeedSlider.setMinimum(1)\n self.slewElSpeedSlider.setValue(9)\n self.slewElSpeedSlider.setStyleSheet(\"QSlider {background-color:rgb(0,0,0); color:rgb(200,0,0)}\")\n self.slewElLabel = Qt.QLabel(\"{:d}\".format(self.el_speed))\n self.slewElLabel.setAlignment(QtCore.Qt.AlignLeft|QtCore.Qt.AlignVCenter)\n self.slewElLabel.setStyleSheet(\"QLabel {color:rgb(255,0,0);}\")\n el_hbox = Qt.QHBoxLayout()\n el_hbox.addWidget(lbl)\n el_hbox.addWidget(self.slewElSpeedSlider)\n el_hbox.addWidget(self.slewElLabel)\n\n self.lock_cb = Qt.QCheckBox('Synchronize Speeds', self)\n self.lock_cb.setStyleSheet(\"QCheckBox { font:10pt; background-color:rgb(0,0,0); color:rgb(255,0,0); }\")\n self.lock_cb.setChecked(True)\n\n vbox = Qt.QVBoxLayout()\n vbox.addWidget(frame_lbl)\n vbox.addLayout(top_hbox)\n vbox.addLayout(middle_hbox)\n vbox.addLayout(bottom_hbox)\n vbox.addLayout(az_hbox)\n vbox.addLayout(el_hbox)\n vbox.addWidget(self.lock_cb)\n vbox.addStretch(1)\n self.setLayout(vbox)\n\n def connect_signals(self):\n self.slewAzSpeedSlider.valueChanged.connect(self.set_az_speed)\n self.slewElSpeedSlider.valueChanged.connect(self.set_el_speed)\n self.lock_cb.stateChanged.connect(self.lock_cb_handler)\n\n self.ulButton.pressed.connect(lambda: self.parent.slew_up_left(self.az_speed, self.el_speed))\n self.ulButton.released.connect(self.parent.slew_stop)\n\n self.uButton.pressed.connect(lambda: self.parent.slew_up(self.el_speed))\n self.uButton.released.connect(self.parent.slew_stop)\n\n self.urButton.pressed.connect(lambda: self.parent.slew_up_right(self.az_speed, self.el_speed))\n self.urButton.released.connect(self.parent.slew_stop)\n\n self.lButton.pressed.connect(lambda: self.parent.slew_left(self.az_speed))\n self.lButton.released.connect(self.parent.slew_stop)\n\n self.stopButton.pressed.connect(self.parent.slew_stop)\n\n self.rButton.pressed.connect(lambda: self.parent.slew_right(self.az_speed))\n self.rButton.released.connect(self.parent.slew_stop)\n\n self.dlButton.pressed.connect(lambda: self.parent.slew_down_left(self.az_speed, self.el_speed))\n self.dlButton.released.connect(self.parent.slew_stop)\n\n self.dButton.pressed.connect(lambda: self.parent.slew_down(self.el_speed))\n self.dButton.released.connect(self.parent.slew_stop)\n\n self.drButton.pressed.connect(lambda: self.parent.slew_down_right(self.az_speed, self.el_speed))\n self.drButton.released.connect(self.parent.slew_stop)\n\n\n def lock_cb_handler(self, state):\n if (state == QtCore.Qt.Checked):\n self.el_speed = self.az_speed\n self.slewElSpeedSlider.setValue(self.el_speed)\n\n def set_az_speed(self, val):\n self.az_speed = val\n print(\"Set Az Slew Speed: {:d}\".format(self.az_speed))\n self.slewAzLabel.setText(\"{:d}\".format(self.az_speed))\n\n if (self.lock_cb.checkState() == Qt.Qt.Checked):\n self.el_speed = val\n self.slewElSpeedSlider.setValue(val)\n\n def set_el_speed(self, val):\n self.el_speed = val\n print(\"Set El Slew Speed: {:d}\".format(self.el_speed))\n self.slewElLabel.setText(\"{:d}\".format(self.el_speed))\n\n if (self.lock_cb.checkState() == Qt.Qt.Checked):\n self.az_speed = val\n self.slewAzSpeedSlider.setValue(val)\n\n def get_slew_speed(self):\n return self.az_speed, self.el_speed\n", "sub_path": "simple_qt/gui/slew_frame.py", "file_name": "slew_frame.py", "file_ext": "py", "file_size_in_byte": 9405, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "PyQt5.Qt.QFrame", "line_number": 9, "usage_type": "attribute"}, {"api_name": "PyQt5.Qt", "line_number": 9, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QFrame", "line_number": 18, "usage_type": "attribute"}, {"api_name": "PyQt5.Qt", "line_number": 18, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QPixmap", "line_number": 23, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 23, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QTransform", "line_number": 24, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 24, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QIcon", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 26, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QIcon", "line_number": 27, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 27, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QTransform", "line_number": 27, "usage_type": "call"}, {"api_name": "PyQt5.Qt.QIcon", "line_number": 28, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 28, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QTransform", "line_number": 28, "usage_type": "call"}, {"api_name": "PyQt5.Qt.QIcon", "line_number": 29, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 29, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QTransform", "line_number": 29, "usage_type": "call"}, {"api_name": "PyQt5.Qt.QTransform", "line_number": 31, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 31, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QIcon", "line_number": 34, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 34, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QIcon", "line_number": 36, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 36, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QTransform", "line_number": 36, "usage_type": "call"}, {"api_name": "PyQt5.Qt.QIcon", "line_number": 37, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 37, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QTransform", "line_number": 37, "usage_type": "call"}, {"api_name": "PyQt5.Qt.QIcon", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 38, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QTransform", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.Qt.QLabel", "line_number": 42, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 42, "usage_type": "name"}, {"api_name": "PyQt5.Qt.Qt", "line_number": 43, "usage_type": "attribute"}, {"api_name": "PyQt5.Qt", "line_number": 43, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QPushButton", "line_number": 49, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 49, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QSize", "line_number": 51, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 51, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QPushButton", "line_number": 53, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 53, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QSize", "line_number": 55, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 55, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QPushButton", "line_number": 57, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 57, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QSize", "line_number": 59, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 59, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QHBoxLayout", "line_number": 61, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 61, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QPushButton", "line_number": 67, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 67, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QSize", "line_number": 69, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 69, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QPushButton", "line_number": 71, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 71, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QPixmap", "line_number": 72, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 72, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QIcon", "line_number": 73, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 73, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QSize", "line_number": 74, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 74, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QPushButton", "line_number": 76, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 76, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QSize", "line_number": 78, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 78, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QHBoxLayout", "line_number": 80, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 80, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QPushButton", "line_number": 86, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 86, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QSize", "line_number": 88, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 88, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QPushButton", "line_number": 90, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 90, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QSize", "line_number": 92, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 92, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QPushButton", "line_number": 94, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 94, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QSize", "line_number": 96, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 96, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QHBoxLayout", "line_number": 98, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 98, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QLabel", "line_number": 104, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 104, "usage_type": "name"}, {"api_name": "PyQt5.Qt.Qt", "line_number": 105, "usage_type": "attribute"}, {"api_name": "PyQt5.Qt", "line_number": 105, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QSlider", "line_number": 108, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 108, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 108, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 108, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QLabel", "line_number": 113, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 113, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 114, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 114, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QHBoxLayout", "line_number": 116, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 116, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QLabel", "line_number": 122, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 122, "usage_type": "name"}, {"api_name": "PyQt5.Qt.Qt", "line_number": 123, "usage_type": "attribute"}, {"api_name": "PyQt5.Qt", "line_number": 123, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QSlider", "line_number": 126, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 126, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 126, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 126, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QLabel", "line_number": 131, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 131, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 132, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 132, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QHBoxLayout", "line_number": 134, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 134, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QCheckBox", "line_number": 139, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 139, "usage_type": "name"}, {"api_name": "PyQt5.Qt.QVBoxLayout", "line_number": 143, "usage_type": "call"}, {"api_name": "PyQt5.Qt", "line_number": 143, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 187, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 187, "usage_type": "name"}, {"api_name": "PyQt5.Qt.Qt", "line_number": 196, "usage_type": "attribute"}, {"api_name": "PyQt5.Qt", "line_number": 196, "usage_type": "name"}, {"api_name": "PyQt5.Qt.Qt", "line_number": 205, "usage_type": "attribute"}, {"api_name": "PyQt5.Qt", "line_number": 205, "usage_type": "name"}]} +{"seq_id": "523079524", "text": "import logging\n\nfrom gemini import GeminiQuery\n\nfrom puzzle.models import (Compound, Variant, Gene, Genotype, Transcript,)\n\nfrom puzzle.utils import (get_most_severe_consequence, get_omim_number,\n get_cytoband_coord, get_gene_info)\n\n\nlogger = logging.getLogger(__name__)\n\nclass VariantMixin(object):\n \"\"\"Class to store variant specific functions for gemini plugin\"\"\"\n\n def variants(self, case_id, skip=0, count=30, filters=None):\n \"\"\"Return count variants for a case.\n\n Args:\n case_id (str): A gemini db\n skip (int): Skip first variants\n count (int): The number of variants to return\n filters (dict): A dictionary with filters. Currently this will\n look like: {\n gene_list: [] (list of hgnc ids),\n frequency: None (float),\n cadd: None (float),\n consequence: [] (list of consequences),\n is_lof: None (Bool),\n genetic_models [] (list of genetic models)\n }\n\n \"\"\"\n filters = filters or {}\n logger.debug(\"Looking for variants in {0}\".format(case_id))\n\n limit = count + skip\n\n gemini_query = \"SELECT * from variants\"\n\n any_filter = False\n\n if filters.get('frequency'):\n frequency = filters['frequency']\n gemini_query += \" WHERE (max_aaf_all < {0} or max_aaf_all is\"\\\n \" Null)\".format(frequency)\n any_filter = True\n\n if filters.get('cadd'):\n cadd_score = filters['cadd']\n if any_filter:\n gemini_query += \" AND (cadd_scaled > {0})\".format(cadd_score)\n else:\n gemini_query += \" WHERE (cadd_scaled > {0})\".format(cadd_score)\n any_filter = True\n\n if filters.get('gene_ids'):\n gene_list = [gene_id.strip() for gene_id in filters['gene_ids']]\n gene_string = \"(\"\n for index, gene_id in enumerate(gene_list):\n if index == 0:\n gene_string += \"'{0}'\".format(gene_id)\n else:\n gene_string += \", '{0}'\".format(gene_id)\n gene_string += \")\"\n\n if any_filter:\n gemini_query += \" AND gene in \" + gene_string\n else:\n gemini_query += \" WHERE gene in \" + gene_string\n\n any_filter = True\n\n filtered_variants = self._variants(\n case_id=case_id,\n gemini_query=gemini_query\n )\n\n if filters.get('consequence'):\n consequences = set(filters['consequence'])\n cons_variants = []\n for variant in filtered_variants:\n for transcript in variant.get('transcripts', []):\n if transcript['consequence'] in consequences:\n cons_variants.append(variant)\n break\n\n filtered_variants = cons_variants\n\n for index, variant_obj in enumerate(filtered_variants):\n if index >= skip:\n if index < limit:\n yield variant_obj\n else:\n break\n\n def variant(self, case_id, variant_id):\n \"\"\"Return a specific variant.\n\n We solve this by building a gemini query and send it to _variants\n\n Args:\n case_id (str): Path to a gemini database\n variant_id (int): A gemini variant id\n\n Returns:\n variant_obj (dict): A puzzle variant\n\n \"\"\"\n variant_id = int(variant_id)\n gemini_query = \"SELECT * from variants WHERE variant_id = {0}\".format(\n variant_id\n )\n\n individuals = []\n # Get the individuals for the case\n for case in self.cases():\n if case['name'] == case_id:\n for individual in case['individuals']:\n individuals.append(individual)\n\n gq = GeminiQuery(self.db)\n\n gq.run(gemini_query)\n\n for gemini_variant in gq:\n variant = self._format_variant(\n gemini_variant=gemini_variant,\n individual_objs=individuals,\n index=gemini_variant['variant_id']\n )\n\n return variant\n\n return None\n\n def _get_genotypes(self, gemini_variant, individual_objs):\n \"\"\"Add the genotypes for a variant for all individuals\n\n Args:\n gemini_variant (GeminiQueryRow): The gemini variant\n individual_objs (list(dict)): A list of Individuals\n\n Returns:\n individuals (list) A list of Genotypes\n \"\"\"\n individuals = []\n for ind in individual_objs:\n index = ind.index\n individuals.append(Genotype(\n sample_id=ind.ind_id,\n genotype=gemini_variant['gts'][index],\n case_id=ind.case_id,\n phenotype=ind.phenotype,\n ref_depth=gemini_variant['gt_ref_depths'][index],\n alt_depth=gemini_variant['gt_alt_depths'][index],\n depth=gemini_variant['gt_depths'][index],\n genotype_quality=gemini_variant['gt_quals'][index]\n ))\n\n return individuals\n\n def _get_genes(self, variant):\n \"\"\"Add the genes for a variant\n\n Get the hgnc symbols from all transcripts and add them\n to the variant\n\n Args:\n variant (dict): A variant dictionary\n\n Returns:\n genes (list): A list of Genes\n \"\"\"\n genes = get_gene_info(variant['transcripts'])\n return genes\n\n def _get_transcripts(self, gemini_variant):\n \"\"\"Return a Transcript object\n\n Gemini stores the information for the most severe transcript\n so only one transcript is connected to one variant.\n\n Args:\n gemini_variant (GeminiQueryRow): The gemini variant\n\n Returns:\n transcripts list: List of affected transcripts\n\n \"\"\"\n query = \"SELECT * from variant_impacts WHERE variant_id = {0}\".format(\n gemini_variant['variant_id']\n )\n gq = GeminiQuery(self.db)\n gq.run(query)\n\n transcripts = []\n for transcript in gq:\n transcripts.append(Transcript(\n hgnc_symbol = transcript['gene'],\n transcript_id = transcript['transcript'],\n consequence=transcript['impact_so'],\n biotype = transcript['biotype'],\n polyphen = transcript['polyphen_pred'],\n sift = transcript['sift_pred'],\n HGVSc = transcript['codon_change'],\n HGVSp = transcript['aa_change']\n )\n )\n\n return transcripts\n\n def _variants(self, case_id, gemini_query):\n \"\"\"Return variants found in the gemini database\n\n Args:\n case_id (str): The case for which we want to see information\n gemini_query (str): What variants should be chosen\n\n Yields:\n variant_obj (dict): A Variant formatted doctionary\n \"\"\"\n\n gq = GeminiQuery(self.db)\n\n gq.run(gemini_query)\n\n individuals = []\n # Get the individuals for the case\n for case in self.cases():\n if case.name == case_id:\n for individual in case.individuals:\n individuals.append(individual)\n\n indexes = [individual.index for individual in individuals]\n\n index = 0\n for gemini_variant in gq:\n # Check if variant is non ref in the individuals\n if self._is_variant(gemini_variant, indexes):\n index += 1\n logger.debug(\"Updating index to: {0}\".format(index))\n\n variant = self._format_variant(\n gemini_variant=gemini_variant,\n individual_objs=individuals,\n index=index\n )\n yield variant\n\n def _format_variant(self, gemini_variant, individual_objs, index=0):\n \"\"\"Make a puzzle variant from a gemini variant\n\n Args:\n gemini_variant (GeminiQueryRow): The gemini variant\n individual_objs (list(dict)): A list of Individuals\n index(int): The index of the variant\n\n Returns:\n variant (dict): A Variant object\n \"\"\"\n variant_dict = {\n 'CHROM':gemini_variant['chrom'].lstrip('chrCHR'),\n 'POS':str(gemini_variant['start']),\n 'ID':gemini_variant['rs_ids'],\n 'REF':gemini_variant['ref'],\n 'ALT':gemini_variant['alt'],\n 'QUAL':gemini_variant['qual'],\n 'FILTER':gemini_variant['filter']\n }\n\n variant = Variant(**variant_dict)\n variant['index'] = index\n\n # Use the gemini id for fast search\n variant.update_variant_id(gemini_variant['variant_id'])\n # Update the individuals\n individual_genotypes = self._get_genotypes(\n gemini_variant=gemini_variant,\n individual_objs=individual_objs\n )\n\n for individual in individual_genotypes:\n # Add the genotype calls to the variant\n variant.add_individual(individual)\n\n for transcript in self._get_transcripts(gemini_variant):\n variant.add_transcript(transcript)\n\n #Add the most severe consequence\n variant['most_severe_consequence'] = gemini_variant['impact_so']\n\n for gene in self._get_genes(variant):\n variant.add_gene(gene)\n\n variant['start'] = int(variant_dict['POS'])\n\n if self.variant_type == 'sv':\n other_chrom = variant['CHROM']\n # If we have a translocation:\n if ':' in variant_dict['ALT']:\n other_coordinates = variant_dict['ALT'].strip('ACGTN[]').split(':')\n other_chrom = other_coordinates[0].lstrip('chrCHR')\n other_position = other_coordinates[1]\n variant['stop'] = other_position\n\n #Set 'infinity' to length if translocation\n variant['sv_len'] = float('inf')\n variant['sv_type'] = 'BND'\n else:\n variant['stop'] = int(gemini_variant['end'])\n variant['sv_len'] = variant['stop'] - variant['start']\n variant['sv_type'] = gemini_variant['sub_type']\n\n variant['stop_chrom'] = other_chrom\n\n else:\n variant['stop'] = int(variant_dict['POS']) + \\\n (len(variant_dict['REF']) - len(variant_dict['ALT']))\n\n variant['cytoband_start'] = get_cytoband_coord(\n chrom=variant['CHROM'],\n pos=variant['start'])\n\n if variant.get('stop_chrom'):\n variant['cytoband_stop'] = get_cytoband_coord(\n chrom=variant['stop_chrom'],\n pos=variant['stop'])\n\n\n #### Check the impact annotations ####\n if gemini_variant['cadd_scaled']:\n variant['cadd_score'] = gemini_variant['cadd_scaled']\n\n # We use the prediction in text\n polyphen = gemini_variant['polyphen_pred']\n if polyphen:\n variant.add_severity('Polyphen', polyphen)\n\n # We use the prediction in text\n sift = gemini_variant['sift_pred']\n if sift:\n variant.add_severity('SIFT', sift)\n\n #### Check the frequencies ####\n thousand_g = gemini_variant['aaf_1kg_all']\n if thousand_g:\n variant['thousand_g'] = float(thousand_g)\n variant.add_frequency(name='1000GAF', value=float(thousand_g))\n\n exac = gemini_variant['aaf_exac_all']\n if exac:\n variant.add_frequency(name='EXaC', value=float(exac))\n\n esp = gemini_variant['aaf_esp_all']\n if esp:\n variant.add_frequency(name='ESP', value=float(esp))\n\n max_freq = gemini_variant['max_aaf_all']\n if max_freq:\n variant.set_max_freq(max_freq)\n\n return variant\n\n\n def _is_variant(self, gemini_variant, indexes):\n \"\"\"Check if the variants is a variation in any of the individuals\n\n Args:\n gemini_variant (GeminiQueryRow): The gemini variant\n indexes (list(int)): A list of indexes for the individuals\n\n Returns:\n bool : If any of the individuals has the variant\n \"\"\"\n\n for index in indexes:\n if gemini_variant['gts'][index] != 0:\n return True\n\n return False\n", "sub_path": "puzzle/plugins/gemini/variant_mixin.py", "file_name": "variant_mixin.py", "file_ext": "py", "file_size_in_byte": 12848, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "gemini.GeminiQuery", "line_number": 122, "usage_type": "call"}, {"api_name": "puzzle.models.Genotype", "line_number": 150, "usage_type": "call"}, {"api_name": "puzzle.utils.get_gene_info", "line_number": 175, "usage_type": "call"}, {"api_name": "gemini.GeminiQuery", "line_number": 194, "usage_type": "call"}, {"api_name": "puzzle.models.Transcript", "line_number": 199, "usage_type": "call"}, {"api_name": "gemini.GeminiQuery", "line_number": 224, "usage_type": "call"}, {"api_name": "puzzle.models.Variant", "line_number": 272, "usage_type": "call"}, {"api_name": "puzzle.utils.get_cytoband_coord", "line_number": 321, "usage_type": "call"}, {"api_name": "puzzle.utils.get_cytoband_coord", "line_number": 326, "usage_type": "call"}]} +{"seq_id": "644703504", "text": "from azureml.core import Environment, ScriptRunConfig\nfrom azureml.core.runconfig import PyTorchConfiguration\nfrom azureml.data import OutputFileDatasetConfig\n\n\ndef launch_run(\n experiment, compute_target, num_epochs=1, output_dataset_storage_path=None\n):\n \"\"\"Launch a run training MNIST on remote compute.\"\"\"\n ws = experiment.workspace\n dstore = ws.get_default_datastore()\n\n env = Environment.get(ws, \"AzureML-ACPT-pytorch-1.11-py38-cuda11.3-gpu\")\n distributed_config = PyTorchConfiguration(process_count=1)\n\n # Set output dataset used for model checkpointing for low-priority runs\n output_dataset_destination = None\n if output_dataset_storage_path:\n output_dataset_destination = (dstore, output_dataset_storage_path)\n output_dataset_config = OutputFileDatasetConfig(\n name=\"model_checkpoints\",\n destination=output_dataset_destination,\n source=\"model_checkpoints/\",\n )\n\n src = ScriptRunConfig(\n source_directory=\"./training_script\",\n script=\"training_script.py\",\n arguments=[output_dataset_config, \"--num_epochs\", num_epochs],\n compute_target=compute_target,\n environment=env,\n distributed_job_config=distributed_config,\n )\n\n run = experiment.submit(src)\n return run\n", "sub_path": "v1/python-sdk/tutorials/train-on-low-priority-aml-compute/helper.py", "file_name": "helper.py", "file_ext": "py", "file_size_in_byte": 1283, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "azureml.core.Environment.get", "line_number": 13, "usage_type": "call"}, {"api_name": "azureml.core.Environment", "line_number": 13, "usage_type": "name"}, {"api_name": "azureml.core.runconfig.PyTorchConfiguration", "line_number": 14, "usage_type": "call"}, {"api_name": "azureml.data.OutputFileDatasetConfig", "line_number": 20, "usage_type": "call"}, {"api_name": "azureml.core.ScriptRunConfig", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "464915977", "text": "# -*- coding: utf-8 -*-\n\n# @Time : 2019/7/30\n# @Author : Lattine\n\n# ======================\nimport os\nimport pickle\nfrom collections import Counter\n\nimport numpy as np\nimport gensim\n\nfrom .base import TrainDataBase\n\n\nclass TrainData(TrainDataBase):\n def __init__(self, config):\n super(TrainData, self).__init__(config)\n\n self._train_data_path = os.path.join(config.BASE_DIR, config.train_data)\n self._output_path = os.path.join(config.BASE_DIR, config.output_path)\n if not os.path.exists(self._output_path):\n os.makedirs(self._output_path)\n self._word_vectors_path = os.path.join(config.BASE_DIR, config.word_vectors_path) if config.word_vectors_path else None\n self._stopwords_path = os.path.join(config.BASE_DIR, config.stopwords) if config.stopwords else None\n\n self._sequence_length = config.sequence_length\n self._batch_size = config.batch_size\n self._embedding_size = config.embedding_size\n\n self.vocab_size = config.vocab_size\n self.word_vectors = None\n\n def read_data(self):\n \"\"\"\n :return: 返回分词后的文本内容和标签，inputs = [[]], labels = []\n \"\"\"\n inputs, labels = [], []\n with open(self._train_data_path, 'r', encoding='utf8') as fr:\n for line in fr:\n try:\n text, label = line.strip().split(\"\")\n inputs.append(text.strip().split(\" \"))\n labels.append(label)\n except:\n continue\n return inputs, labels\n\n def remove_stopwords(self, inputs):\n \"\"\" 去除低频词和停用词\"\"\"\n # 统计词频\n word_counts = Counter()\n for sent in inputs:\n word_counts.update(sent)\n\n # 去除低频词\n words = []\n for k, v in word_counts.most_common(self.vocab_size - 4): # 统计最常用的词，为词表大小减去,,,\n words.append(k)\n\n # 如果设置停用词表，去除停用词\n if self._stopwords_path:\n with open(self._stopwords_path, 'r', encoding=\"utf8\") as fr:\n stopwords = [line.strip() for line in fr]\n words = [w for w in words if w not in stopwords]\n\n return words\n\n def get_word_vectors(self, vocab):\n \"\"\"加载词向量，并获得相应的词向量矩阵\"\"\"\n word_vectors = (1 / np.sqrt(len(vocab)) * (2 * np.random.rand(len(vocab), self._embedding_size) - 1)) # 有待深究\n if os.path.splitext(self._word_vectors_path)[-1] == \".bin\":\n word_vec = gensim.models.KeyedVectors.load_word2vec_format(self._word_vectors_path, binary=True)\n else:\n word_vec = gensim.models.KeyedVectors.load_word2vec_format(self._word_vectors_path)\n for i in range(len(vocab)):\n try:\n vector = word_vec.wv[vocab[i]]\n word_vectors[i, :] = vector\n except:\n print(f\"{vocab[i]} not in w2v file.\")\n return word_vectors\n\n def gen_vocab(self, words, labels):\n \"\"\"\n 生成词汇表\"\n :param words: 训练集所过滤的词汇列表\n :param labels: 标签\n :return: word2vec, label2vec\n \"\"\"\n word_vectors_path = os.path.join(self._output_path, 'word_vectors.npy')\n word_to_index_path = os.path.join(self._output_path, 'word_to_index.pkl')\n label_to_index_path = os.path.join(self._output_path, 'label_to_index.pkl')\n\n # 如果已有词向量，则直接加载\n if os.path.exists(word_vectors_path):\n print(\"load word_vectors.\")\n self.word_vectors = np.load(word_vectors_path)\n # 如果存在词汇表，则直接加载\n if os.path.exists(word_to_index_path) and os.path.exists(label_to_index_path):\n print(\"load word_to_index\")\n with open(word_to_index_path, 'rb') as fr:\n word_to_index = pickle.load(fr)\n print(\"load label to index\")\n with open(label_to_index_path, 'rb') as fr:\n label_to_index = pickle.load(fr)\n self.vocab_size = len(word_to_index)\n\n return word_to_index, label_to_index\n\n words = [\"\", \"\"] + words\n vocab = words[:self.vocab_size] # 词汇表上限\n\n # 如果vocab的长度小于config设置的值，则用实际长度\n self.vocab_size = len(vocab)\n if self._word_vectors_path:\n word_vectors = self.get_word_vectors(vocab)\n self.word_vectors = word_vectors\n np.save(word_vectors_path, self.word_vectors) # 将数据集相关的词向量存入特定目录\n\n word_to_index = {w: i for i, w in enumerate(vocab)}\n\n # 将词汇-索引字典保存\n with open(word_to_index_path, 'wb') as fw:\n pickle.dump(word_to_index, fw)\n\n # 将标签-索引字典保存\n unique_labels = list(set(labels))\n label_to_index = {w: i for i, w in enumerate(unique_labels)}\n with open(label_to_index_path, 'wb') as fw:\n pickle.dump(label_to_index, fw)\n\n return word_to_index, label_to_index\n\n @staticmethod\n def trans_w2ix(inputs, w2ix):\n \"\"\"数据转为索引\"\"\"\n inputs_idx = [[w2ix.get(w, w2ix.get(\"\")) for w in sentence] for sentence in inputs]\n return inputs_idx\n\n @staticmethod\n def trans_t2ix(labels, t2ix):\n labels_idx = [t2ix.get(label) for label in labels]\n return labels_idx\n\n def padding(self, inputs, sequence_length):\n \"\"\"序列填充/截断\"\"\"\n new_inputs = [sentence[:sequence_length] if len(sentence) > sequence_length else sentence + [0] * (sequence_length - len(sentence)) for sentence in inputs]\n return new_inputs\n\n def gen_data(self):\n \"\"\"生成可导入模型的数据\"\"\"\n train_data_path = os.path.join(self._output_path, \"train_data.pkl\")\n label_to_index_path = os.path.join(self._output_path, \"label_to_index.pkl\")\n word_to_index_path = os.path.join(self._output_path, \"word_to_index.pkl\")\n word_vectors_path = os.path.join(self._output_path, \"word_vectors.npy\")\n\n # 如果存在，则直接加载\n if os.path.exists(train_data_path) and os.path.exists(label_to_index_path) and os.path.exists(word_to_index_path):\n print(\"load existed train data\")\n with open(train_data_path, 'rb') as fr:\n train_data = pickle.load(fr)\n with open(word_to_index_path, 'rb') as fr:\n word_to_index = pickle.load(fr)\n with open(label_to_index_path, 'rb') as fr:\n label_to_index = pickle.load(fr)\n self.vocab_size = len(word_to_index)\n\n # 尝试加载词向量\n if os.path.exists(word_vectors_path):\n self.word_vectors = np.load(word_vectors_path)\n return np.array(train_data['inputs_idx']), np.array(train_data['labels_idx']), label_to_index\n\n # --------- 原始处理流程 ----------\n # 1.读取原始数据\n inputs, labels = self.read_data()\n print(\"read finished\")\n\n # 2.去除低频词和停用词\n words = self.remove_stopwords(inputs)\n print(\"word filter process finished\")\n\n # 3.获取词汇-索引字典\n word_to_index, label_to_index = self.gen_vocab(words, labels)\n print(\"vocab process finished\")\n\n # 4.文本转索引\n inputs_idx = self.trans_w2ix(inputs, word_to_index)\n print(\"word to index finished\")\n\n # 5.对文本作PADDING\n inputs_idx = self.padding(inputs_idx, self._sequence_length)\n print(\"padding finished\")\n\n # 6.标签转索引\n labels_idx = self.trans_t2ix(labels, label_to_index)\n print(\"label to index finished\")\n\n # 构建训练数据字典并入库，以备后续直接加载\n train_data = dict(inputs_idx=inputs_idx, labels_idx=labels_idx)\n with open(train_data_path, 'wb') as fw:\n pickle.dump(train_data, fw)\n return np.array(inputs_idx), np.array(labels_idx), label_to_index\n\n def next_batch(self, x, y, batch_size):\n \"\"\"生成批次数据\"\"\"\n # 随机化\n perm = np.arange(len(x))\n np.random.shuffle(perm)\n x = x[perm]\n y = y[perm]\n\n num_batches = len(x) // batch_size\n\n for i in range(num_batches):\n start = i * batch_size\n end = (i + 1) * batch_size\n batch_x = np.array(x[start:end], dtype='int64')\n batch_y = np.array(y[start:end], dtype=\"float32\")\n\n yield dict(x=batch_x, y=batch_y)\n", "sub_path": "N02_TextCNN/data_helper/train_data.py", "file_name": "train_data.py", "file_ext": "py", "file_size_in_byte": 8701, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "base.TrainDataBase", "line_number": 17, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"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": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "collections.Counter", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "gensim.models.KeyedVectors.load_word2vec_format", "line_number": 74, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 74, "usage_type": "attribute"}, {"api_name": "gensim.models.KeyedVectors.load_word2vec_format", "line_number": 76, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 76, "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": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "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": "os.path.exists", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 104, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.save", "line_number": 120, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 126, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 154, "usage_type": "call"}, {"api_name": "os.path", "line_number": 154, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path", "line_number": 155, "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": 157, "usage_type": "call"}, {"api_name": "os.path", "line_number": 157, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path", "line_number": 160, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 163, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 165, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 167, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path", "line_number": 171, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 173, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 204, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 209, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 210, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 219, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 220, "usage_type": "call"}]} +{"seq_id": "460246933", "text": "import matplotlib.pyplot as plt\nimport seaborn as sns\nimport os\nimport numpy as np\nimport pandas as pd\n\nsns.set()\nsns.set_context('paper', font_scale=1.5)\n\nours_name = \"lfiw_tper_adapt_linear\"\nEXP = \"Ant-v2-plot\"\n# paper reward\nAlGOS = [\"sac_full\", \"discor_full\", \"discor_lfiw_full\", ours_name,]\n# paper ablation\n# AlGOS = [\"sac_full\", \"lfiw_full\", \"tper_linear\", ours_name]\n# AlGOS = [\"sac_full\", \"discor_full\", ours_name]\n\n# paper reward\ncolors = {\n 'sac_full': 'blue',\n \"discor_full\": 'green',\n \"discor_lfiw_full\": 'black',\n \"lfiw_full\": 'black',\n ours_name: 'red',\n}\n# paper ablation\n# colors = {\n # 'sac_full': 'blue',\n # \"lfiw_full\": 'green',\n # \"tper_linear\": 'black',\n # ours_name: 'red',\n# }\nlabels = {\n \"discor_lfiw_full\": \"RM-Discor\",\n \"discor_full\": \"Discor\",\n 'lfiw_full': \"Only On-policy Reweight\",\n 'tper_linear': \"Only Step-based Reweight\",\n 'sac_full': 'SAC',\n ours_name: 'RM-TCE'\n}\nMAX_STEP=5e6\nROLLING_STEP=10\n# AlGOS = [\"discor_full\", \"lfiw_sac_full\", \"sac_full\"]\nroot_path = os.path.join(\"../../logs/\"+EXP)\n\nfor algo in AlGOS:\n print(algo)\n file = os.path.join(root_path, \"%s-all.txt\"%algo)\n with open(file, 'r') as f:\n content = f.readlines()\n all_rewards = []\n seed = -100\n for line in content:\n seed += 100\n # if seed != 300:\n # continue\n line_data = []\n for i in line.split(\" \"):\n try:\n line_data.append(eval(i))\n except SyntaxError:\n print(\"Warn: syntax err\")\n print(len(line_data))\n all_rewards.append(line_data[:399])\n all_rewards = np.array(all_rewards)\n rew_mean = np.mean(all_rewards, axis=0)\n print(rew_mean.shape)\n df = pd.DataFrame(rew_mean)\n rew_mean = df[0].rolling(ROLLING_STEP).mean()\n rew_std = np.std(all_rewards, axis=0)\n x = np.arange(0, MAX_STEP, 5e3)[:len(rew_mean)]\n plot_index = np.arange(0, len(x), 1)\n rew_mean = rew_mean[plot_index]\n rew_std = rew_std[plot_index]\n rew_std = np.clip(rew_std, 0, 1500)\n x = x[plot_index]\n plt.plot(x, rew_mean, color=colors[algo], label=labels[algo])\n plt.fill_between(x, rew_mean - rew_std, rew_mean + rew_std, color = colors[algo], alpha = 0.2)\nplt.legend()\nplt.title(EXP)\nplt.xlabel(\"Timestep\")\nplt.ylabel(\"Reward\")\nplt.savefig(\"Reward-%s.png\"%EXP)\n# plt.savefig(\"Ablation-%s.png\"%EXP)\n", "sub_path": "experiments/res_plot/plot.py", "file_name": "plot.py", "file_ext": "py", "file_size_in_byte": 2447, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "seaborn.set", "line_number": 7, "usage_type": "call"}, {"api_name": "seaborn.set_context", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 66, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.fill_between", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "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.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.savefig", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}]} +{"seq_id": "632367901", "text": "#!/usr/bin/python3\nimport os\nimport taglib\nimport sqlite3\n\n\"\"\" Constants \"\"\"\n# mp3, m4a, ogg, and flac are formats that I've tested with taglib\n# and that I know will work.\nMUSIC_FORMATS = [\n \".mp3\",\n \".ogg\",\n \".m4a\",\n \".flac\"\n]\nUNKNOWN = \"Unknown\"\nTAG_TITLE = \"TITLE\"\nTAG_ARTIST = \"ARTIST\"\nTAG_ALBUM = \"ALBUM\"\nTAG_GENRE = \"GENRE\"\n\n\"\"\" Technically not constants but close enough \"\"\"\nHOME_DIR = os.path.expanduser(\"~\")\nMUSIC_DIR = os.path.join(HOME_DIR, \"music\")\nDB_LOCATION = os.path.join(HOME_DIR, \"music.db\")\n\n\ndef create_db(path_to_db):\n \"\"\" Creates a music database at the given path \"\"\"\n connection = sqlite3.connect(path_to_db)\n cursor = connection.cursor()\n\n sql = \"create table songs ( \\\n id INTEGER PRIMARY KEY AUTOINCREMENT, \\\n artist VARCHAR DEFAULT 'Unknown', \\\n album VARCHAR DEFAULT 'Unknown', \\\n song_title VARCHAR NOT NULL, \\\n location VARCHAR NOT NULL \\\n );\"\n\n cursor.execute(sql)\n\n connection.commit()\n connection.close()\n\n\ndef find_songs(dir):\n \"\"\"\n Searches recursively in the directory it is given for song files. Returns\n a generator of paths to the song files.\n \"\"\"\n for root, directory, filenames in os.walk(dir):\n for file in filenames:\n path = os.path.join(root, file)\n name, ext = os.path.splitext(path)\n if ext in MUSIC_FORMATS:\n yield path\n\n\ndef extract_metadata(song_paths):\n \"\"\"\n Takes a generator, iterator, or list of paths to music files and returns\n the metadata in a list of tuples. The are structured as follows:\n (artist, album, song_title, location). (Went with list of tuples becuase then\n you can pass it right to an sqlite3 executemany function. Would liked to have\n returned a generator for memory convservation but executemany doesn't take a\n generator)\n \"\"\"\n rows = []\n for song_path in song_paths:\n song = taglib.File(song_path)\n file_name, ext = name, ext = os.path.splitext(song_path)\n file_name = name.split(\"/\")[-1]\n\n tags = song.tags\n artist = UNKNOWN if TAG_ARTIST not in tags else tags[TAG_ARTIST][0]\n album = UNKNOWN if TAG_ALBUM not in tags else tags[TAG_ALBUM][0]\n title = file_name if TAG_TITLE not in tags else tags[TAG_TITLE][0]\n\n rows += [(artist, album, title, song_path)]\n\n return rows\n\n\ndef main():\n print(\"Creating the database...\")\n create_db(DB_LOCATION)\n\n print(\"Searching for songs...\")\n song_paths = find_songs(MUSIC_DIR)\n\n num_songs = (1 for song_path in song_paths)\n if num_songs == 0:\n print(\"No songs found.\")\n return\n\n print(\"Extracting metadata...\")\n metadata = extract_metadata(song_paths)\n\n print(\"Opening db connection...\")\n db_connection = sqlite3.connect(DB_LOCATION)\n db_cursor = db_connection.cursor()\n\n print(\"Inserting metadata into db...\")\n query_string = \"insert into songs (artist, album, song_title, location) values (?, ?, ?, ?)\"\n db_cursor.executemany(query_string, metadata)\n\n print(\"Commiting and closing connection...\")\n db_connection.commit()\n db_connection.close()\n\n print(\"Done.\")\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "music_db.py", "file_name": "music_db.py", "file_ext": "py", "file_size_in_byte": 3245, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.expanduser", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 29, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 51, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "taglib.File", "line_number": 70, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "sqlite3.connect", "line_number": 100, "usage_type": "call"}]} +{"seq_id": "39354109", "text": "#encoding:utf-8\n\nfrom utils import get_url\nfrom utils import SupplyResult\n\n\nsubreddit = 'wheredidthesodago'\nt_channel = '@r_wheredidthesodago'\nfooter = 'by {}'.format(t_channel)\n\n\ndef send_post(submission, r2t):\n what, url = get_url(submission)\n title = submission.title\n link = submission.shortlink\n text = '{}\\n{}\\n\\n{}'.format(title, link, footer)\n if what == 'gif':\n if r2t.dup_check_and_mark(url) is True:\n return SupplyResult.DO_NOT_WANT_THIS_SUBMISSION\n return r2t.send_gif_img(what, url, text)\n else:\n return SupplyResult.DO_NOT_WANT_THIS_SUBMISSION\n", "sub_path": "reddit2telegram/channels/r_wheredidthesodago/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 608, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "utils.get_url", "line_number": 13, "usage_type": "call"}, {"api_name": "utils.SupplyResult.DO_NOT_WANT_THIS_SUBMISSION", "line_number": 19, "usage_type": "attribute"}, {"api_name": "utils.SupplyResult", "line_number": 19, "usage_type": "name"}, {"api_name": "utils.SupplyResult.DO_NOT_WANT_THIS_SUBMISSION", "line_number": 22, "usage_type": "attribute"}, {"api_name": "utils.SupplyResult", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "198453039", "text": "import requests\nfrom lxml import etree\nimport xlwt\nimport re\n\n# 要访问的地址\n# 未登录状态获取数据受限，需登录后拷贝cookie\nurl = \"https://movie.douban.com/subject/1303967/\"\n\nheaders = {\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.61 Safari/537.36',\n 'Cookie': 'bid=cISTxamqZx4; __utmc=30149280; __utmz=30149280.1632654805.1.1.utmcsr=(direct)|utmccn=(direct)|utmcmd=(none); ll=\"108288\"; __utmz=223695111.1632654807.1.1.utmcsr=(direct)|utmccn=(direct)|utmcmd=(none); __utmc=223695111; __gads=ID=0d3e97fa31910b1e-2256505febcb00fe:T=1632654809:RT=1632654809:S=ALNI_Mb-YhHaLeblpoTYEYjZxYJ4u2lF6A; _vwo_uuid_v2=D4605DF7A3F3F6BF497F2B01AF70DEAED|878f40575bedeb04d67e9798dbb6c101; __yadk_uid=A3rNILD6PVzPl6pBHBY2l3WhOLFBbNKo; dbcl2=\"247328215:KJUIbBeOj64\"; ck=Brqk; push_noty_num=0; push_doumail_num=0; __utmv=30149280.24732; ap_v=0,6.0; __utmb=30149280.0.10.1632663981; __utma=30149280.1911116475.1632654805.1632657647.1632663981.3; __utma=223695111.991280750.1632654807.1632657647.1632663981.3; __utmb=223695111.0.10.1632663981; _pk_ses.100001.4cf6=*; _pk_id.100001.4cf6=1a3464067bab4ed1.1632654805.3.1632664033.1632662076.'\n}\n\n\nresp = requests.get(url=url, headers=headers)\nwb_data = resp.content.decode()\n\nhtml = etree.HTML(wb_data)\nhtml_data = html.xpath('//*[@id=\"content\"]/h1/span[1]')\nrating_num = html.xpath('//*[@id=\"interest_sectl\"]/div[1]/div[2]/strong')\ncomments_url = html.xpath('//*[@id=\"comments-section\"]/div[1]/h2/span/a/@href')\ncounters = html.xpath('//*[@id=\"comments-section\"]/div[1]/h2/span/a/text()')\ncounters_num = int(re.findall('\\d+', counters[0])[0])\n# counters_num = 40\nif counters_num > 1000:\n counters_num = 1000\ndata_list = []\n\n\ndef sub_net(index):\n global data_list\n params = {\n \"percent_type\": '',\n 'start': index * 20,\n 'limit': 20,\n 'status': 'P',\n 'sort': 'new_score',\n 'comments_only': 1\n }\n\n resp = requests.get(url=comments_url[0], headers=headers, params=params)\n final_html = resp.json().get('html')\n final_htm = etree.HTML(final_html)\n comment_divs = final_htm.xpath('//*[@class=\"comment-item \"]')\n username = comment_divs[0].xpath('//div[@class=\"comment\"]//span[@class=\"comment-info\"]/a/text()')\n rating = comment_divs[0].xpath('//div[@class=\"comment\"]//span[@class=\"comment-info\"]/span[2]/@title')\n comment_time = comment_divs[0].xpath('//div[@class=\"comment\"]//span[@class=\"comment-info\"]/span[@class=\"comment-time \"]/@title')\n comment = comment_divs[0].xpath('//div[@class=\"comment\"]/p/span/text()')\n each = zip(username, comment_time, rating, comment)\n print('获取数据中...', username, comment_time, rating, comment)\n data_list.extend(list(each))\n\n\ndef write2execl(rows_num, data_list):\n excelpath = ('./douban_data.xls') # 新建excel文件\n workbook = xlwt.Workbook(encoding='utf-8') # 写入excel文件\n sheet = workbook.add_sheet('Sheet1', cell_overwrite_ok=True)\n headlist = [u'序号', u'评论人', u'评论时间', u'评分', u'评论'] # 写入数据头\n row = 0\n col = 0\n for head in headlist:\n sheet.write(row, col, head)\n col = col + 1\n for i in range(1, rows_num + 1):\n\n username = data_list[i-1][0]\n comment_time = data_list[i-1][1]\n rating = data_list[i-1][2]\n comment = data_list[i-1][3]\n\n sheet.write(i, 0, i)\n sheet.write(i, 1, username)\n sheet.write(i, 2, comment_time)\n sheet.write(i, 3, rating)\n sheet.write(i, 4, comment)\n workbook.save(excelpath) # 保存\n\n\nif __name__ == '__main__':\n print(\"任务开始执行...\")\n times = (counters_num // 20)\n for index in range(times):\n try:\n sub_net(index)\n except:\n print(\"网页访问受限\")\n try:\n write2execl(counters_num, data_list)\n except:\n pass\n print(\"任务执行完成...\")\n\n", "sub_path": "douban_spider.py", "file_name": "douban_spider.py", "file_ext": "py", "file_size_in_byte": 3960, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "requests.get", "line_number": 16, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 19, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 19, "usage_type": "name"}, {"api_name": "re.findall", "line_number": 24, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 42, "usage_type": "call"}, {"api_name": "lxml.etree.HTML", "line_number": 44, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 44, "usage_type": "name"}, {"api_name": "xlwt.Workbook", "line_number": 57, "usage_type": "call"}]} +{"seq_id": "506204857", "text": "from sklearn.cluster import MiniBatchKMeans\nimport pandas as pd\nimport numpy as np\n\n\ndef classification(clusters, data):\n # add 'cluster' column to original DataFrame\n data['cluster'] = clusters\n\n with open('logs/classified_tweets.txt', 'w', encoding=\"utf-8\") as f:\n for cluster_type in set(clusters):\n cluster = data[data.cluster == cluster_type]['text'].values\n for tweet in cluster:\n f.write(str(cluster_type) + ', ' + tweet + '\\n')\n f.write('\\n')\n\n\ndef get_top_keywords(tfidf_matrix, clusters, labels, top_n):\n # group by 'cluster', then calculate average tf-idf score of each word per cluster\n df = pd.DataFrame(tfidf_matrix.todense()).groupby(clusters).mean()\n\n with open('logs/keywords_per_cluster.txt', 'w', encoding=\"utf-8\") as f:\n for i, r in df.iterrows():\n f.write('Cluster ' + str(i) + '\\n')\n f.write(','.join([labels[t] for t in np.argsort(r)[-top_n:]]) + '\\n\\n')\n\n\ndef classification_n_keywords(tfidf_matrix, data, vectorizer, k, top_n):\n k_means = MiniBatchKMeans(n_clusters=k)\n clusters = k_means.fit_predict(tfidf_matrix)\n # classify tweets into k clusters and log them in 'classified_tweets.txt'\n classification(clusters, data)\n # log top n keywords of each cluster in 'keywords_per_cluster.txt'\n get_top_keywords(tfidf_matrix, clusters, vectorizer.get_feature_names(), top_n)\n", "sub_path": "classification.py", "file_name": "classification.py", "file_ext": "py", "file_size_in_byte": 1416, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "pandas.DataFrame", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 25, "usage_type": "call"}, {"api_name": "sklearn.cluster.MiniBatchKMeans", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "157260476", "text": "from itertools import permutations\nfrom math import sqrt, factorial\nn = int(input())\n\ntowns = []\nfor i in range(n):\n towns.append(list(map(int, input().split())))\n\nperms = permutations(towns)\n\nsum_dists = 0\nfor path in list(perms):\n for i in range(n-1):\n dist = sqrt((path[i+1][0] - path[i][0])**2 +\n (path[i+1][1] - path[i][1])**2)\n sum_dists += dist\n\nprint(sum_dists / factorial(n))\n", "sub_path": "abc145/c.py", "file_name": "c.py", "file_ext": "py", "file_size_in_byte": 424, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "itertools.permutations", "line_number": 9, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 14, "usage_type": "call"}, {"api_name": "math.factorial", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "219863530", "text": "import sys\nimport os\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport rosbag\n\nspawn_point=np.array([[0,0],[0,1.36],[0,1.23],[-2.1,2.1],[0,2.18]])\nbag_mouse = rosbag.Bag(os.path.expanduser('~/.ros/mouse_path.bag'))\nbag_cat = rosbag.Bag(os.path.expanduser('~/.ros/cat_path.bag'))\nbag_cat = rosbag.Bag(os.path.expanduser('~/.ros/path.bag'))\npoint_list_mouse=np.array([[0,0]])\npoint_list_cat=np.array([[0,0]])\nmouse_time=[]\ncat_time=[]\nall_topics=['/mouse/base_pose_ground_truth','/cat/base_pose_ground_truth']\nfor topic, msgs, t in bag_mouse.read_messages(topics=['/mouse/base_pose_ground_truth']):\n\tpoint_list_mouse=np.append(point_list_mouse,[[msgs.pose.pose.position.x,msgs.pose.pose.position.y]],axis=0)\n\tmouse_time.append(t)\nfor topic, msgs, t in bag_cat.read_messages(topics=['/cat/base_pose_ground_truth']):\n\tpoint_list_cat=np.append(point_list_cat,[[msgs.pose.pose.position.x,msgs.pose.pose.position.y]],axis=0)\n\tcat_time.append(t)\npoint_list_cat=list(point_list_cat)\ncat_time=list(cat_time)\n\n\npoint_list_cat.pop(0)\nwhile len(point_list_cat) > len(point_list_mouse):\n\tpoint_list_cat.pop(0)\n\tcat_time.pop(0)\n\npoint_list_cat=np.array(point_list_cat)\ncat_time=np.array(cat_time)\n\nplt.scatter(point_list_mouse[1:,1],point_list_mouse[1:,0],label=\"mouse\")\nplt.scatter(point_list_cat[1:,1],point_list_cat[1:,0],label=\"cat\")\nplt.ylabel(\"Y Position\")\nplt.xlabel(\"X Position\")\nplt.title(\"Pfad der Roboter\")\nplt.axis('equal')\nplt.legend()\nplt.savefig(\"report_pfad.png\", dpi=600)\nplt.clf()\n\n\ndiff = (point_list_cat- point_list_mouse)**2\n\n\nplt.plot( np.sqrt(diff[:,0]+diff[:,1]),label=\"Roboterabstand\")\nplt.title(\"Roboterabstand\")\nplt.ylabel(\"Distanz\")\nplt.xlabel(\"Zeit\")\nplt.legend()\nplt.savefig(\"report_distance.png\", dpi=600)\nplt.clf()\n\n\n\n\n\n", "sub_path": "fusion/scripts/create_visualisation.py", "file_name": "create_visualisation.py", "file_ext": "py", "file_size_in_byte": 1745, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.array", "line_number": 7, "usage_type": "call"}, {"api_name": "rosbag.Bag", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "rosbag.Bag", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "rosbag.Bag", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "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.title", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "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.savefig", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.clf", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}]} +{"seq_id": "285535894", "text": "import logging\r\nfrom .base_loader import BaseLoader\r\n\r\ntry:\r\n import boto3\r\nexcept ImportError:\r\n boto3 = None\r\n\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\n\r\nclass SSMLoader(BaseLoader):\r\n def __init__(self, path, aws_region):\r\n if boto3 is None:\r\n raise Exception(\"To use SSMPath, please install the boto3 library.\")\r\n\r\n self.path = path\r\n self.aws_region = aws_region\r\n self._settings = _load_from_ssm(self.path, self.aws_region)\r\n\r\n def get_setting(self, section, key):\r\n return self._settings[f\"{section}/{key}\"]\r\n\r\n def __str__(self):\r\n return f\"SSM from {self.path} region {self.aws_region}\"\r\n\r\n\r\ndef _load_from_ssm(path, aws_region):\r\n \"\"\" Return a dict of {section/key} -> value \"\"\"\r\n\r\n # For production (aka ECS and docker) read from SSM store.\r\n ssm = boto3.client(\"ssm\", region_name=aws_region)\r\n\r\n def get_parameters_by_path(next_token=None):\r\n params = {\"Path\": path, \"Recursive\": True, \"WithDecryption\": True}\r\n if next_token is not None:\r\n params[\"NextToken\"] = next_token\r\n return ssm.get_parameters_by_path(**params)\r\n\r\n def parameters():\r\n next_token = None\r\n while True:\r\n response = get_parameters_by_path(next_token)\r\n parameters = response[\"Parameters\"]\r\n if len(parameters) == 0:\r\n break\r\n for parameter in parameters:\r\n yield parameter\r\n if \"NextToken\" not in response:\r\n break\r\n next_token = response[\"NextToken\"]\r\n\r\n _ssm_parameters = {}\r\n _using_local_config = False\r\n for param in parameters():\r\n # Take the entire key and strip off the path prefix.\r\n # param['Name'] will be eg /site/env/section/key\r\n # and ssm_key might be like section/key\r\n ssm_key = param[\"Name\"][len(path) :]\r\n _ssm_parameters[ssm_key] = param[\"Value\"]\r\n\r\n return _ssm_parameters\r\n", "sub_path": "layered_settings/loaders/ssm_loader.py", "file_name": "ssm_loader.py", "file_ext": "py", "file_size_in_byte": 1969, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 10, "usage_type": "call"}, {"api_name": "base_loader.BaseLoader", "line_number": 13, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "301528303", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun May 14 20:13:28 2017\n\n@author: etami\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport scipy.misc\n\n\ndef numerov_step(x, y, k, n, eigenvalue):\n '''\n ay[n+1] = by[n]+cy[n-1]\n '''\n x[n+1] = x[n] + h\n a = 1 + 1 / 12 * h**2 * k(x[n+1], eigenvalue)\n b = 2 * (1 - 5 / 12 * h**2 * k(x[n], eigenvalue))\n c = -(1 + 1 / 12 * h**2 * k(x[n-1], eigenvalue))\n y[n+1] = (b * y[n] + c * y[n-1])/a \n \ndef numerov_routine(k, x, y, h, eigenvalue):\n for i in range(1,x.size-1):\n numerov_step(x, y, k, i, eigenvalue)\n return x, y\n\nif __name__ == \"__main__\":\n def k(x, eigenvalue = 0.5):\n #return 1\n return 2 * eigenvalue - x**2\n\n def hermite(x, n):\n if (n==0):\n return 1\n elif(n==1):\n return 2 * x\n else:\n return 2 * x * hermite(x, n-1) - 2 * (n-1) * hermite(x, n-2) \n\n def set_init(x, y, h, a, level, eigenvalue):\n if (level % 2 == 0): \n x[0] = 0\n y[0] = a\n y[1] = a - h**2 * k(x[0], eigenvalue) / 2\n else:\n x[0] = 0\n y[0] = 0\n y[1] = a\n \n def std_result(init_x, final_x, level, final_y):\n x = np.linspace(init_x, final_x, 250)\n y = hermite(x, level) / (2**level *\n scipy.misc.factorial(level) / (np.pi)**0.5)**0.5 * np.exp(-x**2 / 2)\n #normalize the reslt\n ratio = final_y / y[-1]\n y = y * ratio\n return (x,y)\n\n #parameters\n init_x = 0\n final_x = 2\n level = 10\n h = 0.001\n a = 1\n \n #initiation\n eigenvalue = level + 0.5\n step_number = int((final_x - init_x)/h)\n x = np.zeros(step_number+1)\n y = np.zeros(step_number+1)\n x[0] = init_x\n set_init(x, y, h, a, level,eigenvalue)\n \n #calculation and plot\n numerov_routine(k, x, y, h, eigenvalue)\n plt.plot(x, y, label=\"numerov result\")\n plt.plot(*std_result(init_x, final_x, level, y[-1]),label=\"standard result\")\n #xline = np.linspace(init_x, final_x, 201)\n #plt.plot(xline, np.exp(-xline**2/2)*(1-2*xline**2))\n #plt.plot(xline, np.exp(-xline**2/2)*(4*xline**4-12*xline**2+3))\n plt.legend()\n plt.show()\n \n", "sub_path": "tutorial04/Neutrons.py", "file_name": "Neutrons.py", "file_ext": "py", "file_size_in_byte": 2230, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "numpy.linspace", "line_number": 51, "usage_type": "call"}, {"api_name": "scipy.misc.misc.factorial", "line_number": 53, "usage_type": "call"}, {"api_name": "scipy.misc.misc", "line_number": 53, "usage_type": "attribute"}, {"api_name": "scipy.misc", "line_number": 53, "usage_type": "name"}, {"api_name": "numpy.pi", "line_number": 53, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}]} +{"seq_id": "93675306", "text": "from datetime import datetime\nfrom pddoctest.tools import StandardDevice\nfrom StringIO import StringIO\nfrom unittest.result import TestResult, failfast\nimport sys\n\nSUCCESS, ERROR, FAILURE, SKIP, EXPECTED_FAILURE, UNEXPECTED_SUCCESS = range(6)\n\nclass DataTestResult(TestResult):\n\n def __init__(self, stream=None, descriptions=None, verbosity=None):\n self.result = {\n \"success_count\": 0,\n \"error_count\": 0,\n \"failure_count\": 0,\n \"skip_count\": 0,\n \"expected_failure_count\": 0,\n \"unexpected_success_count\": 0,\n \"class_list\": {},\n }\n TestResult.__init__(self, stream=stream, descriptions=descriptions,\n verbosity=verbosity)\n self._stderr = StandardDevice(sys.stderr)\n self._stdout = StandardDevice(sys.stdout)\n self.org_stderr = None\n self.org_stdout = None\n self.stream = stream\n self.verbosity = verbosity\n\n @failfast\n def addError(self, test, err):\n self.result[\"error_count\"] += 1\n self.result[\"class_list\"][self.class_name][\"error_count\"] += 1\n self.result[\"class_list\"][self.class_name][\"methods\"][self.method_name]\\\n [\"status\"] = ERROR\n TestResult.addError(self, test, err)\n self.result[\"class_list\"][self.class_name][\"methods\"][self.method_name]\\\n [\"message\"] = self.silence_output()\n self.result[\"class_list\"][self.class_name][\"methods\"][self.method_name]\\\n [\"error\"] = self.errors[-1][1]\n\n @failfast\n def addFailure(self, test, err):\n self.result[\"failure_count\"] += 1\n self.result[\"class_list\"][self.class_name][\"failure_count\"] += 1\n self.result[\"class_list\"][self.class_name][\"methods\"][self.method_name]\\\n [\"status\"] = FAILURE\n TestResult.addFailure(self, test, err)\n self.result[\"class_list\"][self.class_name][\"methods\"][self.method_name]\\\n [\"message\"] = self.silence_output()\n self.result[\"class_list\"][self.class_name][\"methods\"][self.method_name]\\\n [\"error\"] = self.failures[-1][1]\n\n def addSuccess(self, test):\n self.result[\"success_count\"] += 1\n self.result[\"class_list\"][self.class_name][\"success_count\"] += 1\n self.result[\"class_list\"][self.class_name][\"methods\"][self.method_name]\\\n [\"status\"] = SUCCESS\n TestResult.addSuccess(self, test)\n self.result[\"class_list\"][self.class_name][\"methods\"][self.method_name]\\\n [\"message\"] = self.silence_output()\n\n def addSkip(self, test, reason):\n self.result[\"skip_count\"] += 1\n self.result[\"class_list\"][self.class_name][\"skip_count\"] += 1\n self.result[\"class_list\"][self.class_name][\"methods\"][self.method_name]\\\n [\"status\"] = SKIP\n TestResult.addSkip(self, test, reason)\n self.result[\"class_list\"][self.class_name][\"methods\"][self.method_name]\\\n [\"message\"] = self.silence_output()\n self.result[\"class_list\"][self.class_name][\"methods\"][self.method_name]\\\n [\"error\"] = reason\n\n def addExpectedFailure(self, test, err):\n self.result[\"expected_failure_count\"] += 1\n self.result[\"class_list\"][self.class_name][\"expected_failure_count\"] \\\n += 1\n self.result[\"class_list\"][self.class_name][\"methods\"][self.method_name]\\\n [\"status\"] = EXPECTED_FAILURE\n TestResult.addExpectedFailure(self, test, err)\n self.result[\"class_list\"][self.class_name][\"methods\"][self.method_name]\\\n [\"message\"] = self.silence_output()\n self.result[\"class_list\"][self.class_name][\"methods\"][self.method_name]\\\n [\"error\"] = self.expectedFailures[-1][1]\n\n def addUnexpectedSuccess(self, test):\n self.result[\"unexpected_success_count\"] += 1\n self.result[\"class_list\"][self.class_name][\"unexpected_success_count\"] \\\n += 1\n self.result[\"class_list\"][self.class_name][\"methods\"][self.method_name]\\\n [\"status\"] = UNEXPECTED_SUCCESS\n TestResult.addUnexpectedSuccess(self, test)\n self.result[\"class_list\"][self.class_name][\"methods\"][self.method_name]\\\n [\"message\"] = self.silence_output()\n\n def silence_output(self):\n \"\"\"\n Redirect the output to set in the call of test\n \"\"\"\n if self.org_stdout:\n sys.stdout = self.org_stdout\n sys.stderr = self.org_stderr\n self.org_stdout = None\n self.org_stderr = None\n return self.output.getvalue()\n\n def startTest(self, test):\n \"\"\"\n Starting the test we redirect the output to not show in the screen\n \"\"\"\n TestResult.startTest(self, test)\n # Start a new output text result\n self.output = StringIO()\n self._stdout.fp = self.output\n self._stderr.fp = self.output\n # Conserve the original output\n self.org_stderr = sys.stderr\n self.org_stdout = sys.stdout\n sys.stdout = self._stdout\n sys.stderr = self._stderr\n # Define the structure\n self.class_name = test.__class__.__name__\n self.method_name = test._testMethodName\n if self.class_name not in self.result[\"class_list\"].keys():\n class_doc = [] if test.__doc__ is None else \\\n [ item.strip() for item in test.__doc__.splitlines() \\\n if item.strip() != \"\" ]\n self.result[\"class_list\"][self.class_name] = {\n \"module\": test.__module__,\n \"description\": class_doc,\n \"success_count\": 0,\n \"error_count\": 0,\n \"failure_count\": 0,\n \"skip_count\": 0,\n \"expected_failure_count\": 0,\n \"unexpected_success_count\": 0,\n \"methods\": {}\n }\n if self.method_name not in \\\n self.result[\"class_list\"][self.class_name][\"methods\"].keys():\n method_doc = [\"\"] if test._testMethodDoc is None else \\\n [ item.strip() for item in \\\n test._testMethodDoc.splitlines() if item.strip() != \"\"\n ]\n self.result[\"class_list\"][self.class_name][\"methods\"][self.method_name] = \\\n {\n \"started\": datetime.now(),\n \"status\": None,\n \"stopped\": None,\n \"message\": \"\",\n \"error\": None,\n \"description\": method_doc\n }\n\n def stopTest(self, test):\n self.silence_output()\n self.result[\"class_list\"][self.class_name][\"methods\"][self.method_name]\\\n [\"stopped\"] = datetime.now()\n TestResult.stopTest(self, test)\n", "sub_path": "pddoctest/result.py", "file_name": "result.py", "file_ext": "py", "file_size_in_byte": 7137, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "unittest.result.TestResult", "line_number": 9, "usage_type": "name"}, {"api_name": "unittest.result.TestResult.__init__", "line_number": 21, "usage_type": "call"}, {"api_name": "unittest.result.TestResult", "line_number": 21, "usage_type": "name"}, {"api_name": "pddoctest.tools.StandardDevice", "line_number": 23, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 23, "usage_type": "attribute"}, {"api_name": "pddoctest.tools.StandardDevice", "line_number": 24, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 24, "usage_type": "attribute"}, {"api_name": "unittest.result.TestResult.addError", "line_number": 36, "usage_type": "call"}, {"api_name": "unittest.result.TestResult", "line_number": 36, "usage_type": "name"}, {"api_name": "unittest.result.failfast", "line_number": 30, "usage_type": "name"}, {"api_name": "unittest.result.TestResult.addFailure", "line_number": 48, "usage_type": "call"}, {"api_name": "unittest.result.TestResult", "line_number": 48, "usage_type": "name"}, {"api_name": "unittest.result.failfast", "line_number": 42, "usage_type": "name"}, {"api_name": "unittest.result.TestResult.addSuccess", "line_number": 59, "usage_type": "call"}, {"api_name": "unittest.result.TestResult", "line_number": 59, "usage_type": "name"}, {"api_name": "unittest.result.TestResult.addSkip", "line_number": 68, "usage_type": "call"}, {"api_name": "unittest.result.TestResult", "line_number": 68, "usage_type": "name"}, {"api_name": "unittest.result.TestResult.addExpectedFailure", "line_number": 80, "usage_type": "call"}, {"api_name": "unittest.result.TestResult", "line_number": 80, "usage_type": "name"}, {"api_name": "unittest.result.TestResult.addUnexpectedSuccess", "line_number": 92, "usage_type": "call"}, {"api_name": "unittest.result.TestResult", "line_number": 92, "usage_type": "name"}, {"api_name": "sys.stdout", "line_number": 101, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 102, "usage_type": "attribute"}, {"api_name": "unittest.result.TestResult.startTest", "line_number": 111, "usage_type": "call"}, {"api_name": "unittest.result.TestResult", "line_number": 111, "usage_type": "name"}, {"api_name": "StringIO.StringIO", "line_number": 113, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 117, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 118, "usage_type": "attribute"}, {"api_name": "sys.stdout", "line_number": 119, "usage_type": "attribute"}, {"api_name": "sys.stderr", "line_number": 120, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 147, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 147, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 158, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 158, "usage_type": "name"}, {"api_name": "unittest.result.TestResult.stopTest", "line_number": 159, "usage_type": "call"}, {"api_name": "unittest.result.TestResult", "line_number": 159, "usage_type": "name"}]} +{"seq_id": "583078584", "text": "import re, sys, threading\nfrom enum import Enum\nfrom collections import namedtuple\n\nimport requests, bs4\n\nfrom .base_request import BaseHttpRequest\nfrom private_modules.dcms_shovel.page_parser import DcmsWebPage\n\n\nclass RegExp:\n rlk = re.compile(r\"'([A-Z0-9]{32})'\")\n reply_code = re.compile(r'[\\u4e00-\\u9fa5]*复\\[\\d{4}\\]\\d{4}')\n\nclass SearchBy(Enum):\n con_num = 'ref_no'\n customer_name = 'CUSTOMER_NM'\n cf_num = 'CREDIT_FILE_NO'\n customer_code = 'CUSTOMER_NO'\n\n\nclass DcmsHttpRequest(BaseHttpRequest):\n origin_url = 'http://110.17.1.21:9081/'\n base_params = {\n 'do': 'Search',\n 'searchBranchCode': 'HQ',\n 'scope': 'A',\n 'searchCriteria': None,\n 'searchValue': None\n }\n\n class DcmsType(Enum):\n cp = 'DCMSCP'\n sme = 'SMEDCMS'\n cs = 'DCMSCS'\n\n def setDcmsType(self, dcms_type):\n self.dcms_type = 'sme' if dcms_type == self.DcmsType.sme.value else ''\n self.post_urls = {\n 'search_cp': self.UrlPath(\n 'dcms/consumer/application/inquiry/application_inquiry.view' if self.applicationCode == 'DCMSCS' else (self.dcms_type + 'dcms/corporate/application/inquiry/application_inquiry.view'),\n self.base_params.copy()\n ),\n 'search_cf': self.UrlPath(self.dcms_type + 'mcif/credit_file_setup/credit_file.view', self.base_params.copy()),\n 'search_lu': self.UrlPath(self.dcms_type + 'dcma/limit_utilization/application/application.view', self.base_params.copy()),\n 'search_customer': self.UrlPath('mcif/customer_search.view', self.base_params.copy()),\n }\n self.get_urls = {\n 'keep_connection': (self.dcms_type + '_' if self.dcms_type == 'sme' else '') + 'dcms_index.view',\n }\n\n def login(self, userId='czfzc', password='hxb123', applicationCode='DCMSCP', dcms_type='DCMSCP', keep_long=False):\n '''\n\n :param userId:\n :param password:\n :param applicationCode: DCMSCP SMEDCMS DCMSCS\n :return:\n '''\n self.applicationCode = applicationCode\n # self.login_dcms = dcms_type\n self.setDcmsType(dcms_type)\n self.userId = userId\n self.password = password\n r = self.post(self.UrlPath('dcmscp/login.view', {'step': 'defined', 'post': '登录'}), userId=self.userId, password=self.password, applicationCode=self.applicationCode)\n if not 'HXB_DCMS_WINDOW_' in r.text:\n return False\n self.setDcmsType(dcms_type)\n if keep_long:\n self.keepConnection()\n return self\n\n def keepConnection(self):\n r = self.get(self.get_urls['keep_connection'])\n if 'frame' not in r.text:\n self.login(self.userId, self.password, self.applicationCode)\n threading.Timer(180, self.keepConnection).start()\n\n def search_cf(self, name_or_cf_code):\n if name_or_cf_code.startswith('CF'):\n searchCriteria = SearchBy.cf_num.value\n elif name_or_cf_code.startswith('C'):\n searchCriteria = SearchBy.customer_code.value\n else:\n searchCriteria = SearchBy.customer_name.value\n # searchCriteria = SearchBy.cf_num if name_or_cf.startswith('CF') else SearchBy.customer_name\n response = self.post(self.post_urls['search_cf'], searchCriteria=searchCriteria, searchValue=name_or_cf_code)\n try:\n search_result = DcmsWebPage(response.text, None).lists[0].parse_to_tag_dict_list()\n index = 0\n if len(search_result) > 1:\n print('条件【' + name_or_cf_code + '】查找到' + str(len(search_result)) + '个客户，请选择：')\n for i in range(len(search_result)):\n print(str(i + 1) + '.' + search_result[i]['客户'].text.strip())\n index = int(input('>>>')) - 1\n result = search_result[index]['信贷文件编号']\n cf_num = result.text.strip()\n cf_rlk = RegExp.rlk.search(str(result)).group(1)\n return (cf_num, cf_rlk)\n except:\n return (None, None)\n\n def search_customer(self, name_or_code):\n '''\n\n :param name_or_code: 客户名称或客户编号C247551\n :return:\n '''\n customerType = 'CP'\n if name_or_code.startswith('C'):\n searchCriteria = 'no'\n elif name_or_code.startswith('P'):\n searchCriteria = 'no'\n customerType = 'CS'\n else:\n searchCriteria = 'nm'\n r = self.post(\n self.post_urls['search_customer'],\n do='AllScopeSearch',\n customerType=customerType,\n cardCategory='I',\n searchCriteria=searchCriteria,\n searchValue=name_or_code\n )\n try:\n rlk = RegExp.rlk.findall(r.text)[0]\n except:\n print('未在DCMS中查询到客户', name_or_code)\n return None\n else:\n search_result = DcmsWebPage(r.text)\n customer_info = search_result.lists[0].parse_to_dict_list()\n index = 0\n if len(customer_info) > 1:\n print('搜索', name_or_code, '获得超过一个结果：')\n for i in range(len(customer_info)):\n print(i, customer_info[i]['客户名称'], customer_info[i]['客户编号'])\n index = input('请选择>>>')\n shallow_info = customer_info[int(index)]\n deep_info = search_result.lists[0].parse_to_tag_dict_list()[int(index)]\n return (shallow_info, deep_info)\n\n def search_cp(self, cp_num):\n r = self.post(\n self.post_urls['search_cp'],\n searchValue=cp_num,\n searchCriteria=SearchBy.con_num.value\n )\n try:\n rlk = RegExp.rlk.findall(r.text)[0]\n return rlk\n except:\n return None\n\n def search_lu(self, lu_num):\n r = self.post(\n self.post_urls['search_lu'],\n searchValue=lu_num,\n searchCriteria=SearchBy.con_num.value,\n stopLimit='N',\n isOnlineLoan='N',\n )\n rlk = RegExp.rlk.findall(r.text)[0]\n return rlk\n\n", "sub_path": "apps/scraper/dcms_request.py", "file_name": "dcms_request.py", "file_ext": "py", "file_size_in_byte": 6219, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "re.compile", "line_number": 12, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 13, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 15, "usage_type": "name"}, {"api_name": "base_request.BaseHttpRequest", "line_number": 22, "usage_type": "name"}, {"api_name": "enum.Enum", "line_number": 32, "usage_type": "name"}, {"api_name": "threading.Timer", "line_number": 77, "usage_type": "call"}, {"api_name": "private_modules.dcms_shovel.page_parser.DcmsWebPage", "line_number": 89, "usage_type": "call"}, {"api_name": "private_modules.dcms_shovel.page_parser.DcmsWebPage", "line_number": 131, "usage_type": "call"}]} +{"seq_id": "518974396", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jan 23 19:26:46 2018\n\n@author: yume\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport copy\nfrom decide_robot_absolute_position import decide_robot_absolute_position\nfrom decide_robot_absolute_position import avg_vector\nfrom agents_ver3 import Human\nfrom states import AgentState\n\n\n# meは人間の実測データ、 youはモデルとする\ndef display(l_s, f_s):\n l_p = []\n f_p = []\n for i in range(len(l_s)):\n l_p.append(l_s[i])\n f_p.append(f_s[i])\n l_p = np.array(l_p)\n f_p = np.array(f_p)\n# relative_distance = np.sqrt((l_p - f_p) * 2)\n plt.plot(*l_p.T, \"-o\", label=\"Robot\")\n plt.plot(*f_p.T, \"*\", label=\"Human\")\n plt.plot(-0.2, 3, \"^\", label=\"Goal\")\n# plt.plot(self.obstacles_p.T[0], self.obstacles_p.T[1], \"^\",\n# label=\"obstacle\")\n# print(\"relative_distance\", relative_distance[-1])\n plt.xticks(np.arange(-1.0, 3.0, 0.5))\n plt.yticks(np.arange(-0.8, 5.5, 0.5)) # 表の軸を0~20に固定\n plt.grid()\n plt.legend()\n plt.gca().set_aspect('equal')\n plt.show()\n# print(\"leader.p\", self.l_p[-1])\n# print(\"follower.p\", self.f_p[-1])\n\n\ndef make_trajectory(ps):\n ps = np.array(ps)\n trajectory = []\n prev_p = ps[0]\n state = AgentState(prev_p)\n trajectory.append(state)\n for p in ps[1:]:\n d = p - prev_p\n state = AgentState(p, d)\n trajectory.append(state)\n prev_p = p\n return trajectory\n\n\nif __name__ == '__main__':\n length_step = 52\n n = 0\n lim = 5\n trajectory_a = make_trajectory([\n [0.98094249456, 0.731414990608],\n [0.88094249456, 0.631414990608],\n [0.75264596, 0.55928402]\n ])\n trajectory_b = make_trajectory([\n [1.73578850047, -0.99751806081],\n [1.74111587829, -0.937682491898],\n [1.58094249456, -0.831414990608],\n [1.46679422611, -0.745349506114]\n ])\n# # KUBO\n# trajectory_a = make_trajectory([\n# [-1.88094249456, 1.931414990608],\n# [-1.68094249456, 1.831414990608],\n# [-1.45264596, 1.75928402]\n# ])\n# trajectory_b = make_trajectory([\n# [-0.60533186, 1.12512445],\n# [-0.53173167, 1.00566862],\n# [-0.64863302, 1.21137899]\n# ])\n initial_state_b = trajectory_b[-1]\n social_distance = 1.5\n d_t = 0.1\n prev_p = np.array([1.46679422611, -0.745349506114])\n# prev_p = np.array([-0.64863302, 1.21137899])\n d_num_for_avg = 15\n d_lst = []\n l_p = []\n f_p = []\n\n human_b = Human(\n initial_state_b, d_t, trajectory_b, trajectory_a)\n\n while n < length_step:\n temp_lst = []\n\n human_b.measure(human_b.s)\n human_b.decide_action()\n human_b.move()\n\n x_you = human_b.s.p[0]\n y_you = human_b.s.p[1]\n p = np.array([x_you, y_you])\n\n d_lst.append(human_b.s.d)\n if len(d_lst) < d_num_for_avg:\n d_sum = np.sum(np.array(d_lst), axis=0)\n else:\n for i in range(d_num_for_avg):\n temp_lst.append(d_lst[-1 - i])\n d_sum = np.sum(np.array(temp_lst), axis=0)\n d = avg_vector(d_sum, d_num_for_avg)\n x_me, y_me = decide_robot_absolute_position(p, d, social_distance)\n l_p.append(np.array([x_me, y_me]))\n f_p.append(np.array([x_you, y_you]))\n\n print(\"frame\", n)\n plt.title(\"blue = Robot, red = Human\")\n plt.plot(x_you, y_you, '*', color=\"r\")\n# plt.quiver(x_you, y_you, d[0]*20, d[1]*20, angles='xy',scale_units='xy',scale=1)\n plt.plot(x_me, y_me, '.', color=\"b\")\n plt.xlim(-lim, lim)\n plt.ylim(-lim, lim)\n plt.axes().set_aspect('equal')\n plt.grid()\n plt.show()\n plt.draw()\n# print(\"d\", d)\n print(\"robot position\", l_p[-1])\n print(\"\")\n print(\"human position\", f_p[-1])\n print(\"\")\n print(\"relative distance\", np.linalg.norm(np.array(l_p[-1]) - np.array(f_p[-1])))\n prev_p = np.array([x_you, y_you])\n# display(np.array(l_p), np.array(f_p))\n print(\"--------------------------------------------------------------\")\n n += 1 # インクリメント\n", "sub_path": "visualization_ideal_walking.py", "file_name": "visualization_ideal_walking.py", "file_ext": "py", "file_size_in_byte": 4282, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.array", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 35, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 35, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "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"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "states.AgentState", "line_number": 47, "usage_type": "call"}, {"api_name": "states.AgentState", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 86, "usage_type": "call"}, {"api_name": "agents_ver3.Human", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 113, "usage_type": "call"}, {"api_name": "decide_robot_absolute_position.avg_vector", "line_number": 114, "usage_type": "call"}, {"api_name": "decide_robot_absolute_position.decide_robot_absolute_position", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axes", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.draw", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 135, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 136, "usage_type": "call"}]} +{"seq_id": "590119129", "text": "from pathlib import Path\nimport tempfile\nimport time\nimport os\nimport requests\nimport pytest\nimport sys\nfrom typing import Dict, Optional, Tuple\nfrom unittest.mock import Mock, patch\nfrom ray._private.test_utils import (\n format_web_url,\n wait_for_condition,\n wait_until_server_available,\n)\n\nfrom ray.dashboard.modules.dashboard_sdk import (\n ClusterInfo,\n DEFAULT_DASHBOARD_ADDRESS,\n parse_cluster_info,\n)\nfrom ray.dashboard.modules.job.sdk import JobSubmissionClient, JobStatus\nfrom ray.dashboard.tests.conftest import * # noqa\nfrom ray.tests.conftest import _ray_start\nimport ray\nimport ray.experimental.internal_kv as kv\n\n\ndef _check_job_succeeded(client: JobSubmissionClient, job_id: str) -> bool:\n status = client.get_job_status(job_id)\n if status == JobStatus.FAILED:\n logs = client.get_job_logs(job_id)\n raise RuntimeError(f\"Job failed\\nlogs:\\n{logs}\")\n return status == JobStatus.SUCCEEDED\n\n\ndef check_internal_kv_gced():\n return len(kv._internal_kv_list(\"gcs://\")) == 0\n\n\n@pytest.mark.parametrize(\n \"address_param\",\n [\n (\"ray://1.2.3.4:10001\", \"ray\", \"1.2.3.4:10001\"),\n (\"other_module://\", \"other_module\", \"\"),\n (\"other_module://address\", \"other_module\", \"address\"),\n ],\n)\n@pytest.mark.parametrize(\"create_cluster_if_needed\", [True, False])\n@pytest.mark.parametrize(\"cookies\", [None, {\"test_cookie_key\": \"test_cookie_val\"}])\n@pytest.mark.parametrize(\"metadata\", [None, {\"test_metadata_key\": \"test_metadata_val\"}])\n@pytest.mark.parametrize(\"headers\", [None, {\"test_headers_key\": \"test_headers_val\"}])\ndef test_parse_cluster_info(\n address_param: Tuple[str, str, str],\n create_cluster_if_needed: bool,\n cookies: Optional[Dict[str, str]],\n metadata: Optional[Dict[str, str]],\n headers: Optional[Dict[str, str]],\n):\n \"\"\"\n Test ray.dashboard.modules.dashboard_sdk.parse_cluster_info for different\n format of addresses.\n \"\"\"\n mock_get_job_submission_client_cluster = Mock(return_value=\"Ray ClusterInfo\")\n mock_module = Mock()\n mock_module.get_job_submission_client_cluster_info = Mock(\n return_value=\"Other module ClusterInfo\"\n )\n mock_import_module = Mock(return_value=mock_module)\n\n address, module_string, inner_address = address_param\n\n with patch.multiple(\n \"ray.dashboard.modules.dashboard_sdk\",\n get_job_submission_client_cluster_info=mock_get_job_submission_client_cluster,\n ), patch.multiple(\"importlib\", import_module=mock_import_module):\n if module_string == \"ray\":\n with pytest.raises(ValueError, match=\"ray://\"):\n parse_cluster_info(\n address,\n create_cluster_if_needed=create_cluster_if_needed,\n cookies=cookies,\n metadata=metadata,\n headers=headers,\n )\n elif module_string == \"other_module\":\n assert (\n parse_cluster_info(\n address,\n create_cluster_if_needed=create_cluster_if_needed,\n cookies=cookies,\n metadata=metadata,\n headers=headers,\n )\n == \"Other module ClusterInfo\"\n )\n mock_import_module.assert_called_once_with(module_string)\n mock_module.get_job_submission_client_cluster_info.assert_called_once_with(\n inner_address,\n create_cluster_if_needed=create_cluster_if_needed,\n cookies=cookies,\n metadata=metadata,\n headers=headers,\n )\n\n\ndef test_parse_cluster_info_default_address():\n assert (\n parse_cluster_info(\n address=None,\n )\n == ClusterInfo(address=DEFAULT_DASHBOARD_ADDRESS)\n )\n\n\n@pytest.mark.parametrize(\"expiration_s\", [0, 10])\ndef test_temporary_uri_reference(monkeypatch, expiration_s):\n \"\"\"Test that temporary GCS URI references are deleted after expiration_s.\"\"\"\n monkeypatch.setenv(\n \"RAY_RUNTIME_ENV_TEMPORARY_REFERENCE_EXPIRATION_S\", str(expiration_s)\n )\n # We can't use a fixture with a shared Ray runtime because we need to set the\n # expiration_s env var before Ray starts.\n with _ray_start(include_dashboard=True, num_cpus=1) as ctx:\n headers = {\"Connection\": \"keep-alive\", \"Authorization\": \"TOK:\"}\n address = ctx.address_info[\"webui_url\"]\n assert wait_until_server_available(address)\n client = JobSubmissionClient(format_web_url(address), headers=headers)\n with tempfile.TemporaryDirectory() as tmp_dir:\n path = Path(tmp_dir)\n\n hello_file = path / \"hi.txt\"\n with hello_file.open(mode=\"w\") as f:\n f.write(\"hi\\n\")\n\n start = time.time()\n\n client.submit_job(\n entrypoint=\"echo hi\", runtime_env={\"working_dir\": tmp_dir}\n )\n\n # Give time for deletion to occur if expiration_s is 0.\n time.sleep(2)\n # Need to connect to Ray to check internal_kv.\n # ray.init(address=\"auto\")\n\n print(\"Starting Internal KV checks at time \", time.time() - start)\n if expiration_s > 0:\n assert not check_internal_kv_gced()\n wait_for_condition(check_internal_kv_gced, timeout=2 * expiration_s)\n assert expiration_s < time.time() - start < 2 * expiration_s\n print(\"Internal KV was GC'ed at time \", time.time() - start)\n else:\n wait_for_condition(check_internal_kv_gced)\n print(\"Internal KV was GC'ed at time \", time.time() - start)\n\n\n@pytest.fixture\ndef mock_candidate_number():\n os.environ[\"CANDIDATE_AGENT_NUMBER\"] = \"2\"\n yield\n os.environ.pop(\"CANDIDATE_AGENT_NUMBER\", None)\n\n\n@pytest.mark.parametrize(\n \"ray_start_cluster_head\", [{\"include_dashboard\": True}], indirect=True\n)\ndef test_job_head_choose_job_agent_E2E(mock_candidate_number, ray_start_cluster_head):\n cluster = ray_start_cluster_head\n assert wait_until_server_available(cluster.webui_url) is True\n webui_url = cluster.webui_url\n webui_url = format_web_url(webui_url)\n client = JobSubmissionClient(webui_url)\n\n def get_register_agents_number():\n response = requests.get(webui_url + \"/internal/node_module\")\n response.raise_for_status()\n result = response.json()\n data = result[\"data\"]\n return data[\"registeredAgents\"]\n\n def submit_job_and_wait_finish():\n submission_id = client.submit_job(entrypoint=\"echo hello\")\n\n wait_for_condition(_check_job_succeeded, client=client, job_id=submission_id)\n\n # make sure list(cluster.worker_nodes)[0] will be the owner of a supervisor actor.\n cluster.add_node(dashboard_agent_listen_port=52366)\n wait_for_condition(lambda: get_register_agents_number() == 2, timeout=20)\n assert len(cluster.worker_nodes) == 1\n node_try_to_kill = list(cluster.worker_nodes)[0]\n submit_job_and_wait_finish()\n\n cluster.add_node(dashboard_agent_listen_port=52367)\n wait_for_condition(lambda: get_register_agents_number() == 3, timeout=20)\n\n submit_job_and_wait_finish()\n submit_job_and_wait_finish()\n submit_job_and_wait_finish()\n\n def get_all_new_supervisor_actor_info(old_supervisor_actor):\n all_actors = ray.state.state.actor_table(None)\n res = dict()\n for actor_id, actor_info in all_actors.items():\n if actor_id in old_supervisor_actor:\n continue\n if not actor_info[\"Name\"].startswith(\"_ray_internal_job_actor\"):\n continue\n res[actor_id] = actor_info\n return res\n\n old_supervisor_actor = set()\n new_supervisor_actor = get_all_new_supervisor_actor_info(old_supervisor_actor)\n new_owner_port = set()\n for actor_id, actor_info in new_supervisor_actor.items():\n old_supervisor_actor.add(actor_id)\n new_owner_port.add(actor_info[\"OwnerAddress\"][\"Port\"])\n\n assert len(new_owner_port) == 2\n old_owner_port = new_owner_port\n\n node_try_to_kill.kill_raylet()\n\n # make sure the head updates the info of the dead node.\n wait_for_condition(lambda: get_register_agents_number() == 2, timeout=20)\n\n submit_job_and_wait_finish()\n submit_job_and_wait_finish()\n submit_job_and_wait_finish()\n\n new_supervisor_actor = get_all_new_supervisor_actor_info(old_supervisor_actor)\n new_owner_port = set()\n for actor_id, actor_info in new_supervisor_actor.items():\n old_supervisor_actor.add(actor_id)\n new_owner_port.add(actor_info[\"OwnerAddress\"][\"Port\"])\n assert len(new_owner_port) == 2\n assert len(old_owner_port - new_owner_port) == 1\n assert len(new_owner_port - old_owner_port) == 1\n\n\nif __name__ == \"__main__\":\n sys.exit(pytest.main([\"-v\", __file__]))\n", "sub_path": "dashboard/modules/job/tests/test_sdk.py", "file_name": "test_sdk.py", "file_ext": "py", "file_size_in_byte": 8869, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "ray.dashboard.modules.job.sdk.JobSubmissionClient", "line_number": 28, "usage_type": "name"}, {"api_name": "ray.dashboard.modules.job.sdk.JobStatus.FAILED", "line_number": 30, "usage_type": "attribute"}, {"api_name": "ray.dashboard.modules.job.sdk.JobStatus", "line_number": 30, "usage_type": "name"}, {"api_name": "ray.dashboard.modules.job.sdk.JobStatus.SUCCEEDED", "line_number": 33, "usage_type": "attribute"}, {"api_name": "ray.dashboard.modules.job.sdk.JobStatus", "line_number": 33, "usage_type": "name"}, {"api_name": "ray.experimental.internal_kv._internal_kv_list", "line_number": 37, "usage_type": "call"}, {"api_name": "ray.experimental.internal_kv", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 53, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 55, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 55, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 56, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 57, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 57, "usage_type": "name"}, {"api_name": "unittest.mock.Mock", "line_number": 63, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 64, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 65, "usage_type": "call"}, {"api_name": "unittest.mock.Mock", "line_number": 68, "usage_type": "call"}, {"api_name": "unittest.mock.patch.multiple", "line_number": 72, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 72, "usage_type": "name"}, {"api_name": "unittest.mock.patch.multiple", "line_number": 75, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 75, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 77, "usage_type": "call"}, {"api_name": "ray.dashboard.modules.dashboard_sdk.parse_cluster_info", "line_number": 78, "usage_type": "call"}, {"api_name": "ray.dashboard.modules.dashboard_sdk.parse_cluster_info", "line_number": 87, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 40, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 40, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 48, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 49, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 49, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 50, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pytest.mark.parametrize", "line_number": 51, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 51, "usage_type": "attribute"}, {"api_name": "ray.dashboard.modules.dashboard_sdk.parse_cluster_info", "line_number": 108, "usage_type": "call"}, {"api_name": "ray.dashboard.modules.dashboard_sdk.ClusterInfo", "line_number": 111, "usage_type": "call"}, {"api_name": "ray.dashboard.modules.dashboard_sdk.DEFAULT_DASHBOARD_ADDRESS", "line_number": 111, "usage_type": "name"}, {"api_name": "ray.tests.conftest._ray_start", "line_number": 123, "usage_type": "call"}, {"api_name": "ray._private.test_utils.wait_until_server_available", "line_number": 126, "usage_type": "call"}, {"api_name": "ray.dashboard.modules.job.sdk.JobSubmissionClient", "line_number": 127, "usage_type": "call"}, {"api_name": "ray._private.test_utils.format_web_url", "line_number": 127, "usage_type": "call"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 128, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 129, "usage_type": "call"}, {"api_name": "time.time", "line_number": 135, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 142, "usage_type": "call"}, {"api_name": "time.time", "line_number": 146, "usage_type": "call"}, {"api_name": "ray._private.test_utils.wait_for_condition", "line_number": 149, "usage_type": "call"}, {"api_name": "time.time", "line_number": 150, "usage_type": "call"}, {"api_name": "time.time", "line_number": 151, "usage_type": "call"}, {"api_name": "ray._private.test_utils.wait_for_condition", "line_number": 153, "usage_type": "call"}, {"api_name": "time.time", "line_number": 154, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 115, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 115, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 159, "usage_type": "attribute"}, {"api_name": "os.environ.pop", "line_number": 161, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 161, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 157, "usage_type": "attribute"}, {"api_name": "ray._private.test_utils.wait_until_server_available", "line_number": 169, "usage_type": "call"}, {"api_name": "ray._private.test_utils.format_web_url", "line_number": 171, "usage_type": "call"}, {"api_name": "ray.dashboard.modules.job.sdk.JobSubmissionClient", "line_number": 172, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 175, "usage_type": "call"}, {"api_name": "ray._private.test_utils.wait_for_condition", "line_number": 184, "usage_type": "call"}, {"api_name": "ray._private.test_utils.wait_for_condition", "line_number": 188, "usage_type": "call"}, {"api_name": "ray._private.test_utils.wait_for_condition", "line_number": 194, "usage_type": "call"}, {"api_name": "ray.state.state.actor_table", "line_number": 201, "usage_type": "call"}, {"api_name": "ray.state", "line_number": 201, "usage_type": "attribute"}, {"api_name": "ray._private.test_utils.wait_for_condition", "line_number": 224, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 164, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 164, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 241, "usage_type": "call"}, {"api_name": "pytest.main", "line_number": 241, "usage_type": "call"}]} +{"seq_id": "66673634", "text": "from django.conf.urls.defaults import patterns, include, url\n\nurlpatterns = patterns('reports',\n # Examples:\n # url(r'^$', 'beacon_reports.views.home', name='home'),\n # url(r'^beacon_reports/', include('beacon_reports.foo.urls')),\n url(r'^$', 'views.index'),\n url(r'^cp/human/$', 'views.report_cp_human'),\n url(r'^cp/csv/$', 'views.report_cp_csv'),\n url(r'^cc/$', 'views.report_cc'),\n url(r'^ce/$', 'views.report_ce'),\n)\n", "sub_path": "reports/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 445, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.conf.urls.defaults.patterns", "line_number": 3, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.defaults.url", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "512423096", "text": "import pandas as pd\nimport numpy as np\nimport json\n\n\ndef squad_to_dataframe(data_path, record_path=['data', 'paragraphs', 'qas',\n 'answers']):\n\n # record_path: path to deepest level in json file\n\n #\n jsonData = json.loads(open(data_path).read())\n\n # parsing different levels\n dfDeep = pd.json_normalize(jsonData, record_path)\n dfDeepMinus1 = pd.json_normalize(jsonData, record_path[:-1])\n dfDeepMinus2 = pd.json_normalize(jsonData, record_path[:-2])\n\n # concatinating into single dataframe\n contex = np.repeat(dfDeepMinus2['context'].values, dfDeepMinus2.qas.str.len())\n qid = np.repeat(dfDeepMinus1['id'].values, dfDeepMinus1['answers'].str.len())\n dfDeepMinus1['context'] = contex\n dfDeep['q_idx'] = qid\n finalDf = pd.concat([dfDeepMinus1[['id', 'question', 'context']].set_index('id'), dfDeep.set_index('q_idx')], 1,\n sort=False).reset_index()\n finalDf['c_id'] = finalDf['context'].factorize()[0]\n\n return finalDf\n\n\n\n# training data\ndataPath = 'Data/train-v2.0.json'\nrecordpath = ['data', 'paragraphs', 'qas', 'answers']\ndf = squad_to_dataframe(data_path=dataPath, record_path=recordpath)\n\n\ndf.columns\ndf.head()", "sub_path": "src/getData.py", "file_name": "getData.py", "file_ext": "py", "file_size_in_byte": 1199, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "json.loads", "line_number": 12, "usage_type": "call"}, {"api_name": "pandas.json_normalize", "line_number": 15, "usage_type": "call"}, {"api_name": "pandas.json_normalize", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.json_normalize", "line_number": 17, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "578302895", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jun 14 19:58:11 2017\n\n@author: AntoineP\n\"\"\"\nfrom skimage.io import imread, imshow\n\n# Show a sub-part of an image\ndef show_inner_img(path, posX, posY, width, hight):\n image = imread(path)\n image = image[posY:(posY+hight), posX:(posX+width)]\n imshow(image)\n \n# show_inner_img(apprFiles[188], 207, 5, 258, 378)", "sub_path": "tool_func.py", "file_name": "tool_func.py", "file_ext": "py", "file_size_in_byte": 385, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "skimage.io.imread", "line_number": 12, "usage_type": "call"}, {"api_name": "skimage.io.imshow", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "202061602", "text": "from os.path import join, exists\nfrom os import makedirs\nimport time\n\nfrom argparse import ArgumentParser\n\n# project imports\nfrom database.data_loader import DataLoaderBlock\nfrom database import read_slice_info\nfrom src import datasets\nfrom src.utils import algorithm_utils\nfrom src.utils.image_utils import deform2D\nfrom scripts import config_dev as configFile, config_data\nfrom src.algorithm import *\n\nfile = 'slice_separation.csv'\nslice_num_dict = read_slice_info(file, stain=['MRI', 'IHC', 'NISSL'])\naffine = configFile.HISTO_AFFINE\n\nparameter_dict_MRI = configFile.CONFIG_DICT['MRI']\nparameter_dict_NISSL = configFile.CONFIG_DICT['NISSL']\nparameter_dict_IHC = configFile.CONFIG_DICT['IHC']\n\nif __name__ == '__main__':\n\n # Parameters\n arg_parser = ArgumentParser(description='Computes the prediction of certain models')\n arg_parser.add_argument('--nc', type=int, default=2, choices=[2, 3], help='Number of contrasts')\n arg_parser.add_argument('--c1', type=str, default='NISSL', choices=['IHC', 'NISSL'], help='Contrast 1')\n arg_parser.add_argument('--c2', type=str, default='', choices=['IHC', 'NISSL', ''], help='Contrast 2')\n arg_parser.add_argument('--cost', type=str, default='l1', choices=['l1', 'l2'], help='Likelihood cost function')\n arg_parser.add_argument('--nn', type=int, default=2, help='Number of neighbours')\n arg_parser.add_argument('--mdil', type=int, default=7, help='Mask dilation factor')\n\n arguments = arg_parser.parse_args()\n ref = 'MRI'\n N_CONTRASTS = arguments.nc\n c1 = arguments.c1\n c2 = arguments.c2\n nneighbours = arguments.nn\n cost = arguments.cost\n mdil = arguments.mdil\n\n observations_dir = config_data.OBSERVATIONS_DIR_REGNET\n algorithm_dir = config_data.ALGORITHM_DIR\n results_dir = join(algorithm_dir, 'ST' + str(N_CONTRASTS) + '_RegNet', cost, 'NN' + str(nneighbours))\n if not exists(results_dir):\n makedirs(results_dir)\n\n data_loader = DataLoaderBlock(parameter_dict_MRI)\n block_list = data_loader.subject_list\n block_shape = data_loader.image_shape\n\n dataset = datasets.IntraModalRegistrationDataset(\n data_loader,\n rotation_params=parameter_dict_MRI['ROTATION'],\n nonlinear_params=parameter_dict_MRI['NONLINEAR'],\n tf_params=parameter_dict_MRI['TRANSFORM'],\n )\n image_shape = dataset.image_shape\n cp_shape = tuple([int(i / parameter_dict_MRI['UPSAMPLE_LEVELS']) for i in image_shape])\n\n num_tree_pos_prev = 0\n for it_sbj, sbj in enumerate(block_list):\n print('[START] Processing subject: ' + str(sbj.id))\n input_dir = join(observations_dir, sbj.id)\n subject_dir = join(parameter_dict_MRI['DB_CONFIG']['BASE_DIR'], sbj.id)\n\n if not exists(join(input_dir, c1 + '.' + str(nneighbours) + 'N.field_x.tree.nii.gz')):\n print('[WARNING] No observations found for subject ' + sbj.id + ', contrast ' + c1 + ' and RegNet ')\n continue\n\n nslices = len(sbj.slice_list)\n results_dir_sbj = join(results_dir, sbj.id)\n if not exists(join(results_dir_sbj)):\n makedirs(results_dir_sbj)\n elif exists(join(results_dir_sbj, c1 + '.nii.gz')):\n print('[DONE] Subject ' + sbj.id + ' has already been processed')\n num_tree_pos_prev += nslices\n continue\n\n ####################################################################################################\n ####################################################################################################\n\n t_init = time.time()\n print('[' + str(sbj.id) + ' - BUILDING GRAPH] Reading SVFs ...')\n if N_CONTRASTS == 2:\n graph_structure = init_st2(subject_dir, input_dir, cp_shape, nslices,\n nneighbours=nneighbours, se=np.ones((mdil, mdil)))\n\n R, M, W, d_inter, d_Ref, d_C1, NK = graph_structure\n print('[' + str(sbj.id) + ' - BUILDING GRAPH] Total Elapsed time: ' + str(time.time() - t_init))\n\n t_init = time.time()\n print('[' + str(sbj.id) + ' - ALGORITHM] Running the algorithm ...')\n if cost == 'L2':\n Tres = st2_L2(R, M, W, d_inter, d_Ref, d_C1, nslices, niter=5)\n\n else:\n Tres = st2_L1(R, M, W, nslices)\n\n T_C1 = Tres[..., :nslices]\n T_Ref = Tres[..., nslices:]\n\n img = nib.Nifti1Image(T_C1[0], affine)\n nib.save(img, join(results_dir_sbj, c1 + '.field_x.tree.nii.gz'))\n img = nib.Nifti1Image(T_C1[1], affine)\n nib.save(img, join(results_dir_sbj, c1 + '.field_y.tree.nii.gz'))\n\n img = nib.Nifti1Image(T_Ref[0], affine)\n nib.save(img, join(results_dir_sbj, ref + '.field_x.tree.nii.gz'))\n img = nib.Nifti1Image(T_Ref[1], affine)\n nib.save(img, join(results_dir_sbj, ref + '.field_y.tree.nii.gz'))\n\n\n elif N_CONTRASTS == 3:\n graph_structure = init_st3(subject_dir, input_dir, cp_shape, nslices,\n nneighbours=nneighbours, se=np.ones((mdil, mdil)))\n\n R, M, W, d_inter, d_Ref, d_C1, d_C2, NK = graph_structure\n print('[' + str(sbj.id) + ' - BUILDING GRAPH] Total Elapsed time: ' + str(time.time() - t_init))\n\n t_init = time.time()\n print('[' + str(sbj.id) + ' - ALGORITHM] Running the algorithm ...')\n if cost == 'L2':\n Tres = st3_L2(R, M, W, d_inter, d_Ref, d_C1, d_C2, nslices, niter=5)\n\n else:\n Tres = st3_L1(R, M, W, nslices)\n\n T_C1 = Tres[..., :nslices]\n T_C2 = Tres[..., nslices:2 * nslices]\n T_Ref = Tres[..., 2 * nslices:]\n\n img = nib.Nifti1Image(T_C1[0], affine)\n nib.save(img, join(results_dir_sbj, c1 + '.field_x.tree.nii.gz'))\n img = nib.Nifti1Image(T_C1[1], affine)\n nib.save(img, join(results_dir_sbj, c1 + '.field_y.tree.nii.gz'))\n\n img = nib.Nifti1Image(T_C2[0], affine)\n nib.save(img, join(results_dir_sbj, c2 + '.field_x.tree.nii.gz'))\n img = nib.Nifti1Image(T_C2[1], affine)\n nib.save(img, join(results_dir_sbj, c2 + '.field_y.tree.nii.gz'))\n\n img = nib.Nifti1Image(T_Ref[0], affine)\n nib.save(img, join(results_dir_sbj, ref + '.field_x.tree.nii.gz'))\n img = nib.Nifti1Image(T_Ref[1], affine)\n nib.save(img, join(results_dir_sbj, ref + '.field_y.tree.nii.gz'))\n\n\n ####################################################################################################\n ####################################################################################################\n\n\n t_init = time.time()\n print('[' + str(sbj.id) + ' - INTEGRATION] Computing deformation field ... ')\n if not exists(join(results_dir_sbj, c1 + '.flow.tree.nii.gz')):\n flow_c1 = algorithm_utils.integrate_RegNet(T_C1, block_shape, parameter_dict_MRI)\n img = nib.Nifti1Image(flow_c1, affine)\n nib.save(img, join(results_dir_sbj, c1 + '.flow.tree.nii.gz'))\n\n if N_CONTRASTS==3 and not exists(join(results_dir_sbj, c2 + '.flow.tree.nii.gz')):\n flow_c2 = algorithm_utils.integrate_RegNet(T_C2, block_shape, parameter_dict_MRI)\n\n img = nib.Nifti1Image(flow_c2, affine)\n nib.save(img, join(results_dir_sbj, c2 + '.flow.tree.nii.gz'))\n\n print('[' + str(sbj.id) + ' - INTEGRATION] Total Elapsed time: ' + str(time.time() - t_init))\n\n ####################################################################################################\n ####################################################################################################\n t_init = time.time()\n print('[' + str(sbj.id) + ' - DEFORM] Deforming images ... ')\n\n # IHC\n if c1 == 'IHC' or c2 == 'IHC':\n data_loader_IHC = DataLoaderBlock(parameter_dict_IHC)\n block_list_IHC = data_loader_IHC.subject_list\n\n proxy = nib.load(join(results_dir_sbj, 'IHC.flow.tree.nii.gz'))\n flow = np.asarray(proxy.dataobj)\n\n image_deformed = np.zeros(block_shape + (nslices,))\n mask_deformed = np.zeros(block_shape + (nslices,))\n\n for sl in block_list_IHC[it_sbj].slice_list:\n it_sl = sl.tree_pos - num_tree_pos_prev\n print(' Slice: ' + str(it_sl) + '/' + str(nslices))\n\n image = sl.load_ref()\n mask = sl.load_ref_mask()\n\n image_deformed[..., it_sl] = deform2D(image, flow[..., it_sl])\n mask_deformed[..., it_sl] = deform2D(mask, flow[..., it_sl], mode='nearest')\n\n del image\n del mask\n\n img = nib.Nifti1Image(image_deformed, affine)\n nib.save(img, join(results_dir_sbj, 'IHC.tree.nii.gz'))\n\n img = nib.Nifti1Image(mask_deformed, affine)\n nib.save(img, join(results_dir_sbj, 'IHC.mask.tree.nii.gz'))\n\n\n if c1 == 'NISSL' or c2 == 'NISSL':\n # NISSL\n data_loader_NISSL = DataLoaderBlock(parameter_dict_NISSL)\n block_list_NISSL = data_loader_NISSL.subject_list\n\n proxy = nib.load(join(results_dir_sbj, 'NISSL.flow.tree.nii.gz'))\n flow = np.asarray(proxy.dataobj)\n\n image_deformed = np.zeros(block_shape + (nslices,))\n mask_deformed = np.zeros(block_shape + (nslices,))\n\n for sl in block_list_NISSL[it_sbj].slice_list:\n it_sl = sl.tree_pos - num_tree_pos_prev\n print(' Slice: ' + str(it_sl) + '/' + str(nslices))\n\n image = sl.load_ref()\n mask = sl.load_ref_mask()\n\n # NISSL\n image_deformed[..., it_sl] = deform2D(image, flow[..., it_sl])\n mask_deformed[..., it_sl] = deform2D(mask, flow[..., it_sl], mode='nearest')\n\n\n img = nib.Nifti1Image(image_deformed, affine)\n nib.save(img, join(results_dir_sbj, 'NISSL.tree.nii.gz'))\n\n img = nib.Nifti1Image(mask_deformed, affine)\n nib.save(img, join(results_dir_sbj, 'NISSL.mask.tree.nii.gz'))\n\n\n num_tree_pos_prev += nslices\n\n print('[' + str(sbj.id) + ' - DEFORM] Total Elapsed time: ' + str(time.time() - t_init))\n", "sub_path": "scripts/algorithm/algorithm_RegNet.py", "file_name": "algorithm_RegNet.py", "file_ext": "py", "file_size_in_byte": 10361, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "database.read_slice_info", "line_number": 17, "usage_type": "call"}, {"api_name": "scripts.config_dev.HISTO_AFFINE", "line_number": 18, "usage_type": "attribute"}, {"api_name": "scripts.config_dev", "line_number": 18, "usage_type": "name"}, {"api_name": "scripts.config_dev.CONFIG_DICT", "line_number": 20, "usage_type": "attribute"}, {"api_name": "scripts.config_dev", "line_number": 20, "usage_type": "name"}, {"api_name": "scripts.config_dev.CONFIG_DICT", "line_number": 21, "usage_type": "attribute"}, {"api_name": "scripts.config_dev", "line_number": 21, "usage_type": "name"}, {"api_name": "scripts.config_dev.CONFIG_DICT", "line_number": 22, "usage_type": "attribute"}, {"api_name": "scripts.config_dev", "line_number": 22, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 27, "usage_type": "call"}, {"api_name": "scripts.config_data.OBSERVATIONS_DIR_REGNET", "line_number": 44, "usage_type": "attribute"}, {"api_name": "scripts.config_data", "line_number": 44, "usage_type": "name"}, {"api_name": "scripts.config_data.ALGORITHM_DIR", "line_number": 45, "usage_type": "attribute"}, {"api_name": "scripts.config_data", "line_number": 45, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 47, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 48, "usage_type": "call"}, {"api_name": "database.data_loader.DataLoaderBlock", "line_number": 50, "usage_type": "call"}, {"api_name": "src.datasets.IntraModalRegistrationDataset", "line_number": 54, "usage_type": "call"}, {"api_name": "src.datasets", "line_number": 54, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 66, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 67, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 74, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 75, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 76, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 77, "usage_type": "call"}, {"api_name": "time.time", "line_number": 85, "usage_type": "call"}, {"api_name": "time.time", "line_number": 92, "usage_type": "call"}, {"api_name": "time.time", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 106, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 113, "usage_type": "call"}, {"api_name": "time.time", "line_number": 121, "usage_type": "call"}, {"api_name": "time.time", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 143, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 148, "usage_type": "call"}, {"api_name": "time.time", "line_number": 155, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 157, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 157, "usage_type": "call"}, {"api_name": "src.utils.algorithm_utils.integrate_RegNet", "line_number": 158, "usage_type": "call"}, {"api_name": "src.utils.algorithm_utils", "line_number": 158, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 162, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 162, "usage_type": "call"}, {"api_name": "src.utils.algorithm_utils.integrate_RegNet", "line_number": 163, "usage_type": "call"}, {"api_name": "src.utils.algorithm_utils", "line_number": 163, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 166, "usage_type": "call"}, {"api_name": "time.time", "line_number": 168, "usage_type": "call"}, {"api_name": "time.time", "line_number": 172, "usage_type": "call"}, {"api_name": "database.data_loader.DataLoaderBlock", "line_number": 177, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 180, "usage_type": "call"}, {"api_name": "src.utils.image_utils.deform2D", "line_number": 193, "usage_type": "call"}, {"api_name": "src.utils.image_utils.deform2D", "line_number": 194, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 200, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 203, "usage_type": "call"}, {"api_name": "database.data_loader.DataLoaderBlock", "line_number": 208, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 211, "usage_type": "call"}, {"api_name": "src.utils.image_utils.deform2D", "line_number": 225, "usage_type": "call"}, {"api_name": "src.utils.image_utils.deform2D", "line_number": 226, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 230, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 233, "usage_type": "call"}, {"api_name": "time.time", "line_number": 238, "usage_type": "call"}]} +{"seq_id": "366540013", "text": "import json\nfrom datetime import timedelta\nfrom itertools import chain\nfrom operator import attrgetter\nfrom solariat_bottle.configurable_apps import APP_JOURNEYS\nfrom werkzeug.utils import cached_property\nfrom bson.objectid import ObjectId\n\nfrom solariat.db import fields\nfrom solariat.db.abstract import SonDocument\nfrom solariat.utils.timeslot import utc, TIMESLOT_EPOCH, now, parse_datetime\n\nfrom solariat_bottle.db.sequences import NumberSequences\nfrom solariat_bottle.db.user import User, get_user\nfrom solariat_bottle.db.user_profiles.user_profile import UserProfile\nfrom solariat_bottle.db.channel.base import (\n ChannelsAuthDocument, Channel, ChannelsAuthManager, SmartTagChannel)\nfrom solariat_bottle.db.predictors.abc_predictor import ABCPredictor\nfrom solariat_bottle.settings import LOGGER, get_var\nfrom solariat_bottle.utils.id_encoder import pack_event_id, unpack_event_id\nfrom solariat_bottle.db.auth import Document\nfrom solariat_bottle.db.dynamic_profiles import DynamicImportedProfile\nfrom solariat_bottle.db.events.event_type import BaseEventType\n\n\nclass EventManager(ChannelsAuthManager):\n\n def create_by_user(self, user, **kw):\n kw.pop('safe_create', None)\n event = super(EventManager, self).create_by_user(user=user, **kw)\n return event\n\n def create(self, _id=None, **kw):\n '''\n For directly creating an event with params provided.\n '''\n self._handle_create_parameters(_id, kw)\n event = super(EventManager, self).create(**kw)\n self.postprocess_event(event)\n return event\n\n def postprocess_event(self, event):\n if isinstance(event, ObjectId):\n event = self.get(event)\n for account, channels in event.channels_by_account.viewitems():\n if account and APP_JOURNEYS in account.available_apps:\n event.assign_smart_tags()\n # event.compute_journey_information(account)\n\n def get(self, *args, **kwargs):\n if len(args) == 1 and isinstance(args[0], (str, unicode)) and args[0].isdigit():\n id_ = long(args[0]) if str(args[0]).isdigit() else args[0]\n return super(EventManager, self).get(id_, **kwargs)\n if not args and kwargs.get('id', False):\n kwargs['id'] = long(kwargs['id']) if str(kwargs['id']).isdigit() else kwargs['id']\n return super(EventManager, self).get(*args, **kwargs)\n\n def events_for_actor(self, start, end, actor_num):\n id_lower_bound = pack_event_id(actor_num, utc(start))\n id_upper_bound = pack_event_id(actor_num, utc(end))\n if id_lower_bound == id_upper_bound:\n res = self.find(id=id_upper_bound)\n else:\n res = self.find(id__lte=id_upper_bound, id__gte=id_lower_bound)\n return res\n\n def range_query(self, start, end, customer, skip=0, limit=None):\n assert isinstance(customer, DynamicImportedProfile)\n actor_nums = [profile.actor_num for profile in customer.linked_profiles]\n actor_nums.append(customer.actor_num)\n\n from solariat.utils.iterfu import merge\n queries = [self.events_for_actor(start, end, actor_num) for actor_num in set(actor_nums)]\n # merge(*queries, key=attrgetter('created_at')) Removing key to sort by for PRR-174 -- Sabr\n for event in merge(*queries):\n for i in range(skip):\n continue\n if limit is not None:\n limit -= 1\n if limit == 0:\n raise StopIteration\n yield event\n\n def range_query_count(self, start, end, customer, skip=0, limit=0):\n '''\n Fetch all events for a customer. This will look at the linked profiles for the\n customer so that we can obtain a complete event sequence across channels in one\n shot.\n '''\n actor_nums = [profile.actor_num for profile in customer.linked_profiles]\n actor_nums.append(customer.actor_num)\n queries = [self.events_for_actor(start, end, actor_num) for actor_num in set(actor_nums)]\n return sum(query.skip(skip).limit(limit).count() for query in queries)\n\n def lookup_history(self, event, lookback_window):\n actor_num, _ = unpack_event_id(event.id)\n\n id_lower_bound = pack_event_id(actor_num, utc(event._created - timedelta(seconds=lookback_window)))\n id_upper_bound = pack_event_id(actor_num, utc(event._created))\n event_sequence = self.find(id__lte=id_upper_bound, id__gte=id_lower_bound)[:]\n return event_sequence\n\n def customer_history(self, customer, limit=10):\n raise RuntimeError(\"call Event.customer_history\")\n assert isinstance(customer, DynamicImportedProfile)\n id_upper_bound = pack_event_id(customer.actor_num, now())\n id_lower_bound = pack_event_id(customer.actor_num, now() - timedelta(seconds=120))\n return self.find(id__lte=id_upper_bound, id__gte=id_lower_bound).limit(limit).sort(id=-1)[:limit]\n\n def _handle_create_parameters(self, _id, kw):\n '''\n This function handles pre-processing of parameters so that events are loaded\n with correct parameter mapping for native ids and links to pervious events.\n\n Sets:\n * _user_profile from user_profile or actor_id\n * _created - will be used ot set with current time\n * in_reply_to_native_id\n * parent_event\n * id creation\n\n kw already must contain:\n * actor_id - usually created in tasks.normalize_post_params\n * is_inbound - to determine and bind event exact to customer\n * event_type - display_name or instance of BaseEventType\n '''\n if 'user_profile' in kw:\n if not get_var('ON_TEST', False): # _obtain_user_profile creates anonymous UserProfile\n assert isinstance(kw['user_profile'], self.doc_class.PROFILE_CLASS), \\\n \"(%s) %s is not instance of %s\\ndata:\\n%s\" % \\\n (type(kw['user_profile']),\n kw['user_profile'],\n self.doc_class.PROFILE_CLASS,\n str(kw['user_profile'].data))\n\n assert _id, 'Id must be generated earlier, with corresponding dynamic ustomer profile'\n kw['_user_profile'] = kw.pop('user_profile').id\n\n if 'channels' in kw:\n kw['channels'] = [(c.id if isinstance(c, Channel) else c) for c in kw['channels']]\n\n # actor_id must be set on kw earlier, we bind event to actor_id\n actor_id = kw['actor_id']\n if not kw.get('_created'):\n kw['_created'] = now()\n\n in_reply_to_native_id = kw.pop('in_reply_to_native_id', None)\n parent_event = kw.pop('parent_event', None)\n event_type = kw.get('event_type')\n if isinstance(event_type, BaseEventType):\n kw['event_type'] = event_type.display_name\n\n # TODO: pass actor_num instead actor_id\n kw['id'] = long(\n _id or self.doc_class.gen_id(\n kw['is_inbound'],\n actor_id, kw['_created'],\n in_reply_to_native_id,\n parent_event\n )\n )\n\n # Make sure we strip out reference to 'native_id' key. Use it if it is there. If not\n # then try for _native_id, or as a last resort, set the native_id to be based directly\n # in the id.\n kw['_native_id'] = kw.pop('native_id', kw.get('_native_id', str(kw['id'])))\n\n def _prepare_post_checking_duplicates(self, klass, **kw):\n \"\"\"Generates post_id, checks for duplicates and creates post\"\"\"\n\n actual_channels = [(c.id if isinstance(c, Channel) else c) for c in list(kw['channels'])]\n lang_data = kw.pop('lang', None)\n native_id = kw.pop('native_id', None)\n\n from pymongo.errors import DuplicateKeyError\n\n # Some events lack ms resolution on time stamps. So we\n # need to pad the timestampe with additional resolution. We compute\n # this as a hash of the native_id.\n from solariat_bottle.utils.hash import mhash\n from solariat_bottle.utils.id_encoder import MILLISECONDS_PER_SECOND\n padding = mhash(native_id, n=20) % MILLISECONDS_PER_SECOND\n\n p_id = kw.pop('_id', self.gen_id(padding=padding, **kw))\n\n # Now reset the native id if we were not provided one.\n native_id = native_id if native_id else str(p_id)\n\n if lang_data:\n kw['_lang_code'] = lang_data.lang\n\n try:\n post = klass.create(self, _id=p_id, _native_id=native_id, **kw)\n return post, False\n except DuplicateKeyError:\n # If it is a duplicate, fetch the original and update the channels. Note that this use case can\n # probably ne handled with an UPSERT and just set the new channels to this actual_channels\n # list. Not clear which some channels would not be passed in but are still necessary.\n post = self.find_one(_id=p_id)\n post.channels = list(set(post.channels) | set(actual_channels))\n return post, False\n\n return None, True\n\nclass JourneyTypeStagePair(SonDocument):\n journey_type_id = fields.ObjectIdField('t')\n journey_stage_id = fields.ObjectIdField('s')\n\n @property\n def journey_type(self):\n from solariat_bottle.db.journeys.journey_type import JourneyType\n\n return JourneyType.objects.get(id=self.journey_type_id)\n\n @property\n def journey_stage(self):\n from solariat_bottle.db.journeys.journey_type import JourneyStageType\n\n return JourneyStageType.objects.get(self.journey_stage_id)\n\n @property\n def id(self):\n return str(self.journey_type_id), str(self.journey_stage_id)\n\n\nclass Score(SonDocument):\n name = fields.StringField()\n score = fields.NumField()\n\n def __hash__(self):\n return hash(self.id)\n\n @property\n def id(self):\n return self.name, str(self.score)\n\n\nclass Event(ChannelsAuthDocument):\n\n manager = EventManager\n collection = \"Post\"\n allow_inheritance = True\n\n id = fields.EventIdField(db_field='_id', unique=True, required=True)\n\n _created = fields.DateTimeField(db_field='_created',\n default=now, required=True)\n\n # TODO: [gsejop] actor_id and is_inbound might not be needed\n actor_id = fields.BaseField(db_field='ad')\n is_inbound = fields.BooleanField(db_field='ii')\n\n stage_metadata = fields.StringField(db_field='smd')\n\n assigned_tags = fields.ListField(fields.StringField())\n rejected_tags = fields.ListField(fields.StringField())\n # Single event tags\n # computed_single_tags = fields.ListField(fields.StringField())\n # single_tag_scores = fields.ListField(fields.DictField())\n #\n # # Multi event tags\n # computed_multi_tags = fields.ListField(fields.StringField())\n # multi_tag_scores = fields.ListField(fields.DictField())\n\n _computed_tags = fields.ListField(fields.StringField(), db_field='cts')\n computed_scores = fields.ListField(fields.EmbeddedDocumentField(Score), db_field='css')\n\n reward_data = fields.DictField(db_field='rd') # Any reward information we have about a specific event\n _journey_stages = fields.ListField(fields.ObjectIdField(), db_field='js')\n # More client friendly way of passing in journey information. JourneyName__StageName\n journey_mapping = fields.ListField(fields.StringField(), db_field='jm')\n\n # Optional and channel specific (e.g. Twitter: tweet/retweet/PM, Web: click/search)\n event_type = fields.StringField()\n\n # for dynamic events importing\n import_id = fields.NumField()\n _was_processed = fields.BooleanField(db_field='_wp', default=False)\n\n _native_id = fields.StringField(db_field='_n', required=True, unique=True)\n\n PROFILE_CLASS = UserProfile\n\n indexes = ['_was_processed']\n\n\n @property\n def native_id(self):\n return self._native_id\n\n def __init__(self, *args, **kw):\n if '_created' not in kw:\n kw['_created'] = now()\n\n if '_native_id' not in kw:\n kw['_native_id'] = str(now())\n\n super(Event, self).__init__(*args, **kw)\n\n def set_dynamic_class(self, inheritance):\n ''' Support dynamic events '''\n\n if DynamicEvent.__name__ in inheritance:\n # from solariat_bottle.db.dynamic_event import EventType\n from solariat_bottle.db.events.event_type import BaseEventType\n\n event_type_name = self.data[DynamicEvent.event_type.db_field]\n # event_type = EventType.objects.find_one(event_type_id)\n # TODO: check using get_user() is correct here\n acc_id = get_user().account.id\n LOGGER.debug('Set event dynamic class, use acc: %s for find event types', acc_id)\n event_type = BaseEventType.objects.find_one_by_display_name(acc_id, event_type_name)\n\n if event_type:\n dyn_cls = event_type.get_data_class() # initialize class in Registry\n self.__class__ = dyn_cls\n else:\n LOGGER.error('Cannot find suitable event type for dynamic class!')\n\n def _is_inbound(self, account=None):\n if account is None:\n return None\n\n from solariat_bottle.db.account import Account\n assert isinstance(account, Account)\n account_channels = Channel.objects(id__in=self.channels, account=account)\n is_inbound = {channel.is_inbound for channel in account_channels}\n if len(is_inbound) == 0:\n return None\n if len(is_inbound) != 1:\n LOGGER.warning(\"Channel misconfiguration for account %s. \"\n \"Event splitted between inbound and outbound channels.\" % account)\n return None\n return all(is_inbound)\n\n def customer_profile(self, account):\n CustomerProfile = account.get_customer_profile_class()\n customer_profile = CustomerProfile.objects.find_one(id=self.actor_id)\n if customer_profile:\n return customer_profile\n\n customer_profile = None\n if self.is_inbound or self._is_inbound(account):\n actor_num, _ = unpack_event_id(self.id)\n customer_profile = CustomerProfile.objects.find_one(actor_num=actor_num)\n elif hasattr(self, 'parent'):\n try:\n parent = self.parent\n if parent and parent.is_inbound:\n actor_id = parent.actor_id\n customer_profile = CustomerProfile.objects.find_one(id=actor_id)\n else:\n actor_num, _ = unpack_event_id(self.id)\n customer_profile = CustomerProfile.objects.find_one(actor_num=actor_num)\n except (AttributeError, Event.DoesNotExist):\n LOGGER.info(\"Could not get actor_id from parent post of {} {}\".format(self, account))\n\n return customer_profile\n\n def get_event_type(self, account):\n from solariat_bottle.db.events.event_type import BaseEventType\n return BaseEventType.objects.get_by_display_name(account.id, self.event_type)\n\n @property\n def journey_stages(self):\n from solariat_bottle.db.journeys.journey_type import JourneyStageType, JourneyType\n from solariat_bottle.db.journeys.customer_journey import CustomerJourney, JourneyStage\n\n if self._journey_stages:\n # Already computed them or were passed in\n return JourneyStageType.objects.find(id__in=self._journey_stages)\n\n channels_by_account = self.channels_by_account\n accounts = set(channels_by_account)\n result = []\n\n for account in accounts:\n # Need to compute any that apply\n\n journey_types_ids = [jt.id for jt in JourneyType.objects.find(account_id=account.id)]\n if self.event_type:\n event_type = self.get_event_type(account)\n\n # TODO: change JourneyStageType to use event_type name instead of ID?\n candidates = JourneyStageType.objects.find(account_id=account.id,\n event_types=event_type.id,\n journey_type_id__in=journey_types_ids)[:]\n if not candidates:\n return result\n for candidate in candidates:\n if candidate.evaluate_event(self):\n result.append(candidate)\n\n if not result and self.event_type:\n # Nothing new, consider all current active journeys\n # The actual candidates then are the current stages on all journeys\n customer = self.customer_profile(account)\n if customer:\n customer_journeys = CustomerJourney.objects.find(\n account_id=account.id,\n customer_id=customer.id,\n status=JourneyStageType.IN_PROGRESS)[:]\n for journey in customer_journeys:\n current_stage = JourneyStage.objects.get(journey.current_stage)\n current_stage_type = JourneyStageType.objects.get(current_stage.stage_type_id)\n result.append(current_stage_type)\n self._journey_stages = [stage.id for stage in result]\n return result\n\n @property\n def datetime_from_id(self):\n return unpack_event_id(self.id)[1]\n\n @property\n def computed_tags(self):\n if hasattr(self, 'accepted_smart_tags'):\n res = [str(smt.id) for smt in self.accepted_smart_tags]\n else:\n res = []\n res = list(set(self._computed_tags + res + self.assigned_tags))\n return res\n\n @property\n def json_computed_tags(self):\n computed_tags = []\n for tag_id in self.computed_tags:\n try:\n tag = SmartTagChannel.objects.get(tag_id)\n tag_title = tag.title\n except:\n tag = ABCPredictor.objects.get(tag_id)\n tag_title = tag.display_name\n computed_tags.append({'id': tag_id, 'title': tag_title})\n return computed_tags\n\n @property\n def journey_tags(self):\n j_tags = ABCPredictor.objects.find(id__in=self.computed_tags)[:]\n res = []\n if j_tags is not None:\n res = [dict(id=str(tag.id), title=tag.display_name) for tag in j_tags]\n return res\n\n @property\n def computed_single_tags(self):\n from solariat_bottle.db.predictors.multi_channel_smart_tag import SingleEventTag\n return SingleEventTag.objects.find(id__in=self.computed_tags)[:]\n\n @property\n def computed_multi_tags(self):\n from solariat_bottle.db.predictors.multi_channel_smart_tag import MultiEventTag\n return MultiEventTag.objects.find(id__in=self.computed_tags)[:]\n\n @property\n def computed_tag_objects(self):\n return self.computed_single_tags + self.computed_multi_tags\n\n @staticmethod\n def platform_created_at(platform_data):\n if not platform_data:\n return None\n return platform_data.get('created_at', None)\n\n @classmethod\n def patch_post_kw(cls, kw, native_data=None):\n if not kw.get('_created'):\n created_at = native_data and cls.platform_created_at(native_data) or None\n created_time = parse_datetime(created_at, default=now())\n # some facebook entities like images may have created_at=1999-01-01T08:00:00 +0000\n # which is before TIMESLOT_EPOCH and causes AssertError in pack_event_id\n if created_time <= TIMESLOT_EPOCH:\n wrapped_data = native_data.get('_wrapped_data', {})\n if not isinstance(wrapped_data, dict):\n try:\n wrapped_data = json.loads(wrapped_data)\n except (ValueError, TypeError):\n wrapped_data = {}\n updated_at = native_data.get('updated_time') or wrapped_data.get('updated_time', None)\n created_time = parse_datetime(updated_at, default=now())\n if created_time < TIMESLOT_EPOCH:\n created_time = now()\n kw['_created'] = created_time\n channel = kw.get('channel') or kw['channels'][0]\n channel = channel if isinstance(channel, Channel) else Channel.objects.get(id=channel)\n # account = channel.account\n user_profile = kw.get('user_profile')\n # TODO: [gsejop] is_inbound should not be a posted property,\n # event is routed to a set of channels\n # Apparently, is_inbound and actor_id are just extra helping information\n # sent from jop data generation script\n kw['is_inbound'] = kw.pop('is_inbound', channel.is_inbound)\n # if not kw.get('actor_id', False):\n # if kw['is_inbound'] == True:\n # kw['actor_id'] = user_profile.customer_profile.id\n # elif kw['is_inbound'] == False:\n # kw['actor_id'] = user_profile.agent_profile.id\n # else:\n # raise Exception('is_inbound should be set')\n kw.setdefault('actor_id', user_profile.id)\n return kw\n\n @classmethod\n def gen_id(cls, is_inbound, actor_id, _created, in_reply_to_native_id, parent_event=None):\n # Read The actor. This can result in multiple reads across different profile classes\n # which is very expensive. Need to cache this or find a more efficient concept that\n # does not require a read. TODO: pass actor_num directly\n actor_num = cls.get_actor(is_inbound, actor_id).actor_num\n\n # This is required so that we can efficiently query an event sequence with a range\n # query and a customer id. To do that we must figure out if there is a parent event\n # that is inbound. Only need to worry about this case if this event is outbound!\n if in_reply_to_native_id and not is_inbound:\n try:\n parent_event = Event.get_by_native_id(in_reply_to_native_id)\n except cls.DoesNotExist:\n pass\n else:\n actor_num = parent_event.actor.actor_num\n elif parent_event:\n assert isinstance(parent_event.actor, (UserProfile, DynamicImportedProfile)), parent_event.actor\n actor_num = parent_event.actor.actor_num\n\n # Finally, the id is an actor number and a time stamp, and will always be a Customer.\n return pack_event_id(actor_num, _created)\n\n @classmethod\n def get_by_native_id(cls, native_id):\n # Use native id field to fetch the required key\n return cls.objects.get(_native_id=native_id)\n\n @property\n def actor(self):\n return Event.get_actor(self.is_inbound, self.actor_id)\n\n @classmethod\n def get_actor(cls, is_inbound, actor_id, account=None):\n if account == None:\n from solariat_bottle.db.user import get_user\n account = get_user().account\n\n if is_inbound:\n DynProfileClass = account.get_customer_profile_class()\n else:\n DynProfileClass = account.get_agent_profile_class()\n\n # profile already must be created in tasks._obtain_user_profile\n try:\n profile = DynProfileClass.objects.get(actor_id)\n except DynProfileClass.DoesNotExist:\n # TODO: move out for dynamic events imports\n LOGGER.debug('TODO: this is only case for importing dyn events.'\n 'move creating profile outside (like in normalize_post_params)')\n profile = DynProfileClass(id=actor_id)\n profile.save()\n return profile\n\n # @cached_property\n # def customer_id(self):\n # customer_id, _ = unpack_event_id(self.id)\n # return customer_id\n\n def to_dict(self, fields2show=None):\n base_dict = super(Event, self).to_dict(fields2show=fields2show)\n base_dict['id'] = str(base_dict['id']) # Make sure we work with strings instead of longs so we don't lose precision\n base_dict.pop('_journey_stages')\n return base_dict\n\n @property\n def created_at(self):\n return utc(self.created)\n\n @property\n def created(self):\n if utc(self._created) == TIMESLOT_EPOCH:\n self._created = self.parse_created_at() or now()\n self.save()\n return utc(self._created)\n\n def parse_created_at(self):\n return None\n\n @property\n def event_tags(self):\n from solariat_bottle.db.predictors.multi_channel_smart_tag import EventTag\n tag_ids = [t.id for t in (self.computed_single_tags + self.computed_multi_tags)] + self.assigned_tags\n for rejected_tag in self.rejected_tags:\n if rejected_tag in tag_ids:\n tag_ids.remove(rejected_tag)\n return EventTag.objects(id__in=tag_ids)[:]\n\n def assign_tags(self, tag_class):\n scores = []\n assigned_tags = []\n for intention_tag in tag_class.objects(channels__in=self.channels):\n if str(intention_tag.id) in self.assigned_tags:\n score = 1\n elif str(intention_tag.id) in self.rejected_tags:\n return 0\n else:\n score = intention_tag.score(self)\n scores.append(Score(name=intention_tag.display_name, score=score))\n if score > intention_tag.inclusion_threshold:\n assigned_tags.append(str(intention_tag.id))\n self._computed_tags = list(set(assigned_tags).union(set(self._computed_tags)))\n self.computed_scores = list(set(scores).union(set(self.computed_scores)))\n self.save()\n\n def assign_smart_tags(self):\n from solariat_bottle.db.predictors.multi_channel_smart_tag import SingleEventTag\n from solariat_bottle.db.predictors.multi_channel_smart_tag import MultiEventTag\n self.computed_scores = []\n self._computed_tags = []\n self.assign_tags(SingleEventTag)\n self.assign_tags(MultiEventTag)\n # self.assign_single_event_tags() # First, so we can use them as features for multi\n # self.assign_multi_event_tags()\n self.save()\n\n def get_customer_journeys(self, account):\n from solariat_bottle.db.journeys.customer_journey import CustomerJourney\n\n customer = self.customer_profile(account)\n if customer:\n return CustomerJourney.objects(customer_id=customer.id)[:]\n else:\n return []\n\n # def compute_journey_information(self, account):\n # from solariat_bottle.db.journeys.customer_journey import CustomerJourney\n #\n # customer = self.customer_profile(account)\n # if customer:\n # customer_id = customer.id\n # else:\n # return\n # customer_journey = None\n # journey_stages = self.journey_stages\n # if journey_stages:\n # # Journey information was specifically passed in, could be transition of stage\n # # or just event relevant for a specific stage\n # for stage in journey_stages:\n # journey_type_id = stage.journey_type_id\n # try:\n # from solariat_bottle.db.journeys.journey_type import JourneyType\n # customer_journey = CustomerJourney.objects.get(customer_id=customer_id,\n # journey_type_id=journey_type_id,\n # account_id=stage.account_id)\n # except CustomerJourney.DoesNotExist:\n # customer_journey = CustomerJourney.objects.create(customer_id=customer_id,\n # journey_type_id=journey_type_id,\n # start_date=self.created_at,\n # account_id=stage.account_id)\n # customer_journey.process_event(self, stage, account)\n # # else:\n # for journey in CustomerJourney.objects(customer_id=customer_id):\n # # Also process rest of the journeys\n # # if journey != customer_journey:\n # if (not customer_journey or journey.id != customer_journey.id\n # or journey.id == customer_journey.id and self.reward_data):\n # journey.process_event(self, account=account)\n\n def add_tag(self, tag):\n self.assigned_tags.append(str(tag.id))\n if str(tag.id) in self.rejected_tags:\n self.rejected_tags.remove(str(tag.id))\n tag.accept(self)\n self.save()\n\n def remove_tag(self, tag):\n self.rejected_tags.append(str(tag.id))\n if str(tag.id) in self.assigned_tags:\n self.assigned_tags.remove(str(tag.id))\n tag.reject(self)\n self.save()\n\n def apply_smart_tags_to_journeys(self):\n for account in self.channels_by_account.viewkeys():\n if APP_JOURNEYS in account.available_apps:\n [j.apply_smart_tags(self) for j in self.get_customer_journeys(account)]\n\n @classmethod\n def import_data(cls, user, channel, data_loader):\n from solariat_bottle.db.post.utils import factory_by_user\n from solariat_bottle.db.events.event_type import StaticEventType\n\n static_event_types_map = {et.id: et for et in StaticEventType.objects.find()}\n\n stats = {'total': 0, 'success': 0}\n for idx, raw_data in enumerate(data_loader.load_data()):\n if isinstance(raw_data, tuple): # JsonDataLoader returns (event_type, data) tuples\n event_type_id, raw_data = raw_data\n # check we can import this event to this channel\n event_type = static_event_types_map.get(event_type_id) # TODO: wrong!\n if event_type and event_type.platform != channel.platform.lower():\n continue\n\n stats['total'] += 1\n raw_data.pop('channel', None)\n raw_data.pop('channels', None)\n raw_data['channel'] = channel\n try:\n factory_by_user(user, **raw_data)\n except:\n LOGGER.warning(\"Cannot import post #%d %s\", idx, raw_data, exc_info=True)\n else:\n stats['success'] += 1\n return stats\n\n\nclass DynamicEventManager(EventManager):\n\n # for creating via factory_by_user / create_post\n def create_by_user(self, user, **kw):\n event_type = kw['event_type']\n kw['is_inbound'] = all([ch.is_inbound for ch in kw['channels']])\n\n native_field = event_type.native_id_field\n if native_field and native_field in kw:\n kw['_native_id'] = kw.pop(native_field)\n\n kw.pop('lang', None)\n kw.pop('content', None)\n kw.pop('speech_acts', None)\n kw.pop('_platform', None)\n kw.pop('sync', None)\n kw.pop('add_to_queue', None)\n\n return super(DynamicEventManager, self).create_by_user(user=user, **kw)\n\n\nclass DynamicEvent(Event):\n\n manager = DynamicEventManager\n\n event_type_id = fields.ObjectIdField()\n\n # def set_dynamic_class(self, inheritance):\n # # we won't change class if it is created from event_type.create_data_class()\n # pass\n", "sub_path": "db/events/event.py", "file_name": "event.py", "file_ext": "py", "file_size_in_byte": 31337, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "solariat_bottle.db.channel.base.ChannelsAuthManager", "line_number": 26, "usage_type": "name"}, {"api_name": "bson.objectid.ObjectId", "line_number": 43, "usage_type": "argument"}, {"api_name": "solariat_bottle.configurable_apps.APP_JOURNEYS", "line_number": 46, "usage_type": "name"}, {"api_name": "solariat_bottle.utils.id_encoder.pack_event_id", "line_number": 59, "usage_type": "call"}, {"api_name": "solariat.utils.timeslot.utc", "line_number": 59, "usage_type": "call"}, {"api_name": "solariat_bottle.utils.id_encoder.pack_event_id", "line_number": 60, "usage_type": "call"}, {"api_name": "solariat.utils.timeslot.utc", "line_number": 60, "usage_type": "call"}, {"api_name": "solariat_bottle.db.dynamic_profiles.DynamicImportedProfile", "line_number": 68, "usage_type": "argument"}, {"api_name": "solariat.utils.iterfu.merge", "line_number": 75, "usage_type": "call"}, {"api_name": "solariat_bottle.utils.id_encoder.unpack_event_id", "line_number": 96, "usage_type": "call"}, {"api_name": "solariat_bottle.utils.id_encoder.pack_event_id", "line_number": 98, "usage_type": "call"}, {"api_name": "solariat.utils.timeslot.utc", "line_number": 98, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 98, "usage_type": "call"}, {"api_name": "solariat_bottle.utils.id_encoder.pack_event_id", "line_number": 99, "usage_type": "call"}, {"api_name": "solariat.utils.timeslot.utc", "line_number": 99, "usage_type": "call"}, {"api_name": "solariat_bottle.db.dynamic_profiles.DynamicImportedProfile", "line_number": 105, "usage_type": "argument"}, {"api_name": "solariat_bottle.utils.id_encoder.pack_event_id", "line_number": 106, "usage_type": "call"}, {"api_name": "solariat.utils.timeslot.now", "line_number": 106, "usage_type": "call"}, {"api_name": "solariat_bottle.utils.id_encoder.pack_event_id", "line_number": 107, "usage_type": "call"}, {"api_name": "solariat.utils.timeslot.now", "line_number": 107, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 107, "usage_type": "call"}, {"api_name": "solariat_bottle.settings.get_var", "line_number": 128, "usage_type": "call"}, {"api_name": "solariat_bottle.db.channel.base.Channel", "line_number": 140, "usage_type": "argument"}, {"api_name": "solariat.utils.timeslot.now", "line_number": 145, "usage_type": "call"}, {"api_name": "solariat_bottle.db.events.event_type.BaseEventType", "line_number": 150, "usage_type": "argument"}, {"api_name": "solariat_bottle.db.channel.base.Channel", "line_number": 171, "usage_type": "argument"}, {"api_name": "solariat_bottle.utils.hash.mhash", "line_number": 182, "usage_type": "call"}, {"api_name": "solariat_bottle.utils.id_encoder.MILLISECONDS_PER_SECOND", "line_number": 182, "usage_type": "name"}, {"api_name": "pymongo.errors.DuplicateKeyError", "line_number": 195, "usage_type": "name"}, {"api_name": "solariat.db.abstract.SonDocument", "line_number": 205, "usage_type": "name"}, {"api_name": "solariat.db.fields.ObjectIdField", "line_number": 206, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 206, "usage_type": "name"}, {"api_name": "solariat.db.fields.ObjectIdField", "line_number": 207, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 207, "usage_type": "name"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyType.objects.get", "line_number": 213, "usage_type": "call"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyType.objects", "line_number": 213, "usage_type": "attribute"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyType", "line_number": 213, "usage_type": "name"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyStageType.objects.get", "line_number": 219, "usage_type": "call"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyStageType.objects", "line_number": 219, "usage_type": "attribute"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyStageType", "line_number": 219, "usage_type": "name"}, {"api_name": "solariat.db.abstract.SonDocument", "line_number": 226, "usage_type": "name"}, {"api_name": "solariat.db.fields.StringField", "line_number": 227, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 227, "usage_type": "name"}, {"api_name": "solariat.db.fields.NumField", "line_number": 228, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 228, "usage_type": "name"}, {"api_name": "solariat_bottle.db.channel.base.ChannelsAuthDocument", "line_number": 238, "usage_type": "name"}, {"api_name": "solariat.db.fields.EventIdField", "line_number": 244, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 244, "usage_type": "name"}, {"api_name": "solariat.db.fields.DateTimeField", "line_number": 246, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 246, "usage_type": "name"}, {"api_name": "solariat.utils.timeslot.now", "line_number": 247, "usage_type": "name"}, {"api_name": "solariat.db.fields.BaseField", "line_number": 250, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 250, "usage_type": "name"}, {"api_name": "solariat.db.fields.BooleanField", "line_number": 251, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 251, "usage_type": "name"}, {"api_name": "solariat.db.fields.StringField", "line_number": 253, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 253, "usage_type": "name"}, {"api_name": "solariat.db.fields.ListField", "line_number": 255, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 255, "usage_type": "name"}, {"api_name": "solariat.db.fields.StringField", "line_number": 255, "usage_type": "call"}, {"api_name": "solariat.db.fields.ListField", "line_number": 256, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 256, "usage_type": "name"}, {"api_name": "solariat.db.fields.StringField", "line_number": 256, "usage_type": "call"}, {"api_name": "solariat.db.fields.ListField", "line_number": 265, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 265, "usage_type": "name"}, {"api_name": "solariat.db.fields.StringField", "line_number": 265, "usage_type": "call"}, {"api_name": "solariat.db.fields.ListField", "line_number": 266, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 266, "usage_type": "name"}, {"api_name": "solariat.db.fields.EmbeddedDocumentField", "line_number": 266, "usage_type": "call"}, {"api_name": "solariat.db.fields.DictField", "line_number": 268, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 268, "usage_type": "name"}, {"api_name": "solariat.db.fields.ListField", "line_number": 269, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 269, "usage_type": "name"}, {"api_name": "solariat.db.fields.ObjectIdField", "line_number": 269, "usage_type": "call"}, {"api_name": "solariat.db.fields.ListField", "line_number": 271, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 271, "usage_type": "name"}, {"api_name": "solariat.db.fields.StringField", "line_number": 271, "usage_type": "call"}, {"api_name": "solariat.db.fields.StringField", "line_number": 274, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 274, "usage_type": "name"}, {"api_name": "solariat.db.fields.NumField", "line_number": 277, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 277, "usage_type": "name"}, {"api_name": "solariat.db.fields.BooleanField", "line_number": 278, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 278, "usage_type": "name"}, {"api_name": "solariat.db.fields.StringField", "line_number": 280, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 280, "usage_type": "name"}, {"api_name": "solariat_bottle.db.user_profiles.user_profile.UserProfile", "line_number": 282, "usage_type": "name"}, {"api_name": "solariat.utils.timeslot.now", "line_number": 293, "usage_type": "call"}, {"api_name": "solariat.utils.timeslot.now", "line_number": 296, "usage_type": "call"}, {"api_name": "solariat_bottle.db.user.get_user", "line_number": 310, "usage_type": "call"}, {"api_name": "solariat_bottle.settings.LOGGER.debug", "line_number": 311, "usage_type": "call"}, {"api_name": "solariat_bottle.settings.LOGGER", "line_number": 311, "usage_type": "name"}, {"api_name": "solariat_bottle.db.events.event_type.BaseEventType.objects.find_one_by_display_name", "line_number": 312, "usage_type": "call"}, {"api_name": "solariat_bottle.db.events.event_type.BaseEventType.objects", "line_number": 312, "usage_type": "attribute"}, {"api_name": "solariat_bottle.db.events.event_type.BaseEventType", "line_number": 312, "usage_type": "name"}, {"api_name": "solariat_bottle.settings.LOGGER.error", "line_number": 318, "usage_type": "call"}, {"api_name": "solariat_bottle.settings.LOGGER", "line_number": 318, "usage_type": "name"}, {"api_name": "solariat_bottle.db.account.Account", "line_number": 325, "usage_type": "name"}, {"api_name": "solariat_bottle.db.channel.base.Channel.objects", "line_number": 326, "usage_type": "call"}, {"api_name": "solariat_bottle.db.channel.base.Channel", "line_number": 326, "usage_type": "name"}, {"api_name": "solariat_bottle.settings.LOGGER.warning", "line_number": 331, "usage_type": "call"}, {"api_name": "solariat_bottle.settings.LOGGER", "line_number": 331, "usage_type": "name"}, {"api_name": "solariat_bottle.utils.id_encoder.unpack_event_id", "line_number": 344, "usage_type": "call"}, {"api_name": "solariat_bottle.utils.id_encoder.unpack_event_id", "line_number": 353, "usage_type": "call"}, {"api_name": "{'BaseEventType': 'solariat_bottle.db.events.event_type.BaseEventType', 'Account': 'solariat_bottle.db.account.Account'}.DoesNotExist", "line_number": 355, "usage_type": "attribute"}, {"api_name": "solariat_bottle.settings.LOGGER.info", "line_number": 356, "usage_type": "call"}, {"api_name": "solariat_bottle.settings.LOGGER", "line_number": 356, "usage_type": "name"}, {"api_name": "solariat_bottle.db.events.event_type.BaseEventType.objects.get_by_display_name", "line_number": 362, "usage_type": "call"}, {"api_name": "solariat_bottle.db.events.event_type.BaseEventType.objects", "line_number": 362, "usage_type": "attribute"}, {"api_name": "solariat_bottle.db.events.event_type.BaseEventType", "line_number": 362, "usage_type": "name"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyStageType.objects.find", "line_number": 371, "usage_type": "call"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyStageType.objects", "line_number": 371, "usage_type": "attribute"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyStageType", "line_number": 371, "usage_type": "name"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyType.objects.find", "line_number": 380, "usage_type": "call"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyType.objects", "line_number": 380, "usage_type": "attribute"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyType", "line_number": 380, "usage_type": "name"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyStageType.objects.find", "line_number": 385, "usage_type": "call"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyStageType.objects", "line_number": 385, "usage_type": "attribute"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyStageType", "line_number": 385, "usage_type": "name"}, {"api_name": "solariat_bottle.db.journeys.customer_journey.CustomerJourney.objects.find", "line_number": 399, "usage_type": "call"}, {"api_name": "solariat_bottle.db.journeys.customer_journey.CustomerJourney.objects", "line_number": 399, "usage_type": "attribute"}, {"api_name": "solariat_bottle.db.journeys.customer_journey.CustomerJourney", "line_number": 399, "usage_type": "name"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyStageType.IN_PROGRESS", "line_number": 402, "usage_type": "attribute"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyStageType", "line_number": 402, "usage_type": "name"}, {"api_name": "solariat_bottle.db.journeys.customer_journey.JourneyStage.objects.get", "line_number": 404, "usage_type": "call"}, {"api_name": "solariat_bottle.db.journeys.customer_journey.JourneyStage.objects", "line_number": 404, "usage_type": "attribute"}, {"api_name": "solariat_bottle.db.journeys.customer_journey.JourneyStage", "line_number": 404, "usage_type": "name"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyStageType.objects.get", "line_number": 405, "usage_type": "call"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyStageType.objects", "line_number": 405, "usage_type": "attribute"}, {"api_name": "solariat_bottle.db.journeys.journey_type.JourneyStageType", "line_number": 405, "usage_type": "name"}, {"api_name": "solariat_bottle.utils.id_encoder.unpack_event_id", "line_number": 412, "usage_type": "call"}, {"api_name": "solariat_bottle.db.channel.base.SmartTagChannel.objects.get", "line_number": 428, "usage_type": "call"}, {"api_name": "solariat_bottle.db.channel.base.SmartTagChannel.objects", "line_number": 428, "usage_type": "attribute"}, {"api_name": "solariat_bottle.db.channel.base.SmartTagChannel", "line_number": 428, "usage_type": "name"}, {"api_name": "solariat_bottle.db.predictors.abc_predictor.ABCPredictor.objects.get", "line_number": 431, "usage_type": "call"}, {"api_name": "solariat_bottle.db.predictors.abc_predictor.ABCPredictor.objects", "line_number": 431, "usage_type": "attribute"}, {"api_name": "solariat_bottle.db.predictors.abc_predictor.ABCPredictor", "line_number": 431, "usage_type": "name"}, {"api_name": "solariat_bottle.db.predictors.abc_predictor.ABCPredictor.objects.find", "line_number": 438, "usage_type": "call"}, {"api_name": "solariat_bottle.db.predictors.abc_predictor.ABCPredictor.objects", "line_number": 438, "usage_type": "attribute"}, {"api_name": "solariat_bottle.db.predictors.abc_predictor.ABCPredictor", "line_number": 438, "usage_type": "name"}, {"api_name": "solariat_bottle.db.predictors.multi_channel_smart_tag.SingleEventTag.objects.find", "line_number": 447, "usage_type": "call"}, {"api_name": "solariat_bottle.db.predictors.multi_channel_smart_tag.SingleEventTag.objects", "line_number": 447, "usage_type": "attribute"}, {"api_name": "solariat_bottle.db.predictors.multi_channel_smart_tag.SingleEventTag", "line_number": 447, "usage_type": "name"}, {"api_name": "solariat_bottle.db.predictors.multi_channel_smart_tag.MultiEventTag.objects.find", "line_number": 452, "usage_type": "call"}, {"api_name": "solariat_bottle.db.predictors.multi_channel_smart_tag.MultiEventTag.objects", "line_number": 452, "usage_type": "attribute"}, {"api_name": "solariat_bottle.db.predictors.multi_channel_smart_tag.MultiEventTag", "line_number": 452, "usage_type": "name"}, {"api_name": "solariat.utils.timeslot.parse_datetime", "line_number": 468, "usage_type": "call"}, {"api_name": "solariat.utils.timeslot.now", "line_number": 468, "usage_type": "call"}, {"api_name": "solariat.utils.timeslot.TIMESLOT_EPOCH", "line_number": 471, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 475, "usage_type": "call"}, {"api_name": "solariat.utils.timeslot.parse_datetime", "line_number": 479, "usage_type": "call"}, {"api_name": "solariat.utils.timeslot.now", "line_number": 479, "usage_type": "call"}, {"api_name": "solariat.utils.timeslot.TIMESLOT_EPOCH", "line_number": 480, "usage_type": "name"}, {"api_name": "solariat.utils.timeslot.now", "line_number": 481, "usage_type": "call"}, {"api_name": "solariat_bottle.db.channel.base.Channel", "line_number": 484, "usage_type": "argument"}, {"api_name": "solariat_bottle.db.channel.base.Channel.objects.get", "line_number": 484, "usage_type": "call"}, {"api_name": "solariat_bottle.db.channel.base.Channel.objects", "line_number": 484, "usage_type": "attribute"}, {"api_name": "{'BaseEventType': 'solariat_bottle.db.events.event_type.BaseEventType', 'Account': 'solariat_bottle.db.account.Account', 'JourneyStageType': 'solariat_bottle.db.journeys.journey_type.JourneyStageType', 'JourneyType': 'solariat_bottle.db.journeys.journey_type.JourneyType', 'CustomerJourney': 'solariat_bottle.db.journeys.customer_journey.CustomerJourney', 'JourneyStage': 'solariat_bottle.db.journeys.customer_journey.JourneyStage', 'SingleEventTag': 'solariat_bottle.db.predictors.multi_channel_smart_tag.SingleEventTag', 'MultiEventTag': 'solariat_bottle.db.predictors.multi_channel_smart_tag.MultiEventTag'}.get_by_native_id", "line_number": 514, "usage_type": "call"}, {"api_name": "solariat_bottle.db.user_profiles.user_profile.UserProfile", "line_number": 520, "usage_type": "name"}, {"api_name": "solariat_bottle.db.dynamic_profiles.DynamicImportedProfile", "line_number": 520, "usage_type": "name"}, {"api_name": "solariat_bottle.utils.id_encoder.pack_event_id", "line_number": 524, "usage_type": "call"}, {"api_name": "{'BaseEventType': 'solariat_bottle.db.events.event_type.BaseEventType', 'Account': 'solariat_bottle.db.account.Account', 'JourneyStageType': 'solariat_bottle.db.journeys.journey_type.JourneyStageType', 'JourneyType': 'solariat_bottle.db.journeys.journey_type.JourneyType', 'CustomerJourney': 'solariat_bottle.db.journeys.customer_journey.CustomerJourney', 'JourneyStage': 'solariat_bottle.db.journeys.customer_journey.JourneyStage', 'SingleEventTag': 'solariat_bottle.db.predictors.multi_channel_smart_tag.SingleEventTag', 'MultiEventTag': 'solariat_bottle.db.predictors.multi_channel_smart_tag.MultiEventTag'}.get_actor", "line_number": 533, "usage_type": "call"}, {"api_name": "solariat_bottle.db.user.get_user", "line_number": 539, "usage_type": "call"}, {"api_name": "solariat_bottle.settings.LOGGER.debug", "line_number": 551, "usage_type": "call"}, {"api_name": "solariat_bottle.settings.LOGGER", "line_number": 551, "usage_type": "name"}, {"api_name": "solariat.utils.timeslot.utc", "line_number": 570, "usage_type": "call"}, {"api_name": "solariat.utils.timeslot.utc", "line_number": 574, "usage_type": "call"}, {"api_name": "solariat.utils.timeslot.TIMESLOT_EPOCH", "line_number": 574, "usage_type": "name"}, {"api_name": "solariat.utils.timeslot.now", "line_number": 575, "usage_type": "call"}, {"api_name": "solariat.utils.timeslot.utc", "line_number": 577, "usage_type": "call"}, {"api_name": "solariat_bottle.db.predictors.multi_channel_smart_tag.EventTag.objects", "line_number": 589, "usage_type": "call"}, {"api_name": "solariat_bottle.db.predictors.multi_channel_smart_tag.EventTag", "line_number": 589, "usage_type": "name"}, {"api_name": "solariat_bottle.db.predictors.multi_channel_smart_tag.SingleEventTag", "line_number": 613, "usage_type": "name"}, {"api_name": "solariat_bottle.db.predictors.multi_channel_smart_tag.MultiEventTag", "line_number": 614, "usage_type": "name"}, {"api_name": "solariat_bottle.db.journeys.customer_journey.CustomerJourney.objects", "line_number": 624, "usage_type": "call"}, {"api_name": "solariat_bottle.db.journeys.customer_journey.CustomerJourney", "line_number": 624, "usage_type": "name"}, {"api_name": "solariat_bottle.configurable_apps.APP_JOURNEYS", "line_number": 678, "usage_type": "name"}, {"api_name": "solariat_bottle.db.events.event_type.StaticEventType.objects.find", "line_number": 686, "usage_type": "call"}, {"api_name": "solariat_bottle.db.events.event_type.StaticEventType.objects", "line_number": 686, "usage_type": "attribute"}, {"api_name": "solariat_bottle.db.events.event_type.StaticEventType", "line_number": 686, "usage_type": "name"}, {"api_name": "solariat_bottle.db.post.utils.factory_by_user", "line_number": 702, "usage_type": "call"}, {"api_name": "solariat_bottle.settings.LOGGER.warning", "line_number": 704, "usage_type": "call"}, {"api_name": "solariat_bottle.settings.LOGGER", "line_number": 704, "usage_type": "name"}, {"api_name": "solariat.db.fields.ObjectIdField", "line_number": 735, "usage_type": "call"}, {"api_name": "solariat.db.fields", "line_number": 735, "usage_type": "name"}]} +{"seq_id": "393351041", "text": "# -*- coding: utf-8 -*-\nimport scrapy\nfrom Search_Engine.items import five_one_ctoItem\nimport re\nimport datetime\nfrom Search_Engine.utils.common import get_md5\n\nz=datetime.datetime.now().strftime('%Y-%m-%d').split('-')\nclass A51ctoSpider(scrapy.Spider):\n name = '51cto'\n allowed_domains = ['51cto.com']\n def start_requests(self):#url='https://blog.51cto.com/original'\n url='https://blog.51cto.com/artcommend/p270'\n yield scrapy.Request(url=url,callback=self.url_parse)\n #列表url解析 传递了标题url 收藏数 阅读数 评论数\n def url_parse(self,response):\n for article in response.xpath('//ul[@class=\"artical-list\"]/li'):\n url=article.xpath(\".//a[@class='tit']/@href\").extract()[0]\n Collection_num = article.xpath('.//div[@class=\"bot\"]/span[3]/text()').extract()\n Collection_num= re.findall(\"\\d+\", Collection_num[0])[0]\n read_num = article.xpath('.//div[@class=\"bot\"]/span[1]/text()').extract()\n read_num = re.findall(\"\\d+\", read_num[0])[0]\n comment_num = article.xpath('.//div[@class=\"bot\"]/span[2]/text()').extract()\n comment_num = re.findall(\"\\d+\", comment_num[0])[0]\n yield scrapy.Request(url=url,callback=self.parse,meta={\"link_url\":url,\n \"Collection_num\":Collection_num,\n \"read_num\":read_num,\n \"comment_num\":comment_num\n })\n #列表翻页\n next_page = response.xpath(\"//li[@class='next']/a/@href\").extract()\n if next_page:\n url = next_page[0]\n page_num = re.findall('p\\d+', url)[0].replace('p', '')\n print(\"正在爬取\" + page_num + \"页\")\n yield scrapy.Request(url=url, callback=self.url_parse)\n #详情解析\n def parse(self, response):\n item=five_one_ctoItem()\n item['title'] = response.xpath(\"//h1[@class='artical-title']/text()\").extract()[0]\n item['link_url'] = response.meta['link_url']\n content = response.xpath(\"//div[@class='con artical-content editor-preview-side']//text()\").extract()\n item['content'] =','.join(content).strip()\n item['time'] = response.xpath(\"//a[@class='time fr']/text()\").extract()[0]\n item['Collection_num'] = response.meta['Collection_num']\n item['read_num'] = response.meta['read_num']\n item['comment_num'] = response.meta['comment_num']\n item['source'] = \"51cto\"\n item['url_object_id'] = get_md5(item['link_url'])\n item['tag']= \",\".join(response.xpath(\"//div[@class='for-tag mt26']/a/text()\").extract())\n yield item\n\n", "sub_path": "Search_Engine/spiders/51cto.py", "file_name": "51cto.py", "file_ext": "py", "file_size_in_byte": 2810, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "datetime.datetime.now", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 8, "usage_type": "attribute"}, {"api_name": "scrapy.Spider", "line_number": 9, "usage_type": "attribute"}, {"api_name": "scrapy.Request", "line_number": 14, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 20, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 22, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 24, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 25, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 34, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 36, "usage_type": "call"}, {"api_name": "Search_Engine.items.five_one_ctoItem", "line_number": 39, "usage_type": "call"}, {"api_name": "Search_Engine.utils.common.get_md5", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "460409383", "text": "from users.models import User\nfrom django.db import models\nfrom django.core.validators import MaxValueValidator, MinValueValidator\n\n\nclass Category(models.Model):\n name = models.CharField(verbose_name='Название категории',\n max_length=200)\n\n slug = models.SlugField(verbose_name=\"Адрес для категории\",\n max_length=100,\n unique=True,\n help_text='Используйте латиницу')\n\n class Meta:\n ordering = ['id']\n verbose_name = 'category'\n\n def __str__(self):\n return self.name\n\n\nclass Genre(models.Model):\n name = models.CharField(verbose_name='Название жанра',\n max_length=200)\n slug = models.SlugField(verbose_name=\"Адрес для жанра\",\n max_length=100,\n unique=True,\n help_text='Используйте латиницу')\n\n class Meta:\n ordering = ['id']\n verbose_name = 'genre'\n\n def __str__(self):\n return self.name\n\n\nclass Title(models.Model):\n name = models.TextField(verbose_name='Название')\n year = models.PositiveSmallIntegerField()\n category = models.ForeignKey(Category,\n verbose_name='Категория',\n help_text='Выберите категорию',\n on_delete=models.SET_NULL,\n related_name='titles',\n blank=True,\n null=True)\n genre = models.ManyToManyField(Genre,\n verbose_name='Жанр',\n help_text='Выберите жанр',\n related_name='titles',\n blank=True,\n null=True)\n description = models.TextField(verbose_name='Описание',\n blank=True,\n null=True)\n\n class Meta:\n ordering = ['id']\n\n def __str__(self):\n return self.name\n\n\nclass Review(models.Model):\n text = models.TextField()\n author = models.ForeignKey(\n User, on_delete=models.CASCADE\n )\n title = models.ForeignKey(\n Title, on_delete=models.CASCADE, related_name='reviews'\n )\n pub_date = models.DateTimeField(\n 'Дата добавления', auto_now_add=True, db_index=True\n )\n score = models.PositiveSmallIntegerField(\n validators=[MinValueValidator(1), MaxValueValidator(10)],\n blank=True\n )\n\n class Meta:\n ordering = ['id']\n verbose_name = 'review'\n constraints = [\n models.UniqueConstraint(fields=['title', 'author'],\n name='unique_pair')\n ]\n\n def __str__(self):\n return self.text\n\n\nclass Comment(models.Model):\n text = models.TextField()\n author = models.ForeignKey(\n User, on_delete=models.CASCADE\n )\n pub_date = models.DateTimeField(\n 'Дата добавления', auto_now_add=True, db_index=True\n )\n review = models.ForeignKey(\n Review, on_delete=models.CASCADE, related_name='comments'\n )\n\n class Meta:\n ordering = ['id']\n verbose_name = 'comment'\n\n def __str__(self):\n return self.text\n", "sub_path": "api/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 3536, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "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": 7, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.SlugField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.SlugField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "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.TextField", "line_number": 40, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 40, "usage_type": "name"}, {"api_name": "django.db.models.PositiveSmallIntegerField", "line_number": 41, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 42, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 42, "usage_type": "name"}, {"api_name": "django.db.models.SET_NULL", "line_number": 45, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 45, "usage_type": "name"}, {"api_name": "django.db.models.ManyToManyField", "line_number": 49, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 49, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 55, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 55, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 66, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 66, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 67, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 67, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 68, "usage_type": "call"}, {"api_name": "users.models.User", "line_number": 69, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 68, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 69, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 69, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 71, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 71, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 72, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 72, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 74, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 74, "usage_type": "name"}, {"api_name": "django.db.models.PositiveSmallIntegerField", "line_number": 77, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 77, "usage_type": "name"}, {"api_name": "django.core.validators.MinValueValidator", "line_number": 78, "usage_type": "call"}, {"api_name": "django.core.validators.MaxValueValidator", "line_number": 78, "usage_type": "call"}, {"api_name": "django.db.models.UniqueConstraint", "line_number": 86, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 86, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 94, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 94, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 95, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 95, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 96, "usage_type": "call"}, {"api_name": "users.models.User", "line_number": 97, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 96, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 97, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 97, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 99, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 99, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 102, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 102, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 103, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 103, "usage_type": "name"}]} +{"seq_id": "636381155", "text": "from flask import Flask, Response, render_template, request\nfrom flask import jsonify\nfrom flask import json\nfrom movies_information import collect_movies_information\nfrom flask_cache import Cache\nfrom werkzeug.contrib.cache import SimpleCache\n\n\napp = Flask(__name__)\napp.config['JSON_AS_ASCII'] = False\ncache = SimpleCache()\n\n\ndef get_movies_from_cache():\n movies = cache.get('movies')\n afisha_link = cache.get('afisha_link')\n if movies or afisha_link is None:\n movies, afisha_link = collect_movies_information()\n cache.set('movies', movies, timeout=86400)\n cache.set('afisha_link', afisha_link, timeout=86400)\n return movies ,afisha_link\n\n\n@app.route('/todo/api/full_information.json', methods=['GET'])\ndef get_api():\n movies,afisha_link = get_movies_from_cache()\n return Response(json.dumps(movies,indent=4),\n content_type='application/json; charset=utf-8')\n\n\n@app.route('/api_description')\ndef api_description():\n return render_template('api.html')\n\n\n@app.route('/')\ndef films_list():\n movies, afisha_link = get_movies_from_cache()\n return render_template('films_list.html',movies = movies,\n afisha_link = afisha_link)\n \n\nif __name__ == \"__main__\":\n app.run(host='0.0.0.0')", "sub_path": "server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1292, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Flask", "line_number": 9, "usage_type": "call"}, {"api_name": "werkzeug.contrib.cache.SimpleCache", "line_number": 11, "usage_type": "call"}, {"api_name": "movies_information.collect_movies_information", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.Response", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.json.dumps", "line_number": 27, "usage_type": "call"}, {"api_name": "flask.json", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 39, "usage_type": "call"}]} +{"seq_id": "118336780", "text": "import pandas as pd\nimport pprint as pp\nimport json\n\n# list with stand-ins for empty cells\nmissing_values = [\"n/a\", \"na\", \"unknown\", \"-\", \"\"]\n\n# set missing values to NaN\ndf = pd.read_csv(\"data_journalists.csv\", na_values = missing_values, skipinitialspace = True, error_bad_lines=False)\n\n# columns\ncolumns_keep = ['year', 'fullName', 'gender', 'typeOfDeath', 'employedAs', 'organizations', 'jobs', 'coverage', 'mediums', 'country', 'photoUrl']\n\nsmall_df = df[columns_keep]\n\nsmall_df['photoUrl'] = small_df['photoUrl'].fillna('https://bit.ly/2Yxo3Jh')\nsmall_df['coverage'] = small_df['coverage'].fillna('\"War\"')\n\n\n# print(small_df)\n\nsmall_dict = small_df.to_dict(orient='index')\n\n# with pd.option_context('display.max_rows', None, 'display.max_columns', None): # more options can be specified also\n# print(small_df)\n\n# create dict with country-column as index\n# df_dict = small_df.set_index('fullName').T.to_dict('dict')\n\n# print(df_dict)\n\n# make json file from the dict\nwith open('result3.json', 'w') as fp:\n json.dump(small_dict, fp)\n\n # use pretty print to see if dict matches the json example in the exercise\npp.pprint(\"result3.json\")\n", "sub_path": "Homework/Week_6/converter.py", "file_name": "converter.py", "file_ext": "py", "file_size_in_byte": 1150, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 34, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 37, "usage_type": "call"}]} +{"seq_id": "40641495", "text": "\"\"\"Credit to cast42/parse_strava_gpx_minidom.py.\"\"\"\nimport collections\nimport datetime\nimport logging\nimport numpy\nfrom xml.dom import minidom\n\n\nTrack = collections.namedtuple('Track', ['name', 'points'])\n\n\ndef ValueOrNone(v_str, dtype):\n try:\n return dtype(v_str)\n except:\n return None\n\ndef NoneToZero(value):\n return 0 if value is None else value\n\ndef GetTime(t_str):\n try:\n return datetime.datetime.strptime(t_str, '%Y-%m-%dT%H:%M:%S.%fZ')\n except ValueError:\n return datetime.datetime.strptime(t_str, '%Y-%m-%dT%H:%M:%SZ')\n\n\ndef CastVote(point, poll):\n for key, val in point.items():\n if val is not None:\n poll[key] += 1\n\ndef ParseTrk(trk, poll):\n name = trk.getElementsByTagName('name')[0].firstChild.data\n points = []\n for trkseg in trk.getElementsByTagName('trkseg'):\n for trkpt in trkseg.getElementsByTagName('trkpt'):\n lat = ValueOrNone(trkpt.getAttribute('lat'), float)\n lon = ValueOrNone(trkpt.getAttribute('lon'), float)\n ele = ValueOrNone(trkpt.getElementsByTagName('ele')[0].firstChild.data, float)\n dt = GetTime(trkpt.getElementsByTagName('time')[0].firstChild.data)\n extensions = trkpt.getElementsByTagName('extensions')[0]\n try:\n power = ValueOrNone(extensions.getElementsByTagName('power')[0].firstChild.data, float)\n except:\n power = 0\n trkPtExtension = extensions.getElementsByTagName('gpxtpx:TrackPointExtension')[0]\n hl, cad = None, None\n if trkPtExtension:\n hr = ValueOrNone(trkPtExtension.getElementsByTagName('gpxtpx:hr')[0].firstChild.data, int)\n cad = ValueOrNone(trkPtExtension.getElementsByTagName('gpxtpx:cad')[0].firstChild.data, int)\n point = {'Lat': lat, 'Long': lon, 'Ele': ele, 'Time': dt, 'Hr': hr, 'cad': cad, 'Power': power}\n CastVote(point, poll)\n points.append(point)\n\n return Track(name, points)\n\n\ndef PointsIn(tracks):\n for track in tracks:\n for point in track.points:\n yield point\n\n\ndef PointsToSequences(tracks, poll):\n \"\"\"Conver list of points to sequences.\n\n Ignore keys which has too many missing values.\n \"\"\"\n max_vote = max(poll.values())\n keys = []\n for key, vote in poll.items():\n if max_vote - vote < 20:\n keys.append(key)\n else:\n logging.error('Too many missing values for %s(%d < %d)', key, vote, max_vote)\n\n data = collections.defaultdict(list)\n\n for key in keys:\n for point in PointsIn(tracks):\n data[key].append(NoneToZero(point[key]))\n data[key] = numpy.asarray(data[key])\n print('data[{}](shape={})={}'.format(key, data[key].shape, data[key][:5]))\n \n return data\n\n\ndef ParseGpx(filename):\n doc = minidom.parse(filename)\n doc.normalize()\n gpx = doc.documentElement\n tracks = []\n poll = collections.defaultdict(int)\n for node in gpx.getElementsByTagName('trk'):\n tracks.append(ParseTrk(node, poll))\n\n return PointsToSequences(tracks, poll)\n", "sub_path": "gpxparse.py", "file_name": "gpxparse.py", "file_ext": "py", "file_size_in_byte": 2886, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "collections.namedtuple", "line_number": 9, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 23, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 23, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "attribute"}, {"api_name": "logging.error", "line_number": 76, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 83, "usage_type": "call"}, {"api_name": "xml.dom.minidom.parse", "line_number": 90, "usage_type": "call"}, {"api_name": "xml.dom.minidom", "line_number": 90, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 94, "usage_type": "call"}]} +{"seq_id": "591955712", "text": "import os\nimport tempfile\nimport subprocess\n\nfrom concurrent.futures import ProcessPoolExecutor as Pool\n\nimport re\n\nfrom pandaharvester.harvesterconfig import harvester_config\nfrom pandaharvester.harvestercore import core_utils\nfrom pandaharvester.harvestercore.plugin_base import PluginBase\n\n# logger\nbaseLogger = core_utils.setup_logger('htcondor_submitter')\n\n\n# submit a worker\ndef submit_a_worker(data):\n workspec = data['workspec']\n template = data['template']\n log_dir = data['log_dir']\n n_core_per_node = data['n_core_per_node']\n workspec.reset_changed_list()\n # make logger\n tmpLog = core_utils.make_logger(baseLogger, 'workerID={0}'.format(workspec.workerID),\n method_name='submit_a_worker')\n # make batch script\n batchFile = make_batch_script(workspec, template, n_core_per_node, log_dir)\n # command\n comStr = 'condor_submit {0}'.format(batchFile)\n # submit\n tmpLog.debug('submit with {0}'.format(batchFile))\n try:\n p = subprocess.Popen(comStr.split(),\n shell=False,\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n # check return code\n stdOut, stdErr = p.communicate()\n retCode = p.returncode\n except:\n stdOut = ''\n stdErr = core_utils.dump_error_message(tmpLog, no_message=True)\n retCode = 1\n tmpLog.debug('retCode={0}'.format(retCode))\n if retCode == 0:\n # extract batchID\n job_id_match = None\n for tmp_line_str in stdOut.split('\\n'):\n job_id_match = re.search('^(\\d+) job[(]s[)] submitted to cluster (\\d+)\\.$', tmp_line_str)\n if job_id_match:\n break\n if job_id_match is not None:\n workspec.batchID = job_id_match.group(2)\n tmpLog.debug('batchID={0}'.format(workspec.batchID))\n tmpRetVal = (True, '')\n else:\n errStr = 'batchID cannot be found'\n tmpLog.error(errStr)\n tmpRetVal = (False, errStr)\n else:\n # failed\n errStr = stdOut + ' ' + stdErr\n tmpLog.error(errStr)\n tmpRetVal = (False, errStr)\n return tmpRetVal, workspec.get_changed_attributes()\n\n\n# make batch script\ndef make_batch_script(workspec, template, n_core_per_node, log_dir):\n tmpFile = tempfile.NamedTemporaryFile(delete=False, suffix='_submit.sdf', dir=workspec.get_access_point())\n # Note: In workspec, unit of minRamCount and of maxDiskCount are both MB.\n # In HTCondor SDF, unit of request_memory is MB, and request_disk is KB.\n tmpFile.write(template.format(\n nCorePerNode=n_core_per_node,\n nCoreTotal=workspec.nCore,\n nNode=(workspec.nCore // n_core_per_node + min(workspec.nCore % n_core_per_node, 1)),\n requestRam=workspec.minRamCount,\n requestDisk=(workspec.maxDiskCount * 1024),\n requestWalltime=workspec.maxWalltime,\n accessPoint=workspec.accessPoint,\n harvesterID=harvester_config.master.harvester_id,\n workerID=workspec.workerID,\n computingSite=workspec.computingSite,\n logDir=log_dir)\n )\n tmpFile.close()\n return tmpFile.name\n\n\n# parse log, stdout, stderr filename\ndef parse_batch_job_filename(value_str, file_dir, batchID):\n _filename = os.path.basename(value_str)\n _sanitized_list = re.sub('\\{(\\w+)\\}|\\[(\\w+)\\]|\\((\\w+)\\)|#(\\w+)#|\\$', '', _filename).split('.')\n _prefix = _sanitized_list[0]\n _suffix = _sanitized_list[-1] if len(_sanitized_list) > 1 else ''\n\n for _f in os.listdir(file_dir):\n if re.match('{prefix}(.*)\\.{batchID}\\.(.*)\\.{suffix}'.format(prefix=_prefix, suffix=_suffix, batchID=batchID), _f):\n return _f\n return None\n\n\n# submitter for HTCONDOR batch system\nclass HTCondorSubmitter(PluginBase):\n # constructor\n def __init__(self, **kwarg):\n self.nProcesses = 1\n self.logBaseURL = None\n PluginBase.__init__(self, **kwarg)\n # template for batch script\n tmpFile = open(self.templateFile)\n self.template = tmpFile.read()\n tmpFile.close()\n # number of processes\n if self.nProcesses < 1:\n self.nProcesses = None\n\n # submit workers\n def submit_workers(self, workspec_list):\n tmpLog = core_utils.make_logger(baseLogger, method_name='submit_workers')\n tmpLog.debug('start nWorkers={0}'.format(len(workspec_list)))\n dataList = []\n for workSpec in workspec_list:\n data = {'workspec': workSpec,\n 'template': self.template,\n 'log_dir': self.logDir,\n 'n_core_per_node': self.nCorePerNode}\n dataList.append(data)\n # exec with mcore\n with Pool(self.nProcesses) as pool:\n retValList = pool.map(submit_a_worker, dataList)\n\n # get batch_log, stdout, stderr filename\n for _line in self.template.split('\\n'):\n if _line.startswith('#'):\n continue\n _match_batch_log = re.match('log = (.+)', _line)\n _match_stdout = re.match('output = (.+)', _line)\n _match_stderr = re.match('error = (.+)', _line)\n if _match_batch_log:\n batch_log_value = _match_batch_log.group(1)\n continue\n if _match_stdout:\n stdout_value = _match_stdout.group(1)\n continue\n if _match_stderr:\n stderr_value = _match_stderr.group(1)\n continue\n\n\n # propagate changed attributes\n retList = []\n for workSpec, tmpVal in zip(workspec_list, retValList):\n retVal, tmpDict = tmpVal\n workSpec.set_attributes_with_dict(tmpDict)\n # URLs for log files\n if self.logBaseURL is not None and workSpec.batchID is not None:\n batch_log_filename = parse_batch_job_filename(value_str=batch_log_value, file_dir=self.logDir, batchID=workSpec.batchID)\n stdout_path_file_name = parse_batch_job_filename(value_str=stdout_value, file_dir=self.logDir, batchID=workSpec.batchID)\n stderr_path_filename = parse_batch_job_filename(value_str=stderr_value, file_dir=self.logDir, batchID=workSpec.batchID)\n workSpec.set_log_file('batch_log', '{0}/{1}'.format(self.logBaseURL, batch_log_filename))\n workSpec.set_log_file('stdout', '{0}/{1}'.format(self.logBaseURL, stdout_path_file_name))\n workSpec.set_log_file('stderr', '{0}/{1}'.format(self.logBaseURL, stderr_path_filename))\n tmpLog.debug('Done set_log_file')\n for jobSpec in workSpec.get_jobspec_list():\n # using batchLog and stdOut URL as pilotID and pilotLog\n jobSpec.set_one_attribute('pilotID', workSpec.workAttributes['stdOut'])\n jobSpec.set_one_attribute('pilotLog', workSpec.workAttributes['batchLog'])\n tmpLog.debug('Done jobspec attribute setting')\n retList.append(retVal)\n tmpLog.debug('done')\n return retList\n", "sub_path": "pandaharvester/harvestersubmitter/htcondor_submitter.py", "file_name": "htcondor_submitter.py", "file_ext": "py", "file_size_in_byte": 7104, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pandaharvester.harvestercore.core_utils.setup_logger", "line_number": 14, "usage_type": "call"}, {"api_name": "pandaharvester.harvestercore.core_utils", "line_number": 14, "usage_type": "name"}, {"api_name": "pandaharvester.harvestercore.core_utils.make_logger", "line_number": 25, "usage_type": "call"}, {"api_name": "pandaharvester.harvestercore.core_utils", "line_number": 25, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 34, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 36, "usage_type": "attribute"}, {"api_name": "subprocess.PIPE", "line_number": 37, "usage_type": "attribute"}, {"api_name": "pandaharvester.harvestercore.core_utils.dump_error_message", "line_number": 43, "usage_type": "call"}, {"api_name": "pandaharvester.harvestercore.core_utils", "line_number": 43, "usage_type": "name"}, {"api_name": "re.search", "line_number": 50, "usage_type": "call"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 71, "usage_type": "call"}, {"api_name": "pandaharvester.harvesterconfig.harvester_config.master", "line_number": 82, "usage_type": "attribute"}, {"api_name": "pandaharvester.harvesterconfig.harvester_config", "line_number": 82, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 94, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 98, "usage_type": "call"}, {"api_name": "re.match", "line_number": 99, "usage_type": "call"}, {"api_name": "pandaharvester.harvestercore.plugin_base.PluginBase", "line_number": 105, "usage_type": "name"}, {"api_name": "pandaharvester.harvestercore.plugin_base.PluginBase.__init__", "line_number": 110, "usage_type": "call"}, {"api_name": "pandaharvester.harvestercore.plugin_base.PluginBase", "line_number": 110, "usage_type": "name"}, {"api_name": "pandaharvester.harvestercore.core_utils.make_logger", "line_number": 121, "usage_type": "call"}, {"api_name": "pandaharvester.harvestercore.core_utils", "line_number": 121, "usage_type": "name"}, {"api_name": "concurrent.futures.ProcessPoolExecutor", "line_number": 131, "usage_type": "call"}, {"api_name": "re.match", "line_number": 138, "usage_type": "call"}, {"api_name": "re.match", "line_number": 139, "usage_type": "call"}, {"api_name": "re.match", "line_number": 140, "usage_type": "call"}]} +{"seq_id": "615494860", "text": "\"\"\"This is the final project for CSSE120, Introduction to Software Development. For this project, the robot can be\ndriven using keystrokes on the computer. When the up button on the ev3 is pressed, the robot sends back the color\nsensed by the color sensor to the computer. The pc then interprets the data and prints an image in tkinter. The\nrobots imitates a tourist robot. Code was written primarily by Kyle Mehringer. Example code was used from Dave Fisher\nin earlier modules and from Stack Overflow.\"\"\"\n\n\n\nimport tkinter\nfrom tkinter import ttk\nimport mqtt_remote_method_calls as com\nfrom tkinter import *\nfrom PIL import ImageTk, Image\n\nCOLOR_NAMES = [\"None\", \"Black\", \"Blue\", \"Green\", \"Yellow\", \"Red\", \"White\", \"Brown\"]\nCOLOR_NUMBERS = [0, 1, 2, 3, 4, 5, 6, 7]\n\n\nclass DataContainer(object):\n \"\"\" Helper class that might be useful to communicate between different callbacks.\"\"\"\n\n def __init__(self, root):\n self.root = root\n self.panel = None\n self.img = None\n self.running = True\n\n def on_color_received(self, color):\n print(COLOR_NAMES[color])\n\n if color == COLOR_NUMBERS[5]:\n print(\"Mountains\")\n self.img = ImageTk.PhotoImage(Image.open(\"clingmans_dome1.png\"))\n self.panel = Label(self.root, image=self.img)\n self.panel.grid()\n if color == COLOR_NUMBERS[6]:\n print(\"Lake\")\n self.img = ImageTk.PhotoImage(Image.open(\"water.png\"))\n self.panel = Label(self.root, image=self.img)\n self.panel.grid()\n\n\ndef main():\n\n root = Tk()\n root.title(\"Pictures\")\n\n main_frame = ttk.Frame(root, padding=20, relief='raised')\n main_frame.grid()\n\n forward_button = ttk.Button(main_frame, text=\"Forward\")\n forward_button.grid(row=2, column=1)\n # forward_button and '' key is done for your here...\n forward_button['command'] = lambda: drive_forward(mqtt_client, 500, 500)\n root.bind('', lambda event: drive_forward(mqtt_client, 500, 500))\n\n left_button = ttk.Button(main_frame, text=\"Left\")\n left_button.grid(row=3, column=0)\n # left_button and '' key\n left_button['command'] = lambda: turn_left(mqtt_client, 500, 500)\n root.bind('', lambda event: turn_left(mqtt_client, 500, 500))\n\n stop_button = ttk.Button(main_frame, text=\"Stop\")\n stop_button.grid(row=3, column=1)\n # stop_button and '' key (note, does not need left_speed_entry, right_speed_entry)\n stop_button['command'] = lambda: stop(mqtt_client)\n root.bind('', lambda event: stop(mqtt_client))\n\n right_button = ttk.Button(main_frame, text=\"Right\")\n right_button.grid(row=3, column=2)\n # right_button and '' key\n right_button['command'] = lambda: turn_right(mqtt_client, 500, 500)\n root.bind('', lambda event: turn_right(mqtt_client, 500, 500))\n\n back_button = ttk.Button(main_frame, text=\"Back\")\n back_button.grid(row=4, column=1)\n # back_button and '' key\n back_button['command'] = lambda: drive_back(mqtt_client, 500, 500)\n root.bind('', lambda event: drive_back(mqtt_client, 500, 500))\n\n up_button = ttk.Button(main_frame, text=\"Up\")\n up_button.grid(row=5, column=0)\n up_button['command'] = lambda: send_up(mqtt_client)\n root.bind('', lambda event: send_up(mqtt_client))\n\n down_button = ttk.Button(main_frame, text=\"Down\")\n down_button.grid(row=6, column=0)\n down_button['command'] = lambda: send_down(mqtt_client)\n root.bind('', lambda event: send_down(mqtt_client))\n\n # root.bind('', lambda event: drive_forward(mqtt_client, 500, 500))\n # root.bind('', lambda event: turn_left(mqtt_client, 500, 500))\n # root.bind('', lambda event: stop(mqtt_client))\n # root.bind('', lambda event: turn_right(mqtt_client, 500, 500))\n # root.bind('', lambda event: drive_back(mqtt_client, 500, 500))\n # root.bind('', lambda event: send_up(mqtt_client))\n # root.bind('', lambda event: send_down(mqtt_client))\n\n # Buttons for quit and exit\n q_button = ttk.Button(main_frame, text=\"Quit\")\n q_button.grid(row=5, column=2)\n q_button['command'] = (lambda: quit_program(mqtt_client, False))\n\n e_button = ttk.Button(main_frame, text=\"Exit\")\n e_button.grid(row=6, column=2)\n e_button['command'] = (lambda: quit_program(mqtt_client, True))\n\n my_delegate = DataContainer(root)\n mqtt_client = com.MqttClient(my_delegate)\n mqtt_client.connect_to_ev3()\n\n root.mainloop()\n\n\n# ----------------------------------------------------------------------\n# Tkinter callbacks\n# ----------------------------------------------------------------------\n\ndef drive_forward(mqtt_client, left_speed_entry, right_speed_entry):\n print(\"drive_forward\")\n mqtt_client.send_message(\"drive_forward\", [int(left_speed_entry), int(right_speed_entry)])\n\n\ndef turn_left(mqtt_client, left_speed_entry, right_speed_entry):\n print(\"turn_left\")\n mqtt_client.send_message(\"turn_left\", [int(left_speed_entry), int(right_speed_entry)])\n\n\ndef stop(mqtt_client):\n print(\"stop\")\n mqtt_client.send_message(\"stop\")\n\n\ndef turn_right(mqtt_client, left_speed_entry, right_speed_entry):\n print(\"turn_right\")\n mqtt_client.send_message(\"turn_right\", [int(left_speed_entry), int(right_speed_entry)])\n\n\ndef drive_back(mqtt_client, left_speed_entry, right_speed_entry):\n print(\"drive_backward\")\n mqtt_client.send_message(\"drive_backward\", [int(left_speed_entry), int(right_speed_entry)])\n\n\n# Arm command callbacks\ndef send_up(mqtt_client):\n print(\"arm_up\")\n mqtt_client.send_message(\"arm_up\")\n\n\ndef send_down(mqtt_client):\n print(\"arm_down\")\n mqtt_client.send_message(\"arm_down\")\n\n\n# Quit and Exit button callbacks\ndef quit_program(mqtt_client, shutdown_ev3):\n if shutdown_ev3:\n print(\"shutdown\")\n mqtt_client.send_message(\"shutdown\")\n mqtt_client.close()\n exit()\n\n\n# ----------------------------------------------------------------------\n# Calls main to start the ball rolling.\n# ----------------------------------------------------------------------\nmain()\n", "sub_path": "projects/mehrinka/project_2.py", "file_name": "project_2.py", "file_ext": "py", "file_size_in_byte": 6108, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "PIL.ImageTk.PhotoImage", "line_number": 33, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 33, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 33, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 33, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 38, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 38, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 38, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 38, "usage_type": "name"}, {"api_name": "tkinter.ttk.Frame", "line_number": 48, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 48, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 51, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 51, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 57, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 57, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 63, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 63, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 69, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 69, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 75, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 75, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 81, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 81, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 86, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 86, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 100, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 100, "usage_type": "name"}, {"api_name": "tkinter.ttk.Button", "line_number": 104, "usage_type": "call"}, {"api_name": "tkinter.ttk", "line_number": 104, "usage_type": "name"}, {"api_name": "mqtt_remote_method_calls.MqttClient", "line_number": 109, "usage_type": "call"}]} +{"seq_id": "102204098", "text": "import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torchvision\nimport torchvision.transforms as transforms\nfrom vgg import vgg16\nimport numpy as np\nfrom torch.optim import lr_scheduler\nfrom torch.autograd import Variable\nfrom torchvision.utils import make_grid\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nfrom dataLoader import getAnimalDataloader,getSmokeDataloader,getFireDataloader\nfrom collections import OrderedDict\nimport torch\nimport math\nimport cv2\n\n#-------------加载网络结构----------\n\npath = '../models/'+'VGG_fire15.pth'\ndevice = torch.device(\"cuda:1\" if torch.cuda.is_available() else \"cpu\")\n\ndef load_model(path):\n net = vgg16(pretrained=False,progress=False)\n classifier = nn.Sequential(OrderedDict([\n ('fc1',nn.Linear(25088,4096)),\n ('relu1',nn.ReLU()),\n ('fc2',nn.Linear(4096,1000)),\n ('relu2',nn.ReLU()),\n ('fc3',nn.Linear(1000,10)),\n ('output',nn.LogSoftmax(dim=1))\n ]))\n # 替换\n net.classifier = classifier\n net.to(device)\n net.load_state_dict(torch.load(path))\n return net\n\nmodel = load_model(path)\n\nprint(\"model:\\n\",model)\n\n\n#定义数据加载器\n\n_,test_loader = getFireDataloader(227,1)\n#-------------显示处理前的图片----------\ndef imshow(img):\n img = img / 2 + 0.5 # unnormalize\n npimg = img.cpu().detach().numpy()\n plt.imshow(np.transpose(npimg, (1, 2, 0)))\n plt.show()\n\n# 随机获取训练图片\ndataiter = iter(test_loader)\nimages, labels = dataiter.next()\n\n# 显示图片\n#imshow(torchvision.utils.make_grid(images))\n\n#-----------保存各层处理后的图片-------------------\n\ndef save_img_gray(tensor, name):\n #替换深度和batch_size所在的纬度值\n tensor = tensor.permute((1, 0, 2, 3))#将[1, 6, 28, 28]转化成[1, 6, 28, 28]\n print('output permute:',tensor.shape)\n im = make_grid(tensor, normalize=True, scale_each=True, nrow=8, padding=2).permute((1, 2, 0))\n im = (im.cpu().data.numpy() * 255.).astype(np.uint8)#将0~1之间的像素值，转化成0~255\n Image.fromarray(im).save(name + '.jpg')\n\n\n\ndef save_img_heatmap(tensor, name):\n #替换深度和batch_size所在的纬度值\n #tensor = tensor.permute((1, 0, 2, 3))#将[1, 6, 28, 28]转化成[6, 1, 28, 28]\n temp_img = tensor.cpu().detach().permute((1, 0, 2, 3))\n print('output permute----------:',temp_img.size())\n featureMapSize = temp_img.size()[0]\n row = 8\n line = math.ceil(featureMapSize/8)\n imagesList=[]\n for i in range(featureMapSize):\n img1 = temp_img[i][0]\n #print(i,img1.shape)\n plt.subplot(line,row,i+1)\n plt.xticks([])\n plt.yticks([])\n plt.imshow(img1)\n imagesList.append(img1)\n plt.savefig(name,dpi=300)\n\ndef save_img_linear(tensor, name):\n #替换深度和batch_size所在的纬度值\n tensor = tensor.permute((1, 0))\n print('output permute:',tensor.shape)\n im = make_grid(tensor, normalize=True, scale_each=True, nrow=8, padding=2).permute((1, 2, 0))\n im = (im.cpu().data.numpy() * 255.).astype(np.uint8)#将0~1之间的像素值，转化成0~255\n Image.fromarray(im).save(name + '.jpg')\n\n\n#features各层输出\nif False:\n layerCount=30\n for i in range(layerCount+1):\n \n print('------features%d------'%i)\n current_layer = model.features[i]\n if 'Conv2d' in str(current_layer):\n print('input:',images.shape)\n print('layer:',current_layer)\n layer_out = current_layer(images.to(device))\n print('output',layer_out.shape)\n save_img_gray(layer_out, 'features_gray'+str(i))\n images=layer_out\n\n#features各层输出\nif True:\n layerCount=len(model.features)\n for i in range(layerCount+1):\n\n print('------features%d------'%i)\n current_layer = model.features[i]\n nextLayer='NULL'\n if i+2 (dict, str):\n datas = re.match(r'---(?P[\\S\\s]+?)---', md).group('datas')\n\n info = dict()\n if \"\" in datas:\n for each in re.findall(r'<(?P<name>\\S+?)>([\\S\\s]*?)</\\1>', datas.strip().replace(\" \", \"\")):\n info[each[0]] = each[1]\n \n if not info.get(\"tags\"):\n info[\"tags\"] = []\n else:\n info[\"tags\"] = info[\"tags\"].split(\",\")\n else:\n _md = markdown.Markdown(extensions=['markdown.extensions.meta'])\n _md.convert(md)\n info = _md.Meta\n for key, value in info.items():\n if key == \"tags\":\n continue\n info[key] = value[0]\n\n if not info.get(\"corpus\") or info[\"corpus\"][0] == \"\":\n if info.get(\"date\"):\n info[\"corpus\"] = DEFAULT_CORPUS\n\n md = re.sub(r\"^---[\\s\\S]+?---\", \"\", md)\n return info, md\n\n\ndef render_article(md: str, info: dict) -> str:\n extend_javascript = \"\"\n if \"```mermaid\" in md:\n extend_javascript += \"\"\"\n <link rel=\"stylesheet\" href=\"/STATIC/css/mermaid.7.0.0.min.css\">\n <script type=\"text/javascript\" src=\"/STATIC/script/mermaid.7.0.0.min.js\"></script>\n\"\"\"\n md = re.sub(r\"```mermaid([\\s\\S]+?)```\",\n r'<div class=\"mermaid\">\\g<1></div>', md)\n\n md = re.sub(r\"<web-preview>([\\s\\S]*?)</web-preview>\", r\"\"\"\\g<1>\n```html\n\\g<1>\n```\"\"\", md)\n\n data = markdown.markdown(md, extensions=[\n 'markdown.extensions.extra',\n 'markdown.extensions.tables',\n 'markdown.extensions.codehilite',\n 'markdown.extensions.nl2br',\n ])\n\n html = f\"\"\"<!DOCTYPE html>\n<html lang=\"zh-cn\">\n <head>\n <meta content=\"text/html; charset=utf-8\" http-equiv=\"content-type\" />\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\">\n <title>{info[\"title\"]}\n \n \n{extend_javascript}\n \n \n
\n{data}\n
\n \n\n\"\"\"\n return html\n", "sub_path": "src/render.py", "file_name": "render.py", "file_ext": "py", "file_size_in_byte": 2208, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "re.match", "line_number": 9, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 13, "usage_type": "call"}, {"api_name": "markdown.Markdown", "line_number": 21, "usage_type": "call"}, {"api_name": "settings.DEFAULT_CORPUS", "line_number": 31, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 33, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 44, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 47, "usage_type": "call"}, {"api_name": "markdown.markdown", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "304424516", "text": "\"\"\" Splitter function modeled after Mike's code to make it easier to test. \"\"\"\nimport numpy as np\nfrom misc import *\nimport scipy.linalg as la\nfrom disentanglers import renyi_2_disentangler\n\ndef split_psi(psi, split_dim, trunc_params, disentangler_params = None,\n disentangler = renyi_2_disentangler, init_from_polar = True):\n \"\"\" Splits a tripartite state psi. Finds an approximation psi ~= A.lambda,\n where A is an isometry and lambda is a TEBD style wavefunction. \n \n Parameters\n ----------\n psi: Rank 3 tensor\n\n split_dim: A tuple (dL, dR) that describes the dimensions of the first split\n\n trunc_params: Dictionary of trunc_params. Keys are chi_max\n See Figure 2b in https://arxiv.org/pdf/1902.05100.pdf\n\n disentangler_params: Optional parameters for disentangler. Valid parameters\n are given in disentangle()\n\n init_from_polar: Use a polar decomposition as the initial guess\n \"\"\"\n \n dL, dR = split_dim \n if disentangler_params is None:\n disentangler_params = {}\n\n d, mL, mR = psi.shape\n dL = np.min([dL, mL])\n dR = np.min([dR, mR])\n\n # Mike's version -- better than your factors version\n if dL * dR > d:\n dR = min([int(np.rint(np.sqrt(d))), dR])\n dL = min([d // dR, dL])\n \n # How much are we throwing away by not using get_closest_factors()?\n # dL, dR = get_closest_factors(d)\n tp = dict(p_trunc = 0.0, eta = dL * dR)\n# X, y, Z, trunc_info_H = svd_trunc(psi.reshape((-1, mL * mR)), **tp)\n X,y, Z, D2, trunc_leg = svd_theta_UsV(psi.reshape((-1, mL*mR)), dL*dR, 0.)\n\n A = X\n theta = (Z.T * y).T\n D2 = len(y)\n init_from_polar = False\n\n\n # This next section involves truncating the legs of psi based on eigenvalues\n # of the reduced density matrix (Hermitian and positive so EVs are SVs)\n if init_from_polar:\n psi = theta.reshape([D2, mL, mR])\n if mL > dL:\n rho = np.tensordot(psi, psi.conj(), axes = [[0,2],[0,2]])\n e, u = la.eigh(rho)\n u = u[:, -dL:] # dL largest eigenvalues\n psi = np.tensordot(psi, u.conj(), [1,0]).transpose([0,2,1])\n\n if mR > dR:\n rho = np.tensordot(psi, psi.conj(), axes = [[0,1],[0,1]])\n e, u = la.eigh(rho)\n u = u[:, -dR:] \n psi = np.tensordot(psi, u.conj(), [2,0])\n\n psi /= la.norm(psi)\n u, s, v = svd(psi.reshape(D2, D2))\n Zp = np.dot(u, v)\n A = X @ Zp\n theta = np.dot(Zp.T.conj(), theta)\n # Disentangler\n theta = np.reshape(theta, (dL, dR, mL, mR)) \n theta = theta.transpose([2, 0, 1, 3]) # left to right\n \n theta, U = disentangler(theta, **disentangler_params)\n\n A = np.tensordot(A, np.reshape(np.conj(U), (dL, dR, dL * dR)), [1, 2])\n # Second splitting\n theta = theta.transpose([1,0,2,3])\n theta = np.reshape(theta, (dL * mL, dR * mR))\n\n X, s, Z, chi_C, trunc_bond = svd_theta_UsV(theta, trunc_params['chi_max'], p_trunc=3e-16)\n errH = trunc_leg\n errV = trunc_bond\n\n info = dict(error = errH + errV, d_error = errH, sLambda = s)\n S = np.reshape(X, (dL, mL, len(s)))\n S = S * s\n B = np.reshape(Z, (len(s), dR, mR))\n B = B.transpose([1,0,2])\n return(A, S, B, info) # Returns sum of errors\n\n\n", "sub_path": "new_splitter.py", "file_name": "new_splitter.py", "file_ext": "py", "file_size_in_byte": 3258, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "disentanglers.renyi_2_disentangler", "line_number": 8, "usage_type": "name"}, {"api_name": "numpy.min", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.rint", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.tensordot", "line_number": 57, "usage_type": "call"}, {"api_name": "scipy.linalg.eigh", "line_number": 58, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 58, "usage_type": "name"}, {"api_name": "numpy.tensordot", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.tensordot", "line_number": 63, "usage_type": "call"}, {"api_name": "scipy.linalg.eigh", "line_number": 64, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 64, "usage_type": "name"}, {"api_name": "numpy.tensordot", "line_number": 66, "usage_type": "call"}, {"api_name": "scipy.linalg.norm", "line_number": 68, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 68, "usage_type": "name"}, {"api_name": "numpy.dot", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.tensordot", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.conj", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 91, "usage_type": "call"}]} +{"seq_id": "243070817", "text": "__author__ = 'xz1809' # set author to this py file\n\nimport sys\nimport json\n\ntry:\n import urllib2 as urllib\nexcept ImportError:\n import urllib.request as urllib\n \ndef get_jsonparsed_data(url):\n '''\n Parameters\n ----------\n url : str\n \n Returns\n -------\n dict\n \n '''\n response = urllib.urlopen(url)\n data = response.read().decode(\"utf-8\")\n return json.loads(data)\n\nif __name__ == '__main__':\n key, bus_line = sys.argv[1:]\n print('BUS LINE:', bus_line)\n \nbus_url = 'http://api.prod.obanyc.com/api/siri/vehicle-monitoring.json?key=' + key + \\\n '&VehicleMonitoringDetailLevel=calls&LineRef=' + bus_line\n \nbusdata = get_jsonparsed_data(bus_url)\n\n# counting the active buses\nnbusses = len(busdata['Siri']['ServiceDelivery']['VehicleMonitoringDelivery'][0]['VehicleActivity'])\n\n\n# extracting and printing info for each bus\nfor i in range(nbusses):\n busi = busdata['Siri']['ServiceDelivery']['VehicleMonitoringDelivery'][0]['VehicleActivity'][i]['MonitoredVehicleJourney']['VehicleLocation']\n\n print('Bus %d is at latitude %f and longitude %f' % (i,\n busi['Latitude'],\n busi['Longitude']))", "sub_path": "HW2_xz1809/show_bus_location_redo.py", "file_name": "show_bus_location_redo.py", "file_ext": "py", "file_size_in_byte": 1283, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "urllib.request.urlopen", "line_number": 22, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 22, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 24, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 27, "usage_type": "attribute"}]} +{"seq_id": "383856960", "text": "from sklearn import tree, cross_validation\nimport pandas as pd\nfrom os import system\n\nimport evaluation\n\ndf = pd.read_csv('train.csv')\n\ny = df['target']\ndf = df.drop('target', 1)\ndf = df.drop('id', 1)\n\nkf = cross_validation.KFold(len(df), n_folds=5, shuffle=True)\nfor train_index, test_index in kf:\n X_train, X_test = df.ix[train_index], df.ix[test_index]\n y_train, y_test = y[train_index], y[test_index]\n\n X_test.index = range(0, len(X_test))\n y_test.index = range(0, len(y_test))\n\n clf = tree.DecisionTreeClassifier()\n t = clf.fit(X_train, y_train)\n \n probs = clf.predict_proba(X_test)\n print(evaluation.logloss(probs, y_test, t.classes_))\n", "sub_path": "otto/test_decision_tree.py", "file_name": "test_decision_tree.py", "file_ext": "py", "file_size_in_byte": 669, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "pandas.read_csv", "line_number": 7, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.KFold", "line_number": 13, "usage_type": "call"}, {"api_name": "sklearn.cross_validation", "line_number": 13, "usage_type": "name"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 21, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 21, "usage_type": "name"}, {"api_name": "evaluation.logloss", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "142886674", "text": "import math\nimport re\nimport time\nfrom enum import Enum\n\nimport fitz\nimport numpy as np\nimport pandas as pd\n\nimport utils.validations as val\nfrom implementations.table_proc_impl import TableImpl\nfrom utils.pdf_impl_utils import (dataframe_to_dictionary_maker,\n dictionary_generator, \n font_data_assembler,\n get_bbox_dict, \n header_footer_assigner,\n ord_unord_1, \n relationship_establisher, \n excluder,\n tag_assigner, \n filter_df,\n get_space_dist,\n block_mergerer)\nfrom utils.utils import (check_table_area, \n in_table_area)\n\n\nclass PdfImpl(object):\n filename: str\n NAME_LIST: str = \"Order/Unorder List\"\n # adding for reference to customise user output\n tags: list = ['Heading', 'Paragraph', 'Page Number', 'Header', 'Footer']\n\n def __init__(self, fpath: str):\n self.filename = fpath\n\n def is_native(self, password: str=None) -> bool:\n \"\"\"is_native \n Checks nativity of pdf by gathering fonts used across pages\n\n Args:\n password (str, optional): Password to decrypt the pdf. Defaults to None.\n\n Raises:\n PermissionError: If password is not supplied and the document is encrypted\n\n Returns:\n bool: True or False based on the text content available in the document\n \"\"\"\n # Read and create doc object\n self.process()\n if self.doc.isEncrypted and password is None:\n raise PermissionError('Document Encrypted please supply password')\n elif password is not None:\n _ = self.doc.authenticate(password=password)\n else:\n # getting list of fonts used in each page\n non_img_pages = [self.doc.getPageFontList(pno, full=False)\n for pno in range(self.doc.pageCount)\n if self.doc.getPageFontList(pno, full=False)]\n\n pct_of_text_pages = (len(non_img_pages)/self.doc.pageCount)*100\n\n print(pct_of_text_pages, '% of pages have text')\n\n self.metadata['pdfType'] = 'Native PDF' if pct_of_text_pages else 'Scanned PDF'\n\n # Return True if the pdf has any text in it else False\n return True if pct_of_text_pages else False\n\n\n def process(self, **options):\n \"\"\"process \n Processes pdf path\n\n Raises:\n FileNotFoundError: When file is not found or corrupted\n \"\"\"\n if val.check_file(self.filename):\n self.doc = fitz.Document(self.filename, filetype='pdf', **options)\n self.metadata = {\n \"No of Pages\": self.doc.pageCount,\n \"PDF Type\": None,\n \"Author\": self.doc.metadata['author'],\n \"Creator\": self.doc.metadata['creator'],\n \"Producer\": self.doc.metadata['producer'],\n \"Creation Date\": self.doc.metadata['creationDate']\n }\n \n else:\n raise FileNotFoundError('File not found or invalid', self.filename)\n\n\n def pdf_extractor(self, page_range, tbl_obj: TableImpl = None):\n\n image_df = []\n font_metadata = {}\n word_df_list = []\n text_df_list = []\n line_df_list = []\n\n for page in page_range:\n # since the input values are 1 - indexed\n page = page-1\n blocks = self.doc.loadPage(page).getText('dict')['blocks']\n words = self.doc.loadPage(page).getText('words')\n\n # space_dist = get_space_dist(words)\n space_dist = 10\n\n word_df = pd.DataFrame(words, columns=['bbox_left', 'bbox_top', 'bbox_right', 'bbox_bottom',\n 'word', 'block_no', 'line_no', 'word_no'])\n word_df = word_df.assign(page_no=page)\n\n # Apply table filter to Words (ie) words within detected table regions are excluded for now\n if tbl_obj:\n table_bboxes = tbl_obj.get_table_area(page)\n table_mask = word_df.apply(\n check_table_area, axis='columns', result_type='reduce', table_bboxes=table_bboxes)\n word_df = word_df[table_mask]\n\n block_counter = 0\n text_df = []\n line_df = []\n for internal_block in blocks:\n\n if internal_block['type'] == 1:\n print('Skipping image block')\n\n '''\n # image_bytes = internal_block['image']\n # # nparr = np.frombuffer(image_bytes, np.uint8)\n # img=image_bytes.decode('utf-8')\n # image_df.append({'page_no':page,'image':img,\n # 'bbox_top':internal_block['bbox'][1],\n # 'bbox_left':internal_block['bbox'][0],\n # 'bbox_bottom':internal_block['bbox'][3],\n # 'bbox_right':internal_block['bbox'][2]})\n '''\n\n\n # elif PdfBlockTypes(internal_block['type']) is PdfBlockTypes.NORMAL and \\\n # not any([in_table_area(t_bbox, internal_block['bbox']) for t_bbox in table_bboxes]):\n elif internal_block['type'] == 0:\n block_text = ''\n font_size = []\n font_name = []\n lines = internal_block['lines']\n line_counter = 0\n\n for line in lines:\n # line_id = f'page{page}_block{block_counter}_line{line_counter}'\n line_text = ''\n spans = line['spans']\n line_bbox = line['bbox']\n line_font_size = []\n line_font_name = []\n\n initial_space = True\n\n for span in spans:\n \n if initial_space:\n prev = span['bbox']\n line_text = span['text']\n initial_space = False\n else:\n curr = span['bbox']\n text_ = span['text']\n dist = prev[3]-curr[1]\n \n if dist > space_dist:\n line_text = f\"{line_text}{text_}\"\n else:\n line_text = f\"{line_text}|||{text_}\"\n prev = curr\n font_size.append(span['size'])\n font_name.append(span['font'])\n line_font_size.append(span['size'])\n line_font_name.append(span['font'])\n key = f\"{span['size']}\"\n if key in font_metadata.keys():\n font_metadata[key] += 1\n else:\n font_metadata[key] = 1\n\n initial_space = True\n line_text = line_text.strip('|||').strip()\n \n\n if len(line_text) == 0:\n line_counter += 1\n else:\n\n bbox_dict = get_bbox_dict(line_bbox)\n line_dict = dict(\n page_no=page,\n block_no=block_counter,\n line_no=line_counter,\n # line_id=line_id,\n line=line_text,\n size=line_font_size,\n font_name=line_font_name\n )\n # Combine bbox and line dict\n full_line_dict = {**line_dict, **bbox_dict}\n\n line_df.append(full_line_dict)\n line_counter += 1\n block_text = f\"{block_text}|||{line_text.encode('utf-8','ignore').decode('utf-8').strip()}\".strip('|||'\n ).strip()\n # block_text = f\"{block_text} {line_text.strip()}\".strip(\n # )\n\n if len(block_text) == 0:\n block_counter += 1\n else:\n # block_id = f'page{page}_block{block_counter}'\n \n bbox_dict = get_bbox_dict(internal_block['bbox'])\n block_dict = dict(\n page_no= page,\n block_no= block_counter,\n # block_id= block_id,\n text= block_text,\n size= font_size,\n font_name= font_name\n )\n # combine block and bbox information\n block_data = {**block_dict, **bbox_dict}\n text_df.append(block_data)\n block_counter += 1\n\n #now perform check here to merge or delete the block\n text_df = pd.DataFrame(text_df)\n line_df = pd.DataFrame(line_df)\n\n # --- perfrom checks --- \n text_df, line_df, word_df = block_mergerer(text_df, line_df, word_df)\n\n text_df_list.append(text_df)\n line_df_list.append(line_df)\n word_df_list.append(word_df)\n\n text_df = pd.concat(text_df_list,ignore_index=True)\n line_df = pd.concat(line_df_list,ignore_index=True)\n image_df = pd.DataFrame(image_df)\n word_df = pd.concat(word_df_list,ignore_index=True)\n\n text_df = text_df.assign(child = text_df.apply(relationship_establisher,args=(line_df,\n 'page_no','block_no'),axis=1))\n line_df = line_df.assign(child = line_df.apply(relationship_establisher,args=(word_df,\n 'page_no','block_no','line_no'),axis=1))\n \n font_information = font_data_assembler(font_metadata)\n self.metadata['Font Information'] = font_information\n\n text_df = text_df.assign(block_type = text_df.apply(tag_assigner,\n args=(font_information,'blocks'),axis=1))\n line_df = line_df.assign(block_type = line_df.apply(tag_assigner,\n args=(font_information,'lines'),axis=1))\n text_df = header_footer_assigner(text_df.copy(), 'text')\n text_df = text_df.assign(block=text_df['text'].apply(ord_unord_1))\n\n line_df = header_footer_assigner(line_df.copy(),'line')\n line_df = line_df.assign(block=line_df['line'].apply(ord_unord_1))\n\n list_filt = text_df['block'] == PdfImpl.NAME_LIST\n\n text_df.loc[list_filt, 'block_type'] = PdfImpl.NAME_LIST\n\n self.image_df = image_df\n self.text_df = text_df\n self.line_df = line_df\n self.word_df = word_df\n return self\n\n\n\n def get_json_data(self, pages_to_extract, exclude=None):\n block_group = self.text_df.groupby(['page_no'])\n line_group = self.line_df.groupby(['page_no'])\n word_group = self.word_df.groupby(['page_no'])\n # page_no = len(list(block_group['page_no'].count()))\n\n\n pages = []\n for page_ in pages_to_extract:\n print(\"Page no: \", page_)\n page = {'Index': page_, 'Blocks': []}\n # Since the input list is 1-indexed\n page_ = page_-1\n block_group_df = block_group.get_group(page_)\n\n # For output customisation\n if exclude:\n block_group_df = filter_df(block_group_df.copy(), exclude_list=exclude)\n\n data = list(block_group_df.apply(dataframe_to_dictionary_maker,axis=1,args=('Yes','text','No')).values)\n page['Blocks'].extend(data)\n\n line_group_df = line_group.get_group(page_)\n # For output customisation\n if exclude:\n line_group_df = filter_df(line_group_df.copy(), exclude_list=exclude)\n\n line_data = list(line_group_df.apply(dataframe_to_dictionary_maker,axis=1,args=('Yes','line','Yes')).values)\n page['Blocks'].extend(line_data)\n try:\n word_group_df = word_group.get_group(page_)\n word_data = list(word_group_df.apply(dataframe_to_dictionary_maker,axis=1,args=('No','word','No')).values)\n page['Blocks'].extend(word_data)\n pages.append(page)\n except KeyError as e:\n print(\"Skipping this page: \", page_, \" for word df\")\n print(\"Error: \", e)\n\n json_data = {\n \"Metadata\": self.metadata,\n \"Pages\": pages\n }\n return json_data\n\n\n def get_page_data(self, pno: int, extract_type: str):\n \"\"\"get_page_data \n Get page data from the pdf according to extract_type\n\n Arguments:\n pno {int} -- Page no to extract\n extract_type {str} -- Type of extraction\n\n Returns:\n dict, list, str -- data type based on the block\n \"\"\"\n page_data = self.doc.getPageText(pno, extract_type)\n return page_data\n\n\nclass PdfBlockTypes(Enum):\n \"\"\"PdfBlockTypes \n Block type enumerations\n\n Numbers representing the block types of a pdf (maping with PyMupdf)\n \"\"\"\n NORMAL = 0\n IMAGE = 1\n", "sub_path": "DT#23062020/pdfExtraction/implementations/pdf_reader_impl.py", "file_name": "pdf_reader_impl.py", "file_ext": "py", "file_size_in_byte": 13957, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "utils.validations.check_file", "line_number": 79, "usage_type": "call"}, {"api_name": "utils.validations", "line_number": 79, "usage_type": "name"}, {"api_name": "fitz.Document", "line_number": 80, "usage_type": "call"}, {"api_name": "implementations.table_proc_impl.TableImpl", "line_number": 94, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 111, "usage_type": "call"}, {"api_name": "utils.utils.check_table_area", "line_number": 119, "usage_type": "argument"}, {"api_name": "utils.pdf_impl_utils.get_bbox_dict", "line_number": 195, "usage_type": "call"}, {"api_name": "utils.pdf_impl_utils.get_bbox_dict", "line_number": 220, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 235, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 236, "usage_type": "call"}, {"api_name": "utils.pdf_impl_utils.block_mergerer", "line_number": 239, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 245, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 246, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 247, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 248, "usage_type": "call"}, {"api_name": "utils.pdf_impl_utils.relationship_establisher", "line_number": 250, "usage_type": "argument"}, {"api_name": "utils.pdf_impl_utils.relationship_establisher", "line_number": 252, "usage_type": "argument"}, {"api_name": "utils.pdf_impl_utils.font_data_assembler", "line_number": 255, "usage_type": "call"}, {"api_name": "utils.pdf_impl_utils.tag_assigner", "line_number": 258, "usage_type": "argument"}, {"api_name": "utils.pdf_impl_utils.tag_assigner", "line_number": 260, "usage_type": "argument"}, {"api_name": "utils.pdf_impl_utils.header_footer_assigner", "line_number": 262, "usage_type": "call"}, {"api_name": "utils.pdf_impl_utils.ord_unord_1", "line_number": 263, "usage_type": "argument"}, {"api_name": "utils.pdf_impl_utils.header_footer_assigner", "line_number": 265, "usage_type": "call"}, {"api_name": "utils.pdf_impl_utils.ord_unord_1", "line_number": 266, "usage_type": "argument"}, {"api_name": "utils.pdf_impl_utils.filter_df", "line_number": 297, "usage_type": "call"}, {"api_name": "utils.pdf_impl_utils.dataframe_to_dictionary_maker", "line_number": 299, "usage_type": "argument"}, {"api_name": "utils.pdf_impl_utils.filter_df", "line_number": 305, "usage_type": "call"}, {"api_name": "utils.pdf_impl_utils.dataframe_to_dictionary_maker", "line_number": 307, "usage_type": "argument"}, {"api_name": "utils.pdf_impl_utils.dataframe_to_dictionary_maker", "line_number": 311, "usage_type": "argument"}, {"api_name": "enum.Enum", "line_number": 340, "usage_type": "name"}]} +{"seq_id": "321624133", "text": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Rectangle\nimport fire\n\ndef get_high(data):\n data = data[:, 1]\n xy = []\n for idx in range(1, len(data)-1):\n a, b, c = idx-1, idx, idx+1\n if data[a] < data[b] > data[c]:\n xy.append([idx, data[b]])\n return np.array(xy)\n\ndef get_low(data):\n data = data[:, 1]\n xy = []\n for idx in range(1, len(data)-1):\n a, b, c = idx-1, idx, idx+1\n if data[a] > data[b] < data[c]:\n xy.append([idx, data[b]])\n return np.array(xy)\n\ndef candlestick(data, ax):\n '''\n bull_candle = []\n bear_candle = []\n for idx, d in enumerate(data):\n if d[0] < d[1]:\n bull_candle.append(idx)\n else:\n bear_candle.append(idx)\n ax.vlines(x=bull_candle, ymin=data[bull_candle,1],\n ymax=data[bull_candle,0], colors='green', lw=3)\n ax.vlines(x=bull_candle, ymin=data[bull_candle,3],\n ymax=data[bull_candle,2], colors='green', lw=1)\n\n ax.vlines(x=bear_candle, ymin=data[bear_candle,1],\n ymax=data[bear_candle,0], colors='red', lw=3)\n ax.vlines(x=bear_candle, ymin=data[bear_candle,3],\n ymax=data[bear_candle,2], colors='red', lw=1)\n '''\n\n lw = 1\n for idx, d in enumerate(data):\n if d[0] < d[1]:\n #bull_candle.append(idx)\n #print(f'open: {idx}, {d[0]:5f}, {d[1]-d[0]:.5f}')\n #ax.add_patch(Rectangle((idx,d[0]), 2, d[1]-d[0]))\n ax.add_patch(Rectangle((idx-lw/2,d[0]),lw,d[1]-d[0],\n edgecolor='green',\n facecolor='green'))\n ax.add_patch(Rectangle((idx,d[3]),0.1,d[2]-d[3],\n edgecolor='green',\n facecolor='green'))\n else:\n #bear_candle.append(idx)\n #ax.add_patch(Rectangle((idx,d[1]), 1, d[0]-d[1]))\n #print(f'close: {idx}, {d[0]:5f}, {d[0]-d[1]:.5f}')\n ax.add_patch(Rectangle((idx-lw/2,d[1]),lw,d[0]-d[1],\n edgecolor='red',\n facecolor='red'))\n ax.add_patch(Rectangle((idx,d[3]),0.1,d[2]-d[3],\n edgecolor='red',\n facecolor='red'))\n plt.show()\n \n\ndef back_test():\n import os\n import sys\n sys.path.append(os.getcwd())\n from practice_data import PracticeData2\n\n ptd = PracticeData2('EURUSD', 1)\n all_data = ptd.data\n sample = all_data[:10]\n high = get_high(sample)\n low = get_low(sample)\n\n fig, ax = plt.subplots(figsize=(10,8))\n ax.plot(sample[:, 1], alpha=0)\n #ax.scatter(high[:, 0], high[:, 1], c='green')\n #ax.scatter(low[:, 0], low[:, 1], c='red')\n candlestick(sample, ax)\n \n\n plt.show()\n plt.close()\n\n\nif __name__ == '__main__':\n fire.Fire()\n\n", "sub_path": "strategies/candlestick_bible.py", "file_name": "candlestick_bible.py", "file_ext": "py", "file_size_in_byte": 2788, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "numpy.array", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "sys.path.append", "line_number": 73, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 73, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 73, "usage_type": "call"}, {"api_name": "practice_data.PracticeData2", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "fire.Fire", "line_number": 94, "usage_type": "call"}]} +{"seq_id": "571498718", "text": "# Copyright (c) 2020 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\nfrom __future__ import print_function\n\nimport inspect\nimport unittest\n\nimport numpy as np\nimport paddle.fluid as fluid\nfrom paddle.fluid.core import EnforceNotMet\nfrom paddle.fluid.dygraph.dygraph_to_static.error import ERROR_DATA, ErrorData\nfrom paddle.fluid.dygraph.dygraph_to_static.origin_info import unwrap\nfrom paddle.fluid.dygraph.jit import declarative\n\n\ndef inner_func():\n fluid.layers.fill_constant(shape=[1, 2], value=9, dtype=\"int\")\n return\n\n\n@declarative\ndef func_error_in_compile_time(x):\n x = fluid.dygraph.to_variable(x)\n inner_func()\n if fluid.layers.mean(x) < 0:\n x_v = x - 1\n else:\n x_v = x + 1\n return x_v\n\n\n@declarative\ndef func_error_in_compile_time_2(x):\n x = fluid.dygraph.to_variable(x)\n x = fluid.layers.reshape(x, shape=[1, 2])\n return x\n\n\n@declarative\ndef func_error_in_runtime(x, iter_num=3):\n x = fluid.dygraph.to_variable(x)\n two = fluid.layers.fill_constant(shape=[1], value=2, dtype=\"int32\")\n x = fluid.layers.reshape(x, shape=[1, two])\n return x\n\n\nclass TestErrorInCompileTime(unittest.TestCase):\n def setUp(self):\n self.set_func()\n self.set_input()\n self.set_exception_type()\n\n def set_func(self):\n self.func = func_error_in_compile_time\n\n def set_exception_type(self):\n self.exception_type = TypeError\n\n def set_input(self):\n self.input = np.ones([3, 2])\n\n def set_message(self):\n self.expected_message = \\\n ['File \"{}\", line 36, in func_error_in_compile_time'.format(self.filepath),\n 'inner_func()',\n 'File \"{}\", line 29, in inner_func'.format(self.filepath),\n 'fluid.layers.fill_constant(shape=[1, 2], value=9, dtype=\"int\")',\n ]\n\n def _test_create_message(self, error_data):\n self.filepath = inspect.getfile(unwrap(self.func))\n self.set_message()\n error_message = error_data.create_message()\n\n self.assertIn('In user code:', error_message)\n for m in self.expected_message:\n self.assertIn(m, error_message)\n\n def test(self):\n with fluid.dygraph.guard():\n with self.assertRaises(self.exception_type) as cm:\n self.func(self.input)\n exception = cm.exception\n error_data = getattr(exception, ERROR_DATA)\n self.assertIsInstance(error_data, ErrorData)\n self._test_create_message(error_data)\n\n\nclass TestErrorInCompileTime2(TestErrorInCompileTime):\n def set_func(self):\n self.func = func_error_in_compile_time_2\n\n def set_exception_type(self):\n self.exception_type = EnforceNotMet\n\n def set_message(self):\n\n self.expected_message = \\\n [\n 'File \"{}\", line 47, in func_error_in_compile_time_2'.format(self.filepath),\n 'x = fluid.layers.reshape(x, shape=[1, 2])'\n ]\n\n\nclass TestErrorInRuntime(TestErrorInCompileTime):\n def set_func(self):\n self.func = func_error_in_runtime\n\n def set_exception_type(self):\n self.exception_type = EnforceNotMet\n\n def set_message(self):\n self.expected_message = \\\n [\n 'File \"{}\", line 55, in func_error_in_runtime'.format(self.filepath),\n 'x = fluid.layers.reshape(x, shape=[1, two])'\n ]\n\n def _test_create_message(self, error_data):\n self.filepath = inspect.getfile(unwrap(self.func))\n self.set_message()\n\n with self.assertRaises(ValueError):\n error_data.create_message()\n\n error_data.in_runtime = False\n error_message = error_data.create_message()\n\n self.assertIn('In user code:', error_message)\n for m in self.expected_message:\n self.assertIn(m, error_message)\n\n\nif __name__ == '__main__':\n unittest.main()\n", "sub_path": "python/paddle/fluid/tests/unittests/dygraph_to_static/test_error.py", "file_name": "test_error.py", "file_ext": "py", "file_size_in_byte": 4421, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "paddle.fluid.layers.fill_constant", "line_number": 29, "usage_type": "call"}, {"api_name": "paddle.fluid.layers", "line_number": 29, "usage_type": "attribute"}, {"api_name": "paddle.fluid", "line_number": 29, "usage_type": "name"}, {"api_name": "paddle.fluid.dygraph.to_variable", "line_number": 35, "usage_type": "call"}, {"api_name": "paddle.fluid.dygraph", "line_number": 35, "usage_type": "attribute"}, {"api_name": "paddle.fluid", "line_number": 35, "usage_type": "name"}, {"api_name": "paddle.fluid.layers.mean", "line_number": 37, "usage_type": "call"}, {"api_name": "paddle.fluid.layers", "line_number": 37, "usage_type": "attribute"}, {"api_name": "paddle.fluid", "line_number": 37, "usage_type": "name"}, {"api_name": "paddle.fluid.dygraph.jit.declarative", "line_number": 33, "usage_type": "name"}, {"api_name": "paddle.fluid.dygraph.to_variable", "line_number": 46, "usage_type": "call"}, {"api_name": "paddle.fluid.dygraph", "line_number": 46, "usage_type": "attribute"}, {"api_name": "paddle.fluid", "line_number": 46, "usage_type": "name"}, {"api_name": "paddle.fluid.layers.reshape", "line_number": 47, "usage_type": "call"}, {"api_name": "paddle.fluid.layers", "line_number": 47, "usage_type": "attribute"}, {"api_name": "paddle.fluid", "line_number": 47, "usage_type": "name"}, {"api_name": "paddle.fluid.dygraph.jit.declarative", "line_number": 44, "usage_type": "name"}, {"api_name": "paddle.fluid.dygraph.to_variable", "line_number": 53, "usage_type": "call"}, {"api_name": "paddle.fluid.dygraph", "line_number": 53, "usage_type": "attribute"}, {"api_name": "paddle.fluid", "line_number": 53, "usage_type": "name"}, {"api_name": "paddle.fluid.layers.fill_constant", "line_number": 54, "usage_type": "call"}, {"api_name": "paddle.fluid.layers", "line_number": 54, "usage_type": "attribute"}, {"api_name": "paddle.fluid", "line_number": 54, "usage_type": "name"}, {"api_name": "paddle.fluid.layers.reshape", "line_number": 55, "usage_type": "call"}, {"api_name": "paddle.fluid.layers", "line_number": 55, "usage_type": "attribute"}, {"api_name": "paddle.fluid", "line_number": 55, "usage_type": "name"}, {"api_name": "paddle.fluid.dygraph.jit.declarative", "line_number": 51, "usage_type": "name"}, {"api_name": "unittest.TestCase", "line_number": 59, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 72, "usage_type": "call"}, {"api_name": "inspect.getfile", "line_number": 83, "usage_type": "call"}, {"api_name": "paddle.fluid.dygraph.dygraph_to_static.origin_info.unwrap", "line_number": 83, "usage_type": "call"}, {"api_name": "paddle.fluid.dygraph.guard", "line_number": 92, "usage_type": "call"}, {"api_name": "paddle.fluid.dygraph", "line_number": 92, "usage_type": "attribute"}, {"api_name": "paddle.fluid", "line_number": 92, "usage_type": "name"}, {"api_name": "paddle.fluid.dygraph.dygraph_to_static.error.ERROR_DATA", "line_number": 96, "usage_type": "argument"}, {"api_name": "paddle.fluid.dygraph.dygraph_to_static.error.ErrorData", "line_number": 97, "usage_type": "argument"}, {"api_name": "paddle.fluid.core.EnforceNotMet", "line_number": 106, "usage_type": "name"}, {"api_name": "paddle.fluid.core.EnforceNotMet", "line_number": 122, "usage_type": "name"}, {"api_name": "inspect.getfile", "line_number": 132, "usage_type": "call"}, {"api_name": "paddle.fluid.dygraph.dygraph_to_static.origin_info.unwrap", "line_number": 132, "usage_type": "call"}, {"api_name": "unittest.main", "line_number": 147, "usage_type": "call"}]} +{"seq_id": "553156920", "text": "import web\nimport app\nimport json\nimport csv\n\nrender = web.template.render('application/controllers/') #En esta no se ocupa\n\nclass Alumnos:\n def GET(self):\n try:\n datos=web.input() #Los datos introducidos por el usuario se almacenaran en datos\n if datos['token']==\"1234\": #Si el usuario ingresa bien el token se declarara lo siguiente\n result=[] #Un arreglo\n result2={} #Un diccionario\n if datos['action']==\"get\": #Si accion es get va a hacer lo siguiente\n with open('static/csv/alumnos.csv','r') as csvfile: #Ruta del archivo csv que va a leer, r es de lectura, csvfile es una variable cualquiera\n reader = csv.DictReader(csvfile) #Lector del archivo, DictReader te almacena los datos como en diccionario en este caso en la variable reader\n for row in reader: #Lee la primer fila y la manda la arreglo\n result.append(row) #Lo manda al arreglo result\n result['Version']=\"0.1.0\"\n result2['status']=\"200 OK\"\n result2['alumnos']=result #Result2 en la posicion alumnos, sera lo que va a almacenar en result\n return json.dumps(result2) #Va a regresar un json del result2 que es lo que va almacenando el arreglo\n else: #Si accion no es get va a poner comando no encontrado\n result2={}\n result['Version']=\"0.1.0\"\n result2['status']=\"Command not found\"\n return json.dumps(result2)\n else:\n result={}\n result['Version']=\"0.1.0\"\n result['status']=\"Los datos insertados son incorrectos\"\n return json.dumps(result)\n except Exception:\n result={}\n result['Version']=\"0.1.0\"\n result['status']=\"Faltan valores por insertar\"\n return json.dumps(result)", "sub_path": "application/controllers/alumnos.py", "file_name": "alumnos.py", "file_ext": "py", "file_size_in_byte": 2123, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "web.template.render", "line_number": 6, "usage_type": "call"}, {"api_name": "web.template", "line_number": 6, "usage_type": "attribute"}, {"api_name": "web.input", "line_number": 11, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 17, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 23, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 28, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 33, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "637811942", "text": "#!/usr/bin/env python\nimport time\nimport rospy\nfrom geometry_msgs.msg import Twist\nimport sys\nimport signal\nfrom std_msgs.msg import Empty\nimport numpy as np\nimport serial\nfrom std_msgs.msg import Empty\nimport os\nimport math\nfrom datetime import datetime\n#from tkinter import *\nfrom Tkinter import *\nimport json\nfrom nav_msgs.msg import Odometry\n\nfrom tf.msg import tfMessage\nfrom tf.transformations import quaternion_from_euler\nfrom tf.transformations import euler_from_quaternion\n#from tf.transformations import quaternion_multilply\nfrom math import pi\nimport tf.transformations\nfrom ar_track_alvar_msgs.msg import AlvarMarker, AlvarMarkers\n\n\nwaypointFile = \"square\"\nwptsfl = open(waypointFile)\nfolderName = raw_input(\"Enter the folder_name \")\nfileName = raw_input(\"Enter the file_name \")\nif not os.path.exists(folderName):\n os.makedirs(folderName)\nlog = open(folderName + \"/\" + fileName, 'w')\nar_data = open(\"ar_dat\", \"w\")\nser = serial.Serial(\"/dev/ttyACM0\", 576000)\nanchorSigmas = [0.000803, 0.000502, 0.000487]\n# MODE_SHORT_DATA_FAST_ACCURACY [-0.591, -0.572, -0.72]\nanchorOffsets = [-0.61, -0.58, -0.50]\n\nline = []\nposArray = []\ntheta = 0\nwptline = []\n\nyaw = [0.0, 0.0]\n\nrotVel = 0.1\n\n#position[0] = float(raw_input(\"Enter x \"))\n#position[1] = float(raw_input(\"Enter y \"))\n#master = Tk()\n\n#master.title('PID Tuning')\n# You can set the geometry attribute to change the root windows size\n# master.geometry(\"500x500\") # You want the size of the app to be 500x500\n# master.resizable(0, 0) # Don't allow resizing in the x or y direction\n\n\n##rollFrame = LabelFrame(master)\n#rollFrame.pack(fill=\"both\", expand=\"yes\")\n#\n#pitchFrame = LabelFrame(master)\n#pitchFrame.pack(fill=\"both\", expand=\"yes\")\n\n\npitchConsts = {}\nrollConsts = {}\nyawConsts = {}\n\npitchConsts[\"h\"] = 1.0\npitchConsts[\"kp\"] = 0.2\npitchConsts[\"ki\"] = 0\npitchConsts[\"kd\"] = 2 # 0.8\npitchConsts[\"u0\"] = 0.0\npitchConsts[\"e0\"] = 0.0\n\nrollConsts[\"h\"] = 1.0\nrollConsts[\"kp\"] = 0.1\nrollConsts[\"ki\"] = 0\nrollConsts[\"kd\"] = 2 # 0.8\nrollConsts[\"u0\"] = 0.0\nrollConsts[\"e0\"] = 0.0\n\nyawConsts[\"h\"] = 1.0\nyawConsts[\"kp\"] = 0.009\nyawConsts[\"ki\"] = 0\nyawConsts[\"kd\"] = 2 # 0.8\nyawConsts[\"u0\"] = 0.0\nyawConsts[\"e0\"] = 0.0\n\nconstantsFile = open(folderName + '/constants', 'w')\nconstants = {}\n\n\ndef getRollProp(event):\n rollConsts[\"kp\"] = RollProp.get()\n\n\ndef getRollInt(event):\n rollConsts[\"ki\"] = RollInt.get()\n\n\ndef getRollDer(event):\n rollConsts[\"kd\"] = RollDer.get()\n\n\ndef getPitchProp(event):\n pitchConsts[\"kp\"] = PitchProp.get()\n\n\ndef getPitchInt(event):\n pitchConsts[\"ki\"] = PitchInt.get()\n\n\ndef getPitchDer(event):\n pitchConsts[\"kd\"] = PitchDer.get()\n\n\n# RollProp = Scale(rollFrame, from_=0, to=1, orient=HORIZONTAL,\n# command=getRollProp, label=\"Roll Proportional\", resolution=0.001, length=500)\n# RollProp.set(rollConsts[\"kp\"])\n# RollProp.pack()\n#\n# RollInt = Scale(rollFrame, from_=0, to=0.01, orient=HORIZONTAL,\n# command=getRollInt, label=\"Roll Integration\", resolution=0.0001, length=1000)\n# RollInt.set(rollConsts[\"ki\"])\n# RollInt.pack()\n#\n# RollDer = Scale(rollFrame, from_=0, to=2, orient=HORIZONTAL,\n# command=getRollDer, label=\"Roll Deriative\", resolution=0.001, length=500)\n# RollDer.set(rollConsts[\"kd\"])\n# RollDer.pack()\n#\n#\n# PitchProp = Scale(pitchFrame, from_=0, to=1, orient=HORIZONTAL,\n# command=getPitchProp, label=\"Pitch Proportional\", resolution=0.001, length=500)\n# PitchProp.set(pitchConsts[\"kp\"])\n# PitchProp.pack()\n#\n# PitchInt = Scale(pitchFrame, from_=0, to=0.01, orient=HORIZONTAL,\n# command=getPitchInt, label=\"Pitch Integration\", resolution=0.0001, length=1000)\n# PitchInt.set(pitchConsts[\"ki\"])\n# PitchInt.pack()\n#\n# PitchDer = Scale(pitchFrame, from_=0, to=2, orient=HORIZONTAL,\n# command=getPitchDer, label=\"Pitch Deriative\", resolution=0.001, length=500)\n# PitchDer.set(pitchConsts[\"kd\"])\n# PitchDer.pack()\n\n\n#anchor1Pos = (0.0, 6.0)\n#anchor2Pos = (6.0, 0.0)\n#anchor3Pos = (0.0, 0.0)\n\nanchor1Pos = (0.0, 1.16)\nanchor2Pos = (0.0, 0.0)\nanchor3Pos = (0.78, 0.0)\n\nax = [min(anchor1Pos[0], anchor2Pos[0], anchor3Pos[0])-2.0, max(anchor1Pos[0], anchor2Pos[0], anchor3Pos[0])+2.0,\n min(anchor1Pos[1], anchor2Pos[1], anchor3Pos[1])-2.0, max(anchor1Pos[1], anchor2Pos[1], anchor3Pos[1])+2.0]\n\n\nglobal currAngle\n\n\n# Assume origin is p1\ndef getPos(p1, p2, p3, r1, r2, r3):\n\n A = -2*p1[0] + 2*p2[0]\n B = -2*p1[1] + 2*p2[1]\n C = r1**2 - r2**2 - p1[0]**2 + p2[0]**2 - p1[1]**2 + p2[1]**2\n\n D = -2*p2[0] + 2*p3[0]\n E = -2*p2[1] + 2*p3[1]\n F = r2**2 - r3**2 - p2[0]**2 + p3[0]**2 - p2[1]**2 + p3[1]**2\n\n pos = (round((C*E - F*B)/(E*A - B*D), 2),\n round((C*D - A*F)/(B*D - A*E), 2))\n return pos\n\n\nvel = Twist()\n\n\ndef signalHandler(a, b):\n global contants\n global pitchConsts\n global rollConsts\n if a == 2:\n print(\"terminated\")\n emp = Empty()\n # os.killpg(os.getpgid(process.pid), signal.SIGTERM)\n # os.killpg(os.getpgid(process.pid), signal.SIGTERM)\n pub_land.publish(emp)\n print(\"landed\")\n\n constants[\"pitchConstants\"] = pitchConsts\n constants[\"rollConstants\"] = rollConsts\n jsn = json.dumps(constants)\n constantsFile.write(jsn)\n sys.exit(0)\n\n\ndef node_init():\n rospy.init_node(\"bebop_node\")\n runloop()\n\n\ndef set_para(lx, ly, lz, ax, ay, az):\n vel.linear.x = lx\n vel.linear.y = ly\n vel.linear.z = lz\n vel.angular.x = ax\n vel.angular.y = ay\n vel.angular.z = az\n\n\ndef hover():\n set_para(0.0, 0.0, 0.0, 0.0, 0.0, 0.0)\n pub.publish(vel)\n\n\n\"\"\"\nPid output calculating function\n e - error between measured and desired poistions\n k - current time\n Constants :- \n h - time step \n kp - proporitional error\n ki - integration time constant\n kd - derivative time constant\n u0 - initial integration state\n e0 - initial error\n\"\"\"\n\n\ndef calcPid(e, e_prev, ui_prev, deltaTime, consts):\n\n h = consts[\"h\"]\n kp = consts[\"kp\"]\n ki = consts[\"ki\"]\n kd = consts[\"kd\"]\n u0 = consts[\"u0\"]\n e0 = consts[\"e0\"]\n\n ui = ui_prev + deltaTime*e\n ud = (e-e_prev)/deltaTime\n u = kp*e + ki*ui + kd*ud\n\n return u, e, ui\n\n\nrangeNumSamples = 15\nalpha = 2.0/(rangeNumSamples + 1.0)\n\n\ndef quaternion_to_euler_angle(w, x, y, z):\n ysqr = y * y\n t0 = +2.0 * (w * x + y * z)\n t1 = +1.0 - 2.0 * (x * x + ysqr)\n X = math.degrees(math.atan2(t0, t1))\n t2 = +2.0 * (w * y - z * x)\n t2 = +1.0 if t2 > +1.0 else t2\n t2 = -1.0 if t2 < -1.0 else t2\n Y = math.degrees(math.asin(t2))\n t3 = +2.0 * (w * z + x * y)\n t4 = +1.0 - 2.0 * (ysqr + z * z)\n Z = math.degrees(math.atan2(t3, t4))\n return X, Y, Z\n\n\ndef getAngle():\n a = []\n stri = rospy.wait_for_message(\"/bebop/odom\", Odometry)\n val = stri.pose.pose.orientation\n # q = [stri.pose.pose.orientation.x,\n # stri.pose.pose.orientation.y,\n # stri.pose.pose.orientation.z,\n # stri.pose.pose.orientation.w]\n a = quaternion_to_euler_angle(stri.pose.pose.orientation.w,\n stri.pose.pose.orientation.x,\n stri.pose.pose.orientation.y,\n stri.pose.pose.orientation.z)\n return(a[2])\n\n\ndef runloop():\n global position\n k = 0\n now = 0.0\n deltaTime = 1\n t_old = 0.0\n converged = 0\n print(\"Starting\")\n raw_input(\"Press any key to start\")\n emp = Empty()\n pub_takeoff.publish(emp)\n startTime = time.time()\n started = 1\n startAngle = getAngle() # Get drone take off angle\n print(\"Took off at\" + str(startAngle))\n count = 0\n while(True):\n signal.signal(signal.SIGINT, signalHandler)\n try:\n wptline = wptsfl.readline()\n position = [float(i) for i in wptline.split(',')]\n waypointAngle = position[2]\n count = count+1\n k = 0\n mag = 5\n print(\"current position = \" + str(position))\n line = ser.readline()\n line = ser.readline()\n\n line = ser.readline()\n radialPos = [float(i) for i in line.split(',')]\n prevRadialPos = radialPos\n xMax, yMax = getPos(anchor1Pos, anchor2Pos, anchor3Pos,\n radialPos[0]+anchorOffsets[0], radialPos[1]+anchorOffsets[1], radialPos[2]+anchorOffsets[2])\n xMax = abs(xMax)\n yMax = abs(yMax)\n\n waypointAngle = waypointAngle + startAngle\n restoreAngle = startAngle\n if(waypointAngle > 180):\n waypointAngle -= 360\n if(waypointAngle < -180):\n waypointAngle += 360\n print(\"New angle is\", str(waypointAngle))\n\n eX_prev = 1\n eY_prev = 1\n eTheta_prev = 1\n\n eX_max = abs(position[0] - xMax)\n eY_max = abs(position[1] - yMax)\n\n if(eX_max < 1):\n eX_max = 1\n if(eY_max < 1):\n eY_max = 1\n\n print(\" exMax = \" + str(eX_max) + \"eYMax = \" + str(eY_max))\n uX_prev = 0\n uY_prev = 0\n uTheta_prev = 0\n\n # master.update()\n\n while(converged < 50):\n try:\n # master.update()\n line = ser.readline()\n radialPos = [float(i) for i in line.split(',')]\n radialPos = [radialPos[z]*alpha +\n prevRadialPos[z]*(1-alpha) for z in range(0, 3)]\n prevRadialPos = radialPos\n datx, daty = getPos(anchor1Pos, anchor2Pos, anchor3Pos,\n radialPos[0]+anchorOffsets[0], radialPos[1]+anchorOffsets[1], radialPos[2]+anchorOffsets[2])\n print(\"Current Position = \" + str(datx) + ',' + str(daty))\n #Error in x and y\n eX = (position[0] - datx)/eX_max # Distance\n eY = (position[1] - daty)/eY_max\n\n print(\"Ex \" + str(eX) + \" Ey \" +\n str(eY))\n#\n log.write(str(time.time()-startTime) + ',' +\n str(datx) + ',' + str(daty) + '\\n')\n\n rSteer, eX_prev, uX_prev = calcPid(\n eX, eX_prev, uX_prev, deltaTime, rollConsts)\n pSteer, eY_prev, uY_prev = calcPid(\n eY, eY_prev, uY_prev, deltaTime, pitchConsts)\n\n print(\"Pitch Steer = \" + str(pSteer) +\n \" Roll Steer = \" + str(-rSteer))\n print(\"\\n\")\n mag = math.sqrt(eX**2 + eY**2)\n if mag < 0.25:\n converged += 1\n # Motion constraints\n if(pSteer > 0.5):\n pSteer = 0.5\n if(pSteer < -0.5):\n pSteer = -0.5\n if(rSteer > 0.5):\n rSteer = 0.5\n if(pSteer < -0.5):\n rSteer = -0.5\n print(\"X = \" + str(position[0]))\n print(\"Y = \" + str(position[1]))\n # print(\"Steer Pitch\" + str(pSteer))\n # print(\"Steer Roll\" + str(rSteer))\n #set_para((pSteer), (-rSteer), 0.0, 0.0, 0.0, direction*thetaSteer)\n set_para((pSteer), (-rSteer), 0.0, 0.0, 0.0, 0.0)\n pub.publish(vel)\n except Exception as e:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n fname = os.path.split(\n exc_tb.tb_frame.f_code.co_filename)[1]\n print(exc_type, fname, exc_tb.tb_lineno)\n print(e)\n\n currAngle = getCurrAngle()\n eTheta = waypointAngle-currAngle\n while(abs(eTheta) > 2):\n #Error in yaw\n currAngle = getCurrAngle()\n eTheta = waypointAngle-currAngle\n direction = np.sign(eTheta)\n if(abs(eTheta) > 180):\n direction = direction*(-1)\n eTheta = eTheta + direction*360\n thetaSteer, eTheta_prev, uTheta_prev = calcPid(\n abs(eTheta), eTheta_prev, uTheta_prev, deltaTime, yawConsts)\n print(thetaSteer)\n set_para(0.0, 0.0, 0.0, 0.0, 0.0, thetaSteer*direction)\n pub.publish(vel)\n print(\"waypointTheta = \", str(waypointAngle), \" Magnitude = \",\n str(abs(eTheta)), \" Direction = \", str(direction))\n\n emp = Empty()\n pub_snap.publish(emp)\n emp = Empty()\n dat = rospy.wait_for_message(\"/ar_pose_marker\", AlvarMarker)\n ar_data.write(str(count)+\"\\n\"+str(dat)+\"\\n\\n\\n\\n\\n\\n\\n\")\n pub_snap.publish(emp)\n emp = Empty()\n pub_snap.publish(emp)\n\n currAngle = getCurrAngle()\n eTheta = restoreAngle - currAngle\n print(\"Restoring Theta\" + str(eTheta))\n while(abs(eTheta) > 2):\n #Error in yaw\n print(\"Yawing Back\")\n currAngle = getCurrAngle()\n eTheta = restoreAngle - currAngle\n direction = np.sign(eTheta)\n if(abs(eTheta) > 180):\n direction = direction*(-1)\n eTheta = eTheta + direction*360\n thetaSteer, eTheta_prev, uTheta_prev = calcPid(\n abs(eTheta), eTheta_prev, uTheta_prev, deltaTime, yawConsts)\n set_para(0.0, 0.0, 0.0, 0.0, 0.0, thetaSteer*direction)\n pub.publish(vel)\n print(\"waypointTheta = \", str(waypointAngle), \" Magnitude = \",\n str(abs(eTheta)), \" Direction = \", str(direction))\n\n converged = 0\n print(\"Popped\")\n\n except ValueError:\n exc_type, exc_obj, exc_tb = sys.exc_info()\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\n print(exc_type, fname, exc_tb.tb_lineno)\n\n\ndef odomCallback(data):\n\n global currAngle\n val = data.pose.pose.orientation\n a = quaternion_to_euler_angle(data.pose.pose.orientation.w,\n data.pose.pose.orientation.x,\n data.pose.pose.orientation.y,\n data.pose.pose.orientation.z)\n\n currAngle = a[2]\n recvOdom = True\n\n\ndef getCurrAngle():\n global currAngle\n return currAngle\n\n\ndef marker_callback(data):\n pass\n\n\nif __name__ == '__main__':\n\n pub = rospy.Publisher('/bebop/cmd_vel', Twist, queue_size=0)\n pub_land = rospy.Publisher('/bebop/land', Empty, queue_size=100)\n pub_takeoff = rospy.Publisher('/bebop/takeoff', Empty, queue_size=100)\n pub_snap = rospy.Publisher('/bebop/snapshot', Empty, queue_size=100)\n odom_sub = rospy.Subscriber('/bebop/odom', Odometry, odomCallback)\n markder_data = rospy.Subscriber(\n \"/ar_pose_marker\", AlvarMarkers, marker_callback)\n node_init()\n", "sub_path": "src/vision_ws/src/sort.py", "file_name": "sort.py", "file_ext": "py", "file_size_in_byte": 15202, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.exists", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 33, "usage_type": "call"}, {"api_name": "serial.Serial", "line_number": 36, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.Twist", "line_number": 183, "usage_type": "call"}, {"api_name": "std_msgs.msg.Empty", "line_number": 192, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 200, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 202, "usage_type": "call"}, {"api_name": "rospy.init_node", "line_number": 206, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 262, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 262, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 266, "usage_type": "call"}, {"api_name": "math.asin", "line_number": 266, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 269, "usage_type": "call"}, {"api_name": "math.atan2", "line_number": 269, "usage_type": "call"}, {"api_name": "rospy.wait_for_message", "line_number": 275, "usage_type": "call"}, {"api_name": "nav_msgs.msg.Odometry", "line_number": 275, "usage_type": "argument"}, {"api_name": "std_msgs.msg.Empty", "line_number": 297, "usage_type": "call"}, {"api_name": "time.time", "line_number": 299, "usage_type": "call"}, {"api_name": "signal.signal", "line_number": 305, "usage_type": "call"}, {"api_name": "signal.SIGINT", "line_number": 305, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 370, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 381, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 401, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 402, "usage_type": "call"}, {"api_name": "os.path", "line_number": 402, "usage_type": "attribute"}, {"api_name": "numpy.sign", "line_number": 413, "usage_type": "call"}, {"api_name": "std_msgs.msg.Empty", "line_number": 425, "usage_type": "call"}, {"api_name": "std_msgs.msg.Empty", "line_number": 427, "usage_type": "call"}, {"api_name": "rospy.wait_for_message", "line_number": 428, "usage_type": "call"}, {"api_name": "ar_track_alvar_msgs.msg.AlvarMarker", "line_number": 428, "usage_type": "argument"}, {"api_name": "std_msgs.msg.Empty", "line_number": 431, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 442, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 457, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 458, "usage_type": "call"}, {"api_name": "os.path", "line_number": 458, "usage_type": "attribute"}, {"api_name": "rospy.Publisher", "line_number": 486, "usage_type": "call"}, {"api_name": "geometry_msgs.msg.Twist", "line_number": 486, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 487, "usage_type": "call"}, {"api_name": "std_msgs.msg.Empty", "line_number": 487, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 488, "usage_type": "call"}, {"api_name": "std_msgs.msg.Empty", "line_number": 488, "usage_type": "argument"}, {"api_name": "rospy.Publisher", "line_number": 489, "usage_type": "call"}, {"api_name": "std_msgs.msg.Empty", "line_number": 489, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 490, "usage_type": "call"}, {"api_name": "nav_msgs.msg.Odometry", "line_number": 490, "usage_type": "argument"}, {"api_name": "rospy.Subscriber", "line_number": 491, "usage_type": "call"}, {"api_name": "ar_track_alvar_msgs.msg.AlvarMarkers", "line_number": 492, "usage_type": "argument"}]} +{"seq_id": "608841897", "text": "# Copyright 2012 Google Inc. 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\"\"\"Controllers for interactive and non-interactive widgets.\"\"\"\n\n__author__ = 'sll@google.com (Sean Lip)'\n\nimport collections\n\nfrom core.controllers import base\nfrom core.domain import widget_domain\nfrom core.platform import models\nuser_services = models.Registry.import_user_services()\nimport feconf\n\n\nclass WidgetRepositoryPage(base.BaseHandler):\n \"\"\"Displays the widget repository page.\"\"\"\n\n def get(self):\n \"\"\"Returns the widget repository page.\"\"\"\n if self.request.get('iframed') == 'true':\n self.values['iframed'] = True\n if self.request.get('interactive') == 'true':\n self.values['interactive'] = True\n if 'parent_index' in self.request.GET.keys():\n self.values['parent_index'] = self.request.get('parent_index')\n if user_services.is_current_user_admin(self.request):\n self.values['admin'] = True\n self.render_template('editor/widget_repository.html')\n\n\nclass WidgetRepositoryHandler(base.BaseHandler):\n \"\"\"Populates the widget repository pages.\"\"\"\n\n def get(self, widget_type):\n \"\"\"Handles GET requests.\"\"\"\n try:\n widget_list = widget_domain.Registry.get_widgets_of_type(\n widget_type)\n except Exception:\n raise self.PageNotFoundException\n\n widgets = collections.defaultdict(list)\n for widget in widget_list:\n widgets[widget.category].append(\n widget.get_widget_instance_dict({}, {}))\n\n for category in widgets:\n widgets[category].sort()\n\n response = {'widgets': widgets}\n\n if widget_type == feconf.NONINTERACTIVE_PREFIX:\n parent_index = self.request.get('parent_index')\n if parent_index is None:\n raise Exception(\n 'Non-interactive widgets require a parent_index.')\n else:\n response['parent_index'] = parent_index\n\n self.render_json(response)\n\n\nclass WidgetHandler(base.BaseHandler):\n \"\"\"Returns instance dicts for individual widgets.\"\"\"\n\n REQUIRE_PAYLOAD_CSRF_CHECK = False\n\n def post(self, widget_type, widget_id):\n \"\"\"Handles POST requests for parameterized widgets.\"\"\"\n\n customization_args = self.payload.get('customization_args', {})\n\n widget = widget_domain.Registry.get_widget_by_id(\n widget_type, widget_id)\n\n response = {\n 'widget': widget.get_widget_instance_dict(\n customization_args, {}, preview_mode=True),\n }\n\n if widget_type == feconf.NONINTERACTIVE_PREFIX:\n parent_index = self.request.get('parent_index')\n if parent_index is None:\n raise Exception(\n 'Non-interactive widgets require a parent_index.')\n else:\n response['parent_index'] = parent_index\n\n self.render_json(response)\n", "sub_path": "core/controllers/widgets.py", "file_name": "widgets.py", "file_ext": "py", "file_size_in_byte": 3495, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "core.platform.models.Registry.import_user_services", "line_number": 24, "usage_type": "call"}, {"api_name": "core.platform.models.Registry", "line_number": 24, "usage_type": "attribute"}, {"api_name": "core.platform.models", "line_number": 24, "usage_type": "name"}, {"api_name": "core.controllers.base.BaseHandler", "line_number": 28, "usage_type": "attribute"}, {"api_name": "core.controllers.base", "line_number": 28, "usage_type": "name"}, {"api_name": "core.controllers.base.BaseHandler", "line_number": 44, "usage_type": "attribute"}, {"api_name": "core.controllers.base", "line_number": 44, "usage_type": "name"}, {"api_name": "core.domain.widget_domain.Registry.get_widgets_of_type", "line_number": 50, "usage_type": "call"}, {"api_name": "core.domain.widget_domain.Registry", "line_number": 50, "usage_type": "attribute"}, {"api_name": "core.domain.widget_domain", "line_number": 50, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 55, "usage_type": "call"}, {"api_name": "feconf.NONINTERACTIVE_PREFIX", "line_number": 65, "usage_type": "attribute"}, {"api_name": "core.controllers.base.BaseHandler", "line_number": 76, "usage_type": "attribute"}, {"api_name": "core.controllers.base", "line_number": 76, "usage_type": "name"}, {"api_name": "core.domain.widget_domain.Registry.get_widget_by_id", "line_number": 86, "usage_type": "call"}, {"api_name": "core.domain.widget_domain.Registry", "line_number": 86, "usage_type": "attribute"}, {"api_name": "core.domain.widget_domain", "line_number": 86, "usage_type": "name"}, {"api_name": "feconf.NONINTERACTIVE_PREFIX", "line_number": 94, "usage_type": "attribute"}]} +{"seq_id": "202535865", "text": "from django.contrib.auth import get_user_model\nfrom django.contrib.auth.models import Permission, Group\nfrom rest_framework import serializers\n\nfrom apps.user import models\n\nUser = get_user_model()\n\n\nclass PermissionsSerializers(serializers.ModelSerializer):\n \"\"\"\n 权限表序列化\n \"\"\"\n content_type_id = serializers.IntegerField(source=\"content_type.id\")\n content_type = serializers.CharField(source=\"content_type.app_label\")\n\n class Meta:\n model = Permission\n fields = ('id', 'name', 'content_type_id', 'content_type', 'codename')\n\n\nclass GroupsSerializers(serializers.ModelSerializer):\n \"\"\"\n 用户组序列化\n \"\"\"\n # 用户组和权限多对多关系\n permissions = serializers.SerializerMethodField()\n\n # 序列化用户组对应的权限\n # 钩子函数序列化必须以get_开头\n def get_permissions(self, obj):\n permission = obj.permissions.all()\n perm = PermissionsSerializers(permission, many=True)\n return perm.data\n\n class Meta:\n model = Group\n fields = ('id', 'name', 'permissions')\n\n\nclass UserSerializers(serializers.ModelSerializer):\n \"\"\"\n 用户表序列化\n \"\"\"\n gender = serializers.CharField(source=\"get_gender_display\")\n groups = serializers.SerializerMethodField()\n permissions = serializers.SerializerMethodField()\n\n # 序列化用户对应的权限组\n def get_groups(self, obj):\n groups = obj.groups.all()\n group = GroupsSerializers(groups, many=True)\n group_names = []\n for g in group.data:\n group_names.append(g['name'])\n\n return group_names\n\n # 序列化用户对应的权限\n def get_permissions(self, obj):\n permissions = obj.user_permissions.all()\n perm = PermissionsSerializers(permissions, many=True)\n return obj.get_all_permissions()\n\n class Meta:\n model = User\n fields = ('id', 'username', 'name', 'email', 'gender', 'cellphone', 'groups', 'permissions')\n\n\n\n", "sub_path": "apps/user/serializers.py", "file_name": "serializers.py", "file_ext": "py", "file_size_in_byte": 2007, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.contrib.auth.get_user_model", "line_number": 7, "usage_type": "call"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 10, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 10, "usage_type": "name"}, {"api_name": "rest_framework.serializers.IntegerField", "line_number": 14, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 14, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 15, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 15, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.Permission", "line_number": 18, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 22, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 22, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 27, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 27, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.Group", "line_number": 37, "usage_type": "name"}, {"api_name": "rest_framework.serializers.ModelSerializer", "line_number": 41, "usage_type": "attribute"}, {"api_name": "rest_framework.serializers", "line_number": 41, "usage_type": "name"}, {"api_name": "rest_framework.serializers.CharField", "line_number": 45, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 45, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 46, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 46, "usage_type": "name"}, {"api_name": "rest_framework.serializers.SerializerMethodField", "line_number": 47, "usage_type": "call"}, {"api_name": "rest_framework.serializers", "line_number": 47, "usage_type": "name"}]} +{"seq_id": "323099771", "text": "import logging\n\nfrom itertools import islice\nfrom django.db import connection\nfrom django.db import connections\nfrom django.core.management.base import BaseCommand\n\nfrom sale_portal.merchant.models import QrMerchant\nfrom sale_portal.utils.cronjob_util import cron_create, cron_update\n\n\nclass Command(BaseCommand):\n help = 'Synchronize table: qr_merchant-mms to table: qr_merchant daily'\n\n def get_query(self, limit=1000, offset=0):\n query = 'select * from qr_merchant order by \"ID\" limit ' + str(limit) + ' offset ' + str(offset)\n return query\n\n def get_count_qr_merchant(self):\n cursor = connections['mms'].cursor()\n cursor.execute(\"select count(*) as total from qr_merchant\")\n row = cursor.fetchone()\n return row[0] if len(row) == 1 else 0\n\n def handle(self, *args, **options):\n cronjob = cron_create(name='qr_merchant_sync_daily', type='merchant')\n\n try:\n\n self.stdout.write(self.style.WARNING('Start qr_merchant sync daily processing...'))\n\n limit, offset = 1000, 0\n\n count_qr_merchant = self.get_count_qr_merchant()\n if count_qr_merchant == 0:\n raise Exception('Exception: qr_merchant count == 0')\n\n # Truncate table qr_merchant before synchronize all data from MMS\n cursor = connection.cursor()\n cursor.execute('TRUNCATE TABLE \"{0}\" RESTART IDENTITY'.format(QrMerchant._meta.db_table))\n\n print('Truncate table qr_merchant before synchronize all data from MMS')\n\n while offset < count_qr_merchant:\n query = self.get_query(limit=limit, offset=offset)\n with connections['mms'].cursor() as cursor:\n cursor.execute(query)\n columns = [col[0] for col in cursor.description]\n data_cursor = [\n dict(zip(columns, row))\n for row in cursor.fetchall()\n ]\n objs = (QrMerchant(\n id=int(item['ID']),\n merchant_code=item['MERCHANT_CODE'],\n service_code=item['SERVICE_CODE'],\n merchant_brand=item['MERCHANT_BRAND'],\n merchant_name=item['MERCHANT_NAME'],\n merchant_type=item['MERCHANT_TYPE'],\n address=item['ADDRESS'],\n description=item['DESCRIPTION'],\n status=item['STATUS'],\n website=item['WEBSITE'],\n master_merchant_code=item['MASTER_MERCHANT_CODE'],\n province_code=item['PROVINCE_CODE'],\n district_code=item['DISTRICT_CODE'],\n department=item['DEPARTMENT_ID'],\n staff=item['STAFF_ID'],\n genqr_checksum=item['GENQR_CHECKSUM'],\n genqr_accesskey=item['GENQR_ACCESSKEY'],\n switch_code=item['SWITCH_CODE'],\n created_date=item['CREATED_DATE'],\n modify_date=item['MODIFY_DATE'],\n process_user=item['PROCESS_USER'],\n denied_approve_desc=item['DENIED_APPROVE_DESC'],\n create_user=item['CREATE_USER'],\n org_status=item['ORG_STATUS'],\n email_vnpay=item['EMAIL_VNPAY'],\n pass_email_vnpay=item['PASS_EMAIL_VNPAY'],\n process_addition=item['PROCESS_ADDITION'],\n denied_approve_code=item['DENIED_APPROVE_CODE'],\n business_address=item['BUSINESS_ADDRESS'],\n app_user=item['APP_USER'],\n pin_code=item['PIN_CODE'],\n provider_code=item['PROVIDER_CODE'],\n wards_code=item['WARDS_CODE'],\n ) for item in data_cursor)\n\n batch = list(islice(objs, limit))\n\n QrMerchant.objects.bulk_create(batch, limit)\n\n print('QrMerchant synchronize processing. Row: ', offset)\n\n offset = offset + limit\n\n self.stdout.write(self.style.SUCCESS('Finish qr_merchant synchronize processing!'))\n\n cron_update(cronjob, status=1)\n\n except Exception as e:\n logging.error('Job qr_merchant_sync_daily exception: %s', e)\n cron_update(cronjob, status=2, description=str(e))\n", "sub_path": "sale_portal/merchant/management/commands/qr_merchant_sync_daily.py", "file_name": "qr_merchant_sync_daily.py", "file_ext": "py", "file_size_in_byte": 4415, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.core.management.base.BaseCommand", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.connections", "line_number": 20, "usage_type": "name"}, {"api_name": "sale_portal.utils.cronjob_util.cron_create", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.connection.cursor", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.connection", "line_number": 39, "usage_type": "name"}, {"api_name": "sale_portal.merchant.models.QrMerchant._meta", "line_number": 40, "usage_type": "attribute"}, {"api_name": "sale_portal.merchant.models.QrMerchant", "line_number": 40, "usage_type": "name"}, {"api_name": "django.db.connections", "line_number": 46, "usage_type": "name"}, {"api_name": "sale_portal.merchant.models.QrMerchant", "line_number": 53, "usage_type": "call"}, {"api_name": "itertools.islice", "line_number": 89, "usage_type": "call"}, {"api_name": "sale_portal.merchant.models.QrMerchant.objects.bulk_create", "line_number": 91, "usage_type": "call"}, {"api_name": "sale_portal.merchant.models.QrMerchant.objects", "line_number": 91, "usage_type": "attribute"}, {"api_name": "sale_portal.merchant.models.QrMerchant", "line_number": 91, "usage_type": "name"}, {"api_name": "sale_portal.utils.cronjob_util.cron_update", "line_number": 99, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 102, "usage_type": "call"}, {"api_name": "sale_portal.utils.cronjob_util.cron_update", "line_number": 103, "usage_type": "call"}]} +{"seq_id": "250676605", "text": "\n\n######################################################################################\n#\n# YOU CAN / SHOULD EDIT THE FOLLOWING SETTING\n#\n######################################################################################\n\nPKG_NAME = 'dynamet'\n\n\nVERSION = (2, 8, 6)\n\n### install package as emzed extension ? #############################################\n# -> package will appear in emzed.ext namespace after installation\n\nIS_EXTENSION = True\n\n\n### install package as emzed app ? ##################################################\n# -> can be started as app.dynamet()\n# set this variable to None if this is a pure extension and not an emzed app\n\nAPP_MAIN = \"dynamet.app:run\"\n\n\n### author information ###############################################################\n\nAUTHOR = 'Patrick Kiefer'\nAUTHOR_EMAIL = 'kiefer@micro.biol.ethz.ch'\nAUTHOR_URL = ''\n\n# HINT: to modify version edit dynamet/version.py !!!\n\n\n### package descriptions #############################################################\n\nDESCRIPTION = \"an automated pipeline for LC-MS based dynamic labeling data\"\nLONG_DESCRIPTION = \"\"\"\nDynaMet is a fully automated workflow for liquid chromatography mass spectrometry (LC-MS) raw\n data analyses allowing for metabolome-wide investigations of dynamic isotope labeling experiments. \n DynaMet enables untargeted extraction of labeling profiles by grouping metabolite features \n in different samples with isotopic patterns changing over time. \n Integrated tools for expressive data visualization enhance result inspection. \n REMARK: For straight forward installation of DynaMet type command `!pip install dynamet` in emzed\n IPython console and press enter. DynaMet app will be available after opening a new IPython \n console.\n Changes in Version 2.8.0:\n - provides additional fitting function weibull.\n - allows opening results from dynamet projects with read rights only\n - improved feature grouping (low abundant peaks, UPLC data)\n - includes update to use latest hires package 0.0.14\n - requires emzed version 2.24.5 or higher\n \n\n\"\"\"\n\nLICENSE = \"http://opensource.org/licenses/GPL-3.0\"\n\n\n######################################################################################\n# #\n# DO NOT TOUCH THE CODE BELOW UNLESS YOU KNOW WHAT YOU DO !!!! #\n# #\n# #\n# _.--\"\"--._ #\n# / _ _ \\ #\n# _ ( (_\\ /_) ) _ #\n# { \\._\\ /\\ /_./ } #\n# /_\"=-.}______{.-=\"_\\ #\n# _ _.=('\"\"')=._ _ #\n# (_'\"_.-\"`~~`\"-._\"'_) #\n# {_\" \"_} #\n# #\n######################################################################################\n\n\n#from dynamet.version import version as VERSION\n\n\nVERSION_STRING = \"%s.%s.%s\" % VERSION\n\nENTRY_POINTS = dict()\nENTRY_POINTS['emzed_package'] = [ \"package = \" + PKG_NAME, ]\nif IS_EXTENSION:\n ENTRY_POINTS['emzed_package'].append(\"extension = \" + PKG_NAME)\nif APP_MAIN is not None:\n ENTRY_POINTS['emzed_package'].append(\"main = %s\" % APP_MAIN)\n\n\nif __name__ == \"__main__\": # allows import setup.py for version checking\n\n from setuptools import setup # import distutils.config\n\n def patched(self):\n return dict(realm=\"pypi\",\n username='pkiefer',\n password='12karcher@dynamet',\n repository='http://uweschmitt.info:37614',\n server=\"local\",\n )\n # distutils.config.PyPIRCCommand._read_pypirc = patched\n\n\n # from setuptools import setup\n setup(name=PKG_NAME,\n packages=[ PKG_NAME ],\n author=AUTHOR,\n author_email=AUTHOR_EMAIL,\n url=AUTHOR_URL,\n description=DESCRIPTION,\n long_description=LONG_DESCRIPTION,\n license=LICENSE,\n version=VERSION_STRING,\n entry_points = ENTRY_POINTS,\n\tinclude_package_data = True,\n install_requires=['hires==0.0.14', 'pacer==0.5.3']\n )\n \n", "sub_path": "pypi_install_script/dynamet-2.8.6/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 4687, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "setuptools.setup", "line_number": 106, "usage_type": "call"}]} +{"seq_id": "404935558", "text": "import tensorflow as tf\nfrom config.defaults import Config\n\n# NOTE: this confile file can further be overridden by user-defined params provided at the command line\n\n\nconfig = dict(\n\n path_to_impl='model.efficientnet_model_v1',\n\n\n \n #data-related model params\n num_classes=1000, # must be the same as data.num_classes\n input_channels= 3,\n rescale_input= 1, # binary,\n mean_rgb=(0.485 * 255, 0.456 * 255, 0.406 * 255), # used when rescale_input=True\n std_rgb=(0.229 * 255, 0.224 * 255, 0.225 * 255), # used when rescale_input=True\n dtype= tf.float32, #used for input image normalization/casting, # tf.float32, tf.bfloat16, tf.float16, tf.float32, tf.bfloat16,\n \n \n # GUIDE\n # width depth resolution dropout\n # efficientnet_v1-b0 1.0 1.0 224 0.2\n # 'efficientnet_v1-b1 1.0 1.1 240 0.2\n # 'efficientnet_v1-b2 1.1 1.2 260 0.3\n # 'efficientnet_v1-b3 1.2 1.4 300 0.3\n # 'efficientnet_v1-b4 1.4 1.8 380 0.4\n # 'efficientnet_v1-b5 1.6 2.2 456 0.4\n # 'efficientnet_v1-b6 1.8 2.6 528 0.5\n # 'efficientnet_v1-b7 2.0 3.1 600 0.5\n # 'efficientnet_v1-b8 2.2 3.6 672 0.5 \n # 'efficientnet_v1-l2 4.3 5.3 800 0.5\n width_coefficient= 1.0,\n depth_coefficient= 1.0,\n dropout_rate= 0.2,\n # image resolution must be set in tr/eval/predict configs below\n \n drop_connect_rate= 0.2,\n stem_base_filters= 32,\n top_base_filters= 1280,\n activation= 'swish',\n depth_divisor= 8,\n min_depth= None,\n use_se= 1, # binary\n batch_norm= 'syncbn',\n bn_momentum= 0.99,\n bn_epsilon= 1e-3,\n weight_init= 'fan_out',\n \n blocks= (\n # (input_filters, output_filters, kernel_size, num_repeat,expand_ratio, strides, se_ratio)\n # pylint: disable=bad-whitespace\n dict(input_filters=32, output_filters=16, kernel_size=3, num_repeat=1, expand_ratio=1, strides=(1, 1), se_ratio=0.25,id_skip=True,fused_conv=False,conv_type='depthwise'),\n dict(input_filters=16, output_filters=24, kernel_size=3, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25,id_skip=True,fused_conv=False,conv_type='depthwise'),\n dict(input_filters=24, output_filters=40, kernel_size=5, num_repeat=2, expand_ratio=6, strides=(2, 2), se_ratio=0.25,id_skip=True,fused_conv=False,conv_type='depthwise'),\n dict(input_filters=40, output_filters=80, kernel_size=3, num_repeat=3, expand_ratio=6, strides=(2, 2), se_ratio=0.25,id_skip=True,fused_conv=False,conv_type='depthwise'),\n dict(input_filters=80, output_filters=112, kernel_size=5, num_repeat=3, expand_ratio=6, strides=(1, 1), se_ratio=0.25,id_skip=True,fused_conv=False,conv_type='depthwise'),\n dict(input_filters=112, output_filters=192, kernel_size=5, num_repeat=4, expand_ratio=6, strides=(2, 2), se_ratio=0.25,id_skip=True,fused_conv=False,conv_type='depthwise'),\n dict(input_filters=192, output_filters=320, kernel_size=3, num_repeat=1, expand_ratio=6, strides=(1, 1), se_ratio=0.25,id_skip=True,fused_conv=False,conv_type='depthwise'),\n # pylint: enable=bad-whitespace\n ),\n \n )\n\n# train_config = dict(lr_decay='cosine',\n#\n# max_epochs=500,\n# img_size=224,\n# batch_size=256,\n# save_checkpoint_freq=5,\n# lr_init=0.005,\n# weight_decay=5e-6,\n# epsilon=0.001,\n# resume_checkpoint=1,\n# enable_tensorboard=0\n# )\n#\n# eval_config = dict(img_size=224,\n# batch_size=256)\n#\n# data_config = dict(\n# data_dir='/data/',\n# augmenter_name='autoaugment',\n# mixup_alpha=0.0,\n#\n#\n# )\n# runtime_config = dict(mode='train_and_eval',\n# model_dir='./output/',\n# use_amp=1,\n# use_xla=1,\n# log_steps=100\n# )\n#\n# config = dict(model=model_config,\n# train=train_config,\n# eval=eval_config,\n# data=data_config,\n# runtime=runtime_config,\n# )\n", "sub_path": "TensorFlow2/Classification/ConvNets/config/efficientnet_v1/b0_cfg.py", "file_name": "b0_cfg.py", "file_ext": "py", "file_size_in_byte": 4742, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "config.defaults", "line_number": 7, "usage_type": "name"}, {"api_name": "tensorflow.float32", "line_number": 19, "usage_type": "attribute"}]} +{"seq_id": "627008979", "text": "from mrjob.job import MRJob\nfrom mrjob.step import MRStep\nfrom collections import defaultdict\n\nimport codecs\n\nFILE_NAME_RATING=\"ratings.csv\"\nFILE_NAME_MOVIES=\"movies.csv\"\nDELIMITTER_RATING=\",\"\nDELIMITTER_MOVIES=\",\"\n\nclass RelatedMoviesIndexProcess(MRJob):\n\n def configure_options(self):\n super(RelatedMoviesIndexProcess, self).configure_options()\n self.add_file_option(\"--item\", help = \"File location to prep userId and movie rating index\")\n self.add_file_option(\"--movieNameFile\", help = \"File location to prep movieId and Movie name index\")\n\n def steps(self):\n return [\n MRStep(mapper = self.mapper, reducer_init = self.reducer_init, reducer = self.reducer)\n ]\n\n def reducer_init(self):\n self.userIdMovieIdsIndex = defaultdict(list)\n with open(FILE_NAME_RATING) as f:\n for line in f:\n (userId, movieId, rating, _) = line.split(DELIMITTER_RATING)\n if (userId == \"userId\"):\n continue\n if (float(rating) > 2):\n self.userIdMovieIdsIndex[userId].append((movieId, rating))\n f.close()\n\n self.movieIdNameIndex = {}\n with codecs.open(FILE_NAME_MOVIES, 'r', encoding='utf-8', errors='ignore') as f:\n for line in f:\n fields = line.split(DELIMITTER_MOVIES)\n if (fields[0] == \"movieId\"):\n continue\n self.movieIdNameIndex[fields[0]] = fields[1]\n f.close()\n\n def mapper(self, _, line):\n (userId, movieId, rating, _) = line.split(DELIMITTER_RATING)\n if (userId != \"userId\"):\n yield movieId, (userId, rating)\n\n def reducer(self, movieId, userIdRatingPairs):\n for userIdRatingPair in userIdRatingPairs:\n userId = userIdRatingPair[0]\n rating = float(userIdRatingPair[1])\n for relatedMovieIdRatingPair in self.userIdMovieIdsIndex[userId]:\n possibleRelatedMovieId = relatedMovieIdRatingPair[0]\n if (possibleRelatedMovieId == movieId):\n continue\n possibleRelatedRating = float(relatedMovieIdRatingPair[1])\n if (((rating >= possibleRelatedRating) and (rating - possibleRelatedRating < 2))\n or ((rating <= possibleRelatedRating) and possibleRelatedRating - rating < 2)):\n yield self.movieIdNameIndex[movieId], self.movieIdNameIndex[possibleRelatedMovieId]\n\nif __name__ == '__main__':\n RelatedMoviesIndexProcess.run()\n", "sub_path": "movies/related-movies-index.py", "file_name": "related-movies-index.py", "file_ext": "py", "file_size_in_byte": 2556, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "mrjob.job.MRJob", "line_number": 12, "usage_type": "name"}, {"api_name": "mrjob.step.MRStep", "line_number": 21, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 25, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "199960447", "text": "from . import base\nfrom . import mixins\n\nfrom datetime import date\n\n\nclass TransformedRecord(mixins.GenericCompensationMixin,\n mixins.GenericDepartmentMixin, mixins.GenericIdentifierMixin,\n mixins.GenericJobTitleMixin, mixins.GenericPersonMixin,\n mixins.MembershipMixin, mixins.OrganizationMixin, mixins.PostMixin,\n mixins.RaceMixin, mixins.LinkMixin, base.BaseTransformedRecord):\n\n MAP = {\n 'last_name': 'Last Name',\n 'first_name': 'First Name',\n 'department': 'Location',\n 'job_title': 'Job Name',\n 'hire_date': 'Hire Date',\n 'compensation': 'Base Salary',\n 'gender': 'Sex',\n 'race': 'Race',\n }\n\n NAME_FIELDS = ('first_name', 'last_name', )\n\n ORGANIZATION_NAME = 'North East ISD'\n\n ORGANIZATION_CLASSIFICATION = 'School District'\n\n DATE_PROVIDED = date(2014, 6, 30)\n # Y/M/D agency provided the data\n\n URL = \"http://raw.texastribune.org.s3.amazonaws.com/north_east_isd/salaries/2014-06/TPIA%20Response%20%20-%20Texas%20Tribune%20June%202014.xlsx\"\n\n compensation_type = 'FT'\n description = 'Base Salary'\n\n @property\n def is_valid(self):\n # Adjust to return False on invalid fields. For example:\n return self.last_name.strip() != ''\n\n @property\n # department names have three digits and a whitespace in front\n def department_as_child(self):\n return [{'name': self.department[4:], }, ]\n\ntransform = base.transform_factory(TransformedRecord)\n", "sub_path": "tx_salaries/utils/transformers/north_east_isd.py", "file_name": "north_east_isd.py", "file_ext": "py", "file_size_in_byte": 1491, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "datetime.date", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "560390930", "text": "#!/usr/bin/python\r\n# _*_ coding:utf-8 _*_\r\nimport math\r\nimport random\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom mpl_toolkits.mplot3d import Axes3D\r\nimport sys\r\nfrom numpy.matlib import rand\r\nfrom matplotlib.mlab import dist\r\nfrom matplotlib.artist import getp\r\nimport copy\r\n\r\n'''\r\n记录错误，数组直接复制是复制地址\r\n例如， current = route\r\n想要得到一个新的有同样内容的数组，应该用： current = copy.copy(route) \r\n'''\r\n\r\n# 初始三十个城市坐标\r\n# city_x = [1, 3, 6, 12, 19, 22, 23, 20, 21, 22.5, 40, 44, 42, 36, 39, 58, 62, 88, 90, 83, 71, 67, 64, 52, 84, 87, 71, 71,\r\n# 58, 80]\r\n# city_y = [99, 50, 64, 40, 41, 42, 37, 54, 60, 60.5, 26, 20, 35, 83, 95, 33, 30.5, 6, 38, 44, 42, 57, 59, 62, 65, 74, 70,\r\n# 77, 68, 66]\r\n# # 城市数量\r\n# n = 30\r\ncity_x = np.loadtxt(\"city1_40.txt\")\r\ncity_y=np.loadtxt(\"city2_40.txt\")\r\nn = 40\r\ndistance = [[0 for col in range(n)] for raw in range(n)]\r\n# 初始温度 结束温度\r\nT0 = 30\r\nTend = 1e-8\r\n# 循环控制常数\r\nL = 10\r\n# 温度衰减系数\r\na = 0.98\r\n\r\n\r\n# 构建初始参考距离矩阵\r\ndef getdistance():\r\n # distance=np.loadtxt(\"city_30.txt\")\r\n for i in range(n):\r\n for j in range(n):\r\n x = pow(city_x[i] - city_x[j], 2)\r\n y = pow(city_y[i] - city_y[j], 2)\r\n distance[i][j] = pow(x + y, 0.5)\r\n for i in range(n):\r\n for j in range(n):\r\n if distance[i][j] == 0:\r\n distance[i][j] = sys.maxsize\r\n\r\n\r\n# 计算总距离\r\ndef cacl_best(rou):\r\n sumdis = 0.0\r\n for i in range(n - 1):\r\n sumdis += distance[rou[i]][rou[i + 1]]\r\n sumdis += distance[rou[n - 1]][rou[0]]\r\n return sumdis\r\n\r\n\r\n# 得到新解\r\ndef getnewroute(route, time):\r\n # 如果是偶数次，二变换法\r\n current = copy.copy(route)\r\n\r\n if time % 2 == 0:\r\n u = random.randint(0, n - 1)\r\n v = random.randint(0, n - 1)\r\n temp = current[u]\r\n current[u] = current[v]\r\n current[v] = temp\r\n # 如果是奇数次，三变换法\r\n else:\r\n temp2 = random.sample(range(0, n), 3)\r\n temp2.sort()\r\n u = temp2[0]\r\n v = temp2[1]\r\n w = temp2[2]\r\n w1 = w + 1\r\n temp3 = [0 for col in range(v - u + 1)]\r\n j = 0\r\n for i in range(u, v + 1):\r\n temp3[j] = current[i]\r\n j += 1\r\n\r\n for i2 in range(v + 1, w + 1):\r\n current[i2 - (v - u + 1)] = current[i2]\r\n w = w - (v - u + 1)\r\n j = 0\r\n for i3 in range(w + 1, w1):\r\n current[i3] = temp3[j]\r\n j += 1\r\n\r\n return current\r\n\r\n\r\ndef draw(best):\r\n result_x = [0 for col in range(n + 1)]\r\n result_y = [0 for col in range(n + 1)]\r\n\r\n for i in range(n):\r\n result_x[i] = city_x[best[i]]\r\n result_y[i] = city_y[best[i]]\r\n result_x[n] = result_x[0]\r\n result_y[n] = result_y[0]\r\n print(result_x)\r\n print(result_y)\r\n plt.xlim(0, 100) # 限定横轴的范围\r\n plt.ylim(0, 100) # 限定纵轴的范围\r\n plt.plot(result_x, result_y, marker='>', mec='r', mfc='w', label=u'Route')\r\n plt.legend() # 让图例生效\r\n plt.margins(0)\r\n plt.subplots_adjust(bottom=0.15)\r\n plt.xlabel(u\"x\") # X轴标签\r\n plt.ylabel(u\"y\") # Y轴标签\r\n plt.title(\"TSP Solution\") # 标题\r\n\r\n plt.show()\r\n plt.close(0)\r\n\r\n\r\ndef solve():\r\n # 得到距离矩阵\r\n # distance=np.loadtxt(\"city_30.txt\")\r\n getdistance()\r\n # 得到初始解以及初始距离\r\n route = random.sample(range(0, n), n)\r\n total_dis = cacl_best(route)\r\n # print(\"初始路线：\", route)\r\n # print(\"初始距离：\", total_dis)\r\n # 新解\r\n newroute = []\r\n new_total_dis = 0.0\r\n best = route\r\n best_total_dis = total_dis\r\n t = T0\r\n\r\n while True:\r\n if t <= Tend:\r\n break\r\n # 令温度为初始温度\r\n for rt2 in range(L):\r\n newroute = getnewroute(route, rt2)\r\n new_total_dis = cacl_best(newroute)\r\n delt = new_total_dis - total_dis\r\n if delt <= 0:\r\n route = newroute\r\n total_dis = new_total_dis\r\n if best_total_dis > new_total_dis:\r\n best = newroute\r\n best_total_dis = new_total_dis\r\n elif delt > 0:\r\n p = math.exp(-delt / t)\r\n ranp = random.uniform(0, 1)\r\n if ranp < p:\r\n route = newroute\r\n total_dis = new_total_dis\r\n t = t * a\r\n # print(\"现在温度为：\", t)\r\n # print(\"最佳路线：\", best)\r\n print(\"最佳距离：\", best_total_dis)\r\n # draw(best)\r\n return best_total_dis\r\n\r\n\r\nif __name__ == \"__main__\":\r\n dif=np.zeros(100)\r\n for i in range(100):\r\n dif[i]=(solve()-556.536)/556.536\r\n print(dif[i])\r\n np.savetxt(\"dif_40.txt\",dif)\r\n\r\n", "sub_path": "fucking.py", "file_name": "fucking.py", "file_ext": "py", "file_size_in_byte": 4906, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.loadtxt", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.loadtxt", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.maxsize", "line_number": 51, "usage_type": "attribute"}, {"api_name": "copy.copy", "line_number": 66, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 69, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 70, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.margins", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "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.show", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "random.sample", "line_number": 129, "usage_type": "call"}, {"api_name": "math.exp", "line_number": 155, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 173, "usage_type": "call"}]} +{"seq_id": "314392723", "text": "from glob import glob\nimport sqlite3\nfrom datetime import datetime\n\nclass DatabaseManager:\n def __init__(self, path_to_database='/var/www/adambberger/db/posts.db'):\n self.conn = sqlite3.connect(path_to_database, check_same_thread=False)\n self.conn.row_factory = sqlite3.Row\n\n self.create_tables_if_needed()\n\n def create_tables_if_needed(self):\n self.conn.execute(\"\"\"\n CREATE TABLE IF NOT EXISTS Posts (\n title TEXT PRIMARY KEY UNIQUE,\n anchor_title TEXT UNIQUE,\n searchable BOOLEAN,\n local_path TEXT UNIQUE,\n remote_path TEXT,\n parent_path TEXT,\n html TEXT,\n summary TEXT,\n views UNSIGNED BIG INT,\n date_posted DATETIME,\n date_updated DATETIME,\n date_scanned DATETIME DEFAULT CURRENT_TIMESTAMP\n )\n \"\"\")\n\n self.conn.execute(\"\"\"\n CREATE TABLE IF NOT EXISTS Folders (\n title TEXT PRIMARY KEY,\n anchor_title TEXT,\n searchable BOOLEAN,\n local_path TEXT,\n remote_path TEXT,\n parent_path TEXT,\n date_scanned DATETIME DEFAULT CURRENT_TIMESTAMP\n )\n \"\"\")\n\n def insert_post(self, post, parent_path):\n self.conn.execute('REPLACE INTO Posts (title, anchor_title, searchable, local_path, html, remote_path, summary, views, date_posted, date_updated, parent_path) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)',\n [post.title, post.anchor_title, post.searchable, post.path, post.html, post.remote_path, post.summary, 0, post.date, post.date_updated, parent_path])\n self.conn.commit()\n\n def insert_folder(self, folder, parent_path):\n self.conn.execute('REPLACE INTO Folders (title, anchor_title, local_path, remote_path, parent_path) VALUES (?, ?, ?, ?, ?)',\n [folder.title, folder.anchor_title, folder.path, folder.remote_path, parent_path])\n self.conn.commit()\n\n def get_posts_for_directory_order_by_date(self, directory):\n if directory:\n rows = self.conn.execute('SELECT * FROM Posts WHERE parent_path = ? ORDER BY date_posted DESC', (directory,)).fetchall()\n\n posts = []\n for row in rows:\n posts += [Post.fromDict(row)]\n\n rows = self.conn.execute('SELECT * FROM Folders WHERE parent_path = ?', (directory,)).fetchall()\n folders = []\n for row in rows:\n title = row['title']\n remote_path = row['remote_path']\n local_path = row['local_path']\n folders += [Folder(title, title, remote_path, local_path)]\n\n return (posts, folders)\n \n def get_post_by_local_path(self, local_path):\n rows = self.conn.execute('SELECT * FROM Posts WHERE local_path = ?', (local_path,)).fetchall()\n if len(rows) > 0:\n return Post.fromDict(rows[0])\n else:\n raise Exception('No such post')\n\n def get_most_recent_n_posts(self, n):\n rows = self.conn.execute('SELECT * FROM Posts WHERE searchable = 1 ORDER BY date_posted DESC LIMIT ?', (n,)).fetchall()\n if len(rows) > 0:\n posts = []\n for row in rows:\n posts += [Post.fromDict(row)]\n return posts\n else:\n raise Exception('No such post')\n\n def get_post_by_title(self, title):\n rows = self.conn.execute('SELECT * FROM Posts WHERE title = ? ORDER BY date_posted DESC', (title,)).fetchall()\n if len(rows) > 0:\n return Post.fromDict(rows[0])\n else:\n raise Exception('No such post')\n\n def title_is_dir(self, title):\n rows = self.conn.execute('SELECT * FROM Posts WHERE title = ?', (title,)).fetchall()\n return len(rows) <= 0\n\n def get_post_by_anchor_title(self, anchor_title):\n rows = self.conn.execute('SELECT * FROM Posts WHERE anchor_title = ? ORDER BY date_posted DESC', (anchor_title,)).fetchall()\n if len(rows) > 0:\n return Post.fromDict(rows[0])\n else:\n raise Exception('No such post')\n\n def anchor_title_is_not_a_valid_post(self, anchor_title):\n rows = self.conn.execute('SELECT * FROM Posts WHERE anchor_title = ?', (anchor_title,)).fetchall()\n return len(rows) <= 0\n\n def path_is_a_valid_folder(self, remote_path):\n rows = self.conn.execute('SELECT * FROM Folders WHERE remote_path = ?', (remote_path,)).fetchall()\n return len(rows) > 0\n\n def path_is_dir(self, local_path):\n rows = self.conn.execute('SELECT * FROM Posts WHERE local_path = ?', (local_path,)).fetchall()\n return len(rows) <= 0\n\nclass Post:\n def __init__(self, title, anchor_title, remote_path, local_path, date_posted, date_updated, views, summary, html, searchable):\n self.title = title\n self.html = html\n self.remote_path = remote_path\n self.local_path = local_path\n self.date_posted = self._parse_db_date_to_date_string(date_posted)\n self.date_updated = self._parse_db_date_to_date_string(date_updated)\n self.views = views\n self.searchable = searchable\n self.summary = summary\n self.anchor_title = anchor_title\n self.is_post = True\n\n def _parse_db_date_to_date_string(self, db_date):\n return datetime.strptime(str(db_date), '%Y%m%d').strftime('%B %d, %Y')\n\n @staticmethod\n def fromDict(row):\n html = row['html']\n title = row['title']\n anchor_title = row['anchor_title']\n local_path = row['local_path']\n remote_path = row['remote_path']\n local_path = row['local_path']\n summary = row['summary']\n views = row['views']\n searchable = row['searchable']\n date_posted = row['date_posted']\n date_updated = row['date_updated']\n\n return Post(title, anchor_title, remote_path, local_path, date_posted, date_updated, views, summary, html, searchable)\n\nclass Folder:\n def __init__(self, title, anchor_title, remote_path, local_path):\n self.title = title\n self.anchor_title = anchor_title\n self.remote_path = remote_path\n self.local_path = local_path\n self.is_post = False\n\nimport fnmatch\nimport os\nfrom model import MarkdownPost, MarkdownFolder\n\nclass PostScanner():\n def __init__(self, root, db_manager=DatabaseManager()):\n self.db_manager = db_manager\n self.root = root\n\n def scan(self):\n markdown_file_paths = []\n folders = []\n for root, dirnames, filenames in os.walk(self.root):\n for filename in fnmatch.filter(filenames, '*.md'):\n markdown_file_paths.append((os.path.join(root, filename), root))\n for dirname in dirnames:\n folders += [(os.path.join(root, dirname), root)]\n\n posts = []\n for (filepath, parent_path) in markdown_file_paths:\n self.db_manager.insert_post(MarkdownPost(filepath), parent_path)\n\n for (folder, parent_path) in folders:\n self.db_manager.insert_folder(MarkdownFolder(folder), parent_path)\n\n def scan_file_overwrite_in_database(self, absolute_path):\n self.db_manager.insert_post(MarkdownPost(absolute_path), os.path.split(absolute_path)[0])\n", "sub_path": "post_scanner.py", "file_name": "post_scanner.py", "file_ext": "py", "file_size_in_byte": 7443, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sqlite3.connect", "line_number": 7, "usage_type": "call"}, {"api_name": "sqlite3.Row", "line_number": 8, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 132, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 132, "usage_type": "name"}, {"api_name": "os.walk", "line_number": 170, "usage_type": "call"}, {"api_name": "fnmatch.filter", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 172, "usage_type": "call"}, {"api_name": "os.path", "line_number": 172, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 174, "usage_type": "call"}, {"api_name": "os.path", "line_number": 174, "usage_type": "attribute"}, {"api_name": "model.MarkdownPost", "line_number": 178, "usage_type": "call"}, {"api_name": "model.MarkdownFolder", "line_number": 181, "usage_type": "call"}, {"api_name": "model.MarkdownPost", "line_number": 184, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 184, "usage_type": "call"}, {"api_name": "os.path", "line_number": 184, "usage_type": "attribute"}]} +{"seq_id": "373582715", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\n\n\"\"\"\n\nimport datetime\nfrom scarecrow import Base\nfrom sqlalchemy import Column, ForeignKey, Integer, String\n\nclass Customer(Base):\n __tablename__ = 'customer'\n id = Column(Integer, primary_key=True)\n customer_name = Column(String(128),unique=True, nullable=False) # 客户名称\n status = Column(Integer, default=1) # 客户状态\n area = Column(String(48)) # 客户所在地区\n note = Column(String(256)) # 备注\n customer_ID = Column(String(2), unique=True, nullable=False) # 客户ID\n model_ID = Column(String(2), nullable=False) # 机型ID\n left_point = Column(Integer) # 左端点\n right_point = Column(Integer) # 右端点\n offset_point = Column(Integer) # 偏移量\n test_offset_point = Column(Integer, default=90000000) # 测试偏移量\n created_timestamp = Column(String(19), default=str(datetime.datetime.now())[:19])\n updated_timestamp = Column(String(19), default=str(datetime.datetime.now())[:19])\n\nclass Order(Base):\n __tablename__ = 'order'\n id = Column(Integer, primary_key=True)\n order_number = Column(String(128), unique=True, nullable=False)# 订单编号\n order_type = Column(Integer, default=1) # 订单类型：1，订单；0，测试\n customer_name = Column(String(128), ForeignKey(Customer.customer_name, ondelete='CASCADE', onupdate='CASCADE'))\n product_amount= Column(Integer, nullable=False) # 生产数量\n spare_amount = Column(Integer, default=0) # 备品数量\n note = Column(String(2048)) # 备注\n status = Column(Integer, default=0) # 审核状态\n left_point = Column(Integer) # 左端点\n right_point = Column(Integer) # 右端点\n storage_file_name = Column(String(256), nullable=False)\n relative_file_location= Column(String(1024), nullable=False)\n created_timestamp = Column(String(19), default=str(datetime.datetime.now())[:19])\n updated_timestamp = Column(String(19), default=str(datetime.datetime.now())[:19])\n\nclass SerialNumber(Base):\n __tablename__ = 'serial_number'\n id = Column(Integer, primary_key=True)\n customer_name = Column(String(128), ForeignKey(Customer.customer_name, ondelete='CASCADE', onupdate='CASCADE'))\n order_number = Column(String(128), ForeignKey(Order.order_number, ondelete='CASCADE', onupdate='CASCADE'))\n username = Column(String(128),unique=True)\n password = Column(String(128))\n status = Column(Integer, default=0) # 状态\n sn = Column(String(128),unique=True, nullable=False)\n created_timestamp = Column(String(19), default=str(datetime.datetime.now())[:19])\n updated_timestamp = Column(String(19), default=str(datetime.datetime.now())[:19])", "sub_path": "models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 3236, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "scarecrow.Base", "line_number": 11, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 13, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 13, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 14, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 14, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 15, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 15, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 16, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 16, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 17, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 17, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 18, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 18, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 19, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 20, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 20, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 21, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 21, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 22, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 23, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 24, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 24, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 24, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 25, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 25, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 25, "usage_type": "attribute"}, {"api_name": "scarecrow.Base", "line_number": 27, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 29, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 29, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 30, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 31, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 31, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 32, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 33, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 33, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 34, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 34, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 35, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 35, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 36, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 36, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 37, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 37, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 38, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 38, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 39, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 40, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 41, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 41, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 41, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 42, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 42, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 42, "usage_type": "attribute"}, {"api_name": "scarecrow.Base", "line_number": 44, "usage_type": "name"}, {"api_name": "sqlalchemy.Column", "line_number": 46, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 46, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 47, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 47, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 47, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlalchemy.ForeignKey", "line_number": 48, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 50, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 50, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 51, "usage_type": "call"}, {"api_name": "sqlalchemy.Integer", "line_number": 51, "usage_type": "argument"}, {"api_name": "sqlalchemy.Column", "line_number": 52, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 52, "usage_type": "call"}, {"api_name": "sqlalchemy.Column", "line_number": 53, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 53, "usage_type": "attribute"}, {"api_name": "sqlalchemy.Column", "line_number": 54, "usage_type": "call"}, {"api_name": "sqlalchemy.String", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 54, "usage_type": "attribute"}]} +{"seq_id": "238693768", "text": "\nimport os\n\n# Imports\nimport tensorflow as tf\n\nfrom api import object_counting_api\n# Object detection imports\nfrom utils import backbone\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\nprint(tf.version)\n\n\ninput_video = \"./input_images_and_videos/pedestrian_survaillance.mp4\"\n\n# By default I use an \"SSD with Mobilenet\" model here. See the detection model zoo (https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.\ndetection_graph, category_index = backbone.set_model(\n 'ssd_mobilenet_v1_coco_2018_01_28', 'mscoco_label_map.pbtxt')\n\n# set it to 1 for enabling the color prediction for the detected objects\nis_color_recognition_enabled = 0\n\n# the constant that represents the object counting area\ndeviation = 1\n\n# roi line position\nroi = 250\n\n# axis for te object detection\naxis = 'x'\n\n# main counting all the objects\nif (axis == 'y'):\n object_counting_api.cumulative_object_counting_y_axis(\n input_video, detection_graph, category_index, is_color_recognition_enabled, roi, deviation)\nelse:\n object_counting_api.cumulative_object_counting_x_axis(\n input_video, detection_graph, category_index, is_color_recognition_enabled, roi, deviation)\n", "sub_path": "pedestrian_counting.py", "file_name": "pedestrian_counting.py", "file_ext": "py", "file_size_in_byte": 1300, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.environ", "line_number": 11, "usage_type": "attribute"}, {"api_name": "tensorflow.version", "line_number": 12, "usage_type": "attribute"}, {"api_name": "utils.backbone.set_model", "line_number": 18, "usage_type": "call"}, {"api_name": "utils.backbone", "line_number": 18, "usage_type": "name"}, {"api_name": "api.object_counting_api.cumulative_object_counting_y_axis", "line_number": 35, "usage_type": "call"}, {"api_name": "api.object_counting_api", "line_number": 35, "usage_type": "name"}, {"api_name": "api.object_counting_api.cumulative_object_counting_x_axis", "line_number": 38, "usage_type": "call"}, {"api_name": "api.object_counting_api", "line_number": 38, "usage_type": "name"}]} +{"seq_id": "66802489", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Sep 14 13:32:20 2019\n\n@author: atsumilab\n@code: utf-8\n\"\"\"\n\nimport os\nimport numpy as np\nimport cv2\nfrom chainer import serializers, Variable\nimport chainer.functions as F\n#from yolov2_darknet_predict import Predictor\nfrom object_detector import ObjectDetector\nimport argparse\n\ndef save_images(orig_img, bboxes, output_dir, file):\n counter=0\n for bbox in bboxes:\n # 指定した物体が存在したら…/ If there is a specified object\n #if name in object_list:\n # 画像からその物体の領域を切り取って保存\n top = int(bbox[0])\n bottom = int(bbox[2])\n left = int(bbox[1])\n right = int(bbox[3])\n #left, top = result['box'].int_left_top()\n #right, bottom = result['box'].int_right_bottom()\n filename = os.path.join(output_dir,'img',file+'_'+str(counter)+'.jpg')\n cv2.imwrite(filename, orig_img[top:bottom, left:right])\n counter+=1\n\ndef save_feature(features, layer_name_list, output_dir, file):\n # 指定したレイヤーの特徴を保存 / Save the specified layer's feature\n for feature, layer_name in zip(features, layer_name_list):\n save_name=os.path.join(output_dir, layer_name, file)\n np.savez(save_name, feature)\n\ndef save_ebox(bboxes, labels, layer_ids, img_h, img_w, output_dir, file):\n with open(os.path.join(output_dir, 'ebox', file),'w') as w:\n for bbox, label, layer_id in zip(bboxes, labels, layer_ids):\n # 指定した物体が存在したら… / If there is a specified object\n #if name in object_list:\n # ラベルと相対座標をファイルに書き込む / Write the label and relative coordinates to a file\n width = bbox[3] - bbox[1]\n height = bbox[2] - bbox[0]\n center_x = bbox[1] + width/2\n center_y = bbox[0] + height/2\n \n wlist=[str(label),\n str(center_x/img_w),\n str(center_y/img_h),\n str(width/img_w),\n str(height/img_h),\n str(layer_id)]\n w.write(' '.join(wlist)+'\\n')\n\ndef save_specfile(output_dir, features):\n filename = os.path.join(os.path.dirname(output_dir), 'ds_spec.txt')\n with open(filename, 'w') as w:\n w.write('feature: raw\\nlayer: ')\n for i, layer_name in enumerate(img_features.keys()):\n lc,lh,lw=features[layer_name].data.shape[1:]\n wlist=[layer_name,layer_name,str(lh),str(lw),str(lc)]\n w.write(','.join(wlist))\n if(i!=len(img_features)-1): w.write('; ')\n\ndef copy_file(outpath):\n copy_filelist = ['tbox']\n path = 'F:\\AtsumiLabMDS-2\\Trip\\Dashcam\\ds2' #defined path\n\n files = os.listdir(path)\n datasets = []\n for file in files:\n if file[0]!=\"X\":\n datasets.append(file)\n\n for dataset in datasets:\n video_path = os.path.join(path, dataset)\n out_video_path = os.path.join(outpath, dataset)\n videos = os.listdir(video_path)\n for video in videos:\n feature_path = os.path.join(video_path, video)\n out_feature_path = os.path.join(out_video_path, video)\n if not video[-4:] =='.txt':\n feature = os.listdir(feature_path)\n print(os.path.join(feature_path, feature[8]), os.path.join(out_feature_path, feature[8]))\n shutil.copytree(os.path.join(feature_path, feature[8]), os.path.join(out_feature_path, feature[8]))\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description='dataset_maker')\n parser.add_argument('--object_model_type', choices=('yolo_v2', 'yolo_v3'), default='yolo_v3')\n parser.add_argument('--object_model_path', default='../model_v3/accident_KitDash_8000.npz')\n parser.add_argument('--object_label_path', default='../model_v3/obj.names') # must be specified other than 'coco' and 'voc' \n parser.add_argument('--object_cfg_path', default='../model_v3/yolo-obj.cfg')\n parser.add_argument('--object_detection_threshold', type=float, default=0.1)\n parser.add_argument('--gpu', type=int, default=0)\n\n #parser.add_argument('--input_dir', default=r'E:\\AtsumiLabMDS-2\\TRIP\\Dataset\\DashcamAccidentDataset\\images\\testing\\positive_', help='input directory')\n parser.add_argument('--input_dir', default=r'E:\\AtsumiLabMDS-2\\TRIP\\Dataset\\DashcamAccidentDataset\\images\\testing\\positive', help='input directory')\n parser.add_argument('--output_dir', default=r'E:\\AtsumiLabMDS-2\\TRIP\\Trip2018Q1\\Dashcam\\ds3\\_test', help='directory where the dataset will be created')\n parser.add_argument('--layer_name_list', default='conv33,conv39,conv45', help='list of hidden layers name to extract features')\n #parser.add_argument('--object_list', default='car,truck,person,tram,bicycle,motorbike,bus', help='list of object to get box coords')\n parser.add_argument('--save_img', type=bool, default=True, help='save_img option')\n parser.add_argument('--video', type=bool, default=False, help='video option')\n args = parser.parse_args()\n\n input_dir = args.input_dir\n layer_name_list = args.layer_name_list.split(',')\n output_dir = args.output_dir\n thresh = args.object_detection_threshold\n save_img = args.save_img\n video = args.video\n predictor = ObjectDetector(args.object_model_type, args.object_model_path, \n args.object_label_path, args.object_cfg_path, args.object_detection_threshold,\n device=args.gpu)\n lable_names = np.array(predictor.get_bbox_labels())\n # フォルダ内が動画の時 / When folder contains a video\n if video:\n orig_input_dir = input_dir\n orig_output_dir = output_dir\n # フォルダ内のビデオ数だけループ / Loop only the number of videos in a folder \n video_files = os.listdir(input_dir)\n for video_file in video_files:\n print('save %s feature...' % video_file)\n input_dir = orig_input_dir + '/' + video_file\n output_dir = orig_output_dir + '/' + video_file[:-4]\n # フォルダが無ければ新規作成 / Create a new folder if there is none previously\n if not os.path.isdir(output_dir):\n os.makedirs(output_dir)\n for layer in layer_name_list:\n if not os.path.isdir(os.path.join(output_dir, layer)):\n os.mkdir(os.path.join(output_dir, layer))\n if save_img and not os.path.isdir(os.path.join(output_dir, 'img')):\n os.mkdir(os.path.join(output_dir, 'img'))\n if video and not os.path.isdir(os.path.join(output_dir, 'orig_img')):\n os.mkdir(os.path.join(output_dir, 'orig_img'))\n if not os.path.isdir(os.path.join(output_dir, 'ebox')):\n os.mkdir(os.path.join(output_dir, 'ebox'))\n\n # 動画から画像ファイルのリストを作成 / Create a list of image files from a video \n print('load video...')\n i=1\n cap = cv2.VideoCapture(input_dir)\n\n while(cap.isOpened()):\n flag, frame = cap.read()\n if flag == False:\n break\n cv2.imwrite(output_dir + '/orig_img/' + str(i).zfill(6) + '.jpg', frame)\n i+=1\n cap.release()\n\n file_list = os.listdir(output_dir + '/orig_img')\n img_files = [f for f in file_list if os.path.isfile(os.path.join(output_dir + '/orig_img', f))]\n#\n # 最初の画像を読み込み / Loading the first image\n orig_img = cv2.imread(os.path.join(output_dir, img_files[0]))\n # 基準となる画像の高さと幅を取得 / Get the height and width of the base image\n #img_h, img_w = orig_img.shape[:2]\n img_h = 720\n img_w = 1280\n#\n # ファイル数分繰り返す / Repeat files for a few minutes \n for img_file in img_files:\n # 拡張子とファイル名を分ける / Separate file names from extensions\n file, ext = os.path.splitext(img_file)\n # 画像読み込み / Image loading\n orig_img = cv2.imread(os.path.join(output_dir + '/orig_img', img_file))\n#\n # サイズが異なる場合は変形してから入力 / If the size is different, deform and then enter\n if (img_h, img_w) != orig_img.shape[:2] :\n orig_img = cv2.resize(orig_img, (img_w, img_h))\n#\n # 画像の高さと幅を取得 / Get the height and width of the image \n img_h, img_w = orig_img.shape[:2]\n # 検出結果と特徴を取得 / Get detection results and features\n #results, img_features = predictor(orig_img, thresh, layer_list)\n bboxes, labels, scores, layer_ids, features = predictor(orig_img)\n \n # 検出結果を利用し、画像を切り取って保存 / Use the detection results to cut and save images\n #if save_img: save_images(orig_img, results, object_list, output_dir, file)\n if save_img: save_images(orig_img, bboxes, output_dir, file)\n # 特徴ファイルを保存 / Save feature file\n #save_feature(img_features, output_dir, file+'.npz')\n save_feature(features, layer_name_list, output_dir, file+'.npz')\n # 指定した物体の座標を保存 / Save the coordinates of the specified object\n #save_ebox(results, object_list, img_h, img_w, output_dir, 'e'+file+'.txt')\n save_ebox(bboxes, labels, layer_ids, img_h, img_w, output_dir, 'e'+file+'.txt')\n # specfileを保存 / save specfile\n #save_specfile(output_dir, img_features)\n\n\n\n # フォルダ内が画像のとき / When the folder contains an image\n else:\n orig_input_dir = input_dir\n orig_output_dir = output_dir\n # フォルダ内のビデオ数だけループ / Loop only the number of videos in a folder\n video_files = os.listdir(input_dir)\n for video_file in video_files:\n print(video_file)\n print('save %s feature...' % video_file)\n input_dir = orig_input_dir + '/' + video_file\n\n print('load image...')\n#\n # 最初の画像を読み込み / Loading the first image\n orig_img = cv2.imread(os.path.join(input_dir, '000001.png'))\n # 基準となる画像の高さと幅を取得 / Get the height and width of the base image\n img_h, img_w = orig_img.shape[:2]\n#\n # ファイル数分繰り返す / Repeat files for a few minutes \n for i in range(1,101):\n img_file = str(i).zfill(6)+'.png'\n if i < 51:\n file = str(i).zfill(6)\n output_dir = orig_output_dir + '_0/' + video_file + '_0'\n else:\n file = str(i-50).zfill(6)\n output_dir = orig_output_dir + '_1/' + video_file + '_1'\n # フォルダが無ければ新規作成 / Create a new folder if there is none previously\n if not os.path.isdir(output_dir):\n os.makedirs(output_dir)\n for layer in layer_name_list:\n if not os.path.isdir(os.path.join(output_dir, layer)):\n os.mkdir(os.path.join(output_dir, layer))\n if save_img and not os.path.isdir(os.path.join(output_dir, 'img')):\n os.mkdir(os.path.join(output_dir, 'img'))\n if not os.path.isdir(os.path.join(output_dir, 'ebox')):\n os.mkdir(os.path.join(output_dir, 'ebox'))\n if not os.path.isdir(os.path.join(output_dir, 'orig_img')):\n os.mkdir(os.path.join(output_dir, 'orig_img'))\n\n # 画像読み込み / load image\n orig_img = cv2.imread(os.path.join(input_dir, img_file))\n#\n cv2.imwrite(output_dir + '/orig_img/' + file + '.jpg', orig_img)\n # サイズが異なる場合は変形してから入力 / If the size is different, deform and then enter\n if (img_h, img_w) != orig_img.shape[:2] :\n orig_img = cv2.resize(orig_img, (img_w, img_h))\n#\n # 検出結果と特徴を取得 / retrieve detection results and features\n bboxes, labels, scores, layer_ids, features = predictor(orig_img)\n\n # 検出結果を利用し、画像を切り取って保存 / Use the detection results to cut and save images\n if save_img: save_images(orig_img, bboxes, output_dir, file)\n # 特徴ファイルを保存 / save feature file\n save_feature(features, layer_name_list, output_dir, file+'.npz')\n # 指定した物体の座標を保存 / save specified object's coordinates\n save_ebox(bboxes, labels, layer_ids, img_h, img_w, output_dir, 'e'+file+'.txt')\n #copy_file(output_dir)", "sub_path": "estimation/dataset_generator/dataset_generator_img.py", "file_name": "dataset_generator_img.py", "file_ext": "py", "file_size_in_byte": 13173, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 31, "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": "numpy.savez", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 60, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path", "line_number": 80, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 81, "usage_type": "call"}, {"api_name": "os.path", "line_number": 81, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "os.listdir", "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": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 93, "usage_type": "call"}, {"api_name": "object_detector.ObjectDetector", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 119, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path", "line_number": 131, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 132, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 134, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 135, "usage_type": "call"}, {"api_name": "os.path", "line_number": 135, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 136, "usage_type": "call"}, {"api_name": "os.path", "line_number": 136, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 136, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 137, "usage_type": "call"}, {"api_name": "os.path", "line_number": 137, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 138, "usage_type": "call"}, {"api_name": "os.path", "line_number": 138, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 138, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 140, "usage_type": "call"}, {"api_name": "os.path", "line_number": 140, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 140, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "attribute"}, {"api_name": "cv2.VideoCapture", "line_number": 146, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 152, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 156, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 157, "usage_type": "call"}, {"api_name": "os.path", "line_number": 157, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 157, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path", "line_number": 160, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 169, "usage_type": "call"}, {"api_name": "os.path", "line_number": 169, "usage_type": "attribute"}, {"api_name": "cv2.imread", "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": "cv2.resize", "line_number": 175, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 202, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 211, "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.path.isdir", "line_number": 225, "usage_type": "call"}, {"api_name": "os.path", "line_number": 225, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 226, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 228, "usage_type": "call"}, {"api_name": "os.path", "line_number": 228, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 228, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 229, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 229, "usage_type": "call"}, {"api_name": "os.path", "line_number": 229, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 230, "usage_type": "call"}, {"api_name": "os.path", "line_number": 230, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 230, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 231, "usage_type": "call"}, {"api_name": "os.path", "line_number": 231, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 232, "usage_type": "call"}, {"api_name": "os.path", "line_number": 232, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 232, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 233, "usage_type": "call"}, {"api_name": "os.path", "line_number": 233, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 234, "usage_type": "call"}, {"api_name": "os.path", "line_number": 234, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 234, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 235, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 235, "usage_type": "call"}, {"api_name": "os.path", "line_number": 235, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 238, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 238, "usage_type": "call"}, {"api_name": "os.path", "line_number": 238, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 240, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 243, "usage_type": "call"}]} +{"seq_id": "479394244", "text": "from abc import ABCMeta, abstractmethod\nimport numpy as np\nimport datetime\nimport uuid\n\n\nclass EventObserver(metaclass=ABCMeta):\n \"\"\" EventObserver class.\n\n This abstract class defines the minimum requirements of a data source.\n It provides an interface to access entries, streamed from certain source.\n\n Raises\n ------\n NotImplementedError: This is an abstract class.\n\n \"\"\"\n\n @abstractmethod\n def update(self, event):\n raise NotImplementedError\n\n\nclass BufferDataEventObserver(EventObserver):\n\n def __init__(self):\n self.buffer = []\n\n def update(self, event):\n if(event is not None):\n self.buffer.append(event)\n\n def get_buffer(self):\n return self.buffer\n\n def get_name(self):\n return \"BufferDataEventObserver\"\n\n\nclass EvaluationEventObserver(EventObserver):\n \"\"\" EvaluationEventObserver class.\n \"\"\"\n\n def __init__(self, algorithm, train_eval_trigger, results_observers, expected_target_values=None):\n \"\"\" EvaluationEventObserver class constructor.\"\"\"\n self.expected_target_values = expected_target_values\n self.train_eval_trigger = train_eval_trigger\n self.results_observers = results_observers\n self.algorithm = algorithm\n self.result_buffer_id = []\n self.result_buffer_ytrue = []\n self.result_buffer_ypred = []\n\n self.event_buffer_t = []\n self.event_buffer_x = []\n self.event_buffer_y = []\n\n def update(self, event):\n \"\"\"For each new event, we follow this sequence:\n - update the train_eval_trigger, so it gains new context\n - check if we shall fit the algorithm with buffered data\n - update the buffer, according to the trigger policy\n - shall predict?\n - shall buffer new instance?\n - get instances to fit from buffer\n - update the buffer\n \"\"\"\n if event is not None:\n algorithm_type = self.algorithm.algorithm_type()\n if 'id' in event:\n id = event['id']\n else:\n id = uuid.uuid1()\n if 't' in event:\n t = event['t']\n else:\n t = datetime.datetime.now()\n x = event['X']\n\n y_true = None\n if algorithm_type == 'CLASSIFICATION' or algorithm_type == 'REGRESSION':\n y_true = event['y']\n\n t_array = np.array(t)\n x_array = np.array(x)\n y_array = np.array(y_true)\n\n self.train_eval_trigger.update(event)\n\n bx_to_fit, by_to_fit = self.train_eval_trigger.instances_to_fit(self.event_buffer_t, self.event_buffer_x, self.event_buffer_y)\n for idx in range(len(bx_to_fit)):\n self.fit_algorithm(bx_to_fit[idx], by_to_fit[idx])\n\n self.event_buffer_t, self.event_buffer_x, self.event_buffer_y = self.train_eval_trigger.remaining_buffer(self.event_buffer_t, self.event_buffer_x, self.event_buffer_y)\n\n if self.train_eval_trigger.shall_predict():\n y_pred = self.algorithm.predict(x_array)\n self.result_buffer_ytrue.append(y_true)\n self.result_buffer_ypred.append(y_pred)\n self.result_buffer_id.append(id)\n\n if self.train_eval_trigger.shall_buffer():\n self.event_buffer_t.append(t_array)\n self.event_buffer_x.append(x_array)\n self.event_buffer_y.append(y_array)\n\n bx_to_fit, by_to_fit = self.train_eval_trigger.instances_to_fit(self.event_buffer_t, self.event_buffer_x, self.event_buffer_y)\n for idx in range(len(bx_to_fit)):\n self.fit_algorithm(bx_to_fit[idx], by_to_fit[idx])\n\n self.event_buffer_t, self.event_buffer_x, self.event_buffer_y = self.train_eval_trigger.remaining_buffer(self.event_buffer_t, self.event_buffer_x, self.event_buffer_y)\n\n def fit_algorithm(self, x, y):\n if len(self.result_buffer_id) > 0:\n for result_observer in self.results_observers:\n result_observer.report(self.result_buffer_id, self.result_buffer_ypred, self.result_buffer_ytrue)\n self.result_buffer_id = []\n self.result_buffer_ypred = []\n self.result_buffer_ytrue = []\n\n if self.expected_target_values is not None:\n self.algorithm.partial_fit(x, y, self.expected_target_values)\n else:\n self.algorithm.partial_fit(x, y)\n\n\nclass StreamSpeedObserver(EventObserver):\n \"\"\"Supports same functionality originaly envisioned in EvaluateStreamGenerationSpeed\n while providing greater flexibility on how we deal with streams as well\n as metrics we report\"\"\"\n\n def __init__(self, last_n_samples):\n self.last_n_samples = last_n_samples\n self.buffer = []\n\n def update(self, event):\n if(event is not None):\n self.buffer.append(datetime.datetime.now())\n self.buffer = self.buffer[-self.last_n_samples:]\n\n def get_buffer(self):\n return self.buffer\n\n def get_name(self):\n return \"StreamSpeedObserver\"\n", "sub_path": "src/skmultiflow/data/observer/event_observer.py", "file_name": "event_observer.py", "file_ext": "py", "file_size_in_byte": 5138, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "abc.ABCMeta", "line_number": 7, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 19, "usage_type": "name"}, {"api_name": "uuid.uuid1", "line_number": 73, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 77, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 85, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 86, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 138, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 138, "usage_type": "attribute"}]} +{"seq_id": "466282012", "text": "from django.shortcuts import render,redirect\nfrom .serializer import LoginSerializer,RegisterSerializer\nfrom rest_framework import generics,mixins,status\nfrom rest_framework.response import Response\nfrom django.contrib.auth import authenticate,login,get_user_model,logout\nfrom rest_framework.authentication import SessionAuthentication,TokenAuthentication\nfrom rest_framework.authtoken.models import Token\n\n\nUser = get_user_model()\n\nclass LoginWithTokenAuthenticationAPIView(generics.GenericAPIView):\n serializer_class = LoginSerializer\n permission_classes = []\n authentication_classes = []\n\n def post(self,request):\n print(request.data)\n username = request.data.get('mobile')\n print(username)\n user = User.objects.get(username=str(username))\n print(user)\n if user is not None:\n # login(user,request)\n #TOKEN STUFF\n token, _ = Token.objects.get_or_create(user = user)\n login(request,user)\n #token_expire_handler will check, if the token is expired it will generate new one\n # is_expired, token = token_expire_handler(token) # The implementation will be described further\n\n return Response({ \n 'token': token.key,\n }, status=status.HTTP_200_OK)\n response = {\n \"data\": {\n \"message\": \"Your login information is invalid\",\n \"status\": \"invalid\"\n }\n }\n return Response(response, status=status.HTTP_200_OK)\n\n\nclass RegisterAPIView(generics.CreateAPIView):\n queryset = User.objects.all()\n permission_classes = []\n authentication_classes = []\n serializer_class = RegisterSerializer\n\n def post(self,request,*args,**kwargs):\n username = request.data.get('mobile')\n email = request.data.get('email')\n user = User.objects.create_user(username=username,email=email)\n user =\tUser.objects.get(username=username)\n if user is not None:\n \ttoken, _ = Token.objects.get_or_create(user = user)\n \treturn Response({ \n 'token': token.key,\n }, status=status.HTTP_200_OK)\n return Response({'message':'invalid form'})\n\n\ndef login_user(request):\n\tif request.method==\"POST\":\n\t\tusername\t\t=\trequest.POST['mobile']\n\t\ttry:\n\t\t\tuser \t= User.objects.get(username=str(username))\n\t\t\tlogin(request,user)\n\t\t\treturn redirect('index')\n\t\texcept:\n\t\t\treturn render(request,'login.html',{})\n\treturn render(request,'login.html',{})\n\n\ndef logout_user(request):\n\tlogout(request)\n\treturn redirect('login')", "sub_path": "authentication/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2661, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.contrib.auth.get_user_model", "line_number": 10, "usage_type": "call"}, {"api_name": "rest_framework.generics.GenericAPIView", "line_number": 12, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 12, "usage_type": "name"}, {"api_name": "serializer.LoginSerializer", "line_number": 13, "usage_type": "name"}, {"api_name": "rest_framework.authtoken.models.Token.objects.get_or_create", "line_number": 26, "usage_type": "call"}, {"api_name": "rest_framework.authtoken.models.Token.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "rest_framework.authtoken.models.Token", "line_number": 26, "usage_type": "name"}, {"api_name": "django.contrib.auth.login", "line_number": 27, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 31, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 33, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 33, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 40, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 40, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 40, "usage_type": "name"}, {"api_name": "rest_framework.generics.CreateAPIView", "line_number": 43, "usage_type": "attribute"}, {"api_name": "rest_framework.generics", "line_number": 43, "usage_type": "name"}, {"api_name": "serializer.RegisterSerializer", "line_number": 47, "usage_type": "name"}, {"api_name": "rest_framework.authtoken.models.Token.objects.get_or_create", "line_number": 55, "usage_type": "call"}, {"api_name": "rest_framework.authtoken.models.Token.objects", "line_number": 55, "usage_type": "attribute"}, {"api_name": "rest_framework.authtoken.models.Token", "line_number": 55, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 56, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 58, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 58, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 59, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 67, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 68, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 70, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 71, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 75, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "83402037", "text": "import argparse\nimport logging\nimport subprocess\nimport sys\nimport os\nfrom pathlib import Path\n\ndef main():\n parser = argparse.ArgumentParser()\n # Logging\n parser.add_argument('-v', '--verbose',\n action=\"store_const\", const=logging.INFO, default=logging.WARNING)\n parser.add_argument('-d', '--debug',\n action=\"store_const\", const=logging.DEBUG, default=logging.WARNING)\n\n subparser = parser.add_subparsers(dest='command')\n\n garnet_parser = subparser.add_parser('garnet', add_help=False)\n # garnet_parser.add_argument('args', nargs=argparse.REMAINDER)\n\n\n args, extra_args = parser.parse_known_args()\n print(args)\n\n logging.basicConfig(level=min(args.verbose, args.debug))\n\n if args.command == 'garnet':\n subprocess.call(\n [sys.executable, 'garnet.py'] + extra_args,\n cwd=Path(os.getcwd()) / 'garnet',\n )\n\n\nif __name__ == '__main__':\n main()\n\n", "sub_path": "aha.py", "file_name": "aha.py", "file_ext": "py", "file_size_in_byte": 959, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 12, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 12, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 14, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 14, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 25, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 29, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 30, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "282140924", "text": "from termcolor import colored\n\n\nclass Grid:\n def __init__(self):\n self.grid = []\n self.states = dict()\n self.sigma_ordered = []\n self.sigma = dict()\n self.states_reversed = []\n #TODO: IMPLEMENT INTO ADD_STATE\n\n #TODO:TEST\n def add_state(self, state):\n if len(state) - 1 != len(self.sigma):\n raise ValueError(\"State line does not fit to this Sigma\")\n index = len(self.grid)\n self.states[state.pop(0)] = index\n self.grid.append(state)\n return True\n\n def set_sigma(self, sigma: list):\n if len(self.grid) > 0:\n result = input(\n \"There was already something in this Grid. Should I erase it, or ignore new Sigma? Y-erase/N-ignore\")\n if result == \"Y\":\n self.grid.clear()\n self.sigma.clear()\n self.states.clear()\n elif result == \"N\":\n return\n else:\n print(\"Did not recognize \" + result)\n self.set_sigma(sigma)\n return\n\n counter = 0\n for letter in sigma:\n if letter in self.sigma_ordered:\n raise ValueError(\"There cannot be same letters in sigma!\" + str(letter))\n self.sigma_ordered.append(letter)\n self.sigma[letter] = counter\n counter += 1\n\n print(colored(\"Successfully set Sigma\", \"green\"))\n\n def check_states(self):\n for state in self.states:\n state_index = self.states[state]\n for letter in self.sigma:\n letter_index = self.sigma[letter]\n if not self.grid[state_index][letter_index] in self.states:\n print(colored(\"'\" + self.grid[state_index][\n letter_index] + \"' in: (state: '\" + state + \"' letter: '\" + letter +\n \"') does not point to any valid state!\", \"red\"))\n self.fill_new_state_def(state)\n return True\n\n def has_state(self, state):\n return state in self.states\n\n def has_letter(self, letter):\n return letter in self.sigma\n\n def get_sigma(self):\n return self.sigma_ordered\n\n def get_states(self):\n states = []\n for state in self.states:\n states.append(state)\n\n return states\n\n def get_sigma_length(self):\n return len(self.sigma)\n\n def fill_new_state_def(self, state):\n output = \"state\"\n for i in self.get_sigma():\n output += \" \" + i\n print(output)\n result = input(state + \" \")\n result = result.split()\n if len(result) != self.get_sigma_length():\n print(colored(\"Does not fit in current sigma! \",\"red\"))\n self.fill_new_state_def(state)\n return\n\n self.grid[self.states[state]] = result\n\n def get_max_length(self):\n max_len = 0\n for i in self.states:\n max_len = max(max_len, len(i))\n\n for i in self.sigma:\n max_len = max(max_len, len(i))\n return max_len\n\n def leads_to_state(self, current):\n #TODO: COMPLETE\n pass\n\n '''\n Current as NAME\n '''\n def step(self, current, letter):\n if letter not in self.sigma:\n print(colored(\"Letter: \" + letter + \" is not in Sigma!\", \"red\"))\n return current\n else:\n line = self.states[current]\n index = self.sigma[current]\n pass\n #return self.states self.grid[line][index]", "sub_path": "Grid.py", "file_name": "Grid.py", "file_ext": "py", "file_size_in_byte": 3562, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "termcolor.colored", "line_number": 45, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 53, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 86, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 110, "usage_type": "call"}]} +{"seq_id": "396651061", "text": "# encoding: utf-8\n\nfrom scrapy.contrib.spiders import CrawlSpider\nfrom scrapy.http import Request\n\nimport simplejson\nimport math\nimport urllib\nimport bisect\nimport random\n\nclass AmazonSpider(CrawlSpider):\n '''大城市拆分计算路口间距离'''\n \n name = \"part_missing\"\n \n API = 'http://api.map.baidu.com/direction/v1/routematrix?'\n \n \n def __init__(self, *a, **kw):\n\n super(self.__class__, self).__init__(*a, **kw)\n \n def getPair(self):\n \n pid2pidset = dict()\n with open(\"city_map/all_map_37_part1.txt\") as f:\n for line in f:\n items = line.strip().split(\"\\t\")\n pid1 = int(items[0])\n pid2 = int(items[1])\n if pid1 not in pid2pidset:\n pid2pidset[pid1] = set([pid2,])\n else:\n pid2pidset[pid1].add(pid2)\n \n return pid2pidset\n \n def start_requests(self):\n pid2pidset = self.getPair()\n \n #上半部分\n city_border_points = open('city_map/all_map_371.txt').readlines()\n \n points = []\n for line in city_border_points:\n items = line.strip().split()\n pointid = int(items[0])\n lon = float(items[1])\n lat = float(items[2])\n \n point = (pointid,lon,lat)\n points.append(point)\n \n groups = []\n group_size = len(points)/5\n if len(points) % 5 > 0 :\n group_size += 1\n \n for i in range(group_size):\n groups.append(points[i*5:(i+1)*5])\n \n #下半部分\n city_border_points2 = open('city_map/all_map_372.txt').readlines()\n \n points2 = []\n for line in city_border_points2:\n items = line.strip().split()\n pointid = int(items[0])\n lon = float(items[1])\n lat = float(items[2])\n \n point = (pointid,lon,lat)\n points2.append(point)\n \n groups2 = []\n group_size2 = len(points2)/5\n if len(points2) % 5 > 0 :\n group_size2 += 1\n \n for i in range(group_size2):\n groups2.append(points2[i*5:(i+1)*5])\n \n #每次发送5*5的线路请求\n for i in range(group_size):\n group_start = groups[i]\n \n for j in range(group_size2):\n group_end = groups2[j]\n \n start_point_ids = []\n origins = []\n for point in group_start:\n start_point_ids.append(point[0])\n origins.append(\"%s,%s\"%(point[2],point[1]))\n \n end_point_ids = []\n destinations = []\n for point in group_end:\n end_point_ids.append(point[0])\n destinations.append(\"%s,%s\"%(point[2],point[1]))\n \n #判断是否应该发起请求\n exist = False\n for pid1 in start_point_ids:\n for pid2 in end_point_ids:\n if pid1 in pid2pidset and pid2 in pid2pidset[pid1]:\n exist = True\n \n if exist == True:\n continue\n \n origins = \"|\".join(origins) \n destinations = \"|\".join(destinations)\n \n parameter = {\n 'mode':'driving',\n 'origins':origins,\n 'destinations':destinations,\n 'output':'json',\n 'ak':random.choice(['61oEbKGqEBkE5jN2xXx3CiZI',#我\n #'uf77T9ZHeLvZgDkc5IGgPzoO',#子健\n 'mHG6eTMkxTsNBrwTgUtAqvKf',#孙博文\n 'qrrrbv0aCsIj6AlaUVXUtI34',#杜辉\n #'eUR5ah6MQ5y3Sg6ST0penkoX',\n 'dmtP3rufYrXvFwBxxkkMW1Y9',#玉石\n '5ru0ndvN4MCvykmtZOIlAZaf'#三虎\n ]),\n }\n \n parmeters = urllib.urlencode(parameter)\n \n meta = {\n 'start_point_ids':start_point_ids,\n 'end_point_ids':end_point_ids,\n }\n \n url = self.API + parmeters\n\n yield Request(url, callback=self.parse_routine, meta=meta)\n \n def parse_routine(self, response):\n \n meta = response.meta\n \n data = simplejson.loads(response.body)\n \n fp = open('city_map/all_map_37_part1_1.txt','a')\n index = 0\n\n end_points_size = len(meta['end_point_ids'])\n \n for element in data['result']['elements']:\n distance = element['distance']['value']\n \n start_point_id = meta['start_point_ids'][index / end_points_size]\n end_point_id = meta['end_point_ids'][index % end_points_size]\n \n index += 1\n \n if start_point_id == end_point_id:\n continue\n \n if int(distance) == 0:\n distance = 99999999\n result = '%s\\t%s\\t%s\\n' % (start_point_id, end_point_id, distance)\n \n #print result\n fp.write(result)", "sub_path": "baidumap/spiders/part_missing.py", "file_name": "part_missing.py", "file_ext": "py", "file_size_in_byte": 5522, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "scrapy.contrib.spiders.CrawlSpider", "line_number": 12, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 121, "usage_type": "call"}, {"api_name": "urllib.urlencode", "line_number": 131, "usage_type": "call"}, {"api_name": "scrapy.http.Request", "line_number": 140, "usage_type": "call"}, {"api_name": "simplejson.loads", "line_number": 146, "usage_type": "call"}]} +{"seq_id": "451741389", "text": "\"\"\"User objects.\"\"\"\n\nfrom collections import namedtuple\nimport logging\n\n\nlogger = logging.getLogger(__name__)\nDEFAULT_NAME = 'Unknown'\n\nUserID = namedtuple('UserID', ['chat_id', 'gaia_id'])\n\n\nclass User(object):\n\n \"\"\"A chat user.\n\n Handles full_name or first_name being None by creating an approximate\n first_name from the full_name, or setting both to DEFAULT_NAME.\n \"\"\"\n\n def __init__(self, user_id, full_name, first_name, photo_url, emails,\n is_self):\n \"\"\"Initialize a User.\"\"\"\n self.id_ = user_id\n self.full_name = full_name if full_name != '' else DEFAULT_NAME\n self.first_name = (first_name if first_name != ''\n else self.full_name.split()[0])\n self.photo_url = photo_url\n self.emails = emails\n self.is_self = is_self\n\n @staticmethod\n def from_entity(entity, self_user_id):\n \"\"\"Initialize from a Entity.\n\n If self_user_id is None, assume this is the self user.\n \"\"\"\n user_id = UserID(chat_id=entity.id.chat_id,\n gaia_id=entity.id.gaia_id)\n return User(user_id, entity.properties.display_name,\n entity.properties.first_name,\n entity.properties.photo_url,\n entity.properties.email,\n (self_user_id == user_id) or (self_user_id is None))\n\n @staticmethod\n def from_conv_part_data(conv_part_data, self_user_id):\n \"\"\"Initialize from ConversationParticipantData.\n\n If self_user_id is None, assume this is the self user.\n \"\"\"\n user_id = UserID(chat_id=conv_part_data.id.chat_id,\n gaia_id=conv_part_data.id.gaia_id)\n return User(user_id, conv_part_data.fallback_name, None, None, [],\n (self_user_id == user_id) or (self_user_id is None))\n\n\nclass UserList(object):\n\n \"\"\"Collection of User instances.\"\"\"\n\n def __init__(self, client, self_entity, entities, conv_parts):\n \"\"\"Initialize the list of Users.\n\n Creates users from the given Entity and ConversationParticipantData\n instances. The latter is used only as a fallback, because it doesn't\n include a real first_name.\n \"\"\"\n self._client = client\n self._self_user = User.from_entity(self_entity, None)\n # {UserID: User}\n self._user_dict = {self._self_user.id_: self._self_user}\n # Add each entity as a new User.\n for entity in entities:\n user_ = User.from_entity(entity, self._self_user.id_)\n self._user_dict[user_.id_] = user_\n # Add each conversation participant as a new User if we didn't already\n # add them from an entity.\n for participant in conv_parts:\n self.add_user_from_conv_part(participant)\n logger.info('UserList initialized with {} user(s)'\n .format(len(self._user_dict)))\n\n self._client.on_state_update.add_observer(self._on_state_update)\n\n def get_user(self, user_id):\n \"\"\"Return a User by their UserID.\n\n Raises KeyError if the User is not available.\n \"\"\"\n try:\n return self._user_dict[user_id]\n except KeyError:\n logger.warning('UserList returning unknown User for UserID {}'\n .format(user_id))\n return User(user_id, DEFAULT_NAME, None, None, [], False)\n\n def get_all(self):\n \"\"\"Returns all the users known\"\"\"\n return self._user_dict.values()\n\n def add_user_from_conv_part(self, conv_part):\n \"\"\"Add new User from ConversationParticipantData\"\"\"\n user_ = User.from_conv_part_data(conv_part, self._self_user.id_)\n if user_.id_ not in self._user_dict:\n logging.warning('Adding fallback User: {}'.format(user_))\n self._user_dict[user_.id_] = user_\n return user_\n\n def _on_state_update(self, state_update):\n \"\"\"Receive a StateUpdate\"\"\"\n if state_update.HasField('conversation'):\n self._handle_conversation(state_update.conversation)\n\n def _handle_conversation(self, conversation):\n \"\"\"Receive Conversation and update list of users\"\"\"\n for participant in conversation.participant_data:\n self.add_user_from_conv_part(participant)\n", "sub_path": "hangups/user.py", "file_name": "user.py", "file_ext": "py", "file_size_in_byte": 4310, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 7, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 10, "usage_type": "call"}, {"api_name": "logging.warning", "line_number": 106, "usage_type": "call"}]} +{"seq_id": "652102112", "text": "from enum import Enum\r\n\r\nfrom selene.elements import SeleneElement\r\nfrom pteromyini.core.com.space import *\r\nfrom pteromyini.interpreter.interpreter_facade import *\r\nfrom pteromyini.interpreter.xpath import XPath\r\nfrom pteromyini.lib.debug.profiler import ProfileTime\r\nfrom pteromyini.lib.design_pattern.ref import Ref\r\nfrom pteromyini.lib.web.wait import retry_get_result, TimeoutWaitResultError\r\n\r\n\r\nclass TypeElement(Enum):\r\n BUTTON = \"button\"\r\n INPUT = \"input\"\r\n TEXTAREA = \"textarea\"\r\n FORM = \"form\"\r\n SELECT = \"select\"\r\n SPAN = \"span\"\r\n SPACE = \"space\"\r\n CHECKBOX = \"checkbox\"\r\n\r\n\r\nxpaths = {\r\n TypeElement.BUTTON: [\r\n './/*[@type=\"submit\" and @value=\"{value}\"]',\r\n './/*[@type=\"button\" and @value=\"{value}\"]',\r\n './/*[./label[text() = \"{value}\"]]/input', # check button in admga\r\n './/*[./td[text() = \"{value}\"]]//input',\r\n # This type is used into https://stage.admga.com/microgaming/bonus-campaigns/new page for set checkbox (like \"Подтвержденный email\")\r\n './/*[text() = \"{value}\"]',\r\n './/*[normalize-space(text()) = \"{value}\"]',\r\n './/*[contains(text(), \"{value}\")]',\r\n './/*[contains(normalize-space(text()), \"{value}\")]',\r\n '(.//*[contains(., \"{value}\")])[last()]' # use if svg\r\n ],\r\n\r\n TypeElement.INPUT: [\r\n './/input[@id=\"{value}\"]',\r\n './/input[@name=\"{value}\"]',\r\n './/td[.//*[text()=\"{value}\"]]//input',\r\n './/td[.//*[contains(text(), \"{value}\")]]//input', # in admga search form (Email, id, login ..)\r\n './/tr[.//*[text()=\"{value}\"]]//input',\r\n './/tr[.//*[contains(text(), \"{value}\")]]//input', # in admga profile (Email, Имя ..)\r\n './/*[./*[contains(text(),\"{value}\")]]/input',\r\n # work in find player: https://stage.admga.com/microgaming/player/list\r\n './/*[.//*[contains(text(),\"{value}\")]][.//input]//input',\r\n # work in player profile add bonus amount: https://stage.admga.com/microgaming/player/profile/659825/id\r\n './/*[.//*[contains(text(),\"{value}\")]][.//textarea]//textarea',\r\n # work in player profile add bonus comments: https://stage.admga.com/microgaming/player/profile/659825/id\r\n './/input[@placeholder=\"{value}\"]',\r\n './/input[contains(@placeholder, \"{value}\")]'\r\n ],\r\n\r\n TypeElement.TEXTAREA: [\r\n './/textarea[@id=\"{value}\"]',\r\n './/textarea[@name=\"{value}\"]',\r\n './/*[./label[text() = \"{value}\"]]/textarea',\r\n '//*[contains(@placeholder, \"{value}\")]',\r\n './/*[.//*[contains(text(),\"{value}\")]][.//textarea]//textarea'\r\n # work in player profile add bonus comments: https://stage.admga.com/microgaming/player/profile/659825/id\r\n ],\r\n\r\n TypeElement.SELECT: [\r\n './/select[@id=\"{value}\"]',\r\n './/*[./*[contains(text(),\"{value}\")]]/select',\r\n './/*[.//*[contains(text(),\"{value}\")]][.//select]//select'\r\n ],\r\n\r\n TypeElement.SPAN: [\r\n './/*[./*[contains(text(),\"{value}\")]]/span',\r\n './/*[.//*[contains(text(),\"{value}\")]]//span'\r\n ],\r\n\r\n TypeElement.SPACE: [\r\n '[.//*[.=\"{text}\"]]',\r\n '[.//*[normalize-space(.)=\"{text}\"]]',\r\n '[.//*[contains(., \"{text}\")]]',\r\n '[.//*[text()=\"{text}\"]]',\r\n '[.//*[contains(text(), \"{text}\")]]'\r\n ],\r\n\r\n TypeElement.CHECKBOX: [\r\n './/input[@type=\"checkbox\"][contains(@id,\"{value}\")]',\r\n './/input[@type=\"checkbox\"][contains(@name,\"{value}\")]',\r\n './/input[@type=\"checkbox\"]'\r\n ]\r\n}\r\n\r\n\r\ndef set_xpaths(key, values: list):\r\n xpaths[key] = values\r\n\r\n\r\ndef find_elements(context, value=None, element_type=None, ref_xpath: Ref = Ref.none(), ignore_paths: list = None,\r\n _xpaths=None):\r\n def check_path(path):\r\n return ignore_paths == None or path not in ignore_paths\r\n\r\n if _xpaths is None:\r\n _xpaths = xpaths[element_type]\r\n sp = space(context)\r\n if value is not None:\r\n value = check_text_is_list(value)\r\n for path in _xpaths:\r\n if not check_path(path):\r\n continue\r\n if type(value) is list:\r\n v = value.copy()\r\n path = path.format(value=v.pop(0))\r\n while len(v) > 0:\r\n path += f'[.//*[contains(.,\"{v.pop(0)}\")]]'\r\n elif value is not None:\r\n path = path.format(value=value)\r\n if not check_path(path):\r\n continue\r\n if path == '':\r\n return [sp]\r\n else:\r\n if path.startswith('//'):\r\n path = f'.{path}'\r\n r = sp.ss(by.xpath(path))\r\n if len(r) > 0:\r\n ref_xpath(path)\r\n return r\r\n return []\r\n\r\n\r\ndef find_buttons(context, value, ref_xpath: Ref = Ref.none(), ignore_paths: list = None):\r\n return find_elements(context, value, TypeElement.BUTTON, ref_xpath, ignore_paths)\r\n\r\n\r\ndef find_inputs(context, value):\r\n return find_elements(context, value, TypeElement.INPUT)\r\n\r\n\r\ndef find_textarea(context, value):\r\n return find_elements(context, value, TypeElement.TEXTAREA)\r\n\r\n\r\ndef find_selectors(context, value):\r\n return find_elements(context, value, TypeElement.SELECT)\r\n\r\n\r\ndef find_span(context, value, ref_xpath: Ref = Ref.none()):\r\n return find_elements(context, value, TypeElement.SPAN, ref_xpath)\r\n\r\n\r\ndef find_spaces(context, value=None, tag='*', wait=30, ref_xpath: Ref = Ref.none()):\r\n \"\"\"\r\n use in step: \"set context object\"\r\n\r\n :param ref_xpath: return Ref(), contains XPATH string\r\n :param wait:\r\n :param context:\r\n :param value: it can be [\"asdf\", \"{player.login}\"], \"{player.login}\", \"asdf\"\r\n if value[n][0] == * like [\"*asd\"] then use xpath: [contains(.//*[text()], \"asd\")]\r\n :param tag: div, table, form\r\n :return: SeleneCollection or empty list\r\n \"\"\"\r\n with ProfileTime(find_spaces.__name__):\r\n if value is not None:\r\n value = check_text_is_list(value)\r\n if type(value) is str:\r\n if value[0] + value[-1] == '\"\"': # remove \"\"\r\n value = value[1:-1]\r\n value = [value] # it's conver to common format\r\n\r\n for i in range(len(value)): # find and execute interpreter code, like {player.login}\r\n if is_code(value[i]):\r\n value[i] = code_to_objects(value[i])[0]\r\n\r\n def try_find():\r\n def run_xpath(local_xpath, ref_find: Ref):\r\n xpath = space_xpath(context, local_xpath) # convert local xpath to global\r\n # print(f'try space xpath:{xpath}')\r\n _find = ss(by.xpath(xpath))\r\n if len(_find) > 0:\r\n ref_xpath(local_xpath)\r\n ref_find(_find)\r\n return True\r\n return False\r\n\r\n find = Ref()\r\n if value is None:\r\n if run_xpath(f'//{tag}', find):\r\n return find()\r\n else:\r\n def condition_factory(pattern, text):\r\n if type(text) is XPath:\r\n text = str(text)\r\n return f'[.{text}]'\r\n if text[0] == '*': # if text is node\r\n return f'[contains(.//*[text()], \"{text[1:]}\")]'\r\n return pattern.format(text=text)\r\n\r\n patterns = xpaths[TypeElement.SPACE]\r\n for pattern in patterns:\r\n conditions = ''.join([condition_factory(pattern, v) for v in value])\r\n if run_xpath(f'//{tag}{conditions}', find):\r\n return find()\r\n\r\n try:\r\n return retry_get_result(try_find, wait=wait)\r\n except TimeoutWaitResultError:\r\n return []\r\n\r\n\r\ndef find_texts_in_element(element: SeleneElement):\r\n \"\"\"\r\n find only visible text in element\r\n :param element: context\r\n :return: list with texts\r\n \"\"\"\r\n import xml.etree.ElementTree as ET\r\n\r\n true_text = lambda text: text is not None and text != ''\r\n result = []\r\n\r\n t_elements = element.ss(by.xpath('.//*[text()]'))\r\n if len(t_elements) > 0:\r\n result.extend([e.text for e in t_elements if e.is_displayed()])\r\n\r\n t_elements = element.ss(by.xpath('.//*[@value]'))\r\n if len(t_elements) > 0:\r\n result.extend([e.get_attribute('value') for e in t_elements])\r\n\r\n if len(result) == 0:\r\n if element.is_displayed():\r\n t = element.text\r\n if true_text(t):\r\n result.append(t)\r\n t = element.get_attribute('value')\r\n if true_text(t):\r\n result.append(t)\r\n else:\r\n try:\r\n # [find_texts_in_element] fix bug find 'text' in textother\r\n xml_tree_text = ET.fromstring(f'{element.get_attribute(\"innerHTML\")}').text\r\n if type(xml_tree_text) is str:\r\n xml_tree_text = xml_tree_text.strip()\r\n if xml_tree_text:\r\n result.append(xml_tree_text)\r\n except:\r\n pass\r\n return result\r\n", "sub_path": "custom_library/pteromyini/pteromyini/core/com/find_element.py", "file_name": "find_element.py", "file_ext": "py", "file_size_in_byte": 9115, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "enum.Enum", "line_number": 12, "usage_type": "name"}, {"api_name": "pteromyini.lib.design_pattern.ref.Ref", "line_number": 94, "usage_type": "name"}, {"api_name": "pteromyini.lib.design_pattern.ref.Ref.none", "line_number": 94, "usage_type": "call"}, {"api_name": "pteromyini.lib.design_pattern.ref.Ref", "line_number": 128, "usage_type": "name"}, {"api_name": "pteromyini.lib.design_pattern.ref.Ref.none", "line_number": 128, "usage_type": "call"}, {"api_name": "pteromyini.lib.design_pattern.ref.Ref", "line_number": 144, "usage_type": "name"}, {"api_name": "pteromyini.lib.design_pattern.ref.Ref.none", "line_number": 144, "usage_type": "call"}, {"api_name": "pteromyini.lib.design_pattern.ref.Ref", "line_number": 148, "usage_type": "name"}, {"api_name": "pteromyini.lib.design_pattern.ref.Ref.none", "line_number": 148, "usage_type": "call"}, {"api_name": "pteromyini.lib.debug.profiler.ProfileTime", "line_number": 160, "usage_type": "call"}, {"api_name": "pteromyini.lib.design_pattern.ref.Ref", "line_number": 173, "usage_type": "name"}, {"api_name": "pteromyini.lib.design_pattern.ref.Ref", "line_number": 183, "usage_type": "call"}, {"api_name": "pteromyini.interpreter.xpath.XPath", "line_number": 189, "usage_type": "name"}, {"api_name": "pteromyini.lib.web.wait.retry_get_result", "line_number": 203, "usage_type": "call"}, {"api_name": "pteromyini.lib.web.wait.TimeoutWaitResultError", "line_number": 204, "usage_type": "name"}, {"api_name": "selene.elements.SeleneElement", "line_number": 208, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.fromstring", "line_number": 238, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree", "line_number": 238, "usage_type": "name"}]} +{"seq_id": "304927775", "text": "import tweepy\nfrom cassandra.cluster import Cluster\nfrom textblob import TextBlob\nimport requests\ncluster = Cluster(['localhost'])\nsession = cluster.connect()\n\nsession.execute(\"drop keyspace IF EXISTS appSenai\")\nsession.execute(\"create keyspace appSenai WITH replication = {'class':'SimpleStrategy', 'replication_factor' : 3};\")\nsession.execute(\"use appSenai\")\nsession.execute(\"create table twitter(id int PRIMARY KEY, texto text)\")\n\n\n# Twitter API credentials\nconsumer_key = \"tRi7F4pKteNpgWSUeuU8sB920\"\nconsumer_secret = \"bTCqBbrx37JMozlno53FLge55zwozy0xNZWJFa7Bkd9F3L977S\"\naccess_key = \"27323857-tGcMG0MpcoywbyT7oVJwGvl1sxmPFWnuqcT7B9mZZ\"\naccess_secret = \"z2IAzwX4D3dJw3poDMfNPRkBjMnTYKxNNNENu18doDvZp\"\n\n\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_key, access_secret)\n\napi = tweepy.API(auth)\n\npublic_tweets = api.search('Trump')\n\n\n\n\nfor tweet in public_tweets:\n print(tweet.text)\n frase = TextBlob(tweet.text)\n print('Tweet: {0} - Sentimento: {1}'.format(tweet.text, frase.sentiment)) #retornará um número entre -1 e 1. Quanto maior, mais feliz.\n # if frase.detect_language() != 'en':\n # traducao = TextBlob(str(frase.translate(to='en')))\n # print('Tweet: {0} - Sentimento: {1}'.format(tweet.text, traducao.sentiment))\n # else:\n # print('Tweet: {0} - Sentimento: {1}'.format(tweet.text, frase.sentiment))\n\n", "sub_path": "twitterAnaliseSentimentos.py", "file_name": "twitterAnaliseSentimentos.py", "file_ext": "py", "file_size_in_byte": 1427, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "cassandra.cluster.Cluster", "line_number": 5, "usage_type": "call"}, {"api_name": "tweepy.OAuthHandler", "line_number": 21, "usage_type": "call"}, {"api_name": "tweepy.API", "line_number": 24, "usage_type": "call"}, {"api_name": "textblob.TextBlob", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "423517950", "text": "# Copyright (c) 2019 Digital Asset (Switzerland) GmbH and/or its affiliates. All rights reserved.\n# SPDX-License-Identifier: Apache-2.0\n\n\"\"\"\nThis module contains all of the magic for defining Expr and its subclasses.\n\"\"\"\n\nfrom typing import Dict, Any, Optional, NamedTuple, Set\n\nfrom ..util.typing import unpack_optional\n\nTRIGGER_ANY_TIME = 'TRIGGER_ANY_TIME'\nTRIGGER_ON_INIT = 'TRIGGER_ON_INIT'\n\nEQUALS = '='\n\n\nclass _Unquoted:\n __slots__ = ('value',)\n\n def __init__(self, value: str):\n self.value = value\n\n def __repr__(self):\n return self.value\n\n\ndef coerce_type(cls, obj):\n core_optional_type = unpack_optional(cls)\n if core_optional_type is not None:\n if obj is None:\n return None\n else:\n return coerce_type(core_optional_type, obj)\n\n if hasattr(cls, 'coerce'):\n return cls.coerce(obj)\n return obj\n\n\nclass FieldInfo(NamedTuple):\n name: str\n assignment: str\n type_annotation: Any\n type_imports: Set[Any]\n\n\ndef _field_assignment(field_name, type_annotation) -> FieldInfo:\n optional_type = unpack_optional(type_annotation)\n if optional_type is not None:\n assignment = f'self.{field_name} = coerce_type({optional_type.__name__}, {field_name}) ' \\\n f'if {field_name} is not None else None'\n return FieldInfo(\n name=field_name,\n assignment=assignment,\n type_annotation=type_annotation,\n type_imports={optional_type})\n elif hasattr(type_annotation, '__name__'):\n return FieldInfo(\n name=field_name,\n assignment=f'self.{field_name} = coerce_type({type_annotation.__name__}, {field_name})',\n type_annotation=type_annotation,\n type_imports={type_annotation})\n else:\n # TODO: Should this emit a warning?\n return FieldInfo(\n name=field_name,\n assignment=f'self.{field_name} = {field_name}',\n type_annotation=type_annotation,\n type_imports=set())\n\n\ndef _define_expr_data_class(name: str, annotated_fields: Dict[str, Any]) -> Optional[type]:\n \"\"\"\n Create a simple type that defines some basic\n\n :return:\n \"\"\"\n\n if not annotated_fields:\n return None\n\n field_infos = [_field_assignment(field_name, type_annotation)\n for field_name, type_annotation in annotated_fields.items()]\n type_annotations = {im for fi in field_infos for im in fi.type_imports}\n\n data_class = f'_{name}Data'\n init_arg_list = ', '.join(annotated_fields.keys())\n\n init_assignments = '\\n '.join(fi.assignment for fi in field_infos)\n repr_fields = ', '.join(f'{field}={{self.{field}}}' for field in annotated_fields)\n newargs = repr(tuple(_Unquoted(f'self.{field}') for field in annotated_fields))\n\n ns = dict(coerce_type=coerce_type)\n ns.update({typ.__name__: typ for typ in type_annotations})\n exec_locals = {}\n\n class_def = f'''\nclass {data_class}:\n def __init__(self, {init_arg_list}):\n {init_assignments}\n\n def __repr__(self):\n return f'<{name}({repr_fields})>'\n\n def __getnewargs__(self):\n return {newargs}\n'''\n try:\n exec(class_def, ns, exec_locals)\n except:\n print('Failed to construct an Expr representation from this string:')\n print(class_def)\n raise\n\n type_def = exec_locals[data_class]\n type_def.__annotations__ = annotated_fields\n return type_def\n\n\nclass _ExprMeta(type):\n \"\"\"\n Metaclass for :class:`Expr` instances. Not meant to be used directly by outside callers.\n\n This expects a :class:`NamedTuple`-style syntax class definition, and does all the magic\n to actually turn it into an implementation. The generated class is compatible with rewriting.\n \"\"\"\n def __new__(mcs, name, bases, dct):\n data_base_class = _define_expr_data_class(name, dct.get('__annotations__', {}))\n if data_base_class is not None:\n bases = (data_base_class,) + bases\n return super().__new__(mcs, name, bases, dct)\n\n\nclass Expr(metaclass=_ExprMeta):\n \"\"\"\n An expression.\n \"\"\"\n\n @classmethod\n def coerce(cls, obj):\n if isinstance(obj, cls):\n return obj\n else:\n raise TypeError(f\"Couldn't convert to a {cls.__name__!r}: {obj!r}\")\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n ################################################################################################\n # COMPARISON FUNCTIONS\n\n def __lt__(self, other: 'Expr') -> 'Expr':\n \"\"\"\n Return an expression that is the result of a less-than comparison of this expression with\n another expression.\n \"\"\"\n from .expr_impl import AppExpr\n from .builtins import BUILTIN_LT\n return AppExpr(BUILTIN_LT, (self, exprify_value(other)))\n\n def __le__(self, other: 'Expr') -> 'Expr':\n \"\"\"\n Return an expression that is the result of a less-than-or-equal comparison of this\n expression with another expression.\n \"\"\"\n from .expr_impl import AppExpr\n from .builtins import BUILTIN_LE\n return AppExpr(BUILTIN_LE, (self, exprify_value(other)))\n\n def __eq__(self, other: 'Expr') -> 'Expr':\n \"\"\"\n Return an expression that is the result of comparing this expression with another\n expression for equality.\n \"\"\"\n from .expr_impl import AppExpr\n from .builtins import BUILTIN_EQ\n return AppExpr(BUILTIN_EQ, (self, exprify_value(other)))\n\n def __ne__(self, other: 'Expr') -> 'Expr':\n \"\"\"\n Return an expression that is the result of comparing this expression with another\n expression for inequality.\n \"\"\"\n from .expr_impl import AppExpr\n from .builtins import BUILTIN_NE\n return AppExpr(BUILTIN_NE, (self, exprify_value(other)))\n\n def __gt__(self, other: 'Expr') -> 'Expr':\n \"\"\"\n Return an expression that is the result of a greater-than comparison of this expression with\n another expression.\n \"\"\"\n from .expr_impl import AppExpr\n from .builtins import BUILTIN_GT\n return AppExpr(BUILTIN_GT, (self, exprify_value(other)))\n\n def __ge__(self, other: 'Expr') -> 'Expr':\n \"\"\"\n Return an expression that is the result of a greater-than-or-equal comparison of this\n expression with another expression.\n \"\"\"\n from .expr_impl import AppExpr\n from .builtins import BUILTIN_GE\n return AppExpr(BUILTIN_GE, (self, exprify_value(other)))\n\n ################################################################################################\n # MATHEMATICAL FUNCTIONS\n\n def __add__(self, other: 'Expr') -> 'Expr':\n \"\"\"\n Return an expression that is the result of summing this expression with another expression.\n \"\"\"\n from .expr_impl import AppExpr\n from .builtins import BUILTIN_ADD\n return AppExpr(BUILTIN_ADD, (self, exprify_value(other)))\n\n def __sub__(self, other: 'Expr') -> 'Expr':\n \"\"\"\n Return an expression that is the result of subtracting another expression from this\n expression.\n \"\"\"\n from .expr_impl import AppExpr\n from .builtins import BUILTIN_SUB\n return AppExpr(BUILTIN_SUB, (self, exprify_value(other)))\n\n def __mul__(self, other: 'Expr') -> 'Expr':\n \"\"\"\n Return an expression that is the result of multiplying this expression by another\n expression.\n \"\"\"\n from .expr_impl import AppExpr\n from .builtins import BUILTIN_MUL\n return AppExpr(BUILTIN_MUL, (self, exprify_value(other)))\n\n def __truediv__(self, other: 'Expr') -> 'Expr':\n \"\"\"\n Return an expression that is the result of dividing this expression by another\n expression.\n \"\"\"\n from .expr_impl import AppExpr\n from .builtins import BUILTIN_MUL\n return AppExpr(BUILTIN_MUL, (self, exprify_value(other)))\n\n def __concat__(self, other: 'Expr') -> 'Expr':\n \"\"\"\n Return an expression that is the result of summing this expression with another expression.\n \"\"\"\n from .expr_impl import AppExpr\n from .builtins import BUILTIN_CONCAT\n return AppExpr(BUILTIN_CONCAT, (self, exprify_value(other)))\n\n def __pow__(self, other: 'Expr') -> 'Expr':\n \"\"\"\n Return an expression that is the result of raising this expression by another expression.\n \"\"\"\n from .expr_impl import AppExpr\n from .builtins import BUILTIN_POW\n return AppExpr(BUILTIN_POW, (self, exprify_value(other)))\n\n\ndef exprify_args(arguments):\n return {key: exprify_value(value) for key, value in arguments.items()}\n\n\ndef exprify_value(value):\n \"\"\"\n Converts the specified unknown value to a subclass of `Expr` that is suitable to be used as an\n argument to a contract creation or choice exercising.\n \"\"\"\n from .expr_impl import ConstantExpr, GetContractIdExpr, TemplateExpr, TemplateSelectExpr\n if isinstance(value, (TemplateExpr, TemplateSelectExpr)):\n # in an argument context, a bare template reference actually needs to resolve to the\n # contract ID of the default alias for a given template ID\n return GetContractIdExpr.coerce(value)\n\n elif isinstance(value, Expr):\n return value\n\n return ConstantExpr(value)\n", "sub_path": "python/dazl/rules/expr_base.py", "file_name": "expr_base.py", "file_ext": "py", "file_size_in_byte": 9458, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "util.typing.unpack_optional", "line_number": 29, "usage_type": "call"}, {"api_name": "typing.NamedTuple", "line_number": 41, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 44, "usage_type": "name"}, {"api_name": "typing.Set", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 45, "usage_type": "name"}, {"api_name": "util.typing.unpack_optional", "line_number": 49, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.Any", "line_number": 73, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 73, "usage_type": "name"}, {"api_name": "expr_impl.AppExpr", "line_number": 160, "usage_type": "call"}, {"api_name": "builtins.BUILTIN_LT", "line_number": 160, "usage_type": "name"}, {"api_name": "expr_impl.AppExpr", "line_number": 169, "usage_type": "call"}, {"api_name": "builtins.BUILTIN_LE", "line_number": 169, "usage_type": "name"}, {"api_name": "expr_impl.AppExpr", "line_number": 178, "usage_type": "call"}, {"api_name": "builtins.BUILTIN_EQ", "line_number": 178, "usage_type": "name"}, {"api_name": "expr_impl.AppExpr", "line_number": 187, "usage_type": "call"}, {"api_name": "builtins.BUILTIN_NE", "line_number": 187, "usage_type": "name"}, {"api_name": "expr_impl.AppExpr", "line_number": 196, "usage_type": "call"}, {"api_name": "builtins.BUILTIN_GT", "line_number": 196, "usage_type": "name"}, {"api_name": "expr_impl.AppExpr", "line_number": 205, "usage_type": "call"}, {"api_name": "builtins.BUILTIN_GE", "line_number": 205, "usage_type": "name"}, {"api_name": "expr_impl.AppExpr", "line_number": 216, "usage_type": "call"}, {"api_name": "builtins.BUILTIN_ADD", "line_number": 216, "usage_type": "name"}, {"api_name": "expr_impl.AppExpr", "line_number": 225, "usage_type": "call"}, {"api_name": "builtins.BUILTIN_SUB", "line_number": 225, "usage_type": "name"}, {"api_name": "expr_impl.AppExpr", "line_number": 234, "usage_type": "call"}, {"api_name": "builtins.BUILTIN_MUL", "line_number": 234, "usage_type": "name"}, {"api_name": "expr_impl.AppExpr", "line_number": 243, "usage_type": "call"}, {"api_name": "builtins.BUILTIN_MUL", "line_number": 243, "usage_type": "name"}, {"api_name": "expr_impl.AppExpr", "line_number": 251, "usage_type": "call"}, {"api_name": "builtins.BUILTIN_CONCAT", "line_number": 251, "usage_type": "name"}, {"api_name": "expr_impl.AppExpr", "line_number": 259, "usage_type": "call"}, {"api_name": "builtins.BUILTIN_POW", "line_number": 259, "usage_type": "name"}, {"api_name": "expr_impl.TemplateExpr", "line_number": 272, "usage_type": "name"}, {"api_name": "expr_impl.TemplateSelectExpr", "line_number": 272, "usage_type": "name"}, {"api_name": "expr_impl.GetContractIdExpr.coerce", "line_number": 275, "usage_type": "call"}, {"api_name": "expr_impl.GetContractIdExpr", "line_number": 275, "usage_type": "name"}, {"api_name": "expr_impl.ConstantExpr", "line_number": 280, "usage_type": "call"}]} +{"seq_id": "638752728", "text": "from django.contrib import admin\n\nfrom sponsors.models import Company\n\n\nclass CompanyAdmin (admin.ModelAdmin):\n model = Company\n search_fields = (\"name\", )\n filter_fields = (\"is_published\")\n list_display = (\"name\", \"description\", \"is_published\")\n\n\nadmin.site.register(Company, CompanyAdmin)\n", "sub_path": "sponsors/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 303, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.contrib.admin.ModelAdmin", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 6, "usage_type": "name"}, {"api_name": "sponsors.models.Company", "line_number": 7, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 13, "usage_type": "call"}, {"api_name": "sponsors.models.Company", "line_number": 13, "usage_type": "argument"}, {"api_name": "django.contrib.admin.site", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 13, "usage_type": "name"}]} +{"seq_id": "191359829", "text": "from datetime import datetime\nfrom io import BytesIO\nfrom typing import Optional, List\nfrom xml.etree.ElementTree import Element\n\nimport attr\nfrom defusedxml.ElementTree import parse\n\nfrom .utils import get_child, get_text, get_int, get_datetime, FeedParseError\n\n\n@attr.s\nclass RSSImage:\n url: str = attr.ib()\n title: str = attr.ib()\n link: str = attr.ib()\n width: int = attr.ib()\n height: int = attr.ib()\n description: Optional[str] = attr.ib()\n\n\n@attr.s\nclass RSSEnclosure:\n url: str = attr.ib()\n length: int = attr.ib()\n type: str = attr.ib()\n\n\n@attr.s\nclass RSSSource:\n title: str = attr.ib()\n url: str = attr.ib()\n\n\n@attr.s\nclass RSSItem:\n title: Optional[str] = attr.ib()\n link: Optional[str] = attr.ib()\n description: Optional[str] = attr.ib()\n author: Optional[str] = attr.ib()\n categories: List[str] = attr.ib()\n comments: Optional[str] = attr.ib()\n enclosures: List[RSSEnclosure] = attr.ib()\n guid: Optional[str] = attr.ib()\n pub_date: Optional[datetime] = attr.ib()\n source: Optional[RSSSource] = attr.ib()\n\n # Extension\n content_encoded: Optional[str] = attr.ib()\n\n\n@attr.s\nclass RSSChannel:\n title: str = attr.ib()\n link: str = attr.ib()\n description: str = attr.ib()\n\n language: Optional[str] = attr.ib()\n copyright: Optional[str] = attr.ib()\n managing_editor: Optional[str] = attr.ib()\n web_master: Optional[str] = attr.ib()\n pub_date: Optional[datetime] = attr.ib()\n last_build_date: Optional[datetime] = attr.ib()\n categories: List[str] = attr.ib()\n generator: Optional[str] = attr.ib()\n docs: Optional[str] = attr.ib()\n ttl: Optional[int] = attr.ib()\n image: Optional[RSSImage] = attr.ib()\n\n items: List[RSSItem] = attr.ib()\n\n # Extension\n content_encoded: Optional[str] = attr.ib()\n\n\ndef _get_image(element: Element, name,\n optional: bool=False) -> Optional[RSSImage]:\n child = get_child(element, name, optional)\n if child is None:\n return None\n\n return RSSImage(\n get_text(child, 'url'),\n get_text(child, 'title'),\n get_text(child, 'link'),\n get_int(child, 'width', optional=True) or 88,\n get_int(child, 'height', optional=True) or 31,\n get_text(child, 'description', optional=True)\n )\n\n\ndef _get_source(element: Element, name,\n optional: bool=False) -> Optional[RSSSource]:\n child = get_child(element, name, optional)\n if child is None:\n return None\n\n return RSSSource(\n child.text.strip(),\n child.attrib['url'],\n )\n\n\ndef _get_enclosure(element: Element) -> RSSEnclosure:\n return RSSEnclosure(\n element.attrib['url'],\n int(element.attrib['length']),\n element.attrib['type'],\n )\n\n\ndef _get_item(element: Element) -> RSSItem:\n root = element\n\n title = get_text(root, 'title', optional=True)\n link = get_text(root, 'link', optional=True)\n description = get_text(root, 'description', optional=True)\n author = get_text(root, 'author', optional=True)\n categories = [e.text for e in root.findall('category')]\n comments = get_text(root, 'comments', optional=True)\n enclosure = [_get_enclosure(e) for e in root.findall('enclosure')]\n guid = get_text(root, 'guid', optional=True)\n pub_date = get_datetime(root, 'pubDate', optional=True)\n source = _get_source(root, 'source', optional=True)\n\n content_encoded = get_text(root, 'content:encoded', optional=True)\n\n return RSSItem(\n title,\n link,\n description,\n author,\n categories,\n comments,\n enclosure,\n guid,\n pub_date,\n source,\n content_encoded\n )\n\n\ndef _parse_rss(root: Element) -> RSSChannel:\n rss_version = root.get('version')\n if rss_version != '2.0':\n raise FeedParseError('Cannot process RSS feed version \"{}\"'\n .format(rss_version))\n\n root = root.find('channel')\n\n # Mandatory\n title = get_text(root, 'title')\n link = get_text(root, 'link')\n description = get_text(root, 'description')\n\n # Optional\n language = get_text(root, 'language', optional=True)\n copyright = get_text(root, 'copyright', optional=True)\n managing_editor = get_text(root, 'managingEditor', optional=True)\n web_master = get_text(root, 'webMaster', optional=True)\n pub_date = get_datetime(root, 'pubDate', optional=True)\n last_build_date = get_datetime(root, 'lastBuildDate', optional=True)\n categories = [e.text for e in root.findall('category')]\n generator = get_text(root, 'generator', optional=True)\n docs = get_text(root, 'docs', optional=True)\n ttl = get_int(root, 'ttl', optional=True)\n\n image = _get_image(root, 'image', optional=True)\n items = [_get_item(e) for e in root.findall('item')]\n\n content_encoded = get_text(root, 'content:encoded', optional=True)\n\n return RSSChannel(\n title,\n link,\n description,\n language,\n copyright,\n managing_editor,\n web_master,\n pub_date,\n last_build_date,\n categories,\n generator,\n docs,\n ttl,\n image,\n items,\n content_encoded\n )\n\n\ndef parse_rss_file(filename: str) -> RSSChannel:\n \"\"\"Parse an RSS feed from a local XML file.\"\"\"\n root = parse(filename).getroot()\n return _parse_rss(root)\n\n\ndef parse_rss_bytes(data: bytes) -> RSSChannel:\n \"\"\"Parse an RSS feed from a byte-string containing XML data.\"\"\"\n root = parse(BytesIO(data)).getroot()\n return _parse_rss(root)\n", "sub_path": "atoma/rss.py", "file_name": "rss.py", "file_ext": "py", "file_size_in_byte": 5571, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "attr.ib", "line_number": 14, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 15, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 16, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 17, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 18, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 19, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 19, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 12, "usage_type": "attribute"}, {"api_name": "attr.ib", "line_number": 24, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 25, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 26, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 22, "usage_type": "attribute"}, {"api_name": "attr.ib", "line_number": 31, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 32, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 29, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 37, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 37, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 38, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 38, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 39, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 39, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 40, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 40, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 41, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 41, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 42, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 42, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 43, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 43, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 44, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 44, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 45, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 45, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 45, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 46, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 46, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 49, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 49, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 35, "usage_type": "attribute"}, {"api_name": "attr.ib", "line_number": 54, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 55, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 56, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 58, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 58, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 59, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 59, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 60, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 60, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 61, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 61, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 62, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 62, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 62, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 63, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 63, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 63, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 64, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 64, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 65, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 65, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 66, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 66, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 67, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 67, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 68, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 68, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 70, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 70, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 73, "usage_type": "name"}, {"api_name": "attr.ib", "line_number": 73, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 52, "usage_type": "attribute"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 76, "usage_type": "name"}, {"api_name": "utils.get_child", "line_number": 78, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 83, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 84, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 85, "usage_type": "call"}, {"api_name": "utils.get_int", "line_number": 86, "usage_type": "call"}, {"api_name": "utils.get_int", "line_number": 87, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 88, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 77, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 92, "usage_type": "name"}, {"api_name": "utils.get_child", "line_number": 94, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 93, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 104, "usage_type": "name"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 112, "usage_type": "name"}, {"api_name": "utils.get_text", "line_number": 115, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 116, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 117, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 118, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 120, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 122, "usage_type": "call"}, {"api_name": "utils.get_datetime", "line_number": 123, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 126, "usage_type": "call"}, {"api_name": "xml.etree.ElementTree.Element", "line_number": 143, "usage_type": "name"}, {"api_name": "utils.FeedParseError", "line_number": 146, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 152, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 153, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 154, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 157, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 158, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 159, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 160, "usage_type": "call"}, {"api_name": "utils.get_datetime", "line_number": 161, "usage_type": "call"}, {"api_name": "utils.get_datetime", "line_number": 162, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 164, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 165, "usage_type": "call"}, {"api_name": "utils.get_int", "line_number": 166, "usage_type": "call"}, {"api_name": "utils.get_text", "line_number": 171, "usage_type": "call"}, {"api_name": "defusedxml.ElementTree.parse", "line_number": 195, "usage_type": "call"}, {"api_name": "defusedxml.ElementTree.parse", "line_number": 201, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 201, "usage_type": "call"}]} +{"seq_id": "448543372", "text": "# coding=utf-8\n# --------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for\n# license information.\n#\n# Code generated by Microsoft (R) AutoRest Code Generator.\n# Changes may cause incorrect behavior and will be lost if the code is\n# regenerated.\n# --------------------------------------------------------------------------\n\nfrom msrest.serialization import Model\n\n\nclass SyncActivityStatus(Model):\n \"\"\"Sync Session status object.\n\n Variables are only populated by the server, and will be ignored when\n sending a request.\n\n :ivar timestamp: Timestamp when properties were updated\n :vartype timestamp: datetime\n :ivar per_item_error_count: Per item error count\n :vartype per_item_error_count: long\n :ivar applied_item_count: Applied item count.\n :vartype applied_item_count: long\n :ivar total_item_count: Total item count (if available)\n :vartype total_item_count: long\n :ivar applied_bytes: Applied bytes\n :vartype applied_bytes: long\n :ivar total_bytes: Total bytes (if available)\n :vartype total_bytes: long\n \"\"\"\n\n _validation = {\n 'timestamp': {'readonly': True},\n 'per_item_error_count': {'readonly': True},\n 'applied_item_count': {'readonly': True},\n 'total_item_count': {'readonly': True},\n 'applied_bytes': {'readonly': True},\n 'total_bytes': {'readonly': True},\n }\n\n _attribute_map = {\n 'timestamp': {'key': 'timestamp', 'type': 'iso-8601'},\n 'per_item_error_count': {'key': 'perItemErrorCount', 'type': 'long'},\n 'applied_item_count': {'key': 'appliedItemCount', 'type': 'long'},\n 'total_item_count': {'key': 'totalItemCount', 'type': 'long'},\n 'applied_bytes': {'key': 'appliedBytes', 'type': 'long'},\n 'total_bytes': {'key': 'totalBytes', 'type': 'long'},\n }\n\n def __init__(self, **kwargs):\n super(SyncActivityStatus, self).__init__(**kwargs)\n self.timestamp = None\n self.per_item_error_count = None\n self.applied_item_count = None\n self.total_item_count = None\n self.applied_bytes = None\n self.total_bytes = None\n", "sub_path": "sdk/storage/azure-mgmt-storagesync/azure/mgmt/storagesync/models/sync_activity_status.py", "file_name": "sync_activity_status.py", "file_ext": "py", "file_size_in_byte": 2270, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "msrest.serialization.Model", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "25998941", "text": "\"\"\"\nSummary of this code:\n Create a GUI by designer.exe\n And use use_test_gui to use it.\n \n@author:\n Gao-zl\n \n@time:\n 2020.05.08\n\"\"\"\n# -*- coding: utf-8 -*-\n\n# Form implementation generated from reading ui file 'test.ui'\n#\n# Created by: PyQt5 UI code generator 5.13.0\n#\n# WARNING! All changes made in this file will be lost!\n\n\nfrom PyQt5 import QtCore, QtGui, QtWidgets\n\n\nclass Ui_Form(object):\n def setupUi(self, Form):\n Form.setObjectName(\"Form\")\n Form.resize(400, 300)\n self.pushButton = QtWidgets.QPushButton(Form)\n self.pushButton.setGeometry(QtCore.QRect(140, 100, 75, 23))\n self.pushButton.setObjectName(\"pushButton\")\n self.buttonBox = QtWidgets.QDialogButtonBox(Form)\n self.buttonBox.setGeometry(QtCore.QRect(120, 190, 156, 23))\n self.buttonBox.setStandardButtons(QtWidgets.QDialogButtonBox.Cancel|QtWidgets.QDialogButtonBox.Ok)\n self.buttonBox.setObjectName(\"buttonBox\")\n self.toolButton = QtWidgets.QToolButton(Form)\n self.toolButton.setGeometry(QtCore.QRect(230, 100, 37, 18))\n self.toolButton.setObjectName(\"toolButton\")\n self.commandLinkButton = QtWidgets.QCommandLinkButton(Form)\n self.commandLinkButton.setGeometry(QtCore.QRect(100, 130, 185, 41))\n self.commandLinkButton.setObjectName(\"commandLinkButton\")\n\n self.retranslateUi(Form)\n QtCore.QMetaObject.connectSlotsByName(Form)\n\n def retranslateUi(self, Form):\n _translate = QtCore.QCoreApplication.translate\n Form.setWindowTitle(_translate(\"Form\", \"Form\"))\n self.pushButton.setText(_translate(\"Form\", \"test\"))\n self.buttonBox.setToolTip(_translate(\"Form\", \"

Yes


\"))\n self.toolButton.setText(_translate(\"Form\", \"...\"))\n self.commandLinkButton.setText(_translate(\"Form\", \"CommandLinkButton\"))\n", "sub_path": "GUI/test.py", "file_name": "test.py", "file_ext": "py", "file_size_in_byte": 1893, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 28, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 28, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 29, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 29, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDialogButtonBox", "line_number": 31, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 31, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 32, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 32, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QDialogButtonBox", "line_number": 33, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 33, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QToolButton", "line_number": 35, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 35, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 36, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 36, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QCommandLinkButton", "line_number": 38, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 38, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 39, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 39, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QMetaObject.connectSlotsByName", "line_number": 43, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QMetaObject", "line_number": 43, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 43, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QCoreApplication", "line_number": 46, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 46, "usage_type": "name"}]} +{"seq_id": "40151317", "text": "#bfs\n#shortest operations to target\n#forward A: speed*2, position += speed\n#R: speed = -1 if speed > 0 else 1, position no change\nclass Solution:\n def racecar(self, target: int) -> int:\n if target == 0: return 0\n if target == 1: return 1\n visited = set()\n visited.add((0, 1)) #position, speed\n from collections import deque\n queue = deque([(0, 1)])\n res = 0\n while queue:\n n = len(queue)\n for _ in range(n):\n position, speed = queue.popleft()\n if position == target:\n return res\n for neighbor in self.get_neighbors(position, speed, target):\n if neighbor not in visited:\n visited.add(neighbor)\n queue.append(neighbor)\n res += 1\n return res\n\n def get_neighbors(self, position, speed, target):\n neighbors = []\n new_position = position + speed\n if new_position > 0 and new_position <= target << 1:\n neighbors.append((new_position, speed << 1))\n if speed > 0:\n neighbors.append((position, -1))\n else:\n neighbors.append((position, 1))\n return neighbors\n\n \n#dp\n#dp[i]: minimum operations to reach i\n#two scenarios:\n# > i, then back\n#forward = 1<< cnt_forward , \n# int:\n dp = [float('inf')] * (target + 1)\n dp[0] = 0\n for i in range(1, target + 1):\n k = i.bit_length()\n if i == (1 << k) - 1:\n dp[i] = k\n continue\n dp[i] = min(dp[i], k + 1 + dp[(1 << k) - 1 - i]) #7\n for m in range(k - 1):\n dp[i] = min(dp[i], k + 1 + m + dp[i - (1 << (k - 1)) + (1 << m)])\n return dp[target]\n\n \n", "sub_path": "Week_09/赛车.py", "file_name": "赛车.py", "file_ext": "py", "file_size_in_byte": 1893, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "collections.deque", "line_number": 12, "usage_type": "call"}]} +{"seq_id": "349823465", "text": "import itertools\nimport collide\nimport body\nfrom base.vector2 import *\nfrom base.aabb import *\nimport time\n\nclass BroadPhaseNode(object):\n def __init__(self, body):\n self.body = body\n self.update_world_aabb()\n\n def update_world_aabb(self):\n local_aabb = self.body.get_aabb()\n body_transform = self.body.transform\n wld_vertices = []\n for v in local_aabb.get_vertices():\n wld_v = body_transform * v\n wld_vertices.append(wld_v)\n\n self.wld_aabb = Aabb.build_from_vertices(wld_vertices)\n\n\nclass Broadphase(object):\n def __init__(self, world):\n self.world = world\n self.bp_nodes = []\n\n # timing infos\n self._node_update_t = 0\n self._collide_t = 0\n\n def add_body(self, body):\n node = BroadPhaseNode(body)\n self.bp_nodes.append(node)\n\n return node\n\n def update_bp_nodes(self):\n for np_node in self.bp_nodes:\n np_node.update_world_aabb()\n\n def update(self):\n t0 = time.clock()\n self.update_bp_nodes()\n t1 = time.clock()\n self.body_pairs = []\n\n for (p1, p2) in itertools.combinations(self.bp_nodes, 2):\n if p1 == p2:\n continue\n\n if Aabb.overlap(p1.wld_aabb, p2.wld_aabb):\n self.body_pairs.append((p1, p2))\n t2 = time.clock()\n\n self._node_update_t = t1 - t0\n self._collide_t = t2 - t1\n\n # print 'bp node update time = {0}'.format(self._node_update_t)\n # print 'bp collide time = {0}'.format(self._collide_t)\n\n", "sub_path": "core/broadphase.py", "file_name": "broadphase.py", "file_ext": "py", "file_size_in_byte": 1583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "time.clock", "line_number": 44, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 46, "usage_type": "call"}, {"api_name": "itertools.combinations", "line_number": 49, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "250761846", "text": "from aiogram import types\nfrom aiogram.dispatcher import FSMContext\n\nfrom loader import dp\nfrom keyboards.inline.main_menu_keyboard import getMenu\n\n\n# Эхо хендлер, куда летят текстовые сообщения без указанного состояния\n@dp.message_handler(state=None)\nasync def bot_echo(message: types.Message):\n markup = await getMenu()\n await message.answer(text=\"Вот тебе меню оно все может!\", reply_markup=markup)\n\n\n# Эхо хендлер, куда летят ВСЕ сообщения с указанным состоянием\n@dp.message_handler(state=\"*\", content_types=types.ContentTypes.ANY)\nasync def bot_echo_all(message: types.Message, state: FSMContext):\n state = await state.get_state()\n await message.answer(f\"Эхо в состоянии {state}.\\n\"\n f\"\\nСодержание сообщения:\\n\"\n f\"{message}\")\n\n", "sub_path": "handlers/users/echo.py", "file_name": "echo.py", "file_ext": "py", "file_size_in_byte": 984, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "aiogram.types.Message", "line_number": 10, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 10, "usage_type": "name"}, {"api_name": "keyboards.inline.main_menu_keyboard.getMenu", "line_number": 11, "usage_type": "call"}, {"api_name": "loader.dp.message_handler", "line_number": 9, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 9, "usage_type": "name"}, {"api_name": "aiogram.types.Message", "line_number": 17, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 17, "usage_type": "name"}, {"api_name": "aiogram.dispatcher.FSMContext", "line_number": 17, "usage_type": "name"}, {"api_name": "loader.dp.message_handler", "line_number": 16, "usage_type": "call"}, {"api_name": "loader.dp", "line_number": 16, "usage_type": "name"}, {"api_name": "aiogram.types.ContentTypes", "line_number": 16, "usage_type": "attribute"}, {"api_name": "aiogram.types", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "492833768", "text": "from bs4 import BeautifulSoup\nimport pandas as pd\nimport requests\n\nproxies = {\"http\": \"http://pac.invzb.uk.corporg.net:3128\", \"https\": \"http://pac.invzb.uk.corporg.net:3128\"}\nurl = \"https://en.wikipedia.org/wiki/Special:Random\"\n\n\ndef get_wiki_articles(quantity):\n\n global proxies, url\n headers = []\n bodyContents = []\n\n for _ in range(quantity):\n article = requests.get(url, proxies=proxies)\n soup = BeautifulSoup(article.content, features=\"lxml\")\n header = soup.find(\"h1\", {\"id\": \"firstHeading\"})\n headers.append(header.text)\n bodyContent = soup.find(\"div\", {\"id\": \"bodyContent\"}).find_all(\"p\")\n bodyContents.append(bodyContent[0].text)\n\n \n return headers, bodyContents\n \n\n\nif __name__ == \"__main__\":\n\n headers, bodyContents = get_wiki_articles(3)\n\n print(headers)\n print(bodyContents)", "sub_path": "tests/tests/wikipedia.py", "file_name": "wikipedia.py", "file_ext": "py", "file_size_in_byte": 855, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "requests.get", "line_number": 16, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 17, "usage_type": "call"}]} +{"seq_id": "592829360", "text": "# 这里将url进行统一的管理\n# 每个模块下的urls文件中必须有MODEL_NAME及routing_dict\nimport os\nimport sys\nimport importlib\nfrom flask import Blueprint\nfrom base.configs import DefaultConfig\n\ninstance = Blueprint('risk_Control', __name__)\n\nurls = ()\n\nrouting_dict = dict()\nv1_routing_dict = dict()\n\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n\nfor module in DefaultConfig.MODULES:\n if os.path.exists(os.path.join(BASE_DIR, '{}/urls.py'.format(module))):\n app_router = importlib.import_module('{}.urls'.format(module))\n try:\n MODEL_NAME = app_router.MODEL_NAME\n model_routing_dict = app_router.routing_dict\n except:\n print(\"模块缺少路由配置routing_dict/MODEL_NAME\")\n sys.exit()\n\n for k, v in model_routing_dict.items():\n routing_dict[\"/{0}{1}\".format(MODEL_NAME, k)] = v\n\nmethods = ['GET', 'POST', 'PUT', 'DELETE']\nfor path, view in routing_dict.items():\n instance.add_url_rule(\"{0}\".format(path),\n view_func=view.as_view(path),\n methods=methods)\n", "sub_path": "base/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1152, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Blueprint", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 16, "usage_type": "call"}, {"api_name": "base.configs.DefaultConfig.MODULES", "line_number": 18, "usage_type": "attribute"}, {"api_name": "base.configs.DefaultConfig", "line_number": 18, "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.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "importlib.import_module", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "157204979", "text": "# -*- coding: utf-8 -*- \n\nimport sys\nimport re\nfrom PyQt5.QtWidgets import (QWidget , QHBoxLayout , QVBoxLayout , QApplication, QPushButton, QLineEdit ,QLabel , QSplitter , QTableView , QHeaderView , QMessageBox )\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtSql import QSqlDatabase , QSqlQueryModel , QSqlQuery\n\ndef createTableAndInit():\n\t# 新建資料庫\n\tdb = QSqlDatabase.addDatabase('QSQLITE')\n\t# 設定資料庫名稱\n\tdb.setDatabaseName('./db/database.db')\n\t# 判斷是否開啟\n\tif not db.open():\t\t\t\n\t\treturn False\n\n\t# 宣告資料庫查詢物件\n\tquery = QSqlQuery()\n\t# 建立資料表\n\tquery.exec(\"create table student(id int primary key, name vchar, sex vchar, age int, deparment vchar)\")\n\t\n\t# 插入記錄\n\tquery.exec(\"insert into student values(1,'張三1','男',20,'電腦')\")\n\tquery.exec(\"insert into student values(2,'李四1','男',19,'經管')\")\n\tquery.exec(\"insert into student values(3,'王五1','男',22,'機械')\")\n\tquery.exec(\"insert into student values(4,'趙六1','男',21,'法律')\")\n\tquery.exec(\"insert into student values(5,'小明1','男',20,'英語')\")\n\tquery.exec(\"insert into student values(6,'小李1','女',19,'電腦')\")\n\tquery.exec(\"insert into student values(7,'小張1','男',20,'機械')\")\n\tquery.exec(\"insert into student values(8,'小剛1','男',19,'經管')\")\n\tquery.exec(\"insert into student values(9,'張三2','男',21,'電腦')\")\n\tquery.exec(\"insert into student values(10,'張三3','女',20,'法律')\")\n\tquery.exec(\"insert into student values(11,'王五2','男',19,'經管')\")\n\tquery.exec(\"insert into student values(12,'張三4','男',20,'電腦')\")\n\tquery.exec(\"insert into student values(13,'小李2','男',20,'機械')\")\n\tquery.exec(\"insert into student values(14,'李四2','女',19,'經管')\")\n\tquery.exec(\"insert into student values(15,'趙六3','男',21,'英語')\")\n\tquery.exec(\"insert into student values(16,'李四2','男',19,'法律')\")\n\tquery.exec(\"insert into student values(17,'小張2','女',22,'經管')\")\n\tquery.exec(\"insert into student values(18,'李四3','男',21,'英語')\")\n\tquery.exec(\"insert into student values(19,'小李3','女',19,'法律')\")\n\tquery.exec(\"insert into student values(20,'王五3','女',20,'機械')\")\n\tquery.exec(\"insert into student values(21,'張三4','男',22,'電腦')\")\n\tquery.exec(\"insert into student values(22,'小李2','男',20,'法律')\")\n\tquery.exec(\"insert into student values(23,'張三5','男',19,'經管')\")\n\tquery.exec(\"insert into student values(24,'小張3','女',20,'電腦')\")\n\tquery.exec(\"insert into student values(25,'李四4','男',22,'英語')\")\n\tquery.exec(\"insert into student values(26,'趙六2','男',20,'機械')\")\n\tquery.exec(\"insert into student values(27,'小李3','女',19,'英語')\")\n\tquery.exec(\"insert into student values(28,'王五4','男',21,'經管')\")\n\t\n\treturn True\t\t\n \t\t\nclass DataGrid(QWidget):\n\n\tdef __init__(self):\n\t\tsuper().__init__()\n\t\tself.setWindowTitle(\"分頁查詢範例\")\n\t\tself.resize(750,300)\n\t\t\n\t\t# 查詢模型\t\t\n\t\tself.queryModel = None\n\t\t# 資料表\n\t\tself.tableView = None\t\t\n\t\t# 總頁數\n\t\tself.totalPageLabel = None\n\t\t# 目前頁\n\t\tself.currentPageLabel = None\n\t\t# 轉到頁輸入框\t\t\n\t\tself.switchPageLineEdit = None\n\t\t# 前一頁按鈕\n\t\tself.prevButton = None\t\t\n\t\t# 後一頁按鈕\n\t\tself.nextButton = None\n\t\t# 轉到頁按鈕\n\t\tself.switchPageButton = None\t\n\t\t# 目前頁\t\n\t\tself.currentPage = 0\n\t\t# 總頁數\n\t\tself.totalPage = 0\t\t\n\t\t# 總記錄數\n\t\tself.totalRecrodCount = 0\n\t\t# 每頁顯示記錄數\n\t\tself.PageRecordCount = 5\t\t\t\n\t\n\t\tself.initUI()\n\n\tdef initUI(self):\n\t\t# 建立視窗\n\t\tself.createWindow()\n\t\t# 設定表格\n\t\tself.setTableView()\n\t\t\n\t\t# 訊號/槽連接\n\t\tself.prevButton.clicked.connect(self.onPrevButtonClick )\t\t\n\t\tself.nextButton.clicked.connect(self.onNextButtonClick )\t\n\t\tself.switchPageButton.clicked.connect(self.onSwitchPageButtonClick )\t\n\n\t\n # 建立視窗\t\n\tdef createWindow(self):\n\t\t# 操作佈局\n\t\toperatorLayout = QHBoxLayout()\n\t\tself.prevButton = QPushButton(\"前一頁\")\n\t\tself.nextButton = QPushButton(\"後一頁\")\n\t\tself.switchPageButton = QPushButton(\"Go\")\n\t\tself.switchPageLineEdit = QLineEdit()\n\t\tself.switchPageLineEdit.setFixedWidth(40)\t\n\t\t\n\t\tswitchPage = QLabel(\"轉到第\")\n\t\tpage = QLabel(\"頁\")\n\t\toperatorLayout.addWidget(self.prevButton)\n\t\toperatorLayout.addWidget(self.nextButton)\n\t\toperatorLayout.addWidget(switchPage)\n\t\toperatorLayout.addWidget(self.switchPageLineEdit)\n\t\toperatorLayout.addWidget(page)\n\t\toperatorLayout.addWidget(self.switchPageButton)\n\t\toperatorLayout.addWidget( QSplitter())\n\t\n\t # 狀態佈局\n\t\tstatusLayout = QHBoxLayout()\n\t\tself.totalPageLabel = QLabel()\n\t\tself.totalPageLabel.setFixedWidth(70)\n\t\tself.currentPageLabel = QLabel()\n\t\tself.currentPageLabel.setFixedWidth(70)\n\t\t\n\t\tself.totalRecordLabel = QLabel()\n\t\tself.totalRecordLabel.setFixedWidth(70)\n\t\t\n\t\tstatusLayout.addWidget(self.totalPageLabel)\n\t\tstatusLayout.addWidget(self.currentPageLabel)\n\t\tstatusLayout.addWidget( QSplitter() )\t\n\t\tstatusLayout.addWidget(self.totalRecordLabel)\n\t\t\n\t\t# 設定表格屬性\n\t\tself.tableView = QTableView()\n\t\t# 表格寬度的自我調整\n\t\tself.tableView.horizontalHeader().setStretchLastSection(True)\n\t\tself.tableView.horizontalHeader().setSectionResizeMode(QHeaderView.Stretch)\n\t\t\n\t\t# 建立介面\n\t\tmainLayout = QVBoxLayout(self);\n\t\tmainLayout.addLayout(operatorLayout);\n\t\tmainLayout.addWidget(self.tableView);\n\t\tmainLayout.addLayout(statusLayout);\n\t\tself.setLayout(mainLayout)\n\n\t# 設定表格\t\n\tdef setTableView(self):\t\n\t\tprint('*** step2 SetTableView' )\n\t\t\n\t\t# 宣告查詢模型\n\t\tself.queryModel = QSqlQueryModel(self)\n\t\t# 設定目前頁\n\t\tself.currentPage = 1;\n\t\t# 取得總記錄數\n\t\tself.totalRecrodCount = self.getTotalRecordCount()\n\t\t# 取得總頁數\n\t\tself.totalPage = self.getPageCount()\n\t\t# 刷新狀態\n\t\tself.updateStatus()\n\t\t# 設定總頁數標籤\n\t\tself.setTotalPageLabel()\n\t\t# 設定總記錄數標籤\n\t\tself.setTotalRecordLabel()\n\t\t\n\t\t# 記錄查詢\n\t\tself.recordQuery(0)\n\t\t# 設定模型\n\t\tself.tableView.setModel(self.queryModel)\n\n\t\tprint('totalRecrodCount=' + str(self.totalRecrodCount) )\t\t\n\t\tprint('totalPage=' + str(self.totalPage) )\n \t\t\n\t\t# 設定表格表頭\n\t\tself.queryModel.setHeaderData(0,Qt.Horizontal,\"編號\") \n\t\tself.queryModel.setHeaderData(1,Qt.Horizontal,\"姓名\")\n\t\tself.queryModel.setHeaderData(2,Qt.Horizontal,\"性別\")\n\t\tself.queryModel.setHeaderData(3,Qt.Horizontal,\"年齡\")\n\t\tself.queryModel.setHeaderData(4,Qt.Horizontal,\"院系\")\n\n\t# 取得記錄數\t\n\tdef getTotalRecordCount(self):\t\t\t\n\t\tself.queryModel.setQuery(\"select * from student\")\n\t\trowCount = self.queryModel.rowCount()\n\t\tprint('rowCount=' + str(rowCount) )\n\t\treturn rowCount\n\t\t\t\n\t# 取得頁數\t\t\n\tdef getPageCount(self):\t\t\t\n\t\tif self.totalRecrodCount % self.PageRecordCount == 0 :\n\t\t\treturn (self.totalRecrodCount / self.PageRecordCount )\n\t\telse :\n\t\t\treturn (self.totalRecrodCount / self.PageRecordCount + 1)\n\n\t# 記錄查詢\t\t\n\tdef recordQuery(self, limitIndex ):\t\n\t\tszQuery = (\"select * from student limit %d,%d\" % ( limitIndex , self.PageRecordCount ) )\n\t\tprint('query sql=' + szQuery )\n\t\tself.queryModel.setQuery(szQuery)\n\t\t\n\t# 刷新狀態\t\t\n\tdef updateStatus(self):\t\t\t\t\n\t\tszCurrentText = (\"目前第%d頁\" % self.currentPage )\n\t\tself.currentPageLabel.setText( szCurrentText )\n \n\t\t#設定按鈕是否可用\n\t\tif self.currentPage == 1 :\n\t\t\tself.prevButton.setEnabled( False )\n\t\t\tself.nextButton.setEnabled( True )\n\t\telif self.currentPage == self.totalPage :\n\t\t\tself.prevButton.setEnabled( True )\n\t\t\tself.nextButton.setEnabled( False )\n\t\telse :\n\t\t\tself.prevButton.setEnabled( True )\n\t\t\tself.nextButton.setEnabled( True )\n\n\t# 設取總數頁標籤\t\t\n\tdef setTotalPageLabel(self):\t\n\t\tszPageCountText = (\"總共%d頁\" % self.totalPage )\n\t\tself.totalPageLabel.setText(szPageCountText)\n\n\t# 設定總記錄數標籤\t\t\n\tdef setTotalRecordLabel(self):\t\n\t\tszTotalRecordText = (\"共%d筆\" % self.totalRecrodCount )\n\t\tprint('*** setTotalRecordLabel szTotalRecordText=' + szTotalRecordText )\n\t\tself.totalRecordLabel.setText(szTotalRecordText)\n\t\t\n\t# 按下前一頁按鈕\t\t\n\tdef onPrevButtonClick(self):\t\n\t\tprint('*** onPrevButtonClick ');\n\t\tlimitIndex = (self.currentPage - 2) * self.PageRecordCount\n\t\tself.recordQuery( limitIndex) \n\t\tself.currentPage -= 1 \n\t\tself.updateStatus() \n\n\t# 按下後一頁按鈕\t\n\tdef onNextButtonClick(self):\n\t\tprint('*** onNextButtonClick ');\n\t\tlimitIndex = self.currentPage * self.PageRecordCount\n\t\tself.recordQuery( limitIndex) \n\t\tself.currentPage += 1\n\t\tself.updateStatus() \n\t\t\n\t# 按下轉到頁按鈕\n\tdef onSwitchPageButtonClick(self):\t\t\t\n\t\t# 取得輸入字串\n\t\tszText = self.switchPageLineEdit.text()\n\t\t# 數字規則運算式\n\t\tpattern = re.compile(r'^[-+]?[0-9]+\\.[0-9]+$')\n\t\tmatch = pattern.match(szText)\n\t\t\n\t\t# 判斷是否為數字\n\t\tif not match :\n\t\t\tQMessageBox.information(self, \"提示\", \"請輸入數字\" )\n\t\t\treturn\n\t\t\t\n\t\t# 是否為空\n\t\tif szText == '' :\n\t\t\tQMessageBox.information(self, \"提示\" , \"請輸入跳越頁面\" )\n\t\t\treturn\n\n\t\t# 取得頁數\n\t\tpageIndex = int(szText)\n\t\t# 判斷是否有指定頁\n\t\tif pageIndex > self.totalPage or pageIndex < 1 :\n\t\t\tQMessageBox.information(self, \"提示\", \"沒有指定的頁面，請重新輸入\" )\n\t\t\treturn\n\t\t\t\n\t\t# 取得查詢起始列號\n\t\tlimitIndex = (pageIndex-1) * self.PageRecordCount\t\t\t\n\t\t\n\t\t# 記錄查詢\n\t\tself.recordQuery(limitIndex);\n\t\t# 設定目前頁\n\t\tself.currentPage = pageIndex\n\t\t# 刷新狀態\n\t\tself.updateStatus();\n\t\t\t\nif __name__ == '__main__':\n\tapp = QApplication(sys.argv)\n\t\n\tif createTableAndInit(): \t\t\n\t\t# 建立視窗\n\t\texample = DataGrid() \n\t\t# 顯示視窗\n\t\texample.show() \n\t\n\tsys.exit(app.exec_())\n\n", "sub_path": "Chapter05/DataGrid.py", "file_name": "DataGrid.py", "file_ext": "py", "file_size_in_byte": 9611, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "PyQt5.QtSql.QSqlDatabase.addDatabase", "line_number": 11, "usage_type": "call"}, {"api_name": "PyQt5.QtSql.QSqlDatabase", "line_number": 11, "usage_type": "name"}, {"api_name": "PyQt5.QtSql.QSqlQuery", "line_number": 19, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 55, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 104, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 105, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 106, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 107, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 108, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 111, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 112, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QSplitter", "line_number": 119, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHBoxLayout", "line_number": 122, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 123, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 125, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 128, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QSplitter", "line_number": 133, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QTableView", "line_number": 137, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QHeaderView.Stretch", "line_number": 140, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets.QHeaderView", "line_number": 140, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QVBoxLayout", "line_number": 143, "usage_type": "call"}, {"api_name": "PyQt5.QtSql.QSqlQueryModel", "line_number": 154, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 177, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 177, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 178, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 178, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 179, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 179, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 180, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 180, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.Qt.Horizontal", "line_number": 181, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore.Qt", "line_number": 181, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 251, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 256, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 256, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 261, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 261, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QMessageBox.information", "line_number": 268, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMessageBox", "line_number": 268, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 282, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 282, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 290, "usage_type": "call"}]} +{"seq_id": "524270071", "text": "import numpy as np\nimport pandas as pd\n\nfrom matplotlib import pyplot as plt\nplt.rcParams[\"figure.figsize\"] = (15, 10)\n\n\ndef plot_candles2(start_time, end_time, pricing, title=None,\n volume_bars=False,\n color_function=None,\n overlays=None,\n technicals=None,\n technicals_titles=None):\n \"\"\" Plots a candlestick chart using quantopian pricing data.\n\n Author: Daniel Treiman\n\n Args:\n pricing: A pandas dataframe with columns ['open_price', 'close_price', 'high', 'low', 'volume']\n title: An optional title for the chart\n volume_bars: If True, plots volume bars\n color_function: A function which, given a row index and price series, returns a candle color.\n overlays: A list of additional data series to overlay on top of pricing. Must be the same length as pricing.\n technicals: A list of additional data series to display as subplots.\n technicals_titles: A list of titles to display for each technical indicator.\n \"\"\"\n\n pricing = pricing[start_time:end_time]\n if overlays is not None:\n overlays = [o[start_time:end_time] for o in overlays]\n if technicals is not None:\n technicals = [t[start_time:end_time] for t in technicals]\n\n def default_color(_open_price, _close_price, low, high):\n return 'g' if _open_price > _close_price else 'r'\n color_function = color_function or default_color\n overlays = overlays or []\n technicals = technicals or []\n technicals_titles = technicals_titles or []\n open_price = pricing['open']\n close_price = pricing['close']\n low = pricing['low']\n high = pricing['high']\n #\n oc_min = pd.concat([open_price, close_price], axis=1).min(axis=1)\n oc_max = pd.concat([open_price, close_price], axis=1).max(axis=1)\n\n subplot_count = 1\n if volume_bars:\n subplot_count = 2\n if technicals:\n subplot_count += len(technicals)\n\n if subplot_count == 1:\n fig, ax1 = plt.subplots(1, 1)\n else:\n\n ratios = np.insert(np.full(subplot_count - 1, 1), 0, 3)\n fig, subplots = plt.subplots(subplot_count, 1, sharex=True, gridspec_kw={\n 'height_ratios': ratios})\n ax1 = subplots[0]\n if title:\n ax1.set_title(title)\n x = np.arange(len(pricing))\n # 獲得每日顏色列表\n candle_colors = [color_function(\n open_price[i], close_price[i], low, high) for i in x]\n print(pd.concat([open_price, close_price,(oc_max-oc_min)], axis=1))\n candles = ax1.bar(x, [ 0.001 if x<=0 else x for x in (oc_max-oc_min)], bottom=oc_min,\n color=candle_colors)\n lines = ax1.vlines(x, low, high, color=candle_colors, linewidth=1) # + 0.4\n ax1.xaxis.grid(True)\n ax1.xaxis.set_tick_params(\n which='major', length=10.0, direction='in', top='off')\n # Assume minute frequency if first two bars are in the same day.\n frequency = 'minute' if (\n pricing.index[1] - pricing.index[0]).days == 0 else 'day'\n time_format = '%Y/%m/%d'\n if frequency == 'minute':\n time_format = '%H:%M'\n # Set X axis tick labels.\n ticks = [date.strftime(time_format) for date in pricing.index]\n space = max(int(len(ticks) / 20), 1)\n\n for i, t in enumerate(ticks):\n ticks[i] = t if i % space == 0 or i == len(ticks) - 1 else ''\n\n plt.xticks(x, ticks, rotation='0')\n for overlay in overlays:\n ax1.plot(x, overlay)\n # Plot volume bars if needed\n if volume_bars:\n ax2 = subplots[1]\n volume = pricing['volume']\n volume_scale = None\n scaled_volume = volume\n if volume.max() > 1000000:\n volume_scale = 'M'\n scaled_volume = volume / 1000000\n elif volume.max() > 1000:\n volume_scale = 'K'\n scaled_volume = volume / 1000\n ax2.bar(x, scaled_volume, color=candle_colors)\n volume_title = 'Volume'\n if volume_scale:\n volume_title = 'Volume (%s)' % volume_scale\n ax2.set_title(volume_title)\n ax2.xaxis.grid(False)\n # Plot additional technical indicators\n for (i, technical) in enumerate(technicals):\n # Technical indicator plots are shown last\n ax = subplots[i - len(technicals)]\n ax.plot(x, technical)\n if i < len(technicals_titles):\n ax.set_title(technicals_titles[i])\n\n\n\n#######------------------------------------------#######\n# import pandas as pd\nimport sqlite3\nimport os\n\nfrom matplotlib.font_manager import FontProperties\nchf = FontProperties(fname=r\"c:\\windows\\fonts\\msjhbd.ttc\", size=14)\n###########獲得數據#############\n# 建立連線\nconn = sqlite3.connect(os.path.join('data', \"data.db\"))\n\n# 利用SQL獲取結果\ndf = pd.read_sql('select stock_id, date, 開盤價, 收盤價, 最高價, 最低價, 成交股數 from price where stock_id=\"0050\" AND date >DATE(\"2013-01-02\")', conn,\n index_col=['date'], parse_dates=['date'])\n# df = pd.read_sql('select xxx from price where stock_id = \"0050\"', conn, index_col=['stock_id', 'date'], parse_dates=['date'])\n#\n\n# 欄位名稱轉英文\ndf.rename(columns={'收盤價': 'close', '開盤價': 'open', '最高價': 'high',\n '最低價': 'low', '成交股數': 'volume'}, inplace=True)\n# print(df.head())\n\n\n##########繪製圖形##############\n\n# import matplotlib.pylab as plt\n# plt.rcParams['figure.figsize'] = (20, 10)\n# plt.plot(df['close'], color='black', label='收盤價')\n\nfrom talib import abstract\nSMA = abstract.SMA(df)\nSTOCH = abstract.STOCH(df)\nRSI = abstract.RSI(df)\n\n\nfrom finlab.plot_candles import plot_candles \nplot_candles(\n start_time='2013-09-02',\n end_time='2013-12-31',\n pricing=df,\n title='Candles',\n volume_bars=True,\n overlays=[SMA],\n technicals=[RSI, STOCH],\n technicals_titles=['RSI', 'KD']\n )\n# from finlab.plot_candles_brad import plot_candles as pc\n# pc(\n# start_time='2013-09-02',\n# end_time='2013-12-31',\n# pricing=df,\n# title='Candles',\n# volume_bars=True,\n# overlays=[SMA],\n# technicals=[RSI, STOCH],\n# technicals_titles=['RSI', 'KD']\n# )\n#\nplot_candles2(\n start_time='2013-09-02',\n end_time='2013-12-31',\n pricing=df,\n title='Candles',\n volume_bars=True,\n overlays=[SMA],\n technicals=[RSI, STOCH],\n technicals_titles=['RSI', 'KD']\n)\n\n\n\nplt.plot(SMA, color='red', label='SMA',)\n# plt.legend(loc='best', prop=chf) # 圖例自動調位置\n# plt.show()\n\n\n# plt.plot(RSI)\n# plt.plot(STOCH)\n", "sub_path": "Python課程/用Python理財_打造小資族選股策略/12.看盤軟體.py", "file_name": "12.看盤軟體.py", "file_ext": "py", "file_size_in_byte": 6538, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "matplotlib.pyplot.rcParams", "line_number": 5, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 5, "usage_type": "name"}, {"api_name": "pandas.concat", "line_number": 45, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 55, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 55, "usage_type": "name"}, {"api_name": "numpy.insert", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 64, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.font_manager.FontProperties", "line_number": 125, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 128, "usage_type": "call"}, {"api_name": "os.path", "line_number": 128, "usage_type": "attribute"}, {"api_name": "pandas.read_sql", "line_number": 131, "usage_type": "call"}, {"api_name": "talib.abstract.SMA", "line_number": 149, "usage_type": "call"}, {"api_name": "talib.abstract", "line_number": 149, "usage_type": "name"}, {"api_name": "talib.abstract.STOCH", "line_number": 150, "usage_type": "call"}, {"api_name": "talib.abstract", "line_number": 150, "usage_type": "name"}, {"api_name": "talib.abstract.RSI", "line_number": 151, "usage_type": "call"}, {"api_name": "talib.abstract", "line_number": 151, "usage_type": "name"}, {"api_name": "finlab.plot_candles.plot_candles", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}]} +{"seq_id": "226801078", "text": "# Importing what we need\nimport tkinter as tk \t# It is a Python binding to the Tk GUI toolkit\nfrom tkinter import *\nimport easygui \t\t# EasyGUI is a module for very simple, very easy GUI programming in python\nimport cv2 \t\t\t# This is a module from the OpenCV library, it will be used for the image processing\nimport matplotlib.pyplot as plt #This library is used for visualization and plotting. Thus, it is imported to form the plot of images\nimport os \t\t\t\t# For OS interaction. Here, to read the path and save images to that path\nimport sys\t\t\t# This module provides access to some variables used or maintained by the interpreter and to functions that interact strongly with the interpreter\n\n\n# Making the GUI main window\ntop = tk.Tk()\ntop.geometry('400x400')\ntop.title('Cartoonify Your Image !')\ntop.configure(background='#0f2c33')\nlabel = Label(top, background='#CDCDCD', font=('calibri', 20, 'bold'))\n\n\n# fileopenbox Function opens the box to choose file and help us store file path as string\n# fileopenbox() is a method from easyGUI module and it returned a string for the path chosen\ndef upload():\n image_path = easygui.fileopenbox()\n cartoonify(image_path)\n\n\n# Enhancing the save button\ndef save(resize_image6, image_path):\n\t# saving an image using imwrite function\n\tnew_name = \"Cartoonified_Image\"\n\tpath1 = os.path.dirname(image_path)\n\textension = os.path.splitext(image_path)[1]\n\tpath = os.path.join(path1, new_name + extension)\n\tcv2.imwrite(path, cv2.cvtColor(resize_image6, cv2.COLOR_RGB2BGR))\n\tI = \"Image saved by name \" + new_name + \" at \" + path\n\ttk.messagebox.showinfo(title=None, message=I)\n\n\n# Storing the Image\ndef cartoonify(image_path):\n\t# Read image\n\t# Imread is a method in cv2 which is used to store images in the form of numbers\n\toriginal_image = cv2.imread(image_path)\n\n\t# To confirm if it is an image that was chosing\n\tif original_image is None:\n\t\tprint(\"Can't find any image! Choose appropriate file. \")\n\t\tsys.exit()\n\n\toriginal_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)\n\tprint(original_image) # this will be stored in form of number\n\t# Resize the image after each transformation\n\t# We resize the image after each transformation to display all the images on a similar scale at last\n\tresize_image1 = cv2.resize(original_image, (960, 540))\n\tplt.imshow(resize_image1, cmap='gray')\n\n\t# Transform Image to grayscale\n\t# Our first step is to convert the image into grayscale\n\tgrayscale_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB)\n\t# cvtColor(image, flag) is a method in cv2 which is used to transform an image into the colour-space mentioned as ‘flag’\n\t# we use the BGR2GRAY flag. This returns the image in grayscale\n\n\t# After each transformation, we resize the resultant image using the resize() method in cv2\n\t# This is done to get more clear insights into every single transformation step\n\tresize_image2 = cv2.resize(grayscale_image, (960, 540))\n\t# And display it using imshow() method\n\tplt.imshow(resize_image2, cmap=\"gray\")\n\n\t# Smoothening a grayscale image\n\tsmooth_grayscale_image = cv2.medianBlur(grayscale_image, 5)\n\t# The medianBlur() function from cv2 is used in smoothening the grayscale image\n\tresize_image3 = cv2.resize(smooth_grayscale_image, (960, 540))\n\tplt.imshow(resize_image3, cmap=\"gray\")\n\n\t# Extracting the edges in the image\n\t# Retrieving the edges for cartoon effect\n\tget_edge = cv2.adaptiveThreshold(smooth_grayscale_image, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 9)\n\tresize_image4 = cv2.resize(get_edge, (960, 540))\n\tplt.imshow(resize_image4, cmap=\"gray\")\n\n\t# Smooth Colours\n\t# Applying bilateral filter to remove noise\n\tcolor_image = cv2.bilateralFilter(original_image, 9, 300, 300)\n\tresize_image5 = cv2.resize(color_image, (960, 540))\n\tplt.imshow(resize_image5, cmap=\"gray\")\n\n\t# Giving a Cartoon Effect\n\t# Masking edged image with our “BEAUTIFY” image\n\tcartoon_image = cv2.bitwise_and(color_image, color_image, mask=get_edge)\n\tresize_image6 = cv2.resize(cartoon_image, (960, 540))\n\tplt.imshow(resize_image6, cmap=\"gray\")\n\n\t# Plotting all the transitions together\n\timages = [resize_image1, resize_image2, resize_image3, resize_image4, resize_image5, resize_image6]\n\tfig, axes = plt.subplots(3, 2, figsize=(8, 8), subplot_kw = {'xticks': [], 'yticks': []}, gridspec_kw=dict(hspace=0.1, wspace=0.1))\n\t\n\tfor i, ax in enumerate(axes.flat):\n\t\tax.imshow(images[i], cmap='gray')\n\n\t# This code makes a button when the picture is transformed. It gives an alternative to save the cartoonified picture\n\tsavel = Button(top, text=\"Save cartoon image\", command=lambda: save(resize_image6, image_path), padx=30, pady=5)\n\tsavel.configure(background=\"#364156\", foreground=\"white\", font=(\"calibri\", 10, \"bold\"))\n\tsavel.pack(side=TOP, pady=50)\n\tsys.exit()\n\t# save button code\n\tplt.show()\n\n# This is all about the button creation, calling of upload function, setting background, font, and other specifications\nupload = Button(top, text=\"Cartoonify an Image\", command=upload, padx=10, pady=5)\nupload.configure(background=\"#374256\", foreground=\"wheat\", font=('calibri', 10, 'bold'))\nupload.pack(side=TOP, pady=50)\n\n\n\ntop.mainloop()\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 5111, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "tkinter.Tk", "line_number": 12, "usage_type": "call"}, {"api_name": "easygui.fileopenbox", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.COLOR_RGB2BGR", "line_number": 33, "usage_type": "attribute"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 35, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 35, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 42, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "cv2.cvtColor", "line_number": 58, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 58, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "cv2.medianBlur", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}, {"api_name": "cv2.adaptiveThreshold", "line_number": 76, "usage_type": "call"}, {"api_name": "cv2.ADAPTIVE_THRESH_MEAN_C", "line_number": 76, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 76, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "cv2.bilateralFilter", "line_number": 82, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "cv2.bitwise_and", "line_number": 88, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 94, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}]} +{"seq_id": "493635724", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nAuthor: LIURLN\n\"\"\"\n\nfrom flask import Flask\nfrom flask_cors import CORS\nfrom thid import THID\nimport threading\nimport json\nfrom fingerprintSensor import Fingerprint\n\nfrom weight import weight_sensor\nfrom height import height_sensor\nfrom pulse import pulse_sensor\nfrom temperature import temperature_sensor\n\napp = Flask(__name__)\nCORS(app)\nthidSensor = THID()\nfingerprintSensor = Fingerprint()\nthreads = []\n\nweight_sens = weight_sensor()\nheight_sens = height_sensor()\npulse_sens = pulse_sensor()\ntemperature_sens = temperature_sensor()\n\n\n@app.route(\"/\", methods=['GET'])\ndef hello():\n return 'Welcome to Healthios!'\n\n\n@app.route(\"/thid/valid\", methods=['GET'])\ndef thidValidCheck():\n res = {}\n # Call THID to check card inserted\n res['status'] = thidSensor.isInserted()\n return json.dumps(res)\n\n\n@app.route(\"/thid/readable\", methods=['GET'])\ndef thidReadableCheck():\n res = {}\n # Call THID to check card inserted\n res['status'] = thidSensor.isReadalbe()\n return json.dumps(res)\n\n\n@app.route(\"/thid/\", methods=['GET'])\ndef thidGetData():\n res = {}\n # Call THID to get all data in card\n res['status'] = thidSensor.isReadalbe()\n res['data'] = thidSensor.getInformation()\n return json.dumps(res)\n\n\n@app.route(\"/thid/start\", methods=['GET'])\ndef thidStartThread():\n if checkRunningThread('THID') is False:\n t = threading.Thread(name='THID', target=thidThread)\n threads.append(t)\n t.start()\n\n res = {}\n res['status'] = thidSensor.isOpen()\n return json.dumps(res)\n\n\n@app.route(\"/finger/valid/template\", methods=['GET'])\ndef fingerprintTemplateValidCheck():\n res = {}\n # Call THID to check card inserted\n res['status'] = fingerprintSensor.fingerprintTemplateValid\n return json.dumps(res)\n\n\n@app.route(\"/finger/start/template\", methods=['GET'])\ndef fingerprintStartCreateTemplate():\n res = {}\n # Call THID to check card inserted\n if fingerprintSensor.fingerprintTemplateValid is False:\n t = threading.Thread(name='THID', target=fingerprintThread)\n threads.append(t)\n t.start()\n res['status'] = True\n return json.dumps(res)\n\n\n@app.route(\"/finger/template\", methods=['GET'])\ndef fingerprintValidCheck():\n res = {}\n # Call THID to check card inserted\n res['status'] = fingerprintSensor.fingerprintTemplateValid\n res['data'] = fingerprintSensor.fingerprintTemplate\n return json.dumps(res)\n\n\n@app.route(\"/finger/valid/scan\", methods=['GET'])\ndef fingerprintScanValidCheck():\n res = {}\n # Call THID to check card inserted\n res['status'] = fingerprintSensor.fingerprintCharValid\n return json.dumps(res)\n\n\n@app.route(\"/finger/start/scan\", methods=['GET'])\ndef fingerprintStartScan():\n res = {}\n # Call THID to check card inserted\n if fingerprintSensor.fingerprintTemplateValid is False:\n t = threading.Thread(name='THID', target=fingerprintThread2)\n threads.append(t)\n t.start()\n res['status'] = True\n return json.dumps(res)\n\n\n@app.route(\"/finger/valid/compare\", methods=['GET'])\ndef fingerprintCompareValidCheck():\n res = {}\n # Call THID to check card inserted\n res['status'] = fingerprintSensor.fingerptintCompareValid\n return json.dumps(res)\n\n\n@app.route(\"/finger/start/compare\", methods=['GET'])\ndef fingerprintStartCompare():\n res = {}\n # Call THID to check card inserted\n if fingerprintSensor.fingerprintTemplateValid is False:\n t = threading.Thread(name='THID', target=fingerprintThread3)\n threads.append(t)\n t.start()\n res['status'] = True\n return json.dumps(res)\n\n\n@app.route(\"/finger/\", methods=['GET'])\ndef fingerprintGetData():\n res = {}\n # Call THID to get all data in card\n res['status'] = thidSensor.fingerptintCompareValid\n res['data'] = thidSensor.userID\n return json.dumps(res)\n\n\n@app.route(\"/pulse/\", methods=['GET'])\ndef pulseGetData():\n res = {}\n res['valid'] = pulse_sens.isValid\n res['finish'] = pulse_sens.isComplete\n res['data'] = pulse_sens.current_bpm\n return json.dumps(res)\n\n\n@app.route(\"/pulse/valid\", methods=['GET'])\ndef pulseValidCheck():\n # Call pulseSensor for check user is using\n res = {}\n if pulse_sens.isValid:\n res['status'] = True\n else:\n res['status'] = False\n return json.dumps(res)\n\n\n@app.route(\"/pulse/finish\", methods=['GET'])\ndef pulseIsComplete():\n # Call pulseSensor for check finish checking\n res = {}\n if pulse_sens.isComplete:\n pulse_sens.stop_measure()\n res['status'] = True\n else:\n res['status'] = False\n return json.dumps(res)\n\n\n@app.route(\"/pulse/start\", methods=['GET'])\ndef pulseStart():\n pulse_sens.start_measure()\n res = {}\n res['status'] = True\n return json.dumps(res)\n\n\n@app.route(\"/thermal/\", methods=['GET'])\ndef thermalGetData():\n res = {}\n res['valid'] = temperature_sens.isValid\n res['finish'] = temperature_sens.isComplete\n res['data'] = temperature_sens.current_temperature\n return json.dumps(res)\n\n\n@app.route(\"/thermal/valid\", methods=['GET'])\ndef thermalValidCheck():\n # Call thermalSensor for check user is using\n res = {}\n res['status'] = temperature_sens.isValid\n return json.dumps(res)\n\n\n@app.route(\"/thermal/finish\", methods=['GET'])\ndef thermalIsComplete():\n # Call thermalSensor for check finish checking\n res = {}\n if temperature_sens.isComplete:\n temperature_sens.stop_measure()\n res['status'] = True\n else:\n res['status'] = False\n return json.dumps(res)\n\n\n@app.route(\"/thermal/start\", methods=['GET'])\ndef thermalStart():\n # Call thermalSensor for check finish checking\n temperature_sens.start_measure()\n res = {}\n res['status'] = True\n return json.dumps(res)\n\n\n@app.route(\"/pressure/\", methods=['GET'])\ndef pressureGetData():\n res = {}\n res['valid'] = True\n res['finish'] = True\n data = [60, 80, 130]\n res['data'] = data\n return json.dumps(res)\n\n\n@app.route(\"/pressure/valid\", methods=['GET'])\ndef pressureValidCheck():\n # Call pressureSensor for check user is using\n res = {}\n res['status'] = True\n return json.dumps(res)\n\n\n@app.route(\"/pressure/finish\", methods=['GET'])\ndef pressureIsComplete():\n # Call pressureSensor for check finish checking\n res = {}\n res['status'] = True\n return json.dumps(res)\n\n\n@app.route(\"/pressure/start\", methods=['GET'])\ndef pressureStart():\n # Call pressureSensor for check finish checking\n res = {}\n res['status'] = True\n return json.dumps(res)\n\n\n@app.route(\"/weight/\", methods=['GET'])\ndef weightGetData():\n res = {}\n res['valid'] = weight_sens.isValid\n res['finish'] = weight_sens.isComplete\n res['data'] = weight_sens.current_weight\n return json.dumps(res)\n\n\n@app.route(\"/weight/valid\", methods=['GET'])\ndef weightValidCheck():\n # Call weightSensor for check user is using\n res = {}\n res['status'] = weight_sens.isValid\n return json.dumps(res)\n\n\n@app.route(\"/weight/finish\", methods=['GET'])\ndef weightIsComplete():\n # Call weightSensor for check finish checking\n res = {}\n res['status'] = weight_sens.isComplete\n return json.dumps(res)\n\n\n@app.route(\"/weight/start\", methods=['GET'])\ndef weightStart():\n # Call weightSensor for check finish checking\n res = {}\n weight_sens.start_measure()\n res['status'] = True\n return json.dumps(res)\n\n\n@app.route(\"/height/\", methods=['GET'])\ndef heightGetData():\n res = {}\n res['valid'] = height_sens.isValid\n res['finish'] = height_sens.isComplete\n res['data'] = height_sens.current_height\n return json.dumps(res)\n\n\n@app.route(\"/height/valid\", methods=['GET'])\ndef heightValidCheck():\n # Call heightSensor for check user is using\n res = {}\n res['status'] = height_sens.isValid\n return json.dumps(res)\n\n\n@app.route(\"/height/finish\", methods=['GET'])\ndef heightIsComplete():\n # Call heightSensor for check finish checking\n res = {}\n res['status'] = height_sens.isComplete\n return json.dumps(res)\n\n\n@app.route(\"/height/start\", methods=['GET'])\ndef heightStart():\n # Call heightSensor for check finish checking\n res = {}\n height_sens.start_measure()\n res['status'] = True\n return json.dumps(res)\n\n\ndef thidThread():\n print('--------------------------------------------------')\n print('Start Thead THID!')\n thidSensor.readCard()\n print('Exit Thread THID!')\n print('--------------------------------------------------')\n\n\ndef fingerprintThread():\n print('--------------------------------------------------')\n print('Start Thead FINGERPRINT TEMPLATE!')\n fingerprintSensor.createTemplate()\n print('Exit Thread FINGERPRINT TEMPLATE!')\n print('--------------------------------------------------')\n\n\ndef fingerprintThread2():\n print('--------------------------------------------------')\n print('Start Thead FINGERPRINT TEMPLATE!')\n fingerprintSensor.scanOneTime()\n print('Exit Thread FINGERPRINT TEMPLATE!')\n print('--------------------------------------------------')\n\n\ndef fingerprintThread3():\n print('--------------------------------------------------')\n print('Start Thead FINGERPRINT TEMPLATE!')\n fingerprintSensor.compare()\n print('Exit Thread FINGERPRINT TEMPLATE!')\n print('--------------------------------------------------')\n\n\ndef checkRunningThread(threadType):\n for thread in threads:\n if thread.name == threadType:\n if thread.is_alive() is False:\n print('Thread is end')\n threads.remove(thread)\n return False\n return True\n\n return False\n\n\ndef main():\n app.run('0.0.0.0', port=54322)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "src/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 9738, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Flask", "line_number": 20, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 21, "usage_type": "call"}, {"api_name": "thid.THID", "line_number": 22, "usage_type": "call"}, {"api_name": "fingerprintSensor.Fingerprint", "line_number": 23, "usage_type": "call"}, {"api_name": "weight.weight_sensor", "line_number": 26, "usage_type": "call"}, {"api_name": "height.height_sensor", "line_number": 27, "usage_type": "call"}, {"api_name": "pulse.pulse_sensor", "line_number": 28, "usage_type": "call"}, {"api_name": "temperature.temperature_sensor", "line_number": 29, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 42, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 50, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 59, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 65, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 71, "usage_type": "call"}, {"api_name": "fingerprintSensor.fingerprintTemplateValid", "line_number": 78, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 79, "usage_type": "call"}, {"api_name": "fingerprintSensor.fingerprintTemplateValid", "line_number": 86, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 87, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 91, "usage_type": "call"}, {"api_name": "fingerprintSensor.fingerprintTemplateValid", "line_number": 98, "usage_type": "attribute"}, {"api_name": "fingerprintSensor.fingerprintTemplate", "line_number": 99, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 100, "usage_type": "call"}, {"api_name": "fingerprintSensor.fingerprintCharValid", "line_number": 107, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 108, "usage_type": "call"}, {"api_name": "fingerprintSensor.fingerprintTemplateValid", "line_number": 115, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 116, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 120, "usage_type": "call"}, {"api_name": "fingerprintSensor.fingerptintCompareValid", "line_number": 127, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 128, "usage_type": "call"}, {"api_name": "fingerprintSensor.fingerprintTemplateValid", "line_number": 135, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 136, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 140, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 149, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 158, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 169, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 181, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 189, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 198, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 206, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 218, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 227, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 237, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 245, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 253, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 261, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 270, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 278, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 286, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 295, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 304, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 312, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 320, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 329, "usage_type": "call"}, {"api_name": "fingerprintSensor.createTemplate", "line_number": 343, "usage_type": "call"}, {"api_name": "fingerprintSensor.scanOneTime", "line_number": 351, "usage_type": "call"}, {"api_name": "fingerprintSensor.compare", "line_number": 359, "usage_type": "call"}]} +{"seq_id": "228983259", "text": "import time\r\nimport fileinput\r\nfrom collections import defaultdict\r\n\r\n\r\nINPUT_FILE = \"aoc_2018_11.dat\"\r\nstart = time.time()\r\n\r\n\r\ndef summed_area_table(n):\r\n t = defaultdict(int)\r\n for y in range(1, 301):\r\n for x in range(1, 301):\r\n # compute the value of this cell using the specified formula\r\n r = x + 10\r\n p = (((r * y + n) * r) // 100) % 10 - 5\r\n # store the result in summed-area form\r\n t[(x, y)] = p + t[(x, y - 1)] + t[(x - 1, y)] - t[(x - 1, y - 1)]\r\n return t\r\n\r\n\r\n# derive the sum of this region by checking four corners in the summed-area table\r\ndef region_sum(t, s, x, y):\r\n x0, y0, x1, y1 = x - 1, y - 1, x + s - 1, y + s - 1\r\n return t[(x0, y0)] + t[(x1, y1)] - t[(x1, y0)] - t[(x0, y1)]\r\n\r\n\r\n# using the summed-area table `t` and a region size `s` find the sub region with a maximal sum\r\ndef best(t, s):\r\n rs = []\r\n for y in range(1, 301 - s + 1):\r\n for x in range(1, 301 - s + 1):\r\n r = region_sum(t, s, x, y)\r\n rs.append((r, x, y))\r\n return max(rs)\r\n\r\n# build the summed area table\r\nt = summed_area_table(int(next(fileinput.input(INPUT_FILE))))\r\n\r\n# find the best 3x3 region\r\nprint('%d,%d' % best(t, 3)[1:])\r\n\r\n# find the best region of any size\r\n\r\nprint('%d,%d,%d' % max(best(t, s) + (s,) for s in range(1, 301))[1:])\r\nprint(\"Seconds spent: \", round(time.time() - start, 5))\r\n", "sub_path": "advent_of_code/2018/11_fuel_power/aoc_2018_11_alt_1.py", "file_name": "aoc_2018_11_alt_1.py", "file_ext": "py", "file_size_in_byte": 1408, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "time.time", "line_number": 7, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 11, "usage_type": "call"}, {"api_name": "fileinput.input", "line_number": 38, "usage_type": "call"}, {"api_name": "time.time", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "516030280", "text": "from django.shortcuts import render, redirect\nimport random\nfrom time import gmtime, strftime\n\ndef index(request):\n if 'gold' not in request.session:\n request.session['gold']=0\n request.session['activities'] =[]\n request.session['color']=\"\"\n\n return render(request,\"index.html\")\n\ndef process(request):\n\n if request.method == 'POST':\n data ={\n 'text' : '',\n 'color' : 'green'\n\n\n }\n if request.POST['location'] == \"farm\":\n gold_amount = random.randint(10,20)\n time = strftime(\"%Y-%m-%d %H:%M %p\", gmtime())\n data['text'] = f\"Earned {gold_amount} from farm! ({time}) \"\n request.session['gold'] += gold_amount\n\n elif request.POST['location'] == \"cave\":\n gold_amount = random.randint(5,10)\n time = strftime(\"%Y-%m-%d %H:%M %p\", gmtime())\n data['text'] = f\"Earned {gold_amount} from cave! ({time}) \"\n request.session['gold'] += gold_amount\n\n elif request.POST['location'] == \"house\":\n gold_amount = random.randint(2,5)\n time = strftime(\"%Y-%m-%d %H:%M %p\", gmtime())\n data['text'] = f\"Earned {gold_amount} from house! ({time})\"\n request.session['gold'] += gold_amount\n\n elif request.POST['location'] == \"casino\":\n gold_amount = random.randint(-50,50)\n time = strftime(\"%Y-%m-%d %H:%M %p\", gmtime())\n if gold_amount > 0:\n data['text'] = f\"Earned {gold_amount} from casino! ({time})\"\n else:\n postive = abs(gold_amount) \n data['text'] = f\"Entered a casino and lost {postive} golds... Ouch.. ({time})\"\n data['color'] = 'red'\n request.session['gold'] += gold_amount\n\n\n\n request.session['activities'].append(data)\n\n\n return redirect(\"/\")", "sub_path": "Python/Python_Django/ninja_gold/ninja_gold_app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1764, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.shortcuts.render", "line_number": 11, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 23, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 24, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 24, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 29, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 30, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 30, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 35, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 36, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 36, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 41, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 42, "usage_type": "call"}, {"api_name": "time.gmtime", "line_number": 42, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 56, "usage_type": "call"}]} +{"seq_id": "67970655", "text": "from __future__ import division\n\nimport cv2\n\nfrom scripts.videocapture.AbstractVideoCapture import AbstractVideoCapture\n\n\nclass VideoCapture(AbstractVideoCapture):\n def __init__(self):\n self.VIDEO_SIZE = (400, 300)\n self.cap = cv2.VideoCapture(0) # 0 for camera\n self.failedFramesLimit = 10\n\n def get_frame(self):\n failed_frames = 0\n while True:\n ret, frame = self.cap.read()\n if ret == true:\n break\n\n failed_frames = failed_frames + 1\n if failed_frames > self.failedFramesLimit:\n raise Exception(\n \"VideoCapture cannot receive stable video stream. Possible reason is camera driver issue.\"\n )\n\n frame = cv2.resize(cv2.flip(frame, 1), self.VIDEO_SIZE) # vertical flip+ resize\n return frame\n", "sub_path": "scripts/videocapture/VideoCapture.py", "file_name": "VideoCapture.py", "file_ext": "py", "file_size_in_byte": 854, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "scripts.videocapture.AbstractVideoCapture.AbstractVideoCapture", "line_number": 8, "usage_type": "name"}, {"api_name": "cv2.VideoCapture", "line_number": 11, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 27, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "262618959", "text": "from http import HTTPStatus\nfrom uuid import uuid4\n\nfrom fastapi.testclient import TestClient\nfrom sqlalchemy.orm import Session\n\n\ndef test_project(db: Session, client: TestClient) -> None:\n name1 = f\"prj-{uuid4().hex}\"\n prj1 = {\n \"name\": name1,\n \"owner\": \"u0\",\n \"description\": \"banana\",\n # 'users': ['u1', 'u2'],\n }\n resp = client.post(\"/api/project\", json=prj1)\n assert resp.status_code == HTTPStatus.OK.value, \"add\"\n resp = client.get(f\"/api/project/{name1}\")\n out = {key: val for key, val in resp.json()[\"project\"].items() if val}\n # out['users'].sort()\n for key, value in prj1.items():\n assert out[key] == value\n\n data = {\"description\": \"lemon\", \"name\": name1}\n resp = client.post(f\"/api/project/{name1}\", json=data)\n assert resp.status_code == HTTPStatus.OK.value, \"update\"\n resp = client.get(f\"/api/project/{name1}\")\n assert name1 == resp.json()[\"project\"][\"name\"], \"name after update\"\n\n name2 = f\"prj-{uuid4().hex}\"\n prj2 = {\n \"name\": name2,\n \"owner\": \"u0\",\n \"description\": \"banana\",\n # 'users': ['u1', 'u3'],\n }\n resp = client.post(\"/api/project\", json=prj2)\n assert resp.status_code == HTTPStatus.OK.value, \"add (2)\"\n\n resp = client.get(\"/api/projects\")\n expected = {name1, name2}\n assert expected.issubset(set(resp.json()[\"projects\"])), \"list\"\n\n resp = client.get(\"/api/projects?full=true\")\n projects = resp.json()[\"projects\"]\n assert {dict} == set(type(p) for p in projects), \"dict\"\n", "sub_path": "tests/api/api/test_projects.py", "file_name": "test_projects.py", "file_ext": "py", "file_size_in_byte": 1533, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sqlalchemy.orm.Session", "line_number": 8, "usage_type": "name"}, {"api_name": "fastapi.testclient.TestClient", "line_number": 8, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 9, "usage_type": "call"}, {"api_name": "http.HTTPStatus.OK", "line_number": 17, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 17, "usage_type": "name"}, {"api_name": "http.HTTPStatus.OK", "line_number": 26, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 26, "usage_type": "name"}, {"api_name": "uuid.uuid4", "line_number": 30, "usage_type": "call"}, {"api_name": "http.HTTPStatus.OK", "line_number": 38, "usage_type": "attribute"}, {"api_name": "http.HTTPStatus", "line_number": 38, "usage_type": "name"}]} +{"seq_id": "639818736", "text": "# -*- coding: utf-8 -*-\nfrom django.db import migrations\n\n\ndef move_parse_log_to_parse_status_objects_forward(apps, schema_editor):\n \"\"\"\n Copy parse status data to new objects.\n \"\"\"\n RasterLayer = apps.get_model(\"raster\", \"RasterLayer\")\n RasterLayerParseStatus = apps.get_model(\"raster\", \"RasterLayerParseStatus\")\n for lyr in RasterLayer.objects.all():\n status, created = RasterLayerParseStatus.objects.get_or_create(rasterlayer=lyr)\n status.log = lyr.parse_log\n if 'Successfully finished parsing raster' in lyr.parse_log:\n status.status = 5 # Finished\n else:\n status.status = 6 # Failed\n status.save()\n\n\ndef move_parse_log_to_parse_status_objects_backward(apps, schema_editor):\n \"\"\"\n Copy the srids back to the raster layers.\n \"\"\"\n RasterLayer = apps.get_model(\"raster\", \"RasterLayer\")\n for lyr in RasterLayer.objects.all():\n if hasattr(lyr, 'parsestatus'):\n lyr.parse_log = lyr.parsestatus.log\n lyr.save()\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('raster', '0021_rasterlayerparsestatus'),\n ]\n\n operations = [\n migrations.RunPython(\n move_parse_log_to_parse_status_objects_forward,\n move_parse_log_to_parse_status_objects_backward\n )\n ]\n", "sub_path": "raster/migrations/0022_auto_20151110_0810.py", "file_name": "0022_auto_20151110_0810.py", "file_ext": "py", "file_size_in_byte": 1335, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 32, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.migrations.RunPython", "line_number": 39, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "90488710", "text": "\"\"\"Multicast Use cases.\n\nThis will include connecting to a multicast stream via Iperf, ip mroute\nor smcroute.\n\"\"\"\nfrom contextlib import contextmanager\nfrom dataclasses import dataclass\nfrom ipaddress import ip_address\nfrom typing import Generator, List, Optional, Tuple, Union\n\nimport pandas\n\nfrom boardfarm.devices.debian_lan import DebianLAN\nfrom boardfarm.devices.debian_wan import DebianWAN\nfrom boardfarm.devices.debian_wifi import DebianWifi\nfrom boardfarm.exceptions import BFTypeError, UseCaseFailure\nfrom boardfarm.lib.network_testing import kill_process, tcpdump_capture\n\nIperfDevice = Union[DebianLAN, DebianWAN, DebianWifi]\n\n\n@dataclass\nclass IPerfSession:\n \"\"\"Store details of IPerf session.\"\"\"\n\n device: IperfDevice\n pid: str\n address: str\n port: int\n output_file: str\n\n\n@dataclass\nclass IPerfStream:\n \"\"\"Store details of IPerf stream.\"\"\"\n\n device: IperfDevice\n pid: str\n address: str\n port: int\n output_file: str\n time: int = 0\n\n\n@dataclass\nclass IPerfResult:\n \"\"\"Store results of IPerf server session.\"\"\"\n\n _data: Optional[pandas.DataFrame]\n\n @property\n def bandwidth(self) -> Optional[str]:\n \"\"\"Return resultant bandwidth in Mbps.\n\n :return: resultant bandwidth, None if iperf failed\n :rtype: Optional[str]\n \"\"\"\n return (\n self._data[\"bandwidth\"].iloc[-1] / 1000000\n if self._data is not None\n else None\n )\n\n @property\n def total_loss(self) -> Optional[str]:\n \"\"\"Return no. of datagrams lost.\n\n :return: resultant total loss, None if iperf failed\n :rtype: Optional[str]\n \"\"\"\n return self._data[\"lost\"].iloc[-1] if self._data is not None else None\n\n @property\n def result(self) -> Optional[pandas.DataFrame]:\n \"\"\"Return the entire result as a dataframe.\n\n :return: iperf result in tablular format, None if iperf failed\n :rtype: Optional[pandas.DataFrame]\n \"\"\"\n return self._data\n\n\ndef kill_all_iperf(device_list: List[IperfDevice]):\n \"\"\"Kill all iperf session on target devices.\n\n This should be called for cleaning purposes.\n\n :param device_list: list of target devices\n :type device_list: List[IperfDevice]\n \"\"\"\n for obj in device_list:\n dev = obj\n dev.sendcontrol(\"c\")\n dev.expect_prompt()\n dev.check_output(\"for i in $(pgrep iperf); do kill -9 $i; done\")\n\n\ndef _iperf_session_check(dev, multicast_address: str, port: int):\n if not ip_address(multicast_address).is_multicast:\n raise BFTypeError(f\"{multicast_address} is not a multicast address\")\n\n # check before running that there should be no iperf sessions with same port\n if dev.check_output(f\"pgrep iperf -a | grep {port} | grep {multicast_address}\"):\n raise UseCaseFailure(\n f\"{dev.name} already has an iperf session with \"\n f\"port {port} and multicast address {multicast_address}\"\n )\n\n\n@contextmanager\ndef tcpdump(\n dev: IperfDevice, fname: str, filters: Optional[str] = \"ip multicast\"\n) -> Generator[str, None, None]:\n \"\"\"Start packet capture using tcpdump and kills the process at the end.\n\n Applies specific filter for multicast traffic only.\n\n .. hint::This Use Case implements statements from the test suite such as:\n\n - capturing packets sent from and to eRouter WAN\n and eRouter LAN interfaces\n\n :param dev: device object for a VoiceServer\n :type dev: IperfDevice\n :param fname: name of the pcap file to which the capture will be stored\n :type fname: str\n :param filters: additional filters for capture, defaults to \"ip multicast\"\n :type filters: Optional[str]\n :yield: process id\n :rtype: Generator[str, None, None]\n \"\"\"\n pid: str = \"\"\n device = dev\n try:\n pid = tcpdump_capture(\n device,\n device.iface_dut,\n capture_file=fname,\n return_pid=True,\n additional_filters=f\"-s0 {filters}\",\n )\n yield pid\n finally:\n kill_process(device, process=\"tcpdump\", pid=pid)\n\n\ndef _read_mcast_trace(dev: IperfDevice, fname: str) -> List[Tuple[str, ...]]:\n \"\"\"Read and filter multicast packets from the captured file.\n\n Multicast traffic include UDP stream packets as well as IGMP Group\n membership reports.\n\n IP packets will be parsed and following values are returned in a list:\n\n - IP source\n - IP destination\n - MAC source\n - MAC destination\n - IP protocol number (1 - ICMP, 2 - IGMP, 6 - TCP, 17 - UDP)\n - IGMP version (v3 by default)\n - IGMP Record Type number (5 - Allow new sources, 6 - Block old sources)\n - IGMP Multicast Address (if provided in group records)\n - IGMP Source Address (if provided in group records)\n\n :param dev: Descriptor of iperf capable device with PCAP file\n :type dev: IperfDevice\n :param fname: PCAP file to be read\n :type fname: str\n :return: list of parsed IP multicast packets\n :rtype: List[Tuple[str, ...]]\n \"\"\"\n device = dev\n cmd = f'tshark -r {fname} -E separator=, -Y \"igmp or udp\" '\n fields = (\n \"-T fields -e ip.src -e ip.dst -e eth.src -e eth.dst \"\n \"-e ip.proto -e igmp.type -e igmp.record_type \"\n \"-e igmp.maddr -e igmp.saddr\"\n )\n\n device.sudo_sendline(cmd + fields)\n device.expect(device.prompt)\n out = device.before.splitlines()\n for _i, o in enumerate(out):\n if \"This could be dangerous.\" in o:\n break\n out = out[_i + 1 :]\n\n return [tuple(line.strip().split(\",\")) for line in out]\n\n\ndef parse_mcast_trace(\n dev: IperfDevice,\n fname: str,\n expected_sequence: List[Tuple[str, ...]],\n ip_version: int = 4,\n) -> List[Tuple[str, ...]]:\n \"\"\"Compare captured PCAP file against an expected sequence of packets.\n\n This returns a matched subset of the whole packet trace.\n The sequence of the matched packets must align with expected sequence.\n The length of the matched sequence is equal to expected sequence.\n\n In case a packet is missing in captured sequence, an empty value is\n maintained in output at the same index as that of the expected sequence.\n\n IP packets in expected sequence must follow the following order:\n\n - IP source\n - IP destination\n - MAC source\n - MAC destination\n - IP protocol number (1 - ICMP, 2 - IGMP, 6 - TCP, 17 - UDP)\n - IGMP message type (0x00000011 - IGMP Query, 0x00000022 - IGMP Report)\n - IGMP Record Type number (5 - Allow new sources, 6 - Block old sources)\n - IGMP Multicast Address (if provided in group records)\n - IGMP Source Address (if provided in group records)\n\n IPv6 packets will be parsed and following values are returned in a list:\n\n - IPv6 source\n - IPv6 destination\n - MAC source\n - MAC destination\n - IPv6 Next Header (0 - ICMPv6, 6 - TCP, 17 - UDP)\n - MLDv2 version (130 - MLDv2 Query, 143 - MLDv2 Report)\n - MLDv2 Record Type number (5 - Allow new sources, 6 - Block old sources)\n - MLDv2 Multicast Address (if provided in group records)\n - MLDv2 Source Address (if provided in group records)\n\n You can use * to mark a field as Any\n\n .. hint:: This Use Case assists in validating statements from the\n test suite such as:\n\n - Check IGMPv3 report to subscribe to (S,G) from LAN on eRouter\n LAN interface\n - Check Multicast traffic from WAN multicast server is received\n on eRouter WAN interface and forwarded to Ethernet LAN client\n\n :param dev: Descriptor of iperf capable device with PCAP file\n :type dev: IperfDevice\n :param fname: name of the PCAP file\n :type fname: str\n :param expected_sequence: expected sequence to match against captured sequence\n :type expected_sequence: List[Tuple[str, ...]]\n :param ip_version: IP version, defaults to 4\n :type ip_version: int\n :return: matched captured sequence against the expected sequence\n :rtype: List[Tuple[str, ...]]\n :raises ValueError: if invalid IP version is provided\n \"\"\"\n if ip_version == 4:\n captured_sequence = _read_mcast_trace(dev, fname)\n elif ip_version == 6:\n captured_sequence = _read_mcast6_trace(dev, fname)\n else:\n raise ValueError(f\"Invalid IP version: {ip_version}\")\n last_check = 0\n final_result = []\n for packet in expected_sequence:\n for i in range(last_check, len(captured_sequence)):\n if all(\n expected == actual\n for expected, actual in zip(packet, captured_sequence[i])\n if expected != \"*\"\n ):\n last_check = i\n print(\n f\"Verified IP Multicast:\\t{packet[0]}--->{packet[1]},\\t\"\n f\"MAC: {packet[2]}--->{packet[3]}\"\n )\n final_result.append(captured_sequence[i])\n break\n else:\n print(\n f\"Failed IP Multicast verification:\"\n f\"\\t{packet[0]}--->{packet[1]},\\t\"\n f\"MAC: {packet[2]}--->{packet[3]}\"\n )\n final_result.append(())\n return final_result\n\n\ndef _read_mcast6_trace(dev: IperfDevice, fname: str) -> List[Tuple[str, ...]]:\n \"\"\"Read and filter multicast packets from the captured file.\n\n Multicast traffic include UDP stream packets as well as MLDv2 Group\n membership reports.\n\n IPv6 packets will be parsed and following values are returned in a list:\n\n - IP source\n - IP destination\n - MAC source\n - MAC destination\n - IPv6 Next Header (0 - ICMPv6, 6 - TCP, 17 - UDP)\n - MLDv2 version (130 - MLDv2 Query, 143 - MLDv2 Report)\n - MLDv2 Record Type number (5 - Allow new sources, 6 - Block old sources)\n - MLDv2 Multicast Address (if provided in group records)\n - MLDv2 Source Address (if provided in group records)\n\n :param dev: Descriptor of iperf capable device with PCAP file\n :type dev: IperfDevice\n :param fname: PCAP file to be read\n :type fname: str\n :return: list of parsed IP multicast packets\n :rtype: List[Tuple[str, ...]]\n \"\"\"\n device = dev\n cmd = (\n f\"tshark -r {fname} -E separator=, -Y \"\n '\"icmpv6.type==143 or icmpv6.type==130 or udp\" '\n )\n fields = (\n \"-T fields -e ipv6.src -e ipv6.dst -e eth.src \"\n \"-e eth.dst -e ipv6.nxt -e icmpv6.type \"\n \"-e icmpv6.mldr.mar.record_type \"\n \"-e icmpv6.mldr.mar.multicast_address \"\n \"-e icmpv6.mldr.mar.source_address\"\n )\n\n device.sudo_sendline(cmd + fields)\n device.expect(device.prompt)\n out = device.before.splitlines()\n for _i, o in enumerate(out):\n if \"This could be dangerous.\" in o:\n break\n out = out[_i + 1 :]\n\n return [tuple(line.strip().split(\",\")) for line in out]\n", "sub_path": "boardfarm/use_cases/multicast.py", "file_name": "multicast.py", "file_ext": "py", "file_size_in_byte": 10934, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "typing.Union", "line_number": 19, "usage_type": "name"}, {"api_name": "boardfarm.devices.debian_lan.DebianLAN", "line_number": 19, "usage_type": "name"}, {"api_name": "boardfarm.devices.debian_wan.DebianWAN", "line_number": 19, "usage_type": "name"}, {"api_name": "boardfarm.devices.debian_wifi.DebianWifi", "line_number": 19, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 22, "usage_type": "name"}, {"api_name": "dataclasses.dataclass", "line_number": 33, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 49, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 49, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 52, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 65, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 74, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 74, "usage_type": "attribute"}, {"api_name": "dataclasses.dataclass", "line_number": 45, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 83, "usage_type": "name"}, {"api_name": "ipaddress.ip_address", "line_number": 99, "usage_type": "call"}, {"api_name": "boardfarm.exceptions.BFTypeError", "line_number": 100, "usage_type": "call"}, {"api_name": "boardfarm.exceptions.UseCaseFailure", "line_number": 104, "usage_type": "call"}, {"api_name": "typing.Optional", "line_number": 112, "usage_type": "name"}, {"api_name": "boardfarm.lib.network_testing.tcpdump_capture", "line_number": 135, "usage_type": "call"}, {"api_name": "boardfarm.lib.network_testing.kill_process", "line_number": 144, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 110, "usage_type": "name"}, {"api_name": "typing.Generator", "line_number": 113, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 147, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 147, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 194, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 194, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 196, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 196, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 284, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 284, "usage_type": "name"}]} +{"seq_id": "182894048", "text": "\n# coding: utf-8\n\nfrom time import time\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.mlab as mlab\nimport torch\nimport os\n\n\ndef extract_conv_weight(model_path = 'model_best_CUB__Z.pth.tar', layer = 10): # for cifar layer = 28\n net = torch.load(model_path)\n w = net['state_dict']['features.{}.weight'.format(layer)].cpu()\n del net\n return w\n\n\nmodel_path = 'model_best_CUB__poorData.pth.tar'\n\nw = extract_conv_weight(model_path)\nnum_filter = w.size()[0]\nfilters = w.numpy().reshape(num_filter,-1)\n\ntmp = model_path.split('.')\nmodel = ''\nfor t in tmp[:-2]:\n model += t\n\nsave_path = './W_distribution/'+model+'/'\n\nif not os.path.exists(save_path):\n os.mkdir(save_path)\n\nfor i in range(20):\n n, bins, patches = plt.hist((filters[134,:],), bins=50,rwidth = 0.8, histtype='barstacked',alpha = 0.5, stacked=0)\n plt.title(r'$Num of filter: {}/{}$ model: {}'.format(i,num_filter,model))\n plt.savefig(save_path+'{}.png'.format(i))\n\n\n\n", "sub_path": "PCA/ilsvrc/visualize_w.py", "file_name": "visualize_w.py", "file_ext": "py", "file_size_in_byte": 974, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "torch.load", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.hist", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}]} +{"seq_id": "340086284", "text": "from sklearn import tree\nfrom random import random\nfrom ImportCsv import importcsv\nimport numpy as np\n\n\n\n\n# Load data\n\nrowData = importcsv(\"../spambase.data\")\ndata = []\nfor line in rowData:\n listLine = []\n for value in line:\n listLine.append(float(value))\n data.append(listLine)\n\n\n#print(data[0])\n#print(len(data))\n\n#### Specific fields####\nusedData = []\nusedValue = []\nusedData1 = []\nusedValue1 = []\nfor line in data:\n listLine = []\n listLine1 = []\n for k in range(len(line)):\n #if k in [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 46, 51, 52, 53]:\n if k in [0, 1, 3, 4, 5, 6, 7, 8, 9, 10, 12, 13, 14, 15, 16, 17, 19, 20, 21, 22, 23, 37, 46, 51, 52, 53] and k not in [27, 28, 31, 57]:\n listLine.append(line[k])\n if k != 27 and k != 28 and k != 31 and k!=57:\n listLine1.append(line[k])\n usedValue.append(line[-1])\n usedData.append(listLine)\n usedValue1.append(line[-1])\n usedData1.append(listLine1)\n'''data = np.array(data)\nprint(data)\n'''\n\n\ntestSetX = []\ntestSetY = []\ntestSetX1 = []\ntestSetY1 = []\nfor k in range (20):\n placeToTakeForTest = int(random() * len(usedData))\n x = usedData.pop(placeToTakeForTest)\n y = usedValue.pop(placeToTakeForTest)\n testSetX.append(x)\n testSetY.append(y)\n x = usedData1.pop(placeToTakeForTest)\n y = usedValue1.pop(placeToTakeForTest)\n testSetX1.append(x)\n testSetY1.append(y)\n\n\n\n\n\nusedData1 = np.array(usedData1)\nusedValue1 = np.array(usedValue1)\ntestSetX1 = np.array(testSetX1)\ntestSetY1 = np.array(testSetY1)\n\n#print(usedData[:1])\nclf1 = tree.DecisionTreeClassifier()\nclf1 = clf1.fit(usedData1, usedValue1)\nprint(\"Everything\")\n#print(clf1.score(testSetX1, testSetY1))\nprint(clf1.predict(testSetX1))\n\n\nusedData = np.array(usedData)\nusedValue = np.array(usedValue)\ntestSetX = np.array(testSetX)\ntestSetY = np.array(testSetY)\n\n#print(usedData[:1])\nclf = tree.DecisionTreeClassifier()\nclf = clf.fit(usedData, usedValue)\nprint(\"Specific\")\n#print(clf.score(testSetX, testSetY))\nprint(clf.predict(testSetX))\n\nprint('jjj')\nprint(testSetY)\n\n\n\n\n\n#a = clf.predict(testSetX)\n#print(a)\n#print(testSetY)\n'''\ngood = 0\nbad = 0\nfor k in range(len(a)):\n if a[k] == testSetY[k]:\n good+=1\n else:\n bad+=1\n\nprint((good/(good+bad))*100)\nprint((bad/(good+bad))*100)'''\n", "sub_path": "decision_tree/decision_tree_specific_Fields.py", "file_name": "decision_tree_specific_Fields.py", "file_ext": "py", "file_size_in_byte": 2341, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "ImportCsv.importcsv", "line_number": 11, "usage_type": "call"}, {"api_name": "random.random", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 68, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 71, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 71, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 81, "usage_type": "call"}, {"api_name": "sklearn.tree.DecisionTreeClassifier", "line_number": 84, "usage_type": "call"}, {"api_name": "sklearn.tree", "line_number": 84, "usage_type": "name"}]} +{"seq_id": "155905769", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Nov 24 15:18:23 2014\n\n@author: vivianapetrescu\n\"\"\"\nimport numpy as np\nimport time\n\nimport scipy\nimport theano\nimport theano.tensor as T\nfrom theano.tensor.signal import downsample\nfrom theano.tensor.signal import conv\n\nclass LeNetLayerConvPoolSeparableNonSymbolic:\n def __init__(self, rng):\n self.rng = rng\n def run_batch(self, input_images, image_shape, filter_shape, Pstruct, b, poolsize):\n assert image_shape[1] == filter_shape[1]\n # the bias is a 1D tensor -- one bias per output feature map\n # convolve input feature maps with filters\n batch_size = image_shape[0] \n fwidth = Pstruct[0]['U1'].shape[0]\n fheight = Pstruct[0]['U2'].shape[0]\n self.nbr_channels = image_shape[1]\n self.nbr_filters = Pstruct[0]['U3'].shape[0]\n initial_n_rows = image_shape[2]\n initial_n_cols = image_shape[3]\n\n # Final number of rows and columns \n final_n_rows = initial_n_rows - fwidth + 1\n final_n_cols = initial_n_cols - fheight + 1\n # The convolved input images\n self.input4D = np.zeros((batch_size, self.nbr_filters, final_n_rows, final_n_cols))\n one_image_shape = (self.nbr_channels, initial_n_rows, initial_n_cols)\n # assert one_image_shape == (1,28,28)\n nbr_filters = Pstruct[0]['U3'].shape[0]\n rank = Pstruct[0]['U1'].shape[1]\n pcoef = np.ndarray((nbr_filters, self.nbr_channels, rank))\n for filter_index in xrange(nbr_filters):\n for chanel in xrange(self.nbr_channels):\n pcoef[filter_index,chanel, :] = Pstruct[chanel]['U3'][filter_index, :] * Pstruct[chanel]['lmbda'][:]; \n U1 = np.ndarray((self.nbr_channels, rank ,1, fwidth))\n U2 = np.ndarray((self.nbr_channels, rank, fheight, 1))\n for chanel in range(self.nbr_channels):\n U1[chanel,:,0, :] = np.transpose(Pstruct[chanel]['U1']); \n U2[chanel,:,:, 0] = np.transpose(Pstruct[chanel]['U2']);\n start = time.time()\n for image_index in range(batch_size):\n # Convolve image with index image_index in the batch\n self.convolve_one_image(input_images[image_index,:,:,:],\n one_image_shape,\n Pstruct, pcoef, U1, U2,\n filter_shape, \n image_index) \n end = time.time()\n\n self.t_conv = (end - start)*1000/ batch_size\n # print 'convolution time of batch image {0}'.format(self.t_conv)\n # print 'before downsample', self.input4D\n # downsample each feature map individually, using maxpooling\n start = time.time()\n pooled_out = downsample.max_pool_2d(input=self.input4D,\n ds=poolsize,\n ignore_border=True)\n\n # add the bias term. Since the bias is a vector (1D array), we first\n # reshape it to a tensor of shape (1,n_filters,1,1). Each bias will\n # thus be broadcasted across mini-batches and feature map\n # width & height\n # sb = theano.shared(b)\n self.b_params = [b]\n self.output = T.tanh(pooled_out + b.dimshuffle('x', 0, 'x', 'x'))\n end = time.time()\n self.t_downsample_activ = (end - start)*1000/ image_shape[0] \n # print 'pool+tanh time of batch image {0}'.format(self.t_downsample_activ) \n return self.output\n\n \"\"\"TODO change to have an image such as nbr channels as well\"\"\"\n def convolve_one_image(self, one_image, img_shape, \n Pstruct, pcoef, U1, U2, filter_shape,\n image_index):\n rank = Pstruct[0]['U1'].shape[1]\n fwidth = Pstruct[0]['U1'].shape[0]\n fheight = Pstruct[0]['U2'].shape[0]\n #\n num_input_feature_maps = img_shape[0]\n n_rows = img_shape[1] - fwidth + 1\n n_cols = img_shape[2] - fheight + 1\n # horizontal_conv_out = np.ndarray((img_shape[1], n_cols))\n vertical_conv_out = np.ndarray(( n_rows, n_cols,num_input_feature_maps, rank))\n start = time.time()\n # vertical_filter_shape = (rank, fheight,1)\n # vertical_filters = np.ndarray(vertical_filter_shape) \n # horizontal_filter_shape = (rank, 1, fwidth)\n # horizontal_filters = np.ndarray(horizontal_filter_shape)\n for chanel in xrange(num_input_feature_maps): \n for r in xrange(rank):\n # horizontal_conv_out = scipy.signal.convolve2d(one_image[chanel,:,:], \n # U1[chanel,r,:,:], mode='valid')\n vertical_conv_out[:,:, chanel,r] = scipy.signal.convolve2d(scipy.signal.convolve2d(one_image[chanel,:,:], \n U1[chanel,r,:,:], mode='valid'),\n U2[chanel, r,:,:], mode='valid')\n end = (time.time() - start)*1000\n # print 'part 1 ', end\n start = time.time() \n output_image = np.zeros((num_input_feature_maps,n_rows, n_cols))\n for filter_index in xrange(self.nbr_filters): \n # for chanel in xrange(num_input_feature_maps):\n # temp = vertical_conv_out[chanel,:,:,:]*pcoef[filter_index, chanel,:]\n # output_image[chanel, :, :] = np.sum(temp, axis=2)\n output_image = vertical_conv_out[:,:,:,:]*pcoef[filter_index,:,:] \n self.input4D[image_index,filter_index,:,:] = np.sum(np.sum(output_image, axis=3), axis=2)\n end = (time.time() - start)*1000;\n # print 'part2 ', end\n", "sub_path": "proto_cnn/src/core/lenet_layer_conv_pool_separable_non_symbolic.py", "file_name": "lenet_layer_conv_pool_separable_non_symbolic.py", "file_ext": "py", "file_size_in_byte": 5760, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.zeros", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.transpose", "line_number": 48, "usage_type": "call"}, {"api_name": "time.time", "line_number": 49, "usage_type": "call"}, {"api_name": "time.time", "line_number": 57, "usage_type": "call"}, {"api_name": "time.time", "line_number": 63, "usage_type": "call"}, {"api_name": "theano.tensor.signal.downsample.max_pool_2d", "line_number": 64, "usage_type": "call"}, {"api_name": "theano.tensor.signal.downsample", "line_number": 64, "usage_type": "name"}, {"api_name": "theano.tensor.tanh", "line_number": 74, "usage_type": "call"}, {"api_name": "theano.tensor", "line_number": 74, "usage_type": "name"}, {"api_name": "time.time", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 92, "usage_type": "call"}, {"api_name": "time.time", "line_number": 93, "usage_type": "call"}, {"api_name": "scipy.signal.convolve2d", "line_number": 102, "usage_type": "call"}, {"api_name": "scipy.signal", "line_number": 102, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 105, "usage_type": "call"}, {"api_name": "time.time", "line_number": 107, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 114, "usage_type": "call"}, {"api_name": "time.time", "line_number": 115, "usage_type": "call"}]} +{"seq_id": "236224662", "text": "import cv2\nimport mediapipe as mp\nimport time\nimport socketio\n\ncap = cv2.VideoCapture(0)\n\nmpHands = mp.solutions.hands\nhands = mpHands.Hands()\nmpDraw = mp.solutions.drawing_utils\nptime = 0\nctime = 0\n\nwhile True:\n\n\n sucess, img = cap.read()\n imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n result = hands.process(imgRGB)\n #print(result.multi_hand_landmarks)\n if result.multi_hand_landmarks:\n for handLms in result.multi_hand_landmarks:\n mpDraw.draw_landmarks(img, handLms, mpHands.HAND_CONNECTIONS)\n\n ctime = time.time()\n fps =1/(ctime-ptime)\n ptime = ctime\n\n cv2.putText(img, str(int(fps)), (10,70),cv2.FONT_HERSHEY_SIMPLEX,3,(255,255,255),3)\n cv2.imshow(\"Image2\", img)\n cv2.waitKey(1)", "sub_path": "venv/handtrackingmin.py", "file_name": "handtrackingmin.py", "file_ext": "py", "file_size_in_byte": 744, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "cv2.VideoCapture", "line_number": 6, "usage_type": "call"}, {"api_name": "mediapipe.solutions", "line_number": 8, "usage_type": "attribute"}, {"api_name": "mediapipe.solutions", "line_number": 10, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 18, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 18, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 29, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 29, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "551446999", "text": "import json\nfrom faker import faker\n\nfake = Faker()\nwith open(\"file.json\", \"r\") as f:\n family = json.load(f) #f is what we are loading it from i.e. from within\nprint(\"janes children:\")\n\nfor child in family[\"children\"]:\n child[\"favorite_color\"] = fake.color()\n for key, value in child.items():\n print(f'my {key} is {value}')\n\nwith open(\"file.json\", \"w\") as f:\n json.dump(family, f, indent=2)\n\n#", "sub_path": "Week5/Day4/Neuer Ordner/index.py", "file_name": "index.py", "file_ext": "py", "file_size_in_byte": 412, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "json.load", "line_number": 6, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 15, "usage_type": "call"}]} +{"seq_id": "98067049", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nimport django.db.models.deletion\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('myhpom', '0004_auto_20180708_0954'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='HealthNetwork',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('name', models.CharField(max_length=1024)),\n ('priority', models.PositiveSmallIntegerField(choices=[(0, 'Primary Network'), (1, 'Additional Network'), (2, 'Independent System')])),\n ('state', models.ForeignKey(to='myhpom.State')),\n ],\n options={\n 'ordering': ['priority', 'name'],\n },\n ),\n migrations.AddField(\n model_name='userdetails',\n name='custom_provider',\n field=models.CharField(max_length=1024, null=True, blank=True),\n ),\n migrations.AddField(\n model_name='userdetails',\n name='health_network',\n field=models.ForeignKey(on_delete=django.db.models.deletion.SET_NULL, to='myhpom.HealthNetwork', null=True),\n ),\n migrations.AlterUniqueTogether(\n name='healthnetwork',\n unique_together=set([('state', 'name')]),\n ),\n ]\n", "sub_path": "myhpom/migrations/0005_auto_20180710_1058.py", "file_name": "0005_auto_20180710_1058.py", "file_ext": "py", "file_size_in_byte": 1437, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "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.CreateModel", "line_number": 15, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.AutoField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.PositiveSmallIntegerField", "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.AddField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.migrations.AddField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.db", "line_number": 35, "usage_type": "attribute"}, {"api_name": "django.db", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.migrations.AlterUniqueTogether", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 37, "usage_type": "name"}]} +{"seq_id": "167918195", "text": "from django.shortcuts import get_object_or_404\nfrom django.shortcuts import redirect\nfrom django.shortcuts import render\nfrom django.urls import reverse\n\nfrom polls.forms import AnswerForm\nfrom polls.models import Answer\nfrom polls.models import PollUser\nfrom polls.models import Question\n\n\ndef main_view(request):\n return render(request, \"main.html\", {\"appname\": \"Skilltest\"})\n\n\ndef poll_user_view(request, poll_user_guid):\n poll_user = get_object_or_404(PollUser, guid=poll_user_guid)\n ctx = {\"poll_user\": poll_user}\n if not poll_user.completed_at:\n question = poll_user.next_question\n ctx[\"question\"] = question\n return render(request, \"poll_user.html\", ctx)\n\n\ndef poll_user_question_view(request, poll_user_guid, question_pk):\n poll_user = get_object_or_404(PollUser, guid=poll_user_guid)\n question = get_object_or_404(Question, pk=question_pk)\n if request.method == \"POST\":\n form = AnswerForm(request.POST)\n if form.is_valid():\n answer = Answer(\n poll_user=poll_user,\n poll=poll_user.poll,\n question=question,\n answer=form.cleaned_data[\"answer\"],\n )\n answer.save()\n next_question = poll_user.next_question\n if next_question:\n params = {\"poll_user_guid\": poll_user.guid,\n \"question_pk\": next_question.id}\n path = reverse(poll_user_question_view, kwargs=params)\n else:\n poll_user.complete_poll()\n params = {\"poll_user_guid\": poll_user.guid}\n path = reverse(poll_user_view, kwargs=params)\n return redirect(path)\n else:\n form = AnswerForm()\n\n context = {\"poll_user\": poll_user, \"question\": question, \"form\": form}\n return render(request, \"poll_user_question.html\", context)\n", "sub_path": "polls/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1880, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.shortcuts.render", "line_number": 13, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 17, "usage_type": "call"}, {"api_name": "polls.models.PollUser", "line_number": 17, "usage_type": "argument"}, {"api_name": "django.shortcuts.render", "line_number": 22, "usage_type": "call"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 26, "usage_type": "call"}, {"api_name": "polls.models.PollUser", "line_number": 26, "usage_type": "argument"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 27, "usage_type": "call"}, {"api_name": "polls.models.Question", "line_number": 27, "usage_type": "argument"}, {"api_name": "polls.forms.AnswerForm", "line_number": 29, "usage_type": "call"}, {"api_name": "polls.models.Answer", "line_number": 31, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 42, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 46, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 47, "usage_type": "call"}, {"api_name": "polls.forms.AnswerForm", "line_number": 49, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "264266513", "text": "#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n#\r\n#@Author:PawełGrochowski\r\n#\r\n\r\nimport traceback\r\nimport os\r\nimport numpy\r\nimport matplotlib.pyplot\r\nimport pprint\r\nimport string\r\n\r\nfrom DataSource import DataSource\r\nfrom DataStats import DataStats\r\n\r\nif __name__ == \"__main__\":\r\n try:\r\n srcs = [\r\n {\r\n DataSource.NAME:'Amazon',\r\n DataSource.PATH:'../amazon_cells_labelled.txt'\r\n },\r\n {\r\n DataSource.NAME:'PhoneArena',\r\n DataSource.PATH:'../scraping/data/sentences_data.txt'\r\n },\r\n {\r\n DataSource.NAME:'Twitter',\r\n DataSource.PATH:'../../data/SemEval-2014.csv'\r\n }\r\n ]\r\n \r\n srcs = [{DataSource.NAME:src[DataSource.NAME],\r\n DataSource.PATH:os.path.abspath(src[DataSource.PATH]),\r\n DataSource.TYPE:os.path.splitext(src[DataSource.PATH])[1]} for src in srcs]\r\n \r\n for src in srcs:\r\n print(\"Name: '%s'\\nType: '%s'\" % (src[DataSource.NAME], src[DataSource.TYPE]))\r\n dSrc = DataSource(src)\r\n srcStats = dSrc.getStats()\r\n print(\"\\nUsable data:\")\r\n pprint.pprint(srcStats[DataSource.REGULAR])\r\n print(\"\\nUnusable data:\")\r\n pprint.pprint(srcStats[DataSource.BROKEN])\r\n print(\"-----------------\")\r\n \r\n print(\"Finished!\")\r\n \r\n for src in srcs:\r\n srcName = src[DataSource.NAME]\r\n dSrc = DataSource(src)\r\n srcStats = dSrc.getStats()\r\n kws,ocs = [],[]\r\n for word in srcStats[DataSource.REGULAR].items():\r\n ocs.append(word[1][DataStats.AMOUNT])\r\n kws.append(word[0]+'\\n['+str(ocs[-1])+']')\r\n kwb,ocb = [],[]\r\n desc,lbls = [],[]\r\n alpha = list(string.ascii_uppercase)\r\n idx = 0\r\n for reason in srcStats[DataSource.BROKEN][DataStats.REASON].items():\r\n ocb.append(reason[1])\r\n kwb.append(alpha[idx]+'\\n['+str(ocb[-1])+']')\r\n lbls.append(alpha[idx])\r\n desc.append(reason[0])\r\n idx += 1\r\n \r\n lDesc = [matplotlib.lines.Line2D([0], [0], linestyle='none', mfc='black',\r\n mec='none', marker=r'$\\mathregular{{{}}}$'.format(lbl))for lbl in lbls]\r\n \r\n barWidth=0.8\r\n matplotlib.pyplot.figure(srcName)\r\n matplotlib.pyplot.suptitle('Data origin: \\'%s\\'' % srcName)\r\n \r\n xAxis=numpy.arange(len(ocs))\r\n matplotlib.pyplot.subplot(121)\r\n matplotlib.pyplot.bar(xAxis,ocs,barWidth)\r\n matplotlib.pyplot.xticks(xAxis+barWidth*0.5,kws)\r\n matplotlib.pyplot.title('Histogram of usable data:')\r\n matplotlib.pyplot.xlabel('keyword [name : count]')\r\n matplotlib.pyplot.ylabel('sentences [count]')\r\n \r\n xAxis=numpy.arange(len(ocb))\r\n matplotlib.pyplot.subplot(122)\r\n matplotlib.pyplot.bar(xAxis,ocb,barWidth)\r\n matplotlib.pyplot.xticks(xAxis+barWidth*0.5,kwb)\r\n matplotlib.pyplot.legend(lDesc, desc, numpoints=1, markerscale=2, bbox_to_anchor=(1.3, 1.02), fontsize=10)\r\n matplotlib.pyplot.title('Histogram of unusable data:')\r\n matplotlib.pyplot.xlabel('reason [label : count]')\r\n \r\n kWordsCount = len(srcStats[DataSource.REGULAR].items())\r\n idx = 0\r\n barWidth=0.8\r\n labeled = False\r\n matplotlib.pyplot.figure(srcName+':RATES')\r\n matplotlib.pyplot.suptitle('Histograms of keywords ratings: [Data origin: \\'%s\\']' % srcName)\r\n for keyWord in srcStats[DataSource.REGULAR].items():\r\n kwRate = []\r\n rCount = []\r\n for rating in srcStats[DataSource.REGULAR][keyWord[0]][DataStats.RATING].items():\r\n rCount.append(rating[1])\r\n kwRate.append(str(rating[0])+'\\n['+str(rCount[-1])+']')\r\n \r\n idx += 1\r\n xAxis=numpy.arange(len(rCount))\r\n matplotlib.pyplot.subplot(int('1'+str(kWordsCount)+str(idx)))\r\n matplotlib.pyplot.bar(xAxis,rCount,barWidth)\r\n matplotlib.pyplot.xticks(xAxis+barWidth*0.5,kwRate)\r\n matplotlib.pyplot.title('\\'%s\\':' % keyWord[0])\r\n matplotlib.pyplot.xlabel('rate [value : count]')\r\n if not labeled: matplotlib.pyplot.ylabel('count [count]')\r\n \r\n matplotlib.pyplot.show()\r\n \r\n except KeyboardInterrupt:\r\n print(\"Interrupted by user!\")\r\n except:\r\n traceback.print_exc()\r\n##\r\n", "sub_path": "students_project/_tests/_dataReview.py", "file_name": "_dataReview.py", "file_ext": "py", "file_size_in_byte": 4862, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "DataSource.DataSource.NAME", "line_number": 21, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource", "line_number": 21, "usage_type": "name"}, {"api_name": "DataSource.DataSource.PATH", "line_number": 22, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource", "line_number": 22, "usage_type": "name"}, {"api_name": "DataSource.DataSource.NAME", "line_number": 25, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource", "line_number": 25, "usage_type": "name"}, {"api_name": "DataSource.DataSource.PATH", "line_number": 26, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource", "line_number": 26, "usage_type": "name"}, {"api_name": "DataSource.DataSource.NAME", "line_number": 29, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource", "line_number": 29, "usage_type": "name"}, {"api_name": "DataSource.DataSource.PATH", "line_number": 30, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource", "line_number": 30, "usage_type": "name"}, {"api_name": "DataSource.DataSource.NAME", "line_number": 34, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource", "line_number": 34, "usage_type": "name"}, {"api_name": "DataSource.DataSource.PATH", "line_number": 35, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource", "line_number": 35, "usage_type": "name"}, {"api_name": "DataSource.DataSource.TYPE", "line_number": 36, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource", "line_number": 36, "usage_type": "name"}, {"api_name": "os.path.abspath", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource.PATH", "line_number": 36, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource.NAME", "line_number": 39, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource", "line_number": 39, "usage_type": "name"}, {"api_name": "DataSource.DataSource.TYPE", "line_number": 39, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource", "line_number": 40, "usage_type": "call"}, {"api_name": "pprint.pprint", "line_number": 43, "usage_type": "call"}, {"api_name": "DataSource.DataSource.REGULAR", "line_number": 43, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource", "line_number": 43, "usage_type": "name"}, {"api_name": "pprint.pprint", "line_number": 45, "usage_type": "call"}, {"api_name": "DataSource.DataSource.BROKEN", "line_number": 45, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource", "line_number": 45, "usage_type": "name"}, {"api_name": "DataSource.DataSource.NAME", "line_number": 51, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource", "line_number": 51, "usage_type": "name"}, {"api_name": "DataSource.DataSource", "line_number": 52, "usage_type": "call"}, {"api_name": "DataSource.DataSource.REGULAR", "line_number": 55, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource", "line_number": 55, "usage_type": "name"}, {"api_name": "DataStats.DataStats.AMOUNT", "line_number": 56, "usage_type": "attribute"}, {"api_name": "DataStats.DataStats", "line_number": 56, "usage_type": "name"}, {"api_name": "string.ascii_uppercase", "line_number": 60, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource.BROKEN", "line_number": 62, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource", "line_number": 62, "usage_type": "name"}, {"api_name": "DataStats.DataStats.REASON", "line_number": 62, "usage_type": "attribute"}, {"api_name": "DataStats.DataStats", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.lines.Line2D", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.lines", "line_number": 69, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.figure", "line_number": 73, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 73, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 73, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.suptitle", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 74, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot.subplot", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 77, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.bar", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 78, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.xticks", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 79, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.title", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 80, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 80, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.xlabel", "line_number": 81, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 81, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 81, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.ylabel", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 82, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot.subplot", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 85, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.bar", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 86, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.xticks", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 87, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.legend", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 88, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.title", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 89, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.xlabel", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 90, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "DataSource.DataSource.REGULAR", "line_number": 92, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource", "line_number": 92, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.figure", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 96, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 96, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.suptitle", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 97, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "DataSource.DataSource.REGULAR", "line_number": 98, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource", "line_number": 98, "usage_type": "name"}, {"api_name": "DataSource.DataSource.REGULAR", "line_number": 101, "usage_type": "attribute"}, {"api_name": "DataSource.DataSource", "line_number": 101, "usage_type": "name"}, {"api_name": "DataStats.DataStats.RATING", "line_number": 101, "usage_type": "attribute"}, {"api_name": "DataStats.DataStats", "line_number": 101, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot.subplot", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 107, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.bar", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 108, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.xticks", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 109, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.title", "line_number": 110, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 110, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 110, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.xlabel", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 111, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.ylabel", "line_number": 112, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 112, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 112, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.pyplot.show", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pyplot", "line_number": 114, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "traceback.print_exc", "line_number": 119, "usage_type": "call"}]} +{"seq_id": "649015579", "text": "# Complete this class for all parts of the project\n\nfrom pacman_module.game import Agent\nfrom pacman_module.pacman import Directions\nfrom collections import deque\n\n\n#Fonction de base à jamais remplir.\n\nclass PacmanAgent(Agent):\n def __init__(self, args):\n \"\"\"\n Arguments:\n ----------\n - `args`: Namespace of arguments from command-line prompt.\n \"\"\"\n self.args = args\n self.foodPos = []\n self.firstCall = True\n self.path = []\n self.statesToCheck = deque()\n\n def getPath(self,state):\n\n self.statesToCheck.append((state,[],[state.getPacmanPosition()]))\n\n while len(self.statesToCheck) != 0:\n currentState, currentPath, exploredNodes = self.statesToCheck.popleft()\n x,y = currentState.getPacmanPosition()\n if self.foodPos[x][y]:\n self.statesToCheck.clear()\n return currentPath\n successors = currentState.generatePacmanSuccessors()\n for succ in successors:\n if succ[0].getPacmanPosition() in exploredNodes:\n continue\n tempList = exploredNodes[:]\n tempList.append(succ[0].getPacmanPosition())\n self.statesToCheck.append((succ[0], currentPath + [succ[1]],tempList))\n\n\n\n\n def get_action(self, state):\n \"\"\"\n Given a pacman game state, returns a legal move.\n\n Arguments:\n ----------\n - `state`: the current game state. See FAQ and class\n `pacman.GameState`.\n\n Return:\n -------\n - A legal move as defined in `game.Directions`.\n \"\"\"\n if state.isWin():\n return Directions.STOP\n x,y = state.getPacmanPosition()\n if self.firstCall:\n self.foodPos = state.getFood()\n self.path = self.getPath(state)\n self.firstCall = False\n else :\n if self.foodPos[x][y]:\n self.foodPos = state.getFood()\n self.path= self.getPath(state)\n\n return self.path.pop(0)\n", "sub_path": "P1/old/bfsNatanNul.py", "file_name": "bfsNatanNul.py", "file_ext": "py", "file_size_in_byte": 2090, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pacman_module.game.Agent", "line_number": 10, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 21, "usage_type": "call"}, {"api_name": "pacman_module.pacman.Directions.STOP", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pacman_module.pacman.Directions", "line_number": 58, "usage_type": "name"}]} +{"seq_id": "540847710", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n\"\"\"\n\n \n \n \n \n \n \n\n\"\"\"\nimport Domoticz\nimport datetime\nfrom math import degrees as deg\nimport sys\nfrom enum import IntEnum, unique # , auto\n\ntry:\n import ephem\nexcept:\n pass\n\n\n@unique\nclass used(IntEnum):\n \"\"\"\n Constants which can be used to create the devices. Look at onStart where\n the devices are created.\n used.NO, the user has to add this device manually\n used.YES, the device will be directly available\n \"\"\"\n\n NO = 0\n YES = 1\n\n\nclass images:\n PREFIX_IMAGE = \"xfr_sunmoon_\"\n PREFIX_PHASE = \"phase\"\n #\n SUN = PREFIX_IMAGE + \"sun\"\n SUNRISE = PREFIX_IMAGE + \"sunrise\"\n SUNSET = PREFIX_IMAGE + \"sunset\"\n MOON = PREFIX_IMAGE + \"moon\"\n MOONRISE = PREFIX_IMAGE + \"moonrise\"\n MOONSET = PREFIX_IMAGE + \"moonset\"\n MOONPHASE0 = MOONNEW = PREFIX_IMAGE + PREFIX_PHASE + \"0\" # New moon\n MOONPHASE1 = PREFIX_IMAGE + PREFIX_PHASE + \"1\" # Waxing crescent\n MOONPHASE2 = MOONFIRSTQUARTER = PREFIX_IMAGE + PREFIX_PHASE + \"2\" # First quarter\n MOONPHASE3 = PREFIX_IMAGE + PREFIX_PHASE + \"3\" # Waxing gibbous\n MOONPHASE4 = MOONFULL = PREFIX_IMAGE + PREFIX_PHASE + \"4\" # Full moon\n MOONPHASE5 = PREFIX_IMAGE + PREFIX_PHASE + \"5\" # Waning gibbous\n MOONPHASE6 = MOONLASTQUARTER = PREFIX_IMAGE + PREFIX_PHASE + \"6\" # Last quarter\n MOONPHASE7 = PREFIX_IMAGE + PREFIX_PHASE + \"7\" # Waning crescent\n ALL = {\n SUN,\n SUNRISE,\n SUNSET,\n MOON,\n MOONRISE,\n MOONSET,\n MOONPHASE0,\n MOONPHASE1,\n MOONPHASE2,\n MOONPHASE3,\n MOONPHASE4,\n MOONPHASE5,\n MOONPHASE6,\n MOONPHASE7,\n }\n\n\n@unique\nclass unit(IntEnum):\n \"\"\"\n Device Unit numbers\n\n Define here your units numbers. These can be used to update your devices.\n Be sure the these have a unique number!\n \"\"\"\n\n SUN_RISE = 1\n SUN_RISE_CIVIL = 2\n SUN_RISE_NAUTICAL = 3\n SUN_RISE_ASTRONOMICAL = 4\n SUN_TRANSIT = 5\n SUN_SET = 6\n SUN_SET_CIVIL = 7\n SUN_SET_NAUTICAL = 8\n SUN_SET_ASTRONOMICAL = 9\n SUN_AZ = 10\n SUN_ALT = 11\n SUN_DIST = 12\n #\n DAY_LENGTH_M = 18\n DAY_LENGTH_T = 19\n #\n MOON_RISE = 20\n MOON_SET = 21\n MOON_AZ = 22\n MOON_ALT = 23\n MOON_DIST = 24\n MOON_PHASE = 25\n MOON_NEXT_NEW = 26\n MOON_NEXT_FIRST_QUARTER = 27\n MOON_NEXT_FULL = 28\n MOON_NEXT_LAST_QUARTER = 29\n MOON_ILLUMINATION = 30\n\n\nclass BasePlugin:\n\n __DEBUG_NONE = 0\n __DEBUG_ALL = 1\n\n __HEARTBEATS2MIN = 6\n __MINUTES = 1\n\n __SEC30 = datetime.timedelta(seconds=30)\n __D_FORMAT = \"%Y-%m-%d\"\n __T_FORMAT = \"%H:%M\"\n __DT_FORMAT = \"{} {}\".format(__D_FORMAT, __T_FORMAT)\n\n # Twilights, their horizons and whether to use the centre of the Sun or not\n __TWILIGHTS = [(\"0\", False), (\"-6\", True), (\"-12\", True), (\"-18\", True)]\n\n # Device units\n __UNITS = [\n # Unit, Name, Type, Subtype, Options, Used, image\n [unit.SUN_RISE, \"Sunrise\", 243, 19, {}, used.YES, images.SUNRISE],\n [unit.SUN_RISE_CIVIL, \"Sunrise civil\", 243, 19, {}, used.YES, images.SUNRISE],\n [\n unit.SUN_RISE_NAUTICAL,\n \"Sunrise nautical\",\n 243,\n 19,\n {},\n used.YES,\n images.SUNRISE,\n ],\n [\n unit.SUN_RISE_ASTRONOMICAL,\n \"Sunrise astronomical\",\n 243,\n 19,\n {},\n used.YES,\n images.SUNRISE,\n ],\n [unit.SUN_SET, \"Sunset\", 243, 19, {}, used.YES, images.SUNSET],\n [unit.SUN_SET_CIVIL, \"Sunset civil\", 243, 19, {}, used.YES, images.SUNSET],\n [\n unit.SUN_SET_NAUTICAL,\n \"Sunset nautical\",\n 243,\n 19,\n {},\n used.YES,\n images.SUNSET,\n ],\n [\n unit.SUN_SET_ASTRONOMICAL,\n \"Sunset astronomical\",\n 243,\n 19,\n {},\n used.YES,\n images.SUNSET,\n ],\n [\n unit.SUN_ALT,\n \"Sun Altitude\",\n 243,\n 31,\n {\"Custom\": \"0;°\"},\n used.YES,\n images.SUN,\n ],\n [\n unit.SUN_AZ,\n \"Sun Azimuth\",\n 243,\n 31,\n {\"Custom\": \"0;°\"},\n used.YES,\n images.SUN,\n ],\n [\n unit.SUN_DIST,\n \"Sun Distance\",\n 243,\n 31,\n {\"Custom\": \"0;km\"},\n used.YES,\n images.SUN,\n ],\n [unit.SUN_TRANSIT, \"Sun transit\", 243, 19, {}, used.YES, images.SUN],\n [\n unit.DAY_LENGTH_M,\n \"Day length\",\n 243,\n 31,\n {\"Custom\": \"0;min\"},\n used.YES,\n images.SUN,\n ],\n [unit.DAY_LENGTH_T, \"Daylength\", 243, 19, {}, used.YES, images.SUN],\n #\n [unit.MOON_RISE, \"Moon rise\", 243, 19, {}, used.YES, images.MOONRISE],\n [unit.MOON_SET, \"Moon set\", 243, 19, {}, used.YES, images.MOONSET],\n [\n unit.MOON_AZ,\n \"Moon Azimuth\",\n 243,\n 31,\n {\"Custom\": \"0;°\"},\n used.YES,\n images.MOON,\n ],\n [\n unit.MOON_ALT,\n \"Moon Altitude\",\n 243,\n 31,\n {\"Custom\": \"0;°\"},\n used.YES,\n images.MOON,\n ],\n [\n unit.MOON_DIST,\n \"Moon Distance\",\n 243,\n 31,\n {\"Custom\": \"0;km\"},\n used.YES,\n images.MOON,\n ],\n [unit.MOON_PHASE, \"Moon Phase\", 243, 19, {}, used.YES, images.MOON],\n [unit.MOON_NEXT_NEW, \"Next new moon\", 243, 19, {}, used.YES, images.MOONNEW],\n [\n unit.MOON_NEXT_FIRST_QUARTER,\n \"Next first quarter\",\n 243,\n 19,\n {},\n used.YES,\n images.MOONFIRSTQUARTER,\n ],\n [unit.MOON_NEXT_FULL, \"Next full moon\", 243, 19, {}, used.YES, images.MOONFULL],\n [\n unit.MOON_NEXT_LAST_QUARTER,\n \"Next last quarter\",\n 243,\n 19,\n {},\n used.YES,\n images.MOONLASTQUARTER,\n ],\n [\n unit.MOON_ILLUMINATION,\n \"Moon Illumination\",\n 243,\n 31,\n {\"Custom\": \"0;%\"},\n used.YES,\n images.MOON,\n ],\n ]\n __MOON_PHASE_DESCRIPTIONS = [\n \"New moon\",\n \"Waxing crescent\",\n \"First quarter\",\n \"Waxing gibbous\",\n \"Full moon\",\n \"Waning gibbous\",\n \"Last quarter\",\n \"Waning crescent\",\n ]\n\n def __init__(self):\n self.__runAgain = 0\n if \"ephem\" in sys.modules:\n self.__ephem_exist = True\n else:\n self.__ephem_exist = False\n\n def onCommand(self, Unit, Command, Level, Hue):\n Domoticz.Debug(\"onCommand: {}, {}, {}, {}\".format(Unit, Command, Level, Hue))\n\n def onConnect(self, Connection, Status, Description):\n Domoticz.Debug(\n \"onConnect: {}, {}, {}\".format(Connection.Name, Status, Description)\n )\n\n def onDeviceAdded(self, Unit):\n Domoticz.Debug(\"onDeviceAdded: {}\".format(Unit))\n\n def onDeviceModified(self, Unit):\n Domoticz.Debug(\"onDeviceModified: {}\".format(Unit))\n\n def onDeviceRemoved(self, Unit):\n Domoticz.Debug(\"onDeviceRemoved: {}\".format(Unit))\n\n def onStart(self):\n Domoticz.Debug(\"onStart\")\n if not self.__ephem_exist:\n Domoticz.Error(\"Ephem not available\")\n return False\n #\n # Parameters\n if Parameters[\"Mode6\"] == \"Debug\":\n Domoticz.Debugging(self.__DEBUG_ALL)\n else:\n Domoticz.Debugging(self.__DEBUG_NONE)\n #\n # Get Domoticz location\n loc = Settings[\"Location\"].split(\";\")\n self.__lat = loc[0]\n self.__lon = loc[1]\n if self.__lat is None or self.__lon is None:\n Domoticz.Error(\"Unable to parse coordinates\")\n return False\n self.__observer = ephem.Observer()\n self.__observer.lat = self.__lat\n self.__observer.lon = self.__lon\n self.__observer.date = datetime.datetime.utcnow()\n self.__sun = ephem.Sun()\n self.__moon = ephem.Moon()\n #\n # Load images\n # Check if images are in database\n for image in images.ALL:\n if image not in Images:\n zip = \"{}.zip\".format(image)\n Domoticz.Image(zip).Create()\n #\n # Create devices\n for unit in self.__UNITS:\n if unit[0] not in Devices:\n Domoticz.Device(\n Unit=unit[0],\n Name=unit[1],\n Type=unit[2],\n Subtype=unit[3],\n Options=unit[4],\n Used=unit[5],\n Image=Images[unit[6]].ID,\n ).Create()\n # Log config\n DumpAllToLog()\n\n def onStop(self):\n Domoticz.Debug(\"onStop\")\n\n def onMessage(self, Connection, Data):\n Domoticz.Debug(\"onMessage: {}, {}\".format(Connection.Name, Data))\n\n def onNotification(self, Name, Subject, Text, Status, Priority, Sound, ImageFile):\n Domoticz.Debug(\n \"onNotification: {}, {}, {}, {}, {}, {}, {}\".format(\n Name, Subject, Text, Status, Priority, Sound, ImageFile\n )\n )\n\n def onDisconnect(self, Connection):\n Domoticz.Debug(\"onDisconnect: {}\".format(Connection.Name))\n\n def onHeartbeat(self):\n Domoticz.Debug(\"onHeartbeat\")\n self.__runAgain -= 1\n if self.__runAgain <= 0:\n self.__runAgain = self.__HEARTBEATS2MIN * self.__MINUTES\n #\n utc_now = datetime.datetime.utcnow()\n target_date = datetime.datetime.now().date()\n #\n self.__observer.date = utc_now\n self.__sun.compute(self.__observer)\n #\n ################################################################################\n # Sun data\n ################################################################################\n #\n # -------------------------------------------------------------------------------\n # Sun altitude\n # -------------------------------------------------------------------------------\n value = round(deg(self.__sun.alt), 2)\n UpdateDevice(unit.SUN_ALT, int(value), str(value))\n #\n # -------------------------------------------------------------------------------\n # Sun azimuth\n # -------------------------------------------------------------------------------\n value = round(deg(self.__sun.az), 2)\n UpdateDevice(unit.SUN_AZ, int(value), str(value))\n #\n # -------------------------------------------------------------------------------\n # Sun distance\n # -------------------------------------------------------------------------------\n value = round(self.__sun.earth_distance * ephem.meters_per_au / 1000)\n UpdateDevice(unit.SUN_DIST, int(value), str(value))\n #\n # -------------------------------------------------------------------------------\n # Sun transit\n # -------------------------------------------------------------------------------\n value = (\n ephem.localtime(self.__observer.next_transit(self.__sun)) + self.__SEC30\n )\n UpdateDevice(\n unit.SUN_TRANSIT, 0, \"{}\".format(value.strftime(self.__DT_FORMAT))\n )\n #\n # -------------------------------------------------------------------------------\n # Sun rise & set today\n # -------------------------------------------------------------------------------\n self.__observer.date = target_date\n self.__sun.compute(self.__observer)\n i = 0\n for t in self.__TWILIGHTS:\n # Zero the horizon\n self.__observer.horizon = t[0]\n try:\n next_rising = (\n ephem.localtime(\n self.__observer.next_rising(self.__sun, use_center=t[1])\n )\n + self.__SEC30\n )\n UpdateDevice(\n unit.SUN_RISE + i,\n 0,\n \"{}\".format(next_rising.strftime(self.__DT_FORMAT)),\n )\n except:\n UpdateDevice(\n unit.SUN_RISE + i,\n 0,\n \"{}\".format(\"No time available\"),\n )\n try:\n next_setting = (\n ephem.localtime(\n self.__observer.next_setting(self.__sun, use_center=t[1])\n )\n + self.__SEC30\n )\n UpdateDevice(\n unit.SUN_SET + i,\n 0,\n \"{}\".format(next_setting.strftime(self.__DT_FORMAT)),\n )\n except:\n UpdateDevice(\n unit.SUN_RISE + i,\n 0,\n \"{}\".format(\"No time available\"),\n )\n if i == 0:\n value = (next_setting - next_rising).total_seconds()\n hh = divmod(value, 3600)\n mm = divmod(hh[1], 60)\n min = int(divmod(value, 60)[0])\n UpdateDevice(\n unit.DAY_LENGTH_M,\n min,\n \"{}\".format(min),\n )\n UpdateDevice(\n unit.DAY_LENGTH_T,\n 0,\n \"{:02}:{:02}\".format(int(hh[0]), int(mm[0])),\n )\n\n i += 1\n #\n # Reset horizon for further calculations\n self.__observer.horizon = \"0\"\n #\n ################################################################################\n # Moon data\n ################################################################################\n #\n self.__observer.date = utc_now\n self.__moon.compute(self.__observer)\n #\n # -------------------------------------------------------------------------------\n # Moon rise\n # -------------------------------------------------------------------------------\n value = (\n ephem.localtime(self.__observer.next_rising(self.__moon)) + self.__SEC30\n )\n UpdateDevice(\n unit.MOON_RISE, 0, \"{}\".format(value.strftime(self.__DT_FORMAT))\n )\n #\n # -------------------------------------------------------------------------------\n # Moon set\n # -------------------------------------------------------------------------------\n value = (\n ephem.localtime(self.__observer.next_setting(self.__moon))\n + self.__SEC30\n )\n UpdateDevice(\n unit.MOON_SET, 0, \"{}\".format(value.strftime(self.__DT_FORMAT))\n )\n #\n # -------------------------------------------------------------------------------\n # Moon altitude\n # -------------------------------------------------------------------------------\n self.__moon.compute(self.__observer)\n #\n value = round(deg(self.__moon.alt), 2)\n UpdateDevice(unit.MOON_ALT, int(value), str(value))\n #\n # -------------------------------------------------------------------------------\n # Moon azimuth\n # -------------------------------------------------------------------------------\n value = round(deg(self.__moon.az), 2)\n UpdateDevice(unit.MOON_AZ, int(value), str(value))\n #\n # -------------------------------------------------------------------------------\n # Moon distance\n # -------------------------------------------------------------------------------\n value = round(self.__moon.earth_distance * ephem.meters_per_au / 1000)\n UpdateDevice(unit.MOON_DIST, int(value), str(value))\n #\n # -------------------------------------------------------------------------------\n # Next new moon\n # -------------------------------------------------------------------------------\n next_new = ephem.localtime(ephem.next_new_moon(utc_now))\n value = next_new + self.__SEC30\n UpdateDevice(\n unit.MOON_NEXT_NEW, 0, \"{}\".format(value.strftime(self.__DT_FORMAT))\n )\n #\n # -------------------------------------------------------------------------------\n # Next first quarter\n # -------------------------------------------------------------------------------\n next_first_quarter = ephem.localtime(ephem.next_first_quarter_moon(utc_now))\n value = next_first_quarter + self.__SEC30\n UpdateDevice(\n unit.MOON_NEXT_FIRST_QUARTER,\n 0,\n \"{}\".format(value.strftime(self.__DT_FORMAT)),\n )\n #\n # -------------------------------------------------------------------------------\n # Next full moon\n # -------------------------------------------------------------------------------\n next_full = ephem.localtime(ephem.next_full_moon(utc_now))\n value = next_full + self.__SEC30\n UpdateDevice(\n unit.MOON_NEXT_FULL,\n 0,\n \"{}\".format(value.strftime(self.__DT_FORMAT)),\n )\n #\n # -------------------------------------------------------------------------------\n # Next last quarter\n # -------------------------------------------------------------------------------\n next_last_quarter = ephem.localtime(ephem.next_last_quarter_moon(utc_now))\n value = next_last_quarter + self.__SEC30\n UpdateDevice(\n unit.MOON_NEXT_LAST_QUARTER,\n 0,\n \"{}\".format(value.strftime(self.__DT_FORMAT)),\n )\n #\n # -------------------------------------------------------------------------------\n # Moon phase\n # -------------------------------------------------------------------------------\n next_full = next_full.date()\n next_new = next_new.date()\n next_last_quarter = next_last_quarter.date()\n next_first_quarter = next_first_quarter.date()\n previous_full = ephem.localtime(ephem.previous_full_moon(utc_now)).date()\n previous_new = ephem.localtime(ephem.previous_new_moon(utc_now)).date()\n previous_last_quarter = ephem.localtime(\n ephem.previous_last_quarter_moon(utc_now)\n ).date()\n previous_first_quarter = ephem.localtime(\n ephem.previous_first_quarter_moon(utc_now)\n ).date()\n #\n # Domoticz.Debug(\"target_date: {}\".format(target_date))\n # Domoticz.Debug(\"next_full: {}\".format(next_full))\n # Domoticz.Debug(\"next_new: {}\".format(next_new))\n # Domoticz.Debug(\"next_last_quarter: {}\".format(next_last_quarter))\n # Domoticz.Debug(\"next_first_quarter: {}\".format(next_first_quarter))\n # Domoticz.Debug(\"previous_full: {}\".format(previous_full))\n # Domoticz.Debug(\"previous_new: {}\".format(previous_new))\n # Domoticz.Debug(\"previous_last_quarter: {}\".format(previous_last_quarter))\n # Domoticz.Debug(\"previous_first_quarter: {}\".format(previous_first_quarter))\n\n if target_date in (next_new, previous_new):\n phase = 0\n elif target_date in (next_first_quarter, previous_first_quarter):\n phase = 2\n elif target_date in (next_full, previous_full):\n phase = 4\n elif target_date in (next_last_quarter, previous_last_quarter):\n phase = 6\n elif (\n previous_new\n < next_first_quarter\n < next_full\n < next_last_quarter\n < next_new\n ):\n phase = 1\n elif (\n previous_first_quarter\n < next_full\n < next_last_quarter\n < next_new\n < next_first_quarter\n ):\n phase = 3\n elif (\n previous_full\n < next_last_quarter\n < next_new\n < next_first_quarter\n < next_full\n ):\n phase = 5\n elif (\n previous_last_quarter\n < next_new\n < next_first_quarter\n < next_full\n < next_last_quarter\n ):\n phase = 7\n else:\n phase = 4\n UpdateDevice(unit.MOON_PHASE, 0, self.__MOON_PHASE_DESCRIPTIONS[phase])\n UpdateDeviceImage(\n unit.MOON_PHASE, images.PREFIX_IMAGE + images.PREFIX_PHASE + str(phase)\n )\n #\n self.__moon.compute(self.__observer)\n #\n # -------------------------------------------------------------------------------\n # Moon illumination\n # -------------------------------------------------------------------------------\n value = round(deg(self.__moon.moon_phase), 2)\n UpdateDevice(unit.MOON_ILLUMINATION, int(value), str(value))\n UpdateDeviceImage(\n unit.MOON_ILLUMINATION,\n images.PREFIX_IMAGE + images.PREFIX_PHASE + str(phase),\n )\n #\n else:\n Domoticz.Debug(\n \"onHeartbeat called, run again in {} heartbeats.\".format(\n self.__runAgain\n )\n )\n\n\nglobal _plugin\n_plugin = BasePlugin()\n\n\ndef onCommand(Unit, Command, Level, Color):\n global _plugin\n _plugin.onCommand(Unit, Command, Level, Color)\n\n\ndef onConnect(Connection, Status, Description):\n global _plugin\n _plugin.onConnect(Connection, Status, Description)\n\n\ndef onDeviceAdded(Unit):\n global _plugin\n _plugin.onDeviceAdded(Unit)\n\n\ndef onDeviceModified(Unit):\n global _plugin\n _plugin.onDeviceModified(Unit)\n\n\ndef onDeviceRemoved(Unit):\n global _plugin\n _plugin.onDeviceRemoved(Unit)\n\n\ndef onDisconnect(Connection):\n global _plugin\n _plugin.onDisconnect(Connection)\n\n\ndef onHeartbeat():\n global _plugin\n _plugin.onHeartbeat()\n\n\ndef onMessage(Connection, Data):\n global _plugin\n _plugin.onMessage(Connection, Data)\n\n\ndef onNotification(Name, Subject, Text, Status, Priority, Sound, ImageFile):\n global _plugin\n _plugin.onNotification(Name, Subject, Text, Status, Priority, Sound, ImageFile)\n\n\ndef onStart():\n global _plugin\n _plugin.onStart()\n\n\ndef onStop():\n global _plugin\n _plugin.onStop()\n\n\n################################################################################\n# Generic helper functions\n################################################################################\ndef DumpDevicesToLog():\n # Show devices\n Domoticz.Debug(\"Device count.........: {}\".format(len(Devices)))\n for x in Devices:\n Domoticz.Debug(\"Device...............: {} - {}\".format(x, Devices[x]))\n Domoticz.Debug(\"Device Idx...........: {}\".format(Devices[x].ID))\n Domoticz.Debug(\n \"Device Type..........: {} / {}\".format(Devices[x].Type, Devices[x].SubType)\n )\n Domoticz.Debug(\"Device Name..........: '{}'\".format(Devices[x].Name))\n Domoticz.Debug(\"Device nValue........: {}\".format(Devices[x].nValue))\n Domoticz.Debug(\"Device sValue........: '{}'\".format(Devices[x].sValue))\n Domoticz.Debug(\"Device Options.......: '{}'\".format(Devices[x].Options))\n Domoticz.Debug(\"Device Used..........: {}\".format(Devices[x].Used))\n Domoticz.Debug(\"Device ID............: '{}'\".format(Devices[x].DeviceID))\n Domoticz.Debug(\"Device LastLevel.....: {}\".format(Devices[x].LastLevel))\n Domoticz.Debug(\"Device Image.........: {}\".format(Devices[x].Image))\n\n\ndef DumpImagesToLog():\n # Show images\n Domoticz.Debug(\"Image count..........: {}\".format((len(Images))))\n for x in Images:\n Domoticz.Debug(\"Image '{}'...: '{}'\".format(x, Images[x]))\n\n\ndef DumpParametersToLog():\n # Show parameters\n Domoticz.Debug(\"Parameters count.....: {}\".format(len(Parameters)))\n for x in Parameters:\n if Parameters[x] != \"\":\n Domoticz.Debug(\"Parameter '{}'...: '{}'\".format(x, Parameters[x]))\n\n\ndef DumpSettingsToLog():\n # Show settings\n Domoticz.Debug(\"Settings count.......: {}\".format(len(Settings)))\n for x in Settings:\n Domoticz.Debug(\"Setting '{}'...: '{}'\".format(x, Settings[x]))\n\n\ndef DumpAllToLog():\n DumpDevicesToLog()\n DumpImagesToLog()\n DumpParametersToLog()\n DumpSettingsToLog()\n\n\ndef DumpHTTPResponseToLog(httpDict):\n if isinstance(httpDict, dict):\n Domoticz.Debug(\"HTTP Details (\" + str(len(httpDict)) + \"):\")\n for x in httpDict:\n if isinstance(httpDict[x], dict):\n Domoticz.Debug(\"....'\" + x + \" (\" + str(len(httpDict[x])) + \"):\")\n for y in httpDict[x]:\n Domoticz.Debug(\"........'\" + y + \"':'\" + str(httpDict[x][y]) + \"'\")\n else:\n Domoticz.Debug(\"....'\" + x + \"':'\" + str(httpDict[x]) + \"'\")\n\n\ndef UpdateDevice(Unit, nValue, sValue, TimedOut=0, AlwaysUpdate=False):\n if Unit in Devices:\n if (\n Devices[Unit].nValue != nValue\n or Devices[Unit].sValue != sValue\n or Devices[Unit].TimedOut != TimedOut\n or AlwaysUpdate\n ):\n Devices[Unit].Update(nValue=nValue, sValue=str(sValue), TimedOut=TimedOut)\n # Domoticz.Debug(\"Update {}: {} - '{}'\".format(Devices[Unit].Name, nValue, sValue))\n\n\ndef UpdateDeviceOptions(Unit, Options={}):\n if Unit in Devices:\n if Devices[Unit].Options != Options:\n Devices[Unit].Update(\n nValue=Devices[Unit].nValue,\n sValue=Devices[Unit].sValue,\n Options=Options,\n )\n Domoticz.Debug(\n \"Device Options update: {} = {}\".format(Devices[Unit].Name, Options)\n )\n\n\ndef UpdateDeviceImage(Unit, Image):\n if Unit in Devices and Image in Images:\n if Devices[Unit].Image != Images[Image].ID:\n Devices[Unit].Update(\n nValue=Devices[Unit].nValue,\n sValue=Devices[Unit].sValue,\n Image=Images[Image].ID,\n )\n Domoticz.Debug(\n \"Device Image update: {} = {}\".format(\n Devices[Unit].Name, Images[Image].ID\n )\n )\n", "sub_path": "plugin.py", "file_name": "plugin.py", "file_ext": "py", "file_size_in_byte": 28106, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "enum.IntEnum", "line_number": 29, "usage_type": "name"}, {"api_name": "enum.unique", "line_number": 28, "usage_type": "name"}, {"api_name": "enum.IntEnum", "line_number": 78, "usage_type": "name"}, {"api_name": "enum.unique", "line_number": 77, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 123, "usage_type": "call"}, {"api_name": "sys.modules", "line_number": 286, "usage_type": "attribute"}, {"api_name": "Domoticz.Debug", "line_number": 292, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 295, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 300, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 303, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 306, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 309, "usage_type": "call"}, {"api_name": "Domoticz.Error", "line_number": 311, "usage_type": "call"}, {"api_name": "Domoticz.Debugging", "line_number": 316, "usage_type": "call"}, {"api_name": "Domoticz.Debugging", "line_number": 318, "usage_type": "call"}, {"api_name": "Domoticz.Error", "line_number": 325, "usage_type": "call"}, {"api_name": "ephem.Observer", "line_number": 327, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 330, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 330, "usage_type": "attribute"}, {"api_name": "ephem.Sun", "line_number": 331, "usage_type": "call"}, {"api_name": "ephem.Moon", "line_number": 332, "usage_type": "call"}, {"api_name": "Domoticz.Image", "line_number": 339, "usage_type": "call"}, {"api_name": "Domoticz.Device", "line_number": 344, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 357, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 360, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 363, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 370, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 373, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 378, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 378, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 379, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 379, "usage_type": "attribute"}, {"api_name": "math.degrees", "line_number": 391, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 397, "usage_type": "call"}, {"api_name": "ephem.meters_per_au", "line_number": 403, "usage_type": "attribute"}, {"api_name": "ephem.localtime", "line_number": 410, "usage_type": "call"}, {"api_name": "ephem.localtime", "line_number": 427, "usage_type": "call"}, {"api_name": "ephem.localtime", "line_number": 445, "usage_type": "call"}, {"api_name": "ephem.localtime", "line_number": 493, "usage_type": "call"}, {"api_name": "ephem.localtime", "line_number": 503, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 515, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 521, "usage_type": "call"}, {"api_name": "ephem.meters_per_au", "line_number": 527, "usage_type": "attribute"}, {"api_name": "ephem.localtime", "line_number": 533, "usage_type": "call"}, {"api_name": "ephem.next_new_moon", "line_number": 533, "usage_type": "call"}, {"api_name": "ephem.localtime", "line_number": 542, "usage_type": "call"}, {"api_name": "ephem.next_first_quarter_moon", "line_number": 542, "usage_type": "call"}, {"api_name": "ephem.localtime", "line_number": 553, "usage_type": "call"}, {"api_name": "ephem.next_full_moon", "line_number": 553, "usage_type": "call"}, {"api_name": "ephem.localtime", "line_number": 564, "usage_type": "call"}, {"api_name": "ephem.next_last_quarter_moon", "line_number": 564, "usage_type": "call"}, {"api_name": "ephem.localtime", "line_number": 579, "usage_type": "call"}, {"api_name": "ephem.previous_full_moon", "line_number": 579, "usage_type": "call"}, {"api_name": "ephem.localtime", "line_number": 580, "usage_type": "call"}, {"api_name": "ephem.previous_new_moon", "line_number": 580, "usage_type": "call"}, {"api_name": "ephem.localtime", "line_number": 581, "usage_type": "call"}, {"api_name": "ephem.previous_last_quarter_moon", "line_number": 582, "usage_type": "call"}, {"api_name": "ephem.localtime", "line_number": 584, "usage_type": "call"}, {"api_name": "ephem.previous_first_quarter_moon", "line_number": 585, "usage_type": "call"}, {"api_name": "math.degrees", "line_number": 650, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 658, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 729, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 731, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 732, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 733, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 736, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 737, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 738, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 739, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 740, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 741, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 742, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 743, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 748, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 750, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 755, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 758, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 763, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 765, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 777, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 780, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 782, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 784, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 807, "usage_type": "call"}, {"api_name": "Domoticz.Debug", "line_number": 820, "usage_type": "call"}]} +{"seq_id": "428537382", "text": "import base64\n\nimport aiohttp_security\nimport aiohttp_session\nfrom aiohttp import web\nfrom aiohttp_security import SessionIdentityPolicy\nfrom aiohttp_session.cookie_storage import EncryptedCookieStorage\nfrom jibrel_aiohttp_swagger import setup_swagger\n\nfrom billing import settings\nfrom billing.api.handlers import auth, monitoring, wallets\nfrom billing.auth.authorization import DbAuthorizationPolicy\nfrom billing.db.wrapper import Database\n\n\ndef init_app(\n db_dsn: str,\n secret_key: str,\n session_cookie_name: str,\n) -> web.Application:\n app = web.Application()\n app['db'] = Database(db_dsn)\n app.on_startup.append(on_startup)\n app.on_shutdown.append(on_shutdown)\n\n app.router.add_post('/v1/auth/login', auth.login)\n app.router.add_post('/v1/auth/logout', auth.logout)\n app.router.add_post('/v1/auth/register', auth.register)\n\n app.router.add_get('/v1/wallets', wallets.retrieve)\n app.router.add_get('/v1/wallets/operations', wallets.operations)\n app.router.add_post('/v1/wallets/deposit', wallets.deposit)\n app.router.add_post('/v1/wallets/transfer', wallets.transfer)\n\n app.router.add_get('/healthcheck', monitoring.healthcheck)\n\n setup_session(app, secret_key, session_cookie_name)\n setup_security(app)\n setup_swagger(\n app,\n spec_path=settings.SPEC_FILEPATH,\n version_file_path=settings.VERSION_FILEPATH,\n )\n\n return app\n\n\ndef setup_session(\n app: web.Application,\n secret_key: str,\n session_cookie_name: str,\n) -> None:\n aiohttp_session.setup(\n app, EncryptedCookieStorage(\n base64.urlsafe_b64decode(secret_key),\n cookie_name=session_cookie_name,\n ),\n )\n\n\ndef setup_security(app: web.Application) -> None:\n aiohttp_security.setup(\n app, SessionIdentityPolicy(), DbAuthorizationPolicy(app['db']),\n )\n\n\nasync def on_startup(app: web.Application) -> None:\n await app['db'].start()\n\n\nasync def on_shutdown(app: web.Application) -> None:\n await app['db'].stop()\n", "sub_path": "billing/api/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2039, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "aiohttp.web.Application", "line_number": 21, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 21, "usage_type": "name"}, {"api_name": "billing.db.wrapper.Database", "line_number": 22, "usage_type": "call"}, {"api_name": "billing.api.handlers.auth.login", "line_number": 26, "usage_type": "attribute"}, {"api_name": "billing.api.handlers.auth", "line_number": 26, "usage_type": "name"}, {"api_name": "billing.api.handlers.auth.logout", "line_number": 27, "usage_type": "attribute"}, {"api_name": "billing.api.handlers.auth", "line_number": 27, "usage_type": "name"}, {"api_name": "billing.api.handlers.auth.register", "line_number": 28, "usage_type": "attribute"}, {"api_name": "billing.api.handlers.auth", "line_number": 28, "usage_type": "name"}, {"api_name": "billing.api.handlers.wallets.retrieve", "line_number": 30, "usage_type": "attribute"}, {"api_name": "billing.api.handlers.wallets", "line_number": 30, "usage_type": "name"}, {"api_name": "billing.api.handlers.wallets.operations", "line_number": 31, "usage_type": "attribute"}, {"api_name": "billing.api.handlers.wallets", "line_number": 31, "usage_type": "name"}, {"api_name": "billing.api.handlers.wallets.deposit", "line_number": 32, "usage_type": "attribute"}, {"api_name": "billing.api.handlers.wallets", "line_number": 32, "usage_type": "name"}, {"api_name": "billing.api.handlers.wallets.transfer", "line_number": 33, "usage_type": "attribute"}, {"api_name": "billing.api.handlers.wallets", "line_number": 33, "usage_type": "name"}, {"api_name": "billing.api.handlers.monitoring.healthcheck", "line_number": 35, "usage_type": "attribute"}, {"api_name": "billing.api.handlers.monitoring", "line_number": 35, "usage_type": "name"}, {"api_name": "jibrel_aiohttp_swagger.setup_swagger", "line_number": 39, "usage_type": "call"}, {"api_name": "billing.settings.SPEC_FILEPATH", "line_number": 41, "usage_type": "attribute"}, {"api_name": "billing.settings", "line_number": 41, "usage_type": "name"}, {"api_name": "billing.settings.VERSION_FILEPATH", "line_number": 42, "usage_type": "attribute"}, {"api_name": "billing.settings", "line_number": 42, "usage_type": "name"}, {"api_name": "aiohttp.web.Application", "line_number": 20, "usage_type": "attribute"}, {"api_name": "aiohttp.web", "line_number": 20, "usage_type": "name"}, {"api_name": "aiohttp.web.Application", "line_number": 49, "usage_type": "attribute"}, {"api_name": "aiohttp.web", "line_number": 49, "usage_type": "name"}, {"api_name": "aiohttp_session.setup", "line_number": 53, "usage_type": "call"}, {"api_name": "aiohttp_session.cookie_storage.EncryptedCookieStorage", "line_number": 54, "usage_type": "call"}, {"api_name": "base64.urlsafe_b64decode", "line_number": 55, "usage_type": "call"}, {"api_name": "aiohttp.web.Application", "line_number": 61, "usage_type": "attribute"}, {"api_name": "aiohttp.web", "line_number": 61, "usage_type": "name"}, {"api_name": "aiohttp_security.setup", "line_number": 62, "usage_type": "call"}, {"api_name": "aiohttp_security.SessionIdentityPolicy", "line_number": 63, "usage_type": "call"}, {"api_name": "billing.auth.authorization.DbAuthorizationPolicy", "line_number": 63, "usage_type": "call"}, {"api_name": "aiohttp.web.Application", "line_number": 67, "usage_type": "attribute"}, {"api_name": "aiohttp.web", "line_number": 67, "usage_type": "name"}, {"api_name": "aiohttp.web.Application", "line_number": 71, "usage_type": "attribute"}, {"api_name": "aiohttp.web", "line_number": 71, "usage_type": "name"}]} +{"seq_id": "15597359", "text": "import os\nimport random\n\nimport numpy as np\nfrom keras.callbacks import ModelCheckpoint\nfrom keras.layers import Dense, Activation\nfrom keras.models import Sequential\nfrom keras.optimizers import Adam\n\nfrom .agent import Agent\nfrom ..utils import ReplayMemory\n\n\nclass DDQNAgent(Agent):\n def __init__(\n self,\n name,\n state_shape,\n action_size,\n learning_rate=0.01,\n discount_factor=0.95,\n exploration_rate=1,\n exploration_rate_decay=0.005,\n exploration_rate_min=0.01,\n memory_size=10000,\n replay_sample_size=32,\n update_interval=250,\n production=False,\n auto_save=True,\n auto_load=True\n ):\n self.production = production\n self.auto_save = auto_save\n self.auto_load = auto_load\n self._name = name\n\n self._model_path = 'models/{}-DDQN.h5'.format(self._name)\n self._state_shape = state_shape\n self._action_size = action_size\n\n self._learning_rate = learning_rate # alpha\n self._discount_factor = discount_factor # gamma\n\n self.exploration_rate = exploration_rate # epsilon\n self._exploration_rate_decay = exploration_rate_decay\n self._exploration_rate_min = exploration_rate_min\n\n self._memory = ReplayMemory(memory_size)\n self._replay_sample_size = replay_sample_size\n\n self.target_network = self._build_model(self._model_path)\n self.online_network = self._build_model(self._model_path)\n\n self._iteration = 0\n self._update_interval = update_interval\n\n def _build_model(self, filepath):\n model = Sequential([\n Dense(24, input_shape=self._state_shape),\n Activation('relu'),\n\n Dense(24),\n Activation('relu'),\n\n Dense(self._action_size),\n Activation('linear')\n ])\n\n model.compile(loss='mse', optimizer=Adam(lr=self._learning_rate))\n\n if self.auto_load and os.path.isfile(filepath):\n model.load_weights(filepath)\n\n return model\n\n def remember(self, state, action, reward, next_state, done):\n if not self.production:\n self._memory.append((state, action, reward, next_state, done))\n\n def act(self, state):\n if self.production or random.uniform(0, 1) > self.exploration_rate:\n # Follow policy\n action = np.argmax(self.online_network.predict(np.expand_dims(state, axis=0)))\n else:\n # Explore\n action = random.randrange(self._action_size)\n\n return action\n\n def replay(self, batch_size):\n if len(self._memory) >= batch_size:\n batch = random.sample(self._memory, batch_size)\n targets = []\n states = []\n\n for state, action, reward, next_state, done in batch:\n target = self.target_network.predict(np.expand_dims(state, axis=0)).squeeze()\n\n if done:\n target[action] = reward\n else:\n target[action] = \\\n reward + \\\n self._discount_factor * \\\n np.max(self.online_network.predict(np.expand_dims(next_state, axis=0)))\n\n states.append(state)\n targets.append(target)\n\n callbacks = []\n if self.auto_save and not self.production:\n callbacks.append(ModelCheckpoint(filepath=self._model_path))\n\n self.target_network.fit(\n np.array(states),\n np.array(targets),\n verbose=0,\n epochs=1,\n callbacks=callbacks\n )\n\n def update_online_network(self):\n self.online_network.set_weights(self.target_network.get_weights())\n\n def before(self):\n pass\n\n def after(self):\n self._iteration += 1\n\n if not self.production:\n self.replay(self._replay_sample_size)\n\n self.exploration_rate = max(\n self._exploration_rate_min,\n self.exploration_rate * (1 - self._exploration_rate_decay)\n )\n\n if self._iteration % self._update_interval == 0:\n self.update_online_network()\n\n def __setattr__(self, key, value):\n if key == 'production' and value and value != self.__dict__[key]:\n self.update_online_network()\n\n self.__dict__[key] = value\n", "sub_path": "stachrl/agents/ddqn.py", "file_name": "ddqn.py", "file_ext": "py", "file_size_in_byte": 4501, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "agent.Agent", "line_number": 14, "usage_type": "name"}, {"api_name": "utils.ReplayMemory", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 59, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 63, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 66, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 71, "usage_type": "call"}, {"api_name": "os.path", "line_number": 71, "usage_type": "attribute"}, {"api_name": "random.uniform", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 83, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 86, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 105, "usage_type": "call"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 116, "usage_type": "call"}]} +{"seq_id": "203094320", "text": "#Amen\nfrom itertools import groupby, chain\nfrom copy import deepcopy\nfrom random import shuffle\nimport sys\nimport logging\n#~ sys.setrecursionlimit(5000)\nimport socket\nimport time\nNONE = '.'\nwhite = '0'\nblack = '1'\nNbrCols=5\nNbrRows=4\ndef diagonalsPos (matrix, cols, rows):\n\t\"\"\"Get positive diagonals, going from bottom-left to top-right.\"\"\"\n\tfor di in ([(j, i - j) for j in range(cols)] for i in range(cols + rows -1)):\n\t\tyield [matrix[i][j] for i, j in di if i >= 0 and j >= 0 and i < cols and j < rows]\n\ndef diagonalsNeg (matrix, cols, rows):\n\t\"\"\"Get negative diagonals, going from top-left to bottom-right.\"\"\"\n\tfor di in ([(j, i - cols + j + 1) for j in range(cols)] for i in range(cols + rows - 1)):\n\t\tyield [matrix[i][j] for i, j in di if i >= 0 and j >= 0 and i < cols and j < rows]\n\nclass Game:\n\tdef __init__ (self, cols, rows, requiredToWin = 3):\n\t\t\"\"\"Create a new game.\"\"\"\n\t\tself.cols = cols\n\t\tself.rows = rows\n\t\tself.win = requiredToWin\n\t\t\"\"\"\"Initial configuration\"\"\"\n\t\tself.board = [[NONE] * rows for _ in range(cols)]\n\t\tif NbrCols==5:\n\t\t\tfor jj in range(NbrRows):\n\t\t\t\tif jj % 2 == 0:\n\t\t\t\t\tself.board[0][jj] = white\n\t\t\t\t\tself.board[4][jj] = black\n\t\t\t\telse:\n\t\t\t\t\tself.board[4][jj] = white\n\t\t\t\t\tself.board[0][jj] = black\n\t\telif NbrCols==7:\n\t\t\tself.board[1][1]=white\n\t\t\tself.board[1][2]=black\n\t\t\tself.board[1][3]=white\n\t\t\tself.board[1][4]=black\n\t\t\tself.board[5][4]=white\n\t\t\tself.board[5][3]=black\n\t\t\tself.board[5][2]=white\n\t\t\tself.board[5][1]=black\n\t\n\tdef move (self, command):\n\t\t\"\"\"Move in the given direction.\"\"\"\n\t\tself.command=command\n\t\t#~ self.color=color\n\t\tcolumn=int(command[0])-1\n\t\trow=int(command[1])-1\n\t\tdirection=command[2]\n\t\tc = self.board[column][row]\n\t\tif direction == 'S':\n\t\t\trow2=row+1\n\t\t\tcolumn2=column\n\t\telif direction == 'N':\n\t\t\trow2=row-1\n\t\t\tcolumn2=column\n\t\telif direction == 'E':\n\t\t\trow2=row\n\t\t\tcolumn2=column+1\n\t\telse:\n\t\t\trow2=row\n\t\t\tcolumn2=column-1\n\t\t\n\t\tself.board[column2][row2]=c\n\t\tself.board[column][row]=NONE\n\t\t\n\n\t\n\t##################################\n\t\n\tdef minimax(self, player,depth):\n\t\tif self.checkForWin()==white:\n\t\t\treturn (10000,None)\n\t\telif self.checkForWin()==black:\n\t\t\treturn (-10000,None)\n\t\t\treturn (-10000,None)\n\t\telif depth == 0:\n\t\t\treturn (self.heuristic(),None)\n\t\t#elif self.tied(): #here, need to def tied function and specify tie conditions\n\t\t\t#return (0,None)\n\t\telif player==white:\n\t\t\tbest = (-1000,None)\n\t\t\t#~ print( 'AM1= {}'.format(self.checkPossibleMove(player)))\n\t\t\tg_aux=deepcopy(self)\n\t\t\tfor poss_move in self.checkPossibleMove(player):\n\t\t\t\t#~ print(poss_move)\n\t\t\t\tself.move(poss_move[0])\n\t\t\t\t#~ self.printBoard()\n\t\t\t\tvalue = self.minimax(self.getEnemy(player),depth-1)[0]\n\t\t\t\tif value>best[0]:\n\t\t\t\t\tbest = (value,poss_move[0])\n\t\t\t\tself=deepcopy(g_aux)\n\t\t\treturn best\n\t\telse:\n\t\t\tbest = (+1000,None)\n\t\t\t#~ print( 'AM2= {}'.format(self.checkPossibleMove(player)))\n\t\t\tg_aux=deepcopy(self)\n\t\t\tfor poss_move in self.checkPossibleMove(player):\n\t\t\t\t#~ print(poss_move)\n\t\t\t\tself.move(poss_move[0])\n\t\t\t\t#~ self.printBoard()\n\t\t\t\tvalue = self.minimax(self.getEnemy(player),depth-1)[0]\n\t\t\t\tif valuealpha:\n\t\t\t\t\talpha=value\n\t\t\t\t\tif alpha >= beta:\n\t\t\t\t\t\treturn beta, poss_move[0]\n\t\t\t\t\tbest = (alpha,poss_move[0])\n\t\t\t\tself=deepcopy(g_aux)\n\t\t\t\t#~ print(alpha,beta)\n\t\t\t\t#~ print( 'Alpha,beta= {},{}'.format(alpha,beta))\n\t\t\t#~ print(best[1])\n\t\t\t\tif depth==7:\n\t\t\t\t\tprint('move, value= {}, {}'.format(poss_move,value))\n\t\t\treturn best\n\t\telse:\n\t\t\tbest = (beta,None)\n\t\t\t#~ print( 'AM2= {}'.format(self.checkPossibleMove(player)))\n\t\t\tg_aux=deepcopy(self)\n\t\t\tfor poss_move in self.checkPossibleMove(player):\n\t\t\t\t#~ print('min')\n\t\t\t\t#~ print(poss_move)\n\t\t\t\tself.move(poss_move[0])\n\t\t\t\t#~ self.printBoard()\n\t\t\t\tvalue = self.__minimaxAlphaBeta(self.getEnemy(player),depth-1,alpha,beta)[0]\n\t\t\t\tif value= self.win:\n\t\t\t\t\treturn color\n\t\n\tdef getEnemy(self, player):\n\t\tif player == white:\n\t\t\treturn black\n\t\treturn white\n\t\t\n\tdef printBoard (self):\n\t\t\"\"\"Print the board.\"\"\"\n\t\t#print(' '.join(map(str, range(self.cols))))\n\t\tfor y in range(self.rows):\n\t\t\tprint(' '.join(str(self.board[x][y]) for x in range(self.cols)))\n\t\tprint()\n\n\nif __name__ == '__main__':\n\tlogging.basicConfig(level=logging.DEBUG, filename=\"logfile\", filemode=\"a+\", format=\"%(asctime)-15s %(message)s\")\n\tboard_size = raw_input(\"Please enter 1 for 5*4 board or 2 for 7*6 board:\")\n\tif board_size=='1':\n\t\tNbrCols=5\n\t\tNbrRows=4\n\telse:\n\t\tNbrCols=7\n\t\tNbrRows=6\n\tg = Game(NbrCols,NbrRows)\n\tlogging.info(\"Game created\")\n\tgameID, player_color = raw_input(\"Enter gameID and player\\'s color: \").split()\n\t#~ logging.info(str(gameID), str(player_color))\n\tif player_color == 'white':\n\t\tplayer=white\n\t\tcpu=black\n\telif player_color=='black':\n\t\tplayer=black\n\t\tcpu=white\n\tturn = white\n\talpha=-17\n\tbeta=17\n\tw=None\n\twhile w==None:\n\t\tprint('This is the current board:')\n\t\tg.printBoard()\n\t\tif turn == player:\n\t\t\tcommand = raw_input('{}\\'s turn: '.format('white' if turn == white else 'black'))\n\t\t\tg.move(command)\n\t\telse:\n\t\t\tt = time.time()\n\t\t\tg_aux=deepcopy(g)\n\t\t\tprint( 'AM1= {}'.format(g.checkPossibleMove(turn)))\n\t\t\tmove=g_aux.bestAlphaBeta(turn,6,alpha,beta)\n\t\t\t#~ move=g_aux.best(turn,2)\n\t\t\tprint(move)\n\t\t\tg.move(move)\n\t\t\tprint('elapsed time= {}'.format(time.time() - t))\n\t\t\t\n\t\tturn = black if turn == white else white\n\t\tw= g.checkForWin()\n\tg.printBoard()\n\tturn = black if turn == white else white\n\tprint('{} won! '.format(turn))", "sub_path": "human_vs_ai.py", "file_name": "human_vs_ai.py", "file_ext": "py", "file_size_in_byte": 9227, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "copy.deepcopy", "line_number": 92, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 100, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 105, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 113, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 129, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 143, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 153, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 165, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 242, "usage_type": "call"}, {"api_name": "itertools.groupby", "line_number": 243, "usage_type": "call"}, {"api_name": "itertools.chain", "line_number": 260, "usage_type": "call"}, {"api_name": "itertools.groupby", "line_number": 261, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 279, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 279, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 288, "usage_type": "call"}, {"api_name": "time.time", "line_number": 308, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 309, "usage_type": "call"}, {"api_name": "time.time", "line_number": 315, "usage_type": "call"}]} +{"seq_id": "477281373", "text": "from torch.utils.data import Dataset, DataLoader, ConcatDataset\nfrom torchvision import transforms\nimport os\nfrom PIL import Image\nimport numpy as np\nimport torch\nimport sys\n\ndata_transforms = {\n 'train': transforms.Compose([\n transforms.RandomResizedCrop(224),\n transforms.RandomHorizontalFlip(),\n transforms.ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n ]),\n 'val': transforms.Compose([\n transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n ]),\n 'test': transforms.Compose([\n transforms.Resize(256),\n transforms.CenterCrop(224),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n ])\n}\n\n# looks up data count in orig data file and creates the dataset according to the specified thresholds\nclass Threshold_Dataset(Dataset):\n\n def __init__(self, root, orig_txt, txt, low_threshold=0, high_threshold=100000, open_ratio=1, use_open=True, transform=None, picker='generalist'):\n\n # loading train file (from orig_txt)\n self.orig_labels = []\n with open(orig_txt) as f:\n for line in f:\n self.orig_labels.append(int(line.split()[1]))\n self.orig_labels = np.array(self.orig_labels).astype(int)\n\n # loading class counts\n self.tot_num_classes = self.orig_labels.max() + 1\n self.train_class_count = np.zeros(self.tot_num_classes, dtype=np.int32)\n for l in np.unique(self.orig_labels):\n self.train_class_count[l] = len(self.orig_labels[self.orig_labels == l])\n\n self.img_path, open_set = [], []\n self.labels = []\n self.transform = transform\n self.use_open = use_open\n self.open_ratio = open_ratio\n\n # loading data from txt file\n with open(txt) as f:\n \n for line in f:\n img_path, label = os.path.join(root, line.split()[0]), int(line.split()[1])\n \n if(picker=='experts' or picker=='generalist'):\n\n if(self.train_class_count[label]>=low_threshold and self.train_class_count[label] List[str]:\n self.d = collections.defaultdict(list)\n for ticket in tickets:\n self.d[ticket[0]].append(ticket[1])\n for key in self.d:\n self.d[key].sort(reverse=True)\n self.res = []\n self.dfs(\"JFK\", tickets)\n return self.res[::-1]\n \n def dfs(self, start, tickets):\n if start in self.d and len(self.d[start]) > 0:\n while len(self.d[start]) > 0:\n next_stop = self.d[start].pop()\n self.dfs(next_stop, tickets)\n self.res.append(start)\n \nfrom collections import deque\nclass Solution3:\n def findItinerary(self, tickets: List[List[str]]) -> List[str]:\n self.d = collections.defaultdict(list)\n for flight in tickets:\n self.d[flight[0]].append(flight[1])\n self.route = [\"JFK\"]\n length = len(tickets)\n self.dfs(\"JFK\", length)\n return self.route\n \n def dfs(self, start, k):\n if len(self.route) == k + 1:\n return self.route\n cities = sorted(self.d[start])\n for city in cities:\n self.d[start].remove(city)\n self.route.append(city)\n valid = self.dfs(city, k)\n if valid:\n return valid\n self.route.pop()\n self.d[start].append(city)\n \n\n \nsolution = Solution()\n#tickets = [[\"MUC\",\"LHR\"],[\"JFK\",\"MUC\"],[\"SFO\",\"SJC\"],[\"LHR\",\"SFO\"]]\ntickets = [[\"JFK\",\"SFO\"],[\"JFK\",\"ATL\"],[\"SFO\",\"ATL\"],[\"ATL\",\"JFK\"],[\"ATL\",\"SFO\"]]\nprint(solution.findItinerary(tickets))\n", "sub_path": "Graph/332. Reconstruct Itinerary.py", "file_name": "332. Reconstruct Itinerary.py", "file_ext": "py", "file_size_in_byte": 2363, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "collections.defaultdict", "line_number": 14, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 33, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "231001057", "text": "# -- coding: utf-8 --\n\"\"\"This module implements a solution to read the header information of mp3 files.\nAdditionally an estimate for the length of the mp3 files is implemented.\n\nExamples\n--------\n\n>>> import mp3header\n\nThe parse() function is very simple it just returns\nthe content of the header as follows:\n\n>>> header = mp3header.parse('Test.mp3')\n>>> header\nreturns:\n{'BitRate': 128,\n 'ChannelMode': (2, 'Stereo'),\n 'Copyright': False,\n 'Emphasis': 'no emphasis',\n 'ErrorProtection': 'keine CRC',\n 'Layer': 'Layer III',\n 'ModeExtension': None,\n 'Original': True,\n 'Padding': 'Frame wird nicht aufgef\\xc3\\xbcllt',\n 'Private': False,\n 'SampleRate': 44100,\n 'Sync': True,\n 'Version': 'MPEG 1'}\n\nA bit fancier: object oriented with an estimate of the length in seconds\nuse the Mp3Info() object:\n\n>>> mp3info = mp3header.Mp3Info('Test.mp3')\n>>> mp3info.header\n>>> mp3info.len_sec_estimate\n\n\nAuthor: Siegfried Gündert\nMIT licensed.\n\n\"\"\"\nimport os\nfrom json import dumps\n\n# the length of the mp3 header is 4 bytes\nnum_bytes_mp3_header = 4\n\n# list of header element names and the\n# number of bits reserved for the element:\nelement_bits = [\n ('Sync', 11),\n ('Version', 2),\n ('Layer', 2),\n ('ErrorProtection', 1),\n ('BitRate', 4),\n ('SampleRate', 2),\n ('Padding', 1),\n ('Private', 1),\n ('ChannelMode', 2),\n ('ModeExtension', 2),\n ('Copyright', 1),\n ('Original', 1),\n ('Emphasis', 2),\n]\n\n# Each header element covers a possible range of integers\n# some elements are dependent on other elements of the header.\n# For example the element 'SampleRate' depends on the element\n# 'Version'. This data structure is implemented in the\n# following dictionary:\nelement_description = {\n 'Sync': {\n 2047: True\n },\n 'Version': {\n 0: 'MPEG 2.5',\n 1: None,\n 2: 'MPEG 2',\n 3: 'MPEG 1',\n },\n 'Layer': {\n 0: None,\n 1: 'Layer III',\n 2: 'Layer II',\n 3: 'Layer I',\n },\n 'ErrorProtection': {\n \t0: '16-Bit CRC behind the header.',\n 1: 'No CRC.',\n },\n 'BitRate': {\n 'Version': {\n 'MPEG 1': {\n 'Layer': {\n 'Layer I': {\n 1: 32, 2: 64, 3: 96, 4: 128, 5: 160, 6: 192, 7: 224, 8: 256,\n 9: 288, 10: 320, 11: 352, 12: 384, 13: 416, 14: 448,\n },\n 'Layer II': {\n 1: 32, 2: 48, 3: 56, 4: 64, 5: 80, 6: 96, 7: 112, 8: 128,\n 9: 160, 10: 192, 11: 224, 12: 256, 13: 320, 14: 384,\n },\n 'Layer III': {\n 1: 32, 2: 40, 3: 48, 4: 56, 5: 64, 6: 80, 7: 96, 8: 112,\n 9: 128, 10: 160, 11: 192, 12: 224, 13: 256, 14: 320,\n },\n },\n },\n 'MPEG 2': {\n 'Layer': {\n 'Layer I': {\n 1: 32, 2: 48, 3: 56, 4: 64, 5: 80, 6: 96, 7: 112, 8: 128,\n 9: 144, 10: 160, 11: 176, 12: 192, 13: 224, 14: 256,\n },\n 'Layer II': {\n 1: 8, 2: 16, 3: 24, 4: 32, 5: 40, 6: 48, 7: 56, 8: 64,\n 9: 80, 10: 96, 11: 112, 12: 128, 13: 144, 14: 160,\n },\n },\n },\n 'MPEG 2.5': {\n 'Layer': {\n 'Layer I': {\n 1: 32, 2: 48, 3: 56, 4: 64, 5: 80, 6: 96, 7: 112, 8: 128,\n 9: 144, 10: 160, 11: 176, 12: 192, 13: 224, 14: 256,\n },\n 'Layer II': {\n 1: 8, 2: 16, 3: 24, 4: 32, 5: 40, 6: 48, 7: 56, 8: 64,\n 9: 80, 10: 96, 11: 112, 12: 128, 13: 144, 14: 160,\n },\n },\n },\n },\n },\n 'SampleRate': {\n 'Version': {\n 'MPEG 1': {\n 0: 44100,\n 1: 48000,\n 2: 32000,\n },\n 'MPEG 2': {\n 0: 22050,\n 1: 24000,\n 2: 16000,\n },\n 'MPEG 2.5': {\n 0: 11025,\n 1: 12000,\n 2: 8000,\n },\n },\n },\n 'Padding': {\n 0: 'Frame will not be filled up.',\n 1: 'Frame will be filled with extra slot.',\n },\n 'Private': {\n 0: False,\n 1: True\n },\n 'ChannelMode': {\n 0: (2, 'Stereo'),\n 1: (2, 'Joint Stereo'),\n 2: (2, '2 Mono Channels'),\n 3: (1, 'Mono'),\n },\n 'ModeExtension': { # TODO\n 'Layer': {\n 'Layer I': {\n 0: 'Subbands 4 to 31',\n 1: 'Subbands 8 to 31',\n 2: 'Subbands 12 to 31',\n 3: 'Subbands 16 to 31',\n },\n 'Layer II': {\n 0: 'Subbands 4 to 31',\n 1: 'Subbands 8 to 31',\n 2: 'Subbands 12 to 31',\n 3: 'Subbands 16 to 31',\n },\n 'Layer III': {\n 0: 'Intensity-Stereo: off; M/S-Stereo: off',\n 1: 'Intensity-Stereo: on; M/S-Stereo: off',\n 2: 'Intensity-Stereo: off; M/S-Stereo: on',\n 3: 'Intensity-Stereo: on; M/S-Stereo: on',\n },\n },\n },\n 'Copyright': {\n 0: False,\n 1: True\n },\n 'Original': {\n 0: False,\n 1: True,\n },\n 'Emphasis': {\n 0: 'No emphasis',\n 1: '50/15 ms',\n 2: 'Reserved',\n 3: 'ITU-T J.17',\n },\n}\n\n\ndef _parse_header_bytes_as_bitstr(path, num_bytes=num_bytes_mp3_header):\n \"\"\"Returns a string containing zeros and ones.\n The function parses the first num_bytes from the file specified by `path`.\n The string has 8*`num_bytes` digits.\n\n Parameters\n ----------\n path : str\n String, containing path to your mp3-file.\n num_bytes : int\n Number of bytes to parse from the beginning of the file.\n\n Returns\n -------\n header_bytes_bit_string : str\n String with zeros an ones denoting the bits.\n\n \"\"\"\n\n header_bytes_bit_string = list()\n with open(path, 'rb') as f:\n for i in range(num_bytes):\n byte = f.read(1)\n byte = ord(byte)\n byte = bin(byte)[2:].rjust(8, '0')\n header_bytes_bit_string.append(byte)\n return ''.join(header_bytes_bit_string)\n\n\ndef _get_header_values_dict_from_header_bytes(header_bytes_bit_string,\n element_bits=element_bits):\n \"\"\"Returns a dict with integer values from n bits defined by\n the list element_bits= [('key', nbits), ('',...)]\n\n Parameters\n ----------\n header_bytes_bit_string : str\n String with zeros an ones denoting the bits.\n element_bits : list\n List of header element names and the\n number of bits reserved for the element (example: [('Elmnt1', 4), ...]).\n\n Returns\n -------\n header_values_dict : dict\n Keys are the elements and values are\n integers values of the bits.\n\n \"\"\"\n\n header_values_dict = dict()\n start = 0\n for element, bits in element_bits:\n end = start + bits\n header_values_dict[element] = int(header_bytes_bit_string[start:end], 2)\n start = end\n return header_values_dict\n\n\ndef _get_description_from_header_values_dict(header_values_dict,\n element_description=element_description):\n \"\"\"Returns a dictionary containing the descriptions of the header values\n mapped by `element_description`.\n This function performs something like a hierarchical mapping of key-value pairs.\n It turns the integer values in the `header_values_dict`\n to the according descriptions. For example the specifications\n of mp3 files.\n\n Parameters\n ----------\n header_values_dict : dict\n Dictionary, keys are the header elements and values are\n integer values of the bits.\n\n element_description : dict\n Dictionary, keys are header element names and the\n values are possible descriptions for given integer values. See the\n strucure of `mp3header.element_description` and the corresponding docstring.\n\n Returns\n -------\n header_describtions_dict : dict\n Dictionary, keys are the elements of the header and\n and values are the descriptions for the specific integer value\n given by header_values_dict.\n\n \"\"\"\n\n def get_description_recursive(description, value):\n if any(description):\n if all([type(key) is int for key in description.keys()]):\n return description[value]\n elif all([type(key) is str for key in description.keys()]):\n element = list(description.keys())[0]\n description = description[element][element_description[element][header_values_dict[element]]]\n header_description = get_description_recursive(description, value)\n return header_description\n return None\n\n header_describtions_dict = dict()\n\n for element, value in header_values_dict.items():\n description = element_description[element]\n header_describtions_dict[element] = get_description_recursive(description, value)\n\n return header_describtions_dict\n\n\ndef parse(path):\n \"\"\"Returns mp3 header information as dictionary.\n\n Parameters\n ----------\n path : str\n String, containing the path to an mp3-file.\n\n Returns\n -------\n header_descriptionsd : dict\n Dictionary, keys are the elements of the header and\n and values are the descriptions for the specific integer value\n given by header_values_dict.\n\n \"\"\"\n header_bitstr = _parse_header_bytes_as_bitstr(path)\n header_valuesd = _get_header_values_dict_from_header_bytes(header_bitstr)\n header_descriptionsd = _get_description_from_header_values_dict(header_valuesd)\n return header_descriptionsd\n\n\nclass Mp3Info:\n \"\"\"Returns an object providing\n header information about a specified mp3-file\n and an estimate about the length in seconds.\n\n Parameters\n ----------\n path : str\n String, containing the path to an mp3-file\n\n Returns\n -------\n obj : Mp3Info object\n\n Examples\n --------\n mp3i = Mp3Info('Test.mp3')\n print(mp3i.len_sec)\n print(mp3i.header)\n\n \"\"\"\n def __init__(self, path):\n self._path = path\n self._bits_per_byte = 8\n\n self._header_bitstr = _parse_header_bytes_as_bitstr(\n self._path)\n\n self._header_valuesd = _get_header_values_dict_from_header_bytes(\n self._header_bitstr)\n\n if not self._header_valuesd['Sync']==2047:\n raise(IOError(\"\"\"The header from '{}' is not in sync\n with the mp3 header specifications. It seems not\n to be a mp3-file.\"\"\".format(self._path))\n )\n\n self._header_descriptionsd = _get_description_from_header_values_dict(\n self._header_valuesd)\n\n def __repr__(self):\n return dumps(self.header, indent=4)\n\n @property\n def header(self):\n \"\"\"Returns header information as dictionary.\"\"\"\n return dict(self._header_descriptionsd)\n\n @property\n def header_valuesd(self):\n \"\"\"Returns the header as dictionary, keys are\n the elements and values are integers.\n \"\"\"\n return dict(self._header_valuesd)\n\n @property\n def header_bitstr(self):\n \"\"\"Returns the header as bit string.\"\"\"\n return dict(self._header_bitstr)\n\n @property\n def bit_rate(self):\n \"\"\"Returns bit rate in kbyte/s.\"\"\"\n return self._header_descriptionsd['BitRate']\n\n @property\n def sample_rate(self):\n \"\"\"Returns sample rate in hertz.\"\"\"\n return self._header_descriptionsd['SampleRate']\n\n @property\n def channels(self):\n \"\"\"Returns the number of channels.\"\"\"\n return self._header_descriptionsd['ChannelMode'][0]\n\n @property\n def mode(self):\n \"\"\"Returns the mode of the mp3-file (Stereo, Mono, Joint Stereo).\"\"\"\n return self._header_descriptionsd['ChannelMode'][1]\n\n @property\n def size(self):\n \"\"\"Returns the file size in byte.\"\"\"\n return os.path.getsize(self._path)\n\n @property\n def len_sec_estimate(self):\n \"\"\"Returns estimate of the mp3-file length in seconds.\"\"\"\n return 1e-3 * self.size * self._bits_per_byte / self.bit_rate\n", "sub_path": "mp3header.py", "file_name": "mp3header.py", "file_ext": "py", "file_size_in_byte": 12514, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "json.dumps", "line_number": 378, "usage_type": "call"}, {"api_name": "os.path.getsize", "line_number": 420, "usage_type": "call"}, {"api_name": "os.path", "line_number": 420, "usage_type": "attribute"}]} +{"seq_id": "517640376", "text": "#!/usr/bin/python\n\nfrom __future__ import print_function\nimport tensorflow as tf\nfrom tensorflow.contrib.rnn import GRUCell\nfrom tensorflow.python.ops.rnn import dynamic_rnn as rnn\nfrom tensorflow.python.ops.rnn import bidirectional_dynamic_rnn as bi_rnn\nfrom keras.datasets import imdb\n\nfrom attention import attention\nfrom utils import *\n\nfrom tabulate import tabulate\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport pandas as pd\nimport numpy as np\nfrom gensim.models.word2vec import Word2Vec\nfrom collections import Counter, defaultdict\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.ensemble import ExtraTreesClassifier\nfrom sklearn.naive_bayes import BernoulliNB, MultinomialNB\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.cross_validation import cross_val_score\nfrom sklearn.cross_validation import StratifiedShuffleSplit\nfrom itertools import izip\nfrom nltk.tokenize import TweetTokenizer\nfrom sklearn.cluster import KMeans\n\n\n\ntrain_data= \"../train dataset/Tweet.csv\"\ntrain_topic= \"../train dataset/Target.csv\"\ntrain_label= \"../train dataset/Stance.csv\"\n\ntest_data= \"../test dataset/Tweet.csv\"\ntest_topic= \"../test dataset/Target.csv\"\ntest_label= \"../test dataset/Stance.csv\"\n\n\ntarget_dict={}\nstance_dict={}\ninv_target_dict={}\ninv_stance_dict={}\n\nx=set()\nwith open(\"../train dataset/Target.csv\", \"rb\") as f:\n for row in f:\n x.add(row.strip())\nx=list(x)\ni=0\nfor tar in x:\n target_dict[tar]=i\n inv_target_dict[i]=tar\n i+=1\n\nx=set()\nwith open(\"../train dataset/Stance.csv\", \"rb\") as f:\n for row in f:\n x.add(row.strip())\nx=list(x)\ni=0\nfor tar in x:\n stance_dict[tar]=i\n inv_stance_dict[i]=tar \n i+=1\n\n\n# print target_dict,stance_dict \ntknzr=TweetTokenizer()\nx_train, y_train = [[] for i in range(5)], [[] for i in range(5)]\nX_train, Y_train = [[] for i in range(5)], [[] for i in range(5)]\n\nwith open(\"../train dataset/Tweet.csv\", \"rb\") as f1, open(\"../train dataset/Target.csv\", \"rb\") as f2, open(\"../train dataset/Stance.csv\", \"rb\") as f3:\n for l1,l2,l3 in izip(f1,f2,f3):\n \n tweet=tknzr.tokenize(l1.strip())\n x_train[target_dict[l2.strip()]].append(tweet)\n y_train[target_dict[l2.strip()]].append(l3.strip())\n\nx_dev, y_dev = [[] for i in range(5)], [[] for i in range(5)]\nX_dev, Y_dev = [[] for i in range(5)], [[] for i in range(5)]\n\n\nwith open(\"../dev dataset/Tweet.csv\", \"rb\") as f1, open(\"../dev dataset/Target.csv\", \"rb\") as f2, open(\"../dev dataset/Stance.csv\", \"rb\") as f3:\n for l1,l2,l3 in izip(f1,f2,f3):\n\n tweet=tknzr.tokenize(l1.strip())\n x_dev[target_dict[l2.strip()]].append(tweet)\n y_dev[target_dict[l2.strip()]].append(l3.strip()) \n\nx_test, y_test = [[] for i in range(5)], [[] for i in range(5)]\nX_test, Y_test = [[] for i in range(5)], [[] for i in range(5)]\n\n\nwith open(\"../test dataset/Tweet.csv\", \"rb\") as f1, open(\"../test dataset/Target.csv\", \"rb\") as f2, open(\"../test dataset/Stance.csv\", \"rb\") as f3:\n for l1,l2,l3 in izip(f1,f2,f3):\n\n tweet=tknzr.tokenize(l1.strip())\n x_test[target_dict[l2.strip()]].append(tweet)\n y_test[target_dict[l2.strip()]].append(l3.strip())\n\n\n\n\nall_words=[set(w for sen in x_train[i] for w in sen) for i in range(5)]\n \nword_idx=[{} for i in range(5)]\nfor i in xrange(5):\n j=0;\n for word in all_words[i]:\n word_idx[i][word]=j\n j+=1\n\nNUM_WORDS = 10000\nINDEX_FROM = 3\nSEQUENCE_LENGTH = 250\nEMBEDDING_DIM = 100\nHIDDEN_SIZE = 150\nATTENTION_SIZE = 50\nKEEP_PROB = 0.8\nBATCH_SIZE = 20\nNUM_EPOCHS = 10000\nDELTA = 0.5\nlearning_rate=0.05\n\n\n \n\nvocabulary_size=[None for _ in range(5)]\nf=open(\"Prediction.csv\",\"wb\")\nfrom random import shuffle\n\ndef classifier(X):\n pred=np.array([[x] for x in X])\n kmeans = KMeans(n_clusters=3, random_state=0).fit(pred)\n centres=np.sort(kmeans.cluster_centers_) \n res=[]\n for elem in X:\n val=0\n dist=float(\"inf\")\n for i in xrange(3):\n if(abs(elem-centres[i])/')\ndef books_by_author(pk):\n \"\"\"\n Display books by the author\n \"\"\"\n try:\n author = Author.select().where(Author.id == pk).get()\n except DoesNotExist:\n return abort(404)\n return render_template('details.html', table_name=author, page_name='Author ✍🏻')\n\n\n@app.route('/year//')\ndef books_by_year(pk):\n \"\"\"\n Display books by the year\n \"\"\"\n books = Book.select().where(Book.year == pk)\n return render_template('main.html', books=books, page_name=pk)\n\n\n@app.route('/genre//')\ndef books_by_genre(pk):\n \"\"\"\n Display books by genre\n \"\"\"\n try:\n genre = Genre.select().where(Genre.id == pk).get()\n except DoesNotExist:\n return abort(404)\n return render_template('details.html', table_name=genre, page_name='Genre 📚')\n\n\n# /create/\n@app.route('/create/author/', methods=['GET', 'POST'])\ndef create_author():\n \"\"\"\n Create author object in db\n \"\"\"\n form = AuthorForm(obj=Author.select())\n if request.method == \"POST\":\n form = AuthorForm(request.form)\n if form.validate():\n form.save()\n flash(\"You've added a new author! ✍️\")\n return redirect('/')\n return render_template('create.html', form=form, page_name=\"Add Author ✍🏻\")\n\n\n@app.route('/create/genre/', methods=['GET', 'POST'])\ndef create_genre():\n \"\"\"\n Create genre object in db\n \"\"\"\n form = GenreForm()\n if request.method == 'POST':\n form = GenreForm(request.form)\n if form.validate():\n form.save()\n flash(\"You've added a new genre!📚\")\n return redirect('/')\n\n return render_template('create.html', form=form, page_name=\"Add Genre 📚\")\n\n\n@app.route('/create/book/', methods=['GET', 'POST'])\ndef create_book():\n \"\"\"\n Create book object in db\n \"\"\"\n form = BookForm()\n\n # If you put these choices in forms.py, the new author or genre won't show up in the drop-down list.\n # This has to do with database queries.\n form.genre.choices = [(x.id, x.name) for x in Genre.select()]\n form.author.choices = [(x.id, x.name) for x in Author.select()]\n\n if request.method == 'POST':\n form = BookForm(request.form)\n # reassigned form, you must pass the choices again, otherwise there will be a validation error.\n form.genre.choices = [(x.id, x.name) for x in Genre.select()]\n form.author.choices = [(x.id, x.name) for x in Author.select()]\n if form.validate():\n form.save()\n flash(\"You've added a new book!📕\")\n return redirect('/')\n\n return render_template('create.html', form=form, page_name='Add Book 📕')\n\n\n# /update/\n@app.route('/update/book//', methods=['GET', 'POST'])\ndef update_book(pk):\n \"\"\"\n Update book object in db\n \"\"\"\n try:\n book = Book.get_by_id(pk)\n except DoesNotExist:\n return abort(404)\n form = BookForm(request.form, book)\n if request.method == 'POST' and form.validate():\n Book.update(request.form).where(Book.id == pk).execute()\n return redirect('/')\n\n return render_template('update.html', form=form, book=book)\n\n\n# /delete/\n@app.route('/delete/book/', methods=['POST'])\ndef delete_book():\n \"\"\"\n Delete book object in db\n \"\"\"\n Book.delete().where(Book.id == int(request.form['id'])).execute()\n return redirect('/')\n\n\nif __name__ == '__main__':\n app.run(\n host='127.0.0.1',\n port=5000,\n debug=True\n )\n", "sub_path": "hw4-flask-peewee-wtforms/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 4023, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Flask", "line_number": 6, "usage_type": "call"}, {"api_name": "models.Book", "line_number": 16, "usage_type": "argument"}, {"api_name": "models.Genre", "line_number": 16, "usage_type": "argument"}, {"api_name": "models.Author", "line_number": 16, "usage_type": "argument"}, {"api_name": "models.Book.select", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 17, "usage_type": "call"}, {"api_name": "models.Author.select", "line_number": 26, "usage_type": "call"}, {"api_name": "models.Author", "line_number": 26, "usage_type": "name"}, {"api_name": "models.Author.id", "line_number": 26, "usage_type": "attribute"}, {"api_name": "peewee.DoesNotExist", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 29, "usage_type": "call"}, {"api_name": "models.Book.select", "line_number": 37, "usage_type": "call"}, {"api_name": "models.Book", "line_number": 37, "usage_type": "name"}, {"api_name": "models.Book.year", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 38, "usage_type": "call"}, {"api_name": "models.Genre.select", "line_number": 47, "usage_type": "call"}, {"api_name": "models.Genre", "line_number": 47, "usage_type": "name"}, {"api_name": "models.Genre.id", "line_number": 47, "usage_type": "attribute"}, {"api_name": "peewee.DoesNotExist", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 49, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 50, "usage_type": "call"}, {"api_name": "forms.AuthorForm", "line_number": 59, "usage_type": "call"}, {"api_name": "models.Author.select", "line_number": 59, "usage_type": "call"}, {"api_name": "models.Author", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 60, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 60, "usage_type": "name"}, {"api_name": "forms.AuthorForm", "line_number": 61, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 61, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 61, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 66, "usage_type": "call"}, {"api_name": "forms.GenreForm", "line_number": 74, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 75, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 75, "usage_type": "name"}, {"api_name": "forms.GenreForm", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 76, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 82, "usage_type": "call"}, {"api_name": "forms.BookForm", "line_number": 90, "usage_type": "call"}, {"api_name": "models.Genre.select", "line_number": 94, "usage_type": "call"}, {"api_name": "models.Genre", "line_number": 94, "usage_type": "name"}, {"api_name": "models.Author.select", "line_number": 95, "usage_type": "call"}, {"api_name": "models.Author", "line_number": 95, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 97, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 97, "usage_type": "name"}, {"api_name": "forms.BookForm", "line_number": 98, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 98, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 98, "usage_type": "name"}, {"api_name": "models.Genre.select", "line_number": 100, "usage_type": "call"}, {"api_name": "models.Genre", "line_number": 100, "usage_type": "name"}, {"api_name": "models.Author.select", "line_number": 101, "usage_type": "call"}, {"api_name": "models.Author", "line_number": 101, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 104, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 105, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 107, "usage_type": "call"}, {"api_name": "models.Book.get_by_id", "line_number": 117, "usage_type": "call"}, {"api_name": "models.Book", "line_number": 117, "usage_type": "name"}, {"api_name": "peewee.DoesNotExist", "line_number": 118, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 119, "usage_type": "call"}, {"api_name": "forms.BookForm", "line_number": 120, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 120, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 120, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 121, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 121, "usage_type": "name"}, {"api_name": "models.Book.update", "line_number": 122, "usage_type": "call"}, {"api_name": "models.Book", "line_number": 122, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 122, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 122, "usage_type": "name"}, {"api_name": "models.Book.id", "line_number": 122, "usage_type": "attribute"}, {"api_name": "flask.redirect", "line_number": 123, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 125, "usage_type": "call"}, {"api_name": "models.Book.delete", "line_number": 134, "usage_type": "call"}, {"api_name": "models.Book", "line_number": 134, "usage_type": "name"}, {"api_name": "models.Book.id", "line_number": 134, "usage_type": "attribute"}, {"api_name": "flask.request.form", "line_number": 134, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 134, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 135, "usage_type": "call"}]} +{"seq_id": "239036291", "text": "import logging\nfrom enum import Enum\n\nimport numpy as np\nimport serial\nimport serial.tools.list_ports\nimport voluptuous as vol\n\nfrom ledfx.devices import Device\n\n_LOGGER = logging.getLogger(__name__)\n\n\nclass ColorOrder(Enum):\n RGB = 1\n RBG = 2\n GRB = 3\n BRG = 4\n GBR = 5\n BGR = 6\n\n\nCOLOR_ORDERS = {\n \"RGB\": ColorOrder.RGB,\n \"RBG\": ColorOrder.RBG,\n \"GRB\": ColorOrder.GRB,\n \"BRG\": ColorOrder.BRG,\n \"GBR\": ColorOrder.GBR,\n \"BGR\": ColorOrder.BGR,\n}\n\n\nclass AvailableCOMPorts:\n ports = serial.tools.list_ports.comports()\n\n available_ports = []\n\n for p in ports:\n available_ports.append(p.device)\n\n\nclass AdalightDevice(Device):\n \"\"\"Adalight device support\"\"\"\n\n CONFIG_SCHEMA = vol.Schema(\n {\n vol.Required(\n \"com_port\",\n description=\"COM port for Adalight compatible device\",\n ): vol.In(list(AvailableCOMPorts.available_ports)),\n vol.Required(\n \"baudrate\", description=\"baudrate\", default=500000\n ): vol.All(vol.Coerce(int), vol.Range(min=115200)),\n vol.Required(\n \"pixel_count\",\n description=\"Number of individual pixels\",\n ): vol.All(vol.Coerce(int), vol.Range(min=1)),\n vol.Required(\n \"color_order\", description=\"Color order\", default=\"RGB\"\n ): vol.In(list(COLOR_ORDERS.keys())),\n }\n )\n\n def __init__(self, ledfx, config):\n super().__init__(ledfx, config)\n self.serial = None\n self.baudrate = self._config[\"baudrate\"]\n self.com_port = self._config[\"com_port\"]\n self.color_order = COLOR_ORDERS[self._config[\"color_order\"]]\n\n # adalight header\n # Byte Value\n # 0 'A' (0x41)\n # 1 'd' (0x64)\n # 2 'a' (0x61)\n # 3 pixel count, high byte\n # 4 pixel count, low byte\n # 5 Checksum (high byte XOR low byte XOR 0x55)\n\n buffer_size = 6 + self.pixel_count * 3\n self.buffer = bytearray(buffer_size)\n\n self.buffer[0] = ord(\"A\")\n self.buffer[1] = ord(\"d\")\n self.buffer[2] = ord(\"a\")\n pixel_count_in_bytes = (self.pixel_count).to_bytes(2, byteorder=\"big\")\n self.buffer[3] = pixel_count_in_bytes[0]\n self.buffer[4] = pixel_count_in_bytes[1]\n self.buffer[5] = self.buffer[3] ^ self.buffer[4] ^ 0x55\n\n def activate(self):\n try:\n self.serial = serial.Serial(self.com_port, self.baudrate)\n if self.serial.isOpen:\n super().activate()\n\n except serial.SerialException:\n _LOGGER.critical(\n \"Serial Error: Please ensure your device is connected, functioning and the correct COM port is selected.\"\n )\n # Todo: Trigger the UI to refresh after the clear effect call. Currently it still shows as active.\n self.clear_effect()\n\n def deactivate(self):\n super().deactivate()\n self.serial.close()\n\n @property\n def pixel_count(self):\n return int(self._config[\"pixel_count\"])\n\n def flush(self, data):\n\n byteData = data.astype(np.dtype(\"B\"))\n\n i = 3\n for rgb in byteData:\n i += 3\n rgb_bytes = rgb.tobytes()\n self.buffer[i], self.buffer[i + 1], self.buffer[i + 2] = (\n rgb_bytes[0],\n rgb_bytes[1],\n rgb_bytes[2],\n )\n\n if self.color_order == ColorOrder.RGB:\n continue\n elif self.color_order == ColorOrder.GRB:\n self.swap(self.buffer, i, i + 1)\n elif self.color_order == ColorOrder.BGR:\n self.swap(self.buffer, i, i + 2)\n elif self.color_order == ColorOrder.RBG:\n self.swap(self.buffer, i + 1, i + 2)\n elif self.color_order == ColorOrder.BRG:\n self.swap(self.buffer, i, i + 1)\n self.swap(self.buffer, i + 1, i + 2)\n elif self.color_order == ColorOrder.GBR:\n self.swap(self.buffer, i, i + 1)\n self.swap(self.buffer, i, i + 2)\n try:\n self.serial.write(self.buffer)\n\n except serial.SerialException:\n _LOGGER.critical(\n \"Serial Connection Interrupted. Please check connections and ensure your device is functioning correctly.\"\n )\n self.deactivate()\n\n def swap(self, array, pos1, pos2):\n array[pos1], array[pos2] = array[pos2], array[pos1]\n", "sub_path": "ledfx/devices/adalight.py", "file_name": "adalight.py", "file_ext": "py", "file_size_in_byte": 4553, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 11, "usage_type": "call"}, {"api_name": "enum.Enum", "line_number": 14, "usage_type": "name"}, {"api_name": "serial.tools.list_ports.comports", "line_number": 34, "usage_type": "call"}, {"api_name": "serial.tools", "line_number": 34, "usage_type": "attribute"}, {"api_name": "ledfx.devices.Device", "line_number": 42, "usage_type": "name"}, {"api_name": "voluptuous.Schema", "line_number": 45, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 47, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 51, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 54, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 58, "usage_type": "call"}, {"api_name": "voluptuous.In", "line_number": 50, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 53, "usage_type": "call"}, {"api_name": "voluptuous.Coerce", "line_number": 53, "usage_type": "call"}, {"api_name": "voluptuous.Range", "line_number": 53, "usage_type": "call"}, {"api_name": "voluptuous.All", "line_number": 57, "usage_type": "call"}, {"api_name": "voluptuous.Coerce", "line_number": 57, "usage_type": "call"}, {"api_name": "voluptuous.Range", "line_number": 57, "usage_type": "call"}, {"api_name": "voluptuous.In", "line_number": 60, "usage_type": "call"}, {"api_name": "ledfx.devices", "line_number": 65, "usage_type": "argument"}, {"api_name": "serial.Serial", "line_number": 93, "usage_type": "call"}, {"api_name": "serial.SerialException", "line_number": 97, "usage_type": "attribute"}, {"api_name": "numpy.dtype", "line_number": 114, "usage_type": "call"}, {"api_name": "serial.SerialException", "line_number": 143, "usage_type": "attribute"}]} +{"seq_id": "316392135", "text": "import sys\nif __name__ == '__main__':\n pathDir = \"../../utils\"\n sys.path.insert(0,pathDir)\nfrom stopwatch import *\nfrom itertools import permutations\n\nSW = STOPWATCH()\nSW.start()\nsol = 1 # extra line I added\nn = 10\ncols = range(n)\n\nfor vec in permutations(cols):\n\t# The next if statement is where all the magic happens. See [A] below\n if (n == len(set(vec[i]+i for i in cols))\n == len(set(vec[i]-i for i in cols))):\n print(\"[{0}] {1}\").format(sol, vec)\n sol += 1 # extra line I added\nSW.stopAndPrint()\n\n# [A] The secret to solving the queens problem is having a way to determine if 2 queens\n# are on the same diagonal. \n#\n# Preventing them being on same row and column is easy and \n# with the approach above and the vector/collection data structure being used we ensure that\n# they are never on same row or column.\n#\n# If queens are on the same diagonal then:\n# vec[i]+i for i in cols OR\n# vec[i]-i for i in cols\n# will have one or more duplicate items.\n# To really understand this draw a chess board grid. Number the rows and columns \n# and do the maths for a number of positions where queens are on the same diagonal.\n# From this you'll see that the above is always true.\n", "sub_path": "experiment/queens/queens.py", "file_name": "queens.py", "file_ext": "py", "file_size_in_byte": 1214, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.path.insert", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "itertools.permutations", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "348994400", "text": "\"\"\"\nThis script creates & populates the users & the trips cassandra tables\n\"\"\"\n# !/usr/bin/env python3\n\nimport os\nimport csv\nfrom utilities.cassandra_utilities import createCassandraConnection, createKeySpace\nimport numpy as np\nfrom numpy.random import choice\nimport uuid\n\ndef create_products_table():\n create_products_table = \"\"\"CREATE TABLE IF NOT EXISTS products(\n productid text,\n vendor text,\n name text, \n image_url text,\n price float,\n cost float,\n category text,\n popularity int,\n inventory bigint,\n PRIMARY KEY(productid)) ;\n \"\"\"\n dbsession.execute(create_products_table)\n\n\ndef populate_productsid_table(csv_file):\n insert_trip_data_point = \"\"\"INSERT INTO products(productid, vendor, name, price, cost, category, popularity, inventory) VALUES(%s,%s,%s,%s,%s,%s, %s, %s);\"\"\"\n\n with open(csv_file, newline='') as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader:\n price = float(row['price'])\n percent_profit_draw = choice([.05, .1, .2, .25, .3], 1, p=[.2, .3, .3, .1, .1])\n popularity = int(np.random.normal(50,15))\n if popularity < 15:\n popularity = 15\n mean_cost = price * (1-percent_profit_draw[0])\n cost = mean_cost + np.random.normal(5)\n dbsession.execute(insert_trip_data_point, [str(uuid.uuid4()),row['vendor'], row['name'], float(row['price']), cost,row['category'],popularity, 3])\n\n\ndef populate_productsid():\n CSV_DIRECTORY = 'data/products'\n csv_files = []\n for file in os.listdir(CSV_DIRECTORY):\n file_path = '{}/{}'.format(CSV_DIRECTORY, file)\n if file_path.split('.')[-1] == 'csv':\n csv_files.append(file_path)\n for file in csv_files:\n populate_productsid_table(file)\n\n\n\n\n\n\n#\n#\n#\n# def create_prooducts_table():\n# create_products_table = \"\"\"CREATE TABLE IF NOT EXISTS products(\n# vendor text,\n# name text,\n# image_url text,\n# price float,\n# cost float,\n# category text,\n# popularity int,\n# inventory bigint,\n# PRIMARY KEY((vendor, name)));\n# \"\"\"\n# dbsession.execute(create_products_table)\n#\n#\n# def populate_products_table(csv_file):\n# insert_trip_data_point = \"\"\"INSERT INTO products(vendor, name, image_url, price, cost,category,popularity, inventory) VALUES(%s,%s,%s,%s,%s,%s, %s, %s);\"\"\"\n#\n# with open(csv_file, newline='') as csvfile:\n# reader = csv.DictReader(csvfile)\n# for row in reader:\n# price = float(row['price'])\n# percent_profit_draw = choice([.05, .1, .2, .25, .3], 1, p=[.2, .3, .3, .1, .1])\n# popularity = int(np.random.normal(50,15))\n# if popularity < 15:\n# popularity = 15\n# mean_cost = price * (1-percent_profit_draw[0])\n# cost = mean_cost + np.random.normal(5)\n# dbsession.execute(insert_trip_data_point, [row['vendor'], row['name'], row['image_url'], float(row['price']), cost,row['category'],popularity, 3])\n#\n#\n# def populate_products():\n# CSV_DIRECTORY = 'data/products'\n# csv_files = []\n# for file in os.listdir(CSV_DIRECTORY):\n# file_path = '{}/{}'.format(CSV_DIRECTORY, file)\n# if file_path.split('.')[-1] == 'csv':\n# csv_files.append(file_path)\n# for file in csv_files:\n# populate_products_table(file)\n\n\ndef create_orders_table():\n create_orders_table = \"\"\"CREATE TABLE IF NOT EXISTS orders(\n orderid text,\n customerid text,\n quantities list,\n products list,\n vendors list,\n order_status text,\n PRIMARY KEY((orderID, customerID)));\n \"\"\"\n dbsession.execute(create_orders_table)\n\n\ndef populate_orders():\n CSV_FILE = 'data/orders.csv'\n insert_trip_data_point = \"\"\"INSERT INTO orders(orderID, customerID, quantities, products, vendors, order_status) VALUES(%s,%s,%s, %s, %s, %s);\"\"\"\n with open(CSV_FILE, newline='') as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader:\n quantities = [int(q) for q in row['quantities'][1:-1].split(',')]\n products = [q for q in row['products'][1:-1].split(',')]\n vendors = [q for q in row['vendors'][1:-1].split(',')]\n\n dbsession.execute(insert_trip_data_point, [row['orderID'], row['customerID'], quantities, products, vendors, row['order_status']]) #, float(row['total_price']))\n\n\n\ndef create_customers_table():\n create_customers_table = \"\"\"CREATE TABLE IF NOT EXISTS customers(\n customerid text,\n first_name text,\n last_name text,\n email text,\n password text,\n purchases_per_month int,\n average_purchase_amount int,\n city text,\n PRIMARY KEY(customerid));\n \"\"\"\n dbsession.execute(create_customers_table)\n\n\ndef populate_customers():\n CSV_FILE = 'data/customers/customers.csv'\n insert_customer = \"\"\"INSERT INTO customers(customerid, first_name, last_name, email, password, purchases_per_month, average_purchase_amount, city) VALUES(%s,%s,%s, %s, %s, %s, %s, %s);\"\"\"\n with open(CSV_FILE, newline='') as csvfile:\n reader = csv.DictReader(csvfile)\n for row in reader:\n\n dbsession.execute(insert_customer, [row['customerid'], row['first_name'], row['last_name'], row['email'], row['password'], int(row['purchases_per_month']),int(row['average_purchase_amount']), row['city']])\n\n\nif __name__ == '__main__':\n dbsession = createCassandraConnection()\n createKeySpace(\"ks1\", dbsession)\n try:\n dbsession.set_keyspace('ks1')\n except Exception as e:\n print(e)\n create_orders_table()\n create_products_table()\n create_customers_table()\n\n populate_productsid()\n\n # populate_products()\n # populate_orders()\n # populate_customers()\n print(\"THE CASSANDRA DATABASE HAS BEEN SEEDED\")", "sub_path": "data_model/seed_cassandra_database.py", "file_name": "seed_cassandra_database.py", "file_ext": "py", "file_size_in_byte": 5971, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "csv.DictReader", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 41, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 42, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 48, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 122, "usage_type": "call"}, {"api_name": "csv.DictReader", "line_number": 151, "usage_type": "call"}, {"api_name": "utilities.cassandra_utilities.createCassandraConnection", "line_number": 158, "usage_type": "call"}, {"api_name": "utilities.cassandra_utilities.createKeySpace", "line_number": 159, "usage_type": "call"}]} +{"seq_id": "543522941", "text": "# -*- coding: utf-8 -*-\n\nimport logging\nimport sys\nimport os\nimport io\nfrom subprocess import Popen, PIPE\nfrom datetime import timedelta, datetime\n\nfrom error import *\nfrom ontology import Ontology\nimport threading\n\nclass Command(object):\n def __init__(self, name, context, ontology=None):\n self.log = logging.getLogger('Command')\n self.context = context\n self.valid = False\n self.process = None\n self.stdin = None\n self.stdout = None\n self.stderr = None\n self.output = None\n self.error = None\n self._prototype = None\n self._preset = None\n self._errorcode = None\n self._need_work_directory = False\n self.node = {\n 'name': name,\n 'debug': False,\n 'return code': None,\n 'executable': [],\n 'buffer size': 0,\n 'cwd': None,\n 'env': None,\n 'shell': False,\n 'ontology': None,\n }\n\n if self.prototype is not None:\n if self.prototype['available']:\n self.valid = True\n\n self.executable.append(self.prototype['executable'])\n if self.implementation == 'interpreted':\n self.executable.append(self.prototype['script'])\n\n elif self.implementation == 'java':\n if 'jvm arguments' in self.prototype:\n for argument in self.prototype['jvm arguments']:\n self.executable.append(argument)\n self.executable.append('-jar')\n self.executable.append(self.prototype['jar'])\n\n if 'namespace' in self.prototype and self.prototype['namespace']:\n self.node['ontology'] = Ontology(self.env, self.prototype['namespace'])\n else:\n self.node['ontology'] = Ontology(self.env, 'ns/program/default')\n\n # If an environment is specificed it will override the current process environment\n if 'environment' in self.prototype and self.prototype['environment']:\n self.environment = self.prototype['environment']\n\n # If a sub command is present append it to the executable list\n if 'sub command' in self.prototype:\n self.executable.append(self.prototype['sub command'])\n\n if 'task execution' in self.context and 'task' in self.context['task execution']:\n # Populate the ontology with command parameters from the preset\n self.ontology.overlay(self.preset)\n\n # Than override with parameters provided to the task\n self.ontology.overlay(self.context['task execution']['task'])\n self.node['debug'] = self.context['task execution']['task']['debug']\n\n # Than overlay with explicitly provided parameters\n if ontology is not None:\n self.ontology.overlay(ontology)\n\n # Work directory taken from task execution\n if 'work directory' not in self.ontology:\n if 'task execution' in self.context:\n self.ontology['work directory'] = self.context['task execution']['work directory']\n\n # By default a task is executed in a shell rooted at the work directory\n if 'work directory' in self.ontology:\n self.cwd = self.ontology['work directory']\n\n # add the command node to the context\n self.context['task execution']['commands'].append(self.node)\n\n else:\n self.log.debug('command %s is unavailable', self.name)\n else:\n self.log.error('unknown command %s', self.name)\n\n @property\n def env(self):\n return self.context.env\n\n @property\n def name(self):\n return self.node['name']\n\n @property\n def implementation(self):\n return self.prototype['implementation']\n\n @property\n def prototype(self):\n if self._prototype is None:\n if self.name in self.env.command:\n self._prototype = self.env.command[self.name]\n return self._prototype\n\n @property\n def ontology(self):\n return self.node['ontology']\n\n @property\n def simulated(self):\n return self.node['debug']\n\n @property\n def pid(self):\n if self.process is not None:\n return self.process.pid\n else:\n return None\n\n @property\n def returncode(self):\n return self.node['return code']\n\n @property\n def errorcode(self):\n if self._errorcode is None and 'error code' in self.prototype:\n self._errorcode = dict((code['code'], code) for code in self.prototype['error code'])\n return self._errorcode\n\n @property\n def cwd(self):\n return self.node['cwd']\n\n @cwd.setter\n def cwd(self, value):\n self.node['cwd'] = value\n\n @property\n def environment(self):\n return self.node['env']\n\n @environment.setter\n def environment(self, value):\n self.node['env'] = value\n\n @property\n def executable(self):\n return self.node['executable']\n\n @property\n def preset(self):\n if self._preset is None:\n if self.context['task execution']['task']['preset'] in self.env.preset:\n preset = self.env.preset[self.context['task execution']['task']['preset']]\n\n if preset and 'action' in preset and \\\n self.context['task execution']['task']['action'] in preset['action'] and \\\n preset['action'][self.context['task execution']['task']['action']] and \\\n self.name in preset['action'][self.context['task execution']['task']['action']]:\n self._preset = preset['action'][self.context['task execution']['task']['action']][self.name]\n if self._preset is None: self._preset = {}\n return self._preset\n\n def kill(self):\n if self.process is not None:\n self.log.info('sending SIGKILL to %s with pid %s', self.name, self.pid)\n try:\n self.process.kill()\n except ProcessLookupError: pass\n\n def terminate(self):\n if self.process is not None:\n try:\n self.process.terminate()\n self.log.info('SIGTERM sent to %s with pid %s', self.name, self.pid)\n except ProcessLookupError: pass\n\n def encode(self, safe=False):\n # assemble the command line\n assembled = self.assemble(safe)\n\n encoded = []\n for e in assembled:\n if self.env.constant['space'] in e:\n encoded.append('\"{}\"'.format(e))\n else:\n encoded.append(e)\n return self.env.constant['space'].join(encoded)\n\n def assemble(self, safe=False):\n assembled = []\n\n # Start with the executing binary or script\n assembled.extend(self.executable)\n\n # encode the optional parameters\n if self.prototype['style'] == 'POSIX':\n for prototype in self.ontology.namespace.element.values():\n if prototype.key in self.ontology:\n value = self.ontology[prototype.key]\n if value is not None and 'cli' in prototype.node and prototype.node['cli'] is not None:\n if prototype.plural:\n for v in value:\n assembled.append(prototype.node['cli'])\n assembled.append(str(v))\n else:\n if prototype.type == 'boolean':\n if value: assembled.append(prototype.node['cli'])\n else:\n assembled.append(prototype.node['cli'])\n if prototype.key == 'password' and safe:\n assembled.append(self.ontology['hidden password'])\n else:\n assembled.append(str(value))\n\n elif self.prototype['style'] == 'picard':\n for prototype in self.ontology.namespace.element.values():\n if prototype.key in self.ontology:\n value = self.ontology[prototype.key]\n if value is not None and 'cli' in prototype.node and prototype.node['cli'] is not None:\n if prototype.plural:\n for v in value:\n assembled.append('{}={}'.format(prototype.node['cli'], str(v)))\n else:\n if prototype.type == 'boolean':\n assembled.append('{}={}'.format(prototype.node['cli'], str(value).lower()))\n else:\n assembled.append('{}={}'.format(prototype.node['cli'], str(value)))\n\n elif self.prototype['style'] == 'mediainfo':\n for prototype in self.ontology.namespace.element.values():\n if prototype.key in self.ontology:\n value = self.ontology[prototype.key]\n if value is not None and 'cli' in prototype.node and prototype.node['cli'] is not None:\n if prototype.plural:\n for v in value:\n assembled.append('{}={}'.format(prototype.node['cli'], str(v)))\n else:\n if prototype.type == 'boolean':\n if value: assembled.append(prototype.node['cli'])\n else:\n assembled.append('{}={}'.format(prototype.node['cli'], str(value)))\n\n # encode positional parameters\n if 'positional' in self.ontology and self.ontology['positional']:\n assembled.extend(self.ontology['positional'])\n\n return assembled\n\n def execute(self):\n if not self.simulated:\n self.node['started'] = datetime.utcnow()\n\n self.env.prepare_directory(self.ontology['work directory'])\n if self.stdout is None and self.prototype['stdout'] is not None:\n if self.prototype['stdout'] == 'file':\n # attempt to open stdout file for appending\n if self.ontology['stdout path'] is not None:\n try:\n self.stdout = io.open(self.ontology['stdout path'], 'ab')\n except OSError as error:\n self.log.error(str(error))\n\n elif self.prototype['stdout'] == 'pipe':\n # will not redirect to caller's stdout\n self.stdout = PIPE\n\n if self.stderr is None:\n if self.prototype['stderr'] == 'file':\n # attempt to open stderr file for appending\n if self.ontology['stderr path'] is not None:\n try:\n self.stderr = io.open(self.ontology['stderr path'], 'ab')\n except OSError as error:\n self.log.error(str(error))\n\n elif self.prototype['stderr'] == 'pipe':\n # will not redirect to caller's stderr\n self.stderr = PIPE\n\n # assemble the command line\n assembled = self.assemble()\n\n # log the command\n self.log.debug('execute: %s', self.encode(True))\n\n # Start a process object\n self.process = Popen(\n args=assembled,\n bufsize=self.node['buffer size'],\n cwd=self.node['cwd'],\n env=self.node['env'],\n shell=self.node['shell'],\n stdin=self.stdin,\n stdout=self.stdout,\n stderr=self.stderr\n )\n\n # add a reference to the global running process table\n self.context['queue'].process_table.add(self)\n\n # execute the process\n self.output, self.error = self.process.communicate()\n\n # remove the reference from the global running process table\n self.context['queue'].process_table.remove(self)\n\n # convert the byte output and error to utf8 string\n if self.output is not None: self.output = self.output.decode('utf8')\n if self.error is not None: self.error = self.error.decode('utf8')\n\n # set the return code\n self.node['return code'] = self.process.returncode\n\n # attempt to close stdout file\n if self.prototype['stdout'] == 'file':\n try:\n self.stdout.close()\n self.stdout = None\n except OSError as error:\n self.log.error(str(error))\n\n # attempt to close stderr file\n if self.prototype['stderr'] == 'file':\n try:\n self.stderr.close()\n self.stderr = None\n except OSError as error:\n self.log.error(str(error))\n\n self.node['ended'] = datetime.utcnow()\n self.node['duration'] = (self.node['ended'] - self.node['started']).total_seconds()\n\n # if the command returned an error abort the task and log the error\n if self.returncode != 0:\n message = '{} returned {}'.format(self.name, self.returncode)\n if self.errorcode is not None:\n if self.returncode in self.errorcode:\n message = '{} : {}'.format(message, self.errorcode[self.returncode]['message'])\n raise UnsuccessfulTerminationError(message)\n\n # debug mode only prints the encoded command\n else: print(self.encode(True))\n", "sub_path": "command.py", "file_name": "command.py", "file_ext": "py", "file_size_in_byte": 14006, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 16, "usage_type": "call"}, {"api_name": "ontology.Ontology", "line_number": 57, "usage_type": "call"}, {"api_name": "ontology.Ontology", "line_number": 59, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 264, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 264, "usage_type": "name"}, {"api_name": "io.open", "line_number": 272, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 278, "usage_type": "name"}, {"api_name": "io.open", "line_number": 285, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 291, "usage_type": "name"}, {"api_name": "subprocess.Popen", "line_number": 300, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 343, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 343, "usage_type": "name"}]} +{"seq_id": "610200341", "text": "import numpy as np\nimport cv2\nfrom random import randint, uniform\nimport base64\n\nW = 80\nH = 80\n\nVERTICAL_LAYER = 2\nHORIZONTAL_LAYER = 3\n\nfont = cv2.FONT_ITALIC\n\nINPUT_CHOICES = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T',\n 'U', 'V', 'W', 'X', 'Y', 'Z', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\n\n\nclass Captcha:\n\n def generate_captcha(self):\n try:\n GENERATED_CHOICES = []\n ans = \"\"\n root_layer = np.full((H * VERTICAL_LAYER, W * HORIZONTAL_LAYER, 3), fill_value=[255, 255, 255],\n dtype=np.uint8)\n\n for x in range(VERTICAL_LAYER):\n for y in range(HORIZONTAL_LAYER):\n R, G, B = randint(0, 255), randint(0, 255), randint(0, 255)\n\n generated_index = randint(0, len(INPUT_CHOICES) - 1)\n gi = 0\n while generated_index in GENERATED_CHOICES and gi < len(INPUT_CHOICES):\n generated_index = randint(0, len(INPUT_CHOICES) - 1)\n gi += 1\n\n GENERATED_CHOICES.append(generated_index)\n input_text = INPUT_CHOICES[generated_index]\n ans += input_text\n\n temp_image = np.full((H, W, 3), fill_value=[R, G, B], dtype=np.uint8)\n font_scale = uniform(1.2, 2.0)\n cv2.putText(temp_image, str(input_text), (10, 55), font, font_scale, (255 - R, 255 - G, 255 - B), 4)\n\n if randint(2, 9) % 3 == 0 and (255 - R, 255 - G, 255 - B) != (0, 0, 0):\n kernel = np.ones((2, 2), np.uint8)\n temp_image = cv2.morphologyEx(temp_image, cv2.MORPH_GRADIENT, kernel)\n\n root_layer[x * H:(x + 1) * H, y * W:(y + 1) * W] = temp_image\n\n r, buffer = cv2.imencode('.jpg', root_layer)\n return {\"data\": 'data:image/jpeg;base64, ' + base64.b64encode(buffer).decode(), \"answer\": ans }\n\n\n except Exception as e:\n return \"Exception as {}\".format(e)\n", "sub_path": "Captcha/Captcha/CaptchaGenerator.py", "file_name": "CaptchaGenerator.py", "file_ext": "py", "file_size_in_byte": 2131, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "cv2.FONT_ITALIC", "line_number": 12, "usage_type": "attribute"}, {"api_name": "numpy.full", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 25, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 29, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 31, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.full", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 41, "usage_type": "attribute"}, {"api_name": "random.uniform", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.putText", "line_number": 43, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 46, "usage_type": "attribute"}, {"api_name": "cv2.morphologyEx", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.MORPH_GRADIENT", "line_number": 47, "usage_type": "attribute"}, {"api_name": "cv2.imencode", "line_number": 51, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "522678888", "text": "#!/usr/bin/env python2.5\r\n# -*- coding: utf-8 -*-\r\n\n# Copyright (C) 2009 emijrp\r\n# This program is free software: you can redistribute it and/or modify\r\n# it under the terms of the GNU General Public License as published by\r\n# the Free Software Foundation, either version 3 of the License, or\r\n# (at your option) any later version.\r\n# \r\n# This program is distributed in the hope that it will be useful,\r\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\r\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\r\n# GNU General Public License for more details.\r\n# \r\n# You should have received a copy of the GNU General Public License\r\n# along with this program. If not, see .\n\r\nimport wikipedia, re\r\n \r\nensite = wikipedia.Site('en', 'wikipedia')\r\nessite = wikipedia.Site('es', 'wikipedia')\r\nentool = wikipedia.Page(ensite, u'Template:Toolserver')\r\nestool = wikipedia.Page(essite, u'Template:Toolserver')\r\n\r\nenwtext=entool.get()\r\nupdate=enwtext.split(\"\")[1]\r\nupdate=update.replace(\"January\", \"de enero de\")\r\nupdate=update.replace(\"February\", \"de febrero de\")\r\nupdate=update.replace(\"March\", \"de marzo de\")\r\nupdate=update.replace(\"April\", \"de abril de\")\r\nupdate=update.replace(\"May\", \"de mayo de\")\r\nupdate=update.replace(\"June\", \"de junio de\")\r\nupdate=update.replace(\"July\", \"de julio de\")\r\nupdate=update.replace(\"August\", \"de agosto de\")\r\nupdate=update.replace(\"September\", \"de septiembre de\")\r\nupdate=update.replace(\"October\", \"de octubre de\")\r\nupdate=update.replace(\"November\", \"de noviembre de\")\r\nupdate=update.replace(\"December\", \"de diciembre de\")\r\nrosemary=enwtext.split(\"\")[1]\r\ndaphne=enwtext.split(\"\")[1]\r\nyarrow=enwtext.split(\"\")[1]\r\ns1=enwtext.split(\"\")[1]\r\ns2=enwtext.split(\"\")[1]\r\ns3=enwtext.split(\"\")[1]\ns4=enwtext.split(\"\")[1]\ns5=enwtext.split(\"\")[1]\r\n\r\ndic={u'up':u'En línea',u'down':u'Fuera de línea'}\r\n\r\nsalida=u'''{| class=\"infobox\" style=\"width: {{{width|auto}}}; float: {{{float|right}}}; clear: {{{clear|right}}};\"\r\n|-\r\n! colspan=\"2\" | Estado de [[m:Toolserver|Toolserver]]\r\n|-\r\n! Última actualización\r\n| %s\r\n|-\r\n!MySQL rosemary\r\n| %s\r\n|-\r\n!MySQL daphne\r\n| %s \r\n|-\r\n!MySQL yarrow\r\n| %s\r\n|-\r\n!Lag en s1\r\n| %s\r\n|-\r\n!Lag en s2\r\n| %s\r\n|-\r\n!Lag en s3\r\n| %s\n|-\r\n!Lag en s4\r\n| %s\n|-\r\n!Lag en s5\r\n| %s\r\n|}{{documentación}}''' % (update, dic[rosemary], dic[daphne], dic[yarrow], s1, s2, s3, s4, s5)\r\n\r\nestool.put(salida, u'BOT - Estado: rosemary: %s; daphne %s; yarrow %s; Replag: s1 %s; s2 %s; s3 %s; s4 %s; s5 %s' % (dic[rosemary], dic[daphne], dic[yarrow], s1, s2, s3, s4, s5))\r\n\r\n\r\n", "sub_path": "tarea026.py", "file_name": "tarea026.py", "file_ext": "py", "file_size_in_byte": 2874, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "wikipedia.Site", "line_number": 20, "usage_type": "call"}, {"api_name": "wikipedia.Site", "line_number": 21, "usage_type": "call"}, {"api_name": "wikipedia.Page", "line_number": 22, "usage_type": "call"}, {"api_name": "wikipedia.Page", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "507040535", "text": "from ftw.builder import Builder\nfrom ftw.builder import create\nfrom ftw.testbrowser import browsing\nfrom ftw.testbrowser.pages import statusmessages\nfrom ftw.testing import freeze\nfrom ftw.zipexport.zipfilestream import ZipFile\nfrom opengever.meeting.browser.meetings.agendaitem_list import GenerateAgendaItemList\nfrom opengever.meeting.zipexport import MeetingDocumentZipper\nfrom opengever.meeting.zipexport import MeetingJSONSerializer\nfrom opengever.testing import IntegrationTestCase\nfrom opengever.testing import set_preferred_language\nfrom opengever.testing.helpers import localized_datetime\nfrom StringIO import StringIO\nimport json\n\n\nclass TestMeetingZipExportView(IntegrationTestCase):\n features = ('meeting',)\n maxDiff = None\n\n @browsing\n def test_zip_export_includes_generated_protocol(self, browser):\n self.login(self.committee_responsible, browser)\n self.meeting.model.update_protocol_document()\n self.assertTrue(self.meeting.model.has_protocol_document())\n\n browser.open(self.meeting, view='export-meeting-zip')\n zip_file = ZipFile(StringIO(browser.contents), 'r')\n self.assertIn('Protocol-9. Sitzung der Rechnungspruefungskommission.docx',\n zip_file.namelist())\n\n @browsing\n def test_zip_export_agenda_items_attachments(self, browser):\n browser.append_request_header('Accept-Language', 'de-ch')\n self.login(self.committee_responsible, browser)\n self.schedule_proposal(self.meeting, self.submitted_proposal)\n\n browser.open(self.meeting, view='export-meeting-zip')\n zip_file = ZipFile(StringIO(browser.contents), 'r')\n self.assertIn(\n 'Traktandum 1/Beilage/1_Vertraegsentwurf.docx',\n zip_file.namelist())\n\n @browsing\n def test_zip_export_skips_agenda_items_attachments_without_file(self, browser):\n browser.append_request_header('Accept-Language', 'de-ch')\n self.login(self.committee_responsible, browser)\n\n self.proposal.submit_additional_document(self.empty_document)\n self.proposal.submit_additional_document(self.subdocument)\n self.schedule_proposal(self.meeting, self.submitted_proposal)\n\n browser.open(self.meeting, view='export-meeting-zip')\n zip_file = ZipFile(StringIO(browser.contents), 'r')\n self.assertItemsEqual(\n ['Traktandum 1/Vertraege.docx',\n 'Traktandum 1/Beilage/1_Vertraegsentwurf.docx',\n 'Traktandum 1/Beilage/2_Uebersicht der Vertraege von 2016.xlsx',\n 'meeting.json'],\n zip_file.namelist())\n\n @browsing\n def test_export_proposal_word_documents(self, browser):\n browser.append_request_header('Accept-Language', 'de-ch')\n self.login(self.committee_responsible, browser)\n self.schedule_proposal(self.meeting, self.submitted_proposal)\n browser.open(self.meeting, view='export-meeting-zip')\n zip_file = ZipFile(StringIO(browser.contents), 'r')\n self.assertIn(\n 'Traktandum 1/Vertraege.docx',\n zip_file.namelist())\n\n @browsing\n def test_excerpt_is_not_exported(self, browser):\n browser.append_request_header('Accept-Language', 'de-ch')\n self.login(self.committee_responsible, browser)\n agenda_item = self.schedule_proposal(self.meeting,\n self.submitted_proposal)\n agenda_item.decide()\n agenda_item.generate_excerpt(title='Ahoi McEnroe!')\n\n browser.open(self.meeting, view='export-meeting-zip')\n zip_file = ZipFile(StringIO(browser.contents), 'r')\n self.assertItemsEqual(\n ['Traktandum 1/Beilage/1_Vertraegsentwurf.docx',\n 'Traktandum 1/Vertraege.docx',\n 'meeting.json'],\n zip_file.namelist())\n\n @browsing\n def test_zip_export_agenda_items_list(self, browser):\n self.login(self.committee_responsible, browser)\n browser.open(GenerateAgendaItemList.url_for(self.meeting.model))\n browser.open(self.meeting, view='export-meeting-zip')\n zip_file = ZipFile(StringIO(browser.contents), 'r')\n self.assertIn(\n 'Agendaitem list-9. Sitzung der Rechnungspruefungskommission.docx',\n zip_file.namelist())\n\n @browsing\n def test_meeting_can_be_exported_to_zip_when_proposal_related_to_mail(self, browser):\n self.login(self.committee_responsible, browser)\n\n submitted_proposal = create(\n Builder('proposal')\n .within(self.dossier)\n .having(\n title=u'Vertragsentwurf f\\xfcr weitere Bearbeitung bewilligen',\n committee=self.committee.load_model(),\n )\n .relate_to(self.mail_eml)\n .as_submitted()\n )\n\n self.schedule_proposal(self.meeting, submitted_proposal)\n\n browser.open(self.meeting, view='export-meeting-zip')\n statusmessages.assert_no_error_messages()\n self.assertEquals('application/zip', browser.contenttype)\n\n @browsing\n def test_meeting_can_be_exported_to_zip_when_agenda_item_list_template_is_missing(self, browser):\n self.login(self.committee_responsible, browser)\n self.committee.agendaitem_list_template = None\n self.committee_container.agendaitem_list_template = None\n browser.open(self.meeting, view='export-meeting-zip')\n statusmessages.assert_no_error_messages()\n self.assertEquals('application/zip', browser.contenttype)\n\n @browsing\n def test_exported_meeting_contains_json(self, browser):\n self.login(self.committee_responsible, browser)\n browser.open(self.meeting, view='export-meeting-zip')\n self.assertEquals('application/zip', browser.contenttype)\n\n zip_file = ZipFile(StringIO(browser.contents), 'r')\n self.assertEquals(\n ['meeting.json'],\n zip_file.namelist())\n\n def test_meeting_data_for_zip_export_json(self):\n set_preferred_language(self.portal.REQUEST, 'de-ch')\n self.login(self.committee_responsible)\n self.schedule_paragraph(self.meeting, u'A Gesch\\xfcfte')\n with freeze(localized_datetime(2017, 12, 13)):\n self.schedule_ad_hoc(self.meeting, u'Ad-hoc Traktand\\xfem')\n self.schedule_proposal(self.meeting, self.submitted_proposal)\n\n serializer = MeetingJSONSerializer(\n self.meeting.model,\n MeetingDocumentZipper(self.meeting.model, None))\n serializer.traverse()\n expected_agenda_items = {\n 'agenda_items': [\n {'opengever_id': 2, 'sort_order': 1, 'title': u'A Gesch\\xfcfte'},\n {\n 'number': '1.', 'number_raw': 1, 'opengever_id': 3, 'proposal': {\n 'checksum': 'e00d6c8fb32c30d3ca3a3f8e5d873565482567561023016d9ca18243ff1cfa14',\n 'file': 'Traktandum 1/Ad-hoc Traktandthm.docx',\n 'modified': u'2017-12-13T00:00:00+01:00',\n },\n 'sort_order': 2,\n 'title': u'Ad-hoc Traktand\\xfem'\n },\n {\n 'attachments': [{\n 'checksum': '51d6317494eccc4a73154625a6820cb6b50dc1455eb4cf26399299d4f9ce77b2',\n 'file': 'Traktandum 2/Beilage/1_Vertraegsentwurf.docx',\n 'modified': u'2016-08-31T16:09:37+02:00',\n 'title': u'Vertr\\xe4gsentwurf',\n }],\n 'number': '2.',\n 'number_raw': 2,\n 'opengever_id': 4,\n 'proposal': {\n 'checksum': '114e7a059dc34c7459dab90904685584e331089d80bb6310183a0de009b66c3b',\n 'file': 'Traktandum 2/Vertraege.docx',\n 'modified': u'2016-08-31T16:09:35+02:00',\n },\n 'sort_order': 3,\n 'title': u'Vertr\\xe4ge',\n },\n ],\n 'committee': {'oguid': u'plone:1009313300', 'title': u'Rechnungspr\\xfcfungskommission'},\n 'end': u'2016-09-12T17:00:00+00:00',\n 'location': u'B\\xfcren an der Aare',\n 'opengever_id': 1,\n 'start': u'2016-09-12T15:30:00+00:00',\n 'title': u'9. Sitzung der Rechnungspr\\xfcfungskommission',\n }\n\n self.assertEquals(expected_agenda_items, serializer.data)\n\n @browsing\n def test_exported_meeting_json_has_correct_file_names(self, browser):\n set_preferred_language(self.portal.REQUEST, 'de-ch')\n browser.append_request_header('Accept-Language', 'de-ch')\n self.login(self.committee_responsible, browser)\n\n self.meeting.model.title = u'9. Sitzung der Rechnungspr\\xfcfungs' \\\n u'kommission, ordentlich'\n self.schedule_paragraph(self.meeting, u'A Gesch\\xfcfte')\n with freeze(localized_datetime(2017, 12, 13)):\n self.schedule_ad_hoc(\n self.meeting, u'Ad-hoc Traktand\\xfem'\n ).decide()\n agenda_item = self.schedule_proposal(self.meeting, self.submitted_proposal)\n self.decide_agendaitem_generate_and_return_excerpt(agenda_item)\n with freeze(localized_datetime(2017, 12, 14)):\n self.meeting.model.close()\n\n browser.open(self.meeting, view='export-meeting-zip')\n self.assertEquals('application/zip', browser.contenttype)\n\n zip_file = ZipFile(StringIO(browser.contents), 'r')\n\n meeting_json = json.loads(zip_file.read('meeting.json'))\n\n # the protocol is generated during the tests and its checksum cannot\n # be predicted\n meeting_json['meetings'][0]['protocol']['checksum'] = 'unpredictable'\n meeting_json['meetings'][0].pop('opengever_id')\n for agenda_item in meeting_json['meetings'][0]['agenda_items']:\n agenda_item.pop('opengever_id')\n\n expected_meeting_json = {\n u'meetings': [{\n u'agenda_items': [\n {u'sort_order': 1, u'title': u'A Gesch\\xfcfte'},\n {\n u'number': u'1.',\n u'number_raw': 1,\n u'proposal': {\n u'checksum': u'e00d6c8fb32c30d3ca3a3f8e5d873565482567561023016d9ca18243ff1cfa14',\n u'file': u'Traktandum 1/Ad-hoc Traktandthm.docx',\n u'modified': u'2017-12-13T00:00:00+01:00',\n },\n u'sort_order': 2,\n u'title': u'Ad-hoc Traktand\\xfem',\n },\n {\n u'attachments': [{\n u'checksum': u'51d6317494eccc4a73154625a6820cb6b50dc1455eb4cf26399299d4f9ce77b2',\n u'file': u'Traktandum 2/Beilage/1_Vertraegsentwurf.docx',\n u'modified': u'2016-08-31T16:09:37+02:00',\n u'title': u'Vertr\\xe4gsentwurf',\n }],\n u'number': u'2.',\n u'number_raw': 2,\n u'proposal': {\n u'checksum': u'114e7a059dc34c7459dab90904685584e331089d80bb6310183a0de009b66c3b',\n u'file': u'Traktandum 2/Vertraege.docx',\n u'modified': u'2016-08-31T16:09:35+02:00',\n },\n u'sort_order': 3,\n u'title': u'Vertr\\xe4ge',\n },\n ],\n u'committee': {u'oguid': u'plone:1009313300', u'title': u'Rechnungspr\\xfcfungskommission'},\n u'end': u'2016-09-12T17:00:00+00:00',\n u'location': u'B\\xfcren an der Aare',\n u'protocol': {\n u'checksum': 'unpredictable',\n u'file': u'Protokoll-9. Sitzung der Rechnungspruefungskommission- ordentlich.docx',\n u'modified': u'2017-12-14T00:00:00+01:00',\n },\n u'start': u'2016-09-12T15:30:00+00:00',\n u'title': u'9. Sitzung der Rechnungspr\\xfcfungskommission, ordentlich',\n }],\n u'version': u'1.0.0',\n }\n self.assert_json_structure_equal(expected_meeting_json, meeting_json)\n\n expected_file_names = [\n 'Protokoll-9. Sitzung der Rechnungspruefungskommission- ordentlich.docx',\n 'Traktandum 1/Ad-hoc Traktandthm.docx',\n 'Traktandum 2/Beilage/1_Vertraegsentwurf.docx',\n 'Traktandum 2/Vertraege.docx',\n 'meeting.json',\n ]\n file_names = sorted(zip_file.namelist())\n self.assertEqual(expected_file_names, file_names)\n\n @browsing\n def test_filename_conflicts_are_avoided_by_prefixing_attachment_number(self, browser):\n set_preferred_language(self.portal.REQUEST, 'de-ch')\n browser.append_request_header('Accept-Language', 'de-ch')\n self.login(self.committee_responsible, browser)\n\n documents = [\n create(Builder('document')\n .within(self.dossier)\n .titled('The same title')\n .with_dummy_content())\n for i in range(3)]\n proposal, submitted_proposal = create(Builder('proposal')\n .within(self.dossier)\n .having(committee=self.committee.load_model())\n .with_submitted()\n .relate_to(*documents))\n self.schedule_proposal(self.meeting, submitted_proposal)\n\n browser.open(self.meeting, view='export-meeting-zip')\n self.assertEquals('application/zip', browser.contenttype)\n zip_file = ZipFile(StringIO(browser.contents), 'r')\n meeting_json = json.loads(zip_file.read('meeting.json'))\n\n expected_file_names = [u'Traktandum 1/Beilage/1_The same title.doc',\n u'Traktandum 1/Beilage/2_The same title.doc',\n u'Traktandum 1/Beilage/3_The same title.doc']\n json_file_names = [attachment.get(\"file\") for attachment in\n meeting_json[\"meetings\"][0]['agenda_items'][0][\"attachments\"]]\n\n self.assertItemsEqual(expected_file_names, json_file_names)\n\n expected_file_names.extend(['meeting.json', 'Traktandum 1/Fooo.docx'])\n\n file_names = zip_file.namelist()\n self.assertItemsEqual(expected_file_names, file_names)\n", "sub_path": "opengever/meeting/tests/test_meeting_zipexport.py", "file_name": "test_meeting_zipexport.py", "file_ext": "py", "file_size_in_byte": 14573, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "opengever.testing.IntegrationTestCase", "line_number": 17, "usage_type": "name"}, {"api_name": "ftw.zipexport.zipfilestream.ZipFile", "line_number": 28, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 28, "usage_type": "call"}, {"api_name": "ftw.testbrowser.browsing", "line_number": 21, "usage_type": "name"}, {"api_name": "ftw.zipexport.zipfilestream.ZipFile", "line_number": 39, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 39, "usage_type": "call"}, {"api_name": "ftw.testbrowser.browsing", "line_number": 32, "usage_type": "name"}, {"api_name": "ftw.zipexport.zipfilestream.ZipFile", "line_number": 54, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 54, "usage_type": "call"}, {"api_name": "ftw.testbrowser.browsing", "line_number": 44, "usage_type": "name"}, {"api_name": "ftw.zipexport.zipfilestream.ZipFile", "line_number": 68, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 68, "usage_type": "call"}, {"api_name": "ftw.testbrowser.browsing", "line_number": 62, "usage_type": "name"}, {"api_name": "ftw.zipexport.zipfilestream.ZipFile", "line_number": 83, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 83, "usage_type": "call"}, {"api_name": "ftw.testbrowser.browsing", "line_number": 73, "usage_type": "name"}, {"api_name": "opengever.meeting.browser.meetings.agendaitem_list.GenerateAgendaItemList.url_for", "line_number": 93, "usage_type": "call"}, {"api_name": "opengever.meeting.browser.meetings.agendaitem_list.GenerateAgendaItemList", "line_number": 93, "usage_type": "name"}, {"api_name": "ftw.zipexport.zipfilestream.ZipFile", "line_number": 95, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 95, "usage_type": "call"}, {"api_name": "ftw.testbrowser.browsing", "line_number": 90, "usage_type": "name"}, {"api_name": "ftw.builder.create", "line_number": 104, "usage_type": "call"}, {"api_name": "ftw.builder.Builder", "line_number": 105, "usage_type": "call"}, {"api_name": "ftw.testbrowser.pages.statusmessages.assert_no_error_messages", "line_number": 118, "usage_type": "call"}, {"api_name": "ftw.testbrowser.pages.statusmessages", "line_number": 118, "usage_type": "name"}, {"api_name": "ftw.testbrowser.browsing", "line_number": 100, "usage_type": "name"}, {"api_name": "ftw.testbrowser.pages.statusmessages.assert_no_error_messages", "line_number": 127, "usage_type": "call"}, {"api_name": "ftw.testbrowser.pages.statusmessages", "line_number": 127, "usage_type": "name"}, {"api_name": "ftw.testbrowser.browsing", "line_number": 121, "usage_type": "name"}, {"api_name": "ftw.zipexport.zipfilestream.ZipFile", "line_number": 136, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 136, "usage_type": "call"}, {"api_name": "ftw.testbrowser.browsing", "line_number": 130, "usage_type": "name"}, {"api_name": "opengever.testing.set_preferred_language", "line_number": 142, "usage_type": "call"}, {"api_name": "ftw.testing.freeze", "line_number": 145, "usage_type": "call"}, {"api_name": "opengever.testing.helpers.localized_datetime", "line_number": 145, "usage_type": "call"}, {"api_name": "opengever.meeting.zipexport.MeetingJSONSerializer", "line_number": 149, "usage_type": "call"}, {"api_name": "opengever.meeting.zipexport.MeetingDocumentZipper", "line_number": 151, "usage_type": "call"}, {"api_name": "opengever.testing.set_preferred_language", "line_number": 196, "usage_type": "call"}, {"api_name": "ftw.testing.freeze", "line_number": 203, "usage_type": "call"}, {"api_name": "opengever.testing.helpers.localized_datetime", "line_number": 203, "usage_type": "call"}, {"api_name": "ftw.testing.freeze", "line_number": 209, "usage_type": "call"}, {"api_name": "opengever.testing.helpers.localized_datetime", "line_number": 209, "usage_type": "call"}, {"api_name": "ftw.zipexport.zipfilestream.ZipFile", "line_number": 215, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 215, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 217, "usage_type": "call"}, {"api_name": "ftw.testbrowser.browsing", "line_number": 194, "usage_type": "name"}, {"api_name": "opengever.testing.set_preferred_language", "line_number": 286, "usage_type": "call"}, {"api_name": "ftw.builder.create", "line_number": 291, "usage_type": "call"}, {"api_name": "ftw.builder.Builder", "line_number": 291, "usage_type": "call"}, {"api_name": "ftw.builder.create", "line_number": 296, "usage_type": "call"}, {"api_name": "ftw.builder.Builder", "line_number": 296, "usage_type": "call"}, {"api_name": "ftw.zipexport.zipfilestream.ZipFile", "line_number": 305, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 305, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 306, "usage_type": "call"}, {"api_name": "ftw.testbrowser.browsing", "line_number": 284, "usage_type": "name"}]} +{"seq_id": "434057378", "text": "from rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import status\n\nfrom .serializers import *\n\n\nclass SubjectAPI(APIView):\n def get(self, request, format=None):\n serializer = SubjectSerializer(Subject.objects.all(), many=True)\n return Response(serializer.data)\n\n def post(self, request, format=None):\n serializer = SubjectSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n def put(self, request, format=None):\n subject_id = self.request.query_params.get('id', False)\n subject = Subject.objects.get(id=subject_id)\n\n serializer = SubjectSerializer(subject, data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n def delete(self, request, format=None):\n subject_id = self.request.query_params.get('id', False)\n subject = Subject.objects.filter(id=subject_id)\n subject.delete()\n return Response(status=status.HTTP_204_NO_CONTENT)", "sub_path": "profiles/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 1348, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "rest_framework.views.APIView", "line_number": 8, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 11, "usage_type": "call"}, {"api_name": "rest_framework.response.Response", "line_number": 17, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 17, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 17, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 18, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 18, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 18, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 27, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 27, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 27, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 28, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 28, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 28, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 34, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_204_NO_CONTENT", "line_number": 34, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "505231856", "text": "# -*- coding: utf-8 -*-\n\nfrom azure.cosmosdb.table.tableservice import TableService\nfrom azure.cosmosdb.table.models import Entity\nfrom azure.cosmosdb.table.tablebatch import TableBatch\n\n# Azure Cosmos DB への接続（接続文字列を設定）\ntable_service = TableService(connection_string='DefaultEndpointsProtocol=https;AccountName=dbforfaceapitest;AccountKey=fNpt9FKY2FjKa0q1ANN7KT9Gn/s2c8NY1gB8jenN9jqide2eWdz+zw8rX6RlU+DmSLW4tVvLpvyUUnPFhaH6PQ==;EndpointSuffix=core.windows.net')\n\n'''\n# テーブルを作成する\ntable_service.create_table('deskStatusTable')\n\n# エンティティをテーブルに追加する\nentity = Entity()\nentity.PartitionKey = 'userId'\nentity.RowKey = 'S7223'\nentity.userName = '安井誠良'\ntable_service.insert_entity('userMaster', entity)\n\n\n# エンティティをまとめてテーブルに追加する\nbatch = TableBatch()\ndesk001 = {'PartitionKey': 'deskId', 'RowKey':'0000001', 'floorId':'017', 'x':170, 'y':180, 'userId':'', 'userName':'', 'statusCd':''}\ndesk002 = {'PartitionKey': 'deskId', 'RowKey':'0000002', 'floorId':'017', 'x':170, 'y':270, 'userId':'A001', 'userName':'高橋加奈', 'statusCd':'1'}\ndesk003 = {'PartitionKey': 'deskId', 'RowKey':'0000003', 'floorId':'017', 'x':170, 'y':360, 'userId':'', 'userName':'', 'statusCd':''}\ndesk004 = {'PartitionKey': 'deskId', 'RowKey':'0000004', 'floorId':'017', 'x':170, 'y':450, 'userId':'A002', 'userName':'山口良太', 'statusCd':'3'}\ndesk005 = {'PartitionKey': 'deskId', 'RowKey':'0000005', 'floorId':'017', 'x':260, 'y':180, 'userId':'S7223', 'userName':'安井誠良', 'statusCd':'2'}\ndesk006 = {'PartitionKey': 'deskId', 'RowKey':'0000006', 'floorId':'017', 'x':260, 'y':270, 'userId':'', 'userName':'', 'statusCd':''}\ndesk007 = {'PartitionKey': 'deskId', 'RowKey':'0000007', 'floorId':'017', 'x':260, 'y':360, 'userId':'A003', 'userName':'金森祐', 'statusCd':'4'}\ndesk008 = {'PartitionKey': 'deskId', 'RowKey':'0000008', 'floorId':'017', 'x':260, 'y':450, 'userId':'', 'userName':'', 'statusCd':''}\n\nbatch.insert_entity(desk001)\nbatch.insert_entity(desk002)\nbatch.insert_entity(desk003)\nbatch.insert_entity(desk004)\nbatch.insert_entity(desk005)\nbatch.insert_entity(desk006)\nbatch.insert_entity(desk007)\nbatch.insert_entity(desk008)\n\ntable_service.commit_batch('deskStatusTable', batch)\n'''\n\n# エンティティをまとめて更新する\nbatch = TableBatch()\ndesk001 = {'PartitionKey': 'deskId', 'RowKey':'0000001', 'floorId':'017', 'x':170, 'y':60, 'userId':'A001', 'userName':'高橋加奈', 'statusCd':'2'}\ndesk002 = {'PartitionKey': 'deskId', 'RowKey':'0000002', 'floorId':'017', 'x':170, 'y':110, 'userId':'', 'userName':'', 'statusCd':''}\ndesk003 = {'PartitionKey': 'deskId', 'RowKey':'0000003', 'floorId':'017', 'x':170, 'y':170, 'userId':'', 'userName':'', 'statusCd':''}\ndesk004 = {'PartitionKey': 'deskId', 'RowKey':'0000004', 'floorId':'017', 'x':170, 'y':220, 'userId':'', 'userName':'', 'statusCd':''}\ndesk005 = {'PartitionKey': 'deskId', 'RowKey':'0000005', 'floorId':'017', 'x':260, 'y':60, 'userId':'', 'userName':'', 'statusCd':''}\ndesk006 = {'PartitionKey': 'deskId', 'RowKey':'0000006', 'floorId':'017', 'x':260, 'y':110, 'userId':'', 'userName':'', 'statusCd':''}\ndesk007 = {'PartitionKey': 'deskId', 'RowKey':'0000007', 'floorId':'017', 'x':260, 'y':170, 'userId':'', 'userName':'', 'statusCd':''}\ndesk008 = {'PartitionKey': 'deskId', 'RowKey':'0000008', 'floorId':'017', 'x':260, 'y':220, 'userId':'', 'userName':'', 'statusCd':''}\n\nbatch.insert_or_replace_entity(desk001)\nbatch.insert_or_replace_entity(desk002)\nbatch.insert_or_replace_entity(desk003)\nbatch.insert_or_replace_entity(desk004)\nbatch.insert_or_replace_entity(desk005)\nbatch.insert_or_replace_entity(desk006)\nbatch.insert_or_replace_entity(desk007)\nbatch.insert_or_replace_entity(desk008)\n\ntable_service.commit_batch('deskStatusTable', batch)\n\n'''\n# エンティティを更新する\nentity = Entity()\nentity.PartitionKey = 'deskId'\nentity.RowKey = '0000001'\nentity.floorId = '017'\nentity.userId = 'A001'\nentity.userName = '高橋加奈'\nentity.statusCd = '2'\ntable_service.merge_entity('deskStatusTable', entity, if_match=\"*\")\n'''\n\nstatusList = {'1':'在席', '2':'話し中', '3':'トラブル発生', '4':'離席中'}\n\n# エンティティを照会する\ndesks = table_service.query_entities('deskStatusTable', filter=\"PartitionKey eq 'deskId'\")\nfor desk in desks:\n status = '不明'\n if len(desk['statusCd'])>0:\n status = statusList[desk['statusCd']]\n\n print('deskId:{0}, userName:{1}, status:{2}'.format(desk['RowKey'], desk['userName'], status))\n", "sub_path": "sonota_sample/sample_cosmosDB.py", "file_name": "sample_cosmosDB.py", "file_ext": "py", "file_size_in_byte": 4567, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "azure.cosmosdb.table.tableservice.TableService", "line_number": 8, "usage_type": "call"}, {"api_name": "azure.cosmosdb.table.tablebatch.TableBatch", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "411806251", "text": "from imp import reload\nfrom nltk.corpus import stopwords\nfrom collections import Counter\nimport pandas as pd\nimport numpy as np\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport nltk,re,pprint\nimport sys,glob,os\nimport operator, string, argparse, math, random\nimport matplotlib.pyplot as plot\n\nclass flingPretrained:\n def __init__(self,data):\n self.data = data\n self.nDocs = len(self.data)\n self.allDistances = {}\n self.wordVecModel = None\n print(\"\\nDBSCAN initialized!\\n\")\n \n def loadPretrainedWordVectors(self,vecType):\n if vecType == 'glove':\n self.wordVecModel = self.loadGloveModel()\n print(\"GloVe Vectors Loaded!\\n\")\n\n def loadGloveModel(self):\n print(\"Loading Glove Model\\n\")\n try:\n f = open('../datasets/glove.6B/glove.6B.50d.txt','r')\n except:\n f = open('datasets/glove.6B/glove.6B.50d.txt','r')\n gloveModel = {}\n for line in f:\n splitLines = line.split()\n word = splitLines[0]\n wordEmbedding = np.array([float(value) for value in splitLines[1:]])\n gloveModel[word] = wordEmbedding\n print(len(gloveModel),\" words loaded!\\n\")\n return gloveModel\n \n def getDocVector(self,doc_Id):\n gvl=self.getGloveVectorList(listx)\n glove_dv = np.mean(gvl,axis=0)\n return(glove_dv)\n \n def addDocumentGloveVector(self):\n vecL = []\n for indx in range(self.nDocs):\n listWords_1 = set(list(self.data['tfMatrix'][int(indx)]['word']))\n gvl=self.getGloveVectorList(listWords_1)\n vecL.append(np.mean(gvl,axis=0))\n self.data['glove-vector'] = vecL\n\n # distance between two documents using TF-IDF\n def distanceBtnTwoDocs(self, docId_1, docId_2):\n listWords_1 = set(list(self.data['tfMatrix'][int(docId_1)]['word']))\n listWords_2 = set(list(self.data['tfMatrix'][int(docId_2)]['word']))\n common = listWords_1.intersection(listWords_2)\n diff1_2 = listWords_1.difference(listWords_2)\n diff2_1 = listWords_2.difference(listWords_1)\n sumwt1 = self.data['sumTFIDF'][docId_1]\n sumwt2 = self.data['sumTFIDF'][docId_2]\n score_common, score_doc1, score_doc2 = 0,0,0\n #print(len(common),len(diff1_2),len(diff2_1))\n for word_c in common:\n score_1 = float(self.data['tfMatrix'][docId_1].loc[self.data['tfMatrix'][docId_1]['word'] == word_c]['tf-idf'])\n score_2 = float(self.data['tfMatrix'][docId_2].loc[self.data['tfMatrix'][docId_2]['word'] == word_c]['tf-idf'])\n score_common += abs(score_1/float(sumwt1) - score_2/float(sumwt2))\n for word_d12 in diff1_2:\n score_1 = float(self.data['tfMatrix'][docId_1].loc[self.data['tfMatrix'][docId_1]['word'] == word_d12]['tf-idf'])\n score_doc1 += score_1/float(sumwt1)\n for word_d21 in diff2_1:\n score_2 = float(self.data['tfMatrix'][docId_2].loc[self.data['tfMatrix'][docId_2]['word'] == word_d21]['tf-idf'])\n score_doc2 += score_2/float(sumwt2)\n score_total = score_common + score_doc1 + score_doc2\n return(score_total)\n \n def getGloveVectorList(self,listx):\n vecList = []\n nf = []\n for w in listx:\n try:\n vecList.append(self.wordVecModel[w])\n except:\n nf.append(w)\n #print(w,\"not found in glove model!\")\n continue \n if len(vecList)==0:\n return([[0]*50])\n vecArray = np.stack(vecList, axis=0 )\n return vecArray\n \n def getDocVector(self,listx):\n gvl=self.getGloveVectorList(listx)\n glove_dv = np.mean(gvl,axis=0)\n return(glove_dv)\n \n def getGloveDistance(self,docId_1,docId_2,method):\n listWords_1 = set(list(self.data['tfMatrix'][int(docId_1)]['word']))\n listWords_2 = set(list(self.data['tfMatrix'][int(docId_2)]['word']))\n if method == 'average':\n dv_1 = self.getDocVector(listWords_1)\n dv_2 = self.getDocVector(listWords_2)\n #print(\"dv_1\",dv_1)\n #print(\"dv_2\",dv_2)\n dist = np.linalg.norm(dv_1-dv_2)\n return dist\n \n def drawProgressBar(self, percent, barLen = 50):\t\t\t#just a progress bar so that you dont lose patience\n sys.stdout.write(\"\\r\")\n progress = \"\"\n for i in range(barLen):\n if i\n \"\"\"\n \n # Construct the parser (which is stored in parser)\n # Module docstring lives in __doc__\n # See http://python-forum.com/pythonforum/viewtopic.php?f=3&t=36847\n # And a formatter class so our examples in the docstring look good. Isn't it\n # convenient how we already wrapped it to 80 characters?\n # See http://docs.python.org/library/argparse.html#formatter-class\n parser = argparse.ArgumentParser(description=__doc__, \n formatter_class=argparse.RawDescriptionHelpFormatter)\n \n parser.add_argument(\"--input\", type=argparse.FileType('r'), default=sys.stdin,\n help=\"line-oriented JSON GAM to process\")\n parser.add_argument(\"outdir\",\n help=\"directory to place output in\")\n \n # The command line arguments start with the program name, which we don't\n # want to treat as an argument for argparse. So we remove it.\n args = args[1:]\n \n return parser.parse_args(args)\n \ndef make_stats(read, stages=STAGES):\n \"\"\"\n Given a read dict parsed from JSON, compute a stats dict for the read.\n \n Run on an empty dict, makes a zero-value stats dict.\n \n A stats dict maps from stage name to a dict of stage stats.\n The stage stats include:\n \n - 'time' for just the stage.\n - 'cumulative_time' for the stage and all prior stages.\n - 'substage_time' for each substage, which is a dict from substage name to time.\n - 'correct_stop' which is the count of correct mappings that stop at this stage.\n - 'results' which is the count of results produced from this stage.\n \n All values are filled, even if 0 or not applicable.\n \n There is a special 'overall' stage, with just the 'time' key, recording the overall read time.\n \"\"\"\n \n # This is the annotation dict from the read\n annot = read.get('annotation', {})\n \n # This is the stats dict we will fill in\n stats = {}\n \n # This is the cumulative time accumulator\n cumulative_time = 0.0\n \n for stage in stages:\n # For each stage in order\n \n # Prepare stats for the stage\n stage_dict = {}\n \n # Grab its runtime from the read\n stage_dict['time'] = annot.get(\n 'stage_' + stage + '_seconds', 0.0)\n \n # Add into and store the cumulative time\n cumulative_time += stage_dict['time']\n stage_dict['cumulative_time'] = cumulative_time\n \n # Grab its result count from the read\n stage_dict['results'] = int(annot.get(\n 'stage_' + stage + '_results', 0))\n \n # No correct mappings end here\n stage_dict['correct_stop'] = 0\n if annot.get('last_correct_stage', None) == stage:\n # Unless one does\n stage_dict['correct_stop'] += 1\n elif stage == 'minimizer' and annot.get('last_correct_stage', None) == 'none':\n # Reads that do not have correct seeds end at the minimizer stage\n stage_dict['correct_stop'] += 1\n \n # Set up substage times\n substage_time = {}\n for substage in SUBSTAGES[stage]:\n # For each substage it has, record substage time.\n # TODO: no good way to iterate the substages provided in the file.\n substage_time['substage'] = annot.get(\n 'stage_' + stage + '_' + substage + '_seconds', 0.0)\n \n stage_dict['substage_time'] = substage_time\n \n # Save the stats for the stage\n stats[stage] = stage_dict\n \n # Add the overall runtime\n stats['overall'] = {'time': read.get('annotation', {}).get('map_seconds', 0.0)}\n \n return stats\n\ndef add_in_stats(destination, addend):\n \"\"\"\n Add the addend stats dict into the destinatin stats dict.\n Implements += for stats dicts.\n \"\"\"\n \n # Internally we're just recursive += on dicts.\n for k, v in addend.items():\n if isinstance(v, dict):\n # Recurse into dict\n add_in_stats(destination[k], v)\n else:\n # Use real += and hope it works\n destination[k] += v\n\ndef read_line_oriented_json(lines):\n \"\"\"\n For each line in the given stream, yield it as a parsed JSON object.\n \"\"\"\n \n for line in lines:\n yield json.loads(line)\n \nclass Table(object):\n \"\"\"\n Format a table of output nicely in fixed-width text.\n \"\"\"\n \n # Interface\n \n def __init__(self, widths, out=sys.stdout):\n \"\"\"\n Start a table with the given column widths (a list of integers) in\n characters, printing to the given stream.\n \"\"\"\n \n # Remember the base widths\n self.widths = widths\n \n # Remember the out stream\n self.out = out\n \n # Remember the previous actual column widths used, if any.\n # None if no wor has been produced.\n self.last_widths = None\n \n # Remember if we need a dividing line\n self.need_line = False\n \n def line(self):\n \"\"\"\n Say to divide the previous row from the next row.\n \"\"\"\n \n self.need_line = True\n \n def row(self, values, justify='l', merge=None, line_top=False, line_bottom=False):\n \"\"\"\n Given a list of values, one per column, for up to the number of columns\n in the table, draw a table row.\n \n Justify can be 'l', 'r', 'c' or a list/string of those per value.\n \n If merge is given, it must be a list of the number of cells to merge\n horizontally for each value.\n \n Different merge values without a line_top separator will look bad.\n If line_top is set, divide from the previous row.\n If line_bottom is set, divide from the next row.\n \"\"\"\n \n # Compute new merged widths\n merged_widths = self.compute_merges(merge)\n \n # Start or continue the table\n if self.last_widths is None:\n # Start the table\n self.start(merged_widths)\n elif self.need_line or line_top:\n # Divide from the previous row.\n self.sep(self.last_widths, merged_widths)\n \n # Print the actual row\n self.cells(values, justify, merged_widths)\n \n # Remember this row's widths for next time.\n self.last_widths = merged_widths\n \n # Remember if we need a line\n self.need_line = line_bottom\n \n def close(self):\n \"\"\"\n Close off the table at the bottom.\n \"\"\"\n \n if self.last_widths is None:\n self.last_widths = self.widths\n \n self.end(self.last_widths)\n \n self.last_widths = None\n \n def inner_width(self):\n \"\"\"\n Get the total width of the table across all columns, between the outer edges.\n \"\"\"\n \n return sum(self.widths) + len(self.widths) - 1\n \n # Internal methods\n \n def box(self, part):\n \"\"\"\n Return the box-drawing character to draw the given part of a box.\n Parts are {(t)op, (m)iddle, (b)ottom} crossed with {(l)eft, (m)iddle,\n (r)ight} as two-character strings, plus (v)ertical and (h)orizontal as one-character strings.\n \"\"\"\n \n skin = {\n 'tl': '┌',\n 'tm': '┬',\n 'tr': '┐',\n 'bl': '└',\n 'bm': '┴',\n 'br': '┘',\n 'ml': '├',\n 'mm': '┼',\n 'mr': '┤',\n 'v': '│',\n 'h': '─'\n }\n \n return skin[part]\n \n def horizontal(self, left, junction, right, column, widths=None):\n \"\"\"\n Print a line across (either top, middle, or bottom).\n \n Takes the leftmost, between-column, rightmost, and in-column characters\n as box() character ID strings.\n \n Can use a specified widths list, usually self.widths.\n \"\"\"\n \n if widths is None:\n widths = self.widths\n \n # Start edge\n self.out.write(self.box(left))\n \n for i, width in enumerate(widths):\n # For each column\n # Do its top line\n self.out.write(self.box(column) * width)\n if i + 1 != len(widths):\n # Do the separator\n self.out.write(self.box(junction))\n \n # End edge\n self.out.write(self.box(right))\n \n self.out.write('\\n')\n \n def start(self, widths_after):\n \"\"\"\n Print an opening line at the top of the table.\n Needs to know the widths of the cells on the next table line.\n \"\"\"\n \n self.horizontal('tl', 'tm', 'tr', 'h', widths_after)\n \n def end(self, widths_before):\n \"\"\"\n Print a closing line at the bottom of the table.\n Needs to know the widths of the cells on the previous table line.\n \"\"\"\n \n self.horizontal('bl', 'bm', 'br', 'h', widths_before)\n \n def sep(self, widths_before, widths_after):\n \"\"\"\n Print a middle separator line across the table.\n Needs to know the widths of the cells on the previous and next table lines.\n Both sets of widths must describe a table of the same total width.\n \"\"\"\n \n # Start edge\n self.out.write(self.box('ml'))\n \n # Compute total width (cells and separators), not counting final border\n total_width = sum(widths_before) + len(widths_before) - 1\n \n # Track what cell we are in on top\n before_cursor = 0\n # And what column its trailing border is at\n before_border = widths_before[before_cursor]\n # Track what cell we are in on the bottom\n after_cursor = 0\n # And what column its trailing border is at\n after_border = widths_after[after_cursor]\n # Track what column of internal table width we are in.\n col = 0\n \n while col < total_width:\n if col == before_border:\n if col == after_border:\n # Junction on both sides\n char = self.box('mm')\n \n # Advance after\n after_cursor += 1\n after_border += widths_after[after_cursor] + 1\n else:\n # Junction on top only\n char = self.box('bm')\n \n # Advance before\n before_cursor += 1\n before_border += widths_before[before_cursor] + 1\n elif col == after_border:\n # Junction on bottom only\n char = self.box('tm')\n \n # Advance after\n after_cursor += 1\n after_border += widths_after[after_cursor] + 1\n else:\n # No junction\n char = self.box('h')\n \n # Print the character\n self.out.write(char)\n \n # Go to the next column\n col += 1\n \n \n # End edge\n self.out.write(self.box('mr'))\n \n self.out.write('\\n')\n \n def compute_merges(self, merges=None):\n \"\"\"\n Given a list of cell counts to merge horizontally, compute new widths from self.widths.\n \n If merges is None, use self.widths.\n \"\"\"\n \n widths = self.widths\n \n if merges is not None:\n new_widths = []\n width_cursor = 0\n for merge in merges:\n # Compute a new column by merging the given number of old columns.\n merged_width = 0\n for i in range(merge):\n # Take the widths of all cells\n merged_width += widths[width_cursor]\n width_cursor += 1\n # Take the separating columns between cells\n merged_width += merge - 1\n new_widths.append(merged_width)\n while width_cursor < len(widths):\n # Copy any unmerged columns\n new_widths.append(widths[i])\n \n widths = new_widths\n \n return widths\n \n def cells(self, values, justify, widths):\n \"\"\"\n Given a list of values, one per column, for up to the number of columns\n in the table, draw a table row.\n \n Justify can be 'l', 'r', 'c', or a list/string of those per value.\n \n Column count/widths must be passed.\n \"\"\"\n \n # Start the row\n self.out.write(self.box('v'))\n \n for i, (value, width) in enumerate(itertools.zip_longest(values, widths)):\n # For each item and its column and width...\n if width is None:\n # Too many items\n raise RuntimeError(\"Ran out of table width values ({}) for {} columns\".format(\n len(widths), len(values)))\n \n # Compute the item string\n item_string = str(value) if value is not None else ''\n \n # Decide on justification for this item\n if justify == 'l':\n item_just = 'l'\n elif justify == 'r':\n item_just = 'r'\n if justify == 'c':\n item_just = 'c'\n elif i < len(justify):\n item_just = justify[i]\n else:\n item_just = 'l'\n \n # Actually justify it in a field of the necessary width\n if item_just == 'l':\n justified_item = item_string.ljust(width)\n elif item_just == 'r':\n justified_item = item_string.rjust(width)\n elif item_just == 'c':\n justified_item = item_string.center(width)\n else:\n raise RuntimeError('Invalid justification: {}'.format(item_just))\n \n # Output the content\n self.out.write(justified_item)\n \n if (i + 1 != len(widths)):\n # This isn't the last item. Do a separator.\n self.out.write(self.box('v'))\n \n \n # End the row\n # TODO: Same as the separator\n self.out.write(self.box('v'))\n \n self.out.write('\\n')\n \ndef print_table(read_count, stats_total, have_times, stages=STAGES, out=sys.stdout):\n \"\"\"\n Take the read count, and the accumulated total stats dict.\n \n Print a nicely formatted table to the given stream.\n \n If have_times is false, elides the time and speed columns which would be full of NANs.\n \"\"\"\n \n # Now do a table\n \n # First header line for each column\n headers = []\n # Second header line for wach column\n headers2 = []\n # Column min widths from headers\n header_widths = []\n \n # How long is the longest stage name\n stage_width = max((len(x) for x in STAGES))\n # Leave room for the header\n stage_header = \"Stage\"\n stage_width = max(stage_width, len(stage_header))\n # And for the \"Overall\" entry\n stage_overall = \"Overall\"\n stage_width = max(stage_width, len(stage_overall))\n \n headers.append(stage_header)\n headers2.append('')\n header_widths.append(stage_width)\n \n if have_times:\n # How about the reads per second column\n rps_header = \"Reads/Second\"\n rps_header2 = \"(cumulative)\"\n rps_width = max(len(rps_header), len(rps_header2))\n \n headers.append(rps_header)\n headers2.append(rps_header2)\n header_widths.append(rps_width)\n \n # And the time percent column\n time_header = \"Time\"\n time_header2 = \"(%)\"\n # Make sure to leave room for \"100%\"\n time_width = max(len(time_header), len(time_header2), 4)\n \n headers.append(time_header)\n headers2.append(time_header2)\n header_widths.append(time_width)\n \n # And the result count column (average)\n results_header = \"Candidates\"\n results_header2 = \"(Avg.)\"\n results_width = max(len(results_header), len(results_header2))\n \n headers.append(results_header)\n headers2.append(results_header2)\n header_widths.append(results_width)\n \n # And the correct result lost count header\n lost_header = \"Correct\"\n lost_header2 = \"Lost\"\n # How big a number will we need to hold?\n # Look at the reads lost at all stages except final (because if you make it to the final stage nothing is lost)\n lost_reads = [stats_total[stage]['correct_stop'] for stage in STAGES[:-1]]\n max_stage_stop = max(lost_reads)\n # How many reads are lost overall?\n overall_lost = sum(lost_reads)\n lost_width = max(len(lost_header), len(lost_header2), len(str(max_stage_stop)), len(str(overall_lost)))\n \n headers.append(lost_header)\n headers2.append(lost_header2)\n header_widths.append(lost_width)\n \n # Start the table\n table = Table(header_widths)\n \n table.row([\"Giraffe Facts\"], 'c', merge=[len(header_widths)])\n table.line()\n table.row(['Reads' + str(read_count).rjust(table.inner_width() - 5)], merge=[len(header_widths)])\n table.line()\n table.row(headers, 'c')\n table.row(headers2, 'c')\n table.line()\n \n \n # Get the total overall time for all reads\n overall_time = stats_total['overall']['time']\n \n for stage in stages:\n # Grab total cumulative time for this stage and all before\n total_cumulative_time = stats_total[stage]['cumulative_time']\n # Compute cumulative reads per second value\n reads_per_second = read_count / total_cumulative_time if total_cumulative_time != 0 else float('NaN')\n \n # Grab total time for just this stage\n stage_time = stats_total[stage]['time']\n # And get the portion that is this stage out of all time\n stage_portion = stage_time / overall_time if overall_time != 0 else float('NaN')\n # And make a percent\n stage_percent = stage_portion * 100\n \n # Grab average results at this stage\n total_results = stats_total[stage]['results']\n average_results = total_results / read_count if read_count != 0 else float('NaN')\n \n # Grab reads that are lost.\n # No reads are lost at the final stage.\n lost = stats_total[stage]['correct_stop'] if stage != stages[-1] else '-'\n \n row = [stage]\n align = 'c'\n if have_times:\n # Include the time columns\n row += ['{:.2f}'.format(reads_per_second), '{:.0f}%'.format(stage_percent)]\n align += 'rr'\n # Add the provenance columns\n row += ['{:.2f}'.format(average_results), lost]\n align += 'rr'\n \n # Output the final row\n table.row(row, align)\n \n table.line()\n \n # And do overall reads per second\n reads_per_second_overall = read_count / overall_time if overall_time != 0 else float('NaN')\n \n # Compose the overall row\n row = [stage_overall]\n align = 'c'\n if have_times:\n # Include the time columns\n row += ['{:.2f}'.format(reads_per_second_overall), '100%']\n align += 'rr'\n # Add the provenance columns\n row += ['', overall_lost]\n align += 'rr'\n \n table.row(row, align)\n \n # Close off table\n table.close()\n\n\ndef main(args):\n \"\"\"\n Parses command line arguments and do the work of the program.\n \"args\" specifies the program arguments, with args[0] being the executable\n name. The return value should be used as the program's exit code.\n \"\"\"\n \n print(random.choice(FACTS), file = sys.stderr)\n \n options = parse_args(args) # This holds the nicely-parsed options object\n \n # Make the output directory if it doesn't exist\n os.makedirs(options.outdir, exist_ok=True)\n \n # Open a TSV file to draw a histogram from\n tsv_path = os.path.join(options.outdir, 'times.tsv')\n tsv = open(tsv_path, 'w')\n \n # Make a place to total up all the stats\n stats_total = make_stats({})\n \n # Count all the reads\n read_count = 0\n \n # See if wa have any times actually.\n # If they are all 0 (which they are now that we have ripped out timing) we\n # can't actually draw the plot.\n have_times = False\n \n for stats in (make_stats(read) for read in read_line_oriented_json(options.input)):\n # For the stats dict for each read\n \n for stage in STAGES:\n # For each stage, grab the cumulative time\n cumulative_time = stats[stage]['cumulative_time']\n \n # Dump cumulative times to the TSV we will plot a histogram from\n tsv.write('{}\\t{}\\n'.format(stage, cumulative_time))\n \n if cumulative_time > 0:\n have_times = True\n \n # Also include total time\n tsv.write('total\\t{}\\n'.format(stats['overall']['time']))\n \n # Sum up all the stats\n add_in_stats(stats_total, stats)\n \n # Count the read\n read_count += 1\n \n # After processing all the reads\n \n # Close the TSV\n tsv.close()\n \n # Print the table now in case histogram plotting fails\n print_table(read_count, stats_total, have_times)\n \n if have_times:\n # Plot the histogram\n svg_path = os.path.join(options.outdir, 'times.svg')\n histogram.main(['histogram.py', tsv_path, '--save', svg_path,\n '--title', 'Runtime Histogram',\n '--x_label', 'Time (seconds)',\n '--line',\n '--bins', '100',\n '--log',\n '--cumulative',\n '--y_label', 'Cumulative Count',\n '--legend_overlay', 'lower right',\n '--categories', 'total'] + STAGES)\n \n \n \ndef entrypoint():\n \"\"\"\n 0-argument entry point for setuptools to call.\n \"\"\"\n \n # Provide main with its arguments and handle exit codes\n sys.exit(main(sys.argv))\n \nif __name__ == \"__main__\" :\n entrypoint()\n \n\n", "sub_path": "scripts/giraffe-facts.py", "file_name": "giraffe-facts.py", "file_ext": "py", "file_size_in_byte": 27889, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 105, "usage_type": "call"}, {"api_name": "argparse.RawDescriptionHelpFormatter", "line_number": 106, "usage_type": "attribute"}, {"api_name": "argparse.FileType", "line_number": 108, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 108, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 214, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 223, "usage_type": "attribute"}, {"api_name": "itertools.zip_longest", "line_number": 479, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 525, "usage_type": "attribute"}, {"api_name": "random.choice", "line_number": 678, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 678, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 683, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 686, "usage_type": "call"}, {"api_name": "os.path", "line_number": 686, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 732, "usage_type": "call"}, {"api_name": "os.path", "line_number": 732, "usage_type": "attribute"}, {"api_name": "histogram.main", "line_number": 733, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 752, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 752, "usage_type": "attribute"}]} +{"seq_id": "501846504", "text": "#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\n Created on Mon Mar 5 14:41:11 2018\n\n @author: Taewoo\n https://github.com/jeonghoonkang\n \"\"\"\n\n\nfrom __future__ import print_function\n\nimport time\nimport requests\nimport json\n\nfrom collections import OrderedDict\nfrom multiprocessing import current_process\n\nimport Utils\n\nprint_head = ' '*16 + '[LIB_OPENTSDB]'\n\nMAX_BUFFER = 1000000\n#requests.adapters.DEFAULT_RETRIES = 8\n\n\"\"\" 실제 작업에 필요한 query parameter 예 - 초기값 \"\"\"\n\nquery_parameter = {\n \"start\" : \"2014-06-01 00:00:00\",\n \"end\": \"2014-06-02 00:00:00\",\n \"aggregator\" : \"none\",\n \"metric\" : \"____test____\"\n}\n\n\"\"\" 쿼리 하려는 tag \"\"\"\nquery_tags = {\n}\n\n\ndef convertTimeToEpoch(_time):\n date_time = \"%s.%s.%s %s:%s:%s\" %(_time[8:10], _time[5:7], _time[:4], _time[-8:-6], _time[-5:-3], _time[-2:])\n #print date_time\n pattern = '%d.%m.%Y %H:%M:%S'\n epoch = int (time.mktime(time.strptime(date_time, pattern)))\n return epoch\n\n\ndef _processingResponse(in_data):\n '''openTSDB에서 전송받은, string 들을 dictionary로 변경하여\n dict 를 회신함. 형태를 변경하고, dict의 갯수를 알려줌'''\n _d = in_data\n _r = _d.json()\n # queryData.content is string, thus convert this to list\n _l = len(_r)\n\n return _r, _l\n\n\ndef query_by_timedelta(_date, meta, dys=None, hrs=None, mins=None):\n global query_parameter\n \n if dys != None : \n _t_scale = meta['days']\n _type = 'days'\n elif hrs != None : \n _t_scale = meta['hrs']\n _type = 'hrs'\n elif mins != None : \n _t_scale = meta['mins']\n _type = 'mins'\n\n assert type(_t_scale)==int, 'not integer for t_scale'\n query_parameter['start'] = _date\n # delta 시간만큼 시간 변환한 string\n query_parameter['end'] = Utils.strday_delta(_date, _type, _t_scale)\n query_parameter['aggregator'] = meta['aggregator']\n query_parameter['metric'] = meta['in_metric']\n\n query_tags['VEHICLE_NUM'] = meta['carid']\n query_tags['fieldname'] = meta['content']\n\n _q_para = query_parameter\n _url = 'http://' + meta['ip'] + ':' + meta['port'] + '/api/query'\n\n queryData = QueryData(_url, _q_para, query_tags)\n _dictbuf, _dictlen = _processingResponse(queryData)\n \n return _dictbuf, query_parameter['end']\n\n# 여러 메트릭 \ndef query_by_timedelta_v3(_date, meta, dys=None, hrs=None, mins=None):\n global query_parameter\n \n if dys != None : \n _t_scale = meta['days']\n _type = 'days'\n elif hrs != None : \n _t_scale = meta['hrs']\n _type = 'hrs'\n elif mins != None : \n _t_scale = meta['mins']\n _type = 'mins'\n\n assert type(_t_scale)==int, 'not integer for t_scale'\n query_parameter['start'] = _date\n # delta 시간만큼 시간 변환한 string\n query_parameter['end'] = Utils.strday_delta(_date, _type, _t_scale)\n query_parameter['aggregator'] = meta['aggregator']\n \n metric_list = meta['in_metric'].split('|')\n return_dictbuf=[]\n for _metric in metric_list:\n query_parameter['metric'] = _metric\n\n query_tags['VEHICLE_NUM'] = meta['carid']\n \n _q_para = query_parameter\n _url = 'http://' + meta['ip'] + ':' + meta['port'] + '/api/query'\n\n queryData = QueryData(_url, _q_para, query_tags)\n \n _dictbuf, _dictlen = _processingResponse(queryData)\n for _dict in _dictbuf:\n return_dictbuf.append(_dict)\n \n return return_dictbuf, query_parameter['end']\n\ndef query_by_non_repetitive_time(q_start_time, q_end_time, meta):\n global query_parameter\n\n #print(print_head, __file__, 'query starting...')\n\n\n query_parameter['start'] = q_start_time\n query_parameter['end'] = q_end_time\n query_parameter['aggregator'] = meta['aggregator']\n query_parameter['metric'] = meta['in_metric']\n\n query_tags['VEHICLE_NUM'] = meta['carid']\n\n _q_para = query_parameter\n _url = 'http://' + meta['ip'] + ':' + meta['port'] + '/api/query'\n\n queryData = QueryData(_url, _q_para, query_tags)\n _dictbuf, _dictlen = _processingResponse(queryData)\n\n return _dictbuf\n\n\ndef QueryData(_url, _required, _tags=None):\n headers = {'content-type': 'application/json'}\n\n dp = OrderedDict() # dp (Data Point)\n dp[\"start\"] = convertTimeToEpoch(_required[\"start\"])\n dp[\"end\"] = convertTimeToEpoch(_required[\"end\"]) - int(1) # not exactly required\n\n temp = OrderedDict()\n temp[\"aggregator\"] = _required[\"aggregator\"]\n temp[\"metric\"] = _required[\"metric\"]\n if _tags != None:\n temp[\"tags\"] = _tags\n\n dp[\"queries\"] = []\n dp[\"queries\"].append(temp)\n #print (print_head, json.dumps(dp))\n\n #print \" [Querying]\" + json.dumps(dp, ensure_ascii=False, indent=4)\n response = requests.post(_url, data=json.dumps(dp), headers= headers)\n\n while response.status_code > 204:\n print(print_head,\" [Bad Request] Query status: %s\" % (response.status_code))\n print(print_head,\" [Bad Request] We got bad request, Query will be restarted after 3 sec!\\n\")\n time.sleep(3)\n\n print(print_head,\" [Querying]\" + json.dumps(dp, ensure_ascii=False, indent=4))\n response = requests.post(_url, data=json.dumps(dp), headers= headers)\n\n pout = \" [Query is done, got reponse from server]\" + __file__\n pout += \" : now starting processing, writing and more \"\n #print(print_head,pout)\n return response\n\n\n\n\n''' 실질적으로 OpenTSDB에 HTTP POST 방식으로 PUT Request를 보내는 함수\n 50개로 multiple data 를 put 하는게 가장 좋으며, 늘어날 때는 테스트 필요함\n http://opentsdb.net/docs/build/html/api_http/put.html '''\ndef putRequest(_session, _url, _buffer):\n ''' put sends json packs to opentsdb, since opentsdb runs on a multi-thread mode\n putRequest runs efficiently parallelized '''\n headers = {'content-type': 'application/json'}\n\n for i in xrange(0, len(_buffer), 50):\n #print json.dumps(_buffer[i:i+50], ensure_ascii=False, indent=4)\n response = _session.post(_url, data=json.dumps(_buffer[i:i+50]), headers= headers)\n while response.status_code > 204:\n print (print_head, \"error!\")\n print (print_head, response)\n response = _session.post(_url, data=json.dumps(_buffer[i:i+50]), headers= headers)\n \n\n if i+1 % 10000 == 0:\n print (\"\\tputData: %s / %s finished\" % (i+1, len(_buffer)))\n\ndef guaranteePutRetry(_session, _url, _buf, _time):\n ''' Prevent sending too many requests from same ip address in short period of time '''\n while(True):\n try:\n putRequest(_session, _url, _buf)\n except requests.exceptions.ConnectionError:\n print (\"<%s> -> RETRY after %ssec\" % (current_process().name, str(_time)))\n time.sleep(_time)\n continue\n break\n\n\n''' 데이터를 받아와서 JSON 형태로 만들어 buf에 담고 putRequest 함수로 전달 '''\ndef PutData(_session, _url, _metric, _content, _carid, _data):\n buf = []\n keys = _data.keys()\n\n for k in keys:\n dp = OrderedDict() # dp (Data Point)\n\n dp[\"metric\"] = _metric\n dp[\"timestamp\"] = int(k)\n dp[\"value\"] = int(_data[k])\n #print dp[\"value\"]\n\n dp[\"tags\"] = OrderedDict()\n dp[\"tags\"][\"content\"] = _content\n dp[\"tags\"][\"carid\"] = _carid\n\n buf.append(dp)\n\n if len(buf) >= MAX_BUFFER:\n guaranteePutRetry(_session, _url, buf, 3)\n buf = []\n\n guaranteePutRetry(_session, _url, buf ,3)\n", "sub_path": "opentsdb_metric_copy/src/lib/opentsdb/HTTP_POST_request.py", "file_name": "HTTP_POST_request.py", "file_ext": "py", "file_size_in_byte": 7579, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "time.mktime", "line_number": 45, "usage_type": "call"}, {"api_name": "time.strptime", "line_number": 45, "usage_type": "call"}, {"api_name": "Utils.strday_delta", "line_number": 76, "usage_type": "call"}, {"api_name": "Utils.strday_delta", "line_number": 108, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 154, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 158, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 169, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 169, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 174, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 176, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 177, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 177, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 197, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 201, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 212, "usage_type": "attribute"}, {"api_name": "multiprocessing.current_process", "line_number": 213, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 214, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 225, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 232, "usage_type": "call"}]} +{"seq_id": "165025682", "text": "#import certain functions into the global\n#namespace\nfrom app import app\nfrom markdown import markdown\n#from flask import render_template_string\nfrom app.blog_helpers import render_markdown\nfrom os import listdir\nfrom os.path import isfile, join\nfrom flask import render_template, render_template_string, request, session\nimport os\n\n#safe global import (okay to use)\nimport flask\n\n#global import (try to avoid)\n#from flask import *\n\n#home page\n@app.route(\"/\")\ndef home():\n\n onlyfiles = (\n [file \n for file in listdir('app/views') \n if isfile(join('app/views', file))\n ])\n return ', '.join(onlyfiles)\n\n@app.route(\"/favicon.ico\")\ndef favicon():\n return \"\"\n\n@app.route(\"/edit/\", methods=['GET', 'POST'])\ndef page(view_name):\n view_data = {}\n view_data['page_name'] = view_name\n dir_path = 'app/views'\n path = os.path.join(dir_path, view_name) \n if request.method == 'POST':\n f = open(path, \"w\")\n newcontent = request.values[\"content\"]\n f.write(newcontent)\n f.close()\n tempfile = open(path)\n contents = tempfile.read()\n view_data[\"content\"] = contents\n view_edit = 'edit.html'\n edit_path = os.path.join(dir_path, view_edit)\n temp_edit = open(edit_path)\n edit_read = temp_edit.read()\n return render_template_string(edit_read, data = view_data)\n\n#get login page\n@app.route(\"/login.html\", methods=['GET', 'POST'])\ndef login_page():\n view_data = {}\n username = 'admin'\n password = 'testpass'\n view_data['login']\n dir_path = 'app/views'\n if request.method == 'POST':\n view_data['name'] = request.values['user_name']\n view_data['pass'] = request.values['password']\n if view_data['name'] == username and view_data['pass'] == password:\n session['success'] = True \n else: \n session['success'] = False\n view_login = 'login.html'\n new_path = os.path.join(dir_path, view_login)\n temp_login = open(new_path)\n login_read = temp_login.read()\n return render_template_string(login_read, data=view_data)\n\n@app.route(\"/click_tracker\", methods=['GET', 'POST'])\ndef click_tracker():\n view_data = {}\n view_data[\"click_count\"] = 0\n if request.method == 'POST':\n view_data[\"click_count\"] = request.values[\"click_count\"]\n view_data[\"click_count\"] = int(view_data[\"click_count\"]) + 1\n return render_template('click_tracker.html', data=view_data)\n\n#generic page\n@app.route(\"/\")\n\n#input parameter name must match route parameter\ndef render_page(view_name):\n #file for file in listdir('app/views')\n view_data = {}\n view_data['page_name'] = view_name\n if view_name.endswith(\"md\"):\n # os.path.exists(\"app/views/\" + view_name + \".md\")\n html = render_markdown(view_name)\n view_data['content'] = html #figure this out\n # return render_template_string(html, view_name = view_name)\n return html\n elif view_name.endswith(\"html\"):\n# os.path.exists(\"app/views/\" + view_name + \".html\")\n html = render_markdown(view_name)\n view_data['content'] = html #figure this out\n return render_template_string(html, data = view_data)\n# return html", "sub_path": "routes.py", "file_name": "routes.py", "file_ext": "py", "file_size_in_byte": 3210, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.listdir", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 19, "usage_type": "call"}, {"api_name": "app.app", "line_number": 19, "usage_type": "name"}, {"api_name": "app.app.route", "line_number": 29, "usage_type": "call"}, {"api_name": "app.app", "line_number": 29, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request.method", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.request.values", "line_number": 41, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 41, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 48, "usage_type": "call"}, {"api_name": "os.path", "line_number": 48, "usage_type": "attribute"}, {"api_name": "flask.render_template_string", "line_number": 51, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 33, "usage_type": "call"}, {"api_name": "app.app", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 61, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 61, "usage_type": "name"}, {"api_name": "flask.request.values", "line_number": 62, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.request.values", "line_number": 63, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 63, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 65, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 67, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 69, "usage_type": "call"}, {"api_name": "os.path", "line_number": 69, "usage_type": "attribute"}, {"api_name": "flask.render_template_string", "line_number": 72, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 54, "usage_type": "call"}, {"api_name": "app.app", "line_number": 54, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 78, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.request.values", "line_number": 79, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 81, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 74, "usage_type": "call"}, {"api_name": "app.app", "line_number": 74, "usage_type": "name"}, {"api_name": "app.blog_helpers.render_markdown", "line_number": 93, "usage_type": "call"}, {"api_name": "app.blog_helpers.render_markdown", "line_number": 99, "usage_type": "call"}, {"api_name": "flask.render_template_string", "line_number": 101, "usage_type": "call"}, {"api_name": "app.app.route", "line_number": 84, "usage_type": "call"}, {"api_name": "app.app", "line_number": 84, "usage_type": "name"}]} +{"seq_id": "42094216", "text": "from rest_framework import viewsets\nfrom rest_framework import permissions\nfrom rest_framework import status\nfrom rest_framework.response import Response\nfrom rest_framework.decorators import api_view\nfrom rest_framework import generics\n\n\nfrom registration.serializers import *\nfrom PandeminoApp.models import Account\n\n\n@api_view(['POST'])\ndef registration_view(request):\n\n if request.method == 'POST':\n serializer = RegistrationSerlizer(data = request.data)\n\n data = {}\n if serializer.is_valid():\n account = serializer.save()\n data['response'] = \"Sucsefully registred a new user\"\n data['mail'] = account.mail\n data['username'] = account.username\n data['password'] = account.password\n else:\n data = serializer.errors\n return Response(data)\n\n\n@api_view(['GET'])\ndef registration_detail_all(request):\n\n accounts = Account.objects.all()\n if request.method == \"GET\":\n serializer = RegistrationSerlizer(accounts, many=True)\n return Response(serializer.data)\n", "sub_path": "registration/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1077, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "rest_framework.response.Response", "line_number": 28, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 13, "usage_type": "call"}, {"api_name": "PandeminoApp.models.Account.objects.all", "line_number": 34, "usage_type": "call"}, {"api_name": "PandeminoApp.models.Account.objects", "line_number": 34, "usage_type": "attribute"}, {"api_name": "PandeminoApp.models.Account", "line_number": 34, "usage_type": "name"}, {"api_name": "rest_framework.response.Response", "line_number": 37, "usage_type": "call"}, {"api_name": "rest_framework.decorators.api_view", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "549945360", "text": "import argparse\nimport os\nimport time\nimport copy\nimport six\nimport sys\nimport numpy as np\nfrom torch.utils.data.dataloader import DataLoader\n\nimport matplotlib.pyplot as plt\n\n### tensorboard\nfrom torch.utils.tensorboard import SummaryWriter\nfrom torch.optim import lr_scheduler\nfrom torchvision import transforms\nimport torch\n\n\nfrom networks.DIM_model import *\nfrom networks.train_nets import *\nfrom pre_proc.loader import LoadDataset,data_split,data_split_Tr_CV,LoadFeat\nfrom pre_proc.transf import Transform \nfrom networks.model import _classifier\nfrom networks.train_prior_disc import save_prior_dist\n\n\nclass NI_wrap():\n def __init__(self,dataset,val_data,device,path,load=False,replay=True):\n '''\n Class to wrapp architecture and training/testing function for the CVPR challenge\n \n '''\n self.load = load\n self.replay = replay\n self.stats = {\"ram\": [], \"disk\": []}\n self.dataset = dataset\n self.val_data = val_data\n \n self.tr = transforms.Compose([\n \n transforms.ToPILImage(),\n transforms.RandomChoice([\n transforms.ColorJitter(brightness=0.6),\n transforms.ColorJitter(contrast=0.4),\n transforms.ColorJitter(saturation=0.4),\n ]),\n transforms.RandomChoice([\n transforms.RandomHorizontalFlip(p=1),\n transforms.RandomVerticalFlip(p=1),\n transforms.RandomRotation(180, resample=3, expand=False, center=None, fill=0),\n transforms.RandomAffine(30, translate=(.1,.1), scale=(0.95,1.05), shear=5, resample=False, fillcolor=0)\n ]),\n\n transforms.ToTensor(),\n transforms.Normalize([0.60010594, 0.57207793, 0.54166424], [0.10679197, 0.10496728, 0.10731174])\n ])\n self.trT = transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize([0.60010594, 0.57207793, 0.54166424], [0.10679197, 0.10496728, 0.10731174])\n ])\n self.device = device\n self.path = path \n \n def train(self):\n \"\"\" \n algorithm 1 in report\n \n collect data from self.dataset and train the architecture: 1 step DIM 2 step classifier as a regularized\"\"\"\n acc_time = []\n data_test = self.val_data[0][0][0]\n labels_test = self.val_data[0][0][1]\n for i, train_batch in enumerate(self.dataset):\n \n writerDIM = SummaryWriter('runs/experiment_DIM'+str(i))\n data,labels, t = train_batch\n\n index_tr,index_cv,coreset = data_split(data.shape[0],777)\n\n # adding eventual replay patterns to the current batch\n if i == 0:\n ext_mem = [data[coreset], labels[coreset]]\n dataC = np.concatenate((data[index_tr], data[index_cv]),axis=0)\n labC = np.concatenate((labels[index_tr],labels[index_cv]),axis=0)\n else:\n dataP = ext_mem[0]\n labP = ext_mem[1]\n\n ext_mem = [\n np.concatenate((data[coreset], ext_mem[0])),\n np.concatenate((labels[coreset], ext_mem[1]))]\n if self.replay:\n dataC = np.concatenate((data[index_tr], data[index_cv],dataP),axis=0)\n labC = np.concatenate((labels[index_tr],labels[index_cv],labP),axis=0)\n else:\n dataC = np.concatenate((data[index_tr], data[index_cv]),axis=0)\n labC = np.concatenate((labels[index_tr],labels[index_cv]),axis=0)\n\n\n\n print(\"----------- batch {0} -------------\".format(i))\n print(\"Task Label: \", t)\n trC,cvC = data_split_Tr_CV(dataC.shape[0],777)\n\n train_set = LoadDataset(dataC,labC,transform=self.tr,indices=trC)\n val_set = LoadDataset(dataC,labC,transform=self.tr,indices=cvC)\n print('Training set: {0} \\nValidation Set {1}'.format(train_set.__len__(),val_set.__len__()))\n batch_size=32\n train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\n valid_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False)\n dataloaders = {'train':train_loader,'val':valid_loader}\n \n ####### Set hyperparameters for the training\n if i ==0: \n prior = False\n ep=40\n dim_model = DIM_model(batch_s=32,num_classes =128,feature=True) \n dim_model.to(self.device)\n classifierM = _classifier(n_input=128,n_class=50,n_neurons=[256,256,128])\n classifierM = classifierM.to(self.device)\n writer = SummaryWriter('runs/experiment_C'+str(i))\n lr_new = 0.00001\n lrC=0.0001\n \n else:\n prior = True\n ep=6\n \n lr_new =0.000005\n lrC = 0.00005\n\n optimizer = torch.optim.Adam(dim_model.parameters(),lr=lr_new)\n scheduler = lr_scheduler.StepLR(optimizer,step_size=40,gamma=0.1) #there is also MultiStepLR\n\n tr_dict_enc = {'ep':ep,'writer':writerDIM,'best_loss':1e10,'t_board':True,\n 'gamma':.5,'beta':.5,'Prior_Flag':prior,'discriminator':classifierM} \n tr_dict_cl = {'ep':40,'writer':writer,'best_loss':1e10,'t_board':True,'gamma':1}#40\n\n if i==0 and self.load:\n print('Load DIM model weights first step')\n dim_model.load_state_dict(torch.load(self.path + 'weights/weightsDIM_T0.pt'))\n else:\n ############################## Train Encoder########################################\n dim_model,self.stats = trainEnc_MI(self.stats,dim_model, optimizer, scheduler,dataloaders,self.device,tr_dict_enc)\n ####################################################################################\n if i==0:\n torch.save(dim_model.state_dict(), self.path + 'weights/weightsDIM_T'+str(i)+'.pt')\n\n ####\n #Conversion of image into latent space representation for classifier training\n ####\n dim_model.requires_grad_(False)\n for phase in ['train','val']:\n dataF = None\n labF = None\n for inputs, labels in dataloaders[phase]:\n torch.cuda.empty_cache()\n if len(inputs.shape)==5:\n\n inputs = inputs[:,:,:,:,0].to(self.device)\n else:\n inputs = inputs.to(self.device)\n\n _,_,pred = dim_model(inputs)\n pred_l = pred.data.cpu().numpy()\n if dataF is None:\n dataF = pred_l\n labF = labels.data.cpu().numpy()\n else:\n dataF = np.concatenate((dataF,pred_l),axis=0)\n labF = np.concatenate((labF,labels.data.cpu().numpy()),axis=0)\n\n if phase == 'train':\n dataTr_f = dataF\n labTr_f = labF\n else:\n dataCv_f = dataF\n labCv_f = labF\n \n dim_model.requires_grad_(True)\n train_set = LoadFeat(dataTr_f,labTr_f)\n val_set = LoadFeat(dataCv_f,labCv_f)\n batch_size=32\n\n train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True)\n valid_loader = DataLoader(val_set, batch_size=batch_size, shuffle=False)\n dataloaderC = {'train':train_loader,'val':valid_loader}\n\n optimizerC = torch.optim.Adam(classifierM.parameters(),lr=lrC)\n schedulerC = lr_scheduler.StepLR(optimizerC,step_size=40,gamma=0.1)\n classifierM.requires_grad_(True)\n\n ############################## Train Classifier ########################################\n classifierM,self.stats = train_classifier(self.stats,classifierM, optimizerC, schedulerC,dataloaderC,self.device,tr_dict_cl) \n #################################### #################################### ##############\n\n torch.save(classifierM.state_dict(), self.path + 'weights/weightsC_T'+str(i)+'.pt')\n dim_model.eval()\n classifierM.eval()\n #### Cross_val Testing\n \n test_set = LoadDataset(data_test,labels_test,transform=self.trT)\n batch_size=32\n test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False)\n score= []\n\n for inputs, labels in test_loader:\n torch.cuda.empty_cache()\n inputs = inputs.to(self.device)\n labels = labels.to(self.device) \n _,_,ww =dim_model(inputs)\n pred = classifierM(ww)\n pred_l = pred.data.cpu().numpy()\n score.append(np.sum(np.argmax(pred_l,axis=1)==labels.data.cpu().numpy())/pred_l.shape[0])\n print('TEST PERFORMANCES:', np.asarray(score).mean())\n acc_time.append(np.asarray(score).mean())\n del test_set,test_loader\n \n self.dim_model = dim_model\n self.classifierM = classifierM\n acc_time = np.asarray(acc_time)\n return self.stats,acc_time\n \n def test(self,test_data,standalone=False):\n \n if standalone:\n self.dim_model = DIM_model(batch_s=32,num_classes =128,feature=True) \n self.dim_model.to(self.device)\n \n self.classifierM = _classifier(n_input=128,n_class=50,n_neurons=[256,256,128])\n self.classifierM = self.classifierM.to(self.device) \n \n self.dim_model.load_state_dict(torch.load(self.path + 'weights/weightsDIM_T7.pt'))\n self.classifierM.load_state_dict(torch.load(self.path + 'weights/weightsC_T7.pt'))\n\n \n test_set = LoadDataset(test_data[0][0][0],transform=self.trT)\n batch_size=32\n test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False)\n score = None\n self.dim_model.eval()\n self.classifierM.eval()\n for inputs in test_loader:\n torch.cuda.empty_cache()\n inputs = inputs.to(self.device)\n _,_,ww =self.dim_model(inputs)\n pred = self.classifierM(ww)\n pred_l = pred.data.cpu().numpy()\n if score is None:\n score = np.argmax(pred_l,axis=1)\n else:\n score = np.concatenate((score,np.argmax(pred_l,axis=1)),axis=0) \n return score\n\n", "sub_path": "DIM/wrapperNI.py", "file_name": "wrapperNI.py", "file_ext": "py", "file_size_in_byte": 10804, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "torchvision.transforms.Compose", "line_number": 39, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 39, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToPILImage", "line_number": 41, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 41, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomChoice", "line_number": 42, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 42, "usage_type": "name"}, {"api_name": "torchvision.transforms.ColorJitter", "line_number": 43, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 43, "usage_type": "name"}, {"api_name": "torchvision.transforms.ColorJitter", "line_number": 44, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 44, "usage_type": "name"}, {"api_name": "torchvision.transforms.ColorJitter", "line_number": 45, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 45, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomChoice", "line_number": 47, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 47, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomHorizontalFlip", "line_number": 48, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 48, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomVerticalFlip", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 49, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomRotation", "line_number": 50, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 50, "usage_type": "name"}, {"api_name": "torchvision.transforms.RandomAffine", "line_number": 51, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 51, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 54, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 54, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 55, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 55, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 57, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 57, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 58, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 58, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 59, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.utils.tensorboard.SummaryWriter", "line_number": 74, "usage_type": "call"}, {"api_name": "pre_proc.loader.data_split", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 96, "usage_type": "call"}, {"api_name": "pre_proc.loader.data_split_Tr_CV", "line_number": 102, "usage_type": "call"}, {"api_name": "pre_proc.loader.LoadDataset", "line_number": 104, "usage_type": "call"}, {"api_name": "pre_proc.loader.LoadDataset", "line_number": 105, "usage_type": "call"}, {"api_name": "torch.utils.data.dataloader.DataLoader", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.utils.data.dataloader.DataLoader", "line_number": 109, "usage_type": "call"}, {"api_name": "networks.model._classifier", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.utils.tensorboard.SummaryWriter", "line_number": 120, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 131, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 131, "usage_type": "attribute"}, {"api_name": "torch.optim.lr_scheduler.StepLR", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler", "line_number": 132, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 140, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 146, "usage_type": "call"}, {"api_name": "torch.cuda.empty_cache", "line_number": 156, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 156, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 170, "usage_type": "call"}, {"api_name": "pre_proc.loader.LoadFeat", "line_number": 180, "usage_type": "call"}, {"api_name": "pre_proc.loader.LoadFeat", "line_number": 181, "usage_type": "call"}, {"api_name": "torch.utils.data.dataloader.DataLoader", "line_number": 184, "usage_type": "call"}, {"api_name": "torch.utils.data.dataloader.DataLoader", "line_number": 185, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 188, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 188, "usage_type": "attribute"}, {"api_name": "torch.optim.lr_scheduler.StepLR", "line_number": 189, "usage_type": "call"}, {"api_name": "torch.optim.lr_scheduler", "line_number": 189, "usage_type": "name"}, {"api_name": "torch.save", "line_number": 196, "usage_type": "call"}, {"api_name": "pre_proc.loader.LoadDataset", "line_number": 201, "usage_type": "call"}, {"api_name": "torch.utils.data.dataloader.DataLoader", "line_number": 203, "usage_type": "call"}, {"api_name": "torch.cuda.empty_cache", "line_number": 207, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 207, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 214, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 220, "usage_type": "call"}, {"api_name": "networks.model._classifier", "line_number": 229, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 232, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 233, "usage_type": "call"}, {"api_name": "pre_proc.loader.LoadDataset", "line_number": 236, "usage_type": "call"}, {"api_name": "torch.utils.data.dataloader.DataLoader", "line_number": 238, "usage_type": "call"}, {"api_name": "torch.cuda.empty_cache", "line_number": 243, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 243, "usage_type": "attribute"}, {"api_name": "numpy.argmax", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 251, "usage_type": "call"}]} +{"seq_id": "392018681", "text": "\"\"\"\nCreated on Thu Aug 8 11:24:56 2019\nMULTIPLE BATCH\n@author: LohJZ\n\"\"\"\n# from skil_client.models.base64_nd_array_body import Base64NDArrayBody\nimport skil_client,cv2,base64\nfrom skil_client.rest import ApiException\nfrom pprint import pprint\nimport requests\nimport tensorflow as tf\nfrom utils import ImageCoder\nimport tensorflow as tf # Default graph is initialized when the library is imported\nimport uuid\nfrom tensorflow.python.platform import gfile\nimport time\nimport numpy as np\n\nfrom scipy import misc\n# import matplotlib.pyplot as plt\n\nfrom utils import ImageCoder\nfrom data import inputs, standardize_image\n\nstart = time.time()\n\nRESIZE_FINAL = 227\nimage_file = 'D:/tmp/227/64x64x3-2-c.jpg'\nlabel_list = ['(0, 2)','(4, 6)','(8, 12)','(15, 20)','(25, 32)','(38, 43)','(48, 53)','(60, 100)']\n\ndef convert_indarray(np_array):\n \"\"\"Convert a numpy array to `skil_client.INDArray` instance.\n\n # Arguments\n np_array: `numpy.ndarray` instance.\n\n # Returns\n `skil_client.INDArray` instance.\n \"\"\"\n return skil_client.INDArray(\n ordering='c',\n shape=list(np_array.shape),\n data=np_array.reshape(-1).tolist()\n )\ndef _is_png(filename):\n \"\"\"Determine if a file contains a PNG format image.\n Args:\n filename: string, path of the image file.\n Returns:\n boolean indicating if the image is a PNG.\n \"\"\"\n return '.png' in filename\n\n\ndef image_to_base64(image_np):\n\timage = cv2.imencode('.jpg',image_np)[1]\n\timage_code = str(base64.b64encode(image))[2:-1]\n\treturn image_code\n\n\ndef make_multi_crop_batch(filename, coder):\n \"\"\"Process a single image file.\n Args:\n filename: string, path to an image file e.g., '/path/to/example.JPG'.\n coder: instance of ImageCoder to provide TensorFlow image coding utils.\n Returns:\n image_buffer: string, JPEG encoding of RGB image.\n \"\"\"\n # Read the image file.\n with tf.gfile.FastGFile(filename, 'rb') as f:\n image_data = f.read()\n\n # Convert any PNG to JPEG's for consistency.\n if _is_png(filename):\n print('Converting PNG to JPEG for %s' % filename)\n image_data = coder.png_to_jpeg(image_data)\n \n image = coder.decode_jpeg(image_data)\n image = image_to_base64(image)\n\n crops = []\n print('Running multi-cropped image')\n h = image.shape[0]\n w = image.shape[1]\n hl = h - RESIZE_FINAL\n wl = w - RESIZE_FINAL\n\n crop = tf.image.resize_images(image, (RESIZE_FINAL, RESIZE_FINAL))\n crops.append(standardize_image(crop))\n crops.append(standardize_image(tf.image.flip_left_right(crop)))\n\n corners = [ (0, 0), (0, wl), (hl, 0), (hl, wl), (int(hl/2), int(wl/2))]\n for corner in corners:\n ch, cw = corner\n cropped = tf.image.crop_to_bounding_box(image, ch, cw, RESIZE_FINAL, RESIZE_FINAL)\n crops.append(standardize_image(cropped))\n flipped = standardize_image(tf.image.flip_left_right(cropped))\n crops.append(standardize_image(flipped))\n\n image_batch = tf.stack(crops)\n return image_batch\n\ndef make_single_image_batch(image_path, coder):\n image_data = tf.gfile.FastGFile(image_path, 'rb').read()\n image = coder.decode_jpeg(image_data)\n crop = tf.image.resize_images(image, (227,227))\n image_batch = tf.stack([crop])\n return image_batch\n\nwith tf.Session() as sess:\n coder = ImageCoder()\n image_batch = make_multi_crop_batch(image_file, coder)\n image_batch = image_batch.eval()\n\nconfiguration = skil_client.Configuration()\nconfiguration.host = 'http://192.168.1.128:9008'\nconfiguration.username = 'admin'\nconfiguration.password = '123456'\n\nr = requests.post(\"http://192.168.1.128:9008/login\", json={\"userId\": \"admin\", \"password\": \"123456\"})\ntoken = r.json()['token']\n\nconfiguration.api_key['authorization'] = f'Bearer {token}'\napi_instance = skil_client.DefaultApi(skil_client.ApiClient(configuration))\n\n\nlist_ind_array = [[convert_indarray(np.expand_dims(image_batch[i,:,:,:], axis=0))] for i in range(12)]\n\nbatch_results = []\nindex = 0\nfor data in list_ind_array:\n print(\"getting response for batch image \", index)\n body_data = skil_client.MultiPredictRequest(\n id=str(uuid.uuid1()),\n needs_pre_processing=False,\n inputs=data\n )\n \n response = api_instance.multipredict(\"age\", \"default\", \"outputgraphwithsoftmax\", body=body_data )\n response = response.to_dict()\n output = response['outputs'][0]\n probabilities = output['data']\n probabilities = np.array(probabilities)\n batch_results.append(probabilities)\n index+=1\n time.sleep(0.5) #prevent spamming\n\noutput = batch_results[0] \nbatch_sz = len(batch_results)\narg_max_each_batch = []\nfor i in range(1, batch_sz):\n arg_max_each_batch.append(np.argmax(batch_results[i]))\nfor i in range(1, batch_sz):\n output = output + batch_results[i]\n\noutput /= batch_sz\nprint(\"Output: \", output)\nbest = np.argmax(output)\nbest_choice = (label_list[best], output[best])\nprint('best index is : ', best)\nprint('Guess @ 1 %s, prob = %.2f' % best_choice)\n\nnlabels = len(label_list)\nif nlabels > 2:\n output[best] = 0\n second_best = np.argmax(output)\n print('second best index is : ', second_best)\n print('Guess @ 2 %s, prob = %.2f' % (label_list[second_best], output[second_best]))\n\n\nend = time.time()\nprint(\"完成时间: %f s\" % (end - start))\n#\n# label = \"小人\" if best < 3 else \"大人\"\n#\n# import cv2\n# from PIL import ImageFont, ImageDraw, Image\n# import numpy as np\n#\n# bk_img = cv2.imread(image_file)\n# bk_img = cv2.resize(bk_img,(500,500))\n# # 设置需要显示的字体\n# fontpath = \"font/simsun.ttc\" # 32为字体大小\n# font = ImageFont.truetype(fontpath, 32)\n# img_pil = Image.fromarray(bk_img)\n# draw = ImageDraw.Draw(img_pil)\n# # 绘制文字信息
# (100,300/350)为字体的位置，(255,255,255)为白色，(0,0,0)为黑色\n# draw.text((230, 50), label, font=font, fill=(0,0,255))\n# # draw.text((100, 350), \"你好\", font=font, fill=(255, 255, 255))\n# bk_img = np.array(img_pil)\n#\n# cv2.imshow(\" \", bk_img)\n# cv2.waitKey()", "sub_path": "skil/age_on_skil_v3.py", "file_name": "age_on_skil_v3.py", "file_ext": "py", "file_size_in_byte": 6089, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "time.time", "line_number": 25, "usage_type": "call"}, {"api_name": "skil_client.INDArray", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.imencode", "line_number": 56, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 57, "usage_type": "call"}, {"api_name": "tensorflow.gfile.FastGFile", "line_number": 70, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 70, "usage_type": "attribute"}, {"api_name": "tensorflow.image.resize_images", "line_number": 88, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 88, "usage_type": "attribute"}, {"api_name": "data.standardize_image", "line_number": 89, "usage_type": "call"}, {"api_name": "data.standardize_image", "line_number": 90, "usage_type": "call"}, {"api_name": "tensorflow.image.flip_left_right", "line_number": 90, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 90, "usage_type": "attribute"}, {"api_name": "tensorflow.image.crop_to_bounding_box", "line_number": 95, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 95, "usage_type": "attribute"}, {"api_name": "data.standardize_image", "line_number": 96, "usage_type": "call"}, {"api_name": "data.standardize_image", "line_number": 97, "usage_type": "call"}, {"api_name": "tensorflow.image.flip_left_right", "line_number": 97, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 97, "usage_type": "attribute"}, {"api_name": "data.standardize_image", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.stack", "line_number": 100, "usage_type": "call"}, {"api_name": "tensorflow.gfile.FastGFile", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorflow.gfile", "line_number": 104, "usage_type": "attribute"}, {"api_name": "tensorflow.image.resize_images", "line_number": 106, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 106, "usage_type": "attribute"}, {"api_name": "tensorflow.stack", "line_number": 107, "usage_type": "call"}, {"api_name": "tensorflow.Session", "line_number": 110, "usage_type": "call"}, {"api_name": "utils.ImageCoder", "line_number": 111, "usage_type": "call"}, {"api_name": "skil_client.Configuration", "line_number": 115, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 120, "usage_type": "call"}, {"api_name": "skil_client.DefaultApi", "line_number": 124, "usage_type": "call"}, {"api_name": "skil_client.ApiClient", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 127, "usage_type": "call"}, {"api_name": "skil_client.MultiPredictRequest", "line_number": 133, "usage_type": "call"}, {"api_name": "uuid.uuid1", "line_number": 134, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 143, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 166, "usage_type": "call"}, {"api_name": "time.time", "line_number": 171, "usage_type": "call"}]} +{"seq_id": "54945288", "text": "#Nguyen Phuoc Sang\r\n#IU7-46B\r\n# Lab 01 - Graphic\r\n\r\nfrom math import *\r\nimport matplotlib.pyplot as plt\r\n\r\n'''\r\n***************\r\ninput\r\n***************\r\n'''\r\nEPS = 1e-10\r\ndata = []\r\n\r\n#input points\r\ndef point_input(notif):\r\n while (1):\r\n try:\r\n point = list(map(float,input(notif).split()))\r\n if (len(point) < 2):\r\n print(\"\\n!!! Wrong Input, please input again\\n\")\r\n else:\r\n return point[0:2]\r\n except:\r\n print(\"\\n!!!Coordinates must be float, pleas input again !!!\\n\")\r\n \r\n \r\n\r\ndef data_input():\r\n\r\n n = int(input(\"Input Number of point: \"))\r\n if (n < 0):\r\n print(\"\\n!!! Number must be greater than 0\\n\")\r\n return n\r\n else:\r\n if (n <= 2):\r\n print(\"\\n!!! Notice: Number must be greater than 2 \\n\")\r\n print(\"Input Data: \")\r\n for i in range(n):\r\n point = point_input(\"Input New Point: \")\r\n while (point in data):\r\n print(\"\\n!!! Point was in List, please input again\")\r\n point = point_input(\"Input New Point: \")\r\n data.append(point)\r\n return n\r\n\r\n\r\ndef print_menu():\r\n print(\"0. Continue\")\r\n print(\"1. Delete all points\")\r\n print(\"2. Delete 1 point\")\r\n print(\"3. Insert 1 point\")\r\n print(\"4. Change point\")\r\n print(\"5. Print All Points\")\r\n# delete point\r\ndef del_point():\r\n point = point_input(\"Input Delete Point: \")\r\n if (point in data):\r\n data.pop(data.index(point))\r\n else:\r\n print(\"\\n!!! Point not in List\\n\")\r\n\r\n# insert new point\r\ndef insert_point():\r\n point = point_input(\"Input New Point: \")\r\n if (point in data):\r\n print(\"\\n!!! Point was n List\\n\")\r\n else:\r\n data.append(point)\r\n\r\n# change a point\r\ndef change_point():\r\n point1 = point_input('Input Old Point: ')\r\n if (point1 not in data):\r\n print(\"\\n!!! Point not in list\\n\")\r\n return\r\n point2 = point_input(\"Input New Point: \")\r\n if (point2 not in data):\r\n data[data.index(point1)] = point2\r\n else:\r\n data.pop(data.index(point1))\r\n\r\n#output all point\r\ndef print_point():\r\n if (len(data) == 0):\r\n print(\"\\n Empty!!!\\n\")\r\n return\r\n print()\r\n for i in range(len(data)):\r\n print(\"Point {}: ({}, {})\".format( i, data[i][0], data[i][1]))\r\n print()\r\n\r\ndef data_change():\r\n ch = 1\r\n while (ch != 0):\r\n print_menu()\r\n while (1):\r\n try:\r\n ch = int(input(\"Input your choise: \"))\r\n if (ch in range(0, 6)):\r\n break\r\n else:\r\n print(\"\\n!!! ERR: Your choise must be in range [0..5]\")\r\n except:\r\n print(\"\\n!!! ERR: Not an Interger\")\r\n \r\n if (ch == 1):\r\n data = []\r\n elif (ch == 2):\r\n del_point()\r\n elif (ch == 3):\r\n insert_point()\r\n elif (ch == 4):\r\n change_point()\r\n elif (ch == 5):\r\n print_point()\r\n else:\r\n break\r\n \r\n#*****************************************************************************************************************************\r\n'''\r\n***************\r\nProcess\r\n***************\r\n'''\r\n\r\n#check data\r\n\r\ndef check_data(data):\r\n n = len(data)\r\n if (n <= 2):\r\n return -1\r\n \r\n l = line(data[0], data[1])\r\n for i in range(2, n, 1):\r\n if (check_in_line(data[i], l) == False):\r\n return 0\r\n return -2\r\n\r\n# a,b - 2 points\r\ndef distance(a, b):\r\n return sqrt(pow(a[0] - b[0], 2) + pow(a[1] - b[1], 2))\r\n\r\n# a, b - 2 point\r\ndef vector_n(a, b):\r\n return list([a[1] - b[1], b[0] - a[0]]) \r\n\r\n# a, b - 2 points\r\ndef line(a, b):\r\n vec_n = vector_n(a, b)\r\n return [vec_n[0], vec_n[1], -1 * (vec_n[0] * a[0] + vec_n[1] * a[1])]\r\n\r\n# a - point\r\n# l - line\r\ndef check_in_line(a, l):\r\n if (l[0] * a[0] + l[1] * a[1] + l[2] != 0):\r\n return False\r\n return True\r\n\r\n# find point I ( median)\r\ndef mid_point(a, b):\r\n return [ (a[0] + b[0]) / 2, (a[1] + b[1]) / 2]\r\n\r\n#fie point D (bisector)\r\ndef bisector(A, B, C):\r\n vecAC = [C[0] - A[0], C[1] - A[1]]\r\n AB = distance(A, B)\r\n BC = distance(B, C)\r\n k = AB / BC + 1\r\n xD = C[0] - vecAC[0] / k\r\n yD = C[1] - vecAC[1] / k\r\n return [xD, yD]\r\n\r\n# find angle ABC\r\ndef angle_ABC(A, B, C):\r\n AB = distance(A, B)\r\n BC = distance(B, C)\r\n AC = distance(A, C)\r\n try:\r\n cosB = (BC**2 + AB**2 - AC**2) / (2 * BC * AB)\r\n except:\r\n return 4\r\n return acos(cosB)\r\n\r\n# ABC - Triangle\r\n# BI - Median from B\r\n# BD - Bisector from B\r\ndef find_angle(A, B, C):\r\n D = bisector(A, B, C)\r\n I = mid_point(A, C)\r\n IBD = angle_ABC(I, B, D)\r\n return [IBD, D, I]\r\n\r\ndef min_angle(A, B, C):\r\n angle_B = find_angle(A, B, C)\r\n angle_C = find_angle(B, C, A)\r\n angle_A = find_angle(C, A, B)\r\n minn = min(angle_A[0], angle_B[0], angle_C[0])\r\n if (fabs(angle_A[0] - minn) < EPS):\r\n return angle_A + [C, A, B]\r\n if (fabs(angle_B[0] - minn) < EPS):\r\n return angle_B + [A, B, C]\r\n if (fabs(angle_C[0] - minn) < EPS):\r\n return angle_C + [B, C, A]\r\n\r\ndef draw_line(A, B, color):\r\n plt.plot([A[0], B[0]], [A[1], B[1]], color, linewidth = 0.8)\r\n\r\ndef draw_point(A, color, text, k):\r\n plt.plot([A[0]], [A[1]], color + 'o')\r\n plt.text(A[0] + k, A[1] + k, '{}.({}, {})'.format(text, round(A[0], 2), round(A[1], 2)), fontsize = 7)\r\n\r\ndata = []\r\nn = data_input()\r\n#print(data)\r\n#data = list([[13.0, -10.0], [-5.0, 17.0], [8, 13], [-3.0, 23.0], [25.0, 27.0]])\r\n#n = len(data)\r\nwhile (1):\r\n if ( n >= 0):\r\n ch = 1\r\n while (ch != 0):\r\n print_menu()\r\n while (1):\r\n try:\r\n ch = int(input(\"Input your choise: \"))\r\n if (ch in range(0, 6)):\r\n break\r\n else:\r\n print(\"\\n!!! ERR: Your choise must be in range [0..5]\")\r\n except:\r\n print(\"\\n!!! ERR: Not an Interger\")\r\n \r\n if (ch == 1):\r\n data = []\r\n elif (ch == 2):\r\n del_point()\r\n elif (ch == 3):\r\n insert_point()\r\n elif (ch == 4):\r\n change_point()\r\n elif (ch == 5):\r\n print_point()\r\n else:\r\n break\r\n\r\n n = len(data)\r\n if (n >= 2):\r\n X = [data[i][0] for i in range(n)] \r\n Y = [data[i][1] for i in range(n)]\r\n\r\n minX = min(X)\r\n maxX = max(X)\r\n minY = min(Y)\r\n maxY = max(Y)\r\n minn = min(minX, minY)\r\n maxx = max(maxX, maxY)\r\n k = (maxx - minn) * 0.01\r\n \r\n \r\n err = check_data(data)\r\n if (err == -1):\r\n print(\"\\n!!!ERR: Number of point must be > 2\")\r\n \r\n elif (err == -2):\r\n print(\"\\n!!!ERR: All Points in a line\\n\")\r\n for i in range(n):\r\n draw_point(data[i], 'b', i, k)\r\n draw_line([minX, minY], [maxX, maxY], 'r')\r\n \r\n else:\r\n result = []\r\n minrc = 5.0\r\n for i in range(n - 2):\r\n for j in range(i + 1, n - 1, 1):\r\n for t in range(j + 1, n, 1):\r\n if (check_data([data[i], data[j], data[t]]) == 0):\r\n rc = min_angle(data[i], data[j], data[t])\r\n if (minrc > rc[0]):\r\n minrc = rc[0]\r\n result = [i, j, t] + rc\r\n A = result[6]\r\n B = result[7]\r\n C = result[8]\r\n D = result[4]\r\n D = [round(i, 2) for i in D]\r\n I = result[5]\r\n I = [round(i, 2) for i in I]\r\n\r\n print(\"\\nInput Point: \")\r\n print_point()\r\n print(\"\\nResult: \\n\")\r\n print('Points:')\r\n print(\"Point {} ({}, {})\".format(data.index(A), A[0], A[1]))\r\n print(\"Point {} ({}, {})\".format(data.index(B), B[0], B[1]))\r\n print(\"Point {} ({}, {})\".format(data.index(C), C[0], C[1]))\r\n print(\"Min Angle: {} (Rad)(from point {})\".format(round(result[3], 2), data.index(B)))\r\n print(\"Point I ( median )() ({}, {})\".format(I[0], I[1]))\r\n print(\"Point D (Bisector) ({}, {})\".format(D[0], D[1]))\r\n\r\n\r\n\r\n '''\r\n *************************\r\n Phần Đồ Hoạ\r\n *************************\r\n '''\r\n\r\n plt.figure(num=None, figsize=(8, 8), dpi=100, facecolor='w', edgecolor='k')\r\n for i in range(n):\r\n draw_point(data[i], 'b', i, k)\r\n\r\n if ( I == D):\r\n draw_point(I, 'g', 'I=D', k)\r\n else:\r\n draw_point(I, 'y', 'I', k)\r\n draw_point(D, 'r', 'D', k) \r\n plt.axis([minn - 3, maxx + 3, minn - 3, maxx + 3])\r\n if (maxx * minn < 0):\r\n # ve Ox\r\n draw_line([0, minn - 2], [0, maxx + 2], 'k')\r\n plt.plot([maxx + 2], [0], 'k>')\r\n plt.text(maxx + 1, k, 'X')\r\n # ve Oy\r\n draw_line([minn - 2, 0], [maxx + 2, 0], 'k')\r\n plt.plot([0], [maxx + 2], 'k^')\r\n plt.text(k, maxx + 1, 'Y')\r\n # ve O\r\n draw_point([0, 0], 'k', \"O\", k)\r\n else:\r\n # ve Ox\r\n draw_line([minn - 1, minn - 2], [minn - 1, maxx + 2], 'k')\r\n plt.plot([maxx + 2], [minn - 1], 'k>')\r\n plt.text(maxx + 1, minn - 1 + k, \"X\")\r\n # ve Oy\r\n draw_line([minn - 2, minn - 1], [maxx + 2, minn - 1], 'k')\r\n plt.plot([minn - 1], [maxx + 2], 'k^')\r\n plt.text(minn - 1 + k, maxx + 1, \"Y\")\r\n # ve O\r\n draw_point([minn - 1, minn - 1], 'k', \"O\", k)\r\n # draw ABCDI\r\n plt.grid()\r\n draw_line(A, B, 'r')\r\n draw_line(B, C, 'b')\r\n draw_line(A, C, 'g')\r\n draw_line(B, I, 'y')\r\n draw_line(B, D, 'k')\r\n\r\n plt.axis(\"scaled\")\r\n plt.show()\r\n \r\n \r\n\r\n", "sub_path": "PRAKTIKA 2020/Code Prak/Lab_Graphic/Lab_01/Lab_01_Graphic_Test.py", "file_name": "Lab_01_Graphic_Test.py", "file_ext": "py", "file_size_in_byte": 10014, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "matplotlib.pyplot.plot", "line_number": 208, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 208, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 211, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 309, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 309, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 318, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 318, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 322, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 322, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 323, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 323, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 326, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 326, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "line_number": 327, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 327, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 333, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 333, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.text", "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.text", "line_number": 338, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 338, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 342, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 342, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 349, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 349, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 350, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 350, "usage_type": "name"}]} +{"seq_id": "98096203", "text": "import h5py\nimport scipy\n\nimport numpy as np\n\n\ndef split_array(l, n):\n return [l[i:i + n] for i in range(0, len(l), n)]\n\n\nclass Matrix:\n\n def __init__(self, m, T_flag=0):\n self.m = m\n self.n, self.k = m.shape\n self.T_flag = T_flag\n\n @property\n def T(self):\n return Matrix(self.m, 1 - self.T_flag)\n\n def dot(self, vector):\n if self.T_flag:\n return self.m.T.dot(vector)\n else:\n return self.m.dot(vector)\n\n def cov(self, diag=None):\n\n if self.T_flag == 1:\n raise NotImplemented\n\n if diag is None:\n diag = np.ones(self.n)\n\n cov = (self.m.T * diag).dot(self.m)\n return cov\n\n def solveLowerTriangularSquaredSum(self, lhs, correction):\n\n if self.T_flag == 1:\n raise NotImplemented\n\n sol = scipy.linalg.solve_triangular(lhs, self.m.T * correction, lower=True)\n\n return np.sum(sol ** 2, axis=0)\n\n def __getitem__(self, idx):\n if not(isinstance(idx, np.ndarray) and (idx.dtype is np.dtype('bool'))):\n raise TypeError\n\n return Matrix(self.m[idx], self.T_flag)\n\n\nclass h5Matrix:\n\n def __init__(self, f, key_prefix, T_flag=0, comp_dim=0):\n\n self.f = f\n self.key_prefix = key_prefix\n self.h5file = h5py.File(f)\n self.keys = sorted(filter(lambda x: x.startswith(key_prefix), self.h5file))\n\n if comp_dim:\n self.n = sum([self.h5file[key].shape[0] for key in self.keys])\n if len(self.h5file[self.keys[0]].shape) > 1:\n self.k = self.h5file[self.keys[0]].shape[1]\n assert(np.all(np.array([self.h5file[key].shape[1] for key in self.keys]) == self.k))\n else:\n self.k = None\n\n self.T_flag = T_flag\n\n assert(self.keys)\n\n def __getitem__(self, idx):\n if not(isinstance(idx, np.ndarray) and (idx.dtype is np.dtype('bool'))):\n raise TypeError\n\n i = 0\n F_out = []\n\n for key in self.keys:\n F = np.array(self.h5file[key])\n n = F.shape[0]\n F_out.append(F[idx[i:i + n]])\n i += n\n\n return Matrix(np.vstack(F_out))\n\n @property\n def T(self):\n return h5Matrix(self.f, self.key_prefix, 1 - self.T_flag)\n\n def get_matrix(self, glue, selected_images=[]):\n if len(selected_images) > 0:\n keys = [self.keys[j] for j in selected_images]\n else:\n keys = self.keys\n return glue([np.array(self.h5file[key]) for key in keys])\n\n def get_matrix_fixed_dim(self, n, k, selected_images=[]):\n A = np.zeros((n, k))\n\n i = 0\n\n if len(selected_images) > 0:\n keys = [self.keys[j] for j in selected_images]\n else:\n keys = self.keys\n\n for key in keys:\n a = np.array(self.h5file[key])\n l = len(a)\n A[i:i + l] = a\n i += l\n assert(i == n)\n\n return A\n\n def dot(self, vector):\n\n v = [] if self.T_flag == 0 else 0.0\n i = 0\n\n for keys in split_array(self.keys, 1000):\n\n F = []\n n = 0\n\n for key in keys:\n FF = np.array(self.h5file[key])\n F.append(FF)\n nn, _ = FF.shape\n n += nn\n\n F = np.vstack(F)\n\n if self.T_flag == 0:\n v.append(F.dot(vector))\n if self.T_flag == 1:\n n, k = F.shape\n v += F.T.dot(vector[i:i + n])\n i += n\n\n return np.hstack(v) if self.T_flag == 0 else v\n\n def cov(self, diag=None):\n\n if self.T_flag == 1:\n raise NotImplemented\n\n cov = 0.0\n\n i = 0\n for keys in split_array(self.keys, 1000):\n\n F = []\n n = 0\n\n for key in keys:\n FF = np.array(self.h5file[key])\n F.append(FF)\n nn, _ = FF.shape\n n += nn\n\n F = np.vstack(F)\n\n if diag is None:\n cov += F.T.dot(F)\n else:\n cov += (F.T * diag[i:i + n]).dot(F)\n\n i += n\n\n assert(i == len(diag))\n\n return cov\n\n def solveLowerTriangularSquaredSum(self, lhs, correction):\n\n if self.T_flag == 1:\n raise NotImplemented\n\n v = []\n i = 0\n for keys in split_array(self.keys, 1000):\n\n F = []\n n = 0\n\n for key in keys:\n FF = np.array(self.h5file[key])\n F.append(FF)\n nn, _ = FF.shape\n n += nn\n\n F = np.vstack(F)\n\n sol = scipy.linalg.solve_triangular(lhs, F.T * correction[i: i + n], lower=True)\n i += n\n\n v_current = np.sum(sol ** 2, axis=0)\n v.append(v_current)\n\n return np.hstack(v)\n\n\nif __name__ == \"__main__\":\n 0\n", "sub_path": "pyutils/h5mat.py", "file_name": "h5mat.py", "file_ext": "py", "file_size_in_byte": 4928, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.ones", "line_number": 34, "usage_type": "call"}, {"api_name": "scipy.linalg.solve_triangular", "line_number": 44, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 49, "usage_type": "attribute"}, {"api_name": "numpy.dtype", "line_number": 49, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.all", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 77, "usage_type": "attribute"}, {"api_name": "numpy.dtype", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 100, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 132, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 198, "usage_type": "call"}, {"api_name": "scipy.linalg.solve_triangular", "line_number": 200, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 200, "usage_type": "attribute"}, {"api_name": "numpy.sum", "line_number": 203, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 206, "usage_type": "call"}]} +{"seq_id": "246796915", "text": "import json\n\n\nclass StandardTools:\n\tst = {}\n\n\tdef __init__(self):\n\t\tpass\n\n\tdef loadObject(self, obj):\n\t\tself.st = obj\n\n\tdef loadObjectString(self, jsonString):\n\t\tself.st = json.loads(jsonString)\n\n\t# next goes json<-->object mapping\n\tdef getColdWaterStandart(self, heatSystem, waterSystem, isSewer, isBoiler, isShower):\n\t\tSEWER = BOILER = SHOWER = True\n\t\tNOSEWER = NOBOILER = NOSHOWER = False\n\t\tdictCase = {\n\t\t\t\"OPEN\" : {\n\t\t\t\t\"BOTH\" : {\n\t\t\t\t\tSEWER: {\n\t\t\t\t\t\tNOBOILER : {\n\t\t\t\t\t\t\tSHOWER : \"13\",\n\t\t\t\t\t\t\tNOSHOWER: \"23\"\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t},\n\t\t\t},\n\t\t\t\"CLOSED\" : {\n\t\t\t\t\"BOTH\" : {\n\t\t\t\t\tSEWER: {\n\t\t\t\t\t\tNOBOILER: {\n\t\t\t\t\t\t\tSHOWER : \"14\",\n\t\t\t\t\t\t\tNOSHOWER: \"24\"\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t},\n\t\t\t\t\"COLD\" : {\n\t\t\t\t\tSEWER: {\n\t\t\t\t\t\tBOILER: {\n\t\t\t\t\t\t\tSHOWER : \"34\",\n\t\t\t\t\t\t\tNOSHOWER: \"44\"\n\t\t\t\t\t\t},\n\t\t\t\t\t\tNOBOILER: {\n\t\t\t\t\t\t\tNOSHOWER: \"54\"\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t},\n\t\t\t},\n\t\t\t\"NONE\" : {\n\t\t\t\t\"COLD\" : {\n\t\t\t\t\tSEWER: {\n\t\t\t\t\t\tBOILER: {\n\t\t\t\t\t\t\tSHOWER : \"35\",\n\t\t\t\t\t\t\tNOSHOWER: \"45\"\n\t\t\t\t\t\t},\n\t\t\t\t\t\tNOBOILER: {\n\t\t\t\t\t\t\tNOSHOWER: \"56\"\n\t\t\t\t\t\t}\n\t\t\t\t\t},\n\t\t\t\t\tNOSEWER: {\n\t\t\t\t\t\tNOBOILER: {\n\t\t\t\t\t\t\tNOSHOWER: \"65\"\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t},\n\t\t\t},\n\t\t}\n\t\treturn self.st[\"water\"][\"cold\"][dictCase[heatSystem][waterSystem][isSewer][isBoiler][isShower]]\n\n\tdef getHotWaterStandart(self, isShower):\n\t\tSHOWER = True\n\t\tNOSHOWER = False\n\t\tdictCase = {\n\t\t\tSHOWER : \"1\",\n\t\t\tNOSHOWER: \"2\"\n\t\t}\n\t\treturn self.st[\"water\"][\"hot\"][dictCase[isShower]]\n\n\tdef getEnergyStandart(self, roomCount, pCount, isGas, isBoiler):\n\t\troomCount = int(roomCount)\n\t\tif roomCount > 4: roomCount = 4\n\t\tpCount = int(pCount)\n\t\tif pCount > 5: pCount = 5\n\t\tGAS = BOILER = True\n\t\tNOGAS = NOBOILER = False\n\t\tdictCase = {\n\t\t\t\tGAS : {\n\t\t\t\t\tBOILER : str(pCount) + \"5\",\n\t\t\t\t\tNOBOILER: str(pCount) + \"4\"\n\t\t\t\t},\n\t\t\t\tNOGAS: {\n\t\t\t\t\tBOILER : str(pCount) + \"7\",\n\t\t\t\t\tNOBOILER: str(pCount) + \"6\"\n\t\t\t\t}\n\t\t}\n\t\treturn self.st[\"energy\"][\"multy\"][str(roomCount)][dictCase[isGas][isBoiler]]\n\nif __name__ == '__main__':\n\tpass", "sub_path": "python/standardTools.py", "file_name": "standardTools.py", "file_ext": "py", "file_size_in_byte": 1912, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "json.loads", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "651637654", "text": "# Copyright 2021 Huawei\n# Copyright 2021 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\nimport base64\nimport io\nimport os\n\nimport cv2\nimport mmcv\nimport torch\nfrom ts.torch_handler.base_handler import BaseHandler\n\nfrom mmseg.apis import inference_segmentor, init_segmentor\n\n\nclass MMsegHandler(BaseHandler):\n\n def initialize(self, context):\n properties = context.system_properties\n self.map_location = 'cuda' if torch.cuda.is_available() else 'cpu'\n self.device = torch.device(self.map_location + ':' +\n str(properties.get('gpu_id')) if torch.cuda.\n is_available() else self.map_location)\n self.manifest = context.manifest\n\n model_dir = properties.get('model_dir')\n serialized_file = self.manifest['model']['serializedFile']\n checkpoint = os.path.join(model_dir, serialized_file)\n self.config_file = os.path.join(model_dir, 'config.py')\n\n self.model = init_segmentor(self.config_file, checkpoint, self.device)\n self.initialized = True\n\n def preprocess(self, data):\n images = []\n\n for row in data:\n image = row.get('data') or row.get('body')\n if isinstance(image, str):\n image = base64.b64decode(image)\n image = mmcv.imfrombytes(image)\n images.append(image)\n\n return images\n\n def inference(self, data, *args, **kwargs):\n results = [inference_segmentor(self.model, img) for img in data]\n return results\n\n def postprocess(self, data):\n output = []\n for image_result in data:\n buffer = io.BytesIO()\n _, buffer = cv2.imencode('.png', image_result[0].astype('uint8'))\n output.append(buffer.tobytes())\n return output\n", "sub_path": "PyTorch/contrib/cv/semantic_segmentation/DeeplabV3_for_Pytorch/tools/mmseg_handler.py", "file_name": "mmseg_handler.py", "file_ext": "py", "file_size_in_byte": 2383, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "ts.torch_handler.base_handler.BaseHandler", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 33, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path", "line_number": 42, "usage_type": "attribute"}, {"api_name": "mmseg.apis.init_segmentor", "line_number": 44, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 53, "usage_type": "call"}, {"api_name": "mmcv.imfrombytes", "line_number": 54, "usage_type": "call"}, {"api_name": "mmseg.apis.inference_segmentor", "line_number": 60, "usage_type": "call"}, {"api_name": "io.BytesIO", "line_number": 66, "usage_type": "call"}, {"api_name": "cv2.imencode", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "287983332", "text": "import psycopg2\r\nimport datetime\r\nf=open(\"report.csv\",\"w\")\r\ntry:\r\n conn=psycopg2.connect(user=\"postgres\",\\\r\n password=\"GoRDubbPn9mCg4pgEJh++snBVMb\",\\\r\n host=\"172.16.11.11\",\\\r\n port=\"5432\",\\\r\n database=\"nnm\")\r\n cursor = conn.cursor()\r\n sql='''SELECT \r\n nms_incidents.node_name, \r\n nms_incidents.node_uuid,\r\n/* nms_incidents.time_first, \r\n nms_incidents.time_last,\r\n nms_incidents.reg_modified,*/\r\n sum(nms_incidents.reg_modified-nms_incidents.time_last) as duration\r\n \r\nFROM \r\n public.nms_incidents\r\nWHERE \r\n (nms_incidents.time_first > '2019-08-01 00:00:00' AND \r\n nms_incidents.time_last < '2019-09-01 00:00:00') AND\r\n nms_incidents.name ='NodeDown'\r\nGROUP BY\r\n nms_incidents.node_name,nms_incidents.node_uuid\r\nORDER BY\r\n nms_incidents.node_name ASC;\r\n'''\r\n cursor.execute(sql)\r\n #cursor.execute(sql,{\"ip\":ip})\r\n sel_data=cursor.fetchall()# cursor.fetchone() #cursor.fetchall() ;cursor.fetchmany(SIZE)\r\n\r\n \r\n\r\n for i in range(len(sel_data)):\r\n print (\"\\n\")\r\n sql='''select nms_node.id,nms_node.uuid,nms_node.name from nms_node where nms_node.uuid=%(uuid)s'''\r\n cursor.execute(sql,{\"uuid\":sel_data[i][1]})\r\n sel_data_id=cursor.fetchone()\r\n #if not(sel_data_id is None): print (sel_data_id[0],end=\"\\t\")\r\n if not (sel_data_id is None):\r\n sql='''select nms_node_cna.name,nms_node_cna.value from nms_node_cna where nms_node_cna.parent=%(parent)s'''\r\n cursor.execute(sql,{\"parent\":sel_data_id[0]})\r\n sel_data_attr=cursor.fetchall()\r\n if not(sel_data_attr is None):\r\n if len(sel_data_attr)>1: #print (sel_data_attr)\r\n #print (dict(sel_data_attr))\r\n attr_dict=dict(sel_data_attr)\r\n #print (sel_data[i][0],end=\";\")\r\n print (sel_data_id[2],end=\"\\t\")\r\n print (str(sel_data[i][2]),end=\";\\t\")\r\n print (attr_dict.get(\"ParentOrganisationName\"),end=\";\\t\")\r\n print (attr_dict.get(\"OrganisationName\"))\r\n f.write(str(sel_data_id[2]))\r\n f.write(\";\")\r\n f.write(str(sel_data[i][2]))\r\n f.write(\";\")\r\n f.write(str(attr_dict.get(\"ParentOrganisationName\")))\r\n f.write(\";\")\r\n f.write(str(attr_dict.get(\"OrganisationName\")))\r\n f.write(\";\\n\")\r\n\r\n else:\r\n print (sel_data[i][0],end=\";\")\r\n print (str(sel_data[i][2]),end=\";\\n\")\r\n f.write(str(sel_data[i][0]))\r\n f.write(\";\")\r\n f.write(str(sel_data[i][2]))\r\n f.write(\";\\n\")\r\nexcept (Exception, psycopg2.Error) as error :\r\n if(conn):\r\n print(\"Failed to select record into mobile table\", error)\r\nfinally:\r\n#closing database connection.\r\n if(conn):\r\n cursor.close()\r\n conn.close()\r\n f.close()\r\n# print (sel_data)\r\n print(\"\\n\\nPostgreSQL connection is closed\") ", "sub_path": "report.py", "file_name": "report.py", "file_ext": "py", "file_size_in_byte": 3296, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "psycopg2.connect", "line_number": 5, "usage_type": "call"}, {"api_name": "psycopg2.Error", "line_number": 71, "usage_type": "attribute"}]} +{"seq_id": "112226879", "text": "import json\nfrom django.core.serializers.json import DjangoJSONEncoder\nimport django\ndjango.setup()\nfrom atlas.models import Product, Review, Analysis\nfrom django.db.models import Count,Avg\nfrom django.utils.dateformat import format\nimport time , datetime\n\n\ndef getBrand(kw):\n brands = Product.objects.filter(pCategory=kw).distinct().values('pBrand') # to return dictionary of values for each column\n #brands = Product.objects.filter(pCategory=request).distinct().values_list('pBrand', flat=True) # to return only values of that column\n brands_json = json.dumps(list(brands), cls=DjangoJSONEncoder)\n return brands_json\n\n\ndef getSource(kw, brand):\n sources = Product.objects.filter(pCategory=kw, pBrand__in=brand).distinct().values('siteCode') # to return dictionary of values for each column\n sources_json = json.dumps(list(sources), cls=DjangoJSONEncoder)\n return sources_json\n\n\ndef getSku(kw, brand, source):\n sku = Product.objects.filter(pCategory=kw, pBrand__in=brand, siteCode__in=source).distinct().values('pModel') # to return dictionary of values for each column\n sku_json = json.dumps(list(sku), cls=DjangoJSONEncoder)\n return sku_json\n\n\ndef getChart1(kw, brand, source, sku, fromDate, toDate):\n dates = Review.objects.values_list('rDate')\n dates2 = Review.objects.values_list('rDate2')\n data = dates.annotate(Count('rid')).filter(pid__pCategory=kw, pid__pBrand__in=brand, pid__siteCode__in=source\n , pid__pModel__in=sku).order_by('rDate')\n if (fromDate == \"\" or toDate == \"\"):\n data2 = dates2.annotate(Count('rid')).filter(pid__pCategory=kw, pid__pBrand__in=brand, pid__siteCode__in=source\n , pid__pModel__in=sku).order_by('rDate2')\n else:\n data2 = dates2.annotate(Count('rid')).filter(pid__pCategory=kw, pid__pBrand__in=brand, pid__siteCode__in=source\n , pid__pModel__in=sku, rDate2__range=[fromDate, toDate]).order_by('rDate2')\n\n\n\n d2 = list(data2)\n a = list(data)\n # print(a)\n # print(d2)\n c = [[int(time.mktime(b[0].timetuple()))*1000, b[1]] for b in d2]\n #print(\"--------------------------------------------------------------\")\n #print(dir(data))\n #print(c)\n #print(\"--------------------------------------------------------------\")\n #print(\"Query= \", data.query)\n data_json = json.dumps(c, cls=DjangoJSONEncoder)\n #print(data_json)\n return data_json\n\n\ndef neighborhood(iterable):\n iterator = iter(iterable)\n prev_item = None\n current_item = next(iterator) # throws StopIteration if empty.\n for next_item in iterator:\n yield (prev_item, current_item, next_item)\n prev_item = current_item\n current_item = next_item\n yield (prev_item, current_item, None)\n\n\ndef getChart2(kw, brand, source, sku, fromDate, toDate):\n if (fromDate == \"\" or toDate == \"\"):\n data2 = Review.objects.filter\\\n (pid__pCategory=kw, pid__pBrand__in=brand, pid__siteCode__in=source, pid__pModel__in=sku)\\\n .values_list('pid__pBrand', 'pid__pModel')\\\n .annotate(average_rating=Avg('pid__pRating'))\n data1 = Review.objects.filter\\\n (pid__pCategory=kw, pid__pBrand__in=brand, pid__siteCode__in=source, pid__pModel__in=sku).values_list('pid__pBrand')\\\n .annotate(average_rating=Avg('pid__pRating'))\n else:\n data2 = Review.objects.filter\\\n (pid__pCategory=kw, pid__pBrand__in=brand, pid__siteCode__in=source, pid__pModel__in=sku, rDate2__range=[fromDate, toDate])\\\n .values_list('pid__pBrand','pid__pModel')\\\n .annotate(average_rating=Avg('pid__pRating'))\n data1 = Review.objects.filter\\\n (pid__pCategory=kw, pid__pBrand__in=brand, pid__siteCode__in=source, pid__pModel__in=sku, rDate2__range=[fromDate, toDate])\\\n .values_list('pid__pBrand')\\\n .annotate(average_rating=Avg('pid__pRating'))\n\n a = list(data1)\n b = list(data2)\n #print(\"--------------------------------------------------------------\")\n #print(\"--------------------------------------------------------------\")\n #print(\"Query= \", data2.query)\n dict1 = {}\n response1 = []\n #print(data_json1)\n #print(\"--------------------------------------------------------------\")\n #a\n # [[\"Element\", 4.0], [\"Samsung\", 1.0], [\"Sceptre\", 4.03975], [\"Seiki\", 3.0], [\"TCL\", 4.33846], [\"VIZIO\", 4.5]]\n #b\n # [[\"Element\", \"B01HQS8UZA\", 4.0], [\"Samsung\", \"301688015\", 1.0], [\"Sceptre\", \"27678567\", 4.0],\n # [\"Sceptre\", \"55042148\", 4.5], [\"Sceptre\", \"55427159\", 3.5], [\"Sceptre\", \"B00W2T70IM\", 4.2],\n # [\"Seiki\", \"55277725\", 3.0], [\"TCL\", \"B01MTGM5I9\", 4.8], [\"TCL\", \"B01MU1GBLL\", 4.2], [\"VIZIO\", \"49228250\", 4.5]]\n for i in a:\n dict1[\"name\"] = i[0]\n dict1[\"y\"] = i[1]\n dict1[\"drilldown\"] = i[0]\n response1.append(dict1)\n dict1 = {}\n\n series = []\n temp_list = []\n temp1 = []\n dict2 = {}\n\n for prev, item, next in neighborhood(b):\n if next is not None:\n if item[0] == next[0]:\n temp_list.append(item[1])\n temp_list.append(item[2])\n temp1.append(temp_list)\n temp_list = []\n continue\n else:\n temp_list.append(item[1])\n temp_list.append(item[2])\n temp1.append(temp_list)\n else:\n temp_list.append(item[1])\n temp_list.append(item[2])\n temp1.append(temp_list)\n\n dict2[\"name\"] = item[0]\n dict2[\"id\"] = item[0]\n dict2[\"data\"] = temp1\n series.append(dict2)\n dict2 = {}\n temp1 = []\n temp_list = []\n\n dict2['series'] = series\n response = {}\n response['response1'] = response1\n response['dict2'] = dict2\n #print response\n response_json = json.dumps(response, cls=DjangoJSONEncoder)\n return response_json\n\n\ndef getChart3(kw, brand, source, sku, fromDate, toDate):\n dates = Review.objects.values_list('rDate')\n dates2 = Review.objects.values_list('rDate2')\n\n if (fromDate == \"\" or toDate == \"\"):\n data2 = Review.objects.filter \\\n (pid__pCategory=kw, pid__pBrand__in=brand, pid__siteCode__in=source, pid__pModel__in=sku) \\\n .values_list('pid__pBrand', 'pid__pModel') \\\n .annotate(Count('pid__pModel'))\n else:\n data2 = Review.objects.filter \\\n (pid__pCategory=kw, pid__pBrand__in=brand, pid__siteCode__in=source, pid__pModel__in=sku, rDate2__range=[fromDate, toDate]) \\\n .values_list('pid__pBrand', 'pid__pModel') \\\n .annotate(Count('pid__pModel'))\n\n\n print (brand)\n d2 = list(data2)\n series = []\n temp_list = []\n temp1 = []\n dict2 = {}\n temp_dict = {}\n temp_dict1 = {}\n for prev, item, next in neighborhood(d2):\n if next is not None:\n if item[0] == next[0]:\n temp_dict[item[1]] = item[2]\n else:\n temp_dict[item[1]] = item[2]\n temp_dict1[item[0]] = temp_dict\n temp_dict = {}\n else:\n if item[0] == prev[0]:\n temp_dict[item[1]] = item[2]\n temp_dict1[item[0]] = temp_dict\n else:\n temp_dict = {}\n temp_dict[item[1]] = item[2]\n temp_dict1[item[0]] = temp_dict\n\n\n #print(temp_dict1)\n #a = list(data)\n dict1 = {}\n # for i in d2:\n # dict\n #print(a)\n #print(d2)\n #c = [[int(time.mktime(b[0].timetuple()))*1000, b[1]] for b in d2]\n #print(\"--------------------------------------------------------------\")\n #print(dir(data))\n #print(c)\n print(\"--------------------------------------------------------------\")\n #print(\"Query= \", data.query)\n data_json = json.dumps(temp_dict1, cls=DjangoJSONEncoder)\n #print(data_json)\n return data_json\n", "sub_path": "mysite/atlas/services/summary_service.py", "file_name": "summary_service.py", "file_ext": "py", "file_size_in_byte": 7990, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.setup", "line_number": 4, "usage_type": "call"}, {"api_name": "atlas.models.Product.objects.filter", "line_number": 12, "usage_type": "call"}, {"api_name": "atlas.models.Product.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "atlas.models.Product", "line_number": 12, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 14, "usage_type": "call"}, {"api_name": "django.core.serializers.json.DjangoJSONEncoder", "line_number": 14, "usage_type": "name"}, {"api_name": "atlas.models.Product.objects.filter", "line_number": 19, "usage_type": "call"}, {"api_name": "atlas.models.Product.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "atlas.models.Product", "line_number": 19, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 20, "usage_type": "call"}, {"api_name": "django.core.serializers.json.DjangoJSONEncoder", "line_number": 20, "usage_type": "name"}, {"api_name": "atlas.models.Product.objects.filter", "line_number": 25, "usage_type": "call"}, {"api_name": "atlas.models.Product.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "atlas.models.Product", "line_number": 25, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 26, "usage_type": "call"}, {"api_name": "django.core.serializers.json.DjangoJSONEncoder", "line_number": 26, "usage_type": "name"}, {"api_name": "atlas.models.Review.objects.values_list", "line_number": 31, "usage_type": "call"}, {"api_name": "atlas.models.Review.objects", "line_number": 31, "usage_type": "attribute"}, {"api_name": "atlas.models.Review", "line_number": 31, "usage_type": "name"}, {"api_name": "atlas.models.Review.objects.values_list", "line_number": 32, "usage_type": "call"}, {"api_name": "atlas.models.Review.objects", "line_number": 32, "usage_type": "attribute"}, {"api_name": "atlas.models.Review", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models.Count", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models.Count", "line_number": 39, "usage_type": "call"}, {"api_name": "time.mktime", "line_number": 48, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 54, "usage_type": "call"}, {"api_name": "django.core.serializers.json.DjangoJSONEncoder", "line_number": 54, "usage_type": "name"}, {"api_name": "atlas.models.Review.objects.filter", "line_number": 72, "usage_type": "call"}, {"api_name": "atlas.models.Review.objects", "line_number": 72, "usage_type": "attribute"}, {"api_name": "atlas.models.Review", "line_number": 72, "usage_type": "name"}, {"api_name": "django.db.models.Avg", "line_number": 75, "usage_type": "call"}, {"api_name": "atlas.models.Review.objects.filter", "line_number": 76, "usage_type": "call"}, {"api_name": "atlas.models.Review.objects", "line_number": 76, "usage_type": "attribute"}, {"api_name": "atlas.models.Review", "line_number": 76, "usage_type": "name"}, {"api_name": "django.db.models.Avg", "line_number": 78, "usage_type": "call"}, {"api_name": "atlas.models.Review.objects.filter", "line_number": 80, "usage_type": "call"}, {"api_name": "atlas.models.Review.objects", "line_number": 80, "usage_type": "attribute"}, {"api_name": "atlas.models.Review", "line_number": 80, "usage_type": "name"}, {"api_name": "django.db.models.Avg", "line_number": 83, "usage_type": "call"}, {"api_name": "atlas.models.Review.objects.filter", "line_number": 84, "usage_type": "call"}, {"api_name": "atlas.models.Review.objects", "line_number": 84, "usage_type": "attribute"}, {"api_name": "atlas.models.Review", "line_number": 84, "usage_type": "name"}, {"api_name": "django.db.models.Avg", "line_number": 87, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 146, "usage_type": "call"}, {"api_name": "django.core.serializers.json.DjangoJSONEncoder", "line_number": 146, "usage_type": "name"}, {"api_name": "atlas.models.Review.objects.values_list", "line_number": 151, "usage_type": "call"}, {"api_name": "atlas.models.Review.objects", "line_number": 151, "usage_type": "attribute"}, {"api_name": "atlas.models.Review", "line_number": 151, "usage_type": "name"}, {"api_name": "atlas.models.Review.objects.values_list", "line_number": 152, "usage_type": "call"}, {"api_name": "atlas.models.Review.objects", "line_number": 152, "usage_type": "attribute"}, {"api_name": "atlas.models.Review", "line_number": 152, "usage_type": "name"}, {"api_name": "atlas.models.Review.objects.filter", "line_number": 155, "usage_type": "call"}, {"api_name": "atlas.models.Review.objects", "line_number": 155, "usage_type": "attribute"}, {"api_name": "atlas.models.Review", "line_number": 155, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 158, "usage_type": "call"}, {"api_name": "atlas.models.Review.objects.filter", "line_number": 160, "usage_type": "call"}, {"api_name": "atlas.models.Review.objects", "line_number": 160, "usage_type": "attribute"}, {"api_name": "atlas.models.Review", "line_number": 160, "usage_type": "name"}, {"api_name": "django.db.models.Count", "line_number": 163, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 205, "usage_type": "call"}, {"api_name": "django.core.serializers.json.DjangoJSONEncoder", "line_number": 205, "usage_type": "name"}]} +{"seq_id": "541006663", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n###############################################################################\n# Copyright Kitware Inc. and Epidemico 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###############################################################################\nimport json\nimport os\nimport errno\nimport shutil\nfrom fiona import crs as fiona_crs\nimport gaia\nfrom gaia import GaiaException, get_abspath\n\ntry:\n import osr\nexcept ImportError:\n from osgeo import osr\nimport gaia.formats as formats\n\n\nclass MissingParameterError(GaiaException):\n \"\"\"Raise when a required parameter is missing\"\"\"\n pass\n\n\nclass MissingDataException(GaiaException):\n \"\"\"Raise when required data is missing\"\"\"\n pass\n\n\nclass UnsupportedFormatException(GaiaException):\n \"\"\"Raise when an unsupported data format is used\"\"\"\n pass\n\n\nclass GaiaIO(object):\n \"\"\"Abstract IO class for importing/exporting data from a certain source\"\"\"\n data = None\n filters = None\n\n type = None\n format = None\n default_output = None\n\n def __init__(self, **kwargs):\n \"\"\"\n Create a GaiaIO object, assigning attributes based on kwargs\n\n :param kwargs: Keyword arguments\n \"\"\"\n for k, v in kwargs.items():\n setattr(self, k, v)\n\n def read(self, *args, **kwargs):\n \"\"\"\n Abstract method for reading data\n\n :param args: Required arguments\n :param kwargs: Keyword arguments\n \"\"\"\n raise NotImplementedError()\n\n def write(self, *args, **kwargs):\n \"\"\"\n Abstract method for writing data\n\n :param args: Required arguments\n :param kwargs: Keyword arguments\n \"\"\"\n pass\n\n def create_output_dir(self, filepath):\n \"\"\"\n Create an output directory if it doesn't exist\n\n :param filepath: Directory to create\n \"\"\"\n if not os.path.exists(os.path.dirname(filepath)):\n try:\n os.makedirs(os.path.dirname(filepath))\n except OSError as exc:\n if exc.errno != errno.EEXIST:\n raise\n\n def get_epsg(self):\n \"\"\"\n Get the EPSG code of the data\n\n :return: EPSG code (integer)\n \"\"\"\n if self.data is None:\n self.read()\n if self.data.__class__.__name__ == 'GeoDataFrame':\n if self.data.crs is None:\n # Make educated guess about projection based on longitude coords\n minx = min(self.data.geometry.bounds['minx'])\n maxx = max(self.data.geometry.bounds['maxx'])\n if minx >= -180.0 and maxx <= 180.0:\n self.data.crs = fiona_crs.from_epsg(4326)\n self.epsg = 4326\n elif minx >= -20026376.39 and maxx <= 20026376.39:\n self.data.crs = fiona_crs.from_epsg(3857)\n self.epsg = 3857\n else:\n raise GaiaException('Could not determine data projection.')\n return self.epsg\n else:\n crs = self.data.crs.get('init', None)\n if crs and ':' in crs:\n crs = crs.split(':')[1]\n if crs.isdigit():\n self.epsg = int(crs)\n return self.epsg\n else:\n # Assume EPSG:4326\n self.epsg = 4326\n self.data.crs = fiona_crs.from_epsg(4326)\n return self.epsg\n elif self.data.__class__.__name__ == 'Dataset':\n projection = self.data.GetProjection()\n data_crs = osr.SpatialReference(wkt=projection)\n try:\n self.epsg = int(data_crs.GetAttrValue('AUTHORITY', 1))\n return self.epsg\n except KeyError:\n raise GaiaException(\"EPSG code coud not be determined\")\n\n def delete(self):\n \"\"\"\n Abstract method for deleting the IO source\n \"\"\"\n raise NotImplementedError()\n\n\nclass FileIO(GaiaIO):\n \"\"\"Abstract class to read and write file data.\"\"\"\n\n def __init__(self, uri='', **kwargs):\n \"\"\"\n :param uri: Filepath of IO object\n :param kwargs:\n :return:\n \"\"\"\n if uri and not self.allowed_folder(uri):\n raise GaiaException(\n \"Access to this directory is not permitted : {}\".format(\n os.path.dirname(uri)))\n self.uri = uri\n super(FileIO, self).__init__(uri=uri, **kwargs)\n if self.uri:\n self.ext = os.path.splitext(self.uri)[1].lower()\n\n def allowed_folder(self, folder):\n \"\"\"\n Return true or false if folder is in list of\n allowed folders from config\n\n :param folder: folder to check\n :return: True or False\n \"\"\"\n allowed_dirs = gaia.config['gaia']['fileio_paths'].split(',')\n if not allowed_dirs[0] or allowed_dirs[0] == '':\n return True\n filepath = os.path.abspath(os.path.dirname(folder))\n allowed = False\n for path in allowed_dirs:\n if filepath.startswith(get_abspath(path)):\n allowed = True\n break\n return allowed\n\n def delete(self):\n \"\"\"\n Remove file of IO object\n\n :return: None\n \"\"\"\n if os.path.exists(self.uri):\n shutil.rmtree(os.path.dirname(self.uri))\n\n\nclass JsonFileIO(FileIO):\n \"\"\"Read json and write json file data (such as .json)\"\"\"\n\n default_output = formats.JSON\n\n def read(self, format=formats.JSON):\n \"\"\"\n Load JSON data into a python object\n\n :param format: input format\n :return: Python dict object\n \"\"\"\n if self.ext not in formats.JSON:\n raise UnsupportedFormatException(\n \"Only the following weight formats are supported: {}\".format(\n ','.join(formats.JSON)\n )\n )\n if self.data is None:\n with open(self.uri, 'r') as f:\n self.data = json.load(f)\n return self.data\n\n def write(self, filename=None, as_type='json'):\n \"\"\"\n Write data (assumed dictionary object) to json file\n\n :param filename: Base filename\n :param as_type: json\n :return: location of file\n \"\"\"\n if not filename:\n filename = self.uri\n self.create_output_dir(filename)\n if as_type == 'json':\n with open(filename, 'w') as f:\n json.dump(self.data, f)\n else:\n raise NotImplementedError('{} not a valid type'.format(as_type))\n return self.uri\n", "sub_path": "gaia/inputs.py", "file_name": "inputs.py", "file_ext": "py", "file_size_in_byte": 7207, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "gaia.GaiaException", "line_number": 34, "usage_type": "name"}, {"api_name": "gaia.GaiaException", "line_number": 39, "usage_type": "name"}, {"api_name": "gaia.GaiaException", "line_number": 44, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 91, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 93, "usage_type": "call"}, {"api_name": "os.path", "line_number": 93, "usage_type": "attribute"}, {"api_name": "errno.EEXIST", "line_number": 95, "usage_type": "attribute"}, {"api_name": "fiona.crs.from_epsg", "line_number": 112, "usage_type": "call"}, {"api_name": "fiona.crs", "line_number": 112, "usage_type": "name"}, {"api_name": "fiona.crs.from_epsg", "line_number": 115, "usage_type": "call"}, {"api_name": "fiona.crs", "line_number": 115, "usage_type": "name"}, {"api_name": "gaia.GaiaException", "line_number": 118, "usage_type": "call"}, {"api_name": "fiona.crs.from_epsg", "line_number": 130, "usage_type": "call"}, {"api_name": "fiona.crs", "line_number": 130, "usage_type": "name"}, {"api_name": "osgeo.osr.SpatialReference", "line_number": 134, "usage_type": "call"}, {"api_name": "osgeo.osr", "line_number": 134, "usage_type": "name"}, {"api_name": "gaia.GaiaException", "line_number": 139, "usage_type": "call"}, {"api_name": "gaia.GaiaException", "line_number": 158, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path", "line_number": 160, "usage_type": "attribute"}, {"api_name": "os.path.splitext", "line_number": 164, "usage_type": "call"}, {"api_name": "os.path", "line_number": 164, "usage_type": "attribute"}, {"api_name": "gaia.config", "line_number": 174, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 177, "usage_type": "call"}, {"api_name": "os.path", "line_number": 177, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 177, "usage_type": "call"}, {"api_name": "gaia.get_abspath", "line_number": 180, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 191, "usage_type": "call"}, {"api_name": "os.path", "line_number": 191, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path", "line_number": 192, "usage_type": "attribute"}, {"api_name": "gaia.formats.JSON", "line_number": 198, "usage_type": "attribute"}, {"api_name": "gaia.formats", "line_number": 198, "usage_type": "name"}, {"api_name": "gaia.formats.JSON", "line_number": 200, "usage_type": "attribute"}, {"api_name": "gaia.formats", "line_number": 200, "usage_type": "name"}, {"api_name": "gaia.formats.JSON", "line_number": 207, "usage_type": "attribute"}, {"api_name": "gaia.formats", "line_number": 207, "usage_type": "name"}, {"api_name": "gaia.formats.JSON", "line_number": 210, "usage_type": "attribute"}, {"api_name": "gaia.formats", "line_number": 210, "usage_type": "name"}, {"api_name": "json.load", "line_number": 215, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 231, "usage_type": "call"}]} +{"seq_id": "27511437", "text": "from flask import Flask, render_template, request, redirect\nfrom datetime import datetime,timedelta\nimport pandas as pd\nimport simplejson as json\nimport time\nimport requests\nfrom bokeh.plotting import figure, show, output_file\nfrom bokeh import embed\nimport cgi\n\n\napp = Flask(__name__)\n\n@app.route('/')\ndef main():\n return redirect('/stock')\n\n# main page\n@app.route('/stock', methods = ['GET','POST'])\ndef stock():\n return render_template('stock.html')\n\n# collect user input - ticker symbol and requested features\ndef get_req():\n \n features = request.form.getlist('feature')\n \n ticker = request.form['ticker']\n \n # setup date and time to collect 3 months' data up to last week\n curr_date = datetime.now()\n start_date = (curr_date - timedelta(days = 90))\n end_date = (curr_date - timedelta(days = 7))\n \n start_date = start_date.strftime('%Y-%m-%d')\n end_date = end_date.strftime('%Y-%m-%d')\n \n # get the data from Quandl's wiki\n URL = 'https://www.quandl.com/api/v3/datasets/WIKI/'+ticker+'.json?start_date='+start_date+'&end_date='+end_date+'&order=asc&api_key=eFoXAcyvLhyuB3Rsvg6o'\n req = requests.get(URL)\n \n return req, features, ticker\n \n \n\n\n@app.route('/chart',methods=['GET','POST'])\ndef chart():\n req, features, ticker = get_req()\n # if the stock ticker isn't valid, reload with warning message\n if req.status_code != 200:\n msg = \"Sorry, that ticker isn't valid. Please try again.\"\n return render_template('stock.html', msg=msg)\n\t\t \n else: \n \n # convert the data to a pandas dataframe\n req_data = pd.DataFrame(req.json())\n data = pd.DataFrame(req_data.ix['data', 'dataset'], columns = req_data.ix['column_names', 'dataset']) \n #df.columns = [x.lower() for x in df.columns]\n data = data.set_index(['Date'])\n data.index = pd.to_datetime(data.index)\n \n # create the plot and output to 'chart.html'\n #output_file(\"chart.html\", title=\"Stock prices changes for last month\")\n stock_plot = figure(x_axis_type = \"datetime\", title = 'Stock prices for '+ticker, x_axis_label = 'Date', y_axis_label = 'Stock price ($)')\n if 'open' in features:\n stock_plot.line(data.index, data['Open'], color='blue', legend='Open')\n if 'adj_open' in features:\n stock_plot.line(data.index, data['Adj. Open'], color='green', legend='Adj. Open')\n if 'close' in features:\n stock_plot.line(data.index, data['Close'], color='black', legend='Close')\n if 'adj_close' in features:\n stock_plot.line(data.index, data['Adj. Close'], color='red', legend='Adj. Close')\n \n script, div = embed.components(stock_plot)\n return render_template('chart.html', script=script, div=div)\n \n\n\nif __name__ == '__main__':\n app.run(port=33500, debug = True)", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2855, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Flask", "line_number": 12, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 16, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 21, "usage_type": "call"}, {"api_name": "flask.request.form.getlist", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 31, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 32, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 33, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 58, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 59, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 62, "usage_type": "call"}, {"api_name": "bokeh.plotting.figure", "line_number": 66, "usage_type": "call"}, {"api_name": "bokeh.embed.components", "line_number": 76, "usage_type": "call"}, {"api_name": "bokeh.embed", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "54553613", "text": "\"\"\"\nUsage IN TERMINAL .. :\n\tpython build.py py2app\n\"\"\"\n\nfrom setuptools import setup\n\nAPP = ['AshCode.py']\nDATA_FILES = [(\"\", [\"images\"]), (\"\", [\"Audio\"])]\nOPTIONS = {\n\t\"iconfile\": \"images/icon.icns\"\n}\n\nsetup(\n\tname = \"Ash Code\",\n\tversion = \"1.0.0\",\n\tapp = APP,\n\tdata_files = DATA_FILES,\n\toptions = {\"py2app\": OPTIONS},\n\t setup_requires = [\"py2app\"]\n)", "sub_path": "setup_mac.py", "file_name": "setup_mac.py", "file_ext": "py", "file_size_in_byte": 352, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "setuptools.setup", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "452135272", "text": "import datetime\nimport logging\nimport random\n\nfrom selenium import webdriver\nfrom selenium.common.exceptions import (\n RemoteDriverServerException,\n SessionNotCreatedException,\n)\n\nfrom common import enums\nfrom uptime.exceptions import NoProxyError, SeleniumError\nfrom uptime.models import Proxy\n\nlogger = logging.getLogger(\"uptime\")\n\nfrom app import settings\n\n# These are from https://techblog.willshouse.com/2012/01/03/most-common-user-agents/\nAGENTS = [\n \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36\t\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36\",\n \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.141 Safari/537.36\",\n \"Mozilla/5.0 (Macintosh; Intel Mac OS X 11_1_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36\",\n \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/87.0.4280.88 Safari/537.36\",\n]\n\n\ndef get_driver(proxy):\n options = webdriver.ChromeOptions()\n options.add_argument(f\"--proxy-server=socks5://{proxy.address}\")\n\n # https://stackoverflow.com/questions/48450594/selenium-timed-out-receiving-message-from-renderer\n options.add_argument(\"--disable-gpu\")\n options.add_argument(\"enable-automation\")\n options.add_argument(\"--headless\")\n options.add_argument(\"--window-size=1024,1080\")\n options.add_argument(\"--no-sandbox\")\n options.add_argument(\"--disable-extensions\")\n options.add_argument(\"--dns-prefetch-disable\")\n options.add_argument(f\"--user-agent={random.choice(AGENTS)}\")\n options.add_argument(\n \"--disable-browser-side-navigation\"\n ) # https://stackoverflow.com/a/49123152/1689770\n\n # workaround for fargate, since we can't -v /dev/sdm:/dev/shm\n # see: https://stackoverflow.com/questions/48084977/alternative-to-mounting-dev-shm-volume-in-selenium-grid-aws-fargate-setup\n options.add_argument(\"--disable-dev-shm-usage\")\n\n # random independent user dir\n # options.add_argument(f\"--user-data-dir=/tmp/chrome-user-data-{uuid.uuid4()}\")\n\n caps = webdriver.DesiredCapabilities.CHROME.copy()\n caps[\"pageLoadStrategy\"] = \"normal\"\n\n driver = webdriver.Remote(\n command_executor=settings.SELENIUM_URL,\n desired_capabilities=caps,\n options=options,\n )\n driver.set_page_load_timeout(settings.SELENIUM_DRIVER_TIMEOUT)\n logger.info(f\"Created driver for {proxy}\")\n return driver\n\n\ndef get_drivers():\n drivers = []\n\n unused_proxies = list(\n Proxy.objects.filter(\n status=enums.ProxyStatus.UP, last_used__isnull=True\n ).order_by(\"failure_count\", \"created_at\")\n )\n used_proxies = list(\n Proxy.objects.filter(status=enums.ProxyStatus.UP).order_by(\n \"failure_count\", \"last_used\",\n )\n )\n\n # always try to keep a fresh proxy in reserve, if we can\n if unused_proxies and len(unused_proxies) + len(used_proxies) > 2:\n reserve = unused_proxies.pop()\n logger.debug(f\"reserve {reserve}\")\n\n proxies = unused_proxies + used_proxies\n\n # randomly shuffle remaining proxies\n random.shuffle(proxies)\n\n # verify the proxy is responding before we try to use it\n verified = []\n while len(verified) < 2:\n from uptime.proxy import proxy_is_up\n\n if not proxies:\n logger.warning(f\"failed to find 2 working proxies\")\n raise NoProxyError(f\"failed to find 2 working proxies\")\n\n proxy = proxies.pop()\n if proxy_is_up(proxy.address):\n verified.append(proxy)\n\n backup = verified[0]\n primary = verified[1]\n logger.info(f\"backup {backup} last_used {backup.last_used}\")\n logger.info(f\"primary {primary} last_used {primary.last_used}\")\n drivers.append([get_driver(primary), primary])\n drivers.append([get_driver(backup), backup])\n\n primary.last_used = datetime.datetime.utcnow().replace(tzinfo=datetime.timezone.utc)\n primary.save()\n\n return drivers\n\n\ndef test_driver(driver):\n try:\n nonce = str(random.randint(1000000, 9999999))\n url = f\"{settings.PRIMARY_ORIGIN}/test/{nonce}/\"\n driver.get(url)\n if nonce in driver.page_source:\n return True\n return False\n except SessionNotCreatedException as e:\n raise e\n except RemoteDriverServerException as e:\n raise e\n except Exception as e:\n logger.info(e)\n return False\n\n\ndef load_site(driver, url):\n error = None\n timeout = None\n title = \"\"\n content = \"\"\n png = None\n\n try:\n driver.get(url)\n title = driver.title\n content = driver.page_source\n png = driver.get_screenshot_as_png()\n except SessionNotCreatedException as e:\n raise e\n except RemoteDriverServerException as e:\n raise e\n except Exception as e:\n if \"Timed out receiving message from renderer: -\" in str(e):\n # if we get a negatime timeout it's because the worker is broken\n raise SeleniumError(f\"Problem talking to selenium worker: {e}\")\n if \"establishing a connection\" in str(e):\n raise e\n if \"marionette\" in str(e):\n raise e\n if \"timeout\" in str(e):\n # we may tolerate timeout in some cases; see below\n timeout = str(e)\n else:\n error = str(e)\n\n return error, timeout, title, content, png\n", "sub_path": "app/uptime/selenium.py", "file_name": "selenium.py", "file_ext": "py", "file_size_in_byte": 5507, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "selenium.webdriver.ChromeOptions", "line_number": 30, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 30, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 41, "usage_type": "call"}, {"api_name": "selenium.webdriver.DesiredCapabilities.CHROME.copy", "line_number": 53, "usage_type": "call"}, {"api_name": "selenium.webdriver.DesiredCapabilities", "line_number": 53, "usage_type": "attribute"}, {"api_name": "selenium.webdriver", "line_number": 53, "usage_type": "name"}, {"api_name": "selenium.webdriver.Remote", "line_number": 56, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 56, "usage_type": "name"}, {"api_name": "app.settings.SELENIUM_URL", "line_number": 57, "usage_type": "attribute"}, {"api_name": "app.settings", "line_number": 57, "usage_type": "name"}, {"api_name": "app.settings.SELENIUM_DRIVER_TIMEOUT", "line_number": 61, "usage_type": "attribute"}, {"api_name": "app.settings", "line_number": 61, "usage_type": "name"}, {"api_name": "uptime.models.Proxy.objects.filter", "line_number": 70, "usage_type": "call"}, {"api_name": "uptime.models.Proxy.objects", "line_number": 70, "usage_type": "attribute"}, {"api_name": "uptime.models.Proxy", "line_number": 70, "usage_type": "name"}, {"api_name": "common.enums.ProxyStatus", "line_number": 71, "usage_type": "attribute"}, {"api_name": "common.enums", "line_number": 71, "usage_type": "name"}, {"api_name": "uptime.models.Proxy.objects.filter", "line_number": 75, "usage_type": "call"}, {"api_name": "uptime.models.Proxy.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "uptime.models.Proxy", "line_number": 75, "usage_type": "name"}, {"api_name": "common.enums.ProxyStatus", "line_number": 75, "usage_type": "attribute"}, {"api_name": "common.enums", "line_number": 75, "usage_type": "name"}, {"api_name": "random.shuffle", "line_number": 88, "usage_type": "call"}, {"api_name": "uptime.exceptions.NoProxyError", "line_number": 97, "usage_type": "call"}, {"api_name": "uptime.proxy.proxy_is_up", "line_number": 100, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 110, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 110, "usage_type": "attribute"}, {"api_name": "datetime.timezone", "line_number": 110, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 118, "usage_type": "call"}, {"api_name": "app.settings.PRIMARY_ORIGIN", "line_number": 119, "usage_type": "attribute"}, {"api_name": "app.settings", "line_number": 119, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.SessionNotCreatedException", "line_number": 124, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.RemoteDriverServerException", "line_number": 126, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.SessionNotCreatedException", "line_number": 145, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.RemoteDriverServerException", "line_number": 147, "usage_type": "name"}, {"api_name": "uptime.exceptions.SeleniumError", "line_number": 152, "usage_type": "call"}]} +{"seq_id": "104691775", "text": "from urllib.request import urlopen\nfrom bs4 import BeautifulSoup as BS\nfrom urllib.parse import quote\nimport ssl\nimport requests\n\nurl_str = 'https://search.naver.com/search.naver?date_from=&date_option=0&date_to=&dup_remove=1&nso=&post_blogurl=&post_blogurl_without=&query='\nurl_page = '&sm=tab_pge&srchby=all&st=sim&where=post&start='\n\n# 사용자가 입력한 검색어에 대한 블로그 조회\ndef call_sch_url(keyword, start, stop):\n for i in range(start, stop, 10):\n url = url_str + quote(keyword) + url_page + str(i)\n\n context = ssl._create_unverified_context()\n html = urlopen(url, context=context)\n bs_obj = BS(html, \"html.parser\")\n\n scr_htm = bs_obj.find('ul', {'class': 'type01'})\n\n href_htm = scr_htm.find_all('dt')\n for hhtm in href_htm:\n href = hhtm.find('a')\n link_txt = href['href'].replace('?Redirect=Log&logNo=', '/')\n links.append(link_txt)\n #print(link_txt)\n\n\n# 검색 후 페이지 텍스트 추출 및 저장\ndef call_url_text(links):\n for url in links:\n #print(url)\n html = requests.get(url)\n sub_temp = BS(html.text, \"html.parser\")\n # print(soup_temp)\n order_temp = sub_temp.find(id='screenFrame')\n\n if order_temp != None:\n ss_url = order_temp.get('src')\n res = urlopen(ss_url)\n sub_temp = BS(res, 'html.parser')\n area_temp = sub_temp.find(id='mainFrame')\n # print(area_temp)\n url_2 = area_temp.get('src')\n # print(url_2)\n\n url = \"https://blog.naver.com\" + url_2\n res = urlopen(url)\n soup = BS(res, 'html.parser')\n # print(soup)\n\n texts = soup.find_all('div', {'class': 'se-module se-module-text'})\n if len(texts) != 0:\n call_txt_prn(texts)\n\n f.close()\n print('작업이 종료되었습니다.')\n # print(text_prn)\n\n\ndef call_txt_prn(texts):\n for prn in texts:\n txt = prn.text.replace('\\u200b', '')\n txt = txt.replace('\\xa0\\n', '')\n txt = txt.replace('\\n\\n', '')\n\n if txt != '':\n f.write(txt + '\\n')\n # text_prn.append(txt+\"\\n\")\n # print(txt)\n f.write('\\n\\n')\n # print('============================================')\n\n\nkeyword = input('검색어를 입력하세요 : ')\nf = open('' + keyword + '.txt', 'w', encoding='utf-8')\n\nlinks = []\ncall_sch_url(keyword, 1, 10)\ncall_url_text(links)", "sub_path": "crawling/Naver_blog_crawling.py", "file_name": "Naver_blog_crawling.py", "file_ext": "py", "file_size_in_byte": 2445, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "urllib.parse.quote", "line_number": 13, "usage_type": "call"}, {"api_name": "ssl._create_unverified_context", "line_number": 15, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 16, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 17, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 33, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 34, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 40, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 41, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 48, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "487949863", "text": "# -*- coding: utf-8 -*-\n\napp_name = 'blog'\n\nfrom django.urls import path\n\nfrom .views import blog_list, blog_detail, blog_type, blog_date\n\n\nurlpatterns = [\n path('blog_list/', blog_list, name='blog_list'),\n path('blog_detail//', blog_detail, name='blog_detail'),\n path('blog_types//', blog_type, name='blog_type'),\n path('blog_dates///', blog_date, name='blog_date'),\n]", "sub_path": "blog/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 433, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "views.blog_list", "line_number": 11, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "views.blog_detail", "line_number": 12, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "views.blog_type", "line_number": 13, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "views.blog_date", "line_number": 14, "usage_type": "argument"}]} +{"seq_id": "72733908", "text": "\"\"\"Example 005: Audit users. \"\"\"\n\nimport json\nfrom os import path\n\nfrom docusign_admin.client.api_exception import ApiException\nfrom flask import Blueprint, render_template, current_app\n\nfrom app.docusign import authenticate\nfrom app.error_handlers import process_error\nfrom .controller import Eg005Controller\nfrom ....ds_config import DS_CONFIG\n\neg = \"eg005\" # Reference (and URL) for this example\neg005 = Blueprint(eg, __name__)\n\n@eg005.route(\"/eg005\", methods=[\"POST\"])\n@authenticate(eg=eg)\ndef audit_users():\n \"\"\"\n 1. Get required arguments\n 2. Call the worker method\n 3. Render the response\n \"\"\"\n \n # 1. Get required arguments\n args = Eg005Controller.get_args()\n try:\n # 2. Call the worker method to get your monitor data\n results = Eg005Controller.worker(args)\n current_app.logger.info(f\"\"\"Auditing users\"\"\")\n except ApiException as err:\n return process_error(err)\n\n return render_template(\n \"example_done.html\",\n title=\"Audit users\",\n h1=\"Audit users\",\n message=\"Results from eSignUserManagement:getUserProfiles method:\",\n json=json.dumps(json.dumps(results, default=str))\n )\n\n@eg005.route(\"/eg005\", methods=[\"GET\"])\n@authenticate(eg=eg)\ndef get_view():\n \"\"\" Responds with the form for the example\"\"\"\n\n\n return render_template(\n \"eg005_audit_users.html\",\n title=\"Audit users\",\n source_file=path.basename(path.dirname(__file__)) + \"/controller.py\",\n source_url=DS_CONFIG[\"admin_github_url\"] + path.basename(path.dirname(__file__)) + \"/controller.py\",\n documentation=DS_CONFIG[\"documentation\"] + eg,\n )\n\n", "sub_path": "app/admin/examples/eg005_audit_users/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1653, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Blueprint", "line_number": 15, "usage_type": "call"}, {"api_name": "controller.Eg005Controller.get_args", "line_number": 27, "usage_type": "call"}, {"api_name": "controller.Eg005Controller", "line_number": 27, "usage_type": "name"}, {"api_name": "controller.Eg005Controller.worker", "line_number": 30, "usage_type": "call"}, {"api_name": "controller.Eg005Controller", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.current_app.logger.info", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 31, "usage_type": "name"}, {"api_name": "docusign_admin.client.api_exception.ApiException", "line_number": 32, "usage_type": "name"}, {"api_name": "app.error_handlers.process_error", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 35, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 40, "usage_type": "call"}, {"api_name": "app.docusign.authenticate", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 52, "usage_type": "call"}, {"api_name": "os.path", "line_number": 52, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 52, "usage_type": "call"}, {"api_name": "ds_config.DS_CONFIG", "line_number": 53, "usage_type": "name"}, {"api_name": "os.path.basename", "line_number": 53, "usage_type": "call"}, {"api_name": "os.path", "line_number": 53, "usage_type": "name"}, {"api_name": "os.path.dirname", "line_number": 53, "usage_type": "call"}, {"api_name": "ds_config.DS_CONFIG", "line_number": 54, "usage_type": "name"}, {"api_name": "app.docusign.authenticate", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "570034576", "text": "#!/usr/bin/env python\n\nimport sys\nimport numpy as np\nimport matplotlib.ticker as ticker\nimport scipy.spatial.distance as spd \nimport scipy.cluster.hierarchy as sph\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use('Agg')\nimport pylab\nimport pandas as pd\nfrom matplotlib.patches import Rectangle\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nimport cPickle as pickle\nimport math \n\nsys.setrecursionlimit(10000)\n\n# samples on rows\n\nclass SqrtNorm(matplotlib.colors.Normalize):\n \"\"\"\n Normalize a given value to the 0-1 range on a square root scale\n \"\"\"\n def __call__(self, value, clip=None):\n if clip is None:\n clip = self.clip\n\n result, is_scalar = self.process_value(value)\n\n result = np.ma.masked_less_equal(result, 0, copy=False)\n\n self.autoscale_None(result)\n vmin, vmax = self.vmin, self.vmax\n if vmin > vmax:\n raise ValueError(\"minvalue must be less than or equal to maxvalue\")\n elif vmin <= 0:\n raise ValueError(\"values must all be positive\")\n elif vmin == vmax:\n result.fill(0)\n else:\n if clip:\n mask = np.ma.getmask(result)\n result = np.ma.array(np.clip(result.filled(vmax), vmin, vmax),\n mask=mask)\n # in-place equivalent of above can be much faster\n resdat = result.data\n mask = result.mask\n if mask is np.ma.nomask:\n mask = (resdat <= 0)\n else:\n mask |= resdat <= 0\n matplotlib.cbook._putmask(resdat, mask, 1)\n np.sqrt(resdat, resdat)\n resdat -= np.sqrt(vmin)\n resdat /= (np.sqrt(vmax) - np.sqrt(vmin))\n result = np.ma.array(resdat, mask=mask, copy=False)\n if is_scalar:\n result = result[0]\n return result\n\n def inverse(self, value):\n if not self.scaled():\n raise ValueError(\"Not invertible until scaled\")\n vmin, vmax = self.vmin, self.vmax\n\n if matplotlib.cbook.iterable(value):\n val = np.ma.asarray(value)\n return vmin * np.ma.power((vmax / vmin), val)\n else:\n return vmin * pow((vmax / vmin), value)\n\n def autoscale(self, A):\n '''\n Set *vmin*, *vmax* to min, max of *A*.\n '''\n A = np.ma.masked_less_equal(A, 0, copy=False)\n self.vmin = np.ma.min(A)\n self.vmax = np.ma.max(A)\n\n def autoscale_None(self, A):\n ' autoscale only None-valued vmin or vmax'\n if self.vmin is not None and self.vmax is not None:\n return\n A = np.ma.masked_less_equal(A, 0, copy=False)\n if self.vmin is None:\n self.vmin = np.ma.min(A)\n if self.vmax is None:\n self.vmax = np.ma.max(A)\n\nclass DataMatrix:\n datatype = 'data_matrix'\n \n @staticmethod\n def input_parameters( parser ):\n dm_param = parser.add_argument_group('Input data matrix parameters')\n arg = dm_param.add_argument\n\n arg( '--sep', type=str, default='\\t' )\n arg( '--out_table', type=str, default=None,\n help = 'Write processed data matrix to file' )\n arg( '--fname_row', type=int, default=0,\n help = \"row number containing the names of the features \"\n \"[default 0, specify -1 if no names are present in the matrix\")\n arg( '--sname_row', type=int, default=0,\n help = \"column number containing the names of the samples \"\n \"[default 0, specify -1 if no names are present in the matrix\")\n arg( '--metadata_rows', type=str, default=None,\n help = \"Row numbers to use as metadata\"\n \"[default None, meaning no metadata\")\n arg( '--skip_rows', type=str, default=None,\n help = \"Row numbers to skip (0-indexed, comma separated) from the input file\"\n \"[default None, meaning no rows skipped\")\n arg( '--sperc', type=int, default=90, \n help = \"Percentile of sample value distribution for sample selection\" )\n arg( '--fperc', type=int, default=90, \n help = \"Percentile of feature value distribution for sample selection\" )\n arg( '--stop', type=int, default=None, \n help = \"Number of top samples to select (ordering based on percentile specified by --sperc)\" )\n arg( '--ftop', type=int, default=None, \n help = \"Number of top features to select (ordering based on percentile specified by --fperc)\" )\n arg( '--def_na', type=float, default=None,\n help = \"Set the default value for missing values [default None which means no replacement]\")\n\n def __init__( self, input_file, args ):\n self.args = args\n self.metadata_rows = [] \n self.metadata_table = None\n toskip = [int(l) for l in self.args.skip_rows.split(\",\")] if self.args.skip_rows else [] \n if self.args.metadata_rows:\n self.metadata_rows = list([int(a) for a in self.args.metadata_rows.split(\",\")])\n mdr = self.metadata_rows[::]\n for t in toskip:\n for i,m in enumerate(mdr):\n if t <= m:\n self.metadata_rows[i] -= 1\n if self.metadata_rows:\n header = [self.args.fname_row]+self.metadata_rows if self.args.fname_row > -1 else self.metadata_rows\n else:\n header = self.args.fname_row if self.args.fname_row > -1 else None \n self.table = pd.read_table( \n input_file, sep = self.args.sep, # skipinitialspace = True, \n skiprows = sorted(toskip) if isinstance(toskip, list) else toskip,\n header = sorted(header) if isinstance(header, list) else header,\n index_col = self.args.sname_row if self.args.sname_row > -1 else None\n )\n \n def select( perc, top ): \n self.table['perc'] = self.table.apply(lambda x: stats.scoreatpercentile(x,perc),axis=1)\n m = sorted(self.table['perc'])[-top]\n self.table = self.table[self.table['perc'] >= m ]\n del self.table['perc'] \n \n if not self.args.def_na is None:\n self.table = self.table.fillna( self.args.def_na )\n\n if self.args.ftop:\n select( self.args.fperc, self.args.ftop )\n \n if self.args.stop:\n self.table = self.table.T \n select( self.args.sperc, self.args.stop ) \n self.table = self.table.T\n \n\n # add missing values\n \n def get_numpy_matrix( self ): \n return np.matrix(self.table)\n \n #def get_metadata_matrix( self ):\n # return self.table.columns\n \n def get_snames( self ):\n #return list(self.table.index)\n return self.table.columns\n \n def get_fnames( self ):\n #print self.table.columns.names\n #print self.table.columns\n return list(self.table.index)\n \n def get_averages(self, by_row = True) :\n return self.table.mean(axis = 1 if by_row else 0)\n \n def save_matrix( self, output_file ):\n self.table.to_csv( output_file, sep = '\\t' )\n\nclass DistMatrix:\n datatype = 'distance_matrix'\n\n @staticmethod\n def input_parameters( parser ):\n dm_param = parser.add_argument_group('Distance parameters')\n arg = dm_param.add_argument\n\n dist_funcs = [ \"euclidean\",\"minkowski\",\"cityblock\",\"seuclidean\",\n \"sqeuclidean\",\"cosine\",\"correlation\",\"hamming\",\n \"jaccard\",\"chebyshev\",\"canberra\",\"braycurtis\",\n \"mahalanobis\",\"yule\",\"matching\",\"dice\",\n \"kulsinski\",\"rogerstanimoto\",\"russellrao\",\"sokalmichener\",\n \"sokalsneath\",\"wminkowski\",\"ward\" ]\n\n arg( '--f_dist_f', type=str, default=\"correlation\",\n help = \"Distance function for features [default correlation]\")\n arg( '--s_dist_f', type=str, default=\"euclidean\",\n help = \"Distance function for sample [default euclidean]\")\n arg( '--load_dist_matrix_f', type=str, default=None,\n help = \"Load the distance matrix to be used for features [default None].\")\n arg( '--load_dist_matrix_s', type=str, default=None,\n help = \"Load the distance matrix to be used for samples [default None].\")\n arg( '--load_pickled_dist_matrix_f', type=str, default=None,\n help = \"Load the distance matrix to be used for features as previously saved as pickle file using hclust2 itself [default None].\")\n arg( '--load_pickled_dist_matrix_s', type=str, default=None,\n help = \"Load the distance matrix to be used for samples as previously saved as pickle file using hclust2 itself [default None].\")\n arg( '--save_pickled_dist_matrix_f', type=str, default=None,\n help = \"Save the distance matrix for features to file [default None].\")\n arg( '--save_pickled_dist_matrix_s', type=str, default=None,\n help = \"Save the distance matrix for samples to file [default None].\")\n \n def __init__( self, data, args = None ):\n self.sdf = args.s_dist_f\n self.fdf = args.f_dist_f\n\n self.s_cdist_matrix, self.f_cdist_matrix = None, None\n\n self.numpy_full_matrix = (data if \n type(data) == np.matrixlib.defmatrix.matrix else None)\n \n def compute_f_dists( self ):\n if args.load_pickled_dist_matrix_f:\n with open( args.load_pickled_dist_matrix_f ) as inp:\n self.f_cdist_matrix = pickle.load( inp )\n elif args.load_dist_matrix_f:\n self.f_cdist_matrix = spd.squareform( np.matrix( pd.read_table( args.load_dist_matrix_f, sep ='\\t', index_col = None, header = None ) ) )\n else:\n dt = self.numpy_full_matrix\n \n if self.fdf == \"spearman\":\n dt_ranked = np.matrix([stats.rankdata(d) for d in dt])\n self.f_cdist_matrix = spd.pdist( dt_ranked, \"correlation\" )\n return\n \n if self.fdf == 'mhamming':\n dt_ranked = np.matrix([[(0 if l == 0 else 1) for l in np.nditer(d)] for d in dt])\n self.f_cdist_matrix = spd.pdist( dt_ranked, \"hamming\" )\n return\n \n if self.fdf == 'lbraycurtis':\n dt_ranked = np.matrix([[(math.log(l) if l else 0.0) for l in np.nditer(d)] for d in dt])\n self.f_cdist_matrix = spd.pdist( dt_ranked, \"braycurtis\" )\n return\n\n if self.fdf == \"pearson\":\n self.fdf = 'correlation'\n\n self.f_cdist_matrix = spd.pdist( dt, self.fdf )\n\n if args.save_pickled_dist_matrix_f:\n with open( args.save_pickled_dist_matrix_f, \"wb\" ) as outf:\n pickle.dump( self.f_cdist_matrix, outf )\n \n def compute_s_dists( self ):\n if args.load_pickled_dist_matrix_s:\n with open( args.load_pickled_dist_matrix_s ) as inp:\n self.s_cdist_matrix = pickle.load( inp )\n elif args.load_dist_matrix_s:\n self.s_cdist_matrix = spd.squareform( np.matrix( pd.read_table( args.load_dist_matrix_s, sep ='\\t', index_col = None, header = None ) ) )\n else: \n dt = self.numpy_full_matrix.transpose()\n \n if self.sdf == \"spearman\":\n dt_ranked = np.matrix([stats.rankdata(d) for d in dt])\n self.s_cdist_matrix = spd.pdist( dt_ranked, \"correlation\" )\n return\n \n if self.sdf == 'mhamming':\n dt_ranked = np.matrix([[(0 if l == 0 else 1) for l in np.nditer(d)] for d in dt])\n self.s_cdist_matrix = spd.pdist( dt_ranked, \"hamming\" )\n return\n \n if self.sdf == 'lbraycurtis':\n dt_ranked = np.matrix([[(math.log(l) if l else 0.0) for l in np.nditer(d)] for d in dt])\n self.s_cdist_matrix = spd.pdist( dt_ranked, \"braycurtis\" )\n return\n \n if self.sdf == 'sbraycurtis':\n dt_ranked = np.matrix([[(math.sqrt(l) if l else 0.0) for l in np.nditer(d)] for d in dt])\n self.s_cdist_matrix = spd.pdist( dt_ranked, \"braycurtis\" )\n return\n \n if self.sdf == \"pearson\":\n self.sdf = 'correlation'\n \n self.s_cdist_matrix = spd.pdist( dt, self.sdf )\n \n if args.save_pickled_dist_matrix_s:\n with open( args.save_pickled_dist_matrix_s, \"wb\" ) as outf:\n pickle.dump( self.s_cdist_matrix, outf )\n\n def get_s_dm( self ):\n return self.s_cdist_matrix\n\n def get_f_dm( self ):\n return self.f_cdist_matrix\n\nclass HClustering:\n datatype = 'hclustering'\n\n @staticmethod\n def input_parameters( parser ):\n cl_param = parser.add_argument_group('Clustering parameters')\n arg = cl_param.add_argument\n\n linkage_method = [ \"single\",\"complete\",\"average\", \n \"weighted\",\"centroid\",\"median\",\n \"ward\" ]\n arg( '--no_fclustering', action='store_true',\n help = \"avoid clustering features\" )\n arg( '--no_sclustering', action='store_true',\n help = \"avoid clustering samples\" )\n arg( '--flinkage', type=str, default=\"average\",\n help = \"Linkage method for feature clustering [default average]\")\n arg( '--slinkage', type=str, default=\"average\",\n help = \"Linkage method for sample clustering [default average]\")\n\n def get_reordered_matrix( self, matrix, sclustering = True, fclustering = True ):\n if not sclustering and not fclustering:\n return matrix\n \n idx1 = self.sdendrogram['leaves'] if sclustering else None # !!!!!!!!!!!\n idx2 = self.fdendrogram['leaves'][::-1] if fclustering else None\n\n if sclustering and fclustering:\n return matrix[idx2,:][:,idx1]\n if fclustering:\n return matrix[idx2,:][:]\n if sclustering: # !!!!!!!!!!!!\n return matrix[:][:,idx1]\n\n def get_reordered_sample_labels( self, slabels ):\n return [slabels[i] for i in self.sdendrogram['leaves']]\n\n def get_reordered_feature_labels( self, flabels ):\n return [flabels[i] for i in self.fdendrogram['leaves']]\n \n def __init__( self, s_dm, f_dm, args = None ):\n self.s_dm = s_dm\n self.f_dm = f_dm\n self.args = args\n self.sclusters = None\n self.fclusters = None\n self.sdendrogram = None\n self.fdendrogram = None\n\n def shcluster( self, dendrogram = True ):\n self.shclusters = sph.linkage( self.s_dm, args.slinkage ) \n if dendrogram:\n self.sdendrogram = sph.dendrogram( self.shclusters, no_plot=True )\n\n def fhcluster( self, dendrogram = True ):\n self.fhclusters = sph.linkage( self.f_dm, args.flinkage ) \n if dendrogram:\n self.fdendrogram = sph.dendrogram( self.fhclusters, no_plot=True )\n \n def get_shclusters( self ):\n return self.shclusters\n \n def get_fhclusters( self ):\n return self.fhclusters\n \n def get_sdendrogram( self ):\n return self.sdendrogram\n \n def get_fdendrogram( self ):\n return self.fdendrogram\n\n\nclass Heatmap:\n datatype = 'heatmap'\n \n bbcyr = {'red': ( (0.0, 0.0, 0.0),\n (0.25, 0.0, 0.0),\n (0.50, 0.0, 0.0),\n (0.75, 1.0, 1.0),\n (1.0, 1.0, 1.0)),\n 'green': ( (0.0, 0.0, 0.0),\n (0.25, 0.0, 0.0),\n (0.50, 1.0, 1.0),\n (0.75, 1.0, 1.0),\n (1.0, 0.0, 1.0)),\n 'blue': ( (0.0, 0.0, 0.0),\n (0.25, 1.0, 1.0),\n (0.50, 1.0, 1.0),\n (0.75, 0.0, 0.0),\n (1.0, 0.0, 1.0))}\n\n bbcry = {'red': ( (0.0, 0.0, 0.0),\n (0.25, 0.0, 0.0),\n (0.50, 0.0, 0.0),\n (0.75, 1.0, 1.0),\n (1.0, 1.0, 1.0)),\n 'green': ( (0.0, 0.0, 0.0),\n (0.25, 0.0, 0.0),\n (0.50, 1.0, 1.0),\n (0.75, 0.0, 0.0),\n (1.0, 1.0, 1.0)),\n 'blue': ( (0.0, 0.0, 0.0),\n (0.25, 1.0, 1.0),\n (0.50, 1.0, 1.0),\n (0.75, 0.0, 0.0),\n (1.0, 0.0, 1.0))}\n\n bcry = {'red': ( (0.0, 0.0, 0.0),\n (0.33, 0.0, 0.0),\n (0.66, 1.0, 1.0),\n (1.0, 1.0, 1.0)),\n 'green': ( (0.0, 0.0, 0.0),\n (0.33, 1.0, 1.0),\n (0.66, 0.0, 0.0),\n (1.0, 1.0, 1.0)),\n 'blue': ( (0.0, 1.0, 1.0),\n (0.33, 1.0, 1.0),\n (0.66, 0.0, 0.0),\n (1.0, 0.0, 1.0))}\n \n\n my_colormaps = [ ('bbcyr',bbcyr),\n ('bbcry',bbcry),\n ('bcry',bcry)]\n \n #dcols = ['#ca0000','#0087ff','#00ba1d','#cf00ff','#00dbe2','#ffaf00','#0017f4','#006012','#e175ff','#877878','#050505','#b5cf00','#ff8a8a','#aa6400','#50008a','#00ff58']\n dcols = ['#ca0000','#0087ff','#00ba1d','#cf00ff','#00dbe2','#ffaf00','#0017f4','#006012','#e175ff','#877878','#505050','#b5cf00','#ff8a8a','#aa6400','#50008a','#00ff58','#6F1A1A','#FFCC99','#33FF33','#009999','#CC0066','#99004c','#C0C0C0',\"#666600\",\"#CCFF99\",\"#660066\",\"#9370DB\",\"#D8BFD8\",\"#BC8F8F\",\"#2F4F4F\",\"#FF6347\",\"#CD5C5C\",\"#FF0000\",\"#00FF00\",\"#000080\"]\n\n\n @staticmethod\n def input_parameters( parser ):\n hm_param = parser.add_argument_group('Heatmap options')\n arg = hm_param.add_argument\n\n arg( '--dpi', type=int, default=150,\n help = \"Image resolution in dpi [default 150]\")\n arg( '-l', '--log_scale', action='store_true',\n help = \"Log scale\" )\n arg( '--title', type=str, default=None,\n help = \"Title of the plot\" )\n arg( '-s', '--sqrt_scale', action='store_true',\n help = \"Square root scale\" )\n arg( '--no_slabels', action='store_true',\n help = \"Do not show sample labels\" )\n arg( '--minv', type=float, default=None,\n help = \"Minimum value to display in the color map [default None meaning automatic]\" )\n arg( '--maxv', type=float, default=None,\n help = \"Maximum value to display in the color map [default None meaning automatic]\" )\n arg( '--no_flabels', action='store_true',\n help = \"Do not show feature labels\" )\n arg( '--max_slabel_len', type=int, default=25,\n help = \"Max number of chars to report for sample labels [default 15]\" )\n arg( '--max_flabel_len', type=int, default=25,\n help = \"Max number of chars to report for feature labels [default 15]\" )\n arg( '--flabel_size', type=int, default=10,\n help = \"Feature label font size [default 10]\" )\n arg( '--slabel_size', type=int, default=10,\n help = \"Sample label font size [default 10]\" )\n arg( '--fdend_width', type=float, default=1.0,\n help = \"Width of the feature dendrogram [default 1 meaning 100%% of default heatmap width]\")\n arg( '--sdend_height', type=float, default=1.0,\n help = \"Height of the sample dendrogram [default 1 meaning 100%% of default heatmap height]\")\n arg( '--metadata_height', type=float, default=.05,\n help = \"Height of the metadata panel [default 0.05 meaning 5%% of default heatmap height]\")\n arg( '--metadata_separation', type=float, default=.01,\n help = \"Distance between the metadata and data panels. [default 0.001 meaning 0.1%% of default heatmap height]\")\n arg( '--image_size', type=float, default=8,\n help = \"Size of the largest between width and eight size for the image in inches [default 8]\")\n arg( '--cell_aspect_ratio', type=float, default=1.0,\n help = \"Aspect ratio between width and height for the cells of the heatmap [default 1.0]\")\n col_maps = ['Accent', 'Blues', 'BrBG', 'BuGn', 'BuPu', 'Dark2', 'GnBu',\n 'Greens', 'Greys', 'OrRd', 'Oranges', 'PRGn', 'Paired',\n 'Pastel1', 'Pastel2', 'PiYG', 'PuBu', 'PuBuGn', 'PuOr',\n 'PuRd', 'Purples', 'RdBu', 'RdGy', 'RdPu', 'RdYlBu', 'RdYlGn',\n 'Reds', 'Set1', 'Set2', 'Set3', 'Spectral', 'YlGn', 'YlGnBu',\n 'YlOrBr', 'YlOrRd', 'afmhot', 'autumn', 'binary', 'bone',\n 'brg', 'bwr', 'cool', 'copper', 'flag', 'gist_earth',\n 'gist_gray', 'gist_heat', 'gist_ncar', 'gist_rainbow',\n 'gist_stern', 'gist_yarg', 'gnuplot', 'gnuplot2', 'gray',\n 'hot', 'hsv', 'jet', 'ocean', 'pink', 'prism', 'rainbow',\n 'seismic', 'spectral', 'spring', 'summer', 'terrain', 'winter'] + [n for n,c in Heatmap.my_colormaps]\n for n,c in Heatmap.my_colormaps:\n my_cmap = matplotlib.colors.LinearSegmentedColormap(n,c,256)\n pylab.register_cmap(name=n,cmap=my_cmap)\n arg( '-c','--colormap', type=str, choices = col_maps, default = 'bbcry' )\n arg( '--bottom_c', type=str, default = None,\n help = \"Color to use for cells below the minimum value of the scale [default None meaning bottom color of the scale]\")\n arg( '--top_c', type=str, default = None,\n help = \"Color to use for cells below the maximum value of the scale [default None meaning bottom color of the scale]\")\n arg( '--nan_c', type=str, default = None,\n help = \"Color to use for nan cells [default None]\")\n\n \n\n \"\"\"\n arg( '--', type=str, default=\"average\",\n help = \"Linkage method for feature clustering [default average]\")\n arg( '--slinkage', type=str, default=\"average\",\n help = \"Linkage method for sample clustering [default average]\")\n \"\"\"\n\n def __init__( self, numpy_matrix, sdendrogram, fdendrogram, snames, fnames, fnames_meta, args = None ):\n self.numpy_matrix = numpy_matrix\n self.sdendrogram = sdendrogram\n self.fdendrogram = fdendrogram\n self.snames = snames\n self.fnames = fnames\n self.fnames_meta = fnames_meta\n self.ns,self.nf = self.numpy_matrix.shape\n self.args = args\n\n def make_legend( self, dmap, titles, out_fn ): \n figlegend = plt.figure(figsize=(1+3*len(titles),2), frameon = False)\n\n gs = gridspec.GridSpec( 1, len(dmap), wspace = 2.0 )\n\n for i,(d,title) in enumerate(zip(dmap,titles)):\n legax = plt.subplot(gs[i],frameon = False)\n for k,v in sorted(d.items(),key=lambda x:x[1]):\n rect = Rectangle( [0.0, 0.0], 0.0, 0.0,\n facecolor = self.dcols[v%len(self.dcols)],\n label = k,\n edgecolor='b', lw = 0.0)\n\n legax.add_patch(rect)\n #remove_splines( legax )\n legax.set_xticks([])\n legax.set_yticks([])\n legax.legend( loc = 2, frameon = False, title = title) \n \"\"\"\n ncol = legend_ncol, bbox_to_anchor=(1.01, 3.),\n borderpad = 0.0, labelspacing = 0.0,\n handlelength = 0.5, handletextpad = 0.3,\n borderaxespad = 0.0, columnspacing = 0.3,\n prop = {'size':fontsize}, frameon = False)\n \"\"\"\n if out_fn:\n figlegend.savefig(out_fn, bbox_inches='tight')\n \n def draw( self ):\n\n rat = float(self.ns)/self.nf\n rat *= self.args.cell_aspect_ratio\n x,y = (self.args.image_size,rat*self.args.image_size) if rat < 1 else (self.args.image_size/rat,self.args.image_size)\n fig = plt.figure( figsize=(x,y), facecolor = 'w' )\n\n cm = pylab.get_cmap(self.args.colormap)\n bottom_col = [ cm._segmentdata['red'][0][1],\n cm._segmentdata['green'][0][1],\n cm._segmentdata['blue'][0][1] ]\n if self.args.bottom_c:\n bottom_col = self.args.bottom_c\n cm.set_under( bottom_col )\n top_col = [ cm._segmentdata['red'][-1][1],\n cm._segmentdata['green'][-1][1],\n cm._segmentdata['blue'][-1][1] ]\n if self.args.top_c:\n top_col = self.args.top_c\n cm.set_over( top_col )\n\n if self.args.nan_c:\n cm.set_bad( self.args.nan_c )\n\n def make_ticklabels_invisible(ax):\n for tl in ax.get_xticklabels() + ax.get_yticklabels():\n tl.set_visible(False)\n ax.set_xticks([])\n ax.set_yticks([])\n \n def remove_splines( ax ):\n for v in ['right','left','top','bottom']:\n ax.spines[v].set_color('none')\n\n def shrink_labels( labels, n ):\n shrink = lambda x: x[:n/2]+\" [...] \"+x[-n/2:]\n return [(shrink(str(l)) if len(str(l)) > n else l) for l in labels]\n \n\n #gs = gridspec.GridSpec( 4, 2, \n # width_ratios=[1.0-fr_ns,fr_ns], \n # height_ratios=[.03,0.03,1.0-fr_nf,fr_nf], \n # wspace = 0.0, hspace = 0.0 )\n \n fr_ns = float(self.ns)/max([self.ns,self.nf])\n fr_nf = float(self.nf)/max([self.ns,self.nf])\n \n buf_space = 0.05\n minv = min( [buf_space*8, 8*rat*buf_space] )\n if minv < 0.05:\n buf_space /= minv/0.05\n metadata_height = self.args.metadata_height if type(snames[0]) is tuple and len(snames[0]) > 1 else 0.000001 \n gs = gridspec.GridSpec( 6, 4, \n width_ratios=[ buf_space, buf_space*2, .08*self.args.fdend_width,0.9], \n height_ratios=[ buf_space, buf_space*2, .08*self.args.sdend_height, metadata_height, self.args.metadata_separation, 0.9], \n wspace = 0.0, hspace = 0.0 )\n\n ax_hm = plt.subplot(gs[23], axisbg = bottom_col )\n ax_metadata = plt.subplot(gs[15], axisbg = bottom_col )\n ax_hm_y2 = ax_hm.twinx() \n\n norm_f = matplotlib.colors.Normalize\n if self.args.log_scale:\n norm_f = matplotlib.colors.LogNorm\n elif self.args.sqrt_scale:\n norm_f = SqrtNorm\n minv, maxv = 0.0, None\n\n maps, values, ndv = [], [], 0\n if type(snames[0]) is tuple and len(snames[0]) > 1:\n metadata = zip(*[list(s[1:]) for s in snames])\n for m in metadata:\n mmap = dict([(v[1],ndv+v[0]) for v in enumerate(list(set(m)))])\n values.append([mmap[v] for v in m])\n ndv += len(mmap)\n maps.append(mmap)\n dcols = [] \n mdmat = np.matrix(values)\n while len(dcols) < ndv:\n dcols += self.dcols\n cmap = matplotlib.colors.ListedColormap(dcols[:ndv]) \n bounds = [float(f)-0.5 for f in range(ndv+1)]\n imm = ax_metadata.imshow( mdmat, #origin='lower', \n interpolation = 'nearest', \n aspect='auto', \n extent = [0, self.nf, 0, self.ns], \n cmap=cmap,\n vmin=bounds[0],\n vmax=bounds[-1],\n )\n remove_splines( ax_metadata )\n ax_metadata_y2 = ax_metadata.twinx() \n ax_metadata_y2.set_ylim(0,len(self.fnames_meta))\n ax_metadata.set_yticks([])\n ax_metadata_y2.set_ylim(0,len(self.fnames_meta))\n ax_metadata_y2.tick_params(length=0)\n ax_metadata_y2.set_yticks(np.arange(len(self.fnames_meta))+0.5)\n ax_metadata_y2.set_yticklabels(self.fnames_meta[::-1], va='center',size=self.args.flabel_size)\n else:\n ax_metadata.set_yticks([])\n\n ax_metadata.set_xticks([])\n \n im = ax_hm.imshow( self.numpy_matrix, #origin='lower', \n interpolation = 'nearest', aspect='auto', \n extent = [0, self.nf, 0, self.ns], \n cmap=cm, \n vmin=self.args.minv,\n vmax=self.args.maxv, \n norm = norm_f( vmin=minv if minv > 0.0 else None, vmax=maxv)\n )\n \n #ax_hm.set_ylim([0,800])\n ax_hm.set_xticks(np.arange(len(list(snames)))+0.5)\n if not self.args.no_slabels:\n snames_short = shrink_labels( list([s[0] for s in snames]) if type(snames[0]) is tuple else snames, self.args.max_slabel_len )\n ax_hm.set_xticklabels(snames_short,rotation=90,va='top',ha='center',size=self.args.slabel_size)\n else:\n ax_hm.set_xticklabels([])\n ax_hm_y2.set_ylim([0,self.ns])\n ax_hm_y2.set_yticks(np.arange(len(fnames))+0.5)\n if not self.args.no_flabels:\n fnames_short = shrink_labels( fnames, self.args.max_flabel_len )\n ax_hm_y2.set_yticklabels(fnames_short,va='center',size=self.args.flabel_size)\n else:\n ax_hm_y2.set_yticklabels( [] )\n ax_hm.set_yticks([])\n remove_splines( ax_hm )\n ax_hm.tick_params(length=0)\n ax_hm_y2.tick_params(length=0)\n #ax_hm.set_xlim([0,self.ns])\n ax_cm = plt.subplot(gs[3], axisbg = 'r', frameon = False)\n #fig.colorbar(im, ax_cm, orientation = 'horizontal', spacing = 'proportional', format = ticker.LogFormatterMathtext() )\n fig.colorbar(im, ax_cm, orientation = 'horizontal', spacing='proportional' if self.args.sqrt_scale else 'uniform' ) # , format = ticker.LogFormatterMathtext() )\n\n if not self.args.no_sclustering:\n ax_den_top = plt.subplot(gs[11], axisbg = 'r', frameon = False)\n sph._plot_dendrogram( self.sdendrogram['icoord'], self.sdendrogram['dcoord'], self.sdendrogram['ivl'],\n self.ns + 1, self.nf + 1, 1, 'top', no_labels=True,\n color_list=self.sdendrogram['color_list'] )\n ymax = max([max(a) for a in self.sdendrogram['dcoord']])\n ax_den_top.set_ylim([0,ymax])\n make_ticklabels_invisible( ax_den_top )\n if not self.args.no_fclustering:\n ax_den_right = plt.subplot(gs[22], axisbg = 'b', frameon = False)\n sph._plot_dendrogram( self.fdendrogram['icoord'], self.fdendrogram['dcoord'], self.fdendrogram['ivl'],\n self.ns + 1, self.nf + 1, 1, 'right', no_labels=True,\n color_list=self.fdendrogram['color_list'] )\n xmax = max([max(a) for a in self.fdendrogram['dcoord']])\n ax_den_right.set_xlim([xmax,0])\n make_ticklabels_invisible( ax_den_right )\n\n if self.args.title:\n fig.suptitle(self.args.title, x = 0.5, horizontalalignment = 'center')\n if not self.args.out:\n plt.show( )\n else:\n fig.savefig( self.args.out, bbox_inches='tight', dpi = self.args.dpi )\n if maps: \n self.make_legend( maps, fnames_meta, self.args.legend_file ) \n\n\n\nclass ReadCmd:\n\n def __init__( self ):\n import argparse as ap\n import textwrap\n\n p = ap.ArgumentParser( description= \"TBA\" )\n arg = p.add_argument\n \n arg( '-i', '--inp', '--in', metavar='INPUT_FILE', type=str, nargs='?', default=sys.stdin,\n help= \"The input matrix\" )\n arg( '-o', '--out', metavar='OUTPUT_FILE', type=str, nargs='?', default=None,\n help= \"The output image file [image on screen of not specified]\" )\n arg( '--legend_file', metavar='LEGEND_FILE', type=str, nargs='?', default=None,\n help= \"The output file for the legend of the provided metadata\" )\n\n input_types = [DataMatrix.datatype,DistMatrix.datatype]\n arg( '-t', '--input_type', metavar='INPUT_TYPE', type=str, choices = input_types, \n default='data_matrix',\n help= \"The input type can be a data matrix or distance matrix [default data_matrix]\" )\n\n DataMatrix.input_parameters( p )\n DistMatrix.input_parameters( p )\n HClustering.input_parameters( p )\n Heatmap.input_parameters( p )\n\n self.args = p.parse_args()\n\n def check_consistency( self ):\n pass\n\n def get_args( self ):\n return self.args\n\nif __name__ == '__main__':\n \n read = ReadCmd( )\n read.check_consistency()\n args = read.get_args()\n \n if args.input_type == DataMatrix.datatype:\n dm = DataMatrix( args.inp, args ) \n if args.out_table:\n dm.save_matrix( args.out_table )\n \n distm = DistMatrix( dm.get_numpy_matrix(), args = args )\n if not args.no_sclustering:\n distm.compute_s_dists()\n if not args.no_fclustering:\n distm.compute_f_dists()\n elif args.input_type == DataMatrix.datatype:\n # distm = read...\n pass\n else:\n pass\n\n cl = HClustering( distm.get_s_dm(), distm.get_f_dm(), args = args )\n if not args.no_sclustering:\n cl.shcluster()\n if not args.no_fclustering:\n cl.fhcluster()\n \n hmp = dm.get_numpy_matrix()\n fnames = dm.get_fnames()\n snames = dm.get_snames()\n fnames_meta = snames.names[1:]\n #if not args.no_sclustering or not args.no_fclustering ):\n \n hmp = cl.get_reordered_matrix( hmp, sclustering = not args.no_sclustering, fclustering = not args.no_fclustering )\n if not args.no_sclustering:\n snames = cl.get_reordered_sample_labels( snames )\n if not args.no_fclustering:\n fnames = cl.get_reordered_feature_labels( fnames )\n else:\n fnames = fnames[::-1]\n\n hm = Heatmap( hmp, cl.sdendrogram, cl.fdendrogram, snames, fnames, fnames_meta, args = args )\n hm.draw()\n\n\n\n\n\n\n", "sub_path": "utils/hclust2/hclust2.py", "file_name": "hclust2.py", "file_ext": "py", "file_size_in_byte": 34671, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "matplotlib.use", "line_number": 10, "usage_type": "call"}, {"api_name": "sys.setrecursionlimit", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.ma.masked_less_equal", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.ma.getmask", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 46, "usage_type": "attribute"}, {"api_name": "numpy.ma.array", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.clip", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 52, "usage_type": "attribute"}, {"api_name": "matplotlib.cbook._putmask", "line_number": 56, "usage_type": "call"}, {"api_name": "matplotlib.cbook", "line_number": 56, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.ma.array", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 60, "usage_type": "attribute"}, {"api_name": "matplotlib.cbook.iterable", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.cbook", "line_number": 70, "usage_type": "attribute"}, {"api_name": "numpy.ma.asarray", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 71, "usage_type": "attribute"}, {"api_name": "numpy.ma.power", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 72, "usage_type": "attribute"}, {"api_name": "numpy.ma.masked_less_equal", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.ma.min", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 81, "usage_type": "attribute"}, {"api_name": "numpy.ma.max", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 82, "usage_type": "attribute"}, {"api_name": "numpy.ma.masked_less_equal", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 88, "usage_type": "attribute"}, {"api_name": "numpy.ma.min", "line_number": 90, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 90, "usage_type": "attribute"}, {"api_name": "numpy.ma.max", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pandas.read_table", "line_number": 144, "usage_type": "call"}, {"api_name": "scipy.stats.scoreatpercentile", "line_number": 152, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 152, "usage_type": "name"}, {"api_name": "numpy.matrix", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.matrixlib", "line_number": 231, "usage_type": "attribute"}, {"api_name": "cPickle.load", "line_number": 236, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.squareform", "line_number": 238, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 238, "usage_type": "name"}, {"api_name": "numpy.matrix", "line_number": 238, "usage_type": "call"}, {"api_name": "pandas.read_table", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 243, "usage_type": "call"}, {"api_name": "scipy.stats.rankdata", "line_number": 243, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 243, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.pdist", "line_number": 244, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 244, "usage_type": "name"}, {"api_name": "numpy.matrix", "line_number": 248, "usage_type": "call"}, {"api_name": "numpy.nditer", "line_number": 248, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.pdist", "line_number": 249, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 249, "usage_type": "name"}, {"api_name": "numpy.matrix", "line_number": 253, "usage_type": "call"}, {"api_name": "math.log", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.nditer", "line_number": 253, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.pdist", "line_number": 254, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 254, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.pdist", "line_number": 260, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 260, "usage_type": "name"}, {"api_name": "cPickle.dump", "line_number": 264, "usage_type": "call"}, {"api_name": "cPickle.load", "line_number": 269, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.squareform", "line_number": 271, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 271, "usage_type": "name"}, {"api_name": "numpy.matrix", "line_number": 271, "usage_type": "call"}, {"api_name": "pandas.read_table", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.matrix", "line_number": 276, "usage_type": "call"}, {"api_name": "scipy.stats.rankdata", "line_number": 276, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 276, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.pdist", "line_number": 277, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 277, "usage_type": "name"}, {"api_name": "numpy.matrix", "line_number": 281, "usage_type": "call"}, {"api_name": "numpy.nditer", "line_number": 281, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.pdist", "line_number": 282, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 282, "usage_type": "name"}, {"api_name": "numpy.matrix", "line_number": 286, "usage_type": "call"}, {"api_name": "math.log", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.nditer", "line_number": 286, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.pdist", "line_number": 287, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 287, "usage_type": "name"}, {"api_name": "numpy.matrix", "line_number": 291, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 291, "usage_type": "call"}, {"api_name": "numpy.nditer", "line_number": 291, "usage_type": "call"}, {"api_name": "scipy.spatial.distance.pdist", "line_number": 292, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 292, "usage_type": "name"}, {"api_name": "scipy.spatial.distance.pdist", "line_number": 298, "usage_type": "call"}, {"api_name": "scipy.spatial.distance", "line_number": 298, "usage_type": "name"}, {"api_name": "cPickle.dump", "line_number": 302, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy.linkage", "line_number": 360, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy", "line_number": 360, "usage_type": "name"}, {"api_name": "scipy.cluster.hierarchy.dendrogram", "line_number": 362, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy", "line_number": 362, "usage_type": "name"}, {"api_name": "scipy.cluster.hierarchy.linkage", "line_number": 365, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy", "line_number": 365, "usage_type": "name"}, {"api_name": "scipy.cluster.hierarchy.dendrogram", "line_number": 367, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy", "line_number": 367, "usage_type": "name"}, {"api_name": "matplotlib.colors.LinearSegmentedColormap", "line_number": 492, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 492, "usage_type": "attribute"}, {"api_name": "pylab.register_cmap", "line_number": 493, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 522, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 522, "usage_type": "name"}, {"api_name": "matplotlib.gridspec.GridSpec", "line_number": 524, "usage_type": "call"}, {"api_name": "matplotlib.gridspec", "line_number": 524, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 527, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 527, "usage_type": "name"}, {"api_name": "matplotlib.patches.Rectangle", "line_number": 529, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 554, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 554, "usage_type": "name"}, {"api_name": "pylab.get_cmap", "line_number": 556, "usage_type": "call"}, {"api_name": "matplotlib.gridspec.GridSpec", "line_number": 601, "usage_type": "call"}, {"api_name": "matplotlib.gridspec", "line_number": 601, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 606, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 606, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 607, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 607, "usage_type": "name"}, {"api_name": "matplotlib.colors", "line_number": 610, "usage_type": "attribute"}, {"api_name": "matplotlib.colors", "line_number": 612, "usage_type": "attribute"}, {"api_name": "numpy.matrix", "line_number": 626, "usage_type": "call"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 629, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 629, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 645, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 662, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 669, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 680, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 680, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 685, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 685, "usage_type": "name"}, {"api_name": "scipy.cluster.hierarchy._plot_dendrogram", "line_number": 686, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy", "line_number": 686, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 693, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 693, "usage_type": "name"}, {"api_name": "scipy.cluster.hierarchy._plot_dendrogram", "line_number": 694, "usage_type": "call"}, {"api_name": "scipy.cluster.hierarchy", "line_number": 694, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 704, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 704, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 718, "usage_type": "call"}, {"api_name": "sys.stdin", "line_number": 721, "usage_type": "attribute"}]} +{"seq_id": "38746174", "text": "import sys\r\nimport argparse\r\nimport re\r\nimport os\r\nimport os.path\r\nfrom PyQt4.QtGui import *\r\nfrom PyQt4.Qt import *\r\n\r\n\r\ndef saveWrap(dir='.', letter='A', font=\"Arial\", size=40, align=Qt.AlignCenter):\r\n png_file = dir + \"/\" + font + \"_\" + letter + \"_\" + str(size) + \".png\"\r\n save(png_file, letter, font, size, align)\r\n\r\n\r\ndef save(png_file, letter='A', font=\"Arial\", size=40, align=Qt.AlignCenter):\r\n img = QImage(64, 64, QImage.Format_RGB32)\r\n img.fill(Qt.white)\r\n p = QPainter(img)\r\n p.setPen(Qt.black)\r\n p.setFont(QFont(font, size))\r\n p.drawText(img.rect(), align, letter)\r\n p.end()\r\n img.save(png_file)\r\n\r\n\r\ndef main():\r\n app = QApplication([])\r\n p = argparse.ArgumentParser(description='Symbols image generator')\r\n p.add_argument('-f', '--font', default='Arial', help='Font name, default=Arial')\r\n p.add_argument('-s', '--size', type=int, default=40, help='Font size, default=40')\r\n p.add_argument('-d', '--dir', default='.', help='Output directory, default=current')\r\n p.add_argument('letters', help='Array of letters(abc) or range (a-z)')\r\n args = p.parse_args()\r\n path = os.path.abspath(args.dir)\r\n if not os.path.exists(path):\r\n print(\"Directory not exists, created!\")\r\n os.makedirs(path)\r\n if re.match('^([a-z]-[a-z])|([A-Z]-[A-Z])$', args.letters):\r\n begin = args.letters[0]\r\n end = args.letters[2]\r\n if (ord(end) - ord(begin)) > 26:\r\n print(\"Error using letters. Only A-Z or a-z available, not A-z.\")\r\n p.print_help()\r\n return\r\n letters = [chr(a) for a in range(ord(begin), ord(end) + 1)]\r\n else:\r\n letters = args.letters\r\n for lett in letters:\r\n saveWrap(path, lett, args.font, args.size)\r\n return 0\r\n\r\nif __name__ == \"__main__\":\r\n sys.exit(main())", "sub_path": "gen_pic.py", "file_name": "gen_pic.py", "file_ext": "py", "file_size_in_byte": 1823, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.abspath", "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": 37, "usage_type": "call"}, {"api_name": "re.match", "line_number": 38, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "250520044", "text": "from flask import Flask, request\n\nfrom modules.bot import BotConfig\n\nimport os\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\napp = Flask(__name__)\n\ntoken = os.getenv('TELEGRAM_TOKEN')\nwebhook_url = os.getenv('WEBHOOK')\nport = os.getenv('PORT')\n\n\n@app.route('/' + token, methods=['POST'])\ndef getMessage():\n BotConfig.process_updates(request.stream.read().decode(\"utf-8\"))\n return \"!\", 200\n\n\n@app.route(\"/\")\ndef set_webhook():\n BotConfig.set_webhook(webhook_url + '/' + token)\n return \"!\", 200\n\n\nif __name__ == \"__main__\":\n app.run()\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 549, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "dotenv.load_dotenv", "line_number": 8, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 10, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 12, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 13, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 14, "usage_type": "call"}, {"api_name": "modules.bot.BotConfig.process_updates", "line_number": 19, "usage_type": "call"}, {"api_name": "modules.bot.BotConfig", "line_number": 19, "usage_type": "name"}, {"api_name": "flask.request.stream.read", "line_number": 19, "usage_type": "call"}, {"api_name": "flask.request.stream", "line_number": 19, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 19, "usage_type": "name"}, {"api_name": "modules.bot.BotConfig.set_webhook", "line_number": 25, "usage_type": "call"}, {"api_name": "modules.bot.BotConfig", "line_number": 25, "usage_type": "name"}]} +{"seq_id": "240700318", "text": "#! /usr/bin/python2.7\n# -*- coding: utf-8 -*-\n#\n# pagesendmail.py\n#\n# Страница отправки сообщений.\n#\n# Декабрь, 2015\n# Луганск\n# Автор сценария: Иванов Юрий aka HeaTTheatR\n#\n# Email: gleb.assert@mail.ru\n# gorodage@gmail.com\n#\n\nfrom __future__ import print_function\nimport os\n\ntry:\n from kivy.uix.boxlayout import BoxLayout\n from kivy.uix.gridlayout import GridLayout\n from kivy.uix.stacklayout import StackLayout\n from kivy.uix.label import Label\n from kivy.uix.image import Image\n from kivy.uix.behaviors import ButtonBehavior\n from kivy.uix.checkbox import CheckBox\n from kivy.uix.button import Button\n from kivy.uix.textinput import TextInput\n from kivy.uix.accordion import (Accordion, AccordionItem)\n from kivy.uix.popup import Popup\n from kivy.uix.widget import Widget\n from kivy.utils import platform\n from kivy.properties import (StringProperty, ObjectProperty,\n ListProperty, BooleanProperty)\nexcept Exception as text_error:\n raise text_error\n\n\n__version__ = \"0.0.1\"\n\nif callable(platform):\n platform = platform()\n\n\ndef p(*args):\n pass\n\n\nclass SettingSpacer(Widget):\n pass\n\n\nclass ImageButton(ButtonBehavior, Image):\n pass\n\n\nclass PageSendMail(BoxLayout):\n title = StringProperty(\"Screen page created message:\")\n \"\"\"Подпись макета окна.\"\"\"\n message_to = StringProperty(\"[size=18][color=#ffffffff]Message for \"\n \"[color=#2fbfe0]Name Profile:[/size]\")\n \"\"\"Подпись, кому адресовано письмо.\"\"\"\n accordion_panel_text_title = StringProperty(\"Text\")\n \"\"\"Подпись панели текста.\"\"\"\n accordion_panel_smiles_title = StringProperty(\"Smiles\")\n \"\"\"Подпись панели смайлов.\"\"\"\n hint_text = StringProperty(\"Enter text message...\")\n \"\"\"Текст подсказки в поле ввода сообщения.\"\"\"\n default_text = StringProperty(\"\")\n \"\"\"Текст по умолчанию.\"\"\"\n background_image = StringProperty(\n \"atlas://data/images/defaulttheme/modalview-background\")\n \"\"\"Фоновое изображение окна.\"\"\"\n\n # Подписи чеков приоритета сообщения. Низкий/Обычный/Высокий.\n label_priority = StringProperty(\"Priority\")\n priority_low = StringProperty(\"Low\")\n priority_normal = StringProperty(\"Normal\")\n priority_tall = StringProperty(\"Tall\")\n\n # Подписи чеков приоритета сообщения.\n # Отчет о прочтении/Сохранить в отправленных/Добавить файл.\n label_report = StringProperty(\"Report\")\n read_report = StringProperty(\"Read report\")\n save_in_sent = StringProperty(\"Save in sent\")\n add_files = StringProperty(\"Add files\")\n\n text_button_ok = StringProperty(\"Yes\")\n text_button_cancel = StringProperty(\"Cancel\")\n\n # Изображение активного и не активного пункта аккордиона.\n background_selected = StringProperty(\n \"atlas://data/images/defaulttheme/button_pressed\")\n background_normal = StringProperty(\n \"atlas://data/images/defaulttheme/button\")\n\n # Кастомные изображения для checkbox.\n background_checkbox_normal = StringProperty(\n 'atlas://data/images/defaulttheme/checkbox_off')\n background_checkbox_down = StringProperty(\n 'atlas://data/images/defaulttheme/checkbox_on')\n background_radio_normal = StringProperty(\n 'atlas://data/images/defaulttheme/checkbox_radio_off')\n background_radio_down = StringProperty(\n 'atlas://data/images/defaulttheme/checkbox_radio_on')\n\n path_to_folder_images_smiles = StringProperty(\"\")\n \"\"\"Путь к папке с набором смайлов.\"\"\"\n list_images_for_format_text = ListProperty([])\n \"\"\"Список путей к изображениям, используемых в панели для форматирования\n текста.\"\"\"\n events_callback = ObjectProperty(p)\n\n forum = StringProperty(\"mail\")\n \"\"\"Если 'forum' - пишется ответ в форум - форма избавляется от чеков\n приоритета сообщения.\"\"\"\n\n def __init__(self, **kvargs):\n super(PageSendMail, self).__init__(**kvargs)\n self.orientation = \"vertical\"\n\n self.add_widget(Label(text=self.message_to, markup=True,\n size_hint=(1, .1)))\n accordion = Accordion(orientation=\"vertical\")\n\n # Панель смайлов.\n self.smiles = \\\n AccordionItem(title=self.accordion_panel_smiles_title,\n background_selected=self.background_selected,\n background_normal=self.background_normal)\n smiles_box = StackLayout()\n\n if os.path.exists(self.path_to_folder_images_smiles):\n for name_image in os.listdir(self.path_to_folder_images_smiles):\n smile = \\\n ImageButton(\n source=\"{}/{}\".format(\n self.path_to_folder_images_smiles, name_image),\n id=\":{}:\".format(name_image.split(\".\")[0]),\n size_hint=(.09, .12))\n smile.bind(on_press=self.events_callback)\n smiles_box.add_widget(smile)\n\n self.smiles.add_widget(smiles_box)\n accordion.add_widget(self.smiles)\n\n # Панель форматирования и поле ввода.\n text = AccordionItem(title=self.accordion_panel_text_title,\n background_selected=self.background_selected,\n background_normal=self.background_normal)\n content = BoxLayout(orientation=\"vertical\")\n\n # TODO: Слишком маленькие иконки на панели форматирования текста.\n # Сделать иконки в два ряда.\n if platform == \"android\":\n size_hint = (1, .12)\n else:\n size_hint = (1, .06)\n\n formats_panel = BoxLayout(size_hint=size_hint)\n\n # Иконки форматирования.\n for name_image in self.list_images_for_format_text:\n format_button = \\\n ImageButton(id=os.path.split(name_image)[1].split(\".\")[0],\n source=name_image)\n format_button.bind(on_press=self.events_callback)\n formats_panel.add_widget(format_button)\n\n self.text_input = TextInput(hint_text=self.hint_text,\n size_hint=(1, .4), auto_indent=True)\n if self.default_text != \"\":\n self.text_input.text = self.default_text\n\n content.add_widget(formats_panel)\n content.add_widget(self.text_input)\n text.add_widget(content)\n accordion.add_widget(text)\n\n # Подписи и чекбоксы.\n message_to = GridLayout(cols=2, size_hint=(1, .22), spacing=5)\n\n if self.forum == \"mail\":\n message_to.add_widget(Label(text=self.label_priority,\n markup=True))\n message_to.add_widget(Widget())\n\n message_to.add_widget(Label(text=self.priority_low))\n self.low_check = \\\n CheckBox(background_radio_down=self.background_radio_down,\n background_radio_normal=self.background_radio_normal,\n id=\"low\", group=\"priority\")\n self.low_check.bind(active=self.events_callback)\n message_to.add_widget(self.low_check)\n\n message_to.add_widget(Label(text=self.priority_normal))\n self.normal_check = \\\n CheckBox(background_radio_down=self.background_radio_down,\n background_radio_normal=self.background_radio_normal,\n active=True, id=\"normal\", group=\"priority\")\n self.normal_check.bind(active=self.events_callback)\n message_to.add_widget(self.normal_check)\n\n message_to.add_widget(Label(text=self.priority_tall))\n self.tall_check = \\\n CheckBox(background_radio_down=self.background_radio_down,\n background_radio_normal=self.background_radio_normal,\n id=\"tall\", group=\"priority\")\n self.tall_check.bind(active=self.events_callback)\n message_to.add_widget(self.tall_check)\n\n additional = GridLayout(cols=2, size_hint=(1, .22), spacing=5)\n\n if self.forum == \"mail\":\n additional.add_widget(Label(text=self.label_report,\n markup=True))\n additional.add_widget(Widget())\n\n additional.add_widget(Label(text=self.save_in_sent))\n self.save_in_sent_check = \\\n CheckBox(\n background_checkbox_down=self.background_checkbox_down,\n background_checkbox_normal=self.background_checkbox_normal,\n active=True, id=\"save in sent\")\n self.save_in_sent_check.bind(active=self.events_callback)\n additional.add_widget(self.save_in_sent_check)\n\n additional.add_widget(Label(text=self.read_report))\n self.read_report_check = \\\n CheckBox(\n background_checkbox_down=self.background_checkbox_down,\n background_checkbox_normal=self.background_checkbox_normal,\n id=\"read report\")\n self.read_report_check.bind(active=self.events_callback)\n additional.add_widget(self.read_report_check)\n\n self.add_files_label = Label(text=self.add_files)\n additional.add_widget(self.add_files_label)\n self.add_files_check = \\\n CheckBox(\n background_checkbox_down=self.background_checkbox_down,\n background_checkbox_normal=self.background_checkbox_normal,\n id=\"add files\",)\n self.add_files_check.bind(active=self.events_callback)\n additional.add_widget(self.add_files_check)\n\n # Кнопки выбора.\n button_panel = BoxLayout(size_hint=(1, .15))\n for name_button in [self.text_button_ok, self.text_button_cancel]:\n select_button = Button(text=name_button, size_hint=(1, 1),\n id=name_button)\n select_button.bind(on_press=self.events_callback)\n button_panel.add_widget(select_button)\n\n self.add_widget(accordion)\n\n self.add_widget(Widget(size_hint=(None, .02)))\n if self.forum == \"mail\":\n self.add_widget(message_to)\n self.add_widget(Widget(size_hint=(None, .02)))\n self.add_widget(SettingSpacer())\n\n self.add_widget(additional)\n self.add_widget(Widget(size_hint=(None, .02)))\n self.add_widget(SettingSpacer())\n self.add_widget(Widget(size_hint=(None, .02)))\n\n self.add_widget(button_panel)\n\n self.body = Popup(title=self.title, content=self, size_hint=(.9, .99),\n background=self.background_image)\n self.body.open()\n\n def get_status_checks(self):\n return {self.low_check.id: self.low_check.active,\n self.normal_check.id: self.normal_check.active,\n self.tall_check.id: self.tall_check.active,\n self.save_in_sent_check.id: self.save_in_sent_check.active,\n self.read_report_check.id: self.read_report_check.active,\n self.add_files_check.id: self.add_files_check.active}\n\n\nif __name__ in [\"__main__\", \"__android__\"]:\n import kivy\n\n kivy.require(\"1.9.1\")\n\n from kivy.app import App\n\n\n class Test(App):\n def events_callback(self, *args):\n print(args)\n\n def show_setting(self, *args):\n PageSendMail()\n\n def build(self):\n return Button(text=\"Press me!\", on_release=self.show_setting)\n\n\n Test().run()\n", "sub_path": "Libs/uix/pagesendmail.py", "file_name": "pagesendmail.py", "file_ext": "py", "file_size_in_byte": 12185, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "kivy.utils.platform", "line_number": 41, "usage_type": "argument"}, {"api_name": "kivy.utils.platform", "line_number": 42, "usage_type": "name"}, {"api_name": "kivy.uix.widget.Widget", "line_number": 49, "usage_type": "name"}, {"api_name": "kivy.uix.behaviors.ButtonBehavior", "line_number": 53, "usage_type": "name"}, {"api_name": "kivy.uix.image.Image", "line_number": 53, "usage_type": "name"}, {"api_name": "kivy.uix.boxlayout.BoxLayout", "line_number": 57, "usage_type": "name"}, {"api_name": "kivy.properties.StringProperty", "line_number": 58, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 60, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 63, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 65, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 67, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 69, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 71, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 76, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 77, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 78, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 79, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 83, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 84, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 85, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 86, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 88, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 89, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 92, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 94, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 98, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 100, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 102, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 104, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 107, "usage_type": "call"}, {"api_name": "kivy.properties.ListProperty", "line_number": 109, "usage_type": "call"}, {"api_name": "kivy.properties.ObjectProperty", "line_number": 112, "usage_type": "call"}, {"api_name": "kivy.properties.StringProperty", "line_number": 114, "usage_type": "call"}, {"api_name": "kivy.uix.label.Label", "line_number": 122, "usage_type": "call"}, {"api_name": "kivy.uix.accordion.Accordion", "line_number": 124, "usage_type": "call"}, {"api_name": "kivy.uix.accordion.AccordionItem", "line_number": 128, "usage_type": "call"}, {"api_name": "kivy.uix.stacklayout.StackLayout", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path", "line_number": 133, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 134, "usage_type": "call"}, {"api_name": "kivy.uix.accordion.AccordionItem", "line_number": 148, "usage_type": "call"}, {"api_name": "kivy.uix.boxlayout.BoxLayout", "line_number": 151, "usage_type": "call"}, {"api_name": "kivy.utils.platform", "line_number": 155, "usage_type": "name"}, {"api_name": "kivy.uix.boxlayout.BoxLayout", "line_number": 160, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 165, "usage_type": "call"}, {"api_name": "os.path", "line_number": 165, "usage_type": "attribute"}, {"api_name": "kivy.uix.textinput.TextInput", "line_number": 170, "usage_type": "call"}, {"api_name": "kivy.uix.gridlayout.GridLayout", "line_number": 181, "usage_type": "call"}, {"api_name": "kivy.uix.label.Label", "line_number": 184, "usage_type": "call"}, {"api_name": "kivy.uix.widget.Widget", "line_number": 186, "usage_type": "call"}, {"api_name": "kivy.uix.label.Label", "line_number": 188, "usage_type": "call"}, {"api_name": "kivy.uix.checkbox.CheckBox", "line_number": 190, "usage_type": "call"}, {"api_name": "kivy.uix.label.Label", "line_number": 196, "usage_type": "call"}, {"api_name": "kivy.uix.checkbox.CheckBox", "line_number": 198, "usage_type": "call"}, {"api_name": "kivy.uix.label.Label", "line_number": 204, "usage_type": "call"}, {"api_name": "kivy.uix.checkbox.CheckBox", "line_number": 206, "usage_type": "call"}, {"api_name": "kivy.uix.gridlayout.GridLayout", "line_number": 212, "usage_type": "call"}, {"api_name": "kivy.uix.label.Label", "line_number": 215, "usage_type": "call"}, {"api_name": "kivy.uix.widget.Widget", "line_number": 217, "usage_type": "call"}, {"api_name": "kivy.uix.label.Label", "line_number": 219, "usage_type": "call"}, {"api_name": "kivy.uix.checkbox.CheckBox", "line_number": 221, "usage_type": "call"}, {"api_name": "kivy.uix.label.Label", "line_number": 228, "usage_type": "call"}, {"api_name": "kivy.uix.checkbox.CheckBox", "line_number": 230, "usage_type": "call"}, {"api_name": "kivy.uix.label.Label", "line_number": 237, "usage_type": "call"}, {"api_name": "kivy.uix.checkbox.CheckBox", "line_number": 240, "usage_type": "call"}, {"api_name": "kivy.uix.boxlayout.BoxLayout", "line_number": 248, "usage_type": "call"}, {"api_name": "kivy.uix.button.Button", "line_number": 250, "usage_type": "call"}, {"api_name": "kivy.uix.widget.Widget", "line_number": 257, "usage_type": "call"}, {"api_name": "kivy.uix.widget.Widget", "line_number": 260, "usage_type": "call"}, {"api_name": "kivy.uix.widget.Widget", "line_number": 264, "usage_type": "call"}, {"api_name": "kivy.uix.widget.Widget", "line_number": 266, "usage_type": "call"}, {"api_name": "kivy.uix.popup.Popup", "line_number": 270, "usage_type": "call"}, {"api_name": "kivy.require", "line_number": 286, "usage_type": "call"}, {"api_name": "kivy.app.App", "line_number": 291, "usage_type": "name"}, {"api_name": "kivy.uix.button.Button", "line_number": 299, "usage_type": "call"}]} +{"seq_id": "576447787", "text": "import keras\nimport numpy as np\nimport resnet\nfrom net_keras import Net\nimport os\nimport glob\nimport time\nimport click\nimport pickle\nimport h5py\nfrom scipy import io as spio\nfrom losses import qwk_loss, make_cost_matrix\nfrom metrics import np_quadratic_weighted_kappa, top_2_accuracy, top_3_accuracy, \\\n\tminimum_sensitivity, accuracy_off1\nfrom dataset import Dataset\nfrom sklearn.metrics import confusion_matrix\nimport math\nimport gc\nfrom keras import backend as K\n\nclass Experiment:\n\t\"\"\"\n\tClass that represents a single experiment that can be run and evaluated.\n\t\"\"\"\n\n\tdef __init__(self, name='unnamed', db='100', net_type='vgg19', batch_size=128, epochs=100,\n\t\t\t\t checkpoint_dir='checkpoint', loss='categorical_crossentropy', activation='relu',\n\t\t\t\t final_activation='softmax', f_a_params = {}, use_tau=True, spp_alpha=1.0, lr=0.1, momentum=0.9, dropout=0, task='both', workers=4,\n\t\t\t\t queue_size=1024, val_metrics=['loss', 'acc'], rescale_factor=0, augmentation={}):\n\t\tself._name = name\n\t\tself._db = db\n\t\tself._net_type = net_type\n\t\tself._batch_size = batch_size\n\t\tself._epochs = epochs\n\t\tself._checkpoint_dir = checkpoint_dir\n\t\tself._loss = loss\n\t\tself._activation = activation\n\t\tself._use_tau = use_tau\n\t\tself._final_activation = final_activation\n\t\tself._f_a_params = f_a_params\n\t\tself._spp_alpha = spp_alpha\n\t\tself._lr = lr\n\t\tself._momentum = momentum\n\t\tself._dropout = dropout\n\t\tself._task = task\n\t\tself._finished = False\n\t\tself._workers = workers\n\t\tself._queue_size = queue_size\n\t\tself._val_metrics = val_metrics\n\t\tself._rescale_factor = rescale_factor\n\t\tself._augmentation = augmentation\n\n\t\tself._best_metric = None\n\n\t\t# Model and results file names\n\t\tself.model_file = 'model.h5'\n\t\tself.best_model_file = 'best_model.h5'\n\t\tself.model_file_extra = 'model.txt'\n\t\tself.csv_file = 'results.csv'\n\t\tself.evaluation_file = 'evaluation.pickle'\n\n\tdef set_auto_name(self):\n\t\t\"\"\"\n\t\tSet experiment name based on experiment parameters.\n\t\t:return: None\n\t\t\"\"\"\n\t\tself.name = self.get_auto_name()\n\n\tdef get_auto_name(self):\n\t\t\"\"\"\n\t\tGet experiment auto-generated name based on experiment parameters.\n\t\t:return: experiment auto-generated name.\n\t\t\"\"\"\n\t\treturn \"{}_{}_{}_{}_{}_{}_{}_{}_{}_{}\".format(self.db, self.net_type, self.batch_size, self.activation,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t self.loss,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t self.final_activation,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t self.spp_alpha, self.lr,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t self.momentum, self.dropout)\n\n\t# PROPERTIES\n\n\t@property\n\tdef name(self):\n\t\treturn self._name\n\n\t@name.setter\n\tdef name(self, name):\n\t\tself._name = name\n\n\t@name.deleter\n\tdef name(self):\n\t\tdel self._name\n\n\t@property\n\tdef db(self):\n\t\treturn self._db\n\n\t@db.setter\n\tdef db(self, db):\n\t\tself._db = db\n\n\t@db.deleter\n\tdef db(self):\n\t\tdel self._db\n\n\t@property\n\tdef net_type(self):\n\t\treturn self._net_type\n\n\t@net_type.setter\n\tdef net_type(self, net_type):\n\t\tself._net_type = net_type\n\n\t@net_type.deleter\n\tdef net_type(self):\n\t\tdel self._net_type\n\n\t@property\n\tdef batch_size(self):\n\t\treturn self._batch_size\n\n\t@batch_size.setter\n\tdef batch_size(self, batch_size):\n\t\tself._batch_size = batch_size\n\n\t@batch_size.deleter\n\tdef batch_size(self):\n\t\tdel self._batch_size\n\n\t@property\n\tdef epochs(self):\n\t\treturn self._epochs\n\n\t@epochs.setter\n\tdef epochs(self, epochs):\n\t\tself._epochs = epochs\n\n\t@epochs.deleter\n\tdef epochs(self):\n\t\tdel self._epochs\n\n\t@property\n\tdef checkpoint_dir(self):\n\t\treturn self._checkpoint_dir\n\n\t@checkpoint_dir.setter\n\tdef checkpoint_dir(self, checkpoint_dir):\n\t\tself._checkpoint_dir = checkpoint_dir\n\n\t@checkpoint_dir.deleter\n\tdef checkpoint_dir(self):\n\t\tdel self._checkpoint_dir\n\n\t@property\n\tdef loss(self):\n\t\treturn self._loss\n\n\t@loss.setter\n\tdef loss(self, loss):\n\t\tself._loss = loss\n\n\t@loss.deleter\n\tdef loss(self):\n\t\tdel self._loss\n\n\t@property\n\tdef activation(self):\n\t\treturn self._activation\n\n\t@activation.setter\n\tdef activation(self, activation):\n\t\tself._activation = activation\n\n\t@activation.deleter\n\tdef activation(self):\n\t\tdel self._activation\n\n\t@property\n\tdef final_activation(self):\n\t\treturn self._final_activation\n\n\t@final_activation.setter\n\tdef final_activation(self, final_activation):\n\t\tself._final_activation = final_activation\n\n\t@final_activation.deleter\n\tdef final_activation(self):\n\t\tdel self._final_activation\n\n\t@property\n\tdef f_a_params(self):\n\t\treturn self._f_a_params\n\n\t@f_a_params.setter\n\tdef f_a_params(self, f_a_params):\n\t\tself._f_a_params = f_a_params\n\n\t@f_a_params.deleter\n\tdef f_a_params(self):\n\t\tdel self._f_a_params\n\n\t@property\n\tdef use_tau(self):\n\t\treturn self._use_tau\n\n\t@use_tau.setter\n\tdef use_tau(self, use_tau):\n\t\tself._use_tau = use_tau\n\n\t@use_tau.deleter\n\tdef use_tau(self):\n\t\tdel self._use_tau\n\n\t@property\n\tdef spp_alpha(self):\n\t\treturn self._spp_alpha\n\n\t@spp_alpha.setter\n\tdef spp_alpha(self, spp_alpha):\n\t\tself._spp_alpha = spp_alpha\n\n\t@spp_alpha.deleter\n\tdef spp_alpha(self):\n\t\tdel self._spp_alpha\n\n\t@property\n\tdef lr(self):\n\t\treturn self._lr\n\n\t@lr.setter\n\tdef lr(self, lr):\n\t\tself._lr = lr\n\n\t@lr.deleter\n\tdef lr(self):\n\t\tdel self._lr\n\n\t@property\n\tdef momentum(self):\n\t\treturn self._momentum\n\n\t@momentum.setter\n\tdef momentum(self, momentum):\n\t\tself._momentum = momentum\n\n\t@momentum.deleter\n\tdef momentum(self):\n\t\tdel self._momentum\n\n\t@property\n\tdef dropout(self):\n\t\treturn self._dropout\n\n\t@dropout.setter\n\tdef dropout(self, dropout):\n\t\tself._dropout = dropout\n\n\t@dropout.deleter\n\tdef dropout(self):\n\t\tdel self._dropout\n\n\t@property\n\tdef task(self):\n\t\treturn self._task\n\n\t@task.setter\n\tdef task(self, task):\n\t\tself._task = task\n\n\t@task.deleter\n\tdef task(self):\n\t\tdel self._task\n\n\t@property\n\tdef finished(self):\n\t\treturn self._finished\n\n\t@finished.setter\n\tdef finished(self, finished):\n\t\tself._finished = finished\n\n\t@finished.deleter\n\tdef finished(self):\n\t\tdel self._finished\n\n\t@property\n\tdef workers(self):\n\t\treturn self._workers\n\n\t@workers.setter\n\tdef workers(self, workers):\n\t\tself._workers = workers\n\n\t@workers.deleter\n\tdef workers(self):\n\t\tdel self._workers\n\n\t@property\n\tdef queue_size(self):\n\t\treturn self._workers\n\n\t@queue_size.setter\n\tdef queue_size(self, queue_size):\n\t\tself._queue_size = queue_size\n\n\t@queue_size.deleter\n\tdef queue_size(self):\n\t\tdel self._queue_size\n\n\t@property\n\tdef val_metrics(self):\n\t\treturn self._val_metrics\n\n\t@val_metrics.setter\n\tdef val_metrics(self, val_metrics):\n\t\tself._val_metrics = val_metrics\n\n\t@val_metrics.deleter\n\tdef val_metrics(self):\n\t\tdel self._val_metrics\n\n\t@property\n\tdef rescale_factor(self):\n\t\treturn self._rescale_factor\n\n\t@rescale_factor.setter\n\tdef rescale_factor(self, rescale_factor):\n\t\tself._rescale_factor = rescale_factor\n\n\t@rescale_factor.deleter\n\tdef rescale_factor(self):\n\t\tdel self._rescale_factor\n\n\t@property\n\tdef augmentation(self):\n\t\treturn self._augmentation\n\n\t@augmentation.setter\n\tdef augmentation(self, augmentation):\n\t\tself._augmentation = augmentation\n\n\t@augmentation.deleter\n\tdef augmentation(self):\n\t\tdel self._augmentation\n\n\t@property\n\tdef best_metric(self):\n\t\treturn self._best_metric\n\n\tdef new_metric(self, metric, maximize=False):\n\t\t\"\"\"\n\t\tUpdates best metric if metric provided is better than the best metric stored.\n\t\t:param metric: new metric.\n\t\t:param maximize: maximize metric instead of minimizing.\n\t\t:return: True if new metric is better than best metric or False otherwise.\n\t\t\"\"\"\n\t\tif self._best_metric is None or (\n\t\t\t\t\t\tmaximize and metric > self._best_metric or not maximize and metric <= self._best_metric):\n\t\t\tself._best_metric = metric\n\t\t\treturn True\n\t\treturn False\n\n\t# # # # # # #\n\n\tdef run(self):\n\t\t\"\"\"\n\t\tRun training process.\n\t\t:return: None\n\t\t\"\"\"\n\n\t\tprint('=== RUNNING {} ==='.format(self.name))\n\n\t\t# Garbage collection\n\t\tgc.collect()\n\n\t\t# Initial epoch. 0 by default\n\t\tstart_epoch = 0\n\n\t\t# Load training status\n\t\tif os.path.isfile(os.path.join(self.checkpoint_dir, self.model_file_extra)):\n\t\t\t# Continue from the epoch where we were and load the best metric\n\t\t\twith open(os.path.join(self.checkpoint_dir, self.model_file_extra), 'r') as f:\n\t\t\t\tstart_epoch = int(f.readline())\n\t\t\t\tself.new_metric(float(f.readline()))\n\n\t\tif start_epoch >= self.epochs:\n\t\t\tprint(\"Training already finished. Skipping...\")\n\t\t\treturn\n\n\t\t# Train data generator\n\t\ttrain_datagen = keras.preprocessing.image.ImageDataGenerator(\n\t\t\trescale=self.rescale_factor,\n\t\t\t**self.augmentation\n\t\t)\n\n\t\t# Augmentation for validation / test\n\t\teval_augmentation = {k: v for k, v in self.augmentation.items() if\n\t\t\t\t\t\t\t k == 'featurewise_center' or k == 'featurewise_std_normalization'}\n\n\t\t# Validation data generator\n\t\tval_datagen = keras.preprocessing.image.ImageDataGenerator(rescale=self.rescale_factor, **eval_augmentation)\n\n\t\t# Get database paths\n\t\ttrain_path, val_path, _ = self.get_db_path(self.db)\n\n\t\t# Check that database exists and paths are correct\n\t\tif train_path == '' or val_path == '':\n\t\t\traise Exception('Invalid database. Choose one of: Retinopathy or Adience.')\n\n\t\t# Load datasets\n\t\tds_train = Dataset(train_path)\n\t\t# ds_train.standardize_data()\n\t\tds_val = Dataset(val_path)\n\t\t# ds_val.standardize_data()\n\n\t\t# Get dataset details\n\t\tnum_classes = ds_train.num_classes\n\t\tnum_channels = ds_train.num_channels\n\t\timg_size = ds_train.img_size\n\n\t\t# Fit for zca_whitening, featurewise_center, featurewise_std_normalization\n\t\tif 'zca_whitening' in self.augmentation or 'featurewise_center' in self.augmentation or 'featurewise_std_normalization' in self.augmentation:\n\t\t\ttrain_datagen.fit(ds_train.x)\n\t\t\tval_datagen.mean = train_datagen.mean\n\t\t\tval_datagen.std = train_datagen.std\n\n\t\t# Train data generator used for training\n\t\ttrain_generator = train_datagen.flow(\n\t\t\tds_train.x,\n\t\t\tds_train.y,\n\t\t\tbatch_size=self.batch_size\n\t\t)\n\n\t\t# Validation generator\n\t\tval_generator = val_datagen.flow(\n\t\t\tds_val.x,\n\t\t\tds_val.y,\n\t\t\tbatch_size=self.batch_size,\n\t\t\tshuffle=True\n\t\t)\n\n\t\t# Calculate the number of steps per epoch\n\t\tsteps = (len(ds_train.y) * 1) // self.batch_size\n\t\tsteps_val = ds_val.num_batches(self.batch_size)\n\n\t\t# Get class weights based on frequency\n\t\tclass_weight = ds_train.get_class_weights()\n\n\t\t# Free dataset object\n\t\tdel ds_train\n\t\tdel ds_val\n\t\tgc.collect()\n\n\t\t# Learning rate scheduler callback\n\t\tdef learning_rate_scheduler(epoch):\n\t\t\tlr = self.lr * np.exp(-0.01 * epoch)\n\n\t\t\treturn lr\n\n\t\t# Save epoch callback for training process\n\t\tdef save_epoch(epoch, logs):\n\t\t\t# Check whether new metric is better than best metric\n\t\t\tif (self.new_metric(logs['val_loss'])):\n\t\t\t\tmodel.save(os.path.join(self.checkpoint_dir, self.best_model_file))\n\t\t\t\tprint(\"Best model saved.\")\n\n\t\t\twith open(os.path.join(self.checkpoint_dir, self.model_file_extra), 'w') as f:\n\t\t\t\tf.write(str(epoch + 1))\n\t\t\t\tf.write('\\n' + str(self.best_metric))\n\n\n\t\tsave_epoch_callback = keras.callbacks.LambdaCallback(\n\t\t\ton_epoch_end=save_epoch\n\t\t)\n\n\t\t# NNet object\n\t\tnet_object = Net(img_size, self.activation, self.final_activation, self.f_a_params, self.use_tau, num_channels, num_classes,\n\t\t\t\t\t\t self.spp_alpha,\n\t\t\t\t\t\t self.dropout)\n\n\t\tmodel = self.get_model(net_object, self.net_type)\n\n\t\t# Create checkpoint dir if not exists\n\t\tif not os.path.isdir(self.checkpoint_dir):\n\t\t\tos.makedirs(self.checkpoint_dir)\n\n\t\t# Check whether a saved model exists\n\t\tif os.path.isfile(os.path.join(self.checkpoint_dir, self.model_file)):\n\t\t\tprint(\"===== RESTORING SAVED MODEL =====\")\n\t\t\tmodel.load_weights(os.path.join(self.checkpoint_dir, self.model_file))\n\n\t\t# Create the cost matrix that will be used to compute qwk\n\t\tcost_matrix = K.constant(make_cost_matrix(num_classes), dtype=K.floatx())\n\n\t\t# Cross-entropy loss by default\n\t\tloss = 'categorical_crossentropy'\n\n\t\t# Quadratic Weighted Kappa loss\n\t\tif self.loss == 'qwk':\n\t\t\tloss = qwk_loss(cost_matrix)\n\n\t\t# Only accuracy for training.\n\t\t# Computing QWK for training properly is too expensive\n\t\tmetrics = ['accuracy']\n\n\t\t# Compile the keras model\n\t\tmodel.compile(\n\t\t\toptimizer=keras.optimizers.Adam(lr=self.lr), # keras.optimizers.SGD(self.lr, 0.9),\n\t\t\tloss=loss,\n\t\t\tmetrics=metrics\n\t\t)\n\n\t\t# Print model summary\n\t\tmodel.summary()\n\n\t\t# Run training\n\t\tmodel.fit_generator(train_generator, epochs=self.epochs,\n\t\t\t\t\t\t\tinitial_epoch=start_epoch,\n\t\t\t\t\t\t\tsteps_per_epoch=steps,\n\t\t\t\t\t\t\tcallbacks=[keras.callbacks.LearningRateScheduler(learning_rate_scheduler),\n\t\t\t\t\t\t\t\t\t keras.callbacks.ModelCheckpoint(\n\t\t\t\t\t\t\t\t\t\t os.path.join(self.checkpoint_dir, self.model_file)),\n\t\t\t\t\t\t\t\t\t save_epoch_callback,\n\t\t\t\t\t\t\t\t\t keras.callbacks.CSVLogger(os.path.join(self.checkpoint_dir, self.csv_file),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tappend=True),\n\t\t\t\t\t\t\t\t\t keras.callbacks.TensorBoard(log_dir=self.checkpoint_dir),\n\t\t\t\t\t\t\t\t\t keras.callbacks.TerminateOnNaN(),\n\t\t\t\t\t\t\t\t\t ],\n\t\t\t\t\t\t\tworkers=self.workers,\n\t\t\t\t\t\t\tuse_multiprocessing=False,\n\t\t\t\t\t\t\tmax_queue_size=self.queue_size,\n\t\t\t\t\t\t\tclass_weight=class_weight,\n\t\t\t\t\t\t\tvalidation_data=val_generator,\n\t\t\t\t\t\t\tvalidation_steps=steps_val,\n\t\t\t\t\t\t\tverbose=2\n\t\t\t\t\t\t\t)\n\n\n\t\tself.finished = True\n\n\t\t# Mark the training as finished in the checkpoint file\n\t\twith open(os.path.join(self.checkpoint_dir, self.model_file_extra), 'w') as f:\n\t\t\tf.write(str(self.epochs))\n\t\t\tf.write('\\n' + str(self.best_metric))\n\n\t\t# Free objects\n\t\tdel model\n\t\tdel cost_matrix\n\t\tdel train_datagen\n\t\tdel train_generator\n\t\tdel val_datagen\n\t\tdel val_generator\n\n\tdef evaluate(self):\n\t\t\"\"\"\n\t\tRun evaluation on test data.\n\t\t:return: None\n\t\t\"\"\"\n\t\tprint('=== EVALUATING {} ==='.format(self.name))\n\n\t\t# Garbage collection\n\t\tgc.collect()\n\n\t\t# Check if best model file exists\n\t\tif not os.path.isfile(os.path.join(self.checkpoint_dir, self.best_model_file)):\n\t\t\tprint('Best model file not found')\n\t\t\treturn\n\n\t\t# Check if model was already evaluated\n\t\tif os.path.isfile(os.path.join(self.checkpoint_dir, self.evaluation_file)):\n\t\t\tprint('Model already evaluated')\n\t\t\treturn\n\n\t\tpaths = self.get_db_path(self.db)\n\n\t\tall_metrics = {}\n\n\t\t# Augmentation for validation / test\n\t\teval_augmentation = {k: v for k, v in self.augmentation.items() if\n\t\t\t\t\t\t\t k == 'featurewise_center' or k == 'featurewise_std_normalization'}\n\n\t\tmean = 0\n\t\tstd = 0\n\n\t\tfor path, set in zip(paths, ['Train', 'Validation', 'Test']):\n\t\t\tprint('\\n=== {} dataset ===\\n'.format(set))\n\n\t\t\t# Load test dataset\n\t\t\tds_test = Dataset(path)\n\t\t\t# ds_test.standardize_data()\n\n\t\t\t# Get dataset details\n\t\t\tnum_classes = ds_test.num_classes\n\t\t\tnum_channels = ds_test.num_channels\n\t\t\timg_size = ds_test.img_size\n\n\t\t\t# Validation data generator\n\t\t\ttest_datagen = keras.preprocessing.image.ImageDataGenerator(rescale=self.rescale_factor,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t **eval_augmentation)\n\n\t\t\t# Save mean and std of train set\n\t\t\tif set == 'Train':\n\t\t\t\t# Fit for zca_whitening, featurewise_center, featurewise_std_normalization\n\t\t\t\tif 'zca_whitening' in self.augmentation or 'featurewise_center' in self.augmentation or 'featurewise_std_normalization' in self.augmentation:\n\t\t\t\t\ttest_datagen.fit(ds_test.x)\n\t\t\t\t\tmean = test_datagen.mean\n\t\t\t\t\tstd = test_datagen.std\n\t\t\telse:\n\t\t\t\tif 'zca_whitening' in self.augmentation or 'featurewise_center' in self.augmentation or 'featurewise_std_normalization' in self.augmentation:\n\t\t\t\t\ttest_datagen.mean = mean\n\t\t\t\t\ttest_datagen.std = std\n\n\t\t\t# Test generator\n\t\t\ttest_generator = test_datagen.flow(\n\t\t\t\tds_test.x,\n\t\t\t\tds_test.y,\n\t\t\t\tbatch_size=self.batch_size,\n\t\t\t\tshuffle=False\n\t\t\t)\n\n\t\t\t# NNet object\n\t\t\tnet_object = Net(img_size, self.activation, self.final_activation, self.f_a_params, self.use_tau, num_channels,\n\t\t\t\t\t\t\t num_classes,\n\t\t\t\t\t\t\t self.spp_alpha,\n\t\t\t\t\t\t\t self.dropout)\n\n\t\t\tmodel = self.get_model(net_object, self.net_type)\n\n\t\t\t# Restore weights\n\t\t\tmodel.load_weights(os.path.join(self.checkpoint_dir, self.best_model_file))\n\n\t\t\t# Get predictions\n\t\t\ttest_generator.reset()\n\t\t\tpredictions = model.predict_generator(test_generator, verbose=1)\n\n\t\t\tmetrics = self.compute_metrics(ds_test.y, predictions, num_classes)\n\t\t\tself.print_metrics(metrics)\n\n\t\t\tall_metrics[set] = metrics\n\n\t\t\t# Free objects\n\t\t\tdel ds_test\n\t\t\tdel test_datagen\n\t\t\tdel test_generator\n\t\t\tdel net_object\n\t\t\tdel model\n\t\t\tdel predictions\n\t\t\tdel metrics\n\t\t\tgc.collect()\n\n\t\twith open(os.path.join(self.checkpoint_dir, self.evaluation_file), 'wb') as f:\n\t\t\tpickle.dump({'config': self.get_config(), 'metrics': all_metrics}, f)\n\n\tdef compute_metrics(self, y_true, y_pred, num_classes):\n\t\t# Calculate metric\n\t\tsess = keras.backend.get_session()\n\t\tqwk = np_quadratic_weighted_kappa(np.argmax(y_true, axis=1), np.argmax(y_pred, axis=1), 0,\n\t\t\t\t\t\t\t\t\t\t num_classes - 1)\n\t\tms = minimum_sensitivity(y_true, y_pred)\n\t\tmae = sess.run(K.mean(keras.losses.mean_absolute_error(y_true, y_pred)))\n\t\tmse = sess.run(K.mean(keras.losses.mean_squared_error(y_true, y_pred)))\n\t\tacc = sess.run(K.mean(keras.metrics.categorical_accuracy(y_true, y_pred)))\n\t\ttop2 = sess.run(top_2_accuracy(y_true, y_pred))\n\t\ttop3 = sess.run(top_3_accuracy(y_true, y_pred))\n\t\toff1 = accuracy_off1(y_true, y_pred)\n\t\tconf_mat = confusion_matrix(np.argmax(y_true, axis=1), np.argmax(y_pred, axis=1))\n\n\t\tmetrics = {\n\t\t\t'QWK': qwk,\n\t\t\t'MS': ms,\n\t\t\t'MAE': mae,\n\t\t\t'MSE': mse,\n\t\t\t'CCR': acc,\n\t\t\t'Top-2': top2,\n\t\t\t'Top-3': top3,\n\t\t\t'1-off': off1,\n\t\t\t'Confusion matrix': conf_mat\n\t\t}\n\n\t\treturn metrics\n\n\tdef print_metrics(self, metrics):\n\t\tprint('Confusion matrix :\\n{}'.format(metrics['Confusion matrix']))\n\t\tprint('QWK: {:.4f}'.format(metrics['QWK']))\n\t\tprint('CCR: {:.4f}'.format(metrics['CCR']))\n\t\tprint('Top-2: {:.4f}'.format(metrics['Top-2']))\n\t\tprint('Top-3: {:.4f}'.format(metrics['Top-3']))\n\t\tprint('1-off: {:.4f}'.format(metrics['1-off']))\n\t\tprint('MAE: {:.4f}'.format(metrics['MAE']))\n\t\tprint('MSE: {:.4f}'.format(metrics['MSE']))\n\t\tprint('MS: {:.4f}'.format(metrics['MS']))\n\n\tdef get_model(self, net_object, name):\n\t\tif name == 'conv128':\n\t\t\tmodel = net_object.conv128()\n\t\telif name == 'beckhamresnet':\n\t\t\tmodel = net_object.beckham_resnet()\n\t\telse:\n\t\t\traise Exception('Invalid net type. You must select one of these: vgg19, conv128')\n\n\t\treturn model\n\n\tdef get_db_path(self, db):\n\t\t\"\"\"\n\t\tGet dataset path for train, validation and test for a given database name.\n\t\t:param db: database name.\n\t\t:return: train path, validation path, test path.\n\t\t\"\"\"\n\t\tif db.lower() == 'retinopathy':\n\t\t\treturn \"../../retinopathy/128/train\", \"../../retinopathy/128/val\", \"../../retinopathy/128/test\"\n\t\telif db.lower() == 'adience':\n\t\t\treturn \"../../adience/adience_train_256.h5\", \"../../adience/adience_val_256.h5\", \"../../adience/adience_test_256.h5\"\n\t\telse:\n\t\t\treturn \"\", \"\", \"\"\n\n\tdef get_config(self):\n\t\t\"\"\"\n\t\tGet config dictionary from object config.\n\t\t:return: config dictionary.\n\t\t\"\"\"\n\t\treturn {\n\t\t\t'name': self.name,\n\t\t\t'db': self.db,\n\t\t\t'net_type': self.net_type,\n\t\t\t'batch_size': self.batch_size,\n\t\t\t'epochs': self.epochs,\n\t\t\t'checkpoint_dir': self.checkpoint_dir,\n\t\t\t'loss': self.loss,\n\t\t\t'activation': self.activation,\n\t\t\t'use_tau' : self.use_tau,\n\t\t\t'final_activation': self.final_activation,\n\t\t\t'f_a_params': self.f_a_params,\n\t\t\t'spp_alpha': self.spp_alpha,\n\t\t\t'lr': self.lr,\n\t\t\t'momentum': self.momentum,\n\t\t\t'dropout': self.dropout,\n\t\t\t'task': self.task,\n\t\t\t'workers': self.workers,\n\t\t\t'queue_size': self.queue_size,\n\t\t\t'val_metrics': self.val_metrics,\n\t\t\t'rescale_factor': self.rescale_factor,\n\t\t\t'augmentation': self.augmentation\n\t\t}\n\n\tdef set_config(self, config):\n\t\t\"\"\"\n\t\tSet object config from config dictionary\n\t\t:param config: config dictionary.\n\t\t:return: None\n\t\t\"\"\"\n\t\tself.db = 'db' in config and config['db'] or '10'\n\t\tself.net_type = 'net_type' in config and config['net_type'] or 'vgg19'\n\t\tself.batch_size = 'batch_size' in config and config['batch_size'] or 128\n\t\tself.epochs = 'epochs' in config and config['epochs'] or 100\n\t\tself.checkpoint_dir = 'checkpoint_dir' in config and config['checkpoint_dir'] or 'results'\n\t\tself.loss = 'loss' in config and config['loss'] or 'crossentropy'\n\t\tself.activation = 'activation' in config and config['activation'] or 'relu'\n\t\tself.final_activation = 'final_activation' in config and config['final_activation'] or 'softmax'\n\t\tself.f_a_params = config['f_a_params'] if 'f_a_params' in config else {}\n\t\tself.use_tau = config['use_tau'] if 'use_tau' in config and config['use_tau'] else False\n\t\tself.spp_alpha = 'spp_alpha' in config and config['spp_alpha'] or 0\n\t\tself.lr = 'lr' in config and config['lr'] or 0.1\n\t\tself.momentum = 'momentum' in config and config['momentum'] or 0\n\t\tself.dropout = 'dropout' in config and config['dropout'] or 0\n\t\tself.task = 'task' in config and config['task'] or 'both'\n\t\tself.workers = 'workers' in config and config['workers'] or 4\n\t\tself.queue_size = 'queue_size' in config and config['queue_size'] or 1024\n\t\tself.val_metrics = 'val_metrics' in config and config['val_metrics'] or ['acc', 'loss']\n\t\tself.rescale_factor = 'rescale_factor' in config and config['rescale_factor'] or 0\n\t\tself.augmentation = 'augmentation' in config and config['augmentation'] or {}\n\n\t\tif 'name' in config:\n\t\t\tself.name = config['name']\n\t\telse:\n\t\t\tself.set_auto_name()\n\n\tdef save_to_file(self, path):\n\t\t\"\"\"\n\t\tSave experiment to pickle file.\n\t\t:param path: path where pickle file will be saved.\n\t\t:return: None\n\t\t\"\"\"\n\t\tpickle.dump(self.get_config(), path)\n\n\tdef load_from_file(self, path):\n\t\t\"\"\"\n\t\tLoad experiment from pickle file.\n\t\t:param path: path where pickle file is located.\n\t\t:return: None\n\t\t\"\"\"\n\t\tif os.path.isfile(path):\n\t\t\tself.set_config(pickle.load(path))\n", "sub_path": "src/experiment.py", "file_name": "experiment.py", "file_ext": "py", "file_size_in_byte": 21013, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "gc.collect", "line_number": 374, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 380, "usage_type": "call"}, {"api_name": "os.path", "line_number": 380, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 380, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 382, "usage_type": "call"}, {"api_name": "os.path", "line_number": 382, "usage_type": "attribute"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 391, "usage_type": "call"}, {"api_name": "keras.preprocessing", "line_number": 391, "usage_type": "attribute"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 401, "usage_type": "call"}, {"api_name": "keras.preprocessing", "line_number": 401, "usage_type": "attribute"}, {"api_name": "dataset.Dataset", "line_number": 411, "usage_type": "call"}, {"api_name": "dataset.Dataset", "line_number": 413, "usage_type": "call"}, {"api_name": "gc.collect", "line_number": 452, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 456, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 464, "usage_type": "call"}, {"api_name": "os.path", "line_number": 464, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 467, "usage_type": "call"}, {"api_name": "os.path", "line_number": 467, "usage_type": "attribute"}, {"api_name": "keras.callbacks.LambdaCallback", "line_number": 472, "usage_type": "call"}, {"api_name": "keras.callbacks", "line_number": 472, "usage_type": "attribute"}, {"api_name": "net_keras.Net", "line_number": 477, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 484, "usage_type": "call"}, {"api_name": "os.path", "line_number": 484, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 485, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 488, "usage_type": "call"}, {"api_name": "os.path", "line_number": 488, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 488, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 490, "usage_type": "call"}, {"api_name": "os.path", "line_number": 490, "usage_type": "attribute"}, {"api_name": "keras.backend.constant", "line_number": 493, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 493, "usage_type": "name"}, {"api_name": "losses.make_cost_matrix", "line_number": 493, "usage_type": "call"}, {"api_name": "keras.backend.floatx", "line_number": 493, "usage_type": "call"}, {"api_name": "losses.qwk_loss", "line_number": 500, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 508, "usage_type": "call"}, {"api_name": "keras.optimizers", "line_number": 508, "usage_type": "attribute"}, {"api_name": "keras.callbacks.LearningRateScheduler", "line_number": 520, "usage_type": "call"}, {"api_name": "keras.callbacks", "line_number": 520, "usage_type": "attribute"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 521, "usage_type": "call"}, {"api_name": "keras.callbacks", "line_number": 521, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 522, "usage_type": "call"}, {"api_name": "os.path", "line_number": 522, "usage_type": "attribute"}, {"api_name": "keras.callbacks.CSVLogger", "line_number": 524, "usage_type": "call"}, {"api_name": "keras.callbacks", "line_number": 524, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 524, "usage_type": "call"}, {"api_name": "os.path", "line_number": 524, "usage_type": "attribute"}, {"api_name": "keras.callbacks.TensorBoard", "line_number": 526, "usage_type": "call"}, {"api_name": "keras.callbacks", "line_number": 526, "usage_type": "attribute"}, {"api_name": "keras.callbacks.TerminateOnNaN", "line_number": 527, "usage_type": "call"}, {"api_name": "keras.callbacks", "line_number": 527, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 542, "usage_type": "call"}, {"api_name": "os.path", "line_number": 542, "usage_type": "attribute"}, {"api_name": "gc.collect", "line_number": 562, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 565, "usage_type": "call"}, {"api_name": "os.path", "line_number": 565, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 565, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 570, "usage_type": "call"}, {"api_name": "os.path", "line_number": 570, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 570, "usage_type": "call"}, {"api_name": "dataset.Dataset", "line_number": 589, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 598, "usage_type": "call"}, {"api_name": "keras.preprocessing", "line_number": 598, "usage_type": "attribute"}, {"api_name": "net_keras.Net", "line_number": 622, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 630, "usage_type": "call"}, {"api_name": "os.path", "line_number": 630, "usage_type": "attribute"}, {"api_name": "gc.collect", "line_number": 649, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 651, "usage_type": "call"}, {"api_name": "os.path", "line_number": 651, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 652, "usage_type": "call"}, {"api_name": "keras.backend.get_session", "line_number": 656, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 656, "usage_type": "attribute"}, {"api_name": "metrics.np_quadratic_weighted_kappa", "line_number": 657, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 657, "usage_type": "call"}, {"api_name": "metrics.minimum_sensitivity", "line_number": 659, "usage_type": "call"}, {"api_name": "keras.backend.mean", "line_number": 660, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 660, "usage_type": "name"}, {"api_name": "keras.losses.mean_absolute_error", "line_number": 660, "usage_type": "call"}, {"api_name": "keras.losses", "line_number": 660, "usage_type": "attribute"}, {"api_name": "keras.backend.mean", "line_number": 661, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 661, "usage_type": "name"}, {"api_name": "keras.losses.mean_squared_error", "line_number": 661, "usage_type": "call"}, {"api_name": "keras.losses", "line_number": 661, "usage_type": "attribute"}, {"api_name": "keras.backend.mean", "line_number": 662, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 662, "usage_type": "name"}, {"api_name": "keras.metrics.categorical_accuracy", "line_number": 662, "usage_type": "call"}, {"api_name": "keras.metrics", "line_number": 662, "usage_type": "attribute"}, {"api_name": "metrics.top_2_accuracy", "line_number": 663, "usage_type": "call"}, {"api_name": "metrics.top_3_accuracy", "line_number": 664, "usage_type": "call"}, {"api_name": "metrics.accuracy_off1", "line_number": 665, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 666, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 666, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 783, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 791, "usage_type": "call"}, {"api_name": "os.path", "line_number": 791, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 792, "usage_type": "call"}]} +{"seq_id": "474309189", "text": "import datetime\n\nfrom django.conf import settings\nfrom django.contrib.auth import get_user_model\nfrom django.test import TestCase\nfrom django.utils import timezone\nfrom rest_framework.test import APIRequestFactory, APIClient\n\nfrom oauth2_provider.models import Application, AccessToken\nfrom oauth2_provider.settings import oauth2_settings\n\nfrom ... import models\n\n\nUserModel = get_user_model()\n\n\nclass MyGeneRankTestCase(TestCase):\n\n def setUp(self):\n # Set up activities\n for study_id in settings.DEFAULT_STUDY_IDS:\n models.Activity.objects.create(study_task_identifier=study_id)\n\n self.test_user = UserModel.objects.create_user(\"bar_user\", \"dev@example.com\")\n\n self.application = Application(\n name=\"Test Application\",\n redirect_uris=\"http://localhost http://example.com http://example.org\",\n user=self.test_user,\n client_type=Application.CLIENT_CONFIDENTIAL,\n authorization_grant_type=Application.GRANT_AUTHORIZATION_CODE,\n )\n self.application.save()\n\n self.valid_token = AccessToken.objects.create(\n user=self.test_user, token=\"12345678901\",\n application=self.application,\n expires=timezone.now() + datetime.timedelta(days=1),\n scope=\"read write\"\n )\n\n oauth2_settings._SCOPES = [\"read\", \"write\", \"introspection\", \"dolphin\"]\n oauth2_settings.READ_SCOPE = \"read\"\n oauth2_settings.WRITE_SCOPE = \"write\"\n\n def tearDown(self):\n oauth2_settings._SCOPES = [\"read\", \"write\"]\n AccessToken.objects.all().delete()\n Application.objects.all().delete()\n UserModel.objects.all().delete()\n models.Activity.objects.all().delete()\n\n\nclass PublicAPITestMixin(object):\n RESOURCE_URL = None\n\n # GET\n\n def test_get(self):\n r = self.client.get(self.RESOURCE_URL)\n self.assertEqual(r.status_code, 200)\n\n # POST\n\n def test_post(self):\n r = self.client.post(self.RESOURCE_URL)\n self.assertEqual(r.status_code, 405)\n\n # PUT\n\n def test_put(self):\n r = self.client.put(self.RESOURCE_URL)\n self.assertEqual(r.status_code, 405)\n\n # PATCH\n\n def test_patch(self):\n r = self.client.patch(self.RESOURCE_URL)\n self.assertEqual(r.status_code, 405)\n\n # DELETE\n\n def test_delete(self):\n r = self.client.delete(self.RESOURCE_URL)\n self.assertEqual(r.status_code, 405)\n\n\nclass AuthorizationRequiredAPITestMixin(object):\n RESOURCE_URL = None\n\n @property\n def auth_headers(self):\n return {\n \"HTTP_AUTHORIZATION\": \"Bearer \" + self.valid_token.token,\n }\n\n @property\n def invalid_auth_headers(self):\n return {\n \"HTTP_AUTHORIZATION\": \"Bearer \" + 'fake_token',\n }\n\n # GET\n\n def test_authorized_get(self):\n r = self.client.get(self.RESOURCE_URL, **self.auth_headers)\n self.assertEqual(r.status_code, 200)\n\n def test_unauthorized_get(self):\n r = self.client.get(self.RESOURCE_URL, **self.invalid_auth_headers)\n self.assertEqual(r.status_code, 401)\n\n # POST\n\n def test_authorized_post(self):\n r = self.client.post(self.RESOURCE_URL, **self.auth_headers)\n self.assertEqual(r.status_code, 405)\n\n def test_unauthorized_post(self):\n r = self.client.post(self.RESOURCE_URL, **self.invalid_auth_headers)\n self.assertEqual(r.status_code, 401)\n\n # PUT\n\n def test_authorized_put(self):\n r = self.client.put(self.RESOURCE_URL, **self.auth_headers)\n self.assertEqual(r.status_code, 405)\n\n def test_unauthorized_put(self):\n r = self.client.put(self.RESOURCE_URL, **self.invalid_auth_headers)\n self.assertEqual(r.status_code, 401)\n\n # PATCH\n\n def test_authorized_patch(self):\n r = self.client.patch(self.RESOURCE_URL, **self.auth_headers)\n self.assertEqual(r.status_code, 405)\n\n def test_unauthorized_patch(self):\n r = self.client.patch(self.RESOURCE_URL, **self.invalid_auth_headers)\n self.assertEqual(r.status_code, 401)\n\n # DELETE\n\n def test_authorized_delete(self):\n r = self.client.delete(self.RESOURCE_URL, **self.auth_headers)\n self.assertEqual(r.status_code, 405)\n\n def test_unauthorized_delete(self):\n r = self.client.delete(self.RESOURCE_URL, **self.invalid_auth_headers)\n self.assertEqual(r.status_code, 401)\n\n", "sub_path": "generank/api/tests/views/base.py", "file_name": "base.py", "file_ext": "py", "file_size_in_byte": 4442, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.contrib.auth.get_user_model", "line_number": 15, "usage_type": "call"}, {"api_name": "django.test.TestCase", "line_number": 18, "usage_type": "name"}, {"api_name": "django.conf.settings.DEFAULT_STUDY_IDS", "line_number": 22, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 22, "usage_type": "name"}, {"api_name": "oauth2_provider.models.Application", "line_number": 27, "usage_type": "call"}, {"api_name": "oauth2_provider.models.Application.CLIENT_CONFIDENTIAL", "line_number": 31, "usage_type": "attribute"}, {"api_name": "oauth2_provider.models.Application", "line_number": 31, "usage_type": "name"}, {"api_name": "oauth2_provider.models.Application.GRANT_AUTHORIZATION_CODE", "line_number": 32, "usage_type": "attribute"}, {"api_name": "oauth2_provider.models.Application", "line_number": 32, "usage_type": "name"}, {"api_name": "oauth2_provider.models.AccessToken.objects.create", "line_number": 36, "usage_type": "call"}, {"api_name": "oauth2_provider.models.AccessToken.objects", "line_number": 36, "usage_type": "attribute"}, {"api_name": "oauth2_provider.models.AccessToken", "line_number": 36, "usage_type": "name"}, {"api_name": "django.utils.timezone.now", "line_number": 39, "usage_type": "call"}, {"api_name": "django.utils.timezone", "line_number": 39, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 39, "usage_type": "call"}, {"api_name": "oauth2_provider.settings.oauth2_settings._SCOPES", "line_number": 43, "usage_type": "attribute"}, {"api_name": "oauth2_provider.settings.oauth2_settings", "line_number": 43, "usage_type": "name"}, {"api_name": "oauth2_provider.settings.oauth2_settings.READ_SCOPE", "line_number": 44, "usage_type": "attribute"}, {"api_name": "oauth2_provider.settings.oauth2_settings", "line_number": 44, "usage_type": "name"}, {"api_name": "oauth2_provider.settings.oauth2_settings.WRITE_SCOPE", "line_number": 45, "usage_type": "attribute"}, {"api_name": "oauth2_provider.settings.oauth2_settings", "line_number": 45, "usage_type": "name"}, {"api_name": "oauth2_provider.settings.oauth2_settings._SCOPES", "line_number": 48, "usage_type": "attribute"}, {"api_name": "oauth2_provider.settings.oauth2_settings", "line_number": 48, "usage_type": "name"}, {"api_name": "oauth2_provider.models.AccessToken.objects.all", "line_number": 49, "usage_type": "call"}, {"api_name": "oauth2_provider.models.AccessToken.objects", "line_number": 49, "usage_type": "attribute"}, {"api_name": "oauth2_provider.models.AccessToken", "line_number": 49, "usage_type": "name"}, {"api_name": "oauth2_provider.models.Application.objects.all", "line_number": 50, "usage_type": "call"}, {"api_name": "oauth2_provider.models.Application.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "oauth2_provider.models.Application", "line_number": 50, "usage_type": "name"}]} +{"seq_id": "502059557", "text": "import cv2 as cv\nimport numpy as np\n#from SimpleTracker import SimpleTracker\n\n\ndef track():\n\n # Load the classifier with the trained weights\n drone_cascade = cv.CascadeClassifier('/home/user/sommarjobb/drone_classifier/classifier/cascade.xml')\n\n # Load video\n cap = cv.VideoCapture('/home/user/sommarjobb/drone_classifier/drone_video.mp4')\n\n # Use tracker algorithm\n #tracker = SimpleTracker()\n\n while True:\n\n # Read from video frame by frame\n ret, img = cap.read() \n gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n\n # Detect the drone\n drones = drone_cascade.detectMultiScale(gray, 1.3, 5)\n \n # Draw rectangle around detected drone\n for (x,y,w,h) in drones:\n cv.rectangle(img,(x,y),(x+w,y+h),(255,255,0),2)\n font = cv.FONT_HERSHEY_SIMPLEX\n cv.putText(img, 'Drone', (x-w, y-h), font, 0.5, (0,255,255), 2, cv.LINE_AA)\n\n # Update trackers\n #if drones is not None:\n # objects = tracker.update([(x,y,x+w,y+h)])\n #else:\n # objects = tracker.update([])\n\n # Show frame\n cv.imshow('img',img)\n \n # Wait for key to show next frame\n k = cv.waitKey(0)\n\n # Quit if q or esc is pressed\n if k == 27 or k ==ord('q'):\n break\n\n cap.release()\n\n cv.destroyAllWindows()\n\ntrack()\n", "sub_path": "cascade_train/drone_detection.py", "file_name": "drone_detection.py", "file_ext": "py", "file_size_in_byte": 1375, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "cv2.CascadeClassifier", "line_number": 9, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 21, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 21, "usage_type": "attribute"}, {"api_name": "cv2.rectangle", "line_number": 28, "usage_type": "call"}, {"api_name": "cv2.FONT_HERSHEY_SIMPLEX", "line_number": 29, "usage_type": "attribute"}, {"api_name": "cv2.putText", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.LINE_AA", "line_number": 30, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "520028116", "text": "#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n\n#############################################\n# File Name: setup.py\n# Author: JeffreyCao\n# Mail: jeffreycao1024@gmail.com\n# Created Time: 2019-11-16 21:48:34\n# https://packaging.python.org/guides/distributing-packages-using-setuptools/#package-data\n#############################################\n\n# from setuptools import setup, find_packages # 这个包没有的可以pip一下\nimport setuptools\nwith open(\"README.md\", \"r\") as fh:\n long_description = fh.read()\n\nsetuptools.setup(\n name=\"ezutils\",\n version=\"0.0.5\",\n keywords=[\"color\", \"file\", \"progress\", \"ezutils\"],\n description=\"Utils to save your time on python coding\",\n long_description=long_description,\n long_description_content_type=\"text/markdown\",\n license=\"MIT Licence\",\n\n url=\"https://github.com/caojianfeng/ezutils\",\n author=\"JeffreyCao\",\n author_email=\"jeffreycao1024@gmail.com\",\n\n packages=setuptools.find_packages(),\n include_package_data=True,\n platforms=\"any\",\n install_requires=[],\n classifiers=[\n \"Programming Language :: Python :: 3\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n ],\n python_requires='>=3.6',\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1244, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "setuptools.setup", "line_number": 17, "usage_type": "call"}, {"api_name": "setuptools.find_packages", "line_number": 30, "usage_type": "call"}]} +{"seq_id": "550574458", "text": "import sys\nimport torch\n\nimport sentence_data\nfrom translator_model import TranslatorModel\n\ndataset = sentence_data.SentenceData(\"dataset/data_full.txt\")\n\nmodel = TranslatorModel(dataset.english_word_size(),\n dataset.japanese_word_size())\n\nmodel.load_state_dict(torch.load(\"trained_model/translator_full.model\"))\n\n# 入力された文章を単語に分割する\nsentence = input(\"input an english sentence : \").split(' ')\n# 単語IDのリストに変換する\nsentence_id = []\nfor word in sentence:\n if not word:\n # 単語が空だったら飛ばす\n continue\n word = word.lower()\n id = dataset.english_word_id(word)\n if id is None:\n sys.stderr.write(\"Error : Unknown word \" + word + \"\\n\")\n sys.exit()\n else:\n id = torch.tensor(id,dtype=torch.long).unsqueeze(-1)\n sentence_id.append(id)\n\njapanese = model(torch.stack(sentence_id))\nfor id in japanese:\n print(dataset.japanese_word(id), end='')\nprint()\n", "sub_path": "nlp/translator_model_test.py", "file_name": "translator_model_test.py", "file_ext": "py", "file_size_in_byte": 984, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sentence_data.SentenceData", "line_number": 7, "usage_type": "call"}, {"api_name": "translator_model.TranslatorModel", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 12, "usage_type": "call"}, {"api_name": "sys.stderr.write", "line_number": 25, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 25, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.long", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.stack", "line_number": 31, "usage_type": "call"}]} +{"seq_id": "449656421", "text": "# Mengimpor library yang diperlukan\r\nimport numpy as np\r\nfrom featureExtractor import featureExtractor\r\nimport matplotlib.pyplot as plt\r\nimport pandas as pd\r\n\r\n\r\n# Mengimpor dataset\r\ndataset = pd.read_csv('Iklan_sosmed.csv')\r\nX = dataset.iloc[:, [2, 3]].values\r\ny = dataset.iloc[:, 4].values\r\ndef replaceZeroes(data):\r\n min_nonzero = np.min(data[np.nonzero(data)])\r\n data[data == 0] = min_nonzero\r\n return data\r\n# Menjadi dataset ke dalam Training set and Test set\r\nfrom sklearn.model_selection import train_test_split\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)\r\n \r\n# Feature Scaling\r\nfrom sklearn.preprocessing import StandardScaler\r\nsc = StandardScaler()\r\nX_train = sc.fit_transform(X_train)\r\nX_test = sc.transform(X_test)\r\n \r\n# Membuat model Naive Bayes terhadap Training set\r\nfrom sklearn.naive_bayes import GaussianNB\r\nclassifier = GaussianNB()\r\nclassifier.fit(X_train, y_train)\r\n \r\n# Memprediksi hasil test set\r\ny_pred = classifier.predict(X_test)\r\n \r\n# Membuat confusion matrix\r\nfrom sklearn.metrics import confusion_matrix\r\ncm = confusion_matrix(y_test, y_pred)\r\n \r\n# Visualisasi hasil model Naive Bayes dari Training set\r\nfrom matplotlib.colors import ListedColormap\r\nX_set, y_set = X_train, y_train\r\nX1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\r\n np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\r\nplt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\r\n alpha = 0.75, cmap = ListedColormap(('red', 'green')))\r\nplt.xlim(X1.min(), X1.max())\r\nplt.ylim(X2.min(), X2.max())\r\nfor i, j in enumerate(np.unique(y_set)):\r\n plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\r\n c = ListedColormap(('red', 'green'))(i), label = j)\r\nplt.title('Naive Bayes (Normal)')\r\nplt.xlabel('Protocol')\r\nplt.ylabel('Length')\r\nplt.legend()\r\nplt.show()\r\nnumpy.seterr(divide = 'ignore') \r\n# Visualisasi hasil model Naive Bayes dari Test set\r\nfrom matplotlib.colors import ListedColormap\r\nX_set, y_set = X_test, y_test\r\nX1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),\r\n np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))\r\nplt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),\r\n alpha = 0.75, cmap = ListedColormap(('red', 'green')))\r\nplt.xlim(X1.min(), X1.max())\r\nplt.ylim(X2.min(), X2.max())\r\nfor i, j in enumerate(np.unique(y_set)):\r\n plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],\r\n c = ListedColormap(('red', 'green'))(i), label = j)\r\nplt.title('Naive Bayes (Test set)')\r\nplt.xlabel('Protocol')\r\nplt.ylabel('Length')\r\nplt.legend()\r\nplt.show()\r\n", "sub_path": "tes1.py", "file_name": "tes1.py", "file_ext": "py", "file_size_in_byte": 2871, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pandas.read_csv", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.nonzero", "line_number": 13, "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": "sklearn.naive_bayes.GaussianNB", "line_number": 28, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 45, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 45, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 46, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 46, "usage_type": "name"}, {"api_name": "numpy.unique", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 48, "usage_type": "name"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 50, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 50, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 52, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 52, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 53, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "numpy.seterr", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "numpy.unique", "line_number": 65, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 66, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 66, "usage_type": "name"}, {"api_name": "matplotlib.colors.ListedColormap", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 69, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 69, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 71, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 71, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 72, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 72, "usage_type": "name"}]} +{"seq_id": "322265002", "text": "from __future__ import unicode_literals\nfrom textx.scoping.rrel import rrel_standalone, parse\nfrom arpeggio import ParserPython\nfrom textx import metamodel_from_str\nfrom textx.scoping.rrel import find\n\n\ndef test_rrel_basic_parser1():\n parser = ParserPython(rrel_standalone)\n parse_tree = parser.parse(\"^pkg*.cls\")\n assert len(parse_tree) == 2 # always true (one path, one EOF)\n\n parse_tree = parser.parse(\"obj.ref.~extension *.methods\")\n assert len(parse_tree) == 2 # always true (one path, one EOF)\n\n parse_tree = parser.parse(\"instance.(type.vals)*\")\n assert len(parse_tree) == 2 # always true (one path, one EOF)\n\n\ndef test_rrel_basic_parser2():\n tree = parse(\"^pkg*.cls\")\n assert str(tree) == '(..)*.(pkg)*.cls'\n tree = parse(\"obj.ref.~extension *.methods\")\n assert str(tree) == 'obj.ref.(~extension)*.methods'\n tree = parse(\"type.vals\")\n assert str(tree) == 'type.vals'\n tree = parse(\"(type.vals)\")\n assert str(tree) == '(type.vals)'\n tree = parse(\"(type.vals)*\")\n assert str(tree) == '(type.vals)*'\n tree = parse(\"instance . ( type.vals ) *\")\n assert str(tree) == 'instance.(type.vals)*'\n tree = parse(\"a,b,c\")\n assert str(tree) == 'a,b,c'\n\n\nmetamodel_str = '''\n Model:\n packages*=Package\n ;\n\n Package:\n 'package' name=ID '{'\n packages*=Package\n classes*=Class\n '}'\n ;\n\n Class:\n 'class' name=ID '{'\n attributes*=Attribute\n '}'\n ;\n\n Attribute:\n 'attr' name=ID ';'\n ;\n\n Comment: /#.*/;\n FQN: ID('.'ID)*;\n '''\n\nmodeltext = '''\n package P1 {\n class Part1 {\n }\n }\n package P2 {\n package Inner {\n class Inner {\n attr inner;\n }\n }\n class Part2 {\n attr rec;\n }\n class C2 {\n attr p1;\n attr p2a;\n attr p2b;\n }\n class rec {\n attr p1;\n }\n }\n '''\n\n\ndef test_rrel_basic_lookup():\n \"\"\"\n This is a basic test for the find function:\n we use a model with some structure\n and query this structure with RREL expressions.\n \"\"\"\n #################################\n # META MODEL DEF\n #################################\n\n my_metamodel = metamodel_from_str(metamodel_str)\n\n #################################\n # MODEL PARSING\n #################################\n\n my_model = my_metamodel.model_from_str(modeltext)\n\n #################################\n # TEST\n #################################\n\n P2 = find(my_model, \"P2\", \"packages\")\n assert P2.name == \"P2\"\n Part2 = find(my_model, \"P2.Part2\", \"packages.classes\")\n assert Part2.name == \"Part2\"\n rec = find(my_model, \"P2.Part2.rec\", \"packages.classes.attributes\")\n assert rec.name == \"rec\"\n assert rec.parent == Part2\n\n P2 = find(my_model, \"P2\", \"(packages)\")\n assert P2.name == \"P2\"\n\n from textx import get_model\n assert get_model(my_model) is my_model\n\n P2 = find(my_model, \"P2\", \"packages*\")\n assert P2.name == \"P2\"\n Part2 = find(my_model, \"P2.Part2\", \"packages*.classes\")\n assert Part2.name == \"Part2\"\n rec = find(my_model, \"P2.Part2.rec\", \"packages*.classes.attributes\")\n assert rec.name == \"rec\"\n assert rec.parent == Part2\n\n Part2_tst = find(rec, \"\", \"..\")\n assert Part2_tst is Part2\n\n P2_from_inner_node = find(rec, \"P2\", \"(packages)\")\n assert P2_from_inner_node is P2\n\n P2_tst = find(rec, \"\", \"parent(Package)\")\n assert P2_tst is P2\n\n P2_tst = find(rec, \"\", \"...\")\n assert P2_tst is P2\n\n P2_tst = find(rec, \"\", \".(..).(..)\")\n assert P2_tst is P2\n\n P2_tst = find(rec, \"\", \"(..).(..)\")\n assert P2_tst is P2\n\n P2_tst = find(rec, \"\", \"...(.).(.)\")\n assert P2_tst is P2\n\n P2_tst = find(rec, \"\", \"..(.).(..)\")\n assert P2_tst is P2\n\n P2_tst = find(rec, \"\", \"..((.)*)*.(..)\")\n assert P2_tst is P2\n\n none = find(my_model, \"\", \"..\")\n assert none is None\n\n inner = find(my_model, \"inner\", \"~packages.~packages.~classes.attributes\")\n assert inner.name == \"inner\"\n\n # expensive version of a \"Plain Name\" scope provider:\n inner = find(my_model, \"inner\", \"~packages*.~classes.attributes\")\n assert inner.name == \"inner\"\n\n rec2 = find(my_model, \"P2.Part2.rec\", \"other1,other2,packages*.classes.attributes\")\n assert rec2 is rec\n\n rec2 = find(my_model, \"P2.Part2.rec\", \"other1,packages*.classes.attributes,other2\")\n assert rec2 is rec\n\n rec2 = find(my_model, \"P2::Part2::rec\", \"other1,packages*.classes.attributes,other2\",\n split_string=\"::\")\n assert rec2 is rec\n\n rec2 = find(my_model, \"P2.Part2.rec\", \"other1,other2,other3\")\n assert rec2 is None\n\n rec2 = find(my_model, \"P2.Part2.rec\", \"(packages,classes,attributes)*\")\n assert rec2 is rec\n\n rec2 = find(my_model, \"P2.Part2.rec\", \"(packages,(classes,attributes)*)*.attributes\")\n assert rec2 is rec\n\n rec2 = find(my_model, \"rec\", \"(~packages,~classes,attributes,classes)*\")\n assert rec2.name == \"rec\"\n\n rec2 = find(my_model, \"rec\",\n \"(~packages,~classes,attributes,classes)*\", my_metamodel[\"OBJECT\"])\n assert rec2.name == \"rec\"\n\n rec2 = find(my_model, \"rec\",\n \"(~packages,~classes,attributes,classes)*\", my_metamodel[\"Attribute\"])\n assert rec2 is rec\n\n rec2 = find(my_model, \"rec\",\n \"(~packages,~classes,attributes,classes)*\", my_metamodel[\"Package\"])\n assert rec2 is None\n\n rec2 = find(my_model, \"rec\",\n \"(~packages,classes,attributes,~classes)*\", my_metamodel[\"Class\"])\n assert rec2.name == \"rec\"\n assert rec2 is not rec # it is the class...\n\n rec2 = find(my_model, \"rec\",\n \"(~packages,~classes,attributes,classes)*\", my_metamodel[\"Class\"])\n assert rec2.name == \"rec\"\n assert rec2 is not rec # it is the class...\n\n t = find(my_model, \"\", \".\")\n assert t is my_model\n\n t = find(my_model, \"\", \"(.)\")\n assert t is my_model\n\n t = find(my_model, \"\", \"(.)*\")\n assert t is my_model\n\n t = find(my_model, \"\", \"(.)*.no_existent\") # inifite recursion stopper\n assert t is None\n\n rec2 = find(my_model, \"rec\",\n \"(.)*.(~packages,~classes,attributes,classes)*\", my_metamodel[\"Class\"])\n assert rec2.name == \"rec\"\n assert rec2 is not rec # it is the class...\n\n # Here, we test the start_from_root/start_locally logic:\n P2t = find(rec, \"P2\", \"(.)*.packages\")\n assert P2t is None\n P2t = find(rec, \"P2\", \"(.,not_existent_but_root)*.packages\")\n assert P2t is P2\n rect = find(rec, \"rec\", \"(~packages)*.(..).attributes\")\n assert rect is None\n rect = find(rec, \"rec\", \"(.,~packages)*.(..).attributes\")\n assert rect is rec\n\n\ndef test_rrel_repetitions():\n \"\"\"\n This is a basic extra test to demonstrate `()*`\n in RREL expressions.\n \"\"\"\n\n my_metamodel = metamodel_from_str(r'''\n Model: entries*=Entry;\n Entry: name=ID (':' ref=[Entry])?;\n ''')\n\n my_model = my_metamodel.model_from_str(r'''\n a: b\n c\n b: a\n ''')\n\n a = find(my_model, \"a\", \"entries.ref*\")\n assert a.name == 'a'\n b = find(my_model, \"b\", \"entries.ref*\")\n assert b.name == 'b'\n c = find(my_model, \"c\", \"entries.ref*\")\n assert c.name == 'c'\n\n a2 = find(my_model, \"a.b.a\", \"entries.ref*\")\n assert a2 == a\n\n b2 = find(my_model, \"b.a.b\", \"entries.ref*\")\n assert b2 == b\n\n a2 = find(my_model, \"b.a.b.a\", \"entries.ref*\")\n assert a2 == a\n\n a2 = find(my_model, \"b.a.b.a.b.a.b.a.b.a\", \"entries.ref*\")\n assert a2 == a\n", "sub_path": "tests/functional/test_scoping/test_rrel.py", "file_name": "test_rrel.py", "file_ext": "py", "file_size_in_byte": 7581, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "arpeggio.ParserPython", "line_number": 9, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.rrel_standalone", "line_number": 9, "usage_type": "argument"}, {"api_name": "textx.scoping.rrel.parse", "line_number": 21, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.parse", "line_number": 23, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.parse", "line_number": 25, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.parse", "line_number": 27, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.parse", "line_number": 29, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.parse", "line_number": 31, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.parse", "line_number": 33, "usage_type": "call"}, {"api_name": "textx.metamodel_from_str", "line_number": 99, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 111, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 113, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 115, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 119, "usage_type": "call"}, {"api_name": "textx.get_model", "line_number": 123, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 125, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 127, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 129, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 133, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 136, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 139, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 142, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 145, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 148, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 151, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 154, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 157, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 160, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 163, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 167, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 170, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 173, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 176, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 180, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 183, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 186, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 189, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 192, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 196, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 200, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 204, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 209, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 214, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 217, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 220, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 223, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 226, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 232, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 234, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 236, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 238, "usage_type": "call"}, {"api_name": "textx.metamodel_from_str", "line_number": 248, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 259, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 261, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 263, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 266, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 269, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 272, "usage_type": "call"}, {"api_name": "textx.scoping.rrel.find", "line_number": 275, "usage_type": "call"}]} +{"seq_id": "311043944", "text": "import speech_recognition as sr\nimport webbrowser\nimport time\nimport playsound\nimport os\nimport random\nfrom gtts import gTTS\nfrom time import ctime\n\n\n\n\nr = sr.Recognizer()\n\n\n\n\ndef record_audio(ask = False):\n with sr.Microphone() as source:\n if ask:\n kes_speak(ask)\n\n audio = r.listen(source)\n voice_data = ''\n try:\n voice_data = r.recognize_google(audio)\n except sr.UnknownValueError:\n kes_speak('Sorry, I did not understand that')\n except sr.RequestError:\n kes_speak('Damn my speech service is down, try again')\n return voice_data\n\n\ndef kes_speak(audio_string):\n tts = gTTS(text=audio_string, lang='en')\n r = random.randint(1,10000000)\n audio_file = 'audio-' + str(r) + '.mp3'\n tts.save(audio_file)\n playsound.playsound(audio_file)\n print(audio_string)\n os.remove(audio_file)\n\ndef respond(voice_data):\n if 'what is your name' in voice_data:\n kes_speak('Kes, my name is Kes Popinga')\n if 'what time is it' in voice_data:\n kes_speak(ctime())\n if 'search' in voice_data:\n search = record_audio('What do you want to search for?')\n url = 'https://google.com/search?q=' + search\n webbrowser.get().open(url)\n kes_speak('Here is what I found for ' + search)\n if 'find location' in voice_data:\n location = record_audio('What is the location?')\n url = 'https://google.nl/maps/place/' + location + '/&'\n webbrowser.get().open(url)\n kes_speak('Here is the location of ' + location)\n if 'best book ' in voice_data:\n kes_speak('I am a character from a literature classic')\n if 'The man who watched trains go by' in voice_data:\n kes_speak('You must be a genious!!!')\n if 'exit' in voice_data:\n kes_speak('Oh Nooooooooooooo you are going to leave me alone again, ok...')\n exit()\n\n\ntime.sleep(0.5)\nkes_speak('How can I help you')\nwhile 0.5:\n voice_data = record_audio()\n respond(voice_data)\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2027, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "speech_recognition.Recognizer", "line_number": 13, "usage_type": "call"}, {"api_name": "speech_recognition.Microphone", "line_number": 19, "usage_type": "call"}, {"api_name": "speech_recognition.UnknownValueError", "line_number": 27, "usage_type": "attribute"}, {"api_name": "speech_recognition.RequestError", "line_number": 29, "usage_type": "attribute"}, {"api_name": "gtts.gTTS", "line_number": 35, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 36, "usage_type": "call"}, {"api_name": "playsound.playsound", "line_number": 39, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 41, "usage_type": "call"}, {"api_name": "time.ctime", "line_number": 47, "usage_type": "call"}, {"api_name": "webbrowser.get", "line_number": 51, "usage_type": "call"}, {"api_name": "webbrowser.get", "line_number": 56, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "633037920", "text": "# -*- coding: utf-8 -*-\r\nimport numpy as np\r\nfrom numpy.random import seed\r\nimport time\r\nimport sys\r\nfrom matplotlib import pyplot as plt\r\n\r\nGraphDataX = []\r\nGraphDataY = []\r\n'''\r\n This function will be used to read data from train and test files.\r\n This function is used for both train and test files because the format is\r\n the same for both the files.\r\n \r\n This function will be returning a list which has all the image data\r\n from the text file - the name is passed as an argument.\r\n'''\r\ndef ReadFile(FileName):\r\n OpenedFile = open(FileName, 'r')\r\n \r\n DataList = []\r\n Line = 'starting'\r\n \r\n # Iterating through the file - line after line - till an empty string is not read\r\n while Line != '':\r\n List = []\r\n \r\n Line = OpenedFile.readline()\r\n \r\n while True:\r\n Length = len(Line)\r\n length = 0\r\n Result = ''\r\n check = True\r\n \r\n for x in range(Length):\r\n if Line[x] == ']':\r\n Result = Line[:length]\r\n check = False\r\n else:\r\n length += 1\r\n \r\n EndOrNotResult = check\r\n \r\n if EndOrNotResult == False:\r\n break\r\n \r\n LineSplit = Line.split()\r\n NumberOfElementsInLine = len(LineSplit)\r\n \r\n if NumberOfElementsInLine <= 0:\r\n break\r\n elif LineSplit[0] == '[':\r\n for x in range(1, NumberOfElementsInLine):\r\n Number = LineSplit[x]\r\n Number = int(Number)\r\n # Normalisation\r\n Number = Number/float(255)\r\n \r\n List.append(Number)\r\n Line = OpenedFile.readline()\r\n else:\r\n for x in range(NumberOfElementsInLine):\r\n Number = LineSplit[x]\r\n Number = int(Number)\r\n # Normalisation\r\n Number = Number/float(255)\r\n \r\n List.append(Number)\r\n \r\n Line = OpenedFile.readline()\r\n \r\n Length = len(Line)\r\n length = 0\r\n Result = ''\r\n check = True\r\n \r\n for x in range(Length):\r\n if Line[x] == ']':\r\n Result = Line[:length]\r\n check = False\r\n else:\r\n length += 1\r\n \r\n EndOrNotResult = check\r\n \r\n \r\n if EndOrNotResult == False:\r\n \r\n LineSplit = Result.split()\r\n NumberOfElementsInLine = len(LineSplit)\r\n \r\n for x in range(NumberOfElementsInLine):\r\n Number = LineSplit[x]\r\n Number = int(Number)\r\n Number = Number/float(255)\r\n \r\n List.append(Number)\r\n break\r\n DataList.append(List)\r\n return DataList\r\n\r\n'''\r\n This function will be used to read data from train-labels and\r\n test-labels files.\r\n This function is used for both train-labels and test-labels files\r\n because the format is the same for both the files.\r\n \r\n This function will be returning a list which has all the labels for\r\n each of the images corresponding to each row of the train.txt and\r\n test.txt from the text file - the name is passed as an argument.\r\n'''\r\ndef ReadLabels(FileName):\r\n OpenedFile = open(FileName, 'r')\r\n \r\n Labels = []\r\n \r\n label = OpenedFile.readline()\r\n while label != '':\r\n label = int(label)\r\n Labels.append(label)\r\n label = OpenedFile.readline()\r\n return Labels\r\n\r\n'''\r\n This function implements the sigmoid function using numpy\r\n Sigmoid function is 1/(1+e^(-x))\r\n'''\r\ndef sigmoid(x):\r\n exp = np.exp(-x)\r\n denominator = 1.0 + exp\r\n Final = 1.0/denominator\r\n \r\n return Final\r\n\r\n'''\r\n This function returns your label into one hot encoding array\r\n label is a digit\r\n Ex-> If label is 1 then one hot encoding should be [0,1,0,0,0,0,0,0,0,0]\r\n Ex-> If label is 9 then one hot encoding shoudl be [0,0,0,0,0,0,0,0,0,1]\r\n'''\r\ndef generate_label(label):\r\n List = np.zeros(10)\r\n List[label] = 1\r\n List = np.array(List)\r\n return List\r\n\r\n'''\r\n This function will be training the model.\r\n'''\r\ndef TrainModel(TrainingData, TrainingLabels, LearningRate):\r\n # initialising weights\r\n HiddenLayerWeightRange = (784, 30)\r\n seed(1)\r\n HiddenLayerWeight = 2 * np.random.random(HiddenLayerWeightRange) - 1\r\n seed(1)\r\n OutputLayerWeightRange = (30, 10)\r\n OutputLayerWeight = 2 * np.random.random(OutputLayerWeightRange) - 1\r\n \r\n # training now\r\n Epoches = 2\r\n for x in range(Epoches):\r\n print('Epoch Number: ', x + 1)\r\n LengthOfTrainingLabels = len(TrainingLabels)\r\n \r\n for y in range(LengthOfTrainingLabels):\r\n HiddenLayer = np.dot(TrainingData[y], HiddenLayerWeight)\r\n HiddenLayer = sigmoid(HiddenLayer)\r\n HiddenLayer = np.array(HiddenLayer, dtype = float)\r\n \r\n \r\n OutputLayer = np.dot(HiddenLayer, OutputLayerWeight)\r\n Activation = sigmoid(OutputLayer)\r\n Activation = np.array(Activation, dtype = float)\r\n \r\n TargetLabelArray = generate_label(TrainingLabels[y])\r\n Difference = Activation - TargetLabelArray\r\n \r\n x = 1 - TargetLabelArray\r\n \r\n LogValue = np.log(Activation)\r\n Dotproduct = x.dot(LogValue)\r\n \r\n Result = LogValue + Dotproduct\r\n \r\n Error = TargetLabelArray.dot(Result)\r\n \r\n Size = len(TrainingLabels)\r\n Error = Error/Size\r\n Error = -1 * Error\r\n\r\n if Error < 0.0000002:\r\n break\r\n\r\n HiddenLayerArray = np.array([HiddenLayer])\r\n HiddenLayerArrayTranspose = HiddenLayerArray.transpose()\r\n \r\n TargetLabelDifferenceArray = np.array([Difference])\r\n \r\n HiddenLayerError = np.matmul(HiddenLayerArrayTranspose, TargetLabelDifferenceArray)\r\n \r\n \r\n DeltaOutputLayer = LearningRate*HiddenLayerError \r\n OutputLayerWeight = OutputLayerWeight - DeltaOutputLayer\r\n \r\n TargetLabelDifferenceArray = np.array([Difference])\r\n TargetLabelDifferenceArrayTranspose = TargetLabelDifferenceArray.transpose()\r\n \r\n E1 = DerivativeFunction(HiddenLayer)\r\n E1 = np.array([E1])\r\n \r\n E2 = np.dot(OutputLayerWeight, TargetLabelDifferenceArrayTranspose)\r\n E2Transpose = E2.transpose()\r\n \r\n E3 = np.multiply(E1, E2Transpose)\r\n \r\n TrainingDataArray = np.array([TrainingData[y]])\r\n TrainingDataArrayTranspose = TrainingDataArray.transpose()\r\n \r\n InputToHiddenErrorValue = np.dot(TrainingDataArrayTranspose, E3)\r\n \r\n \r\n DeltaHiddenLayer = LearningRate*InputToHiddenErrorValue\r\n HiddenLayerWeight = HiddenLayerWeight - DeltaHiddenLayer\r\n \r\n print('Writing to netWights.txt\\n')\r\n # writing weights to file 'netWeights.txt'\r\n WeightsFile = open('netWeights.txt', 'w')\r\n \r\n WeightsFile.write('Hidden Layer\\n')\r\n np.savetxt(WeightsFile, HiddenLayerWeight)\r\n \r\n WeightsFile.write('Output Layer\\n')\r\n np.savetxt(WeightsFile, OutputLayerWeight)\r\n \r\n WeightsFile.write('End\\n')\r\n WeightsFile.close()\r\n\r\ndef DerivativeFunction(x):\r\n Result = x * (1 - x)\r\n return Result\r\n\r\n\r\n'''\r\n This function reads file which contains the netWeights - the ones which\r\n formed during the training of the model\r\n'''\r\ndef ReadNetWeights(FileName):\r\n OpenedFile = open(FileName, 'r')\r\n \r\n HiddenLayer = []\r\n \r\n Line = OpenedFile.readline()\r\n \r\n Line = OpenedFile.readline()\r\n while Line != 'Output Layer\\n':\r\n OldLine = Line.split()\r\n Line = map(float, OldLine)\r\n Line = list(Line)\r\n \r\n if OldLine != []:\r\n HiddenLayer.append(Line)\r\n Line = OpenedFile.readline()\r\n HiddenLayer = np.array(HiddenLayer)\r\n \r\n \r\n OutputLayer = []\r\n \r\n Line = OpenedFile.readline()\r\n while Line != 'End\\n':\r\n OldLine = Line.split()\r\n Line = map(float, OldLine)\r\n Line = list(Line)\r\n \r\n if OldLine != []:\r\n OutputLayer.append(Line)\r\n Line = OpenedFile.readline()\r\n OutputLayer = np.array(OutputLayer)\r\n \r\n OpenedFile.close()\r\n \r\n return (HiddenLayer, OutputLayer)\r\n \r\n'''\r\n This function will be testing the model.\r\n'''\r\ndef TestModel(TestingData, TestingLabels, HiddenLayerWeight, OutputLayerWeight):\r\n print('Testing now...\\n')\r\n \r\n LengthOfTestingLabels = len(TestingLabels)\r\n Epoches = 1\r\n for num in range(Epoches):\r\n Accurate = 0\r\n for x in range(LengthOfTestingLabels):\r\n GraphDataX.append(time.time())\r\n GraphDataY.append(Accurate*100/len(TestingLabels))\r\n HiddenLayer = np.dot(TestingData[x], HiddenLayerWeight)\r\n \r\n HiddenLayer = sigmoid(HiddenLayer)\r\n HiddenLayer = np.array(HiddenLayer, dtype = float)\r\n \r\n OutputLayer = np.dot(HiddenLayer, OutputLayerWeight)\r\n OutputLayer = sigmoid(OutputLayer)\r\n OutputLayer = np.array(OutputLayer, dtype = float)\r\n \r\n MaxNumber = OutputLayer[0]\r\n MaxNumberIndex = 0\r\n LengthOfOutputLayer = len(OutputLayer)\r\n for y in range( LengthOfOutputLayer):\r\n if MaxNumber < OutputLayer[y]:\r\n \r\n MaxNumber = OutputLayer[y]\r\n MaxNumberIndex = y\r\n \r\n Label = MaxNumberIndex\r\n if(TestingLabels[x] == Label):\r\n Accurate += 1\r\n \r\n print('Epoch Number ', num+1, ' -------> ', Accurate, '/', len(TestingLabels), 'images correctly classified.\\n')\r\n Percentage = Accurate*100/len(TestingLabels)\r\n Error = 100 - Percentage\r\n print('Accuracy ', Percentage, ' % ------------------- Error ', Error, ' %\\n')\r\n print('Testing ended...\\n')\r\n\r\n\r\ndef makeGraph():\r\n plt.plot(GraphDataX, GraphDataY)\r\n plt.title('Accuracy % vs Time')\r\n plt.xlabel('Time(s)')\r\n plt.ylabel('Accuracy %')\r\n plt.show()\r\n\r\ndef main(Argument1, Argument2, Argument3, Argument4):\r\n if Argument1 == 'train':\r\n print('Loading training data... \\n')\r\n TrainingData = ReadFile(Argument2)\r\n TrainingData = np.array(TrainingData)\r\n\r\n print('Loading training labels... \\n')\r\n TrainingLabels = ReadLabels(Argument3)\r\n TrainingLabels = np.array(TrainingLabels)\r\n\r\n LearningRate = Argument4\r\n LearningRate = float(LearningRate)\r\n\r\n print('Training model now....\\n')\r\n\r\n start_time = time.time()\r\n TrainModel(TrainingData, TrainingLabels, LearningRate)\r\n print('Training ended...\\n')\r\n end_time = time.time()\r\n Duration = end_time - start_time\r\n print('Time taken to train is ', Duration/float(60), ' minutes.')\r\n \r\n elif Argument1 == 'test':\r\n print('Loading testing data... \\n')\r\n TestingData = ReadFile(Argument2)\r\n TestingData = np.array(TestingData)\r\n \r\n print('Loading testing labels... \\n')\r\n TestingLabels = ReadLabels(Argument3)\r\n TestingLabels = np.array(TestingLabels)\r\n \r\n print('Loading netWeights.txt \\n')\r\n HiddenLayerWeight, OutputLayerWeight = ReadNetWeights(Argument4)\r\n \r\n TestModel(TestingData, TestingLabels, HiddenLayerWeight, OutputLayerWeight)\r\n makeGraph()\r\n \r\n \r\n \r\nmain(sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4])\r\n\r\n", "sub_path": "MyNetwork.py", "file_name": "MyNetwork.py", "file_ext": "py", "file_size_in_byte": 12349, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.exp", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 155, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 158, "usage_type": "attribute"}, {"api_name": "numpy.dot", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 210, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 212, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 215, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 217, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 264, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 278, "usage_type": "call"}, {"api_name": "time.time", "line_number": 295, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 297, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 300, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 302, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 304, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 327, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 327, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 328, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 328, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 329, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 329, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 330, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 330, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 331, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 331, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 337, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 341, "usage_type": "call"}, {"api_name": "time.time", "line_number": 348, "usage_type": "call"}, {"api_name": "time.time", "line_number": 351, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 358, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 362, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 372, "usage_type": "attribute"}]} +{"seq_id": "153610479", "text": "\"\"\"\ntraining and validation on the same image folder\n\"\"\"\n\n\n\n\n\n\nfrom hourglass_dann_v10 import HourglassModel\nfrom time import time\nimport numpy as np\nimport cv2\nfrom python_code.predictclass2 import PredictProcessor\nfrom python_code.datagen import DataGenerator\nfrom python_code.filters import VideoFilters\nimport configparser\n\ndef process_config(conf_file):\n\t\"\"\"\n\t\"\"\"\n\tparams = {}\n\tconfig = configparser.ConfigParser()\n\tconfig.read(conf_file)\n\n\tfor section in config.sections():\n\t\tif section == 'DataSetHG':\n\t\t\tfor option in config.options(section):\n\t\t\t\tparams[option] = eval(config.get(section, option))\n\t\tif section == 'Network':\n\t\t\tfor option in config.options(section):\n\t\t\t\tparams[option] = eval(config.get(section, option))\n\t\tif section == 'Train':\n\t\t\tfor option in config.options(section):\n\t\t\t\tparams[option] = eval(config.get(section, option))\n\t\tif section == 'Validation':\n\t\t\tfor option in config.options(section):\n\t\t\t\tparams[option] = eval(config.get(section, option))\n\t\tif section == 'Saver':\n\t\t\tfor option in config.options(section):\n\t\t\t\tparams[option] = eval(config.get(section, option))\n\treturn params\n\n\nclass Inference():\n \"\"\" Inference Class\n Use this file to make your prediction\n Easy to Use\n Images used for inference should be RGB images (int values in [0,255])\n Methods:\n webcamSingle : Single Person Pose Estimation on Webcam Stream\n webcamMultiple : Multiple Person Pose Estimation on Webcam Stream\n webcamPCA : Single Person Pose Estimation with reconstruction error (PCA)\n webcamYOLO : Object Detector\n predictHM : Returns Heat Map for an input RGB Image\n predictJoints : Returns joint's location (for a 256x256 image)\n pltSkeleton : Plot skeleton on image\n runVideoFilter : SURPRISE !!!\n \"\"\"\n\n def __init__(self, config_file='config.cfg', model='hg_refined_tiny_200', yoloModel='YOLO_small.ckpt'):\n \"\"\" Initilize the Predictor\n Args:\n config_file \t \t: *.cfg file with model's parameters\n model \t \t \t \t: *.index file's name. (weights to load)\n yoloModel \t \t: *.ckpt file (YOLO weights to load)\n \"\"\"\n t = time()\n params = process_config(config_file)\n self.predict = PredictProcessor(params)\n self.predict.color_palette()\n self.predict.LINKS_JOINTS()\n self.predict.model_init()\n self.predict.load_model(load=model)\n self.predict.yolo_init()\n self.predict.restore_yolo(load=yoloModel)\n self.predict._create_prediction_tensor()\n self.filter = VideoFilters()\n print('Done: ', time() - t, ' sec.')\n\n # ----------------------- Heat Map Prediction ------------------------------\n\n def predictHM(self, img):\n \"\"\" Return Sigmoid Prediction Heat Map\n Args:\n img : Input Image -shape=(256x256x3) -value= uint8 (in [0, 255])\n \"\"\"\n # \t\treturn self.predict.pred(self, img / 255, debug = False, sess = None)\n return self.predict.pred(img / 255, debug=False, sess=None)\n\n # ------------------------- Joint Prediction -------------------------------\n\n def predictJoints(self, img, mode='cpu', thresh=0.2):\n \"\"\" Return Joint Location\n /!\\ Location with respect to 256x256 image\n Args:\n img : Input Image -shape=(256x256x3) -value= uint8 (in [0, 255])\n mode : 'cpu' / 'gpu' Select a mode to compute joints' location\n thresh : Joint Threshold\n \"\"\"\n SIZE = False\n if len(img.shape) == 3:\n batch = np.expand_dims(img, axis=0)\n SIZE = True\n elif len(img.shape) == 4:\n batch = np.copy(img)\n SIZE = True\n if SIZE:\n if mode == 'cpu':\n return self.predict.joints_pred_numpy(batch / 255, coord='img', thresh=thresh, sess=None)\n elif mode == 'gpu':\n return self.predict.joints_pred(batch / 255, coord='img', debug=False, sess=None)\n else:\n print(\"Error : Mode should be 'cpu'/'gpu'\")\n else:\n print('Error : Input is not a RGB image nor a batch of RGB images')\n\n # ----------------------------- Plot Skeleton ------------------------------\n\n def pltSkeleton(self, img, thresh, pltJ, pltL):\n \"\"\" Return an image with plotted joints and limbs\n Args:\n img : Input Image -shape=(256x256x3) -value= uint8 (in [0, 255])\n thresh: Joint Threshold\n pltJ: (bool) True to plot joints\n pltL: (bool) True to plot limbs\n \"\"\"\n return self.predict.pltSkeleton(img, thresh=thresh, pltJ=pltJ, pltL=pltL, tocopy=True, norm=True)\n\n # -------------------------- Process Stream --------------------------------\n\n def centerStream(self, img):\n img = cv2.flip(img, 1)\n img[:,\n self.predict.cam_res[1] // 2 - self.predict.cam_res[0] // 2:self.predict.cam_res[1] // 2 + self.predict.cam_res[\n 0] // 2]\n img_hg = cv2.resize(img, (256, 256))\n img_res = cv2.resize(img, (800, 800))\n img_hg = cv2.cvtColor(img_hg, cv2.COLOR_BGR2RGB)\n return img_res, img_hg\n\n def plotLimbs(self, img_res, j):\n \"\"\"\n \"\"\"\n for i in range(len(self.predict.links)):\n l = self.predict.links[i]['link']\n good_link = True\n for p in l:\n if np.array_equal(j[p], [-1, -1]):\n good_link = False\n if good_link:\n pos = self.predict.givePixel(l, j)\n cv2.line(img_res, tuple(pos[0])[::-1], tuple(pos[1])[::-1], self.predict.links[i]['color'][::-1],\n thickness=5)\n\n\n\n\nif __name__ == '__main__':\n for dr in np.linspace(0.1,0.3,20):\n\n network_name = '../trained_networks/hg_test_32_dr_' + str(round(dr,2))\n\n try :\n print('--Parsing Config File')\n params = process_config('config.cfg')\n\n print('--Creating Dataset')\n dataset = DataGenerator(params['joint_list'], params['img_directory'], params['training_txt_file'],\n remove_joints=params['remove_joints'])\n dataset._create_train_table()\n dataset._randomize()\n dataset._create_sets()\n # model = HourglassModel(nFeat=params['nfeats'], nStack=params['nstacks'], nModules=params['nmodules'], nLow=params['nlow'], outputDim=params['num_joints'], batch_size=params['batch_size'], attention = params['mcam'], training=True, drop_rate= params['dropout_rate'], lear_rate=params['learning_rate'], decay=params['learning_rate_decay'], decay_step=params['decay_step'], dataset=dataset, name=params['name'], logdir_train=params['log_dir_train'], logdir_test=params['log_dir_test'], tiny= params['tiny'], w_loss=params['weighted_loss'], joints= params['joint_list'], modif=False)\n\n model = HourglassModel(nFeat=params['nfeats'], nStack=params['nstacks'], nModules=params['nmodules'],\n nLow=params['nlow'], outputDim=params['num_joints'], batch_size=params['batch_size'],\n drop_rate=params['dropout_rate'], lear_rate=round(dr,2),\n decay=params['learning_rate_decay'], decay_step=params['decay_step'], dataset=dataset,\n training=True, logdir_train=params['log_dir_train'], logdir_test=params['log_dir_test'],\n name=network_name, joints=params['joint_list'])\n\n model.generate_model()\n model.training_init(nEpochs=50, epochSize=params['epoch_size'], saveStep=params['saver_step'],\n dataset=None)\n\n\n\n except:\n pass\n\n", "sub_path": "train_validation_script.py", "file_name": "train_validation_script.py", "file_ext": "py", "file_size_in_byte": 7768, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "configparser.ConfigParser", "line_number": 23, "usage_type": "call"}, {"api_name": "time.time", "line_number": 68, "usage_type": "call"}, {"api_name": "python_code.predictclass2.PredictProcessor", "line_number": 70, "usage_type": "call"}, {"api_name": "python_code.filters.VideoFilters", "line_number": 78, "usage_type": "call"}, {"api_name": "time.time", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 103, "usage_type": "call"}, {"api_name": "numpy.copy", "line_number": 106, "usage_type": "call"}, {"api_name": "cv2.flip", "line_number": 133, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 137, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 138, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 139, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 139, "usage_type": "attribute"}, {"api_name": "numpy.array_equal", "line_number": 149, "usage_type": "call"}, {"api_name": "cv2.line", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 160, "usage_type": "call"}, {"api_name": "python_code.datagen.DataGenerator", "line_number": 169, "usage_type": "call"}, {"api_name": "hourglass_dann_v10.HourglassModel", "line_number": 176, "usage_type": "call"}]} +{"seq_id": "47253936", "text": "#!/usr/bin/env python3\n\nimport sys\nimport argparse\n\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nplt.rcParams['font.family'] = 'Times New Roman'\n\n\nprettify = {\n 'puffer_ttp_cl/bbr': ['Fugu', 'C3'],\n 'linear_bba/bbr': ['BBA', 'C2'],\n 'mpc/bbr': ['MPC-HM', 'C0'],\n 'robust_mpc/bbr': ['RobustMPC-HM', 'C5'],\n 'pensieve/bbr': ['Pensieve', 'C4']\n}\n\n\ndef plot_data(data, output_figure):\n fig, ax = plt.subplots()\n ax.set_xlabel('Time spent stalled (%)')\n ax.set_ylabel('Average SSIM (dB)')\n\n for name in data:\n x = data[name]['stall'][2]\n y = data[name]['ssim'][2]\n\n pretty_name = prettify[name][0]\n pretty_color = prettify[name][1]\n\n ax.scatter(x, y, color=pretty_color)\n ax.errorbar(x, y,\n xerr=[[x - data[name]['stall'][0]], [data[name]['stall'][1] - x]],\n yerr=[[y - data[name]['ssim'][1]], [data[name]['ssim'][0] - y]],\n ecolor=pretty_color,\n capsize=4.0)\n ax.annotate(pretty_name, (x, y),\n xytext=(4,5), textcoords='offset pixels')\n\n ax.invert_xaxis()\n\n # Hide the right and top spines\n ax.spines['right'].set_visible(False)\n ax.spines['top'].set_visible(False)\n\n fig.savefig(output_figure)\n sys.stderr.write('Saved plot to {}\\n'.format(output_figure))\n\n\ndef parse_data(input_data_path):\n data = {}\n\n with open(input_data_path) as fh:\n for line in fh:\n if line[0] == '#':\n continue\n line = line.replace(',', '').replace(';', '').replace('%', '')\n\n items = line.split()\n\n name = items[0]\n data[name] = {}\n\n # stall_low, stall_high, stall_mean\n data[name]['stall'] = [float(x) for x in items[5:11:2]]\n\n # ssim_low, ssim_high, ssim_mean\n data[name]['ssim'] = [float(x) for x in items[13:19:2]]\n\n return data\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument('input_data', help='input data (output of confint)')\n parser.add_argument('output_figure', help='output figure')\n args = parser.parse_args()\n\n data = parse_data(args.input_data)\n plot_data(data, args.output_figure)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "plots/plot_ssim_stall_confint.py", "file_name": "plot_ssim_stall_confint.py", "file_ext": "py", "file_size_in_byte": 2263, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "matplotlib.use", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 9, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 9, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "sys.stderr.write", "line_number": 49, "usage_type": "call"}, {"api_name": "sys.stderr", "line_number": 49, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "652508683", "text": "import logging\r\nfrom enum import Enum\r\nfrom .Position import Position\r\nfrom ..utils.CustomLogger import CustomLogger\r\n\r\n# Simple wrapper to log the calling of a function\r\n# to enable set the logger to debug mode\r\ndef logfunc(func):\r\n def wrapper(*args, **kwargs):\r\n args[0].logger.debug(\"['{0}'] params: {1} kwargs: {2}\".\r\n format(func.__name__, args, kwargs))\r\n return func(*args, **kwargs)\r\n return wrapper\r\n\r\nclass PositionError(Exception):\r\n ''' \r\n An Error that is thrown whenever there is a problem with opening,\r\n creating or updating a position.\r\n '''\r\n def __init__(self, message, errors=None):\r\n # Pass the message to the base class\r\n super().__init__(message)\r\n self.errors = errors\r\n\r\nclass OrderType(Enum):\r\n '''\r\n An Enum used to represent the diffrent types of orders that \r\n are possible.\r\n '''\r\n MARKET = 1\r\n EXCHANGE_MARKET = 2\r\n LIMIT = 3\r\n EXCHANGE_LIMIT = 4\r\n\r\nclass PositionManager(object):\r\n\r\n ############################\r\n # Close Position functions #\r\n ############################\r\n\r\n @logfunc\r\n def closePosition(self, *args, **kwargs):\r\n self.closePositionWithOrder(*args, **kwargs)\r\n \r\n @logfunc\r\n def closeOpenPositions(self):\r\n openPositions = list(self.positions.values())\r\n count = len(openPositions)\r\n for pos in openPositions:\r\n price, mts = self.getLastPrice(pos.symbol)\r\n self.closePositionMarket(\r\n symbol=pos.symbol, price=price, mtsCreate=mts, tag='Close all positions')\r\n self.logger.trade('CLOSED_ALL {} open positions.'.format(count))\r\n \r\n @logfunc\r\n def closePositionLimit(self, *args, **kwargs):\r\n orderType = OrderType.LIMIT if hasattr(self, 'margin') else OrderType.EXCHANGE_LIMIT\r\n return self.closePosition(*args, **kwargs, type=orderType)\r\n\r\n @logfunc\r\n def closePositionMarket(self, *args, **kwargs):\r\n orderType = OrderType.MARKET if hasattr(self, 'margin') else OrderType.EXCHANGE_MARKET\r\n return self.closePosition(*args, **kwargs, type=orderType)\r\n\r\n @logfunc\r\n def closePositionWithOrder(self, price, mtsCreate, symbol=None, **kwargs):\r\n symbol = symbol or self.symbol\r\n position = self.getPosition(symbol)\r\n \r\n if position == None:\r\n raise PositionError('No position exists for %s' % (symbol))\r\n\r\n amount = position.amount * -1\r\n def submit(self):\r\n order, trade = self.OrderManager.submitTrade(symbol, price, amount,\r\n mtsCreate, **kwargs)\r\n position.addTrade(trade)\r\n position.close()\r\n self.removePosition(position)\r\n self.logger.info(\"Position closed:\")\r\n self.logger.trade(\"CLOSED \" + str(trade))\r\n self.onOrderFill({ trade: trade, order: order })\r\n self.onTrade(trade)\r\n self.onPositionClose({\r\n 'position': position,\r\n 'order': order,\r\n 'trade': trade\r\n })\r\n self._startNewThread(submit)\r\n\r\n ###########################\r\n # Open Position functions #\r\n ###########################\r\n\r\n @logfunc\r\n def openPosition(self, *args, **kwargs):\r\n return self.openPositionWithOrder(*args, **kwargs)\r\n\r\n @logfunc\r\n def openShortPosition(self, amount, *args, **kwargs):\r\n return self.openPosition(amount=-amount, *args, **kwargs)\r\n\r\n @logfunc\r\n def openLongPosition(self, *args, **kwargs):\r\n return self.openPosition(*args, **kwargs)\r\n\r\n @logfunc\r\n def openPositionLimit(self, *args, **kwargs):\r\n orderType = OrderType.LIMIT if hasattr(self, 'margin') else OrderType.EXCHANGE_LIMIT\r\n return self.openPosition(type=orderType, *args, **kwargs)\r\n\r\n @logfunc\r\n def openPositionMarket(self, *args, **kwargs):\r\n orderType = OrderType.MARKET if hasattr(self, 'margin') else OrderType.EXCHANGE_MARKET\r\n return self.openPosition(type=orderType, *args, **kwargs)\r\n\r\n @logfunc\r\n def openPositionWithOrder(self, amount, price, mtsCreate, symbol=None, \r\n stop=None, target=None, tag='', **kwargs):\r\n symbol = symbol or self.symbol\r\n # check for open positions\r\n if self.getPosition(symbol) != None:\r\n raise PositionError('A position already exists for %s' % (symbol))\r\n\r\n # create submit functions so its easier to pass onto\r\n # a new thread\r\n def submit(self):\r\n order, trade = self.OrderManager.submitTrade(symbol, price, amount,\r\n mtsCreate, **kwargs)\r\n position = Position(symbol, stop, target, tag)\r\n position.addTrade(trade)\r\n self.addPosition(position)\r\n self.logger.info(\"New Position opened:\")\r\n self.logger.trade(\"OPENED \" + str(trade))\r\n self.onOrderFill({ trade: trade, order: order })\r\n self.onTrade(trade)\r\n self.onPositionUpdate({\r\n 'position': position,\r\n 'order': order,\r\n 'trade': trade\r\n })\r\n self._startNewThread(submit)\r\n\r\n @logfunc\r\n def openShortPositionMarket(self, amount, *args, **kwargs):\r\n return self.openPositionMarket(amount=-amount, *args, **kwargs)\r\n\r\n @logfunc\r\n def openShortPositionLimit(self, amount, *args, **kwargs):\r\n return self.openPositionMarket(amount=-amount, *args, **kwargs)\r\n\r\n @logfunc\r\n def openLongPositionMarket(self, *args, **kwargs):\r\n return self.openPositionMarket(*args, **kwargs)\r\n\r\n @logfunc\r\n def openLongPositionLimit(self, *args, **kwargs):\r\n return self.openPositionLimit(*args, **kwargs)\r\n\r\n #############################\r\n # Update Position functions #\r\n #############################\r\n\r\n @logfunc\r\n def updatePosition(self, *args, **kwargs):\r\n return self.updatePositionWithOrder(*args, **kwargs)\r\n\r\n @logfunc\r\n def updateShortPosition(self, amount, *args, **kwargs):\r\n return self.updatePosition(amount=amount, *args, **kwargs)\r\n\r\n @logfunc\r\n def updateLongPosition(self, *args, **kwargs):\r\n return self.updatePosition(*args, **kwargs)\r\n\r\n @logfunc\r\n def updateLongPositionLimit(self, *args, **kwargs):\r\n orderType = OrderType.LIMIT if hasattr(self, 'margin') else OrderType.EXCHANGE_LIMIT\r\n return self.updatePosition(type=orderType, *args, **kwargs)\r\n\r\n @logfunc\r\n def updateLongPositionMarket(self, *args, **kwargs):\r\n orderType = OrderType.MARKET if hasattr(self, 'margin') else OrderType.EXCHANGE_MARKET\r\n return self.updatePosition(type=orderType, *args, **kwargs)\r\n\r\n @logfunc\r\n def updatePositionLimit(self, *args, **kwargs):\r\n orderType = OrderType.LIMIT if hasattr(self, 'margin') else OrderType.EXCHANGE_LIMIT\r\n return self.updatePosition(type=orderType, *args, **kwargs)\r\n\r\n @logfunc\r\n def updatePositionMarket(self, *args, **kwargs):\r\n orderType = OrderType.MARKET if hasattr(self, 'margin') else OrderType.EXCHANGE_MARKET\r\n return self.updatePosition(type=orderType, *args, **kwargs)\r\n\r\n @logfunc\r\n def updatePositionWithOrder(self, price, amount, mtsCreate, symbol=None, **kwargs):\r\n symbol = symbol or self.symbol\r\n position = self.getPosition(symbol)\r\n\r\n # check for open positions\r\n if self.getPosition(symbol) == None:\r\n raise PositionError('No position exists for %s' % (symbol))\r\n\r\n def update(self):\r\n order, trade = self.OrderManager.submitTrade(symbol, price, amount,\r\n mtsCreate, tag='Update position' **kwargs)\r\n position.addTrade(trade)\r\n self.logger.info(\"Position updated:\")\r\n self.logger.trade(\"UPDATED POSITION \" + str(trade))\r\n self.onOrderFill({ trade: trade, order: order })\r\n self.onTrade(trade)\r\n self.onPositionUpdate({\r\n 'position': position,\r\n 'order': order,\r\n 'trade': trade\r\n })\r\n self._startNewThread(update)\r\n \r\n @logfunc\r\n def updateShortPositionLimit(self, amount, *args, **kwargs):\r\n return self.updatePosition(amount=-amount, *args, **kwargs)\r\n \r\n @logfunc\r\n def updateShortPositionMarket(self, amount, *args, **kwargs):\r\n return self.updatePosition(amount=-amount, *args, **kwargs)\r\n\r\n ############################\r\n # Other Position functions #\r\n ############################\r\n\r\n def setPositionStop(self, stop, symbol):\r\n position = self.getPosition(symbol or self.symbol)\r\n position.stop = stop\r\n", "sub_path": "HFStrategy/Strategy/PositionManager.py", "file_name": "PositionManager.py", "file_ext": "py", "file_size_in_byte": 8030, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "enum.Enum", "line_number": 25, "usage_type": "name"}, {"api_name": "Position.Position", "line_number": 130, "usage_type": "call"}]} +{"seq_id": "590246701", "text": "import sys\nimport json\nimport os\nimport time\nimport glob\nfrom scipy.stats import pearsonr\nimport shutil\nimport hashlib\nimport sys\nimport pandas as pd\nfrom sklearn.metrics import r2_score\nimport time\nimport numpy as np\nimport tensorflow as tf\nfrom natsort import natsorted\nimport tensorflow as tf\n# import metrics\nimport scipy\nimport yaml\n ################################################################\n # functions for loading tfr files into tfr dataset\n ################################################################\ndef bin_resolution(y,bin_size):\n y_dim = y.shape\n y_bin = tf.math.reduce_mean(tf.reshape(y,(y_dim[0],int(y_dim[1]/bin_size),bin_size,y_dim[2])),axis = 2)\n return y_bin\n\ndef make_dir(dir_path):\n if not os.path.isdir(dir_path):\n os.mkdir(dir_path)\n return dir_path\n\ndef replace_all(text):\n dic = {'X': '23', 'Y': '24'}\n for i, j in dic.items():\n text = text.replace(i, j)\n return text.split('_')\n\ndef load_stats(data_dir):\n data_stats_file = '%s/statistics.json' % data_dir\n with open(data_stats_file) as data_stats_open:\n data_stats = json.load(data_stats_open)\n return data_stats\n\ndef batches_per_epoch(num_seqs, batch_size):\n return num_seqs // batch_size\n\ndef file_to_records(filename):\n return tf.data.TFRecordDataset(filename, compression_type='ZLIB')\n\ndef generate_parser(seq_length, target_length, num_targets, coords):\n def parse_proto(example_protos):\n \"\"\"Parse TFRecord protobuf.\"\"\"\n # TFRecord constants\n TFR_COORD = 'coordinate'\n TFR_INPUT = 'sequence'\n TFR_OUTPUT = 'target'\n\n # define features\n features = {\n TFR_COORD: tf.io.FixedLenFeature([], tf.string),\n TFR_INPUT: tf.io.FixedLenFeature([], tf.string),\n TFR_OUTPUT: tf.io.FixedLenFeature([], tf.string)\n }\n\n # parse example into features\n parsed_features = tf.io.parse_single_example(example_protos, features=features)\n\n # decode coords\n coordinate = parsed_features[TFR_COORD]\n\n # decode sequence\n # sequence = tf.io.decode_raw(parsed_features[TFR_INPUT], tf.uint8)\n sequence = tf.io.decode_raw(parsed_features[TFR_INPUT], tf.float16)\n sequence = tf.reshape(sequence, [seq_length, 4])\n sequence = tf.cast(sequence, tf.float32)\n\n # decode targets\n targets = tf.io.decode_raw(parsed_features[TFR_OUTPUT], tf.float16)\n targets = tf.reshape(targets, [target_length, num_targets])\n targets = tf.cast(targets, tf.float32)\n if coords:\n return coordinate, sequence, targets\n else:\n return sequence, targets\n\n return parse_proto\n\n\n\ndef make_dataset(data_dir, split_label, data_stats, batch_size=64, seed=None, shuffle=True, coords=False):\n seq_length = data_stats['seq_length']\n target_length = data_stats['target_length']\n num_targets = data_stats['num_targets']\n tfr_path = '%s/tfrecords/%s-*.tfr' % (data_dir, split_label)\n num_seqs = data_stats['%s_seqs' % split_label]\n\n tfr_files = natsorted(glob.glob(tfr_path))\n dataset = tf.data.Dataset.list_files(tf.constant(tfr_files), shuffle=False)\n\n # train\n # if split_label == 'train':\n if (split_label == 'train'):\n # repeat\n #dataset = dataset.repeat()\n\n # interleave files\n dataset = dataset.interleave(map_func=file_to_records,\n cycle_length=4,\n num_parallel_calls=tf.data.experimental.AUTOTUNE)\n\n # shuffle\n dataset = dataset.shuffle(buffer_size=32,\n reshuffle_each_iteration=True)\n\n # valid/test\n else:\n # flat mix files\n dataset = dataset.flat_map(file_to_records)\n\n dataset = dataset.map(generate_parser(seq_length, target_length, num_targets, coords))\n if shuffle:\n if seed:\n dataset = dataset.shuffle(32, seed=seed)\n else:\n dataset = dataset.shuffle(32)\n # dataset = dataset.batch(64)\n # batch\n dataset = dataset.batch(batch_size)\n\n # prefetch\n dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)\n return dataset\n\ndef make_dataset_binned(data_dir, split_label, data_stats, batch_size=64, seed=None, shuffle=True, coords=False, bin_size=1):\n seq_length = data_stats['seq_length']\n target_length = data_stats['target_length']\n num_targets = data_stats['num_targets']\n tfr_path = '%s/tfrecords/%s-*.tfr' % (data_dir, split_label)\n num_seqs = data_stats['%s_seqs' % split_label]\n\n tfr_files = natsorted(glob.glob(tfr_path))\n dataset = tf.data.Dataset.list_files(tf.constant(tfr_files), shuffle=False)\n\n dataset = dataset.flat_map(file_to_records)\n\n dataset = dataset.map(generate_parser_with_binning(seq_length, target_length, num_targets, coords, bin_size=128))\n if shuffle:\n if seed:\n dataset = dataset.shuffle(32, seed=seed)\n else:\n dataset = dataset.shuffle(32)\n dataset = dataset.batch(64)\n # batch\n dataset = dataset.batch(batch_size)\n\n # prefetch\n dataset = dataset.prefetch(tf.data.experimental.AUTOTUNE)\n return dataset\n\ndef tfr_to_np(data, choose, array_shape):\n if choose=='x':\n data_part = data.map(lambda z,x,y: x)\n elif choose=='y':\n data_part = data.map(lambda z,x,y: y)\n data_np = np.zeros(array_shape)\n # load data to a numpy array\n iter_data = iter(data_part)\n j=0\n for i in iter_data:\n n_seqs = i.shape[0]\n data_np[j:j+n_seqs,:,:] = i\n j+=n_seqs\n return data_np\n\ndef window_shift(X,Y,window_size,shift_num):\n if len(X.shape) == 2:\n X = np.expand_dims(X,axis = 0)\n\n chop_size = X.shape[1]\n input_seq_num = X.shape[0]\n output_num = shift_num*input_seq_num\n #Shift X around\n ori_X = np.repeat(X,shift_num,axis=0)\n shift_idx = (np.arange(window_size) +\n np.random.randint(low = 0,high = chop_size-window_size,\n size = output_num)[:,np.newaxis])\n col_idx = shift_idx.reshape(window_size *output_num)\n row_idx = np.repeat(range(0,output_num),window_size)\n f_index = np.vstack((row_idx,col_idx)).T.reshape(output_num,window_size,2)\n shift_x = tf.gather_nd(ori_X,f_index)\n\n #shift Y accordingly\n ori_Y = np.repeat(Y,shift_num,axis=0)\n shift_y = tf.gather_nd(ori_Y,f_index)\n\n shift_idx = shift_idx[:,0]\n center_idx = int(0.5*(chop_size-window_size))\n relative_shift_idx =shift_idx - center_idx\n\n return np.array(shift_x),np.array(shift_y),relative_shift_idx\n\ndef evaluate_shift(X,Y,model,window_size,shift_num):\n shift_x,shift_y,shift_idx = window_shift(X,Y,window_size,shift_num)\n pred_y = model.predict(shift_x)\n if pred_y.shape[1] < shift_y.shape[1]:\n bin_size = int(shift_y.shape[1] / pred_y.shape[1])\n pred_y = np.repeat(pred_y,bin_size,axis = 1)\n shift_y = bin_resolution(shift_y,bin_size)\n shift_y = np.repeat(shift_y,bin_size,axis=1)\n p_r_list = []\n for i,pred in enumerate(pred_y):\n task_pr = []\n for task in range(pred.shape[1]):\n p_r = scipy.stats.pearsonr(shift_y[i,task],pred[task])[0]\n task_pr.append(p_r)\n p_r_list.append(task_pr)\n return np.array(p_r_list),np.array(shift_idx)\n\n\nclass SeabornFig2Grid():\n '''Class for plotting multiple sns jointplots'''\n\n def __init__(self, seaborngrid, fig, subplot_spec):\n self.fig = fig\n self.sg = seaborngrid\n self.subplot = subplot_spec\n if isinstance(self.sg, sns.axisgrid.FacetGrid) or isinstance(self.sg, sns.axisgrid.PairGrid):\n self._movegrid()\n elif isinstance(self.sg, sns.axisgrid.JointGrid):\n self._movejointgrid()\n self._finalize()\n\n def _movegrid(self):\n \"\"\" Move PairGrid or Facetgrid \"\"\"\n self._resize()\n n = self.sg.axes.shape[0]\n m = self.sg.axes.shape[1]\n self.subgrid = gridspec.GridSpecFromSubplotSpec(n,m, subplot_spec=self.subplot)\n for i in range(n):\n for j in range(m):\n self._moveaxes(self.sg.axes[i,j], self.subgrid[i,j])\n\n def _movejointgrid(self):\n \"\"\" Move Jointgrid \"\"\"\n h= self.sg.ax_joint.get_position().height\n h2= self.sg.ax_marg_x.get_position().height\n r = int(np.round(h/h2))\n self._resize()\n self.subgrid = gridspec.GridSpecFromSubplotSpec(r+1,r+1, subplot_spec=self.subplot)\n\n self._moveaxes(self.sg.ax_joint, self.subgrid[1:, :-1])\n self._moveaxes(self.sg.ax_marg_x, self.subgrid[0, :-1])\n self._moveaxes(self.sg.ax_marg_y, self.subgrid[1:, -1])\n\n def _moveaxes(self, ax, gs):\n #https://stackoverflow.com/a/46906599/4124317\n ax.remove()\n ax.figure=self.fig\n self.fig.axes.append(ax)\n self.fig.add_axes(ax)\n ax._subplotspec = gs\n ax.set_position(gs.get_position(self.fig))\n ax.set_subplotspec(gs)\n\n def _finalize(self):\n plt.close(self.sg.fig)\n self.fig.canvas.mpl_connect(\"resize_event\", self._resize)\n self.fig.canvas.draw()\n\n def _resize(self, evt=None):\n self.sg.fig.set_size_inches(self.fig.get_size_inches())\n", "sub_path": "util.py", "file_name": "util.py", "file_ext": "py", "file_size_in_byte": 9021, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "tensorflow.math.reduce_mean", "line_number": 25, "usage_type": "call"}, {"api_name": "tensorflow.math", "line_number": 25, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 30, "usage_type": "call"}, {"api_name": "json.load", "line_number": 42, "usage_type": "call"}, {"api_name": "tensorflow.data.TFRecordDataset", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 49, "usage_type": "attribute"}, {"api_name": "tensorflow.io.FixedLenFeature", "line_number": 61, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 61, "usage_type": "attribute"}, {"api_name": "tensorflow.string", "line_number": 61, "usage_type": "attribute"}, {"api_name": "tensorflow.io.FixedLenFeature", "line_number": 62, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 62, "usage_type": "attribute"}, {"api_name": "tensorflow.string", "line_number": 62, "usage_type": "attribute"}, {"api_name": "tensorflow.io.FixedLenFeature", "line_number": 63, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 63, "usage_type": "attribute"}, {"api_name": "tensorflow.string", "line_number": 63, "usage_type": "attribute"}, {"api_name": "tensorflow.io.parse_single_example", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 67, "usage_type": "attribute"}, {"api_name": "tensorflow.io.decode_raw", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 74, "usage_type": "attribute"}, {"api_name": "tensorflow.float16", "line_number": 74, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 76, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 76, "usage_type": "attribute"}, {"api_name": "tensorflow.io.decode_raw", "line_number": 79, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 79, "usage_type": "attribute"}, {"api_name": "tensorflow.float16", "line_number": 79, "usage_type": "attribute"}, {"api_name": "tensorflow.reshape", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 81, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 81, "usage_type": "attribute"}, {"api_name": "natsort.natsorted", "line_number": 98, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.data.Dataset.list_files", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 99, "usage_type": "attribute"}, {"api_name": "tensorflow.constant", "line_number": 99, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 110, "usage_type": "attribute"}, {"api_name": "tensorflow.data", "line_number": 132, "usage_type": "attribute"}, {"api_name": "natsort.natsorted", "line_number": 142, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 142, "usage_type": "call"}, {"api_name": "tensorflow.data.Dataset.list_files", "line_number": 143, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 143, "usage_type": "attribute"}, {"api_name": "tensorflow.constant", "line_number": 143, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 158, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 185, "usage_type": "call"}, {"api_name": "numpy.random.randint", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 186, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 187, "usage_type": "attribute"}, {"api_name": "numpy.repeat", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 190, "usage_type": "call"}, {"api_name": "tensorflow.gather_nd", "line_number": 191, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 194, "usage_type": "call"}, {"api_name": "tensorflow.gather_nd", "line_number": 195, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 201, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 208, "usage_type": "call"}, {"api_name": "numpy.repeat", "line_number": 210, "usage_type": "call"}, {"api_name": "scipy.stats.pearsonr", "line_number": 215, "usage_type": "call"}, {"api_name": "scipy.stats", "line_number": 215, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 218, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 248, "usage_type": "call"}]} +{"seq_id": "130307917", "text": "import os\r\nimport tensorflow as tf\r\nimport time\r\nimport numpy as np\r\nimport multiprocessing\r\nimport matplotlib.pyplot as plt\r\n\r\n# РЕАЛИЗАЦИЯ МНОГОПОТОЧНОГО ЧТЕНИЯ ФАЙЛОВ С ПОМОЩЬЮ ГЕНЕРАТОРА СПИСКОВ, АВТОМАТИЧЕСКИ\r\n#===========================================================================================\r\n\r\ndef getArray(FilePath, Filename): #Чтение данных из отдельных файлов\r\n _file = open(FilePath + '\\\\' + Filename)\r\n line = _file.read()\r\n vec = line.split(' ')\r\n vec_sl = vec[2:402]\r\n vec2 = []\r\n for i in range(len(vec_sl)):\r\n vec2.append(float(vec_sl[i]))\r\n _file.close()\r\n return vec2\r\n\r\n\r\ndef DbFill(A, B, Arr, Arr2, Path, Files):\r\n for i in range(A,B):\r\n Arr += getArray(Path, Files[i]) #Формируем список аргументов\r\n Arr2 += [float(Files[i][:4])] #Порождаем значения функции в соответствии с количеством аргументов\r\n\r\n\r\ndef ReadNames(A, B, Path, Files): #Преобразование имен файлов в значения функций\r\n for item in range(A, B):\r\n Files.append(os.listdir(Path)[item])\r\n\r\n\r\n#=======================================================================================\r\n\r\n#Реализация с параллелизмом\r\nif __name__ == '__main__':\r\n\r\n #start_time = time.time()\r\n #print(\"--- %s seconds ---\" % (time.time() - start_time))\r\n\r\n start_time = time.time()\r\n\r\n manager = multiprocessing.Manager()\r\n\r\n #directory = r\"C:\\ANALYZER\\train_db\" #Для тренировочных данных\r\n directory = r\"C:\\ANALYZER\\test_db\" #Для тестовых данных\r\n filenames = []\r\n\r\n #=============================================================================================\r\n print('Формирую список файлов...')\r\n\r\n numproc = 4 #Количество процессов\r\n numf = 150 #Количество файлов\r\n\r\n\r\n F = [] #Фрагменты списка имен файлов для быстрого доступа к ним\r\n p1 = [] #Процессы\r\n for i in range(numproc):\r\n F.append(manager.list())\r\n Icurr = i*round(numf / numproc)\r\n Inext = (i + 1) * round(numf / numproc)\r\n if (i == (numproc-1)): #Если цикл - последний\r\n if (Inext == numf): #Если итеративное количество файлов равно конечному\r\n #print('Количество равно конечному! Смотрю файлы с ', Icurr, 'по ', Inext)\r\n p1.append(multiprocessing.Process(target=ReadNames, args=(Icurr, Inext, directory, F[i])))\r\n else: #Иначе приравниваем конечное число количеству файлов, чтобы не потерять последние файлы\r\n #print('Количество не равно конечному!!! Смотрю файлы с ', Icurr, 'по ', numf)\r\n p1.append(multiprocessing.Process(target=ReadNames, args=(Icurr, numf, directory, F[i])))\r\n else: #Если цикл не последний\r\n #print('Смотрю файлы с ', Icurr, 'по ', Inext)\r\n p1.append(multiprocessing.Process(target=ReadNames, args=(Icurr, Inext, directory, F[i])))\r\n p1[i].start()\r\n p1[i].join()\r\n p1[i].terminate()\r\n\r\n for i in range(numproc): #Последовательная сборка результатов в единый список имен файлов\r\n filenames += list(F[i])\r\n\r\n #print(filenames) #Файлы в списке\r\n\r\n print('СПИСОК ФАЙЛОВ ВЫПОЛНЕН! --- %s seconds ---' % (time.time() - start_time))\r\n\r\n #========================================================================================================\r\n\r\n\r\n start_time = time.time()\r\n\r\n print('Создаю список аргументов и значений функций...')\r\n\r\n BigAr = [] #Полный массив данных аргументов и функций из файлов (52284 файлов * 400 чисел)\r\n BigAr2 = [] #Полный массив данных аргументов и функций из файлов (52284 значений)\r\n\r\n # Создание прокси-списков для доступа к ним в общей памяти===\r\n LX = []\r\n LY = []\r\n #============================================================\r\n\r\n p2 = [] # Процессы\r\n\r\n for i in range(numproc):\r\n LX.append(manager.list())\r\n LY.append(manager.list())\r\n Icurr = i*round(numf/numproc)\r\n Inext = (i + 1) * round(numf / numproc)\r\n if (i == (numproc-1)): #Если цикл - последний\r\n if (Inext == numf): #Если итеративное количество файлов равно конечному\r\n #print('Количество равно конечному! Смотрю файлы с ', Icurr, 'по ', Inext)\r\n p2.append(multiprocessing.Process(target=DbFill, args=(Icurr, Inext, LX[i], LY[i], directory, filenames)))\r\n else: #Иначе приравниваем конечное число количеству файлов, чтобы не потерять последние файлы\r\n #print('Количество не равно конечному!!! Смотрю файлы с ', Icurr, 'по ', numf)\r\n p2.append(multiprocessing.Process(target=DbFill, args=(Icurr, numf, LX[i], LY[i], directory, filenames)))\r\n else: #Если цикл не последний\r\n #print('Смотрю файлы с ', Icurr, 'по ', Inext)\r\n p2.append(multiprocessing.Process(target=DbFill, args=(Icurr, Inext, LX[i], LY[i], directory, filenames)))\r\n p2[i].start()\r\n p2[i].join()\r\n p2[i].terminate()\r\n\r\n for i in range(numproc): # Последовательная сборка результатов\r\n BigAr += list(LX[i])\r\n BigAr2 += list(LY[i])\r\n\r\n #print(BigAr); print(BigAr2)\r\n print('СПИСКИ АГРУМЕНТОВ И ЗНАЧЕНИЙ ФУНКЦИИ ВЫПОЛНЕН! --- %s seconds ---' % (time.time() - start_time))\r\n\r\n\r\n #Запись в отдельные файлы прочитанных результатов============================\r\n print('Записываю в файл...')\r\n # Тренировочные данные ======================================================\r\n '''\r\n X_File = open(directory + '\\\\' + 'FULLX.txt', 'w')\r\n Y_File = open(directory + '\\\\' + 'FULLY.txt', 'w')\r\n StrX = ''.join(str(BigAr)[1:-1])\r\n StrY = ''.join(str(BigAr2)[1:-1])\r\n X_File.write(StrX)\r\n Y_File.write(StrY)\r\n X_File.close()\r\n Y_File.close()\r\n '''\r\n # Тестовые данные ============================================================\r\n\r\n X_File = open(directory + '\\\\' + 'TEST_X.txt', 'w')\r\n Y_File = open(directory + '\\\\' + 'TEST_Y.txt', 'w')\r\n StrX = ''.join(str(BigAr)[1:-1])\r\n StrY = ''.join(str(BigAr2)[1:-1])\r\n X_File.write(StrX)\r\n Y_File.write(StrY)\r\n X_File.close()\r\n Y_File.close()\r\n\r\n print('ЗАПИСЬ В ФАЙЛ ЗАВЕРШЕНА!!!')\r\n #=============================================================================\r\n\r\n", "sub_path": "ANN_v2.py", "file_name": "ANN_v2.py", "file_ext": "py", "file_size_in_byte": 7685, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.listdir", "line_number": 31, "usage_type": "call"}, {"api_name": "time.time", "line_number": 42, "usage_type": "call"}, {"api_name": "multiprocessing.Manager", "line_number": 44, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 66, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 69, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 72, "usage_type": "call"}, {"api_name": "time.time", "line_number": 82, "usage_type": "call"}, {"api_name": "time.time", "line_number": 87, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 109, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 112, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 115, "usage_type": "call"}, {"api_name": "time.time", "line_number": 125, "usage_type": "call"}]} +{"seq_id": "327545547", "text": "#!/usr/bin/python\n#coding=utf-8\nfrom xbmcswift2 import Plugin , xbmc , xbmcgui , xbmcaddon\nimport requests , re , urllib , os , zipfile , json , uuid , shutil , pickle\nif 64 - 64: i11iIiiIii\nOO0o = Plugin ( )\nOo0Ooo = xbmcaddon . Addon ( \"plugin.video.kodi4vn.launcher.adult\" )\nO0O0OO0O0O0 = \"plugin://plugin.video.kodi4vn.launcher.adult\"\niiiii = \"http://echipstore.com:8000\"\nif 64 - 64: iIIi1iI1II111 + ii11i / oOooOoO0Oo0O\ndef iI1 ( url ) :\n i1I11i = requests . get ( url + \"%st=%s\" % ( \"&\" if \"?\" in url else \"?\" , urllib . quote_plus ( OO0o . get_setting ( \"token\" ) ) ) )\n i1I11i . encoding = \"utf-8\"\n OoOoOO00 = i1I11i . json ( )\n return OoOoOO00\n if 27 - 27: OOOo0 / Oo - Ooo00oOo00o . I1IiI\ndef o0OOO ( url ) :\n OoOoOO00 = iI1 ( url )\n return OoOoOO00\n if 13 - 13: ooOo + Ooo0O\ndef IiiIII111iI ( source , dest_dir ) :\n with zipfile . ZipFile ( source ) as IiII :\n for iI1Ii11111iIi in IiII . infolist ( ) :\n i1i1II = iI1Ii11111iIi . filename . split ( '/' )\n O0oo0OO0 = dest_dir\n for I1i1iiI1 in i1i1II [ : - 1 ] :\n iiIIIII1i1iI , I1i1iiI1 = os . path . splitdrive ( I1i1iiI1 )\n o0oO0 , I1i1iiI1 = os . path . split ( I1i1iiI1 )\n if I1i1iiI1 in ( os . curdir , os . pardir , '' ) : continue\n O0oo0OO0 = os . path . join ( O0oo0OO0 , I1i1iiI1 )\n IiII . extract ( iI1Ii11111iIi , O0oo0OO0 )\n if 100 - 100: i11Ii11I1Ii1i\n@ OO0o . route ( '/warning/' )\ndef Ooo ( s = \"\" ) :\n o0oOoO00o ( \"Warning\" , '/warning/%s' % s )\n i1 = xbmcgui . Dialog ( )\n i1 . ok ( 'Chú ý: User %s' % OO0o . get_setting ( \"email\" ) , s )\n return OO0o . finish ( )\n if 64 - 64: oo % O0Oooo00\n@ OO0o . route ( '/search/' )\ndef Ooo0 ( ) :\n o0oOoO00o ( \"Browse\" , '/search' )\n oo00000o0 = OO0o . keyboard ( heading = 'Tìm kiếm' )\n if oo00000o0 :\n I11i1i11i1I = '%s/yts/none/video/%s/' % ( O0O0OO0O0O0 , urllib . quote_plus ( oo00000o0 ) )\n Iiii = OO0o . get_storage ( 'search_history' )\n if 'keywords' in Iiii :\n Iiii [ \"keywords\" ] = [ oo00000o0 ] + Iiii [ \"keywords\" ]\n else :\n Iiii [ \"keywords\" ] = [ oo00000o0 ]\n OO0o . redirect ( I11i1i11i1I )\n if 87 - 87: oO0o0o0ooO0oO / I1i1I - OoOoo0 % iIiiI1 % OOooO % OOoO00o\n@ OO0o . route ( '/searchlist/' )\ndef II111iiii ( ) :\n o0oOoO00o ( \"Browse\" , '/searchlist' )\n OoOoOO00 = [ ]\n II = [ {\n \"label\" : \"[B]Search[/B]\" ,\n \"path\" : \"%s/search\" % ( O0O0OO0O0O0 ) ,\n \"thumbnail\" : \"https://lh3.googleusercontent.com/jH1IxHp7MbOx62G1aboX2kj1vgtt3kercFVPYTxh7Yr0kMoVZARVNZIYjFZQOY1FzK7DisXyfHo=s256-no\"\n } ]\n Iiii = OO0o . get_storage ( 'search_history' )\n if 'keywords' in Iiii :\n for oOoOo00oOo in Iiii [ 'keywords' ] :\n Ooo00O00O0O0O = [ {\n \"label\" : oOoOo00oOo ,\n \"path\" : '%s/yts/none/video/%s/' % ( O0O0OO0O0O0 , urllib . quote_plus ( oOoOo00oOo ) ) ,\n \"thumbnail\" : \"https://lh3.googleusercontent.com/jH1IxHp7MbOx62G1aboX2kj1vgtt3kercFVPYTxh7Yr0kMoVZARVNZIYjFZQOY1FzK7DisXyfHo=s256-no\"\n } ]\n OoOoOO00 += Ooo00O00O0O0O\n OoOoOO00 = II + OoOoOO00\n return OO0o . finish ( OoOoOO00 )\n if 90 - 90: i1IIiiiii + ii11i - oO0o0o0ooO0oO / iIIi1iI1II111 % ooOo\n@ OO0o . route ( '/login' )\ndef oO0O ( ) :\n o0oOoO00o ( \"Login\" , \"/login\" )\n xbmc . executebuiltin ( 'Dialog.Close(busydialog)' )\n try :\n OOoO000O0OO = requests . get ( \"http://echipstore.com/get-code/?nocache=true\" ) . json ( )\n iiI1IiI = OOoO000O0OO [ \"message\" ] % OOoO000O0OO [ \"user_code\" ] . upper ( )\n IIooOoOoo0O = xbmcgui . DialogProgress ( )\n IIooOoOoo0O . create ( 'Login' , iiI1IiI )\n if 76 - 76: iIIi1iI1II111 / i11Ii11I1Ii1i . Ooo00oOo00o * OoOoo0 - oO0o0o0ooO0oO\n Oooo = 0\n while Oooo < 60 :\n O00o = int ( ( Oooo / 60.0 ) * 100 )\n if IIooOoOoo0O . iscanceled ( ) :\n break\n IIooOoOoo0O . update ( O00o , \"\" )\n Oooo = Oooo + 1\n xbmc . sleep ( 5000 )\n i1I11i = requests . get ( \"http://echipstore.com/device?device_code=%s&nocache=true\" % urllib . quote_plus ( OOoO000O0OO [ \"device_code\" ] ) )\n if \"token\" in i1I11i . text :\n Oo0Ooo . setSetting ( \"token\" , i1I11i . json ( ) [ \"token\" ] )\n Oo0Ooo . setSetting ( \"email\" , i1I11i . json ( ) [ \"email\" ] )\n break\n IIooOoOoo0O . close ( )\n del IIooOoOoo0O\n xbmc . executebuiltin ( 'XBMC.Container.Update(%s)' % O0O0OO0O0O0 )\n except :\n O00 = xbmcgui . Dialog ( )\n O00 . ok ( \"Oops!\" , \"Có lỗi xảy ra. Xin quý vị vui lòng login vào dịp khác\" )\n if 11 - 11: Ooo00oOo00o\n@ OO0o . route ( '/' )\ndef O0o0Oo ( ) :\n if xbmc . getSkinDir ( ) in ( 'skin.titan' ) :\n if xbmc . getInfoLabel ( 'Skin.String(Widgets_DisplayTags)' ) != \"enable\" :\n xbmc . executebuiltin ( 'Skin.SetString(Widgets_DisplayTags.label,Enable)' )\n xbmc . executebuiltin ( 'Skin.SetString(Widgets_DisplayTags,enable)' )\n xbmc . executebuiltin ( 'XBMC.ReloadSkin()' )\n o0oOoO00o ( )\n dir ( \"1490889723\" )\n if 78 - 78: ii11i - OoOoo0 * ooOo + i11Ii11I1Ii1i + iIiiI1 + iIiiI1\n@ OO0o . route ( '/ytslive//' , name = \"ytslive_firstpage\" )\n@ OO0o . route ( '/ytslive//' )\ndef I11I11i1I ( order = \"viewcount\" , page = \"\" ) :\n o0oOoO00o ( \"Browse YT Live News\" , \"/ytslive/%s/%s\" % ( order , page ) )\n OoOoOO00 = o0OOO ( \"%s/ytslive/%s/%s\" % ( iiiii , order , page ) )\n for Ooo00O00O0O0O in OoOoOO00 :\n Ooo00O00O0O0O [ \"path\" ] = O0O0OO0O0O0 + Ooo00O00O0O0O [ \"path\" ]\n if OO0o . get_setting ( 'thumbview' , bool ) :\n if xbmc . getSkinDir ( ) in ( 'skin.confluence' , 'skin.eminence' ) :\n return OO0o . finish ( OoOoOO00 , view_mode = 500 )\n elif xbmc . getSkinDir ( ) == 'skin.xeebo' :\n return OO0o . finish ( OoOoOO00 , view_mode = 52 )\n else :\n return OO0o . finish ( OoOoOO00 )\n else :\n return OO0o . finish ( OoOoOO00 )\n if 49 - 49: Oo % iIiiI1 * iIIi1iI1II111\n@ OO0o . route ( '/yts////' , name = 'yts_firstpage' )\n@ OO0o . route ( '/yts////' )\ndef oOOo0oo ( order , t , q = \"\" , page = \"\" ) :\n o0oOoO00o ( \"Browse YT by topics %s\" % q , \"/yts/%s/%s/%s/%s\" % ( order , t , q , page ) )\n OoOoOO00 = [ ]\n if t in [ \"channel\" , \"playlist\" ] and order == \"date\" :\n order = \"videocount\"\n OoOoOO00 = o0OOO ( \"%s/yts/%s/%s?q=%s&page=%s\" % ( iiiii , order , t , q , page ) )\n for Ooo00O00O0O0O in OoOoOO00 :\n if \"plugin://\" not in Ooo00O00O0O0O [ \"path\" ] :\n Ooo00O00O0O0O [ \"path\" ] = O0O0OO0O0O0 + Ooo00O00O0O0O [ \"path\" ]\n if t == \"video\" :\n o0oo0o0O00OO = [ {\n \"label\" : \"[B]Channels[/B]\" ,\n \"path\" : \"%s/yts/%s/channel/%s\" % ( O0O0OO0O0O0 , order , q ) ,\n \"thumbnail\" : \"http://thong.viettv24.com/kodi4vn/images/yt.png\"\n } ]\n o0oO = [ {\n \"label\" : \"[B]Playlist[/B]\" ,\n \"path\" : \"%s/yts/%s/playlist/%s\" % ( O0O0OO0O0O0 , order , q ) ,\n \"thumbnail\" : \"https://lh3.googleusercontent.com/184S-U4BBN7f55qcTQFUQSsBjYlJZ246A01J-n_BKa4bwe74nANMPkj58I8DSPzlxYyWocyYYYj89D-1qHXfEkVENdA6O1weJZOVZAMCAIhK8vfZ9bgKpw-eY4pwpaCzfQ0MS4wlwnjZE28jmTZejHIVRflEUcPS-SLJ6xGTAVIHXbIP1uEKugegwL9ULD0vfwD92FWzz9_abZ70VNeBTBRCjE3-gfQ-IKVUmGJlnJeEJcS1fUAo6_qvrBf9NX1n0gLp24lVdTj-ml6VmDtr5bVwQBBes-7zTKthqeLqZoo-Zr0ZDY2hhw871xrXDeUtlwVeK-EnAEgFRAWyRa9HjijEEED81GDYkCc5r0qK3xjqqPvo3aJ-urdVH2TcOkbmTgx2l7jHIMo4WuE9-d8hAMzGXJfLp4NNGty3vYLk-0RG_MjvUp4qeNcmPMHrX8fWih2z-hAXhfvjXZ1SJq_BEnFzSgVCyW44inHkLUallDmcbFyz5EuYgEAVYHMUikabDj2eLrsMbHTM94a_ljcBV9X4jS0Dz5EMjLl5veXQmCA=w175-h107-no\"\n } ]\n OoOoOO00 = o0oo0o0O00OO + o0oO + OoOoOO00\n if OO0o . get_setting ( 'thumbview' , bool ) :\n if xbmc . getSkinDir ( ) in ( 'skin.confluence' , 'skin.eminence' ) :\n return OO0o . finish ( OoOoOO00 , view_mode = 500 )\n elif xbmc . getSkinDir ( ) == 'skin.xeebo' :\n return OO0o . finish ( OoOoOO00 , view_mode = 52 )\n else :\n return OO0o . finish ( OoOoOO00 )\n else :\n return OO0o . finish ( OoOoOO00 )\n if 48 - 48: I1i1I + I1i1I / Oo / ii11i\n@ OO0o . route ( '/dir/' )\n@ OO0o . route ( '/dir//' )\ndef dir ( url , title = \"\" ) :\n OoOoOO00 = [ ]\n o0oOoO00o ( \"Browse Menu [%s]\" % title , \"/dir/%s/%s\" % ( title , url ) )\n try :\n if \"://\" in url :\n pass\n else :\n OoOoOO00 = o0OOO ( \"%s/dir/%s\" % ( iiiii , urllib . quote_plus ( url ) ) )\n for Ooo00O00O0O0O in OoOoOO00 :\n if \"plugin://\" not in Ooo00O00O0O0O [ \"path\" ] :\n Ooo00O00O0O0O [ \"path\" ] = O0O0OO0O0O0 + Ooo00O00O0O0O [ \"path\" ]\n i1iiI11I ( Ooo00O00O0O0O )\n if 29 - 29: oOooOoO0Oo0O\n iI = ( \"\" if OO0o . get_setting ( \"email\" ) == \"\" else ( \"Chào %s. \" % OO0o . get_setting ( \"email\" ) ) )\n I1i1I1II = [ {\n \"label\" : \"[COLOR yellow][B]%sVào đây để Login/Relogin[/B][/COLOR]\" % iI ,\n \"path\" : O0O0OO0O0O0 + \"/login\" ,\n \"thumbnail\" : \"https://cdn3.iconfinder.com/data/icons/gray-toolbar-2/512/login_user_profile_account-512.png\"\n } ]\n OoOoOO00 = OoOoOO00\n except :\n O0oo0OO0 = xbmc . translatePath ( xbmcaddon . Addon ( ) . getAddonInfo ( 'path' ) ) . decode ( \"utf-8\" )\n i1IiIiiI = xbmc . translatePath ( os . path . join ( O0oo0OO0 , \"error_icon.jpg\" ) )\n I1I = xbmc . translatePath ( os . path . join ( O0oo0OO0 , \"error_bg.jpg\" ) )\n oOO00oOO = xbmc . translatePath ( os . path . join ( O0oo0OO0 , \"error_fullscreen.jpg\" ) )\n if 75 - 75: OOOo0 / oOooOoO0Oo0O - iIIi1iI1II111 / Ooo0O . Oo - OOOo0\n O000OO0 = [ {\n \"label\" : \"Connection Error! OK Here for more details\" ,\n \"path\" : \"%s/showimage/%s\" % ( O0O0OO0O0O0 , urllib . quote_plus ( oOO00oOO ) ) ,\n \"thumbnail\" : i1IiIiiI ,\n \"properties\" : { 'fanart_image' : I1I }\n } ]\n OoOoOO00 += O000OO0\n if 43 - 43: OOoO00o - iIIi1iI1II111 % Ooo00oOo00o . I1i1I\n return OO0o . finish ( OoOoOO00 )\n if 57 - 57: oO0o0o0ooO0oO . oO0o0o0ooO0oO\n@ OO0o . route ( '/ytp/<pid>' , name = 'ytp_firstpage' )\n@ OO0o . route ( '/ytp/<pid>/<page>' )\ndef OooOooo ( pid , page = \"\" ) :\n O000oo0O = \"\"\n if \" - \" in pid :\n OOOO = pid . split ( \" - \" )\n O000oo0O = \" - \" . join ( OOOO [ 1 : ] )\n pid = OOOO [ 0 ]\n if 10 - 10: oO0o0o0ooO0oO / Ooo00oOo00o * oO0o0o0ooO0oO\n o0oOoO00o ( \"Browse YT Videos by Playlist [%s]\" % O000oo0O , \"/ytp/%s/%s/%s\" % ( O000oo0O , pid , page ) )\n OoOoOO00 = o0OOO ( \"%s/ytp/%s/%s\" % ( iiiii , pid , page ) )\n for Ooo00O00O0O0O in OoOoOO00 :\n Ooo00O00O0O0O [ \"path\" ] = O0O0OO0O0O0 + Ooo00O00O0O0O [ \"path\" ]\n i1iiI11I ( Ooo00O00O0O0O )\n if OO0o . get_setting ( 'thumbview' , bool ) :\n if xbmc . getSkinDir ( ) in ( 'skin.confluence' , 'skin.eminence' ) :\n return OO0o . finish ( OoOoOO00 , view_mode = 500 )\n elif xbmc . getSkinDir ( ) == 'skin.xeebo' :\n return OO0o . finish ( OoOoOO00 , view_mode = 52 )\n else :\n return OO0o . finish ( OoOoOO00 )\n else :\n return OO0o . finish ( OoOoOO00 )\n if 29 - 29: oo % Ooo00oOo00o + i1IIiiiii / i11Ii11I1Ii1i + oO0o0o0ooO0oO * i11Ii11I1Ii1i\n@ OO0o . route ( '/ytu/<uid>' )\ndef i1I1iI ( uid ) :\n oo0OooOOo0 = requests . get ( \"%s/ytu/%s\" % ( iiiii , uid ) ) . text\n o0O ( oo0OooOOo0 + \" - \" + uid , \"\" )\n if 72 - 72: iIiiI1 / OOOo0 * I1IiI - OOoO00o\n@ OO0o . route ( '/ytc/<cid>' , name = 'ytc_firstpage' )\n@ OO0o . route ( '/ytc/<cid>/<page>' )\ndef o0O ( cid , page = \"\" ) :\n O000oo0O = \"\"\n if \" - \" in cid :\n Oo0O0O0ooO0O = cid . split ( \" - \" )\n O000oo0O = \" - \" . join ( Oo0O0O0ooO0O [ 1 : ] )\n cid = Oo0O0O0ooO0O [ 0 ]\n if 15 - 15: oo + Ooo0O - oOooOoO0Oo0O / oO0o0o0ooO0oO\n o0oOoO00o ( \"Browse YT Videos by Channel [%s]\" % O000oo0O , \"/ytc/%s/%s/%s\" % ( O000oo0O , cid , page ) )\n OoOoOO00 = [ {\n \"label\" : \"[B]Playlist[/B]\" ,\n \"path\" : \"%s/ytcp/%s/%s\" % ( O0O0OO0O0O0 , cid . split ( \"@\" ) [ 0 ] , \"\" ) ,\n \"thumbnail\" : \"https://lh3.googleusercontent.com/184S-U4BBN7f55qcTQFUQSsBjYlJZ246A01J-n_BKa4bwe74nANMPkj58I8DSPzlxYyWocyYYYj89D-1qHXfEkVENdA6O1weJZOVZAMCAIhK8vfZ9bgKpw-eY4pwpaCzfQ0MS4wlwnjZE28jmTZejHIVRflEUcPS-SLJ6xGTAVIHXbIP1uEKugegwL9ULD0vfwD92FWzz9_abZ70VNeBTBRCjE3-gfQ-IKVUmGJlnJeEJcS1fUAo6_qvrBf9NX1n0gLp24lVdTj-ml6VmDtr5bVwQBBes-7zTKthqeLqZoo-Zr0ZDY2hhw871xrXDeUtlwVeK-EnAEgFRAWyRa9HjijEEED81GDYkCc5r0qK3xjqqPvo3aJ-urdVH2TcOkbmTgx2l7jHIMo4WuE9-d8hAMzGXJfLp4NNGty3vYLk-0RG_MjvUp4qeNcmPMHrX8fWih2z-hAXhfvjXZ1SJq_BEnFzSgVCyW44inHkLUallDmcbFyz5EuYgEAVYHMUikabDj2eLrsMbHTM94a_ljcBV9X4jS0Dz5EMjLl5veXQmCA=w175-h107-no\"\n } ]\n if \"@\" not in cid :\n cid = requests . get ( \"%s/ytc/%s\" % ( iiiii , cid ) ) . text\n if \"@\" in cid :\n o0oo0o0O00OO = o0OOO ( \"%s/ytp/%s/%s\" % ( iiiii , cid . split ( \"@\" ) [ 1 ] , page ) )\n for Ooo00O00O0O0O in o0oo0o0O00OO :\n Ooo00O00O0O0O [ \"path\" ] = O0O0OO0O0O0 + Ooo00O00O0O0O [ \"path\" ]\n i1iiI11I ( Ooo00O00O0O0O )\n OoOoOO00 += o0oo0o0O00OO\n if OO0o . get_setting ( 'thumbview' , bool ) :\n if xbmc . getSkinDir ( ) in ( 'skin.confluence' , 'skin.eminence' ) :\n return OO0o . finish ( OoOoOO00 , view_mode = 500 )\n elif xbmc . getSkinDir ( ) == 'skin.xeebo' :\n return OO0o . finish ( OoOoOO00 , view_mode = 52 )\n else :\n return OO0o . finish ( OoOoOO00 )\n else :\n return OO0o . finish ( OoOoOO00 )\n if 58 - 58: i11iIiiIii % I1i1I\n@ OO0o . route ( '/ytcp/<cid>' , name = 'ytcp_firstpage' )\n@ OO0o . route ( '/ytcp/<cid>/<page>' )\ndef OO00Oo ( cid , page = \"\" ) :\n O000oo0O = \"\"\n if \" - \" in cid :\n Oo0O0O0ooO0O = cid . split ( \" - \" )\n O000oo0O = \" - \" . join ( Oo0O0O0ooO0O [ 1 : ] )\n cid = Oo0O0O0ooO0O [ 0 ]\n if 51 - 51: OOooO * i11Ii11I1Ii1i + I1i1I + ooOo\n o0oOoO00o ( \"Browse YT Playlist by Channel [%s]\" % O000oo0O , \"/ytcp/%s/%s/%s\" % ( O000oo0O , cid , page ) )\n OoOoOO00 = o0OOO ( \"%s/ytcp/%s/%s\" % ( iiiii , cid , page ) )\n for Ooo00O00O0O0O in OoOoOO00 :\n Ooo00O00O0O0O [ \"path\" ] = O0O0OO0O0O0 + Ooo00O00O0O0O [ \"path\" ]\n i1iiI11I ( Ooo00O00O0O0O )\n if OO0o . get_setting ( 'thumbview' , bool ) :\n if xbmc . getSkinDir ( ) in ( 'skin.confluence' , 'skin.eminence' ) :\n return OO0o . finish ( OoOoOO00 , view_mode = 500 )\n elif xbmc . getSkinDir ( ) == 'skin.xeebo' :\n return OO0o . finish ( OoOoOO00 , view_mode = 52 )\n else :\n return OO0o . finish ( OoOoOO00 )\n else :\n return OO0o . finish ( OoOoOO00 )\n if 66 - 66: Ooo0O\n@ OO0o . route ( '/play/<url>' )\n@ OO0o . route ( '/play/<url>/<title>' )\ndef oO000Oo000 ( url , title = \"\" ) :\n o0oOoO00o ( \"Play [%s]\" % title , \"/play/%s/%s\" % ( title , url ) )\n IIooOoOoo0O = xbmcgui . DialogProgress ( )\n IIooOoOoo0O . create ( 'Kodi4VN Launcher' , 'Loading video. Please wait...' )\n OO0o . set_resolved_url ( i111IiI1I ( url ) , subtitles = \"https://docs.google.com/spreadsheets/d/16l-nMNyOvrtu4FKLm-ctGDNClCjI09XKp3lcOKPOXMk/export?format=tsv&gid=0\" )\n IIooOoOoo0O . close ( )\n del IIooOoOoo0O\n if 70 - 70: OoOoo0 . I1IiI / i11Ii11I1Ii1i . OoOoo0 - iIIi1iI1II111 / OOooO\ndef i111IiI1I ( url ) :\n if \"youtube\" in url :\n ooOooo000oOO = re . compile ( '(youtu\\.be\\/|youtube-nocookie\\.com\\/|youtube\\.com\\/(watch\\?(.*&)?v=|(embed|v|user)\\/))([^\\?&\"\\'>]+)' ) . findall ( url )\n Oo0oOOo = ooOooo000oOO [ 0 ] [ len ( ooOooo000oOO [ 0 ] ) - 1 ] . replace ( 'v/' , '' )\n return 'plugin://plugin.video.youtube/play/?video_id=%s' % Oo0oOOo\n elif \"://\" not in url :\n Oo0OoO00oOO0o = \"http://www.viettv24.com/main/getStreamingServer.php\"\n OOO00O = { 'strname' : '%s-' % url }\n return requests . post ( Oo0OoO00oOO0o , data = OOO00O ) . text . strip ( )\n else :\n return url\n if 84 - 84: O0Oooo00 * ooOo / I1i1I - iIIi1iI1II111\n@ OO0o . route ( '/showimage/<url>' )\ndef IiI1 ( url ) :\n O0oo0OO0 = xbmc . translatePath ( xbmcaddon . Addon ( ) . getAddonInfo ( 'path' ) ) . decode ( \"utf-8\" )\n Oo0O00Oo0o0 = xbmc . translatePath ( os . path . join ( O0oo0OO0 , \"tmp\" ) )\n if os . path . exists ( Oo0O00Oo0o0 ) :\n shutil . rmtree ( Oo0O00Oo0o0 )\n os . makedirs ( Oo0O00Oo0o0 )\n if \".zip\" in url :\n O00O0oOO00O00 = xbmc . translatePath ( os . path . join ( Oo0O00Oo0o0 , \"temp.zip\" ) )\n urllib . urlretrieve ( url , O00O0oOO00O00 )\n IiiIII111iI ( O00O0oOO00O00 , Oo0O00Oo0o0 )\n xbmc . executebuiltin ( \"SlideShow(%s,recursive)\" % Oo0O00Oo0o0 )\n if 11 - 11: OOooO . oo\n if 92 - 92: iIiiI1 . OOoO00o\n else :\n i1i = xbmcgui . WindowDialog ( )\n iiI111I1iIiI = xbmcgui . ControlImage ( 0 , 0 , 1280 , 720 , url )\n i1i . addControl ( iiI111I1iIiI )\n i1i . doModal ( )\n if 41 - 41: I1IiI . i1IIiiiii + iIIi1iI1II111 * i11Ii11I1Ii1i % I1IiI * I1IiI\ndef o0oOoO00o ( title = \"Home\" , page = \"/\" ) :\n try :\n iIIIIi1iiIi1 = \"http://www.google-analytics.com/collect\"\n iii1i1iiiiIi = open ( IiiiOO0OoO0o00 ) . read ( )\n ooOO0O0ooOooO = {\n 'v' : '1' ,\n 'tid' : 'UA-52209804-5' ,\n 'cid' : iii1i1iiiiIi ,\n 't' : 'pageview' ,\n 'dp' : \"Kodi4VNLauncher%s\" % page ,\n 'dt' : \"[Kodi4VNLauncher] - %s\" % title\n }\n requests . post ( iIIIIi1iiIi1 , data = urllib . urlencode ( ooOO0O0ooOooO ) )\n except :\n pass\n if 55 - 55: i11Ii11I1Ii1i * Ooo0O\ndef i1iiI11I ( item ) :\n o0O00oOoOO = \"/\" if item [ \"path\" ] [ - 1 ] != \"/\" else \"\"\n iIIi1i1 = re . compile ( '/dir/\\d+/*$' ) . findall ( item [ \"path\" ] )\n i1IIIiiII1 = [ \"/ytc/\" , \"/ytcp/\" , \"ytp\" ]\n if len ( iIIi1i1 ) > 0 or 'launcher/play/' in item [ \"path\" ] :\n item [ \"path\" ] += o0O00oOoOO + urllib . quote_plus ( item [ \"label\" ] . encode ( \"utf8\" ) )\n elif any ( word in item [ \"path\" ] for word in i1IIIiiII1 ) :\n O0oo0OO0 = urllib . unquote_plus ( item [ \"path\" ] )\n if \" - \" not in O0oo0OO0 :\n OOOOoOoo0O0O0 = re . compile ( '/yt[c,p]p?/(.+?)/?$' ) . findall ( O0oo0OO0 ) [ 0 ] . split ( \"/\" ) [ 0 ]\n OOOo00oo0oO = \"%s - %s\" % ( OOOOoOoo0O0O0 , item [ \"label\" ] )\n IIiIi1iI = urllib . quote_plus ( OOOOoOoo0O0O0 )\n i1IiiiI1iI = urllib . quote_plus ( OOOo00oo0oO . encode ( \"utf8\" ) )\n item [ \"path\" ] = item [ \"path\" ] . replace ( IIiIi1iI , i1IiiiI1iI )\n if 49 - 49: OoOoo0 / ooOo . Oo\nooOOoooooo = xbmc . translatePath ( 'special://userdata' )\nif os . path . exists ( ooOOoooooo ) == False :\n os . mkdir ( ooOOoooooo )\nIiiiOO0OoO0o00 = os . path . join ( ooOOoooooo , 'cid' )\nII1I = os . path . join ( ooOOoooooo , 'search.p' )\nif 84 - 84: OOooO . i11iIiiIii . OOooO * oo - I1i1I\nif os . path . exists ( IiiiOO0OoO0o00 ) == False :\n with open ( IiiiOO0OoO0o00 , \"w\" ) as ii :\n ii . write ( str ( uuid . uuid1 ( ) ) )\n if 81 - 81: OOoO00o % iIiiI1 . oo / i11Ii11I1Ii1i\nif __name__ == '__main__' :\n OO0o . run ( )\n if 40 - 40: Ooo00oOo00o + oOooOoO0Oo0O\n# dd678faae9ac167bc83abf78e5cb2f3f0688d3a3\n", "sub_path": "plugin.video.kodi4vn.launcher.adult/default.py", "file_name": "default.py", "file_ext": "py", "file_size_in_byte": 17758, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "xbmcswift2.Plugin", "line_number": 6, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmcaddon.Addon", "line_number": 7, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmcaddon", "line_number": 7, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 12, "usage_type": "call"}, {"api_name": "urllib.quote_plus", "line_number": 12, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.splitdrive", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.split", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.curdir", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.pardir", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path", "line_number": 30, "usage_type": "attribute"}, {"api_name": "xbmcswift2.xbmcgui.Dialog", "line_number": 36, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmcgui", "line_number": 36, "usage_type": "name"}, {"api_name": "urllib.quote_plus", "line_number": 45, "usage_type": "call"}, {"api_name": "urllib.quote_plus", "line_number": 67, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc.executebuiltin", "line_number": 77, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 77, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 79, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmcgui.DialogProgress", "line_number": 81, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmcgui", "line_number": 81, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmc.sleep", "line_number": 91, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 91, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 92, "usage_type": "call"}, {"api_name": "urllib.quote_plus", "line_number": 92, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc.executebuiltin", "line_number": 99, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 99, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmcgui.Dialog", "line_number": 101, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmcgui", "line_number": 101, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmc.getSkinDir", "line_number": 106, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 106, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmc.getInfoLabel", "line_number": 107, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 107, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmc.executebuiltin", "line_number": 108, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 108, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmc.executebuiltin", "line_number": 109, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 109, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmc.executebuiltin", "line_number": 110, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 110, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmc.getSkinDir", "line_number": 122, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 122, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmc.getSkinDir", "line_number": 124, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 124, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmc.getSkinDir", "line_number": 155, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 155, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmc.getSkinDir", "line_number": 157, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 157, "usage_type": "name"}, {"api_name": "urllib.quote_plus", "line_number": 173, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc.translatePath", "line_number": 187, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 187, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmcaddon.Addon", "line_number": 187, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmcaddon", "line_number": 187, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmc.translatePath", "line_number": 188, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 188, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 188, "usage_type": "call"}, {"api_name": "os.path", "line_number": 188, "usage_type": "attribute"}, {"api_name": "xbmcswift2.xbmc.translatePath", "line_number": 189, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 189, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 189, "usage_type": "call"}, {"api_name": "os.path", "line_number": 189, "usage_type": "attribute"}, {"api_name": "xbmcswift2.xbmc.translatePath", "line_number": 190, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 190, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 190, "usage_type": "call"}, {"api_name": "os.path", "line_number": 190, "usage_type": "attribute"}, {"api_name": "urllib.quote_plus", "line_number": 194, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc.getSkinDir", "line_number": 217, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 217, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmc.getSkinDir", "line_number": 219, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 219, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 228, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 247, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc.getSkinDir", "line_number": 255, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 255, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmc.getSkinDir", "line_number": 257, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 257, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmc.getSkinDir", "line_number": 279, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 279, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmc.getSkinDir", "line_number": 281, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 281, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmcgui.DialogProgress", "line_number": 292, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmcgui", "line_number": 292, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 300, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 306, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc.translatePath", "line_number": 312, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 312, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmcaddon.Addon", "line_number": 312, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmcaddon", "line_number": 312, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmc.translatePath", "line_number": 313, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 313, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 313, "usage_type": "call"}, {"api_name": "os.path", "line_number": 313, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 314, "usage_type": "call"}, {"api_name": "os.path", "line_number": 314, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 315, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 316, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc.translatePath", "line_number": 318, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 318, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 318, "usage_type": "call"}, {"api_name": "os.path", "line_number": 318, "usage_type": "attribute"}, {"api_name": "urllib.urlretrieve", "line_number": 319, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc.executebuiltin", "line_number": 321, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 321, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmcgui.WindowDialog", "line_number": 325, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmcgui", "line_number": 325, "usage_type": "name"}, {"api_name": "xbmcswift2.xbmcgui.ControlImage", "line_number": 326, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmcgui", "line_number": 326, "usage_type": "name"}, {"api_name": "requests.post", "line_number": 342, "usage_type": "call"}, {"api_name": "urllib.urlencode", "line_number": 342, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 348, "usage_type": "call"}, {"api_name": "urllib.quote_plus", "line_number": 351, "usage_type": "call"}, {"api_name": "urllib.unquote_plus", "line_number": 353, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 355, "usage_type": "call"}, {"api_name": "urllib.quote_plus", "line_number": 357, "usage_type": "call"}, {"api_name": "urllib.quote_plus", "line_number": 358, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc.translatePath", "line_number": 361, "usage_type": "call"}, {"api_name": "xbmcswift2.xbmc", "line_number": 361, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 362, "usage_type": "call"}, {"api_name": "os.path", "line_number": 362, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 363, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 364, "usage_type": "call"}, {"api_name": "os.path", "line_number": 364, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 365, "usage_type": "call"}, {"api_name": "os.path", "line_number": 365, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 367, "usage_type": "call"}, {"api_name": "os.path", "line_number": 367, "usage_type": "attribute"}, {"api_name": "uuid.uuid1", "line_number": 369, "usage_type": "call"}]} +{"seq_id": "461795834", "text": "from gpiozero import LED, PWMLED\n\nfrom abc import ABC, abstractmethod\n\nclass Motor(ABC):\n\n \"\"\"\n 单个电机类\n \"\"\"\n @abstractmethod\n def forward(self):\n '''\n 正转\n '''\n pass\n\n @abstractmethod\n def reverse(self):\n '''\n 反转\n '''\n pass\n\n\nclass SingleMotor(Motor):\n \"\"\"\n 单个电机的控制类\n \"\"\"\n def __init__(self, C1: int, C2: int, S:float):\n \"\"\"\n C1 控制端1\n C2 控制端2\n S 调速端\n \"\"\"\n self._IN1 = LED(C1)\n self._IN2 = LED(C2)\n self._EN = PWMLED(S)\n self._speed = 0\n\n @property\n def speed(self) -> float:\n return self._speed\n\n @speed.setter\n def speed(self, speed: float):\n self._EN.value = speed\n self._speed = speed\n\n def forward(self):\n '''\n 正转\n '''\n self._EN.value = self._speed\n self._IN1.on()\n self._IN2.off()\n\n def reverse(self):\n '''\n 反转\n '''\n self._EN.value = self._speed\n self._IN1.off()\n self._IN2.on()\n\n\n\nif __name__ == \"__main__\":\n import time\n # 左电机\n # s1 = SingleMotor(26, 19, 20)\n # 右电机\n s1 = SingleMotor(13, 6, 21)\n for i in range(10):\n s1.speed = i/10\n s1.forward()\n\n print(i/10)\n time.sleep(1)\n\n s1.reverse()\n time.sleep(1)\n", "sub_path": "component/Motor.py", "file_name": "Motor.py", "file_ext": "py", "file_size_in_byte": 1418, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "abc.ABC", "line_number": 5, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 10, "usage_type": "name"}, {"api_name": "abc.abstractmethod", "line_number": 17, "usage_type": "name"}, {"api_name": "gpiozero.LED", "line_number": 35, "usage_type": "call"}, {"api_name": "gpiozero.LED", "line_number": 36, "usage_type": "call"}, {"api_name": "gpiozero.PWMLED", "line_number": 37, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 78, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 81, "usage_type": "call"}]} +{"seq_id": "651858321", "text": "import os\nimport sys\nimport time\nimport win32api\nimport win32con\nimport win32gui\nimport pymouse\nimport pykeyboard\n\nimport redis\nimport pickle\n\nfrom datetime import datetime\nimport pyautogui as pag\nfrom struct import unpack\nfrom collections import namedtuple, deque, OrderedDict\nfrom win32gui import IsWindow, IsWindowEnabled, IsWindowVisible, GetWindowText, EnumWindows\nfrom pymouse import PyMouse\nfrom pykeyboard import PyKeyboard\nfrom ctypes import windll as win32\n\n\nRed = redis.Redis() # key whfuture_min1\nMin = []\n\ntry:\n BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\n BASE_DIR = os.path.join(BASE_DIR, 'AndBefore_2018_3')\n sys.path.append(BASE_DIR)\n from MyUtil import get_conn\nexcept:\n from myconn.myconn import get_conn\n\n\"\"\"\n读取文华财经dat数据，并存储到数据库\n\"\"\"\n\n\nclass WHCJ:\n def __init__(self):\n self.conn = get_conn('carry_investment')\n N = namedtuple('N', ['month', 'code'])\n # 代码所对应的合约\n self.hsi = OrderedDict({\n '00034150.dat': N(1, 'HSIF9'),\n '00034151.dat': N(2, 'HSIG9'),\n '00034152.dat': N(3, 'HSIH9'),\n '00034154.dat': N(4, 'HSIJ9'),\n '00034155.dat': N(5, 'HSIK9'),\n '00034157.dat': N(6, 'HSIM9'),\n '00034158.dat': N(7, 'HSIN9'),\n '00034161.dat': N(8, 'HSIQ9'),\n '00034165.dat': N(9, 'HSIU9'),\n '00034166.dat': N(10, 'HSIV9'),\n '00034168.dat': N(11, 'HSIX9'),\n '00034170.dat': N(12, 'HSIZ9'),\n })\n # 7214 # HSI 00034182.dat\n # 7253 # MHI 00034233.dat\n # 7234 # HHI 00034214.dat\n # 恒生期货当月\n self.same_month = {'00034182.dat': 'HSI', '00034233.dat': 'MHI', '00034214.dat': 'HHI'}\n # 恒生指数 HSI, 国内 IF,IH,IC 主连\n self.index = {\n '00033906.dat': 'HSI',\n '00010067.dat': 'IF',\n '00010083.dat': 'IH',\n '00010213.dat': 'IC',\n }\n\n def transfer_min1(self, files):\n ''' 解析一分钟、五分钟数据 '''\n contrast = None\n with open(files, 'rb') as f:\n buf = f.read()\n num = len(buf)\n no = (num - 4) / 36 # 32\n b = 4 # 0\n e = 40 # 32\n\n for i in range(int(no) - 1):\n a = unpack('iffffffff', buf[b:e])\n dd = a[0]\n openPrice = a[1]\n close = a[2]\n high = a[3]\n low = a[4]\n vol = a[5]\n amount = a[6]\n\n dd = datetime.fromtimestamp(dd)\n\n b += 36 # 32\n e += 36 # 32\n if i != 0:\n t = dd - contrast\n if dd < contrast or t.days > 100:\n break\n contrast = dd\n yield [dd, openPrice, high, low, close, vol, amount, a[7], a[8]]\n else:\n contrast = dd\n yield [dd, openPrice, high, low, close, vol, amount, a[7], a[8]]\n\n def transfer_day(self, files):\n ''' 解析日线数据 '''\n contrast = None\n with open(files, 'rb') as f:\n buf = f.read()\n num = len(buf)\n no = num / 37\n b = 0\n e = 37\n\n for i in range(int(no) - 1):\n a = unpack('iffffffffB', buf[b:e])\n dd = a[0]\n openPrice = a[1]\n close = a[2]\n high = a[3]\n low = a[4]\n vol = a[5]\n amount = a[6]\n\n dd = datetime.fromtimestamp(dd)\n\n b += 37 # 32\n e += 37 # 32\n if i != 0:\n t = dd - contrast\n if dd < contrast or t.days > 100:\n break\n contrast = dd\n yield [dd, openPrice, high, low, close, vol, amount, a[7], a[8]]\n else:\n contrast = dd\n yield [dd, openPrice, high, low, close, vol, amount, a[7], a[8]]\n\n def to_sql(self, conn, data):\n cur = conn.cursor()\n sql = \"INSERT INTO wh_min(prodcode,datetime,open,high,low,close,vol) VALUES(%s,%s,%s,%s,%s,%s,%s)\"\n count = 0\n cur.execute(\"SELECT prodcode,datetime FROM wh_min ORDER BY datetime DESC LIMIT 1\")\n d = cur.fetchone()\n for i in data:\n try:\n cur.execute(sql, (i[0], i[1], i[2], i[3], i[4], i[5], i[6]))\n except Exception as exc:\n print(exc)\n count += 1\n if not count % 10000:\n conn.commit()\n else:\n conn.commit()\n\n def file_to_sql(self, file_path, dat, code_time):\n \"\"\" 获得文件数据，插入数据库 \"\"\"\n global Min\n cur = self.conn.cursor()\n count = 0\n insert_size = 0\n folder = file_path.split('\\\\')[-2]\n if folder == 'min1' and dat in self.hsi:\n code = self.hsi[dat].code\n next_data = self.transfer_min1(file_path)\n sql = \"INSERT INTO wh_min(prodcode,datetime,open,high,low,close,vol,position,settlement,ratio) VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\n elif folder == 'min1' and dat in self.same_month:\n code = self.same_month[dat]\n next_data = self.transfer_min1(file_path)\n sql = \"INSERT INTO wh_same_month_min(prodcode,datetime,open,high,low,close,vol,position,settlement,ratio) VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\n elif folder == 'min1' and dat in self.index:\n code = self.index[dat]\n next_data = self.transfer_min1(file_path)\n sql = \"INSERT INTO wh_index_min(prodcode,datetime,open,high,low,close,vol,position,settlement,ratio) VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\n elif folder == 'day' and dat in self.same_month:\n code = self.same_month[dat]\n next_data = self.transfer_day(file_path)\n sql = \"INSERT INTO wh_same_month_day(prodcode,datetime,open,high,low,close,vol,position,settlement,ratio) VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\n elif folder == 'min5' and dat in self.same_month:\n code = self.same_month[dat]\n next_data = self.transfer_min1(file_path)\n sql = \"INSERT INTO wh_same_month_min5(prodcode,datetime,open,high,low,close,vol,position,settlement,ratio) VALUES(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\n else:\n return None\n\n # 保存到数据库\n for i in next_data:\n if code_time and i[0] <= code_time[1]:\n continue\n try:\n cur.execute(sql, (code, str(i[0])[:19], i[1], i[2], i[3], i[4], i[5], i[6], i[7], i[8]))\n insert_size += 1\n if dat == '00034182.dat' and folder == 'min1':\n Min.append((code, str(i[0])[:19], i[1], i[2], i[3], i[4], i[5], i[6], i[7], i[8]))\n except Exception as exc:\n print(exc)\n count += 1\n if not count % 10000:\n self.conn.commit()\n if dat == '00034182.dat' and folder == 'min1':\n Min = Min[-100:] # 限制为只保存100条分钟数据\n Red.set('whfuture_min1', pickle.dumps(Min))\n return insert_size\n\n def main(self, to_file=None):\n \"\"\" to_file: 如果有传入to_file，则只使用to_file这个文件更新数据库，否则检查所有指定文件并更新到数据库\"\"\"\n dirs = r'C:\\wh6模拟版\\Data\\恒生期指\\min1'\n\n t = datetime.now()\n hsi = self.hsi\n index = self.index\n same_month = self.same_month\n dat = to_file.split('\\\\')[-1] if to_file else 0\n folder = to_file.split('\\\\')[-2] if to_file else ''\n cur = self.conn.cursor()\n if folder == 'min1' and dat in same_month:\n code_time_sql = \"SELECT prodcode,datetime FROM wh_same_month_min WHERE prodcode='%s' ORDER BY datetime DESC LIMIT 1\" % \\\n same_month[dat]\n elif folder == 'min1' and dat in hsi:\n code_time_sql = \"SELECT prodcode,datetime FROM wh_min ORDER BY datetime DESC LIMIT 1\"\n elif folder == 'min1' and dat in index:\n code_time_sql = \"SELECT prodcode,datetime FROM wh_index_min WHERE prodcode='%s' ORDER BY datetime DESC LIMIT 1\" % \\\n index[dat]\n elif folder == 'day' and dat in same_month:\n code_time_sql = \"SELECT prodcode,datetime FROM wh_same_month_day ORDER BY datetime DESC LIMIT 1\"\n elif folder == 'min5' and dat in same_month:\n code_time_sql = \"SELECT prodcode,datetime FROM wh_same_month_min5 ORDER BY datetime DESC LIMIT 1\"\n else:\n return\n cur.execute(code_time_sql)\n code_time = cur.fetchone()\n self.conn.commit()\n\n numbers = [nu for nu in os.listdir(dirs) if (nu in hsi and hsi[nu].month <= t.month) or nu in same_month]\n\n if to_file:\n insert_size = self.file_to_sql(to_file, dat, code_time)\n self.conn.commit()\n return insert_size\n\n for number in numbers:\n file_path = dirs + os.sep + number\n insert_size = self.file_to_sql(file_path, dat, code_time)\n\n self.conn.commit()\n\n return insert_size\n\n def sbdj(self, x, y, enter=None):\n \"\"\" 鼠标左击 与 按回车键 \"\"\"\n win32api.SetCursorPos([x, y]) # 为鼠标焦点设定一个位置\n win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN, 0, 0, 0, 0)\n win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, 0, 0, 0, 0)\n # win32api.keybd_event(0,0,win32con.KEYEVENTF_KEYUP,0)\n if enter is not None:\n # 按下回车键\n time.sleep(0.1)\n win32api.keybd_event(13, 0, 0, 0)\n win32api.keybd_event(13, 0, win32con.KEYEVENTF_KEYUP, 0)\n\n def get_ct(self):\n \"\"\" 获取所有Windows打开的窗体 \"\"\"\n titles = set()\n\n def foo(hwnd, mouse):\n # 去掉下面这句就能获取所有，但是我不需要那么多\n if IsWindow(hwnd) and IsWindowEnabled(hwnd) and IsWindowVisible(hwnd):\n titles.add(GetWindowText(hwnd))\n\n EnumWindows(foo, 0)\n return titles\n\n def start_wh(self):\n \"\"\" 启动文华财经，并返回窗口句柄\"\"\"\n # 启动文华财经\n os.system('start C:\\wh6模拟版\\mytrader_wh.exe')\n cts = self.get_ct()\n count = 0\n while '赢顺云交易' not in ''.join(cts): # 若窗口没打开，则过一秒后再次检查\n time.sleep(1)\n count += 1\n cts = self.get_ct()\n if not count % 30:\n os.system('taskkill /F /IM mytrader_wh.exe')\n os.system('start C:\\wh6模拟版\\mytrader_wh.exe')\n d2 = [i for i in cts if '赢顺云交易' in i][0]\n win = win32gui.FindWindow(None, d2)\n return win\n\n def runs(self):\n \"\"\" 循环点击文华财经以刷新本地文件 \"\"\"\n win = self.start_wh()\n ct = []\n for i in range(65536):\n tid = win32gui.FindWindowEx(win, None, i, None)\n if tid != 0:\n ct.append(tid)\n if len(ct) == 2:\n break\n hEdit = win32.user32.FindWindowExW(ct[-1], None, 'Edit', None)\n WM_CHAR = 0x0102\n min1_zb = (258, 32)\n # aj=OrderedDict({'wp':(16,330), 'wpzlhy':(906,999), 'hz=':(131,94), 'min1':(260,35), 'back_off':(19,31)})\n x, y = pag.position() # 原来鼠标坐标\n # win32gui.ShowWindow(win, win32con.SW_MAXIMIZE) # 全屏\n time.sleep(0.1)\n self.sbdj(*min1_zb)\n time.sleep(0.5)\n win32api.SetCursorPos([x, y]) # 为鼠标还原到原来的坐标\n win32api.mouse_event(win32con.MOUSEEVENTF_LEFTDOWN, 0, 0, 0, 0)\n win32api.mouse_event(win32con.MOUSEEVENTF_LEFTUP, 0, 0, 0, 0)\n win32gui.CloseWindow(win) # 最小化\n start_time = 0\n\n # 打开代码为name的产品\n def start_name(name):\n len_name = len(name)\n for i in range(len_name):\n win32.user32.SendMessageW(hEdit, WM_CHAR, ord(name[i]), None)\n time.sleep(0.05)\n if i == len_name - 1:\n time.sleep(0.1)\n try:\n # 进行回车确认\n # win32gui.SetForegroundWindow(hEdit)\n # win32api.keybd_event(13, 0, 0, 0)\n # win32api.keybd_event(13, 0, win32con.KEYEVENTF_KEYUP, 0)\n win32gui.PostMessage(hEdit, win32con.WM_KEYDOWN, win32con.VK_RETURN, 0)\n win32gui.PostMessage(hEdit, win32con.WM_KEYUP, win32con.VK_RETURN, 0)\n except:\n self.runs()\n\n while 1:\n t2 = time.time()\n t = time.localtime(t2)\n t_min = t.tm_hour * 60 + t.tm_min\n if t.tm_hour == 12 or (16 * 60 + 30 < t_min < 17 * 60 + 15) or (0 < t_min < 9 * 60 + 15):\n continue\n\n # 要更新的产品代码\n names = {'7204': 'HSIJ9', '7121': 'HSI', '7253': 'MHI',\n '7234': 'HHI', '8618': 'IF',\n '8633': 'IH', '8693': 'IC', '7214': 'HSI'}\n for name in names:\n if name not in {'7253', '7214'}:\n if t2 - start_time < 600:\n continue\n elif name in {'8618', '8633', '8693'} and t.tm_hour > 14:\n continue\n else:\n start_time = 1\n print('更新产品：', name, names[name], '...')\n start_name(name)\n time.sleep(5)\n # win32gui.SetForegroundWindow(win) # 指定句柄设置为前台，也就是激活\n # win32gui.SetBkMode(win, win32con.TRANSPARENT) # 设置为后台\n start_time = t2 if start_time == 1 else start_time\n time.sleep(60)\n\n\nif __name__ == '__main__':\n # res = main(d)\n # with open('res.txt','w') as f:\n #\tf.write(json.dumps(res))\n # to_sql(conn,res[:-1])\n whcj = WHCJ()\n print(whcj.main())\n", "sub_path": "2018/wh_dat.py", "file_name": "wh_dat.py", "file_ext": "py", "file_size_in_byte": 14146, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "redis.Redis", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "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": "sys.path.append", "line_number": 29, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "myconn.myconn.get_conn", "line_number": 41, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 42, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 44, "usage_type": "call"}, {"api_name": "struct.unpack", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 91, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 91, "usage_type": "name"}, {"api_name": "struct.unpack", "line_number": 116, "usage_type": "call"}, {"api_name": "datetime.datetime.fromtimestamp", "line_number": 125, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 125, "usage_type": "name"}, {"api_name": "pickle.dumps", "line_number": 202, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 209, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 209, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 234, "usage_type": "call"}, {"api_name": "os.sep", "line_number": 242, "usage_type": "attribute"}, {"api_name": "win32api.SetCursorPos", "line_number": 251, "usage_type": "call"}, {"api_name": "win32api.mouse_event", "line_number": 252, "usage_type": "call"}, {"api_name": "win32con.MOUSEEVENTF_LEFTDOWN", "line_number": 252, "usage_type": "attribute"}, {"api_name": "win32api.mouse_event", "line_number": 253, "usage_type": "call"}, {"api_name": "win32con.MOUSEEVENTF_LEFTUP", "line_number": 253, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 257, "usage_type": "call"}, {"api_name": "win32api.keybd_event", "line_number": 258, "usage_type": "call"}, {"api_name": "win32api.keybd_event", "line_number": 259, "usage_type": "call"}, {"api_name": "win32con.KEYEVENTF_KEYUP", "line_number": 259, "usage_type": "attribute"}, {"api_name": "win32gui.IsWindow", "line_number": 267, "usage_type": "call"}, {"api_name": "win32gui.IsWindowEnabled", "line_number": 267, "usage_type": "call"}, {"api_name": "win32gui.IsWindowVisible", "line_number": 267, "usage_type": "call"}, {"api_name": "win32gui.GetWindowText", "line_number": 268, "usage_type": "call"}, {"api_name": "win32gui.EnumWindows", "line_number": 270, "usage_type": "call"}, {"api_name": "os.system", "line_number": 276, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 280, "usage_type": "call"}, {"api_name": "os.system", "line_number": 284, "usage_type": "call"}, {"api_name": "os.system", "line_number": 285, "usage_type": "call"}, {"api_name": "win32gui.FindWindow", "line_number": 287, "usage_type": "call"}, {"api_name": "win32gui.FindWindowEx", "line_number": 295, "usage_type": "call"}, {"api_name": "ctypes.windll.user32.FindWindowExW", "line_number": 300, "usage_type": "call"}, {"api_name": "ctypes.windll.user32", "line_number": 300, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 300, "usage_type": "name"}, {"api_name": "pyautogui.position", "line_number": 304, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 306, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 308, "usage_type": "call"}, {"api_name": "win32api.SetCursorPos", "line_number": 309, "usage_type": "call"}, {"api_name": "win32api.mouse_event", "line_number": 310, "usage_type": "call"}, {"api_name": "win32con.MOUSEEVENTF_LEFTDOWN", "line_number": 310, "usage_type": "attribute"}, {"api_name": "win32api.mouse_event", "line_number": 311, "usage_type": "call"}, {"api_name": "win32con.MOUSEEVENTF_LEFTUP", "line_number": 311, "usage_type": "attribute"}, {"api_name": "win32gui.CloseWindow", "line_number": 312, "usage_type": "call"}, {"api_name": "ctypes.windll.user32.SendMessageW", "line_number": 319, "usage_type": "call"}, {"api_name": "ctypes.windll.user32", "line_number": 319, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 319, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 320, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 322, "usage_type": "call"}, {"api_name": "win32gui.PostMessage", "line_number": 328, "usage_type": "call"}, {"api_name": "win32con.WM_KEYDOWN", "line_number": 328, "usage_type": "attribute"}, {"api_name": "win32con.VK_RETURN", "line_number": 328, "usage_type": "attribute"}, {"api_name": "win32gui.PostMessage", "line_number": 329, "usage_type": "call"}, {"api_name": "win32con.WM_KEYUP", "line_number": 329, "usage_type": "attribute"}, {"api_name": "win32con.VK_RETURN", "line_number": 329, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 334, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 335, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 354, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 358, "usage_type": "call"}]} +{"seq_id": "613501501", "text": "import caffe\nimport numpy as np\nfrom sklearn.metrics import confusion_matrix\n\nclass Measure_Layer(caffe.Layer):\n\t# Setup method\n\tdef setup(self, bottom, top):\n\t\t# We want two bottom blobs, the labels and the predictions\n\t\tif len(bottom) != 2:\n\t\t\traise Exception(\n\t\t\t\t\"Wrong number of bottom blobs (prediction and label)\")\n\n\t\t# And some top blobs, depending on the phase\n\t\tif len(top) != 4:\n\t\t\traise Exception(\"Wrong number of top blobs (acc, specificity, sensitivity, precision)\")\n\n\t\tself.accuracy = -1\n\t\tself.specificity = -1\n\t\tself.sensitivity = -1\n\t\tself.precision = -1\n\n\tdef reshape(self, bottom, top):\n\t\t\"\"\"\n\t\tWe don't need to reshape or instantiate anything that is input-size sensitive\n\t\t\"\"\"\n\t\ttop[0].reshape(1)\n\t\ttop[1].reshape(1)\n\t\ttop[2].reshape(1)\n\t\ttop[3].reshape(1)\n\n\t# Forward method\n\tdef forward(self, bottom, top):\n\t\tpredictions = bottom[0].data\n\t\tlabel = bottom[1].data\n\t\tpred = predictions[:,1,:,:]\n\n\t\ty_true = np.reshape(label,(label.size))\n\t\ty_scores = np.reshape(pred,(pred.size))\n\t\t#Confusion matrix\n\t\tthreshold_confusion = 0.5\n\t\ty_pred = np.zeros(y_scores.shape[0])\n\t\ty_pred[y_scores>=threshold_confusion] = 1\n\n\t\tconfusion = confusion_matrix(y_true, y_pred)\n\t\tif float(np.sum(confusion))!=0:\n\t\t self.accuracy = float(confusion[0,0]+confusion[1,1])/float(np.sum(confusion))\n\t\tif float(confusion[0,0]+confusion[0,1])!=0:\n\t\t self.specificity = float(confusion[0,0])/float(confusion[0,0]+confusion[0,1])\n\t\tif float(confusion[1,1]+confusion[1,0])!=0:\n\t\t self.sensitivity = float(confusion[1,1])/float(confusion[1,1]+confusion[1,0])\n\t\tif float(confusion[1,1]+confusion[0,1])!=0:\n\t\t self.precision = float(confusion[1,1])/float(confusion[1,1]+confusion[0,1])\n\n\t\ttop[0].data[0] = self.accuracy\n\t\ttop[1].data[0] = self.specificity\n\t\ttop[2].data[0] = self.sensitivity\n\t\ttop[3].data[0] = self.precision \n\n\tdef backward(self, bottom, top):\n\t\t\"\"\"\n\t\tThis layer does not back propagate\n\t\t\"\"\"\n\t\tpass\n", "sub_path": "measure.py", "file_name": "measure.py", "file_ext": "py", "file_size_in_byte": 1920, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "caffe.Layer", "line_number": 5, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 41, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "632604251", "text": "from typing import List\nimport collections\n\nclass Solution:\n # DFS\n #时间复杂度：O(R×C)。其中 RR 是给定网格中的行数，CC 是列数。我们访问每个网格最多一次。\n #空间复杂度：O(R×C)，递归的深度最大可能是整个网格的大小，因此最大可能使用 O(R×C) 的栈空间。\n\n def maxAreaOfIsland(self, grid: List[List[int]]) -> int:\n def dfs(r, c):\n if r < 0 or r >= R or c < 0 or c >= C or (r, c) in seen or grid[r][c] == 0:\n return 0\n seen.add((r, c))\n return 1 + dfs(r + 1, c) + dfs(r, c + 1) + dfs(r - 1, c) + dfs(r, c - 1)\n\n\n R = len(grid)\n C = len(grid[0])\n seen = set()\n mx = 0\n for i in range(R):\n for j in range(C):\n if grid[i][j] == 1:\n mx = max(mx, dfs(i, j))\n return mx\n # DFS + stack\n # 把接下来想要遍历的土地放在栈里，然后在取出这些土地的时候访问它们。\n # 访问每一片土地时，我们将对围绕它四个方向进行探索，找到还未访问的土地，加入到栈 stack 中；\n # 另外，只要栈 stack 不为空，就说明我们还有土地待访问，那么就从栈中取出一个元素并访问\n def maxAreaOfIsland2(self, grid: List[List[int]]) -> int:\n R = len(grid)\n C = len(grid[0])\n stack = []\n mx = 0\n for i, l in enumerate(grid):\n for j, val in enumerate(l):\n cur = 0\n stack.append((i, j))\n while stack:\n r, c = stack.pop()\n if r < 0 or r >= R or c < 0 or c >= C or grid[r][c] == 0:\n continue\n cur += 1\n grid[r][c] = 0\n for di, dj in [[0, 1], [0, -1],[1, 0],[-1,0]]:\n next_r, next_c = r + di, c + di\n stack.append((next_r,next_c))\n mx = max(mx, cur)\n # BFS\n def maxAreaOfIsland3(self, grid: List[List[int]]) -> int:\n ans = 0\n for i, l in enumerate(grid):\n for j, n in enumerate(l):\n cur = 0\n q = collections.deque([(i, j)])\n while q:\n cur_i, cur_j = q.popleft()\n if cur_i < 0 or cur_j < 0 or cur_i == len(grid) or cur_j == len(grid[0]) or grid[cur_i][cur_j] != 1:\n continue\n cur += 1\n grid[cur_i][cur_j] = 0\n for di, dj in [[0, 1], [0, -1], [1, 0], [-1, 0]]:\n next_i, next_j = cur_i + di, cur_j + dj\n q.append((next_i, next_j))\n ans = max(ans, cur)\n return ans\n\n\n\nso = Solution()\ngrid = [[0,0,1,0,0,0,0,1,0,0,0,0,0],\n [0,0,0,0,0,0,0,1,1,1,0,0,0],\n [0,1,1,0,1,0,0,0,0,0,0,0,0],\n [0,1,0,0,1,1,0,0,1,0,1,0,0],\n [0,1,0,0,1,1,0,0,1,1,1,0,0],\n [0,0,0,0,0,0,0,0,0,0,1,0,0],\n [0,0,0,0,0,0,0,1,1,1,0,0,0],\n [0,0,0,0,0,0,0,1,1,0,0,0,0]]\n\n\nprint(so.maxAreaOfIsland(grid))", "sub_path": "DFS-BFS/695-MaxAreaofIsland.py", "file_name": "695-MaxAreaofIsland.py", "file_ext": "py", "file_size_in_byte": 3083, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "typing.List", "line_number": 9, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 30, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 50, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "619854597", "text": "#\n# Copyright (C) 2019 Luca Pasqualini\n# University of Siena - Artificial Intelligence Laboratory - SAILab\n#\n#\n# TicTacToeRL project is licensed under a BSD 3-Clause.\n#\n# You should have received a copy of the license along with this\n# work. If not, see <https://opensource.org/licenses/BSD-3-Clause>.\n\n# Import packages\n\nimport tensorflow\nimport logging\nimport os\n\n# Import usienarl\n\nfrom usienarl import Config, LayerType, run_experiment, command_line_parse\nfrom usienarl.td_models import DuelingDeepQLearning\nfrom usienarl.exploration_policies import BoltzmannExplorationPolicy\n\n# Import required src\n\nfrom src.dddql_tictactoe_agent import DDDQLTicTacToeAgent\nfrom src.tictactoe_experiment import TicTacToeExperiment\nfrom src.tictactoe_environment_random import TicTacToeEnvironmentRandom, Player\nfrom src.tictactoe_environment_fixed import TicTacToeEnvironmentFixed\nfrom src.tictactoe_pass_through_interface import TicTacToePassThroughInterface\n\n# Define utility functions to run the experiment\n\n\ndef _define_dddqn_model(config: Config, learning_rate: float) -> DuelingDeepQLearning:\n # Define attributes\n discount_factor: float = 0.99\n buffer_capacity: int = 100000\n minimum_sample_probability: float = 0.01\n random_sample_trade_off: float = 0.6\n importance_sampling_value_increment: float = 0.4\n importance_sampling_value: float = 0.001\n error_clip: bool = True\n # Return the _model\n return DuelingDeepQLearning(\"model\",\n learning_rate, discount_factor,\n buffer_capacity,\n minimum_sample_probability, random_sample_trade_off,\n importance_sampling_value, importance_sampling_value_increment,\n config, error_clip)\n\n\ndef _define_boltzmann_exploration_policy(temperature_max: float, temperature_min: float) -> BoltzmannExplorationPolicy:\n # Define attributes\n temperature_decay: float = 0.00002\n # Return the explorer\n return BoltzmannExplorationPolicy(temperature_max, temperature_min, temperature_decay)\n\n\ndef _define_curriculum_agent(model: DuelingDeepQLearning,\n exploration_policy: BoltzmannExplorationPolicy,\n warmup_random_action_probability: float) -> DDDQLTicTacToeAgent:\n # Define attributes\n weight_copy_step_interval: int = 100\n batch_size: int = 150\n # Return the agent\n return DDDQLTicTacToeAgent(\"dddqn_curriculum_agent\", model, exploration_policy, weight_copy_step_interval, batch_size, warmup_random_action_probability)\n\n\nif __name__ == \"__main__\":\n # Parse the command line arguments\n workspace_path, experiment_iterations_number, cuda_devices, render_during_training, render_during_validation, render_during_test = command_line_parse()\n # Define the CUDA devices in which to run the experiment\n os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = cuda_devices\n # Define the logger\n logger: logging.Logger = logging.getLogger(__name__)\n logger.setLevel(logging.INFO)\n # Generate Tic Tac Toe environments with random and fixed environment player and using the O player as the environment player with two reward types\n # Tic Tac Toe random environment:\n # - success threshold to consider both the training completed and the experiment successful is around 95% of match won by the agent (depending on reward assigned)\n environment_name: str = 'TicTacToeRandom'\n environment_random_low_reward: TicTacToeEnvironmentRandom = TicTacToeEnvironmentRandom(environment_name, Player.o,\n 1.0, -0.1, 0.0)\n environment_random_high_reward: TicTacToeEnvironmentRandom = TicTacToeEnvironmentRandom(environment_name, Player.o,\n 100.0, -10.0, 0.0)\n # Tic Tac Toe fixed environment:\n # - success threshold to consider both the training completed and the experiment successful is around 65% of match won by the agent (depending on reward assigned)\n environment_name: str = 'TicTacToeFixed'\n environment_fixed_low_reward: TicTacToeEnvironmentFixed = TicTacToeEnvironmentFixed(environment_name, Player.o,\n 1.0, -0.1, 0.0)\n environment_fixed_high_reward: TicTacToeEnvironmentFixed = TicTacToeEnvironmentFixed(environment_name, Player.o,\n 100.0, -10.0, 0.0)\n # Define Neural Network layers\n nn_config: Config = Config()\n nn_config.add_hidden_layer(LayerType.dense, [1024, tensorflow.nn.relu, True, tensorflow.contrib.layers.xavier_initializer()])\n nn_config.add_hidden_layer(LayerType.dense, [1024, tensorflow.nn.relu, True, tensorflow.contrib.layers.xavier_initializer()])\n nn_config.add_hidden_layer(LayerType.dense, [1024, tensorflow.nn.relu, True, tensorflow.contrib.layers.xavier_initializer()])\n # Define model\n inner_model_first: DuelingDeepQLearning = _define_dddqn_model(nn_config, 0.000001)\n inner_model_second: DuelingDeepQLearning = _define_dddqn_model(nn_config, 0.000001)\n # Define exploration policies\n exploration_policy_first: BoltzmannExplorationPolicy = _define_boltzmann_exploration_policy(1.0, 0.1)\n exploration_policy_second: BoltzmannExplorationPolicy = _define_boltzmann_exploration_policy(0.85, 0.1)\n # Define agents\n dddqn_curriculum_agent_first: DDDQLTicTacToeAgent = _define_curriculum_agent(inner_model_first,\n exploration_policy_first,\n 1.0)\n dddqn_curriculum_agent_second: DDDQLTicTacToeAgent = _define_curriculum_agent(inner_model_second,\n exploration_policy_second,\n 0.25)\n # Define interfaces\n interface_low_reward_random: TicTacToePassThroughInterface = TicTacToePassThroughInterface(environment_random_low_reward)\n interface_high_reward_random: TicTacToePassThroughInterface = TicTacToePassThroughInterface(environment_random_high_reward)\n interface_low_reward_fixed: TicTacToePassThroughInterface = TicTacToePassThroughInterface(environment_fixed_low_reward)\n interface_high_reward_fixed: TicTacToePassThroughInterface = TicTacToePassThroughInterface(environment_fixed_high_reward)\n # Define experiments\n success_threshold: float = 0.95\n experiment_low_reward_random: TicTacToeExperiment = TicTacToeExperiment(\"experiment_low_reward\", success_threshold,\n environment_random_low_reward,\n dddqn_curriculum_agent_first, interface_low_reward_random)\n success_threshold: float = 0.65\n experiment_low_reward_fixed: TicTacToeExperiment = TicTacToeExperiment(\"experiment_low_reward\", success_threshold,\n environment_fixed_low_reward,\n dddqn_curriculum_agent_second, interface_low_reward_fixed)\n success_threshold: float = 95.0\n experiment_high_reward_random: TicTacToeExperiment = TicTacToeExperiment(\"experiment_high_reward\", success_threshold,\n environment_random_high_reward,\n dddqn_curriculum_agent_first, interface_high_reward_random)\n success_threshold: float = 65.0\n experiment_high_reward_fixed: TicTacToeExperiment = TicTacToeExperiment(\"experiment_low_reward\", success_threshold,\n environment_fixed_high_reward,\n dddqn_curriculum_agent_second, interface_high_reward_fixed)\n # Define experiments data\n testing_episodes: int = 100\n test_cycles: int = 10\n training_episodes: int = 1000\n validation_episodes: int = 100\n max_training_episodes: int = 50000\n episode_length_max: int = 20\n # Run curriculum experiments for low reward\n saved_metagraph_paths: [] = run_experiment(experiment_low_reward_random,\n training_episodes,\n max_training_episodes, episode_length_max,\n validation_episodes,\n testing_episodes, test_cycles,\n render_during_training, render_during_validation, render_during_test,\n workspace_path, __file__,\n logger, None, experiment_iterations_number)\n for metagraph_path in saved_metagraph_paths:\n run_experiment(experiment_low_reward_fixed,\n training_episodes,\n max_training_episodes, episode_length_max,\n validation_episodes,\n testing_episodes, test_cycles,\n render_during_training, render_during_validation, render_during_test,\n workspace_path, __file__,\n logger, metagraph_path)\n # Run curriculum experiments for high reward\n saved_metagraph_paths: [] = run_experiment(experiment_high_reward_random,\n training_episodes,\n max_training_episodes, episode_length_max,\n validation_episodes,\n testing_episodes, test_cycles,\n render_during_training, render_during_validation, render_during_test,\n workspace_path, __file__,\n logger, None, experiment_iterations_number)\n for metagraph_path in saved_metagraph_paths:\n run_experiment(experiment_high_reward_fixed,\n training_episodes,\n max_training_episodes, episode_length_max,\n validation_episodes,\n testing_episodes, test_cycles,\n render_during_training, render_during_validation, render_during_test,\n workspace_path, __file__,\n logger, metagraph_path)\n\n\n", "sub_path": "tictactoe_dddql_curriculum_random_fixed.py", "file_name": "tictactoe_dddql_curriculum_random_fixed.py", "file_ext": "py", "file_size_in_byte": 10980, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "usienarl.Config", "line_number": 34, "usage_type": "name"}, {"api_name": "usienarl.td_models.DuelingDeepQLearning", "line_number": 44, "usage_type": "call"}, {"api_name": "usienarl.td_models.DuelingDeepQLearning", "line_number": 34, "usage_type": "name"}, {"api_name": "usienarl.exploration_policies.BoltzmannExplorationPolicy", "line_number": 56, "usage_type": "call"}, {"api_name": "usienarl.exploration_policies.BoltzmannExplorationPolicy", "line_number": 52, "usage_type": "name"}, {"api_name": "usienarl.td_models.DuelingDeepQLearning", "line_number": 59, "usage_type": "name"}, {"api_name": "usienarl.exploration_policies.BoltzmannExplorationPolicy", "line_number": 60, "usage_type": "name"}, {"api_name": "src.dddql_tictactoe_agent.DDDQLTicTacToeAgent", "line_number": 66, "usage_type": "call"}, {"api_name": "src.dddql_tictactoe_agent.DDDQLTicTacToeAgent", "line_number": 61, "usage_type": "name"}, {"api_name": "usienarl.command_line_parse", "line_number": 71, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 73, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 74, "usage_type": "attribute"}, {"api_name": "logging.Logger", "line_number": 76, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 76, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 77, "usage_type": "attribute"}, {"api_name": "src.tictactoe_environment_random.TicTacToeEnvironmentRandom", "line_number": 82, "usage_type": "name"}, {"api_name": "src.tictactoe_environment_random.Player.o", "line_number": 82, "usage_type": "attribute"}, {"api_name": "src.tictactoe_environment_random.Player", "line_number": 82, "usage_type": "name"}, {"api_name": "src.tictactoe_environment_random.TicTacToeEnvironmentRandom", "line_number": 84, "usage_type": "name"}, {"api_name": "src.tictactoe_environment_random.Player.o", "line_number": 84, "usage_type": "attribute"}, {"api_name": "src.tictactoe_environment_random.Player", "line_number": 84, "usage_type": "name"}, {"api_name": "src.tictactoe_environment_fixed.TicTacToeEnvironmentFixed", "line_number": 89, "usage_type": "name"}, {"api_name": "src.tictactoe_environment_random.Player.o", "line_number": 89, "usage_type": "attribute"}, {"api_name": "src.tictactoe_environment_random.Player", "line_number": 89, "usage_type": "name"}, {"api_name": "src.tictactoe_environment_fixed.TicTacToeEnvironmentFixed", "line_number": 91, "usage_type": "name"}, {"api_name": "src.tictactoe_environment_random.Player.o", "line_number": 91, "usage_type": "attribute"}, {"api_name": "src.tictactoe_environment_random.Player", "line_number": 91, "usage_type": "name"}, {"api_name": "usienarl.Config", "line_number": 94, "usage_type": "name"}, {"api_name": "usienarl.LayerType.dense", "line_number": 95, "usage_type": "attribute"}, {"api_name": "usienarl.LayerType", "line_number": 95, "usage_type": "name"}, {"api_name": "tensorflow.nn", "line_number": 95, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.layers.xavier_initializer", "line_number": 95, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 95, "usage_type": "attribute"}, {"api_name": "usienarl.LayerType.dense", "line_number": 96, "usage_type": "attribute"}, {"api_name": "usienarl.LayerType", "line_number": 96, "usage_type": "name"}, {"api_name": "tensorflow.nn", "line_number": 96, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.layers.xavier_initializer", "line_number": 96, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 96, "usage_type": "attribute"}, {"api_name": "usienarl.LayerType.dense", "line_number": 97, "usage_type": "attribute"}, {"api_name": "usienarl.LayerType", "line_number": 97, "usage_type": "name"}, {"api_name": "tensorflow.nn", "line_number": 97, "usage_type": "attribute"}, {"api_name": "tensorflow.contrib.layers.xavier_initializer", "line_number": 97, "usage_type": "call"}, {"api_name": "tensorflow.contrib", "line_number": 97, "usage_type": "attribute"}, {"api_name": "usienarl.td_models.DuelingDeepQLearning", "line_number": 99, "usage_type": "name"}, {"api_name": "usienarl.td_models.DuelingDeepQLearning", "line_number": 100, "usage_type": "name"}, {"api_name": "usienarl.exploration_policies.BoltzmannExplorationPolicy", "line_number": 102, "usage_type": "name"}, {"api_name": "usienarl.exploration_policies.BoltzmannExplorationPolicy", "line_number": 103, "usage_type": "name"}, {"api_name": "src.dddql_tictactoe_agent.DDDQLTicTacToeAgent", "line_number": 105, "usage_type": "name"}, {"api_name": "src.dddql_tictactoe_agent.DDDQLTicTacToeAgent", "line_number": 108, "usage_type": "name"}, {"api_name": "src.tictactoe_pass_through_interface.TicTacToePassThroughInterface", "line_number": 112, "usage_type": "name"}, {"api_name": "src.tictactoe_pass_through_interface.TicTacToePassThroughInterface", "line_number": 113, "usage_type": "name"}, {"api_name": "src.tictactoe_pass_through_interface.TicTacToePassThroughInterface", "line_number": 114, "usage_type": "name"}, {"api_name": "src.tictactoe_pass_through_interface.TicTacToePassThroughInterface", "line_number": 115, "usage_type": "name"}, {"api_name": "src.tictactoe_experiment.TicTacToeExperiment", "line_number": 118, "usage_type": "name"}, {"api_name": "src.tictactoe_experiment.TicTacToeExperiment", "line_number": 122, "usage_type": "name"}, {"api_name": "src.tictactoe_experiment.TicTacToeExperiment", "line_number": 126, "usage_type": "name"}, {"api_name": "src.tictactoe_experiment.TicTacToeExperiment", "line_number": 130, "usage_type": "name"}, {"api_name": "usienarl.run_experiment", "line_number": 141, "usage_type": "call"}, {"api_name": "usienarl.run_experiment", "line_number": 150, "usage_type": "call"}, {"api_name": "usienarl.run_experiment", "line_number": 159, "usage_type": "call"}, {"api_name": "usienarl.run_experiment", "line_number": 168, "usage_type": "call"}]} +{"seq_id": "481085561", "text": "from __future__ import division\nimport argparse\n\nfrom PIL import Image\nimport numpy as np\nimport gym\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Activation, Flatten, Convolution2D, Permute, LSTM, TimeDistributed, Input, Lambda\nfrom keras.optimizers import Adam\nimport keras.backend as K\n\nfrom rl.agents.dqn import DQNAgent\nfrom rl.policy import LinearAnnealedPolicy, BoltzmannQPolicy, EpsGreedyQPolicy\nfrom rl.core import Processor\nfrom rl.callbacks import FileLogger, ModelIntervalCheckpoint\n\nbatch_size = 32\nINPUT_SHAPE = (84, 84)\nIN_SHAPE = (batch_size, None, 1, 84, 84)\nIN_SHAPE_I = (None, 1, 84, 84)\n\nfrom rl.core import Agent\nfrom rl.agents.dqn import AbstractDQNAgent\nfrom rl.util import *\nfrom rl.memory import *\n\nfrom keras.layers import Input, Dense, Reshape, Flatten, Permute, TimeDistributed, Activation, Lambda, multiply, subtract, concatenate\nfrom keras.layers import SimpleRNN, LSTM, GRU\nfrom keras.models import Model\nfrom keras.losses import kullback_leibler_divergence\nfrom keras.regularizers import L1L2, Regularizer\nfrom keras import backend as K\nfrom keras.engine.topology import Layer\nimport numpy as np\n\nlatent_dim = 32 # Latent space dimensionality\nreg_lambda = 1e-4 # Global regularization coefficient\ndecoder_loss = 1.0 # Weight of the decoder loss\n\ndef linear_objective(y_true, y_pred):\n return 1.0 - 1.0 * K.mean(y_pred, axis=-1)\n\ndef linear_regularizer(x):\n return K.mean(x)\n\ndef mse_regularizer(x):\n return K.mean(K.square(x))\n\ntime_dim = 1\n\n## Define Extra Layers to Use\nclass Regularize(Layer):\n def __init__(self, regularizer_function, **kwargs):\n super(Regularize, self).__init__(**kwargs)\n self.supports_masking = True\n self.activity_regularizer = regularizer_function\n def get_config(self):\n config = {'regularizer_function': regularizer_function}\n base_config = super(Regularize, self).get_config()\n return dict(list(base_config.items()) + list(config.items()))\n\nclass ApplyRegularization(Layer):\n def __init__(self, **kwargs):\n super(ApplyRegularization, self).__init__(**kwargs)\n def build(self, input_shape):\n super(ApplyRegularization, self).build(input_shape)\n def call(self, x):\n return x[0]\n\nclass CELayer(Layer):\n def __init__(self, **kwargs):\n super(CELayer, self).__init__(**kwargs)\n def build(self, input_shape):\n super(CELayer, self).build(input_shape)\n def call(self, x):\n return -1. * kullback_leibler_divergence(x[0], x[1])\n def compute_output_shape(self, input_shape):\n return (None, IN_SHAPE_I[1]-1)\n\nclass CPLayer(Layer):\n def __init__(self, **kwargs):\n super(CPLayer, self).__init__(**kwargs)\n def build(self, input_shape):\n super(CPLayer, self).build(input_shape)\n def call(self, x):\n y_true = K.l2_normalize(x[0], axis=-1)\n y_pred = K.l2_normalize(x[1], axis=-1)\n return K.sum(y_true * y_pred, axis=-1)\n def compute_output_shape(self, input_shape):\n return (None, IN_SHAPE_I[1]-1)\n\nclass SoftmaxDropout(Layer):\n def __init__(self, mean, stddev, **kwargs):\n super(SoftmaxDropout, self).__init__(**kwargs)\n self.supports_masking = True\n self.mean = mean\n self.stddev = stddev\n\n def call(self, x, training=None):\n return K.cast(K.greater(x + K.random_normal(shape=K.shape(x),\n mean=self.mean,\n stddev=self.stddev), 0.5), 'float32')\n\nclass LinearRegularizer(Regularizer):\n def __init__(self, c=0.):\n self.c = c\n def __call__(self, x):\n regularization = 0.\n regularization += K.sum(self.c * x)\n return regularization\n def get_config(self):\n return {'c': float(self.c)}\n\n## Define the Representation, Consciousness and Generator models\ndef representation_rnn():\n i = Input(shape=IN_SHAPE[1:])\n i = Permute((0, 1, 2, 3), batch_input_shape = IN_SHAPE)(i)\n x = TimeDistributed(Flatten())(i)\n x = GRU(latent_dim, return_sequences = True)(x)\n model = Model(inputs=i, outputs=x, name='Representation')\n return model\n\ndef consciousness_rnn():\n i = Input(shape=(IN_SHAPE_I[1], latent_dim))\n x_gru = GRU(latent_dim, return_sequences = True)(i)\n x = TimeDistributed(Dense(latent_dim, activation='sigmoid'))(x_gru)\n xa_probabilities = x\n xa = SoftmaxDropout(0.,1.0)(x)\n x = TimeDistributed(Dense(latent_dim, activation='sigmoid'))(x_gru)\n xb_probabilities = x\n xb = SoftmaxDropout(0.,1.0)(x)\n model = Model(inputs=i, outputs=[xa, xb, xa_probabilities, xb_probabilities], name='Consciousness')\n return model\n\ndef generator_rnn():\n ia = Input(shape=(IN_SHAPE_I[1], latent_dim))\n ib = Input(shape=(IN_SHAPE_I[1], latent_dim))\n ic = Input(shape=(IN_SHAPE_I[1], latent_dim))\n i = concatenate([ia, ib, ic])\n x = GRU(latent_dim, return_sequences = True)(i)\n model = Model(inputs=[ia, ib, ic], outputs=x, name='Generator')\n return model\n\ndef decoder_rnn(output_dim):\n i = Input(shape=(IN_SHAPE_I[1], latent_dim))\n x = GRU(32, return_sequences = True)(i)\n x = Dense(output_dim)(x)\n model = Model(inputs=i, outputs=x, name='Decoder')\n return model\n\nprint(IN_SHAPE)\n## Start constructing the circuit\n\ndef mean_q(y_true, y_pred):\n return K.mean(K.max(y_pred, axis=-1))\n\nINPUT_SHAPE = (84, 84)\nWINDOW_LENGTH = 4\n\n\nEpisodicTimestep = namedtuple('EpisodicTimestep', 'observation, action, reward, terminal')\n\n\nclass RecurrentDQNAgent(AbstractDQNAgent):\n def __init__(self, model, policy=EpsGreedyQPolicy(), enable_double_dqn=True,\n target_model=None, policy_model=None,\n nb_max_steps_recurrent_unrolling=100, *args, **kwargs):\n super(RecurrentDQNAgent, self).__init__(*args, **kwargs)\n\n # Validate (important) input.\n if hasattr(model.output, '__len__') and len(model.output) > 1:\n raise ValueError('Model \"{}\" has more than one output. DQN expects a model that has a single output.'.format(model))\n if model.output._keras_shape[-1] != self.nb_actions:\n raise ValueError('Model output \"{}\" has invalid shape. DQN expects a model that has one dimension for each action, in this case {}.'.format(model.output, self.nb_actions))\n\n # Validate settings for recurrent DQN.\n self.is_recurrent = True\n if self.is_recurrent:\n if enable_double_dqn:\n raise ValueError('DoubleDQN (`enable_double_dqn = True`) is currently not supported for recurrent Q learning.')\n memory = kwargs['memory']\n if not memory.is_episodic:\n raise ValueError('Recurrent Q learning requires an episodic memory. You are trying to use it with memory={} instead.'.format(memory))\n if nb_max_steps_recurrent_unrolling and not model.stateful:\n raise ValueError('Recurrent Q learning with max. unrolling requires a stateful model.')\n if policy_model is None or not policy_model.stateful:\n raise ValueError('Recurrent Q learning requires a separate stateful policy model with batch_size=1. Please refer to an example to see how to properly set it up.')\n\n # Parameters.\n self.enable_double_dqn = enable_double_dqn\n self.nb_max_steps_recurrent_unrolling = nb_max_steps_recurrent_unrolling\n\n # Related objects.\n self.model = model\n self.target_model = target_model\n self.policy_model = policy_model if policy_model is not None else model\n self.policy = policy\n\n # State.\n self.reset_states()\n\n def get_config(self):\n config = super(RecurrentDQNAgent, self).get_config()\n config['enable_double_dqn'] = self.enable_double_dqn\n config['nb_max_steps_recurrent_unrolling'] = self.nb_max_steps_recurrent_unrolling\n config['model'] = get_object_config(self.model)\n config['policy'] = get_object_config(self.policy)\n if self.compiled:\n config['target_model'] = get_object_config(self.target_model)\n return config\n\n def compile(self, optimizer, metrics=[]):\n metrics += [mean_q] # register default metrics\n\n # We never train the target model, hence we can set the optimizer and loss arbitrarily.\n if self.target_model is None:\n self.target_model = clone_model(self.model, self.custom_model_objects)\n self.target_model.compile(optimizer='sgd', loss='mse')\n self.model.compile(optimizer='sgd', loss='mse')\n\n # Compile model.\n updates = []\n if self.target_model_update < 1.:\n # We use the `AdditionalUpdatesOptimizer` to efficiently soft-update the target model.\n updates += get_soft_target_model_updates(self.target_model, self.model, self.target_model_update)\n if self.policy_model is not self.model:\n # Update the policy model after every training step.\n updates += get_soft_target_model_updates(self.policy_model, self.model, 1.)\n if len(updates) > 0:\n optimizer = AdditionalUpdatesOptimizer(optimizer, updates)\n\n def clipped_masked_mse(args):\n y_true, y_pred, mask = args\n delta = K.clip(y_true - y_pred, self.delta_clip[0], self.delta_clip[1])\n delta *= mask # apply element-wise mask\n loss = K.mean(K.square(delta), axis=-1)\n # Multiply by the number of actions to reverse the effect of the mean.\n loss *= float(self.nb_actions)\n return loss\n\n # Create trainable model. The problem is that we need to mask the output since we only\n # ever want to update the Q values for a certain action. The way we achieve this is by\n # using a custom Lambda layer that computes the loss. This gives us the necessary flexibility\n # to mask out certain parameters by passing in multiple inputs to the Lambda layer.\n input_shape = (None, self.nb_actions) if self.is_recurrent else (self.nb_actions,)\n output_shape = (None, 1) if self.is_recurrent else (1,)\n\n y_pred = self.model.output\n y_true = Input(name='y_true', shape=input_shape)\n mask = Input(name='mask', shape=input_shape)\n loss_out = Lambda(clipped_masked_mse, output_shape=output_shape, name='loss')([y_pred, y_true, mask])\n ins = [self.model.input] if type(self.model.input) is not list else self.model.input\n trainable_model = Model(input=ins + [y_true, mask], output=[loss_out, y_pred])\n assert len(trainable_model.output_names) == 2\n combined_metrics = {trainable_model.output_names[1]: metrics}\n losses = [\n lambda y_true, y_pred: y_pred, # loss is computed in Lambda layer\n lambda y_true, y_pred: K.zeros_like(y_pred), # we only include this for the metrics\n ]\n trainable_model.compile(optimizer=optimizer, loss=losses, metrics=combined_metrics)\n self.trainable_model = trainable_model\n\n self.update_target_model_hard()\n self.compiled = True\n\n def load_weights(self, filepath):\n self.model.load_weights(filepath)\n self.update_target_model_hard()\n\n def save_weights(self, filepath, overwrite=False):\n self.model.save_weights(filepath, overwrite=overwrite)\n\n def reset_states(self):\n self.recent_action = None\n self.recent_observation = None\n if self.compiled:\n self.model.reset_states()\n self.target_model.reset_states()\n self.policy_model.reset_states()\n\n def update_target_model_hard(self):\n self.target_model.set_weights(self.model.get_weights())\n if self.policy_model is not None:\n self.policy_model.set_weights(self.model.get_weights())\n\n def compute_q_values(self, state):\n batch = self.process_state_batch([state])\n if self.is_recurrent:\n # Add time axis.\n batch = batch.reshape((1,) + batch.shape) # (1, 1, ...)\n q_values = self.policy_model.predict_on_batch(batch).flatten()\n assert q_values.shape == (self.nb_actions,)\n return q_values\n\n def forward(self, observation):\n # Select an action.\n state = self.memory.get_recent_state(observation)\n q_values = self.compute_q_values(state)\n action = self.policy.select_action(q_values=q_values)\n if self.processor is not None:\n action = self.processor.process_action(action)\n\n # Book-keeping.\n self.recent_observation = observation\n self.recent_action = action\n\n return action\n\n def backward(self, reward, terminal):\n # Store most recent experience in memory.\n if self.step % self.memory_interval == 0:\n self.memory.append(self.recent_observation, self.recent_action, reward, terminal,\n training=self.training)\n\n metrics = [np.nan for _ in self.metrics_names]\n if not self.training:\n # We're done here. No need to update the experience memory since we only use the working\n # memory to obtain the state over the most recent observations.\n return metrics\n\n # Train the network on a single stochastic batch.\n if self.step > self.nb_steps_warmup and self.step % self.train_interval == 0:\n experiences = self.memory.sample(self.batch_size)\n assert len(experiences) == self.batch_size\n\n if self.is_recurrent:\n lengths = [len(seq) for seq in experiences]\n maxlen = np.max(lengths)\n\n # Start by extracting the necessary parameters (we use a vectorized implementation).\n state0_batch = [[] for _ in range(len(experiences))]\n reward_batch = [[] for _ in range(len(experiences))]\n action_batch = [[] for _ in range(len(experiences))]\n terminal1_batch = [[] for _ in range(len(experiences))]\n state1_batch = [[] for _ in range(len(experiences))]\n for sequence_idx, sequence in enumerate(experiences):\n for e in sequence:\n state0_batch[sequence_idx].append(e.state0)\n state1_batch[sequence_idx].append(e.state1)\n reward_batch[sequence_idx].append(e.reward)\n action_batch[sequence_idx].append(e.action)\n terminal1_batch[sequence_idx].append(0. if e.terminal1 else 1.)\n\n # Apply padding.\n state_shape = state0_batch[sequence_idx][-1].shape\n while len(state0_batch[sequence_idx]) < maxlen:\n state0_batch[sequence_idx].append(np.zeros(state_shape))\n state1_batch[sequence_idx].append(np.zeros(state_shape))\n reward_batch[sequence_idx].append(0.)\n action_batch[sequence_idx].append(0)\n terminal1_batch[sequence_idx].append(1.)\n\n state0_batch = self.process_state_batch(state0_batch)\n state1_batch = self.process_state_batch(state1_batch)\n terminal1_batch = np.array(terminal1_batch)\n reward_batch = np.array(reward_batch)\n assert reward_batch.shape == (self.batch_size, maxlen)\n assert terminal1_batch.shape == reward_batch.shape\n assert len(action_batch) == len(reward_batch)\n else:\n # Start by extracting the necessary parameters (we use a vectorized implementation).\n state0_batch = []\n reward_batch = []\n action_batch = []\n terminal1_batch = []\n state1_batch = []\n for e in experiences:\n state0_batch.append(e.state0)\n state1_batch.append(e.state1)\n reward_batch.append(e.reward)\n action_batch.append(e.action)\n terminal1_batch.append(0. if e.terminal1 else 1.)\n\n # Prepare and validate parameters.\n state0_batch = self.process_state_batch(state0_batch)\n state1_batch = self.process_state_batch(state1_batch)\n terminal1_batch = np.array(terminal1_batch)\n reward_batch = np.array(reward_batch)\n assert reward_batch.shape == (self.batch_size,)\n assert terminal1_batch.shape == reward_batch.shape\n assert len(action_batch) == len(reward_batch)\n\n # Compute Q values for mini-batch update.\n if self.enable_double_dqn:\n # Double DQN relies on the model for additional predictions, which we cannot use\n # since it must be stateful (we could save the state and re-apply, but this is\n # messy).\n assert not self.is_recurrent\n\n # According to the paper \"Deep Reinforcement Learning with Double Q-learning\"\n # (van Hasselt et al., 2015), in Double DQN, the online network predicts the actions\n # while the target network is used to estimate the Q value.\n if self.is_recurrent:\n self.model.reset_states()\n q_values = self.model.predict_on_batch(state1_batch)\n assert q_values.shape == (self.batch_size, self.nb_actions)\n actions = np.argmax(q_values, axis=1)\n assert actions.shape == (self.batch_size,)\n\n # Now, estimate Q values using the target network but select the values with the\n # highest Q value wrt to the online model (as computed above).\n if self.is_recurrent:\n self.target_model.reset_states()\n target_q_values = self.target_model.predict_on_batch(state1_batch)\n assert target_q_values.shape == (self.batch_size, self.nb_actions)\n q_batch = target_q_values[range(self.batch_size), actions]\n else:\n # Compute the q_values given state1, and extract the maximum for each sample in the batch.\n # We perform this prediction on the target_model instead of the model for reasons\n # outlined in Mnih (2015). In short: it makes the algorithm more stable.\n target_q_values = self.target_model.predict_on_batch(state1_batch)\n if self.is_recurrent:\n assert target_q_values.shape == (self.batch_size, maxlen, self.nb_actions)\n else:\n assert target_q_values.shape == (self.batch_size, self.nb_actions)\n q_batch = np.max(target_q_values, axis=-1)\n if self.is_recurrent:\n assert q_batch.shape == (self.batch_size, maxlen)\n else:\n q_batch = q_batch.flatten()\n assert q_batch.shape == (self.batch_size,)\n\n if self.is_recurrent:\n targets = np.zeros((self.batch_size, maxlen, self.nb_actions))\n dummy_targets = np.zeros((self.batch_size, maxlen, 1))\n masks = np.zeros((self.batch_size, maxlen, self.nb_actions))\n else:\n targets = np.zeros((self.batch_size, self.nb_actions))\n dummy_targets = np.zeros((self.batch_size,))\n masks = np.zeros((self.batch_size, self.nb_actions))\n\n # Compute r_t + gamma * max_a Q(s_t+1, a) and update the target targets accordingly,\n # but only for the affected output units (as given by action_batch).\n discounted_reward_batch = self.gamma * q_batch\n # Set discounted reward to zero for all states that were terminal.\n discounted_reward_batch *= terminal1_batch\n assert discounted_reward_batch.shape == reward_batch.shape\n Rs = reward_batch + discounted_reward_batch\n if self.is_recurrent:\n for batch_idx, (inner_targets, inner_masks, inner_Rs, inner_action_batch, length) in enumerate(zip(targets, masks, Rs, action_batch, lengths)):\n for idx, (target, mask, R, action) in enumerate(zip(inner_targets, inner_masks, inner_Rs, inner_action_batch)):\n target[action] = R # update action with estimated accumulated reward\n dummy_targets[batch_idx, idx] = R\n if idx < length: # only enable loss for valid transitions\n mask[action] = 1. # enable loss for this specific action\n\n else:\n for idx, (target, mask, R, action) in enumerate(zip(targets, masks, Rs, action_batch)):\n target[action] = R # update action with estimated accumulated reward\n dummy_targets[idx] = R\n mask[action] = 1. # enable loss for this specific action\n\n targets = np.array(targets).astype('float32')\n masks = np.array(masks).astype('float32')\n ins = [state0_batch] if type(self.model.input) is not list else state0_batch\n\n # In the recurrent case, we support splitting the sequences into multiple\n # chunks. Each chunk is then used as a training example. The reason for this is that,\n # for too long episodes, the unrolling in time during backpropagation can exceed the\n # memory of the GPU (or, to a lesser degree, the RAM if training on CPU).\n if self.is_recurrent and self.nb_max_steps_recurrent_unrolling:\n assert targets.ndim == 3\n steps = targets.shape[1] # (batch_size, steps, actions)\n nb_chunks = int(np.ceil(float(steps) / float(self.nb_max_steps_recurrent_unrolling)))\n chunks = []\n for chunk_idx in range(nb_chunks):\n start = chunk_idx * self.nb_max_steps_recurrent_unrolling\n t = targets[:, start:start + self.nb_max_steps_recurrent_unrolling, ...]\n m = masks[:, start:start + self.nb_max_steps_recurrent_unrolling, ...]\n iss = [i[:, start:start + self.nb_max_steps_recurrent_unrolling, ...] for i in ins]\n dt = dummy_targets[:, start:start + self.nb_max_steps_recurrent_unrolling, ...]\n chunks.append((iss, t, m, dt))\n else:\n chunks = [(ins, targets, masks, dummy_targets)]\n\n metrics = []\n if self.is_recurrent:\n # Reset states before training on the entire sequence.\n self.trainable_model.reset_states()\n for i, t, m, dt in chunks:\n # Finally, perform a single update on the entire batch. We use a dummy target since\n # the actual loss is computed in a Lambda layer that needs more complex input. However,\n # it is still useful to know the actual target to compute metrics properly.\n ms = self.trainable_model.train_on_batch(i + [t, m], [dt, t])\n ms = [metric for idx, metric in enumerate(ms) if idx not in (1, 2)] # throw away individual losses\n metrics.append(ms)\n metrics = np.mean(metrics, axis=0).tolist()\n metrics += self.policy.metrics\n if self.processor is not None:\n metrics += self.processor.metrics\n\n if self.target_model_update >= 1 and self.step % self.target_model_update == 0:\n self.update_target_model_hard()\n\n return metrics\n\n @property\n def metrics_names(self):\n # Throw away individual losses and replace output name since this is hidden from the user.\n assert len(self.trainable_model.output_names) == 2\n dummy_output_name = self.trainable_model.output_names[1]\n model_metrics = [name for idx, name in enumerate(self.trainable_model.metrics_names) if idx not in (1, 2)]\n model_metrics = [name.replace(dummy_output_name + '_', '') for name in model_metrics]\n\n names = model_metrics + self.policy.metrics_names[:]\n if self.processor is not None:\n names += self.processor.metrics_names[:]\n return names\n\n @property\n def policy(self):\n return self.__policy\n\n @policy.setter\n def policy(self, policy):\n self.__policy = policy\n self.__policy._set_agent(self)\n\n\n\nclass EpisodicMemory(Memory):\n def __init__(self, limit, **kwargs):\n super(EpisodicMemory, self).__init__(**kwargs)\n\n self.limit = limit\n self.episodes = RingBuffer(limit)\n self.terminal = False\n\n def sample(self, batch_size, batch_idxs=None):\n if len(self.episodes) <= 1:\n # We don't have a complete episode yet ...\n return []\n\n if batch_idxs is None:\n # Draw random indexes such that we never use the last episode yet, which is\n # always incomplete by definition.\n batch_idxs = sample_batch_indexes(0, self.nb_entries - 1, size=batch_size)\n assert np.min(batch_idxs) >= 0\n assert np.max(batch_idxs) < self.nb_entries\n assert len(batch_idxs) == batch_size\n\n # Create sequence of experiences.\n sequences = []\n for idx in batch_idxs:\n episode = self.episodes[idx]\n while len(episode) == 0:\n idx = sample_batch_indexes(0, self.nb_entries, size=1)[0]\n\n # Bootstrap state.\n running_state = deque(maxlen=self.window_length)\n for _ in range(self.window_length - 1):\n running_state.append(np.zeros(episode[0].observation.shape))\n assert len(running_state) == self.window_length - 1\n\n states, rewards, actions, terminals = [], [], [], []\n terminals.append(False)\n for idx, timestep in enumerate(episode):\n running_state.append(timestep.observation)\n states.append(np.array(running_state))\n rewards.append(timestep.reward)\n actions.append(timestep.action)\n terminals.append(timestep.terminal) # offset by 1, see `terminals.append(False)` above\n assert len(states) == len(rewards)\n assert len(states) == len(actions)\n assert len(states) == len(terminals) - 1\n\n # Transform into experiences (to be consistent).\n sequence = []\n for idx in range(len(episode) - 1):\n state0 = states[idx]\n state1 = states[idx + 1]\n reward = rewards[idx]\n action = actions[idx]\n terminal1 = terminals[idx + 1]\n experience = Experience(state0=state0, state1=state1, reward=reward, action=action, terminal1=terminal1)\n sequence.append(experience)\n sequences.append(sequence)\n assert len(sequence) == len(episode) - 1\n assert len(sequences) == batch_size\n return sequences\n\n def append(self, observation, action, reward, terminal, training=True):\n super(EpisodicMemory, self).append(observation, action, reward, terminal, training=training)\n\n # This needs to be understood as follows: in `observation`, take `action`, obtain `reward`\n # and weather the next state is `terminal` or not.\n if training:\n timestep = EpisodicTimestep(observation=observation, action=action, reward=reward, terminal=terminal)\n if len(self.episodes) == 0:\n self.episodes.append([]) # first episode\n self.episodes[self.episodes.length-1].append(timestep)\n if self.terminal:\n self.episodes.append([])\n self.terminal = terminal\n\n @property\n def nb_entries(self):\n return len(self.episodes)\n\n def get_config(self):\n config = super(SequentialMemory, self).get_config()\n config['limit'] = self.limit\n return config\n\n @property\n def is_episodic(self):\n return True\n\n\nclass AtariProcessor(Processor):\n def process_observation(self, observation):\n assert observation.ndim == 3 # (height, width, channel)\n img = Image.fromarray(observation)\n img = img.resize(INPUT_SHAPE).convert('L') # resize and convert to grayscale\n processed_observation = np.array(img)\n assert processed_observation.shape == INPUT_SHAPE\n return processed_observation.astype('uint8') # saves storage in experience memory\n\n def process_state_batch(self, batch):\n # We could perform this processing step in `process_observation`. In this case, however,\n # we would need to store a `float32` array instead, which is 4x more memory intensive than\n # an `uint8` array. This matters if we store 1M observations.\n processed_batch = batch.astype('float32') / 255.\n return processed_batch\n\n def process_reward(self, reward):\n return np.clip(reward, -1., 1.)\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--mode', choices=['train', 'test'], default='train')\nparser.add_argument('--env-name', type=str, default='BreakoutDeterministic-v4')\nparser.add_argument('--weights', type=str, default=None)\nargs = parser.parse_args()\n\n# Get the environment and extract the number of actions.\nenv = gym.make(args.env_name)\nnp.random.seed(123)\nenv.seed(123)\nnb_actions = env.action_space.n\n\n# We patch the environment to be closer to what Mnih et al. actually do: The environment\n# repeats the action 4 times and a game is considered to be over during training as soon as a live\n# is lost.\n'''\ndef _step(a):\n reward = 0.0\n action = env._action_set[a]\n lives_before = env.ale.lives()\n for _ in range(4):\n reward += env.ale.act(action)\n ob = env._get_obs()\n done = env.ale.game_over() or (args.mode == 'train' and lives_before != env.ale.lives())\n return ob, reward, done, {}\nenv._step = _step\n'''\n\ndef build_model(stateful, batch_size=None):\n i = Input(shape=IN_SHAPE[1:])\n i = Permute((1, 3, 4, 2), batch_input_shape = IN_SHAPE)(i)\n R = representation_rnn()\n C = consciousness_rnn()\n G = generator_rnn()\n D = decoder_rnn(nb_actions)\n\n h = R(i) # Get h from R\n c_A, c_B, c_A_soft, c_B_soft = C(h) # Get masks c_A and c_B from C\n b = multiply([h, c_B], name = 'b') # Get b through elementwise multiplication\n a_hat = G([c_A, c_B, b]) # Send c_A, c_B and b to G to get a_hat\n\n a_hat = Lambda(lambda x: x[:,:-1,:], output_shape=(None, latent_dim))(a_hat) # Slice dimensions to align vectors\n h_A = Lambda(lambda x: x[:,1:,:], output_shape=(None, latent_dim))(h) # Slice dimensions to align vectors\n c_A = Lambda(lambda x: x[:,:-1,:], output_shape=(None, latent_dim))(c_A) # Slice dimensions to align vectors\n\n h_A = multiply([h_A, c_A]) # Calculate h[A] to compare against a_hat\n a_hat = multiply([a_hat, c_A]) # Mask a_hat\n consciousness_error = subtract([a_hat, h_A])\n consciousness_error = Regularize(L1L2(l1 = 0., l2 =1. * reg_lambda), name='Consciousness_Generator_Error')(consciousness_error)\n\n b_transformed = Dense(latent_dim, activation='linear')(b) # Create a layer that attempts to make b independent from h[A]\n b_transformed = Lambda(lambda x: x[:,:-1,:], output_shape=(None, latent_dim))(b_transformed)\n b_transformed = multiply([b_transformed, c_A])\n transformation_error = subtract([b_transformed, h_A])\n transformation_error = Regularize(L1L2(l1 = 0., l2 =1. * reg_lambda), name='Transformation_Error')(transformation_error)\n\n intelligence_error = concatenate([c_A_soft, c_B_soft]) # The more elements we choose to predict, the more \"intelligent\" we are\n intelligence_error = Flatten()(intelligence_error)\n intelligence_error = Regularize(LinearRegularizer(c = 1. * reg_lambda), name='Intelligence_Level')(intelligence_error)\n\n x_hat = D(a_hat)\n x_hat = ApplyRegularization()([x_hat, consciousness_error, transformation_error, intelligence_error])\n\n ## Compile the model and start training\n CN = Model(inputs=i, outputs=[x_hat])\n\n return CN\n\n\nmodel = build_model(stateful=True, batch_size=batch_size)\npolicy_model = build_model(stateful=True, batch_size=1)\nprint(model.summary())\n\n# Finally, we configure and compile our agent. You can use every built-in Keras optimizer and\n# even the metrics!\nmemory = EpisodicMemory(limit=10000, window_length=1)\nprocessor = AtariProcessor()\n\n# Select a policy. We use eps-greedy action selection, which means that a random action is selected\n# with probability eps. We anneal eps from 1.0 to 0.1 over the course of 1M steps. This is done so that\n# the agent initially explores the environment (high eps) and then gradually sticks to what it knows\n# (low eps). We also set a dedicated eps value that is used during testing. Note that we set it to 0.05\n# so that the agent still performs some random actions. This ensures that the agent cannot get stuck.\npolicy = LinearAnnealedPolicy(EpsGreedyQPolicy(), attr='eps', value_max=1., value_min=.1, value_test=.05,\n nb_steps=1000000)\n\n# The trade-off between exploration and exploitation is difficult and an on-going research topic.\n# If you want, you can experiment with the parameters or use a different policy. Another popular one\n# is Boltzmann-style exploration:\n# policy = BoltzmannQPolicy(tau=1.)\n# Feel free to give it a try!\n\ndqn = RecurrentDQNAgent(model=model, nb_actions=nb_actions, policy=policy, memory=memory,\n processor=processor, nb_steps_warmup=50000, gamma=.99, delta_clip=(-1., 1.),\n target_model_update=10000, train_interval=500, policy_model=policy_model,\n enable_double_dqn=False, batch_size=batch_size)\ndqn.compile(Adam(lr=.00025), metrics=['mae'])\n\nif args.mode == 'train':\n # Okay, now it's time to learn something! We capture the interrupt exception so that training\n # can be prematurely aborted. Notice that you can the built-in Keras callbacks!\n weights_filename = 'dqn_{}_weights.h5f'.format(args.env_name)\n checkpoint_weights_filename = 'dqn_' + args.env_name + '_weights_{step}.h5f'\n log_filename = 'dqn_{}_log.json'.format(args.env_name)\n callbacks = [ModelIntervalCheckpoint(checkpoint_weights_filename, interval=250000)]\n callbacks += [FileLogger(log_filename, interval=100)]\n dqn.fit(env, callbacks=callbacks, nb_steps=1750000, log_interval=10000)\n\n # After training is done, we save the final weights one more time.\n dqn.save_weights(weights_filename, overwrite=True)\n\n # Finally, evaluate our algorithm for 10 episodes.\n dqn.test(env, nb_episodes=10, visualize=False)\nelif args.mode == 'test':\n weights_filename = 'dqn_{}_weights.h5f'.format(args.env_name)\n if args.weights:\n weights_filename = args.weights\n dqn.load_weights(weights_filename)\n dqn.test(env, nb_episodes=10, visualize=True)\n", "sub_path": "src/rl/consciousness_dqn_example.py", "file_name": "consciousness_dqn_example.py", "file_ext": "py", "file_size_in_byte": 34949, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "keras.backend.mean", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 42, "usage_type": "name"}, {"api_name": "keras.backend.mean", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 45, "usage_type": "name"}, {"api_name": "keras.backend.mean", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 48, "usage_type": "name"}, {"api_name": "keras.backend.square", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.engine.topology.Layer", "line_number": 53, "usage_type": "name"}, {"api_name": "keras.engine.topology.Layer", "line_number": 63, "usage_type": "name"}, {"api_name": "keras.engine.topology.Layer", "line_number": 71, "usage_type": "name"}, {"api_name": "keras.losses.kullback_leibler_divergence", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.engine.topology.Layer", "line_number": 81, "usage_type": "name"}, {"api_name": "keras.backend.l2_normalize", "line_number": 87, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 87, "usage_type": "name"}, {"api_name": "keras.backend.l2_normalize", "line_number": 88, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 88, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 89, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 89, "usage_type": "name"}, {"api_name": "keras.engine.topology.Layer", "line_number": 93, "usage_type": "name"}, {"api_name": "keras.backend.cast", "line_number": 101, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 101, "usage_type": "name"}, {"api_name": "keras.backend.greater", "line_number": 101, "usage_type": "call"}, {"api_name": "keras.backend.random_normal", "line_number": 101, "usage_type": "call"}, {"api_name": "keras.backend.shape", "line_number": 101, "usage_type": "call"}, {"api_name": "keras.regularizers.Regularizer", "line_number": 105, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 110, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 110, "usage_type": "name"}, {"api_name": "keras.layers.Input", "line_number": 117, "usage_type": "call"}, {"api_name": "keras.layers.Permute", "line_number": 118, "usage_type": "call"}, {"api_name": "keras.layers.TimeDistributed", "line_number": 119, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 119, "usage_type": "call"}, {"api_name": "keras.layers.GRU", "line_number": 120, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 121, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 125, "usage_type": "call"}, {"api_name": "keras.layers.GRU", "line_number": 126, "usage_type": "call"}, {"api_name": "keras.layers.TimeDistributed", "line_number": 127, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 127, "usage_type": "call"}, {"api_name": "keras.layers.TimeDistributed", "line_number": 130, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 130, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 133, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 137, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 138, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 139, "usage_type": "call"}, {"api_name": "keras.layers.concatenate", "line_number": 140, "usage_type": "call"}, {"api_name": "keras.layers.GRU", "line_number": 141, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 142, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 146, "usage_type": "call"}, {"api_name": "keras.layers.GRU", "line_number": 147, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 148, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 149, "usage_type": "call"}, {"api_name": "keras.backend.mean", "line_number": 156, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 156, "usage_type": "name"}, {"api_name": "keras.backend.max", "line_number": 156, "usage_type": "call"}, {"api_name": "rl.agents.dqn.AbstractDQNAgent", "line_number": 165, "usage_type": "name"}, {"api_name": "rl.policy.EpsGreedyQPolicy", "line_number": 166, "usage_type": "call"}, {"api_name": "keras.backend.clip", "line_number": 235, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 235, "usage_type": "name"}, {"api_name": "keras.backend.mean", "line_number": 237, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 237, "usage_type": "name"}, {"api_name": "keras.backend.square", "line_number": 237, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 250, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 251, "usage_type": "call"}, {"api_name": "keras.layers.Lambda", "line_number": 252, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 254, "usage_type": "call"}, {"api_name": "keras.backend.zeros_like", "line_number": 259, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 259, "usage_type": "name"}, {"api_name": "numpy.nan", "line_number": 316, "usage_type": "attribute"}, {"api_name": "numpy.max", "line_number": 329, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 348, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 349, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 356, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 357, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 378, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 379, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 398, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 417, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 425, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 426, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 427, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 429, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 430, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 431, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 454, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 455, "usage_type": "call"}, {"api_name": "numpy.ceil", "line_number": 465, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 488, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 539, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 540, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 553, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 560, "usage_type": "call"}, {"api_name": "rl.core.Processor", "line_number": 611, "usage_type": "name"}, {"api_name": "PIL.Image.fromarray", "line_number": 614, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 614, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 616, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 628, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 631, "usage_type": "call"}, {"api_name": "gym.make", "line_number": 638, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 639, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 639, "usage_type": "attribute"}, {"api_name": "keras.layers.Input", "line_number": 660, "usage_type": "call"}, {"api_name": "keras.layers.Permute", "line_number": 661, "usage_type": "call"}, {"api_name": "keras.layers.multiply", "line_number": 669, "usage_type": "call"}, {"api_name": "keras.layers.Lambda", "line_number": 672, "usage_type": "call"}, {"api_name": "keras.layers.Lambda", "line_number": 673, "usage_type": "call"}, {"api_name": "keras.layers.Lambda", "line_number": 674, "usage_type": "call"}, {"api_name": "keras.layers.multiply", "line_number": 676, "usage_type": "call"}, {"api_name": "keras.layers.multiply", "line_number": 677, "usage_type": "call"}, {"api_name": "keras.layers.subtract", "line_number": 678, "usage_type": "call"}, {"api_name": "keras.regularizers.L1L2", "line_number": 679, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 681, "usage_type": "call"}, {"api_name": "keras.layers.Lambda", "line_number": 682, "usage_type": "call"}, {"api_name": "keras.layers.multiply", "line_number": 683, "usage_type": "call"}, {"api_name": "keras.layers.subtract", "line_number": 684, "usage_type": "call"}, {"api_name": "keras.regularizers.L1L2", "line_number": 685, "usage_type": "call"}, {"api_name": "keras.layers.concatenate", "line_number": 687, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 688, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 695, "usage_type": "call"}, {"api_name": "rl.policy.LinearAnnealedPolicy", "line_number": 714, "usage_type": "call"}, {"api_name": "rl.policy.EpsGreedyQPolicy", "line_number": 714, "usage_type": "call"}, {"api_name": "keras.optimizers.Adam", "line_number": 727, "usage_type": "call"}, {"api_name": "rl.callbacks.ModelIntervalCheckpoint", "line_number": 735, "usage_type": "call"}, {"api_name": "rl.callbacks.FileLogger", "line_number": 736, "usage_type": "call"}]} +{"seq_id": "194843741", "text": "import yaml\nimport csv\nimport json\nfrom pymongo import MongoClient\nfrom bson import json_util, ObjectId\nimport subprocess\nimport sys\n\nweb = sys.argv[1]\nsnp = sys.argv[2]\nchromosome = sys.argv[3]\nstart = sys.argv[4]\nstop = sys.argv[5]\nrequest = sys.argv[6]\nsubprocess_id = sys.argv[7]\nr2_d = sys.argv[8]\nr2_d_threshold = sys.argv[9]\n\n\n# Set data directories using config.yml\nwith open('config.yml', 'r') as f:\n config = yaml.load(f)\nenv = config['env']\napi_mongo_addr = config['api']['api_mongo_addr']\npop_dir = config['data']['pop_dir']\nvcf_dir = config['data']['vcf_dir']\nreg_dir = config['data']['reg_dir']\nmongo_username = config['database']['mongo_user_readonly']\nmongo_password = config['database']['mongo_password']\nmongo_port = config['database']['mongo_port']\n\ntmp_dir = \"./tmp/\"\n\n# Get population ids\npop_list = open(tmp_dir + \"pops_\" + request + \".txt\").readlines()\nids = []\nfor i in range(len(pop_list)):\n ids.append(pop_list[i].strip())\n\npop_ids = list(set(ids))\n\n# Get VCF region\nvcf_file = vcf_dir + chromosome + \".phase3_shapeit2_mvncall_integrated_v5.20130502.genotypes.vcf.gz\"\ntabix_snp = \"tabix -fh {0} {1}:{2}-{3} | grep -v -e END\".format(vcf_file, chromosome, start, stop)\nproc = subprocess.Popen(tabix_snp, shell=True, stdout=subprocess.PIPE)\n\n# Define function to calculate LD metrics\ndef set_alleles(a1, a2):\n if len(a1) >= 1:\n a1_n = a1\n else:\n a1_n = \"-\"\n if len(a2) >= 1:\n a2_n = a2\n else:\n a2_n = \"_\"\n return(a1_n, a2_n)\n\ndef LD_calcs(hap, allele_n):\n # Extract haplotypes\n A = hap[\"00\"]\n B = hap[\"01\"]\n C = hap[\"10\"]\n D = hap[\"11\"]\n N = A + B + C + D\n delta = float(A*D-B*C)\n Ms = float((A+C)*(B+D)*(A+B)*(C+D))\n if Ms != 0:\n # D prime\n if delta < 0:\n D_prime = abs(delta/min((A+C)*(A+B), (B+D)*(C+D)))\n else:\n D_prime = abs(delta/min((A+C)*(C+D), (A+B)*(B+D)))\n # R2\n r2 = (delta**2)/Ms\n # Non-effect and Effect Alleles and their Allele Frequencies\n allele1 = str(allele_n[\"0\"])\n allele1_freq = str(round(float(A + C) / N, 3)) if N > float(A + C) else \"NA\"\n\n allele2 = str(allele_n[\"1\"])\n allele2_freq = str(round(float(B + D) / N, 3)) if N > float(B + D) else \"NA\"\n return [r2, D_prime, \"=\".join([allele1, allele1_freq]), \"=\".join([allele2, allele2_freq])]\n\n\n# Connect to Mongo database\nif env == 'local':\n mongo_host = api_mongo_addr\nelse: \n mongo_host = 'localhost'\nif web == \"True\":\n client = MongoClient('mongodb://' + mongo_username + ':' + mongo_password + '@' + mongo_host+'/admin', mongo_port)\nelse:\n if env == 'local':\n client = MongoClient('mongodb://' + mongo_username + ':' + mongo_password + '@' + mongo_host+'/admin', mongo_port)\n else:\n client = MongoClient('localhost', mongo_port)\ndb = client[\"LDLink\"]\n\ndef get_coords(db, rsid):\n rsid = rsid.strip(\"rs\")\n query_results = db.dbsnp151.find_one({\"id\": rsid})\n query_results_sanitized = json.loads(json_util.dumps(query_results))\n return query_results_sanitized\n\n# Import SNP VCF files\nvcf = open(tmp_dir+\"snp_no_dups_\"+request+\".vcf\").readlines()\n\nif len(vcf) > 1:\n for i in range(len(vcf)):\n if vcf[i].strip().split()[2] == snp:\n geno = vcf[i].strip().split()\nelse:\n geno = vcf[0].strip().split()\n\n# Import Window around SNP\nvcf = csv.reader([x.decode('utf-8') for x in proc.stdout.readlines()], dialect=\"excel-tab\")\n\n# Loop past file information and find header\nhead = next(vcf, None)\nwhile head[0][0:2] == \"##\":\n head = next(vcf, None)\n\n# Create Index of Individuals in Population\nindex = []\nfor i in range(9, len(head)):\n if head[i] in pop_ids:\n index.append(i)\n\n# Loop through SNPs\nout = []\nfor geno_n in vcf:\n if \",\" not in geno_n[3] and \",\" not in geno_n[4]:\n new_alleles_n = set_alleles(geno_n[3], geno_n[4])\n allele_n = {\"0\": new_alleles_n[0], \"1\": new_alleles_n[1]}\n hap = {\"00\": 0, \"01\": 0, \"10\": 0, \"11\": 0}\n for i in index:\n hap0 = geno[i][0]+geno_n[i][0]\n if hap0 in hap:\n hap[hap0] += 1\n\n if len(geno[i]) == 3 and len(geno_n[i]) == 3:\n hap1 = geno[i][2]+geno_n[i][2]\n if hap1 in hap:\n hap[hap1] += 1\n\n out_stats = LD_calcs(hap, allele_n)\n if out_stats != None:\n if ((r2_d == \"r2\" and out_stats[0] >= float(r2_d_threshold)) or (r2_d == \"d\" and out_stats[1] >= float(r2_d_threshold))):\n bp_n = geno_n[1]\n rs_n = geno_n[2]\n out.append([rs_n, chromosome, bp_n, out_stats[0], out_stats[1], out_stats[2], out_stats[3]])\n\nfor line in out:\n print(\"\\t\".join([str(val) for val in line]))\n", "sub_path": "LDlink/LDexpress_ld_sub.py", "file_name": "LDexpress_ld_sub.py", "file_ext": "py", "file_size_in_byte": 4742, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.argv", "line_number": 9, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 10, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 11, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 13, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 16, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 17, "usage_type": "attribute"}, {"api_name": "yaml.load", "line_number": 22, "usage_type": "call"}, {"api_name": "subprocess.Popen", "line_number": 45, "usage_type": "call"}, {"api_name": "subprocess.PIPE", "line_number": 45, "usage_type": "attribute"}, {"api_name": "pymongo.MongoClient", "line_number": 91, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 94, "usage_type": "call"}, {"api_name": "pymongo.MongoClient", "line_number": 96, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 102, "usage_type": "call"}, {"api_name": "bson.json_util.dumps", "line_number": 102, "usage_type": "call"}, {"api_name": "bson.json_util", "line_number": 102, "usage_type": "name"}, {"api_name": "csv.reader", "line_number": 116, "usage_type": "call"}]} +{"seq_id": "580629927", "text": "from PyQt5.QtWidgets import (\n QApplication,\n QGridLayout,\n QLabel,\n QScrollArea,\n QWidget,\n QLayout,\n QMainWindow,\n)\nfrom pyqtgraph.Qt import QtCore, QtGui\nfrom collections import defaultdict, deque\nfrom functools import partial\nfrom random import randint\n\nimport pyqtgraph as pg\nimport can\n\n\nclass Listener(can.Listener):\n def __init__(self, buffer: defaultdict, *args, **kwargs):\n self.buffer = buffer\n super().__init__(*args, **kwargs)\n\n def on_message_received(self, msg):\n val = int.from_bytes(msg.data, byteorder=\"big\")\n self.buffer[msg.arbitration_id].append(val)\n\n\nclass PlotWidget(pg.PlotWidget):\n height = 75 # px\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.setMenuEnabled(False)\n self.setMouseEnabled(x=False, y=False)\n self.hideAxis(\"left\")\n self.hideAxis(\"bottom\")\n self.setFixedHeight(self.height)\n self.pen = self.rand_pen()\n\n def rand_pen(self):\n r, g, b = [randint(0, 255) for _ in range(3)]\n return pg.mkPen(color=(r, g, b), width=2)\n\n\nclass Label(QLabel):\n font = QtGui.QFont(\"Helvetica\", 15)\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.setFont(self.font)\n self.setAlignment(QtCore.Qt.AlignCenter)\n\n\nclass CanoPy:\n def __init__(self, bus, rate=0.05, buffer_length=200):\n # Buffer\n self.buf_len = buffer_length\n self.buf_int = 0.1\n self.buf_max = 2500\n self.buffer = defaultdict(\n lambda: deque([0] * self.buf_len, maxlen=self.buf_len)\n )\n\n # CAN\n self.bus = bus\n self.listener = Listener(self.buffer)\n self.notifier = can.Notifier(self.bus, [self.listener])\n\n # Application\n self.app = QApplication([])\n self.window = QMainWindow()\n\n self.layout = QGridLayout()\n\n self.widget = QWidget()\n self.widget.setLayout(self.layout)\n self.widget.keyPressEvent = self.key_pressed\n\n self.scroll = QScrollArea()\n self.scroll.setWidgetResizable(True)\n self.scroll.setWidget(self.widget)\n\n self.window.setCentralWidget(self.scroll)\n self.window.setMinimumWidth(400)\n self.window.setWindowTitle(\"CanoPy\")\n self.window.show()\n\n # Key Actions\n self.key_actions = {\n ord(\"I\"): self.ignore_current,\n QtCore.Qt.Key_Up: partial(self.scale_buffer_size, True),\n QtCore.Qt.Key_Down: partial(self.scale_buffer_size, False),\n }\n\n # Plotting\n self.set_x_axis()\n self.plots = {}\n self.rows = []\n self.ignore = []\n\n # Timer\n self.timer = QtCore.QTimer()\n self.timer.timeout.connect(self.update_plots)\n self.timer.start(int(rate * 1000))\n\n def key_pressed(self, event):\n key = event.key()\n try:\n self.key_actions.get(key)()\n except TypeError:\n pass\n\n def set_x_axis(self):\n self.x = list(range(self.buf_len))\n return\n\n def add_plot(self, id_):\n label = Label(hex(id_))\n plt = PlotWidget()\n row = (label, plt)\n self.rows.append(row)\n self.plots[id_] = plt.plot(pen=plt.pen)\n self.layout.addWidget(label, len(self.plots), 0)\n self.layout.addWidget(plt, len(self.plots), 1)\n return\n\n def update_plots(self):\n current = dict(self.buffer)\n for id_, data in current.items():\n if id_ in self.ignore:\n continue\n if id_ not in self.plots:\n self.add_plot(id_)\n self.plots[id_].setData(self.x, data)\n return\n\n def ignore_current(self):\n self.ignore += list(self.buffer)\n for row in self.rows:\n for i in row:\n self.layout.removeWidget(i)\n self.rows.clear()\n return\n\n def scale_buffer_size(self, increase: bool):\n prod = int(self.buf_len * self.buf_int)\n val = prod if increase else -prod\n new_len = self.buf_len + val\n if 10 < new_len < self.buf_max:\n self.buf_len = new_len\n self.update_buffer()\n return\n\n def update_buffer(self):\n if len(self.x) != self.buf_len:\n self.set_x_axis()\n for id_, data in self.buffer.items():\n if data.maxlen != self.buf_len:\n new_buf = deque(data, maxlen=self.buf_len)\n current_len = len(new_buf)\n if current_len != self.buf_len:\n difference = [new_buf[0]] * (self.buf_len - current_len)\n new_buf.extendleft(difference)\n self.buffer[id_] = new_buf\n return\n\n def run(self):\n self.app.exec_()\n", "sub_path": "canopy/canopy.py", "file_name": "canopy.py", "file_ext": "py", "file_size_in_byte": 4838, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "can.Listener", "line_number": 19, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 20, "usage_type": "name"}, {"api_name": "pyqtgraph.PlotWidget", "line_number": 29, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 42, "usage_type": "call"}, {"api_name": "pyqtgraph.mkPen", "line_number": 43, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 46, "usage_type": "name"}, {"api_name": "pyqtgraph.Qt.QtGui.QFont", "line_number": 47, "usage_type": "call"}, {"api_name": "pyqtgraph.Qt.QtGui", "line_number": 47, "usage_type": "name"}, {"api_name": "pyqtgraph.Qt.QtCore.Qt", "line_number": 52, "usage_type": "attribute"}, {"api_name": "pyqtgraph.Qt.QtCore", "line_number": 52, "usage_type": "name"}, {"api_name": "collections.defaultdict", "line_number": 61, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 62, "usage_type": "call"}, {"api_name": "can.Notifier", "line_number": 68, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QApplication", "line_number": 71, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QMainWindow", "line_number": 72, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QGridLayout", "line_number": 74, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QWidget", "line_number": 76, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets.QScrollArea", "line_number": 80, "usage_type": "call"}, {"api_name": "pyqtgraph.Qt.QtCore.Qt", "line_number": 92, "usage_type": "attribute"}, {"api_name": "pyqtgraph.Qt.QtCore", "line_number": 92, "usage_type": "name"}, {"api_name": "pyqtgraph.Qt.QtCore.Qt", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pyqtgraph.Qt.QtCore", "line_number": 93, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 92, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 93, "usage_type": "call"}, {"api_name": "pyqtgraph.Qt.QtCore.QTimer", "line_number": 103, "usage_type": "call"}, {"api_name": "pyqtgraph.Qt.QtCore", "line_number": 103, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 160, "usage_type": "call"}]} +{"seq_id": "239570362", "text": "from __future__ import print_function\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\n# Importing dataset, splitting it into training and testing\r\nfrom tensorflow.keras.datasets import mnist\r\n(X_train, y_train), (X_test, y_test) = mnist.load_data()\r\n\r\n\r\n# Preparing the data for Training and Testing\r\nnum_pixels = X_train.shape[1] * X_train.shape[2]\r\nX_train = X_train.reshape(X_train.shape[0], num_pixels).T\r\nX_test = X_test.reshape(X_test.shape[0], num_pixels).T\r\ny_train = y_train.reshape(y_train.shape[0], 1)\r\ny_test = y_test.reshape(y_test.shape[0], 1)\r\nX_train = X_train.astype('float32')\r\nX_test = X_test.astype('float32')\r\ny_train = y_train.astype('float32')\r\ny_test = y_test.astype('float32')\r\nX_train = X_train / 255\r\nX_test = X_test / 255\r\n\r\n\r\n# Adjust outputs for binary classification: digit 0 is classified 1 \r\n# and all the other digits are classified 0\r\n\r\ny_new = np.zeros(y_train.shape)\r\ny_new[np.where(y_train==0.0)[0]] = 1\r\ny_train = y_new\r\n\r\ny_new = np.zeros(y_test.shape)\r\ny_new[np.where(y_test==0.0)[0]] = 1\r\ny_test = y_new\r\n\r\n\r\ny_train = y_train.T\r\ny_test = y_test.T\r\n\r\n# Number of training instances\r\nm = X_train.shape[1] \r\n\r\n# Shuffle training set\r\nnp.random.seed(138)\r\nshuffle_index = np.random.permutation(m)\r\nX_train, y_train = X_train[:,shuffle_index], y_train[:,shuffle_index]\r\n\r\n# =============================================================================\r\n# For Report Purposes\r\n# \r\n# # Display one image and corresponding label\r\n# import matplotlib\r\n# import matplotlib.pyplot as plt\r\n# i = 3\r\n# print('y[{}]={}'.format(i, y_train[:,i]))\r\n# plt.imshow(X_train[:,i].reshape(28,28), cmap = matplotlib.cm.binary)\r\n# plt.axis(\"off\")\r\n# plt.show()\r\n# =============================================================================\r\n\r\n\r\n\r\n\r\n#Creating a Neural Network\r\n\r\n# Define Sigmoid Activation Function\r\ndef sigmoid(X):\r\n return 1 / (1 + np.exp(-X))\r\n\r\n# For a single Neuron\r\ndef singleNeuron(x, W, b, sigmoid):\r\n return sigmoid(np.dot(W, x) + b)\r\n\r\n# Define a Binary loss function \r\ndef loss_function(y, y_hat):\r\n m = y.shape[1]\r\n return -1/m * ( np.sum(np.multiply(np.log(y_hat),y)) + np.sum(np.multiply(np.log(1-y_hat),(1-y))) )\r\n\r\n# Backpropagate a neuron\r\ndef backpropagation_single_neuron(y, y_hat, w, X):\r\n m = y.shape[1]\r\n dX = y_hat - y\r\n dW = np.dot(dX,X.T)/ m\r\n db = np.sum(dX) / m\r\n return dW, db, dX\r\n\r\n# Weight and Bias Update \r\ndef update_single_neuron(W, dW, b, db, h):\r\n W = W - h * dW\r\n b = b - h * db\r\n return W,b\r\n\r\n# Define the training process for a single layer, single neuron neetowrk\r\ndef train(x, y, epochs = 5, h = 0.9):\r\n print(\"\\n\\n==========START TRAINING FOR SINGLE LAYER ==========\\n\\n\")\r\n layer_output_size = 1\r\n np.random.seed(22)\r\n W = np.random.randn(layer_output_size, 784) * 1/100\r\n b = np.random.randn(layer_output_size,1) * 1/100\r\n\r\n train_losses = []\r\n train_accuracies = []\r\n\r\n for i in range(epochs):\r\n print(\"Starting epoch \" + str(i))\r\n y_hat = singleNeuron(x, W, b, sigmoid)\r\n \r\n loss = loss_function(y, y_hat)\r\n print(\"Train Loss: \" +str(np.round(loss,4)))\r\n train_losses.append(loss)\r\n \r\n accur = accuracy(y_hat,y)\r\n print(\"Train accuracy: \" + str(np.round(accur,4))+\"\\n\")\r\n train_accuracies.append(accur)\r\n \r\n dW, db, dX = backpropagation_single_neuron(y, y_hat, W, x)\r\n W,b = update_single_neuron(W, dW, b, db, h)\r\n\r\n\r\n return train_losses, train_accuracies, W, b\r\n\r\n# Define accuracy based on binary classification. Simple threshold\r\ndef accuracy(y, y_hat):\r\n y_hat_corrected = y_hat > 0.5\r\n errors = np.sum(np.abs(y-y_hat_corrected))\r\n return 1 - errors / y.shape[1]\r\n\r\n# Define a Sequential two layered network\r\ndef two_layers_network(x, W_h, b_h, W_o, b_o):\r\n z = singleNeuron(x, W_h, b_h, sigmoid)\r\n return singleNeuron(z, W_o, b_o, sigmoid)\r\n\r\n\r\n# Define Training Method for a Two Layered Network\r\ndef train_two_layers(x, y, X_test, epochs = 5, h = 0.9):\r\n print(\"\\n\\n==========START TRAINING FOR DOUBLE LAYER ==========\\n\\n\")\r\n np.random.seed(22)\r\n W_h = np.random.randn(64, 784) * 1/100\r\n b_h = np.random.randn(64,1) * 1/100\r\n W_o = np.random.randn(1, 64) * 1/100\r\n b_o =np.random.randn(1,1) * 1/100\r\n\r\n train_losses = []\r\n train_accuracies = []\r\n for i in range(epochs):\r\n \r\n print(\"Starting epoch \" + str(i))\r\n z = singleNeuron(x, W_h, b_h, sigmoid)\r\n y_hat = singleNeuron(z, W_o, b_o, sigmoid)\r\n \r\n loss = loss_function(y, y_hat)\r\n print(\"Train Loss: \" +str(np.round(loss,4)))\r\n train_losses.append(loss)\r\n \r\n accur = accuracy(y_hat,y)\r\n print(\"Train accuracy: \" + str(np.round(accur,4))+\"\\n\")\r\n train_accuracies.append(accur)\r\n \r\n dW_o, db_o, dW_h, db_h = backpropagation_two_layers(y, y_hat, W_h, W_o, x, z)\r\n W_o, b_o, W_h, b_h = update_two_layers(dW_o, db_o, dW_h, db_h, W_h, b_h, W_o, b_o, h)\r\n\r\n return train_losses, train_accuracies, W_o, b_o, W_h, b_h\r\n\r\n# Backpropagate on both layers, neurons\r\ndef backpropagation_two_layers(y, y_hat, W_h, W_o, x, z):\r\n dW_o, db_o, dX_o = backpropagation_single_neuron(y, y_hat, W_o, z)\r\n m = y.shape[1]\r\n dX1 = np.dot(W_o.T,dX_o)\r\n dX1 = np.multiply(np.multiply(dX1, z),1 - z)\r\n dW_h = np.dot(dX1, x.T) / m\r\n db_h = np.sum(dX1) / m\r\n return dW_o, db_o, dW_h, db_h\r\n\r\n# Weights and Bias Updates for double layered network\r\ndef update_two_layers(dW_o, db_o, dW_h, db_h, W_h, b_h, W_o, b_o, h):\r\n W_o = W_o - h * dW_o\r\n b_o = b_o - h * db_o\r\n W_h = W_h - h * dW_h\r\n b_h = b_h - h * db_h\r\n\r\n return W_o, b_o, W_h, b_h\r\n\r\n\r\nepochs=20\r\n#Single Layer\r\ntrain_losses, train_accuracies, W, b = train(X_train, y_train, epochs)\r\ny_hat_test = singleNeuron(X_test, W, b, sigmoid)\r\ntest_loss = loss_function(y_test, y_hat_test)\r\nprint(\"Test Accuracy: \" + str(accuracy(y_test, y_hat_test)))\r\n\r\n#Double Layer\r\ntrain_losses_2, train_accuracies_2, W_o, b_o, W_h, b_h = train_two_layers(X_train, y_train, X_test, epochs)\r\ny_hat_test = two_layers_network(X_test, W_h, b_h, W_o, b_o)\r\ntest_loss = loss_function(y_test, y_hat_test)\r\nprint(\"Test Accuracy: \" + str(accuracy(y_test, y_hat_test)))\r\n\r\nplt.figure()\r\nplt.plot(train_losses)\r\nplt.plot(train_losses_2)\r\nplt.title(\"Training Loss\")\r\nplt.xlabel(\"Iteration\")\r\nplt.ylabel(\"Loss\")\r\nplt.legend(['Single Layer', 'Double Layer'])\r\nplt.show()\r\n\r\nplt.figure()\r\nplt.plot(train_accuracies)\r\nplt.plot(train_accuracies_2)\r\nplt.title(\"Training Accuracy\")\r\nplt.xlabel(\"Iteration\")\r\nplt.ylabel(\"Accuracy\")\r\nplt.legend(['Single Layer', 'Double Layer'])\r\nplt.show()", "sub_path": "2.NN_From_Scratch/Code/Single_Doubled_NN_Scratch.py", "file_name": "Single_Doubled_NN_Scratch.py", "file_ext": "py", "file_size_in_byte": 6671, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "tensorflow.keras.datasets.mnist.load_data", "line_number": 8, "usage_type": "call"}, {"api_name": "tensorflow.keras.datasets.mnist", "line_number": 8, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 44, "usage_type": "attribute"}, {"api_name": "numpy.random.permutation", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 45, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 97, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 98, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 98, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 99, "usage_type": "attribute"}, {"api_name": "numpy.round", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 137, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 137, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 138, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 139, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 140, "usage_type": "attribute"}, {"api_name": "numpy.random.randn", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 141, "usage_type": "attribute"}, {"api_name": "numpy.round", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 156, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 168, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 199, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 199, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 207, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 208, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 208, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 209, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 209, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 210, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 211, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 211, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 213, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 213, "usage_type": "name"}]} +{"seq_id": "181736348", "text": "import contextlib\nimport os\nimport pytest\nimport unittest.mock\n\nfrom app import create_app, db\nfrom app.models import Host\n\n\n@contextlib.contextmanager\ndef set_environment(new_env=None):\n new_env = new_env or {}\n patched_dict = unittest.mock.patch.dict(os.environ, new_env)\n patched_dict.start()\n os.environ.clear()\n os.environ.update(new_env)\n yield\n patched_dict.stop()\n\n\ndef rename_host_table_and_indexes():\n \"\"\"\n Temporarily rename the host table while the tests run. This is done\n to make dropping the table at the end of the tests a bit safer.\n \"\"\"\n temp_table_name_suffix = \"__unit_tests__\"\n if temp_table_name_suffix not in Host.__table__.name:\n Host.__table__.name = Host.__table__.name + temp_table_name_suffix\n if temp_table_name_suffix not in Host.__table__.fullname:\n Host.__table__.fullname = Host.__table__.fullname + temp_table_name_suffix\n\n # Adjust the names of the indices\n for index in Host.__table_args__:\n if temp_table_name_suffix not in index.name:\n index.name = index.name + temp_table_name_suffix\n\n\n@pytest.fixture\ndef flask_app_fixture():\n rename_host_table_and_indexes()\n\n app = create_app(config_name=\"testing\")\n\n # binds the app to the current context\n with app.app_context() as ctx:\n # create all tables\n db.create_all()\n ctx.push()\n yield app\n ctx.pop\n\n db.session.remove()\n db.drop_all()\n", "sub_path": "test_utils.py", "file_name": "test_utils.py", "file_ext": "py", "file_size_in_byte": 1463, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "unittest.mock.mock.patch.dict", "line_number": 13, "usage_type": "call"}, {"api_name": "unittest.mock.mock", "line_number": 13, "usage_type": "attribute"}, {"api_name": "unittest.mock", "line_number": 13, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.environ.clear", "line_number": 15, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.environ.update", "line_number": 16, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 16, "usage_type": "attribute"}, {"api_name": "contextlib.contextmanager", "line_number": 10, "usage_type": "attribute"}, {"api_name": "app.models.Host.__table__", "line_number": 27, "usage_type": "attribute"}, {"api_name": "app.models.Host", "line_number": 27, "usage_type": "name"}, {"api_name": "app.models.Host.__table__", "line_number": 28, "usage_type": "attribute"}, {"api_name": "app.models.Host", "line_number": 28, "usage_type": "name"}, {"api_name": "app.models.Host.__table__", "line_number": 29, "usage_type": "attribute"}, {"api_name": "app.models.Host", "line_number": 29, "usage_type": "name"}, {"api_name": "app.models.Host.__table__", "line_number": 30, "usage_type": "attribute"}, {"api_name": "app.models.Host", "line_number": 30, "usage_type": "name"}, {"api_name": "app.models.Host.__table_args__", "line_number": 33, "usage_type": "attribute"}, {"api_name": "app.models.Host", "line_number": 33, "usage_type": "name"}, {"api_name": "app.create_app", "line_number": 42, "usage_type": "call"}, {"api_name": "app.app_context", "line_number": 45, "usage_type": "call"}, {"api_name": "app.db.create_all", "line_number": 47, "usage_type": "call"}, {"api_name": "app.db", "line_number": 47, "usage_type": "name"}, {"api_name": "app.db.session.remove", "line_number": 52, "usage_type": "call"}, {"api_name": "app.db.session", "line_number": 52, "usage_type": "attribute"}, {"api_name": "app.db", "line_number": 52, "usage_type": "name"}, {"api_name": "app.db.drop_all", "line_number": 53, "usage_type": "call"}, {"api_name": "app.db", "line_number": 53, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 38, "usage_type": "attribute"}]} +{"seq_id": "122891816", "text": "#!/usr/bin/env python3\nfrom sys import stdin\nfrom itertools import combinations\n\n\nif __name__ == '__main__':\n containers = []\n for line in stdin:\n containers.append(int(line))\n\n all_combos = []\n for n in range(1, len(containers)+1):\n all_combos.extend(combinations(containers, n))\n\n # for tup in all_combos:\n # print(tup, end='\\t\\t\\t')\n # print(sum(tup))\n valids = tuple(filter(lambda x: sum(x) == 150, all_combos))\n min_num = min((len(x) for x in valids))\n print(min_num)\n\n # for tup in valids[:24]:\n # print(len(tup))\n\n print(len(list(filter(lambda x: len(x) == 4, valids))))\n", "sub_path": "2015/day17/p02.py", "file_name": "p02.py", "file_ext": "py", "file_size_in_byte": 645, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sys.stdin", "line_number": 8, "usage_type": "name"}, {"api_name": "itertools.combinations", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "171247793", "text": "# -*- coding: utf-8 -*-\n'''\n======================Welcome to Python====================\n/-********Have a good time.********-/\n\n* Copyright (c) 2019,************\n* All rights reserved.\nFILE NAME:\nAUTHOR: Eden·Gabriel \nDATE: Mar-11-Mon/2019 18:31:34\nVERSION: V-1.0\nFunction List: // 主要函数列表，每条记录应包括函数名及功能简要说明\nDESCRIPTION:\n一个数据文件letter-recognition.data它有20000 行\n实际上每一行的第一列是我们的一个字母标记。接\n下来的16 个数字是它的不同特征。\n这些特征来源于UCI Machine LearningRepository\n\n取前10000 个作为训练样本，剩下的10000 个作为测试样本。\n我们应在先把字母表转换成asc 码，因为我们不直接处理字母。\n\nHistory: // 历史修改记录\n<author> <time> <version > <desc>\n build this moudle\n\n'''\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfilename = 'E:\\\\A_BOOM_LEARNING_EDEN_GABRIEL\\\\2018.12.19start_opencv+python\\\\data\\\\letter-recognition.data'\n\ndata = np.loadtxt(filename,dtype = 'float32',delimiter = ',',\n converters = {0:lambda ch:ord(ch)-ord('A')})\n\n# split the data to two, 10000 each for train and test\ntrain, test = np.vsplit(data,2)\n# split trainData and testData to features and responses\nresponses, trainData = np.hsplit(train,[1])\nlabels, testData = np.hsplit(test,[1])\n\n\n#构建KNN分类器\nknn = cv2.ml_KNearest.create()\n#传入一个训练数据集，以及\n#与训练数据对应的分类来训练kNN 分类器（构建搜索树）\nknn.train(trainData, cv2.ml.ROW_SAMPLE,responses)\nret,results,neighbours,dist = knn.findNearest(testData,k = 5)\n\ncorrect = np.count_nonzero(results == labels)\naccuracy = correct*100.0/10000\nprint(accuracy,'%')\n\n\n\n", "sub_path": "Knn英文字母ocr.py", "file_name": "Knn英文字母ocr.py", "file_ext": "py", "file_size_in_byte": 1789, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.loadtxt", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.vsplit", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.hsplit", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.hsplit", "line_number": 40, "usage_type": "call"}, {"api_name": "cv2.ml_KNearest.create", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.ml_KNearest", "line_number": 44, "usage_type": "attribute"}, {"api_name": "cv2.ml", "line_number": 47, "usage_type": "attribute"}, {"api_name": "numpy.count_nonzero", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "608976384", "text": "import copy\n\nfrom lark import Token, Tree\nfrom lark.tree import pydot__tree_to_png\nfrom termcolor import cprint\n\nfrom mll.utils import presentation, clean_tok, get_keras_layers, get_sklearn_models, get_mlxtend_models, stampa, istok, \\\n isTree, group, flatten, match, substitute_comp_SQ_W_SQ, map, apply, wellformed, escape\n\n\nclass superMLL:\n def __init__(self, program: str, env={}) -> None:\n\n # deve essere {\"Conv2D\" : \"from keras.layers import Conv2D\"}\n # contains available libraries such as keras, mlxtend, sklearn\n self.string = \"\"\n self.current_bindings = []\n self.available_libraries = {}\n\n # contains currently used libraries in the program\n # has to be [\"from keras.layers import Conv2D\"]\n self.used_libraries = []\n\n # nomi esterni\n self.env = env\n\n # modelli da esportare\n self.models = {}\n # modelli ordinati per prendere l' ultimo o il penultimo in ordine di esecuzione\n self.ordered_models = []\n\n # macros {ID, Tree}\n self.macros = {}\n\n #parmacs {ID, String 'ID' String}\n self.parmacs = {}\n\n #non so\n self.import_from_glob = {}\n\n # the two ASTs\n self.after_tree = None\n self.before_tree = None\n\n # newer pieces\n self.regressors = []\n self.classifiers = []\n self.current_branch = 0\n self.current_binding_name = None\n\n self.function_trees = {}\n\n # MLL program\n self.program = program.replace(\"with \",\"\")\n\n self.isInner = False\n\n # avviso per programma vuoto\n # if self.program.__len__()==0:\n # cprint(\"WARNING: your program is empty\",'red')\n\n def select_imported_libraries(self, t: Token) -> None:\n s = clean_tok(t).value\n if s in self.available_libraries.keys():\n if self.available_libraries[s] not in self.used_libraries:\n self.used_libraries.append(self.available_libraries[s])\n\n def contained_in_imported_libraries(self, t: Token) -> bool:\n s = clean_tok(t).value\n if s in self.available_libraries.keys():\n return True\n return False\n\n def create_available_imports(self):\n imp = get_keras_layers()\n for i in imp.keys():\n for j in imp[i]:\n a = \"from keras.\"+i + \" import \"+j + \"\\n\"\n self.available_libraries[j] = a\n\n imp = get_sklearn_models()\n for i in imp.keys():\n for j in imp[i]:\n self.available_libraries[j] = \"from sklearn.\"+i + \" import \"+j + \"\\n\"\n\n imp = get_mlxtend_models()\n for i in imp.keys():\n for j in imp[i]:\n self.available_libraries[j] = \"from mlxtend.\"+i + \" import \"+j + \"\\n\"\n\n def get_string(self) -> str:\n return self.string\n\n def last_model(self) -> object:\n return self.models[self.ordered_models[len(self.ordered_models)-1]]\n\n def print_tree(self):\n stampa(self.after_tree)\n\n def image_tree(self, which=\"after\"):\n if which == \"after\":\n pydot__tree_to_png(self.after_tree, \"../tree-after.png\")\n else:\n if which == \"before\":\n pydot__tree_to_png(self.after_tree, \"../tree-before.png\")\n else:\n pydot__tree_to_png(self.after_tree, \"../tree-after.png\")\n\n def get_tree_before(self):\n return self.before_tree\n\n def get_tree_after(self):\n return self.after_tree\n\n def set_current_branch(self, param):\n self.current_branch = param\n\n def put_macros(self, t):\n\n # print(t)\n\n if isinstance(t, Token):\n\n #########################################################\n # caso parmac\n #########################################################\n\n if clean_tok(t).value in self.parmacs.keys():\n # print(\"---before\",clean_tok(t).value,\"; after\",self.parmacs[clean_tok(t).value])\n return self.parmacs[clean_tok(t).value]\n\n #########################################################\n # caso macro\n #########################################################\n\n if clean_tok(t).value in self.macros.keys():\n # print(\"---before\", clean_tok(t).value, \"; after\", self.macros[clean_tok(t).value])\n m = escape(self.macros[clean_tok(t).value])\n return self.put_macros(m)\n\n #########################################################\n # caso pmacro\n #########################################################\n\n if clean_tok(t).value[:3] in self.macros.keys():\n m = self.macros[clean_tok(t).value[:3]]\n m = copy.deepcopy(m)\n m = escape(m)\n export:str = clean_tok(t).value[3:]\n\n # se quindi c'è solo la grandezza del filtro\n if len(export) == 2:\n m.children[0].children[1:1] = [Tree(\"e\",[Token(\"ID\",export)])]\n\n return self.put_macros(m)\n\n # print(\"---last return\", clean_tok(t).value)\n return clean_tok(t)\n\n if isinstance(t, Tree):\n return Tree(t.data, self.put_macros(t.children))\n\n if isinstance(t, list):\n t = escape(t)\n return [self.put_macros(x) for x in t]\n\n raise Exception(\"caso inaspettato\", t, type(t))\n\n def solve_macro(self, t):\n return self.macros[clean_tok(t).value]\n\n def solve_parmac(self, t):\n if clean_tok(t).value in self.parmacs.keys():\n return self.parmacs[clean_tok(t).value]\n else:\n return t\n\n def add_tab_top_level(self, arr2d: []):\n for i in arr2d:\n i.children.insert(0,Token(\"WS\",\"\\n\\t\"))\n\n return arr2d\n\n def substitute_model(self, t: Token):\n if clean_tok(t).value in self.models.keys():\n return Token(\"ID\",\"models['\"+clean_tok(t).value+\"']\")\n else:\n return t\n\n def execute(self):\n s = self.get_string()\n self.env.update({\"models\":self.models})\n\n # cprint(self.env.keys(), \"green\")\n # cprint(\"inputs\" in self.env.keys(),\"red\")\n # cprint(type(self.env[\"inputs\"]), \"green\")\n\n # s = \"print('inputs' in locals())\\n\\nprint('inputs' in globals())\\n\\n\" + s\n\n # print(s)\n\n exec(s,self.env)\n\n # cprint(self.env.keys(), \"blue\")\n # cprint(self.env[\"models\"], \"red\")\n\n return self\n\n def insert_parmac(self, t: Tree):\n id = t.children[0].value\n a = t.children[2:]\n a = flatten(group(a, \"OR\"))\n\n # print(a)\n\n for i in a:\n self.parmacs[clean_tok(i).value] = Tree(\"comp\", [Token(\"ID\", id), Token(\"EQ\", \"=\"), Token(\"ID\", \"'\" + i.value + \"'\")])\n\n", "sub_path": "mll/superMLL.py", "file_name": "superMLL.py", "file_ext": "py", "file_size_in_byte": 6862, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "lark.Token", "line_number": 62, "usage_type": "name"}, {"api_name": "mll.utils.clean_tok", "line_number": 63, "usage_type": "call"}, {"api_name": "lark.Token", "line_number": 68, "usage_type": "name"}, {"api_name": "mll.utils.clean_tok", "line_number": 69, "usage_type": "call"}, {"api_name": "mll.utils.get_keras_layers", "line_number": 75, "usage_type": "call"}, {"api_name": "mll.utils.get_sklearn_models", "line_number": 81, "usage_type": "call"}, {"api_name": "mll.utils.get_mlxtend_models", "line_number": 86, "usage_type": "call"}, {"api_name": "mll.utils.stampa", "line_number": 98, "usage_type": "call"}, {"api_name": "lark.tree.pydot__tree_to_png", "line_number": 102, "usage_type": "call"}, {"api_name": "lark.tree.pydot__tree_to_png", "line_number": 105, "usage_type": "call"}, {"api_name": "lark.tree.pydot__tree_to_png", "line_number": 107, "usage_type": "call"}, {"api_name": "lark.Token", "line_number": 122, "usage_type": "argument"}, {"api_name": "mll.utils.clean_tok", "line_number": 128, "usage_type": "call"}, {"api_name": "mll.utils.clean_tok", "line_number": 130, "usage_type": "call"}, {"api_name": "mll.utils.clean_tok", "line_number": 136, "usage_type": "call"}, {"api_name": "mll.utils.escape", "line_number": 138, "usage_type": "call"}, {"api_name": "mll.utils.clean_tok", "line_number": 138, "usage_type": "call"}, {"api_name": "mll.utils.clean_tok", "line_number": 145, "usage_type": "call"}, {"api_name": "mll.utils.clean_tok", "line_number": 146, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 147, "usage_type": "call"}, {"api_name": "mll.utils.escape", "line_number": 148, "usage_type": "call"}, {"api_name": "mll.utils.clean_tok", "line_number": 149, "usage_type": "call"}, {"api_name": "lark.Tree", "line_number": 153, "usage_type": "call"}, {"api_name": "lark.Token", "line_number": 153, "usage_type": "call"}, {"api_name": "mll.utils.clean_tok", "line_number": 158, "usage_type": "call"}, {"api_name": "lark.Tree", "line_number": 160, "usage_type": "argument"}, {"api_name": "lark.Tree", "line_number": 161, "usage_type": "call"}, {"api_name": "mll.utils.escape", "line_number": 164, "usage_type": "call"}, {"api_name": "mll.utils.clean_tok", "line_number": 170, "usage_type": "call"}, {"api_name": "mll.utils.clean_tok", "line_number": 173, "usage_type": "call"}, {"api_name": "mll.utils.clean_tok", "line_number": 174, "usage_type": "call"}, {"api_name": "lark.Token", "line_number": 180, "usage_type": "call"}, {"api_name": "lark.Token", "line_number": 184, "usage_type": "name"}, {"api_name": "mll.utils.clean_tok", "line_number": 185, "usage_type": "call"}, {"api_name": "lark.Token", "line_number": 186, "usage_type": "call"}, {"api_name": "mll.utils.clean_tok", "line_number": 186, "usage_type": "call"}, {"api_name": "lark.Tree", "line_number": 209, "usage_type": "name"}, {"api_name": "mll.utils.flatten", "line_number": 212, "usage_type": "call"}, {"api_name": "mll.utils.group", "line_number": 212, "usage_type": "call"}, {"api_name": "mll.utils.clean_tok", "line_number": 217, "usage_type": "call"}, {"api_name": "lark.Tree", "line_number": 217, "usage_type": "call"}, {"api_name": "lark.Token", "line_number": 217, "usage_type": "call"}]} +{"seq_id": "358653516", "text": "import os\nimport numpy as np\nimport random\n\nfrom PIL import Image\nfrom face_detector import DLIB\n\nclass PrepareData:\n def __init__(self):\n self.load_path = 'drive/My Drive/project42/KDEF_and_AKDEF/KDEF/'\n self.save_path = 'drive/My Drive/project42/data/'\n self.detector = DLIB()\n self.split = {}\n self.OHE = {'AF': [1, 0, 0, 0, 0, 0, 0], 'AN': [0, 1, 0, 0, 0, 0, 0],\n 'DI': [0, 0, 1, 0, 0, 0, 0], 'HA': [0, 0, 0, 1, 0, 0, 0],\n 'NE': [0, 0, 0, 0, 1, 0, 0], 'SA': [0, 0, 0, 0, 0, 1, 0],\n 'SU': [0, 0, 0, 0, 0, 0, 1], }\n\n def __split(self, val_ratio):\n folders = np.sort(os.listdir(self.load_path))\n shuffle = random.sample(range(len(folders)), len(folders))\n shuffled_folders = [folders[shuffle[i]] for i in range(len(shuffle))]\n\n split_border = int((1 - val_ratio) * len(folders))\n\n self.split['train'] = shuffled_folders[:split_border]\n self.split['val'] = shuffled_folders[split_border:]\n print(self.split)\n\n def __detect_faces(self):\n for key in self.split.keys():\n x, y = [], []\n for foldername in self.split[key]:\n imagenames = np.sort(os.listdir('{}{}/'.format(self.load_path,\n foldername)))\n for imagename in imagenames:\n if imagename[6] == 'S': # straight face\n img = np.asarray(Image.open('{}{}/{}'.format(self.load_path,\n foldername, imagename)))\n self.detector.detect(img)\n x.append(self.detector.faces[0])\n y.append(self.OHE[imagename[4:6]])\n\n x, y = np.asarray(x), np.asarray(y)\n np.savez_compressed('{}kdef_detected_{}.npz'.format(self.save_path, key), x=x, y=y)\n print(x.shape, y.shape)\n\n def __shuffle(self):\n for key in self.split.keys():\n loaded = np.load('{}kdef_detected_{}.npz'.format(self.save_path, key), allow_pickle=True)\n X, Y = loaded['x'], loaded['y']\n\n shuffle = random.sample(range(len(X)), len(X))\n\n x = [X[shuffle[i]] for i in range(len(shuffle))]\n y = [Y[shuffle[i]] for i in range(len(shuffle))]\n\n x, y = np.asarray(x), np.asarray(y)\n np.savez('{}kdef_shuffled_{}.npz'.format(self.save_path, key), x=x, y=y)\n print(x.shape, y.shape)\n\n def __save(self, val_split):\n loaded_train = np.load('{}kdef_detected_train.npz'.format(self.save_path), allow_pickle=True)\n x_train, y_train = loaded_train['x'], loaded_train['y']\n loaded_val = np.load('{}kdef_detected_val.npz'.format(self.save_path), allow_pickle=True)\n x_val, y_val = loaded_val['x'], loaded_val['y']\n np.savez('{}kdef_{}.npz'.format(self.save_path, val_split),\n x_train=x_train, y_train=y_train, x_val=x_val, y_val=y_val)\n print(x_train.shape, y_train.shape, x_val.shape, y_val.shape)\n\n def prepare(self, val_split):\n self.__split(val_split)\n print('Split done...')\n self.__detect_faces()\n print('Face detection done...')\n self.__shuffle()\n print('Shuffle done...')\n self.__save(val_split)\n print('Data saved.')\n\npd = PrepareData()\npd.prepare(0.3)", "sub_path": "data_prep.py", "file_name": "data_prep.py", "file_ext": "py", "file_size_in_byte": 3438, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "face_detector.DLIB", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 20, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 20, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.sort", "line_number": 34, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 38, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 38, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 38, "usage_type": "name"}, {"api_name": "numpy.asarray", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.savez_compressed", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 50, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.savez", "line_number": 67, "usage_type": "call"}]} +{"seq_id": "354016196", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"config.py - Example configuration script for forcepho runs.\n\"\"\"\nimport os\nimport numpy as np\nfrom argparse import Namespace\nexpandpath = os.path.expandvars\npjoin = os.path.join\n\nconfig = Namespace()\n\n# -----------\n# --- Overall ----\nconfig.log = True\n\n# ---------------\n# --- Output -----\nconfig.scene_catalog = \"superscene.fits\"\nconfig.patchlogfile = \"patchlog.dat\"\n\n# -----------------------\n# --- Filters being run ---\nconfig.bandlist = [\"F090W\", \"F115W\", \"F150W\", \"F200W\",\n \"F277W\", \"F335M\", \"F356W\", \"F410M\", \"F444W\"]\n\n# -----------------------\n# --- Data locations ---\nconfig.storename = \"mini-challenge-19-mosaic-st\"\nconfig.pixelstorefile = \"stores/pixels_{}.h5\".format(config.storename)\nconfig.metastorefile = \"stores/meta_{}.dat\".format(config.storename)\nconfig.psfstorefile = \"stores/psf_jades_mosaic_ng4.h5\"\nconfig.splinedatafile = \"stores/sersic_mog_model.smooth=0.0150.h5\"\nconfig.mosaics_directory = os.path.expandvars(\"$SCRATCH/eisenstein_lab/stacchella/mosaic/mosaic/\")\nconfig.frames_directory = os.path.expandvars(\"$SCRATCH/eisenstein_lab/bdjohnson/jades_force/data/2019-mini-challenge/mosaics/st/trimmed/\")\nconfig.initial_catalog = os.path.expandvars(\"$SCRATCH/eisenstein_lab/bdjohnson/jades_force/data/2019-mini-challenge/source_catalogs/forcepho_table_psf_matched_v5.0.fits\")\n\n# ------------------------\n# --- Data Types/Sizes ---\nconfig.pix_dtype = np.float32\nconfig.meta_dtype = np.float32\nconfig.super_pixel_size = 8 # number of pixels along one side of a superpixel\nconfig.nside_full = np.array([11496, 9096]) # number of pixels along one side of a square input frame\nconfig.bitmask = None\n\n# -----------------------\n# --- Patch Generation ---\nconfig.max_active_fraction = 0.1\nconfig.maxactive_per_patch = 15\n\n# -----------------------\n# --- HMC parameters ---\nconfig.n_warm = 250\nconfig.n_iter = 100\nconfig.n_tune = 100\n\n# ------------------------\n# --- PSF information ----\n# Used for building PSF store\n# generally not necessary\nconfig.mixture_directory = \"/Users/bjohnson/Projects/jades_force/data/psf/mixtures\"\nconfig.psf_search = \"gmpsf*ng4.h5\"\n", "sub_path": "cannon/config_mosaic.py", "file_name": "config_mosaic.py", "file_ext": "py", "file_size_in_byte": 2150, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "argparse.Namespace", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.expandvars", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.path.expandvars", "line_number": 36, "usage_type": "call"}, {"api_name": "os.path", "line_number": 36, "usage_type": "attribute"}, {"api_name": "os.path.expandvars", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 41, "usage_type": "attribute"}, {"api_name": "numpy.float32", "line_number": 42, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}]} +{"seq_id": "517609107", "text": "# -*- coding: utf-8 -*-\n# Copyright by Beyondy, 2010-2020\n# All rights reserved.\n\nfrom config.config import Config\nfrom models.stocks import StockProfile, StockDailyPrice\nfrom sqlalchemy import func\nfrom sqlalchemy.orm import aliased\nfrom libs.utils.db import *\nfrom libs.utils.date import *\nfrom libs.utils.app import init_app\nfrom libs.constants import *\nfrom libs.logger import Logger, getTraceback\n\nfrom optparse import OptionParser\nfrom datetime import datetime\nfrom bs4 import BeautifulSoup as BS4\nimport requests\nimport random\nimport time\nimport json\nimport sys\nimport re\n\nCL_DOWNLOAD_MOST_RECENT = 0\nCL_DOWNLOAD_MISSED_QUARTERS = 1\nCL_DOWNLOAD_ALL_AND_UPDATE_LOCALS = 2\n\n\ndef __get_latest_trade_day():\n\ttry:\n\t\turl = 'http://hq.sinajs.cn/rn=1513596251861&list=sh000001'\n\t\trsp = requests.get(url)\n\t\t# var hq_str_sh000001=\"上证指数,3268.0335,3266.1371,3267.9224,3280.5438,3254.1775,0,0,120700389,149738224054,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2017-12-18,15:01:03,00\";\\n\n\t\ttxt = rsp.text.replace('_',' ').replace('\"','').replace(',',' ').replace('=',' ').replace(';','').strip()\n\t\tfds = txt.split()\n\n\t\tymd = int(fds[34].replace('-', '')) #2016-10-18\n\t\thms = fds[35] #15:01:03\n\n\t\treturn ymd\n\texcept Exception as ex:\n\t\tlogger.error(\"%s\", \"get latest trade day from url failed and got exception: {}\".format(url, ex))\n\t\treturn today2day()\n\n\ndef __get_specific_stock_ids(tickers):\n\twith ScopedSession() as db_session:\n\t\ttry:\n\t\t\trows = db_session.query(StockProfile).filter(StockProfile.cn_code.in_(tickers)).all()\n\t\t\tids = [{\"id\": row.stock_id, \"ticker\": row.cn_code} for row in rows]\n\t\t\treturn ids\n\t\texcept Exception as ex:\n\t\t\tlogger.error(\"get all tickers failed and got exception: {}\".format(ex))\n\t\t\treturn []\n\ndef __get_all_stock_ids():\n\twith ScopedSession() as db_session:\n\t\ttry:\n\t\t\trows = db_session.query(StockProfile).order_by(StockProfile.cn_code).all()\n\t\t\tids = [{\"id\": row.stock_id, \"ticker\": row.cn_code} for row in rows]\n\t\t\treturn ids\n\t\texcept Exception as ex:\n\t\t\tlogger.error(\"get all tickers failed and got exception: {}\".format(ex))\n\t\t\treturn []\n\n\ndef __get_next_day(stock_id):\n\twith ScopedSession() as db_session:\n\t\ttry:\n\t\t\trow = db_session.query(func.max(StockDailyPrice.date))\\\n\t\t\t\t\t\t\t.filter(StockDailyPrice.stock_id == stock_id, StockDailyPrice.open != None)\\\n\t\t\t\t\t\t\t.first()\n\t\t\tif row and row[0]:\n\t\t\t\t#TODO: next trade day\n\t\t\t\treturn days_ago(row[0], -1)\n\n\t\t\t#get ipo day\n\t\t\trow = db_session.query(StockProfile)\\\n\t\t\t\t\t\t\t.filter(StockProfile.stock_id == stock_id)\\\n\t\t\t\t\t\t\t.first()\n\t\t\tif row and row.ipo_day is not None:\n\t\t\t\treturn row.ipo_day\n\n\t\t\treturn None\n\t\texcept Exception as ex:\n\t\t\tlogger.error(\"%s\", \"get last day for ticker={} failed and got exception: {}\".format(stock_id, ex))\n\t\t\treturn None\n\ndef __get_ipo_day(stock_id):\n\twith ScopedSession() as db_session:\n\t\ttry:\n\t\t\t#get ipo day\n\t\t\trow = db_session.query(StockProfile).filter(StockProfile.stock_id == stock_id).first()\n\t\t\tif row and row.ipo_day is not None:\n\t\t\t\treturn row.ipo_day\n\n\t\t\treturn None\n\t\texcept Exception as ex:\n\t\t\tlogger.error(\"%s\", \"get IPO day for ticker={} failed and got exception: {}\".format(stock_id, ex))\n\t\t\treturn None\n\ndef __to_float_and_div10(val):\n\ttry:\n\t\treturn float(val) / 10.0 if val else None\n\texcept Exception as ex:\n\t\treturn None\n\ndef __parse_stock_dividend(tab):\n\ttry:\n\t\t\"\"\"\n\t\t公告日期\t 分红方案(每10股) \t\t进度 除权除息日 股权登记日 红股上市日 查看详细\n\t\t\t\t 送股(股)\t转增(股)\t派息(税前)(元)\n\t\t2017-06-02\t0\t\t0\t\t3.3\t\t\t\t实施\t 2017-06-12 2017-06-09 --\t\t 查看\n\t\t2016-05-27\t0\t\t0\t\t3\t\t\t\t实施\t 2016-06-02 2016-06-01\t --\t\t 查看\n\t\t2015-05-21\t0\t\t0\t\t2.5\t\t\t\t实施\t 2015-05-28 2015-05-27\t --\t\t 查看\n\t\t2014-05-13\t0\t\t0\t\t4\t\t\t\t实施\t 2014-05-19 2014-05-16\t --\t\t 查看\n\t\t\"\"\"\n\t\tsdps = []\n\t\ttrs = tab.find_all('tr')\n\t\tif not trs or len(trs) < 3:\n\t\t\treturn []\n\n\t\tfor tr in trs[3:]:\n\t\t\ttds = tr.find_all('td')\n\t\t\tif tds is None or len(tds) < 9:\n\t\t\t\tlogger.warn(\"%s\", \"invalid dividend line: {}\".format(str(tr)))\n\t\t\t\tcontinue\n\n\t\t\tnow = datetime.now()\n\t\t\tsdp = StockDailyPrice(stock_id = None,\n\t\t\t\t\t\t\t\t date = str2day(tds[5].text.strip()),\n\t\t\t\t\t\t\t\t dividend_shares = __to_float_and_div10(tds[1].text.strip()),\n\t\t\t\t\t\t\t\t dividend_capital_shares = __to_float_and_div10(tds[2].text.strip()),\n\t\t\t\t\t\t\t\t dividend_cash = __to_float_and_div10(tds[3].text.strip()),\n\t\t\t\t\t\t\t\t create_time = now,\n\t\t\t\t\t\t\t\t last_update_time = now)\n\t\t\tif (sdp.dividend_shares is None or sdp.dividend_shares < 1e-9) \\\n\t\t\t\tand (sdp.dividend_capital_shares is None or sdp.dividend_capital_shares < 1e-9) \\\n\t\t\t\t\tand (sdp.dividend_cash is None or sdp.dividend_cash < 1e-9):\n\t\t\t\tcontinue\n\t\t\telif sdp.date is None:\n\t\t\t\tlogger.error(\"%s\", \"no date found for dividend: {}\".format(sdp))\n\t\t\t\tcontinue\n\n\t\t\tsdps.append(sdp)\n\t\t\treturn sdps\n\texcept Exception as e:\n\t\tlogger.error(\"%s\", \"parse stock dividend failed and got exception: {}\".format(getTraceback()))\n\t\treturn []\n\ndef __parse_stock_rationed_shares(tab):\n\ttry:\n\t\t\"\"\"\n\t\t配股\n\t\t公告日期\t配股方案(每10股配股股数)\t配股价格(元)\t基准股本(万股)\t除权日\t股权登记日\t\\\n\t\t缴款起始日\t缴款终止日\t配股上市日\t募集资金合计(元)\t查看详细\n\t\t\"\"\"\n\t\ttrs = tab.find_all('tr')\n\t\tif not trs or len(trs) < 3:\n\t\t\treturn []\n\n\t\tsdps = []\n\t\tfor tr in trs[3:]:\n\t\t\ttds = tr.find_all('td')\n\t\t\tif not tds or len(tds) < 11:\n\t\t\t\tlogger.warn(\"%s\", \"invalid rationed shares line: {}\".format(str(tr)))\n\t\t\t\tcontinue\n\n\t\t\tnow = datetime.now()\n\t\t\tsdp = StockDailyPrice(stock_id = None,\n\t\t\t\t\t\t\t\t date = str2day(tds[5].text.strip()),\n\t\t\t\t\t\t\t\t rationed_shares = __to_float_and_div10(tds[1].text.strip()),\n\t\t\t\t\t\t\t\t rationed_price = __to_float_and_div10(tds[1].text.strip()),\n\t\t\t\t\t\t\t\t create_time = now,\n\t\t\t\t\t\t\t\t last_update_time = now)\n\t\t\tif (sdp.rationed_shares is None or sdp.rationed_shares < 1e-9) \\\n\t\t\t\t\tand (sdp.rationed_price is None or sdp.rationed_price < 1e-9):\n\t\t\t\tcontinue\n\t\t\telif sdp.date is None:\n\t\t\t\tlogger.error(\"%s\", \"no date found for rationed-share: {}\".format(sdp))\n\t\t\t\tcontinue\n\n\t\t\tsdps.append(sdp)\n\t\treturn sdps\n\texcept Exception as ex:\n\t\tlogger.error(\"%s\", \"parse stock rationed shares failed and got exception: {}\".format(getTraceback()))\n\t\treturn []\n\n\ndef __download_stock_dividends(http_session, ticker):\n\ttry:\n\t\tfor i in range(Config.number_of_retry):\n\t\t\tt1 = datetime.now().timestamp()\n\t\t\turl = 'http://vip.stock.finance.sina.com.cn/corp/go.php/vISSUE_ShareBonus/stockid/{}.phtml'.format(ticker)\n\t\t\trsp = http_session.get(url, headers = {'Referer': url})\n\t\t\tt2 = datetime.now().timestamp()\n\t\t\tlogger.debug(\"%s\", \"get({}) cost={:.3f}s, rsp-length={}\".format(url, (t2-t1), len(rsp.content)))\n\t\t\tif rsp.status_code != 200:\n\t\t\t\tlogger.error(\"%s\", \"{}th get({}) return status-code={}\".format(i+1, url, rsp.status_code))\n\t\t\t\ttime.sleep(Config.suspend_seconds_when_sina_busy)\n\t\t\t\tcontinue\n\t\t\ttime.sleep(1)\n\t\t\tbreak\n\n\t\tif rsp.encoding == 'ISO-8859-1': rsp.encoding = 'GB2312'\n\t\t# fix html tag error\n\t\ttxt = rsp.text.replace('分红</td>', '分红</th>')\\\n\t\t\t\t\t .replace('配股</td>', '配股</th>')\\\n\t\t\t\t\t .replace('</strong></td>', '</strong></th>')\n\t\thtm = BS4(txt, 'html.parser')\n\t\ttab = htm.find('table', attrs={'id':'sharebonus_1'})\n\t\tif not tab:\n\t\t\tlogger.warn(\"no sharebonus_1 found for ticker: {}\".format(ticker))\n\t\t\treturn []\n\t\tsdps = __parse_stock_dividend(tab)\n\n\t\t# 配股\n\t\ttab2 = htm.find('table', attrs={'id':'sharebonus_2'})\n\t\tif not tab2:\n\t\t\tlogger.warn(\"no sharebonus_2 found for ticker: {}\".format(ticker))\n\t\telse:\n\t\t\tsdps2 = __parse_stock_rationed_shares(tab2)\n\t\t\tif sdps2: sdps.extend(sdps2)\n\n\t\treturn sdps\n\texcept Exception as ex:\n\t\tlogger.error(\"get stock dividends and splits for {} failed and got exception: {}\".format(url, getTraceback()))\n\t\traise\n\ndef __str2float(val):\n\ttry:\n\t\treturn float(val) if val else None\n\texcept:\n\t\treturn None\n\ndef __download_stock_prices(http_session, ticker, year, jidu):\n\ttry:\n\t\tfor i in range(Config.number_of_retry):\n\t\t\tt1 = datetime.now().timestamp()\n\t\t\turl = 'http://vip.stock.finance.sina.com.cn/corp/go.php/vMS_MarketHistory/stockid/{}.phtml?year={}&jidu={}'.format(ticker, year, jidu)\n\t\t\treferer = 'http://vip.stock.finance.sina.com.cn/corp/go.php/vMS_MarketHistory/stockid/{}.phtml'.format(ticker)\n\t\t\trsp = http_session.get(url, headers={'Referer': referer})\n\t\t\tt2 = datetime.now().timestamp()\n\t\t\tlogger.debug(\"%s\", \"get({}) cost={:.3f}s, rsp-length={}\".format(url, (t2 - t1), len(rsp.content)))\n\t\t\tif rsp.status_code != 200:\n\t\t\t\tlogger.error(\"%s\", \"{}th get({}) return status-code={}\".format(i+1, url, rsp.status_code))\n\t\t\t\ttime.sleep(Config.suspend_seconds_when_sina_busy)\n\t\t\t\tcontinue\n\t\t\ttime.sleep(1)\n\t\t\tbreak\n\n\t\tif rsp.encoding == 'ISO-8859-1': rsp.encoding = 'GB2312'\n\t\thtm = BS4(rsp.text, 'html.parser')\n\n\t\t#get oldest year\n\t\toldest_year = None\n\t\tform = htm.find('form', attrs={'name':'daily'})\n\t\tif form:\n\t\t\tyears = form.find('select', attrs={'name':'year'})\n\t\t\tif years:\n\t\t\t\toptions = years.find_all('option')\n\t\t\t\tif len(options) > 0:\n\t\t\t\t\toldest_year = int(options[-1].text.strip())\n\n\t\t#get prices\n\t\ttab = htm.find('table', attrs={'id':'FundHoldSharesTable'})\n\t\tif not tab:\n\t\t\tlogger.warn(\"%s\", \"no FundHoldingSharesTable found for ticker={}, year={}, quarter={}\".format(ticker, year, jidu))\n\t\t\t# add a invalid record, to avoid download this quarter again\n\t\t\tnow = datetime.now()\n\t\t\tsdp = StockDailyPrice(stock_id = None,\n\t\t\t\t\t\t\t\t date = year * 10000 + jidu * 300 + 1,\n\t\t\t\t\t\t\t\t high = 0.0,\n\t\t\t\t\t\t\t\t create_time = now,\n\t\t\t\t\t\t\t\t last_update_time = now)\n\t\t\treturn (oldest_year, [sdp])\n\n\t\tsdps = [] #stock daily prices\n\t\ttrs = tab.find_all('tr')\n\t\tif not trs or len(trs) <= 2:\n\t\t\t# no more prices\n\t\t\tlogger.warn(\"%s\", \"no price found for ticker={}, year={}, quarter={}\".format(ticker, year, jidu))\n\t\t\treturn (oldest_year, [])\n\n\t\tfor tr in trs[2:]: # skip two header rows\n\t\t\ttds = tr.find_all('td')\n\t\t\tif tds is None or len(tds) < 7:\n\t\t\t\tcontinue\n\n\t\t\tnow = datetime.now()\n\t\t\tsdp = StockDailyPrice(stock_id = None,\n\t\t\t\t\t\t\t\t date = str2day(tds[0].text.strip()),\n\t\t\t\t\t\t\t\t open = __str2float(tds[1].text.strip()),\n\t\t\t\t\t\t\t\t high = __str2float(tds[2].text.strip()),\n\t\t\t\t\t\t\t\t close = __str2float(tds[3].text.strip()),\n\t\t\t\t\t\t\t\t low = __str2float(tds[4].text.strip()),\n\t\t\t\t\t\t\t\t volumn = __str2float(tds[5].text.strip()),\n\t\t\t\t\t\t\t\t amount = __str2float(tds[6].text.strip()),\n\t\t\t\t\t\t\t\t create_time = now,\n\t\t\t\t\t\t\t\t last_update_time = now)\n\t\t\tsdps.append(sdp)\n\t\t# end-for\n\n\t\tif not sdps:\n\t\t\tlogger.warn(\"%s\", \"no valid price found for ticker={}, year={}, quarter={}\".format(ticker, year, jidu))\n\t\treturn (oldest_year, sdps)\n\texcept Exception as ex:\n\t\tlogger.error(\"%s\", \"get stock prices for {} failed and got exception: {}\".format(url, getTraceback()))\n\t\treturn (None, None)\n\ndef __update_stock_dividend(sdp, new_sdp):\n\tif sdp.dividend_cash != new_sdp.dividend_cash \\\n\t\tor sdp.dividend_shares != new_sdp.dividend_shares \\\n\t\t\tor sdp.dividend_capital_shares != new_sdp.dividend_capital_shares \\\n\t\t\t\tor sdp.rationed_shares != new_sdp.rationed_shares \\\n\t\t\t\t\tor sdp.rationed_price != new_sdp.rationed_price:\n\t\tsdp.dividend_cash = new_sdp.dividend_cash\n\t\tsdp.dividend_shares = new_sdp.dividend_shares\n\t\tsdp.dividend_capital_shares = new_sdp.dividend_capital_shares\n\t\tsdp.rationed_shares = new_sdp.rationed_shares\n\t\tsdp.rationed_price = new_sdp.rationed_price\n\t\tsdp.last_update_time = new_sdp.last_update_time\n\t\treturn True\n\treturn False\n\n\ndef __save_stock_dividends(stock_id, ticker, dividends):\n\twith ScopedSession() as db_session:\n\t\ttry:\n\t\t\tupdate_cnt = 0\n\t\t\tfor dividend in dividends:\n\t\t\t\told_sdp = db_session.query(StockDailyPrice)\\\n\t\t\t\t\t\t\t\t\t.filter(StockDailyPrice.stock_id == stock_id, StockDailyPrice.date == dividend.date)\\\n\t\t\t\t\t\t\t\t\t.first()\n\t\t\t\tif old_sdp:\n\t\t\t\t\tif __update_stock_dividend(old_sdp, dividend):\n\t\t\t\t\t\t#store into db when commit\n\t\t\t\t\t\tupdate_cnt += 1\n\t\t\t\t\t\tpass\n\t\t\t\telse:\n\t\t\t\t\tlogger.warn(\"%s\", \"no price found for dividend: {}, add directly\".format(dividend))\n\t\t\t\t\tdividend.stock_id = stock_id\n\t\t\t\t\tdb_session.add(dividend)\n\t\t\t\t\tupdate_cnt += 1\n\n\t\t\tdb_session.commit()\n\t\t\treturn update_cnt\n\t\texcept Exception as ex:\n\t\t\tlogger.error(\"%s\", \"save stock dividends for ticker: {}, count-of-prices: {} failed and got exception: {}\".format(ticker, len(dividends), getTraceback()))\n\t\t\tdb_session.rollback()\n\t\t\treturn 0\n\ndef __update_stock_price(sdp, new_sdp):\n\t#TODO: compare more fields\n\tif sdp.open != new_sdp.open or sdp.high != new_sdp.high \\\n\t\tor sdp.close != new_sdp.close or sdp.low != new_sdp.low \\\n\t\t\tor sdp.volumn != new_sdp.volumn or sdp.amount != new_sdp.amount:\n\t\tsdp.open = new_sdp.open\n\t\tsdp.high = new_sdp.high\n\t\tsdp.close = new_sdp.close\n\t\tsdp.low = new_sdp.low\n\t\tsdp.volumn = new_sdp.volumn\n\t\tsdp.amount = new_sdp.amount\n\t\tsdp.last_update_time = new_sdp.last_update_time\n\t\treturn True\n\n\treturn False\n\n\ndef __save_stock_prices(stock_id, ticker, sdps, check_level):\n\twith ScopedSession() as db_session:\n\t\ttry:\n\t\t\tupdate_cnt = 0\n\t\t\tfor sdp in sdps:\n\t\t\t\told_sdp = db_session.query(StockDailyPrice)\\\n\t\t\t\t\t\t\t\t\t.filter(StockDailyPrice.stock_id == stock_id, StockDailyPrice.date == sdp.date)\\\n\t\t\t\t\t\t\t\t\t.first()\n\t\t\t\tif old_sdp is not None:\n\t\t\t\t\tif __update_stock_price(old_sdp, sdp):\n\t\t\t\t\t\t#already update old_sdp, which will be saved into db during commit\n\t\t\t\t\t\tupdate_cnt += 1\n\t\t\t\t\t\tpass\n\t\t\t\telse:\n\t\t\t\t\t# add directly\n\t\t\t\t\tsdp.stock_id = stock_id\n\t\t\t\t\tdb_session.add(sdp)\n\t\t\t\t\tupdate_cnt += 1\n\t\t\t\t# end-if\n\n\t\t\tdb_session.commit()\n\t\t\treturn update_cnt\n\t\texcept Exception as ex:\n\t\t\tlogger.error(\"%s\", \"save stock prices for ticker: {}, count-of-prices: {} failed and got exception: {}\".format(stock_id, len(sdps), getTraceback()))\n\t\t\treturn 0\n\n\ndef __get_quarters_having_prices(stock_id):\n\twith ScopedSession() as db_session:\n\t\ttry:\n\t\t\t#year = aliased(StockDailyPrice.date // 10000, name=\"year\")\n\t\t\t#quarter = aliased((StockDailyPrice.date % 10000) / 400 + 1, name=\"quarter\")\n\t\t\t#rows = db_session.query(func.div(StockDailyPrice.date, 10000).label('year'), func.div(StockDailyPrice.date % 10000, 400).label('quarter'))\\\n\t\t\t#\t\t\t\t .filter(StockDailyPrice.stock_id == stock_id)\\\n\t\t\t#\t\t\t\t .group_by('year', 'quarter')\\\n\t\t\t#\t\t\t\t .all()\n\t\t\trows = db_session.query(StockDailyPrice.date.distinct())\\\n\t\t\t\t\t\t .filter(StockDailyPrice.stock_id == stock_id, StockDailyPrice.high != None)\\\n\t\t\t\t\t\t\t .all()\n\t\t\tquarters = {}\n\t\t\tfor (date,) in rows:\n\t\t\t\tyear = year_of_day(date)\n\t\t\t\tquarter = quarter_of_day(date)\n\t\t\t\tquarters.update({(year, quarter): 1})\n\n\t\t\treturn quarters\n\t\texcept Exception as ex:\n\t\t\tlogger.error(\"%s\", \"get year/quarter(s) for stock: {} failed and got exception: {}\".format(stock_id, getTraceback()))\n\t\t\treturn None\n\n\ndef __calc_stock_daily_return_index(stock_id, oldest_changed_day):\n\twith ScopedSession() as db_session:\n\t\ttry:\n\t\t\tsp = db_session.query(StockProfile).filter(StockProfile.stock_id == stock_id).first()\n\t\t\tsdps = db_session.query(StockDailyPrice)\\\n\t\t\t\t\t\t\t .filter(StockDailyPrice.stock_id == stock_id)\\\n\t\t\t\t\t\t\t .order_by(StockDailyPrice.date.asc())\\\n\t\t\t\t\t\t\t .all()\n\t\t\tif sp is None or not sdps:\n\t\t\t\treturn True\n\n\t\t\t\"\"\"\n\t\t\t像600018, 20061026吸收合并发行，sina没有此之前的行情数据，但有分红。暂时忽略有行情前的分红！！！\n\t\t\t\"\"\"\n\t\t\tstart_day_idx = 0\n\t\t\tsdp0 = None\n\t\t\twhile True:\n\t\t\t\tif start_day_idx >= len(sdps):\n\t\t\t\t\tbreak\n\t\t\t\tif sdps[start_day_idx].close is not None and sdps[start_day_idx].close >= 1e-9:\n\t\t\t\t\tsdp0 = sdps[start_day_idx]\n\t\t\t\t\tbreak\n\t\t\t\tstart_day_idx += 1\n\n\t\t\tif sdp0 is None:\n\t\t\t\t# do not calculate daily-return-index\n\t\t\t\tlogger.warn(\"%s\", \"stock(id={}, ticker={}) has no close price even it has {} days history prices\"\\\n\t\t\t\t\t\t\t\t\t.format(sp.stock_id, sp.cn_code, len(sdps)))\n\t\t\t\treturn True\n\t\t\tif start_day_idx != 0:\n\t\t\t\tlogger.warn(\"%s\", \"stock(id={}, ticker={}) has no close price in the first {} days but it has {} history prices\"\\\n\t\t\t\t\t\t\t\t\t.format(sp.stock_id, sp.cn_code, start_day_idx, len(sdps)))\n\t\t\t# end-if\n\n\t\t\tyesterday_close = sdp0.close\n\t\t\tyesterday_dri = 100.0\n\t\t\treinvest_factor = 1.0 # assume no dividend on the first day\n\n\t\t\t#ipo-day\n\t\t\tif sp.ipo_day is not None and sp.ipo_price is not None and sdp0.date == sp.ipo_day:\n\t\t\t\tsdp0.daily_inc = sdp0.close / sp.ipo_price - 1.0\n\t\t\t\tyesterday_dri = sdp0.close / sp.ipo_price * 100\n\t\t\telse:\n\t\t\t\tlogger.warn(\"%s\", \"stock(id={}, ticker={}) has unmatched data: ipo-day={}, ipo-price={}, first-day={}, first-day-close={}\"\\\n\t\t\t\t .format(sp.stock_id, sp.cn_code, sp.ipo_day, sp.ipo_price, sdp0.date, sdp0.close))\n\t\t\t\tsdp0.daily_inc = 0\n\n\t\t\tsdp0.shares_if_reinvest = reinvest_factor\n\t\t\tsdp0.daily_return_index = yesterday_dri\n\t\t\tsdp0.last_update_time = datetime.now()\n\n\t\t\tfor sdp in sdps[start_day_idx:]:\n\t\t\t\tif sdp.dividend_cash is not None or sdp.dividend_shares is not None \\\n\t\t\t\t\t\tor sdp.dividend_capital_shares is not None:\n\t\t\t\t\t\"\"\"\n\t\t\t\t\thas dividend, need calc reinvest-factor.\n\t\t\t\t\t\tYC + rationed-shares * rationed-price = YC' * factor\n\t\t\t\t\t\tfactor = 1 + dividend-shares + dividend-cash / YC' + rationed-shares\n\t\t\t\t\t==>\n\t\t\t\t\t\tYC' = (YC - dividend-cash + rationed-shares * rationed-price) / (1 + dividend-shares + rationed-shares)\n\t\t\t\t\t\tfactor-without-cash-in = YC / YC'\n\t\t\t\t\t\"\"\"\n\t\t\t\t\tdividend_shares = 0\n\t\t\t\t\tdividend_cash = 0\n\n\t\t\t\t\tif sdp.dividend_shares is not None:\n\t\t\t\t\t\tdividend_shares += sdp.dividend_shares\n\t\t\t\t\tif sdp.dividend_capital_shares is not None:\n\t\t\t\t\t\tdividend_shares += sdp.dividend_capital_shares\n\t\t\t\t\tif sdp.rationed_shares is not None and sdp.rationed_price is not None:\n\t\t\t\t\t\tdividend_shares += sdp.rationed_shares\n\t\t\t\t\tif sdp.dividend_cash is not None:\n\t\t\t\t\t\tdividend_cash = sdp.dividend_cash\n\n\t\t\t\t\t# adjust yesterday close price first\n\t\t\t\t\tadjusted_yesterday_close = yesterday_close - dividend_cash\n\t\t\t\t\tif sdp.rationed_shares is not None and sdp.rationed_price is not None:\n\t\t\t\t\t\tadjusted_yesterday_close += sdp.rationed_shares * sdp.rationed_price\n\t\t\t\t\tadjusted_yesterday_close = adjusted_yesterday_close / (1 + dividend_shares)\n\n\t\t\t\t\t# assume no cash-in for rationed-shares, otherwise \n\t\t\t\t\t# new-factor = (YC + rationed-share * rationed-price) / YC'\n\t\t\t\t\tnew_factor = yesterday_close / adjusted_yesterday_close\n\t\t\t\t\treinvest_factor *= new_factor\n\n\t\t\t\t\t# consider factor!!!\n\t\t\t\t\tif sdp.close is not None:\n\t\t\t\t\t\tsdp.daily_inc = sdp.close / adjusted_yesterday_close - 1.0\n\t\t\t\t\t\tsdp.daily_return_index = yesterday_dri * new_factor * sdp.close / adjusted_yesterday_close\n\t\t\t\t\t\tyesterday_close, yesterday_dri = sdp.close, sdp.daily_return_index\n\t\t\t\t\telse:\n\t\t\t\t\t\tsdp.daily_return_index = yesterday_dri * new_factor\n\t\t\t\t\t\tyesterday_close, yesterday_dri = adjusted_yesterday_close, yesterday_dri * new_factor\n\t\t\t\t\tsdp.shares_if_reinvest = reinvest_factor\n\t\t\t\telif sdp.close is not None:\n\t\t\t\t\t# no dividend, just update daily inc, dri\n\t\t\t\t\tsdp.daily_inc = sdp.close / yesterday_close - 1.0\n\t\t\t\t\tsdp.daily_return_index = yesterday_dri * sdp.close / yesterday_close\n\t\t\t\t\tsdp.shares_if_reinvest = reinvest_factor\n\t\t\t\t\tyesterday_close, yesterday_dri = sdp.close, sdp.daily_return_index\n\t\t\t\telse:\n\t\t\t\t\tlogger.warn(\"%s\", \"{} has no close!!!\".format(sdp))\n\t\t\t\tsdp.last_update_time = datetime.now()\n\n\t\t\tdb_session.commit()\t\n\t\t\treturn True\n\t\texcept Exception as ex:\n\t\t\tdb_session.rollback()\n\t\t\tlogger.error(\"%s\", \"calc stock daily return index for stock-id={} failed and got exception: {}\".format(stock_id, getTraceback()))\n\t\t\treturn False\n\ndef __download_stock_prices_dividends(http_session, stock_id, today_year, today_quarter, check_level):\n\ttry:\n\t\tyear = today_year\n\t\tjidu = today_quarter\n\n\t\toldest_year = None\n\t\toldest_quarter = None\n\n\t\tif check_level == CL_DOWNLOAD_MOST_RECENT:\n\t\t\tnext_trade_day = __get_next_day(stock_id['id'])\n\t\t\tif next_trade_day is not None:\n\t\t\t\tif next_trade_day > today:\n\t\t\t\t\treturn True\n\n\t\t\t\toldest_year = year_of_day(next_trade_day)\n\t\t\t\toldest_quarter = quarter_of_day(next_trade_day)\n\t\telif check_level == CL_DOWNLOAD_MISSED_QUARTERS:\n\t\t\t# get all (year, quarter) which have prices\n\t\t\tquarters_with_prices = __get_quarters_having_prices(stock_id['id'])\n\t\t\tnext_trade_day = __get_next_day(stock_id['id'])\n\t\t\tif next_trade_day is None:\n\t\t\t\t# can not determine next trade day(means no history prices downloaded, no ipo-date info),\n\t\t\t\t# need download the current quarter anyway\n\t\t\t\tquarters_with_prices.pop((today_year, today_quarter), None)\n\t\t\telif next_trade_day <= today:\n\t\t\t\t# today quarter have no quote price, need download, so remove it from quarters having prices\n\t\t\t\tquarters_with_prices.pop((today_year, today_quarter), None)\n\t\t\telse:\n\t\t\t\t# today has quote price, do not need download the current quarter\n\t\t\t\tpass\n\n\t\tif check_level in (CL_DOWNLOAD_MISSED_QUARTERS, CL_DOWNLOAD_ALL_AND_UPDATE_LOCALS):\n\t\t\tipo_day = __get_ipo_day(stock_id['id'])\n\t\t\tif ipo_day is not None:\n\t\t\t\toldest_year = year_of_day(ipo_day)\n\t\t\t\toldest_quarter = quarter_of_day(ipo_day)\n\n\t\tupdate_cnt = 0\n\t\twhile True:\n\t\t\tif check_level == CL_DOWNLOAD_MISSED_QUARTERS and (year, jidu) in quarters_with_prices:\n\t\t\t\t# the quarter has prices, assume has day in that quarter has prices\n\t\t\t\t# do not re-download, skip it\n\t\t\t\t# logger.debug(\"%s\", \"skip year={} quarter={}\".format(year, jidu))\n\t\t\t\tpass\n\t\t\telse:\n\t\t\t\t_oldest_year, prices = __download_stock_prices(http_session, stock_id['ticker'], year, jidu)\n\t\t\t\tif oldest_year is None and _oldest_year is not None and _oldest_year >= 1000:\n\t\t\t\t\toldest_year = _oldest_year\n\t\t\t\t\toldest_quarter = 1\n\n\t\t\t\tif prices is None:\n\t\t\t\t\t# soemthing wrong\n\t\t\t\t\t# TODO: re-try 3 times?\n\t\t\t\t\tbreak\n\n\t\t\t\tif prices is not None and len(prices) < 1:\n\t\t\t\t\t# some stock stop trading for quarters/years, so need continue\n\t\t\t\t\tpass\n\t\t\t\telif (check_level == CL_DOWNLOAD_MISSED_QUARTERS and year == today_year and jidu == today_quarter) \\\n\t\t\t\t\t\tor check_level == CL_DOWNLOAD_MOST_RECENT:\n\t\t\t\t\tif next_trade_day is not None and prices[-1].date < next_trade_day:\n\t\t\t\t\t\t# filter out old prices\n\t\t\t\t\t\tnew_added_prices = [sp for sp in prices if sp.date >= next_trade_day]\n\t\t\t\t\t\tupdate_cnt += __save_stock_prices(stock_id['id'], stock_id['ticker'], new_added_prices, check_level)\n\t\t\t\t\telse:\n\t\t\t\t\t\tupdate_cnt += __save_stock_prices(stock_id['id'], stock_id['ticker'], prices, check_level)\n\t\t\t\telse: # check_level in (CL_DOWNLOAD_MISSED_QUARTERS, CL_DOWNLOAD_ALL_AND_UPDATE_LOCALS):\n\t\t\t\t\tupdate_cnt += __save_stock_prices(stock_id['id'], stock_id['ticker'], prices, check_level)\n\n\t\t\tjidu -= 1\n\t\t\tif jidu <= 0:\n\t\t\t\tjidu = 4\n\t\t\t\tyear -= 1\n\n\t\t\t# check need retrieve more history prices\n\t\t\tif (oldest_year is not None and oldest_quarter is not None and year == oldest_year and jidu < oldest_quarter) \\\n\t\t\t\tor (oldest_year is not None and year < oldest_year) \\\n\t\t\t\t\tor year < 1000:\n\t\t\t\tbreak\n\n\t\t# TODO: how to call it when necessay\n\t\tif update_cnt > 0:\n\t\t\tdividends = __download_stock_dividends(http_session, stock_id['ticker'])\n\t\t\tif dividends:\n\t\t\t\tupdate_cnt += __save_stock_dividends(stock_id['id'], stock_id['ticker'], dividends)\n\n\t\t# re-calc shares-if-re-invest, daily return index since the oldest new date\n\t\tif update_cnt > 0:\n\t\t\t__calc_stock_daily_return_index(stock_id['id'], None)\n\n\t\treturn True\n\texcept Exception as ex:\n\t\tlogger.error(\"%s\", \"get prices/dividens for stock(id={}, ticker={}) failed and got exception: {}\".format(stock_id['id'], stock_id['ticker'], getTraceback()))\n\t\treturn False\n\n\ndef download_stocks_prices_dividends(stock_ids, today, check_level):\n\twith requests.Session() as http_session:\n\t\thttp_session.headers.update({\n\t\t\t'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8',\n\t\t\t'Accept-Encoding': 'gzip, deflate',\n\t\t\t'Accept-Language': 'en-US,en;q=0.9,pt;q=0.8,zh-CN;q=0.7,zh-TW;q=0.6,zh;q=0.5',\n\t\t\t'Cache-Control': 'no-cache',\n\t\t\t'Connection': 'keep-alive',\n\t\t\t'Host': 'vip.stock.finance.sina.com.cn',\n\t\t\t'Pragma': 'no-cache',\n\t\t\t'Upgrade-Insecure-Requests': '1',\n\t\t\t'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_9_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.84 Safari/537.36'\n\t\t})\n\n\t\ttoday_year = year_of_day(today)\n\t\ttoday_quarter = quarter_of_day(today)\n\n\t\tfor stock_id in stock_ids:\n\t\t\t__download_stock_prices_dividends(http_session, stock_id, today_year, today_quarter, check_level)\n\t\t# end-for\n\nif __name__ == '__main__':\n\tinit_app()\n\tglobal logger\n\tlogger = Logger.getLogger(__name__)\n\n\tparser = OptionParser()\n\tkinds_of_returns = [a[0] for a in return_names_arr]; kinds_of_returns.remove(\"yearly\")\n\tparser.add_option('-t', '--tickers', dest='tickers', help='download stock history prices and dividends for specified tickers. default is all tickers in db')\n\tparser.add_option('-d', '--date', dest='date', help='get prices before this date. default is the latest trade day')\n\tparser.add_option('-C', '--check-level', dest='check_level', help='level of check. 1 means check missed prices and re-download. 2 means check all history prices and add missed/update changed prices (call this on weekend!)')\n\n\ttry:\n\t\t(options, args) = parser.parse_args()\n\t\t\n\t\tif options.tickers is not None:\n\t\t\ttickers = [ticker.strip() for ticker in options.tickers.split(',')]\n\t\t\tstock_ids = __get_specific_stock_ids(tickers)\n\t\telse:\n\t\t\tstock_ids = __get_all_stock_ids()\n\n\t\ttoday = int(options.date) if options.date else __get_latest_trade_day()\n\t\tcheck_level = int(options.check_level) if options.check_level is not None else CL_DOWNLOAD_MOST_RECENT\n\t\tif check_level < CL_DOWNLOAD_MOST_RECENT or check_level > CL_DOWNLOAD_ALL_AND_UPDATE_LOCALS:\n\t\t\tprint(\"invalid check-level(now is %d), which must be 0(default), 1 or 2\", check_level)\n\t\t\texit(1)\n\n\t\tlogger.info(\"start to download stock history prices and dividends till {}...\".format(today))\n\t\tdownload_stocks_prices_dividends(stock_ids, today, check_level)\n\t\tlogger.info(\"download done\")\n\t\texit(0)\n\texcept Exception as ex:\n\t\tlogger.error(\"download stock history prices and dividends failed and got exception: {}\".format(getTraceback()))\n\t\texit(1)\n", "sub_path": "bin/download_stock_prices.py", "file_name": "download_stock_prices.py", "file_ext": "py", "file_size_in_byte": 25700, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "15", "api": [{"api_name": "requests.get", "line_number": 33, "usage_type": "call"}, {"api_name": "models.stocks.StockProfile", "line_number": 50, "usage_type": "argument"}, {"api_name": "models.stocks.StockProfile.cn_code.in_", "line_number": 50, "usage_type": "call"}, {"api_name": "models.stocks.StockProfile.cn_code", "line_number": 50, "usage_type": "attribute"}, {"api_name": "models.stocks.StockProfile", "line_number": 60, "usage_type": "argument"}, {"api_name": "models.stocks.StockProfile.cn_code", "line_number": 60, "usage_type": "attribute"}, {"api_name": "sqlalchemy.func.max", "line_number": 71, "usage_type": "call"}, {"api_name": "sqlalchemy.func", "line_number": 71, "usage_type": "name"}, {"api_name": "models.stocks.StockDailyPrice.date", "line_number": 71, "usage_type": "attribute"}, {"api_name": "models.stocks.StockDailyPrice", "line_number": 71, "usage_type": "name"}, {"api_name": "models.stocks.StockDailyPrice.stock_id", "line_number": 72, "usage_type": "attribute"}, {"api_name": "models.stocks.StockDailyPrice", "line_number": 72, "usage_type": "name"}, {"api_name": "models.stocks.StockDailyPrice.open", "line_number": 72, "usage_type": "attribute"}, {"api_name": "models.stocks.StockProfile", "line_number": 79, "usage_type": "argument"}, {"api_name": "models.stocks.StockProfile.stock_id", "line_number": 80, "usage_type": "attribute"}, {"api_name": "models.stocks.StockProfile", "line_number": 80, "usage_type": "name"}, {"api_name": "models.stocks.StockProfile", "line_number": 94, "usage_type": "argument"}, {"api_name": "models.stocks.StockProfile.stock_id", "line_number": 94, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 130, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 130, "usage_type": "name"}, {"api_name": "models.stocks.StockDailyPrice", "line_number": 131, "usage_type": "call"}, {"api_name": "libs.logger.getTraceback", "line_number": 149, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 170, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 170, "usage_type": "name"}, {"api_name": "models.stocks.StockDailyPrice", "line_number": 171, "usage_type": "call"}, {"api_name": "libs.logger.getTraceback", "line_number": 187, "usage_type": "call"}, {"api_name": "config.config.Config.number_of_retry", "line_number": 193, "usage_type": "attribute"}, {"api_name": "config.config.Config", "line_number": 193, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 194, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 194, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 197, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 197, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 201, "usage_type": "call"}, {"api_name": "config.config.Config.suspend_seconds_when_sina_busy", "line_number": 201, "usage_type": "attribute"}, {"api_name": "config.config.Config", "line_number": 201, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 203, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 211, "usage_type": "call"}, {"api_name": "libs.logger.getTraceback", "line_number": 228, "usage_type": "call"}, {"api_name": "config.config.Config.number_of_retry", "line_number": 239, "usage_type": "attribute"}, {"api_name": "config.config.Config", "line_number": 239, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 240, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 240, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 244, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 244, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 248, "usage_type": "call"}, {"api_name": "config.config.Config.suspend_seconds_when_sina_busy", "line_number": 248, "usage_type": "attribute"}, {"api_name": "config.config.Config", "line_number": 248, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 250, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 254, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 271, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 271, "usage_type": "name"}, {"api_name": "models.stocks.StockDailyPrice", "line_number": 272, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 291, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 291, "usage_type": "name"}, {"api_name": "models.stocks.StockDailyPrice", "line_number": 292, "usage_type": "call"}, {"api_name": "libs.logger.getTraceback", "line_number": 309, "usage_type": "call"}, {"api_name": "models.stocks.StockDailyPrice", "line_number": 333, "usage_type": "argument"}, {"api_name": "models.stocks.StockDailyPrice.stock_id", "line_number": 334, "usage_type": "attribute"}, {"api_name": "models.stocks.StockDailyPrice", "line_number": 334, "usage_type": "name"}, {"api_name": "models.stocks.StockDailyPrice.date", "line_number": 334, "usage_type": "attribute"}, {"api_name": "libs.logger.getTraceback", "line_number": 350, "usage_type": "call"}, {"api_name": "models.stocks.StockDailyPrice", "line_number": 376, "usage_type": "argument"}, {"api_name": "models.stocks.StockDailyPrice.stock_id", "line_number": 377, "usage_type": "attribute"}, {"api_name": "models.stocks.StockDailyPrice", "line_number": 377, "usage_type": "name"}, {"api_name": "models.stocks.StockDailyPrice.date", "line_number": 377, "usage_type": "attribute"}, {"api_name": "libs.logger.getTraceback", "line_number": 394, "usage_type": "call"}, {"api_name": "models.stocks.StockDailyPrice.date.distinct", "line_number": 407, "usage_type": "call"}, {"api_name": "models.stocks.StockDailyPrice.date", "line_number": 407, "usage_type": "attribute"}, {"api_name": "models.stocks.StockDailyPrice", "line_number": 407, "usage_type": "name"}, {"api_name": "models.stocks.StockDailyPrice.stock_id", "line_number": 408, "usage_type": "attribute"}, {"api_name": "models.stocks.StockDailyPrice", "line_number": 408, "usage_type": "name"}, {"api_name": "models.stocks.StockDailyPrice.high", "line_number": 408, "usage_type": "attribute"}, {"api_name": "libs.logger.getTraceback", "line_number": 418, "usage_type": "call"}, {"api_name": "models.stocks.StockProfile", "line_number": 425, "usage_type": "argument"}, {"api_name": "models.stocks.StockProfile.stock_id", "line_number": 425, "usage_type": "attribute"}, {"api_name": "models.stocks.StockDailyPrice", "line_number": 426, "usage_type": "argument"}, {"api_name": "models.stocks.StockDailyPrice.stock_id", "line_number": 427, "usage_type": "attribute"}, {"api_name": "models.stocks.StockDailyPrice", "line_number": 427, "usage_type": "name"}, {"api_name": "models.stocks.StockDailyPrice.date.asc", "line_number": 428, "usage_type": "call"}, {"api_name": "models.stocks.StockDailyPrice.date", "line_number": 428, "usage_type": "attribute"}, {"api_name": "models.stocks.StockDailyPrice", "line_number": 428, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 471, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 471, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 524, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 524, "usage_type": "name"}, {"api_name": "libs.logger.getTraceback", "line_number": 530, "usage_type": "call"}, {"api_name": "libs.logger.getTraceback", "line_number": 625, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 630, "usage_type": "call"}, {"api_name": "libs.utils.app.init_app", "line_number": 651, "usage_type": "call"}, {"api_name": "libs.logger.Logger.getLogger", "line_number": 653, "usage_type": "call"}, {"api_name": "libs.logger.Logger", "line_number": 653, "usage_type": "name"}, {"api_name": "optparse.OptionParser", "line_number": 655, "usage_type": "call"}, {"api_name": "libs.logger.getTraceback", "line_number": 681, "usage_type": "call"}]} +{"seq_id": "42234794", "text": "# coding:utf-8\r\n\r\n# https://developers.douban.com/wiki/?title=book_v2#get_isbn_book\r\n# https://github.com/douban/douban-client\r\n\r\nfrom contextlib import closing\r\nimport codecs\r\nimport requests\r\n\r\n\r\ndef query_douban(isbn):\r\n url = 'https://api.douban.com/v2/book/isbn/%s' % isbn\r\n r = requests.get(url)\r\n return r.json()['title']\r\n\r\n\r\ndef gen_isbn_barcode(isbn):\r\n url = 'http://b.wwei.cn/html/image.php'\r\n payload = {\r\n 'filetype':'PNG',\r\n 'dpi': 72,\r\n 'scale': 2,\r\n 'rotation': 0,\r\n 'font_family': 'Arial.ttf',\r\n 'font_size': 12,\r\n 'text': isbn,\r\n 'thickness': 30,\r\n 'code': 'BCGisbn',\r\n }\r\n filename = '%s.png' % isbn\r\n with closing(requests.get(url, params=payload)) as r:\r\n fd = open(filename, 'wb')\r\n for chunk in r.iter_content(4096):\r\n fd.write(chunk)\r\n fd.flush()\r\n fd.close()\r\n return filename\r\n\r\n\r\ndef main():\r\n outfd = codecs.open('out.txt', 'w', 'utf-8')\r\n for line in open('q.txt'):\r\n line = line.strip()\r\n if line:\r\n title = query_douban(line)\r\n outfd.write(u'%s %s\\n' % (line, title))\r\n gen_isbn_barcode(line)\r\n outfd.close()\r\n\r\nif __name__ == '__main__':\r\n main()\r\n", "sub_path": "13-ISBN/query_isbn.py", "file_name": "query_isbn.py", "file_ext": "py", "file_size_in_byte": 1270, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "contextlib.closing", "line_number": 31, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 31, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "343149804", "text": "import discord,asyncio\r\nimport datetime\r\nimport random\r\nimport urllib\r\nimport urllib.request, time, shutil, os\r\nfrom bs4 import BeautifulSoup\r\nimport bs4\r\nimport requests\r\nimport re, io,sys,json\r\nfrom PIL import Image,ImageDraw, ImageFont\r\nfrom translation import bing\r\nimport nltk\r\nimport imageio\r\nimg=0\r\nh=0\r\nw=0\r\n\r\nlangs= {}\r\nf= open('langDictionary.txt','r')\r\nar= f.readlines()\r\nfor a in ar:\r\n a=a.split()\r\n langs[a[0]]=a[1]\r\nf.close() \r\n \r\nclient= discord.Client()\r\nbot=client.get_channel('329463367859175424')\r\nave=client.get_channel('255441087852707846')\r\n\r\n@client.event\r\nasync def on_ready():\r\n print('Logged in as')\r\n print(client.user.name)\r\n print(client.user.id)\r\n print('------')\r\n await client.send_message(client.get_channel('329463367859175424'),'Ready')\r\n\r\n\r\n@client.event\r\nasync def my_background_task():\r\n print(\"STARTING\")\r\n await client.wait_until_ready()\r\n while not client.is_closed:\r\n print('STILL ALIVE')\r\n time=str(datetime.datetime.now().strftime(\"%I:%M:%S %p\"))[:5]\r\n print(time)\r\n if time ==\"06:39\":\r\n link='https://i.redd.it/ltzz7uhpgjpz.jpg'\r\n em= discord.Embed(colour=0x00000)\r\n emImg= discord.Embed.set_image(em,url=link)\r\n await client.send_message(client.get_channel('288910294661726219'), embed=em)\r\n if time==\"04:20\":\r\n await client.send_message(client.get_channel('288910294661726219'),'/img 420')\r\n await client.send_message(client.get_channel('288910294661726219'),'/imgf 420')\r\n await client.send_message(client.get_channel('255441087852707846'),'/img 420')\r\n await client.send_message(client.get_channel('255441087852707846'),'/imgf 420')\r\n \r\n await asyncio.sleep(60) # task runs every 60 seconds\r\n\r\n \r\n@client.event\r\n#ANYTIME A MESSAGE IS SENT, ON ANY SERVER, THIS METHOD BEGINS_______________________________________________________________\r\nasync def on_message(message):\r\n \r\n msg= (message.content).lower()\r\n user= message.author.name\r\n ch= message.channel\r\n if msg.startswith('::'):\r\n await client.delete_message(message)\r\n msg2=[]\r\n for a in msg[2:]:\r\n if ' ' not in a:\r\n msg2.append(''.join((\" :regional_indicator_\",a.lower(),\": \")))\r\n else:\r\n msg2.append(\" \")\r\n await client.send_message(ch, ''.join(msg2)) \r\n #SPELLCHECK______________________________________________________________________________________________________________\r\n if msg.startswith('/meme'):\r\n d=msg[6:]\r\n d=d.split()\r\n print(d)\r\n v=0\r\n sz=1\r\n sz1=0\r\n sz2=0\r\n backs=[]\r\n subs=[]\r\n last=(0,0)\r\n options=[]\r\n custom=False\r\n while(True):\r\n \r\n link= 'https://www.bing.com/images/search?q=',d[0],' wallpaper&qft=+filterui:imagesize-wallpaper&FORM=RESTAB'\r\n soup3 = requests.get(''.join(link))\r\n soup= BeautifulSoup(soup3.content,\"html.parser\")\r\n for a in soup.find_all('a', href=True):\r\n if a['href'].startswith(\"http://\") and a['href'].endswith('.jpg'):\r\n backs.append(a['href'])\r\n if len(backs)<1:\r\n print(\"nothing\")\r\n link=backs[random.randint(0,len(backs)-1)]\r\n r= requests.get(''.join(link))\r\n with open('Z:\\\\py\\\\images\\\\background.jpg', 'wb') as outfile:\r\n outfile.write(r.content)\r\n\r\n try:\r\n Image.open('Z:\\\\py\\\\images\\\\background.jpg').convert(\"RGBA\")\r\n break\r\n except:\r\n continue\r\n\r\n img= Image.open('Z:\\\\py\\\\images\\\\background.jpg').convert(\"RGBA\")\r\n img= img.resize((1920,1080), Image.ANTIALIAS)\r\n w= img.size[0]\r\n h= img.size[1]\r\n newim= Image.new('RGBA',(w,h))\r\n newim.paste(img,(0,0))\r\n for b in d:\r\n custom=False\r\n print(b)\r\n if \"=\" in b:\r\n sz=b.split(\"=\")[1]\r\n try:\r\n sz1=int(sz.split(',')[0])\r\n sz2=int(sz.split(',')[1])\r\n custom=True\r\n except:\r\n sz1=100\r\n sz2=100\r\n custom=False\r\n b=b.split(\"=\")[0]\r\n u=True\r\n if v!=0:\r\n while(u):\r\n \r\n subs=[]\r\n print(b)\r\n link= 'https://www.bing.com/images/search?&q=',b+str(v),'&qft=+filterui:photo-transparent&FORM=IRFLTR'\r\n soup3 = requests.get(''.join(link))\r\n soup= BeautifulSoup(soup3.content,\"html.parser\")\r\n for a in soup.find_all('a', href=True):\r\n if a['href'].startswith(\"http://\") and a['href'].endswith('.png'):\r\n subs.append(a['href'])\r\n options.append(subs[random.randint(0,len(subs)-1)])\r\n link=subs[random.randint(0,len(subs)-1)]\r\n r= requests.get(''.join(link))\r\n print(''.join(('Z:\\\\py\\\\images\\\\',b+str(v),'.png')))\r\n with open(''.join(('Z:\\\\py\\\\images\\\\',b+str(v),'.png')), 'wb') as outfile:\r\n outfile.write(r.content)\r\n try:\r\n img2= Image.open(''.join(('Z:\\\\py\\\\images\\\\',b+str(v),'.png'))).convert(\"RGBA\")\r\n except:\r\n continue\r\n if custom:\r\n print(sz1,' ',sz2)\r\n img2=img2.resize((sz1,sz2), Image.ANTIALIAS)\r\n else:\r\n s=random.randint(50,1000)\r\n img2=img2.resize((s,s), Image.ANTIALIAS)\r\n img2.save(''.join(('Z:\\\\py\\\\images\\\\',b+str(v),'.png')), format=\"png\")\r\n img2= Image.open(''.join(('Z:\\\\py\\\\images\\\\',b+str(v),'.png'))).convert(\"RGBA\")\r\n x= random.randint(25,w-img2.size[0])\r\n y= random.randint(25,h-img2.size[1])\r\n newim.paste(img2, (x-last[0],y-last[1]), img2)\r\n last=(x,y)\r\n os.remove(''.join(('Z:\\\\py\\\\images\\\\',b+str(v),'.png')))\r\n u=False\r\n v+=1\r\n \r\n # get a drawing context\r\n #d = ImageDraw.Draw(newim)\r\n #font = ImageFont.truetype(\"C:\\\\Windows\\\\Fonts\\\\Arial.ttf\", 100)\r\n # draw text, half opacity\r\n # draw text, full opacity\r\n await client.delete_message(message)\r\n newim.save('Z:\\\\py\\\\images\\\\final.png')\r\n await client.send_file(ch,'Z:\\\\py\\\\images\\\\final.png',content=''.join(('```css\\n/meme: ',','.join(d),'\\n```')))\r\n\r\n\r\n \r\n if not(msg.startswith('/')) and str(user)!='Test-bot':\r\n if str(ch)=='bot_test':\r\n try:\r\n goodWords=['tho']\r\n main = requests.get('+'.join(('http://www.bing.com/search?q=',msg)))\r\n soup= BeautifulSoup(main.content,\"html.parser\")\r\n words= msg.split()\r\n badWords= soup.find('div',{'id':'sp_requery'}).text.replace('Including results for','').replace('Showing results for','').split()\r\n print(badWords)\r\n fix=[]\r\n inc=0\r\n inc2=0\r\n for a in words:\r\n if a!=badWords[inc] and a not in goodWords:\r\n \r\n print('UHOH')\r\n print(a)\r\n inc2+=1\r\n fix.append(''.join((str(inc2),'. ',badWords[inc].replace('.','').replace(',','').replace('!','').replace('?',''), ' not ' ,a)))\r\n inc+=1\r\n em= discord.Embed(description='\\n'.join(fix),title='I think you might have misspelled something:',colour=0x08122)\r\n await client.send_message(ch, embed=em)\r\n except:\r\n works=False\r\n \r\n \r\n \r\n #########################################################################################################################\r\n #BING_VIDEO_SEARCH_______________________________________________________________________________________________________________________\r\n if msg.startswith('/vid'):\r\n vids=[]\r\n views=[]\r\n await client.delete_message(message)\r\n inp=msg[5:]\r\n token='CDIQAA'\r\n timeSm=0\r\n yes=True\r\n iD=''\r\n inc=0\r\n tmp= await client.send_message(ch, \"Loading...\".format(msg))\r\n \r\n main = requests.get(''.join((\"https://www.googleapis.com/youtube/v3/search?part=id%2Csnippet&maxResults=50&pageToken=\",token,\"&q=\",inp,\"&&type=video&key=AIzaSyAYf5mIyC5RJxY-u3xiRPsLfjn6niJ9O4o\")))\r\n soup= BeautifulSoup(main.content,\"html.parser\")\r\n mainJ= main.json()\r\n non_bmp_map = dict.fromkeys(range(0x10000, sys.maxunicode + 1), 0xfffd)\r\n token= mainJ['nextPageToken']\r\n for a in mainJ['items']:\r\n if a['id']:\r\n iD=str(a['id']['videoId']).translate(non_bmp_map)\r\n vid = requests.get(''.join((\"https://www.googleapis.com/youtube/v3/videos?id=\",iD,\"&key=AIzaSyAYf5mIyC5RJxY-u3xiRPsLfjn6niJ9O4o&part=contentDetails%2Cstatistics\")))\r\n vidJ= vid.json()\r\n vids.append(''.join(('http://www.youtube.com/watch?v=',iD)))\r\n views.append(int(str(vidJ['items'][0]['statistics']['viewCount']).translate(non_bmp_map)))\r\n choice= views.index(max(views)) \r\n await client.delete_message(tmp)\r\n await client.send_message(ch,vids[choice])\r\n \r\n #OBSCURE_________________________________________________________________________________________________________________\r\n if msg.startswith('/wut'):\r\n #publishedBefore=2007-01-01T00%3A00%3A00Z\r\n await client.delete_message(message)\r\n inp=msg[5:]\r\n token='CDIQAA'\r\n timeSm=0\r\n yes=True\r\n iD=''\r\n inc=0\r\n try:\r\n while(yes):\r\n inc+=1\r\n if inc>5:\r\n await client.send_message(ch, 'No videos found, sorry :expressionless:')\r\n main = requests.get(''.join((\"https://www.googleapis.com/youtube/v3/search?part=id%2Csnippet&eventType=live&maxResults=50&pageToken=\",token,\"&q=\",inp,\"&&type=video&videoDuration=short&key=AIzaSyAYf5mIyC5RJxY-u3xiRPsLfjn6niJ9O4o\")))\r\n soup= BeautifulSoup(main.content,\"html.parser\")\r\n mainJ= main.json()\r\n non_bmp_map = dict.fromkeys(range(0x10000, sys.maxunicode + 1), 0xfffd)\r\n token= mainJ['nextPageToken']\r\n print(token)\r\n for a in mainJ['items']:\r\n if a['id']:\r\n iD=str(a['id']['videoId']).translate(non_bmp_map)\r\n vid = requests.get(''.join((\"https://www.googleapis.com/youtube/v3/videos?id=\",iD,\"&key=AIzaSyAYf5mIyC5RJxY-u3xiRPsLfjn6niJ9O4o&part=contentDetails%2Cstatistics\")))\r\n vidJ= vid.json()\r\n duration=str(vidJ['items'][0]['contentDetails']['duration']).translate(non_bmp_map).replace('PT','').replace('M',' Minutes ').replace('S',' Seconds')\r\n try:\r\n timeSm= int(duration.split('Seconds')[0])\r\n except:\r\n continue\r\n views= int(str(vidJ['items'][0]['statistics']['viewCount']).translate(non_bmp_map))\r\n print(views)\r\n if views <5000:\r\n await client.send_message(ch, ''.join(('https://www.youtube.com/watch?v=',iD)))\r\n print(iD,duration,views) \r\n yes=False\r\n break\r\n except:\r\n print(\"FUUUCK\")\r\n #FOOD____________________________________________________________________________________________________________________\r\n if msg.startswith('/yelp'):\r\n inp= msg[6:].split(',')[0]\r\n loc= msg[6:].split(',')[1]\r\n f=['<address>','</address>','<br/>']\r\n imgurl=[]\r\n title=[]\r\n price=[]\r\n address=[]\r\n url=[]\r\n stars=[]\r\n b=0\r\n soup3 = requests.get(''.join((\"https://www.yelp.com/search?find_loc=\",loc,\"&cflt=\",inp)))\r\n soup= BeautifulSoup(soup3.content,\"html.parser\")\r\n for a in soup.find_all('div',{'class':'biz-listing-large'}):\r\n if b!=0:\r\n imgurl.append(a.find('img',{'class':'photo-box-img'},src=True)['src'])\r\n title.append(a.find('span').text[11:])\r\n stars.append(a.find('img',{'class':'offscreen'},alt=True)['alt'])\r\n try:\r\n price.append(a.find('span',{'class':'bullet-after'}).text.strip()) \r\n except:\r\n price.append('') \r\n address.append(str(a.find('address')).replace(f[0],'').replace(f[1],'').replace(f[2],'\\n').strip()) \r\n url.append(''.join(('http://www.yelp.com',a.find('a',href=True)['href'])))\r\n b+=1 \r\n i= random.randint(0,len(url)-1)\r\n em= discord.Embed(description=''.join((stars[i],'\\n',price[i],'\\n',address[i])),title=title[i],url=url[i],colour=0x48437)\r\n em.set_thumbnail(url=imgurl[i])\r\n await client.send_message(ch, embed=em)\r\n \r\n #AMAZON_!SEMI-FUNCTIONAL!__________________________________________________________________________________________________________________________________\r\n if msg.startswith('https://www.amazon.com'):\r\n await client.delete_message(message)\r\n soup3 = requests.get(msg)\r\n soup= BeautifulSoup(soup3.content,\"html.parser\")\r\n b=0\r\n for a in soup.find_all('img',{'id':'landingImage'},src=True):\r\n b=a['src']\r\n try:\r\n title= soup.find('span',{'id':'productTitle'}).text\r\n price= soup.find('span',{'id':'priceblock_ourprice'}).text\r\n except:\r\n title=\"null\"\r\n price=\"null\"\r\n em= discord.Embed(description=price,url=msg,title=title, colour=0x48437)\r\n emImg= discord.Embed.set_image(em,url=b)\r\n await client.send_message(ch, embed=em)\r\n \r\n #HELP___________________________________________________________________________________________________________________________________ \r\n #if msg.startswith('/help'):\r\n # helplist='''/urb 'word'; Defines a word using Urban Dictionary\\n\r\n # /map'number' 'coordinates or a location'. -Displays a satellite image at the specified \\tzoom level at the specified location. ex: /map15 New York City\\n\r\n # /sp 'word(s)'. -splits a word up by each letter\\n\r\n #/img 'word(s)'. -Displays a random image based on your specified word(s)\\n\r\n #/define 'word'. -Defines a word using Dictionary.com\\n\r\n #/verse 'quran' or 'bible'. -Displays a random verse from the specified religious text\\n\r\n #/apod 'YYMMDD'. -Displays the Astronomy Picture of the Day and the associated text of the specified date.'''\r\n ##em= discord.Embed(title=\"A list of the available commands\",description=helplist, colour=0x48437) \r\n #await client.send_message(ch, embed=em)'''\r\n #RANDOM_ART_____________________________________________________________________________________________________________________________________________\r\n if msg.startswith('/art'):\r\n abc='abcdefghijklmnopqrstuvwxyz'\r\n artist=[]\r\n art=[]\r\n d=0 \r\n abc=abc[random.randint(0,len(abc)-1)]\r\n url= 'http://www.wga.hu/art/',abc\r\n soup3 = requests.get(''.join(url))\r\n soup= BeautifulSoup(soup3.content,\"html.parser\")\r\n for a in soup.find_all('tr'):\r\n if d>2:\r\n artist.append(a.text.split('/')[0])\r\n d+=1\r\n artist=artist[:-1]\r\n \r\n while(True):\r\n try:\r\n artist= artist[random.randint(0,len(artist)-1)]\r\n break \r\n except:\r\n print('retry')\r\n \r\n url= 'http://www.wga.hu/art/',abc,'/',artist\r\n soup3 = requests.get(''.join(url))\r\n soup= BeautifulSoup(soup3.content,\"html.parser\")\r\n for a in soup.find_all('a',href=True):\r\n if a['href'].endswith('.jpg'):\r\n art.append(a['href'])\r\n await client.delete_message(message)\r\n \r\n while(True):\r\n try:\r\n art= art[random.randint(0,len(art))]\r\n break\r\n except:\r\n print('retry')\r\n \r\n link=''.join(('http://www.wga.hu/art/',abc,'/',artist,'/',art))\r\n url=''.join(('http://www.wga.hu/html_m/',abc,'/',artist,'/',art.replace('.jpg','.html')))\r\n soup3 = requests.get(''.join(url))\r\n soup= BeautifulSoup(soup3.content,\"html.parser\")\r\n text=[]\r\n for a in soup.find_all('td'):\r\n text.append(a.text)\r\n text=''.join(text).split('-->')[1].split('Artists')[0] \r\n em= discord.Embed(description=' '.join(text.split()), colour=0x48437)\r\n emImg= discord.Embed.set_image(em,url=link)\r\n await client.send_message(ch,embed=em)\r\n \r\n #WEATHER_FORECAST_!SEMI-FUNCTIONAL!__________________________________________________________________________________________________________________ \r\n if msg.startswith('/fore'):\r\n print(\"WHAT\")\r\n inp= msg[6:]\r\n days=[]\r\n forecast=[]\r\n combine=[]\r\n url= 'http://forecast.weather.gov/MapClick.php?CityName=',inp,'#.WYdo0elOmUm'\r\n print(url)\r\n soup3 = requests.get(''.join(url))\r\n soup= BeautifulSoup(soup3.content,\"html.parser\")\r\n\r\n for a in soup.find_all('div',{'class':'col-sm-2 forecast-label'}):\r\n days.append(a.text)\r\n for a in soup.find_all('div',{'class':'col-sm-10 forecast-text'}):\r\n forecast.append(a.text)\r\n for a in range(0,len(days)):\r\n text= ''.join((days[a],': ',forecast[a],'\\n\\n'))\r\n combine.append(text)\r\n combine= combine.split()\r\n await client.delete_message(message)\r\n em= discord.Embed(title= ' '.join(('Detailed Forecast for',inp)), description=''.join(combine[0]), colour=0x000DA)\r\n await client.send_message(ch,embed=em) \r\n #FORTUNE_______________________________________________________________________________________________________________________________________ \r\n if msg.startswith('/tell'):\r\n fortunes=[]\r\n url= 'http://www.fortunecookiemessage.com/archive.php?start=',str(random.randrange(0,800,50))\r\n print(url)\r\n soup3 = requests.get(''.join(url))\r\n soup= BeautifulSoup(soup3.content,\"html.parser\")\r\n for a in soup.find_all('a',href=True):\r\n if a['href'].startswith('/cookie'):\r\n fortunes.append(a.text)\r\n await client.delete_message(message)\r\n em= discord.Embed(title=' '.join(('Fortune for',user)), description=fortunes[random.randint(0,len(fortunes))], colour=0x000DA)\r\n await client.send_message(ch,embed=em)\r\n #TEXT_TO_AURELIUS_____________________________________________________________________________________________________________________________\r\n if msg.startswith(',,'):\r\n inp=msg[7:]\r\n words=[]\r\n phils=[]\r\n com=[]\r\n sent=[]\r\n for a in inp.split():\r\n words.append(nltk.pos_tag(nltk.word_tokenize(a)))\r\n soup3 = requests.get('http://classics.mit.edu/Antoninus/meditations.5.five.html')\r\n soup= BeautifulSoup(soup3.content,\"html.parser\")\r\n a= soup.text.split('Book Five')[1].split('Table')[0].replace('\\n','\\n').replace(\"\\'\",'').replace(' \"','\"').replace('\" ','\"')\r\n\r\n topic= a.split()[0]\r\n tokens= nltk.word_tokenize(a)\r\n tagged= nltk.pos_tag(tokens)\r\n noPos= ['CD',',','.',';','``',\"''\",':','(',')']\r\n ends=['.','!','?']\r\n f= open('aureliusBank.txt','w')\r\n for a in range(0,len(tagged)):\r\n f.write(tagged[a][1])\r\n f.write(\",\")\r\n f.close()\r\n f= open('aureliusBank.txt','r')\r\n word=f.readline().split(',')\r\n phils.append(word)\r\n f.close()\r\n\r\n sentRue=0\r\n for a in range(0,len(words)):\r\n pos=words[a]\r\n for a in range(0,len(tagged)):\r\n do= tagged[a]\r\n \r\n if do[1] == pos[0][1]:\r\n if pos[0][1] in ends:\r\n print('yes')\r\n com.append(do[0])\r\n sent.append(com[random.randint(0,len(com))])\r\n print(sent)\r\n await client.delete_message(message)\r\n sent= ' '.join(sent)\r\n em= discord.Embed(description=''.join(\"```\\n\"+sent+\"\\n```\"), colour=0x48437)\r\n await client.send_message(ch, embed=em)\r\n #VOLTAIRE_TO_AURELIUS_______________________________________________________________________________________________________________________\r\n '''if msg.startswith('/jared'):\r\n amt= msg[4:] \r\n d=0\r\n phils= []\r\n sent= []\r\n pos= []\r\n words= []\r\n posWords= []\r\n final= []\r\n sentC= []\r\n print('hello')\r\n soup3 = requests.get('http://classics.mit.edu/Antoninus/meditations.5.five.html')\r\n soup= BeautifulSoup(soup3.content,\"html.parser\")\r\n a= soup.text.split('Book Five')[1].split('Table')[0].replace('\\n','\\n').replace(\"\\'\",'').replace(' \"','\"').replace('\" ','\"')\r\n topic= a.split()[0]\r\n tokens= nltk.word_tokenize(a)\r\n tagged= nltk.pos_tag(tokens)\r\n noPos= ['CD',',','.',';','``',\"''\",':','(',')']\r\n print('hello')\r\n f= open('aureliusBank.txt','w')\r\n\r\n for a in range(0,len(tagged)): \r\n f.write(tagged[a][1])\r\n f.write(\",\")\r\n \r\n f.close()\r\n f= open('aureliusBank.txt','r')\r\n words=f.readline().split(',')\r\n phils.append(words)\r\n f.close()\r\n print('hello')\r\n sentRue=0\r\n\r\n for a in range(0,len(tagged)):\r\n if tagged[a][1] =='.':\r\n end=tagged[a][1]\r\n if tagged[a][1]!='.' and tagged[a][0]!='-':\r\n sent.append(tagged[a][1])\r\n else:\r\n sent.append(end)\r\n sentC.append(sent)\r\n sent=[]\r\n print('hello')\r\n url='https://history.hanover.edu/texts/voltaire/volindex.html'\r\n print('hello')\r\n soup3 = requests.get('https://history.hanover.edu/texts/voltaire/volindex.html')\r\n print('hello')\r\n soup= BeautifulSoup(soup3.content,\"html.parser\") \r\n print('hello') \r\n for a in soup.find_all('a',href=True):\r\n d+=1\r\n if d>2 and d<106:\r\n phils.append(''.join(('https://history.hanover.edu',a['href'])))\r\n print('hello')\r\n url=phils[random.randint(0,103)] \r\n soup3 = requests.get(url)\r\n soup= BeautifulSoup(soup3.content,\"html.parser\")\r\n\r\n a= soup.text.split('2001.')[1].split('Hanover')[0].replace('\\n','\\n').replace(\"\\'\",'').replace(' \"','\"').replace('\" ','\"')\r\n topic= a.split()[0]\r\n tokens= nltk.word_tokenize(a)\r\n #print(tokens)\r\n tagged= nltk.pos_tag(tokens)\r\n #print(tagged)\r\n print('hello')\r\n for a in range(len(tagged)):\r\n sent.append(tagged[a])\r\n\r\n for a in sent:\r\n if a[1] not in pos and a[1] not in noPos:\r\n pos.append(a[1])\r\n words.append(a[0])\r\n\r\n choose= sentC[random.randint(0,151)]\r\n print('hello')\r\n numdo= 0\r\n for b in range(len(choose)):\r\n for a in sent:\r\n if a[1]==choose[b] and a[0] not in posWords:\r\n posWords.append(a[0].lower())\r\n \r\n num= random.randrange(0,int(len(posWords)))\r\n \r\n final.append(posWords[num])\r\n posWords=[]\r\n print('hello')\r\n foo= final[0][0].upper()\r\n foo= ''.join((foo,final[0][1:]))\r\n em= discord.Embed(description=' '.join((\"```css\\n\",foo,' '.join(final[1:]),\"\\n```\")), colour=random.randrange(123456,666666))\r\n await client.delete_message(message) \r\n await client.send_message(ch,embed=em)'''\r\n \r\n #WIKI_SEARCH_!SEMI-FUNCTIONAL!__________________________________________________________________________________________________ \r\n if msg.startswith('/wiki'):\r\n await client.delete_message(message)\r\n inp=msg[6:]\r\n print(inp)\r\n link= ''.join(('https://en.wikipedia.org/wiki/',inp))\r\n soup3 = requests.get(link)\r\n rlink= soup3.url\r\n soup3 = requests.get(rlink)\r\n soup= BeautifulSoup(soup3.content,\"html.parser\")\r\n words= []\r\n link = soup.find_all('p')\r\n for a in range(len(link)):\r\n words.append(link[a].get_text())\r\n s= soup.find('h1',{'id':'firstHeading'}).text\r\n d= '\\n'.join(words)\r\n url= soup3.url\r\n \r\n em= discord.Embed(title= s ,description=d, colour=0x000DA)\r\n\r\n await client.send_message(ch, embed=em)\r\n\r\n \r\n \r\n #TRANSLATION____________________________________________________________________________________________________________\r\n if msg.startswith('/tr'):\r\n inp= msg[3:].split()\r\n text= []\r\n for a in range(1,len(inp)):\r\n text.append(inp[a])\r\n \r\n print(text)\r\n print((inp[0])[0].upper())\r\n await client.delete_message(message)\r\n try: \r\n translated= bing(' '.join(text),dst=langs[inp[0]])\r\n em= discord.Embed(title=' '.join(text),description=translated, colour=0x69420)\r\n await client.send_message(ch, embed=em)\r\n except Exception as e:\r\n print(e)\r\n\r\n \r\n #Urban_Dictionary_Search_____________________________________________________________________________________________________________________________________________\r\n if msg.startswith('/urb'):\r\n inp=msg[5:]\r\n await client.delete_message(message)\r\n tmp= await client.send_message(ch, \"Loading urban definition of {0}...\".format(msg[5:]))\r\n try:\r\n link= 'http://www.urbandictionary.com/define.php?term=','+'.join(inp.split())\r\n soup3 = requests.get(''.join(link))\r\n soup= BeautifulSoup(soup3.content,\"html.parser\")\r\n\r\n text= soup.find('div',{'class':'meaning'}).text.replace(''',''),'\\n\\n',soup.find('div',{'class':'example'}).text.replace(''','')\r\n em= discord.Embed(title=msg[5:],description=''.join(text), colour=0x48437)\r\n\r\n await client.delete_message(tmp)\r\n await client.send_message(ch, embed=em)\r\n except:\r\n await client.delete_message(tmp)\r\n await client.send_message(ch, '{0} is not a word in the Urban Dictionary...'.format(inp))\r\n \r\n #GOOGLE_MAPS___________________________________________________________________________________________________________________________________________________\r\n \r\n if msg.startswith('/map'):\r\n inp= msg[4:]\r\n \r\n inp=inp.replace(' ','+')\r\n d=1\r\n h=1\r\n m=20\r\n c=False\r\n size= 500,500\r\n images = []\r\n await client.delete_message(message)\r\n tmp= await client.send_message(ch, \"Please wait... Creating GIF...\")\r\n\r\n while(True):\r\n for a in range(d,m,h):\r\n \r\n linkst= 'https://maps.googleapis.com/maps/api/staticmap?center=',inp,'&zoom=',str(a),'&size=200x200&scale=2&maptype=satellite&key=AIzaSyAYf5mIyC5RJxY-u3xiRPsLfjn6niJ9O4o'\r\n linkst= ''.join(linkst)\r\n print(a)\r\n with urllib.request.urlopen(linkst) as url:\r\n f = io.BytesIO(url.read())\r\n im= Image.open(f)\r\n images.append(im)\r\n if h<1 and c==False:\r\n for a in range(0,360,36):\r\n linkst= 'https://maps.googleapis.com/maps/api/streetview?size=400x400&location=',inp,'&heading=',str(a),'&pitch=0&key=AIzaSyAYf5mIyC5RJxY-u3xiRPsLfjn6niJ9O4o'\r\n linkst= ''.join(linkst)\r\n with urllib.request.urlopen(linkst) as url:\r\n f = io.BytesIO(url.read())\r\n im= Image.open(f)\r\n images.append(im)\r\n c=True\r\n\r\n if h<0:\r\n break\r\n h=h*-1\r\n d=20\r\n m=0\r\n \r\n im.save('/image.gif', save_all=True, append_images=images,duration=250)\r\n\r\n await client.delete_message(tmp)\r\n await client.send_file(ch,'/image.gif')\r\n print(\"done\")\r\n\r\n #S P A C E S_______________________________________________________________________________________________________________________________________________ \r\n if msg.startswith('/sp'):\r\n await client.delete_message(message)\r\n inp= ' '.join(message.content[4:])\r\n await client.send_message(ch, inp)\r\n \r\n #Image Search_______________________________________________________________________________________________________________________________________ \r\n if msg.startswith('/img'):\r\n x=False\r\n imgs= []\r\n msg1=msg[5:]\r\n dif=msg[6:]\r\n dif2=msg[7:]\r\n if not(msg1):\r\n msg1=\"null\"\r\n if not(dif):\r\n dif=\"null\"\r\n \r\n await client.delete_message(message)\r\n tmp= await client.send_message(ch, \"Loading...\".format(msg))\r\n\r\n if msg.startswith('/imgf'):\r\n if msg.startswith('imgfx'):\r\n dif= dif2\r\n main = requests.get(''.join((\"http://api.giphy.com/v1/gifs/search?q=\",dif,\"&api_key=gJa9mzJnBQehCMOHbqy0S4Jy7tnGXpGt&limit=100\")))\r\n mainJ= main.json()\r\n imgs=[]\r\n for a in mainJ['data']:\r\n imgs.append(a['embed_url'])\r\n print(imgs[random.randint(0,len(imgs)-1)])\r\n \r\n if len(imgs)<1:\r\n await client.send_message(ch, 'No gifs were found...')\r\n \r\n else:\r\n link= 'https://www.bing.com/images/search?q=',msg1,'&FORM=RESTAB'\r\n soup3 = requests.get(''.join(link))\r\n soup= BeautifulSoup(soup3.content,\"html.parser\")\r\n for a in soup.find_all('a', href=True):\r\n if a['href'].startswith(\"http://\") and a['href'].endswith('.jpg'):\r\n imgs.append(a['href'])\r\n\r\n if len(imgs)<1:\r\n await client.send_message(ch, 'No images were found...')\r\n \r\n if msg.startswith('/imgx'):\r\n link= imgs[random.randint(0,len(imgs)-1)]\r\n\r\n try:\r\n r= requests.get(''.join(link))\r\n print(r)\r\n with open('/mainImg.jpg', 'wb') as outfile:\r\n outfile.write(r.content)\r\n img= Image.open('/mainImg.jpg')\r\n w= img.size[0]\r\n h= img.size[1]\r\n\r\n img1= img.crop((0,0,w/2,h/2))\r\n\r\n img2= img1.transpose(Image.FLIP_LEFT_RIGHT)\r\n img3= img1.transpose(Image.FLIP_LEFT_RIGHT).rotate(180)\r\n img4= img1.rotate(-180)\r\n\r\n\r\n newim= Image.new('RGB',(w,h))\r\n newim.paste(img1,(0,0))\r\n newim.paste(img2,(int(w/2),0))\r\n newim.paste(img3,(0,int(h/2)))\r\n newim.paste(img4,(int(w/2),int(h/2)))\r\n newim.save('/mainImg.jpg')\r\n await client.send_message(ch, ' '.join((user,' creates: ',dif))) \r\n await client.send_file(ch,'/mainImg.jpg')\r\n print(newim.size)\r\n os.remove('/mainImg.jpg')\r\n x=True\r\n await client.delete_message(tmp)\r\n\r\n except Exception as e:\r\n x=True\r\n print(e)\r\n await client.send_message(ch, \"An error occured, please try again...\")\r\n \r\n if not(x) and not(msg.startswith('/imgfx')):\r\n try:\r\n link=imgs[random.randint(0,len(imgs)-1)]\r\n await client.delete_message(tmp)\r\n \r\n if msg.startswith('/imgf'):\r\n await client.send_message(ch, link)\r\n else:\r\n em= discord.Embed(url=link,title=msg)\r\n discord.Embed.set_author(em,name=user)\r\n discord.Embed.set_image(em,url=link) \r\n await client.send_message(ch, embed=em)\r\n except Exception as errorMsg:\r\n print(errorMsg)\r\n await client.send_message(ch, \"An error occured, please try again...\")\r\n if not(x) and msg.startswith('/imgfx'):\r\n print('1')\r\n try:\r\n print('4')\r\n link=imgs[random.randint(0,len(imgs)-1)]\r\n with open('Z:\\Py\\gifs\\123.gif', 'wb') as f:\r\n f.write(requests.get(link).content)\r\n print('5')\r\n inGif='Z:\\Py\\gifs\\123.gif'\r\n print('5')\r\n frame = Image.open(inGif)\r\n nframes = 0\r\n images=[]\r\n w=0\r\n h=0\r\n print('2')\r\n while frame:\r\n b=''.join(('Z:\\\\Py\\\\gifs\\\\',str(nframes),'.png'))\r\n print(b)\r\n frame.save(b)\r\n\r\n if nframes!=0:\r\n print(nframes)\r\n img= Image.open(b)\r\n w= img.size[0]\r\n h= img.size[1]\r\n\r\n img1= img.crop((0,0,w/2,h/2))\r\n #img1= img.crop((0,0,w/2,h))\r\n img2= img1.transpose(Image.FLIP_LEFT_RIGHT)\r\n img3= img1.transpose(Image.FLIP_LEFT_RIGHT).rotate(180)\r\n img4= img1.rotate(-180)\r\n\r\n\r\n newim= Image.new('RGB',(w,h))\r\n newim.paste(img1,(0,0))\r\n newim.paste(img2,(int(w/2),0))\r\n newim.paste(img3,(0,int(h/2)))\r\n newim.paste(img4,(int(w/2),int(h/2)))\r\n newim.save(b)\r\n images.append(imageio.imread(b))\r\n nframes += 1\r\n print('3')\r\n try:\r\n frame.seek( nframes )\r\n except EOFError:\r\n print('Done')\r\n break\r\n imageio.mimsave('Z:\\Py\\gifs\\gif.gif',images)\r\n await client.delete_message(tmp)\r\n await client.send_file(ch,'Z:\\Py\\gifs\\gif.gif',content=''.join(('```\\n',dif,'\\n```')))\r\n except Exception as errorMsg:\r\n print(errorMsg)\r\n await client.delete_message(tmp)\r\n \r\n\r\n #DEFINITION______________________________________________________________________________________________________________________________________\r\n if msg.startswith(\"/define\"):\r\n try:\r\n inp= msg[8:]\r\n if not(inp):\r\n inp='null'\r\n await client.delete_message(message)\r\n \r\n soup3 = requests.get(''.join(('http://www.dictionary.com/browse/',inp)))\r\n soup= BeautifulSoup(soup3.content,\"html.parser\")\r\n\r\n b= soup.find('span',{'class' : 'dbox-pg'})\r\n b= (' '.join(b.get_text().split())).replace('. ', '.\\n')\r\n\r\n\r\n a= soup.find('div',{'class' : 'def-content'})\r\n a= (' '.join(a.get_text().split())).replace('. ', '.\\n')\r\n\r\n em= discord.Embed(title= '- '.join((inp,b)) , description=a, colour=0xABCDE)\r\n \r\n await client.send_message(ch, embed=em)\r\n\r\n except:\r\n await client.send_message(ch, \"No definition of {0}.\".format(inp))\r\n\r\n #ASTRONOMY PICTURE OF THE DAY______________________________________________________________________________________________________________________ \r\n if msg.startswith(\"/apod\"):\r\n inp= msg[6:]\r\n await client.delete_message(message)\r\n \r\n if len(inp)<6 or len(inp)>6:\r\n await client.send_message(ch, \"Incorrect format; /apod YYMMDD\")\r\n await client.delete_message(message)\r\n \r\n else:\r\n if inp.isdigit():\r\n try:\r\n date= [inp[i:i+2] for i in range(0, len(inp), 2)]\r\n tmp= await client.send_message(ch, \"Loading Astronomy Picture of the Day from {0}.\".format('/'.join(date)))\r\n \r\n link= ''.join(('https://apod.nasa.gov/apod/ap',inp,'.html'))\r\n\r\n soup3 = requests.get(link)\r\n soup= BeautifulSoup(soup3.content,\"html.parser\")\r\n \r\n imgLink= ''.join(('https://apod.nasa.gov/',str((soup.find_all(\"a\", href=True))[1]).split('=\"')[1].split('\">')[0]))\r\n \r\n text= \" \".join((re.sub('<[^>]+>', '', str((soup.find_all('p'))[2]))).split())\r\n text= text.split(\"Tomorrow's picture:\")[0].split(\"digg\")[0]\r\n text= text.replace(\"Explanation: \",\"\").replace(\". \",\".\\n\\n\").replace(\"? \",\"?\\n\\n\").replace(\"! \",\"!\\n\\n\")\r\n\r\n em= discord.Embed(title= '/'.join(date), description=text, colour=0x48437)\r\n emImg= discord.Embed.set_image(em,url=imgLink) \r\n\r\n await client.delete_message(tmp)\r\n await client.send_message(ch, embed=em)\r\n \r\n except IndexError:\r\n \r\n client.send_message(ch, 'Incorrect format; /apod YYMMDD')\r\n await client.delete_message(message)\r\n \r\n elif not inp.isdigit():\r\n \r\n await client.send_message(ch, \"Incorrect format; /apod YYMMDD\")\r\n await client.delete_message(message)\r\n \r\n #RELIGIOUS TEXT_____________________________________________________________________________________________________________________________________ \r\n if msg.startswith(\"/verse\"):\r\n inp= msg[7:]\r\n\r\n #BIBLE______________\r\n if inp.lower() == \"bible\":\r\n \r\n await client.delete_message(message)\r\n req= requests.get('https://dailyverses.net/random-bible-verse')\r\n soup= BeautifulSoup(req.content,\"html.parser\")\r\n\r\n verse= str(soup.select('.bibleVerse'))\r\n verse= verse.split('[<div class=\"bibleVerse\">')[1].split('<div class')[0]\r\n verse= verse.replace(\"<br/>\",\" \")\r\n\r\n verse2= str(soup.select('.bibleChapter'))\r\n verse2= re.sub('<[^>]+>', '', verse2)\r\n verse2= verse2.split('[')[1].split('|')[0]\r\n \r\n em= discord.Embed(title= verse , description= verse2, colour=0x48437)\r\n await client.send_message(ch, embed=em)\r\n \r\n #QURAN____________ \r\n elif inp.lower() == 'quran':\r\n \r\n await client.delete_message(message)\r\n req= requests.get('http://ayatalquran.com/random')\r\n soup= BeautifulSoup(req.content,\"html.parser\")\r\n \r\n verse= str(soup.select('#aya_text'))\r\n verse= verse.split('[<h2 id=\"aya_text\">')[1].split('</h2>]')[0]\r\n\r\n book1= str(soup.select('#sura_id'))\r\n book1= book1.split('[<span id=\"sura_id\">')[1].split('</span>]')[0]\r\n \r\n book2= str(soup.select('#verse_id'))\r\n book2= book2.split('[<span id=\"verse_id\">')[1].split('</span>]')[0]\r\n book= \"The Holy Quran \",book1,\":\",book2\r\n \r\n em= discord.Embed(title= verse , description=\"\".join(book), colour=0x48437)\r\n await client.send_message(ch, embed=em)\r\n\r\n \r\nclient.loop.create_task(my_background_task())\r\nclient.run('token')\r\n", "sub_path": "pybot.py", "file_name": "pybot.py", "file_ext": "py", "file_size_in_byte": 40950, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "discord.Client", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 45, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 49, "usage_type": "call"}, {"api_name": "discord.Embed.set_image", "line_number": 50, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 50, "usage_type": "attribute"}, {"api_name": "asyncio.sleep", "line_number": 58, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 94, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 95, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 101, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 102, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 107, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 107, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 112, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 112, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 113, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 113, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 116, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 116, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 139, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 140, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 144, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 145, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 146, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 151, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 151, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 156, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 156, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 158, "usage_type": "call"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 159, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 159, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 161, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 161, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 162, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 163, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 166, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 185, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 186, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 201, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 222, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 223, "usage_type": "call"}, {"api_name": "sys.maxunicode", "line_number": 225, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 230, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 253, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 254, "usage_type": "call"}, {"api_name": "sys.maxunicode", "line_number": 256, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 262, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 290, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 291, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 304, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 305, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 312, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 313, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 323, "usage_type": "call"}, {"api_name": "discord.Embed.set_image", "line_number": 324, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 324, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 344, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 346, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 347, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 356, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 362, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 363, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 371, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 378, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 379, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 384, "usage_type": "call"}, {"api_name": "discord.Embed.set_image", "line_number": 385, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 385, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 397, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 398, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 409, "usage_type": "call"}, {"api_name": "random.randrange", "line_number": 414, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 416, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 417, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 422, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 422, "usage_type": "call"}, {"api_name": "nltk.pos_tag", "line_number": 432, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 432, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 433, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 434, "usage_type": "call"}, {"api_name": "nltk.word_tokenize", "line_number": 438, "usage_type": "call"}, {"api_name": "nltk.pos_tag", "line_number": 439, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 462, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 466, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 567, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 569, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 570, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 579, "usage_type": "call"}, {"api_name": "translation.bing", "line_number": 596, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 597, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 610, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 611, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 614, "usage_type": "call"}, {"api_name": "urllib.request.urlopen", "line_number": 643, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 643, "usage_type": "attribute"}, {"api_name": "io.BytesIO", "line_number": 644, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 645, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 645, "usage_type": "name"}, {"api_name": "urllib.request.urlopen", "line_number": 651, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 651, "usage_type": "attribute"}, {"api_name": "io.BytesIO", "line_number": 652, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 653, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 653, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 693, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 698, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 705, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 706, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 715, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 718, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 722, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 722, "usage_type": "name"}, {"api_name": "PIL.Image.FLIP_LEFT_RIGHT", "line_number": 728, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 728, "usage_type": "name"}, {"api_name": "PIL.Image.FLIP_LEFT_RIGHT", "line_number": 729, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 729, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 733, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 733, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 742, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 753, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 759, "usage_type": "call"}, {"api_name": "discord.Embed.set_author", "line_number": 760, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 760, "usage_type": "attribute"}, {"api_name": "discord.Embed.set_image", "line_number": 761, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 761, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 770, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 772, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 776, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 776, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 789, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 789, "usage_type": "name"}, {"api_name": "PIL.Image.FLIP_LEFT_RIGHT", "line_number": 795, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 795, "usage_type": "name"}, {"api_name": "PIL.Image.FLIP_LEFT_RIGHT", "line_number": 796, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 796, "usage_type": "name"}, {"api_name": "PIL.Image.new", "line_number": 800, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 800, "usage_type": "name"}, {"api_name": "imageio.imread", "line_number": 806, "usage_type": "call"}, {"api_name": "imageio.mimsave", "line_number": 814, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 830, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 831, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 840, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 864, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 865, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 869, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 873, "usage_type": "call"}, {"api_name": "discord.Embed.set_image", "line_number": 874, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 874, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 897, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 898, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 905, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 908, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 915, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 916, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 928, "usage_type": "call"}]} +{"seq_id": "64978117", "text": "from django.urls import path\nfrom . import views\nfrom django.conf import settings\nfrom django.conf.urls.static import static\n\napp_name = \"client\"\nurlpatterns = [\n path('', views.index, name='index'),\n path('home/', views.home, name='home'),\n path('document/', views.document, name='document'),\n path('document/add/', views.document_add, name='document_add'),\n path('document/edit/<int:id>', views.document_edit, name='document_edit'),\n path('document/del/<int:id>', views.document_del, name='document_del'),\n path('check_face/', views.check_face, name='check_face'),\n path('exit/', views.exit, name='exit'),\n path('add_client/', views.add_client, name='add_client'),\n path('edit_client/<int:id>', views.edit_client, name='edit_client'),\n path('search/', views.search, name='search'),\n] + static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)\n\n", "sub_path": "client/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 886, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.urls.static.static", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.settings.STATIC_URL", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.conf.settings", "line_number": 19, "usage_type": "name"}, {"api_name": "django.conf.settings.STATIC_ROOT", "line_number": 19, "usage_type": "attribute"}]} +{"seq_id": "560659337", "text": "# Discord.py is smoooooooooooooosh!!!!!\nimport discord\nfrom discord.ext import tasks, commands\nimport asyncio\n\nimport os # .env読み込みスターズ。\nimport json\nimport pymongo\n\nclass VoiceChannel(commands.Cog):\n def __init__(self, airlinia):\n self.bot = airlinia #botを受け取る。\n mongo_connection = pymongo.MongoClient(\"ds161505.mlab.com\", 61505, retryWrites=False)\n mongo_db = mongo_connection[\"heroku_stfrs35p\"]\n mongo_db.authenticate(\"heroku_stfrs35p\", os.environ['MONGODB_PASSWORD'])\n self.mongo_coll = mongo_db['voicechannel']\n self.datas = self.mongo_coll.find_one(filter={\"server\": 615849898637656093})\n\n @property\n def category(self):\n return self.bot.get_channel(655274860708364288)\n\n @commands.Cog.listener()\n async def on_voice_state_update(self, member, before, after):\n if (\n after.channel is not None\n and (before.channel is None or before.channel != after.channel)\n ):\n if after.channel.id == 655274902600941579:\n await self._channel_create(member)\n else:\n try:\n text_channel = self.bot.get_channel(self.datas[\"channel_data\"][str(after.channel.id)][\"id\"])\n except KeyError:\n pass\n else:\n embed = discord.Embed(title='ボイスチャンネル入室通知',\n description=f'{member.mention}さんが入室しました。',\n color=0x00ff00)\n await text_channel.send(embed=embed, delete_after=180)\n\n if (\n before.channel is not None\n and (after.channel is None or before.channel != after.channel)\n ):\n try:\n text_channel = self.bot.get_channel(self.datas[\"channel_data\"][str(before.channel.id)][\"id\"])\n except KeyError:\n pass\n else:\n embed = discord.Embed(title='ボイスチャンネル退出通知',\n description=f'{member.mention}さんが退出しました。',\n color=0xff0000)\n await text_channel.send(embed=embed, delete_after=180)\n if len(before.channel.members) == 0:\n await before.channel.delete()\n await text_channel.delete()\n del [\"channel_data\"][str(before.channel.id)]\n self.mongo_coll.update_one({\"server\": 615849898637656093}, {'$set':self.datas})\n\n async def _channel_create(self, member):\n category = self.category\n guild = member.guild\n position = self.bot.get_channel(670473107269746718).position - 1\n overwrites = {\n self.bot.user:\n discord.PermissionOverwrite.from_pair(discord.Permissions.all(), discord.Permissions.none()),\n category.guild.default_role:\n discord.PermissionOverwrite.from_pair(discord.Permissions.none(), discord.Permissions.all()),\n category.guild.get_role(635149066795483137): #ミュート。\n discord.PermissionOverwrite.from_pair(discord.Permissions.none(), discord.Permissions.all()),\n category.guild.get_role(617017694306435073): #閲覧できる役職\n discord.PermissionOverwrite.from_pair(\n discord.Permissions(37080128), discord.Permissions(2 ** 53 - 37080129)),\n }\n text_channel = await guild.create_text_channel(member.display_name, overwrites=overwrites, category=category, position=position)\n voice_channel = await guild.create_voice_channel(member.display_name, overwrites=overwrites, category=category)\n self.datas[\"channel_data\"][str(voice_channel.id)] = {}\n self.datas[\"channel_data\"][str(voice_channel.id)][\"id\"] = text_channel.id\n self.datas[\"channel_data\"][str(voice_channel.id)][\"owner\"] = member.id\n self.mongo_coll.update_one({\"server\": 615849898637656093}, {'$set':self.datas})\n embed = discord.Embed(title='ボイスチャンネル作成通知',\n description=f'{member.mention}さん、ようこそ！',\n color=0x0080ff)\n await text_channel.send(content=member.mention, embed=embed)\n await member.move_to(voice_channel)\n\n @commands.group()\n async def voice(self, ctx):\n embed = discord.Embed(title='💻チャンネル編集',\n description=f'🔐チャンネルロック\\n🔏チャンネル閲覧限定／解除\\n🔓チャンネルロック\\n✅招待\\n❎キック\\n🎟人数制限\\n✒名前変更\\n💻オーナー継承\\n🚫キャンセル',\n color=0x0080ff)\n embed.set_author(name=f'{ctx.channel.name} - 編集しちゃお！',icon_url='https://i.imgur.com/yRCJ26G.gif')\n embed_no = discord.Embed(title='💻🚫チャンネル編集をキャンセル',\n description=f'チャンネルの編集をキャンセルしたよ。',\n color=0xff0000)\n embed_no.set_author(name=f'{ctx.channel.name} - 編集せんのかーい。',icon_url='https://encrypted-tbn0.gstatic.com/images?q=tbn%3AANd9GcQLFHFV5AuInaxeSHFkAtvJV-HT3xa6Ua7M61pXgsADOC6Y0Czj',url=\"https://airlinia.ml\")\n\n msg1 = await ctx.channel.send(embed=embed)\n emojis = ['🔐', '🔓', '🔏', '✅', '❎', '🎟', '✒', '💻', '🚫']\n for emoji1 in emojis:\n await msg1.add_reaction(emoji1)\n try:\n def check1(r, u):\n return r.me and r.message.id == msg1.id and u == ctx.author\n def check2(m):\n return m.author == ctx.author and m.channel.id == msg2.channel.id\n react = await self.bot.wait_for('reaction_add', timeout=60.0, check=check1)\n if react[0].emoji == '🔐':\n await self.lock(ctx)\n elif react[0].emoji == '🔓':\n await self.unlock(ctx)\n elif react[0].emoji == '🔏':\n await self.view_only(ctx)\n elif react[0].emoji == '✅':\n try:\n embed = discord.Embed(title='チャンネルへ招待✅',\n description=f'招待する人を言ってください。\\n> （ID, メンション, 名前に対応しています。）',\n color=0x0080ff)\n msg2 = await ctx.send(embed=embed)\n react = await self.bot.wait_for('message', timeout=30.0, check=check2)\n await self.permit(ctx, react[0])\n await msg1.clear_reactions()\n except asyncio.TimeoutError:\n await msg1.edit(embed=embed_no)\n await msg1.clear_reactions()\n await msg2.delete()\n elif react[0].emoji == '❎':\n try:\n embed = discord.Embed(title='チャンネルからつまみ出す❎',\n description=f'チャンネルからつまみ出す対象を言ってください。\\n> （ID, メンション, 名前に対応しています。）',\n color=0x0080ff)\n msg2 = await ctx.send(embed=embed)\n react = await self.bot.wait_for('message', timeout=30.0, check=check2)\n await self.reject(ctx, react[0])\n except asyncio.TimeoutError:\n await msg1.edit(embed=embed_no)\n await msg1.clear_reactions()\n await msg2.delete()\n elif react[0].emoji == '🎟':\n try:\n embed = discord.Embed(title='チャンネル人数制限🎟',\n description=f'チャンネルの制限人数を言ってください。',\n color=0x0080ff)\n msg2 = await ctx.send(embed=embed)\n react = await self.bot.wait_for('message', timeout=30.0, check=check2)\n await self.limit(ctx, limit)\n await msg1.clear_reactions()\n except asyncio.TimeoutError:\n await msg1.edit(embed=embed_no)\n await msg1.clear_reactions()\n await msg2.delete()\n elif react[0].emoji == '✒':\n try:\n embed = discord.Embed(title='チャンネル名変更✒',\n description=f'チャンネルの名前を変更してください。',\n color=0x0080ff)\n msg2 = await ctx.send(embed=embed)\n react = await self.bot.wait_for('message', timeout=30.0, check=check2)\n await self.name(ctx, name)\n await msg1.clear_reactions()\n except asyncio.TimeoutError:\n await msg1.edit(embed=embed_no)\n await msg1.clear_reactions()\n await msg2.delete()\n elif react[0].emoji == '💻':\n await self.claim(ctx)\n elif react[0].emoji == '🚫':\n await msg1.edit(embed=embed_no)\n await msg1.clear_reactions()\n except asyncio.TimeoutError:\n await msg1.edit(embed=embed_no)\n await msg1.clear_reactions()\n\n @voice.command(name=\"lock\")\n async def _lock(self, ctx):\n await self.lock(ctx)\n\n async def lock(self, ctx):\n channel = ctx.author.voice.channel\n if ctx.author.id == self.datas[\"channel_data\"][str(channel.id)][\"owner\"]:\n text_channel = self.bot.get_channel(self.datas[\"channel_data\"][str(channel.id)][\"id\"])\n role = ctx.guild.get_role(617017694306435073)\n await channel.set_permissions(role, connect=False, speak=False, send_messages=False, read_message_history=False, read_messages=False)\n await text_channel.set_permissions(role, connect=False, speak=False, send_messages=False, read_message_history=False, read_messages=False)\n embed = discord.Embed(title='Channel Moderate!',\n description=f'{ctx.author.mention}さん、チャンネルをロックしました！🔐',\n color=0xffff00)\n await ctx.send(content=ctx.author.mention, embed=embed)\n elif channel is None:\n await ctx.send(content=f\"{ctx.author.mention}さんボイチャ入ってないですやんか\")\n else:\n await ctx.send(content=f\"{ctx.author.mention}さん、多分そこあんたのチャンネルじゃないよ。\")\n\n @voice.command(name=\"view_only\")\n async def _view_only(self, ctx):\n await self.view_only(ctx)\n\n async def view_only(self, ctx):\n channel = ctx.author.voice.channel\n if ctx.author.id == self.datas[\"channel_data\"][str(channel.id)][\"owner\"]:\n text_channel = self.bot.get_channel(self.datas[\"channel_data\"][str(channel.id)][\"id\"])\n role = ctx.guild.get_role(617017694306435073)\n await channel.set_permissions(role, connect=True, speak=False, read_message_history=True, read_messages=True, send_messages=False)\n await text_channel.set_permissions(role, connect=True, speak=False, read_message_history=True, read_messages=True, send_messages=False)\n embed = discord.Embed(title='Channel Moderate!',\n description=f'{ctx.author.mention}さん、チャンネルを閲覧限定にしました！🔏',\n color=0xffff00)\n await ctx.send(content=ctx.author.mention, embed=embed)\n elif channel is None:\n await ctx.send(content=f\"{ctx.author.mention}さんボイチャ入ってないですやんか\")\n else:\n await ctx.send(content=f\"{ctx.author.mention}さん、多分そこあんたのチャンネルじゃないよ。\")\n\n @voice.command(name=\"unlock\")\n async def _unlock(self, ctx):\n await self.unlock(ctx)\n\n async def unlock(self, ctx):\n channel = ctx.author.voice.channel\n if ctx.author.id == self.datas[\"channel_data\"][str(channel.id)][\"owner\"]:\n text_channel = self.bot.get_channel(self.datas[\"channel_data\"][str(channel.id)][\"id\"])\n role = ctx.guild.get_role(617017694306435073)\n await channel.set_permissions(role, connect=True, speak=True, read_message_history=True, read_messages=True, send_messages=True)\n await text_channel.set_permissions(role, connect=True, speak=True, read_message_history=True, read_messages=True, send_messages=True)\n embed = discord.Embed(title='Channel Moderate!',\n description=f'{ctx.author.mention}さん、チャンネルをアンロックしました！🔓',\n color=0xffff00)\n await ctx.send(content=ctx.author.mention, embed=embed)\n elif channel is None:\n await ctx.send(content=f\"{ctx.author.mention}さんボイチャ入ってないですやんか\")\n else:\n await ctx.send(content=f\"{ctx.author.mention}さん、多分そこあんたのチャンネルじゃないよ。\")\n\n @voice.command(name=\"permit\", aliases=[\"allow\"])\n async def _permit(self, ctx, member):\n await self.permit(ctx, member)\n\n async def permit(self, ctx, member: discord.Member):\n channel = ctx.author.voice.channel\n if ctx.author.id == self.datas[\"channel_data\"][str(channel.id)][\"owner\"]:\n text_channel = self.bot.get_channel(self.datas[\"channel_data\"][str(channel.id)][\"id\"])\n await channel.set_permissions(member, connect=True, speak=True, read_message_history=True, read_messages=True, send_messages=True)\n await text_channel.set_permissions(member, connect=True, speak=True, read_message_history=True, read_messages=True, send_messages=True)\n embed = discord.Embed(title='Channel Moderate!',\n description=f'{member.mention}さん、ようこそ。✅',\n color=0x00ff00)\n await ctx.send(content=f\"{ctx.author.mention}、{member.mention}\", embed=embed)\n elif channel is None:\n await ctx.send(content=f\"{ctx.author.mention}さんボイチャ入ってないですやんか\")\n else:\n await ctx.send(content=f\"{ctx.author.mention}さん、多分そこあんたのチャンネルじゃないよ。\")\n\n @voice.command(name=\"reject\", aliases=[\"deny\"])\n async def _reject(self, ctx, member):\n await self.reject(ctx, member)\n\n async def reject(self, ctx, member: discord.Member):\n channel = ctx.author.voice.channel\n if ctx.author.id == self.datas[\"channel_data\"][str(channel.id)][\"owner\"]:\n text_channel = self.bot.get_channel(self.datas[\"channel_data\"][str(channel.id)][\"id\"])\n await channel.set_permissions(member, connect=False, speak=False, read_message_history=False, send_messages=False, read_messages=False)\n await text_channel.set_permissions(member, connect=False, speak=False, read_message_history=False, send_messages=False, read_messages=False)\n await member.move_to(self.bot.get_channel(655272738952314908))\n embed_1 = discord.Embed(title='Channel Moderate!',\n description=f'{member.name}さんをつまみ出しました。❎',\n color=0xff0000)\n embed_2 = discord.Embed(title='Your Reject.',\n description=f'{member.mention}さん、あなたはつまみ出されました。❎',\n color=0xff0000)\n await ctx.send(content=f\"{ctx.author.mention}\", embed=embed_1)\n await member.send(embed=embed_2)\n elif channel is None:\n await ctx.send(content=f\"{ctx.author.mention}さんボイチャ入ってないですやんか\")\n else:\n await ctx.send(content=f\"{ctx.author.mention}さん、多分そこあんたのチャンネルじゃないよ。\")\n\n @voice.command(name=\"limit\")\n async def _limit(self, ctx, limit):\n await self.limit(ctx, limit)\n\n async def limit(self, ctx, limit):\n channel = ctx.author.voice.channel\n if ctx.author.id == self.datas[\"channel_data\"][str(channel.id)][\"owner\"]:\n await channel.edit(user_limit = limit)\n embed = discord.Embed(title='Channel Moderate!',\n description=f'参加人数を{limit}人に制限しました。🎟',\n color=0xffffff)\n await ctx.send(content=f\"{ctx.author.mention}\", embed=embed)\n elif channel is None:\n await ctx.send(content=f\"{ctx.author.mention}さんボイチャ入ってないですやんか\")\n else:\n await ctx.send(content=f\"{ctx.author.mention}さん、多分そこあんたのチャンネルじゃないよ。\")\n\n @voice.command(name=\"name\")\n async def _name(self, ctx, *, name):\n await self.name(ctx, name)\n\n async def name(self, ctx, name):\n channel = ctx.author.voice.channel\n if ctx.author.id == self.datas[\"channel_data\"][str(channel.id)][\"owner\"]:\n text_channel = self.bot.get_channel(self.datas[\"channel_data\"][str(channel.id)][\"id\"])\n await channel.edit(name = name)\n await text_channel.edit(name = name)\n embed = discord.Embed(title='Channel Moderate!',\n description=f'チャンネル名を{name}に変更しました！✒',\n color=0xffffff)\n await ctx.send(content=f\"{ctx.author.mention}\", embed=embed)\n elif channel is None:\n await ctx.send(content=f\"{ctx.author.mention}さんボイチャ入ってないですやんか\")\n else:\n await ctx.send(content=f\"{ctx.author.mention}さん、多分そこあんたのチャンネルじゃないよ。\")\n\n @voice.command(name=\"claim\")\n async def _claim(self, ctx):\n await self.claim(ctx, ctx)\n\n async def claim(self, ctx):\n channel = ctx.author.voice.channel\n if ctx.author.id == self.datas[\"channel_data\"][str(channel.id)][\"owner\"]:\n x = False\n for member in channel.members:\n if member.id == self.datas[channel.id][\"owner\"]:\n await ctx.send(content=f\"とっくにオーナーさんいるやないですか。\")\n x = True\n if x == False:\n self.datas[channel.id][\"owner\"] = ctx.author.id\n self.mongo_coll.update_one({\"server\": 615849898637656093}, {'$set':self.datas})\n embed = discord.Embed(title='Channel Moderate!',\n description=f'{ctx.author.mention}さん、あなたが今ここのオーナーです。💻',\n color=0x80ff00)\n await ctx.send(content=ctx.author.mention, embed=embed)\n elif channel is None:\n await ctx.send(content=f\"{ctx.author.mention}さんボイチャ入ってないですやんか\")\n else:\n await ctx.send(content=f\"{ctx.author.mention}さん、多分そこあんたのチャンネルじゃないよ。\")\n\ndef setup(airlinia):\n airlinia.add_cog(VoiceChannel(airlinia))\n", "sub_path": "airlinia_cogs/voicechannel.py", "file_name": "voicechannel.py", "file_ext": "py", "file_size_in_byte": 18942, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "discord.ext.commands.Cog", "line_number": 10, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 10, "usage_type": "name"}, {"api_name": "pymongo.MongoClient", "line_number": 13, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 37, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 51, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog.listener", "line_number": 23, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 23, "usage_type": "attribute"}, {"api_name": "discord.ext.commands", "line_number": 23, "usage_type": "name"}, {"api_name": "discord.PermissionOverwrite.from_pair", "line_number": 67, "usage_type": "call"}, {"api_name": "discord.PermissionOverwrite", "line_number": 67, "usage_type": "attribute"}, {"api_name": "discord.Permissions.all", "line_number": 67, "usage_type": "call"}, {"api_name": "discord.Permissions", "line_number": 67, "usage_type": "attribute"}, {"api_name": "discord.Permissions.none", "line_number": 67, "usage_type": "call"}, {"api_name": "discord.PermissionOverwrite.from_pair", "line_number": 69, "usage_type": "call"}, {"api_name": "discord.PermissionOverwrite", "line_number": 69, "usage_type": "attribute"}, {"api_name": "discord.Permissions.none", "line_number": 69, "usage_type": "call"}, {"api_name": "discord.Permissions", "line_number": 69, "usage_type": "attribute"}, {"api_name": "discord.Permissions.all", "line_number": 69, "usage_type": "call"}, {"api_name": "discord.PermissionOverwrite.from_pair", "line_number": 71, "usage_type": "call"}, {"api_name": "discord.PermissionOverwrite", "line_number": 71, "usage_type": "attribute"}, {"api_name": "discord.Permissions.none", "line_number": 71, "usage_type": "call"}, {"api_name": "discord.Permissions", "line_number": 71, "usage_type": "attribute"}, {"api_name": "discord.Permissions.all", "line_number": 71, "usage_type": "call"}, {"api_name": "discord.PermissionOverwrite.from_pair", "line_number": 73, "usage_type": "call"}, {"api_name": "discord.PermissionOverwrite", "line_number": 73, "usage_type": "attribute"}, {"api_name": "discord.Permissions", "line_number": 74, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 82, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 90, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 94, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 117, "usage_type": "call"}, {"api_name": "asyncio.TimeoutError", "line_number": 124, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 130, "usage_type": "call"}, {"api_name": "asyncio.TimeoutError", "line_number": 136, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 142, "usage_type": "call"}, {"api_name": "asyncio.TimeoutError", "line_number": 149, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 155, "usage_type": "call"}, {"api_name": "asyncio.TimeoutError", "line_number": 162, "usage_type": "attribute"}, {"api_name": "asyncio.TimeoutError", "line_number": 171, "usage_type": "attribute"}, {"api_name": "discord.ext.commands.group", "line_number": 88, "usage_type": "call"}, {"api_name": "discord.ext.commands", "line_number": 88, "usage_type": "name"}, {"api_name": "discord.Embed", "line_number": 186, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 206, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 226, "usage_type": "call"}, {"api_name": "discord.Member", "line_number": 239, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 245, "usage_type": "call"}, {"api_name": "discord.Member", "line_number": 258, "usage_type": "attribute"}, {"api_name": "discord.Embed", "line_number": 265, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 268, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 286, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 305, "usage_type": "call"}, {"api_name": "discord.Embed", "line_number": 329, "usage_type": "call"}]} +{"seq_id": "409020455", "text": "# -*- coding: utf-8 -*-\n# Part of Odoo. See LICENSE file for full copyright and licensing details.\n\nimport calendar\nfrom datetime import timedelta ,datetime \nfrom dateutil.relativedelta import relativedelta\nfrom odoo import api, fields, models, _\nfrom odoo.exceptions import UserError\nfrom collections import OrderedDict\n\n\nclass DebtReconstructionReport(models.AbstractModel):\n _name = 'report.account_custom_report.report_debt_reconstruction_view'\n _order = 'date desc'\n\n @api.model\n def get_report_values(self, docids, data):\n \n #short_name\n model = data['model']\n account_parent_id = data['account_parent_id']\n accounts=[]\n smalles_move=[]\n move_one_month=[]\n move_three_month=[]\n move_six_month=[]\n move_one_year=[]\n move_two_year=[]\n move_more_year=[]\n balances1=[]\n balances3=[]\n balances6=[]\n years1=[]\n years2=[]\n years=[]\n\n\n # rase Eror if date_from max\n if data['date_from'] > data['date_to']:\n raise UserError(_('Start Date must be equal to or less than Date To'))\n\n\n #search sub accounts thate contant move with not Specific date(date_from,date_to)\n if data['date_from'] == False and data['date_to'] == False :\n moves = self.env['account.move.line'].with_context({'show_parent_account':True}).search([\n ('account_id.parent_id','child_of',account_parent_id[0])])\n \n\n #non_duplicated_accounts\n for line in moves:\n accounts.append(line.account_id)\n non_duplicated_accounts=list(dict.fromkeys(accounts).keys()) \n\n #small_move_for_each_account\n for accountt in non_duplicated_accounts :\n smalles_move_for_each_account = self.env['account.move.line'].with_context({'show_parent_account':True}).search([\n ('account_id','=',accountt.id)],order='date',limit=1)\n smalles_move.append(smalles_move_for_each_account)\n \n #convert date from string to date and short name\n dates = datetime.strptime(smalles_move_for_each_account.date, '%Y-%m-%d').date()\n\n #balance after one month\n one_next_month =dates + relativedelta(months=1)\n move_of_one_month = self.env['account.move.line'].with_context({'show_parent_account':True}).search([('date','>=',dates ),('date','<=',one_next_month ),('account_id','=',accountt.id)])\n move_one_month.append(move_of_one_month)\n for line in move_one_month :\n total_debit = 0.0\n total_credit = 0.0\n for move in line :\n total_debit = total_debit + move.debit\n total_credit = total_credit+ move.credit\n balance1 =(total_debit-total_credit)\n balances1.append(balance1)\n\n #balance after three month\n three_next_month =dates + relativedelta(months=3)\n move_of_three_month = self.env['account.move.line'].with_context({'show_parent_account':True}).search([('date','>=',dates ),('date','<=',three_next_month ),('account_id','=',accountt.id)])\n move_three_month.append(move_of_one_month)\n for line in move_three_month :\n total_debit = 0.0\n total_credit = 0.0\n for move in line :\n total_debit = total_debit + move.debit\n total_credit = total_credit+ move.credit\n balance3 =(total_debit-total_credit)\n balances3.append(balance3)\n\n #balance after six month\n six_next_month =dates + relativedelta(months=6)\n move_of_six_month = self.env['account.move.line'].with_context({'show_parent_account':True}).search([('date','>=',dates ),('date','<=',six_next_month ),('account_id','=',accountt.id)])\n move_six_month.append(move_of_six_month)\n for line in move_six_month :\n total_debit = 0.0\n total_credit = 0.0\n for move in line :\n total_debit = total_debit + move.debit\n total_credit = total_credit+ move.credit\n balance6 =(total_debit-total_credit)\n balances6.append(balance6)\n\n #balance after one year\n one_next_year =dates + relativedelta(years=1)\n move_of_one_year = self.env['account.move.line'].with_context({'show_parent_account':True}).search([('date','>=',dates ),('date','<=',one_next_year ),('account_id','=',accountt.id)])\n move_one_year.append(move_of_one_year)\n for line in move_one_year :\n total_debit = 0.0\n total_credit = 0.0\n for move in line :\n total_debit = total_debit + move.debit\n total_credit = total_credit+ move.credit\n year1 =(total_debit-total_credit)\n years1.append(year1)\n \n #balance after two year\n two_next_year =dates + relativedelta(years=2)\n move_of_two_year = self.env['account.move.line'].with_context({'show_parent_account':True}).search([('date','>=',dates ),('date','<=',two_next_year ),('account_id','=',accountt.id)])\n move_two_year.append(move_of_two_year)\n for line in move_two_year :\n total_debit = 0.0\n total_credit = 0.0\n for move in line :\n total_debit = total_debit + move.debit\n total_credit = total_credit+ move.credit\n year2 =(total_debit-total_credit)\n years2.append(year2)\n\n #balance after more\n more = dates + relativedelta(years=3)\n move_of_more_year = self.env['account.move.line'].with_context({'show_parent_account':True}).search([('date','>=',dates ),('account_id','=',accountt.id)])\n move_more_year.append(move_of_more_year)\n for line in move_more_year :\n total_debit = 0.0\n total_credit = 0.0\n for move in line :\n total_debit = total_debit + move.debit\n total_credit = total_credit+ move.credit\n year =(total_debit-total_credit)\n years.append(year)\n \n\n\n #get_sub accounts && search account thate contant move with Specific date(date_from,date_to)\n else : \n moves = self.env['account.move.line'].with_context({'show_parent_account':True}).search([\n ('account_id.parent_id','child_of',account_parent_id[0]),\n ('date','>=',data['date_from']),\n ('date','<',data['date_to'])\n ])\n \n\n #rase Eror if not moves\n if not moves:\n raise UserError(_(\"this parent Account has not any childs accounts, this report cannot be printed.\"))\n\n\n\n #return to template\n docargs = {\n 'doc_ids' : self.ids,\n 'doc_model' : model,\n 'account_code': data['account_code'],\n 'account_name': data['account_name'],\n 'account_currency': data['account_currency'],\n 'date_from' : data['date_from'],\n 'date_to' : data['date_to'],\n 'moves' :moves,\n 'non_duplicated_accounts':non_duplicated_accounts,\n 'smalles_move' :smalles_move,\n 'move_one_month' :move_one_month,\n 'balances1' : balances1 ,\n 'balances3' : balances3 ,\n 'balances6' : balances6 ,\n 'years1' : years1,\n 'years2' :years2,\n 'years' :years,\n \n }\n \n return docargs", "sub_path": "v_11/EBS-SVN/trunk/account_custom_report/report/account_debt_reconstruction_abstract.py", "file_name": "account_debt_reconstruction_abstract.py", "file_ext": "py", "file_size_in_byte": 8040, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "odoo.models.AbstractModel", "line_number": 12, "usage_type": "attribute"}, {"api_name": "odoo.models", "line_number": 12, "usage_type": "name"}, {"api_name": "odoo.exceptions.UserError", "line_number": 40, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 61, "usage_type": "name"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 64, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 77, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 90, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 103, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 116, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 129, "usage_type": "call"}, {"api_name": "odoo.exceptions.UserError", "line_number": 154, "usage_type": "call"}, {"api_name": "odoo._", "line_number": 154, "usage_type": "call"}, {"api_name": "odoo.api.model", "line_number": 16, "usage_type": "attribute"}, {"api_name": "odoo.api", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "230623623", "text": "\"\"\"Support for UK public transport data provided by transportapi.com.\n\nFor more details about this platform, please refer to the documentation at\nhttps://home-assistant.io/components/sensor.uk_transport/\n\"\"\"\nimport logging\nimport re\nfrom datetime import datetime, timedelta\n\nimport requests\nimport voluptuous as vol\n\nfrom homeassistant.components.sensor import PLATFORM_SCHEMA\nfrom homeassistant.const import ATTR_ATTRIBUTION\n\nfrom homeassistant.helpers.entity import Entity\nimport homeassistant.helpers.config_validation as cv\n\n_LOGGER = logging.getLogger(__name__)\n\nCONF_API_APP_KEY = 'app_key'\nCONF_API_APP_ID = 'app_id'\nCONF_LIVE_BUS_TIME = 'live_bus_time'\nCONF_LIVE_TRAIN_TIME = 'live_train_time'\nCONF_STOP_ATCOCODE = 'stop_atcocode'\nCONF_BUS_DIRECTION = 'direction'\n\n# API codes for travel time details\nATTR_ATCOCODE = 'atcocode'\nATTR_LOCALITY = 'locality'\nATTR_STOP_NAME = 'stop_name'\nATTR_REQUEST_TIME = 'request_time'\nATTR_NEXT_BUSES = 'next_buses'\nATTRIBUTION = \"Data provided by transportapi.com\"\n\nATTR_STATION_CODE = 'station_code'\nATTR_DESTINATION_NAME = 'destination_name'\nATTR_NEXT_TRAINS = 'next_trains'\n\nSCAN_INTERVAL = timedelta(minutes=1)\n\nPLATFORM_SCHEMA = PLATFORM_SCHEMA.extend({\n vol.Required(CONF_API_APP_ID): cv.string,\n vol.Required(CONF_API_APP_KEY): cv.string,\n vol.Optional(CONF_LIVE_BUS_TIME): [{\n vol.Required(CONF_STOP_ATCOCODE): cv.string,\n vol.Required(CONF_BUS_DIRECTION): cv.string}],\n vol.Optional(CONF_LIVE_TRAIN_TIME): [{\n vol.Required(ATTR_STATION_CODE): cv.string,\n vol.Required(ATTR_DESTINATION_NAME): cv.string}]\n})\n\n\ndef setup_platform(hass, config, add_devices, discovery_info=None):\n \"\"\"Get the uk_transport sensor.\"\"\"\n sensors = []\n if config.get(CONF_LIVE_BUS_TIME): # retunrs None if not present\n for live_bus_time in config.get(CONF_LIVE_BUS_TIME): # trhows exception if not present\n stop_atcocode = live_bus_time.get(CONF_STOP_ATCOCODE)\n bus_direction = live_bus_time.get(CONF_BUS_DIRECTION)\n sensors.append(\n UkTransportLiveBusTimeSensor(\n config.get(CONF_API_APP_ID),\n config.get(CONF_API_APP_KEY),\n stop_atcocode,\n bus_direction))\n\n if config.get(CONF_LIVE_TRAIN_TIME):\n for live_train_time in config.get(CONF_LIVE_TRAIN_TIME):\n station_code = live_train_time.get(ATTR_STATION_CODE)\n destination_name = live_train_time.get(ATTR_DESTINATION_NAME)\n sensors.append(\n UkTransportLiveTrainTimeSensor(\n config.get(CONF_API_APP_ID),\n config.get(CONF_API_APP_KEY),\n station_code,\n destination_name))\n\n add_devices(sensors, True)\n\n\nclass UkTransportSensor(Entity):\n \"\"\"\n Sensor that reads the UK transport web API.\n\n transportapi.com provides comprehensive transport data for UK train, tube\n and bus travel across the UK via simple JSON API. Subclasses of this\n base class can be used to access specific types of information.\n \"\"\"\n\n TRANSPORT_API_URL_BASE = \"https://transportapi.com/v3/uk/\"\n ICON = 'mdi:car'\n\n def __init__(self, name, api_app_id, api_app_key, url):\n \"\"\"Initialize the sensor.\"\"\"\n self._data = {}\n self._api_app_id = api_app_id\n self._api_app_key = api_app_key\n self._url = self.TRANSPORT_API_URL_BASE + url\n self._name = name\n self._state = None\n\n @property\n def name(self):\n \"\"\"Return the name of the sensor.\"\"\"\n return self._name\n\n @property\n def state(self):\n \"\"\"Return the state of the sensor.\"\"\"\n return self._state\n\n @property\n def icon(self):\n \"\"\"Icon to use in the frontend, if any.\"\"\"\n return self.ICON\n\n def _do_api_request(self, params):\n \"\"\"Perform an API request.\"\"\"\n request_params = dict({\n 'app_id': self._api_app_id,\n 'app_key': self._api_app_key,\n }, **params)\n\n try:\n response = requests.get(self._url, params=request_params)\n response.raise_for_status()\n self._data = response.json()\n except requests.RequestException as req_exc:\n _LOGGER.warning(\n 'Invalid response from transportapi.com: %s', req_exc\n )\n\n\nclass UkTransportLiveBusTimeSensor(UkTransportSensor):\n \"\"\"Live bus time sensor from UK transportapi.com.\"\"\"\n ICON = 'mdi:bus'\n\n def __init__(self, api_app_id, api_app_key, stop_atcocode, bus_direction):\n \"\"\"Construct a live bus time sensor.\"\"\"\n self._stop_atcocode = stop_atcocode\n self._bus_direction = bus_direction\n self._next_buses = []\n self._destination_re = re.compile(\n '{}'.format(bus_direction), re.IGNORECASE\n )\n\n sensor_name = 'Next bus to {}'.format(bus_direction)\n stop_url = 'bus/stop/{}/live.json'.format(stop_atcocode)\n\n UkTransportSensor.__init__(\n self, sensor_name, api_app_id, api_app_key, stop_url\n )\n\n def update(self):\n \"\"\"Get the latest live departure data for the specified stop.\"\"\"\n params = {'group': 'route', 'nextbuses': 'no'}\n\n self._do_api_request(params)\n\n if self._data != {}:\n self._next_buses = []\n\n for (route, departures) in self._data['departures'].items():\n for departure in departures:\n if self._destination_re.search(departure['direction']):\n self._next_buses.append({\n 'route': route,\n 'direction': departure['direction'],\n 'scheduled': departure['aimed_departure_time'],\n 'estimated': departure['best_departure_estimate']\n })\n\n self._state = min(map(\n _delta_mins, [bus['scheduled'] for bus in self._next_buses]\n ))\n\n @property\n def device_state_attributes(self):\n \"\"\"Return other details about the sensor state.\"\"\"\n if self._data is not None:\n attrs = {ATTR_ATTRIBUTION: ATTRIBUTION}\n for key in [\n ATTR_ATCOCODE, ATTR_LOCALITY, ATTR_STOP_NAME,\n ATTR_REQUEST_TIME\n ]:\n attrs[key] = self._data.get(key)\n attrs[ATTR_NEXT_BUSES] = self._next_buses\n return attrs\n\n @property\n def unit_of_measurement(self):\n \"\"\"Return the unit this state is expressed in.\"\"\"\n return \"min\"\n\n# As per bus but route becomes origin_name, direction becomes destination_name, next_suses becomes next_trains\n\nclass UkTransportLiveTrainTimeSensor(UkTransportSensor):\n \"\"\"Live train time sensor from UK transportapi.com.\"\"\"\n ICON = 'mdi:train'\n\n def __init__(self, api_app_id, api_app_key, station_code, destination_name):\n \"\"\"Construct a live bus time sensor.\"\"\"\n self._station_code = station_code # stick to the naming convention of transportAPI\n self._destination_name = destination_name\n self._next_trains = {}\n\n sensor_name = 'Next train to {}'.format(destination_name)\n query_url = 'train/station/{}/live.json'.format(station_code)\n\n print(query_url)\n # also requires '&darwin=false&destination=WAT&train_status=passenger'\n\n UkTransportSensor.__init__(\n self, sensor_name, api_app_id, api_app_key, query_url\n )\n\n def update(self):\n \"\"\"Get the latest live departure data for the specified stop.\"\"\"\n params = {'darwin': 'false', 'destination': self._destination_name, 'train_status': 'passenger'}\n\n self._do_api_request(params)\n\n if self._data != {}:\n if 'error' in self._data: # if query returns an error\n self._state = 'Error in query'\n else:\n self._next_trains = []\n for departure in self._data['departures']['all']: # don't need a regex search as passing in destination to search\n self._next_trains.append({\n 'origin_name': departure['origin_name'],\n 'destination_name': departure['destination_name'],\n 'status': departure['status'],\n 'scheduled': departure['aimed_departure_time'],\n 'estimated': departure['expected_departure_time'],\n 'platform': departure['platform'],\n 'operator_name': departure['operator_name']\n })\n\n self._state = min(map(\n _delta_mins, [train['scheduled'] for train in self._next_trains]\n ))\n\n @property\n def device_state_attributes(self):\n \"\"\"Return other details about the sensor state.\"\"\"\n if self._data is not None:\n attrs = {ATTR_ATTRIBUTION: ATTRIBUTION} # {'attribution': 'Data provided by transportapi.com'}\n for key in [\n ATTR_STATION_CODE,\n ATTR_DESTINATION_NAME\n ]:\n attrs[key] = self._data.get(key) # place these attributes\n attrs[ATTR_NEXT_TRAINS] = self._next_trains\n return attrs\n\n @property\n def unit_of_measurement(self):\n \"\"\"Return the unit this state is expressed in.\"\"\"\n return \"min\"\n\ndef _delta_mins(hhmm_time_str):\n \"\"\"Calculate time delta in minutes to a time in hh:mm format.\"\"\"\n now = datetime.now()\n hhmm_time = datetime.strptime(hhmm_time_str, '%H:%M')\n\n hhmm_datetime = datetime(\n now.year, now.month, now.day,\n hour=hhmm_time.hour, minute=hhmm_time.minute\n )\n if hhmm_datetime < now:\n hhmm_datetime += timedelta(days=1)\n\n delta_mins = (hhmm_datetime - now).seconds // 60\n return delta_mins\n", "sub_path": "HASS files/uk_transport.py", "file_name": "uk_transport.py", "file_ext": "py", "file_size_in_byte": 9942, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 40, "usage_type": "call"}, {"api_name": "homeassistant.components.sensor.PLATFORM_SCHEMA", "line_number": 42, "usage_type": "name"}, {"api_name": "homeassistant.components.sensor.PLATFORM_SCHEMA.extend", "line_number": 42, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 43, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 44, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 45, "usage_type": "call"}, {"api_name": "voluptuous.Optional", "line_number": 48, "usage_type": "call"}, {"api_name": "homeassistant.helpers.config_validation.string", "line_number": 43, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 43, "usage_type": "name"}, {"api_name": "homeassistant.helpers.config_validation.string", "line_number": 44, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 44, "usage_type": "name"}, {"api_name": "voluptuous.Required", "line_number": 46, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 47, "usage_type": "call"}, {"api_name": "homeassistant.helpers.config_validation.string", "line_number": 46, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 46, "usage_type": "name"}, {"api_name": "homeassistant.helpers.config_validation.string", "line_number": 47, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 47, "usage_type": "name"}, {"api_name": "voluptuous.Required", "line_number": 49, "usage_type": "call"}, {"api_name": "voluptuous.Required", "line_number": 50, "usage_type": "call"}, {"api_name": "homeassistant.helpers.config_validation.string", "line_number": 49, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 49, "usage_type": "name"}, {"api_name": "homeassistant.helpers.config_validation.string", "line_number": 50, "usage_type": "attribute"}, {"api_name": "homeassistant.helpers.config_validation", "line_number": 50, "usage_type": "name"}, {"api_name": "homeassistant.helpers.entity.Entity", "line_number": 82, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 126, "usage_type": "call"}, {"api_name": "requests.RequestException", "line_number": 129, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 144, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 145, "usage_type": "attribute"}, {"api_name": "homeassistant.const.ATTR_ATTRIBUTION", "line_number": 182, "usage_type": "name"}, {"api_name": "homeassistant.const.ATTR_ATTRIBUTION", "line_number": 248, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 264, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 264, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 265, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 265, "usage_type": "name"}, {"api_name": "datetime.datetime", "line_number": 267, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 272, "usage_type": "call"}]} +{"seq_id": "577007005", "text": "from contextlib import contextmanager\nfrom datetime import datetime\n\nimport pytest\nfrom fakeparser import Parser\nfrom fakeparser import ParserThatRemembers\nfrom utils import naive_datetime\nfrom utils import utc_datetime\n\nfrom reader import Entry\nfrom reader import EntryNotFoundError\nfrom reader import Feed\nfrom reader import FeedNotFoundError\nfrom reader import make_reader\nfrom reader import ParseError\nfrom reader._parser import RetrieveResult\nfrom reader._types import EntryData\nfrom reader._types import FeedData\nfrom reader._types import FeedFilterOptions\n\n\n@pytest.mark.parametrize('entry_updated', [utc_datetime(2010, 1, 1), None])\ndef test_update_stale(reader, update_feed, entry_updated):\n \"\"\"When a feed is marked as stale feeds/entries should be updated\n regardless of their .updated or caching headers.\n\n \"\"\"\n from utils import utc_datetime as datetime\n\n parser = ParserThatRemembers()\n parser.http_etag = 'etag'\n parser.http_last_modified = 'last-modified'\n reader._parser = parser\n\n feed = parser.feed(1, datetime(2010, 1, 1))\n entry = parser.entry(1, 1, entry_updated)\n\n with pytest.raises(FeedNotFoundError):\n reader._storage.mark_as_stale(feed.url)\n\n reader.add_feed(feed.url)\n\n reader._now = lambda: naive_datetime(2010, 1, 1)\n update_feed(reader, feed.url)\n\n assert {(f.url, f.title, f.last_updated) for f in reader.get_feeds()} == {\n (feed.url, feed.title, datetime(2010, 1, 1))\n }\n assert {(e.id, e.title, e.last_updated) for e in reader.get_entries()} == {\n (entry.id, entry.title, datetime(2010, 1, 1))\n }\n\n # we can't change feed/entry here because their hash would change,\n # resulting in an update;\n # the only way to check they were updated is through last_updated\n\n # should we deprecate the staleness API? maybe:\n # https://github.com/lemon24/reader/issues/179#issuecomment-663840297\n # OTOH, we may still want an update to happen for other side-effects,\n # even if the hash doesn't change\n\n if entry_updated:\n # nothing changes after update\n reader._now = lambda: naive_datetime(2010, 1, 2)\n update_feed(reader, feed.url)\n assert {(f.url, f.title, f.last_updated) for f in reader.get_feeds()} == {\n (feed.url, feed.title, datetime(2010, 1, 1))\n }\n assert {(e.id, e.title, e.last_updated) for e in reader.get_entries()} == {\n (entry.id, entry.title, datetime(2010, 1, 1))\n }\n\n # but it does if we mark the feed as stale\n parser.calls[:] = []\n reader._storage.mark_as_stale(feed.url)\n reader._now = lambda: naive_datetime(2010, 1, 3)\n update_feed(reader, feed.url)\n assert parser.calls == [(feed.url, None, None)]\n assert {(f.url, f.title, f.last_updated) for f in reader.get_feeds()} == {\n (feed.url, feed.title, datetime(2010, 1, 3))\n }\n assert {(e.id, e.title, e.last_updated) for e in reader.get_entries()} == {\n (entry.id, entry.title, datetime(2010, 1, 3))\n }\n\n\ndef test_update_parse(reader, update_feed):\n \"\"\"Updated feeds should pass caching headers back to ._parser().\"\"\"\n from utils import utc_datetime as datetime\n\n parser = ParserThatRemembers()\n parser.http_etag = 'etag'\n parser.http_last_modified = 'last-modified'\n reader._parser = parser\n\n feed = parser.feed(1, datetime(2010, 1, 1))\n entry = parser.entry(1, 1, datetime(2010, 1, 1))\n\n reader.add_feed(feed.url)\n\n update_feed(reader, feed.url)\n assert parser.calls == [(feed.url, None, None)]\n\n parser.calls[:] = []\n update_feed(reader, feed.url)\n assert parser.calls == [(feed.url, 'etag', 'last-modified')]\n\n\ndef test_make_reader_storage(storage):\n reader = make_reader('', _storage=storage)\n assert reader._storage is storage\n\n\ndef test_delete_entries(reader):\n \"\"\"While Storage.delete_entries() is a storage method,\n we care how it interacts with updates etc.,\n and it will be called by plugins.\n\n \"\"\"\n from utils import utc_datetime as datetime\n\n reader._parser = parser = Parser()\n feed = parser.feed(1, datetime(2010, 1, 1))\n entry = parser.entry(1, 1, datetime(2010, 1, 1))\n reader.add_feed(feed.url)\n\n def get_entry_ids():\n return [e.id for e in reader.get_entries()]\n\n with pytest.raises(EntryNotFoundError) as excinfo:\n reader._storage.delete_entries([entry.resource_id])\n assert (excinfo.value.feed_url, excinfo.value.id) == entry.resource_id\n assert 'no such entry' in excinfo.value.message\n\n assert get_entry_ids() == []\n\n reader.update_feeds()\n assert get_entry_ids() == ['1, 1']\n\n reader._storage.delete_entries([entry.resource_id])\n assert get_entry_ids() == []\n\n with pytest.raises(EntryNotFoundError) as excinfo:\n reader._storage.delete_entries([entry.resource_id])\n\n del parser.entries[1][1]\n reader.update_feeds()\n assert get_entry_ids() == []\n\n parser.entries[1][1] = entry\n reader.update_feeds()\n assert get_entry_ids() == ['1, 1']\n\n\n# TODO: move CustomRetriever and CustomParser to fakes.py\n\n\nclass CustomRetriever:\n slow_to_read = False\n\n @contextmanager\n def __call__(self, url, http_etag, *_, **__):\n self.raise_exc('__call__', url)\n yield RetrieveResult('file', 'x.test', http_etag=http_etag.upper())\n\n def validate_url(self, url):\n pass\n\n def process_feed_for_update(self, feed):\n self.raise_exc('process_feed_for_update', feed.url)\n assert feed.http_etag is None\n return feed._replace(http_etag='etag')\n\n def raise_exc(self, method_name, feed_url):\n pass\n\n\nclass CustomParser:\n http_accept = 'x.test'\n\n def __call__(self, url, file, headers):\n self.raise_exc('__call__', url)\n feed = FeedData(url, title=file.upper())\n entries = [EntryData(url, 'id', title='entry')]\n return feed, entries\n\n def process_entry_pairs(self, url, pairs):\n self.raise_exc('process_entry_pairs', url)\n for new, old in pairs:\n yield new._replace(title=new.title.upper()), old\n\n def raise_exc(self, method_name, feed_url):\n pass\n\n\ndef test_retriever_parser_process_hooks(reader):\n \"\"\"Test retriever.process_feed_for_update() and\n parser.process_entry_pairs() get called\n (both private, but used by plugins).\n\n \"\"\"\n reader._parser.mount_retriever('test:', CustomRetriever())\n reader._parser.mount_parser_by_mime_type(CustomParser())\n\n reader.add_feed('test:one')\n reader.update_feeds()\n\n (feed_for_update,) = reader._storage.get_feeds_for_update(\n FeedFilterOptions('test:one')\n )\n assert feed_for_update.http_etag == 'ETAG'\n\n (entry,) = reader.get_entries()\n assert entry.title == 'ENTRY'\n assert entry.feed.title == 'FILE'\n\n\ndef test_retriever_process_feed_for_update_parse_error(reader):\n cause = Exception('bar')\n\n def process_feed_for_update(feed):\n raise ParseError(feed.url, 'foo') from cause\n\n retriever = CustomRetriever()\n retriever.process_feed_for_update = process_feed_for_update\n\n reader._parser.mount_retriever('test:', retriever)\n reader._parser.mount_parser_by_mime_type(CustomParser())\n\n reader.add_feed('test:one')\n\n with pytest.raises(ParseError) as excinfo:\n reader.update_feed('test:one')\n assert 'foo' == excinfo.value.message\n assert excinfo.value.__cause__ is cause\n\n (feed,) = reader.get_feeds()\n assert feed.last_exception is not None\n assert feed.last_exception.type_name == 'builtins.Exception'\n assert feed.last_exception.value_str == 'bar'\n", "sub_path": "tests/test_reader_private.py", "file_name": "test_reader_private.py", "file_ext": "py", "file_size_in_byte": 7574, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "fakeparser.ParserThatRemembers", "line_number": 30, "usage_type": "call"}, {"api_name": "reader._parser", "line_number": 33, "usage_type": "attribute"}, {"api_name": "utils.utc_datetime", "line_number": 35, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 38, "usage_type": "call"}, {"api_name": "reader.FeedNotFoundError", "line_number": 38, "usage_type": "argument"}, {"api_name": "reader._storage.mark_as_stale", "line_number": 39, "usage_type": "call"}, {"api_name": "reader._storage", "line_number": 39, "usage_type": "attribute"}, {"api_name": "reader.add_feed", "line_number": 41, "usage_type": "call"}, {"api_name": "reader._now", "line_number": 43, "usage_type": "attribute"}, {"api_name": "utils.naive_datetime", "line_number": 43, "usage_type": "call"}, {"api_name": "reader.get_feeds", "line_number": 46, "usage_type": "call"}, {"api_name": "utils.utc_datetime", "line_number": 47, "usage_type": "call"}, {"api_name": "reader.get_entries", "line_number": 49, "usage_type": "call"}, {"api_name": "utils.utc_datetime", "line_number": 50, "usage_type": "call"}, {"api_name": "reader._now", "line_number": 64, "usage_type": "attribute"}, {"api_name": "utils.naive_datetime", "line_number": 64, "usage_type": "call"}, {"api_name": "reader.get_feeds", "line_number": 66, "usage_type": "call"}, {"api_name": "utils.utc_datetime", "line_number": 67, "usage_type": "call"}, {"api_name": "reader.get_entries", "line_number": 69, "usage_type": "call"}, {"api_name": "utils.utc_datetime", "line_number": 70, "usage_type": "call"}, {"api_name": "reader._storage.mark_as_stale", "line_number": 75, "usage_type": "call"}, {"api_name": "reader._storage", "line_number": 75, "usage_type": "attribute"}, {"api_name": "reader._now", "line_number": 76, "usage_type": "attribute"}, {"api_name": "utils.naive_datetime", "line_number": 76, "usage_type": "call"}, {"api_name": "reader.get_feeds", "line_number": 79, "usage_type": "call"}, {"api_name": "utils.utc_datetime", "line_number": 80, "usage_type": "call"}, {"api_name": "reader.get_entries", "line_number": 82, "usage_type": "call"}, {"api_name": "utils.utc_datetime", "line_number": 83, "usage_type": "call"}, {"api_name": "pytest.mark.parametrize", "line_number": 22, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 22, "usage_type": "attribute"}, {"api_name": "utils.utc_datetime", "line_number": 22, "usage_type": "call"}, {"api_name": "fakeparser.ParserThatRemembers", "line_number": 91, "usage_type": "call"}, {"api_name": "reader._parser", "line_number": 94, "usage_type": "attribute"}, {"api_name": "utils.utc_datetime", "line_number": 96, "usage_type": "call"}, {"api_name": "utils.utc_datetime", "line_number": 97, "usage_type": "call"}, {"api_name": "reader.add_feed", "line_number": 99, "usage_type": "call"}, {"api_name": "reader.make_reader", "line_number": 110, "usage_type": "call"}, {"api_name": "reader._storage", "line_number": 111, "usage_type": "attribute"}, {"api_name": "reader._parser", "line_number": 122, "usage_type": "attribute"}, {"api_name": "fakeparser.Parser", "line_number": 122, "usage_type": "call"}, {"api_name": "utils.utc_datetime", "line_number": 123, "usage_type": "call"}, {"api_name": "utils.utc_datetime", "line_number": 124, "usage_type": "call"}, {"api_name": "reader.add_feed", "line_number": 125, "usage_type": "call"}, {"api_name": "reader.get_entries", "line_number": 128, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 130, "usage_type": "call"}, {"api_name": "reader.EntryNotFoundError", "line_number": 130, "usage_type": "argument"}, {"api_name": "reader._storage.delete_entries", "line_number": 131, "usage_type": "call"}, {"api_name": "reader._storage", "line_number": 131, "usage_type": "attribute"}, {"api_name": "reader.update_feeds", "line_number": 137, "usage_type": "call"}, {"api_name": "reader._storage.delete_entries", "line_number": 140, "usage_type": "call"}, {"api_name": "reader._storage", "line_number": 140, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 143, "usage_type": "call"}, {"api_name": "reader.EntryNotFoundError", "line_number": 143, "usage_type": "argument"}, {"api_name": "reader._storage.delete_entries", "line_number": 144, "usage_type": "call"}, {"api_name": "reader._storage", "line_number": 144, "usage_type": "attribute"}, {"api_name": "reader.update_feeds", "line_number": 147, "usage_type": "call"}, {"api_name": "reader.update_feeds", "line_number": 151, "usage_type": "call"}, {"api_name": "reader._parser.RetrieveResult", "line_number": 164, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 161, "usage_type": "name"}, {"api_name": "reader._types.FeedData", "line_number": 183, "usage_type": "call"}, {"api_name": "reader._types.EntryData", "line_number": 184, "usage_type": "call"}, {"api_name": "reader._parser.mount_retriever", "line_number": 202, "usage_type": "call"}, {"api_name": "reader._parser", "line_number": 202, "usage_type": "attribute"}, {"api_name": "reader._parser.mount_parser_by_mime_type", "line_number": 203, "usage_type": "call"}, {"api_name": "reader._parser", "line_number": 203, "usage_type": "attribute"}, {"api_name": "reader.add_feed", "line_number": 205, "usage_type": "call"}, {"api_name": "reader.update_feeds", "line_number": 206, "usage_type": "call"}, {"api_name": "reader._storage.get_feeds_for_update", "line_number": 208, "usage_type": "call"}, {"api_name": "reader._storage", "line_number": 208, "usage_type": "attribute"}, {"api_name": "reader._types.FeedFilterOptions", "line_number": 209, "usage_type": "call"}, {"api_name": "reader.get_entries", "line_number": 213, "usage_type": "call"}, {"api_name": "reader.ParseError", "line_number": 222, "usage_type": "call"}, {"api_name": "reader._parser.mount_retriever", "line_number": 227, "usage_type": "call"}, {"api_name": "reader._parser", "line_number": 227, "usage_type": "attribute"}, {"api_name": "reader._parser.mount_parser_by_mime_type", "line_number": 228, "usage_type": "call"}, {"api_name": "reader._parser", "line_number": 228, "usage_type": "attribute"}, {"api_name": "reader.add_feed", "line_number": 230, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 232, "usage_type": "call"}, {"api_name": "reader.ParseError", "line_number": 232, "usage_type": "argument"}, {"api_name": "reader.update_feed", "line_number": 233, "usage_type": "call"}, {"api_name": "reader.get_feeds", "line_number": 237, "usage_type": "call"}]} +{"seq_id": "517019443", "text": "import gc\nimport yt\nimport trident\nimport sys\nimport numpy as np\nimport os\nfrom mpi4py import MPI\nimport time\n\ncomm = MPI.COMM_WORLD\nrank = comm.Get_rank()\nsize = comm.Get_size()\n\n\n\"\"\"\ncombined spectra and light-ray generator. Uses make_simple_ray functionality of trident.\n call using:\n aprun -n nProcs python fullBoxLightRays.py <sim> <dump> <root-grid-dimension> <data location> <N to keep>\n\n on BW do first:\n > module load bwpy/2.0.0-pre0\n > module load bwpy-mpi\n > source /u/sciteam/wells/my_yt_env/bin/activate\n\"\"\"\nif rank == 0:\n scripttime = time.time()\nlinear = False # whether lightrays are linear along z or randomly oriented.\ndataLoc = sys.argv[4]\nif dataLoc == 'BW':\n workdir = '/u/sciteam/wells/scratch/prodRun'\n scriptsdir = '/u/sciteam/wells/scratch/prodRun/scripts'\n dataRepo = '/u/sciteam/wells/scratch/prodRun/scripts'\nelif dataLoc == 'HD':\n workdir = '/home/azton/simulations'\n dataRepo = '/home/azton/simulations/data'#/media/azton/bigbook/projects/nextGenIGM'\n scriptsdir = '/home/azton/simulations/scripts'\nelif dataLoc == 'bigbook':\n workdir = '/media/azton/bigbook/projects/nextGenIGM'\n dataRepo = '/media/azton/bigbook/projects/nextGenIGM'\n scriptsdir = '/home/azton/simulations/scripts'\nelif dataLoc == 'comet':\n workdir = '/oasis/scratch/comet/azton/temp_project/nextGenIGM'\n dataRepo = '/scratch/azton/%s'%os.getenv('SLURM_JOBID')\n scriptsdir = '%s/scripts'%workdir\nelse:\n print('Data Location not recognized!\\nUse \"BW\" for bluewaters, \"HD\" for harddrive, \"bigbook\" for external!')\n comm.Abort()\nsimname = sys.argv[1]\nd = int(sys.argv[2])\ndim = float(sys.argv[3])\ndataPath = workdir+simname\nresultsPath = workdir+'/analysis/%s'%simname\n\nif (rank == 0) & (dataLoc == 'comet'):\n if not os.path.exists('%s/lin_rays'%(dataRepo)):\n os.makedirs('%s/lin_rays'%(dataRepo))\n if not os.path.exists(\"%s/scripts/rayData/%s/RD%04d/\"%(workdir, simname, d)):\n os.makedirs(\"%s/scripts/rayData/%s/RD%04d/\"%(workdir, simname, d))\nelif (rank == 0) & (dataLoc != \"comet\"):\n if os.path.exists('%s/rayData/%s/RD%04d/lin_rays'%(dataRepo, simname, d)) == False:\n os.makedirs('%s/rayData/%s/RD%04d/lin_rays'%(dataRepo, simname, d))\n if os.path.exists('%s/rayData/%s/RD%04d/lin_spectra'%(dataRepo, simname, d)) == False:\n os.makedirs('%s/rayData/%s/RD%04d/lin_spectra'%(dataRepo, simname, d))\n\n\ndx = 1./dim\nnSamples = int(dim*dim)\ntry:\n ds = yt.load('%s/%s/RD%04d/RedshiftOutput%04d'\\\n %(workdir, simname, d, d))\n print ('using redshift output at z = %0.3f'%(ds.current_redshift))\nexcept:\n ds = yt.load('%s/%s/DD%04d/data%04d'\\\n %(workdir, simname, d, d))\n print ('using data output at z = %0.3f'%(ds.current_redshift))\nz = ds.current_redshift\nwidth = ds.domain_width.to(\"Mpc\")[0]\ndz = ds.current_redshift - ds.cosmology.z_from_t(\\\n ds.current_time + ds.domain_width[0]/ds.quan(2.998*10**8, 'm/s'))\nlambda_shift = 1215.67\nlambda_min = lambda_shift*(z-dz+1)\nlambda_max = lambda_shift*(z+1)\nlocals = range(rank, nSamples, size)\ncount = 0\ndx = 1./dim\n#\n# Make light-rays first and save to disk\n#\ncomm.Barrier()\nfor i in locals:\n startTime = time.time()\n if dataLoc == \"comet\":\n rFile = \"%s/lin_rays/ray_%d.h5\"%(dataRepo,i)\n else:\n rFile = '%s/rayData/%s/RD%04d/lin_rays/ray_%d.h5'\\\n %(dataRepo,simname, d, i)\n row = (i // dim)\n col = (i % dim)\n xstart = (row+0.5) * dx\n ystart = (col+0.5) * dx\n start = np.array([xstart, ystart, 0.0])\n end = start + np.array([5*dx,5*dx,1.0])\n ray = trident.make_simple_ray(ds, start_position=start,\\\n end_position=end, data_filename=rFile,\\\n lines=['H I 1216'], ftype='gas',\\\n fields = ['density','metallicity','temperature','H_p0_number_density'])\n count += 1\n counts = comm.gather(count, root=0)\n \n if rank==0: \n if count % 50 == 0:\n counts=sum(counts)\n print('%d light-ray objects created!'%(counts)\\\n +'\\nIntermediate pipelinefile being created')\n f = open('lin_%s_RD%04d_pipeline.info'%(simname, d), 'w')\n f.write('%s\\n%d\\n%d\\n%s\\n%d\\n%d\\n'\\\n %(simname,d,dim,sys.argv[4], size, counts ))\n f.close()\n os.system('cd %s; tar -cvf lin_rays.tar lin_rays;'%(dataRepo)\\\n +' mv lin_rays.tar %s/rayData/%s/RD%04d/'\\\n %(scriptsdir, simname, d))\ncounts = comm.gather(count, root=0)\nif rank==0: \n counts=sum(counts)\n print('%d light-ray objects created!'%counts)\n os.system('touch lin_%s_RD%04d_pipeline.info'%(simname, d))\n f = open('lin_%s_RD%04d_pipeline.info'%(simname, d), 'w')\n f.write('%s\\n%d\\n%d\\n%s\\n%d\\n%d\\n'%(simname,d,dim,sys.argv[4], size, counts ))\n f.close()\n print('InfoFile: \\n%s\\n%d\\n%d\\n%s\\n%d\\n%d\\n'\\\n %(simname,d,dim,sys.argv[4], size, counts ))\n if dataLoc=='comet':\n os.system('cd %s; tar -cvf lin_rays.tar lin_rays'%(dataRepo))\n if not os.path.exists('%s/scripts/rayData/%s/RD%04d/'\\\n %(workdir, simname, d)):\n os.makedirs('%s/scripts/rayData/%s/RD%04d/'\\\n %(workdir, simname, d))\n os.system('cp %s/lin_rays.tar %s/scripts/rayData/%s/RD%04d/'\\\n %(dataRepo, workdir, simname, d))\n", "sub_path": "SA_makeLightRaysAxial.py", "file_name": "SA_makeLightRaysAxial.py", "file_ext": "py", "file_size_in_byte": 5413, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "mpi4py.MPI.COMM_WORLD", "line_number": 10, "usage_type": "attribute"}, {"api_name": "mpi4py.MPI", "line_number": 10, "usage_type": "name"}, {"api_name": "time.time", "line_number": 26, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 43, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 48, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 49, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 56, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 63, "usage_type": "call"}, {"api_name": "yt.load", "line_number": 69, "usage_type": "call"}, {"api_name": "yt.load", "line_number": 73, "usage_type": "call"}, {"api_name": "time.time", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 102, "usage_type": "call"}, {"api_name": "trident.make_simple_ray", "line_number": 103, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 117, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 119, "usage_type": "call"}, {"api_name": "os.system", "line_number": 126, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 128, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 131, "usage_type": "attribute"}, {"api_name": "os.system", "line_number": 133, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 134, "usage_type": "call"}, {"api_name": "os.path", "line_number": 134, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 136, "usage_type": "call"}, {"api_name": "os.system", "line_number": 138, "usage_type": "call"}]} +{"seq_id": "385526848", "text": "import telegram\nimport logging\nfrom telegram.ext import Updater\nfrom telegram.ext import CommandHandler\nfrom telegram.ext import MessageHandler, Filters\n\ndef start(bot, update, args):\n print(args)\n bot.send_message(chat_id=update.message.chat_id, text=\"Welcome to Home Light manager.\\nGet ready and turn up the lights \\ud83d\\udca1\")\n\ndef echo(bot, update):\n bot.send_message(chat_id=update.message.chat_id, text=update.message.text)\n\nbot = telegram.Bot(token='286206912:AAGSaq_vPv7wdpBzHn7YNf2hi74nbZzKUFQ')\n\nprint(bot.getMe())\n\nupdater = Updater(token='286206912:AAGSaq_vPv7wdpBzHn7YNf2hi74nbZzKUFQ')\ndispatcher = updater.dispatcher\n\nlogging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',level=logging.INFO)\n\nstart_handler = CommandHandler('start',start, pass_args=True)\ntext_handler = MessageHandler(Filters.text, echo)\n\ndispatcher.add_handler(start_handler)\ndispatcher.add_handler(text_handler)\n\nupdater.start_polling()", "sub_path": "bot_test.py", "file_name": "bot_test.py", "file_ext": "py", "file_size_in_byte": 959, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "telegram.Bot", "line_number": 14, "usage_type": "call"}, {"api_name": "telegram.ext.Updater", "line_number": 18, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 21, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 21, "usage_type": "attribute"}, {"api_name": "telegram.ext.CommandHandler", "line_number": 23, "usage_type": "call"}, {"api_name": "telegram.ext.MessageHandler", "line_number": 24, "usage_type": "call"}, {"api_name": "telegram.ext.Filters.text", "line_number": 24, "usage_type": "attribute"}, {"api_name": "telegram.ext.Filters", "line_number": 24, "usage_type": "name"}]} +{"seq_id": "172230827", "text": "from django.contrib import admin\nfrom django.urls import include, path\n\nurlpatterns = [\n path('', include('forum.urls')),\n path('accounts/', include('accounts.urls')),\n path('admin/', admin.site.urls),\n]\n\nhandler404 = \"forum.views.handle_404\"\nhandler403 = \"forum.views.handle_403\"", "sub_path": "workshop2/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 289, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "django.urls.path", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 5, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 6, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 7, "usage_type": "name"}]} +{"seq_id": "381933893", "text": "\"\"\" account validation/suspending \"\"\"\nfrom datetime import timedelta, datetime\nfrom pyramid.view import view_config\nfrom pyramid.security import remember\nfrom pyramid.httpexceptions import HTTPFound\n\nimport ptah\nfrom ptahcrowd.settings import CFG_ID_CROWD\nfrom ptahcrowd.settings import _\n\nTOKEN_TYPE = ptah.token.TokenType(\n 'cd51f14e9b2842608ccadf1a240046c1', timedelta(hours=24))\n\n\ndef initiate_email_validation(principal, request):\n \"\"\" Initiate email validation\n\n :param email: email address of user\n :param principal: principal object\n :param request: current request object\n \"\"\"\n t = ptah.token.service.generate(TOKEN_TYPE, principal.__uri__)\n template = ValidationTemplate(request, principal=principal, token=t)\n template.send()\n\n\n@ptah.auth_checker\ndef validationAndSuspendedChecker(info):\n principal = info.principal\n\n if principal.suspended:\n info.message = _('Account is suspended.')\n info.arguments['suspended'] = True\n return False\n\n if principal.validated:\n return True\n\n CROWD = ptah.get_settings(CFG_ID_CROWD)\n if not CROWD['validation']:\n return True\n\n if CROWD['allow-unvalidated'] or principal.validated:\n return True\n\n info.message = _('Account is not validated.')\n info.arguments['validation'] = False\n return False\n\n\n@ptah.subscriber(ptah.events.PrincipalRegisteredEvent)\ndef principalRegistered(ev):\n ev.principal.joined = datetime.now()\n\n cfg = ptah.get_settings(CFG_ID_CROWD)\n if not cfg['validation']:\n ev.principal.validated = True\n\n\nclass ValidationTemplate(ptah.mail.MailTemplate):\n\n subject = _('Activate Your Account')\n template = 'ptahcrowd:templates/validate_email.txt'\n\n def update(self):\n super(ValidationTemplate, self).update()\n\n self.url = '%s/validateaccount.html?token=%s'%(\n self.request.application_url, self.token)\n\n self.recipients = [ptah.mail.formataddr(\n (self.principal.name, self.principal.email)\n )]\n\n\n@view_config(route_name='ptah-principal-validate')\ndef validate(request):\n \"\"\"Validate account\"\"\"\n t = request.GET.get('token')\n\n data = ptah.token.service.get(t)\n if data is not None:\n user = ptah.resolve(data)\n if user is not None:\n user.validated = True\n ptah.token.service.remove(t)\n request.add_message(_(\"Account has been successfully validated.\"))\n\n request.registry.notify(ptah.events.PrincipalValidatedEvent(user))\n\n headers = remember(request, user.__uri__)\n return HTTPFound(location=request.application_url, headers=headers)\n\n request.add_message(_(\"Can't validate email address.\"), 'warning')\n return HTTPFound(location=request.application_url)\n", "sub_path": "ptahcrowd/validation.py", "file_name": "validation.py", "file_ext": "py", "file_size_in_byte": 2780, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "ptah.token.TokenType", "line_number": 11, "usage_type": "call"}, {"api_name": "ptah.token", "line_number": 11, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 12, "usage_type": "call"}, {"api_name": "ptah.token.service.generate", "line_number": 22, "usage_type": "call"}, {"api_name": "ptah.token", "line_number": 22, "usage_type": "attribute"}, {"api_name": "ptahcrowd.settings._", "line_number": 32, "usage_type": "call"}, {"api_name": "ptah.get_settings", "line_number": 39, "usage_type": "call"}, {"api_name": "ptahcrowd.settings.CFG_ID_CROWD", "line_number": 39, "usage_type": "argument"}, {"api_name": "ptahcrowd.settings._", "line_number": 46, "usage_type": "call"}, {"api_name": "ptah.auth_checker", "line_number": 27, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 53, "usage_type": "name"}, {"api_name": "ptah.get_settings", "line_number": 55, "usage_type": "call"}, {"api_name": "ptahcrowd.settings.CFG_ID_CROWD", "line_number": 55, "usage_type": "argument"}, {"api_name": "ptah.subscriber", "line_number": 51, "usage_type": "call"}, {"api_name": "ptah.events", "line_number": 51, "usage_type": "attribute"}, {"api_name": "ptah.mail", "line_number": 60, "usage_type": "attribute"}, {"api_name": "ptahcrowd.settings._", "line_number": 62, "usage_type": "call"}, {"api_name": "ptah.mail.formataddr", "line_number": 71, "usage_type": "call"}, {"api_name": "ptah.mail", "line_number": 71, "usage_type": "attribute"}, {"api_name": "ptah.token.service.get", "line_number": 81, "usage_type": "call"}, {"api_name": "ptah.token", "line_number": 81, "usage_type": "attribute"}, {"api_name": "ptah.resolve", "line_number": 83, "usage_type": "call"}, {"api_name": "ptah.token.service.remove", "line_number": 86, "usage_type": "call"}, {"api_name": "ptah.token", "line_number": 86, "usage_type": "attribute"}, {"api_name": "ptahcrowd.settings._", "line_number": 87, "usage_type": "call"}, {"api_name": "ptah.events.PrincipalValidatedEvent", "line_number": 89, "usage_type": "call"}, {"api_name": "ptah.events", "line_number": 89, "usage_type": "attribute"}, {"api_name": "pyramid.security.remember", "line_number": 91, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 92, "usage_type": "call"}, {"api_name": "ptahcrowd.settings._", "line_number": 94, "usage_type": "call"}, {"api_name": "pyramid.httpexceptions.HTTPFound", "line_number": 95, "usage_type": "call"}, {"api_name": "pyramid.view.view_config", "line_number": 76, "usage_type": "call"}]} +{"seq_id": "593406182", "text": "import numpy as np\nfrom ViperEnvironment import ViperEnvironment\nfrom tqdm import tqdm\nimport pdb\n\nenv = ViperEnvironment()\ns = env.reset()\n\naction_min, action_max = .2, 2\n\nwith open('output.txt', 'w') as file:\n rand = np.random.rand(3)\n action = action_min * rand + (1-rand) * action_max\n for i in tqdm(range(3600)):\n if i % 50:\n rand = np.random.rand(3)\n action = action_min * rand + (1-rand) * action_max\n # action = np.array([1.5, 1.5, .2])\n state, reward = env.step(action)\n combined = np.hstack([state, action])\n row = ' '.join([str(f) for f in combined])\n file.write(row)\n file.write('\\n')\n if i == 1800:\n s = env.reset()\n file.close()\n", "sub_path": "python/example_simulate.py", "file_name": "example_simulate.py", "file_ext": "py", "file_size_in_byte": 745, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "ViperEnvironment.ViperEnvironment", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 12, "usage_type": "attribute"}, {"api_name": "tqdm.tqdm", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random.rand", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 16, "usage_type": "attribute"}, {"api_name": "numpy.hstack", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "552427046", "text": "\n# coding: utf-8\n\nimport requests\nfrom bs4 import BeautifulSoup\n\n\n\n# Задаем ссылку на статью, которую нужно распарсить\narticle_url = 'http://www.e1.ru/news/spool/news_id-449429-section_id-162.html'\n# article_url = 'http://www.e1.ru/news/spool/news_id-449488.html'\nr = requests.get(article_url)\nsoup = BeautifulSoup(r.text, 'html.parser')\n#for link in soup.findAll('a'):)\n# print (link.get('href'))\nWordsInArticle = len(soup.find('div',{\"id\":\"newscontent\"}).get_text())\nprint(WordsInArticle)\n# Выводим заголовок статьи\ntitleTag = soup.html.head.title\n#print(titleTag)\n#print(titleTag.string)\n#\n## Выводим содержимое статьи\n#print(soup.get_text())\nLinksNumber = len(soup.find_all('a', {\"class\":\"news\"})) \nprint(LinksNumber)\nImagesNumbers = len(soup.html.body.find_all('img')) \nImagesNumber = len(soup.find('div',{\"id\":\"newscontent\"}).find_all('img'))\nprint (str(ImagesNumbers) + ' '+ str(ImagesNumber))\n# Выделяем фрагмент, в котором содержится информация о популярности статьи\n#text = soup.get_text()\n#\n#i1 = text.find('просмотров')\n#i2 = text.find('версия для печати')\n#\n#\n## Выделение количества просмотров\n#fragment = text[i1:i2-3]\n#\n#j1 = fragment.find('просмотров:')\n#j2 = fragment.find('|')\n#print(i1)\n#print(i2)\n#views = int(fragment[j1+12:j2])\n#print(views)\n#\n## Выделение количества комментариев\n#k1 = fragment.find('(')\n#k2 = fragment.find(')')\n#print(k1)\n#print(k2)\n#comments = int(fragment[k1+1:k2])\n\n", "sub_path": "lab_3/example1.py", "file_name": "example1.py", "file_ext": "py", "file_size_in_byte": 1663, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "requests.get", "line_number": 12, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "654336350", "text": "from kivy.uix.screenmanager import Screen\nfrom kivy.uix.button import Button\nfrom kivy.lang import Builder\n\nimport os\nfrom os.path import join\n\nfrom .filewidgets import *\n\nBuilder.load_string('''\n<FileExplorer>:\n canvas.before:\n Color:\n rgba: .1,.1,.1,1\n Rectangle:\n size: self.size\n size_hint_x: None\n width: '160dp'\n GridLayout:\n cols: 1\n Label:\n text: 'Explorer!'\n font_size: '16dp'\n size_hint_y: None\n height: '32dp'\n ScrollView:\n GridLayout:\n id: grid\n cols: 1\n size_hint_y: None\n height: self.minimum_height\n\n''')\n \n\nclass FileExplorer(Screen):\n \n def __init__(self, **k):\n super(FileExplorer, self).__init__(**k)\n direct = '.'\n for node in os.listdir(direct):\n dr = join(direct, node)\n if os.path.isfile(dr):\n self.ids.grid.add_widget(FileWidget(file_path=dr))\n elif os.path.isdir(join(dr)):\n self.ids.grid.add_widget(DirWidget(file_path=dr))\n", "sub_path": "kivystudio/components/sibebar/fileexplorer/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1124, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "kivy.lang.Builder.load_string", "line_number": 10, "usage_type": "call"}, {"api_name": "kivy.lang.Builder", "line_number": 10, "usage_type": "name"}, {"api_name": "kivy.uix.screenmanager.Screen", "line_number": 36, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 42, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}]} +{"seq_id": "573344552", "text": "import os\nimport spotipy\nfrom spotipy.oauth2 import SpotifyClientCredentials\nimport spotipy.util as util\nfrom json.decoder import JSONDecodeError\nimport spotify_credentials as sc\n\nscope = 'user-read-private playlist-modify-public user-library-read'\nusername = sc.username\nclient_id = sc.client_id\nclient_secret = sc.client_secret\nredirect_uri = 'https://www.google.com/callback/'\nplaylist_id = sc.playlist_id\nclient_credentials_manager = SpotifyClientCredentials(client_id=client_id, client_secret=client_secret)\n\n# Erase cache and prompt for user permission\ntry:\n # token = client_credentials_manager.get_access_token()\n token = util.prompt_for_user_token(username=username, scope=scope, client_id=client_id, client_secret=client_secret, redirect_uri=redirect_uri) # add scope\nexcept (AttributeError, JSONDecodeError):\n os.remove(f\".cache-{username}\")\n token = util.prompt_for_user_token(username=username, scope=scope, client_id=client_id, client_secret=client_secret, redirect_uri=redirect_uri) # add scope\n # token = client_credentials_manager.get_access_token()\n\n# Create spotify object with permissions\nspotifyObject = spotipy.Spotify(auth=token)\n\n# due to how results gets put into paginated sections,\n# and due to the issue where there is a limit to how many\n# songs can be moved at a time, every time I get a section\n# I immediately upload them\nresults = spotifyObject.current_user_saved_tracks(offset=0)\ntracks = results['items']\nwhile results['next']:\n track_uris = []\n for track in tracks:\n track_uris.append(track['track']['uri'])\n print(track['track']['name'])\n spotifyObject.user_playlist_add_tracks(username, playlist_id,\n track_uris)\n \n results = spotifyObject.next(results)\n tracks = results['items']\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1810, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "spotify_credentials.username", "line_number": 9, "usage_type": "attribute"}, {"api_name": "spotify_credentials.client_id", "line_number": 10, "usage_type": "attribute"}, {"api_name": "spotify_credentials.client_secret", "line_number": 11, "usage_type": "attribute"}, {"api_name": "spotify_credentials.playlist_id", "line_number": 13, "usage_type": "attribute"}, {"api_name": "spotipy.oauth2.SpotifyClientCredentials", "line_number": 14, "usage_type": "call"}, {"api_name": "spotipy.util.prompt_for_user_token", "line_number": 19, "usage_type": "call"}, {"api_name": "spotipy.util", "line_number": 19, "usage_type": "name"}, {"api_name": "json.decoder.JSONDecodeError", "line_number": 20, "usage_type": "name"}, {"api_name": "os.remove", "line_number": 21, "usage_type": "call"}, {"api_name": "spotipy.util.prompt_for_user_token", "line_number": 22, "usage_type": "call"}, {"api_name": "spotipy.util", "line_number": 22, "usage_type": "name"}, {"api_name": "spotipy.Spotify", "line_number": 26, "usage_type": "call"}]} +{"seq_id": "456992275", "text": "import os\nimport sys\nimport shutil\nimport glob\nfrom argparse import ArgumentParser\nfrom moviepy.editor import *\nfrom moviepy import config_defaults\nfrom pydub import AudioSegment\nfrom pydub.utils import *\nfrom tqdm import tqdm\nimport subprocess\nfrom termcolor import colored\n\nfrom load_im import get_image_magick_executable\n\ndef install(package):\n subprocess.call([sys.executable, \"-m\", \"pip\", \"install\", package])\n\ndef upgrade(package):\n subprocess.call(['pip', \"install\", \"--upgrade\", package])\n\ndef installModule(package):\n install(package)\n upgrade(package)\n\ndef initializeMoviePy():\n installModule(\"moviepy\")\n\ndef identifySilenceMomentsOfVideo(videoFilename, rmsOfSilence, timeOfSilenceInMilliseconds, debug):\n audioFile = AudioSegment.from_file(videoFilename, \"mp4\")\n videoFile = VideoFileClip(videoFilename)\n\n chunksOfAudio = make_chunks(audioFile, timeOfSilenceInMilliseconds)\n currentTime = 0.0\n startSilence = False\n fileCounter = 0\n\n silenceToRemoveTxt = open(\"silenceToRemove.txt\", \"w\")\n if(debug):\n silenceFileTxt = open(\"log.txt\", \"w\")\n it = 0\n listOfClipsToCombine = []\n print(colored(\"Buscando instantes de silêncio ao longo do vídeo...\", \"green\"))\n for chunk in tqdm(chunksOfAudio):\n if(chunk.rms < rmsOfSilence and startSilence == False):\n #detecta um chunk que começa com silêncio\n startSilenceClipTime = currentTime\n startSilence = True\n if debug:\n silenceFileTxt.write(\"Começou: \" + str(currentTime) + \":\" + str(chunk.rms) + \":\" + str(round(chunk.dBFS, 2)) + \"\\n\")\n elif(chunk.rms > rmsOfSilence and startSilence == True and startSilenceClipTime < currentTime - 1.5):\n #achou o fim de um chunk que possui no mínimo 2x segundos, sendo x o tamanho do chunk\n endSilenceClipTime = currentTime - (timeOfSilenceInMilliseconds / 1000.0)\n silenceClip = videoFile.subclip(startSilenceClipTime, endSilenceClipTime)\n silenceFilename = \"silence\" + str(fileCounter) + \".mp4\"\n textClip = TextClip(silenceFilename, fontsize = 80)\n compClip = CompositeVideoClip([silenceClip, textClip]).set_duration(endSilenceClipTime - startSilenceClipTime)\n if debug:\n if os.path.exists(silenceFilename) == False:\n compClip.write_videofile(silenceFilename, logger = None)\n listOfClipsToCombine.append(compClip)\n\n startSilence = False\n fileCounter += 1\n if(debug):\n silenceFileTxt.write(silenceFilename + \"\\n\")\n silenceToRemoveTxt.write(silenceFilename + \":\" + str(startSilenceClipTime) + \":\" + str(endSilenceClipTime) + \"\\n\")\n elif(chunk.rms > rmsOfSilence and startSilence == True):\n #achou um chunk de exatamente x segundos, sendo x o tamanho do chunk. nesse caso ignora\n startSilence = False\n if(debug):\n silenceFileTxt.write(\"Interrompido: \" + str(currentTime) + \":\" + str(chunk.rms) + \":\" + str(round(chunk.dBFS, 2)) + \"\\n\")\n elif(it == len(chunksOfAudio) - 1):\n #última iteração\n if startSilence == True:\n endSilenceClipTime = currentTime - (timeOfSilenceInMilliseconds / 1000.0)\n silenceClip = videoFile.subclip(startSilenceClipTime)\n silenceFilename = \"silence\" + str(fileCounter) + \".mp4\"\n textClip = TextClip(silenceFilename, fontsize = 80)\n compClip = CompositeVideoClip([silenceClip, textClip]).set_duration(endSilenceClipTime - startSilenceClipTime)\n if debug:\n if os.path.exists(silenceFilename) == False:\n compClip.write_videofile(silenceFilename, logger = None)\n listOfClipsToCombine.append(compClip)\n\n #silenceFileTxt.write(\"Final: \" + str(currentTime) + \":\" + str(chunk.rms) + \":\" + str(round(chunk.dBFS, 2)) + \"\\n\")\n if(debug):\n silenceFileTxt.write(silenceFilename)\n silenceToRemoveTxt.write(silenceFilename + \":\" + str(startSilenceClipTime) + \":\" + str(endSilenceClipTime))\n elif(startSilence == True):\n #fins de debug\n if(debug):\n silenceFileTxt.write(str(currentTime) + \":\" + str(chunk.rms) + \":\" + str(round(chunk.dBFS, 2)) + \"\\n\")\n\n currentTime += round(timeOfSilenceInMilliseconds / 1000.0, 2)\n it += 1\n\n if not os.path.exists(\"silence.mp4\") and debug:\n silenceClips = concatenate_videoclips(listOfClipsToCombine)\n silenceClips.write_videofile(\"silence.mp4\")\n silenceClips.close()\n\n if(debug):\n silenceFileTxt.close()\n silenceToRemoveTxt.close()\n videoFile.close()\n\n if os.path.exists(\"silenceToRemoveCOPY.txt\") == False:\n shutil.copyfile(\"silenceToRemove.txt\", \"silenceToRemoveCOPY.txt\")\n\ndef clipSilenceBasedOnTxtFile(videoFilename, txtFile, debug = True):\n silenceToRemoveFile = open(txtFile, \"r\")\n videoFile = VideoFileClip(videoFilename)\n\n print(colored(\"Recortando momentos de silêncio do vídeo...\", \"green\"))\n firstIt = True\n listOfClipsToCombine = []\n i = 0\n for line in list(silenceToRemoveFile):\n if line[0] == \"#\":\n continue\n\n file, startTime, endTime = line.rstrip().split(\":\")\n startTime = float(startTime)\n endTime = float(endTime)\n\n filename = \"clip\" + str(i) + \".mp4\"\n if(startTime == 0.0 and firstIt == True):\n firstIt = False\n lastEndTime = endTime\n elif(firstIt == True):\n #from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip\n #ffmpeg_extract_subclip(videoFilename, 0, startTime, targetname=filename)\n clip = videoFile.subclip(0, startTime)\n listOfClipsToCombine.append(clip)\n if debug:\n clip.write_videofile(filename)\n lastEndTime = endTime\n firstIt = False\n else:\n #ffmpeg_extract_subclip(videoFilename, lastEndTime, startTime, targetname=filename)\n clip = videoFile.subclip(lastEndTime, startTime)\n listOfClipsToCombine.append(clip)\n if debug:\n clip.write_videofile(filename)\n lastEndTime = endTime\n i += 1\n\n totalClips = i\n\n if os.path.exists(\"original_without_silence.mp4\") == False:\n finalVideoClips = concatenate_videoclips(listOfClipsToCombine)\n finalVideoClips.write_videofile(\"original_without_silence.mp4\")\n finalVideoClips.close()\n\n videoFile.close()\n silenceToRemoveFile.close()\n\ndef deleteTempFiles():\n for file in os.listdir(os.getcwd()):\n if file.endswith(\".txt\"):\n os.remove(file)\n if file.endswith(\".mp4\"):\n if \"clip\" in file:\n os.remove(file)\n if \"silence\" in file:\n os.remove(file)\n\n\ndef parse_args():\n parser = ArgumentParser(description = 'Remove os pedaços silenciosos do video.')\n parser.add_argument('file', help = 'arquivo de vídeo mp4')\n parser.add_argument('-r', action = 'store', dest = 'rs', type = int, default = 900, required = False,\n help = 'limiar que demarca intensidade de silêncio')\n parser.add_argument('-t', action = 'store', dest = 'ts', type = int, default = 250, required = False,\n help = 'tempo mínimo de silêncio em milissegundos')\n parser.add_argument('--d', action = 'store_true', dest = 'debug', required = False, help = 'mode debug')\n \n return parser.parse_args()\n\n\ndef main():\n arguments = parse_args()\n \n if not os.path.exists(arguments.file):\n print(f'{arguments.file} não existe.')\n return\n \n #initializeMoviePy() # TODO: descomentar e adc flag no settings para só executar isso na 1a vez\n\t\n\t# define o caminho do imagehack em runtime\n config_defaults.IMAGEMAGICK_BINARY = get_image_magick_executable()\n \n identifySilenceMomentsOfVideo(arguments.file, arguments.rs, arguments.ts, debug = arguments.debug)\n\n #essa função abaixo clipa o vídeo original passado por parâmetro de acordo com a informação de silêncio no arquivo de log\n clipSilenceBasedOnTxtFile(\"pythonfazpramim1-2.mp4\", \"silenceToRemoveCOPY.txt\", debug = arguments.debug)\n\n deleteTempFiles()\n\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 8442, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "subprocess.call", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 17, "usage_type": "attribute"}, {"api_name": "subprocess.call", "line_number": 20, "usage_type": "call"}, {"api_name": "pydub.AudioSegment.from_file", "line_number": 30, "usage_type": "call"}, {"api_name": "pydub.AudioSegment", "line_number": 30, "usage_type": "name"}, {"api_name": "termcolor.colored", "line_number": 43, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "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.path.exists", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "shutil.copyfile", "line_number": 109, "usage_type": "call"}, {"api_name": "termcolor.colored", "line_number": 115, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path", "line_number": 151, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 160, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 160, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 162, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 165, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 167, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 171, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 185, "usage_type": "call"}, {"api_name": "os.path", "line_number": 185, "usage_type": "attribute"}, {"api_name": "moviepy.config_defaults.IMAGEMAGICK_BINARY", "line_number": 192, "usage_type": "attribute"}, {"api_name": "moviepy.config_defaults", "line_number": 192, "usage_type": "name"}, {"api_name": "load_im.get_image_magick_executable", "line_number": 192, "usage_type": "call"}]} +{"seq_id": "87151561", "text": "\r\n#import matplotlib.pyplot as plt\r\n#from matplotlib.pyplot import *\r\n#import matplotlib.patches as mpatches #for stack plots\r\n\r\n#from Inputs import *\r\n#from Inputs_ElecRate import *\r\n##from Inputs_Energy import *\r\nfrom Housing_Class1 import *\r\n#from Appliances_Class import *\r\n#==============================================================================\r\nimport numpy as np\r\nimport copy\r\nimport datetime\r\n\r\naggregageDevices = {} # indexed by year and device type holds the number of devices of that type\r\n\r\ndef getDead(p1_homes, homeType, fuelName, k, yr):\r\n\r\n return sum([homes.devices[0].annualreplacement(yr) for homes in p1_homes if (homes.cznum ==k and homes.type == homeType and homes.devices[0].fuel.name== fuelName)])\r\n\r\ndef computeInitialSnapshot():\r\n \r\n HomesSnapShot = {}\r\n year = PastPastYear\r\n \r\n for k in range(1,Numcz+1): # number of CZs\r\n print('BeginInitstamp: {:%Y-%m-%d %H:%M:%S}'.format(datetime.datetime.now())) \r\n #for i in range(0,1):\r\n p_homes = []\r\n\r\n hhsize = cz[k,ThisYear].HHsize * samplesize * 10**6 #number of households today in cz j #samplesize defined in Inputs.py\r\n num_heaters = HH_withHeat[k,ThisYear] #% of heating in CZ k (current saturation)\r\n num_coolers = HH_withCool[k,ThisYear] #current cooling saturation per CZ\r\n#=============Below stock with heating==================================== ##==============================================================================\r\n Stck_SFNG = hhsize * P1 * num_heaters #P1 is the % of SF, hese are houses with Heating and cooling (NG based heating P1 is assinged in Inputs.py\r\n Stck_SFER = hhsize * P2 * num_heaters # P2 is %of ER houses and this is STck_SFER are the #of homes iwth ER based heating can have cooling too\r\n #==============Below Stock wiht cooling ===================================================\r\n Stck_SFNGCool = int(round(hhsize * num_coolers )) #Houses with cooling \r\n # Stck_SFERCool = int(round(SFShare * hhsize * P2* num_coolers )) #houes with cooling\r\n##==============================================================================\r\n# hhtotal += Stck_SFNG + Stck_SFER + Stck_MFNG+ Stck_MFER\r\n # print \"initinit\", k, year, hhsize, P1, P2, num_heaters, num_coolers, Stck_SFNG, Stck_SFER, Stck_SFNGCool\r\n is_new = False # old homes are created here.\r\n r0 = R0present\r\n r1 = R1present\r\n r2 = R2present\r\n\r\n NG1 = Device(\"NGH\", NG, NG0_EF, 0,k, size1,size2,r0,r1,r2, year , NG_LT, NGIC, OM_NG)\r\n E1 = Device(\"ERH\", Elec, E0_EF,0, k, size1,size2, r0,r1,r2, year ,EL_LT, ERIC, OM_EL)\r\n E1_Cool = Device( \"Cooler\", Elec, 0, AC0_EF, k,size1,size2,r0,r1,r2, year, EL_LT ,ACIC, OM_EL, True, Ref1)\r\n \r\n NGHeat1 = [NG1] # NG Heating alone\r\n NGHeatCool1 = [NG1,E1_Cool] #NG Heating and A.C Cooling\r\n\r\n ERHeat1 = [E1] #ERW heating\r\n #ERHeatCool1 = [E1,E1_Cool]\r\n\r\n SFNG_HH = int(round(Stck_SFNG - Stck_SFNGCool) ) # Houses with just NG heating\r\n SFER_HH = int(round(Stck_SFER) ) # Houses with just ER heating\r\n homesL1 = []\r\n for _ in range(SFNG_HH):\r\n homesL1.append(SFHomes(\"SFNG\",SFNG_HH,k, size1, size2, year, NGHeat1,False)) #is_new== False, means an old home.,,not used\r\n\r\n for _ in range(Stck_SFNGCool):\r\n homesL1.append(SFHomes(\"SFNGCool\",Stck_SFNGCool, k,size1, size2, year,NGHeatCool1,False))\r\n \r\n for _ in range(SFER_HH):\r\n homesL1.append(SFHomes(\"SFER\",SFER_HH, k,size1, size2, year,ERHeat1,False))\r\n\r\n \r\n p_homes.extend(homesL1)\r\n\r\n if (year, k) in HomesSnapShot:\r\n HomesSnapShot[(year,k)] = updateHomeStats(HomesSnapShot[(year, k)], HomesStats(year, k, copy.copy(p_homes)))\r\n else:\r\n HomesSnapShot[(year,k)] = HomesStats(year, k, copy.copy(p_homes))\r\n # print \"Initialization\", k, year, hhsize, Stck_SFNG, Stck_SFER, \"..\",SFNG_HH, Stck_SFNGCool, SFER_HH\r\n\r\n # SnapShotYear = HomesSnapShot[(year,k)]\r\n\r\n # if (year, k) not in HomesSnapShot:\r\n # continue\r\n\r\n # for home in HomesSnapShot[(year,k)].homes:\r\n # hometype = home.type\r\n # dev = home.devices\r\n\r\n # if home.type not in HomesSnapShot[(year,k)].aggregateHomeStats:\r\n # HomesSnapShot[(year,k)].aggregateHomeStats[hometype] = AggregateStats()\r\n\r\n # HomesSnapShot[(year,k)].aggregateHomeStats[hometype].num += 1\r\n # HomesSnapShot[(year,k)].aggregateHomeStats[hometype].numdev = len(dev)\r\n # engheat = home.HHenergyUsage_BTU()[0]/kWh_BTU #heating energy\r\n # engcool = home.HHenergyUsage_BTU()[1]/kWh_BTU # cooling energy\r\n\r\n # hhng = home.HHenergyUsage_units()[0] #NG usage in kWh\r\n # hhelec = home.HHenergyUsage_units()[1] #electricity usage\r\n # hhemis1 = home.HHemissions(year)[0]\r\n # hhemis2 = home.HHemissions(year)[1]\r\n # hhengcost1 = home.HHEnergyCost(year)[0] #NG Cost\r\n # hhengcost2 = home.HHEnergyCost(year)[1]\r\n # hhcapcost = home.HHDevicesCapCost(year)\r\n \r\n\r\n # # print \"mid test\", k,year, hometype, hhng,hhelec, engheat, engcool, hhemis1, hhemis2\r\n\r\n # cnt = HomesSnapShot[(year,k)].aggregateHomeStats[hometype].num\r\n # HomesSnapShot[(year,k)].aggregateHomeStats[hometype].eng1 += hhng\r\n # HomesSnapShot[(year,k)].aggregateHomeStats[hometype].eng2 += hhelec\r\n # HomesSnapShot[(year,k)].aggregateHomeStats[hometype].heateng += engheat\r\n # HomesSnapShot[(year,k)].aggregateHomeStats[hometype].cooleng += engcool\r\n # HomesSnapShot[(year,k)].aggregateHomeStats[hometype].emis1 += hhemis1\r\n # HomesSnapShot[(year,k)].aggregateHomeStats[hometype].emis2 += hhemis2\r\n # HomesSnapShot[(year,k)].aggregateHomeStats[hometype].engCost1 += hhengcost1\r\n # HomesSnapShot[(year,k)].aggregateHomeStats[hometype].engCost2 += hhengcost2\r\n # HomesSnapShot[(year,k)].aggregateHomeStats[hometype].replaceCost += hhcapcost\r\n \r\n # for d in dev:\r\n # dname = d.name\r\n # devcnt = getDeviceCountinHome(HomesSnapShot[(year,k)], dname, hometype, year)\r\n # HomesSnapShot[(year,k)].aggregateHomeStats[hometype].aggDevices[year, dname] = devcnt\r\n # devstypes = getDevices(HomesSnapShot[(year,k)], hometype)\r\n # #devsyear = getDevicesYear(HomesSnapShot[(year,k)], dname, year)\r\n # devcountyear = getDeviceCountYear(HomesSnapShot[(year,k)], dname,year)\r\n # # print \"Dev stats\", year,cnt, hometype,dname, devcnt, devcountyear\r\n\r\n return HomesSnapShot\r\n\r\n", "sub_path": "Initialization.py", "file_name": "Initialization.py", "file_ext": "py", "file_size_in_byte": 6866, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "datetime.datetime.now", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 28, "usage_type": "attribute"}, {"api_name": "copy.copy", "line_number": 75, "usage_type": "call"}, {"api_name": "copy.copy", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "473145016", "text": "#!/usr/bin/env python\n# encoding: utf-8\nimport os\n\nimport requests\nfrom flask import Blueprint, current_app, request, send_from_directory, url_for\nfrom werkzeug.utils import secure_filename\n\nimport application.models as Models\n\nupload_bp = Blueprint('upload', __name__, url_prefix='/uploads')\n\n\nALLOWED_EXTENSIONS = set(['png', 'jpg', 'jpeg', 'gif'])\n\n\ndef allowed_file(filename):\n return '.' in filename and \\\n filename.rsplit('.', 1)[1] in ALLOWED_EXTENSIONS\n\n\ndef upload_to_sm(local_path):\n url = 'https://sm.ms/api/upload'\n files = {'smfile': open(local_path, 'rb')}\n r = requests.post(url, files=files)\n data1 = eval(r.text.encode('utf-8'))\n url1 = data1['data']['url']\n return url1\n\n\n@upload_bp.route('/', methods=['GET', 'POST'])\ndef upload_file():\n if request.method == 'POST':\n # check if the post request has the file part\n if 'uploadimage' not in request.files:\n return \"no upload images\"\n file = request.files['uploadimage']\n # if user does not select file, browser also\n # submit a empty part without filename\n if file.filename == '':\n return \"no filenames\"\n if file and allowed_file(file.filename):\n filename = secure_filename(file.filename)\n local_path = os.path.join(current_app.config['UPLOAD_FOLDER'],\n filename)\n file.save(local_path)\n url = upload_to_sm(local_path)\n Models.Upload(filename=file.filename,\n local_path=local_path,\n url=url).save()\n return '\"{}\"'.format(url_for('upload.uploaded_file',\n filename=filename))\n\n\n@upload_bp.route('/<filename>')\ndef uploaded_file(filename):\n return send_from_directory(current_app.config['UPLOAD_FOLDER'], filename)\n", "sub_path": "application/controllers/upload.py", "file_name": "upload.py", "file_ext": "py", "file_size_in_byte": 1880, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "flask.Blueprint", "line_number": 11, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 33, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 35, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 35, "usage_type": "name"}, {"api_name": "flask.request.files", "line_number": 37, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 37, "usage_type": "name"}, {"api_name": "werkzeug.utils.secure_filename", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask.current_app.config", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 44, "usage_type": "name"}, {"api_name": "application.models.Upload", "line_number": 48, "usage_type": "call"}, {"api_name": "application.models", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.url_for", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.send_from_directory", "line_number": 57, "usage_type": "call"}, {"api_name": "flask.current_app.config", "line_number": 57, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 57, "usage_type": "name"}]} +{"seq_id": "505521918", "text": "\"\"\"\r\n=============================================================================\r\nTitle : Useful functions for general data handling\r\nProject : PeakBlock\r\nFile : data_handling.py\r\n-----------------------------------------------------------------------------\r\n\r\n Description:\r\n\r\n This file contains important functions for general data handling\r\n\r\n\r\n-----------------------------------------------------------------------------\r\nMajor Revisions:\r\n Date Version Name Description\r\n 28-Mar-2020 1.0 Ramy First iteration of the script\r\n\"\"\"\r\n# Python library import\r\nfrom collections import OrderedDict\r\nimport pandas as pd\r\n\r\n\r\ndef fill_nan(old_data_panel, method):\r\n \"\"\"\r\n fill NaN values in a pandas Panel\r\n :param pandas Panel, old_data_panel: pandas Panel with NaN\r\n :param method: string, method for filling (\"ffill\", \"bfill\", etc...)\r\n :return: new filled panel\r\n \"\"\"\r\n new_object = OrderedDict()\r\n for item in old_data_panel.items:\r\n new_object[item] = old_data_panel[item].fillna(method=method)\r\n new_panel = pd.Panel(new_object)\r\n\r\n return new_panel\r\n", "sub_path": "Utilities/data_handling.py", "file_name": "data_handling.py", "file_ext": "py", "file_size_in_byte": 1155, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "collections.OrderedDict", "line_number": 30, "usage_type": "call"}, {"api_name": "pandas.Panel", "line_number": 33, "usage_type": "call"}]} +{"seq_id": "186410046", "text": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# %matplotlib inline\n\ndf = pd.read_csv(\"./dataset_LP_1.csv\", header=None)\n\n\ndef train_test_split(dataframe, split=0.70):\n train_size = int(split * len(dataframe))\n test_size = len(dataframe) - train_size\n dataframe = dataframe.sample(frac=1, random_state=69)\n dataframe.insert(0, 0, np.ones(dataframe.shape[0]), True)\n train = dataframe[:train_size].to_numpy()\n test = dataframe[-test_size:].to_numpy()\n x_train = train[:, :-1]\n y_train = 2 * train[:, -1] - 1\n x_test = test[:, :-1]\n y_test = 2 * test[:, -1] - 1\n return x_train, y_train, x_test, y_test\n\n\ndef perceptron(x_train, y_train, learning_rate=1, epochs=1000000):\n np.random.seed(1)\n weights = np.random.rand(x_train.shape[1])\n cost = 0\n for iter in range(epochs):\n if iter % 25000 == 0:\n print(\"Currently at : \", iter)\n y_predict = 2 * (x_train.dot(weights) > 0) - 1\n misclassified = y_predict != y_train\n cost = misclassified.sum()\n if cost == 0:\n return weights, cost\n weights = weights + learning_rate * (\n x_train[misclassified, :][0] * y_train[misclassified][0]\n )\n return weights, cost\n\n\ndef plot(x_train, y_train):\n fig = plt.figure()\n ax = fig.add_subplot(111, projection=\"3d\")\n ax.scatter(\n x_train[y_train == 1, 1],\n x_train[y_train == 1, 2],\n x_train[y_train == 1, 3],\n color=\"red\",\n )\n ax.scatter(\n x_train[y_train == -1, 1],\n x_train[y_train == -1, 2],\n x_train[y_train == -1, 3],\n color=\"blue\",\n )\n xx, yy = np.meshgrid(range(-10, 10), range(-10, 10))\n zz = (-weights[1] * xx - weights[2] * yy - weights[0]) * 1.0 / weights[3]\n ax.plot_surface(xx, yy, zz, color=\"green\")\n plt.show()\n\n\ndef predict(x_test, y_test, weights):\n y_predict = 2 * (x_test.dot(weights) > 0) - 1\n misclassified = y_predict != y_test\n cost = misclassified.sum()\n accuracy = 1 - cost / y_test.shape[0]\n testing_accuracy = accuracy * 100\n print(\"Testing Accuracy: \", testing_accuracy)\n\n\nx_train, y_train, x_test, y_test = train_test_split(df, 0.70)\nweights, cost = perceptron(x_train, y_train, learning_rate=1, epochs=1000000)\nweights = weights / np.linalg.norm(weights)\nprint(weights)\nprint(\"Training Accuracy : \", (1 - cost / x_train.shape[0]) * 100)\npredict(x_test, y_test, weights)\nplot(x_train, y_train)\nplot(x_test, y_test)\n", "sub_path": "Linear Perceptron/linearperceptron.py", "file_name": "linearperceptron.py", "file_ext": "py", "file_size_in_byte": 2481, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "pandas.read_csv", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 25, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 26, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 43, "usage_type": "name"}, {"api_name": "numpy.meshgrid", "line_number": 57, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "numpy.linalg.norm", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 74, "usage_type": "attribute"}]} +{"seq_id": "270049302", "text": "from face_detection.Rpn import RPN\nimport torch \nfrom torch import nn \nfrom torchvision.models import vgg16\nfrom torchvision.ops import RoIPool\n\nfrom face_detection.Config import cig\nfrom face_detection.utils.initialize import init_with_normal\nfrom face_detection.utils.transform import safe_to_tensor\nfrom face_detection.AbstractFasterRcnn import AbstractFasterRcnn\n\n# decomponent vgg16 into extractor and classifier\ndef get_component_from_vgg16(pretrained : bool):\n model = vgg16(pretrained=pretrained) # download may take some time if this line is execuated initially\n \"\"\"\n model.features:\n Sequential(\n (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n (1): ReLU(inplace=True)\n (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n (3): ReLU(inplace=True)\n (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n (6): ReLU(inplace=True)\n (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n (8): ReLU(inplace=True)\n (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n (11): ReLU(inplace=True)\n (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n (13): ReLU(inplace=True)\n (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n (15): ReLU(inplace=True)\n (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n (18): ReLU(inplace=True)\n (20): ReLU(inplace=True)\n (22): ReLU(inplace=True)\n (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n (25): ReLU(inplace=True)\n (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n (27): ReLU(inplace=True)\n (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n (29): ReLU(inplace=True)\n (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n )\n\n model.classifier:\n Sequential(\n (0): Linear(in_features=25088, out_features=4096, bias=True)\n (1): ReLU(inplace=True)\n (2): Dropout(p=0.5, inplace=False)\n (3): Linear(in_features=4096, out_features=4096, bias=True)\n (4): ReLU(inplace=True)\n (5): Dropout(p=0.5, inplace=False)\n (6): Linear(in_features=4096, out_features=1000, bias=True)\n )\n \"\"\"\n \n extractor = list(model.features)[:-1] # drop the last pooling\n classifier = list(model.classifier)[:-1] # we will redefine the softmax\n\n # the pre 4 layers don't need to be updated\n for layer in extractor[:cig.freeze_layer_num]:\n for param in layer.parameters():\n param.requires_grad = False\n \n # drop all the drop layer if cig.use_classifier_drop is False\n if not cig.use_classifier_drop:\n classifier = [layer for layer in classifier if not isinstance(layer, nn.Dropout)]\n \n # transform to sequence\n extractor = nn.Sequential(*extractor)\n classifier = nn.Sequential(*classifier)\n # component = {\n # \"extractor\" : extractor,\n # \"classifier\" : classifier\n # }\n return extractor, classifier\n\nclass RoIHead(nn.Module):\n def __init__(self, n_class : int, roi_size : int, spatial_scale, classifier):\n \"\"\"\n Args:\n - n_class : class number of foreground\n - roi_size : size of the roi after the RoI pooling, which si assigned as 7 in the original paper\n - spatial_scale\n \"\"\"\n super().__init__()\n self.n_class = n_class\n self.roi_size = roi_size\n self.spatial_scale = spatial_scale\n self.roi = RoIPool(\n output_size=[self.roi_size, self.roi_size], \n spatial_scale=self.spatial_scale\n )\n\n self.classifier = classifier\n self.cls_loc = nn.Linear(4096, n_class * 4)\n self.score = nn.Linear(4096, n_class)\n\n init_with_normal(self.cls_loc, 0, 0.001)\n init_with_normal(self.score, 0, 0.01)\n\n def forward(self, x : torch.Tensor, rois : torch.Tensor, roi_indices : torch.Tensor):\n roi_indices = safe_to_tensor(roi_indices).float()\n rois = safe_to_tensor(rois).float()\n indices_and_rois = torch.cat([roi_indices[:, None], rois], dim=1)\n\n # xy_indices_and_rois = indices_and_rois[:, [0, 2, 1, 4, 3]]\n # indices_and_rois = xy_indices_and_rois.contiguous()\n # RoI pooling\n pool = self.roi(x, indices_and_rois)\n pool = pool.view(pool.size(0), -1)\n fc7 = self.classifier(pool)\n roi_cls_locs = self.cls_loc(fc7)\n roi_scores = self.score(fc7)\n return roi_cls_locs, roi_scores\n\nclass FasterRcnn(AbstractFasterRcnn):\n \"\"\"\n this the final module of the whole model, which is derived from AbstractFasterRcnn\n Args:\n - n_fg_class(int) : number of class of foreground items\n - ratios(list) : ratio of width and height of the generated anchor base\n - anchor_scales(list) : scale from the base bbox\n \"\"\"\n def __init__(self, n_fg_class : int = 1, ratios : list = [0.5, 1, 2], anchor_scales : list = [8, 16, 32]):\n self.stride = 16\n extractor, classifier = get_component_from_vgg16(pretrained=True)\n\n rpn = RPN(\n input_channel=512,\n output_channel=512,\n ratios=ratios,\n anchor_scales=anchor_scales,\n stride=self.stride\n )\n\n roi_head = RoIHead(\n n_class=n_fg_class + 1, # 1 is background\n roi_size=7,\n spatial_scale=1. / self.stride,\n classifier=classifier\n )\n\n super().__init__(\n extractor=extractor,\n rpn=rpn,\n roi_head=roi_head\n )\n ", "sub_path": "face_detection/FasterRcnn.py", "file_name": "FasterRcnn.py", "file_ext": "py", "file_size_in_byte": 6192, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "torchvision.models.vgg16", "line_number": 14, "usage_type": "call"}, {"api_name": "face_detection.Config.cig.freeze_layer_num", "line_number": 65, "usage_type": "attribute"}, {"api_name": "face_detection.Config.cig", "line_number": 65, "usage_type": "name"}, {"api_name": "face_detection.Config.cig.use_classifier_drop", "line_number": 70, "usage_type": "attribute"}, {"api_name": "face_detection.Config.cig", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 71, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "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": "torchvision.ops.RoIPool", "line_number": 94, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 100, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 100, "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": "face_detection.utils.initialize.init_with_normal", "line_number": 103, "usage_type": "call"}, {"api_name": "face_detection.utils.initialize.init_with_normal", "line_number": 104, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 106, "usage_type": "attribute"}, {"api_name": "face_detection.utils.transform.safe_to_tensor", "line_number": 107, "usage_type": "call"}, {"api_name": "face_detection.utils.transform.safe_to_tensor", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 109, "usage_type": "call"}, {"api_name": "face_detection.AbstractFasterRcnn.AbstractFasterRcnn", "line_number": 121, "usage_type": "name"}, {"api_name": "face_detection.Rpn.RPN", "line_number": 133, "usage_type": "call"}]} +{"seq_id": "532263370", "text": "\"\"\"\nView wrappers\n\"\"\"\nfrom inspect import getmembers\n\nfrom sqlalchemy import (\n Column,\n MetaData,\n Table,\n)\nfrom sqlalchemy.ext import compiler\nfrom sqlalchemy.schema import DDLElement\n\n\nclass View:\n \"\"\"Base class for Views.\"\"\"\n\n @staticmethod\n def _make_table(name, selectable, pkeys):\n \"\"\"Create a view.\n\n :param name: The name of the view.\n :param selectable: SQLAlchemy selectable.\n :param pkeys: set of primary keys for the selectable.\n \"\"\"\n columns = [Column(c.name, c.type, primary_key=(c.name in pkeys)) for c in selectable.subquery().columns]\n # We do not use the metadata object from model.mapping.py that contains all the Table objects\n # because that would create a circular import (create_view is called from View objects\n # in model.view; but those View objects are imported into model.mapping.py where the\n # metadata object we need is defined). Thus, we do not use the after_create/before_drop\n # hooks to automate creating/dropping views. Instead, this is taken care of in install_views().\n\n # The metadata object passed to Table() should be empty: this table is internal to a View\n # object and is not intended to be created in the database.\n return Table(name, MetaData(), *columns)\n\n\nclass CreateView(DDLElement):\n def __init__(self, name, selectable):\n self.name = name\n self.selectable = selectable\n\n\nclass DropView(DDLElement):\n def __init__(self, name):\n self.name = name\n\n\n@compiler.compiles(CreateView)\ndef compile_create_view(element, compiler, **kw):\n compiled_selectable = compiler.sql_compiler.process(element.selectable, literal_binds=True)\n return f\"CREATE VIEW {element.name} AS {compiled_selectable}\"\n\n\n@compiler.compiles(DropView)\ndef compile_drop_view(element, compiler, **kw):\n return f\"DROP VIEW IF EXISTS {element.name}\"\n\n\ndef is_view_model(o):\n return hasattr(o, \"__view__\") and issubclass(o, View)\n\n\ndef install_views(engine):\n import galaxy.model.view\n\n views = getmembers(galaxy.model.view, is_view_model)\n for _, view in views:\n # adding DropView here because our unit-testing calls this function when\n # it mocks the app and CreateView will attempt to rebuild an existing\n # view in a database that is already made, the right answer is probably\n # to change the sql that gest emitted when CreateView is rendered.\n with engine.begin() as conn:\n conn.execute(DropView(view.name))\n conn.execute(CreateView(view.name, view.__view__))\n", "sub_path": "lib/galaxy/model/view/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 2598, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "sqlalchemy.Column", "line_number": 26, "usage_type": "call"}, {"api_name": "sqlalchemy.Table", "line_number": 35, "usage_type": "call"}, {"api_name": "sqlalchemy.MetaData", "line_number": 35, "usage_type": "call"}, {"api_name": "sqlalchemy.schema.DDLElement", "line_number": 38, "usage_type": "name"}, {"api_name": "sqlalchemy.schema.DDLElement", "line_number": 44, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.compiler.sql_compiler.process", "line_number": 51, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.compiler.sql_compiler", "line_number": 51, "usage_type": "attribute"}, {"api_name": "sqlalchemy.ext.compiler", "line_number": 51, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.compiler.compiles", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.compiler", "line_number": 49, "usage_type": "name"}, {"api_name": "sqlalchemy.ext.compiler.compiles", "line_number": 55, "usage_type": "call"}, {"api_name": "sqlalchemy.ext.compiler", "line_number": 55, "usage_type": "name"}, {"api_name": "inspect.getmembers", "line_number": 67, "usage_type": "call"}, {"api_name": "galaxy.model.view.model", "line_number": 67, "usage_type": "attribute"}, {"api_name": "galaxy.model.view", "line_number": 67, "usage_type": "name"}]} +{"seq_id": "1697327", "text": "import numpy as np\nimport pandas as pd\nimport scipy.stats\n\ndef log_param(est_mean, est_std):\n miu = np.log(est_mean) - 0.5*np.log(1+((est_std**2)/est_mean))\n std = np.sqrt(np.log(1+((est_std**2)/est_mean)))\n return miu, std\n\n\nclass SPV:\n def __init__(self, simu_frequency, drug_compounds, liaStruct):\n \"\"\"\n expected # of drugs approved and total valuation at given time frequency\n if self.simulation_frequency = 2, it counts every 6 months\n\n drug_compounds = [ (drug_type, num) ... ()]\n\n liaStruct is assumed to contain principal percentage, rate (annual) and duration (year) for each tranche\n e.g. [[0.9, 0.05, 4], [0.1, 0.08, 8]]\n \"\"\"\n self.successCountSeries = []\n self.valuationSeries = []\n self.budgeSeries = []\n\n self.budget = 0\n\n self.simulation_frequency = simu_frequency\n\n self.drugMixedCompounds = []\n for (drugType, num) in drug_compounds:\n self.drugMixedCompounds.append(Drug_compounds(drugType, num))\n\n self.calcBudget()\n self.liability = Liability(self.budget, liaStruct)\n\n self.meanIRR = [0,0,0]\n\n\n \"\"\"calculates budget of SPV i.e. SPV's total liability to investors\"\"\"\n \"\"\"A generic drug compounds takes 690M out-of-pocket; oncology takes 1.2 times more\"\"\"\n \"\"\"the expected development cost for a generic drug compound is 263; 315.6 for oncology compounds\"\"\"\n \"\"\"estimate 80percentile as starting budget\"\"\"\n def calcBudget(self):\n for drug in self.drugMixedCompounds:\n if drug.type == 7:\n self.budget = self.budget + 420 * drug.number\n else:\n self.budget = self.budget + 300 * drug.number #415\n\n\n \"\"\"\n @:param drug: user_defined drug_compound object; contains drug state and drug type\n @:param num_drug: # of drugs in simulation\n @:param transitionProb: user_defined transitionProb object\n @:param simu_time: number of time units (quarter) simulated in each simulation\n @:param simu_num: total number of simulation to run \n corr_val: correlation level in compound valuation \n corr_appro: correlation level in drug success\n \"\"\"\n\n\n def simulate(self, sim_num, simu_time=48, corrSuccessRate=0, corrValuation=0):\n print(\"\\ncorrelation in success: {}, correlation in valuation: {}\".format(corrSuccessRate, corrValuation))\n\n totalDevCost = 0\n count_frequency = self.simulation_frequency\n\n totalSuccessCount = [0 for i in range(simu_time//count_frequency)] # counts average # of approved drugs\n totalApproValue = [0 for i in range(simu_time//count_frequency)] # records cash generated from approved drugs\n budgetSeries = [0 for i in range(simu_time//count_frequency)]\n\n for n in range(sim_num):\n \"\"\"reset for each new simulation\"\"\"\n # reset drug compound state\n for drugCompounds in self.drugMixedCompounds:\n drugCompounds.reset()\n\n self.liability.reset()\n\n self.budget = 0\n self.calcBudget()\n\n\n successCountSeries = [] # counts cumu # of approved drugs at a given time in each simulation\n approValueSeries = [] # records valuation of all approved drugs at a given time in each simulation\n bgtSeries = []\n\n successCount = 0\n approValue = 0\n for t in range(simu_time):\n for drugCompounds in self.drugMixedCompounds:\n succCount, approvValue, bal, devCost = drugCompounds.test(balance=self.budget,\n corrSuccess=corrSuccessRate,\n corrVal=corrValuation, )\n successCount = successCount + succCount\n approValue = approValue + approvValue\n self.budget = bal\n totalDevCost += devCost\n\n if (t+1) % self.liability.couponFrequency == 0:\n # check if enough funds for coupon payment; if not, sell off compounds\n self.budget = self.drugMixedCompounds[0].ICtest(self.budget, self.liability.getExpectedPmt())\n\n cash = min(self.budget, self.liability.getExpectedPmt())\n self.budget -= cash # deducts payment\n # print(\"cash: {}\".format(cash))\n extraCash = self.liability.pay(cash)\n self.budget += extraCash\n\n # records # approved and valuation at given time frequency\n if (t+1) % count_frequency == 0: # every 6 months; (t+1) adjusts overcounting\n successCountSeries.append(successCount)\n approValueSeries.append(approValue)\n bgtSeries.append(self.budget)\n\n \"\"\"after each simulation\"\"\"\n # sum of values for all simulations\n totalSuccessCount = [sum(x) for x in zip(totalSuccessCount, successCountSeries)]\n totalApproValue = [sum(x) for x in zip(totalApproValue, approValueSeries)]\n budgetSeries = [sum(x) for x in zip(budgetSeries, bgtSeries)]\n # calculates IRR\n IRR = self.liability.calcIRR()\n # print(self.liability.sheet[0][4]) # retreieve payment series\n # print(self.liability.sheet[1][4])\n # print(self.liability.equitySeries)\n # print(\"IRR: {}\".format(IRR))\n if IRR[-1] is np.nan:\n IRR[-1] = -1\n self.meanIRR = np.add(self.meanIRR, IRR)\n\n\n # print(\"equity series: {}\".format(self.liability.equitySeries))\n\n\n\n # average # of drugs approved given all simulation cases\n self.successCountSeries = np.array(totalSuccessCount) / sim_num\n self.valuationSeries = np.array(totalApproValue) / sim_num\n self.budgeSeries = np.array(budgetSeries) / sim_num\n self.meanIRR = np.array(self.meanIRR) / sim_num\n\n\n\n def get_success_count(self):\n return self.successCountSeries\n\n def get_valuation(self):\n return self.valuationSeries\n\n\n\nclass Drug_compounds:\n\n \"\"\"drug types and num of drugs in total\n drug type is an integer value with the following correspondence\n 0: Other\n 1: Infectious\n 2: Autoimmune\n 3: Endoctrine\n 4: Respiratory\n 5: Neurology\n 6: Cardio\n 7: Oncology\n \"\"\"\n def __init__(self, drug_type_int, num):\n\n self.type = drug_type_int\n self.number = num\n self.drugs_state = [0 for i in range(num)]\n\n self.string = ['Other','Infectious','Autoimmune','Endoctrine','Rspiratory','Neurology','Cardio','Oncology']\n \"\"\"\n phase \n 0: Pre\n 1: I\n 2: II\n 3: III\n 4: NDA\n 5: Approved\n 6: Failed\n [mean, std, cap] for log-normal distribution\n \"\"\"\n \"\"\"param=0\"\"\"\n self.oncology_val_paramByPhase = {0:[2.36, 0.9393, 100], 1:[2.96, 0.9393, 250], 2:[4, 0.939, 500],\n 3:[5.8, 0.939, 1000], 4:[7.35, 0.939, 2500], 5:[7.24, 0.939, 5000]}\n \"\"\"param=1\"\"\"\n self.val_5Pctlower = {0: [2.3, 0.9393, 100], 1: [2.9, 0.9393, 250], 2: [3.95, 0.939, 500],\n 3: [5.7, 0.939, 1000], 4: [7.15, 0.939, 2500], 5: [7.04, 0.939, 5000]}\n \"\"\"param=2\"\"\"\n self.val_10Pctlower = {0: [2.25, 0.9393, 100], 1: [2.84, 0.9393, 250], 2: [3.9, 0.939, 500],\n 3: [5.5, 0.939, 1000], 4: [6.95, 0.939, 2500], 5: [6.84, 0.939, 5000]}\n \"\"\"param=3\"\"\"\n self.val_20Pctlower = {0: [2.15, 0.9393, 100], 1: [2.74, 0.9393, 250], 2: [3.8, 0.939, 500],\n 3: [5, 0.939, 1000], 4: [6.5, 0.939, 2500], 5: [6.44, 0.939, 5000]}\n\n\n \"\"\"total development cost\n [upfront, mean, std, cap] for log-normal distribution \"\"\"\n self.dev_cost_oncology = {0: [3.6, 1.53, 0.79, 20], 1: [11.3, 2.72, 0.73, 50], 2: [30, 3.65, 0.79, 120],\n 3: [112.6, 5.1, 0.63, 500]}\n self.dev_cost = {0: [3.6, 1, 0.79, 20], 1: [11.3, 2, 0.73, 50], 2: [30, 3, 0.79, 120],\n 3: [112.6, 4.55, 0.63, 500]}\n \"\"\"\n transition matrix by drug type\n each simulation time unit is a quarter (3 months)\n [Pre, I, II, III, NDA, Yes, No]\n \"\"\"\n self.Other = [[0.658, 0.254, 0.000, 0.000, 0.000, 0.000, 0.089],\n [0.0, 0.848, 0.12, 0.000, 0.000, 0.000, 0.032],\n [0.0, 0.000, 0.895, 0.044, 0.000, 0.000, 0.061],\n [0.0, 0.000, 0.000, 0.883, 0.091, 0.000, 0.026],\n [0.0, 0.000, 0.000, 0.000, 0.649, 0.304, 0.047],\n [0.0, 0.000, 0.000, 0.000, 0.000, 1.000, 0.000],\n [0.0, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000]]\n self.Infectious = [[0.658, 0.254, 0.000, 0.000, 0.000, 0.000, 0.089],\n [0.0, 0.857, 0.102, 0.000, 0.000, 0.000, 0.041],\n [0.0, 0.000, 0.896, 0.046, 0.000, 0.000, 0.058],\n [0.0, 0.000, 0.000, 0.890, 0.078, 0.000, 0.032],\n [0.0, 0.000, 0.000, 0.000, 0.621, 0.343, 0.036],\n [0.0, 0.000, 0.000, 0.000, 0.000, 1.000, 0.000],\n [0.0, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000]]\n self.Autoimmune = [[0.658, 0.254, 0.000, 0.000, 0.000, 0.000, 0.089],\n [0.0, 0.854, 0.108, 0.000, 0.000, 0.000, 0.038],\n [0.0, 0.000, 0.889, 0.031, 0.000, 0.000, 0.080],\n [0.0, 0.000, 0.000, 0.886, 0.085, 0.000, 0.029],\n [0.0, 0.000, 0.000, 0.000, 0.649, 0.303, 0.048],\n [0.0, 0.000, 0.000, 0.000, 0.000, 1.000, 0.000],\n [0.0, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000]]\n self.Endoctrine = [[0.658, 0.254, 0.000, 0.000, 0.000, 0.000, 0.089],\n [0.0, 0.863, 0.084, 0.000, 0.000, 0.000, 0.053],\n [0.0, 0.000, 0.889, 0.031, 0.000, 0.000, 0.080],\n [0.0, 0.000, 0.000, 0.888, 0.083, 0.000, 0.030],\n [0.0, 0.000, 0.000, 0.000, 0.606, 0.363, 0.031],\n [0.0, 0.000, 0.000, 0.000, 0.000, 1.000, 0.000],\n [0.0, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000]]\n self.Respiratory = [[0.658, 0.254, 0.000, 0.000, 0.000, 0.000, 0.089],\n [0.0, 0.856, 0.104, 0.000, 0.000, 0.000, 0.040],\n [0.0, 0.000, 0.881, 0.024, 0.000, 0.000, 0.095],\n [0.0, 0.000, 0.000, 0.891, 0.074, 0.000, 0.035],\n [0.0, 0.000, 0.000, 0.000, 0.480, 0.511, 0.009],\n [0.0, 0.000, 0.000, 0.000, 0.000, 1.000, 0.000],\n [0.0, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000]]\n self.Neurology = [[0.658, 0.254, 0.000, 0.000, 0.000, 0.000, 0.089],\n [0.0, 0.860, 0.093, 0.000, 0.000, 0.000, 0.046],\n [0.0, 0.000, 0.885, 0.027, 0.000, 0.000, 0.088],\n [0.0, 0.000, 0.000, 0.893, 0.069, 0.000, 0.038],\n [0.0, 0.000, 0.000, 0.000, 0.639, 0.319, 0.043],\n [0.0, 0.000, 0.000, 0.000, 0.000, 1.000, 0.000],\n [0.0, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000]]\n self.Cardio = [[0.658, 0.254, 0.000, 0.000, 0.000, 0.000, 0.089],\n [0.0, 0.862, 0.089, 0.000, 0.000, 0.000, 0.049],\n [0.0, 0.000, 0.880, 0.023, 0.000, 0.000, 0.098],\n [0.0, 0.000, 0.000, 0.896, 0.056, 0.000, 0.048],\n [0.0, 0.000, 0.000, 0.000, 0.624, 0.339, 0.037],\n [0.0, 0.000, 0.000, 0.000, 0.000, 1.000, 0.000],\n [0.0, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000]]\n self.Oncology = [[0.658, 0.254, 0.000, 0.000, 0.000, 0.000, 0.089],\n [0.0, 0.859, 0.097, 0.000, 0.000, 0.000, 0.044],\n [0.0, 0.000, 0.882, 0.025, 0.000, 0.000, 0.093],\n [0.0, 0.000, 0.000, 0.896, 0.045, 0.000, 0.059],\n [0.0, 0.000, 0.000, 0.000, 0.642, 0.314, 0.044],\n [0.0, 0.000, 0.000, 0.000, 0.000, 1.000, 0.000],\n [0.0, 0.000, 0.000, 0.000, 0.000, 0.000, 1.000]]\n self.tranP = [self.Other, self.Infectious, self.Autoimmune, self.Endoctrine,\n self.Respiratory, self.Neurology, self.Cardio, self.Oncology]\n\n def reset(self):\n self.drugs_state = [0 for i in range(self.number)]\n\n\n def ICtest(self, budget, expectedPmt):\n for index, drug_state in enumerate(self.drugs_state):\n if budget > expectedPmt:\n return budget\n else:\n if drug_state != 5 or drug_state != 6:\n budget += self.valuation(stage=drug_state, syst_factor=0, drugType=7)\n self.drugs_state[index] = 6\n return budget\n\n\n def test(self, balance, corrSuccess, corrVal):\n tranMatrix = self.tranP[self.type]\n successCount = 0\n approvedValue = 0\n sysFactorVal = np.random.normal()\n \"\"\"generates standard uniform and then transformed it to normal for success simulation\"\"\"\n x1 = np.random.random()\n sysFactorSuccess = scipy.stats.norm.ppf(x1) # uniform to normal\n\n\n \"\"\"records total development cost for this time unit\"\"\"\n devCost = 0\n\n for index, drug_state in enumerate(self.drugs_state):\n if drug_state != 5 or drug_state != 6:\n\n prob_list = tranMatrix[self.drugs_state[index]] # e.g. [0.658, 0.254, 0.0, 0.0, 0.0, 0.0, 0.089]\n cumu_prob_list = np.cumsum(prob_list) # e.g. [0.658, 0.912, 0.912, 0.912, 0.912, 0.912, 1.001]\n rand_draw = Drug_compounds.corr_random_UNI(corrLevel=corrSuccess, sysFactor=sysFactorSuccess)\n\n for i, num in enumerate(cumu_prob_list):\n if rand_draw < num:\n if self.drugs_state[index] != 5 and i == 5:\n successCount += 1 # if approved, counts\n self.drugs_state[index] = i # set drug state to success\n \"\"\"choose appropriate valuation parameter\"\"\"\n value = self.valuation(stage=i, syst_factor=sysFactorVal, rho=corrVal, drugType=self.type)\n approvedValue += value # if approved, records expected valuation\n balance += value # adds valuation to current balance\n else:\n if self.drugs_state[index] != i:\n \"\"\"if moving to new state and\n there's enough to cover development cost, proceed\n if not, sell off drug compounds until sufficient budget\"\"\"\n cost = self.devCost(i)\n devCost += cost\n if balance >= cost:\n self.drugs_state[index] = i\n balance -= cost\n else:\n balance += self.valuation(stage=i, syst_factor=sysFactorVal,\n rho=corrVal, drugType=7)\n self.drugs_state[index] = 6\n else:\n self.drugs_state[index] = i\n break\n\n return successCount, approvedValue, balance, devCost\n\n\n\n def devCost(self, stage):\n \"\"\"development cost only applies to pre-clinical through III phases\"\"\"\n if stage == 4 or stage == 5 or stage == 6:\n return 0\n else:\n if self.type is 7:\n p_mean_std_cap = self.dev_cost_oncology.get(stage)\n else:\n p_mean_std_cap = self.dev_cost.get(stage)\n\n upfront = p_mean_std_cap[0]\n mean = p_mean_std_cap[1]\n std = p_mean_std_cap[2]\n cap = p_mean_std_cap[3]\n return min(np.random.lognormal(mean, std), cap)\n\n def valuation(self, stage, drugType, syst_factor, rho=0):\n if stage is not 6:\n if drugType == 0 or drugType == 1:\n mean_std_cap = self.val_20Pctlower.get(stage)\n elif drugType == 2 or drugType == 3:\n mean_std_cap = self.val_10Pctlower.get(stage)\n elif drugType == 4 or drugType == 5 or drugType == 6:\n mean_std_cap = self.val_5Pctlower.get(stage)\n else:\n mean_std_cap = self.oncology_val_paramByPhase.get(stage)\n mean = mean_std_cap[0]\n std = mean_std_cap[1]\n cap = mean_std_cap[2]\n miu = np.random.normal()\n X = np.exp(mean + (rho * syst_factor + np.sqrt(1 - rho**2) * miu) * std)\n return min(X, cap)\n else:\n return 0\n\n\n @classmethod\n def corr_random_UNI(cls, corrLevel, sysFactor):\n if corrLevel == 0:\n return np.random.random()\n else:\n z2 = sysFactor * corrLevel + np.sqrt(1 - corrLevel**2) * np.random.normal()\n return scipy.stats.norm.cdf(z2)\n\n\n\n def toString(self):\n return self.string[self.type]\n @classmethod\n def toString_index(cls, i):\n return ['Other','Infectious','Autoimmune','Endoctrine','Rspiratory','Neurology','Cardio','Oncology'][i]\n\n\nclass Liability:\n \"\"\"liaStruct is assumed to contain principal percentage, rate (annual) and duration (year) for each tranche\"\"\"\n \"\"\"equity tranche is assumed to cover the rest of the principal\"\"\"\n \"\"\"e.g. [[0.9, 0.05, 4], [0.1, 0.08, 8]]\"\"\"\n def __init__(self, totalBalance, liaStruct):\n self.totalBal = totalBalance\n self.liabilityStruct = liaStruct\n self.couponFrequency = 2 # semi-annual payment\n\n self.equity = 0\n self.equityPct = 1\n self.equitySeries = []\n\n self.sheet = []\n for [pct, rate, duration] in liaStruct:\n\n self.equityPct -= pct\n\n balance = self.totalBal * pct\n duration = duration * self.couponFrequency # match duration to coupon paying periods\n rate = rate / self.couponFrequency # match rate to coupon paying periods\n payment = self.calcPayment(balance, rate, duration)\n\n \"\"\"[payment, rate, balance, [balanceSeries], [payment received] [principalPaidSeries], [interestDueSeries], \n [interestPaidSeries], interestShortFall] \"\"\"\n self.sheet.append([payment, rate, balance, [],[], [], [], [], 0])\n\n\n\n def reset(self):\n self.equity = 0\n self.equitySeries = []\n\n self.sheet = []\n for (pct, rate, duration) in self.liabilityStruct:\n balance = self.totalBal * pct\n duration = duration * self.couponFrequency # match duration to coupon paying periods\n rate = rate / self.couponFrequency # match rate to coupon paying periods\n payment = self.calcPayment(balance, rate, duration)\n \"\"\"[payment, rate, balance, [balanceSeries], [payment received] [principalPaidSeries], [interestDueSeries], \n [interestPaidSeries], interestShortFall] \"\"\"\n self.sheet.append([payment, rate, balance, [],[], [], [], [], 0])\n\n\n\n @classmethod\n def calcPayment(cls, balance, rate, duration):\n return (rate * balance) / (1 - (1 + rate)**(-duration))\n\n def getExpectedPmt(self):\n pmt = 0\n if self.sheet[0][2] > 1: # if senior not paid off\n pmt += self.sheet[0][0] + self.sheet[1][1] * self.sheet[1][2] # pay senior interest and principal + junior interest\n else: # if senior paid off\n pmt += self.sheet[1][0]\n return pmt\n\n def pay(self, cash):\n maturedTrancheCount = 0 # counts number of tranches already being paid off\n for tranche in self.sheet:\n if tranche[2] < 1:\n maturedTrancheCount += 1\n tranche[6].append(0) # records interest due\n tranche[7].append(0) # records interest paid\n tranche[5].append(0) # rest of payment goes to pincipal\n tranche[4].append(0) # records payment received\n else:\n payment = min(cash, tranche[0]) # min(cash, expected payment)\n # if tranche[1] == 0.04:\n # print(\"balance: {}, payment: {}\".format(tranche[2], payment))\n cash = cash - payment # cash deducted after paying this tranche\n\n interestSFPaid = min(tranche[-1], payment) # pay off accrued interest\n payment = payment - interestSFPaid\n tranche[-1] = tranche[-1] - interestSFPaid # update interest short fall\n\n interestDue = tranche[1] * tranche[2] # calculates interest due\n tranche[6].append(interestDue) # records interest due\n interestPaid = min(payment, interestDue) # pay interest\n tranche[7].append(interestPaid) # records interest paid\n payment = payment - interestPaid # deducts interest\n tranche[-1] = tranche[-1] + (interestDue - interestPaid) # records interest short fall\n\n principalPaid = min(tranche[2], payment)\n payment = payment - principalPaid\n tranche[5].append(principalPaid) # rest of payment goes to pincipal\n tranche[2] = tranche[2] - principalPaid # calculates new balance after payment made\n\n # if tranche[1] == 0.04:\n # print(\" interest due: {}, interest paid: {}, principal paid: {}\".format(interestDue,\n # interestPaid, principalPaid))\n\n tranche[4].append(interestSFPaid + interestPaid + principalPaid) # records payment received\n\n cash += payment # extra cash left after paying off principal\n\n if tranche[2] < 1:\n tranche[2] = 0\n\n tranche[3].append(tranche[2]) # records balance change\n\n if abs(cash) < 1:\n cash = 0\n if maturedTrancheCount == len(self.sheet) and cash != 0:\n self.equity += cash # remaining cash goes to equity holders\n self.equitySeries.append(self.equity)\n return 0\n else:\n self.equity += 0\n self.equitySeries.append(self.equity)\n return cash\n\n\n\n def calcIRR(self):\n IRRs = []\n \"\"\" (pct, rate, duration) in liabilityStruct\"\"\"\n for (info, tranche) in zip(self.liabilityStruct, self.sheet):\n p = [-self.totalBal * info[0]]\n p.extend(tranche[4])\n # if tranche[1] == 0.04:\n # print(\"\\nseries used to calc IRR: {}\\n\".format(p))\n IRRs.append(np.irr(p))\n\n # for equity\n p = [-self.equityPct * self.totalBal]\n p.extend(self.equitySeries)\n\n IRRs.append(np.irr(p))\n\n return IRRs\n\n\n\nclass TransitionProb:\n def __init__(self):\n\n \"\"\"\n Average transition time (quarter)\n [Pre-I, I-II, II-III, III-NDA, NDA-Yes]\n \"\"\"\n self.transition_time = [4, 10, 13, 13, 5]\n\n\n \"\"\"\n phase-wise transition probability by drug type\n [Pre-I, I-II, II-III, III-NDA, NDA-Yes]\n \"\"\"\n self.other = [0.69, 0.722, 0.442, 0.711, 0.804]\n self.infectious = [0.69, 0.658, 0.459, 0.653, 0.849]\n self.autoimmune = [0.69, 0.68, 0.34, 0.684, 0.803]\n self.endoctrine = [0.69, 0.583, 0.338, 0.674, 0.869]\n self.respiratory = [0.69, 0.667, 0.275, 0.633, 0.96]\n self.neurology = [0.69, 0.624, 0.302, 0.606, 0.822]\n self.cardiovascular = [0.69, 0.606, 0.263, 0.528, 0.845]\n self.oncology = [0.69, 0.639, 0.283, 0.452, 0.817]\n\n def get_transtion_time(self):\n return int(np.sum(self.transition_time))\n\n @staticmethod\n def matrixMul(a, n):\n if n <= 1:\n return a\n else:\n return np.matmul(TransitionProb.matrixMul(a, n - 1), a)\n\n @staticmethod\n def calc_unit_prob(t, phase_prob):\n lamda = -np.log(1 - phase_prob) / t\n print(lamda)\n return 1 - np.exp(-lamda)\n\n\n\n\nif __name__ == \"__main__\":\n pass\n\n\n\n\n\n", "sub_path": "simu.py", "file_name": "simu.py", "file_ext": "py", "file_size_in_byte": 24826, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "numpy.log", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 128, "usage_type": "attribute"}, {"api_name": "numpy.add", "line_number": 130, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 141, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 287, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 287, "usage_type": "attribute"}, {"api_name": "numpy.random.random", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 289, "usage_type": "attribute"}, {"api_name": "scipy.stats.stats.norm.ppf", "line_number": 290, "usage_type": "call"}, {"api_name": "scipy.stats.stats", "line_number": 290, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 290, "usage_type": "name"}, {"api_name": "numpy.cumsum", "line_number": 300, "usage_type": "call"}, {"api_name": "numpy.random.lognormal", "line_number": 348, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 348, "usage_type": "attribute"}, {"api_name": "numpy.random.normal", "line_number": 363, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 363, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 364, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 364, "usage_type": "call"}, {"api_name": "numpy.random.random", "line_number": 373, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 373, "usage_type": "attribute"}, {"api_name": "numpy.sqrt", "line_number": 375, "usage_type": "call"}, {"api_name": "numpy.random.normal", "line_number": 375, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 375, "usage_type": "attribute"}, {"api_name": "scipy.stats.stats.norm.cdf", "line_number": 376, "usage_type": "call"}, {"api_name": "scipy.stats.stats", "line_number": 376, "usage_type": "attribute"}, {"api_name": "scipy.stats", "line_number": 376, "usage_type": "name"}, {"api_name": "numpy.irr", "line_number": 509, "usage_type": "call"}, {"api_name": "numpy.irr", "line_number": 515, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 545, "usage_type": "call"}, {"api_name": "numpy.matmul", "line_number": 552, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 556, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 558, "usage_type": "call"}]} +{"seq_id": "609727753", "text": "import array\nimport curio\nimport functools\nimport logging\nimport os\nimport pytest\nimport signal\nimport subprocess\nimport sys\nimport threading\nimport time\nimport uuid\nimport trio\n\nfrom types import SimpleNamespace\n\nimport caproto as ca\nimport caproto.benchmarking # noqa\nfrom caproto.sync.client import read\nimport caproto.curio # noqa\nimport caproto.threading # noqa\nimport caproto.trio # noqa\nimport caproto.asyncio # noqa\n\n\n_repeater_process = None\n\nREPEATER_PORT = 5065\nSERVER_HOST = '0.0.0.0'\n# make the logs noisy\nlogger = logging.getLogger('caproto')\nlogger.setLevel('DEBUG')\n# except for the broadcaster\nbcast_logger = logging.getLogger('caproto.bcast')\nbcast_logger.setLevel('INFO')\n\narray_types = (array.array,)\ntry:\n import numpy\nexcept ImportError:\n pass\nelse:\n array_types = array_types + (numpy.ndarray,)\n\n\n# Don't import these from numpy because we do not assume that numpy is\n# installed.\n\n\ndef assert_array_equal(arr1, arr2):\n assert len(arr1) == len(arr2)\n for i, j in zip(arr1, arr2):\n assert i == j\n\n\ndef assert_array_almost_equal(arr1, arr2):\n assert len(arr1) == len(arr2)\n for i, j in zip(arr1, arr2):\n assert abs(i - j) < 1e-6\n\n\ndef run_example_ioc(module_name, *, request, pv_to_check, args=None,\n stdin=None, stdout=None, stderr=None, very_verbose=True):\n '''Run an example IOC by module name as a subprocess\n\n Parameters\n ----------\n module_name : str\n request : pytest request\n pv_to_check : str\n args : list, optional\n '''\n if args is None:\n args = []\n\n if module_name == '--script':\n logger.debug(f'Running script {args}')\n else:\n logger.debug(f'Running {module_name}')\n\n if '-vvv' not in args and very_verbose:\n args = list(args) + ['-vvv']\n\n os.environ['COVERAGE_PROCESS_START'] = '.coveragerc'\n\n p = subprocess.Popen([sys.executable, '-um', 'caproto.tests.example_runner',\n module_name] + list(args),\n stdout=stdout, stderr=stderr, stdin=stdin,\n env=os.environ)\n\n def stop_ioc():\n if p.poll() is None:\n if sys.platform != 'win32':\n logger.debug('Sending Ctrl-C to the example IOC')\n p.send_signal(signal.SIGINT)\n logger.debug('Waiting on process...')\n\n try:\n p.wait(timeout=1)\n except subprocess.TimeoutExpired:\n logger.debug('IOC did not exit in a timely fashion')\n p.terminate()\n logger.debug('IOC terminated')\n else:\n logger.debug('IOC has exited')\n else:\n logger.debug('Example IOC has already exited')\n\n if request is not None:\n request.addfinalizer(stop_ioc)\n\n if pv_to_check:\n looks_like_areadetector = 'areadetector' in module_name\n if looks_like_areadetector:\n poll_timeout, poll_attempts = 5.0, 5\n else:\n poll_timeout, poll_attempts = 1.0, 5\n\n poll_readiness(pv_to_check, timeout=poll_timeout,\n attempts=poll_attempts)\n\n return p\n\n\ndef poll_readiness(pv_to_check, attempts=5, timeout=1):\n logger.debug(f'Checking PV {pv_to_check}')\n start_repeater()\n for attempt in range(attempts):\n try:\n read(pv_to_check, timeout=timeout, repeater=False)\n except TimeoutError:\n continue\n else:\n break\n else:\n raise TimeoutError(f\"ioc fixture failed to start in \"\n f\"{attempts * timeout} seconds (pv: {pv_to_check})\")\n\n\ndef run_softioc(request, db, additional_db=None, **kwargs):\n db_text = ca.benchmarking.make_database(db)\n\n if additional_db is not None:\n db_text = '\\n'.join((db_text, additional_db))\n\n err = None\n for attempt in range(3):\n ioc_handler = ca.benchmarking.IocHandler()\n ioc_handler.setup_ioc(db_text=db_text, max_array_bytes='10000000',\n **kwargs)\n\n request.addfinalizer(ioc_handler.teardown)\n\n (pv_to_check, _), *_ = db\n try:\n poll_readiness(pv_to_check)\n except TimeoutError as err_:\n err = err_\n else:\n return ioc_handler\n else:\n # ran out of retry attempts\n raise err\n\n\n@pytest.fixture(scope='function')\ndef prefix():\n 'Random PV prefix for a server'\n return str(uuid.uuid4())[:8] + ':'\n\n\ndef _epics_base_ioc(prefix, request):\n name = 'Waveform and standard record IOC'\n db = {\n ('{}waveform'.format(prefix), 'waveform'):\n dict(FTVL='LONG', NELM=4000),\n ('{}float'.format(prefix), 'ai'): dict(VAL=3.14),\n ('{}enum'.format(prefix), 'bi'):\n dict(VAL=1, ZNAM=\"zero\", ONAM=\"one\"),\n ('{}str'.format(prefix), 'stringout'): dict(VAL='test'),\n ('{}int'.format(prefix), 'longout'): dict(VAL=1),\n ('{}int2'.format(prefix), 'longout'): dict(VAL=1),\n ('{}int3'.format(prefix), 'longout'): dict(VAL=1),\n }\n\n macros = {'P': prefix}\n handler = run_softioc(request, db,\n additional_db=ca.benchmarking.PYEPICS_TEST_DB,\n macros=macros)\n\n process = handler.processes[-1]\n\n exit_lock = threading.RLock()\n monitor_output = []\n\n def ioc_monitor():\n process.wait()\n with exit_lock:\n monitor_output.extend([\n f'***********************************',\n f'********IOC process exited!********',\n f'******* Returned: {process.returncode} ******',\n f'***********************************''',\n ])\n\n stdout, stderr = process.communicate()\n if process.returncode != 0:\n if stdout is not None:\n lines = [f'[Server-stdout] {line}'\n for line in stdout.decode('latin-1').split('\\n')]\n monitor_output.extend(lines)\n\n if stderr is not None:\n lines = [f'[Server-stderr] {line}'\n for line in stdout.decode('latin-1').split('\\n')]\n monitor_output.extend(lines)\n\n def ioc_monitor_output():\n with exit_lock:\n if monitor_output:\n logger.debug('IOC monitor output:')\n for line in monitor_output:\n logger.debug(line)\n\n request.addfinalizer(ioc_monitor_output)\n\n threading.Thread(target=ioc_monitor).start()\n pvs = {pv[len(prefix):]: pv\n for pv, rtype in db\n }\n\n return SimpleNamespace(process=process, prefix=prefix, name=name, pvs=pvs,\n type='epics-base')\n\n\ndef _caproto_ioc(prefix, request):\n name = 'Caproto type varieties example'\n pvs = dict(int=prefix + 'int',\n int2=prefix + 'int2',\n int3=prefix + 'int3',\n float=prefix + 'pi',\n str=prefix + 'str',\n enum=prefix + 'enum',\n waveform=prefix + 'waveform',\n chararray=prefix + 'chararray',\n empty_string=prefix + 'empty_string',\n empty_bytes=prefix + 'empty_bytes',\n empty_char=prefix + 'empty_char',\n empty_float=prefix + 'empty_float',\n )\n process = run_example_ioc('caproto.ioc_examples.type_varieties',\n request=request,\n pv_to_check=pvs['float'],\n args=('--prefix', prefix,))\n return SimpleNamespace(process=process, prefix=prefix, name=name, pvs=pvs,\n type='caproto')\n\n\ncaproto_ioc = pytest.fixture(scope='function')(_caproto_ioc)\nepics_base_ioc = pytest.fixture(scope='function')(_epics_base_ioc)\n\n\n@pytest.fixture(params=['caproto', 'epics-base'], scope='function')\ndef ioc_factory(prefix, request):\n 'A fixture that runs more than one IOC: caproto, epics'\n # Get a new prefix for each IOC type:\n if request.param == 'caproto':\n return functools.partial(_caproto_ioc, prefix, request)\n elif request.param == 'epics-base':\n return functools.partial(_epics_base_ioc, prefix, request)\n\n\n@pytest.fixture(params=['caproto', 'epics-base'], scope='function')\ndef ioc(prefix, request):\n 'A fixture that runs more than one IOC: caproto, epics'\n # Get a new prefix for each IOC type:\n if request.param == 'caproto':\n ioc_ = _caproto_ioc(prefix, request)\n elif request.param == 'epics-base':\n ioc_ = _epics_base_ioc(prefix, request)\n\n return ioc_\n\n\ndef start_repeater():\n global _repeater_process\n if _repeater_process is not None:\n return\n\n logger.info('Spawning repeater process')\n _repeater_process = run_example_ioc('--script',\n args=['caproto-repeater'],\n request=None,\n pv_to_check=None)\n time.sleep(1.0)\n\n\ndef stop_repeater():\n global _repeater_process\n if _repeater_process is None:\n return\n\n logger.info('[Repeater] Sending Ctrl-C to the repeater')\n if sys.platform == 'win32':\n _repeater_process.terminate()\n else:\n _repeater_process.send_signal(signal.SIGINT)\n _repeater_process.wait()\n _repeater_process = None\n logger.info('[Repeater] Repeater exited')\n\n\ndef default_setup_module(module):\n logger.info('-- default module setup {} --'.format(module.__name__))\n start_repeater()\n\n\ndef default_teardown_module(module):\n logger.info('-- default module teardown {} --'.format(module.__name__))\n stop_repeater()\n\n\n@pytest.fixture(scope='function')\ndef pvdb_from_server_example():\n alarm = ca.ChannelAlarm(\n status=ca.AlarmStatus.READ,\n severity=ca.AlarmSeverity.MINOR_ALARM,\n alarm_string='alarm string',\n )\n\n pvdb = {\n 'pi': ca.ChannelDouble(value=3.14,\n lower_disp_limit=3.13,\n upper_disp_limit=3.15,\n lower_alarm_limit=3.12,\n upper_alarm_limit=3.16,\n lower_warning_limit=3.11,\n upper_warning_limit=3.17,\n lower_ctrl_limit=3.10,\n upper_ctrl_limit=3.18,\n precision=5,\n units='doodles',\n alarm=alarm,\n ),\n 'enum': ca.ChannelEnum(value='b',\n enum_strings=['a', 'b', 'c', 'd'],\n ),\n 'enum2': ca.ChannelEnum(value='bb',\n enum_strings=['aa', 'bb', 'cc', 'dd'],\n ),\n 'int': ca.ChannelInteger(value=96,\n units='doodles',\n ),\n 'char': ca.ChannelByte(value=b'3',\n units='poodles',\n lower_disp_limit=33,\n upper_disp_limit=35,\n lower_alarm_limit=32,\n upper_alarm_limit=36,\n lower_warning_limit=31,\n upper_warning_limit=37,\n lower_ctrl_limit=30,\n upper_ctrl_limit=38,\n ),\n 'bytearray': ca.ChannelByte(value=b'1234567890' * 2),\n 'chararray': ca.ChannelChar(value=b'1234567890' * 2),\n 'str': ca.ChannelString(value='hello',\n string_encoding='latin-1',\n alarm=alarm),\n 'str2': ca.ChannelString(value='hello',\n string_encoding='latin-1',\n alarm=alarm),\n 'stra': ca.ChannelString(value=['hello', 'how is it', 'going'],\n string_encoding='latin-1'),\n }\n\n return pvdb\n\n\n@pytest.fixture(scope='function')\ndef curio_server(prefix):\n str_alarm_status = ca.ChannelAlarm(\n status=ca.AlarmStatus.READ,\n severity=ca.AlarmSeverity.MINOR_ALARM,\n alarm_string='alarm string',\n )\n\n caget_pvdb = {\n 'pi': ca.ChannelDouble(value=3.14,\n lower_disp_limit=3.13,\n upper_disp_limit=3.15,\n lower_alarm_limit=3.12,\n upper_alarm_limit=3.16,\n lower_warning_limit=3.11,\n upper_warning_limit=3.17,\n lower_ctrl_limit=3.10,\n upper_ctrl_limit=3.18,\n precision=5,\n units='doodles',\n ),\n 'enum': ca.ChannelEnum(value='b',\n enum_strings=['a', 'b', 'c', 'd'],\n ),\n 'int': ca.ChannelInteger(value=33,\n units='poodles',\n lower_disp_limit=33,\n upper_disp_limit=35,\n lower_alarm_limit=32,\n upper_alarm_limit=36,\n lower_warning_limit=31,\n upper_warning_limit=37,\n lower_ctrl_limit=30,\n upper_ctrl_limit=38,\n ),\n 'char': ca.ChannelByte(value=b'3',\n units='poodles',\n lower_disp_limit=33,\n upper_disp_limit=35,\n lower_alarm_limit=32,\n upper_alarm_limit=36,\n lower_warning_limit=31,\n upper_warning_limit=37,\n lower_ctrl_limit=30,\n upper_ctrl_limit=38,\n ),\n 'str': ca.ChannelString(value='hello',\n alarm=str_alarm_status,\n reported_record_type='caproto'),\n }\n\n # tack on a unique prefix\n caget_pvdb = {prefix + key: value\n for key, value in caget_pvdb.items()}\n\n async def _server(pvdb):\n ctx = caproto.curio.server.Context(pvdb)\n try:\n await ctx.run()\n except caproto.curio.server.ServerExit:\n logger.info('ServerExit caught; exiting')\n except Exception as ex:\n logger.error('Server failed: %s %s', type(ex), ex)\n raise\n\n async def run_server(client, *, pvdb=caget_pvdb):\n server_task = await curio.spawn(_server, pvdb, daemon=True)\n\n try:\n await client()\n except caproto.curio.server.ServerExit:\n ...\n finally:\n await server_task.cancel()\n\n return run_server, prefix, caget_pvdb\n\n\nasync def get_curio_context():\n logger.debug('New curio broadcaster')\n broadcaster = caproto.curio.client.SharedBroadcaster()\n logger.debug('Registering...')\n await broadcaster.register()\n logger.debug('Registered! Returning new context.')\n return caproto.curio.client.Context(broadcaster)\n\n\ndef run_with_trio_context(func, **kwargs):\n async def runner():\n async with trio.open_nursery() as nursery:\n logger.debug('New trio broadcaster')\n broadcaster = caproto.trio.client.SharedBroadcaster(\n nursery=nursery)\n\n logger.debug('Registering...')\n await broadcaster.register()\n logger.debug('Registered! Returning new context.')\n context = caproto.trio.client.Context(broadcaster, nursery=nursery)\n ret = await func(context=context, **kwargs)\n\n logger.debug('Shutting down the broadcaster')\n await broadcaster.disconnect()\n logger.debug('And the context')\n # await context.stop()\n nursery.cancel_scope.cancel()\n return ret\n\n return trio.run(runner)\n\n\n@pytest.fixture(scope='function',\n params=['curio', 'trio', 'asyncio'])\ndef server(request):\n\n def curio_runner(pvdb, client, *, threaded_client=False):\n async def server_main():\n try:\n ctx = caproto.curio.server.Context(pvdb)\n await ctx.run()\n except caproto.curio.server.ServerExit:\n logger.info('Server exited normally')\n except Exception as ex:\n logger.error('Server failed: %s %s', type(ex), ex)\n raise\n\n async def run_server_and_client():\n try:\n server_task = await curio.spawn(server_main)\n # Give this a couple tries, akin to poll_readiness.\n for _ in range(15):\n try:\n if threaded_client:\n await threaded_in_curio_wrapper(client)()\n else:\n await client()\n except TimeoutError:\n continue\n else:\n break\n else:\n raise TimeoutError(f\"ioc failed to start\")\n finally:\n await server_task.cancel()\n\n with curio.Kernel() as kernel:\n kernel.run(run_server_and_client)\n\n def trio_runner(pvdb, client, *, threaded_client=False):\n async def trio_server_main(task_status):\n try:\n ctx = caproto.trio.server.Context(pvdb)\n task_status.started(ctx)\n await ctx.run()\n except Exception as ex:\n logger.error('Server failed: %s %s', type(ex), ex)\n raise\n\n async def run_server_and_client():\n async with trio.open_nursery() as test_nursery:\n server_context = await test_nursery.start(trio_server_main)\n # Give this a couple tries, akin to poll_readiness.\n for _ in range(15):\n try:\n if threaded_client:\n await trio.run_sync_in_worker_thread(client)\n else:\n await client(test_nursery, server_context)\n except TimeoutError:\n continue\n else:\n break\n server_context.stop()\n # don't leave the server running:\n test_nursery.cancel_scope.cancel()\n\n trio.run(run_server_and_client)\n\n def asyncio_runner(pvdb, client, *, threaded_client=False):\n import asyncio\n\n async def asyncio_server_main():\n try:\n ctx = caproto.asyncio.server.Context(pvdb)\n await ctx.run()\n except Exception as ex:\n logger.error('Server failed: %s %s', type(ex), ex)\n raise\n\n async def run_server_and_client(loop):\n tsk = loop.create_task(asyncio_server_main())\n # Give this a couple tries, akin to poll_readiness.\n for _ in range(15):\n try:\n if threaded_client:\n await loop.run_in_executor(client)\n else:\n await client()\n except TimeoutError:\n continue\n else:\n break\n tsk.cancel()\n await asyncio.wait((tsk, ))\n\n loop = asyncio.new_event_loop()\n asyncio.set_event_loop(loop)\n loop.run_until_complete(run_server_and_client(loop))\n\n if request.param == 'curio':\n curio_runner.backend = 'curio'\n return curio_runner\n elif request.param == 'trio':\n trio_runner.backend = 'trio'\n return trio_runner\n elif request.param == 'asyncio':\n asyncio_runner.backend = 'asyncio'\n return asyncio_runner\n\n\ndef pytest_make_parametrize_id(config, val, argname):\n # FIX for python 3.6.3 and/or pytest 3.3.0\n if isinstance(val, bytes):\n return repr(val)\n\n\n@pytest.fixture(scope='function')\ndef circuit_pair(request):\n host = '127.0.0.1'\n port = 5555\n priority = 1\n version = 13\n cli_circuit = ca.VirtualCircuit(ca.CLIENT, (host, port), priority)\n buffers_to_send = cli_circuit.send(ca.VersionRequest(version=version,\n priority=priority))\n\n srv_circuit = ca.VirtualCircuit(ca.SERVER, (host, port), None)\n commands, _ = srv_circuit.recv(*buffers_to_send)\n for command in commands:\n srv_circuit.process_command(command)\n buffers_to_send = srv_circuit.send(ca.VersionResponse(version=version))\n commands, _ = cli_circuit.recv(*buffers_to_send)\n for command in commands:\n cli_circuit.process_command(command)\n return cli_circuit, srv_circuit\n\n\n# Import the pytest-benchmark -> asv shim if both are available\ntry:\n __import__('pytest_benchmark')\n __import__('asv')\nexcept ImportError as ex:\n print('{} is missing'.format(ex))\nelse:\n from ._asv_shim import get_conftest_globals\n globals().update(**get_conftest_globals())\n\n\ndef threaded_in_curio_wrapper(fcn):\n '''Run a threaded test with curio support\n\n Usage\n -----\n Wrap the threaded function using this wrapper, call the wrapped function\n using `curio.run_in_thread` and then await wrapped_function.wait() inside\n the test kernel.\n '''\n uqueue = curio.UniversalQueue()\n\n def wrapped_threaded_func():\n try:\n fcn()\n except Exception as ex:\n uqueue.put(ex)\n else:\n uqueue.put(None)\n\n @functools.wraps(fcn)\n async def test_runner():\n 'Wait for the test function completion'\n await curio.run_in_thread(wrapped_threaded_func)\n res = await uqueue.get()\n if res is not None:\n raise res\n\n return test_runner\n\n\n@pytest.fixture(scope='function', params=['array', 'numpy'])\ndef backends(request):\n from caproto import select_backend, backend\n\n def switch_back():\n select_backend(initial_backend)\n\n initial_backend = backend.backend_name\n request.addfinalizer(switch_back)\n\n try:\n select_backend(request.param)\n except KeyError:\n raise pytest.skip(f'backend {request.param} unavailable')\n\n\ndef dump_process_output(prefix, stdout, stderr):\n print('-- Process stdout --')\n if stdout is not None:\n for line in stdout.decode('latin-1').split('\\n'):\n print(f'[{prefix}-stdout]', line)\n print('-- Process stderr --')\n if stderr is not None:\n for line in stderr.decode('latin-1').split('\\n'):\n print(f'[{prefix}-stderr]', line)\n print('--')\n\n\n@pytest.hookimpl(hookwrapper=True)\ndef pytest_runtest_call(item):\n 'Socket and thread debugging hook'\n from .debug import use_debug_socket, use_thread_counter\n\n with use_thread_counter() as (dangling_threads, thread_counter):\n with use_debug_socket() as (sockets, socket_counter):\n yield\n\n num_dangling = len(dangling_threads)\n num_threads = thread_counter.value\n\n if num_threads:\n if num_dangling:\n thread_info = ', '.join(str(thread) for thread in dangling_threads)\n logger.warning('%d thread(s) left dangling out of %d! %s',\n num_dangling, num_threads, thread_info)\n # pytest.fail() ?\n else:\n logger.debug('%d thread(s) OK', num_threads)\n\n item.user_properties.append(('total_threads', num_threads))\n item.user_properties.append(('dangling_threads', num_dangling))\n\n num_sockets = socket_counter.value\n num_open = len(sockets)\n\n if num_sockets:\n if num_open:\n logger.warning('%d sockets still open of %d', num_open,\n num_sockets)\n # pytest.fail() ?\n else:\n logger.debug('%d sockets OK', socket_counter.value)\n\n item.user_properties.append(('total_sockets', num_sockets))\n item.user_properties.append(('open_sockets', num_open))\n", "sub_path": "caproto/tests/conftest.py", "file_name": "conftest.py", "file_ext": "py", "file_size_in_byte": 24469, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "logging.getLogger", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 34, "usage_type": "call"}, {"api_name": "array.array", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.ndarray", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 84, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 86, "usage_type": "call"}, {"api_name": "sys.executable", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 89, "usage_type": "attribute"}, {"api_name": "sys.platform", "line_number": 93, "usage_type": "attribute"}, {"api_name": "signal.SIGINT", "line_number": 95, "usage_type": "attribute"}, {"api_name": "subprocess.TimeoutExpired", "line_number": 100, "usage_type": "attribute"}, {"api_name": "caproto.sync.client.read", "line_number": 130, "usage_type": "call"}, {"api_name": "caproto.benchmarking.make_database", "line_number": 141, "usage_type": "call"}, {"api_name": "caproto.benchmarking", "line_number": 141, "usage_type": "attribute"}, {"api_name": "caproto.benchmarking.IocHandler", "line_number": 148, "usage_type": "call"}, {"api_name": "caproto.benchmarking", "line_number": 148, "usage_type": "attribute"}, {"api_name": "uuid.uuid4", "line_number": 169, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 166, "usage_type": "call"}, {"api_name": "caproto.benchmarking", "line_number": 188, "usage_type": "attribute"}, {"api_name": "threading.RLock", "line_number": 193, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 227, "usage_type": "call"}, {"api_name": "types.SimpleNamespace", "line_number": 232, "usage_type": "call"}, {"api_name": "types.SimpleNamespace", "line_number": 255, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 259, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 260, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 268, "usage_type": "call"}, {"api_name": "functools.partial", "line_number": 270, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 263, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 273, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 295, "usage_type": "call"}, {"api_name": "sys.platform", "line_number": 304, "usage_type": "attribute"}, {"api_name": "signal.SIGINT", "line_number": 307, "usage_type": "attribute"}, {"api_name": "caproto.ChannelAlarm", "line_number": 325, "usage_type": "call"}, {"api_name": "caproto.AlarmStatus", "line_number": 326, "usage_type": "attribute"}, {"api_name": "caproto.AlarmSeverity", "line_number": 327, "usage_type": "attribute"}, {"api_name": "caproto.ChannelDouble", "line_number": 332, "usage_type": "call"}, {"api_name": "caproto.ChannelEnum", "line_number": 345, "usage_type": "call"}, {"api_name": "caproto.ChannelEnum", "line_number": 348, "usage_type": "call"}, {"api_name": "caproto.ChannelInteger", "line_number": 351, "usage_type": "call"}, {"api_name": "caproto.ChannelByte", "line_number": 354, "usage_type": "call"}, {"api_name": "caproto.ChannelByte", "line_number": 365, "usage_type": "call"}, {"api_name": "caproto.ChannelChar", "line_number": 366, "usage_type": "call"}, {"api_name": "caproto.ChannelString", "line_number": 367, "usage_type": "call"}, {"api_name": "caproto.ChannelString", "line_number": 370, "usage_type": "call"}, {"api_name": "caproto.ChannelString", "line_number": 373, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 323, "usage_type": "call"}, {"api_name": "caproto.ChannelAlarm", "line_number": 382, "usage_type": "call"}, {"api_name": "caproto.AlarmStatus", "line_number": 383, "usage_type": "attribute"}, {"api_name": "caproto.AlarmSeverity", "line_number": 384, "usage_type": "attribute"}, {"api_name": "caproto.ChannelDouble", "line_number": 389, "usage_type": "call"}, {"api_name": "caproto.ChannelEnum", "line_number": 401, "usage_type": "call"}, {"api_name": "caproto.ChannelInteger", "line_number": 404, "usage_type": "call"}, {"api_name": "caproto.ChannelByte", "line_number": 415, "usage_type": "call"}, {"api_name": "caproto.ChannelString", "line_number": 426, "usage_type": "call"}, {"api_name": "caproto.curio.server.Context", "line_number": 436, "usage_type": "call"}, {"api_name": "caproto.curio", "line_number": 436, "usage_type": "attribute"}, {"api_name": "caproto.curio", "line_number": 439, "usage_type": "attribute"}, {"api_name": "curio.spawn", "line_number": 446, "usage_type": "call"}, {"api_name": "caproto.curio", "line_number": 450, "usage_type": "attribute"}, {"api_name": "pytest.fixture", "line_number": 380, "usage_type": "call"}, {"api_name": "caproto.curio.client.SharedBroadcaster", "line_number": 460, "usage_type": "call"}, {"api_name": "caproto.curio", "line_number": 460, "usage_type": "attribute"}, {"api_name": "caproto.curio.client.Context", "line_number": 464, "usage_type": "call"}, {"api_name": "caproto.curio", "line_number": 464, "usage_type": "attribute"}, {"api_name": "trio.open_nursery", "line_number": 469, "usage_type": "call"}, {"api_name": "caproto.trio.client.SharedBroadcaster", "line_number": 471, "usage_type": "call"}, {"api_name": "caproto.trio", "line_number": 471, "usage_type": "attribute"}, {"api_name": "caproto.trio.client.Context", "line_number": 477, "usage_type": "call"}, {"api_name": "caproto.trio", "line_number": 477, "usage_type": "attribute"}, {"api_name": "trio.run", "line_number": 487, "usage_type": "call"}, {"api_name": "caproto.curio.server.Context", "line_number": 497, "usage_type": "call"}, {"api_name": "caproto.curio", "line_number": 497, "usage_type": "attribute"}, {"api_name": "caproto.curio", "line_number": 499, "usage_type": "attribute"}, {"api_name": "curio.spawn", "line_number": 507, "usage_type": "call"}, {"api_name": "curio.Kernel", "line_number": 524, "usage_type": "call"}, {"api_name": "caproto.trio.server.Context", "line_number": 530, "usage_type": "call"}, {"api_name": "caproto.trio", "line_number": 530, "usage_type": "attribute"}, {"api_name": "trio.open_nursery", "line_number": 538, "usage_type": "call"}, {"api_name": "trio.run_sync_in_worker_thread", "line_number": 544, "usage_type": "call"}, {"api_name": "trio.run", "line_number": 555, "usage_type": "call"}, {"api_name": "caproto.asyncio.server.Context", "line_number": 562, "usage_type": "call"}, {"api_name": "caproto.asyncio", "line_number": 562, "usage_type": "attribute"}, {"api_name": "asyncio.wait", "line_number": 582, "usage_type": "call"}, {"api_name": "asyncio.new_event_loop", "line_number": 584, "usage_type": "call"}, {"api_name": "asyncio.set_event_loop", "line_number": 585, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 490, "usage_type": "call"}, {"api_name": "caproto.VirtualCircuit", "line_number": 611, "usage_type": "call"}, {"api_name": "caproto.CLIENT", "line_number": 611, "usage_type": "attribute"}, {"api_name": "caproto.VersionRequest", "line_number": 612, "usage_type": "call"}, {"api_name": "caproto.VirtualCircuit", "line_number": 615, "usage_type": "call"}, {"api_name": "caproto.SERVER", "line_number": 615, "usage_type": "attribute"}, {"api_name": "caproto.VersionResponse", "line_number": 619, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 605, "usage_type": "call"}, {"api_name": "_asv_shim.get_conftest_globals", "line_number": 634, "usage_type": "call"}, {"api_name": "curio.UniversalQueue", "line_number": 646, "usage_type": "call"}, {"api_name": "curio.run_in_thread", "line_number": 659, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 656, "usage_type": "call"}, {"api_name": "caproto.select_backend", "line_number": 672, "usage_type": "call"}, {"api_name": "caproto.backend.backend_name", "line_number": 674, "usage_type": "attribute"}, {"api_name": "caproto.backend", "line_number": 674, "usage_type": "name"}, {"api_name": "caproto.select_backend", "line_number": 678, "usage_type": "call"}, {"api_name": "pytest.skip", "line_number": 680, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 667, "usage_type": "call"}, {"api_name": "debug.use_thread_counter", "line_number": 700, "usage_type": "call"}, {"api_name": "debug.use_debug_socket", "line_number": 701, "usage_type": "call"}, {"api_name": "pytest.hookimpl", "line_number": 695, "usage_type": "call"}]} +{"seq_id": "407636100", "text": "import os\nimport re\nimport numpy as np\nimport pandas as pd\nimport xarray as xr\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom letkf_forecasting import (\n analyse_results,\n letkf_io)\n\n\ndef return_smoothing_data(directory_name):\n runs = ['owp_opt']\n error_stats = []\n for run in runs:\n load_directory = ('/a2/uaren/travis/'\n + 'results/multi_day_error/'\n + f'{directory_name}/{run}')\n adict = {'name': run}\n for stat_file in os.listdir(load_directory):\n stat_name = stat_file.split('.')[0]\n stat_file = os.path.join(load_directory,\n stat_file)\n adict[stat_name] = pd.read_hdf(stat_file)\n error_stats.append(adict)\n for this_stat in error_stats:\n if this_stat['name'] == 'owp_opt':\n owp_rmse = this_stat['rmse']\n owp_sd = this_stat['forecast_sd']\n smoothing_params = np.array(\n [3, 9, 15, 21, 27, 33, 39, 45, 51, 57, 63])\n smoothing_runs = ['opt_flow', 'wrf_no_div']\n these_smoothing_runs = []\n for run in smoothing_runs:\n for this_param in smoothing_params:\n these_smoothing_runs.append(\n run + '_' + str(this_param))\n directory_name = 'smoothing_runs'\n smoothing_stats = []\n for run in these_smoothing_runs:\n load_directory = ('/a2/uaren/travis/'\n + 'results/multi_day_error/'\n + f'{directory_name}/{run}')\n adict = {'name': run}\n for stat_file in os.listdir(load_directory):\n stat_name = stat_file.split('.')[0]\n stat_file = os.path.join(load_directory,\n stat_file)\n adict[stat_name] = pd.read_hdf(stat_file)\n smoothing_stats.append(adict)\n of_rmse = pd.DataFrame(\n index=[15, 30, 45, 60],\n columns=smoothing_params)\n of_corr = of_rmse.copy()\n of_sd = of_rmse.copy()\n wrf_rmse = of_rmse.copy()\n wrf_corr = of_rmse.copy()\n wrf_sd = of_rmse.copy()\n for this_stat in smoothing_stats:\n split_name = this_stat['name'].split('_')\n smoothing_param = int(split_name[-1])\n run_name = split_name[0]\n if run_name == 'wrf':\n wrf_rmse[smoothing_param] = this_stat['rmse']\n wrf_corr[smoothing_param] = this_stat['correlation']\n wrf_sd[smoothing_param] = this_stat['forecast_sd']\n elif run_name == 'opt':\n of_rmse[smoothing_param] = this_stat['rmse']\n of_corr[smoothing_param] = this_stat['correlation']\n of_sd[smoothing_param] = this_stat['forecast_sd']\n one_of_rmse = pd.DataFrame(\n index=[15, 30, 45, 60],\n columns=['rmse'])\n one_of_corr = pd.DataFrame(\n index=[15, 30, 45, 60],\n columns=['correlation'])\n one_of_sd = pd.DataFrame(\n index=[15, 30, 45, 60],\n columns=['forecast_sd'])\n one_wrf_rmse = one_of_rmse.copy()\n one_wrf_corr = one_of_corr.copy()\n one_wrf_sd = one_of_sd.copy()\n for hor in one_of_rmse.index:\n this_sd = owp_sd.loc[hor].values.item()\n to_minimize = np.abs(of_sd.loc[hor].values - this_sd)\n optimal_index = to_minimize.argmin()\n one_of_rmse.loc[hor] = of_rmse.loc[hor].iloc[optimal_index]\n one_of_corr.loc[hor] = of_corr.loc[hor].iloc[optimal_index]\n one_of_sd.loc[hor] = of_sd.loc[hor].iloc[optimal_index]\n\n to_minimize = np.abs(wrf_sd.loc[hor].values - this_sd)\n optimal_index = to_minimize.argmin()\n one_wrf_rmse.loc[hor] = wrf_rmse.loc[hor].iloc[optimal_index]\n one_wrf_corr.loc[hor] = wrf_corr.loc[hor].iloc[optimal_index]\n one_wrf_sd.loc[hor] = wrf_sd.loc[hor].iloc[optimal_index]\n to_return = {\n 'one_of_rmse': one_of_rmse,\n 'one_wrf_rmse': one_wrf_rmse,\n }\n return to_return\n\n\ndef plot_smoothing_data(*, cpal_dict, marker_dict, legend_dict, format, dpi,\n smoothing_error, averaged_error, save_directory):\n save_directory = os.path.join(save_directory,\n 'smoothing_plots')\n if not os.path.exists(save_directory):\n os.makedirs(save_directory)\n # RMSE\n plt.figure()\n plt.plot(smoothing_error['one_of_rmse'],\n color=cpal_dict['opt_flow'],\n marker=marker_dict['opt_flow'])\n plt.plot(smoothing_error['one_wrf_rmse'],\n color=cpal_dict['wrf_no_div'],\n marker=marker_dict['wrf_no_div'])\n plt.plot(averaged_error['owp_opt']['rmse'].loc[slice(15, None)],\n color=cpal_dict['owp_opt'],\n marker=marker_dict['owp_opt'])\n plt.legend([legend_dict['opt_flow'],\n legend_dict['wrf_no_div'],\n legend_dict['owp_opt']],\n ncol=2)\n plt.title('RMSE for all days w/ equal SD')\n plt.xlabel('Forecast horizon (min.)')\n plt.ylabel('RMSE (CI)')\n plt.xlim([15, 60])\n plt.savefig(fname=os.path.join(save_directory,\n f'rmse.{format}'),\n format=format, dpi=dpi)\n\n\ndef return_original_stats(directory_name):\n runs = ['opt_flow', 'wrf_no_div',\n 'owp_opt', 'owp_opt_anly_fore', 'persistence']\n runs = ['owp_opt', 'owp_opt_anly_fore', 'persistence', 'opt_flow',\n 'wrf_no_div', 'wrf_mean', 'radiosonde']\n error_stats = {}\n for run in runs:\n load_directory = ('/a2/uaren/travis/'\n + 'results/multi_day_error/'\n + f'{directory_name}/{run}')\n adict = {'name': run}\n for stat_file in os.listdir(load_directory):\n stat_name = stat_file.split('.')[0]\n stat_file = os.path.join(load_directory,\n stat_file)\n adict[stat_name] = pd.read_hdf(stat_file)\n error_stats[run] = adict\n return error_stats\n\n\ndef plot_original_error(*, cpal_dict, marker_dict,\n legend_dict, format, dpi,\n averaged_error, save_directory):\n save_directory = os.path.join(save_directory,\n 'error_plots')\n if not os.path.exists(save_directory):\n os.makedirs(save_directory)\n # RMSE\n plt.figure()\n this_to_plot = averaged_error[\n 'persistence']['rmse'].loc[slice(15, None)]\n plt.plot(this_to_plot,\n color=cpal_dict['persistence'],\n marker=marker_dict['persistence'],\n )\n this_to_plot = averaged_error[\n 'opt_flow']['rmse'].loc[slice(15, None)]\n plt.plot(this_to_plot,\n color=cpal_dict['opt_flow'],\n marker=marker_dict['opt_flow'],\n )\n this_to_plot = averaged_error[\n 'wrf_no_div']['rmse'].loc[slice(15, None)]\n plt.plot(this_to_plot,\n color=cpal_dict['wrf_no_div'],\n marker=marker_dict['wrf_no_div'],\n )\n this_to_plot = averaged_error[\n 'owp_opt']['rmse'].loc[slice(15, None)]\n plt.plot(this_to_plot,\n color=cpal_dict['owp_opt'],\n marker=marker_dict['owp_opt'],\n )\n this_to_plot = averaged_error[\n 'owp_opt_anly_fore']['rmse'].loc[slice(15, None)]\n plt.plot(this_to_plot,\n color=cpal_dict['anly_fore'],\n marker=marker_dict['anly_fore'],\n )\n plt.legend([legend_dict['persistence'],\n legend_dict['opt_flow'],\n legend_dict['wrf_no_div'],\n legend_dict['owp_opt'],\n legend_dict['anly_fore']],\n ncol=2)\n plt.title('RMSE for all days')\n plt.xlabel('Forecast horizon (min.)')\n plt.ylabel('RMSE (CI)')\n plt.xlim([15, 60])\n plt.xticks([15, 30, 45, 60])\n plt.ylim([None, None])\n plt.savefig(fname=os.path.join(save_directory,\n f'rmse.{format}'),\n format=format, dpi=dpi)\n\n\ndef return_daily_error():\n base_folder = '/a2/uaren/travis/'\n year = 2014\n month_day = [[4, 2], [4, 5], [4, 9],\n [4, 10], [4, 11], [4, 12],\n [4, 15], [4, 18], [4, 19],\n [4, 20], [4, 21], [4, 22],\n [4, 25], [4, 26],\n\n [5, 5], [5, 6], [5, 7],\n [5, 8], [5, 9], [5, 19], [5, 20],\n [5, 21], [5, 22], [5, 23],\n [5, 24], [5, 25], [5, 29],\n [5, 30],\n\n [6, 3], [6, 10], [6, 11],\n [6, 12], [6, 14], [6, 15],\n [6, 16], [6, 17],\n [6, 18], [6, 19], [6, 22]]\n opt_flow = pd.DataFrame(columns=[15, 30, 45, 60])\n wrf_no_div = opt_flow.copy()\n owp_opt = opt_flow.copy()\n anly_fore = opt_flow.copy()\n daily_error = {'opt_flow': opt_flow,\n 'wrf_no_div': wrf_no_div,\n 'owp_opt': owp_opt,\n 'anly_fore': anly_fore}\n for this_month_day in month_day:\n month = this_month_day[0]\n day = this_month_day[1]\n this_date = pd.datetime(year, month, day).date()\n for run_name in daily_error.keys():\n anly_fore_flag = False\n if run_name == 'anly_fore':\n run_name = 'owp_opt'\n anly_fore_flag = True\n results_folder_path = os.path.join(\n base_folder,\n 'results',\n f'{year:04}',\n f'{month:02}',\n f'{day:02}',\n run_name)\n results_folder_path = letkf_io.find_latest_run(\n results_folder_path)\n results_folder_path = os.path.join(\n results_folder_path, 'single_day')\n stat_name = 'rmse'\n if anly_fore_flag:\n stat_name = stat_name + '_anly_fore'\n run_name = 'anly_fore'\n file_path = os.path.join(\n results_folder_path, f'{stat_name}.h5')\n rmse = pd.read_hdf(file_path, stat_name)\n daily_error[run_name].loc[this_date] = rmse['rmse']\n return daily_error\n\n\ndef plot_daily_error(*, cpal_dict, marker_dict, legend_dict,\n format, dpi,\n daily_error, averaged_error, save_directory):\n save_directory = os.path.join(save_directory,\n 'daily_error')\n if not os.path.exists(save_directory):\n os.makedirs(save_directory)\n opt_flow = daily_error['opt_flow']\n xticks = [str(index.month) + ' ' + str(index.day)\n for index in opt_flow.index]\n opt_flow = pd.concat(\n [opt_flow,\n (averaged_error['opt_flow']['rmse'].T)[[15, 30, 45, 60]]])\n wrf_no_div = daily_error['wrf_no_div']\n wrf_no_div = pd.concat(\n [wrf_no_div,\n (averaged_error['wrf_no_div']['rmse'].T)[[15, 30, 45, 60]]])\n owp_opt = daily_error['owp_opt']\n owp_opt = pd.concat(\n [owp_opt,\n (averaged_error['owp_opt']['rmse'].T)[[15, 30, 45, 60]]])\n anly_fore = daily_error['anly_fore']\n anly_fore = pd.concat(\n [anly_fore,\n (averaged_error['owp_opt_anly_fore']['rmse'].T)[[15, 30, 45, 60]]])\n\n y_max = np.max([opt_flow.max(),\n wrf_no_div.max(),\n owp_opt.max()])\n y_min = 0\n\n xticks.append('All days')\n xarange = np.arange(len(xticks))\n figsize = plt.figaspect(0.3)\n width = 0.20\n\n for hor in [15, 30, 45, 60]:\n plt.figure(figsize=figsize)\n plt.bar(xarange,\n opt_flow[hor], width,\n color=cpal_dict['opt_flow'])\n plt.bar(xarange + width,\n wrf_no_div[hor], width,\n color=cpal_dict['wrf_no_div'])\n plt.bar(xarange + 2*width,\n owp_opt[hor], width,\n color=cpal_dict['owp_opt'])\n plt.bar(xarange + 3*width,\n anly_fore[hor], width,\n color=cpal_dict['anly_fore'])\n plt.xticks(xarange + width, xticks, rotation=90)\n plt.title(f'RMSE for a horizon of {hor} minutes')\n plt.legend([legend_dict['opt_flow'],\n legend_dict['wrf_no_div'],\n legend_dict['owp_opt'],\n legend_dict['anly_fore']],\n ncol=2)\n\n plt.xlabel('Date')\n plt.ylabel('RMSE (CI)')\n plt.ylim([y_min, y_max])\n plt.xlim([xarange[0] - 0.5, xarange[-1] + 2*width + 0.5])\n plt.tight_layout()\n plt.savefig(fname=os.path.join(save_directory,\n f'rmse_{hor}.{format}'),\n format=format, dpi=dpi)\n\n\ndef return_spaghetti_error(*, dates_dict, run_names,\n base_folder='/a2/uaren/travis'):\n year = 2014\n spaghetti_error = {}\n for day_type, month_day in dates_dict.items():\n month = month_day[0]\n day = month_day[1]\n this_error = {}\n for run_name in run_names:\n ensemble_flag = False\n analysis_fore_flag = False\n if run_name[0] is 'ensemble':\n ensemble_flag = True\n run_name = run_name[1]\n elif run_name[0] is 'anly_fore':\n analysis_fore_flag = True\n run_name = run_name[1]\n else:\n ensemble_flag = False\n results_folder_path = os.path.join(\n base_folder,\n 'results',\n f'{year:04}',\n f'{month:02}',\n f'{day:02}',\n run_name)\n results_folder_path = letkf_io.find_latest_run(\n results_folder_path)\n results_folder_path = os.path.join(\n results_folder_path, 'single_day')\n\n stat_name = 'rmse'\n if ensemble_flag:\n stat_name = stat_name + '_ens'\n run_name = 'ensemble'\n elif analysis_fore_flag:\n stat_name = stat_name + '_anly_fore'\n run_name = 'owp_opt_anly_fore'\n file_path = os.path.join(\n results_folder_path, f'{stat_name}.h5')\n print(file_path)\n rmse = pd.read_hdf(file_path, stat_name)\n\n stat_name = 'bias'\n if ensemble_flag:\n stat_name = stat_name + '_ens'\n run_name = 'ensemble'\n elif analysis_fore_flag:\n stat_name = stat_name + '_anly_fore'\n run_name = 'owp_opt_anly_fore'\n file_path = os.path.join(\n results_folder_path, f'{stat_name}.h5')\n print(file_path)\n bias = pd.read_hdf(file_path, stat_name)\n\n stat_name = 'correlation'\n if ensemble_flag:\n stat_name = stat_name + '_ens'\n run_name = 'ensemble'\n elif analysis_fore_flag:\n stat_name = stat_name + '_anly_fore'\n run_name = 'owp_opt_anly_fore'\n file_path = os.path.join(\n results_folder_path, f'{stat_name}.h5')\n print(file_path)\n correlation = pd.read_hdf(file_path, stat_name)\n\n all_stats = {'rmse': rmse,\n 'bias': bias,\n 'correlation': correlation}\n this_error[run_name] = all_stats\n spaghetti_error[day_type] = this_error\n return spaghetti_error\n\n\ndef plot_spaghetti(*, cpal_dict, legend_dict, loc_dict, marker_dict,\n format, dpi, dates_dict,\n spaghetti_error,\n save_directory):\n case_study_dict = {'translation': 'Case Study 1',\n 'more_complex': 'Case Study 2',\n 'two_levels': 'Case Study 3'}\n for day_type, month_day in dates_dict.items():\n month = month_day[0]\n day = month_day[1]\n this_title = case_study_dict[day_type]\n this_save_directory = os.path.join(save_directory,\n day_type)\n if not os.path.exists(this_save_directory):\n os.makedirs(this_save_directory)\n this_error = spaghetti_error[day_type]\n analy_fore_rmse = this_error['owp_opt_anly_fore']['rmse']\n ensemble_rmse = this_error['ensemble']['rmse']\n mean_rmse = this_error['owp_opt']['rmse']\n wrf_rmse = this_error['wrf_no_div']['rmse']\n opt_flow_rmse = this_error['opt_flow']['rmse']\n persistence_rmse = this_error['persistence']['rmse']\n horizons = mean_rmse.index.values\n\n # RMSE\n plt.figure()\n plt.plot(horizons, persistence_rmse,\n marker=marker_dict['persistence'],\n color=cpal_dict['persistence'],\n )\n plt.plot(horizons, opt_flow_rmse,\n marker=marker_dict['opt_flow'],\n color=cpal_dict['opt_flow'],\n )\n plt.plot(horizons, wrf_rmse,\n marker=marker_dict['wrf_no_div'],\n color=cpal_dict['wrf_no_div'],\n )\n plt.plot(horizons, mean_rmse,\n marker=marker_dict['owp_opt'],\n color=cpal_dict['owp_opt'],\n )\n plt.plot(horizons, analy_fore_rmse,\n marker=marker_dict['anly_fore'],\n color=cpal_dict['anly_fore'],\n )\n plt.plot(horizons, ensemble_rmse, alpha=0.5, linestyle=':',\n color=cpal_dict['ens_member'])\n plt.xlim([min(horizons), max(horizons)])\n plt.xticks(horizons)\n legend = plt.legend([legend_dict['persistence'],\n legend_dict['opt_flow'],\n legend_dict['wrf_no_div'],\n legend_dict['owp_opt'],\n legend_dict['anly_fore'],\n legend_dict['ens_member']],\n ncol=2,\n loc=loc_dict[day_type])\n y_min = None\n y_max = None\n if day_type == 'translation':\n y_max = 0.1\n if day_type == 'more_complex':\n y_max = 0.29\n plt.ylim([y_min, y_max])\n for handle in legend.legendHandles:\n handle.set_alpha(1)\n plt.xlabel('Forecast horizon (min.)')\n plt.ylabel('RMSE (CI)')\n plt.title(f'RMSE for {this_title}: 2014/{month}/{day}')\n plt.savefig(fname=os.path.join(this_save_directory,\n f'rmse.{format}'),\n format=format, dpi=dpi)\n\n\ndef table_original_error(*, save_directory,\n legend_dict,\n averaged_error):\n file_name = 'all_days'\n decimals = 2\n horizons = [15, 30, 45, 60]\n runs = ['owp_opt', 'owp_opt_anly_fore',\n 'opt_flow', 'wrf_no_div', 'wrf_mean', 'radiosonde', 'persistence']\n rmse = pd.DataFrame(index=horizons, columns=runs)\n rmse.index.name = 'Horizon'\n correlation = rmse.copy()\n bias = rmse.copy()\n truth_sd = rmse.copy()\n for run_name in runs:\n stat_name = 'rmse'\n rmse[run_name] = averaged_error[run_name][stat_name].loc[horizons]\n\n stat_name = 'bias'\n bias[run_name] = averaged_error[run_name][stat_name].loc[horizons]\n\n stat_name = 'correlation'\n correlation[run_name] = (\n averaged_error[run_name][stat_name].loc[horizons])\n\n stat_name = 'truth_sd'\n truth_sd[run_name] = (\n averaged_error[run_name][stat_name].loc[horizons])\n ss_per = (1 - rmse.div(rmse['persistence'], axis='index'))\n peices = [rmse, ss_per, correlation, bias]\n combined = pd.concat(peices, axis=0,\n keys=['RMSE', 'SS_per', 'Corr.', 'Bias'])\n # peices = [rmse, correlation, bias]\n # combined = pd.concat(peices, axis=0,\n # keys=['RMSE', 'Corr.', 'Bias'])\n combined = combined.rename(columns=legend_dict)\n\n def format_table(text, header_num=5, footer_num=2):\n text = text.split(' ')\n text = list(filter(is_empty, text))\n text = ' '.join(text)\n split_text = text.split('\\n')\n split_titles2 = split_text[2]\n removed = split_titles2[-2:]\n split_titles2 = split_titles2[:-2]\n split_titles2 = split_titles2.split('&')\n for count, this in enumerate(split_titles2):\n if len(this) > 2:\n this = this[0] + '{' + this[1:-1] + '}' + this[-1]\n split_titles2[count] = this\n split_text[2] = '&'.join(split_titles2) + removed\n for line_num, line in enumerate(split_text[header_num:-footer_num - 1]):\n split_line = line.split(' ')\n if split_line[0] == 'Corr.':\n Corr = True\n elif split_line[0] == 'SS\\_per':\n split_line[0] = '$\\mbox{SS}_\\mbox{per}$'\n Corr = True\n elif split_line[0] != '':\n Corr = False\n num_slice = slice(4, None, 2)\n numbers_str = split_line[num_slice]\n numbers = np.array(\n split_line[num_slice],\n dtype='float')\n if Corr:\n best_num = numbers.max()\n else:\n best_num = numbers[np.abs(numbers).argmin()]\n argmins = np.where(numbers == best_num)[0]\n for argmin in argmins:\n numbers_str[argmin] = '\\\\B ' + numbers_str[argmin]\n split_line[num_slice] = numbers_str\n split_text[header_num + line_num] = ' '.join(split_line)\n return '\\n'.join(split_text)\n column_format = 'll' + 'S[table-format=-1.3]' * len(runs)\n text = combined.round(decimals=decimals).to_latex(column_format=column_format)\n text2 = format_table(text)\n text2 = re.sub('\\\\\\\\textasciitilde', '~', text2, count=5)\n this_file = os.path.join(save_directory, f'{file_name}_results.tex')\n with open(this_file, 'w') as file:\n file.write(text2)\n\n # Skill Score table\n def format_table_SS(text, header_num=4, footer_num=2):\n text = text.split(' ')\n text = list(filter(is_empty, text))\n text = ' '.join(text)\n split_text = text.split('\\n')\n hor = split_text[3]\n hor = hor.split('&')[0]\n split_text.pop(3)\n split_titles2 = split_text[2]\n removed = split_titles2[-2:]\n split_titles2 = split_titles2[:-2]\n split_titles2 = split_titles2.split('&')\n split_titles2[0] = hor\n for count, this in enumerate(split_titles2):\n if len(this) > 2:\n if this[0] == ' ':\n this = this[1:]\n if this[-1] == ' ':\n this = this[:-1]\n this = ' {' + this + '} '\n split_titles2[count] = this\n split_text[2] = '&'.join(split_titles2) + removed\n for line_num, line in enumerate(split_text[header_num:-footer_num - 1]):\n split_line = line.split(' ')\n num_slice = slice(2, None, 2)\n numbers_str = split_line[num_slice]\n numbers = np.array(\n split_line[num_slice],\n dtype='float')\n best_num = numbers.max()\n argmins = np.where(numbers == best_num)[0]\n for argmin in argmins:\n numbers_str[argmin] = '\\\\B ' + numbers_str[argmin]\n split_line[num_slice] = numbers_str\n split_text[header_num + line_num] = ' '.join(split_line)\n return '\\n'.join(split_text)\n SS_per = (1 - rmse[\n ['owp_opt',\n 'owp_opt_anly_fore',\n 'opt_flow',\n 'wrf_no_div']].div(\n rmse['persistence'], axis='index'))\n SS_per = SS_per.rename(columns=legend_dict)\n column_format = 'l' + 'S[table-format=1.3]' * 4\n text = SS_per.round(\n decimals=decimals).to_latex(\n column_format=column_format)\n text2 = format_table_SS(text)\n text2 = re.sub('\\\\\\\\textasciitilde', '~', text2, count=5)\n this_file = os.path.join(save_directory, f'{file_name}_SS.tex')\n with open(this_file, 'w') as file:\n file.write(text2)\n\n\ndef is_empty(str):\n return str != ''\n\n\ndef table_case_studies(*, save_directory, legend_dict,\n spaghetti_error, dates_dict):\n decimals = 2\n horizons = [15, 30, 45, 60]\n runs = ['owp_opt', 'owp_opt_anly_fore',\n 'opt_flow', 'wrf_no_div', 'wrf_mean', 'radiosonde', 'persistence']\n\n def format_table(text, header_num=5, footer_num=2):\n text = text.split(' ')\n text = list(filter(is_empty, text))\n text = ' '.join(text)\n split_text = text.split('\\n')\n split_titles2 = split_text[2]\n removed = split_titles2[-2:]\n split_titles2 = split_titles2[:-2]\n split_titles2 = split_titles2.split('&')\n for count, this in enumerate(split_titles2):\n if len(this) > 2:\n this = this[0] + '{' + this[1:-1] + '}' + this[-1]\n split_titles2[count] = this\n split_text[2] = '&'.join(split_titles2) + removed\n for line_num, line in enumerate(split_text[header_num:-footer_num - 1]):\n split_line = line.split(' ')\n if split_line[0] == 'Corr.':\n Corr = True\n elif split_line[0] == 'SS\\_per':\n split_line[0] = '$\\mbox{SS}_\\mbox{per}$'\n Corr = True\n elif split_line[0] != '':\n Corr = False\n num_slice = slice(4, None, 2)\n numbers_str = split_line[num_slice]\n numbers = np.array(\n split_line[num_slice],\n dtype='float')\n if Corr:\n best_num = numbers.max()\n else:\n best_num = numbers[np.abs(numbers).argmin()]\n argmins = np.where(numbers == best_num)[0]\n for argmin in argmins:\n numbers_str[argmin] = '\\\\B ' + numbers_str[argmin]\n split_line[num_slice] = numbers_str\n split_text[header_num + line_num] = ' '.join(split_line)\n return '\\n'.join(split_text)\n for day_type, month_day in dates_dict.items():\n this_error = spaghetti_error[day_type]\n rmse = pd.DataFrame(index=horizons, columns=runs)\n rmse.index.name = 'Horizon'\n correlation = rmse.copy()\n bias = rmse.copy()\n for run_name in runs:\n stat_name = 'rmse'\n rmse[run_name] = this_error[run_name][stat_name].loc[horizons]\n\n stat_name = 'bias'\n bias[run_name] = this_error[run_name][stat_name].loc[horizons]\n\n stat_name = 'correlation'\n correlation[run_name] = (\n this_error[run_name][stat_name].loc[horizons])\n ss_per = (1 - rmse.div(rmse['persistence'], axis='index'))\n peices = [rmse, ss_per, correlation, bias]\n combined = pd.concat(peices, axis=0,\n keys=['RMSE', 'SS_per', 'Corr.', 'Bias'])\n combined = combined.rename(columns=legend_dict)\n column_format = 'll' + 'S[table-format=-1.3]' * len(runs)\n text = combined.round(decimals=decimals).to_latex(\n column_format=column_format)\n text2 = format_table(text)\n text2 = re.sub('\\\\\\\\textasciitilde', '~', text2, count=5)\n this_file = os.path.join(save_directory, f'{day_type}_results.tex')\n with open(this_file, 'w') as file:\n file.write(text2)\n\n\ndef main():\n format = 'png'\n dpi = 400\n cpal = sns.color_palette('colorblind')\n sns.set_style('whitegrid')\n sns.set_context('paper', font_scale=1.5,\n rc={'lines.linewidth': 1.0,\n 'lines.markersize': 11})\n cpal_dict = {'opt_flow': cpal[0],\n 'wrf_no_div': cpal[1],\n 'owp_opt': cpal[2],\n 'anly_fore': cpal[5],\n 'persistence': 'gray',\n 'ens_member': cpal[2]}\n legend_dict = {'opt_flow': 'Opt. Flow',\n 'wrf_no_div': 'NWP Winds',\n 'owp_opt': 'ANOC Ens. Mean',\n 'anly_fore': 'ANOC Control',\n 'persistence': 'Persistence',\n 'ens_member': 'ANOC Ens. Members'}\n marker_dict = {'opt_flow': 'o',\n 'wrf_no_div': '^',\n 'owp_opt': 'd',\n 'anly_fore': 'X',\n 'persistence': 's',\n 'ens_member': '1'}\n\n directory_name = 'third_set'\n figure_directory = ('/home/travis/python_code/'\n 'letkf_forecasting/figures/')\n smoothing_error = return_smoothing_data(\n directory_name=directory_name)\n averaged_error = return_original_stats(\n directory_name=directory_name)\n daily_error = return_daily_error()\n\n # plot smoothed\n plot_smoothing_data(cpal_dict=cpal_dict,\n legend_dict=legend_dict,\n marker_dict=marker_dict,\n format=format,\n dpi=dpi,\n smoothing_error=smoothing_error,\n averaged_error=averaged_error,\n save_directory=figure_directory)\n plt.close('all')\n\n # plot original error plots\n plot_original_error(cpal_dict=cpal_dict,\n legend_dict=legend_dict,\n marker_dict=marker_dict,\n format=format,\n dpi=dpi,\n averaged_error=averaged_error,\n save_directory=figure_directory)\n plt.close('all')\n\n # plot error by date plots\n plot_daily_error(cpal_dict=cpal_dict,\n legend_dict=legend_dict,\n marker_dict=marker_dict,\n\n format=format,\n dpi=dpi,\n daily_error=daily_error,\n averaged_error=averaged_error,\n save_directory=figure_directory)\n plt.close('all')\n\n # plot spaghetti data\n dates_dict = {\n 'translation': (4, 15),\n 'more_complex': (5, 29),\n 'two_levels': (4, 26)\n }\n run_names = ['persistence',\n 'opt_flow',\n 'wrf_no_div',\n 'owp_opt',\n 'wrf_mean',\n 'radiosonde',\n ['ensemble', 'owp_opt'],\n ['anly_fore', 'owp_opt']]\n loc_dict = {\n 'translation': 'upper left',\n 'more_complex': 'upper left',\n 'two_levels': 'lower right'\n }\n spaghetti_error = return_spaghetti_error(\n dates_dict=dates_dict,\n run_names=run_names)\n plot_spaghetti(cpal_dict=cpal_dict,\n legend_dict=legend_dict,\n marker_dict=marker_dict,\n loc_dict=loc_dict,\n format=format,\n dpi=dpi,\n dates_dict=dates_dict,\n spaghetti_error=spaghetti_error,\n save_directory=figure_directory)\n plt.close('all')\n\n # tables\n table_directory = '/home2/travis/python_code/letkf_forecasting/tables/'\n table_legend_dict = {'opt_flow': 'Opt.~Flow',\n 'opt_flow_with_div': 'Opt. Flow w/ Div.',\n 'wrf_no_div': 'NWP Winds',\n 'wrf': 'NWP w/ Div.',\n 'owp_opt': 'ANOC Ens.~Mean',\n 'persistence': 'Persis.',\n 'radiosonde': 'Radiosonde',\n 'wrf_mean': 'NWP Avg.~Winds',\n 'ens_member': 'ANOC Ens.~Member',\n 'owp_opt_anly_fore': 'ANOC Control'}\n\n # original error table\n table_original_error(save_directory=table_directory,\n legend_dict=table_legend_dict,\n averaged_error=averaged_error)\n\n # case study tables\n table_case_studies(save_directory=table_directory,\n legend_dict=table_legend_dict,\n spaghetti_error=spaghetti_error,\n dates_dict=dates_dict)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "scripts/generate_plots.py", "file_name": "generate_plots.py", "file_ext": "py", "file_size_in_byte": 32166, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "matplotlib.use", "line_number": 7, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "pandas.read_hdf", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 33, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 48, "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": "pandas.read_hdf", "line_number": 52, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 54, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 74, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 77, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 88, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 94, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 108, "usage_type": "call"}, {"api_name": "os.path", "line_number": 108, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 110, "usage_type": "call"}, {"api_name": "os.path", "line_number": 110, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 114, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 114, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 117, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 117, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 130, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 131, "usage_type": "call"}, {"api_name": "os.path", "line_number": 131, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 147, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 149, "usage_type": "call"}, {"api_name": "os.path", "line_number": 149, "usage_type": "attribute"}, {"api_name": "pandas.read_hdf", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 159, "usage_type": "call"}, {"api_name": "os.path", "line_number": 159, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 161, "usage_type": "call"}, {"api_name": "os.path", "line_number": 161, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 167, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 167, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 202, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 202, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 204, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 206, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 206, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 207, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 207, "usage_type": "call"}, {"api_name": "os.path", "line_number": 207, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 231, "usage_type": "call"}, {"api_name": "pandas.datetime", "line_number": 242, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 248, "usage_type": "call"}, {"api_name": "os.path", "line_number": 248, "usage_type": "attribute"}, {"api_name": "letkf_forecasting.letkf_io.find_latest_run", "line_number": 255, "usage_type": "call"}, {"api_name": "letkf_forecasting.letkf_io", "line_number": 255, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 257, "usage_type": "call"}, {"api_name": "os.path", "line_number": 257, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 263, "usage_type": "call"}, {"api_name": "os.path", "line_number": 263, "usage_type": "attribute"}, {"api_name": "pandas.read_hdf", "line_number": 265, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 273, "usage_type": "call"}, {"api_name": "os.path", "line_number": 273, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 275, "usage_type": "call"}, {"api_name": "os.path", "line_number": 275, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 276, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 280, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 284, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 288, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 292, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 296, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 302, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figaspect", "line_number": 303, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 303, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 307, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 307, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 308, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 308, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 311, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 311, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 314, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 314, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 317, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 317, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 320, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 320, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 321, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 321, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 322, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 322, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 328, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 328, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 329, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 329, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 330, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 330, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 331, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 331, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 332, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 332, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 333, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 333, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 333, "usage_type": "call"}, {"api_name": "os.path", "line_number": 333, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 357, "usage_type": "call"}, {"api_name": "os.path", "line_number": 357, "usage_type": "attribute"}, {"api_name": "letkf_forecasting.letkf_io.find_latest_run", "line_number": 364, "usage_type": "call"}, {"api_name": "letkf_forecasting.letkf_io", "line_number": 364, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 366, "usage_type": "call"}, {"api_name": "os.path", "line_number": 366, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 376, "usage_type": "call"}, {"api_name": "os.path", "line_number": 376, "usage_type": "attribute"}, {"api_name": "pandas.read_hdf", "line_number": 379, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 388, "usage_type": "call"}, {"api_name": "os.path", "line_number": 388, "usage_type": "attribute"}, {"api_name": "pandas.read_hdf", "line_number": 391, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 400, "usage_type": "call"}, {"api_name": "os.path", "line_number": 400, "usage_type": "attribute"}, {"api_name": "pandas.read_hdf", "line_number": 403, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 424, "usage_type": "call"}, {"api_name": "os.path", "line_number": 424, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 426, "usage_type": "call"}, {"api_name": "os.path", "line_number": 426, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 427, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 438, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 438, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 439, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 439, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 443, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 443, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 447, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 447, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 451, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 451, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 455, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 455, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 459, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 459, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 461, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 461, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 462, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 462, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 463, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 463, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 477, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 477, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 480, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 480, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 481, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 481, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 482, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 482, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 483, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 483, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 483, "usage_type": "call"}, {"api_name": "os.path", "line_number": 483, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 496, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 517, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 549, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 555, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 556, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 565, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 566, "usage_type": "call"}, {"api_name": "os.path", "line_number": 566, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 597, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 601, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 619, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 620, "usage_type": "call"}, {"api_name": "os.path", "line_number": 620, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 661, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 667, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 668, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 676, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 692, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 699, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 700, "usage_type": "call"}, {"api_name": "os.path", "line_number": 700, "usage_type": "attribute"}, {"api_name": "seaborn.color_palette", "line_number": 708, "usage_type": "call"}, {"api_name": "seaborn.set_style", "line_number": 709, "usage_type": "call"}, {"api_name": "seaborn.set_context", "line_number": 710, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.close", "line_number": 750, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 750, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 760, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 760, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 772, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 772, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 805, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 805, "usage_type": "name"}]} +{"seq_id": "71597910", "text": "from wirc_drp.constants import *\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport copy\nfrom scipy.ndimage import shift\nfrom scipy import ndimage\nfrom scipy.signal import fftconvolve \nfrom astropy.io import ascii as asci\nfrom astropy.io import fits \nfrom wirc_drp.utils import spec_utils as su\nfrom wirc_drp.utils.image_utils import findTrace\nfrom decimal import Decimal\n\n\n\ndef plot_source_traces(source_list, cmap = None, figsize=(8,8), plot_lims = None):\n '''\n Plot all the traces \n\n '''\n\n fig = plt.figure(figsize=figsize)\n ax1 = fig.add_subplot(221)\n ax2 = fig.add_subplot(222)\n ax3 = fig.add_subplot(223)\n ax4 = fig.add_subplot(224)\n\n #Pick a colormap\n if cmap == None:\n cm1 = plt.get_cmap('bwr')\n\n\n nsources = np.size(source_list)\n\n for i,source in enumerate(source_list):\n\n c = cm1(1.*(i)/nsources)\n\n ax1.plot(source.trace_spectra[0,0,:],source.trace_spectra[0,1,:], color=c)\n ax2.plot(source.trace_spectra[1,0,:],source.trace_spectra[1,1,:], color=c)\n ax3.plot(source.trace_spectra[2,0,:],source.trace_spectra[2,1,:], color=c)\n ax4.plot(source.trace_spectra[3,0,:],source.trace_spectra[3,1,:], color=c)\n\n if plot_lims != None:\n ax1.set_xlim(plot_lims[0:2])\n ax2.set_xlim(plot_lims[0:2])\n ax3.set_xlim(plot_lims[0:2])\n ax4.set_xlim(plot_lims[0:2])\n\n ax1.set_ylim(plot_lims[2:])\n ax2.set_ylim(plot_lims[2:])\n ax3.set_ylim(plot_lims[2:])\n ax4.set_ylim(plot_lims[2:])\n\n\ndef align_spectra(source_list, ref_source = None, xlow=0, xhigh=-1):\n '''\n Align each trace to a reference trace with cross correlation. \n '''\n for i in np.arange(0,len(source_list)):\n for j in range(4):\n new_trace = copy.deepcopy(source_list[i].trace_spectra[j,1,xlow:xhigh])\n\n if ref_source == None:\n ref = source_list[0].trace_spectra[j,1,xlow:xhigh]\n len0 = np.size(source_list[0].trace_spectra[j,1,:])\n else:\n ref = ref_source.trace_spectra[j,1,xlow:xhigh]\n len0 = np.size(source_list[0].trace_spectra[j,1,:])\n\n corr = fftconvolve(np.nan_to_num(ref/np.nanmax(ref)), np.nan_to_num((new_trace/np.nanmax(new_trace))))\n\n # shift_size = np.nanargmax(corr) - len(ref) +1\n shift_size = (np.nanargmax(corr) - len0)/2\n\n source_list[i].trace_spectra[j,1,:] = shift(source_list[i].trace_spectra[j,1,:], -shift_size)\n\ndef get_angles_widths_from_list(filelist, data_dir = '', source_number = 0):\n \"\"\"\n Go through the list of calibrated and extracted files and read out angles for given source_number\n \"\"\"\n angles = []\n widths = []\n\n filelist = asci.read(filelist, format = 'no_header')['col1'] \n\n for j in filelist:\n hdulist = fits.open(data_dir+j)\n i = source_number\n try:\n widths += [np.fromstring(hdulist[(2*i)+2].header[\"WIDTHS\"][1:-1], sep = ' ')]\n angles += [np.fromstring(hdulist[(2*i)+2].header[\"ANGLES\"][1:-1], sep = ' ')]\n except:\n print('Widths or angles not available')\n return np.array(widths), np.array(angles)\n\n#POLARIZATION CALCULATION HELPER\n# q, u, q_err, u_err, q_position, u_position = compute_qu(spec1, spec2, HWP1, HWP2)\n#helper function to compute q and u given two spectra cubes\ndef compute_qu(spec1, spec2, HWP1, HWP2, run_alignment = True, method = 'flux_ratio'):\n \"\"\"\n compute_qu is a helper function that takes two spectral cubes, each with the dimensions of (4,3,spec_pix)\n with two orthogonal HWP angles (0 and 45 or 22.5 and 67.5), then compute q and u\n Inputs:\n spec1, spec2: spectra cubes, each with the dimensions of (4,3,spec_pix). \n First index is the 4 spectra in one WIRC image.\n Second index is (wavelength, flux, flux_error)\n Last index is the spectral pixel direction\n HWP1, HWP2: half wave plate angles for spec1 and spec2 respectively. We need HWP2-HWP1 to be 45 deg (orthogonal)\n run_alignment: booleen indicating whether to run align_spectral_cube and scale_and_combine_spectra. Default is True\n method: Either 'flux_ratio' or 'double_difference'. \n Output:\n q, u: normalized stokes vectors q and u where q, u corresponds to polarization along 0 and 45 degrees respectively\n q_err, u_err: associated uncertainties\n q_ind, u_ind: indices of frames used to compute q and u. This is provided so we can check the results. \n \"\"\" \n #If method is neither 'flux_ratio' nor 'double_difference', revert to 'flux_ratio'\n if method not in ['flux_ratio','double_difference']:\n print(\"method has to be either flux_ratio or double_difference. not %s. revert to flux_ratio\"%method)\n\n #stack spectra\n # if spec1.shape != spec2.shape:\n if ((round(HWP1,2) - round(HWP2,2))%45) >0.01: #add some tolerance\n print(np.abs((HWP1 - HWP2)%45))\n print(\"Error, halfwave plate angles (%f, %f) are not orthogonal.\"%(HWP1,HWP2))\n return None\n else:\n spec_cube = np.stack([spec1, spec2]) #This has to be the same shape\n #align and scale cubes\n if run_alignment:\n aligned_cube = su.align_spectral_cube(spec_cube)\n scaled_cube = su.scale_and_combine_spectra(aligned_cube, return_scaled_cube = True)\n scaled_cube = aligned_cube\n else:\n scaled_cube = spec_cube\n\n if method == 'double_difference':\n #polarization vector and uncertainty. This is (spec1-spec2)/(spec1+spec2)\n pol_vec = (scaled_cube[0,:,1,:] - scaled_cube[1,:,1,:])/(scaled_cube[0,:,1,:] + scaled_cube[1,:,1,:])\n pol_err = (2/(scaled_cube[0,:,1,:] + scaled_cube[1,:,1,:])**2) * np.sqrt( (scaled_cube[0,:,1,:]*scaled_cube[1,:,2,:])**2 + (scaled_cube[0,:,2,:]* scaled_cube[1,:,1,:])**2)\n\n #now determine which is which\n sampling_angles_0 = np.array([135, 45, 90, 0]) #THIS IS FROM UL, LR, UR, LL = U-, U+, Q-, Q+ as determined from twilight. \n sampling_angles_1 = (sampling_angles_0 + 2*(HWP1))%180 #angles are mod 180 deg. \n sampling_angles_2 = (sampling_angles_0 + 2*(HWP2))%180 #angles are mod 180 deg. \n signs = np.sign(sampling_angles_2 - sampling_angles_1) # 0 - 45 is +q, 22.5 - 67.5 is +u\n\n #q's are those with sampling_angles_1 = 0 or 90 and sampling_angles_2 = 90 or 0\n q_ind = np.where(np.logical_or(sampling_angles_1 == 0, sampling_angles_1 == 90))\n u_ind = np.where(np.logical_or(sampling_angles_1 == 45, sampling_angles_1 == 135))\n\n #print(HWP1, HWP2, sampling_angles_1, sampling_angles_2, q_ind, u_ind)\n # print(signs)\n # print(signs[list(q_ind[0])])\n # print('q shape is ',pol_vec[q_ind[0]].shape)\n q_sign = signs[q_ind[0]]\n u_sign = signs[u_ind[0]]\n q = pol_vec[q_ind[0]]*q_sign[:,None] \n u = pol_vec[u_ind[0]]*u_sign[:,None] \n q_err = pol_err[list(q_ind[0])] \n u_err = pol_err[list(u_ind[0])] \n # print(q.shape, q_err.shape)\n return q, u, q_err, u_err, q_ind[0], u_ind[0]\n\n elif method == 'flux_ratio':\n #First, figure out the sampling angles\n sampling_angles_0 = np.array([135, 45, 90, 0]) #THIS IS FROM UL, LR, UR, LL = U-, U+, Q-, Q+ as determined from twilight. \n sampling_angles_1 = (sampling_angles_0 + 2*(HWP1))%180 #angles are mod 180 deg. \n sampling_angles_2 = (sampling_angles_0 + 2*(HWP2))%180 #angles are mod 180 deg. \n\n #indices (non elegant solution...)\n ind0_0 = np.where(sampling_angles_1 == 0)[0]\n ind0_90 = np.where(sampling_angles_1 == 90)[0]\n ind0_45 = np.where(sampling_angles_1 == 45)[0]\n ind0_135 = np.where(sampling_angles_1 == 135)[0]\n ind1_0 = np.where(sampling_angles_2 == 0)[0]\n ind1_90 = np.where(sampling_angles_2 == 90)[0]\n ind1_45 = np.where(sampling_angles_2 == 45)[0]\n ind1_135 = np.where(sampling_angles_2 == 135)[0]\n\n #q computation, \n Rq_sq = (scaled_cube[0,ind0_0,1,:]/scaled_cube[0,ind0_90,1,:]) / (scaled_cube[1,ind1_90,1,:]/scaled_cube[1,ind1_0,1,:])\n Rq_sq_err = Rq_sq * np.sqrt( (scaled_cube[0,ind0_0,2,:]/scaled_cube[0,ind0_0,1,:])**2 +\n (scaled_cube[0,ind0_90,2,:]/scaled_cube[0,ind0_90,1,:])**2 +\n (scaled_cube[0,ind1_0,2,:]/scaled_cube[0,ind1_0,1,:])**2 +\n (scaled_cube[0,ind1_90,2,:]/scaled_cube[0,ind1_90,1,:])**2 )\n\n q = (np.sqrt(Rq_sq) - 1)/(np.sqrt(Rq_sq) + 1)\n q_err = Rq_sq_err /np.sqrt(Rq_sq)/(np.sqrt(Rq_sq)+1)**2 \n\n #u computation, \n Ru_sq = (scaled_cube[0,ind0_45,1,:]/scaled_cube[0,ind0_135,1,:]) / (scaled_cube[1,ind1_135,1,:]/scaled_cube[1,ind1_45,1,:])\n Ru_sq_err = Ru_sq * np.sqrt( (scaled_cube[0,ind0_45,2,:]/scaled_cube[0,ind0_45,1,:])**2 +\n (scaled_cube[0,ind0_135,2,:]/scaled_cube[0,ind0_135,1,:])**2 +\n (scaled_cube[0,ind1_45,2,:]/scaled_cube[0,ind1_45,1,:])**2 +\n (scaled_cube[0,ind1_135,2,:]/scaled_cube[0,ind1_135,1,:])**2 )\n\n u = (np.sqrt(Ru_sq) - 1)/(np.sqrt(Ru_sq) + 1)\n u_err = Ru_sq_err /np.sqrt(Ru_sq)/(np.sqrt(Ru_sq)+1)**2 \n return q, u, q_err, u_err, None, None\n\n \n\ndef group_HWP(HWP_set):\n \"\"\"\n Helper function to compute_qu_for_obs_sequence. It groups given list of HWP angles into sets of two orthogonal observations. \n\n Input: HWP_set, a vector of all half wave plate angles in the observing compute_qu_for_obs_sequence\n Output: Two arrays each for sets of 0/45 deg and another for 22.5/67.5. compute_qu_for_obs_sequence can then use this info to call \n compute_qu to compute qu for each appropriate pair. \n \"\"\"\n # #HWP_index determine which pair is q and which is u. If HWP = 0 or 45, HWP_ind = 0; if HWP = 22.5, 67.5, HWP_ind = 1\n # #So HWP_ind = 0; LL, UR is q, LR, UL is u. Flipped for HWP_ind = 1\n # HWP_ind = (HWP_set//22.5)%2\n\n # group_0 = HWP_set[HWP_ind == 0]\n # group_1 = HWP_set[HWP_ind == 1]\n # #initialize the sets of observations\n # set_0 = []\n # set_1 = []\n # holding_0_ind = []\n # holding_1_ind = []\n # for i, HWP in enumerate(group_0):\n\n set_0 = np.where(HWP_set == 0)\n set_225 = np.where(HWP_set == 22.5)\n set_45 = np.where(HWP_set == 45)\n set_675 = np.where(HWP_set == 67.5)\n\n small_0_45 = np.min( (len(set_0[0]), len(set_45[0])))\n small_225_675 = np.min( (len(set_225[0]), len(set_675[0])))\n\n\n pairs_0 = np.stack([set_0[0][0:small_0_45], set_45[0]][0:small_0_45], axis = 1) #This is an array with shape (N/4, 2), each element is 2 indices of best 0, 45 pair. \n pairs_225 = np.stack([set_225[0][0:small_225_675], set_675[0][0:small_225_675]], axis = 1)\n return pairs_0, pairs_225\n\ndef compute_qu_for_obs_sequence(spectra_cube, HWP_set, HWP_offset = 0, run_alignment = True):\n \"\"\"\n This function takes a set of aligned spectra along with a set of HWP angles, both with the same length, \n and call compute_qu to measure polarization q and u. \n\n Input:\n obs_set: a spectral cube with shape (N, 4, 3, spec_pix) where N is the number of frames. Each element is the 4 spectra from single image. \n HWP_set: a vector of length N, prescribing the half wave plate angle for each of the frame in obs_set. Values should be 0, 45, 22.5, 67.5 for double diff. \n if there is an offset from this orthogonal set, indicae so in HWP_offset\n HWP_offset: a float indicating the zeropoint of the HWP angle. We proceed with HWP_set - HWP_offset.\n\n Output:\n q, q_err, u, u_err **currently single differencing in time. Can do double difference manually afterward. This may change. \n \"\"\"\n #First, check length\n if spectra_cube.shape[0] != len(HWP_set):\n raise ValueError(\"Lengths of spectra_cube and HWP_set are not equal.\")\n\n #Apply HWP_offset\n HWP_final = HWP_set - HWP_offset\n #check if the values are good. \n all_ang = set([0,45,22.5,67.5])\n if set(HWP_final) != all_ang:\n raise ValueError(\"HWP set doesn't have all 4 angles or have wrong angles: %s\"%str(set(HWP_final)))\n\n #Arrange the sequence into best pairs of 0/45 and 22.5/67.5 to compute qu\n pairs_0, pairs_225 = group_HWP(HWP_final)\n\n #First deal with observations with HWP angles 0/45. Go through the list and compute q and u for each pair\n\n all_q0 = []\n all_u0 = []\n all_qerr0 = []\n all_uerr0 = []\n all_qind0 = []\n all_uind0 = []\n\n for i in pairs_0:\n q, u, q_err, u_err, q_ind, u_ind = compute_qu(spectra_cube[i[0]], spectra_cube[i[1]], HWP_final[i[0]], HWP_final[i[1]], run_alignment)\n all_q0 += [q]\n all_u0 += [u]\n all_qerr0 += [q_err]\n all_uerr0 += [u_err]\n all_qind0 += [q_ind]\n all_uind0 += [u_ind]\n\n #Now deal with observations with HWP angles 22.5/67.5. \n\n all_q225 = []\n all_u225 = []\n all_qerr225 = []\n all_uerr225 = []\n all_qind225 = []\n all_uind225 = []\n\n for i in pairs_225:\n q, u, q_err, u_err, q_ind, u_ind = compute_qu(spectra_cube[i[0]], spectra_cube[i[1]], HWP_final[i[0]], HWP_final[i[1]], run_alignment)\n all_q225 += [q]\n all_u225 += [u]\n all_qerr225 += [q_err]\n all_uerr225 += [u_err]\n all_qind225 += [q_ind]\n all_uind225 += [u_ind]\n\n\n all_q = np.array(all_q0 + all_q225 )\n all_u = np.array(all_u0 + all_u225 )\n all_qerr = np.array(all_qerr0 + all_qerr225 )\n all_uerr = np.array(all_uerr0 + all_uerr225 )\n all_qind = np.array(all_qind0 + all_qind225 )\n all_uind = np.array(all_uind0 + all_uind225 )\n\n return all_q, all_u, all_qerr, all_uerr, all_qind, all_uind\n\n\ndef find_best_background(list_of_headers, separation_threshold = 2):\n \"\"\"\n find_best_background takes a list of headers from WIRC+Pol observations and find best background frame for each element. \n Here are the conditions: \n Same HWP angle\n With telescope offset greater than 'separation_threshold' (default at 2 arcsec)\n Closest in time\n Not already used by another frame (this condition is relaxed if every frame is used up. Say we have extra set of exposures at position A)\n\n Input:\n list_of_headers: a list of fits headers of the observations. \n separation_threshold: how far away, in arcsec, the background frame is required to be from the current frame. \n\n Output: \n list of the same length of list_of_headers giving the index of the best background for each frame in list_of_headers\n example: if the best background for file wirc0001 is wirc0005, then best_bkg[1] = 5 \n \"\"\"\n from astropy.time import Time\n from astropy.coordinates import SkyCoord\n import astropy.units as u \n #closest in time, some distance away, same HWP\n sep_threshold = separation_threshold \n all_hdr = list_of_headers\n #get some useful quantities\n coords = np.array([ SkyCoord(x['RA'], x['DEC'], unit = (u.hourangle, u.deg)) for x in all_hdr ])\n names = np.array([x['RAW_FN'] for x in all_hdr])\n hwps = np.array([x['HWP_ANG'] for x in all_hdr])\n times = np.array([Time(x['UTSHUT'], format = 'isot') for x in all_hdr])\n\n #array to keep track of best background frame\n best_bkgs = np.array( [None]*len(all_hdr) )\n #array to keep track of whether this index is already in a pair \n already_in_pair = np.zeros(len(all_hdr))\n\n for i in range(len(all_hdr)):\n if best_bkgs[i] is None:\n # print(i)\n all_dist = np.array([ (coords[i].separation(x)).arcsec for x in coords ])\n far_enough = all_dist > sep_threshold\n same_hwp = hwps == hwps[i]\n\n all_good = np.logical_and(far_enough, same_hwp)\n\n\n \n #are there good background that is not already in a pair?\n not_in_pair = already_in_pair == 0\n if np.any(not_in_pair[all_good]): #if there're some frames available not already in a pair, use those first\n all_good = np.logical_and(all_good, not_in_pair)\n #otherwise, just accept the repeat\n \n\n #time difference\n t0 = Time(all_hdr[i]['UTSHUT'], format = 'isot')\n time_diff = np.array([ np.abs((x - t0).value) for x in times[all_good]])\n\n #Use minimal time difference\n best_bkg = names[all_good][np.where(time_diff == np.min(time_diff))[0]]\n \n\n\n #Save this in best_bkg array\n best_bkgs[i] = np.where( names == best_bkg[0] )[0][0]\n \n #remember that this is already in an AB pair\n already_in_pair[i] = 1\n \n #But also reciprocate, so it's neatly in a pair\n if best_bkgs[np.where(names == best_bkg )[0]][0] is None:\n #print(names[np.where(names == best_bkg )[0]])\n best_bkgs[np.where(names == best_bkg )[0]] = np.where(names == names[i])[0][0]\n #also remember that this frame is already in a pair\n already_in_pair[np.where(names == best_bkg )[0]] = 1\n \n return best_bkgs.astype('int') \n \n # print(all_dist)\n\ndef plot_pol_summary(wvs,spec,q,u,qerr,uerr,mode='mean',xlow=1.15,xhigh=1.325,ylow=-0.02,yhigh=0.02,\n target_name=\"\",date=\"19850625\",t_ext = 0,binsize=1,theta_wrap=180,ldwarf=False,show=True,\n save_path=None,legend_loc =\"bottom left\",all_theta=False,\n fig = None, axes = None):\n '''\n Make a summary plot of polarization. The formatting assumes that the inputs (q,u,qerr,uerr)\n are the output of compute_qu_for_obs_sequence. \n\n Inputs:\n mode - Either \"mean\" or \"median\"\n fig, axes - to plot on existing figure/axes. None if not. \n '''\n\n #First calculate the double_difference values\n q_dd = np.nanmean(q,axis=1)\n u_dd = np.nanmean(u,axis=1)\n p_dd = np.sqrt(q_dd**2+u_dd**2)\n theta_dd = 0.5*np.degrees(np.arctan2(u_dd,q_dd))\n theta_dd[theta_dd < 0] +=180\n\n q_dd_err = np.sqrt(np.sum((qerr**2),axis=1))/qerr.shape[1]\n u_dd_err = np.sqrt(np.sum((uerr**2),axis=1))/uerr.shape[1]\n\n #Now calculate the mean or median\n from astropy import stats\n q_mean = np.zeros([q_dd.shape[1]]) #We name this mean, though it could either be Mean or Median\n q_std = np.zeros([q_dd.shape[1]])\n u_mean = np.zeros([u_dd.shape[1]])\n u_std = np.zeros([u_dd.shape[1]])\n\n for i in range(q_dd.shape[1]):\n\n mn,md,std = stats.sigma_clipped_stats(q_dd[:,i], sigma=3, maxiters=5)\n if mode == 'median':\n q_mean[i] = md\n else:\n q_mean[i] = mn\n q_std[i] = std/np.sqrt(q_dd.shape[0])\n mn,md,std = stats.sigma_clipped_stats(u_dd[:,i], sigma=3, maxiters=5)\n u_std[i] = std/np.sqrt(q_dd.shape[0])\n if mode == 'median':\n u_mean[i] = md\n else:\n u_mean[i] = mn\n\n p_mean = np.sqrt(q_mean**2+u_mean**2)\n theta_mean = 0.5*np.degrees(np.arctan2(u_mean,q_mean))\n theta_mean[theta_mean < theta_wrap] +=180\n\n q_mean_err = np.sqrt(np.sum(q_dd_err**2,axis=0))/q_dd_err.shape[0]\n u_mean_err = np.sqrt(np.sum(u_dd_err**2,axis=0))/q_dd_err.shape[0]\n p_mean_err = np.sqrt(q_mean**2*q_mean_err**2+u_mean**2*u_mean_err**2)/p_mean\n p_std = np.sqrt(q_mean**2*q_std**2+u_mean**2*u_std**2)/p_mean\n theta_mean_err = 0.5*np.degrees( np.sqrt( (u_mean**2*q_mean_err**2+q_mean**2*u_mean_err**2)/(q_mean**2+u_mean**2)**2))\n theta_std = 0.5*np.degrees( np.sqrt( (u_mean**2*q_std**2+q_mean**2*u_std**2)/(q_mean**2+u_mean**2)**2))\n\n\n\n ### Implement Binning\n if binsize != 1:\n snip = q_mean.shape[0] % binsize\n if snip != 0:\n q_mean = np.mean(q_mean[:-snip].reshape(-1,binsize),axis=1)\n u_mean = np.mean(u_mean[:-snip].reshape(-1,binsize),axis=1)\n wvs = np.mean(wvs[:-snip].reshape(-1,binsize),axis=1)\n p_mean = np.sqrt(q_mean**2+u_mean**2)\n theta_mean = 0.5*np.degrees(np.arctan2(u_mean,q_mean))\n if spec is not None:\n spec = np.mean(spec[:-snip].reshape(-1,binsize),axis=1)\n\n q_mean_err = np.sqrt(np.sum(q_mean_err[:-snip].reshape(-1,binsize)**2,axis=1))/binsize\n q_std = np.sqrt(np.sum(q_std[:-snip].reshape(-1,binsize)**2,axis=1))/binsize\n u_mean_err = np.sqrt(np.sum(u_mean_err[:-snip].reshape(-1,binsize)**2,axis=1))/binsize\n u_std = np.sqrt(np.sum(u_std[:-snip].reshape(-1,binsize)**2,axis=1))/binsize\n p_mean_err = np.sqrt(q_mean**2*q_mean_err**2+u_mean**2*u_mean_err**2)/p_mean\n theta_mean_err = 0.5*np.degrees( np.sqrt( (u_mean**2*q_mean_err**2+q_mean**2*u_mean_err**2)/(q_mean**2+u_mean**2)**2))\n \n \n else: \n q_mean = np.mean(q_mean.reshape(-1,binsize),axis=1)\n u_mean = np.mean(u_mean.reshape(-1,binsize),axis=1)\n wvs = np.mean(wvs.reshape(-1,binsize),axis=1)\n p_mean = np.sqrt(q_mean**2+u_mean**2)\n theta_mean = 0.5*np.degrees(np.arctan2(u_mean,q_mean))\n # theta_bin[theta_bin < 0] +=180\n if spec is not None:\n spec = np.mean(spec.reshape(-1,binsize),axis=1)\n\n q_mean_err = np.sqrt(np.sum(q_mean_err.reshape(-1,binsize)**2,axis=1))/binsize\n q_std = np.sqrt(np.sum(q_std.reshape(-1,binsize)**2,axis=1))/binsize\n u_mean_err = np.sqrt(np.sum(u_mean_err.reshape(-1,binsize)**2,axis=1))/binsize\n u_std = np.sqrt(np.sum(u_std.reshape(-1,binsize)**2,axis=1))/binsize\n p_mean_err = np.sqrt(q_mean**2*q_mean_err**2+u_mean**2*u_mean_err**2)/p_mean\n theta_mean_err = 0.5*np.degrees( np.sqrt( (u_mean**2*q_mean_err**2+q_mean**2*u_mean_err**2)/(q_mean**2+u_mean**2)**2))\n p_std = np.sqrt(q_mean**2*q_std**2+u_mean**2*u_std**2)/p_mean\n theta_std = 0.5*np.degrees( np.sqrt( (u_mean**2*q_std**2+q_mean**2*u_std**2)/(q_mean**2+u_mean**2)**2))\n \n #Wrap theta about 180\n theta_mean[theta_mean>theta_wrap] -= 180\n\n\n\n ##Calculate the mean values\n low = 65\n high = 135\n\n inds = (wvs > 1.165) & (wvs<1.31)\n mn_q = np.mean(q_mean[inds])\n mn_u = np.mean(u_mean[inds])\n mn_p = np.mean(p_mean[inds])\n\n mn_q_err = np.mean(q_mean_err[inds])\n mn_u_err = np.mean(u_mean_err[inds])\n mn_p_err = np.mean(p_mean_err[inds])\n\n std_q = np.std(q_mean[inds])\n std_u = np.std(u_mean[inds])\n std_p = np.std(p_mean[inds])\n\n ### Make the plot!!! ###\n if fig is None and axes is None:\n fig,axes = plt.subplots(3,2,figsize=(16,20))\n\n axes[2,1] = plt.subplot(3,2,6, projection='polar')\n ##### Plot Q, U, P and theta ######\n #The mean values\n if mode == \"median\":\n axes[0,0].plot(wvs,q_mean,'k',label=\"Median\")\n else:\n axes[0,0].plot(wvs,q_mean,'k',label=\"Mean\") \n axes[0,1].plot(wvs,u_mean,'k') \n axes[1,0].plot(wvs,p_mean,'k')\n \n\n #Make a line at zero\n axes[0,0].axhline(0.,color='r',linestyle='--')\n axes[0,1].axhline(0.,color='r',linestyle='--')\n\n #Fill in photon/ron error ranges\n axes[0,0].fill_between(wvs,q_mean+q_mean_err,q_mean-q_mean_err,color='k',alpha=0.1,label=\"Propagated Noise\")\n axes[0,1].fill_between(wvs,u_mean+u_mean_err,u_mean-u_mean_err,color='k',alpha=0.1)\n axes[1,0].fill_between(wvs,p_mean+p_mean_err,p_mean-p_mean_err,color='k',alpha=0.1)\n # axes[1,1].fill_between(wvs,theta_mean+theta_mean_err,theta_mean-theta_mean_err,color='k',alpha=0.1)\n\n #Only plot theta where > 3sigma\n if all_theta:\n where_theta = p_mean > 0\n else:\n where_theta = p_mean > 3*p_mean_err\n axes[1,1].errorbar(wvs[where_theta],theta_mean[where_theta],yerr=theta_mean_err[where_theta],linestyle=\"None\",marker='o',color='k')\n axes[2,1].errorbar(np.radians(theta_mean[where_theta]),wvs[where_theta],xerr=np.radians(theta_mean_err[where_theta]),linestyle=\"None\",marker='o',color='k')\n #Fill in photon/ron error ranges from stds\n axes[0,0].plot(wvs,q_mean+q_std,'k--',alpha=0.5,label=\"Standard Error on the Mean\")\n axes[0,0].plot(wvs,q_mean-q_std,'k--',alpha=0.5)\n axes[0,1].plot(wvs,u_mean+u_std,'k--',alpha=0.5)\n axes[0,1].plot(wvs,u_mean-u_std,'k--',alpha=0.5)\n axes[1,0].plot(wvs,p_mean+p_std,'k--',alpha=0.5)\n axes[1,0].plot(wvs,p_mean-p_std,'k--',alpha=0.5)\n # axes[1,1].plot(wvs,theta_mean+theta_std,'k--',alpha=0.5)\n # axes[1,1].plot(wvs,theta_mean-theta_std,'k--',alpha=0.5)\n\n #3-sigma for p\n axes[1,0].plot(inds,3*p_mean_err,'r')\n\n #Axis plot ranges\n axes[0,0].set_xlim(xlow,xhigh)\n axes[0,1].set_xlim(xlow,xhigh)\n axes[1,0].set_xlim(xlow,xhigh)\n axes[1,1].set_xlim(xlow,xhigh)\n\n axes[0,0].set_ylim(ylow,yhigh)\n axes[0,1].set_ylim(ylow,yhigh)\n axes[1,0].set_ylim(0,yhigh)\n axes[1,1].set_ylim(theta_wrap-180,theta_wrap)\n\n axes[0,0].locator_params(nbins=6)\n axes[0,1].locator_params(nbins=6)\n axes[1,0].locator_params(nbins=6)\n axes[1,1].locator_params(nbins=6)\n axes[2,1].locator_params(nbins=3)\n\n axes[0,0].legend(loc=legend_loc,fontsize=14)\n #Figure Title\n fig.suptitle(\"{}, {}, t_exp = {}, Bin size = {}\".format(target_name,date,t_ext,binsize),fontsize=24)\n\n #Some annotations: \n diff=(yhigh-ylow)\n spacing = diff/16\n # #### Add some Text\n axes[0,0].text(1.025*xlow,yhigh-spacing,r\"Mean $q$ = {:.2f}%\".format(mn_q*100),fontsize=20)\n axes[0,0].text(1.025*xlow,yhigh-2*spacing,r\"Mean $q$ Error = {:.2f}%\".format(mn_q_err*100),fontsize=20)\n axes[0,0].text(1.025*xlow,yhigh-3*spacing,r\"Std $q$ = {:.2f}%\".format(std_q*100),fontsize=20)\n\n axes[0,1].text(1.025*xlow,yhigh-spacing,\"Mean $u$ = {:.2f}%\".format(mn_u*100),fontsize=20)\n axes[0,1].text(1.025*xlow,yhigh-2*spacing,\"Mean $u$ Error = {:.2f}%\".format(mn_u_err*100),fontsize=20)\n axes[0,1].text(1.025*xlow,yhigh-3*spacing,\"Std $u$ = {:.2f}%\".format(std_u*100),fontsize=20)\n\n axes[1,0].text(1.025*xlow,yhigh-spacing,\"Mean $p$ = {:.2f}%\".format(mn_p*100),fontsize=20)\n axes[1,0].text(1.025*xlow,yhigh-1.5*spacing,\"Mean $p$ Error = {:.2f}%\".format(mn_p_err*100),fontsize=20)\n axes[1,0].text(1.025*xlow,yhigh-2*spacing,\"Std $p$ = {:.2f}%\".format(std_p*100),fontsize=20)\n\n ## Put in the plot overlaid with the spectrum\n axes[2,0].plot(wvs,p_mean,'k')\n axes[2,0].fill_between(wvs,p_mean+p_mean_err,p_mean-p_mean_err,color='k',alpha=0.1)\n axes[2,0].plot(wvs,3*p_mean_err,'r',label=r\"3$\\sigma$ from zero\")\n axes[1,0].plot(wvs,3*p_mean_err,'r',label=r\"3$\\sigma$ from zero\")\n axes[2,0].legend(fontsize=14)\n if spec is not None:\n #Twin axis to show the mean spectrum\n twin = axes[2,0].twinx()\n p_right, = twin.plot(wvs,spec)\n twin.set_ylim(0,1.3*np.max(spec))\n\n #### Lots of plot setup ####\n\n #Labels\n axes[0,0].set_ylabel(\"q\",fontsize=24)\n axes[0,1].set_ylabel(\"u\",fontsize=24)\n axes[1,0].set_ylabel(\"p\",fontsize=24)\n axes[1,1].set_ylabel(r\"$\\theta$\",fontsize=24)\n axes[1,1].set_xlabel(r\"$\\theta$\",fontsize=24)\n\n axes[1,0].set_xlabel(r\"Wavelength [$\\mu m$]\",fontsize=24)\n axes[1,1].set_xlabel(r\"Wavelength [$\\mu m$]\",fontsize=24)\n axes[2,0].set_xlabel(r\"Wavelength [$\\mu m$]\",fontsize=24)\n axes[2,1].set_xlabel(r\"Wavelength [$\\mu m$]\",fontsize=24)\n if spec is not None:\n twin.set_ylabel(\"Uncalibrated Spectrum\",color=p_right.get_color())\n\n #Shrink the space for the title\n plt.subplots_adjust(top=0.95)\n\n ### Axis plot ranges\n axes[0,0].set_xlim(xlow,xhigh)\n axes[0,1].set_xlim(xlow,xhigh)\n axes[1,0].set_xlim(xlow,xhigh)\n axes[1,1].set_xlim(xlow,xhigh)\n axes[2,0].set_xlim(xlow,xhigh)\n\n axes[0,0].set_ylim(ylow,yhigh)\n axes[0,1].set_ylim(ylow,yhigh)\n axes[1,0].set_ylim(0,yhigh)\n axes[1,1].set_ylim(theta_wrap-180,theta_wrap)\n axes[2,1].set_ylim(xlow,xhigh)\n axes[2,1].set_xlim(np.radians(theta_wrap)-np.pi,np.radians(theta_wrap))\n axes[2,0].set_ylim(0,yhigh)\n axes[2,1].set_rticks([1.15, 1.2, 1.25, 1.30])\n ### Number of ticks\n axes[0,0].locator_params(nbins=6)\n axes[0,1].locator_params(nbins=6)\n axes[1,0].locator_params(nbins=6)\n axes[1,1].locator_params(nbins=6)\n axes[2,0].locator_params(nbins=6)\n\n if ldwarf:\n ############################\n ######## Absorption Lines, Ranges and Bandheads #########\n ############################\n\n #Vo Bandhead\n axes[2,0].plot((1.17,1.22),[0.93*yhigh,0.93*yhigh],'k',label=\"VO\")\n axes[2,0].text((1.195),0.95*yhigh,\"V0\",fontsize=14)#H20 Range\n\n axes[2,0].plot((1.1,1.2),(0.86*yhigh,0.86*yhigh),'k',label=r\"H$_2$0\")\n axes[2,0].text(1.170,0.88*yhigh,r\"H$_2$0\",fontsize=14)\n\n #Na Line\n axes[2,0].plot((1.14,1.14),(0.85*yhigh,0.95*yhigh),'k',label=\"Na\")\n axes[2,0].text(1.141,0.9*yhigh, \"Na\",fontsize=14)\n\n #K lines\n axes[2,0].plot((1.177,1.177),(0.75*yhigh,0.85*yhigh),'k',label=\"K\")\n axes[2,0].text(1.179,0.8*yhigh,\"K\",fontsize=14)\n\n\n axes[2,0].plot((1.2485,1.2485),(0.75*yhigh,0.85*yhigh),'k',label=\"K\")\n axes[2,0].text(1.2505,0.8*yhigh,\"K\",fontsize=14)\n\n #FeH doublet\n axes[2,0].plot((1.1939,1.1939),(0.75*yhigh,0.85*yhigh),\"k\",label=\"FeH\")\n axes[2,0].text(1.1955,0.8*yhigh,\"FeH\",fontsize=14)\n\n axes[2,0].plot((1.2389,1.2389),(0.75*yhigh,0.85*yhigh),\"k\",label=\"FeH\")\n axes[2,0].text(1.2282,0.8*yhigh,\"FeH\",fontsize=14)\n\n #H20\n axes[2,0].plot((1.3,1.51),(0.85*yhigh,0.85*yhigh),'k',label=r\"H$_2$0\") #Changed from Mike's list below to be the range from Cushing\n axes[2,0].text(1.31,0.86*yhigh,r\"H$_2$0\",fontsize=14)\n\n if show:\n plt.show()\n if save_path is not None:\n if binsize > 1:\n fn = \"{}_{}_Binned.png\".format(target_name,date,binsize)\n else: \n fn = \"{}_{}_Binned.png\".format(target_name,date)\n plt.savefig(save_path+fn)\n\ndef plot_pol_summary_time_bins(master_wvs,master_spec,spec_cube,hwp_ang,n_time_bins=1,mode='mean',xlow=1.15,xhigh=1.325,ylow=-0.02,yhigh=0.02,\n target_name=\"\",date=\"19850625\",t_ext = 0,binsize=1,theta_wrap=180,ldwarf=False,show=True,\n save_path=None,legend_loc =\"bottom left\",all_theta=False,cmap=None,dt=None,period=1.):\n '''\n Make a summary plot of polarization. The formatting assumes that the inputs (q,u,qerr,uerr)\n are the output of compute_qu_for_obs_sequence. \n\n Inputs:\n wvs - The wavelengths for the x-axis. Should correspond to the rest of the inputs\n spec - The total intensity spectrum\n spec_cube - A cube that holds All of the spectra (aligned) from your data. Should have shape (nfiles, 4, 3, spec_length)\n hwp_ang - The HWP angles. Should have length nfiles\n mode - Either \"mean\" or \"median\"\n dt - the change in time from the first frame. \n '''\n\n ### Make the plot!!! ###\n fig,axes = plt.subplots(3,2,figsize=(16,20))\n\n #Cycle through the time bins\n\n time_snip = spec_cube.shape[0] % n_time_bins\n time_bin_size = spec_cube.shape[0]//n_time_bins\n\n ### TODO: Add in something so that you can put in the rotational period here and have the colors be cyclic. \n if dt is not None:\n if time_snip != 0:\n dt_bins = np.mean(np.reshape(dt[:-time_snip],(-1,time_bin_size)),axis=1)\n else:\n dt_bins = np.mean(np.reshape(dt,(-1,time_bin_size)),axis=1)\n phase = (dt_bins % period)/period\n else:\n phase = np.linspace(0,1.0,n_time_bins,endpoint=False)\n\n time_inds = np.arange(spec_cube.shape[0])\n if cmap is None:\n colormapp = plt.get_cmap('hsv')\n else:\n colormapp = plt.get_cmap(cmap) \n print(phase)\n colors = colormapp(phase)\n \n pmeans = []\n qmeans = []\n umeans = []\n theta_means = []\n for k in range(n_time_bins):\n # print(\"time_bin_size = {}\".format(time_bin_size)) \n good_inds = time_inds[np.where((time_inds >= k*time_bin_size) & (time_inds < (k+1)*time_bin_size))]\n # print(\"Using inds {}\".format(good_inds))\n q,u,qerr,uerr,qind,uind = compute_qu_for_obs_sequence(spec_cube[good_inds],hwp_ang[good_inds],run_alignment=False)\n\n #First calculate the double_difference values\n q_dd = np.nanmean(q,axis=1)\n u_dd = np.nanmean(u,axis=1)\n p_dd = np.sqrt(q_dd**2+u_dd**2)\n theta_dd = 0.5*np.degrees(np.arctan2(u_dd,q_dd))\n theta_dd[theta_dd < 0] +=180\n\n q_dd_err = np.sqrt(np.sum((qerr**2),axis=1))/qerr.shape[1]\n u_dd_err = np.sqrt(np.sum((uerr**2),axis=1))/uerr.shape[1]\n\n #Now calculate the mean or median\n from astropy import stats\n q_mean = np.zeros([q_dd.shape[1]]) #We name this mean, though it could either be Mean or Median\n q_std = np.zeros([q_dd.shape[1]])\n u_mean = np.zeros([u_dd.shape[1]])\n u_std = np.zeros([u_dd.shape[1]])\n\n for i in range(q_dd.shape[1]):\n\n mn,md,std = stats.sigma_clipped_stats(q_dd[:,i], sigma=3, maxiters=5)\n if mode == 'median':\n q_mean[i] = md\n else:\n q_mean[i] = mn\n q_std[i] = std/np.sqrt(q_dd.shape[0])\n mn,md,std = stats.sigma_clipped_stats(u_dd[:,i], sigma=3, maxiters=5)\n u_std[i] = std/np.sqrt(q_dd.shape[0])\n if mode == 'median':\n u_mean[i] = md\n else:\n u_mean[i] = mn\n\n p_mean = np.sqrt(q_mean**2+u_mean**2)\n theta_mean = 0.5*np.degrees(np.arctan2(u_mean,q_mean))\n theta_mean[theta_mean < theta_wrap] +=180\n\n q_mean_err = np.sqrt(np.sum(q_dd_err**2,axis=0))/q_dd_err.shape[0]\n u_mean_err = np.sqrt(np.sum(u_dd_err**2,axis=0))/q_dd_err.shape[0]\n p_mean_err = np.sqrt(q_mean**2*q_mean_err**2+u_mean**2*u_mean_err**2)/p_mean\n p_std = np.sqrt(q_mean**2*q_std**2+u_mean**2*u_std**2)/p_mean\n theta_mean_err = 0.5*np.degrees( np.sqrt( (u_mean**2*q_mean_err**2+q_mean**2*u_mean_err**2)/(q_mean**2+u_mean**2)**2))\n theta_std = 0.5*np.degrees( np.sqrt( (u_mean**2*q_std**2+q_mean**2*u_std**2)/(q_mean**2+u_mean**2)**2))\n\n\n wvs = copy.deepcopy(master_wvs)\n spec = copy.deepcopy(master_spec)\n ### Implement Binning\n if binsize != 1:\n snip = q_mean.shape[0] % binsize\n if snip != 0:\n q_mean = np.mean(q_mean[:-snip].reshape(-1,binsize),axis=1)\n u_mean = np.mean(u_mean[:-snip].reshape(-1,binsize),axis=1)\n wvs = np.mean(wvs[:-snip].reshape(-1,binsize),axis=1)\n p_mean = np.sqrt(q_mean**2+u_mean**2)\n theta_mean = 0.5*np.degrees(np.arctan2(u_mean,q_mean))\n\n spec = np.mean(spec[:-snip].reshape(-1,binsize),axis=1)\n\n q_mean_err = np.sqrt(np.sum(q_mean_err[:-snip].reshape(-1,binsize)**2,axis=1))/binsize\n q_std = np.sqrt(np.sum(q_std[:-snip].reshape(-1,binsize)**2,axis=1))/binsize\n u_mean_err = np.sqrt(np.sum(u_mean_err[:-snip].reshape(-1,binsize)**2,axis=1))/binsize\n u_std = np.sqrt(np.sum(u_std[:-snip].reshape(-1,binsize)**2,axis=1))/binsize\n p_mean_err = np.sqrt(q_mean**2*q_mean_err**2+u_mean**2*u_mean_err**2)/p_mean\n theta_mean_err = 0.5*np.degrees( np.sqrt( (u_mean**2*q_mean_err**2+q_mean**2*u_mean_err**2)/(q_mean**2+u_mean**2)**2))\n \n \n else: \n q_mean = np.mean(q_mean.reshape(-1,binsize),axis=1)\n u_mean = np.mean(u_mean.reshape(-1,binsize),axis=1)\n wvs = np.mean(wvs.reshape(-1,binsize),axis=1)\n p_mean = np.sqrt(q_mean**2+u_mean**2)\n theta_mean = 0.5*np.degrees(np.arctan2(u_mean,q_mean))\n # theta_bin[theta_bin < 0] +=180\n\n spec = np.mean(spec.reshape(-1,binsize),axis=1)\n\n q_mean_err = np.sqrt(np.sum(q_mean_err.reshape(-1,binsize)**2,axis=1))/binsize\n q_std = np.sqrt(np.sum(q_std.reshape(-1,binsize)**2,axis=1))/binsize\n u_mean_err = np.sqrt(np.sum(u_mean_err.reshape(-1,binsize)**2,axis=1))/binsize\n u_std = np.sqrt(np.sum(u_std.reshape(-1,binsize)**2,axis=1))/binsize\n p_mean_err = np.sqrt(q_mean**2*q_mean_err**2+u_mean**2*u_mean_err**2)/p_mean\n theta_mean_err = 0.5*np.degrees( np.sqrt( (u_mean**2*q_mean_err**2+q_mean**2*u_mean_err**2)/(q_mean**2+u_mean**2)**2))\n p_std = np.sqrt(q_mean**2*q_std**2+u_mean**2*u_std**2)/p_mean\n theta_std = 0.5*np.degrees( np.sqrt( (u_mean**2*q_std**2+q_mean**2*u_std**2)/(q_mean**2+u_mean**2)**2))\n \n pmeans.append(p_mean)\n qmeans.append(q_mean)\n umeans.append(u_mean) \n #Wrap theta about 180\n theta_mean[theta_mean>theta_wrap] -= 180\n theta_means.append(theta_mean) \n\n ##Calculate the mean values\n low = 65\n high = 135\n\n inds = (wvs > 1.165) & (wvs<1.31)\n mn_q = np.mean(q_mean[inds])\n mn_u = np.mean(u_mean[inds])\n mn_p = np.mean(p_mean[inds])\n\n mn_q_err = np.mean(q_mean_err[inds])\n mn_u_err = np.mean(u_mean_err[inds])\n mn_p_err = np.mean(p_mean_err[inds])\n\n std_q = np.std(q_mean[inds])\n std_u = np.std(u_mean[inds])\n std_p = np.std(p_mean[inds])\n\n ### Make the plot!!! ###\n\n axes[2,1] = plt.subplot(3,2,6, projection='polar')\n ##### Plot Q, U, P and theta ######\n #The mean values\n if mode == \"median\":\n axes[0,0].plot(wvs,q_mean,color=colors[k],label=\"Median\")\n else:\n axes[0,0].plot(wvs,q_mean,color=colors[k],label=\"Mean\") \n axes[0,1].plot(wvs,u_mean,color=colors[k]) \n axes[1,0].plot(wvs,p_mean,color=colors[k])\n \n\n #Make a line at zero\n axes[0,0].axhline(0.,color='k',linestyle='--')\n axes[0,1].axhline(0.,color='k',linestyle='--')\n\n #Fill in photon/ron error ranges\n axes[0,0].fill_between(wvs,q_mean+q_mean_err,q_mean-q_mean_err,color=colors[k],alpha=0.1,label=\"Propagated Noise\")\n axes[0,1].fill_between(wvs,u_mean+u_mean_err,u_mean-u_mean_err,color=colors[k],alpha=0.1)\n axes[1,0].fill_between(wvs,p_mean+p_mean_err,p_mean-p_mean_err,color=colors[k],alpha=0.1)\n # axes[1,1].fill_between(wvs,theta_mean+theta_mean_err,theta_mean-theta_mean_err,color='k',alpha=0.1)\n\n #Only plot theta where > 3sigma\n if all_theta:\n where_theta = p_mean > 0\n else:\n where_theta = p_mean > 3*p_mean_err\n axes[1,1].errorbar(wvs[where_theta],theta_mean[where_theta],yerr=theta_mean_err[where_theta],\n linestyle=\"None\",marker='o',color=colors[k])\n axes[2,1].errorbar(np.radians(theta_mean[where_theta]),wvs[where_theta],xerr=np.radians(theta_mean_err[where_theta]),\n linestyle=\"None\",marker='o',color=colors[k])\n #Fill in photon/ron error ranges from stds\n axes[0,0].plot(wvs,q_mean+q_std,'--',color=colors[k],alpha=0.5,label=\"Standard Error on the Mean\")\n axes[0,0].plot(wvs,q_mean-q_std,'--',color=colors[k],alpha=0.5)\n axes[0,1].plot(wvs,u_mean+u_std,'--',color=colors[k],alpha=0.5)\n axes[0,1].plot(wvs,u_mean-u_std,'--',color=colors[k],alpha=0.5)\n axes[1,0].plot(wvs,p_mean+p_std,'--',color=colors[k],alpha=0.5)\n axes[1,0].plot(wvs,p_mean-p_std,'--',color=colors[k],alpha=0.5)\n # axes[1,1].plot(wvs,theta_mean+theta_std,'k--',alpha=0.5)\n # axes[1,1].plot(wvs,theta_mean-theta_std,'k--',alpha=0.5)\n\n #3-sigma for p\n axes[1,0].plot(inds,3*p_mean_err,'k')\n\n #Axis plot ranges\n axes[0,0].set_xlim(xlow,xhigh)\n axes[0,1].set_xlim(xlow,xhigh)\n axes[1,0].set_xlim(xlow,xhigh)\n axes[1,1].set_xlim(xlow,xhigh)\n\n axes[0,0].set_ylim(ylow,yhigh)\n axes[0,1].set_ylim(ylow,yhigh)\n axes[1,0].set_ylim(0,yhigh)\n axes[1,1].set_ylim(theta_wrap-180,theta_wrap)\n\n axes[0,0].locator_params(nbins=6)\n axes[0,1].locator_params(nbins=6)\n axes[1,0].locator_params(nbins=6)\n axes[1,1].locator_params(nbins=6)\n axes[2,1].locator_params(nbins=3)\n\n if k == 0:\n axes[0,0].legend(loc=legend_loc,fontsize=14)\n #Figure Title\n fig.suptitle(\"{}, {}, t_exp = {}, Bin size = {}\".format(target_name,date,t_ext,binsize),fontsize=24)\n\n #Some annotations: \n diff=(yhigh-ylow)\n spacing = diff/16\n # #### Add some Text\n # axes[0,0].text(1.025*xlow,yhigh-spacing,r\"Mean $q$ = {:.2f}%\".format(mn_q*100),fontsize=20)\n # axes[0,0].text(1.025*xlow,yhigh-2*spacing,r\"Mean $q$ Error = {:.2f}%\".format(mn_q_err*100),fontsize=20)\n # axes[0,0].text(1.025*xlow,yhigh-3*spacing,r\"Std $q$ = {:.2f}%\".format(std_q*100),fontsize=20)\n\n # axes[0,1].text(1.025*xlow,yhigh-spacing,\"Mean $u$ = {:.2f}%\".format(mn_u*100),fontsize=20)\n # axes[0,1].text(1.025*xlow,yhigh-2*spacing,\"Mean $u$ Error = {:.2f}%\".format(mn_u_err*100),fontsize=20)\n # axes[0,1].text(1.025*xlow,yhigh-3*spacing,\"Std $u$ = {:.2f}%\".format(std_u*100),fontsize=20)\n\n # axes[1,0].text(1.025*xlow,yhigh-spacing,\"Mean $p$ = {:.2f}%\".format(mn_p*100),fontsize=20)\n # axes[1,0].text(1.025*xlow,yhigh-1.5*spacing,\"Mean $p$ Error = {:.2f}%\".format(mn_p_err*100),fontsize=20)\n # axes[1,0].text(1.025*xlow,yhigh-2*spacing,\"Std $p$ = {:.2f}%\".format(std_p*100),fontsize=20)\n\n ## Put in the plot overlaid with the spectrum\n axes[2,0].plot(wvs,p_mean,color=colors[k])\n axes[2,0].fill_between(wvs,p_mean+p_mean_err,p_mean-p_mean_err,color=colors[k],alpha=0.1)\n axes[2,0].plot(wvs,3*p_mean_err,'r',label=r\"3$\\sigma$ from zero\")\n axes[1,0].plot(wvs,3*p_mean_err,'r',label=r\"3$\\sigma$ from zero\")\n if k == 0:\n axes[2,0].legend(fontsize=14)\n #Twin axis to show the mean spectrum\n twin = axes[2,0].twinx()\n p_right, = twin.plot(wvs,spec)\n twin.set_ylim(0,1.3*np.max(spec))\n\n #### Lots of plot setup ####\n\n #Labels\n axes[0,0].set_ylabel(\"q\",fontsize=24)\n axes[0,1].set_ylabel(\"u\",fontsize=24)\n axes[1,0].set_ylabel(\"p\",fontsize=24)\n axes[1,1].set_ylabel(r\"$\\theta$\",fontsize=24)\n axes[1,1].set_xlabel(r\"$\\theta$\",fontsize=24)\n\n axes[1,0].set_xlabel(\"Wavelength [arb units.]\",fontsize=24)\n axes[1,1].set_xlabel(\"Wavelength [arb units.]\",fontsize=24)\n axes[2,0].set_xlabel(\"Wavelength [arb units.]\",fontsize=24)\n twin.set_ylabel(\"Uncalibrated Spectrum\",color=p_right.get_color())\n\n #Shrink the space for the title\n plt.subplots_adjust(top=0.95)\n\n ### Axis plot ranges\n axes[0,0].set_xlim(xlow,xhigh)\n axes[0,1].set_xlim(xlow,xhigh)\n axes[1,0].set_xlim(xlow,xhigh)\n axes[1,1].set_xlim(xlow,xhigh)\n axes[2,0].set_xlim(xlow,xhigh)\n\n axes[0,0].set_ylim(ylow,yhigh)\n axes[0,1].set_ylim(ylow,yhigh)\n axes[1,0].set_ylim(0,yhigh)\n axes[1,1].set_ylim(theta_wrap-180,theta_wrap)\n axes[2,1].set_ylim(xlow,xhigh)\n axes[2,1].set_xlim(np.radians(theta_wrap)-np.pi,np.radians(theta_wrap))\n axes[2,0].set_ylim(0,yhigh)\n axes[2,1].set_rticks([1.15, 1.2, 1.25, 1.30])\n ### Number of ticks\n axes[0,0].locator_params(nbins=6)\n axes[0,1].locator_params(nbins=6)\n axes[1,0].locator_params(nbins=6)\n axes[1,1].locator_params(nbins=6)\n axes[2,0].locator_params(nbins=6)\n\n if ldwarf and k==0:\n ############################\n ######## Absorption Lines, Ranges and Bandheads #########\n ############################\n\n #Vo Bandhead\n axes[2,0].plot((1.17,1.22),[0.93*yhigh,0.93*yhigh],'k',label=\"VO\")\n axes[2,0].text((1.195),0.95*yhigh,\"V0\",fontsize=14)#H20 Range\n\n axes[2,0].plot((1.1,1.2),(0.86*yhigh,0.86*yhigh),'k',label=r\"H$_2$0\")\n axes[2,0].text(1.170,0.88*yhigh,r\"H$_2$0\",fontsize=14)\n\n #Na Line\n axes[2,0].plot((1.14,1.14),(0.85*yhigh,0.95*yhigh),'k',label=\"Na\")\n axes[2,0].text(1.141,0.9*yhigh, \"Na\",fontsize=14)\n\n #K lines\n axes[2,0].plot((1.177,1.177),(0.75*yhigh,0.85*yhigh),'k',label=\"K\")\n axes[2,0].text(1.179,0.8*yhigh,\"K\",fontsize=14)\n\n\n axes[2,0].plot((1.2485,1.2485),(0.75*yhigh,0.85*yhigh),'k',label=\"K\")\n axes[2,0].text(1.2505,0.8*yhigh,\"K\",fontsize=14)\n\n #FeH doublet\n axes[2,0].plot((1.1939,1.1939),(0.75*yhigh,0.85*yhigh),\"k\",label=\"FeH\")\n axes[2,0].text(1.1955,0.8*yhigh,\"FeH\",fontsize=14)\n\n axes[2,0].plot((1.2389,1.2389),(0.75*yhigh,0.85*yhigh),\"k\",label=\"FeH\")\n axes[2,0].text(1.2282,0.8*yhigh,\"FeH\",fontsize=14)\n\n #H20\n axes[2,0].plot((1.3,1.51),(0.85*yhigh,0.85*yhigh),'k',label=r\"H$_2$0\") #Changed from Mike's list below to be the range from Cushing\n axes[2,0].text(1.31,0.86*yhigh,r\"H$_2$0\",fontsize=14)\n\n if show:\n plt.show()\n if save_path is not None:\n if binsize > 1:\n fn = \"{}_{}_Binned.png\".format(target_name,date,binsize)\n else: \n fn = \"{}_{}_Binned.png\".format(target_name,date)\n plt.savefig(save_path+fn)\n if dt is not None:\n return dt_bins,phase,qmeans,umeans,pmeans,theta_means\n else:\n return qmeans,umeans,pmeans,theta_means\n\n\ndef thresholding_algo(y, lag, threshold, influence):\n \"\"\"\n Helper function for quantize_peaks\n \"\"\"\n signals = np.zeros(len(y))\n filteredY = np.array(y)\n avgFilter = [0]*len(y)\n stdFilter = [0]*len(y)\n avgFilter[lag - 1] = np.mean(y[0:lag])\n stdFilter[lag - 1] = np.std(y[0:lag])\n for i in range(lag, len(y)):\n if abs(y[i] - avgFilter[i-1]) > threshold * stdFilter [i-1]:\n if y[i] > avgFilter[i-1]:\n signals[i] = 1\n else:\n signals[i] = -1\n\n filteredY[i] = influence * y[i] + (1 - influence) * filteredY[i-1]\n avgFilter[i] = np.mean(filteredY[(i-lag+1):i+1])\n stdFilter[i] = np.std(filteredY[(i-lag+1):i+1])\n else:\n signals[i] = 0\n filteredY[i] = y[i]\n avgFilter[i] = np.mean(filteredY[(i-lag+1):i+1])\n stdFilter[i] = np.std(filteredY[(i-lag+1):i+1])\n\n return dict(signals = np.asarray(signals),\n avgFilter = np.asarray(avgFilter),\n stdFilter = np.asarray(stdFilter))\n\ndef quantize_peaks(y, lag=30, threshold=5, influence=0, show_plot=True):\n \"\"\"\n Finds peaks in a data vector and quantizes them\n \"\"\"\n \n result = thresholding_algo(y, lag=lag, threshold=threshold, influence=influence)\n\n if show_plot:\n # Plot result\n plt.subplot(211)\n plt.plot(np.arange(1, len(y)+1), y)\n\n plt.plot(np.arange(1, len(y)+1),\n result[\"avgFilter\"], color=\"cyan\", lw=2)\n\n plt.plot(np.arange(1, len(y)+1),\n result[\"avgFilter\"] + threshold * result[\"stdFilter\"], color=\"green\", lw=2)\n\n plt.plot(np.arange(1, len(y)+1),\n result[\"avgFilter\"] - threshold * result[\"stdFilter\"], color=\"green\", lw=2)\n\n plt.subplot(212)\n plt.step(np.arange(1, len(y)+1), result[\"signals\"], color=\"red\", lw=2)\n plt.ylim(-1.5, 1.5)\n plt.show()\n \n return result\n\ndef missing_elements(L):\n start, end = L[0], L[-1]\n return sorted(set(range(start, end + 1)).difference(L))\n\ndef perp_fit(fit, ctr):\n \"\"\"\n calculates fit to a line perpendicular to trace \n \"\"\"\n x1=0\n x2=len(fit)\n \n y1 = fit[0]\n y2 = fit[-1]\n \n m = (y2-y1)/(x2-x1)\n \n #y_fit = (-1/m)*x_fit + b ==>\n #ctr[1] = (-1/m)*ctr[0] + b ==>\n b = ctr[1] + (1/m)*ctr[0]\n \n x_fit = np.arange(len(fit))\n \n pfit = (-1/m)*x_fit + b\n \n pfit[np.where(pfit<0)] = 0\n pfit[np.where(pfit>len(fit))] = len(fit)-1\n \n return pfit\n\ndef fit_aperture(source, x_stretch=1, y_stretch=1, interp_factor=10, verbose=False, plot=False, savefig=None):\n \"\"\"\n fits racetrack aperture to trace cutouts from a WIRC+Pol source.\n \n returns: list of nan masked apertures \n \"\"\"\n apertures = []\n total_flux = []\n noise = []\n trace_labels = ['UL', 'LR', 'UR', 'LL']\n \n for k in range(4):\n interp_factor = 10\n im = source.trace_images[k]\n\n peak, fit, width, angle = findTrace(im, weighted=True, plot=False)\n\n angle=angle*np.pi/180\n\n trace = np.array([im[fit.astype(int)[i]][i] for i in range(len(fit))])\n\n yy, xx = np.indices(im.shape)\n\n results = quantize_peaks(trace, lag=20, threshold=5, influence=0, show_plot=plot)\n\n mean_peak = np.mean(trace[np.where(results[\"signals\"]>0)][10:-10])\n\n index = np.where(results[\"signals\"]>0)[0]\n\n if verbose:\n print(\"Mean peak flux = {}\".format(mean_peak))\n\n peak_indices = missing_elements(np.where(trace<.5*mean_peak)[0])\n\n FWHM = peak_indices[-1]-peak_indices[0]\n\n if verbose:\n print(\"FWHM = {} px\".format(FWHM))\n\n if plot:\n plt.figure()\n plt.plot(trace[np.where(results[\"signals\"]>0)])\n plt.axvline(peak_indices[0] - index[0], ymin=0, ymax=1, color='r', ls='--', label='FWHM = {}'.format(FWHM))\n plt.axvline(peak_indices[-1] - index[0], ymin=0, ymax=1, color='r', ls='--')\n plt.axhline(mean_peak, 10/len(trace[np.where(results[\"signals\"]>0)]),\n (len(trace[np.where(results[\"signals\"]>0)])-11)/len(trace[np.where(results[\"signals\"]>0)]),\n color='k', ls='--', label='Mean peak value = {}'.format(np.round(mean_peak, 2)))\n plt.legend()\n plt.show()\n plt.close()\n\n peak_index = (peak_indices[0]+peak_indices[-1])//2\n\n p_fit = perp_fit(fit, (peak_index, fit[peak_index]))\n\n p_trace = np.array([im[p_fit.astype(int)[i]][i] for i in range(len(fit))])\n\n #interpolate perp trace to better calculate FWHM\n p_trace_interp = interp1d(np.arange(len(p_trace)), p_trace)(np.linspace(0, len(fit)-1, len(fit)*interp_factor))\n\n if plot:\n plt.figure()\n plt.title('Perpendicular trace')\n plt.plot(p_trace)\n plt.show()\n plt.close()\n\n p_results = quantize_peaks(p_trace_interp, lag=20*interp_factor, threshold=5, influence=0, show_plot=plot)\n\n p_peak = np.max(p_trace_interp)\n\n p_index = np.where(p_results[\"signals\"]>0)[0]\n\n if verbose:\n print(\"Peak flux = {}\".format(p_peak))\n\n p_peak_indices = missing_elements(np.where(p_trace_interp<.5*p_peak)[0])\n\n p_peak_index = int(np.where(p_trace_interp==np.max(p_trace_interp))[0][0]/interp_factor)\n\n p_FWHM = (p_peak_indices[-1]-p_peak_indices[0])/interp_factor\n\n if verbose:\n print(\"FWHM = {} px\".format(p_FWHM))\n\n if plot:\n plt.figure()\n plt.title('Perpendicular trace peak')\n plt.plot(p_trace_interp[np.where(p_results[\"signals\"]>0)])\n plt.axvline(p_peak_indices[0] - p_index[0], ymin=0, ymax=1, color='r', ls='--', label='FWHM = {}'.format(p_FWHM))\n plt.axvline(p_peak_indices[-1] - p_index[0], ymin=0, ymax=1, color='r', ls='--')\n plt.axhline(p_peak, (p_peak_indices[0] - p_index[0])/len(p_trace_interp[np.where(p_results[\"signals\"]>0)]),\n (p_peak_indices[-1] - p_index[0])/len(p_trace_interp[np.where(p_results[\"signals\"]>0)]),\n color='k', ls='--', label='Peak value = {}'.format(np.round(p_peak, 2)))\n plt.legend()\n plt.show()\n plt.close()\n\n if verbose:\n print(\"trace ctr = {}\".format((peak_index, int(fit[peak_index]))))\n x_ctr, y_ctr = peak_index, int(fit[peak_index])\n\n circ1 = (xx-x_ctr-x_stretch*FWHM/2)**2+(yy-y_ctr)**2\n circ2 = (xx-x_ctr+x_stretch*FWHM/2)**2+(yy-y_ctr)**2\n ends = np.logical_or((circ1<y_stretch*p_FWHM/2), (circ2<y_stretch*p_FWHM/2)).astype(float)\n \n box_x = np.logical_and((xx>x_ctr-x_stretch*FWHM/2), (xx<x_ctr+x_stretch*FWHM/2))\n box_y = np.logical_and((yy>y_ctr-y_stretch*p_FWHM/2),(yy<y_ctr+y_stretch*p_FWHM/2))\n box = np.logical_and(box_x, box_y).astype(float)\n \n racetrack = np.logical_or(box, ends).astype(float)\n\n xp = (xx-x_ctr)*np.cos(angle) + (yy-y_ctr)*np.sin(angle) + x_ctr\n yp = -(xx-x_ctr)*np.sin(angle) + (yy-y_ctr)*np.cos(angle) + y_ctr\n\n racetrack = np.nan_to_num(np.round(ndimage.map_coordinates(racetrack, (yp, xp), cval=np.nan))).astype(bool)\n\n aperture = np.copy(im.astype(float))\n aperture[~racetrack] = np.nan\n\n f, ax = plt.subplots(1, 2, figsize=(6, 3))\n f.suptitle(trace_labels[k], fontsize=25)\n ax[0].imshow(im, origin='lower', vmin=0, vmax=np.nanmax(aperture))\n ax[0].plot(fit)\n ax[0].plot(peak)\n ax[0].set_xlim(0, im.shape[1])\n ax[0].set_ylim(0, im.shape[0])\n ax[1].imshow(aperture, origin='lower', vmin=0, vmax=np.nanmax(aperture))\n ax[1].set_xlabel('Total flux = %.2E' % Decimal(str(np.nansum(aperture))))\n if savefig:\n plt.savefig('{}.pdf'.format(trace_labels[k]), dpi=300, bbox_inches='tight')\n plt.show()\n plt.close()\n \n apertures.append(aperture)\n total_flux.append(np.nansum(aperture))\n noise.append(np.nanstd(aperture))\n \n return apertures, total_flux, noise\n", "sub_path": "wirc_drp/utils/source_utils.py", "file_name": "source_utils.py", "file_ext": "py", "file_size_in_byte": 53924, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "14", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "numpy.size", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 60, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.size", "line_number": 69, "usage_type": "call"}, {"api_name": "scipy.signal.fftconvolve", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.nan_to_num", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.nanmax", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.nanargmax", "line_number": 74, "usage_type": "call"}, {"api_name": "scipy.ndimage.shift", "line_number": 76, "usage_type": "call"}, {"api_name": "astropy.io.ascii.read", "line_number": 85, "usage_type": "call"}, {"api_name": "astropy.io.ascii", "line_number": 85, "usage_type": "name"}, {"api_name": "astropy.io.fits.open", "line_number": 88, "usage_type": "call"}, {"api_name": "astropy.io.fits", "line_number": 88, "usage_type": "name"}, {"api_name": "numpy.fromstring", "line_number": 91, "usage_type": "call"}, {"api_name": "numpy.fromstring", "line_number": 92, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 95, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 128, "usage_type": "call"}, {"api_name": "wirc_drp.utils.spec_utils.align_spectral_cube", "line_number": 131, "usage_type": "call"}, {"api_name": "wirc_drp.utils.spec_utils", "line_number": 131, "usage_type": "name"}, {"api_name": "wirc_drp.utils.spec_utils.scale_and_combine_spectra", "line_number": 132, "usage_type": "call"}, {"api_name": "wirc_drp.utils.spec_utils", "line_number": 132, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.sign", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.logical_or", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 172, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 188, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 193, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 199, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 227, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 228, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.stack", "line_number": 235, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 303, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 304, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 307, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 337, "usage_type": "call"}, {"api_name": "astropy.coordinates.SkyCoord", "line_number": 337, "usage_type": "call"}, {"api_name": "astropy.units.hourangle", "line_number": 337, "usage_type": "attribute"}, {"api_name": "astropy.units", "line_number": 337, "usage_type": "name"}, {"api_name": "astropy.units.deg", "line_number": 337, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 338, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 339, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 340, "usage_type": "call"}, {"api_name": "astropy.time.Time", "line_number": 340, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 343, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 345, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 350, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 354, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 360, "usage_type": "call"}, {"api_name": "numpy.logical_and", "line_number": 361, "usage_type": "call"}, {"api_name": "astropy.time.Time", "line_number": 366, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 367, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 367, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 370, "usage_type": "call"}, {"api_name": "numpy.min", "line_number": 370, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 375, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 381, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 383, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 385, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 405, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 406, "usage_type": "call"}, {"api_name": "astropy.units", "line_number": 406, "usage_type": "argument"}, {"api_name": "numpy.sqrt", "line_number": 407, "usage_type": "call"}, {"api_name": "numpy.degrees", "line_number": 408, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 408, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 411, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 411, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 412, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 412, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 416, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 417, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 418, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 419, "usage_type": "call"}, {"api_name": "astropy.stats.sigma_clipped_stats", "line_number": 423, "usage_type": "call"}, {"api_name": "astropy.stats", "line_number": 423, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 428, "usage_type": "call"}, {"api_name": "astropy.stats.sigma_clipped_stats", "line_number": 429, "usage_type": "call"}, {"api_name": "astropy.stats", "line_number": 429, "usage_type": "name"}, {"api_name": "numpy.sqrt", "line_number": 430, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 436, "usage_type": "call"}, {"api_name": "numpy.degrees", "line_number": 437, "usage_type": "call"}, {"api_name": "numpy.arctan2", "line_number": 437, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 440, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 440, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 441, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 441, 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